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The AccessToPostgres tool simplifies migrating data from Microsoft Access databases to PostgreSQL, but large-scale datasets often run into performance bottlenecks due to network latency, indexing overhead, and sub-optimal server configurations. Optimizing large data migrations requires minimizing database overhead during ingestion and maximizing bulk hardware processing power. 1. Disable Indexes and Constraints During the Load

Indexes and foreign key constraints are the most common causes of migration slowdowns.

Drop prior to migration: Drop indexes, foreign keys, and unique checks on the target PostgreSQL database before running the data transfer.

Rebuild concurrently: After the raw data is entirely migrated, rebuild the indexes using CREATE INDEX CONCURRENTLY to avoid table locking.

Add constraints last: Apply your primary keys and foreign key constraints only after the records are fully loaded. 2. Optimize Target PostgreSQL Configuration

Temporary tuning adjustments to PostgreSQL parameters can accelerate writing speeds before a heavy bulk insert operation.

Increase memory allocation: Temporarily increase shared_buffers (up to 25% of RAM) and boost max_wal_size to prevent frequent disk checkpoints.

Manage synchronous commits: Turn synchronous_commit = off to let transactions process without waiting for immediate hard disk confirmation.

Disable autovacuum temporarily: Turn off autovacuum for target tables during migration to prevent the engine from analyzing empty space while it is being actively written to. 3. Leverage Bulk Loading Over Individual Inserts

Row-by-row INSERT statements from Access generate immense networking and logging overhead.

Utilize COPY commands: Ensure AccessToPostgres leverages native PostgreSQL COPY processing, which feeds streams directly into tables.

Batch transactions: If raw statements must be utilized, chunk records into batches (e.g., 5,000 to 10,000 rows per transaction) instead of writing row-by-row. How I Optimised a 13 Million Row PostgreSQL Migration