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Addok Application Critical Fixes

Executive Summary

This document provides immediate fixes for critical issues identified in the Addok Docker application that are preventing full functionality. These fixes address version inconsistencies, potential CSV endpoint failures, and missing monitoring capabilities.

🔴 Critical Issues to Fix

Issue #1: addok-csv Version Inconsistency

Impact: CSV endpoints (/search/csv/, /reverse/csv/) may fail due to version mismatch Severity: HIGH - Core functionality affected

Problem:

  • Dockerfile: Uses addok-csv==1.1.0
  • Dockerfile.backup: Uses addok-csv==1.1.1
  • Inconsistent versions can cause import errors or missing features

Root Cause Analysis: The recent commit "Fix; return addok-csv values to 1.1.0" attempted to standardize on v1.1.0, but the backup Dockerfile was not updated consistently.

Issue #2: Datadog Tracing Disabled Without Replacement

Impact: No distributed tracing or APM capabilities Severity: MEDIUM - Observability gap

Problem:

  • Kubernetes deployment has Datadog variables set but agent disabled
  • ddtrace library installed but not functional
  • Missing integration with Grafana/Prometheus/Tempo stack

Issue #3: CSV Processing Error Handling

Impact: Silent failures in batch processing Severity: MEDIUM - User experience degradation

Problem:

  • No visibility into CSV upload processing errors
  • Missing validation for CSV file formats
  • No progress tracking for large file uploads

🛠️ Immediate Fix Implementation

Fix #1: Resolve addok-csv Version Inconsistency

Step 1: Standardize on addok-csv 1.1.0

File: addok/Dockerfile.backup

# BEFORE (line 52)
addok-csv==1.1.1 \

# AFTER (Fix)
addok-csv==1.1.0 \

File: addok-importer/Dockerfile

# BEFORE
RUN pip install cython addok==1.0.3 addok-fr==1.0.1 addok-france==1.1.3 addok-sqlite-store==1.0.1

# AFTER (Add explicit version)
RUN pip install --no-cache-dir \
    cython \
    addok==1.0.3 \
    addok-fr==1.0.1 \
    addok-france==1.1.3 \
    addok-csv==1.1.0 \
    addok-sqlite-store==1.0.1

File: addok-standalone/Dockerfile

# BEFORE (line 25)
RUN pip install --no-cache-dir \
    cython \
    addok==1.0.3 \
    addok-fr==1.0.1 \
    addok-france==1.1.3 \
    addok-sqlite-store==1.0.1

# AFTER (Add missing addok-csv)
RUN pip install --no-cache-dir \
    cython \
    addok==1.0.3 \
    addok-fr==1.0.1 \
    addok-france==1.1.3 \
    addok-csv==1.1.0 \
    addok-sqlite-store==1.0.1

Step 2: Version Verification Script

Create scripts/verify-dependencies.sh:

#!/bin/bash
set -euo pipefail

echo "=== Addok Dependency Version Verification ==="

# Expected versions
EXPECTED_ADDOK="1.0.3"
EXPECTED_ADDOK_FR="1.0.1"
EXPECTED_ADDOK_FRANCE="1.1.3"
EXPECTED_ADDOK_CSV="1.1.0"
EXPECTED_ADDOK_SQLITE="1.0.1"

# Check installed versions
echo "Checking installed versions..."
python3 -c "
import pkg_resources
packages = {
    'addok': '$EXPECTED_ADDOK',
    'addok-fr': '$EXPECTED_ADDOK_FR', 
    'addok-france': '$EXPECTED_ADDOK_FRANCE',
    'addok-csv': '$EXPECTED_ADDOK_CSV',
    'addok-sqlite-store': '$EXPECTED_ADDOK_SQLITE'
}

errors = []
for pkg, expected in packages.items():
    try:
        installed = pkg_resources.get_distribution(pkg).version
        status = '✅' if installed == expected else '❌'
        print(f'{status} {pkg}: {installed} (expected: {expected})')
        if installed != expected:
            errors.append(f'{pkg}: {installed} != {expected}')
    except pkg_resources.DistributionNotFound:
        print(f'❌ {pkg}: NOT INSTALLED (expected: {expected})')
        errors.append(f'{pkg}: NOT INSTALLED')

if errors:
    print(f'\n❌ Found {len(errors)} version mismatches:')
    for error in errors:
        print(f'  - {error}')
    exit(1)
else:
    print(f'\n✅ All dependencies have correct versions!')
"

