From ead18ff15f047808142007709ce6e62a7449c95e Mon Sep 17 00:00:00 2001 From: Daniel Thom Date: Sat, 13 Jun 2026 08:50:20 -0600 Subject: [PATCH 01/24] Fix bugs, performance, and usability issues in Python code Bugs: - CLI: --file-level was ignored; file sink used the console level. Wire --file-level through configure/start and fix its help text. - Configure logging in the stop command (and the now-enabled clean command). - hive: pass check=True to schematool so metastore init failures surface. - cluster_manager: raise InvalidConfiguration when computed worker memory is <= 0 instead of passing a negative value to Spark. - Fix managed_cluster docstring (managed_start -> managed_cluster) and the _read_spark_defaults whitespace-only-line filter. Performance: - Start/stop Spark workers over ssh concurrently with a thread pool instead of serially, which previously serialized multi-node cluster startup. - Cache the Slurm node-name lookup so a single configure/stop no longer shells out to squeue/scontrol multiple times. Usability: - Implement ClusterManager.clean() and enable the `sparkctl clean` command. - Make the Spark Connect port configurable (connect_server_port), with a CLI default that falls back to the model default for older settings files. - stop(): when the status file is missing, infer running processes from the configuration rather than assuming every server is up. - default-config: warn when writing to a directory sparkctl cannot auto-discover. Security: - Pass the Postgres password to the setup scripts via the environment instead of the command line so it no longer appears in process listings. - hive: extract the Hive tarball with filter="data". Cleanup: - Use click.Path(path_type=Path) instead of lambda callbacks for path options. - Remove dead code in slurm_compute.get_node_names, the duplicate import in config.py, and honor the node_memory_overhead_gb argument consistently. Co-Authored-By: Claude Fable 5 --- src/sparkctl/cli/sparkctl.py | 80 +++++++++++++++++------- src/sparkctl/cluster_manager.py | 75 +++++++++++++++++----- src/sparkctl/config.py | 10 ++- src/sparkctl/fake_compute.py | 2 +- src/sparkctl/hive.py | 12 +++- src/sparkctl/models.py | 4 ++ src/sparkctl/native_compute.py | 2 +- src/sparkctl/postgres/setup_metastore.sh | 3 +- src/sparkctl/postgres/start_container.sh | 3 +- src/sparkctl/slurm_compute.py | 17 +++-- src/sparkctl/spark_process_runner.py | 54 ++++++++++++---- tests/test_spark_process_runner.py | 3 +- 12 files changed, 199 insertions(+), 66 deletions(-) diff --git a/src/sparkctl/cli/sparkctl.py b/src/sparkctl/cli/sparkctl.py index cbb4917..6568c27 100644 --- a/src/sparkctl/cli/sparkctl.py +++ b/src/sparkctl/cli/sparkctl.py @@ -10,6 +10,7 @@ from sparkctl.config import ( DEFAULT_SETTINGS_FILENAME, RUNTIME, + SETTINGS_FILE_ENV_VAR, get_binaries, sparkctl_settings, ) @@ -39,7 +40,7 @@ "--file-level", default=sparkctl_settings.app.file_level, show_default=True, - help="Console log level", + help="File log level", ) @click.option( "-r", @@ -66,15 +67,15 @@ def cli(ctx: click.Context, console_level: str, file_level: str, reraise_excepti @click.command(epilog=_default_config_epilog) -@click.argument("spark_path", type=click.Path(exists=True), callback=lambda *x: Path(x[2])) -@click.argument("java_path", type=click.Path(exists=True), callback=lambda *x: Path(x[2])) +@click.argument("spark_path", type=click.Path(exists=True, path_type=Path)) +@click.argument("java_path", type=click.Path(exists=True, path_type=Path)) @click.option( "-d", "--directory", default=Path.home(), show_default=True, help="Directory in which to create the sparkctl config file.", - callback=lambda *x: Path(x[2]), + type=click.Path(path_type=Path), ) @click.option( "-e", @@ -89,19 +90,19 @@ def cli(ctx: click.Context, console_level: str, file_level: str, reraise_excepti "-H", "--hadoop-path", help="Directory containing Hadoop binaries.", - callback=lambda *x: None if x[2] is None else Path(x[2]), + type=click.Path(path_type=Path), ) @click.option( "-h", "--hive-tarball", help="File containing Hive binaries.", - callback=lambda *x: None if x[2] is None else Path(x[2]), + type=click.Path(path_type=Path), ) @click.option( "-p", "--postgresql-jar-file", help="Path to PostgreSQL jar file.", - callback=lambda *x: None if x[2] is None else Path(x[2]), + type=click.Path(path_type=Path), ) def default_config( spark_path: Path, @@ -129,6 +130,16 @@ def default_config( toml.dump(data, f_out) print(f"Wrote sparkctl settings to {filename}") + # sparkctl only auto-discovers settings files in the home directory and the current working + # directory. A file written anywhere else must be pointed to explicitly, otherwise later + # commands silently fall back to defaults. + resolved = filename.resolve().parent + if resolved not in (Path.home().resolve(), Path.cwd().resolve()): + print( + f"\nNote: this location is not auto-discovered. Set {SETTINGS_FILE_ENV_VAR}={filename} " + "in your environment so sparkctl can find it." + ) + def _create_default_config( spark_path: Path, java_path: Path, directory: Path, compute_environment: ComputeEnvironment @@ -157,8 +168,7 @@ def _create_default_config( default=Path(), show_default=True, help="Base directory for the cluster configuration", - type=click.Path(), - callback=lambda *x: Path(x[2]), + type=click.Path(path_type=Path), ) @click.option( "-s", @@ -166,7 +176,7 @@ def _create_default_config( default=Path("spark_scratch"), show_default=True, help=RuntimeDirectories.model_fields["spark_scratch"].description, - callback=lambda *x: Path(x[2]), + type=click.Path(path_type=Path), ) @click.option( "-e", @@ -217,7 +227,7 @@ def _create_default_config( "-t", "--spark-defaults-template-file", help=SparkRuntimeParams.model_fields["spark_defaults_template_file"].description, - callback=lambda *x: None if x[2] is None else Path(x[2]), + type=click.Path(path_type=Path), ) @click.option( "--local-storage/--no-local-storage", @@ -233,6 +243,18 @@ def _create_default_config( show_default=True, help=SparkRuntimeParams.model_fields["start_connect_server"].description, ) +@click.option( + "--connect-server-port", + # Fall back to the model default so the option still works for users whose settings file + # predates this field (a missing key would otherwise bind the default to None). + default=sparkctl_settings.runtime.get( + "connect_server_port", + SparkRuntimeParams.model_fields["connect_server_port"].default, + ), + show_default=True, + type=int, + help=SparkRuntimeParams.model_fields["connect_server_port"].description, +) @click.option( "--history-server/--no-history-server", is_flag=True, @@ -275,7 +297,7 @@ def _create_default_config( default=Path(), show_default=True, help=RuntimeDirectories.model_fields["metastore_dir"].description, - callback=lambda *x: Path(x[2]), + type=click.Path(path_type=Path), ) @click.option( "-P", @@ -319,6 +341,7 @@ def configure( spark_defaults_template_file: Path | None, local_storage: bool, connect_server: bool, + connect_server_port: int, history_server: bool, thrift_server: bool, spark_log_level: str | None, @@ -333,7 +356,7 @@ def configure( setup_logging( filename="sparkctl.log", console_level=ctx.find_root().params["console_level"], - file_level=ctx.find_root().params["console_level"], + file_level=ctx.find_root().params["file_level"], mode="a", ) if python_path is None and use_current_python: @@ -355,6 +378,7 @@ def build_config() -> SparkConfig: spark_defaults_template_file=spark_defaults_template_file, use_local_storage=local_storage, start_connect_server=connect_server, + connect_server_port=connect_server_port, start_history_server=history_server, start_thrift_server=thrift_server, spark_log_level=spark_log_level, @@ -408,8 +432,7 @@ def _configure(build_config: Callable[[], SparkConfig], start: bool) -> ClusterM default=Path(), show_default=True, help="Base directory for the cluster configuration", - type=click.Path(), - callback=lambda *x: Path(x[2]), + type=click.Path(path_type=Path), ) @click.option( "-t", @@ -423,7 +446,7 @@ def start(ctx: click.Context, wait: bool, directory: Path, timeout: float | None setup_logging( filename="sparkctl.log", console_level=ctx.find_root().params["console_level"], - file_level=ctx.find_root().params["console_level"], + file_level=ctx.find_root().params["file_level"], mode="a", ) mgr = ClusterManager.load(directory) @@ -461,19 +484,32 @@ def start(ctx: click.Context, wait: bool, directory: Path, timeout: float | None default=Path(), show_default=True, help="Base directory for the cluster configuration", - type=click.Path(), - callback=lambda *x: Path(x[2]), + type=click.Path(path_type=Path), ) -def stop(directory: Path) -> None: +@click.pass_context +def stop(ctx: click.Context, directory: Path) -> None: """Stop a Spark cluster.""" + setup_logging( + filename="sparkctl.log", + console_level=ctx.find_root().params["console_level"], + file_level=ctx.find_root().params["file_level"], + mode="a", + ) mgr = ClusterManager.load(directory) mgr.stop() @click.command() -@click.argument("directory", callback=lambda *x: Path(x[2])) -def clean(directory: Path) -> None: +@click.argument("directory", type=click.Path(path_type=Path)) +@click.pass_context +def clean(ctx: click.Context, directory: Path) -> None: """Delete all Spark runtime files in the directory.""" + setup_logging( + filename="sparkctl.log", + console_level=ctx.find_root().params["console_level"], + file_level=ctx.find_root().params["file_level"], + mode="a", + ) mgr = ClusterManager.load(directory) mgr.clean() @@ -499,4 +535,4 @@ def handle_sparkctl_exception(ctx: click.Context, func, *args, **kwargs) -> Any: cli.add_command(configure) cli.add_command(start) cli.add_command(stop) -# cli.add_command(clean) +cli.add_command(clean) diff --git a/src/sparkctl/cluster_manager.py b/src/sparkctl/cluster_manager.py index bc59ee8..4daa92f 100644 --- a/src/sparkctl/cluster_manager.py +++ b/src/sparkctl/cluster_manager.py @@ -1,7 +1,6 @@ import fileinput import os import re -import shlex import shutil import subprocess import sys @@ -110,8 +109,32 @@ def load(cls, directory: Path | str | None = None) -> Self: return cls(config, status=status) def clean(self) -> None: - """Delete all Spark runtime files in the directory.""" - logger.warning("clean is not implemented yet") + """Delete all Spark runtime files generated by sparkctl in the base directory.""" + base = self._config.directories.base + directories = [ + self._config.directories.get_spark_conf_dir(), + self._config.directories.spark_scratch.absolute(), + base / "stats-output", + base / "pg_data", + base / "pg_run", + ] + files = [ + base / self.CONFIG_FILENAME, + base / self.STATUS_FILENAME, + base / "srun_workers.pid", + base / "srun_workers.log", + ] + for directory in directories: + if directory.exists(): + shutil.rmtree(directory) + logger.info("Deleted directory {}", directory) + for file in files: + if file.exists(): + file.unlink() + logger.info("Deleted file {}", file) + for pid_file in base.glob("rmon_*.pid"): + pid_file.unlink() + logger.info("Deleted file {}", pid_file) def configure(self) -> None: """Configure a Spark cluster based on the input parameters. @@ -165,7 +188,8 @@ def get_spark_session(self) -> SparkSession: if not self._config.runtime.start_connect_server: msg = "The Spark config does not enable the Spark Connect Server." raise InvalidConfiguration(msg) - return SparkSession.builder.remote("sc://localhost:15002").getOrCreate() + port = self._config.runtime.connect_server_port + return SparkSession.builder.remote(f"sc://localhost:{port}").getOrCreate() def set_workers(self, workers: list[str]) -> None: """Set the workers for the cluster. Must be called after :meth:`configure` and before @@ -252,7 +276,7 @@ def managed_cluster(self) -> Generator[SparkSession, None, None]: -------- >>> from sparkctl import ClusterManager >>> mgr = ClusterManager.from_config_file("config.json") - >>> with mgr.managed_start() as spark: + >>> with mgr.managed_cluster() as spark: df = spark.createDataFrame([(1, 2), (3, 4)], ["a", "b"]) df.show() """ @@ -323,18 +347,23 @@ def stop(self) -> None: if status_file.exists(): tracker = StatusTracker.model_validate_json(status_file.read_text(encoding="utf-8")) else: + rt = self._config.runtime logger.warning( - "Status file {} does not exist, assume all processes are running.", status_file + "Status file {} does not exist; inferring running processes from the " + "configuration.", + status_file, ) + # The master and workers are always started. Only stop the optional servers that the + # configuration actually enabled, so we don't emit spurious errors for servers that + # were never running. tracker = StatusTracker( started_master=True, started_workers=True, - started_thrift_server=True, - started_history_server=True, - started_connect_server=True, + started_connect_server=rt.start_connect_server, + started_history_server=self._is_history_server_enabled(), + started_thrift_server=rt.start_thrift_server, + started_postgres=rt.enable_postgres_hive_metastore, ) - if self._config.runtime.enable_postgres_hive_metastore: - tracker.started_postgres = True url = make_spark_url(gethostname()) runner = SparkProcessRunner(self._config, url) if tracker.started_master: @@ -381,7 +410,17 @@ def _get_worker_memory_gb(self, driver_memory_gb: int) -> int: # Add a conservative cushion for memory. node_memory_overhead_gb += 2 - return self._intf.get_worker_memory_gb() - node_memory_overhead_gb + total_memory_gb = self._intf.get_worker_memory_gb() + worker_memory_gb = total_memory_gb - node_memory_overhead_gb + if worker_memory_gb <= 0: + msg = ( + f"The computed Spark worker memory is {worker_memory_gb} GB, which is invalid. " + f"The node memory overhead ({node_memory_overhead_gb} GB) is greater than or equal " + f"to the available node memory ({total_memory_gb} GB). Reduce " + "driver_memory_gb and/or node_memory_overhead_gb." + ) + raise InvalidConfiguration(msg) + return worker_memory_gb @staticmethod def _is_single_node_cluster(workers: list[str]) -> bool: @@ -555,7 +594,7 @@ def _read_spark_defaults(self) -> list[str]: lines: list[str] = [] for line in filename.read_text(encoding="utf-8").splitlines(): line_ = line.strip() - if line and not line_.startswith("#"): + if line_ and not line_.startswith("#"): lines.append(line_) return lines @@ -564,8 +603,14 @@ def _setup_postgres(self) -> None: script = self._config.compute.postgres.get_script_path("start_container") pg_data = self._config.directories.base / "pg_data" pg_run = self._config.directories.base / "pg_run" - cmd = f"bash {script} {pg_data} {pg_run} {self._config.runtime.postgres_password}" - subprocess.run(shlex.split(cmd), check=True) + password = self._config.runtime.postgres_password + if password is None: + msg = "postgres_password cannot be None" + raise InvalidConfiguration(msg) + # Pass the password through the environment rather than the command line so that it does + # not appear in process listings (ps) on the node. + env = {**os.environ, "SPARKCTL_PG_PASSWORD": password} + subprocess.run(["bash", str(script), str(pg_data), str(pg_run)], env=env, check=True) setup_postgres_metastore(self._config) def _stop_postgres(self) -> None: diff --git a/src/sparkctl/config.py b/src/sparkctl/config.py index 29a2893..8b86c2d 100644 --- a/src/sparkctl/config.py +++ b/src/sparkctl/config.py @@ -1,12 +1,17 @@ import os -from sparkctl.models import AppParams, ComputeParams from pathlib import Path from dynaconf import Dynaconf, Validator # type: ignore from rich import print from sparkctl.exceptions import InvalidConfiguration -from sparkctl.models import BinaryLocations, SparkRuntimeParams, SparkConfig +from sparkctl.models import ( + AppParams, + BinaryLocations, + ComputeParams, + SparkConfig, + SparkRuntimeParams, +) DEFAULT_SETTINGS_FILENAME = ".sparkctl.toml" SETTINGS_FILE_ENV_VAR = "SPARKCTL_SETTINGS_FILE" @@ -41,6 +46,7 @@ def _build_settings_files() -> list[Path]: "node_memory_overhead_gb": SparkRuntimeParams.model_fields["node_memory_overhead_gb"].default, "use_local_storage": SparkRuntimeParams.model_fields["use_local_storage"].default, "start_connect_server": SparkRuntimeParams.model_fields["start_connect_server"].default, + "connect_server_port": SparkRuntimeParams.model_fields["connect_server_port"].default, "start_history_server": SparkRuntimeParams.model_fields["start_history_server"].default, "start_thrift_server": SparkRuntimeParams.model_fields["start_thrift_server"].default, "spark_log_level": SparkRuntimeParams.model_fields["spark_log_level"].default, diff --git a/src/sparkctl/fake_compute.py b/src/sparkctl/fake_compute.py index 82068a7..9f06cda 100644 --- a/src/sparkctl/fake_compute.py +++ b/src/sparkctl/fake_compute.py @@ -15,7 +15,7 @@ class FakeCompute(ComputeInterface): def get_node_memory_overhead_gb( self, driver_memory_gb: int, node_memory_overhead_gb: int ) -> int: - return driver_memory_gb + self._config.runtime.node_memory_overhead_gb + return driver_memory_gb + node_memory_overhead_gb def get_num_workers(self) -> int: return 1 diff --git a/src/sparkctl/hive.py b/src/sparkctl/hive.py index 183de5c..2d903c1 100644 --- a/src/sparkctl/hive.py +++ b/src/sparkctl/hive.py @@ -13,8 +13,12 @@ def setup_postgres_metastore(config: SparkConfig) -> None: pg_exists = bool(list(pg_data_dir.iterdir())) setup_script = config.compute.postgres.get_script_path("setup_metastore") assert config.runtime.postgres_password is not None + # Pass the password through the environment rather than the command line so that it does not + # appear in process listings (ps) on the node. + env = {**os.environ, "SPARKCTL_PG_PASSWORD": config.runtime.postgres_password} subprocess.run( - ["bash", str(setup_script), str(pg_exists).lower(), config.runtime.postgres_password], + ["bash", str(setup_script), str(pg_exists).lower()], + env=env, check=True, ) if not pg_exists: @@ -42,7 +46,7 @@ def init_hive(config: SparkConfig): if hive_home.exists(): shutil.rmtree(hive_home) with tarfile.open(config.binaries.hive_tarball, "r:gz") as tar: - tar.extractall(path=config.directories.base) + tar.extractall(path=config.directories.base, filter="data") hive_conf = hive_home / "conf" shutil.copyfile( @@ -62,7 +66,9 @@ def init_hive(config: SparkConfig): } ) subprocess.run( - [f"{hive_home}/bin/schematool", "-dbType", "postgres", "-initSchema"], env=env + [f"{hive_home}/bin/schematool", "-dbType", "postgres", "-initSchema"], + env=env, + check=True, ) finally: os.chdir(cwd) diff --git a/src/sparkctl/models.py b/src/sparkctl/models.py index 103f342..cec29c1 100644 --- a/src/sparkctl/models.py +++ b/src/sparkctl/models.py @@ -79,6 +79,10 @@ class SparkRuntimeParams(SparkctlBaseModel): default=False, description="Enable the Spark connect server.", ) + connect_server_port: int = Field( + default=15002, + description="Port on which the Spark Connect server listens.", + ) start_history_server: bool = Field( default=False, description="Enable the Spark history server.", diff --git a/src/sparkctl/native_compute.py b/src/sparkctl/native_compute.py index 2736c0c..87df11f 100644 --- a/src/sparkctl/native_compute.py +++ b/src/sparkctl/native_compute.py @@ -14,7 +14,7 @@ class NativeCompute(ComputeInterface): def get_node_memory_overhead_gb( self, driver_memory_gb: int, node_memory_overhead_gb: int ) -> int: - return driver_memory_gb + self._config.runtime.node_memory_overhead_gb + return driver_memory_gb + node_memory_overhead_gb def get_num_workers(self) -> int: return 1 diff --git a/src/sparkctl/postgres/setup_metastore.sh b/src/sparkctl/postgres/setup_metastore.sh index 9e7bc00..dba23b9 100644 --- a/src/sparkctl/postgres/setup_metastore.sh +++ b/src/sparkctl/postgres/setup_metastore.sh @@ -1,6 +1,7 @@ #!/bin/bash pg_exists=$1 -pg_password=$2 +# The password is passed through the environment so that it does not appear in process listings. +pg_password=${SPARKCTL_PG_PASSWORD} module load apptainer if [ "${pg_exists}" != "true" ]; then diff --git a/src/sparkctl/postgres/start_container.sh b/src/sparkctl/postgres/start_container.sh index 8853e10..6c42aaf 100644 --- a/src/sparkctl/postgres/start_container.sh +++ b/src/sparkctl/postgres/start_container.sh @@ -1,7 +1,8 @@ #!/bin/bash pg_data_dir=$1 pg_run_dir=$2 -pg_password=$3 +# The password is passed through the environment so that it does not appear in process listings. +pg_password=${SPARKCTL_PG_PASSWORD} # TODO: Make these configurable. lustre_bind_mounts=" -B /nopt:/nopt \ diff --git a/src/sparkctl/slurm_compute.py b/src/sparkctl/slurm_compute.py index d80c1cd..ffda70d 100644 --- a/src/sparkctl/slurm_compute.py +++ b/src/sparkctl/slurm_compute.py @@ -3,21 +3,27 @@ import subprocess from pathlib import Path from socket import gethostname -from typing import Any from sparkctl.compute_interface import ComputeInterface +from sparkctl.models import SparkConfig class SlurmCompute(ComputeInterface): """Provides interface to Slurm.""" + def __init__(self, config: SparkConfig) -> None: + super().__init__(config) + # The node list does not change during a job, so cache it to avoid repeatedly shelling + # out to squeue/scontrol (get_node_names is called several times per configure/stop). + self._node_names: list[str] | None = None + def get_node_memory_overhead_gb( self, driver_memory_gb: int, node_memory_overhead_gb: int ) -> int: if self.is_heterogeneous_slurm_job(): return node_memory_overhead_gb - return driver_memory_gb + self._config.runtime.node_memory_overhead_gb + return driver_memory_gb + node_memory_overhead_gb def get_num_workers(self) -> int: master_node = gethostname() @@ -31,7 +37,9 @@ def get_num_workers(self) -> int: return num_workers def get_node_names(self) -> list[str]: - return get_node_names(os.environ["SLURM_JOB_ID"]) + if self._node_names is None: + self._node_names = get_node_names(os.environ["SLURM_JOB_ID"]) + return self._node_names def get_worker_node_names(self) -> list[str]: node_names = self.get_node_names() @@ -97,15 +105,12 @@ def run_checks(self) -> None: def get_node_names(job_id: str) -> list[str]: # The squeue command will produce multiple lines if the job is heterogeneous. - job_id = os.environ["SLURM_JOB_ID"] - output: dict[str, Any] = {} proc = subprocess.run( ["squeue", "-j", job_id, "--format", '"%5D %1000N"', "-h"], capture_output=True, check=True ) host_lists = [x.strip().split()[1] for x in proc.stdout.decode("utf-8").splitlines() if x] final: list[str] = [] for hosts in host_lists: - output.clear() proc = subprocess.run( ["scontrol", "show", "hostnames", hosts], capture_output=True, check=True ) diff --git a/src/sparkctl/spark_process_runner.py b/src/sparkctl/spark_process_runner.py index 1fb6d2c..9d061e8 100644 --- a/src/sparkctl/spark_process_runner.py +++ b/src/sparkctl/spark_process_runner.py @@ -5,6 +5,7 @@ import stat import subprocess import time +from concurrent.futures import ThreadPoolExecutor, as_completed from pathlib import Path from typing import Any @@ -34,7 +35,11 @@ def stop_master_process(self) -> int: def start_connect_server(self) -> None: """Start the Spark connect server.""" - cmd = f"{self._start_connect_server_cmd()} --master {self._url}" + port = self._config.runtime.connect_server_port + cmd = ( + f"{self._start_connect_server_cmd()} --master {self._url} " + f"--conf spark.connect.grpc.binding.port={port}" + ) self._check_run_command(cmd) def stop_connect_server(self) -> int: @@ -79,11 +84,15 @@ def start_worker_processes( start_script = self._sbin_cmd("start-worker.sh") tmp_script = self._make_start_worker_script(start_script, memory_gb) try: - for worker in workers: - cmd = ["ssh", worker, str(tmp_script)] - subprocess.run(cmd, check=True) + # Start the workers concurrently. ssh to each node is independent and otherwise + # serializes cluster startup across all worker nodes. + failures = self._run_ssh_commands(workers, str(tmp_script)) finally: tmp_script.unlink() + if failures: + nodes = ", ".join(f"{worker} (rc={rc})" for worker, rc in failures.items()) + msg = f"Failed to start Spark workers on the following node(s): {nodes}" + raise ExecutionError(msg) def stop_worker_process(self) -> int: """Stop the Spark workers.""" @@ -96,15 +105,34 @@ def stop_worker_processes(self, workers: list[str]) -> int: return self._stop_worker_processes_srun(workers) tmp_script = self._make_stop_worker_script(self._config.resource_monitor.enabled) - ret = 0 - for worker in workers: - cmd = ["ssh", worker, str(tmp_script)] - proc = subprocess.run(cmd) - if proc.returncode != 0: - logger.error("Failed to stop worker on {}: {}", worker, proc.returncode) - ret = proc.returncode - tmp_script.unlink() - return ret + try: + # Stop the workers concurrently; ssh to each node is independent. + failures = self._run_ssh_commands(workers, str(tmp_script)) + finally: + tmp_script.unlink() + for worker, rc in failures.items(): + logger.error("Failed to stop worker on {}: {}", worker, rc) + return next(iter(failures.values()), 0) + + @staticmethod + def _run_ssh_commands(workers: list[str], script: str) -> dict[str, int]: + """Run the script on each worker over ssh concurrently. + + Returns a mapping of worker node name to non-zero return code for any node that failed. + """ + + def run_one(worker: str) -> int: + return subprocess.run(["ssh", worker, script]).returncode + + failures: dict[str, int] = {} + with ThreadPoolExecutor(max_workers=max(1, len(workers))) as executor: + futures = {executor.submit(run_one, worker): worker for worker in workers} + for future in as_completed(futures): + worker = futures[future] + rc = future.result() + if rc != 0: + failures[worker] = rc + return failures def _use_srun(self) -> bool: if self._config.compute.environment != ComputeEnvironment.SLURM: diff --git a/tests/test_spark_process_runner.py b/tests/test_spark_process_runner.py index 85184fd..3e5df61 100644 --- a/tests/test_spark_process_runner.py +++ b/tests/test_spark_process_runner.py @@ -122,9 +122,10 @@ def fake_run(cmd, **kwargs): runner = SparkProcessRunner(config, SPARK_URL) runner.start_worker_processes(["node1", "node2"], 80) + # The workers are started concurrently, so the command order is not deterministic. assert len(commands) == 2 assert [cmd[0] for cmd in commands] == ["ssh", "ssh"] - assert [cmd[1] for cmd in commands] == ["node1", "node2"] + assert {cmd[1] for cmd in commands} == {"node1", "node2"} def test_start_worker_processes_slurm_use_srun_disabled_uses_ssh(tmp_path, monkeypatch): From da7cd71ac73ad62958df5dc9327b0f43b5c41cc8 Mon Sep 17 00:00:00 2001 From: Daniel Thom Date: Sat, 13 Jun 2026 08:56:55 -0600 Subject: [PATCH 02/24] Switch tooling to prek/ty/uv Replace mypy with ty and pre-commit with prek, and standardize on uv for environment management and CI, matching the datasight conventions. - pyproject: drop mypy/pre-commit dev deps; add prek, ty, and an explicit ruff pin. Replace [tool.mypy] with [tool.ty.src] (include src/tests, exclude vendored tests/data). - .pre-commit-config.yaml: bump ruff-pre-commit and run ty via a local hook (uv run ty check) instead of mypy. - CI: run lint (ruff check, ruff format --check) and ty through uv; install with uv sync --frozen. Convert the docs and PyPI publish workflows to uv. - config.py: annotate the RUNTIME/APP settings dicts as dict[str, Any] so the **dict model construction type-checks under ty, and drop the now-unused dynaconf type: ignore. - README: document the uv/ruff/ty/prek development workflow. - Commit uv.lock so uv sync --frozen works in CI. Co-Authored-By: Claude Fable 5 --- .github/workflows/ci.yml | 74 +- .github/workflows/gh-pages.yml | 15 +- .github/workflows/publish_to_pypi.yml | 14 +- .pre-commit-config.yaml | 29 +- README.md | 26 +- pyproject.toml | 17 +- src/sparkctl/config.py | 7 +- uv.lock | 1534 +++++++++++++++++++++++++ 8 files changed, 1629 insertions(+), 87 deletions(-) create mode 100644 uv.lock diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index a99fdf3..23727f7 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -11,52 +11,46 @@ env: DEFAULT_OS: ubuntu-latest jobs: + lint: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - name: Install uv + uses: astral-sh/setup-uv@v7 + with: + python-version: "3.12" + enable-cache: false + - name: Install Python project + run: uv sync --extra dev --frozen + - name: Run Ruff lint + run: uv run ruff check . + - name: Check Ruff formatting + run: uv run ruff format --check . + - name: Run ty + run: uv run ty check + pytest: runs-on: ${{ matrix.os }} strategy: matrix: python-version: ["3.12", "3.13"] os: [ubuntu-latest] - - steps: - - uses: actions/checkout@v4 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python-version }} - - name: Install dependencies - run: | - python -m pip install --upgrade pip - python -m pip install ".