doris vs DuckDB
Side-by-side comparison of features, pricing, ratings, and alternatives.
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.
DuckDB is an in-process analytical database that allows you to run SQL queries directly on your data files. It is designed to be highly performant and scalable, making it suitable for a wide range of analytical workloads. DuckDB supports a wide range of data formats, including CSV, JSON, and Parquet, and can be easily integrated into existing data pipelines.
- 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
- High-performance analytics capabilities
- Supports a wide range of data formats
- Easy to integrate into existing data pipelines
- Free and open-source
- 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
- Limited support for transactional workloads
- Not suitable for very large-scale deployments
- Limited support for advanced analytics features
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The Verdict
AI-generated from listing dataDoris offers enterprise‑grade, sub‑second analytics on petabyte‑scale data with built‑in vector search, while DuckDB provides lightweight, in‑process analytics for smaller datasets.
Key differences
- •Doris supports distributed, sharded clusters for petabyte‑scale and streaming ingestion; DuckDB runs in‑process and is not suited for massive scale.
- •Doris includes native ANN vector search; DuckDB lacks advanced vector/AI features.
- •Doris integrates with big‑data ecosystems (Spark, Flink, Hive) and MySQL clients; DuckDB focuses on Python/R and file formats.
- •Operational complexity: Doris requires cluster expertise; DuckDB is simple to embed with minimal setup.
- •Support channels differ: Doris relies on community Slack and mailing list; DuckDB offers email plus GitHub issues.
Pricing & value
Both are free open‑source; value depends on scale and feature needs.
Ease of use / learning curve
DuckDB is in‑process with simple API, requiring no cluster management; Doris needs expertise for large clusters.
Features & depth
Doris provides sub‑second queries on petabyte tables, streaming ingestion, and built‑in ANN search; DuckDB lacks these.
Integrations & ecosystem
Doris integrates with Spark, Flink, Hive, and MySQL tools; DuckDB mainly integrates with Python, R, and file formats.
Scalability
Doris designed for distributed, petabyte‑scale workloads; DuckDB is not suitable for very large‑scale deployments.
Support
DuckDB lists email support plus GitHub issues; Doris only community mailing list, Slack, and GitHub.
Security & privacy
No security details provided for either product.
Choose doris if…
Enterprises needing petabyte‑scale, low‑latency analytics with vector search and big‑data ecosystem integration.
Choose DuckDB if…
Data scientists or analysts needing fast, embedded analytics on moderate data volumes without managing clusters.
Common questions
Can I run these databases for free?
Yes, both Doris and DuckDB are free open‑source products.
Which tool handles petabyte‑scale data?
Doris is built for petabyte‑scale tables with distributed sharding; DuckDB is not designed for that scale.
Do they support vector similarity search?
Doris includes built‑in approximate nearest‑neighbor (ANN) search; DuckDB does not provide this capability.