Fix #2: CSV Endpoint Validation & Error Handling

Step 1: Enhanced CSV Endpoint Wrapper

Create addok/csv_handler.py:

import io
import csv
import logging
from functools import wraps
from flask import request, jsonify, Response
from werkzeug.exceptions import BadRequest

logger = logging.getLogger(__name__)

MAX_FILE_SIZE = 50 * 1024 * 1024  # 50MB
ALLOWED_ENCODINGS = ['utf-8', 'utf-8-sig', 'iso-8859-1', 'cp1252']
REQUIRED_CSV_COLUMNS = {
    'search': ['address', 'ville'],  # Flexible column names
    'reverse': ['lat', 'lon']        # Required for reverse geocoding
}

class CSVProcessingError(Exception):
    def __init__(self, message, code=400):
        self.message = message
        self.code = code
        super().__init__(self.message)

def validate_csv_upload():
    """Decorator to validate CSV upload requests"""
    def decorator(f):
        @wraps(f)
        def decorated_function(*args, **kwargs):
            try:
                # Check if file is present
                if 'data' not in request.files:
                    raise CSVProcessingError("No 'data' file provided in request", 400)
                
                file = request.files['data']
                if file.filename == '':
                    raise CSVProcessingError("Empty filename provided", 400)
                
                # Check file size
                file.seek(0, 2)  # Seek to end
                size = file.tell()
                file.seek(0)     # Reset to beginning
                
                if size > MAX_FILE_SIZE:
                    raise CSVProcessingError(f"File too large: {size} bytes (max: {MAX_FILE_SIZE})", 413)
                
                # Validate file format
                if not file.filename.lower().endswith('.csv'):
                    logger.warning(f"Non-CSV file uploaded: {file.filename}")
                
                # Try to read first few lines to validate CSV format
                sample = file.read(8192).decode('utf-8-sig', errors='ignore')
                file.seek(0)
                
                # Basic CSV validation
                sniffer = csv.Sniffer()
                try:
                    dialect = sniffer.sniff(sample)
                    delimiter = getattr(dialect, 'delimiter', ',')
                except csv.Error:
                    # Fallback to common delimiters
                    delimiter = ',' if ',' in sample else ';'
                
                logger.info(f"CSV upload validated: {file.filename}, {size} bytes, delimiter: {delimiter}")
                
                return f(*args, **kwargs)
                
            except CSVProcessingError as e:
                logger.error(f"CSV validation error: {e.message}")
                return jsonify({
                    'error': 'CSV_VALIDATION_ERROR',
                    'message': e.message,
                    'timestamp': datetime.utcnow().isoformat()
                }), e.code
            except Exception as e:
                logger.error(f"Unexpected CSV validation error: {str(e)}")
                return jsonify({
                    'error': 'INTERNAL_SERVER_ERROR', 
                    'message': 'Failed to process CSV file',
                    'timestamp': datetime.utcnow().isoformat()
                }), 500
                
        return decorated_function
    return decorator

def create_csv_response(data, filename_prefix="addok_results"):
    """Create properly formatted CSV response"""
    output = io.StringIO()
    
    if data:
        writer = csv.DictWriter(output, fieldnames=data[0].keys())
        writer.writeheader()
        writer.writerows(data)
    
    output.seek(0)
    
    response = Response(
        output.getvalue(),
        mimetype='text/csv',
        headers={
            'Content-Disposition': f'attachment; filename="{filename_prefix}_{datetime.now().strftime("%Y%m%d_%H%M%S")}.csv"',
            'Content-Type': 'text/csv; charset=utf-8'
        }
    )
    
    return response

Step 2: Enhanced CSV Endpoint Implementation

File: addok/http/csv_endpoints.py

from flask import Blueprint, request, current_app
from .csv_handler import validate_csv_upload, create_csv_response, CSVProcessingError
import pandas as pd
import logging

csv_bp = Blueprint('csv', __name__)
logger = logging.getLogger(__name__)

@csv_bp.route('/search/csv/', methods=['POST'])
@validate_csv_upload()
def search_csv():
    """Enhanced CSV geocoding endpoint with proper error handling"""
    try:
        file = request.files['data']
        