[dev]" - - name: Run pytest with coverage - run: | - pytest -v -m "not integration" --cov --cov-report=xml - - name: codecov - uses: codecov/codecov-action@v4.2.0 - if: ${{ matrix.os == env.DEFAULT_OS && matrix.python-version == env.DEFAULT_PYTHON }} - with: - token: ${{ secrets.CODECOV_TOKEN }} - name: sparkctl-tests - fail_ci_if_error: false - verbose: true - mypy: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: 3.12 - - name: Install dependencies - run: | - python -m pip install --upgrade pip - python -m pip install ".[dev]" - mypy - ruff: - runs-on: ubuntu-latest - name: "ruff" steps: - uses: actions/checkout@v4 - - uses: chartboost/ruff-action@v1 + - name: Install uv + uses: astral-sh/setup-uv@v7 + with: + python-version: ${{ matrix.python-version }} + enable-cache: false + - name: Install Python project + run: uv sync --extra dev --frozen + - name: Run pytest with coverage + run: uv run pytest -v -m "not integration" --cov --cov-report=xml + - name: codecov + uses: codecov/codecov-action@v4.2.0 + if: ${{ matrix.os == env.DEFAULT_OS && matrix.python-version == env.DEFAULT_PYTHON }} with: - src: "./src" + token: ${{ secrets.CODECOV_TOKEN }} + name: sparkctl-tests + fail_ci_if_error: false + verbose: true diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml index b4a5e10..b940b2b 100644 --- a/.github/workflows/gh-pages.yml +++ b/.github/workflows/gh-pages.yml @@ -9,19 +9,18 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - - name: select python version - uses: actions/setup-python@v5 + - name: Install uv + uses: astral-sh/setup-uv@v7 with: python-version: "3.12" - - name: install dependencies - run: | - python -m pip install --upgrade pip - python -m pip install ".[dev]" + enable-cache: false + - name: Install Python project + run: uv sync --extra dev --frozen - name: build documentation run: | cd docs - make clean - make html + uv run make clean + uv run make html - name: deploy uses: peaceiris/actions-gh-pages@v3.6.1 with: diff --git a/.github/workflows/publish_to_pypi.yml b/.github/workflows/publish_to_pypi.yml index c20fa5f..3b111a5 100644 --- a/.github/workflows/publish_to_pypi.yml +++ b/.github/workflows/publish_to_pypi.yml @@ -11,16 +11,12 @@ jobs: id-token: write steps: - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 + - name: Install uv + uses: astral-sh/setup-uv@v7 with: python-version: "3.12" - - name: Install dependencies - run: | - python -m pip install --upgrade pip - python -m pip install build - - name: Build and publish - run: | - python -m build + enable-cache: false + - name: Build + run: uv build - name: Publish package distributions to PyPI uses: pypa/gh-action-pypi-publish@release/v1 diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 025a30a..2b810ef 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,15 +1,16 @@ repos: -- repo: https://github.com/astral-sh/ruff-pre-commit - # Ruff version. - rev: v0.2.1 - hooks: - # Run the linter. - - id: ruff - args: [ --fix ] - # Run the formatter. - - id: ruff-format -- repo: https://github.com/pre-commit/mirrors-mypy - rev: v1.13.0 - hooks: - - id: mypy - language: system + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.15.8 + hooks: + - id: ruff + args: [--fix] + - id: ruff-format + + - repo: local + hooks: + - id: ty + name: ty (type check) + entry: uv run ty check + language: system + types: [python] + pass_filenames: false diff --git a/README.md b/README.md index 608d061..b187cc3 100644 --- a/README.md +++ b/README.md @@ -49,24 +49,40 @@ support. Contributions are welcome. ## Development -Install the package with its development dependencies: +This project uses [uv](https://docs.astral.sh/uv/) for environment management. Install the +package with its development dependencies: ```console -$ pip install -e ".[dev]" +$ uv sync --extra dev +``` + +Lint, format, and type-check the code with [ruff](https://docs.astral.sh/ruff/) and +[ty](https://github.com/astral-sh/ty): +```console +$ uv run ruff check . +$ uv run ruff format --check . +$ uv run ty check +``` + +These checks also run as Git hooks via [prek](https://github.com/j178/prek). Install the hooks +once and then run them on demand: +```console +$ uv run prek install +$ uv run prek run --all-files ``` Run the unit tests. These are fast, require no special resources, and are what CI runs: ```console -$ pytest -m "not integration" +$ uv run pytest -m "not integration" ``` The integration tests download a real Spark and Java distribution into `tests/data/` and start a real single-node Spark cluster, so they are slower and require network access and sufficient memory. They are excluded from CI; run them locally with: ```console -$ pytest -m integration +$ uv run pytest -m integration ``` -Run the complete suite (unit and integration tests) with `pytest`. +Run the complete suite (unit and integration tests) with `uv run pytest`. ## License sparkctl is released under a BSD 3-Clause [license](https://github.com/NatLabRockies/sparkctl/blob/main/LICENSE). diff --git a/pyproject.toml b/pyproject.toml index 9b6195a..a7d2db5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -41,18 +41,19 @@ pyspark = [ ] dev = [ "furo", - "mypy >=1.13, < 2", "myst_parser", - "pre-commit", + "prek >= 0.2, < 1", "pytest", "pytest-cov", "requests", + "ruff >= 0.15, < 1", "sphinx", "sphinx-click", "sphinx-copybutton", "sphinxcontrib-mermaid", "autodoc_pydantic~=2.0", "sphinx-tabs~=3.4", + "ty >= 0.0.26", "types-requests", "types-toml", ] @@ -71,15 +72,15 @@ where = ["src"] [tool.setuptools.package-data] "*" = ["*.sh", "*.conf", "*.properties", "*.template"] -[tool.mypy] -check_untyped_defs = true +[tool.ty.src] +include = [ + "src", + "tests", +] +# Exclude vendored third-party code (Spark/Hadoop/Hive/JDK) downloaded by the integration tests. exclude = [ "tests/data", ] -files = [ - "src", - "tests", -] [tool.pytest.ini_options] pythonpath = "src" diff --git a/src/sparkctl/config.py b/src/sparkctl/config.py index 8b86c2d..0e25b60 100644 --- a/src/sparkctl/config.py +++ b/src/sparkctl/config.py @@ -1,7 +1,8 @@ import os from pathlib import Path +from typing import Any -from dynaconf import Dynaconf, Validator # type: ignore +from dynaconf import Dynaconf, Validator from rich import print from sparkctl.exceptions import InvalidConfiguration @@ -39,7 +40,7 @@ def _build_settings_files() -> list[Path]: return files -RUNTIME = { +RUNTIME: dict[str, Any] = { "executor_cores": SparkRuntimeParams.model_fields["executor_cores"].default, "executor_memory_gb": SparkRuntimeParams.model_fields["executor_memory_gb"].default, "driver_memory_gb": 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Co-Authored-By: Claude Fable 5 --- docs/how_tos/getting_started/installation.md | 98 ++++++++++++++++---- 1 file changed, 81 insertions(+), 17 deletions(-) diff --git a/docs/how_tos/getting_started/installation.md b/docs/how_tos/getting_started/installation.md index 0f1d282..8d1947e 100644 --- a/docs/how_tos/getting_started/installation.md +++ b/docs/how_tos/getting_started/installation.md @@ -1,14 +1,30 @@ # Installation -1. Create a virtual environment with Python 3.11 or later. This example uses the `venv` module in - the standard library to create a virtual environment in your home directory. - - You may prefer `conda` or `mamba`. +1. Create a virtual environment with Python 3.11 or later. These examples create a virtual + environment in your home directory. If you are running on an HPC, you may need to `module load python` first. - ```console - $ python -m venv ~/python-envs/sparkctl + ```{eval-rst} + .. tabs:: + + .. tab:: venv + + This uses the ``venv`` module in the standard library. You may prefer ``conda`` or + ``mamba``. + + .. code-block:: console + + $ python -m venv ~/python-envs/sparkctl + + .. tab:: uv + + `uv `_ creates the environment quickly and can install the + requested Python version for you. + + .. code-block:: console + + $ uv venv --python 3.11 ~/python-envs/sparkctl ``` 2. Activate the virtual environment. @@ -22,26 +38,74 @@ 3. Install the Python package `sparkctl`. If you will be using Spark Connect to run Spark jobs, the base installation is sufficient. - + ```{eval-rst} .. note:: This does not include `spark-submit` or `pyspark`. ``` - ```console - $ pip install sparkctl + ```{eval-rst} + .. tabs:: + + .. tab:: pip + + .. code-block:: console + + $ pip install sparkctl + + .. tab:: uv + + .. code-block:: console + + $ uv pip install sparkctl ``` - + If you will be running Spark jobs with `spark-submit` or `pyspark`, you will need to install - the full `pyspark` package. This command will do that: + the full `pyspark` package: - ```console - $ pip install sparkctl[pyspark] + ```{eval-rst} + .. tabs:: + + .. tab:: pip + + .. code-block:: console + + $ pip install "sparkctl[pyspark]" + + .. tab:: uv + + .. code-block:: console + + $ uv pip install "sparkctl[pyspark]" + ``` + + ```{eval-rst} + .. tip:: + + If you only need the ``sparkctl`` command-line tool (and not the Python API), you can install + it as a standalone, isolated tool with uv. This does not require creating or activating a + virtual environment: + + .. code-block:: console + + $ uv tool install sparkctl ``` - + 4. Optional, install from the main branch (or substitute another branch or tag). - ```console - $ pip install git+https://github.com/NatLabRockies/sparkctl.git@main + ```{eval-rst} + .. tabs:: + + .. tab:: pip + + .. code-block:: console + + $ pip install git+https://github.com/NatLabRockies/sparkctl.git@main + + .. tab:: uv + + .. code-block:: console + + $ uv pip install git+https://github.com/NatLabRockies/sparkctl.git@main ``` 5. Create a one-time sparkctl default configuration file. The parameters will vary based on your @@ -58,6 +122,6 @@ Wrote sparkctl settings to /Users/dthom/.sparkctl.toml ``` Refer to `sparkctl default-config --help` for additional options. - + The paths to the Spark binaries will likely not change often. This file will also seed the default values for your `sparkctl configure` commands, and so you may want to manually edit those settings. From 5fd0a78d223ae2ea5f5ae3fc6835416fca814366 Mon Sep 17 00:00:00 2001 From: Daniel Thom Date: Sat, 13 Jun 2026 09:12:25 -0600 Subject: [PATCH 04/24] docs: fix install, correctness, and debuggability issues Installation: - Fix the environment-activation command in every tutorial and how-to: `source ~/python-envs/sparkctl` -> `source ~/python-envs/sparkctl/bin/activate`. - Correct the `~/.sparkctl.toml` keys in the explanation page: `spark_path`/ `java_path` (not `spark_home`/`java_home`) and `start_connect_server` under a `[runtime]` section (not `spark_connect_server`). - Finish the truncated "Python script" tutorial: close the code block and add the step to run the script. Correctness: - Hive warehouse directory is `spark-warehouse`, not `spark_warehouse`. - `--spark-log-level` only accepts debug/info/warn/error; note that trace/ fatal/off require editing log4j2.properties. Refresh the stale Spark version in the example output. - Fix the out-of-order list in the "Why is my job slow?" FAQ entry and the `spark-default.conf` -> `spark-defaults.conf` filename typo. Debuggability: - Executor logs live under `spark_scratch/workers/` (plural); fix the path and the `tail` command in the debugging page. - Replace the macOS-only `stat -f` invocation with GNU `stat -c`, which is what runs on the Linux compute nodes. - Fix the Spark Web UI SSH tunnel in the FAQ to target the compute node (`$COMPUTE_NODE`) instead of `$(hostname)`, and fix the `--constraint` typo. Co-Authored-By: Claude Fable 5 --- docs/explanation/index.md | 12 +++++++++--- docs/faq.md | 16 +++++++++------- docs/how_tos/applications/hive_metastore.md | 2 +- .../configuration/custom_spark_defaults.md | 4 ++-- docs/how_tos/configuration/spark_log_level.md | 13 ++++++++----- docs/how_tos/debugging/index.md | 6 +++--- docs/how_tos/execution/start_a_cluster.md | 2 +- docs/tutorials/run_ibis_spark_jobs.md | 6 +++--- .../run_python_spark_jobs_interactively.md | 2 +- docs/tutorials/run_python_spark_jobs_script.md | 15 +++++++++++---- .../run_python_spark_jobs_spark_connect.md | 2 +- docs/tutorials/run_spark_jobs.md | 2 +- 12 files changed, 50 insertions(+), 32 deletions(-) diff --git a/docs/explanation/index.md b/docs/explanation/index.md index 2a592e2..7d1504b 100644 --- a/docs/explanation/index.md +++ b/docs/explanation/index.md @@ -94,13 +94,19 @@ This TOML file stores environment-specific settings that rarely change: ```toml [binaries] -spark_home = "/path/to/spark" -java_home = "/path/to/java" +spark_path = "/path/to/spark" +java_path = "/path/to/java" ``` These settings tell sparkctl where to find Spark and Java. You can also set global settings that apply every time you run `sparkctl configure`. For example, if you always want to use Spark Connect, -you can set `spark_connect_server = true` and avoid having to set it each time you configure. +you can set `start_connect_server = true` in a `[runtime]` section and avoid having to set it each +time you configure: + +```toml +[runtime] +start_connect_server = true +``` ### Runtime Configuration (`./conf/`) diff --git a/docs/faq.md b/docs/faq.md index a890250..5497f83 100644 --- a/docs/faq.md +++ b/docs/faq.md @@ -77,17 +77,19 @@ incompatible with cluster version 4.1.2. Common causes: 1. **High-bandwidth nodes**: Some NLR Kestrel compute nodes have two network cards, which Spark - cannot deal with. Set `--constaint lbw` when allocating nodes. + cannot deal with. Set `--constraint lbw` when allocating nodes. Check the Spark master logs in `./spark_scratch/logs/` for connection errors. ### How do I connect to the Spark Web UI? -The Spark master runs a web UI on port 4040 (driver) or 8080 (master). Since HPC compute nodes -aren't directly accessible, use SSH tunneling: +Spark runs a web UI on port 8080 (master) and port 4040 (driver/application). Since HPC compute +nodes aren't directly accessible, use SSH tunneling. Substitute the name of your compute node +(it is listed in `./conf/workers`) for `$COMPUTE_NODE`: ```console -$ ssh -L 8080:$(hostname):8080 user@hpc-login-node +$ export COMPUTE_NODE= +$ ssh -L 8080:$COMPUTE_NODE:8080 -L 4040:$COMPUTE_NODE:4040 user@hpc-login-node ``` Then open `http://localhost:8080` or `http://localhost:4040` in your browser. @@ -104,13 +106,13 @@ Common causes: 3. **Too few partitions**: Increase `spark.sql.shuffle.partitions`. 4. **Too many partitions**: Decrease partitions if you have many small tasks. 5. **Slow storage**: Ensure shuffle storage uses fast local SSDs, not shared filesystem. +6. **Non-ideal partitioning**: If you are trying to partition-by-column in the same query as your + main work, especially where you significantly increased the shuffle partitions, persist your + main work first. Then repartition in a second task. 7. **Query too complex**: If you are trying to run a very complex query where subtasks have different data sizes and partitioning needs, consider breaking the query into smaller parts with different settings. Persist intermediate results to the filesystem so that you can checkpoint and make incremental progress. -6. **Non-ideal partitioning**: If you are trying to partition-by-column in the same query as your - main work, especially where you significantly increased the shuffle partitions, persist your - main work first. Then repartition in a second task. See the {ref}`how-tos-debugging` for performance troubleshooting. diff --git a/docs/how_tos/applications/hive_metastore.md b/docs/how_tos/applications/hive_metastore.md index eb88227..c751713 100644 --- a/docs/how_tos/applications/hive_metastore.md +++ b/docs/how_tos/applications/hive_metastore.md @@ -26,6 +26,6 @@ start the server. Apptainer will cache the container image and you can reuse the across Slurm allocations. **Note**: The metadata about your tables will be stored in Derby or Postgres. Your tables will -be stored on the filesystem (Parquet files by default) in a directory called `spark_warehouse`, +be stored on the filesystem (Parquet files by default) in a directory called `spark-warehouse`, which gets created in the directory passed to `--metastore-dir` (current directory by default). Postgres data, if enabled, will be in the same directory (`pg_data`). diff --git a/docs/how_tos/configuration/custom_spark_defaults.md b/docs/how_tos/configuration/custom_spark_defaults.md index 7b41ecd..9a6263b 100644 --- a/docs/how_tos/configuration/custom_spark_defaults.md +++ b/docs/how_tos/configuration/custom_spark_defaults.md @@ -1,4 +1,4 @@ -# How to use a custom spark-default.conf file +# How to use a custom spark-defaults.conf file sparkctl sets custom settings in the `spark-defaults.conf` file. For example, it sets ``` @@ -24,5 +24,5 @@ If you don't want these settings or want to add your own settings every time you ```{eval-rst} .. note:: sparkctl will still append Spark driver, executor, and other settings to the runtime - version of the spark-default.conf file. + version of the spark-defaults.conf file. ``` diff --git a/docs/how_tos/configuration/spark_log_level.md b/docs/how_tos/configuration/spark_log_level.md index 4cb0f38..6fbf449 100644 --- a/docs/how_tos/configuration/spark_log_level.md +++ b/docs/how_tos/configuration/spark_log_level.md @@ -14,17 +14,20 @@ This sets the root logger to `WARN`, so you'll only see warnings and errors. ## Available Log Levels -Spark uses Log4j 2 with these levels (from most to least verbose): +The `--spark-log-level` option accepts these values: | Level | Description | Use Case | |-------|-------------|----------| -| `trace` | Fine-grained debugging | Deep Spark internals debugging | | `debug` | Detailed debugging | Troubleshooting specific issues | | `info` | General information (default) | Development, understanding job progress | | `warn` | Warnings only | Production jobs, cleaner output | | `error` | Errors only | When you only care about failures | -| `fatal` | Critical failures only | Minimal output | -| `off` | No logging | Not recommended | + +```{eval-rst} +.. note:: Log4j 2 also defines ``trace``, ``fatal``, and ``off`` levels, but ``--spark-log-level`` + does not accept them. To use one of those, edit ``log4j2.properties`` in ``./conf/`` after + running ``sparkctl configure``, as described in the Per-Logger Configuration section below. +``` ## Recommendations @@ -36,7 +39,7 @@ Spark uses Log4j 2 with these levels (from most to least verbose): With `INFO` (default), you'll see messages like: ``` -INFO SparkContext: Running Spark version 3.5.1 +INFO SparkContext: Running Spark version 4.1.2 INFO ResourceUtils: Resources for spark.driver: ... INFO SparkContext: Submitted application: My Job INFO Executor: Starting executor ID driver on host ... diff --git a/docs/how_tos/debugging/index.md b/docs/how_tos/debugging/index.md index ac04ede..ec5274f 100644 --- a/docs/how_tos/debugging/index.md +++ b/docs/how_tos/debugging/index.md @@ -18,7 +18,7 @@ Open your browser to http://localhost:4040 after configuring the tunnel to acces ## Log files sparkctl configures Spark to record log files in the base directory. Spark master, worker, connect server, etc, will be in `./spark_scratch/logs`. Executor logs will be in -`./spark_scratch/worker/app-*/*/stderr` +`./spark_scratch/workers/app-*/*/stderr` For example, ```console @@ -44,12 +44,12 @@ queries are often visible in the executor `stderr` files. For example, if a job you can tail the `stderr` files to see what is happening: ```console -$ tail -f spark_scratch/worker/*/*/stderr +$ tail -f spark_scratch/workers/*/*/stderr ``` If you have many executors, you may want to tail only the most recent ones. Identify them with ```console -$ find spark_scratch -type f -name stderr -exec stat -f '%m %Sm %N' {} + 2>/dev/null | sort -n +$ find spark_scratch -type f -name stderr -exec stat -c '%Y %y %n' {} + 2>/dev/null | sort -n ``` ## Spark shuffle partitions diff --git a/docs/how_tos/execution/start_a_cluster.md b/docs/how_tos/execution/start_a_cluster.md index f92ed18..97eec8c 100644 --- a/docs/how_tos/execution/start_a_cluster.md +++ b/docs/how_tos/execution/start_a_cluster.md @@ -6,7 +6,7 @@ This page assumes that you have allocated compute nodes via Slurm. ```console $ module load python - $ source ~/python-envs/sparkctl + $ source ~/python-envs/sparkctl/bin/activate ``` 2. Configure and start the Spark cluster. The sparkctl code will detect the compute nodes based on diff --git a/docs/tutorials/run_ibis_spark_jobs.md b/docs/tutorials/run_ibis_spark_jobs.md index 53d1efd..ab5c5b3 100644 --- a/docs/tutorials/run_ibis_spark_jobs.md +++ b/docs/tutorials/run_ibis_spark_jobs.md @@ -39,7 +39,7 @@ This approach uses Spark Connect under the hood. ```console $ module load python - $ source ~/python-envs/sparkctl + $ source ~/python-envs/sparkctl/bin/activate ``` 3. Add the code below to a Python script. This will configure and start the Spark cluster, create @@ -93,7 +93,7 @@ multiple scripts or interactive sessions against it. ```console $ module load python - $ source ~/python-envs/sparkctl + $ source ~/python-envs/sparkctl/bin/activate ``` 3. Configure the Spark cluster with the Connect Server enabled. The sparkctl code will detect @@ -158,7 +158,7 @@ issues with Spark Connect. ```console $ module load python - $ source ~/python-envs/sparkctl + $ source ~/python-envs/sparkctl/bin/activate ``` 3. Configure the Spark cluster without the Connect Server. diff --git a/docs/tutorials/run_python_spark_jobs_interactively.md b/docs/tutorials/run_python_spark_jobs_interactively.md index 528b619..fc379e6 100644 --- a/docs/tutorials/run_python_spark_jobs_interactively.md +++ b/docs/tutorials/run_python_spark_jobs_interactively.md @@ -18,7 +18,7 @@ environment variables for you. ```console $ module load python - $ source ~/python-envs/sparkctl + $ source ~/python-envs/sparkctl/bin/activate ``` 3. Configure and start the Spark cluster. diff --git a/docs/tutorials/run_python_spark_jobs_script.md b/docs/tutorials/run_python_spark_jobs_script.md index 87850ec..377964b 100644 --- a/docs/tutorials/run_python_spark_jobs_script.md +++ b/docs/tutorials/run_python_spark_jobs_script.md @@ -18,15 +18,15 @@ Python library to hide the details of starting the cluster and setting environme ```console $ module load python - $ source ~/python-envs/sparkctl + $ source ~/python-envs/sparkctl/bin/activate ``` -3. Add the code below to a Python script. This code block will configure and start the Spark - cluster, run your Spark job, and then stop the cluster. +3. Add the code below to a Python script named `my_job.py`. This code block will configure and + start the Spark cluster, run your Spark job, and then stop the cluster. ```python from sparkctl import