ustore VS json_benchmark

Just to clarify, I meant in other projects, like the UKV.

Yes, we also constantly think about that! In the document collections of UKV, for example, we have interoperability between JSON, BSON, and MessagePack objects [1]. CSV is another potential option, but text-based formats aren't ideal for large scale transmissions.

One thing people do - use two protocols. That is the case with Apache Arrow Flight RPC = gRPC for tasks, Arrow for data. It is a viable path, but compiling gRPC is a nightmare, and we don't want to integrate it into our other libraries, as we generally compile everything from sources. Seemingly, UJRPC can replace gRPC, and for the payload we can continue using Arrow. We will see :)

[1]: https://github.com/unum-cloud/ukv/blob/main/src/modality_doc...


Great point! I would be happy to get more input and brain-storm a good pricing model together, one that is fair both for developers and for users.

We have an source project UKV, that partly overlaps with vector-search: https://github.com/unum-cloud/ukv

Another one - UNSW, is a placeholder for now: https://github.com/unum-cloud/unsw

Both will be soon available on cloud marketplaces, but server-less options are a bit harder to cook. Our Discord is the best place to continue conversation: https://discord.gg/Bbh2bjNhvz

Thank you for advice!

Most "apps" are for CI/CD pipelines: testing, coverage reports, reviews automation, deployments, and so on. GitHub is a lot more than Git hosting these days. It comes with a web version of VS Code, GitHub Copilot AI, vulnerability detection tools, discussions, roadmap boards, and so on.

Documentation

I am really stunned by this story. It made me check the MemGraph benchmarks section. Don't get me wrong, it may be 10-100x faster than Neo4J in even the most basic operations. Moreover, given the quality of Neo4J, it is hard not to be that much quicker. Even Postgres and MySQL are better at storing graphs than Neo4J.

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Disclosure: I have worked on Graph Algorithms, Graph Databases, and Database Engines for years, and we are now preparing a commercial solution based on UKV [1]. I don't know anyone at MemGraph or Neo4J. Never used the first. As for the second, I am not a fan.

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Aside from licensing, there are 3 primary complaints. I will address them individually, and I am open to a discussion.

A. Using Python for Benchmarks instead of Gatling. I don't entirely agree with this. Python still has the fastest-growing programming community while already being one of the 2 most popular languages. Gatling, however, never heard of it. Choosing between the two, I would pick Python. But neither works if you want to design a High-Performance benchmark for a fast system. Without automatic memory management and expensive runtimes, you can only implement those in C, C++, Rust, or another systems-programming language. We have faced that too many times that the benchmark itself works worse than the system it is trying to evaluate [2].

B. Using hardware from 2010 [3], weird datasets [4]. This shocked me. When I looked at the charts [5] and the benchmarking section, it seemed highly professional and good-looking. I wouldn't expect less from a startup with $20M VC funding. But the devil is in the details. I would have never expected anyone benchmarking a new DBMS to use now 13-year-old CPUs and an unknown dataset. Assuming current developer salaries, hiring people to design a DBMS doesn't make sense if you will be evaluating on a $1000 machine is just financially irresponsible. We buy expensive servers, they cost like sports cars or even apartments in poorer countries. It is hard to maintain, but they are essential to quality work. It is sad to see companies taking such shortcuts. But to be a devil's advocate, there is no 1 graph benchmark or dataset that everyone agrees on. So I imagine people experimenting with multiple real datasets of different sizes or generating them systemically using one of the Random Generator algorithms. In UKV, we have used Twitter data to construct both document and graph collections. In the past, we have also used `ci-patent`, `bio-mouse-gene`, `human-Jung2015-M87102575`, and hundreds of other public datasets from the Network Repository and SNAP [6]. There are datasets of every shape and size, reaching around 1 Billion edges, in case someone is searching for data. For us the next step is the reconstruction of the Web from the 300 TB CommonCrawl dataset [7]. There is no such Graph benchmark in existence, but it is the biggest public dataset we could find.

C. Running query different number of times for various engines. This can be justified, and it is how current benchmarks are done. You are tracking not just the mean execution time but also variability, so if at some point results converge, you abrupt before hitting the expected iterations number to save time.

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LDBC [8] seems like a good contestant for a potential industry standard, but it needs to be completed. Its "Business Intelligence workload" and "Interactive workload" categories exclude any real "Graph Analytics". Running an All-Pairs-Shortest-Paths algorithm on a large external memory graph could have been a much more interesting integrated benchmark. Similarly, one can make large-scale community detection or personalized recommendations based on Graphs and evaluate the overall cost/performance. It, however, poses another big challenge. Almost all algorithm implementations for those problems are vertex-centric. They scale poorly with large sparse graphs that demand edge-centric algorithms, so a new implementation has to be written from scratch. We will try to allocate more resources towards that in 2023 and invite anyone curious to join.

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[1]: https://github.com/unum-cloud/ukv

If you're primarily targeting Python as an application layer, you may also want to check out my msgspec library[1]. All the perf benefits of e.g. yyjson, but with schema validation like pydantic. It regularly benchmarks[2] as the fastest JSON library for Python. Much of the overhead of decoding JSON -> Python comes from the python layer, and msgspec employs every trick I know to minimize that overhead.

[1]: https://github.com/jcrist/msgspec

[2]: https://github.com/TkTech/json_benchmark

- Adding nvme drive support to SMARTie, https://github.com/tktech/smartie, which is a pure-python cross-platform library for getting disk information like serial number, SMART attributes (like disk temperature) - json_benchmark, https://github.com/tktech/json_benchmark, which is a new benchmark and correctness test for the more modern Python JSON libraries - py_yyjson, https://github.com/tktech/py_yyjson, which is still a WIP and provides Python bindings to the yyjson library, which offers comparable speed to simdjson but more flexibility when parsing (comments, arbitrary sized numbers, Inf/Nan, etc) - And some fixes to https://github.com/TkTech/humanmark, which is a markdown library used to edit the README.md in json_benchmark above.