Doris
Open-source real-time analytics and search database for the AI era.
For teams that can manage their own infrastructure, Apache Doris is a compelling open-source alternative to proprietary solutions like Elasticsearch and ClickHouse, especially when you need hybrid search without stitching together separate systems.
- Teams building customer-facing analytics dashboards
- Data engineers running real-time data warehouses
- Observability engineers handling high-volume logs and metrics
- AI/ML engineers needing hybrid search (vector + text) for RAG
- Teams needing a fully managed SaaS solution with zero operations
- Users seeking a mobile or desktop application interface
- Simple key-value or document workloads better suited to NoSQL databases
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In short
Doris — Open-source real-time analytics and search database for the AI era. Best for Teams building customer-facing analytics dashboards, Data engineers running real-time data warehouses, Observability engineers handling high-volume logs and metrics. Free to use.
Viability Score
How likely is Doris to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Real-time SQL analytics with sub-second query latency
- Native inverted indexes for full-text and hybrid search
- ANN vector indexes (IVF, IVF_ON_DISK) for similarity search
- VARIANT data type for semi-structured JSON data
- Disaggregated storage on cloud object storage (S3, Azure Blob, GCS)
- Shared-nothing multi-warehouse architecture
- High-throughput log and metric ingestion for observability
- Columnar storage engine optimized for OLAP workloads
- ASOF JOIN with benchmark-leading performance
- Unified engine for OLAP, full-text, and vector search
- Data-tiering and elastic scaling for cost efficiency
- Active community with frequent releases (v4.1 as of May 2026)
About Doris
Apache Doris is an open-source, real-time analytics and search database built for modern data and AI workloads. It unifies high-performance SQL analytics with native inverted indexes, vector search, and JSON support in a single engine, enabling hybrid search and real-time data warehousing. Teams use it for customer-facing analytics, observability, data warehousing, and AI agent backends. Doris runs shared-nothing on bare metal or disaggregated on cloud object storage, delivering sub-second query performance at scale. Key features include a unified OLAP, full-text search, and vector search engine; the VARIANT data type for semi-structured JSON data; high-throughput log and metric ingestion; and native ANN vector indexes achieving 900 QPS at 97% recall. The project is backed by the Apache Software Foundation with over 15,600 GitHub stars and is trusted by enterprises including Baidu, Xiaomi, and Ford. Compared to alternatives like ClickHouse and DuckDB, Doris offers superior ASOF JOIN performance and built-in hybrid search capabilities.
Behind the Verdict
Apache Doris solves a real problem: running OLAP, full-text search, and vector search on one engine. That's rare in open source. For teams building customer-facing analytics or observability pipelines, it means fewer moving parts and simpler operations. The native inverted indexes and ANN vector indexes (IVF, IVF_ON_DISK) mean you don't need Elasticsearch for search or Milvus for vectors. Performance is strong — 900 QPS at 97% recall for vector search, and sub-second SQL latencies at scale. But it's not for everyone. Doris is self-managed; there's no fully managed SaaS option. If your team wants zero ops, look elsewhere. The community is active and growing (15.6k stars), but it's smaller than ClickHouse's. Also, Doris is Apache-licensed, which is great for cost, but you won't get a dedicated support contract unless you go with a third-party vendor. We'd reach for Doris when we need hybrid search on real-time data and are willing to handle infrastructure. For pure vector search, Milvus or Pinecone might be simpler. For basic analytics, DuckDB might be easier to embed. But for a unified engine, Doris is a strong bet.
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Use Cases
- Deliver sub-second customer-facing analytics dashboards in production.
- Unify log and metric analytics for observability and incident response.
- Run real-time data warehousing across multiple business domains.
- Build AI agent backends with unified vector, text, and JSON search.
- Analyze player behavior for live operations in gaming.
- Power EV telemetry and supply chain analytics in automotive.
Limitations
- As an open-source project, Apache Doris requires significant operational expertise to deploy and tune.
- It lacks a managed cloud offering (the project does not provide a SaaS version), so teams must handle installation, scaling, and maintenance.
- The advanced features (vector indexes, VARIANT) are available from version 4.x onward, meaning older installations may need upgrades.
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