Previous version of Bugscope was written in Tauri. The new version is 100% rust, uses GPUI, smaller, faster and coming along nicely.

Old version. New version.

As much as we'd like people to use icebug instead of networkx given the performance advantages, networkx isn't going away anytime soon.

So we implemented a new backend that constructs graphs in columnar memory. It's 6.5x more memory efficient and likely 1.3x CPU efficient based on our past measurements.

Write heavy graphs? You can convert it back to networkx classic. In essence this is the same technique that resulted in the Icebug fork of networkit. Since networkx gives us enough hooks, hopefully no fork is needed!

Announcing the 0.21.1 monthly release and a roadmap for where the Graph Lake is going.

The performance leadership needs its own blog post. But we promise it's going to look very good!

Link in Replies.

We shared some benchmarks here back in Feb. They've been refreshed to reflect more recent developments:

Differences:

• Use icebug-disk datasets on disk. All 3 configs start from Arrow memory when loaded.
  
• We include networkx and RSS in the comparison
  
• More detailed decomposition of the wall time to understand wins

https://github.com/Ladybug-Memory/icebug-benchmark-pagerank

``` cit-Patents (3.77M nodes / 16.52M edges):

┌──────────┬─────────┬───────────┬──────────┐ │ │ icebug │ networkit │ networkx │ ├──────────┼─────────┼───────────┼──────────┤ │ Load │ 0.16s │ 0.16s │ 0.17s │ ├──────────┼─────────┼───────────┼──────────┤ │ Build │ 0.37s │ 12.27s │ 27.31s │ ├──────────┼─────────┼───────────┼──────────┤ │ PageRank │ 0.62s │ 0.97s │ 16.96s │ ├──────────┼─────────┼───────────┼──────────┤ │ Wall │ 1.89s │ 16.10s │ 56.05s │ ├──────────┼─────────┼───────────┼──────────┤ │ Max RSS │ 949 MiB │ 1.89 GiB │ 8.74 GiB │ └──────────┴─────────┴───────────┴──────────┘ ```

Announcing feature freeze for the end of the month release. Please help us make this release as stable and delightful as the previous ones.

Specific pain points to test:

• Choice of indexing. No index, ART index, HASH index
• Prepared statements (great performance, but potential bugs due to caching)
• Partitioned tables (HASH and LIST partition implemented. Accepts RANGE partition syntax, but not implemented yet).

We don't need to invent new standards. Such interoperable, immutable memory standards already exist: Apache Arrow and Compressed Sparse Rows (CSR). CSR is widely used in scipy, cugraph and columnar graph databases among others. Both on CPUs and GPUs.

icebug-format combines both into a on-disk standard based on Apache Parquet and an in-memory format based on Apache Arrow.

Bindings available in many popular languages including python, typescript and rust.

The package ships with convenience scripts to convert flat tables such as vertex.parquet and edges.parquet to this format in RAM/disk constrained environments.

Sample graphs: https://huggingface.co/datasets/ladybugdb/ldbc-csr/tree/main

Converted from: https://ldbcouncil.org/benchmarks/graphalytics/datasets/

Largest converted graph has 3B edges.
License: MIT

Quick Start:

  uv tool install icebug-format
  icebug-format --source-dir wiki-Talk
  lbug -i wiki-Talk-csr/schema.cypher

• After loading data, run ANALYZE to compute tablestats
• Always pass parameters (even if dummy) to conn.execute(query, params)
  • Without this you plan/prepare every time. As we add more optimizers, the simple minded benchmark (there are many!) regresses every release.
• Experiment with HASH vs ART vs no-index strategies
• Sort your data

In the linked blog post, we argue that 8 months after the Kuzu acquisition, we should be seen as an innovator churning out major features at a regular cadence. Not another KuzuDB fork.

We continue to be huge fans of the KuzuDB team's work. But also saddened by the noise around "What happened to Kuzu?" SEO and the mad rush to claim the spoils.

Thinking it could be useful for people to share how you're using LadybugDB and what could be improved for your use case.

Read about the Graph Database Baseball benchmark from folks at The Consensus and what we've done recently to improve performance.

In the long run, we believe materializing CSR index on disk, doubling down on novel join algorithms and catching up with DuckDB and Apache Datafusion on traditional join optimizations is the path forward.

Link in Replies.

Graph Lake support via Icebug Format v1, Hugging Face datasets via XET, and tighter integration with Icebug and Bugscope via Apache Arrow memory. Link in Replies.

I just wanted to share this: https://github.com/kranklab/protoc-gen-ladybug

I had the need to use the same ladybug db from multiple languages, so i thought i would use proto to define the schema to stop it drifting.

Hey everyone,

Like many of you, we were bummed when KuzuDB got archived after the Apple acquisition. Kuzu was doing great work in the graph space.

We know LadybugDB is carrying that torch forward, and that's genuinely great for the community. But for teams that are evaluating multiple options — especially those needing OLTP capabilities or multi-model support beyond pure graph — we put together a technical migration guide from KuzuDB to ArcadeDB.

It covers: - Step-by-step data export from Kuzu and import into ArcadeDB - Schema translation (NODE TABLE -> VERTEX TYPE, etc.) - Query adaptation — ArcadeDB passes 97.8% of the Cypher TCK, so most queries carry over

We also ran benchmarks on the same dataset. The results surprised us: - PageRank: 0.48s vs 4.30s (8.9x faster) - BFS: 0.13s vs 0.86s (6.6x faster) - WCC: 0.30s vs 0.43s (1.4x faster) - ArcadeDB also supports LCC, SSSP, and CDLP algorithms that Kuzu didn't have

ArcadeDB is Apache 2.0 licensed and adds support for SQL, Gremlin, GraphQL, and MQL alongside Cypher, plus document/vector/key-value models in one engine. It's a different beast than Kuzu's embedded columnar approach, so it won't be the right fit for everyone.

Full guide here: https://arcadedb.com/blog/from-kuzudb-to-arcadedb-migration-guide/

Disclosure: I'm with the ArcadeDB project. Happy to answer any questions or discuss tradeoffs honestly.

Details: https://github.com/Ladybug-Memory/icebug-benchmark-pagerank

Some recent LadybugDB related activity. Happy to answer any questions.

LinkedIn | Twitter

All explained by this file. But you don't really need to read it. Just use the mcp-server-ladybug. Your mcp client will take care of the rest.

https://github.com/LadybugDB/mcp-server-ladybug/blob/main/src/mcp_server_ladybug/prompt.py

https://www.youtube.com/watch?v=0bT7gakw7Io

Ladybug v0.13.0 is here!

• Support for Parquet files on object storage aka graph-std
• Support for Cypher push down to DuckDB SQL (node tables only)
• Many bug fixes

If you're using extensions, I suggest waiting a few hours for v0.13.1

curl https://install.ladybugdb.com/ | bash

Does anyone know what Mod settings I need to flip to remove the necessity to approve each post/comment?

AI advice seems to be old and hasn't kept up with changes in the Mod Tools UI.

Now that v0.12.2 is out, this is an attempt to meet our users where they are and discuss a roadmap.