starrocks
ClickHouse
starrocks | ClickHouse | |
---|---|---|
12 | 208 | |
7,789 | 34,269 | |
2.6% | 1.6% | |
10.0 | 10.0 | |
4 days ago | 4 days ago | |
Java | 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.
starrocks
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A MySQL compatible database engine written in pure Go
tidb has been around for a while, it is distributed, written in Go and Rust, and MySQL compatible. https://github.com/pingcap/tidb
Somewhat relatedly, StarRocks is also MySQL compatible, written in Java and C++, but it's tackling OLAP use-cases. https://github.com/StarRocks/starrocks
- StarRocks – sub-second MPP OLAP database for full analytics scenarios
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Let's Talk about Joins
I think you're talking about doing denormalization before importing data into an OLAP system to avoid subsequent joins. However, this greatly limits the flexibility of data modeling. Moreover, denormalization can be a headache-inducing process. In fact, I have tested StarRocks (https://github.com/StarRocks/starrocks), and it is capable of performing joins while streaming data imports, and the speed is very fast. It's worth giving it a try.
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Ask HN: Are there any notable Chinese FLOSS projects?
https://github.com/apache/doris Is a great example. Same for it's cousin https://github.com/StarRocks/starrocks that was an early fork of the doris project.
To be fair, these are the only examples I can think of and I only learned of these as I'm standing up new data infra using starrocks.
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Open Source Columnar Databases
ClickHouseClickHouse and Starrocks are similar. They are both columnar databases powered by vectorization tech, which means they are really fast.
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Ask HN: Do you use any software (mainly) developed in China?
StarRocks, it’s a Linux Foundation project now, but a lot of the initial team and community behind it came from China.
https://github.com/StarRocks/starrocks
Funny that I hadn’t heard of them in the database space till they showed up at the top of ClickBench. Makes me wonder what other interesting projects I’m missing out on in China.
- Anyone using StarRocks DB instead of ClickHouse?
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Show HN: A benchmark for analytical databases (Snowflake, Druid, Redshift)
Full disclosure - I work for StarRocks (starrocks.com)
First of all, this is great. Transparent and healthy competition is always great for the customers!
Regarding the joined table queries that are missing in the tests, this is exactly why we built StarRocks - to give people the best performance of complex analytics queries on both joined tables and single tables.
I encourage you to checkout this blog: https://starrocks.medium.com/starrocks-outperforms-clickhous...
And, give us a star if you think we are doing the right thing: https://github.com/StarRocks/starrocks
Follow us on LinkedIn for the latest updates: https://www.linkedin.com/company/starrocks
- We are looking for a very fast database for big data analysis, does anyone know about starrocks, I heard it is very fast
- wow, i found a super fast database for Big Data analytics,it's called StarRocks,come and take a look!
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?
ClickBench - ClickBench: a Benchmark For Analytical Databases
loki - Like Prometheus, but for logs.
doris - Apache Doris is an easy-to-use, high performance and unified analytics database.
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)
TablePlus - TablePlus macOS issue tracker
VictoriaMetrics - VictoriaMetrics: fast, cost-effective monitoring solution and time series database
clickhouse-bulk - Collects many small inserts to ClickHouse and send in big inserts
TimescaleDB - An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.
LakeSoul - LakeSoul is an end-to-end, realtime and cloud native Lakehouse framework with fast data ingestion, concurrent update and incremental data analytics on cloud storages for both BI and AI applications.
datafusion - Apache DataFusion SQL Query Engine