Apache Solr
ClickHouse
Our great sponsors
Apache Solr | ClickHouse | |
---|---|---|
31 | 208 | |
4,365 | 34,153 | |
0.0% | 2.6% | |
0.0 | 10.0 | |
2 months ago | 4 days 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.
Apache Solr
- Iniciando no Elasticsearch: Conceitos básicos
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YaCy, a distributed Web Search Engine, based on a peer-to-peer network
There are already many project about search:
- https://www.marginalia.nu/
- https://searchmysite.net/
- https://lucene.apache.org/
- elastic search
- https://presearch.com/
- https://stract.com/
- https://wiby.me/
I think that all project are fun. I would like to see one succeeding at reaching mainstream level of attention.
I have also been gathering links meta data for some time. Maybe I will use them to feed any eventual self hosted search engine, or language model, if I decide to experiment with that.
- domains for seed https://github.com/rumca-js/Internet-Places-Database
- bookmarks seed https://github.com/rumca-js/RSS-Link-Database
- links for year https://github.com/rumca-js/RSS-Link-Database-2024
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Getting started with Elasticsearch + Python
Elasticsearch is based on Lucene and is used by various companies and developers across the world to build custom search solutions.
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Tools to use to query and index data?
elastic search is kinda heavyweight infra for a small project. Its built on top of apache lucene (https://lucene.apache.org), which you can use directly.
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Top metrics for Elasticsearch monitoring with Prometheus
Elasticsearch is based on Lucene, which is built in Java. This means that monitoring the Java Virtual Machine (JVM) memory is crucial to understand the current usage of the whole system.
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Cross data type search that wasn’t supported well using Elasticsearch
Apache Lucene which seems to have a lot more features than Elasticsearch
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How to find closest keyphrase match in text?
Generally with term vectors and a tf-idf index. Lucene is a good starting place to help.
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Java Library to perform string search
try elasticsearch or solr, behind the scenes they both use https://lucene.apache.org/ if you don't want basically a full nosql database service, but I'd just slap solr up and call it a day.
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Top 8 Open-Source Observability & Testing Tools
OpenSearch is an open-source database to ingest, search, visualize, and analyze data. It’s built on top of Apache Lucerce, a FOSS library for indexing and search, which OpenSearch leverages for more advanced analytics capabilities, like anomaly detection, machine learning, full-text search, and more.
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grep like search with preprocessing
Lucene is the thing you think you need. Elastic Search is a nice wrapper for it. But these are Java, so maybe you want Sphinx Search (C++) or MeiliSearch (Rust).
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?
OpenSearch - 🔎 Open source distributed and RESTful search engine.
loki - Like Prometheus, but for logs.
Typesense - Open Source alternative to Algolia + Pinecone and an Easier-to-Use alternative to ElasticSearch ⚡ 🔍 ✨ Fast, typo tolerant, in-memory fuzzy Search Engine for building delightful search experiences
duckdb - DuckDB is an in-process SQL OLAP Database Management System
MeiliSearch - A lightning-fast search API that fits effortlessly into your apps, websites, and workflow
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
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.
Apache Lucene - Apache Lucene.NET
datafusion - Apache DataFusion SQL Query Engine