copycat
ClickHouse
Our great sponsors
copycat | ClickHouse | |
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13 | 208 | |
316 | 34,153 | |
4.8% | 2.6% | |
8.8 | 10.0 | |
2 months ago | 5 days ago | |
TypeScript | C++ | |
MIT License | 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.
copycat
- Resend – Incident report for February 21st, 2024
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Greenmask: PostgreSQL Dump and Obfuscation Tool
Check out https://www.snaplet.dev (I'm the founder). We do exactly this.
- Pgtemp: The easiest way to write tests with Postgres in Rust, without Docker
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Supabase Branching
Testing with product workloads today? If you need to test with production workloads today, check out Snaplet and Postgres.ai. Both are great partners of Supabase.
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How Modern SQL Databases Are Changing Web Development - #3 Better Developer Experience
It’s worth noting that being able to branch production data for testing easily doesn’t mean you should just do it. It poses a significant risk of leaking sensitive user data. You should consider using tools like Snaplet to transform and anonymize sensitive columns.
- Show HN: Seed your Postgres development database with production-like data
- Show AWS: Snaplet clones a subset of your Postgres RDS instance, whilst anonymizing the data, so that you can restore it into your development environments: Local, staging, and preview.
- Show HN: Seed your Postgres database with production-like data
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Databricks acquires serverless Postgres vendor bit.io
[disclosure: I'm the founder of Snaplet]
I think there are a lot of different reasons why people may want to use a service like bit.io, but if you want a database with data in it to code against, run tests against, reproduce production related data-bugs, and run e2e tests against then check out https://www.snaplet.dev.
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Postgres WASM by Snaplet and Supabase
For now, this is very experimental - but it has a lot of potential. If you want to get involved, please reach out to us or the team at [Snaplet(https://www.snaplet.dev/). The work they're doing over at Snaplet is incredible, and we've had a blast collaborating with them.
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;
What are some alternatives?
Replibyte - Seed your development database with real data ⚡️
loki - Like Prometheus, but for logs.
faker - Generate massive amounts of fake data in the browser and node.js
duckdb - DuckDB is an in-process SQL OLAP Database Management System
falso - All the Fake Data for All Your Real Needs 🙂
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
helm-charts - neondatabase helm charts
VictoriaMetrics - VictoriaMetrics: fast, cost-effective monitoring solution and time series database
fakey - web-based fake data generator.
TimescaleDB - An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.
postgres-wasm - A PostgresQL server in your browser
datafusion - Apache DataFusion SQL Query Engine