Open source alternatives to Elasticsearch

4 projects on starwire do the same job. Ranked by how much of what Elasticsearch does each one covers, read from its README and website. Ties go to the most active.

What Elasticsearch does

  1. Full-text searchcore
  2. Log and event analyticscore
  3. Real-time indexing
  4. Aggregations
  5. Vector search
  6. Distributed scaling
  7. Dashboards with Kibana

Each dot in the table is one job, in this order. Square dots are core jobs and count double. Full means yes, half means partly. "See the evidence" under a project shows the line behind each one. Coverage is what a project documents, not how well it works or how hard it is to move to. How we score this

ProjectCoversStarsCommits, 12 weeksLast releaseClosed vs openedSetupLicense
serenedbSQL database for real-time search and analytics queries Replaces it94%1.1k+144
281
11 days ago50% Needs a server Apache-2.0
Covers all 2 core jobs. Partly: Dashboards with Kibana.See the evidence
  • Full-text searchcoreYesKeyword and text search across large datasets.
  • Log and event analyticscoreYes10x faster than ClickHouse on 10 billion OpenTelemetry logs
  • Real-time indexingYesNew rows become searchable at the next index refresh
  • AggregationsYesQuery, filter, aggregate and analyze data using SQL.
  • Vector searchYesBuilt-in vector search and hybrid ranking
  • Distributed scalingYesHorizontal scalability, high availability
  • Dashboards with KibanaPartlyAn intuitive Postgres-compatible UI
manticoresearchFull-text, vector and hybrid search engine with real-time indexing and SQL access Replaces it89%12k+3
233
2 months ago100% Needs a server GPL-3.0
Covers all 2 core jobs. Not shown: Dashboards with Kibana.See the evidence
  • Full-text searchcoreYesFull-text search works seamlessly with both small and large datasets.
  • Log and event analyticscoreYestypical DevOps queries on 10M Nginx logs
  • Real-time indexingYesReal-time inserts make newly added or updated documents immediately accessible.
  • AggregationsYesFaceted search
  • Vector searchYesVector search
  • Distributed scalingYesSharded tables transparently distribute reads and writes across physical shards
  • Dashboards with KibanaNot shownNeither the README nor the website mentions it.
typesenseIn-memory search engine with typo tolerance, written in C++ Covers one part44%27k+32
103
7 months ago60% Hosted version GPL-3.0
Covers 1 of 2 core jobs. Partly: Aggregations; Distributed scaling. Not shown: Log and event analytics; Real-time indexing; Dashboards with Kibana.See the evidence
  • Full-text searchcoreYesfast, typo-tolerant search engine
  • Log and event analyticscoreNot shownNeither the README nor the website mentions it.
  • Real-time indexingNot shownNeither the README nor the website mentions it.
  • AggregationsPartlyFiltering Faceting
  • Vector searchYesVector Search
  • Distributed scalingPartlyEasy High Availability
  • Dashboards with KibanaNot shownNeither the README nor the website mentions it.
VictoriaLogsFast log database that scales to terabytes of logs Covers one part39%2.5k+30
160
44 days ago76% Needs a server Apache-2.0
Covers 1 of 2 core jobs. Partly: Dashboards with Kibana. Not shown: Full-text search; Real-time indexing; Aggregations; Vector search.See the evidence
  • Full-text searchcoreNot shownNeither the README nor the website mentions it.
  • Log and event analyticscoreYesdatabase for logs, which can efficiently handle terabytes of logs
  • Real-time indexingNot shownNeither the README nor the website mentions it.
  • AggregationsNot shownNeither the README nor the website mentions it.
  • Vector searchNot shownNeither the README nor the website mentions it.
  • Distributed scalingYesscales both vertically and horizontally
  • Dashboards with KibanaPartlybuilt-in web UI, Grafana plugin for VictoriaLogs

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