Performance Issues#

Optimizing Gold Digger performance and memory usage.

Memory Usage#

Large Result Sets#

Gold Digger loads all results into memory. For large datasets:

• Limit Rows: Use LIMIT clauses to reduce result size
• Paginate: Process data in smaller chunks
• Filter Early: Use WHERE clauses to reduce data volume

Memory Monitoring#

Monitor memory usage during execution:

# Linux/macOS
top -p $(pgrep gold_digger)

# Windows
tasklist /fi "imagename eq gold_digger.exe"

Query Optimization#

Efficient Queries#

• Use indexes on filtered columns
• Avoid SELECT * - specify needed columns only
• Use appropriate WHERE clauses

Example Optimizations#

-- Instead of:
SELECT * FROM large_table

-- Use:
SELECT id, name, email FROM large_table WHERE active = 1 LIMIT 1000

Connection Performance#

Connection Pooling#

Gold Digger uses connection pooling internally, but:

• Minimize connection overhead with efficient queries
• Consider database server connection limits

Network Optimization#

• Use local databases when possible
• Optimize network latency for remote connections
• Consider compression for large data transfers

Output Performance#

Format Selection#

• CSV: Fastest for large datasets
• JSON: More overhead but structured
• TSV: Good balance of speed and readability

File I/O#

• Use fast storage (SSD) for output files
• Consider output file location (local vs network)

Troubleshooting Slow Performance#

1. Profile Queries: Use EXPLAIN to analyze query execution
2. Monitor Resources: Check CPU, memory, and I/O usage
3. Database Tuning: Optimize database configuration
4. Network Analysis: Check for network bottlenecks