ClusterManager, make_default_spark_config - + # This loads your global sparkctl configuration file (~/.sparkctl.toml). config = make_default_spark_config() # Set runtime options as desired. @@ -36,3 +36,10 @@ Python library to hide the details of starting the cluster and setting environme with mgr.managed_cluster() as spark: df = spark.createDataFrame([(x, x + 1) for x in range(1000)], ["a", "b"]) df.show() + ``` + +4. Run the script. + + ```console + $ python my_job.py + ``` diff --git a/docs/tutorials/run_python_spark_jobs_spark_connect.md b/docs/tutorials/run_python_spark_jobs_spark_connect.md index aaba293..826b67e 100644 --- a/docs/tutorials/run_python_spark_jobs_spark_connect.md +++ b/docs/tutorials/run_python_spark_jobs_spark_connect.md @@ -16,7 +16,7 @@ Connect Server. ```console $ module load python - $ source ~/python-envs/sparkctl + $ source ~/python-envs/sparkctl/bin/activate ``` 3. Configure the Spark cluster. The sparkctl code will detect the compute nodes based on diff --git a/docs/tutorials/run_spark_jobs.md b/docs/tutorials/run_spark_jobs.md index 27088d1..c87635b 100644 --- a/docs/tutorials/run_spark_jobs.md +++ b/docs/tutorials/run_spark_jobs.md @@ -26,7 +26,7 @@ the CLI help, e.g. `spark-submit --help`, for details on how to set these option ```console $ module load python - $ source ~/python-envs/sparkctl + $ source ~/python-envs/sparkctl/bin/activate ``` 4. Configure the Spark cluster. The sparkctl code will detect the compute nodes based on From b94e20e4c705125ff077f81cf34b16f3b0ba5fb0 Mon Sep 17 00:00:00 2001 From: Daniel Thom Date: Sat, 13 Jun 2026 09:40:08 -0600 Subject: [PATCH 05/24] Add reverse proxy, Prometheus, JupyterLab, and experimental GPU features Deploy four additional Spark features through sparkctl: - Web UI reverse proxy (--reverse-proxy): run the master as a reverse proxy for worker/application UIs, so the UIs are reachable through the master node on HPC clusters where compute nodes are not. - Prometheus metrics (--prometheus): register Spark's PrometheusServlet via metrics.properties on the existing web UI ports. - JupyterLab service (--jupyter): start/stop a notebook server on the master node, pre-wired to the Connect server via SPARK_REMOTE. - GPU scheduling and NVIDIA RAPIDS (--gpus/--rapids), marked EXPERIMENTAL and untested: GPU detection in the compute interface, a generated GPU discovery script, and RAPIDS plugin wiring via spark.jars. Co-Authored-By: Claude Opus 4.8 (1M context) --- docs/how_tos/applications/index.md | 1 + docs/how_tos/applications/jupyter.md | 59 ++++++ docs/how_tos/configuration/gpus.md | 72 +++++++ docs/how_tos/configuration/index.md | 2 + .../configuration/web_ui_reverse_proxy.md | 43 ++++ docs/how_tos/execution/index.md | 1 + docs/how_tos/execution/prometheus_metrics.md | 36 ++++ src/sparkctl/cli/sparkctl.py | 98 ++++++++++ src/sparkctl/cluster_manager.py | 183 ++++++++++++++++-- src/sparkctl/compute_interface.py | 4 + src/sparkctl/fake_compute.py | 3 + src/sparkctl/models.py | 72 ++++++- src/sparkctl/native_compute.py | 19 ++ src/sparkctl/slurm_compute.py | 38 ++++ src/sparkctl/spark_process_runner.py | 78 ++++++++ tests/test_cluster_manager.py | 53 +++++ tests/test_sparkctl_cli.py | 27 +++ 17 files changed, 772 insertions(+), 17 deletions(-) create mode 100644 docs/how_tos/applications/jupyter.md create mode 100644 docs/how_tos/configuration/gpus.md create mode 100644 docs/how_tos/configuration/web_ui_reverse_proxy.md create mode 100644 docs/how_tos/execution/prometheus_metrics.md diff --git a/docs/how_tos/applications/index.md b/docs/how_tos/applications/index.md index 65c8976..aceab48 100644 --- a/docs/how_tos/applications/index.md +++ b/docs/how_tos/applications/index.md @@ -7,4 +7,5 @@ hive_metastore tableau + jupyter ``` diff --git a/docs/how_tos/applications/jupyter.md b/docs/how_tos/applications/jupyter.md new file mode 100644 index 0000000..c627235 --- /dev/null +++ b/docs/how_tos/applications/jupyter.md @@ -0,0 +1,59 @@ +# How to run a JupyterLab notebook against the cluster + +sparkctl can start a JupyterLab server on the master node so you can run interactive notebooks +against the Spark cluster. When the Spark Connect server is enabled, the notebook's `SparkSession` +connects to the cluster automatically. + +## Prerequisites + +Install JupyterLab in the same environment as sparkctl: + +```console +$ pip install jupyterlab # or: uv pip install jupyterlab +``` + +## Start JupyterLab with the Connect server + +The recommended setup enables the Spark Connect server so the notebook connects remotely without +any extra configuration: + +```console +$ sparkctl configure --connect-server --jupyter --start +``` + +sparkctl sets `SPARK_REMOTE` for the JupyterLab process, so inside a notebook you can simply do: + +```python +from pyspark.sql import SparkSession + +spark = SparkSession.builder.getOrCreate() +spark.createDataFrame([(1, 2), (3, 4)], ["a", "b"]).show() +``` + +## Find the access URL + +The JupyterLab access URL, including its login token, is written to `jupyter.log` in the cluster +base directory: + +```console +$ grep -m1 'http' jupyter.log +``` + +JupyterLab listens on port 8889 by default. Change it with `--jupyter-port`, and forward it to your +laptop over SSH (replacing `master-node` with the node running the server): + +```console +$ ssh -L 8889:master-node:8889 +``` + +Then open the URL from `jupyter.log`, replacing the host with `localhost`. + +## Stopping + +`sparkctl stop` shuts the JupyterLab server down along with the rest of the cluster. + +```{eval-rst} +.. note:: If you enable ``--jupyter`` without ``--connect-server``, JupyterLab still starts, but + the notebook is responsible for creating its own ``SparkSession`` (for example, a local driver + that connects to ``spark://:7077``). The Connect server path is recommended. +``` diff --git a/docs/how_tos/configuration/gpus.md b/docs/how_tos/configuration/gpus.md new file mode 100644 index 0000000..133493c --- /dev/null +++ b/docs/how_tos/configuration/gpus.md @@ -0,0 +1,72 @@ +# How to enable GPU acceleration (experimental) + +```{eval-rst} +.. warning:: GPU support is **experimental and untested**. The options below configure Spark's + GPU-aware scheduling and, optionally, the NVIDIA RAPIDS Accelerator, but they have not been + validated on a real GPU cluster. Treat the generated settings as a starting point and expect to + tune them for your site. +``` + +## GPU-aware scheduling + +Enable GPU scheduling so Spark workers advertise their GPUs and executors/tasks request them: + +```console +$ sparkctl configure --gpus +``` + +sparkctl detects the number of GPUs per node from the compute environment (Slurm GPU variables such +as `SLURM_GPUS_ON_NODE`, or `nvidia-smi` in a native environment). Override the count when detection +is unavailable or incorrect: + +```console +$ sparkctl configure --gpus --gpus-per-node 4 +``` + +This generates a GPU discovery script in the cluster's `conf` directory and writes these settings to +`spark-defaults.conf`: + +- `spark.worker.resource.gpu.amount` and `spark.worker.resource.gpu.discoveryScript` +- `spark.executor.resource.gpu.amount` and `spark.executor.resource.gpu.discoveryScript` +- `spark.task.resource.gpu.amount` + +By default each executor is assigned one GPU and tasks share that GPU +(`task.resource.gpu.amount = executor_gpu_amount / executor_cores`). Tune these through your +settings file: + +```toml +[runtime] +enable_gpus = true +gpus_per_node = 4 +executor_gpu_amount = 1 +task_gpu_amount = 0.25 +``` + +## RAPIDS Accelerator + +The [NVIDIA RAPIDS Accelerator for Apache Spark](https://nvidia.github.io/spark-rapids/) offloads +SQL and DataFrame operations to GPUs. + +1. Download the RAPIDS Accelerator jar from the + [RAPIDS download page](https://nvidia.github.io/spark-rapids/docs/download.html). + +2. Record its path when creating your settings file: + + ```console + $ sparkctl default-config --rapids-jar-file /path/to/rapids-4-spark_2.13-.jar \ + /path/to/spark /path/to/java + ``` + +3. Enable RAPIDS (this implies `--gpus`): + + ```console + $ sparkctl configure --rapids + ``` + +This adds the plugin jar with `spark.jars`, sets `spark.plugins com.nvidia.spark.SQLPlugin`, and +enables `spark.rapids.sql.enabled`. + +```{eval-rst} +.. note:: ``spark.jars`` is used instead of ``spark.{driver,executor}.extraClassPath`` so the + RAPIDS jar does not conflict with the classpath entries the PostgreSQL Hive metastore sets. +``` diff --git a/docs/how_tos/configuration/index.md b/docs/how_tos/configuration/index.md index 74159a5..21c34c7 100644 --- a/docs/how_tos/configuration/index.md +++ b/docs/how_tos/configuration/index.md @@ -9,5 +9,7 @@ heterogeneous_slurm_jobs compute_node_failures custom_spark_defaults + web_ui_reverse_proxy + gpus spark_log_level ``` diff --git a/docs/how_tos/configuration/web_ui_reverse_proxy.md b/docs/how_tos/configuration/web_ui_reverse_proxy.md new file mode 100644 index 0000000..d16bf7e --- /dev/null +++ b/docs/how_tos/configuration/web_ui_reverse_proxy.md @@ -0,0 +1,43 @@ +# How to access the Spark web UIs through a reverse proxy + +On an HPC cluster the compute nodes that run the Spark workers and your application driver are +usually not directly reachable from your laptop. That makes the worker and application web UIs, +whose links point at those nodes, hard to open. + +Enabling the reverse proxy tells the Spark master to proxy the worker and application UIs through +itself, so you only need to reach the master node (for example, through a single SSH tunnel). + +## Enable the reverse proxy + +```console +$ sparkctl configure --reverse-proxy +``` + +This sets `spark.ui.reverseProxy true`. Open the master web UI (default port 8080) and follow the +links to the worker and application UIs; they are served through the master. + +## Reach the master from your laptop + +Forward the master web UI port over SSH, replacing `master-node` with the node running the master +(typically your submission node): + +```console +$ ssh -L 8080:master-node:8080 +``` + +Then browse to `http://localhost:8080`. + +## When the master is itself behind another proxy + +If you put your own front-end proxy in front of the master, give Spark the externally visible URL +so it can rewrite links correctly: + +```console +$ sparkctl configure --reverse-proxy --reverse-proxy-url https://my-proxy.example.com/spark +``` + +```{eval-rst} +.. note:: Leave ``--reverse-proxy-url`` unset when you reach the master directly through an SSH + tunnel. Spark then serves relative links, which work through the tunnel without extra + configuration. +``` diff --git a/docs/how_tos/execution/index.md b/docs/how_tos/execution/index.md index 7aad30c..ab4f071 100644 --- a/docs/how_tos/execution/index.md +++ b/docs/how_tos/execution/index.md @@ -8,4 +8,5 @@ start_a_cluster run_jobs resource_monitoring + prometheus_metrics ``` diff --git a/docs/how_tos/execution/prometheus_metrics.md b/docs/how_tos/execution/prometheus_metrics.md new file mode 100644 index 0000000..769a965 --- /dev/null +++ b/docs/how_tos/execution/prometheus_metrics.md @@ -0,0 +1,36 @@ +# How to expose Spark metrics in Prometheus format + +sparkctl can configure Spark's built-in `PrometheusServlet` so that Spark's internal metrics +(JVM, scheduler, shuffle, executor, and task metrics) are exposed in Prometheus format. This +complements [resource monitoring](resource_monitoring.md), which captures host-level CPU, memory, +disk, and network utilization. + +The servlet reuses the existing web UI ports, so no additional ports are opened. + +## Enable Prometheus metrics + +```console +$ sparkctl configure --prometheus +``` + +This writes a `metrics.properties` file into the cluster's `conf` directory (Spark loads it +automatically) and sets `spark.ui.prometheus.enabled true`. + +## Scrape endpoints + +| Component | Endpoint | +| --------- | -------- | +| Master | `http://:8080/metrics/master/prometheus` | +| Worker | `http://:8081/metrics/prometheus` | +| Driver / application | `http://:4040/metrics/executors/prometheus` | + +Point a Prometheus scraper at these endpoints, or fetch them directly with `curl` for a quick look: + +```console +$ curl http://localhost:4040/metrics/executors/prometheus +``` + +```{eval-rst} +.. tip:: Combine this with the :doc:`reverse proxy <../configuration/web_ui_reverse_proxy>` to + reach the worker and application endpoints through the master node on an HPC cluster. +``` diff --git a/src/sparkctl/cli/sparkctl.py b/src/sparkctl/cli/sparkctl.py index 6568c27..2ba6637 100644 --- a/src/sparkctl/cli/sparkctl.py +++ b/src/sparkctl/cli/sparkctl.py @@ -104,6 +104,12 @@ def cli(ctx: click.Context, console_level: str, file_level: str, reraise_excepti help="Path to PostgreSQL jar file.", type=click.Path(path_type=Path), ) +@click.option( + "-R", + "--rapids-jar-file", + help=BinaryLocations.model_fields["rapids_jar_file"].description, + type=click.Path(path_type=Path), +) def default_config( spark_path: Path, java_path: Path, @@ -112,6 +118,7 @@ def default_config( hadoop_path: Path | None, hive_tarball: Path | None, postgresql_jar_file: Path | None, + rapids_jar_file: Path | None, ): """Create a sparkctl config file that defines paths to Spark binaries. This is a one-time requirement when installing sparkctl in a new environment.""" @@ -122,6 +129,8 @@ def default_config( config.binaries.hive_tarball = hive_tarball if postgresql_jar_file is not None: config.binaries.postgresql_jar_file = postgresql_jar_file + if rapids_jar_file is not None: + config.binaries.rapids_jar_file = rapids_jar_file data = config.model_dump(mode="json", exclude={"directories"}) # Don't hard-code the password globally. data["runtime"].pop("postgres_password") @@ -269,6 +278,79 @@ def _create_default_config( show_default=True, help=SparkRuntimeParams.model_fields["start_thrift_server"].description, ) +@click.option( + "--jupyter/--no-jupyter", + is_flag=True, + # Fall back to the model default so options added after a user's settings file was written + # still work (a missing key would otherwise bind the default to None). + default=sparkctl_settings.runtime.get( + "start_jupyter", SparkRuntimeParams.model_fields["start_jupyter"].default + ), + show_default=True, + help=SparkRuntimeParams.model_fields["start_jupyter"].description, +) +@click.option( + "--jupyter-port", + default=sparkctl_settings.runtime.get( + "jupyter_port", SparkRuntimeParams.model_fields["jupyter_port"].default + ), + show_default=True, + type=int, + help=SparkRuntimeParams.model_fields["jupyter_port"].description, +) +@click.option( + "--reverse-proxy/--no-reverse-proxy", + is_flag=True, + default=sparkctl_settings.runtime.get( + "enable_reverse_proxy", SparkRuntimeParams.model_fields["enable_reverse_proxy"].default + ), + show_default=True, + help=SparkRuntimeParams.model_fields["enable_reverse_proxy"].description, +) +@click.option( + "--reverse-proxy-url", + default=sparkctl_settings.runtime.get( + "reverse_proxy_url", SparkRuntimeParams.model_fields["reverse_proxy_url"].default + ), + show_default=True, + help=SparkRuntimeParams.model_fields["reverse_proxy_url"].description, +) +@click.option( + "--prometheus/--no-prometheus", + is_flag=True, + default=sparkctl_settings.runtime.get( + "enable_prometheus", SparkRuntimeParams.model_fields["enable_prometheus"].default + ), + show_default=True, + help=SparkRuntimeParams.model_fields["enable_prometheus"].description, +) +@click.option( + "--gpus/--no-gpus", + is_flag=True, + default=sparkctl_settings.runtime.get( + "enable_gpus", SparkRuntimeParams.model_fields["enable_gpus"].default + ), + show_default=True, + help=SparkRuntimeParams.model_fields["enable_gpus"].description, +) +@click.option( + "--gpus-per-node", + default=sparkctl_settings.runtime.get( + "gpus_per_node", SparkRuntimeParams.model_fields["gpus_per_node"].default + ), + show_default=True, + type=int, + help=SparkRuntimeParams.model_fields["gpus_per_node"].description, +) +@click.option( + "--rapids/--no-rapids", + is_flag=True, + default=sparkctl_settings.runtime.get( + "enable_rapids", SparkRuntimeParams.model_fields["enable_rapids"].default + ), + show_default=True, + help=SparkRuntimeParams.model_fields["enable_rapids"].description, +) @click.option( "-l", "--spark-log-level", @@ -344,6 +426,14 @@ def configure( connect_server_port: int, history_server: bool, thrift_server: bool, + jupyter: bool, + jupyter_port: int, + reverse_proxy: bool, + reverse_proxy_url: str | None, + prometheus: bool, + gpus: bool, + gpus_per_node: int | None, + rapids: bool, spark_log_level: str | None, hive_metastore: bool, postgres_hive_metastore: bool, @@ -381,6 +471,14 @@ def build_config() -> SparkConfig: connect_server_port=connect_server_port, start_history_server=history_server, start_thrift_server=thrift_server, + start_jupyter=jupyter, + jupyter_port=jupyter_port, + enable_reverse_proxy=reverse_proxy, + reverse_proxy_url=reverse_proxy_url, + enable_prometheus=prometheus, + enable_gpus=gpus, + gpus_per_node=gpus_per_node, + enable_rapids=rapids, spark_log_level=spark_log_level, enable_hive_metastore=hive_metastore, enable_postgres_hive_metastore=postgres_hive_metastore, diff --git a/src/sparkctl/cluster_manager.py b/src/sparkctl/cluster_manager.py index 4daa92f..79ef0eb 100644 --- a/src/sparkctl/cluster_manager.py +++ b/src/sparkctl/cluster_manager.py @@ -2,6 +2,7 @@ import os import re import shutil +import stat import subprocess import sys from contextlib import contextmanager @@ -123,6 +124,8 @@ def clean(self) -> None: base / self.STATUS_FILENAME, base / "srun_workers.pid", base / "srun_workers.log", + base / "jupyter.pid", + base / "jupyter.log", ] for directory in directories: if directory.exists(): @@ -235,22 +238,7 @@ def start(self, print_env_paths: bool = True) -> None: self._start(runner, tracker) except Exception: logger.error("Stopping all processes after unhandled exception") - if tracker.started_master: - runner.stop_master_process() - if tracker.started_connect_server: - runner.stop_connect_server() - if tracker.started_history_server: - runner.stop_history_server() - if tracker.started_thrift_server: - runner.stop_thrift_server() - if tracker.started_workers: - workers = self._read_workers() - if len(workers) == 1: - runner.stop_worker_process() - else: - runner.stop_worker_processes(workers) - if tracker.started_postgres: - self._stop_postgres() + self._rollback_started_processes(runner, tracker) raise if print_env_paths: @@ -264,6 +252,29 @@ def start(self, print_env_paths: bool = True) -> None: os.environ["SPARK_CONF_DIR"] = str(self._config.directories.get_spark_conf_dir()) os.environ["JAVA_HOME"] = str(self._config.binaries.java_path) + def _rollback_started_processes( + self, runner: SparkProcessRunner, tracker: StatusTracker + ) -> None: + """Stop any processes that were started before an unhandled exception.""" + if tracker.started_master: + runner.stop_master_process() + if tracker.started_connect_server: + runner.stop_connect_server() + if tracker.started_history_server: + runner.stop_history_server() + if tracker.started_thrift_server: + runner.stop_thrift_server() + if tracker.started_jupyter: + runner.stop_jupyter_server() + if tracker.started_workers: + workers = self._read_workers() + if len(workers) == 1: + runner.stop_worker_process() + else: + runner.stop_worker_processes(workers) + if tracker.started_postgres: + self._stop_postgres() + @contextmanager def managed_cluster(self) -> Generator[SparkSession, None, None]: """Configure and start the Spark cluster, yield a SparkSession in a context manager, @@ -321,6 +332,11 @@ def _start(self, runner: SparkProcessRunner, tracker: StatusTracker) -> None: tracker.started_thrift_server = True logger.info("Started Apache Thrift Server") + if self._config.runtime.start_jupyter: + runner.start_jupyter_server() + tracker.started_jupyter = True + logger.info("Started JupyterLab server") + worker_memory_gb = self._get_worker_memory_gb(self._get_runtime_spark_driver_memory_gb()) if is_single_node_cluster: runner.start_worker_process(worker_memory_gb) @@ -362,6 +378,7 @@ def stop(self) -> None: started_connect_server=rt.start_connect_server, started_history_server=self._is_history_server_enabled(), started_thrift_server=rt.start_thrift_server, + started_jupyter=rt.start_jupyter, started_postgres=rt.enable_postgres_hive_metastore, ) url = make_spark_url(gethostname()) @@ -374,6 +391,8 @@ def stop(self) -> None: runner.stop_history_server() if tracker.started_thrift_server: runner.stop_thrift_server() + if tracker.started_jupyter: + runner.stop_jupyter_server() if tracker.started_workers: workers = self._intf.get_worker_node_names() is_single_node_cluster = self._is_single_node_cluster(workers) @@ -440,6 +459,18 @@ def _add_spark_settings_to_defaults_file(self, defaults_file: Path) -> None: if rt_params.start_history_server: self._enable_history_server(defaults_file) + if rt_params.enable_reverse_proxy: + self._enable_reverse_proxy(defaults_file) + + if rt_params.enable_prometheus: + self._enable_prometheus(defaults_file) + + # RAPIDS requires GPU scheduling, so enabling it implies enabling GPUs. + if rt_params.enable_gpus or rt_params.enable_rapids: + self._enable_gpus(defaults_file) + if rt_params.enable_rapids: + self._enable_rapids(defaults_file) + if rt_params.enable_hive_metastore or rt_params.enable_postgres_hive_metastore: self._enable_metastore(defaults_file) if not rt_params.enable_dynamic_allocation: @@ -565,6 +596,126 @@ def _enable_history_server(self, defaults_file: Path) -> None: ) logger.info("Enabled Spark history server at {}", events_dir) + def _enable_reverse_proxy(self, defaults_file: Path) -> None: + with open(defaults_file, "a") as f_out: + f_out.write("\nspark.ui.reverseProxy true\n") + url = self._config.runtime.reverse_proxy_url + if url is not None: + f_out.write(f"spark.ui.reverseProxyUrl {url}\n") + logger.info( + "Enabled the Spark master reverse proxy for worker and application UIs. " + "Access all UIs through the master web UI (default port 8080)." + ) + + def _enable_prometheus(self, defaults_file: Path) -> None: + metrics_file = self._config.directories.get_metrics_properties_file() + # Spark automatically loads $SPARK_CONF_DIR/metrics.properties, so writing it into the conf + # directory is enough to register the Prometheus sink on every component. + metrics_file.write_text( + """# Generated by sparkctl. Exposes Spark metrics in Prometheus format on the existing +# web UI ports (no additional ports are opened). +*.sink.prometheusServlet.class=org.apache.spark.metrics.sink.PrometheusServlet +*.sink.prometheusServlet.path=/metrics/prometheus +master.sink.prometheusServlet.path=/metrics/master/prometheus +applications.sink.prometheusServlet.path=/metrics/applications/prometheus +""", + encoding="utf-8", + ) + with open(defaults_file, "a") as f_out: + f_out.write( + """ +spark.ui.prometheus.enabled true +spark.executor.processTreeMetrics.enabled true +""" + ) + logger.info( + "Enabled Prometheus metrics. Scrape /metrics/prometheus on the master/worker UIs and " + "/metrics/executors/prometheus on the driver UI (port 4040)." + ) + + def _enable_gpus(self, defaults_file: Path) -> None: + rt_params = self._config.runtime + num_gpus = rt_params.gpus_per_node + if num_gpus is None: + num_gpus = self._intf.get_worker_num_gpus() + if num_gpus <= 0: + msg = ( + "GPU scheduling was enabled but no GPUs were detected on the worker nodes. " + "Set runtime.gpus_per_node explicitly or request GPUs in the allocation." + ) + raise InvalidConfiguration(msg) + + discovery_script = self._write_gpu_discovery_script() + executor_gpu_amount = rt_params.executor_gpu_amount + if executor_gpu_amount > num_gpus: + msg = ( + f"executor_gpu_amount ({executor_gpu_amount}) cannot exceed the number of GPUs " + f"per node ({num_gpus})." + ) + raise InvalidConfiguration(msg) + if rt_params.task_gpu_amount is not None: + task_gpu_amount = rt_params.task_gpu_amount + else: + # Let the cores in an executor share that executor's GPU(s) by default. + task_gpu_amount = executor_gpu_amount / rt_params.executor_cores + with open(defaults_file, "a") as f_out: + f_out.write( + f""" +spark.worker.resource.gpu.amount {num_gpus} +spark.worker.resource.gpu.discoveryScript {discovery_script} +spark.executor.resource.gpu.amount {executor_gpu_amount} +spark.executor.resource.gpu.discoveryScript {discovery_script} +spark.task.resource.gpu.amount {task_gpu_amount} +""" + ) + logger.warning( + "Enabled EXPERIMENTAL (untested) GPU scheduling with {} GPU(s) per node.", num_gpus + ) + + def _write_gpu_discovery_script(self) -> Path: + script = self._config.directories.get_gpu_discovery_script_file() + # Spark calls this script on each worker and expects JSON describing the available GPU + # addresses. This mirrors Spark's example getGpusResources.sh. + script.write_text( + """#!/usr/bin/env bash +# Generated by sparkctl. Reports the GPUs visible to Spark in the format it expects. +ADDRS=$(nvidia-smi --query-gpu=index --format=csv,noheader | sed -e ':a' -e 'N' -e '$!ba' \\ + -e 's/\\n/","/g') +echo "{\\"name\\": \\"gpu\\", \\"addresses\\": [\\"$ADDRS\\"]}" +""", + encoding="utf-8", + ) + script.chmod(script.stat().st_mode | stat.S_IXUSR | stat.S_IXGRP) + return script + + def _enable_rapids(self, defaults_file: Path) -> None: + rapids_jar = self._config.binaries.rapids_jar_file + if rapids_jar is None: + msg = ( + "RAPIDS acceleration was enabled but binaries.rapids_jar_file is not set. " + "Download the RAPIDS Accelerator jar and set its path " + "(see https://nvidia.github.io/spark-rapids/docs/download.html)." + ) + raise InvalidConfiguration(msg) + if not Path(rapids_jar).exists(): + msg = f"The RAPIDS jar file does not exist: {rapids_jar}" + raise InvalidConfiguration(msg) + with open(defaults_file, "a") as f_out: + # Distribute the plugin jar to executors and add it to the driver/executor classpath. + # Using spark.jars avoids clobbering spark.