stargazers
ClickHouse
stargazers | ClickHouse | |
---|---|---|
4 | 208 | |
479 | 34,153 | |
- | 1.3% | |
0.0 | 10.0 | |
almost 3 years ago | 7 days ago | |
Go | C++ | |
Apache 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.
stargazers
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The (Detailed & Creative) Playbook for More GitHub Stars
I used tools (such at this one and this one) to see what other repos our stargazers were starring most frequently. The stargazers of these "other" commonly starred repos became an expanded pool of potential stargazers for Preevy
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Analysing Github Stars - Extracting and analyzing data from Github using Apache NiFi®, Apache Kafka® and Apache Druid®
Spencer Kimball (now CEO at CockroachDB) wrote an interesting article on this topic in 2021 where they created spencerkimball/stargazers based on a Python script. So I started thinking: could I create a data pipeline using Nifi and Kafka (two OSS tools often used with Druid) to get the API data into Druid - and then use SQL to do the analytics? The answer was yes! And I have documented the outcome below. Here’s my analytical pipeline for Github stars data using Nifi, Kafka and Druid.
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Ask HN: Why so many projects set their GitHub links to /stargazers?
It's analytics. Google search brings up
https://www.cockroachlabs.com/blog/what-can-we-learn-from-ou...
"Years ago I dedicated a Flex Friday (our version of 20% time) to stargazers, a tool to query the CockroachDB repository for information about its GitHub stars and analyze the results. At the time of writing, we had 6,000+ stars (which felt like a lot), and the data in this blog will be based on that original set of 6,000 stargazers."
Which links to
https://github.com/spencerkimball/stargazers
"GitHub allows visitors to star a repo to bookmark it for later perusal. Stars represent a casual interest in a repo, and when enough of them accumulate, it's natural to wonder what's driving interest. Stargazers attempts to get a handle on who these users are by finding out what else they've starred, which other repositories they've contributed to, and who's following them on GitHub."
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4500 people starred Appsmith on Github. What do we know about them?
Go ahead and use the Stargazers repo yourself to analyze yours (or anyone else’s) repo’s trends. Depending upon the number of Stargazers you have, it can take some time. It took us about 7-8 hours (with a 5K/Hr rate limit).
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?
awesome-opensource - Best open-source GitHub libraries voted by members 🎤
loki - Like Prometheus, but for logs.
auto-animate - A zero-config, drop-in animation utility that adds smooth transitions to your web app. You can use it with React, Vue, or any other JavaScript application.
duckdb - DuckDB is an in-process SQL OLAP Database Management System
payload - The best way to build a modern backend + admin UI. No black magic, all TypeScript, and fully open-source, Payload is both an app framework and a headless CMS.
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
appsmith - Platform to build admin panels, internal tools, and dashboards. Integrates with 25+ databases and any API.
VictoriaMetrics - VictoriaMetrics: fast, cost-effective monitoring solution and time series database
preevy - Quickly deploy preview environments to the cloud!
TimescaleDB - An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.
cockroach - CockroachDB - the open source, cloud-native distributed SQL database.
datafusion - Apache DataFusion SQL Query Engine