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Quickwit 0.4 is now released. Check out our blog post to discover the new features.
Quickwit is a cloud-native search engine for log management & analytics written in Rust. It is designed to be very cost-effective, easy to operate, and scale to petabytes.
- Cloud-scale: K8s-native, decoupled compute & storage
- Sub-second full-text search on object storage (AWS S3, Azure...)
- Sleep like a log: all your indexed data is safely stored on object storage
- Distributed search
- Distributed indexing with Kafka
- Schemaless or strict schema indexing
- Multi-tenancy
- Add and remove nodes in seconds
- Index with exactly-once semantics with Kafka / Kinesis
- Ingest & Aggregation API Elasticsearch compatible
- Search stream API for full-text search in ClickHouse
- Works out of the box with sensible defaults
- Lightweight Embedded UI
- Quickwit 0.5 - Q2 2023
- Distributed and replicated ingestion queue
- Tiered storage (local drive, block storage, object storage)
- Native support for OpenTelemetry exporters (logs and traces)
- Jaeger integration
- Grafana data source
- Long-term roadmap
- Pipe-based query language
- Security (TLS, authentication, RBAC)
- Transforms
- and more...
| ✅ When to use | ❌ When not to use |
|---|---|
| Your documents are immutable: application logs, system logs, access logs, user actions logs, audit trail (logs), etc. | Your documents are mutable. |
| Your data has a time component. Quickwit includes optimizations and design choices specifically related to time. | You need a low-latency search for e-commerce websites. |
| You want a full-text search in a multi-tenant environment. | You provide a public-facing search with high QPS. |
| You want to index directly from Kafka / Kinesis. | You want to re-score documents at query time. |
| You want to add full-text search to your ClickHouse cluster. | |
| You ingest a tremendous amount of logs and don't want to pay huge bills. | |
| You ingest a tremendous amount of data and you don't want to waste your precious time babysitting your cluster. |
Quickwit compiles to a single binary and we provide various ways to install it. The easiest is to run the command below from your preferred shell:
curl -L https://install.quickwit.io | shYou can now move this executable directory wherever sensible for your environment and possibly add it to your PATH environment.
Take a look at our Quick Start to do amazing things, like Creating your first index or Adding some documents, or take a glance at our full Installation guide!
- Set up a cluster on a local machine
- Set up a distributed search on AWS S3
- Send logs from Vector to Quickwit
- Ingest data from Apache Kafka
- Ingest data from Amazon Kinesis
- Add full-text search to a well-known OLAP database, ClickHouse
In Quickwit 0.3, we released Elasticsearch compatible Ingest-API, so that you can change the configuration of your current log shipper (Vector, Fluent Bit, Syslog, ...) to send data to Quickwit. You can query the logs using the Quickwit Web UI or Search API. We also support ES compatible Aggregation-API.
The core difference and advantage of Quickwit is its architecture that is built from the ground up for cloud and log management. Optimized IO paths make search on object storage sub-second and thanks to the true decoupled compute and storage, search instances are stateless, it is possible to add or remove search nodes within seconds. Last but not least, we implemented a highly-reliable distributed search and exactly-once semantics during indexing so that all engineers can sleep at night. All this slashes costs for log management.
We estimate that Quickwit can be up to 10x cheaper on average than Elastic. To understand how, check out our blog post about searching the web on AWS S3.
Quickwit is open-source under the GNU Affero General Public License Version 3 - AGPLv3. Fundamentally, this means that you are free to use Quickwit for your project, as long as you don't modify Quickwit. If you do, you have to make the modifications public. We also provide a commercial license for enterprises to provide support and a voice on our roadmap.
Not today, but HA is on our roadmap.
Our business model relies on our commercial license. There is no plan to become SaaS in the near future.
- Filtering a Vector with SIMD Instructions
- ChitChat: Cluster Membership with Failure Detection
- How to investigate memory usage of your rust program
- Cost-Efficient Rust in Practice
- Internals of Quickwit & How We Built It
- Stream Ingestion with Kafka & Kinesis
Chat with us in Discord | Follow us on Twitter
We are always super happy to have contributions: code, documentation, issues, feedback, or even saying hello on discord! Here is how you can help us build the future of log management:
- Have a look through GitHub issues labeled "Good first issue".
- Read our Contributor Covenant Code of Conduct.
- Create a fork of Quickwit and submit your pull request!
✨ And to thank you for your contributions, claim your swag by emailing us at hello at quickwit.io.