q
ClickHouse
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q | ClickHouse | |
---|---|---|
46 | 208 | |
10,109 | 34,153 | |
- | 2.3% | |
3.6 | 10.0 | |
3 months ago | about 6 hours ago | |
Python | C++ | |
GNU General Public License v3.0 only | 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.
q
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I wrote this iCalendar (.ics) command-line utility to turn common calendar exports into more broadly compatible CSV files.
CSV utilities (still haven't pick a favorite one...): https://github.com/harelba/q https://github.com/BurntSushi/xsv https://github.com/wireservice/csvkit https://github.com/johnkerl/miller
- Segítség kérés Excel automatizáláshoz
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Show HN: ClickHouse-local – a small tool for serverless data analytics
I think they're talking about https://github.com/harelba/q, which is not very fast.
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sqly - execute SQL against CSV / JSON with shell
Apparently, there were many who thought the same thing; Tools to execute SQL against CSV were trdsql, q, csvq, TextQL. They were highly functional, hoewver, had many options and no input completion. I found it just a little difficult to use.
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Q – Run SQL Directly on CSV or TSV Files
Hi, author of q here.
Regarding the error you got, q currently does not autodetect headers, so you'd need to add -H as a flag in order to use the "country" column name. You're absolutely correct on failing-fast here - It's a bug which i'll fix.
In general regarding speed - q supports automatic caching of the CSV files (through the "-C readwrite" flag). Once it's activated, it will write the data into another file (with a .qsql extension), and will use it automatically in further queries in order to speed things considerably.
Effectively, the .qsql files are regular sqlite3 files (with some metadata), and q can be used to query them directly (or any regular sqlite3 file), including the ability to seamlessly join between multiple sqlite3 files.
http://harelba.github.io/q/#auto-caching-examples
- PostgreSQL alternative for Large amounts of data
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q VS trdsql - a user suggested alternative
2 projects | 25 Jun 2022
- One-liner for running queries against CSV files with SQLite
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?
textql - Execute SQL against structured text like CSV or TSV
loki - Like Prometheus, but for logs.
csvq - SQL-like query language for csv
duckdb - DuckDB is an in-process SQL OLAP Database Management System
octosql - OctoSQL is a query tool that allows you to join, analyse and transform data from multiple databases and file formats using SQL.
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
InquirerPy - :snake: Python port of Inquirer.js (A collection of common interactive command-line user interfaces)
VictoriaMetrics - VictoriaMetrics: fast, cost-effective monitoring solution and time series database
xsv - A fast CSV command line toolkit written in Rust.
TimescaleDB - An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.
ledger - Double-entry accounting system with a command-line reporting interface
arrow-datafusion - Apache DataFusion SQL Query Engine