SirixDB 1.0 Beta – Git-Like Versioning, Diffs, Time-Travel Queries
Hi HN! I've posted SirixDB here before, back in 2019 (https://news.ycombinator.com/item?id=19834681) and again in 2023 (https://news.ycombinator.com/item?id=38252963). The core idea behind SirixDB is, that history is a first-class citizen. Every commit stores a lightweight, queryable revision. You can query any point in time, even individual nodes (for instance JSON values), diff arbitrary revisions, and efficiently track how data evolved without replaying events. Unlike traditional event stores, historical states do not need to be reconstructed by replaying…
In plain words
SirixDB is a database that treats data history as a core feature rather than an afterthought. It stores lightweight, queryable revisions with each commit, allowing users to query any point in time, compare different versions, and track how individual data elements evolved without replaying events. Built for developers who need to audit data changes, correct historical records, or understand data lineage, SirixDB supports JSON and XML data with git-like versioning capabilities and time-travel queries across both transaction time and valid time.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi HN! I've posted SirixDB here before, back in 2019 (https://news.ycombinator.com/item?id=19834681) and again in 2023 (https://news.ycombinator.com/item?id=38252963). The core idea behind SirixDB is, that history is a first-class citizen. Every commit stores a lightweight, queryable revision. You can query any point in time, even individual nodes (for instance JSON values), diff arbitrary revisions, and efficiently track how data evolved without replaying events. Unlike traditional event stores, historical states do not need to be reconstructed by replaying events nor do we have to think about projections. Revisions are directly queryable. A simple example: Jan 1: Record "Price = $100, valid from Jan 1". Stored on Jan 1 (transaction time). Jan 20: Discover price was actually $95 on Jan 1. Commit correction. After correction, you can ask across both axes: - "What did we THINK the price was on Jan 16?" -> $100 (Transaction time) - "What WAS the price on Jan 1?" -> $95 (Valid time) I've worked on this in my spare time since 2013, following its academic precursor (Idefix/Treetank) at the University of Konstanz. The architecture relies on an append-only physical log and a persistent copy-on-write page trie. A high level view of the architecture: Physical Log (append-only, sequential writes) ┌────────────────────────────────────────────────────────────────────────┐ │ [R1:Root] [R1:P1] [R1:P2] [R2:Root] [R2:P1'] [R3:Root] [R3:P2'] ... │ └────────────────────────────────────────────────────────────────────────┘ t=0 t=1 t=2 t=3 t=4 t=5 t=6 → time Each revision is indexed, and unchanged pages are shared: [Rev 1] [Rev 2] [Rev 3] │ │ │ ▼ ▼ ▼ [Root₁] [Root₂] [Root₃] │ │ │ │ │ │ │ └─────────┐ │ └────────┐ │ └─────────┐ ▼ ▼ ▼ ▼ ▼ ▼ ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐ │ P1 │ │ P2 │ │ P1' │ │ P2' │ └──────┘ └──────┘ └──────┘ └──────┘ Rev 1 Rev 1+2 Rev 2+3 Rev 3 (shared) (shared) Beneath the root pages sit node and secondary indexes, using a novel sliding-snapshot algorithm to balance read/write performance. Everything is queryable using JSONiq via the Brackit compiler. Back in 2019, and even in 2023, SirixDB was very slow due to GC pressure. Unlike most other document stores, SirixDB stores fine-grained nodes, and I came to realize that an on-heap (JVM) representation made up of lots of small objects simply didn't make sense. I measured it with async-profiler — with some help from Andrei Pangin himself — and the result was that the poor throughput was due to the sheer amount of allocations which scaled almost linearly with the number of open transactions. Working a full-time software engineering job, I lacked the energy for a massive spare-time rewrite. About a year ago, I started experimenting with AI. It turned out to be ideal for automating the tedious, repetitive parts of migrating the storage layer to Java's Foreign Function & Memory API, storing pages completely off-heap. Looking further ahead, the append-only, immutable-page design maps naturally onto object storage like S3 and distributed logs like Kafka for a cloud version, and initial prototypes already exist. Maybe that becomes a commercial service one day, but for now, I'm just thrilled to see these core design principles finally proven out.There's an interactive demo, documentation, and the code is on GitHub. I'd love feedback and am happy to answer questions! kind regards Johannes [1] https://sirix.io | https://github.com/sirixdb/sirix [2] https://sirix.io/docs/architecture.html [3] https://demo.sirix.io [4] https://sirix.io/docs/ [5] http://brackit.io
Does the same job
all alternatives →- SSSirixDB – Storing and Querying of Temporal Data (Java and Open Source)2018 · ▲13
I'm developing a storage system for versioning data at the subfile level, especially well suited for SSDs due to its log-structured COW nature. It implements a novel versioning algorithm called sliding snapshot, a diff-algorithm which makes use of our stable record-identifiers and optionally hashes, another diff algorithm for importing similar XML-documents as a versioned resource as well as novel XPath axis to navigate not only in space, but also in time. Recently, I've implemented a higher level, asynchronous REST-API with Kotlin (Coroutines) and Vert.x in a seperate module. The system is…
- SVSirixDB – versioning through efficient snapshotting2018 · ▲55
I've already posted yesterday, but I'd really love to get comments, any kind of questions, suggestions and help would be greatly appreciated as it's an Open Source project of mine (and was for others during my studies at the University of Konstanz 6 years ago). Since then I spent countless ours to bring forth the idea of a versioned storage system, especially well suited for analytical tasks for timd-varying data. Especially I'd love to discuss what documentation you need, which next steps are necessary (JSON, Cloud...), API additions or changes... I've updated the README quiet a bit, such…
- LBLearn by rebuilding Redis, Git, a database from scratchJul 2026 · shipthatcode.com · ▲200
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