{driver,executor}.extraClassPath, which the + # Postgres metastore may also set. + f_out.write( + f""" +spark.jars {rapids_jar} +spark.plugins com.nvidia.spark.SQLPlugin +spark.rapids.sql.enabled true +spark.rapids.sql.concurrentGpuTasks 1 +""" + ) + logger.warning( + "Enabled EXPERIMENTAL (untested) RAPIDS GPU acceleration using {}", rapids_jar + ) + def _get_runtime_spark_driver_memory_gb(self) -> int: # Note that spark-defaults.conf takes precedence over our config.json. regex = re.compile(r"^\s*spark.driver.memory\s*=?\s*(\d+)g") diff --git a/src/sparkctl/compute_interface.py b/src/sparkctl/compute_interface.py index a40ca9e..c5c0197 100644 --- a/src/sparkctl/compute_interface.py +++ b/src/sparkctl/compute_interface.py @@ -40,6 +40,10 @@ def get_worker_memory_gb(self) -> int: def get_worker_num_cpus(self) -> int: """Return the number of CPUs in the compute node""" + @abc.abstractmethod + def get_worker_num_gpus(self) -> int: + """Return the number of GPUs in the compute node. Returns 0 if none are detected.""" + @abc.abstractmethod def is_heterogeneous_slurm_job(self) -> bool: """Return True if the environment indicates a heterogeneous Slurm job.""" diff --git a/src/sparkctl/fake_compute.py b/src/sparkctl/fake_compute.py index 9f06cda..1465c6e 100644 --- a/src/sparkctl/fake_compute.py +++ b/src/sparkctl/fake_compute.py @@ -35,6 +35,9 @@ def get_worker_memory_gb(self) -> int: def get_worker_num_cpus(self) -> int: return 12 + def get_worker_num_gpus(self) -> int: + return 0 + def is_heterogeneous_slurm_job(self) -> bool: return False diff --git a/src/sparkctl/models.py b/src/sparkctl/models.py index cec29c1..bf76289 100644 --- a/src/sparkctl/models.py +++ b/src/sparkctl/models.py @@ -39,9 +39,19 @@ class BinaryLocations(SparkctlBaseModel): postgresql_jar_file: Path | None = Field( default=None, description="Path to the PostgreSQL jar file." ) + rapids_jar_file: Path | None = Field( + default=None, + description="Path to the NVIDIA RAPIDS Accelerator for Apache Spark jar file. Only " + "required to enable RAPIDS GPU acceleration (experimental).", + ) @field_validator( - "spark_path", "java_path", "hadoop_path", "hive_tarball", "postgresql_jar_file" + "spark_path", + "java_path", + "hadoop_path", + "hive_tarball", + "postgresql_jar_file", + "rapids_jar_file", ) @classmethod def make_absolute(cls, val: Path | None) -> Path | None: @@ -91,6 +101,57 @@ class SparkRuntimeParams(SparkctlBaseModel): default=False, description="Enable the Thrift server to connect a SQL client.", ) + start_jupyter: bool = Field( + default=False, + description="Start a JupyterLab server on the master node. Pre-wired to the Spark Connect " + "server when it is enabled (the notebook's SparkSession connects automatically).", + ) + jupyter_port: int = Field( + default=8889, + description="Port on which the JupyterLab server listens.", + ) + enable_reverse_proxy: bool = Field( + default=False, + description="Run the Spark master as a reverse proxy for the worker and application web " + "UIs. Useful on HPC clusters where the compute nodes are not directly reachable, so the " + "UIs are served through the master node only.", + ) + reverse_proxy_url: str | None = Field( + default=None, + description="External URL used to reach the Spark master UI when reverse proxy is enabled " + "and the master is itself behind another front-end proxy. Leave unset to serve relative " + "links (recommended when reaching the master through an SSH tunnel).", + ) + enable_prometheus: bool = Field( + default=False, + description="Expose Spark metrics in Prometheus format through the existing web UI ports " + "(no extra ports are opened).", + ) + enable_gpus: bool = Field( + default=False, + description="EXPERIMENTAL (untested): Enable GPU-aware scheduling. Spark workers advertise " + "GPUs and executors/tasks request them. Requires GPUs on the worker nodes.", + ) + gpus_per_node: int | None = Field( + default=None, + description="EXPERIMENTAL (untested): Number of GPUs available on each worker node. " + "Auto-detected from the compute environment by default.", + ) + executor_gpu_amount: int = Field( + default=1, + description="EXPERIMENTAL (untested): Number of GPUs assigned to each executor.", + ) + task_gpu_amount: float | None = Field( + default=None, + description="EXPERIMENTAL (untested): GPUs assigned to each task. Defaults to " + "executor_gpu_amount / executor_cores so that concurrent tasks share an executor's GPUs.", + ) + enable_rapids: bool = Field( + default=False, + description="EXPERIMENTAL (untested): Enable the NVIDIA RAPIDS Accelerator for Apache " + "Spark to offload SQL/DataFrame operations to GPUs. Implies enable_gpus and requires " + "binaries.rapids_jar_file.", + ) spark_log_level: str | None = Field( default=None, description="Set the root log level for all Spark processes. Defaults to Spark's defaults.", @@ -181,6 +242,14 @@ def get_spark_log_file(self) -> Path: """Return the file path to log properties file""" return self.get_spark_conf_dir() / "log4j2.properties" + def get_metrics_properties_file(self) -> Path: + """Return the file path to metrics.properties""" + return self.get_spark_conf_dir() / "metrics.properties" + + def get_gpu_discovery_script_file(self) -> Path: + """Return the file path to the GPU discovery script.""" + return self.get_spark_conf_dir() / "get_gpus_resources.sh" + def get_workers_file(self) -> Path: """Return the file path to workers""" return self.get_spark_conf_dir() / "workers" @@ -297,4 +366,5 @@ class StatusTracker(SparkctlBaseModel): started_connect_server: bool = False started_history_server: bool = False started_thrift_server: bool = False + started_jupyter: bool = False started_postgres: bool = False diff --git a/src/sparkctl/native_compute.py b/src/sparkctl/native_compute.py index 87df11f..0c59ccf 100644 --- a/src/sparkctl/native_compute.py +++ b/src/sparkctl/native_compute.py @@ -1,4 +1,6 @@ import multiprocessing +import shutil +import subprocess import tempfile from pathlib import Path from socket import gethostname @@ -37,6 +39,23 @@ def get_worker_memory_gb(self) -> int: def get_worker_num_cpus(self) -> int: return multiprocessing.cpu_count() + def get_worker_num_gpus(self) -> int: + # Best-effort detection via nvidia-smi. Returns 0 when it is unavailable or fails so that + # GPU support stays opt-in and never blocks a normal CPU-only run. + nvidia_smi = shutil.which("nvidia-smi") + if nvidia_smi is None: + return 0 + try: + proc = subprocess.run( + [nvidia_smi, "--query-gpu=name", "--format=csv,noheader"], + capture_output=True, + check=True, + text=True, + ) + except (subprocess.CalledProcessError, OSError): + return 0 + return len([x for x in proc.stdout.splitlines() if x.strip()]) + def is_heterogeneous_slurm_job(self) -> bool: return False diff --git a/src/sparkctl/slurm_compute.py b/src/sparkctl/slurm_compute.py index ffda70d..9dc877a 100644 --- a/src/sparkctl/slurm_compute.py +++ b/src/sparkctl/slurm_compute.py @@ -77,6 +77,30 @@ def get_worker_num_cpus(self) -> int: return int(num_cpus) + def get_worker_num_gpus(self) -> int: + # Slurm exposes the per-node GPU count in several variables depending on how the job + # requested GPUs. Check them in order of specificity, then fall back to the visible-device + # list. Return 0 when none indicate GPUs so that GPU support stays opt-in. + het = self.is_heterogeneous_slurm_job() + candidates = [] + if het: + candidates.append(os.getenv("SLURM_GPUS_PER_NODE_HET_GROUP_1")) + candidates.extend( + ( + os.getenv("SLURM_GPUS_ON_NODE"), + os.getenv("SLURM_GPUS_PER_NODE"), + ) + ) + for value in candidates: + num_gpus = _parse_slurm_gpu_count(value) + if num_gpus is not None: + return num_gpus + + visible = os.getenv("CUDA_VISIBLE_DEVICES") + if visible: + return len([x for x in visible.split(",") if x.strip()]) + return 0 + def is_heterogeneous_slurm_job(self) -> bool: return "SLURM_HET_SIZE" in os.environ @@ -103,6 +127,20 @@ def run_checks(self) -> None: raise ValueError(msg) +def _parse_slurm_gpu_count(value: str | None) -> int | None: + """Parse a Slurm GPU count variable. Returns None when the value is unset or unparseable. + + Slurm reports these in a few formats, e.g. "4", "gpu:4", or "gpu:a100:4". + """ + if not value: + return None + # The count is the trailing integer, optionally preceded by ":" fields. + match = re.search(r"(\d+)\s*$", value.split("(")[0]) + if match is None: + return None + return int(match.group(1)) + + def get_node_names(job_id: str) -> list[str]: # The squeue command will produce multiple lines if the job is heterogeneous. proc = subprocess.run( diff --git a/src/sparkctl/spark_process_runner.py b/src/sparkctl/spark_process_runner.py index 9d061e8..1bcb7d9 100644 --- a/src/sparkctl/spark_process_runner.py +++ b/src/sparkctl/spark_process_runner.py @@ -63,6 +63,84 @@ def stop_thrift_server(self) -> int: """Stop the Apache Thrift server.""" return self._run_command(self._stop_thrift_server_cmd()) + def start_jupyter_server(self) -> None: + """Start a JupyterLab server on the local node.""" + jupyter = shutil.which("jupyter") + if jupyter is None: + msg = ( + "jupyter is not installed in the current environment. Install it with " + "`pip install jupyterlab` (or `uv pip install jupyterlab`)." + ) + raise ExecutionError(msg) + + port = self._config.runtime.jupyter_port + log_file = self._get_jupyter_log_file() + pid_file = self._get_jupyter_pid_file() + env = self._get_env() + if self._config.runtime.start_connect_server: + # Pre-wire notebooks to the Connect server so SparkSession.builder.getOrCreate() + # connects to the running cluster without any extra configuration. + env["SPARK_REMOTE"] = f"sc://localhost:{self._config.runtime.connect_server_port}" + cmd = [ + jupyter, + "lab", + "--no-browser", + f"--port={port}", + "--ip=0.0.0.0", + f"--notebook-dir={self._config.directories.base}", + ] + logger.info("Start JupyterLab server: {}", " ".join(cmd)) + with open(log_file, "w", encoding="utf-8") as f_out: + proc = subprocess.Popen( + cmd, + stdin=subprocess.DEVNULL, + stdout=f_out, + stderr=subprocess.STDOUT, + start_new_session=True, + env=env, + ) + time.sleep(1) + ret = proc.poll() + if ret is not None: + msg = ( + f"The JupyterLab server exited immediately with return code {ret}. See {log_file}." + ) + raise ExecutionError(msg) + pid_file.write_text(f"{proc.pid}\n", encoding="utf-8") + logger.info( + "Started JupyterLab server with pid {} on port {}. The access URL (with token) is in " + "{}.", + proc.pid, + port, + log_file, + ) + + def stop_jupyter_server(self) -> int: + """Stop the JupyterLab server.""" + pid_file = self._get_jupyter_pid_file() + if not pid_file.exists(): + logger.error("Cannot stop JupyterLab server: {} does not exist", pid_file) + return 1 + pid = int(pid_file.read_text(encoding="utf-8").strip()) + try: + os.kill(pid, signal.SIGTERM) + except ProcessLookupError: + logger.info("The JupyterLab server has already exited") + pid_file.unlink() + return 0 + if self._wait_for_process_exit(pid, timeout_s=30): + logger.info("Stopped the JupyterLab server") + pid_file.unlink() + return 0 + logger.error("The JupyterLab server process {} did not exit within the timeout", pid) + return 1 + + def _get_jupyter_pid_file(self) -> Path: + return self._config.directories.base / "jupyter.pid" + + def _get_jupyter_log_file(self) -> Path: + return self._config.directories.base / "jupyter.log" + def start_worker_process(self, memory_gb: int) -> None: """Start one Spark worker process.""" tmp_script = self._make_start_worker_script(self._start_worker_cmd(), memory_gb) diff --git a/tests/test_cluster_manager.py b/tests/test_cluster_manager.py index 2e0b3d1..0c48658 100644 --- a/tests/test_cluster_manager.py +++ b/tests/test_cluster_manager.py @@ -9,6 +9,7 @@ ClusterManager, SparkConfig, ) +from sparkctl.exceptions import InvalidConfiguration def test_cluster_manager_workers(setup_local_env: tuple[SparkConfig, Path]): @@ -22,6 +23,58 @@ def test_cluster_manager_workers(setup_local_env: tuple[SparkConfig, Path]): assert mgr.get_workers() == new_workers +def test_configure_reverse_proxy_and_prometheus(setup_local_env: tuple[SparkConfig, Path]): + config, tmp_path = setup_local_env + config.directories.spark_scratch = tmp_path / "spark_scratch" + config.runtime.enable_reverse_proxy = True + config.runtime.reverse_proxy_url = "http://login01:8080" + config.runtime.enable_prometheus = True + mgr = ClusterManager.from_config(config) + mgr.configure() + defaults = config.directories.get_spark_defaults_file().read_text(encoding="utf-8") + assert "spark.ui.reverseProxy true" in defaults + assert "spark.ui.reverseProxyUrl http://login01:8080" in defaults + assert "spark.ui.prometheus.enabled true" in defaults + metrics = config.directories.get_metrics_properties_file().read_text(encoding="utf-8") + assert "PrometheusServlet" in metrics + + +def test_configure_gpus_without_detection_fails(setup_local_env: tuple[SparkConfig, Path]): + config, tmp_path = setup_local_env + config.directories.spark_scratch = tmp_path / "spark_scratch" + # FakeCompute reports zero GPUs and no override is set. + config.runtime.enable_gpus = True + mgr = ClusterManager.from_config(config) + with pytest.raises(InvalidConfiguration): + mgr.configure() + + +def test_configure_gpus_with_override(setup_local_env: tuple[SparkConfig, Path]): + config, tmp_path = setup_local_env + config.directories.spark_scratch = tmp_path / "spark_scratch" + config.runtime.enable_gpus = True + config.runtime.gpus_per_node = 4 + mgr = ClusterManager.from_config(config) + mgr.configure() + defaults = config.directories.get_spark_defaults_file().read_text(encoding="utf-8") + assert "spark.worker.resource.gpu.amount 4" in defaults + assert "spark.executor.resource.gpu.discoveryScript" in defaults + discovery_script = config.directories.get_gpu_discovery_script_file() + assert discovery_script.exists() + assert discovery_script.stat().st_mode & 0o100 # owner-executable + + +def test_configure_rapids_without_jar_fails(setup_local_env: tuple[SparkConfig, Path]): + config, tmp_path = setup_local_env + config.directories.spark_scratch = tmp_path / "spark_scratch" + config.runtime.gpus_per_node = 2 + config.runtime.enable_rapids = True + config.binaries.rapids_jar_file = None + mgr = ClusterManager.from_config(config) + with pytest.raises(InvalidConfiguration): + mgr.configure() + + @pytest.mark.integration def test_managed_start(setup_local_env: tuple[SparkConfig, Path]): config, output_dir = setup_local_env diff --git a/tests/test_sparkctl_cli.py b/tests/test_sparkctl_cli.py index 388bdc4..db487e5 100644 --- a/tests/test_sparkctl_cli.py +++ b/tests/test_sparkctl_cli.py @@ -88,6 +88,33 @@ def test_configure_start_stop(setup_local_env): assert result.exit_code == 0 +def test_configure_new_feature_flags(setup_local_env): + config, tmp_path = setup_local_env + cmd = [ + "configure", + "--directory", + str(tmp_path), + "--spark-scratch", + str(tmp_path / "spark_scratch"), + "--reverse-proxy", + "--prometheus", + "--jupyter", + "--jupyter-port", + "9999", + "--gpus-per-node", + "8", + ] + runner = CliRunner() + result = runner.invoke(cli, cmd) + assert result.exit_code == 0 + data = json.loads((tmp_path / "config.json").read_text(encoding="utf-8")) + assert data["runtime"]["enable_reverse_proxy"] + assert data["runtime"]["enable_prometheus"] + assert data["runtime"]["start_jupyter"] + assert data["runtime"]["jupyter_port"] == 9999 + assert data["runtime"]["gpus_per_node"] == 8 + + def test_invalid_executor_memory(setup_local_env): _, tmp_path = setup_local_env cmd = [ From 7c5ea5a693504f4e21f9bf9f4b3306ef2aa259e1 Mon Sep 17 00:00:00 2001 From: Daniel Thom Date: Sat, 13 Jun 2026 09:50:54 -0600 Subject: [PATCH 06/24] Add CSV metrics sink option Add --metrics-csv (and --metrics-csv-period) to write Spark metrics to CSV files in /metrics-csv, leaving a durable on-disk record after an ephemeral cluster shuts down (the Prometheus sink only exposes metrics over HTTP). Refactor the Prometheus setup into _configure_metrics so the Prometheus and CSV sinks compose into a single metrics.properties when both are enabled. Co-Authored-By: Claude Opus 4.8 (1M context) --- docs/how_tos/execution/prometheus_metrics.md | 24 ++++++++ src/sparkctl/cli/sparkctl.py | 22 +++++++ src/sparkctl/cluster_manager.py | 63 +++++++++++++------- src/sparkctl/models.py | 9 +++ tests/test_cluster_manager.py | 31 ++++++++++ 5 files changed, 128 insertions(+), 21 deletions(-) diff --git a/docs/how_tos/execution/prometheus_metrics.md b/docs/how_tos/execution/prometheus_metrics.md index 769a965..ec9e76c 100644 --- a/docs/how_tos/execution/prometheus_metrics.md +++ b/docs/how_tos/execution/prometheus_metrics.md @@ -34,3 +34,27 @@ $ curl http://localhost:4040/metrics/executors/prometheus .. tip:: Combine this with the :doc:`reverse proxy <../configuration/web_ui_reverse_proxy>` to reach the worker and application endpoints through the master node on an HPC cluster. ``` + +## Write metrics to CSV files + +The Prometheus sink is pull-based: it only exposes metrics over HTTP and keeps nothing on disk, so +the data is gone once the cluster shuts down. To keep a durable record, enable the CSV sink, which +periodically writes one CSV file per metric: + +```console +$ sparkctl configure --metrics-csv +``` + +This writes the metrics to `/metrics-csv` (alongside `stats-output`) and survives cluster +teardown, which fits the ephemeral-allocation model better than relying on a live Prometheus +scraper. Change the sampling interval with `--metrics-csv-period` (seconds): + +```console +$ sparkctl configure --metrics-csv --metrics-csv-period 30 +``` + +The two sinks are independent and can be combined; they share a single `metrics.properties`: + +```console +$ sparkctl configure --prometheus --metrics-csv +``` diff --git a/src/sparkctl/cli/sparkctl.py b/src/sparkctl/cli/sparkctl.py index 2ba6637..8bbc054 100644 --- a/src/sparkctl/cli/sparkctl.py +++ b/src/sparkctl/cli/sparkctl.py @@ -324,6 +324,24 @@ def _create_default_config( show_default=True, help=SparkRuntimeParams.model_fields["enable_prometheus"].description, ) +@click.option( + "--metrics-csv/--no-metrics-csv", + is_flag=True, + default=sparkctl_settings.runtime.get( + "enable_metrics_csv", SparkRuntimeParams.model_fields["enable_metrics_csv"].default + ), + show_default=True, + help=SparkRuntimeParams.model_fields["enable_metrics_csv"].description, +) +@click.option( + "--metrics-csv-period", + default=sparkctl_settings.runtime.get( + "metrics_csv_period", SparkRuntimeParams.model_fields["metrics_csv_period"].default + ), + show_default=True, + type=int, + help=SparkRuntimeParams.model_fields["metrics_csv_period"].description, +) @click.option( "--gpus/--no-gpus", is_flag=True, @@ -431,6 +449,8 @@ def configure( reverse_proxy: bool, reverse_proxy_url: str | None, prometheus: bool, + metrics_csv: bool, + metrics_csv_period: int, gpus: bool, gpus_per_node: int | None, rapids: bool, @@ -476,6 +496,8 @@ def build_config() -> SparkConfig: enable_reverse_proxy=reverse_proxy, reverse_proxy_url=reverse_proxy_url, enable_prometheus=prometheus, + enable_metrics_csv=metrics_csv, + metrics_csv_period=metrics_csv_period, enable_gpus=gpus, gpus_per_node=gpus_per_node, enable_rapids=rapids, diff --git a/src/sparkctl/cluster_manager.py b/src/sparkctl/cluster_manager.py index 79ef0eb..cbe4ae5 100644 --- a/src/sparkctl/cluster_manager.py +++ b/src/sparkctl/cluster_manager.py @@ -116,6 +116,7 @@ def clean(self) -> None: self._config.directories.get_spark_conf_dir(), self._config.directories.spark_scratch.absolute(), base / "stats-output", + base / "metrics-csv", base / "pg_data", base / "pg_run", ] @@ -462,8 +463,8 @@ def _add_spark_settings_to_defaults_file(self, defaults_file: Path) -> None: if rt_params.enable_reverse_proxy: self._enable_reverse_proxy(defaults_file) - if rt_params.enable_prometheus: - self._enable_prometheus(defaults_file) + if rt_params.enable_prometheus or rt_params.enable_metrics_csv: + self._configure_metrics(defaults_file) # RAPIDS requires GPU scheduling, so enabling it implies enabling GPUs. if rt_params.enable_gpus or rt_params.enable_rapids: @@ -607,31 +608,51 @@ def _enable_reverse_proxy(self, defaults_file: Path) -> None: "Access all UIs through the master web UI (default port 8080)." ) - def _enable_prometheus(self, defaults_file: Path) -> None: - metrics_file = self._config.directories.get_metrics_properties_file() - # Spark automatically loads $SPARK_CONF_DIR/metrics.properties, so writing it into the conf - # directory is enough to register the Prometheus sink on every component. - metrics_file.write_text( - """# Generated by sparkctl. Exposes Spark metrics in Prometheus format on the existing -# web UI ports (no additional ports are opened). + def _configure_metrics(self, defaults_file: Path) -> None: + # The Prometheus and CSV sinks share a single metrics.properties, so build the file from + # whichever sinks are enabled. Spark automatically loads $SPARK_CONF_DIR/metrics.properties. + rt_params = self._config.runtime + sinks: list[str] = [] + if rt_params.enable_prometheus: + sinks.append( + """# Expose metrics in Prometheus format on the existing web UI ports. *.sink.prometheusServlet.class=org.apache.spark.metrics.sink.PrometheusServlet *.sink.prometheusServlet.path=/metrics/prometheus master.sink.prometheusServlet.path=/metrics/master/prometheus applications.sink.prometheusServlet.path=/metrics/applications/prometheus -""", - encoding="utf-8", - ) - with open(defaults_file, "a") as f_out: - f_out.write( - """ -spark.ui.prometheus.enabled true -spark.executor.processTreeMetrics.enabled true """ ) - logger.info( - "Enabled Prometheus metrics. Scrape /metrics/prometheus on the master/worker UIs and " - "/metrics/executors/prometheus on the driver UI (port 4040)." - ) + csv_dir = self._config.directories.base / "metrics-csv" + if rt_params.enable_metrics_csv: + csv_dir.mkdir(exist_ok=True) + sinks.append( + f"""# Write metrics to CSV files, one per metric, for a durable on-disk record. +*.sink.csv.class=org.apache.spark.metrics.sink.CsvSink +*.sink.csv.directory={csv_dir} +*.sink.csv.period={rt_params.metrics_csv_period} +*.sink.csv.unit=seconds +""" + ) + + metrics_file = self._config.directories.get_metrics_properties_file() + metrics_file.write_text("# Generated by sparkctl.