{
  "id": 273590,
  "title": "Making Postgres 300x faster for analytics: batching, operator fusion, and SIMD",
  "url": "https://urgent.news/2026/08/07/making-postgres-300x-faster-for-analytics-batching-operator-fusion",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-08-07T11:00:35.000Z",
  "source": {
    "name": "Hacker News",
    "slug": "hacker-news",
    "url": "https://malisper.me/how-we-made-postgres-hundreds-of-times-faster-the-query-engine/"
  },
  "original_language": "en",
  "account": "Last week, version 0.2 of pgrust was released, focusing on performance improvements. It is 10x faster than the previous version and 300x faster than Postgres on analytical database benchmarks. The main performance driver is the query engine, which alone contributed ~10x to the overall speedup. To improve performance, the team built a miniature version of the Postgres query engine and sequentially added optimizations.\n\nPostgres was created in the 80s, when disk I/O was the main performance bottleneck. However, modern trends have shifted the focus to CPU and memory speeds. The query engine is the primary CPU consumer in a database, and optimizations have been made to reduce its CPU and memory usage.\n\nA simple example query sums the first 500 million numbers and takes ~20 seconds in Postgres, while a Rust implementation completes in 358ms, around 55x faster. The performance gap is due to the overhead of the Postgres query engine, which involves locking and parsing the Postgres storage format. To focus on the impact of the query engine, a miniature version of the Postgres query engine was created.\n\nThe query engine processes SQL queries by converting them into an internal representation called a \"Query Plan.\" The Postgres query engine executes this plan using the \"Volcano model,\" which processes one row at a time, adding overhead. The team optimized this by implementing batching, reducing query time from 1.3 seconds to ~480ms.\n\nThe next optimization, operator fusion, eliminates the need to copy data between slices, further improving performance. The result is a query that matches the speed of a straightforward for loop, showing the potential for significant performance gains in Postgres.",
  "summary": null,
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 2,
    "also_reported_by": [
      {
        "outlet": "Hacker News Best",
        "title": "Making Postgres 300x faster for analytics: batching, operator fusion, and SIMD",
        "url": "https://urgent.news/2026/08/07/making-postgres-300x-faster-for-analytics-batching-operator-fusion-279791",
        "published": "2026-08-07T11:00:35.000Z"
      }
    ]
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}