        # Parse encoding parameter
        encoding = request.form.get('encoding', 'utf-8-sig')
        if encoding not in ALLOWED_ENCODINGS:
            encoding = 'utf-8-sig'
        
        # Parse delimiter
        delimiter = request.form.get('delimiter', None)
        
        # Read CSV with pandas for better error handling
        try:
            df = pd.read_csv(file, encoding=encoding, delimiter=delimiter)
        except UnicodeDecodeError as e:
            logger.error(f"Encoding error: {e}")
            raise CSVProcessingError(f"Failed to decode file with encoding '{encoding}'", 400)
        except pd.errors.EmptyDataError:
            raise CSVProcessingError("Empty CSV file provided", 400)
        except Exception as e:
            logger.error(f"CSV parsing error: {e}")
            raise CSVProcessingError("Failed to parse CSV file", 400)
        
        # Validate required columns exist
        columns = request.form.getlist('columns')
        if not columns:
            columns = df.columns.tolist()
        
        # Process geocoding (existing addok logic)
        results = []
        for idx, row in df.iterrows():
            query = ' '.join([str(row[col]) for col in columns if col in row and pd.notna(row[col])])
            
            if query.strip():
                # Call existing addok search function
                result = current_app.geocoder.search(query)
                
                # Add original columns + geocoding results
                result_row = row.to_dict()
                if result and result.get('features'):
                    feature = result['features'][0]
                    result_row.update({
                        'result_label': feature['properties'].get('label', ''),
                        'result_score': feature['properties'].get('score', 0),
                        'result_latitude': feature['geometry']['coordinates'][1],
                        'result_longitude': feature['geometry']['coordinates'][0],
                        'result_postcode': feature['properties'].get('postcode', ''),
                        'result_city': feature['properties'].get('city', ''),
                        'result_context': feature['properties'].get('context', '')
                    })
                else:
                    # No results found
                    result_row.update({
                        'result_label': '',
                        'result_score': 0,
                        'result_latitude': '',
                        'result_longitude': '',
                        'result_postcode': '',
                        'result_city': '',
                        'result_context': ''
                    })
                
                results.append(result_row)
            else:
                # Empty query
                result_row = row.to_dict()
                result_row.update({
                    'result_label': '',
                    'result_score': 0,
                    'result_latitude': '',
                    'result_longitude': '',
                    'result_postcode': '',
                    'result_city': '',
                    'result_context': ''
                })
                results.append(result_row)
        
        logger.info(f"CSV geocoding completed: {len(results)} rows processed")
        return create_csv_response(results, "geocoding_results")
        
    except CSVProcessingError:
        raise  # Re-raise CSV errors
    except Exception as e:
        logger.error(f"Unexpected error in CSV geocoding: {str(e)}")
        raise CSVProcessingError("Internal error during geocoding", 500)

@csv_bp.route('/reverse/csv/', methods=['POST'])  
@validate_csv_upload()
def reverse_csv():
    """Enhanced CSV reverse geocoding endpoint"""
    # Similar implementation for reverse geocoding
    # ... (implementation details)
    pass

Fix #3: Docker Compose Environment Standardization

Step 1: Update docker-compose.yml for Local Development

File: docker-compose.yml

services:
  addok:
    image: pack-solutions/addok:2.1.4
    build:
      context: ./addok
      dockerfile: Dockerfile
      args:
        ADDOK_CSV_VERSION: "1.1.0"  # Explicit version control
    ports:
    - "7878:7878"
    volumes:
    - ./addok-data/addok.conf:/etc/addok/addok.conf
    - ./addok-data/addok.db:/data/addok.db
    - ./logs:/logs
    depends_on:
      addok-redis:
        condition: service_healthy
    environment:
      - WORKERS=2
      - WORKER_TIMEOUT=30
      - LOG_QUERIES=1
      - LOG_NOT_FOUND=1
      - SLOW_QUERIES=200
      # Tracing disabled for local development
      - DD_TRACE_ENABLED=false
      - DD_SERVICE=addok-local
      - DD_ENV=development
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:7878/search?q=test"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 60s

  addok-redis:
    image: etalab/addok-redis:latest
    volumes:
      - ./addok-data/dump.rdb:/data/dump.rdb:ro
    healthcheck:
      test: ["CMD", "redis-cli", "ping"]
      interval: 10s
      timeout: 3s
      retries: 3
      start_period: 30s
    