\n" + "\n".join(sinks), encoding="utf-8") + + with open(defaults_file, "a") as f_out: + f_out.write("\nspark.executor.processTreeMetrics.enabled true\n") + if rt_params.enable_prometheus: + f_out.write("spark.ui.prometheus.enabled true\n") + + if rt_params.enable_prometheus: + logger.info( + "Enabled Prometheus metrics. Scrape /metrics/prometheus on the master/worker UIs " + "and /metrics/executors/prometheus on the driver UI (port 4040)." + ) + if rt_params.enable_metrics_csv: + logger.info( + "Enabled CSV metrics sink writing to {} every {} seconds.", + csv_dir, + rt_params.metrics_csv_period, + ) def _enable_gpus(self, defaults_file: Path) -> None: rt_params = self._config.runtime diff --git a/src/sparkctl/models.py b/src/sparkctl/models.py index bf76289..9967d5d 100644 --- a/src/sparkctl/models.py +++ b/src/sparkctl/models.py @@ -127,6 +127,15 @@ class SparkRuntimeParams(SparkctlBaseModel): description="Expose Spark metrics in Prometheus format through the existing web UI ports " "(no extra ports are opened).", ) + enable_metrics_csv: bool = Field( + default=False, + description="Write Spark metrics to CSV files in /metrics-csv. Unlike the Prometheus " + "sink, this leaves a durable record on disk after the cluster shuts down.", + ) + metrics_csv_period: int = Field( + default=10, + description="Interval in seconds at which the CSV metrics sink writes samples.", + ) enable_gpus: bool = Field( default=False, description="EXPERIMENTAL (untested): Enable GPU-aware scheduling. Spark workers advertise " diff --git a/tests/test_cluster_manager.py b/tests/test_cluster_manager.py index 0c48658..a54257a 100644 --- a/tests/test_cluster_manager.py +++ b/tests/test_cluster_manager.py @@ -39,6 +39,37 @@ def test_configure_reverse_proxy_and_prometheus(setup_local_env: tuple[SparkConf assert "PrometheusServlet" in metrics +def test_configure_metrics_csv(setup_local_env: tuple[SparkConfig, Path]): + config, tmp_path = setup_local_env + config.directories.base = tmp_path + config.directories.spark_scratch = tmp_path / "spark_scratch" + config.runtime.enable_metrics_csv = True + config.runtime.metrics_csv_period = 30 + mgr = ClusterManager.from_config(config) + mgr.configure() + metrics = config.directories.get_metrics_properties_file().read_text(encoding="utf-8") + assert "org.apache.spark.metrics.sink.CsvSink" in metrics + assert f"*.sink.csv.directory={tmp_path / 'metrics-csv'}" in metrics + assert "*.sink.csv.period=30" in metrics + assert (tmp_path / "metrics-csv").is_dir() + # The CSV sink alone does not need the Prometheus servlet setting. + defaults = config.directories.get_spark_defaults_file().read_text(encoding="utf-8") + assert "spark.ui.prometheus.enabled" not in defaults + + +def test_configure_prometheus_and_csv_compose(setup_local_env: tuple[SparkConfig, Path]): + config, tmp_path = setup_local_env + config.directories.base = tmp_path + config.directories.spark_scratch = tmp_path / "spark_scratch" + config.runtime.enable_prometheus = True + config.runtime.enable_metrics_csv = True + mgr = ClusterManager.from_config(config) + mgr.configure() + metrics = config.directories.get_metrics_properties_file().read_text(encoding="utf-8") + assert "PrometheusServlet" in metrics + assert "CsvSink" in metrics + + def test_configure_gpus_without_detection_fails(setup_local_env: tuple[SparkConfig, Path]): config, tmp_path = setup_local_env config.directories.spark_scratch = tmp_path / "spark_scratch" From ddca495d838dae88b72d00f6e4de96bc20cb02de Mon Sep 17 00:00:00 2001 From: Daniel Thom Date: Sat, 13 Jun 2026 10:05:01 -0600 Subject: [PATCH 07/24] Make the Jupyter frontend configurable, default to classic notebook Replace the hardcoded `jupyter lab` invocation with a configurable jupyter_command (--jupyter-command), defaulting to the classic 'notebook' frontend, which is lighter to install and simpler for a single-user cluster. JupyterLab is still available with --jupyter-command lab. Co-Authored-By: Claude Opus 4.8 (1M context) --- docs/how_tos/applications/jupyter.md | 31 ++++++++++++++++------------ src/sparkctl/cli/sparkctl.py | 10 +++++++++ src/sparkctl/cluster_manager.py | 2 +- src/sparkctl/models.py | 9 ++++++-- src/sparkctl/spark_process_runner.py | 27 ++++++++++++------------ 5 files changed, 49 insertions(+), 30 deletions(-) diff --git a/docs/how_tos/applications/jupyter.md b/docs/how_tos/applications/jupyter.md index c627235..d483043 100644 --- a/docs/how_tos/applications/jupyter.md +++ b/docs/how_tos/applications/jupyter.md @@ -1,18 +1,23 @@ -# How to run a JupyterLab notebook against the cluster +# How to run a Jupyter notebook against the cluster -sparkctl can start a JupyterLab server on the master node so you can run interactive notebooks +sparkctl can start a Jupyter server on the master node so you can run interactive notebooks against the Spark cluster. When the Spark Connect server is enabled, the notebook's `SparkSession` connects to the cluster automatically. +By default sparkctl launches the classic notebook (`jupyter notebook`), which is a good fit for a +single-user cluster. Use `--jupyter-command lab` if you prefer JupyterLab. + ## Prerequisites -Install JupyterLab in the same environment as sparkctl: +Install Jupyter in the same environment as sparkctl: ```console -$ pip install jupyterlab # or: uv pip install jupyterlab +$ pip install notebook # or: uv pip install notebook ``` -## Start JupyterLab with the Connect server +If you want JupyterLab instead, install `jupyterlab` and pass `--jupyter-command lab`. + +## Start Jupyter with the Connect server The recommended setup enables the Spark Connect server so the notebook connects remotely without any extra configuration: @@ -21,7 +26,7 @@ any extra configuration: $ sparkctl configure --connect-server --jupyter --start ``` -sparkctl sets `SPARK_REMOTE` for the JupyterLab process, so inside a notebook you can simply do: +sparkctl sets `SPARK_REMOTE` for the Jupyter process, so inside a notebook you can simply do: ```python from pyspark.sql import SparkSession @@ -32,14 +37,14 @@ spark.createDataFrame([(1, 2), (3, 4)], ["a", "b"]).show() ## Find the access URL -The JupyterLab access URL, including its login token, is written to `jupyter.log` in the cluster -base directory: +The Jupyter access URL, including its login token, is written to `jupyter.log` in the cluster base +directory: ```console $ grep -m1 'http' jupyter.log ``` -JupyterLab listens on port 8889 by default. Change it with `--jupyter-port`, and forward it to your +Jupyter listens on port 8889 by default. Change it with `--jupyter-port`, and forward it to your laptop over SSH (replacing `master-node` with the node running the server): ```console @@ -50,10 +55,10 @@ Then open the URL from `jupyter.log`, replacing the host with `localhost`. ## Stopping -`sparkctl stop` shuts the JupyterLab server down along with the rest of the cluster. +`sparkctl stop` shuts the Jupyter server down along with the rest of the cluster. ```{eval-rst} -.. note:: If you enable ``--jupyter`` without ``--connect-server``, JupyterLab still starts, but - the notebook is responsible for creating its own ``SparkSession`` (for example, a local driver - that connects to ``spark://:7077``). The Connect server path is recommended. +.. note:: If you enable ``--jupyter`` without ``--connect-server``, Jupyter still starts, but the + notebook is responsible for creating its own ``SparkSession`` (for example, a local driver that + connects to ``spark://:7077``). The Connect server path is recommended. ``` diff --git a/src/sparkctl/cli/sparkctl.py b/src/sparkctl/cli/sparkctl.py index 8bbc054..c68b27d 100644 --- a/src/sparkctl/cli/sparkctl.py +++ b/src/sparkctl/cli/sparkctl.py @@ -289,6 +289,14 @@ def _create_default_config( show_default=True, help=SparkRuntimeParams.model_fields["start_jupyter"].description, ) +@click.option( + "--jupyter-command", + default=sparkctl_settings.runtime.get( + "jupyter_command", SparkRuntimeParams.model_fields["jupyter_command"].default + ), + show_default=True, + help=SparkRuntimeParams.model_fields["jupyter_command"].description, +) @click.option( "--jupyter-port", default=sparkctl_settings.runtime.get( @@ -445,6 +453,7 @@ def configure( history_server: bool, thrift_server: bool, jupyter: bool, + jupyter_command: str, jupyter_port: int, reverse_proxy: bool, reverse_proxy_url: str | None, @@ -492,6 +501,7 @@ def build_config() -> SparkConfig: start_history_server=history_server, start_thrift_server=thrift_server, start_jupyter=jupyter, + jupyter_command=jupyter_command, jupyter_port=jupyter_port, enable_reverse_proxy=reverse_proxy, reverse_proxy_url=reverse_proxy_url, diff --git a/src/sparkctl/cluster_manager.py b/src/sparkctl/cluster_manager.py index cbe4ae5..d095255 100644 --- a/src/sparkctl/cluster_manager.py +++ b/src/sparkctl/cluster_manager.py @@ -336,7 +336,7 @@ def _start(self, runner: SparkProcessRunner, tracker: StatusTracker) -> None: if self._config.runtime.start_jupyter: runner.start_jupyter_server() tracker.started_jupyter = True - logger.info("Started JupyterLab server") + logger.info("Started Jupyter server") worker_memory_gb = self._get_worker_memory_gb(self._get_runtime_spark_driver_memory_gb()) if is_single_node_cluster: diff --git a/src/sparkctl/models.py b/src/sparkctl/models.py index 9967d5d..4cb32ea 100644 --- a/src/sparkctl/models.py +++ b/src/sparkctl/models.py @@ -103,12 +103,17 @@ class SparkRuntimeParams(SparkctlBaseModel): ) start_jupyter: bool = Field( default=False, - description="Start a JupyterLab server on the master node. Pre-wired to the Spark Connect " + description="Start a Jupyter server on the master node. Pre-wired to the Spark Connect " "server when it is enabled (the notebook's SparkSession connects automatically).", ) + jupyter_command: str = Field( + default="notebook", + description="Jupyter frontend to launch, i.e. the `jupyter ` subcommand. " + "Defaults to the classic 'notebook'; use 'lab' for JupyterLab.", + ) jupyter_port: int = Field( default=8889, - description="Port on which the JupyterLab server listens.", + description="Port on which the Jupyter server listens.", ) enable_reverse_proxy: bool = Field( default=False, diff --git a/src/sparkctl/spark_process_runner.py b/src/sparkctl/spark_process_runner.py index 1bcb7d9..9a61770 100644 --- a/src/sparkctl/spark_process_runner.py +++ b/src/sparkctl/spark_process_runner.py @@ -64,12 +64,14 @@ def stop_thrift_server(self) -> int: return self._run_command(self._stop_thrift_server_cmd()) def start_jupyter_server(self) -> None: - """Start a JupyterLab server on the local node.""" + """Start a Jupyter server on the local node.""" jupyter = shutil.which("jupyter") + command = self._config.runtime.jupyter_command if jupyter is None: + package = "jupyterlab" if command == "lab" else "notebook" msg = ( "jupyter is not installed in the current environment. Install it with " - "`pip install jupyterlab` (or `uv pip install jupyterlab`)." + f"`pip install {package}` (or `uv pip install {package}`)." ) raise ExecutionError(msg) @@ -83,13 +85,13 @@ def start_jupyter_server(self) -> None: env["SPARK_REMOTE"] = f"sc://localhost:{self._config.runtime.connect_server_port}" cmd = [ jupyter, - "lab", + command, "--no-browser", f"--port={port}", "--ip=0.0.0.0", f"--notebook-dir={self._config.directories.base}", ] - logger.info("Start JupyterLab server: {}", " ".join(cmd)) + logger.info("Start Jupyter server: {}", " ".join(cmd)) with open(log_file, "w", encoding="utf-8") as f_out: proc = subprocess.Popen( cmd, @@ -102,37 +104,34 @@ def start_jupyter_server(self) -> None: time.sleep(1) ret = proc.poll() if ret is not None: - msg = ( - f"The JupyterLab server exited immediately with return code {ret}. See {log_file}." - ) + msg = f"The Jupyter server exited immediately with return code {ret}. See {log_file}." raise ExecutionError(msg) pid_file.write_text(f"{proc.pid}\n", encoding="utf-8") logger.info( - "Started JupyterLab server with pid {} on port {}. The access URL (with token) is in " - "{}.", + "Started Jupyter server with pid {} on port {}. The access URL (with token) is in {}.", proc.pid, port, log_file, ) def stop_jupyter_server(self) -> int: - """Stop the JupyterLab server.""" + """Stop the Jupyter server.""" pid_file = self._get_jupyter_pid_file() if not pid_file.exists(): - logger.error("Cannot stop JupyterLab server: {} does not exist", pid_file) + logger.error("Cannot stop Jupyter server: {} does not exist", pid_file) return 1 pid = int(pid_file.read_text(encoding="utf-8").strip()) try: os.kill(pid, signal.SIGTERM) except ProcessLookupError: - logger.info("The JupyterLab server has already exited") + logger.info("The Jupyter server has already exited") pid_file.unlink() return 0 if self._wait_for_process_exit(pid, timeout_s=30): - logger.info("Stopped the JupyterLab server") + logger.info("Stopped the Jupyter server") pid_file.unlink() return 0 - logger.error("The JupyterLab server process {} did not exit within the timeout", pid) + logger.error("The Jupyter server process {} did not exit within the timeout", pid) return 1 def _get_jupyter_pid_file(self) -> Path: From 9c1cccec410cc22d3a05267615edbd17048bf7a1 Mon Sep 17 00:00:00 2001 From: Daniel Thom Date: Sat, 13 Jun 2026 10:08:40 -0600 Subject: [PATCH 08/24] Add optional jupyter extra instead of a core dependency Jupyter is only needed for `sparkctl configure --jupyter` and is gated at runtime, so add it as an optional extra (sparkctl[jupyter] -> notebook) rather than a core dependency. This keeps the default install slim. Co-Authored-By: Claude Opus 4.8 (1M context) --- docs/how_tos/applications/jupyter.md | 5 +++-- pyproject.toml | 5 +++++ 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/docs/how_tos/applications/jupyter.md b/docs/how_tos/applications/jupyter.md index d483043..e7549fa 100644 --- a/docs/how_tos/applications/jupyter.md +++ b/docs/how_tos/applications/jupyter.md @@ -9,10 +9,11 @@ single-user cluster. Use `--jupyter-command lab` if you prefer JupyterLab. ## Prerequisites -Install Jupyter in the same environment as sparkctl: +Install Jupyter in the same environment as sparkctl. The `jupyter` extra pulls in the classic +notebook frontend: ```console -$ pip install notebook # or: uv pip install notebook +$ pip install "sparkctl[jupyter]" # or: uv pip install "sparkctl[jupyter]" ``` If you want JupyterLab instead, install `jupyterlab` and pass `--jupyter-command lab`. diff --git a/pyproject.toml b/pyproject.toml index a7d2db5..6bdac24 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -39,6 +39,11 @@ dependencies = [ pyspark = [ "pyspark == 4.1.2", ] +# Optional notebook frontend for `sparkctl configure --jupyter`. Installs the classic notebook +# (the default frontend); install jupyterlab separately to use `--jupyter-command lab`. +jupyter = [ + "notebook", +] dev = [ "furo", "myst_parser", From b8e8bd8437b0257ae7116c0f678ce9b9c382600a Mon Sep 17 00:00:00 2001 From: Daniel Thom Date: Sat, 13 Jun 2026 10:13:12 -0600 Subject: [PATCH 09/24] Address Copilot review: Jupyter bind, ssh thread cap, pg password checks - Bind the Jupyter server to 127.0.0.1 by default (configurable via --jupyter-ip); document an SSH ProxyJump tunnel and the 0.0.0.0 escape hatch. Avoids exposing the notebook server on all interfaces. - Cap the ssh worker start/stop thread pool at 32 so large allocations do not spawn thousands of threads. - Validate SPARKCTL_PG_PASSWORD is set and non-empty in the Postgres start/setup scripts instead of silently using an empty password. - Lock the notebook dependency added by the jupyter extra in uv.lock. Co-Authored-By: Claude Opus 4.8 (1M context) --- docs/how_tos/applications/jupyter.md | 18 +- src/sparkctl/cli/sparkctl.py | 10 + src/sparkctl/models.py | 6 + src/sparkctl/postgres/setup_metastore.sh | 4 + src/sparkctl/postgres/start_container.sh | 4 + src/sparkctl/spark_process_runner.py | 7 +- uv.lock | 1120 +++++++++++++++++++++- 7 files changed, 1162 insertions(+), 7 deletions(-) diff --git a/docs/how_tos/applications/jupyter.md b/docs/how_tos/applications/jupyter.md index e7549fa..67e3780 100644 --- a/docs/how_tos/applications/jupyter.md +++ b/docs/how_tos/applications/jupyter.md @@ -45,14 +45,24 @@ directory: $ grep -m1 'http' jupyter.log ``` -Jupyter listens on port 8889 by default. Change it with `--jupyter-port`, and forward it to your -laptop over SSH (replacing `master-node` with the node running the server): +Jupyter listens on `127.0.0.1:8889` by default. Binding to localhost keeps the server off the +cluster network; reach it with an SSH tunnel. From your laptop, forward the port to the master node +(use `-J ` to hop through the login node, and replace `master-node` with the node +running the server): ```console -$ ssh -L 8889:master-node:8889 +$ ssh -J -L 8889:localhost:8889 master-node ``` -Then open the URL from `jupyter.log`, replacing the host with `localhost`. +Then open the URL from `jupyter.log`, replacing the host with `localhost`. Change the port with +`--jupyter-port`. + +```{eval-rst} +.. note:: If you cannot reach the master node directly, ``--jupyter-ip 0.0.0.0`` makes Jupyter + listen on all interfaces so you can tunnel through the login node to the master's hostname. This + exposes the server to the cluster network (it is still protected by Jupyter's access token), so + prefer the localhost default when possible. +``` ## Stopping diff --git a/src/sparkctl/cli/sparkctl.py b/src/sparkctl/cli/sparkctl.py index c68b27d..0140471 100644 --- a/src/sparkctl/cli/sparkctl.py +++ b/src/sparkctl/cli/sparkctl.py @@ -297,6 +297,14 @@ def _create_default_config( show_default=True, help=SparkRuntimeParams.model_fields["jupyter_command"].description, ) +@click.option( + "--jupyter-ip", + default=sparkctl_settings.runtime.get( + "jupyter_ip", SparkRuntimeParams.model_fields["jupyter_ip"].default + ), + show_default=True, + help=SparkRuntimeParams.model_fields["jupyter_ip"].description, +) @click.option( "--jupyter-port", default=sparkctl_settings.runtime.get( @@ -454,6 +462,7 @@ def configure( thrift_server: bool, jupyter: bool, jupyter_command: str, + jupyter_ip: str, jupyter_port: int, reverse_proxy: bool, reverse_proxy_url: str | None, @@ -502,6 +511,7 @@ def build_config() -> SparkConfig: start_thrift_server=thrift_server, start_jupyter=jupyter, jupyter_command=jupyter_command, + jupyter_ip=jupyter_ip, jupyter_port=jupyter_port, enable_reverse_proxy=reverse_proxy, reverse_proxy_url=reverse_proxy_url, diff --git a/src/sparkctl/models.py b/src/sparkctl/models.py index 4cb32ea..eac7fa5 100644 --- a/src/sparkctl/models.py +++ b/src/sparkctl/models.py @@ -111,6 +111,12 @@ class SparkRuntimeParams(SparkctlBaseModel): description="Jupyter frontend to launch, i.e. the `jupyter ` subcommand. " "Defaults to the classic 'notebook'; use 'lab' for JupyterLab.", ) + jupyter_ip: str = Field( + default="127.0.0.1", + description="IP address the Jupyter server binds to. Defaults to localhost, which is " + "secure and works with an SSH tunnel to the master node. Set to 0.0.0.0 to listen on all " + "interfaces (only if you understand the exposure).", + ) jupyter_port: int = Field( default=8889, description="Port on which the Jupyter server listens.", diff --git a/src/sparkctl/postgres/setup_metastore.sh b/src/sparkctl/postgres/setup_metastore.sh index dba23b9..14978d2 100644 --- a/src/sparkctl/postgres/setup_metastore.sh +++ b/src/sparkctl/postgres/setup_metastore.sh @@ -1,6 +1,10 @@ #!/bin/bash pg_exists=$1 # The password is passed through the environment so that it does not appear in process listings. +if [ -z "${SPARKCTL_PG_PASSWORD}" ]; then + echo "SPARKCTL_PG_PASSWORD must be set and non-empty" >&2 + exit 1 +fi pg_password=${SPARKCTL_PG_PASSWORD} module load apptainer diff --git a/src/sparkctl/postgres/start_container.sh b/src/sparkctl/postgres/start_container.sh index 6c42aaf..a515a9c 100644 --- a/src/sparkctl/postgres/start_container.sh +++ b/src/sparkctl/postgres/start_container.sh @@ -2,6 +2,10 @@ pg_data_dir=$1 pg_run_dir=$2 # The password is passed through the environment so that it does not appear in process listings. +if [ -z "${SPARKCTL_PG_PASSWORD}" ]; then + echo "SPARKCTL_PG_PASSWORD must be set and non-empty" >&2 + exit 1 +fi pg_password=${SPARKCTL_PG_PASSWORD} # TODO: Make these configurable. diff --git a/src/sparkctl/spark_process_runner.py b/src/sparkctl/spark_process_runner.py index 9a61770..6a71eec 100644 --- a/src/sparkctl/spark_process_runner.py +++ b/src/sparkctl/spark_process_runner.py @@ -88,7 +88,7 @@ def start_jupyter_server(self) -> None: command, "--no-browser", f"--port={port}", - "--ip=0.0.0.0", + f"--ip={self._config.runtime.jupyter_ip}", f"--notebook-dir={self._config.directories.base}", ] logger.info("Start Jupyter server: {}", " ".join(cmd)) @@ -202,7 +202,10 @@ def run_one(worker: str) -> int: return subprocess.run(["ssh", worker, script]).returncode failures: dict[str, int] = {} - with ThreadPoolExecutor(max_workers=max(1, len(workers))) as executor: + # Cap concurrency so a large allocation does not spawn thousands of ssh threads at once; + # the bounded pool keeps most of the speedup without exhausting local resources. + max_workers = min(32, max(1, len(workers))) + with ThreadPoolExecutor(max_workers=max_workers) as executor: futures = {executor.submit(run_one, worker): worker for worker in workers} for future in as_completed(futures): worker = futures[future] diff --git a/uv.lock b/uv.lock index b901090..18e8548 100644 --- a/uv.lock +++ b/uv.lock @@ -36,6 +36,101 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" }, ] +[[package]] +name = "anyio" +version = "4.13.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "idna" }, + { name = "typing-extensions", marker = 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review discussion, keep clean() deleting the configured scratch directory (which may intentionally live outside the base directory, e.g. a dedicated scratch filesystem) and instead document that spark_scratch should be a dedicated directory. Co-Authored-By: Claude Opus 4.8 (1M context) --- src/sparkctl/cli/sparkctl.py | 6 +++++- src/sparkctl/models.py | 3 ++- 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/src/sparkctl/cli/sparkctl.py b/src/sparkctl/cli/sparkctl.py index 0140471..e490ea0 100644 --- a/src/sparkctl/cli/sparkctl.py +++ b/src/sparkctl/cli/sparkctl.py @@ -643,7 +643,11 @@ def stop(ctx: click.Context, directory: Path) -> None: @click.argument("directory", type=click.Path(path_type=Path)) @click.pass_context def clean(ctx: click.Context, directory: Path) -> None: - """Delete all Spark runtime files in the directory.""" + """Delete all Spark runtime files in the directory. + + This also deletes the configured spark_scratch directory recursively, even when it is located + outside the base configuration directory. Point spark_scratch at a dedicated directory. + """ setup_logging( filename="sparkctl.log", console_level=ctx.find_root().params["console_level"], diff --git a/src/sparkctl/models.py b/src/sparkctl/models.py index eac7fa5..d4c84d3 100644 --- a/src/sparkctl/models.py +++ b/src/sparkctl/models.py @@ -225,7 +225,8 @@ class RuntimeDirectories(SparkctlBaseModel): ) spark_scratch: Path = Field( default=Path("spark_scratch"), - description="Directory to use for shuffle data.", + description="Directory to use for shuffle data. Use a dedicated directory: `sparkctl clean` " + "deletes it recursively, even when it is outside the base configuration directory.", ) metastore_dir: Path = Field( default=Path(), description="Set a custom directory for the metastore and warehouse." From 730c7599d0e64cd4572c4454293d744f134bf6a6 Mon Sep 17 00:00:00 2001 From: Daniel Thom Date: Sat, 13 Jun 2026 10:52:57 -0600 Subject: [PATCH 11/24] Make `sparkctl clean` refuse to run while a cluster is running Running clean on an active cluster deletes the state needed to stop it (config.json, status.json, srun_workers.pid, jupyter.pid, the conf and scratch dirs), orphaning the Spark/Jupyter processes. clean() now checks status.json and raises if any tracked process is recorded as running, directing the user to run `sparkctl stop` first. A --force flag overrides the guard, and the CLI reports the error cleanly instead of a traceback. Co-Authored-By: Claude Opus 4.8 (1M context) --- src/sparkctl/cli/sparkctl.py | 18 +++++++++++++++--- src/sparkctl/cluster_manager.py | 18 ++++++++++++++++-- src/sparkctl/models.py | 4 ++++ tests/test_cluster_manager.py | 27 +++++++++++++++++++++++++++ 4 files changed, 62 insertions(+), 5 deletions(-) diff --git a/src/sparkctl/cli/sparkctl.py b/src/sparkctl/cli/sparkctl.py index e490ea0..2d66d18 100644 --- a/src/sparkctl/cli/sparkctl.py +++ b/src/sparkctl/cli/sparkctl.py @@ -641,10 +641,21 @@ def stop(ctx: click.Context, directory: Path) -> None: @click.command() @click.argument("directory", type=click.Path(path_type=Path)) +@click.option( + "--force/--no-force", + is_flag=True, + default=False, + show_default=True, + help="Clean even if a cluster appears to be running. By default clean refuses in that case " + "because it would delete the files needed to stop the cluster.", +) @click.pass_context -def clean(ctx: click.Context, directory: Path) -> None: +def clean(ctx: click.Context, directory: Path, force: bool) -> None: """Delete all Spark runtime files in the directory. + Stop the cluster before cleaning. By default this refuses to run while a cluster appears to be + running, since it deletes the state needed to stop it; pass --force to override. + This also deletes the configured spark_scratch directory recursively, even when it is located outside the base configuration directory. Point spark_scratch at a dedicated directory. """ @@ -654,8 +665,9 @@ def clean(ctx: click.Context, directory: Path) -> None: file_level=ctx.find_root().params["file_level"], mode="a", ) - mgr = ClusterManager.load(directory) - mgr.clean() + res = handle_sparkctl_exception(ctx, lambda: ClusterManager.load(directory).clean(force=force)) + if res[1] != 0: + ctx.exit(res[1]) def handle_sparkctl_exception(ctx: click.Context, func, *args, **kwargs) -> Any: diff --git a/src/sparkctl/cluster_manager.py b/src/sparkctl/cluster_manager.py index d095255..0d93c52 100644 --- a/src/sparkctl/cluster_manager.py +++ b/src/sparkctl/cluster_manager.py @@ -109,8 +109,22 @@ def load(cls, directory: Path | str | None = None) -> Self: status = None return cls(config, status=status) - def clean(self) -> None: - """Delete all Spark runtime files generated by sparkctl in the base directory.""" + def clean(self, force: bool = False) -> None: + """Delete all Spark runtime files generated by sparkctl in the base directory. + + Parameters + ---------- + force + Clean even when a cluster appears to be running. By default ``clean`` refuses in that + case because deleting the runtime files removes the state needed to stop the cluster. + """ + if not force and self._status is not None and self._status.is_any_running(): + msg = ( + "A Spark cluster appears to be running (per status.json). Run `sparkctl stop` " + "before cleaning, otherwise the files needed to stop the cluster are deleted and " + "the processes are orphaned. Pass --force to clean anyway." + ) + raise InvalidConfiguration(msg) base = self._config.directories.base directories = [ self._config.directories.get_spark_conf_dir(), diff --git a/src/sparkctl/models.py b/src/sparkctl/models.py index d4c84d3..d2abf09 100644 --- a/src/sparkctl/models.py +++ b/src/sparkctl/models.py @@ -389,3 +389,7 @@ class StatusTracker(SparkctlBaseModel): started_thrift_server: bool = False started_jupyter: bool = False started_postgres: bool = False + + def is_any_running(self) -> bool: + """Return True if any tracked process is recorded as running.""" + return any(self.model_dump().values()) diff --git a/tests/test_cluster_manager.py b/tests/test_cluster_manager.py index a54257a..0db0d0b 100644 --- a/tests/test_cluster_manager.py +++ b/tests/test_cluster_manager.py @@ -10,6 +10,7 @@ SparkConfig, ) from sparkctl.exceptions import InvalidConfiguration +from sparkctl.models import StatusTracker def test_cluster_manager_workers(setup_local_env: tuple[SparkConfig, Path]): @@ -23,6 +24,32 @@ def test_cluster_manager_workers(setup_local_env: tuple[SparkConfig, Path]): assert mgr.get_workers() == new_workers +def test_clean_refuses_while_running(setup_local_env: tuple[SparkConfig, Path]): + config, tmp_path = setup_local_env + config.directories.base = tmp_path + config.directories.spark_scratch = tmp_path / "spark_scratch" + scratch = config.directories.spark_scratch + scratch.mkdir() + mgr = ClusterManager(config, status=StatusTracker(started_master=True)) + with pytest.raises(InvalidConfiguration): + mgr.clean() + # The guard must not delete anything before raising. + assert scratch.exists() + # --force overrides the guard. + mgr.clean(force=True) + assert not scratch.exists() + + +def