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We just released Quickwit 0.8! Read theblog post to learn about the latest powerful features!

• Log management
• Distributed tracing
• Metrics support is on the roadmap

• Search and analytics on Stack Overflow dataset
• Trace analytics with Grafana
• Distributed tracing with Jaeger

• Full-text search and aggregation queries
• Elasticsearch-compatible API, use Quickwit with any Elasticsearch or OpenSearch client
• Jaeger-native
• OTEL-native forlogs andtraces
• Schemaless or strict schema indexing
• Schemaless analytics
• Sub-second search on cloud storage (Amazon S3, Azure Blob Storage, Google Cloud Storage, …)
• Decoupled compute and storage, stateless indexers & searchers
• Grafana data source
• Kubernetes ready - See ourhelm-chart
• RESTful API

• Multipledata sources Kafka / Kinesis / Pulsar native
• Multi-tenancy: indexing with many indexes and partitioning
• Retention policies
• Delete tasks (for GDPR use cases)
• Distributed and highly available* engine that scales out in seconds (*HA indexing only with Kafka)

• Architecture overview
• Log management
• Distributed traces

• Installation
• Log management with Quickwit
• Distributed Tracing with Quickwit
• Ingest data
• REST API

• Blog posts
• Youtube channel
• Discord

• Quickwit 0.9 (July 2024)

  • Indexing and search performance improvements
  • Index configuration updates (retention policy, indexing and search settings)
  • Concatenated field

• Quickwit 0.10 (October 2024)

  • Schema (doc mapping) updates
  • Native distributed ingestion
  • Index templates

Quickwit supports a large subset of Elasticsearch/OpenSearch API.

For instance, it has an ES-compatible ingest API to make it easier to migrate your log shippers (Vector, Fluent Bit, Syslog, ...) to Quickwit.

On the search side, the most popular Elasticsearch endpoints, query DSL, and even aggregations are supported.

The list of available endpoints and queries is availablehere, while the list of supported aggregations is availablehere.

Let us know if part of the API you are using is missing!

If the client you are using is refusing to connect to Quickwit due to missing headers, you can use theextra_headers option in thenode configuration to impersonate any compatible version of Elasticsearch or OpenSearch.

The core difference and advantage of Quickwit is its architecture built from the ground to search on cloud storage. We optimized IO paths, revamped the index data structures and made search stateless and sub-second on cloud storage.

We estimate that Quickwit can be up to 10x cheaper on average than Elastic. To understand how, check out ourblog post about searching the web on AWS S3.

Quickwit is open-source under the Apache License, Version 2.0 - Apache-2.0.

HA is available for search, for indexing it's available only with a Kafka source.

We are always thrilled to receive contributions: code, documentation, issues, or feedback. Here's how you can help us build the future of log management:

• Start by checking out theGitHub issues labeled "Good first issue". These are a great place for newcomers to contribute.
• Read ourContributor Covenant Code of Conduct to understand our community standards.
• Create a fork of Quickwit to have your own copy of the repository where you can make changes.
• To understand how to contribute, read ourcontributing guide.
• Set up your development environment following ourdevelopment setup guide.
• Once you've made your changes and tested them, you can contribute bysubmitting a pull request.

✨ After your contributions are accepted, don't forget to claim your swag by emailing us athello@quickwit.io. Thank you for contributing!

We welcome everyone to our community! Whether you're contributing code or just saying hello, we'd love to hear from you. Here's how you can connect with us:

• Join the conversation onDiscord.
• Follow us onTwitter.
• Check out ourwebsite andblog for the latest updates.
• Watch ourYouTube channel for video content.