HN Debrief

Making Postgres 300x faster for analytics: batching, operator fusion, and SIMD

  • Databases
  • Open Source
  • Performance
  • Programming
  • Developer Tools

The post is a technical walkthrough of why pgrust can run some analytical queries vastly faster than standard Postgres. The core claim is not magic. It is a switch from Postgres’s classic tuple-at-a-time model to a vectorized engine that works on batches, keeps multiple operators in one tight loop, and leans on SIMD so the CPU does more useful work per memory fetch. The author also makes clear this is paired with columnar storage and other engine changes, so the headline number is not a clean “same database, same storage, just smarter code” comparison.

Treat this as evidence that there is still a large performance gap between core Postgres and modern analytical execution, especially on columnar workloads. The practical gating factors are not clever query-engine tricks but trust, license policy, and whether the project can prove boring operational correctness over time.

Discussion mood

Interested but wary. People mostly bought that vectorized execution can crush Postgres on analytics, but the mood stayed skeptical because the comparison is workload-specific, the project is early, the license is a corporate blocker, and a database built through aggressive AI-assisted rewriting has to clear a very high trust bar.

Key insights

  1. 01

    Why this is hard to upstream

    Core Postgres is not just missing a few micro-optimizations. Some of the gains depend on columnar storage, a different execution model, and scheduler choices that do not slot neatly into the existing engine. A reviewer who checked the ClickBench results said standard Postgres is genuinely bad at parts of this workload, but also said the hardest fixes only become visible once columnar storage is integrated deeply instead of bolted on as an extension.

    Do not assume upstream Postgres will absorb this quickly. If analytics performance on Postgres-compatible data is strategic for you, watch the extension and fork ecosystem, not just core release notes.

      Attribution:
    • malisper #1
    • kopirgan #1
    • postgresperf #1
  2. 02

    Adaptive planning is the bigger architectural signal

    The excitement around adaptive planning points to something larger than batch execution. Postgres still commits to plans too early and too rigidly for workloads where cardinality estimates drift or data shape changes mid-query. If pgrust can make runtime adaptation practical in a Postgres-compatible system, that closes a gap with production databases that have treated this as normal for years.

    If you run complex analytical SQL, pay attention to planner behavior as much as raw scan speed. A faster execution engine helps, but bad plan selection can still dominate end-to-end latency.

      Attribution:
    • AsyncBanana #1
  3. 03

    Performance work breaks under real cache behavior

    The exchange on benchmarking was unusually grounded. The author described one optimization that was worthless on an AWS Graviton test box but worth 20 percent on a Mac because the instruction cache bottleneck moved. Another commenter pushed the point further. Production systems live in a messy state of partial memory residency and low-grade contention that is almost impossible to replay once benchmarks warm the page cache. That is a reminder that serious database tuning is often about memory hierarchy pathologies, not algorithm choice in the abstract.

    When you evaluate database speed claims, reproduce them under mixed-residency and contested-memory conditions, not just hot-cache runs. If your production latency is noisy, assume cache and I/O state are part of the workload.

      Attribution:
    • malisper #1
    • marginalia_nu #1
  4. 04

    Correctness work already paid for itself

    The most convincing part of the author’s defense was not the benchmark. It was the testing strategy. Formal equivalence proofs for a slice of user-facing functions plus differential fuzzing against the C implementation already found around a hundred pgrust bugs and about twenty Postgres bugs, including a nasty floating point corner case in quadtree logic. That is exactly the kind of weird failure mode that normal application testing misses for years.

    If you are building systems software with AI assistance, insist on differential testing and property-based fuzzing from day one. For users, ask for evidence of that work before trusting claims of compatibility.

      Attribution:
    • malisper #1
    • wffurr #1
    • jnwatson #1
  5. 05

    Threaded architecture opens new use cases

    The Rust rewrite is not only about faster analytics queries. The author said replacing Postgres’s process-per-connection model with thread-per-connection makes embedding practical, including in test harnesses and WebAssembly deployments. They also pointed to fast template-database cloning for integration tests. That puts pgrust in a different product category than server-only Postgres clones.

