ClickHouse
loki
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ClickHouse | loki | |
---|---|---|
208 | 80 | |
34,054 | 22,149 | |
2.3% | 3.7% | |
10.0 | 9.9 | |
6 days ago | 4 days ago | |
C++ | Go | |
Apache License 2.0 | GNU Affero General Public License v3.0 |
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.
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;
loki
- Loki 3.0 Released
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List of your reverse proxied services
I also needed to make a small patch to Promtail to make this work: https://github.com/grafana/loki/pull/10256
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About reading logs
We don't pull logs, we forward logs to a centralized logging service.
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loki VS openobserve - a user suggested alternative
2 projects | 30 Aug 2023
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Logs monitoring with Loki, Node.js and Fastify.js
Over the past few months, I've been spending a lot of time creating dashboards on Grafana using Loki for MyUnisoft (the company I work for).
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OpenObserve: Open source Elasticsearch alternative in Rust for logs. 140x lower storage cost
For log systems you generally don't migrate data. Logs lose value over time. What you want to do is to go ahead and start ingesting data into the new system (OpenObserve in this case) and slowly, the data in the old system will become stale and then you can retire it. However if you need to export logs anyhow, there is no straightforward way in loki to do this. You could run a script to query loki and export it to a file. If found this thread with a sample script - https://github.com/grafana/loki/issues/409
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Config files of snaps?
That snap is woefully out of date. The upstream repo was recently updated to 2.8.2, but the snap stable channel has 2.4.1 from 18 months ago. https://github.com/grafana/loki/releases/tag/v2.8.2
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i need to visualize all logs from remote dir
Loki
- Loki Helm charts that use DynamoDB
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I can't recommend serious use of an all-in-one local Grafana Loki setup
I installed promtail a few weeks back and I ran into this bug, that has been outstanding for months: https://github.com/grafana/loki/issues/8663 (e.g. a fix had been written but had not been released):
Due to a buffering issue, Loki would exit in case of configuration error without printing any error message or anything at all
There is definitely something weird about how the project is run.
What are some alternatives?
duckdb - DuckDB is an in-process SQL OLAP Database Management System
fluent-bit - Fast and Lightweight Logs and Metrics processor for Linux, BSD, OSX and Windows
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
Zabbix - Real-time monitoring of IT components and services, such as networks, servers, VMs, applications and the cloud.
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.
ElastiFlow - Network flow analytics (Netflow, sFlow and IPFIX) with the Elastic Stack
arrow-datafusion - Apache DataFusion SQL Query Engine
loki-multi-tenant-proxy - Grafana Loki multi-tenant Proxy. Needed to deploy Grafana Loki in a multi-tenant way
RocksDB - A library that provides an embeddable, persistent key-value store for fast storage.
oauth2-proxy - A reverse proxy that provides authentication with Google, Azure, OpenID Connect and many more identity providers.