octosql
ClickHouse
Our great sponsors
octosql | ClickHouse | |
---|---|---|
34 | 208 | |
4,695 | 34,054 | |
- | 2.3% | |
1.2 | 10.0 | |
1 day ago | 7 days ago | |
Go | C++ | |
Mozilla Public License 2.0 | Apache 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.
octosql
-
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
-
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
-
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
-
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
-
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
-
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.
-
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!
-
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
ClickHouse
-
We Built a 19 PiB Logging Platform with ClickHouse and Saved Millions
Yes, we are working on it! :) Taking some of the learnings from current experimental JSON Object datatype, we are now working on what will become the production-ready implementation. Details here: https://github.com/ClickHouse/ClickHouse/issues/54864
Variant datatype is already available as experimental in 24.1, Dynamic datatype is WIP (PR almost ready), and JSON datatype is next up. Check out the latest comment on that issue with how the Dynamic datatype will work: https://github.com/ClickHouse/ClickHouse/issues/54864#issuec...
-
Build time is a collective responsibility
In our repository, I've set up a few hard limits: each translation unit cannot spend more than a certain amount of memory for compilation and a certain amount of CPU time, and the compiled binary has to be not larger than a certain size.
When these limits are reached, the CI stops working, and we have to remove the bloat: https://github.com/ClickHouse/ClickHouse/issues/61121
Although these limits are too generous as of today: for example, the maximum CPU time to compile a translation unit is set to 1000 seconds, and the memory limit is 5 GB, which is ridiculously high.
-
Fair Benchmarking Considered Difficult (2018) [pdf]
I have a project dedicated to this topic: https://github.com/ClickHouse/ClickBench
It is important to explain the limitations of a benchmark, provide a methodology, and make it reproducible. It also has to be simple enough, otherwise it will not be realistic to include a large number of participants.
I'm also collecting all database benchmarks I could find: https://github.com/ClickHouse/ClickHouse/issues/22398
-
How to choose the right type of database
ClickHouse: A fast open-source column-oriented database management system. ClickHouse is designed for real-time analytics on large datasets and excels in high-speed data insertion and querying, making it ideal for real-time monitoring and reporting.
-
Writing UDF for Clickhouse using Golang
Today we're going to create an UDF (User-defined Function) in Golang that can be run inside Clickhouse query, this function will parse uuid v1 and return timestamp of it since Clickhouse doesn't have this function for now. Inspired from the python version with TabSeparated delimiter (since it's easiest to parse), UDF in Clickhouse will read line by line (each row is each line, and each text separated with tab is each column/cell value):
-
The 2024 Web Hosting Report
For the third, examples here might be analytics plugins in specialized databases like Clickhouse, data-transformations in places like your ETL pipeline using Airflow or Fivetran, or special integrations in your authentication workflow with Auth0 hooks and rules.
-
Choosing Between a Streaming Database and a Stream Processing Framework in Python
Online analytical processing (OLAP) databases like Apache Druid, Apache Pinot, and ClickHouse shine in addressing user-initiated analytical queries. You might write a query to analyze historical data to find the most-clicked products over the past month efficiently using OLAP databases. When contrasting with streaming databases, they may not be optimized for incremental computation, leading to challenges in maintaining the freshness of results. The query in the streaming database focuses on recent data, making it suitable for continuous monitoring. Using streaming databases, you can run queries like finding the top 10 sold products where the “top 10 product list” might change in real-time.
-
Proton, a fast and lightweight alternative to Apache Flink
Proton is a lightweight streaming processing "add-on" for ClickHouse, and we are making these delta parts as standalone as possible. Meanwhile contributing back to the ClickHouse community can also help a lot.
Please check this PR from the proton team: https://github.com/ClickHouse/ClickHouse/pull/54870
-
1 billion rows challenge in PostgreSQL and ClickHouse
curl https://clickhouse.com/ | sh
-
We Executed a Critical Supply Chain Attack on PyTorch
But I continue to find garbage in some of our CI scripts.
Here is an example: https://github.com/ClickHouse/ClickHouse/pull/58794/files
The right way is to:
- always pin versions of all packages;
What are some alternatives?
duckdb - DuckDB is an in-process SQL OLAP Database Management System
loki - Like Prometheus, but for logs.
q - q - Run SQL directly on delimited files and multi-file sqlite databases
trdsql - CLI tool that can execute SQL queries on CSV, LTSV, JSON, YAML and TBLN. Can output to various formats.
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
sqlitebrowser - Official home of the DB Browser for SQLite (DB4S) project. Previously known as "SQLite Database Browser" and "Database Browser for SQLite". Website at:
VictoriaMetrics - VictoriaMetrics: fast, cost-effective monitoring solution and time series database
sqlite-utils - Python CLI utility and library for manipulating SQLite databases
TimescaleDB - An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.
textql - Execute SQL against structured text like CSV or TSV
arrow-datafusion - Apache DataFusion SQL Query Engine