PostgreSQL Architecture, Performance & Reliability Engineering
Wolk Inc designs, optimises, and operates PostgreSQL database systems: schema design, query tuning, streaming and logical replication, Patroni HA failover, PgBouncer connection pooling, and partitioning architecture. From performance investigations to zero-downtime major version upgrades.
Patroni
Automatic HA Failover
PgBouncer
Connection Pooling
TimescaleDB
Time-Series Extension
Logical Rep
Zero-Downtime Upgrades
PostgreSQL Consulting Deliverables
Schema Design & Query Optimisation
PostgreSQL schema design: normalisation strategy, data type selection (JSONB vs. columns, enum vs. lookup table), constraint design, and index strategy (B-tree, GIN for JSONB/arrays, BRIN for time-series, partial indexes for sparse queries). EXPLAIN ANALYSE-driven query optimisation: planner statistics tuning, join strategy analysis, sequential scan elimination, and materialised view design for expensive repeated queries.
Replication & High Availability
Streaming replication configuration: primary/replica topology, synchronous vs. asynchronous replication trade-offs, replication slot management, and WAL archiving to S3/GCS. Patroni-based automatic failover with etcd or ZooKeeper consensus. Logical replication for zero-downtime major version upgrades, cross-region read replicas, and selective table replication to analytics databases.
Performance Tuning & Connection Management
PostgreSQL configuration tuning: shared_buffers, work_mem, effective_cache_size, checkpoint parameters, autovacuum tuning for high-write workloads. PgBouncer connection pooler deployment (transaction-mode pooling) to support high-concurrency application workloads without connection exhaustion. Vacuum and bloat analysis with pg_bloat_check, and table maintenance scheduling.
Partitioning & Time-Series Architecture
Declarative table partitioning (range, list, hash) for large tables: partition pruning analysis, partition key selection, and automated partition management with pg_partman. TimescaleDB extension integration for time-series workloads: hypertable design, continuous aggregates, and compression policies. pg_cron for scheduled maintenance and data retention policy enforcement.
Query Plans Analysed. Replication Verified.
PostgreSQL Consulting Questions
When should a PostgreSQL table be partitioned?▾
Table partitioning becomes beneficial when a table exceeds roughly 100–200GB or contains data with a natural time or category dimension that queries consistently filter on. The primary benefit is partition pruning: queries that include the partition key in their WHERE clause skip irrelevant partitions entirely, dramatically reducing I/O. Partitioning also enables efficient bulk data management — archiving old partitions by detaching them, or dropping old time partitions for data retention. Wolk Inc analyses query patterns and table growth projections to recommend partition key and range design.
What is the difference between PostgreSQL streaming replication and logical replication?▾
Streaming replication copies the entire WAL stream (physical, byte-for-byte copy of the primary) to one or more standbys — suitable for high-availability failover where the replica is an exact copy of the primary. Logical replication decodes WAL into SQL-level change events and replicates specific tables to a subscriber — useful for selective replication, cross-version upgrades, migrating to different hardware, or feeding a replica that serves a different purpose (analytics, audit). Zero-downtime major version upgrades using logical replication are a key use case Wolk Inc implements.
How does PgBouncer improve PostgreSQL performance?▾
PostgreSQL creates a new OS process for each client connection, making each connection expensive in memory (roughly 5–10MB) and CPU. Applications with hundreds of concurrent connections (common in microservice architectures or high-traffic web apps) can exhaust PostgreSQL's connection limit or cause significant memory pressure. PgBouncer maintains a small pool of actual PostgreSQL connections and multiplexes many application connections through them. In transaction-mode pooling, a database connection is only held during an active transaction, enabling thousands of application connections to share a pool of 20–50 database connections.
How does Wolk Inc approach a PostgreSQL major version upgrade?▾
Major version upgrades (e.g., PostgreSQL 13 → 16) require careful planning. For zero-downtime upgrades, Wolk Inc uses logical replication: set up a new PostgreSQL instance on the target version, configure logical replication from the current primary, let it catch up to near-real-time, then perform a brief application-level cutover. For acceptable maintenance window upgrades, pg_upgrade provides a faster in-place upgrade. Pre-upgrade testing includes extension compatibility checks (TimescaleDB, PostGIS, pgvector), application query regression testing, and configuration parameter review for deprecated settings.
Can PostgreSQL handle time-series workloads or should we use a dedicated time-series database?▾
PostgreSQL with the TimescaleDB extension handles most enterprise time-series workloads effectively — sensor data, application metrics, financial tick data, IoT telemetry. TimescaleDB adds automatic time-based partitioning (hypertables), continuous aggregates for pre-computed rollups, and native compression that reduces storage by 90%+ for time-series data. For workloads with 100,000+ inserts/second or complex time-series analytics requiring columnar processing, dedicated systems like ClickHouse or InfluxDB may be more appropriate. Wolk Inc assesses your write rate, cardinality, and query patterns to recommend the right architecture.