Eureka
ClickHouse
Our great sponsors
Eureka | ClickHouse | |
---|---|---|
8 | 208 | |
12,220 | 34,153 | |
0.6% | 2.6% | |
5.7 | 10.0 | |
12 days ago | 1 day 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.
Eureka
- How Netflix Uses Java
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Why using Eureka?
I was setting up microservices based on Netflix Eureka and experimenting on top of spring-cloud and after weeks of research and development the question rose!
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[Feedback request] Fuddle service registry
The closest thing I've found is Netflix's Eureka, though its very Java oriented and I found hard to use.
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Kubernetes Microservices on Azure with Cosmos DB
There's an open issue documenting this problem on Spring Cloud Netflix and Netflix Eureka.
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need help with microservices and spring boot please!
if you want to get more knowledge of the services, eureka etc, you can debug it. but I will digest you use the library instead of the annotations: https://github.com/Netflix/eureka
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Programming Microservices Communication With Istio
Service discovery — Traditionally provided by platforms like Netflix Eureka or Consul.
- Ask HN: What are the best the publicly available FAMANG code repos?
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What Is a Service Mesh, and Why Is It Essential for Your Kubernetes Deployments?
With multiple services running, it’s hard to discover where they’re located. The dependencies between multiple services are not always easily found, and new services may be deployed with a new dependency on an older service. Those services can be deployed anywhere in the infrastructure, so what you need is a Service Discovery service. There are plenty available, such as Netflix Eureka or HashiCorp Consul.
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?
service-mesh-istio - A microservice project leveraging Service Mesh with advanced features from Istio
loki - Like Prometheus, but for logs.
consul - Consul is a distributed, highly available, and data center aware solution to connect and configure applications across dynamic, distributed infrastructure.
duckdb - DuckDB is an in-process SQL OLAP Database Management System
Apollo - Java libraries for writing composable microservices
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
consul-api - Java client for Consul HTTP API
VictoriaMetrics - VictoriaMetrics: fast, cost-effective monitoring solution and time series database
SnopEE
TimescaleDB - An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.
restQL-core - Microservice query language
datafusion - Apache DataFusion SQL Query Engine