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Performance & Quality Report

Overview

This document summarizes the performance optimizations and data quality improvements implemented as part of the project's production-readiness phase. The primary goals were to reduce latency, decrease external dependencies, and ensure data consistency across all integrated sources.

1. Performance Optimizations

ISO Client Caching

  • Implementation: Applied @lru_cache to all data loading methods (_load_from_excel, _load_from_csv_or_json) and the main data retrieval method (get_iso14001_certifications).
  • Impact: Drastically reduces redundant file I/O and network requests for ISO 14001 certification data, especially in high-traffic scenarios.

EEA Client Caching

  • Implementation: Leveraged @lru_cache on the _get_parquet_data method, which is the single entry point for downloading large Parquet files from the EEA API.
  • Impact: Prevents re-downloading and re-processing of large datasets for subsequent requests involving the same EEA indicators.

EDGAR Client Caching

  • Implementation: Utilizes a global, in-memory dictionary (_GLOBAL_CACHE) keyed by file path and modification time. This ensures that the large EDGAR Excel file is parsed only once per file version.
  • Impact: The most significant performance gain, as it avoids repeatedly parsing a large and complex spreadsheet. The cache is shared across all instances of the client.

2. Data Quality & Consistency

Comprehensive Country Name Normalization

  • System: A centralized mapping utility was created in app/utils/mappings.py.
  • Features:
    • Maps over 260 variations (common names, official names, ISO codes) for 50+ countries to a single canonical format (e.g., "DE", "Deutschland" -> "germany").
    • Ensures reliable data joining and filtering across all data sources.
  • Integration: This normalization is applied consistently in the EEAClient, ISOClient, and EDGARClient before any filtering or data aggregation occurs.

Enhanced Client Compatibility

  • EEA Client: A generic get_indicator() method was added to provide a stable interface for the API routes, intelligently routing requests to the correct internal data-fetching function based on the indicator type.
  • Schema Normalization: The app/utils/schema.py module ensures that data from different sources (like EPA Envirofacts) is transformed into a consistent, predictable structure before being returned by the API.

3. Test Coverage

Global Routes (tests/test_global_routes.py)

  • Coverage: All /global/* endpoints are covered by dedicated tests.
  • Validation: Tests verify response structures, filter functionality, and error handling for missing or invalid parameters.

CEVS Logic (tests/test_cevs.py)

  • Coverage: Multiple scenario-based tests validate the CEVS calculation logic.
  • Validation: Tests confirm the correct application of bonuses and penalties from different data sources and check for edge cases (e.g., country-specific policy bonuses).

4. Summary of Results

  • Performance: Latency for repeated, complex queries has been significantly reduced due to multi-layer caching.
  • Reliability: Data consistency is greatly improved, making the CEVS score more accurate and reliable.
  • Maintainability: Centralized utilities for mapping and schemas make it easier to add new data sources in the future.

Report Status: Final Phase: Production Readiness