[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-5487":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":25,"hasPages":23,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":37,"readmeContent":38,"aiSummary":39,"trendingCount":16,"starSnapshotCount":16,"syncStatus":40,"lastSyncTime":41,"discoverSource":42},5487,"quickwit","quickwit-oss\u002Fquickwit","quickwit-oss","Cloud-native search engine for observability. An open-source alternative to Datadog, Elasticsearch, Loki, and Tempo.","https:\u002F\u002Fquickwit.io",null,"Rust",11312,553,69,664,0,1,27,121,11,43.23,"Apache License 2.0",false,"main",true,[27,28,29,30,31,32,33,34,35,36],"big-data","cloud-native","cloud-storage","distributed-tracing","log-management","logs","open-source","rust","search-engine","tantivy","2026-06-12 02:01:10","[![CI](https:\u002F\u002Fgithub.com\u002Fquickwit-oss\u002Fquickwit\u002Factions\u002Fworkflows\u002Fci.yml\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002Fquickwit-oss\u002Fquickwit\u002Factions?query=workflow%3ACI+branch%3Amain)\n[![codecov](https:\u002F\u002Fcodecov.io\u002Fgh\u002Fquickwit-oss\u002Fquickwit\u002Fbranch\u002Fmain\u002Fgraph\u002Fbadge.svg?token=06SRGAV5SS)](https:\u002F\u002Fcodecov.io\u002Fgh\u002Fquickwit-oss\u002Fquickwit)\n[![OpenSSF Scorecard](https:\u002F\u002Fapi.scorecard.dev\u002Fprojects\u002Fgithub.com\u002Fquickwit-oss\u002Fquickwit\u002Fbadge)](https:\u002F\u002Fscorecard.dev\u002Fviewer\u002F?uri=github.com\u002Fquickwit-oss\u002Fquickwit)\n[![Contributor Covenant](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FContributor%20Covenant-2.0-4baaaa.svg)](CODE_OF_CONDUCT.md)\n[![License: Apache 2.0](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Flicense-Apache%202.0-blue?style=flat-square)](LICENSE)\n[![Twitter Follow](https:\u002F\u002Fimg.shields.io\u002Ftwitter\u002Ffollow\u002FQuickwit_Inc?color=%231DA1F2&logo=Twitter&style=plastic)](https:\u002F\u002Ftwitter.com\u002FQuickwit_Inc)\n[![Discord](https:\u002F\u002Fimg.shields.io\u002Fdiscord\u002F908281611840282624?logo=Discord&logoColor=%23FFFFFF&style=plastic)](https:\u002F\u002Fdiscord.quickwit.io)\n\u003Cbr\u002F>\n\n\u003Cbr\u002F>\n\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"docs\u002Fassets\u002Fimages\u002Flogo_horizontal.svg#gh-light-mode-only\" alt=\"Quickwit Cloud-Native Search Engine\" height=\"40\">\n  \u003Cimg src=\"docs\u002Fassets\u002Fimages\u002Fquickwit-dark-theme-logo.png#gh-dark-mode-only\" alt=\"Quickwit Cloud-Native Search Engine\" height=\"40\">\n\u003C\u002Fp>\n\n\u003Ch2 align=\"center\">\nCloud-native search engine for observability (logs, traces, and soon metrics!). An open-source alternative to Datadog, Elasticsearch,  Loki, and Tempo.\n\u003C\u002Fh2>\n\n\u003Ch4 align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fquickwit.io\u002Fdocs\u002Fget-started\u002Fquickstart\">Quickstart\u003C\u002Fa> |\n  \u003Ca href=\"https:\u002F\u002Fquickwit.io\u002Fdocs\u002F\">Docs\u003C\u002Fa> |\n  \u003Ca href=\"https:\u002F\u002Fquickwit.io\u002Ftutorials\">Tutorials\u003C\u002Fa> |\n  \u003Ca href=\"https:\u002F\u002Fdiscord.quickwit.io\">Chat\u003C\u002Fa> |\n  \u003Ca href=\"https:\u002F\u002Fquickwit.io\u002Fdocs\u002Fget-started\u002Finstallation\">Download\u003C\u002Fa>\n\u003C\u002Fh4>\n\u003Cbr\u002F>\n\n\u003Cb>We just released Quickwit 0.8! Read the [blog post](https:\u002F\u002Fquickwit.io\u002Fblog\u002Fquickwit-0.8) to learn about the latest powerful features!