Countly
ClickHouse
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Countly | ClickHouse | |
---|---|---|
13 | 207 | |
5,438 | 33,712 | |
1.4% | 2.4% | |
9.9 | 10.0 | |
about 5 hours ago | about 2 hours ago | |
JavaScript | C++ | |
GNU General Public License v3.0 or later | 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.
Countly
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I built an open source Google Analytics alternative (free as in freedom and privacy-first, too!)
Happy cake day. Yeah, nowadays there are so many analytics variants out there: Swetrix, Plausible, offen, count.ly any many more.
- Integration/extension/plugin system in Nodejs
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Self Hostable Open Source Alternatives to Commercial products
Countly (https://github.com/Countly/countly-server)
- Ask HN: Good open source alternatives to Google Analytics?
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Ask HN: Best alternatives to Google Analytics in 2021?
Always surprised more people don’t use countly. Runs nice in docker or digital ocean. https://count.ly. Been self hosting it for years with few issues.
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Open Source Analytics Stack: Bringing Control, Flexibility, and Data-Privacy to Your Analytics
Countly (website, GitHub) is also an open-source product analytics platform that is designed primarily for marketing organizations. It helps marketers track website information (website transactions, campaigns, and sources that led visitors to the website, etc.). Countly also collects real-time mobile analytics metrics like active users, time spent in-app, customer location, etc., in a unified view on your dashboard.
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What services (if any) are you using for analytics and push notifications in 2021?
Countly
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Firebase Analytics Alternative for Kid's Category iOS App
Depending on the server resources at your disposal, you could roll out your own installation of the Countly server together with its iOS SDK. That way you are not using a third party solution for analytics so you are not breaking the rules while still obtaining analytics data.
ClickHouse
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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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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;
Recently, there were similar attempts (two) of supply chain attacks on the ClickHouse repository, but: - it didn't do anything because CI does not run without approval; - the user's account magically disappeared from GitHub with all pull requests within a day.
Also worth reading a similar example: https://blog.cloudflare.com/cloudflares-handling-of-an-rce-v...
Also, let me recommend our bug bounty program: https://github.com/ClickHouse/ClickHouse/issues/38986 It sounds easy - pick your favorite fuzzer, find a segfault (it should be easy because C++ isn't a memory-safe language), and get your paycheck.
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Why does musl make my Rust code so slow? (2020)
It is the case when you use a default malloc, default memcpy, or default string functions from libc.
In ClickHouse, we use jemalloc as a memory allocator and custom memcpy: https://github.com/ClickHouse/ClickHouse/blob/master/base/gl...
So, the Musl build does not imply performance degradations. But the usage of Musl is not related to Docker, because ClickHouse is a single self-contained binary anyway, and it is easy to use without Docker.
What are some alternatives?
loki - Like Prometheus, but for logs.
duckdb - DuckDB is an in-process SQL OLAP Database Management System
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
VictoriaMetrics - VictoriaMetrics: fast, cost-effective monitoring solution and time series database
TimescaleDB - An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.
arrow-datafusion - Apache Arrow DataFusion SQL Query Engine
RocksDB - A library that provides an embeddable, persistent key-value store for fast storage.
materialize - The data warehouse for operational workloads.
PostgreSQL - Mirror of the official PostgreSQL GIT repository. Note that this is just a *mirror* - we don't work with pull requests on github. To contribute, please see https://wiki.postgresql.org/wiki/Submitting_a_Patch
TileDB - The Universal Storage Engine
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
Adminer - Database management in a single PHP file