perspective
ClickHouse
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perspective | ClickHouse | |
---|---|---|
43 | 207 | |
7,384 | 33,579 | |
3.7% | 2.4% | |
9.4 | 10.0 | |
7 days ago | 7 days ago | |
C++ | 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.
perspective
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Show HN: WhatTheDuck – open-source, in-browser SQL on CSV files
SQL workbench also uses https://perspective.finos.org/ for tables. It's a WASM table library which pairs nicely with duckdb and works well with large tables.
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React Spreadsheet 2 – Your Own Google Sheets
Yes. We are working on adding support for aggregation and pivoting using https://github.com/finos/perspective
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Show HN: DataSheetGrid, an Airtable-like React component
I haven't looked extensively at react-datasheet. It looks like it is trying to build more of a full product than the other data tables.
I have used ag-grid extensively, its an impressive product. Some pieces are a little awkward to use, particularly auto-sizing. But generally ag-grid has thought of most functionality and has a solution. The creator of ag-grid had a great interview on Javascript Jabber [1].
The other serious data table component that I have seen is FinOS Perspective [2]. This is extremely high performance, also more specialized and probably harder to customize. I think Perspective renders to a canvas element from Rust/C++ compiled to WASM (not 100% sure). It is also made for streaming updates.
AG-Grid supports streaming updates... but only in the commercial version.
Eventually the data model for these types of tables becomes tricky. I will be investigating parquet-wasm for my use case. Hit me up if you want to collaborate.
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ChDB: Embedded OLAP SQL Engine Powered by ClickHouse
Something like https://github.com/finos/perspective ? We use an OLAP(-y) WASM engine to provide query-ability to our data visualization tool, and doing the calculations in the browser is cheaper and simpler than a server-side database for datasets that fit in browser memory.
- Show HN: Udsv.js – A faster CSV parser in 5KB (min)
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Perspective 2.0, Open Source WebAssembly-Powered BI
It's an open source project. You could create an issue on their GitHub repo, or better yet, create a PR and reference this existing issue:
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Ask HN: Who is hiring? (February 2023)
We're looking for senior product managers and engineers of all experience levels to build the next generation of collaborative data visualization. At the Prospective Co., you'll contribute to our existing open-source project as well as help design our enterprise offering.
https://perspective.finos.org/
We're looking for any of:
- Familiarity with WebAssembly, data visualization, WebGL/OpenGL, data science, Jupyter/notebook, web/desktop/mobile UI development, compiler/language or database design, finance services.
- Primary stack is Rust (targeting WebAssembly). JavaScript, C++ and Python are a big plus.
- We <3 GitHub contributors - opt to discuss your GitHub work in lieu of a technical interview.
Contact [email protected]
- NYC Slice
- Data Visualization Framework for React, Angular, Svelte, TypeScript, JavaScript
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
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
TileDB - The Universal Storage Engine
Adminer - Database management in a single PHP file