PostgreSQL Development, Tuning & Migration

PostgreSQL is open source with no per-core licence, which is often the main reason teams move to it from Oracle or SQL Server. It is also unusually capable: strong SQL standards support, transactional DDL, rich indexing, JSONB for semi-structured data and an extension system that adds geospatial, time-series and vector features without a second database.

Every major cloud offers a managed service - Amazon RDS and Aurora, Azure Database for PostgreSQL and Google Cloud SQL or AlloyDB - so you can choose hosting on cost and operations rather than on database lock-in.

Moving tables and data is the easy part. The real work is converting stored procedures, packages and triggers to PL/pgSQL, adjusting data types and collations, and changing application queries that rely on vendor-specific syntax. We inventory all of this first, convert with tools such as ora2pg or the cloud providers' migration services where they help, and hand-convert the rest.

Data is then synchronised continuously while both databases run, results are compared row by row, and cut-over happens only when performance tests on PostgreSQL match or beat the original.

Much of PostgreSQL's value comes from extensions. pg_stat_statements records how often each query runs and how long it takes, and is the first thing we enable on any performance engagement. PostGIS adds geospatial types and indexes, pgvector adds embeddings, TimescaleDB adds time-series partitioning and compression, and pg_partman automates partition maintenance for large tables.

Managed services only allow extensions from their own supported list, and the lists differ between providers and change over time - TimescaleDB, for example, is not available on every managed platform. If an extension matters to your design, we confirm support on your chosen service before committing to it.

PostgreSQL starts a separate server process for every connection, so hundreds of application instances or serverless functions connecting directly can exhaust memory long before the CPU is busy. A pooler such as PgBouncer, or a provider proxy such as Amazon RDS Proxy, lets many clients share a small set of connections. Transaction-level pooling changes how session state behaves - temporary tables, SET commands and advisory locks need care - so we check the application before switching it on.

Because PostgreSQL keeps old row versions until vacuum removes them, tables with heavy updates and deletes can bloat and slow down. We tune autovacuum per table for high-churn data, track down long-running transactions that block cleanup, monitor transaction ID age, and use tools such as pg_repack to reclaim space without holding a long table lock.

pgvector stores embeddings next to your relational data, so semantic search and retrieval-augmented generation can use the same permissions, backups and transactions as the rest of your application. For many workloads this removes the need for a separate vector database.