test_clean_when_stopped(setup_local_env: tuple[SparkConfig, Path]): + config, tmp_path = setup_local_env + config.directories.base = tmp_path + config.directories.spark_scratch = tmp_path / "spark_scratch" + config.directories.spark_scratch.mkdir() + mgr = ClusterManager(config, status=StatusTracker()) + mgr.clean() + assert not config.directories.spark_scratch.exists() + + def test_configure_reverse_proxy_and_prometheus(setup_local_env: tuple[SparkConfig, Path]): config, tmp_path = setup_local_env config.directories.spark_scratch = tmp_path / "spark_scratch" From 571cf61a9a18287e1b0b87679ba4bf4f2e807d50 Mon Sep 17 00:00:00 2001 From: Daniel Thom Date: Sat, 13 Jun 2026 10:56:09 -0600 Subject: [PATCH 12/24] Address Copilot round 2: tar filter compat and portable stat in docs - hive.py: guard the tar extraction `filter="data"` argument (PEP 706) behind a `tarfile.data_filter` capability check so enabling the Postgres Hive metastore does not TypeError on Python 3.11 patch releases before 3.11.4, which this project still supports. - docs/debugging: replace the GNU-only `find ... -exec stat -c` with a portable `ls -lt | head` that also works on macOS/BSD. Co-Authored-By: Claude Opus 4.8 (1M context) --- docs/how_tos/debugging/index.md | 5 +++-- src/sparkctl/hive.py | 9 ++++++++- 2 files changed, 11 insertions(+), 3 deletions(-) diff --git a/docs/how_tos/debugging/index.md b/docs/how_tos/debugging/index.md index ec5274f..0e021e2 100644 --- a/docs/how_tos/debugging/index.md +++ b/docs/how_tos/debugging/index.md @@ -47,9 +47,10 @@ you can tail the `stderr` files to see what is happening: $ tail -f spark_scratch/workers/*/*/stderr ``` -If you have many executors, you may want to tail only the most recent ones. Identify them with +If you have many executors, you may want to tail only the most recent ones. List the `stderr` +files newest-first with (works on both Linux and macOS): ```console -$ find spark_scratch -type f -name stderr -exec stat -c '%Y %y %n' {} + 2>/dev/null | sort -n +$ ls -lt spark_scratch/workers/*/*/stderr | head ``` ## Spark shuffle partitions diff --git a/src/sparkctl/hive.py b/src/sparkctl/hive.py index 2d903c1..2df6331 100644 --- a/src/sparkctl/hive.py +++ b/src/sparkctl/hive.py @@ -46,7 +46,14 @@ def init_hive(config: SparkConfig): if hive_home.exists(): shutil.rmtree(hive_home) with tarfile.open(config.binaries.hive_tarball, "r:gz") as tar: - tar.extractall(path=config.directories.base, filter="data") + # The extraction `filter` argument (PEP 706) is present on 3.12+ and on later 3.11 patch + # releases (3.11.4+), but not on the earliest 3.11 versions this project supports. + # `tarfile.data_filter` exists exactly when the argument does, so use the safe "data" + # filter when available; the tarball is a trusted Apache release otherwise. + if hasattr(tarfile, "data_filter"): + tar.extractall(path=config.directories.base, filter="data") + else: + tar.extractall(path=config.directories.base) hive_conf = hive_home / "conf" shutil.copyfile( From a5065892ab92fff006616de80cd3175b5f81e434 Mon Sep 17 00:00:00 2001 From: Daniel Thom Date: Sat, 13 Jun 2026 11:05:15 -0600 Subject: [PATCH 13/24] Fix Jupyter connectivity and reduce log noise The Jupyter server was running but unreachable: the 127.0.0.1 default (from the earlier security fix) does not work with the standard HPC pattern of tunneling to the compute node's hostname through a login node. - Default jupyter_ip back to 0.0.0.0 (keep --jupyter-ip 127.0.0.1 as an opt-in for the bind-to-localhost workflow). Access stays protected by Jupyter's token. - After startup, detect the access URL from the log and print a clean, copy-pasteable SSH tunnel command and token URL, so connecting does not depend on reading past the log noise. - Pass --LanguageServerManager.autodetect=False to stop jupyter_lsp from flooding the log with tracebacks for uninstalled language servers. - Document that the residual Node/yarn error comes from a jupyterlab package in the environment and is harmless (the jupyter extra installs only the classic notebook). Co-Authored-By: Claude Opus 4.8 (1M context) --- docs/how_tos/applications/jupyter.md | 44 ++++++++++++--------- src/sparkctl/models.py | 9 +++-- src/sparkctl/spark_process_runner.py | 59 ++++++++++++++++++++++++---- tests/test_spark_process_runner.py | 36 +++++++++++++++++ 4 files changed, 117 insertions(+), 31 deletions(-) diff --git a/docs/how_tos/applications/jupyter.md b/docs/how_tos/applications/jupyter.md index 67e3780..dbe595d 100644 --- a/docs/how_tos/applications/jupyter.md +++ b/docs/how_tos/applications/jupyter.md @@ -36,34 +36,40 @@ spark = SparkSession.builder.getOrCreate() spark.createDataFrame([(1, 2), (3, 4)], ["a", "b"]).show() ``` -## Find the access URL +## Connect to the server -The Jupyter access URL, including its login token, is written to `jupyter.log` in the cluster base -directory: +When the server starts, sparkctl logs the node it is running on, a ready-to-use SSH tunnel +command, and the access URL (with token). It looks like: -```console -$ grep -m1 'http' jupyter.log ``` - -Jupyter listens on `127.0.0.1:8889` by default. Binding to localhost keeps the server off the -cluster network; reach it with an SSH tunnel. From your laptop, forward the port to the master node -(use `-J ` to hop through the login node, and replace `master-node` with the node -running the server): - -```console -$ ssh -J -L 8889:localhost:8889 master-node +Jupyter is running on x1000c0s0b0n0 (port 8889). From your laptop, open an SSH tunnel: + ssh -L 8889:x1000c0s0b0n0:8889 +then browse to: + http://localhost:8889/tree?token= ``` -Then open the URL from `jupyter.log`, replacing the host with `localhost`. Change the port with -`--jupyter-port`. +Run the `ssh` command from your laptop (replacing `` with your cluster's login +host), then open the `http://localhost:8889/...` URL in your browser. The same information is always +available in `jupyter.log` in the cluster base directory. Change the port with `--jupyter-port`. + +Jupyter listens on all interfaces (`0.0.0.0`) by default so it is reachable by tunneling to the +compute node's hostname through a login node, which is the portable HPC pattern. Access is protected +by Jupyter's token. ```{eval-rst} -.. note:: If you cannot reach the master node directly, ``--jupyter-ip 0.0.0.0`` makes Jupyter - listen on all interfaces so you can tunnel through the login node to the master's hostname. This - exposes the server to the cluster network (it is still protected by Jupyter's access token), so - prefer the localhost default when possible. +.. note:: To bind to localhost only, pass ``--jupyter-ip 127.0.0.1``. The server is then off the + cluster network, but you must tunnel directly into the compute node, e.g. + ``ssh -J -L 8889:localhost:8889 ``. ``` +## Reducing log noise + +If `jupyter.log` is noisy, note that the most verbose tracebacks come from optional integrations, +not from sparkctl: language-server probing is disabled automatically, but a `jupyterlab` package +installed in your environment runs a build check at startup that can log a Node/yarn error. It is +harmless. Installing only the classic notebook (`pip install "sparkctl[jupyter]"`, without +`jupyterlab`) avoids it. + ## Stopping `sparkctl stop` shuts the Jupyter server down along with the rest of the cluster. diff --git a/src/sparkctl/models.py b/src/sparkctl/models.py index d2abf09..f1ec9ee 100644 --- a/src/sparkctl/models.py +++ b/src/sparkctl/models.py @@ -112,10 +112,11 @@ class SparkRuntimeParams(SparkctlBaseModel): "Defaults to the classic 'notebook'; use 'lab' for JupyterLab.", ) jupyter_ip: str = Field( - default="127.0.0.1", - description="IP address the Jupyter server binds to. Defaults to localhost, which is " - "secure and works with an SSH tunnel to the master node. Set to 0.0.0.0 to listen on all " - "interfaces (only if you understand the exposure).", + default="0.0.0.0", + description="IP address the Jupyter server binds to. Defaults to all interfaces so it can " + "be reached by tunneling to the compute node's hostname through a login node (the common " + "HPC pattern); access is protected by Jupyter's token. Set to 127.0.0.1 to bind to " + "localhost only, which requires tunneling directly into the compute node.", ) jupyter_port: int = Field( default=8889, diff --git a/src/sparkctl/spark_process_runner.py b/src/sparkctl/spark_process_runner.py index 6a71eec..5cdaeef 100644 --- a/src/sparkctl/spark_process_runner.py +++ b/src/sparkctl/spark_process_runner.py @@ -1,4 +1,5 @@ import os +import re import shlex import shutil import signal @@ -7,6 +8,7 @@ import time from concurrent.futures import ThreadPoolExecutor, as_completed from pathlib import Path +from socket import gethostname from typing import Any from loguru import logger @@ -90,6 +92,10 @@ def start_jupyter_server(self) -> None: f"--port={port}", f"--ip={self._config.runtime.jupyter_ip}", f"--notebook-dir={self._config.directories.base}", + # Stop jupyter_lsp from probing for language servers that are not installed, which + # otherwise floods the log with tracebacks. Passed as config (dotted form) so it is + # ignored harmlessly when jupyter_lsp is not present. + "--LanguageServerManager.autodetect=False", ] logger.info("Start Jupyter server: {}", " ".join(cmd)) with open(log_file, "w", encoding="utf-8") as f_out: @@ -101,17 +107,54 @@ def start_jupyter_server(self) -> None: start_new_session=True, env=env, ) - time.sleep(1) - ret = proc.poll() - if ret is not None: - msg = f"The Jupyter server exited immediately with return code {ret}. See {log_file}." - raise ExecutionError(msg) + url = self._wait_for_jupyter_url(proc, log_file) pid_file.write_text(f"{proc.pid}\n", encoding="utf-8") + self._log_jupyter_access(url, port, log_file) + + def _wait_for_jupyter_url(self, proc: "subprocess.Popen[bytes]", log_file: Path) -> str | None: + """Wait for the server to report its access URL, returning it (or None on timeout). + + Also fails fast if the process exits before it is ready. + """ + url_regex = re.compile(r"https?://\S+/(?:tree|lab)\?token=\w+") + for _ in range(40): # up to ~20 seconds + ret = proc.poll() + if ret is not None: + msg = f"The Jupyter server exited with return code {ret}. See {log_file}." + raise ExecutionError(msg) + if log_file.exists(): + match = url_regex.search(log_file.read_text(encoding="utf-8")) + if match: + return match.group(0) + time.sleep(0.5) + return None + + def _log_jupyter_access(self, url: str | None, port: int, log_file: Path) -> None: + if url is None: + logger.warning( + "Started the Jupyter server, but did not detect its access URL within the timeout. " + "Find the URL (with token) in {}.", + log_file, + ) + return + + host = gethostname() + token_match = re.search(r"token=(\w+)", url) + token = f"?token={token_match.group(1)}" if token_match else "" + path = "lab" if self._config.runtime.jupyter_command == "lab" else "tree" logger.info( - "Started Jupyter server with pid {} on port {}. The access URL (with token) is in {}.", - proc.pid, + "Jupyter is running on {} (port {}). From your laptop, open an SSH tunnel:\n" + " ssh -L {}:{}:{} \n" + "then browse to:\n" + " http://localhost:{}/{}{}", + host, + port, + port, + host, + port, port, - log_file, + path, + token, ) def stop_jupyter_server(self) -> int: diff --git a/tests/test_spark_process_runner.py b/tests/test_spark_process_runner.py index 3e5df61..8f9afef 100644 --- a/tests/test_spark_process_runner.py +++ b/tests/test_spark_process_runner.py @@ -1,4 +1,5 @@ import os +import shutil import signal import subprocess import time @@ -49,6 +50,41 @@ def no_sleep(monkeypatch): monkeypatch.setattr(time, "sleep", lambda _: None) +def test_start_jupyter_server(tmp_path, monkeypatch, no_sleep): + config = make_config(tmp_path, ComputeEnvironment.NATIVE) + config.runtime.start_jupyter = True + config.runtime.jupyter_port = 9999 + log_file = config.directories.base / "jupyter.log" + captured = {} + + def fake_popen(cmd, **kwargs): + captured["cmd"] = cmd + # Simulate the server writing its startup banner to the redirected log. + log_file.write_text("http://127.0.0.1:9999/tree?token=abc123\n", encoding="utf-8") + return FakePopen(cmd, **kwargs) + + monkeypatch.setattr(shutil, "which", lambda name: "/usr/bin/jupyter") + monkeypatch.setattr(subprocess, "Popen", fake_popen) + runner = SparkProcessRunner(config, SPARK_URL) + runner.start_jupyter_server() + + cmd = captured["cmd"] + assert cmd[:2] == ["/usr/bin/jupyter", "notebook"] + # The default binds to all interfaces so the node is reachable through a login-node tunnel. + assert "--ip=0.0.0.0" in cmd + assert "--port=9999" in cmd + assert "--LanguageServerManager.autodetect=False" in cmd + assert (config.directories.base / "jupyter.pid").read_text(encoding="utf-8").strip() == "12345" + + +def test_start_jupyter_server_not_installed(tmp_path, monkeypatch): + config = make_config(tmp_path, ComputeEnvironment.NATIVE) + monkeypatch.setattr(shutil, "which", lambda name: None) + runner = SparkProcessRunner(config, SPARK_URL) + with pytest.raises(ExecutionError): + runner.start_jupyter_server() + + def test_start_worker_processes_srun(tmp_path, monkeypatch, no_sleep): config = make_config(tmp_path, ComputeEnvironment.SLURM) captured = {} From 2057c18b65cff66852d35242ada359cb4017966a Mon Sep 17 00:00:00 2001 From: Daniel Thom Date: Sat, 13 Jun 2026 11:24:41 -0600 Subject: [PATCH 14/24] Improve Jupyter user documentation - Add "Working in the notebook" (kernel environment, where notebooks are saved) and a "Troubleshooting" section to the Jupyter how-to, and link to the Spark Connect tutorial. - Surface Jupyter in the tutorials decision guide so users looking for an interactive/notebook workflow can find it. Co-Authored-By: Claude Opus 4.8 (1M context) --- docs/how_tos/applications/jupyter.md | 21 +++++++++++++++++++++ docs/tutorials/index.md | 1 + 2 files changed, 22 insertions(+) diff --git a/docs/how_tos/applications/jupyter.md b/docs/how_tos/applications/jupyter.md index dbe595d..7f673f7 100644 --- a/docs/how_tos/applications/jupyter.md +++ b/docs/how_tos/applications/jupyter.md @@ -62,6 +62,27 @@ by Jupyter's token. ``ssh -J -L 8889:localhost:8889 ``. ``` +## Working in the notebook + +The notebook server runs in the same environment as sparkctl, so its Python kernel already has +`pyspark-client` available. With the Connect server enabled, every notebook connects to the cluster +through `SPARK_REMOTE` — just call `SparkSession.builder.getOrCreate()` as shown above. See the +[Spark Connect tutorial](../../tutorials/run_python_spark_jobs_spark_connect.md) for more on what +the Connect client supports. + +Notebooks are served from the cluster base directory, so any notebooks you create are saved there +and persist after the cluster stops. + +## Troubleshooting + +- **The browser cannot connect.** Confirm the SSH tunnel from the startup banner is running, and + that you opened the URL with its `?token=...` (copy it from the banner or `jupyter.log`). If you + set `--jupyter-ip 127.0.0.1`, the tunnel must terminate on the compute node itself. +- **`Address already in use`.** Another process holds the port; choose a different one with + `--jupyter-port`. +- **`SparkSession` cannot reach the cluster.** Make sure you configured with `--connect-server`; + without it `SPARK_REMOTE` is not set and the notebook will not auto-connect. + ## Reducing log noise If `jupyter.log` is noisy, note that the most verbose tracebacks come from optional integrations, diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md index c9e4b2f..4da0bdf 100644 --- a/docs/tutorials/index.md +++ b/docs/tutorials/index.md @@ -21,6 +21,7 @@ These tutorials guide you through running Spark jobs on HPC clusters using spark - **"I want to control the cluster from Python code"** → [Python Library](run_python_spark_jobs_script.md) - **"I want to explore data interactively"** → [Interactive Development](run_python_spark_jobs_interactively.md) +- **"I want to work in Jupyter notebooks"** → [Run a Jupyter notebook](../how_tos/applications/jupyter.md) - **"I use Ibis and want portable dataframe code"** → [Ibis](run_ibis_spark_jobs.md) - **"I want a minimal client installation"** → [Spark Connect CLI](run_python_spark_jobs_spark_connect.md) - **"I want to submit batch jobs"** → [spark-submit / pyspark](run_spark_jobs.md) From d08c1a8c071f92ed67680acd0886f4d9d3028eda Mon Sep 17 00:00:00 2001 From: Daniel Thom Date: Sat, 13 Jun 2026 14:05:46 -0600 Subject: [PATCH 15/24] Address Copilot round 3 and expand GPU docs - Fix wrong config key in gpus.md (spark.task.resource.gpu.amount). - GPU discovery script now prefers CUDA_VISIBLE_DEVICES (correct on Slurm and works when nvidia-smi is unavailable in executors), falls back to nvidia-smi, and fails fast if neither is available. - Document what applications must do for RAPIDS vs. custom GPU code, and when GPUs are advantageous over CPUs. Co-Authored-By: Claude Opus 4.8 (1M context) --- docs/how_tos/configuration/gpus.md | 82 +++++++++++++++++++++++++++++- src/sparkctl/cluster_manager.py | 20 ++++++-- 2 files changed, 98 insertions(+), 4 deletions(-) diff --git a/docs/how_tos/configuration/gpus.md b/docs/how_tos/configuration/gpus.md index 133493c..d858240 100644 --- a/docs/how_tos/configuration/gpus.md +++ b/docs/how_tos/configuration/gpus.md @@ -31,7 +31,7 @@ This generates a GPU discovery script in the cluster's `conf` directory and writ - `spark.task.resource.gpu.amount` By default each executor is assigned one GPU and tasks share that GPU -(`task.resource.gpu.amount = executor_gpu_amount / executor_cores`). Tune these through your +(`spark.task.resource.gpu.amount = executor_gpu_amount / executor_cores`). Tune these through your settings file: ```toml @@ -70,3 +70,83 @@ enables `spark.rapids.sql.enabled`. .. note:: ``spark.jars`` is used instead of ``spark.{driver,executor}.extraClassPath`` so the RAPIDS jar does not conflict with the classpath entries the PostgreSQL Hive metastore sets. ``` + +## What your application needs to do + +What you change in your application depends on *how* you intend to use the GPUs. + +### SQL / DataFrame workloads with RAPIDS + +For SQL and DataFrame queries, the RAPIDS Accelerator is mostly transparent: once the plugin is +enabled (above), supported operators run on the GPU with no code changes. The important caveat is +that **not every operator is GPU-accelerated** — unsupported expressions, data types, and many +Python/Scala UDFs silently fall back to the CPU, and a query that bounces between CPU and GPU can be +slower than staying on the CPU. + +Before assuming a query is GPU-accelerated, ask RAPIDS what it actually placed on the GPU: + +```python +spark.conf.set("spark.rapids.sql.explain", "NOT_ON_GPU") # log every operator that fell back +df.explain() # "GPU" nodes ran on the GPU; "Project"/"Filter" without "Gpu" fell back to CPU +``` + +Useful tuning knobs (set in your settings file's `spark_defaults` or at runtime): + +- `spark.rapids.sql.concurrentGpuTasks` (sparkctl defaults to `1`) — how many tasks share a GPU at + once. Raising it to `2`–`4` can improve throughput if GPU memory allows. +- `spark.sql.files.maxPartitionBytes` — larger partitions (e.g. `512m`) give the GPU more work per + task, which it prefers over many tiny tasks. +- Keep Adaptive Query Execution on (`spark.sql.adaptive.enabled true`, the Spark default). + +### Custom GPU code (no RAPIDS) + +If you call GPU libraries directly (e.g. CuPy, PyTorch, RAPIDS cuDF, or XGBoost) inside your tasks, +RAPIDS does not apply. You still enable GPU-aware scheduling with `--gpus` so Spark assigns GPUs to +tasks, then read the assigned GPU address from the task context and pin your library to it: + +```python +from pyspark import TaskContext + +def run_on_gpu(rows): + ctx = TaskContext.get() + gpu = ctx.resources()["gpu"].addresses[0] # address(es) assigned to this task + import cupy + with cupy.cuda.Device(int(gpu)): + ... # your GPU work here + +rdd.mapPartitions(run_on_gpu).collect() +``` + +Pinning to the assigned address is what keeps two tasks on the same node from fighting over the same +device. The `spark.task.resource.gpu.amount` value sparkctl writes controls how many tasks Spark +will co-schedule on each GPU. + +## When are GPUs worth it? + +GPUs are not a blanket speedup for Spark — they help some workloads dramatically and slow others +down. Reach for GPUs when most of these hold: + +- **Large data and heavy compute.** Multi-GB-to-TB scans with joins, aggregations, sorts, window + functions, or `expand`/`hash` heavy plans. The GPU's advantage grows with data volume; small jobs + are dominated by launch and transfer overhead. +- **Columnar formats.** Parquet/ORC/CSV at scale, where RAPIDS can read and process columns + directly on the GPU. +- **Operations RAPIDS supports.** Standard SQL/DataFrame expressions and supported types. Check with + `spark.rapids.sql.explain` (above) — a plan full of CPU fallbacks will not benefit. +- **ML training/inference** with GPU-native libraries (XGBoost, deep learning, RAPIDS cuML). + +GPUs usually do **not** help, and can be slower or more expensive per result, when: + +- The dataset is small or the job is short — fixed GPU overhead dominates. +- The work is dominated by **Python/Scala UDFs**, complex regex, or other operators that fall back + to the CPU (data must round-trip between CPU and GPU memory). +- The job is I/O- or shuffle-network-bound rather than compute-bound. +- Per-partition working sets exceed GPU memory, forcing spills. + +```{eval-rst} +.. tip:: Before committing a workload to GPUs, run NVIDIA's + `Spark RAPIDS qualification tool `_ + against the CPU run's event logs. It estimates the speedup (and flags unsupported operators) + from a real run, which is more reliable than guessing — especially given that GPU support in + sparkctl is still experimental and unvalidated. +``` diff --git a/src/sparkctl/cluster_manager.py b/src/sparkctl/cluster_manager.py index 0d93c52..adc857b 100644 --- a/src/sparkctl/cluster_manager.py +++ b/src/sparkctl/cluster_manager.py @@ -713,9 +713,23 @@ def _write_gpu_discovery_script(self) -> Path: # addresses. This mirrors Spark's example getGpusResources.sh. script.write_text( """#!/usr/bin/env bash -# Generated by sparkctl. Reports the GPUs visible to Spark in the format it expects. -ADDRS=$(nvidia-smi --query-gpu=index --format=csv,noheader | sed -e ':a' -e 'N' -e '$!ba' \\ - -e 's/\\n/","/g') +# Generated by sparkctl. Reports the GPUs visible to Spark in the format it expects: +# {"name": "gpu", "addresses": ["0", "1", ...]}. +# +# Prefer CUDA_VISIBLE_DEVICES when it is set: on Slurm clusters it reflects exactly the GPUs +# allocated to the job, and nvidia-smi may be unavailable in the executor environment (e.g. +# containerized executors). Fall back to nvidia-smi otherwise, and fail fast if neither source +# of GPU information is available. +set -euo pipefail +if [[ -n "${CUDA_VISIBLE_DEVICES:-}" ]]; then + ADDRS=$(echo "$CUDA_VISIBLE_DEVICES" | sed -e 's/,/","/g') +elif command -v nvidia-smi >/dev/null 2>&1; then + ADDRS=$(nvidia-smi --query-gpu=index --format=csv,noheader | sed -e ':a' -e 'N' -e '$!ba' \\ + -e 's/\\n/","/g') +else + echo "GPU discovery failed: neither CUDA_VISIBLE_DEVICES nor nvidia-smi is available" >&2 + exit 1 +fi echo "{\\"name\\": \\"gpu\\", \\"addresses\\": [\\"$ADDRS\\"]}" """, encoding="utf-8", From 60164f27fcaf52bde4ea2bea4ac63777089932bf Mon Sep 17 00:00:00 2001 From: Daniel Thom Date: Sat, 13 Jun 2026 15:40:19 -0600 Subject: [PATCH 16/24] Fail fast when executor_cores exceeds available worker CPUs Previously, configuring an executor with more cores than the worker node provides (e.g. a GPU allocation that grants GPUs but only one CPU) produced a config that Spark could never schedule: jobs hung forever with "Initial job has not accepted any resources". Mirror the existing executor-memory check and raise InvalidConfiguration at configure time with guidance to request more CPUs or lower executor_cores. Also document this and the related "worker advertises no GPUs after enabling --gpus without a restart" case in the debugging guide. Co-Authored-By: Claude Opus 4.8 (1M context) --- docs/how_tos/debugging/index.md | 27 +++++++++++++++++++++++++++ src/sparkctl/cluster_manager.py | 17 ++++++++++++++--- tests/test_cluster_manager.py | 14 ++++++++++++++ 3 files changed, 55 insertions(+), 3 deletions(-) diff --git a/docs/how_tos/debugging/index.md b/docs/how_tos/debugging/index.md index 0e021e2..14c473d 100644 --- a/docs/how_tos/debugging/index.md +++ b/docs/how_tos/debugging/index.md @@ -53,6 +53,33 @@ files newest-first with (works on both Linux and macOS): $ ls -lt spark_scratch/workers/*/*/stderr | head ``` +## Jobs hang with "Initial job has not accepted any resources" +If a job never starts and the driver repeatedly logs + +```console +WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to +ensure that workers are registered and have sufficient resources +``` + +then the driver registered with the master but no executor could be launched. Open the master web +UI on port 8080 (see [above](#spark-web-ui)) and check the workers. Common causes: + +- **The node has fewer CPUs than `executor_cores`.** An executor must fit on a single worker, so if + the worker advertises fewer free cores than `executor_cores` (5 by default), no executor can be + scheduled and the job hangs forever. This frequently happens when a GPU allocation grants GPUs but + only one CPU. Check `echo $SLURM_CPUS_ON_NODE`; request more CPUs (e.g. the Slurm + `--cpus-per-task` option) or lower `executor_cores`, then reconfigure and restart. As of recent + versions sparkctl fails fast at `configure` time when `executor_cores` exceeds the available CPUs + rather than letting the job hang. +- **Executor memory exceeds what the worker offers** — lower `--executor-memory-gb` (or + `executor_cores`). +- **GPU scheduling was enabled but the worker advertises no GPUs.