\u003C\u002Fb>\n\n### **Quickwit is the fastest search engine on cloud storage. It's the perfect fit for observability use cases**\n\n- [Log management](https:\u002F\u002Fquickwit.io\u002Fdocs\u002Flog-management\u002Foverview)\n- [Distributed tracing](https:\u002F\u002Fquickwit.io\u002Fdocs\u002Fdistributed-tracing\u002Foverview)\n- Metrics support is on the roadmap\n\n### 🚀 Quickstart\n\n- [Search and analytics on Stack Overflow dataset](https:\u002F\u002Fquickwit.io\u002Fdocs\u002Fget-started\u002Fquickstart)\n- [Trace analytics with Grafana](https:\u002F\u002Fquickwit.io\u002Fdocs\u002Fget-started\u002Ftutorials\u002Ftrace-analytics-with-grafana)\n- [Distributed tracing with Jaeger](https:\u002F\u002Fquickwit.io\u002Fdocs\u002Fget-started\u002Ftutorials\u002Ftutorial-jaeger)\n\n\u003Cbr\u002F>\n\n\u003Cvideo src=\"https:\u002F\u002Fgithub.com\u002Fquickwit-oss\u002Fquickwit\u002Fassets\u002F653704\u002F020b94b9-deeb-4376-9a3a-b82e1168094c\" controls=\"controls\" style=\"max-width: 1200px;\">\n\u003C\u002Fvideo>\n\n\u003Cbr\u002F>\n\n# 💡 Features\n\n- Full-text search and aggregation queries\n- Elasticsearch-compatible API, use Quickwit with any Elasticsearch or OpenSearch client\n- [Jaeger-native](https:\u002F\u002Fquickwit.io\u002Fdocs\u002Fdistributed-tracing\u002Fplug-quickwit-to-jaeger)\n- OTEL-native for [logs](https:\u002F\u002Fquickwit.io\u002Fdocs\u002Flog-management\u002Foverview) and [traces](https:\u002F\u002Fquickwit.io\u002Fdocs\u002Fdistributed-tracing\u002Foverview)\n- [Schemaless](https:\u002F\u002Fquickwit.io\u002Fdocs\u002Fguides\u002Fschemaless) or strict schema indexing\n- Schemaless analytics\n- Sub-second search on cloud storage (Amazon S3, Azure Blob Storage, Google Cloud Storage, …)\n- Decoupled compute and storage, stateless indexers & searchers\n- [Grafana data source](https:\u002F\u002Fgithub.com\u002Fquickwit-oss\u002Fquickwit-datasource)\n- Kubernetes ready - See our [helm-chart](https:\u002F\u002Fquickwit.io\u002Fdocs\u002Fdeployment\u002Fkubernetes\u002Fhelm)\n- RESTful API\n\n## Enterprise ready\n\n- Multiple [data sources](https:\u002F\u002Fquickwit.io\u002Fdocs\u002Fingest-data\u002F) Kafka \u002F Kinesis \u002F Pulsar native\n- Multi-tenancy: indexing with many indexes and partitioning\n- Retention policies\n- Delete tasks (for GDPR use cases)\n- Distributed and highly available* engine that scales out in seconds (*HA indexing only with Kafka)\n\n# 📑 Architecture overview\n\n![Quickwit Distributed Tracing](.\u002Fdocs\u002Fassets\u002Fimages\u002Fquickwit-overview-light.svg#gh-light-mode-only)![Quickwit Distributed Tracing](.\u002Fdocs\u002Fassets\u002Fimages\u002Fquickwit-overview-dark.svg#gh-dark-mode-only)\n\n- [Architecture overview]([https:\u002F\u002Fquickwit.io\u002Fdocs\u002Fdistributed-tracing\u002Foverview](https:\u002F\u002Fquickwit.io\u002Fdocs\u002Foverview\u002Farchitecture))\n- [Log management](https:\u002F\u002Fquickwit.io\u002Fdocs\u002Flog-management\u002Foverview)\n- [Distributed traces](https:\u002F\u002Fquickwit.io\u002Fdocs\u002Fdistributed-tracing\u002Foverview)\n\n\n# 📕 Documentation\n\n- [Installation](https:\u002F\u002Fquickwit.io\u002Fdocs\u002Fget-started\u002Finstallation)\n- [Log management with Quickwit](https:\u002F\u002Fquickwit.io\u002Fdocs\u002Flog-management\u002Foverview)\n- [Distributed Tracing with Quickwit](https:\u002F\u002Fquickwit.io\u002Fdocs\u002Fdistributed-tracing\u002Foverview)\n- [Ingest data](https:\u002F\u002Fquickwit.io\u002Fdocs\u002Fingest-data\u002F)\n- [REST API](https:\u002F\u002Fquickwit.io\u002Fdocs\u002Freference\u002Frest-api)\n\n# 📚 Resources\n\n- [Blog posts](https:\u002F\u002Fquickwit.io\u002Fblog\u002F)\n- [Youtube channel](https:\u002F\u002Fwww.youtube.com\u002F@quickwit8103)\n- [Discord](https:\u002F\u002Fdiscord.quickwit.io)\n\n# 🙋 FAQ\n\n### How can I switch from Elasticsearch or OpenSearch to Quickwit?