    If your pain point is local-first apps, browser-side SQL, or high-churn test databases, this project may become interesting before it becomes a credible production OLTP system.

      Attribution:
    • malisper #1 #2

Against the grain

  1. 01

    The benchmark advantage is narrower than advertised

    The headline number rides on an in-memory analytical setup, and the author explicitly chose data sizes that fit RAM. A commenter argued that this is where many spectacular database speedups come from, because once the working set spills, the game shifts from CPU efficiency to memory and disk movement. That does not make the result fake. It makes it much more workload-specific than the slogan suggests.

    Map this claim to your own bottleneck before getting excited. If your queries spill, join large tables, or live on cold data, ask for evidence on those cases rather than extrapolating from ClickBench-style wins.

      Attribution:
    • hunterpayne #1
    • malisper #1
    • throwaway7783 #1
  2. 02

    Trust and longevity beat raw speed

    Several readers cut past the technology and focused on institutional trust. A faster fork does not replace the value of decades of operational history, broad ecosystem support, and the confidence that your database will still be maintained ten years from now. The author’s own answer on using it as a WAL-backed analytics mirror was effectively “try it on something non-critical.” That honesty underscored the point.

    Use projects like this first where blast radius is small. Read-only replicas, internal analytics, and test infrastructure are plausible entry points. Core transactional systems are not.

      Attribution:
    • sgt #1
    • f311a #1
    • malisper #1
  3. 03

    AGPL may block adoption before performance matters

    The most practical objection was licensing, not engineering. The author chose AGPL to stop cloud vendors from reselling the work without paying, and offered commercial licensing as the escape hatch. Many readers said that still kills adoption inside larger companies because AGPL is banned or escalated automatically by legal teams, especially for infrastructure software. A huge speedup does not help if procurement will not let you run the binary.

    If you are evaluating pgrust for a company, involve legal early instead of treating the license as cleanup later. If you build infrastructure products yourself, this is a reminder that license strategy determines your reachable market as much as technical merit.

      Attribution:
    • malisper #1
    • mey #1
    • xyzzy_plugh #1
    • ForHackernews #1
    • jacquesm #1

In plain english

AGPL ↩
Affero General Public License, a copyleft software license that requires source code sharing in some networked use cases.
ClickBench ↩
ClickBench is a public benchmark suite commonly used to compare analytical database performance.
columnar storage ↩
Columnar storage keeps values from the same column together on disk or in memory, which is efficient for analytical scans.
formal verification ↩
Formal verification uses mathematical methods to prove that software meets a specified behavior.
fuzzing ↩
Fuzzing is automated testing that feeds many random or unusual inputs into software to find bugs.
instruction cache ↩
The instruction cache is a small fast CPU memory area that stores recently used machine instructions.
OLAP ↩
Online Analytical Processing, workloads focused on large read-heavy analytical queries rather than frequent small updates.
pgrust ↩
pgrust is a Rust-based Postgres-compatible database project discussed in the story.
Postgres ↩
Postgres, or PostgreSQL, is a widely used open source relational database system.
quadtree ↩
A quadtree is a tree data structure used to partition two-dimensional space for spatial indexing.
SIMD ↩
Single Instruction, Multiple Data, a CPU feature that lets one instruction process several data values at once.
tuple-at-a-time ↩
Tuple-at-a-time execution is a database model where rows are processed one by one instead of in batches.
vectorized engine ↩
A vectorized engine processes batches of values together, which often improves cache use and allows SIMD acceleration.
WAL ↩
Write-Ahead Log, a database log used for durability and replication by recording changes before they are applied.
WebAssembly ↩
WebAssembly, often shortened to Wasm, is a portable binary format for running code in browsers and other environments.

Reference links

Project and benchmarks

Testing and verification

Papers and technical references

Related projects and background posts

Books