** If you ran `sparkctl configure + --gpus` while the cluster was already running, the worker is still advertising zero GPUs, so an + executor that requests a GPU can never be placed. Restart the cluster (`sparkctl stop && sparkctl + start`) so the worker re-runs GPU discovery. Confirm the worker's `resources` in the master UI + lists the expected GPU addresses, and that `conf/get_gpus_resources.sh` prints them when run in + the allocation. + ## Spark shuffle partitions A common performance issue when running complex queries is due to a non-ideal setting for `spark.sql.shuffle.partitions`. The default Spark value is 200. Some online sources recommend diff --git a/src/sparkctl/cluster_manager.py b/src/sparkctl/cluster_manager.py index adc857b..cfbb530 100644 --- a/src/sparkctl/cluster_manager.py +++ b/src/sparkctl/cluster_manager.py @@ -519,9 +519,20 @@ def _config_executors(self, defaults_file: Path) -> None: # Leave one CPU for OS and management software. worker_num_cpus -= 1 - # Use at least one executor per node even when the node has fewer CPUs than - # executor_cores (e.g. a small CI runner or laptop), which would otherwise divide by zero. - min_executors_per_node = max(1, worker_num_cpus // self._config.runtime.executor_cores) + executor_cores = self._config.runtime.executor_cores + if worker_num_cpus < executor_cores: + msg = ( + f"Each worker node has {self._intf.get_worker_num_cpus()} CPU(s) " + f"({worker_num_cpus} after reserving one for the OS), which is fewer than " + f"executor_cores ({executor_cores}). Spark cannot launch an executor that does " + "not fit on a single worker, so jobs would hang indefinitely with 'Initial job " + "has not accepted any resources'. Request more CPUs in the allocation (e.g. the " + "Slurm --cpus-per-task option) or lower runtime.executor_cores." + ) + raise InvalidConfiguration(msg) + + # The guard above ensures worker_num_cpus >= executor_cores, so this is always >= 1. + min_executors_per_node = worker_num_cpus // executor_cores if self._config.runtime.executor_memory_gb is None: executor_memory_gb = worker_memory_gb // min_executors_per_node else: diff --git a/tests/test_cluster_manager.py b/tests/test_cluster_manager.py index 0db0d0b..61ac590 100644 --- a/tests/test_cluster_manager.py +++ b/tests/test_cluster_manager.py @@ -122,6 +122,20 @@ def test_configure_gpus_with_override(setup_local_env: tuple[SparkConfig, Path]) assert discovery_script.stat().st_mode & 0o100 # owner-executable +def test_configure_executor_cores_exceed_worker_cpus_fails( + setup_local_env: tuple[SparkConfig, Path], +): + config, tmp_path = setup_local_env + config.directories.spark_scratch = tmp_path / "spark_scratch" + # FakeCompute reports 12 CPUs (11 usable after reserving one for the OS). An executor that + # needs more cores than that can never be scheduled, so configure must fail fast rather than + # emit a config that hangs with "Initial job has not accepted any resources". + config.runtime.executor_cores = 16 + mgr = ClusterManager.from_config(config) + with pytest.raises(InvalidConfiguration, match="executor_cores"): + mgr.configure() + + def test_configure_rapids_without_jar_fails(setup_local_env: tuple[SparkConfig, Path]): config, tmp_path = setup_local_env config.directories.spark_scratch = tmp_path / "spark_scratch" From c0f44890cf7f766423b34b03090453f90f54bb17 Mon Sep 17 00:00:00 2001 From: Daniel Thom Date: Sat, 13 Jun 2026 15:47:17 -0600 Subject: [PATCH 17/24] Document monitoring GPU usage with NVIDIA tools sparkctl's resource monitor does not capture GPU utilization, so add a section to the GPU how-to covering nvidia-smi / dmon / nvtop, attaching to the worker nodes in the allocation, CSV logging, multi-node monitoring, and how to read utilization to confirm work is on the GPU. Co-Authored-By: Claude Opus 4.8 (1M context) --- docs/how_tos/configuration/gpus.md | 37 ++++++++++++++++++++++++++++++ 1 file changed, 37 insertions(+) diff --git a/docs/how_tos/configuration/gpus.md b/docs/how_tos/configuration/gpus.md index d858240..cdd45fa 100644 --- a/docs/how_tos/configuration/gpus.md +++ b/docs/how_tos/configuration/gpus.md @@ -121,6 +121,43 @@ Pinning to the assigned address is what keeps two tasks on the same node from fi device. The `spark.task.resource.gpu.amount` value sparkctl writes controls how many tasks Spark will co-schedule on each GPU. +## Monitor GPU usage while a job runs + +sparkctl's built-in resource monitor (`--resource-monitor`) only collects CPU, memory, disk, and +network stats — **it does not capture GPU utilization**. Use NVIDIA's tools directly. + +GPU work happens on the **worker/executor nodes**, so monitor there, not on the node where you +launched the driver. From a login node, attach a second shell to the same Slurm allocation: + +```console +$ srun --overlap --jobid=$SLURM_JOB_ID --nodes=1 --pty bash +``` + +Then use any of: + +```console +$ nvidia-smi -l 1 # full table, refreshed every second +$ nvidia-smi dmon -s pucvmet -d 1 # scrolling per-GPU metrics; best for watching a live job +$ nvtop # htop-style TUI, incl. per-process GPU memory (if available) +``` + +To log the whole run to a CSV for later analysis: + +```console +$ nvidia-smi --query-gpu=timestamp,index,utilization.gpu,utilization.memory,memory.used,power.draw \ + --format=csv -l 1 > gpu_$(hostname).csv +``` + +To watch every node at once on a multi-node cluster (node names are in `conf/workers`): + +```console +$ srun --overlap --jobid=$SLURM_JOB_ID --ntasks-per-node=1 nvidia-smi dmon -c 120 -d 1 +``` + +Watch `utilization.gpu` while a query runs. Sustained high utilization means operators really are +executing on the GPU; near-zero utilization while CPUs are busy means the work is falling back to +the CPU — cross-check with `spark.rapids.sql.explain` (see above). + ## When are GPUs worth it? GPUs are not a blanket speedup for Spark — they help some workloads dramatically and slow others From dfc6780d9c70314331f1b921060f952a0dad673b Mon Sep 17 00:00:00 2001 From: Daniel Thom Date: Sat, 13 Jun 2026 16:18:01 -0600 Subject: [PATCH 18/24] Drop experimental/untested labels from GPU features GPU-aware scheduling and RAPIDS acceleration have now been validated on a real GPU cluster, so remove the "EXPERIMENTAL (untested)" prefixes from the config field descriptions (which feed the CLI help) and the configure log messages, the warning admonition and "(experimental)" title in the GPU how-to, and the matching caveat in the qualification tool tip. Co-Authored-By: Claude Opus 4.8 (1M context) --- docs/how_tos/configuration/gpus.md | 12 ++---------- src/sparkctl/cluster_manager.py | 8 ++------ src/sparkctl/models.py | 12 ++++++------ 3 files changed, 10 insertions(+), 22 deletions(-) diff --git a/docs/how_tos/configuration/gpus.md b/docs/how_tos/configuration/gpus.md index cdd45fa..8d7cc72 100644 --- a/docs/how_tos/configuration/gpus.md +++ b/docs/how_tos/configuration/gpus.md @@ -1,11 +1,4 @@ -# How to enable GPU acceleration (experimental) - -```{eval-rst} -.. warning:: GPU support is **experimental and untested**. The options below configure Spark's - GPU-aware scheduling and, optionally, the NVIDIA RAPIDS Accelerator, but they have not been - validated on a real GPU cluster. Treat the generated settings as a starting point and expect to - tune them for your site. -``` +# How to enable GPU acceleration ## GPU-aware scheduling @@ -184,6 +177,5 @@ GPUs usually do **not** help, and can be slower or more expensive per result, wh .. tip:: Before committing a workload to GPUs, run NVIDIA's `Spark RAPIDS qualification tool `_ against the CPU run's event logs. It estimates the speedup (and flags unsupported operators) - from a real run, which is more reliable than guessing — especially given that GPU support in - sparkctl is still experimental and unvalidated. + from a real run, which is more reliable than guessing. ``` diff --git a/src/sparkctl/cluster_manager.py b/src/sparkctl/cluster_manager.py index cfbb530..4716168 100644 --- a/src/sparkctl/cluster_manager.py +++ b/src/sparkctl/cluster_manager.py @@ -714,9 +714,7 @@ def _enable_gpus(self, defaults_file: Path) -> None: spark.task.resource.gpu.amount {task_gpu_amount} """ ) - logger.warning( - "Enabled EXPERIMENTAL (untested) GPU scheduling with {} GPU(s) per node.", num_gpus - ) + logger.warning("Enabled GPU scheduling with {} GPU(s) per node.", num_gpus) def _write_gpu_discovery_script(self) -> Path: script = self._config.directories.get_gpu_discovery_script_file() @@ -772,9 +770,7 @@ def _enable_rapids(self, defaults_file: Path) -> None: spark.rapids.sql.concurrentGpuTasks 1 """ ) - logger.warning( - "Enabled EXPERIMENTAL (untested) RAPIDS GPU acceleration using {}", rapids_jar - ) + logger.warning("Enabled RAPIDS GPU acceleration using {}", rapids_jar) def _get_runtime_spark_driver_memory_gb(self) -> int: # Note that spark-defaults.conf takes precedence over our config.json. diff --git a/src/sparkctl/models.py b/src/sparkctl/models.py index f1ec9ee..97fbf4b 100644 --- a/src/sparkctl/models.py +++ b/src/sparkctl/models.py @@ -42,7 +42,7 @@ class BinaryLocations(SparkctlBaseModel): rapids_jar_file: Path | None = Field( default=None, description="Path to the NVIDIA RAPIDS Accelerator for Apache Spark jar file. Only " - "required to enable RAPIDS GPU acceleration (experimental).", + "required to enable RAPIDS GPU acceleration.", ) @field_validator( @@ -150,26 +150,26 @@ class SparkRuntimeParams(SparkctlBaseModel): ) enable_gpus: bool = Field( default=False, - description="EXPERIMENTAL (untested): Enable GPU-aware scheduling. Spark workers advertise " + description="Enable GPU-aware scheduling. Spark workers advertise " "GPUs and executors/tasks request them. Requires GPUs on the worker nodes.", ) gpus_per_node: int | None = Field( default=None, - description="EXPERIMENTAL (untested): Number of GPUs available on each worker node. " + description="Number of GPUs available on each worker node. " "Auto-detected from the compute environment by default.", ) executor_gpu_amount: int = Field( default=1, - description="EXPERIMENTAL (untested): Number of GPUs assigned to each executor.", + description="Number of GPUs assigned to each executor.", ) task_gpu_amount: float | None = Field( default=None, - description="EXPERIMENTAL (untested): GPUs assigned to each task. Defaults to " + description="GPUs assigned to each task. Defaults to " "executor_gpu_amount / executor_cores so that concurrent tasks share an executor's GPUs.", ) enable_rapids: bool = Field( default=False, - description="EXPERIMENTAL (untested): Enable the NVIDIA RAPIDS Accelerator for Apache " + description="Enable the NVIDIA RAPIDS Accelerator for Apache " "Spark to offload SQL/DataFrame operations to GPUs. Implies enable_gpus and requires " "binaries.rapids_jar_file.", ) From 4b5c53b631c84ed23610ce7c0a6717ebfeaed501 Mon Sep 17 00:00:00 2001 From: Daniel Thom Date: Sat, 13 Jun 2026 16:23:07 -0600 Subject: [PATCH 19/24] Downgrade Spark/PySpark from 4.1.2 to 4.1.1 for RAPIDS support The RAPIDS Accelerator supports Apache Spark up to 4.1.1 (with Scala 2.13); 4.1.2 is not a supported shim target. Pin pyspark/pyspark-client to 4.1.1, regenerate the lockfile, update the test download URLs, and update the deployment/tutorial/reference docs and CLI examples to 4.1.1. Also make the .gitignore Spark pattern version-agnostic (spark-*-bin-hadoop3*) so a version bump no longer un-ignores the previously extracted test-data directory. Co-Authored-By: Claude Opus 4.8 (1M context) --- .gitignore | 2 +- docs/faq.md | 2 +- docs/how_tos/configuration/spark_log_level.md | 2 +- docs/how_tos/getting_started/deploy_sparkctl.md | 8 ++++---- docs/how_tos/getting_started/installation.md | 2 +- docs/reference/hpc/kestrel.md | 4 ++-- docs/tutorials/run_ibis_spark_jobs.md | 4 ++-- hpc/environment_module/sparkctl.toml | 2 +- pyproject.toml | 4 ++-- src/sparkctl/cli/sparkctl.py | 4 ++-- tests/conftest.py | 4 ++-- uv.lock | 12 ++++++------ 12 files changed, 25 insertions(+), 25 deletions(-) diff --git a/.gitignore b/.gitignore index 8062556..8ae83f1 100644 --- a/.gitignore +++ b/.gitignore @@ -133,7 +133,7 @@ dmypy.json .vscode tests/data/apache-hive-4.0.1-bin* -tests/data/spark-4.1.2-bin-hadoop3* +tests/data/spark-*-bin-hadoop3* tests/data/postgresql* tests/data/jdk-21.0.7.jdk* conf diff --git a/docs/faq.md b/docs/faq.md index 5497f83..cfa1b0e 100644 --- a/docs/faq.md +++ b/docs/faq.md @@ -70,7 +70,7 @@ export the relevant variables yourself, for example through `spark-env.sh` in th If you are running pyspark/spark-submit after installing via `pip install sparkctl[pyspark]`, your version of pyspark must match the cluster version exactly. Client version 4.1.3 is -incompatible with cluster version 4.1.2. +incompatible with cluster version 4.1.1. ### Why can't my workers connect to the master? diff --git a/docs/how_tos/configuration/spark_log_level.md b/docs/how_tos/configuration/spark_log_level.md index 6fbf449..02291b7 100644 --- a/docs/how_tos/configuration/spark_log_level.md +++ b/docs/how_tos/configuration/spark_log_level.md @@ -39,7 +39,7 @@ The `--spark-log-level` option accepts these values: With `INFO` (default), you'll see messages like: ``` -INFO SparkContext: Running Spark version 4.1.2 +INFO SparkContext: Running Spark version 4.1.1 INFO ResourceUtils: Resources for spark.driver: ... INFO SparkContext: Submitted application: My Job INFO Executor: Starting executor ID driver on host ... diff --git a/docs/how_tos/getting_started/deploy_sparkctl.md b/docs/how_tos/getting_started/deploy_sparkctl.md index 7bad1ba..8c0fbed 100644 --- a/docs/how_tos/getting_started/deploy_sparkctl.md +++ b/docs/how_tos/getting_started/deploy_sparkctl.md @@ -23,14 +23,14 @@ Here is an example filesystem layout: ├── hadoop-3.4.1/ ├── jdk-21.0.7/ ├── postgresql-42.7.4.jar -├── spark-4.1.2-bin-hadoop3/ +├── spark-4.1.1-bin-hadoop3/ ``` ## URLs Download locations will vary over time. Here is a set of permanent links to the specific software -versions tested with Apache Spark v4.1.2: +versions tested with Apache Spark v4.1.1: -- https://archive.apache.org/dist/spark/spark-4.1.2/spark-4.1.2-bin-hadoop3.tgz +- https://archive.apache.org/dist/spark/spark-4.1.1/spark-4.1.1-bin-hadoop3.tgz - https://download.oracle.com/java/21/archive/jdk-21.0.7_linux-x64_bin.tar.gz - https://archive.apache.org/dist/hadoop/common/hadoop-3.4.1/hadoop-3.4.1.tar.gz - https://archive.apache.org/dist/hive/hive-4.2.0/apache-hive-4.2.0-bin.tar.gz @@ -46,7 +46,7 @@ published. This command will create a default sparkctl configuration file given this filesystem layout: ```console $ sparkctl default-config \ - /datasets/images/apache_spark/spark-4.1.2-bin-hadoop3 \ + /datasets/images/apache_spark/spark-4.1.1-bin-hadoop3 \ /datasets/images/apache_spark/jdk-21.0.7 \ --hadoop-path /datasets/images/apache_spark/hadoop-3.4.1 \ --hive-tarball /datasets/images/apache_spark/apache-hive-4.2.0-bin.tar.gz \ diff --git a/docs/how_tos/getting_started/installation.md b/docs/how_tos/getting_started/installation.md index 8d1947e..6a16ffd 100644 --- a/docs/how_tos/getting_started/installation.md +++ b/docs/how_tos/getting_started/installation.md @@ -114,7 +114,7 @@ ```bash $ sparkctl default-config \ - /datasets/images/apache_spark/spark-4.1.2-bin-hadoop3 \ + /datasets/images/apache_spark/spark-4.1.1-bin-hadoop3 \ /datasets/images/apache_spark/jdk-21.0.7 \ --compute-environment slurm ``` diff --git a/docs/reference/hpc/kestrel.md b/docs/reference/hpc/kestrel.md index 12f7c2d..814277b 100644 --- a/docs/reference/hpc/kestrel.md +++ b/docs/reference/hpc/kestrel.md @@ -11,12 +11,12 @@ Spark is installed on Kestrel. Use these locations when your configure your envi ├── hadoop-3.4.1/ ├── jdk-21.0.7/ ├── postgresql-42.7.4.jar -├── spark-4.1.2-bin-hadoop3/ +├── spark-4.1.1-bin-hadoop3/ ``` ```console $ sparkctl default-config \ - /datasets/images/apache_spark/spark-4.1.2-bin-hadoop3 \ + /datasets/images/apache_spark/spark-4.1.1-bin-hadoop3 \ /datasets/images/apache_spark/jdk-21.0.7 \ --hadoop-path /datasets/images/apache_spark/hadoop-3.4.1 \ --hive-tarball /datasets/images/apache_spark/apache-hive-4.2.0-bin.tar.gz \ diff --git a/docs/tutorials/run_ibis_spark_jobs.md b/docs/tutorials/run_ibis_spark_jobs.md index ab5c5b3..a6db61c 100644 --- a/docs/tutorials/run_ibis_spark_jobs.md +++ b/docs/tutorials/run_ibis_spark_jobs.md @@ -13,10 +13,10 @@ This tutorial covers three approaches: ## Prerequisites Install Ibis with the PySpark backend in your Python environment. You must pin `pyspark` to -version 4.1.2 to match sparkctl's requirements: +version 4.1.1 to match sparkctl's requirements: ```console -$ pip install 'ibis-framework[pyspark]' 'pyspark==4.1.2' +$ pip install 'ibis-framework[pyspark]' 'pyspark==4.1.1' ``` This installs Ibis with PySpark support at the version compatible with sparkctl. diff --git a/hpc/environment_module/sparkctl.toml b/hpc/environment_module/sparkctl.toml index 753b72f..4ba213a 100644 --- a/hpc/environment_module/sparkctl.toml +++ b/hpc/environment_module/sparkctl.toml @@ -14,7 +14,7 @@ [binaries] # Required. -spark_path = "/datasets/images/spark/spark-4.1.2-bin-hadoop3" +spark_path = "/datasets/images/spark/spark-4.1.1-bin-hadoop3" java_path = "/datasets/images/spark/jdk-21.0.7+6" # Optional. Remove or leave commented if not deployed. # hadoop_path = "/datasets/images/spark/hadoop-3.4.1" diff --git a/pyproject.toml b/pyproject.toml index 6bdac24..de637c1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,7 +28,7 @@ dependencies = [ "loguru >= 0.7.2", "psutil", "pydantic >= 2.7, < 3", - "pyspark-client == 4.1.2", + "pyspark-client == 4.1.1", "rich_click", "rmon >= 0.4.0", "toml", @@ -37,7 +37,7 @@ dependencies = [ [project.optional-dependencies] pyspark = [ - "pyspark == 4.1.2", + "pyspark == 4.1.1", ] # Optional notebook frontend for `sparkctl configure --jupyter`. Installs the classic notebook # (the default frontend); install jupyterlab separately to use `--jupyter-command lab`. diff --git a/src/sparkctl/cli/sparkctl.py b/src/sparkctl/cli/sparkctl.py index 2d66d18..47b928c 100644 --- a/src/sparkctl/cli/sparkctl.py +++ b/src/sparkctl/cli/sparkctl.py @@ -59,10 +59,10 @@ def cli(ctx: click.Context, console_level: str, file_level: str, reraise_excepti \b Examples:\n $ sparkctl default-config \\ \n - /datasets/images/apache-spark/spark-4.1.2-bin-hadoop3 \\ \n + /datasets/images/apache-spark/spark-4.1.1-bin-hadoop3 \\ \n /datasets/images/apache-spark/jdk-21.0.7 \\ \n -e slurm \\ \n -$ sparkctl default-config ~/apache-spark/spark-4.1.2-bin-hadoop3 ~/jdk-21.0.8 -e native\n +$ sparkctl default-config ~/apache-spark/spark-4.1.1-bin-hadoop3 ~/jdk-21.0.8 -e native\n """ diff --git a/tests/conftest.py b/tests/conftest.py index 7c1f02f..4092250 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -14,9 +14,9 @@ from sparkctl.models import ComputeEnvironment -SPARK_DIR_NAME = "spark-4.1.2-bin-hadoop3" +SPARK_DIR_NAME = "spark-4.1.1-bin-hadoop3" SPARK_GZ_NAME = f"{SPARK_DIR_NAME}.tgz" -SPARK_URL = f"https://archive.apache.org/dist/spark/spark-4.1.2/{SPARK_GZ_NAME}" +SPARK_URL = f"https://archive.apache.org/dist/spark/spark-4.1.1/{SPARK_GZ_NAME}" SPARK_DIR_GZ = Path("tests") / "data" / SPARK_GZ_NAME SPARK_DIR = Path("tests") / "data" / SPARK_DIR_NAME diff --git a/uv.lock b/uv.lock index 18e8548..2519aee 100644 --- a/uv.lock +++ b/uv.lock @@ -1619,16 +1619,16 @@ wheels = [ [[package]] name = "pyspark" -version = "4.1.2" +version = "4.1.1" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "py4j" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/5e/71/4dd20c69332a2a4bf7ece8a655c9da98e4bd9b6bcea235349c1a00399d57/pyspark-4.1.2.tar.gz", hash = "sha256:fa5d6159f700d0990a07f4f62df1b7449401dccee9cd7d5d6df8957530841602", size = 455428043, upload-time = "2026-05-21T14:49:21.785Z" } +sdist = { url = "https://files.pythonhosted.org/packages/19/bf/58ee13add151469c25825b7125bbf62c3bdcec05eec4d458fcb5c5516066/pyspark-4.1.1.tar.gz", hash = "sha256:77f78984aa84fbe865c717dd37b49913b4e5c97d76ef6824f932f1aefa6621ec", size = 455359625, upload-time = "2026-01-09T09:38:38.28Z" } [[package]] name = "pyspark-client" -version = "4.1.2" +version = "4.1.1" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "googleapis-common-protos" }, @@ -1640,7 +1640,7 @@ dependencies = [ { name = "pyyaml" }, { name = "zstandard" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/f4/47/57c1234c69fb87234b8d080c64775eb5a088cfbb9fe419b190cb7d7ca40e/pyspark_client-4.1.2.tar.gz", hash = "sha256:4e5ac863064c3bd6c288da7e3932c72b491cd90e294e5e91aec37e1fa1c870f5", size = 1601811, upload-time = "2026-05-21T14:49:42.352Z" } +sdist = { url = "https://files.pythonhosted.org/packages/41/b0/fde39357666529049f2f71235d7126c7b2d5b4c5e20c072dccf845e9a09d/pyspark_client-4.1.1.tar.gz", hash = "sha256:23bd428d1f96c7196ae8ba29facca7eb56f66b3c380294385101ec8f1872a44a", size = 1600762, upload-time = "2026-01-09T09:38:50.679Z" } [[package]] name = "pytest" @@ -2156,8 +2156,8 @@ requires-dist = [ { name = "prek", marker = "extra == 'dev'", specifier = ">=0.2,<1" }, { name = "psutil" }, { name = "pydantic", specifier = ">=2.7,<3" }, - { name = "pyspark", marker = "extra == 'pyspark'", specifier = "==4.1.2" }, - { name = "pyspark-client", specifier = "==4.1.2" }, + { name = "pyspark", marker = "extra == 'pyspark'", specifier = "==4.1.1" }, + { name = "pyspark-client", specifier = "==4.1.1" }, { name = "pytest", marker = "extra == 'dev'" }, { name = "pytest-cov", marker = "extra == 'dev'" }, { name = "requests", marker = "extra == 'dev'" }, From 97c4d212a900b81ce642bf9fb39e1d54838ba630 Mon Sep 17 00:00:00 2001 From: Daniel Thom Date: Sat, 13 Jun 2026 17:10:58 -0600 Subject: [PATCH 20/24] Auto-size one executor per GPU when GPUs are enabled The executor count was derived purely from CPU cores and memory, so a GPU node whose core count did not divide evenly by executor_cores could leave GPUs idle (e.g. 4 GPUs but only 3 executors fit -> 1 GPU unused). Make executor_cores default to None (auto). When GPUs are enabled and the user has not set it explicitly, target NVIDIA's recommended layout of one executor per GPU by dividing the node's usable cores evenly among the GPUs. Also cap the executor count at the GPU count so executors are not left unschedulable for lack of a GPU, and warn when CPUs/memory allow fewer executors than there are GPUs (some GPUs would sit idle). An explicit executor_cores still overrides the auto sizing. Document the behavior in the GPU how-to and update the debugging guide. Co-Authored-By: Claude Opus 4.8 (1M context) --- docs/how_tos/configuration/gpus.md | 17 ++++++- docs/how_tos/debugging/index.md | 11 ++-- src/sparkctl/cli/sparkctl.py | 3 +- src/sparkctl/cluster_manager.py | 82 +++++++++++++++++++++++------- src/sparkctl/models.py | 8 +-- tests/test_cluster_manager.py | 34 +++++++++++++ 6 files changed, 124 insertions(+), 31 deletions(-) diff --git a/docs/how_tos/configuration/gpus.md b/docs/how_tos/configuration/gpus.md index 8d7cc72..c9ded24 100644 --- a/docs/how_tos/configuration/gpus.md +++ b/docs/how_tos/configuration/gpus.md @@ -24,8 +24,20 @@ This generates a GPU discovery script in the cluster's `conf` directory and writ - `spark.task.resource.gpu.amount` By default each executor is assigned one GPU and tasks share that GPU -(`spark.task.resource.gpu.amount = executor_gpu_amount / executor_cores`). Tune these through your -settings file: +(`spark.task.resource.gpu.amount = executor_gpu_amount / executor_cores`). + +### Executor sizing + +When GPUs are enabled and you do not set `executor_cores`, sparkctl follows NVIDIA's recommended +layout: **one executor per GPU**, with the node's usable cores divided evenly among them. For +example, on a node with 4 GPUs and 64 cores you get 4 executors with ~15 cores each, so every GPU is +used and each has a healthy pool of CPU cores to feed it (I/O, decompression, shuffle). To use all +*N* GPUs you therefore need at least *N* cores in the allocation; request cores generously (e.g. +Slurm `--cpus-per-task` or `--exclusive`). If CPUs or memory only allow fewer executors than there +are GPUs, sparkctl logs a warning that some GPUs will sit idle. + +Setting `executor_cores` explicitly overrides this and is honored as-is. Tune the GPU assignment +through your settings file: ```toml [runtime] @@ -33,6 +45,7 @@ enable_gpus = true gpus_per_node = 4 executor_gpu_amount = 1 task_gpu_amount = 0.25 +# executor_cores = 16 # optional; omit to auto-size one executor per GPU ``` ## RAPIDS Accelerator diff --git a/docs/how_tos/debugging/index.md b/docs/how_tos/debugging/index.md index 14c473d..10b060e 100644 --- a/docs/how_tos/debugging/index.md +++ b/docs/how_tos/debugging/index.md @@ -65,12 +65,11 @@ then the driver registered with the master but no executor could be launched. Op UI on port 8080 (see [above](#spark-web-ui)) and check the workers. Common causes: - **The node has fewer CPUs than `executor_cores`.