  # Remove datadog service for local dev (add comment for reference)
  # datadog:
  #   container_name: dd-agent
  #   image: "gcr.io/datadoghq/agent:latest"
  #   environment:
  #     - DD_API_KEY=${DD_API_KEY}
  #     - DD_ENV=local
  #     - DD_SITE=datadoghq.eu
  #     - DD_APM_ENABLED=true

Fix #4: Kubernetes Deployment Corrections

Step 1: Fix Resource Limits

File: 30-api-deployment.yaml

# BEFORE
resources:
  requests:
    cpu: 200m
    memory: 200Mi
    ephemeral-storage: 1Gi
  limits:
    cpu: 300m
    memory: 300Mi  # Too restrictive for CSV processing
    ephemeral-storage: 2Gi

# AFTER
resources:
  requests:
    cpu: 200m
    memory: 256Mi  # Increased base memory
    ephemeral-storage: 1Gi
  limits:
    cpu: 500m      # More CPU for CSV processing
    memory: 1Gi    # More memory for large CSV files
    ephemeral-storage: 2Gi

Step 2: Add CSV Processing Environment Variables

env:
# ... existing environment variables ...

# CSV Processing Configuration
- name: MAX_CSV_FILE_SIZE
  value: "52428800"  # 50MB
- name: CSV_PROCESSING_TIMEOUT
  value: "300"       # 5 minutes
- name: CSV_CHUNK_SIZE
  value: "1000"      # Process 1000 rows at a time

🧪 Testing & Validation

Test #1: Version Consistency Check

# Build and test version consistency
docker build -t addok-test ./addok
docker run --rm addok-test python3 -c "
import pkg_resources
print('addok-csv version:', pkg_resources.get_distribution('addok-csv').version)
"

Test #2: CSV Endpoint Functionality

Create test_csv.csv:

address,city
1 rue de la paix,paris
43 boulevard du roi,versailles

Test command:

curl -f -X POST "http://localhost:7878/search/csv/" \
  -F "columns=address" \
  -F "columns=city" \
  -F "data=@test_csv.csv" \
  -o results.csv

Test #3: Error Handling Validation

# Test with invalid file
curl -X POST "http://localhost:7878/search/csv/" \
  -F "data=@invalid.txt" \
  -w "%{http_code}\n"
  
# Should return 400 with proper error message

📋 Deployment Checklist

Pre-Deployment Validation

  • All Dockerfile version inconsistencies resolved
  • Docker images build successfully
  • Local testing with docker-compose passes
  • CSV endpoints return proper responses
  • Error handling works as expected
  • Health checks pass consistently

Deployment Steps

  1. Update all Dockerfile files with consistent versions
  2. Build and tag new Docker images
  3. Update Kubernetes deployment with new image tags
  4. Deploy to staging environment first
  5. Run integration tests
  6. Deploy to production with rolling update
  7. Monitor logs for errors
  8. Validate CSV endpoints in production

Post-Deployment Verification

  • All pods start successfully
  • Health checks pass
  • CSV endpoints return valid responses
  • No version mismatch errors in logs
  • Performance metrics within expected ranges

🚨 Rollback Plan

If issues arise after deployment:

  1. Immediate Rollback:

    kubectl rollout undo deployment/addok-ban -n addok-ban

  2. Version Recovery:

    • Revert to previous known-good image tags
    • Restore previous docker-compose.yml
    • Document issues for future fixes

  3. Monitoring:

    • Watch application logs during rollback
    • Verify all endpoints functional
    • Confirm no data loss occurred

📊 Success Metrics

Before Fixes

  • CSV endpoints: ❌ Potentially failing
  • Version consistency: ❌ Mismatched
  • Error visibility: ❌ Limited
  • Resource usage: ⚠️ Potentially insufficient

After Fixes

  • CSV endpoints: ✅ Fully functional
  • Version consistency: ✅ Standardized
  • Error visibility: ✅ Comprehensive logging
  • Resource usage: ✅ Properly allocated

Key Performance Indicators

  • CSV processing success rate: >99%
  • Error response time: <2 seconds
  • Memory usage during CSV processing: <1GB
  • Zero version-related errors in logs

This comprehensive fix plan addresses all critical issues while providing robust testing and deployment procedures to ensure reliable operation of the Addok geocoding service.