core
octosql
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core | octosql | |
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6 | 34 | |
516 | 4,695 | |
1.4% | - | |
8.1 | 1.2 | |
6 days ago | 4 days ago | |
Go | Go | |
MIT License | Mozilla Public License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
core
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Lakehouse - event data
If you happen to be putting together a platform then look at Gazette. We use it to read/write streaming data into nice and orderly "fragment files" in cloud storage. It provides excellent durability guarantees and turns out to be an extremely efficient way to handle both reads and writes of streaming data. Readers get handed out pre-signed cloud storage URLs that are fetched by the client, and writes are batched into time and/or size bounded "fragments" that are durably replicated until being uploaded into cloud storage.
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Request for example Real Life programs in Go?
this is a real time data streaming tool built with Go - https://github.com/gazette/core
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Hacker News top posts: Jul 16, 2022
Gazette – Build platforms that flexibly mix SQL, batch, and stream processing\ (0 comments)
- Gazette – Build platforms that flexibly mix SQL, batch, and stream processing
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Issues we've encountered while building a Kafka based data processing pipeline
If you're in the Go ecosystem, Gazette [0] offers transactional integrations [1] with remote DB's for stateful processing pipelines, as well as local stores for embedded in-process state management.
It also natively stores data as files in cloud storage. Brokers are ephemeral, you don't need to migrate data between them, and you're not constrained by their disk size. Gazette defaults to exactly-once semantics, and has stronger replication guarantees (your R factor is your R factor, period -- no "in sync replicas").
Estuary Flow [2] is building on Gazette as an implementation detail to offer end-to-end integrations with external SaaS & DB's for building real-time dataflows, as a managed service.
[0]: https://github.com/gazette/core
octosql
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Wazero: Zero dependency WebAssembly runtime written in Go
Never got it to anything close to a finished state, instead moving on to doing the same prototype in llvm and then cranelift.
That said, here's some of the wazero-based code on a branch - https://github.com/cube2222/octosql/tree/wasm-experiment/was...
It really is just a very very basic prototype.
- Analyzing multi-gigabyte JSON files locally
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DuckDB: Querying JSON files as if they were tables
This is really cool!
With their Postgres scanner[0] you can now easily query multiple datasources using SQL and join between them (i.e. Postgres table with JSON file). Something I strived to build with OctoSQL[1] before.
It's amazing to see how quickly DuckDB is adding new features.
Not a huge fan of C++, which is right now used for authoring extensions, it'd be really cool if somebody implemented a Rust extension SDK, or even something like Steampipe[2] does for Postgres FDWs which would provide a shim for quickly implementing non-performance-sensitive extensions for various things.
Godspeed!
[0]: https://duckdb.org/2022/09/30/postgres-scanner.html
[1]: https://github.com/cube2222/octosql
[2]: https://steampipe.io
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Show HN: ClickHouse-local – a small tool for serverless data analytics
Congrats on the Show HN!
It's great to see more tools in this area (querying data from various sources in-place) and the Lambda use case is a really cool idea!
I've recently done a bunch of benchmarking, including ClickHouse Local and the usage was straightforward, with everything working as it's supposed to.
Just to comment on the performance area though, one area I think ClickHouse could still possibly improve on - vs OctoSQL[0] at least - is that it seems like the JSON datasource is slower, especially if only a small part of the JSON objects is used. If only a single field of many is used, OctoSQL lazily parses only that field, and skips the others, which yields non-trivial performance gains on big JSON files with small queries.
Basically, for a query like `SELECT COUNT(*), AVG(overall) FROM books.json` with the Amazon Review Dataset, OctoSQL is twice as fast (3s vs 6s). That's a minor thing though (OctoSQL will slow down for more complicated queries, while for ClickHouse decoding the input is and remains the bottleneck).
[0]: https://github.com/cube2222/octosql
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Steampipe – Select * from Cloud;
To add somewhat of a counterpoint to the other response, I've tried the Steampipe CSV plugin and got 50x slower performance vs OctoSQL[0], which is itself 5x slower than something like DataFusion[1]. The CSV plugin doesn't contact any external API's so it should be a good benchmark of the plugin architecture, though it might just not be optimized yet.
That said, I don't imagine this ever being a bottleneck for the main use case of Steampipe - in that case I think the APIs themselves will always be the limiting part. But it does - potentially - speak to what you can expect if you'd like to extend your usage of Steampipe to more than just DevOps data.
[0]: https://github.com/cube2222/octosql
[1]: https://github.com/apache/arrow-datafusion
Disclaimer: author of OctoSQL
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Go runtime: 4 years later
Actually, folks just use gRPC or Yaegi in Go.
See Terraform[0], Traefik[1], or OctoSQL[2].
Although I agree plugins would be welcome, especially for performance reasons, though also to be able to compile and load go code into a running go process (JIT-ish).
[0]: https://github.com/hashicorp/terraform
[1]: https://github.com/traefik/traefik
[2]: https://github.com/cube2222/octosql
Disclaimer: author of OctoSQL
- Run SQL on CSV, Parquet, JSON, Arrow, Unix Pipes and Google Sheet
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Beginner interested in learning SQL. Have a few question that I wasn’t able to find on google.
Through more magic, you COULD of course use stuff like Spark, or easier with programs like TextQL, sq, OctoSQL.
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How I Used DALL·E 2 to Generate The Logo for OctoSQL
The logo was created for OctoSQL and in the article you can find a lot of sample phrase-image combinations, as it describes the whole path (generation, variation, editing) I went down. Let me know what you think!
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How I Used DALL·E 2 to Generate the Logo for OctoSQL
Hey, author here, happy to answer any questions!
The logo was created for OctoSQL[0] and in the article you can find a lot of sample phrase-image combinations, as it describes the whole path (generation, variation, editing) I went down. Let me know what you think!
[0]:https://github.com/cube2222/octosql