How to Use SPRING Database Converter — Step-by-Step Tutorial

SPRING Database Converter — Performance Tips & Troubleshooting

Key performance tips

• Use batch operations: Convert records in configurable batches (e.g., 1k–10k rows) to reduce transaction overhead and memory pressure.
• Tune batch size by testing: Start with ~1,000 rows and increase until you see memory spikes or longer DB locks; reduce if latency or errors rise.
• Disable unnecessary indexes during write-heavy phases: Drop or disable noncritical indexes before large writes and rebuild afterward to speed inserts.
• Use bulk inserts / multi-row statements: Prefer multi-row INSERTs or DB-specific bulk APIs rather than single-row inserts.
• Keep transactions short: Commit frequently enough to avoid long-running transactions that hold locks; use savepoints if supported.
• Stream rather than materialize: Read source data as a stream (cursor) to avoid loading entire tables into memory.
• Parallelize safely: Run multiple converter workers when the source and target DBs can handle concurrent load; shard work by primary key ranges.
• Profile and monitor: Measure CPU, I/O, DB locks, and network; instrument conversion code to capture per-batch timings and error rates.
• Adjust DB settings temporarily: For one-time large conversions, consider temporarily increasing WAL/checkpoint thresholds or write-ahead settings per DB best practices.
• Optimize transformations: Move simple transforms into SQL (pushdown) where possible instead of transforming row-by-row in application code.

Common causes of slow conversions

• Full-table scans without indexes on join/filter columns.
• Small batch sizes causing excessive round-trips.
• Large transactions causing lock contention and long recovery times.
• Memory exhaustion from materializing huge datasets.
• Network latency when source and target are remote.
• Rebuilding many indexes per-row instead of once after bulk load.
• Inefficient transformation logic (e.g., regex per-row, repeated lookups).

Troubleshooting checklist

1. Reproduce and measure: Record time per batch and where time is spent (read, transform, write).
2. Check DB locks: Use DB tooling to identify long-held locks or blocked sessions.
3. Inspect query plans: For slow reads/writes, examine EXPLAIN/EXPLAIN ANALYZE to find missing indexes or bad plans.
4. Monitor resources: Watch CPU, memory, disk I/O, and network—look for saturation.
5. Review logs and error rates: Identify patterns (specific rows, payloads, or transforms) causing failures.
6. Test varying batch sizes: Try larger and smaller batches and compare throughput and stability.
7. Try single-threaded run on problem range: Isolate a key range causing failures to inspect data anomalies.
8. Validate data types and conversions: Mismatched types can cause implicit casts, slow writes, or errors.
9. Check index strategy: Ensure indexes used for reads exist; disable nonessential indexes for writes.
10. Retry and backoff logic: Implement exponential backoff for transient DB errors to avoid cascading failures.

Quick fixes for urgent slowdowns

• Temporarily reduce concurrency and increase batch size if DB is I/O bound.
• Pause index maintenance and rebuild after bulk load.
• Move conversion job closer to the database (same VPC/region) to reduce latency.
• Increase logging level for a short run to capture failing row details.

Post-conversion validation

• Run row counts, checksums, and sampled data comparisons.
• Verify indexes and constraints are re-enabled and that performance after conversion meets expectations.
• Run a small subset