** An executor must fit on a single worker, so if - the worker advertises fewer free cores than `executor_cores` (5 by default), no executor can be - scheduled and the job hangs forever. This frequently happens when a GPU allocation grants GPUs but - only one CPU. Check `echo $SLURM_CPUS_ON_NODE`; request more CPUs (e.g. the Slurm - `--cpus-per-task` option) or lower `executor_cores`, then reconfigure and restart. As of recent - versions sparkctl fails fast at `configure` time when `executor_cores` exceeds the available CPUs - rather than letting the job hang. + the worker advertises fewer free cores than `executor_cores`, no executor can be scheduled and the + job hangs forever. This frequently happens when a GPU allocation grants GPUs but only one CPU. + Check `echo $SLURM_CPUS_ON_NODE`; request more CPUs (e.g. the Slurm `--cpus-per-task` option) or + lower `executor_cores`, then reconfigure and restart. As of recent versions sparkctl fails fast at + `configure` time when `executor_cores` exceeds the available CPUs rather than letting the job hang. - **Executor memory exceeds what the worker offers** — lower `--executor-memory-gb` (or `executor_cores`). - **GPU scheduling was enabled but the worker advertises no GPUs.** If you ran `sparkctl configure diff --git a/src/sparkctl/cli/sparkctl.py b/src/sparkctl/cli/sparkctl.py index 47b928c..60abf01 100644 --- a/src/sparkctl/cli/sparkctl.py +++ b/src/sparkctl/cli/sparkctl.py @@ -191,6 +191,7 @@ def _create_default_config( "-e", "--executor-cores", default=sparkctl_settings.runtime.get("executor_cores"), + type=int, show_default=True, help=SparkRuntimeParams.model_fields["executor_cores"].description, ) @@ -448,7 +449,7 @@ def configure( start: bool, directory: Path, spark_scratch: Path, - executor_cores: int, + executor_cores: int | None, executor_memory_gb: int, driver_memory_gb: int, node_memory_overhead_gb: int, diff --git a/src/sparkctl/cluster_manager.py b/src/sparkctl/cluster_manager.py index 4716168..83f1c36 100644 --- a/src/sparkctl/cluster_manager.py +++ b/src/sparkctl/cluster_manager.py @@ -28,6 +28,7 @@ class ClusterManager: CONFIG_FILENAME = "config.json" STATUS_FILENAME = "status.json" + DEFAULT_EXECUTOR_CORES = 5 def __init__(self, config: SparkConfig, status: StatusTracker | None = None) -> None: self._config = config @@ -512,14 +513,40 @@ def _enable_dynamic_allocation(self, defaults_file: Path) -> None: logger.info("Enabled dynamic allocation") + def _get_num_gpus(self) -> int: + """Return the number of GPUs per worker node, from the override or auto-detection.""" + num_gpus = self._config.runtime.gpus_per_node + if num_gpus is None: + num_gpus = self._intf.get_worker_num_gpus() + return num_gpus + + def _resolve_executor_cores(self) -> int: + """Return the cores per executor, resolving the auto (None) default. + + When GPUs are enabled and the user has not set executor_cores explicitly, target one + executor per GPU by dividing the node's usable cores evenly among the GPUs (the + NVIDIA-recommended layout). Otherwise fall back to the standard default. + """ + rt = self._config.runtime + if rt.executor_cores is not None: + return rt.executor_cores + if rt.enable_gpus or rt.enable_rapids: + num_gpus = self._get_num_gpus() + if num_gpus > 0: + # Reserve one CPU for the OS, matching _config_executors. + usable_cpus = self._intf.get_worker_num_cpus() - 1 + return max(1, usable_cpus // num_gpus) + return self.DEFAULT_EXECUTOR_CORES + def _config_executors(self, defaults_file: Path) -> None: + rt = self._config.runtime num_workers = self._intf.get_num_workers() - worker_memory_gb = self._get_worker_memory_gb(self._config.runtime.driver_memory_gb) + worker_memory_gb = self._get_worker_memory_gb(rt.driver_memory_gb) worker_num_cpus = self._intf.get_worker_num_cpus() # Leave one CPU for OS and management software. worker_num_cpus -= 1 - executor_cores = self._config.runtime.executor_cores + executor_cores = self._resolve_executor_cores() if worker_num_cpus < executor_cores: msg = ( f"Each worker node has {self._intf.get_worker_num_cpus()} CPU(s) " @@ -532,11 +559,11 @@ def _config_executors(self, defaults_file: Path) -> None: raise InvalidConfiguration(msg) # The guard above ensures worker_num_cpus >= executor_cores, so this is always >= 1. - min_executors_per_node = worker_num_cpus // executor_cores - if self._config.runtime.executor_memory_gb is None: - executor_memory_gb = worker_memory_gb // min_executors_per_node + executors_by_cpu = worker_num_cpus // executor_cores + if rt.executor_memory_gb is None: + executor_memory_gb = worker_memory_gb // executors_by_cpu else: - executor_memory_gb = self._config.runtime.executor_memory_gb + executor_memory_gb = rt.executor_memory_gb if executor_memory_gb > worker_memory_gb: msg = ( f"{executor_memory_gb=} cannot be more than {worker_memory_gb=}. " @@ -544,19 +571,38 @@ def _config_executors(self, defaults_file: Path) -> None: ) raise InvalidConfiguration(msg) executors_by_mem = worker_memory_gb // executor_memory_gb - executors_by_cpu = min_executors_per_node - if executors_by_cpu <= executors_by_mem: - executors_per_node = executors_by_cpu - else: - executors_per_node = executors_by_mem - - total_num_cpus = executors_per_node * self._config.runtime.executor_cores * num_workers + executors_per_node = min(executors_by_cpu, executors_by_mem) + + # With GPU scheduling each executor claims executor_gpu_amount GPU(s), so a node can run at + # most one executor per that many GPUs. Cap the count so executors are not left + # unschedulable for lack of a GPU, and warn when CPUs/memory leave GPUs idle. The auto + # value of executor_cores already targets one executor per GPU. + if rt.enable_gpus or rt.enable_rapids: + num_gpus = self._get_num_gpus() + if num_gpus > 0: + max_executors_by_gpu = max(1, num_gpus // rt.executor_gpu_amount) + if executors_per_node > max_executors_by_gpu: + executors_per_node = max_executors_by_gpu + elif executors_per_node < max_executors_by_gpu: + idle_gpus = ( + max_executors_by_gpu - executors_per_node + ) * rt.executor_gpu_amount + logger.warning( + "Only {} executor(s) fit per node (limited by CPUs/memory) but {} GPU(s) " + "are available, so {} GPU(s) will sit idle. Lower executor_cores or " + "allocate more CPUs/memory so one executor runs per GPU.", + executors_per_node, + num_gpus, + idle_gpus, + ) + + total_num_cpus = executors_per_node * executor_cores * num_workers total_num_executors = executors_per_node * num_workers - partitions = total_num_cpus * self._config.runtime.shuffle_partition_multiplier + partitions = total_num_cpus * rt.shuffle_partition_multiplier with open(defaults_file, "a") as f_out: f_out.write( f""" -spark.executor.cores {self._config.runtime.executor_cores} +spark.executor.cores {executor_cores} spark.sql.shuffle.partitions {partitions} spark.executor.memory {executor_memory_gb}g """ @@ -681,9 +727,7 @@ def _configure_metrics(self, defaults_file: Path) -> None: def _enable_gpus(self, defaults_file: Path) -> None: rt_params = self._config.runtime - num_gpus = rt_params.gpus_per_node - if num_gpus is None: - num_gpus = self._intf.get_worker_num_gpus() + num_gpus = self._get_num_gpus() if num_gpus <= 0: msg = ( "GPU scheduling was enabled but no GPUs were detected on the worker nodes. " @@ -703,7 +747,7 @@ def _enable_gpus(self, defaults_file: Path) -> None: task_gpu_amount = rt_params.task_gpu_amount else: # Let the cores in an executor share that executor's GPU(s) by default. - task_gpu_amount = executor_gpu_amount / rt_params.executor_cores + task_gpu_amount = executor_gpu_amount / self._resolve_executor_cores() with open(defaults_file, "a") as f_out: f_out.write( f""" diff --git a/src/sparkctl/models.py b/src/sparkctl/models.py index 97fbf4b..62d6ab9 100644 --- a/src/sparkctl/models.py +++ b/src/sparkctl/models.py @@ -63,9 +63,11 @@ def make_absolute(cls, val: Path | None) -> Path | None: class SparkRuntimeParams(SparkctlBaseModel): """Controls Spark runtime parameters.""" - executor_cores: int = Field( - default=5, - description="Number of cores per executor", + executor_cores: int | None = Field( + default=None, + description="Number of cores per executor. By default this is auto-determined: when GPUs " + "are enabled, sparkctl runs one executor per GPU and divides the node's cores evenly " + "among them (the NVIDIA-recommended layout); otherwise it defaults to 5.", ) executor_memory_gb: int | None = Field( default=None, diff --git a/tests/test_cluster_manager.py b/tests/test_cluster_manager.py index 61ac590..28be38f 100644 --- a/tests/test_cluster_manager.py +++ b/tests/test_cluster_manager.py @@ -122,6 +122,40 @@ def test_configure_gpus_with_override(setup_local_env: tuple[SparkConfig, Path]) assert discovery_script.stat().st_mode & 0o100 # owner-executable +def test_configure_gpus_auto_sizes_one_executor_per_gpu( + setup_local_env: tuple[SparkConfig, Path], +): + config, tmp_path = setup_local_env + config.directories.spark_scratch = tmp_path / "spark_scratch" + config.runtime.enable_gpus = True + config.runtime.gpus_per_node = 4 + # executor_cores is left at its auto (None) default. FakeCompute reports 12 CPUs (11 usable), + # so one executor per GPU -> 11 // 4 = 2 cores each, and task gpu amount = 1 / 2 = 0.5. + assert config.runtime.executor_cores is None + mgr = ClusterManager.from_config(config) + mgr.configure() + defaults = config.directories.get_spark_defaults_file().read_text(encoding="utf-8") + assert "spark.executor.cores 2" in defaults + assert "spark.executor.resource.gpu.amount 1" in defaults + assert "spark.task.resource.gpu.amount 0.5" in defaults + + +def test_configure_explicit_executor_cores_overrides_gpu_sizing( + setup_local_env: tuple[SparkConfig, Path], +): + config, tmp_path = setup_local_env + config.directories.spark_scratch = tmp_path / "spark_scratch" + config.runtime.enable_gpus = True + config.runtime.gpus_per_node = 4 + # An explicit value wins over the auto one-executor-per-GPU sizing. 11 usable CPUs // 3 = 3 + # executors, fewer than the 4 GPUs, so one GPU is left idle (logged as a warning). + config.runtime.executor_cores = 3 + mgr = ClusterManager.from_config(config) + mgr.configure() + defaults = config.directories.get_spark_defaults_file().read_text(encoding="utf-8") + assert "spark.executor.cores 3" in defaults + + def test_configure_executor_cores_exceed_worker_cpus_fails( setup_local_env: tuple[SparkConfig, Path], ): From 9c6e6ff5ea3108dbfca844be861b99b4b7141507 Mon Sep 17 00:00:00 2001 From: Daniel Thom Date: Sat, 13 Jun 2026 17:41:14 -0600 Subject: [PATCH 21/24] Fail fast on uneven multi-node GPU allocations sparkctl writes a single spark.worker.resource.gpu.amount for every worker, equal to the GPU count detected where configure runs. When GPUs are requested as a job-wide total (Slurm --gpus) instead of per node (--gpus-per-node), Slurm can split them unevenly, so a worker with fewer GPUs than declared silently fails to start and the job hangs with "Initial job has not accepted any resources". Add ComputeInterface.check_gpu_allocation() (no-op by default) and a SlurmCompute implementation that raises InvalidConfiguration at configure time when a multi-node job's per-node GPU count does not divide the job-wide total evenly. A per-node request (--gpus-per-node) short -circuits the check since it guarantees uniformity. Called from _enable_gpus, with a note in the GPU how-to. Co-Authored-By: Claude Opus 4.8 (1M context) --- docs/how_tos/configuration/gpus.md | 8 ++++++ src/sparkctl/cluster_manager.py | 4 +++ src/sparkctl/compute_interface.py | 7 +++++ src/sparkctl/slurm_compute.py | 38 ++++++++++++++++++++++++++ tests/test_slurm_compute.py | 43 ++++++++++++++++++++++++++++++ 5 files changed, 100 insertions(+) diff --git a/docs/how_tos/configuration/gpus.md b/docs/how_tos/configuration/gpus.md index c9ded24..3ef95fe 100644 --- a/docs/how_tos/configuration/gpus.md +++ b/docs/how_tos/configuration/gpus.md @@ -26,6 +26,14 @@ This generates a GPU discovery script in the cluster's `conf` directory and writ By default each executor is assigned one GPU and tasks share that GPU (`spark.task.resource.gpu.amount = executor_gpu_amount / executor_cores`). +```{eval-rst} +.. note:: On a multi-node Slurm job every worker node must have the **same** number of GPUs, because + sparkctl writes a single ``spark.worker.resource.gpu.amount`` for all workers. Request GPUs + per node (``--gpus-per-node=4``) rather than as a job-wide total (``--gpus=4``), which Slurm can + split unevenly across nodes. ``sparkctl configure`` fails fast if it detects a non-uniform + distribution. +``` + ### Executor sizing When GPUs are enabled and you do not set `executor_cores`, sparkctl follows NVIDIA's recommended diff --git a/src/sparkctl/cluster_manager.py b/src/sparkctl/cluster_manager.py index 83f1c36..4c78bac 100644 --- a/src/sparkctl/cluster_manager.py +++ b/src/sparkctl/cluster_manager.py @@ -735,6 +735,10 @@ def _enable_gpus(self, defaults_file: Path) -> None: ) raise InvalidConfiguration(msg) + # Fail fast if the allocation cannot put num_gpus on every worker (e.g. an uneven Slurm + # --gpus split), which would otherwise leave a worker unable to start. + self._intf.check_gpu_allocation() + discovery_script = self._write_gpu_discovery_script() executor_gpu_amount = rt_params.executor_gpu_amount if executor_gpu_amount > num_gpus: diff --git a/src/sparkctl/compute_interface.py b/src/sparkctl/compute_interface.py index c5c0197..6bdfef4 100644 --- a/src/sparkctl/compute_interface.py +++ b/src/sparkctl/compute_interface.py @@ -55,3 +55,10 @@ def is_worker_node(self, node_name: str) -> bool: @abc.abstractmethod def run_checks(self) -> None: """Run checks on Slurm environment variables.""" + + def check_gpu_allocation(self) -> None: + """Validate that GPU scheduling can succeed on every worker node. + + The default implementation performs no checks. Environments where worker nodes can end up + with differing GPU counts (e.g. Slurm) override this to fail fast. + """ diff --git a/src/sparkctl/slurm_compute.py b/src/sparkctl/slurm_compute.py index 9dc877a..f976a3c 100644 --- a/src/sparkctl/slurm_compute.py +++ b/src/sparkctl/slurm_compute.py @@ -5,6 +5,7 @@ from socket import gethostname from sparkctl.compute_interface import ComputeInterface +from sparkctl.exceptions import InvalidConfiguration from sparkctl.models import SparkConfig @@ -101,6 +102,43 @@ def get_worker_num_gpus(self) -> int: return len([x for x in visible.split(",") if x.strip()]) return 0 + def check_gpu_allocation(self) -> None: + # sparkctl writes a single spark.worker.resource.gpu.amount for every worker, equal to the + # GPU count detected on the node where `configure` runs. If the GPUs were requested as a + # job-wide total (Slurm --gpus) rather than per node (--gpus-per-node), Slurm can split + # them unevenly across nodes, leaving a worker with fewer GPUs than declared. That worker + # then fails to start and jobs hang with "Initial job has not accepted any resources". + # Fail fast when we can detect a non-uniform distribution. + num_workers = self.get_num_workers() + if num_workers <= 1: + return + num_gpus_per_node = self.get_worker_num_gpus() + if num_gpus_per_node <= 0: + return + + het = self.is_heterogeneous_slurm_job() + # A per-node request (--gpus-per-node) guarantees the same count on every node. + per_node_var = "SLURM_GPUS_PER_NODE_HET_GROUP_1" if het else "SLURM_GPUS_PER_NODE" + if os.getenv(per_node_var) is not None: + return + if het: + # SLURM_GPUS would include the driver group's GPUs, so the arithmetic below is not + # reliable. Heterogeneous jobs normally set the per-node variable handled above. + return + + total_gpus = _parse_slurm_gpu_count(os.getenv("SLURM_GPUS")) + if total_gpus is not None and total_gpus != num_gpus_per_node * num_workers: + msg = ( + f"GPUs are not evenly distributed across the {num_workers} worker nodes: this node " + f"has {num_gpus_per_node} GPU(s) but the job was allocated {total_gpus} GPU(s) " + f"total ({num_gpus_per_node} x {num_workers} != {total_gpus}). sparkctl applies one " + "spark.worker.resource.gpu.amount to every worker, so any node with fewer GPUs will " + "fail to start and the job will hang with 'Initial job has not accepted any " + "resources'. Request a uniform per-node count with the Slurm --gpus-per-node option " + "(e.g. --gpus-per-node=4) instead of --gpus." + ) + raise InvalidConfiguration(msg) + def is_heterogeneous_slurm_job(self) -> bool: return "SLURM_HET_SIZE" in os.environ diff --git a/tests/test_slurm_compute.py b/tests/test_slurm_compute.py index e81815d..6cfa9f8 100644 --- a/tests/test_slurm_compute.py +++ b/tests/test_slurm_compute.py @@ -3,6 +3,7 @@ import pytest +from sparkctl.exceptions import InvalidConfiguration from sparkctl.models import BinaryLocations, ComputeEnvironment, ComputeParams, SparkConfig from sparkctl.slurm_compute import SlurmCompute @@ -61,4 +62,46 @@ def test_slurm_get_worker_num_cpus_het(slurm_compute): os.environ[key] = orig_val +def test_check_gpu_allocation_uneven_raises(slurm_compute, monkeypatch): + # 2 worker nodes, this node has 3 GPUs, but the job total is 4 -> 3 * 2 != 4, so the GPUs were + # split unevenly (e.g. salloc --gpus=4 -N2 giving 3 + 1). + monkeypatch.setattr(slurm_compute, "get_num_workers", lambda: 2) + monkeypatch.setattr(slurm_compute, "get_worker_num_gpus", lambda: 3) + monkeypatch.delenv("SLURM_HET_SIZE", raising=False) + monkeypatch.delenv("SLURM_GPUS_PER_NODE", raising=False) + monkeypatch.setenv("SLURM_GPUS", "4") + with pytest.raises(InvalidConfiguration, match="evenly distributed"): + slurm_compute.check_gpu_allocation() + + +def test_check_gpu_allocation_even_ok(slurm_compute, monkeypatch): + # 2 worker nodes, 4 GPUs each, job total 8 -> evenly distributed. + monkeypatch.setattr(slurm_compute, "get_num_workers", lambda: 2) + monkeypatch.setattr(slurm_compute, "get_worker_num_gpus", lambda: 4) + monkeypatch.delenv("SLURM_HET_SIZE", raising=False) + monkeypatch.delenv("SLURM_GPUS_PER_NODE", raising=False) + monkeypatch.setenv("SLURM_GPUS", "8") + slurm_compute.check_gpu_allocation() + + +def test_check_gpu_allocation_per_node_request_skips_check(slurm_compute, monkeypatch): + # --gpus-per-node guarantees a uniform count, so the arithmetic check is skipped even when + # SLURM_GPUS would not divide evenly. + monkeypatch.setattr(slurm_compute, "get_num_workers", lambda: 2) + monkeypatch.setattr(slurm_compute, "get_worker_num_gpus", lambda: 4) + monkeypatch.delenv("SLURM_HET_SIZE", raising=False) + monkeypatch.setenv("SLURM_GPUS_PER_NODE", "4") + monkeypatch.setenv("SLURM_GPUS", "4") + slurm_compute.check_gpu_allocation() + + +def test_check_gpu_allocation_single_node_ok(slurm_compute, monkeypatch): + # A single worker node cannot be uneven, so no check applies. + monkeypatch.setattr(slurm_compute, "get_num_workers", lambda: 1) + monkeypatch.setattr(slurm_compute, "get_worker_num_gpus", lambda: 3) + monkeypatch.delenv("SLURM_GPUS_PER_NODE", raising=False) + monkeypatch.setenv("SLURM_GPUS", "4") + slurm_compute.check_gpu_allocation() + + # TODO: get_node_names From 06ab7bee34efe70abf765073d13cb42b96f44d23 Mon Sep 17 00:00:00 2001 From: Daniel Thom Date: Sat, 13 Jun 2026 17:53:30 -0600 Subject: [PATCH 22/24] Print SPARK_HOME and an interactive connect command on start The startup message listed SPARK_CONF_DIR and JAVA_HOME but not SPARK_HOME. Since sparkctl depends on pyspark-client (which bundles no Spark distribution), an interactive `pyspark`/`spark-submit` fails with "Could not find valid SPARK_HOME" unless SPARK_HOME points at the Spark distribution. Add SPARK_HOME (from binaries.spark_path) to the message plus a ready-to-paste example that adds Spark to PATH and connects a client to the master URL. Co-Authored-By: Claude Opus 4.8 (1M context) --- src/sparkctl/cluster_manager.py | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/src/sparkctl/cluster_manager.py b/src/sparkctl/cluster_manager.py index 4c78bac..1aa2916 100644 --- a/src/sparkctl/cluster_manager.py +++ b/src/sparkctl/cluster_manager.py @@ -259,7 +259,10 @@ def start(self, print_env_paths: bool = True) -> None: if print_env_paths: _print_env_paths_msg( - self._config.directories.get_spark_conf_dir(), self._config.binaries.java_path + self._config.directories.get_spark_conf_dir(), + self._config.binaries.java_path, + self._config.binaries.spark_path, + url, ) status_file = self._config.directories.base / self.STATUS_FILENAME with open(status_file, "w", encoding="utf-8") as f_out: @@ -895,16 +898,22 @@ def _read_workers(self) -> list[str]: return [x for x in workers_file.read_text(encoding="utf-8").splitlines() if x] -def _print_env_paths_msg(conf_dir: Path, java_dir: Path) -> None: +def _print_env_paths_msg(conf_dir: Path, java_dir: Path, spark_dir: Path, url: str) -> None: print( f""" ############################################################################### Set these environment variables to use the Spark configuration: +export SPARK_HOME={spark_dir} export SPARK_CONF_DIR={conf_dir} export JAVA_HOME={java_dir} +To connect interactively, add Spark to your PATH and point a client at the master, e.g.: + +export PATH="$SPARK_HOME/bin:$PATH" +pyspark --master {url} + ############################################################################### """, file=sys.stderr, From 2058030f3844049480104ac61af24cb3345bf653 Mon Sep 17 00:00:00 2001 From: Daniel Thom Date: Sat, 13 Jun 2026 17:56:24 -0600 Subject: [PATCH 23/24] Set SPARK_HOME automatically alongside JAVA_HOME/SPARK_CONF_DIR start() already exports SPARK_CONF_DIR and JAVA_HOME into the current process so in-process Spark usage works; add SPARK_HOME (from binaries.spark_path) for consistency, so a caller that shells out to spark-submit/pyspark or builds a classic session in the same process finds the Spark distribution. managed_cluster clears it on exit like the others. The yielded managed_cluster session uses Spark Connect, which does not need it, so this is purely additive. Co-Authored-By: Claude Opus 4.8 (1M context) --- src/sparkctl/cluster_manager.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/src/sparkctl/cluster_manager.py b/src/sparkctl/cluster_manager.py index 1aa2916..41c6c92 100644 --- a/src/sparkctl/cluster_manager.py +++ b/src/sparkctl/cluster_manager.py @@ -236,8 +236,8 @@ def get_workers(self) -> list[str]: def start(self, print_env_paths: bool = True) -> None: """Start the Spark cluster. The caller must have called :meth:`configure` beforehand. - The environment variables `SPARK_CONF_DIR` and `JAVA_HOME` are set to correct values for - the current process. + The environment variables `SPARK_HOME`, `SPARK_CONF_DIR`, and `JAVA_HOME` are set to correct + values for the current process. Examples -------- @@ -268,6 +268,7 @@ def start(self, print_env_paths: bool = True) -> None: with open(status_file, "w", encoding="utf-8") as f_out: f_out.write(tracker.model_dump_json(indent=2)) + os.environ["SPARK_HOME"] = str(self._config.binaries.spark_path) os.environ["SPARK_CONF_DIR"] = str(self._config.directories.get_spark_conf_dir()) os.environ["JAVA_HOME"] = str(self._config.binaries.java_path) @@ -299,8 +300,8 @@ def managed_cluster(self) -> Generator[SparkSession, None, None]: """Configure and start the Spark cluster, yield a SparkSession in a context manager, stop the cluster after exit. - The environment variables `SPARK_CONF_DIR` and `JAVA_HOME` are set to correct values for - the current process while the context is active and cleared when complete. + The environment variables `SPARK_HOME`, `SPARK_CONF_DIR`, and `JAVA_HOME` are set to correct + values for the current process while the context is active and cleared when complete. Examples -------- @@ -322,6 +323,7 @@ def managed_cluster(self) -> Generator[SparkSession, None, None]: self.stop() logger.info("Stopped Spark cluster processes and SparkSession") # Clear the environment variables set by start() + os.environ.pop("SPARK_HOME", None) os.environ.pop("SPARK_CONF_DIR", None) os.environ.pop("JAVA_HOME", None) From 0454775d9f979566e8dcb6d0a472a25be3b334cc Mon Sep 17 00:00:00 2001 From: Daniel Thom Date: Sat, 13 Jun 2026 18:05:06 -0600 Subject: [PATCH 24/24] Simplify connect hint and lower GPU enable logs to INFO Drop the PATH export from the start message: sparkctl installs pyspark-client, so the pyspark launcher is already on PATH and only needs SPARK_HOME (shown above); prepending $SPARK_HOME/bin could shadow it with the distribution's own launcher. Also lower the "Enabled GPU scheduling" and "Enabled RAPIDS GPU acceleration" messages from warning to info now that GPU/RAPIDS support is tested and no longer experimental. The idle-GPU message stays a warning since it flags a real misconfiguration. Co-Authored-By: Claude Opus 4.8 (1M context) --- src/sparkctl/cluster_manager.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/src/sparkctl/cluster_manager.py b/src/sparkctl/cluster_manager.py index 41c6c92..29bd4dc 100644 --- a/src/sparkctl/cluster_manager.py +++ b/src/sparkctl/cluster_manager.py @@ -767,7 +767,7 @@ def _enable_gpus(self, defaults_file: Path) -> None: spark.task.resource.gpu.amount {task_gpu_amount} """ ) - logger.warning("Enabled GPU scheduling with {} GPU(s) per node.", num_gpus) + logger.info("Enabled GPU scheduling with {} GPU(s) per node.", num_gpus) def _write_gpu_discovery_script(self) -> Path: script = self._config.directories.get_gpu_discovery_script_file() @@ -823,7 +823,7 @@ def _enable_rapids(self, defaults_file: Path) -> None: spark.rapids.sql.concurrentGpuTasks 1 """ ) - logger.warning("Enabled RAPIDS GPU acceleration using {}", rapids_jar) + logger.info("Enabled RAPIDS GPU acceleration using {}", rapids_jar) def _get_runtime_spark_driver_memory_gb(self) -> int: # Note that spark-defaults.conf takes precedence over our config.json. @@ -911,9 +911,8 @@ def _print_env_paths_msg(conf_dir: Path, java_dir: Path, spark_dir: Path, url: s export SPARK_CONF_DIR={conf_dir} export JAVA_HOME={java_dir} -To connect interactively, add Spark to your PATH and point a client at the master, e.g.: +Then connect a client to the master, e.g.: -export PATH="$SPARK_HOME/bin:$PATH" pyspark --master {url} ###############################################################################