doris

Name: doris
Brand: doris

Real-time analytics and hybrid search database for AI agents

softwareDatabases analyticssearchreal-time

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Our Verdict

Best for

Enterprises and AI developers needing sub‑second analytics on massive data

Skip if

Teams requiring extensive third‑party connectors or easy‑click GUIs

What is doris?

Apache Doris is an open‑source, high‑performance analytical database designed for real‑time reporting and hybrid search workloads. It combines columnar storage with vectorized execution to deliver low‑latency queries on massive data sets. Built for AI‑driven applications, Doris supports both traditional SQL analytics and approximate nearest‑neighbor search, enabling developers to embed fast, searchable analytics directly into intelligent agents and services.

SpecificationsAI-estimated

deploymentSelf-hosted

open source✅ Yes

github stars15,744

api available✅ Yes

support optionsMailing list, GitHub Issues, Community Slack

key integrationsApache Spark, Apache Flink, Apache Hive, MySQL clients

primary languageJava

Key Features of doris

Executes sub‑second SQL queries on petabyte‑scale tables using columnar storage and vectorized processing.

Provides built‑in approximate nearest‑neighbor (ANN) search for high‑dimensional vector data.

Supports both batch ingestion and streaming data pipelines for continuous analytics.

Offers a distributed architecture with automatic sharding and replication for fault tolerance.

Integrates with popular data ecosystems such as Apache Spark, Flink, and Hive.

Provides a native MySQL‑compatible protocol, allowing existing tools to connect without modification.

Includes a cost‑based optimizer that selects the most efficient execution plan for mixed workloads.

Offers a cloud‑hosted Doris service and Docker/Kubernetes images for flexible deployment.

Use Cases for doris

AI Agent Knowledge Base

Fast vector search over embeddings to retrieve relevant context for generative models.

Real‑time Dashboards

Sub‑second analytics on streaming click‑stream or IoT data for operational monitoring.

Hybrid Recommendation Engine

Combine SQL filters with ANN similarity to serve personalized recommendations.

Large‑scale Data Warehouse

Consolidate petabyte‑level logs and perform ad‑hoc BI queries with low latency.

Pros & Cons of doris

Pros

Open‑source and free to use
Sub‑second query latency on massive data sets
Native support for vector similarity search
Compatible with existing MySQL tools and drivers

Cons

Relatively new ecosystem, fewer third‑party connectors than older warehouses
Operational complexity for large clusters requires expertise
Limited built‑in GUI; relies on external BI tools

Frequently Asked Questions

Is Apache Doris suitable for production workloads?

Yes, many enterprises run Doris in production for real‑time analytics and vector search.

Can Doris run on Kubernetes?

Official Docker images and Helm charts are provided for Kubernetes deployments.

Does Doris support ACID transactions?

Doris offers eventual consistency for inserts and supports atomic batch writes, but full ACID semantics are limited.

Is there a managed cloud service?

Apache Doris Cloud offers a fully managed SaaS version with automatic scaling and backups.

Pricing Overview

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Free

Detailed plans are not listed. Visit the official website for pricing information.

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