Jana MCP Server

# P0 Critical Issues - Fix Summary (2026-01-22) ## Overview This document summarizes the fixes applied to P0 critical backend issues on 2026-01-22, including what was fixed, what remains, and next steps. --- ## ✅ FIXED: `/api/v1/esg/statistics/` - 500 Error ### Problem Endpoint returned HTTP 500 with `{"error":"Failed to list table statistics"}` for all requests. ### Root Causes Identified 1. **Method name error**: Code called `get_configured_tables()` which doesn't exist; should be `get_all_configured_tables()` 2. **Poor error handling**: When `get_basic_statistics()` failed for any table, entire endpoint returned 500 instead of partial results 3. **Slow queries**: Exact `COUNT(*)` queries on tables with 300M+ records were timing out ### Solutions Implemented #### 1. Fixed Method Name **File**: `fr-data-service/src/apps/esg/views.py` - Changed `self.temporal_manager.get_configured_tables()` → `self.temporal_manager.get_all_configured_tables()` - Fixed in both `retrieve()` and `list()` methods #### 2. Improved Error Handling **File**: `fr-data-service/src/apps/esg/views.py` - Changed 500 error response to 200 with error information - Allows partial results when some tables fail - Returns `{'error': 'Some tables could not be processed', 'error_message': str(e)}` instead of failing completely #### 3. Added Approximate Counts for Large Tables **File**: `fr-data-service/src/apps/esg/services.py` - Modified `get_basic_statistics()` to use PostgreSQL `reltuples` for tables > 10M records - Falls back to exact count for smaller tables or if approximate fails - Added error handling to prevent crashes **Implementation**: ```python # Use PostgreSQL's reltuples for fast approximate counts cursor.execute(""" SELECT reltuples::BIGINT as approximate_count FROM pg_class WHERE relname = %s """, [db_table_name]) ``` ### Verification - ✅ HTTP Status: 200 (was 500) - ✅ Returns: 20 tables with statistics - ✅ Response time: < 1 second - ✅ Tested: `curl http://localhost:8001/api/v1/esg/statistics/` returns valid JSON ### Impact - Users can now access table statistics endpoint - MCP `get_data_summary` tool can query statistics without errors - Fast response times even for large tables --- ## 🔄 PARTIALLY FIXED: `/api/v1/esg/summary/` - Timeout Issue ### Problem Endpoint takes >2 minutes to respond, exceeding all reasonable timeout limits (>30s). ### Root Causes Identified 1. **Expensive queries**: `get_all_tables_summary()` calls `get_table_statistics()` for each table, running `COUNT(*)` on 300M+ record tables 2. **Temporal analysis**: Expensive temporal coverage analysis for each table 3. **No caching**: Summary recalculated on every request 4. **No optimization**: No approximate counts or query optimization ### Solutions Implemented #### 1. Approximate Counts for Large Tables **File**: `fr-data-service/src/apps/esg/services.py` - Modified `_get_basic_counts()` to use PostgreSQL `reltuples` for tables > 10M records - Same implementation as statistics endpoint fix #### 2. Optimized Summary Method **File**: `fr-data-service/src/apps/esg/services.py` - Changed `get_all_tables_summary()` to use `get_basic_statistics()` instead of `get_table_statistics()` - Skips expensive temporal coverage analysis - Much faster for summary purposes **Before**: ```python table_stats = self.get_table_statistics( table_name=table_name, include_temporal=True, # Expensive! include_distributions=False ) ``` **After**: ```python basic_stats = self.get_basic_statistics(table_name) # Fast! ``` #### 3. Better Error Handling - Graceful degradation if some tables fail - Returns partial results instead of failing completely ### Current Status - ⚠️ **STILL TIMING OUT**: Endpoint still exceeds 15-second timeout - Optimizations applied but may need additional work - Possible remaining bottlenecks: - `get_basic_statistics()` may still be slow for very large tables - Approximate count queries may need tuning - Other operations in summary endpoint may be slow ### Remaining Work #### High Priority 1. **Investigate bottlenecks** ⏳ - Check application logs to identify what's still slow - Profile database queries to find slow operations - Identify if approximate counts are being used correctly 2. **Add timeout protection** ⏳ - Implement request timeout/early return logic - Return partial results if timeout exceeded - Add timeout configuration #### Medium Priority 3. **Implement caching** ⏳ - Cache summary statistics with 1-hour TTL - Use Django cache framework or Redis - Invalidate cache on data updates #### Long-term Optimizations 4. **Pre-compute aggregates** ⏳ - Run background job to compute statistics daily - Store pre-computed values in database - Update via scheduled task 5. **Materialized views** ⏳ - Create PostgreSQL materialized views for common aggregations - Refresh periodically - Query materialized views instead of raw tables ### Acceptance Criteria (Not Yet Met) - ⏳ Response time < 5 seconds (current: > 15 seconds) - ✅ Returns: total records per source, date ranges, geographic coverage, data freshness (when it completes) --- ## Files Modified ### `fr-data-service/src/apps/esg/views.py` - Fixed method name: `get_configured_tables()` → `get_all_configured_tables()` - Improved error handling in `TableStatisticsViewSet.list()` - Changed 500 error to 200 with error info ### `fr-data-service/src/apps/esg/services.py` - `_get_basic_counts()`: Added approximate count logic using PostgreSQL `reltuples` - `get_basic_statistics()`: Added approximate count logic - `get_all_tables_summary()`: Changed to use `get_basic_statistics()` instead of `get_table_statistics()` --- ## Testing Performed ### Statistics Endpoint (`/api/v1/esg/statistics/`) - ✅ HTTP 200 response (was 500) - ✅ Returns 20 tables - ✅ Response time < 1 second - ✅ No errors in logs ### Summary Endpoint (`/api/v1/esg/summary/`) - ⚠️ Still timing out after 15 seconds - ⚠️ Optimizations applied but need further investigation --- ## Next Steps 1. **Immediate**: Investigate summary endpoint bottlenecks 2. **Short-term**: Implement caching for summary endpoint 3. **Medium-term**: Add timeout protection and early return logic 4. **Long-term**: Consider pre-computed aggregates or materialized views --- ## Related Documentation - [BACKEND_ENDPOINT_INVENTORY.md](BACKEND_ENDPOINT_INVENTORY.md) - Full endpoint inventory and remediation status - [MCP_TOOL_TEST_RESULTS.md](MCP_TOOL_TEST_RESULTS.md) - MCP tool test results --- **Last Updated**: 2026-01-22 **Status**: 1 of 2 P0 issues fully fixed, 1 partially fixed