ClickHouse
sqlite
Our great sponsors
ClickHouse | sqlite | |
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208 | 73 | |
34,153 | - | |
2.6% | - | |
10.0 | - | |
5 days ago | - | |
C++ | ||
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.
ClickHouse
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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...
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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.
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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
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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.
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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):
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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.
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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.
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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
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1 billion rows challenge in PostgreSQL and ClickHouse
curl https://clickhouse.com/ | sh
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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;
sqlite
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Show HN: My Go SQLite driver did poorly on a benchmark, so I fixed it
> I would've probably picked the modernc variation
Heads up about the modernc library, it has been stuck on an old version of sqlite for several months [1]. It seems like maintainer time is the limiting factor [2]. There has been a call to arms on that issue page, the maintainer is looking for help, but it looks like not much has arrived. It seems like it might trace back to blockers in the C-to-Go compiler.
It's a major undertaking and a very impressive piece of work, but I'm not surprised it's a struggle when big roadblocks get hit. I hope they find a way to progress, but I'm very relieved to be seeing some CGo-free alternatives like ncruces/go-sqlite3 emerging. I'm going to give it a try for sure and see if I can live with the compromises.
Squinn-go looks very compelling too, but I don't like that it requires the squinn binary to already be installed on a user's machine, I think that gives with one hand and takes with the other: sure, I get to avoid CGo, but I also lose the turnkey, single-command install, static build benefits Go brings out of the box.
Seconding the point about nitty gritty, I'd read it for sure too!
[1]: https://gitlab.com/cznic/sqlite/-/issues/154
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Show HN: Sqinn-Go is a Golang library for accessing SQLite databases in pure Go
No, but that has the disadvantage of being C compiled into Go, then being compiled into native executable.
I'm actually surprised by how readable this came out; props to the Go->C compiler author. But you can guess that pushing this sort of thing through the Go compiler is going to cause some slowdowns due to sheer paradigm mismatch: https://gitlab.com/cznic/sqlite/-/blob/master/lib/sqlite_lin...
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Show HN: MongoDB Protocol for SQLite
FWIW, we use a version of SQLite transpiled into Go to avoid CGI problems: https://gitlab.com/cznic/sqlite
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Go port of SQLite without CGo
It could be clearer in the readme, but note that this is a machine translation from C to Go, repeated for every OS-Arch pair. Example of the one you're most likely to use in production: https://gitlab.com/cznic/sqlite/-/blob/master/lib/sqlite_linux_amd64.go
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What would you rewrite in Golang?
Like this? https://gitlab.com/cznic/sqlite
What are some alternatives?
loki - Like Prometheus, but for logs.
chai - Modern embedded SQL database
duckdb - DuckDB is an in-process SQL OLAP Database Management System
ffi-overhead - comparing the c ffi (foreign function interface) overhead on various programming languages
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
sqlite - Go SQLite3 driver
VictoriaMetrics - VictoriaMetrics: fast, cost-effective monitoring solution and time series database
go-sqlite3 - sqlite3 driver for go using database/sql
TimescaleDB - An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.
sqlparser-rs - Extensible SQL Lexer and Parser for Rust
datafusion - Apache DataFusion SQL Query Engine
proteus - A simple tool for generating an application's data access layer.