preevy
ClickHouse
preevy | ClickHouse | |
---|---|---|
41 | 208 | |
1,996 | 34,359 | |
1.4% | 1.9% | |
9.5 | 10.0 | |
5 days ago | 1 day ago | |
TypeScript | 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.
preevy
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How to Get Preview Environments for Every Pull Request
Feel free to star the Preevy repo here.
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What's New In Docker 2023?
We at Livecycle, a Docker-centric company, are super excited to share some highlights from the event and discuss how we are developing our products, Livecycle Docker Extension and Preevy, to improve collaboration, feedback, etc., in the vision that Docker is moving with their recent development and announcement at DockerCon.
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Anti-FAQs: Use localhost for collaboration?? Seriously?!
To learn more about how to use Preevy, just check out the docs site.
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Docker’s Recent Product Announcement is Bigger than You Realize
Historically, many developers have preferred to facilitate this type of collaboration with ephemeral preview environments that are triggered with every PR or commit. And indeed, we have built robust solutions like “Preevy” to facilitate the creation of these environments for dockerized applications on any cloud or Kubernetes cluster.
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In-Flight Collaboration With The Livecycle Docker Extension
Preevy also integrates into your CI pipeline to convert your pull requests into easily shareable ephemeral environments
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Featured Mod of the Month: Pradumna Saraf
My typical day is filled with a couple of meetings, building the Lifecycle community on Slack, collaborating with projects and people, and creating strategies and content (videos, blogs, short forms for Twitter and LinkedIn) around the product to increase adoption. I heavily focus on the company’s Open Source offering called Preevy which helps create a preview environment using Docker Compose underneath.
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Easy Dockerization with Docker init
Also, at Livecycle, we are building Preevy, which helps you create a preview environment for Docker Compose applications. Do check out https://github.com/livecycle/preevy. It's open source, and don't forget to leave a star to show your support.
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[Showoff Saturday] Preevy: an open source tool for instantly creating ephemeral environments for Dockerized apps on any cloud/Kubernetes cluster
Check out the docs here
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The (Detailed & Creative) Playbook for More GitHub Stars
A dedicated docs site with relevant technical information and how-to guides for people who want to use it
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Why Developers Should Use Preview Environments
About “Preevy” - an open source tool that simplifies the creation of preview environments
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?
Appwrite - Your backend, minus the hassle.
loki - Like Prometheus, but for logs.
Pulumi - Pulumi - Infrastructure as Code in any programming language. Build infrastructure intuitively on any cloud using familiar languages 🚀
duckdb - DuckDB is an in-process SQL OLAP Database Management System
framer/motion - Open source, production-ready animation and gesture library for React
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
zustand - 🐻 Bear necessities for state management in React
VictoriaMetrics - VictoriaMetrics: fast, cost-effective monitoring solution and time series database
traefik - The Cloud Native Application Proxy
TimescaleDB - An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.
helm - The Kubernetes Package Manager
datafusion - Apache DataFusion SQL Query Engine