chore: update usage of latest python sdk (#54)

Author: ShiKaiWi

docs/src/cn/SUMMARY.md

@@ -52,7 +52,7 @@
 # 技术系列文章
 
 - [整体架构](design/architecture.md)
-- [集群](design/clustering.md)
+- [集群](design/cluster.md)
 - [存储](design/storage.md)
 - [WAL](design/wal.md)
   - [WAL on RocksDB](design/wal_on_rocksdb.md)

docs/src/cn/design/storage.md

@@ -22,7 +22,7 @@ CeresDB 是一种基于 share-nothing 架构的分布式存储系统，不同服
 
 由于 memtable 不是实时持久化到底层存储系统，因此需要用 WAL 来保证 memtable 中数据的可靠性。
 
-另一方面，由于[分布式架构](clustering.md)的设计，要求 WAL 本身是高可用的，现在 CeresDB 中，主要有以下几种实现：
+另一方面，由于[分布式架构](cluster.md)的设计，要求 WAL 本身是高可用的，现在 CeresDB 中，主要有以下几种实现：
 
 - [本地磁盘](wal_on_rocksdb.md)（基于 [RocksDB](http://rocksdb.org/)，无分布式高可用）
 - [Oceanbase](https://www.oceanbase.com)

docs/src/cn/sdk/python.md

@@ -91,7 +91,7 @@ async def async_write(client, ctx, req):
     return await client.write(ctx, req)
 
 point_builder = PointBuilder('demo')
-point_builder.set_timestamp(int(round(datetime.datetime.now().timestamp())))
+point_builder.set_timestamp(1000 * int(round(datetime.datetime.now().timestamp())))
 point_builder.set_tag("name", ValueBuilder().string("test_tag1"))
 point_builder.set_field("value", ValueBuilder().double(0.4242))
 point = point_builder.build()
@@ -123,15 +123,21 @@ resp = event_loop.run_until_complete(async_query(client, ctx, req))
 查询到数据后，逐行逐列处理数据的示例如下：
 
 ```python
-for row_idx in range(0, resp.row_num()):
+# Access row by index in the resp.
+for row_idx in range(0, resp.num_rows()):
     row_tokens = []
-
-    row = resp.get_row(row_idx)
+    row = resp.row_by_idx(row_idx)
     for col_idx in range(0, row.num_cols()):
         col = row.column_by_idx(col_idx)
         row_tokens.append(f"{col.name()}:{col.value()}#{col.data_type()}")
+    print(f"row#{row_idx}: {','.join(row_tokens)}")
 
-    print(f"row#{col_idx}: {','.join(row_tokens)}")
+# Access row by iter in the resp.
+for row in resp.iter_rows():
+    row_tokens = []
+    for col in row.iter_columns():
+        row_tokens.append(f"{col.name()}:{col.value()}#{col.data_type()}")
+    print(f"row: {','.join(row_tokens)}")
 ```
 
 ## 删表

docs/src/en/SUMMARY.md

@@ -52,7 +52,7 @@
 # Technical and Design
 
 - [Architecture](design/architecture.md)
-- [Clustering](design/clustering.md)
+- [Cluster](design/cluster.md)
 - [Storage](design/storage.md)
 - [WAL](design/wal.md)
   - [WAL on RocksDB](design/wal_on_rocksdb.md)

docs/src/en/design/storage.md

@@ -24,7 +24,7 @@ A write request will be written to
 
 Since memtable is not persisted to the underlying storage system in real time, so WAL is required to ensure the reliability of the data in memtable.
 
-On the other hand, due to the design of the [distributed architecture](clustering.md), WAL itself is required to be highly available. Now there are following implementations in CeresDB:
+On the other hand, due to the design of the [distributed architecture](cluster.md), WAL itself is required to be highly available. Now there are following implementations in CeresDB:
 
 - [Local disk](wal_on_rocksdb.md) (based on [RocksDB](http://rocksdb.org/), no distributed high availability)
 - [OceanBase](https://www.oceanbase.com)

docs/src/en/sdk/python.md

@@ -91,7 +91,7 @@ async def async_write(client, ctx, req):
     return await client.write(ctx, req)
 
 point_builder = PointBuilder('demo')
-point_builder.set_timestamp(int(round(datetime.datetime.now().timestamp())))
+point_builder.set_timestamp(1000 * int(round(datetime.datetime.now().timestamp())))
 point_builder.set_tag("name", ValueBuilder().string("test_tag1"))
 point_builder.set_field("value", ValueBuilder().double(0.4242))
 point = point_builder.build()
@@ -123,15 +123,21 @@ Currently, the first parameter is necessary for performance on routing.
 With retrieved data, we can process it row by row and column by column:
 
 ```python
-for row_idx in range(0, resp.row_num()):
+# Access row by index in the resp.
+for row_idx in range(0, resp.num_rows()):
     row_tokens = []
-
-    row = resp.get_row(row_idx)
+    row = resp.row_by_idx(row_idx)
     for col_idx in range(0, row.num_cols()):
         col = row.column_by_idx(col_idx)
         row_tokens.append(f"{col.name()}:{col.value()}#{col.data_type()}")
+    print(f"row#{row_idx}: {','.join(row_tokens)}")
 
-    print(f"row#{col_idx}: {','.join(row_tokens)}")
+# Access row by iter in the resp.
+for row in resp.iter_rows():
+    row_tokens = []
+    for col in row.iter_columns():
+        row_tokens.append(f"{col.name()}:{col.value()}#{col.data_type()}")
+    print(f"row: {','.join(row_tokens)}")
 ```
 
 ## Drop Table