Scaling PostgreSQL Past 10TB with GridGain 9: A Practical Guide

This guide breaks down what really starts to strain as data grows into the tens of terabytes and why mixed workloads become harder to keep predictable. It includes how to:

  • Eliminate replica lag
  • Reduce maintenance windows
  • Accelerate mixed workloads
  • Preserve PostgreSQL as the source of truth

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About this white paper

Organizations that scale PostgreSQL into the tens of terabytes encounter sharp inflection points: replica lag despite more readers, maintenance windows that expand with table bloat, and mixed OLTP/analytics workloads that push the planner and storage beyond comfortable limits. This white paper presents a practical approach to scaling to ~10TB and beyond by pairing PostgreSQL, kept as the authoritative system of record, with GridGain 9, a distributed in-memory and persistent SQL layer.

We compare two integration patterns: (A) a CDC-driven speed layer via Kafka for near-real-time, cross-table SQL at scale, and (B) a read/write-through JDBC cache for targeted hot-table acceleration without Kafka.

We outline architecture, sizing, and operational guidance, and quantify impact through representative metrics (latency distributions, write-ahead log (WAL) throughput, and cache hit ratio). Finally, we provide an implementation blueprint and appendices with configuration snippets for rapid adoption. The result is lower tail latency, reduced bloat and I/O cost, and a safer operations envelope, while preserving PostgreSQL as the single source of truth.
 

GridGain provides a low latency, horizontally scalable SQL tier that offloads PostgreSQL, delivers sub second analytics on fresh data, and preserves PostgreSQL as the single source of truth.

Scaling PostgreSQL Past 10TB with GridGain 9: A Practical Guide

Scale PostgreSQL beyond 10TB with this practical guide that explains how to reduce replica lag, improve latency, cut infrastructure cost, and achieve real time distributed SQL performance using GridGain 9.

Get the full white paper