Bedrock
ClickHouse
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Bedrock | ClickHouse | |
---|---|---|
23 | 208 | |
1,044 | 34,054 | |
1.4% | 2.3% | |
9.4 | 10.0 | |
2 days ago | 1 day ago | |
C | C++ | |
GNU Lesser 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.
Bedrock
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Marmot: Multi-writer distributed SQLite based on NATS
Also Expensify's Bedrock, which powers their famous "Scaling SQLite to 4M QPS" article:
https://use.expensify.com/blog/scaling-sqlite-to-4m-qps-on-a...
- I'm All-In on Server-Side SQLite
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SQLite is not a toy database
Lots of things don't need failover, but if you do, you can use Bedrock, which is built on sqlite.
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Amazon announces 'Bedrock,' its ChatGPT and DALL-E rival
At first, I thought Amazon was launching their own SQLite hosted database.
BedrockDB is a SQLite based database with MySQL compatible drivers.
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Ask HN: Hunting for a Framework
Vapor[0] based on Swift. Advantage of this is that you don't have to evaluate multiple frameworks for Swift and suffer paralysis by analysis. All the Swift community is behind one framework.
The next is Actix[1] based on Rust. There are many frameworks in Rust and most of them have not reached 1.0 And which framework will survive becomes a question.
Other not so well-known is Wt[2] based on C++. This actually is created for programmers who are not web developers. The development experience is similar to desktop app development like Qt.
If that is not acceptable then Django[3], based on Python, is the one that will be good for you.
For the front-end I would recommend Flutter[4]. As much as I dislike getting tied to a single company for whom the framework is not their bread-and-butter, I don't see any other viable options to Flutter that will cover all web, mobile and desktop out of the box.
For databases, I would recommend BedrockDB[5], if you are not averse to SQLite. Or FoundationDB[6], if you want NoSQL. But if you are not concerned about horizontal scalability or okay with self-managing database availability, then PostgreSQL[7] is a very good option.
For push notifications, PushPin[8] is a good option.
[1] https://actix.rs
[3] https://www.djangoproject.com
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Databases: 2021 in Review and Predictions for 2022
Recently I stumbled upon BedrockDB[0] from Expensify. It is based on SQLite and has very interesting idea on HA and distributed DB.
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One million queries per second with MySQL
This is not SQLite though, also the test is trivial compared to TPC: https://github.com/Expensify/Bedrock/blob/dbarrett_perftest/...
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Turning SQLite into a Distributed Database
Don’t forget BedrockDB (built on SQLite) that’s used in production at Expensify.
How it scales as well.
https://blog.expensify.com/2018/01/08/scaling-sqlite-to-4m-q...
- Fly.io Buys Litestream
- Ask HN: Have you used SQLite as a primary database?
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?
SQLite - Unofficial git mirror of SQLite sources (see link for build instructions)
loki - Like Prometheus, but for logs.
MySQL - MySQL Server, the world's most popular open source database, and MySQL Cluster, a real-time, open source transactional database.
duckdb - DuckDB is an in-process SQL OLAP Database Management System
rqlite - The lightweight, distributed relational database built on SQLite.
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
LevelDB - LevelDB is a fast key-value storage library written at Google that provides an ordered mapping from string keys to string values.
VictoriaMetrics - VictoriaMetrics: fast, cost-effective monitoring solution and time series database
Adminer - Database management in a single PHP file
TimescaleDB - An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.
MongoDB C++ Driver - C++ Driver for MongoDB
arrow-datafusion - Apache DataFusion SQL Query Engine