\n\nQuickwit supports a large subset of Elasticsearch\u002FOpenSearch API.\n\nFor instance, it has an ES-compatible ingest API to make it easier to migrate your log shippers (Vector, Fluent Bit, Syslog, ...) to Quickwit.\n\nOn the search side, the most popular Elasticsearch endpoints, query DSL, and even aggregations are supported.\n\nThe list of available endpoints and queries is available [here](https:\u002F\u002Fquickwit.io\u002Fdocs\u002Freference\u002Fes_compatible_api), while the list of supported aggregations is available [here](https:\u002F\u002Fquickwit.io\u002Fdocs\u002Freference\u002Faggregation).\n\nLet us know if part of the API you are using is missing!\n\nIf the client you are using is refusing to connect to Quickwit due to missing headers, you can use the `extra_headers` option in the [node configuration](https:\u002F\u002Fquickwit.io\u002Fdocs\u002Fconfiguration\u002Fnode-config#rest-configuration) to impersonate any compatible version of Elasticsearch or OpenSearch.\n\n### How is Quickwit different from traditional search engines like Elasticsearch or Solr?\n\nThe 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.\n\n### How does Quickwit compare to Elastic in terms of cost?\n\nWe estimate that Quickwit can be up to 10x cheaper on average than Elastic. To understand how, check out our [blog post](https:\u002F\u002Fquickwit.io\u002Fblog\u002Fcommoncrawl\u002F) about searching the web on AWS S3.\n\n### What license does Quickwit use?\n\nQuickwit is open-source under the Apache License, Version 2.0 - Apache-2.0.\n\n### Is it possible to set up Quickwit for a High Availability (HA)?\n\nHA is available for search, for indexing it's available only with a Kafka source.\n\n# 🤝 Contribute and spread the word\n\nWe are always thrilled to receive contributions: code, documentation, issues, or feedback. Here's how you can help us build the future of log management:\n\n- Start by checking out the [GitHub issues labeled \"Good first issue\"](https:\u002F\u002Fgithub.com\u002Fquickwit-oss\u002Fquickwit\u002Fissues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22). These are a great place for newcomers to contribute.\n- Read our [Contributor Covenant Code of Conduct](.\u002FCODE_OF_CONDUCT.md) to understand our community standards.\n- [Create a fork of Quickwit](https:\u002F\u002Fgithub.com\u002Fquickwit-oss\u002Fquickwit\u002Ffork) to have your own copy of the repository where you can make changes.\n- To understand how to contribute, read our [contributing guide](.\u002FCONTRIBUTING.md).\n- Set up your development environment following our [development setup guide](.\u002FCONTRIBUTING.md#development).\n- Once you've made your changes and tested them, you can contribute by [submitting a pull request](.\u002FCONTRIBUTING.md#submitting-a-pr).\n\n✨ After your contributions are accepted, don't forget to claim your swag by emailing us at hello@quickwit.io. Thank you for contributing!\n\n# 💬 Join Our Community\n\nWe 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:\n\n- Join the conversation on [Discord](https:\u002F\u002Fdiscord.quickwit.io).\n- Follow us on [Twitter](https:\u002F\u002Ftwitter.com\u002FQuickwit_Inc).\n- Check out our [website](https:\u002F\u002Fquickwit.io\u002F) and [blog](https:\u002F\u002Fquickwit.io\u002Fblog) for the latest updates.\n- Watch our [YouTube](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCvZVuRm2FiDq1_ul0mY85wA) channel for video content.\n","Quickwit 是一个面向可观测性的云原生搜索引擎，适用于日志、追踪以及未来的指标数据。它使用 Rust 语言开发，提供全文搜索和聚合查询功能，并兼容 Elasticsearch API，能够与任何 Elasticsearch 或 OpenSearch 客户端无缝集成。此外，Quickwit 还支持 Jaeger 原生集成，便于进行分布式追踪分析。其设计特别适合需要高效处理大规模数据的场景，如日志管理和系统监控等。",2,"2026-06-11 03:03:36","top_language"]