Software · head to head
DuckDB vs QuestDB
QuestDB
Software
Fast open source time-series database for high throughput ingestion
- From
- Free
- Rated
- -
The short version
- Each has a real cost: DuckDB client-server setup remains in beta and not recommended for production distributed scenarios; QuestDB open-source edition lacks high-availability, distributed architecture, and enterprise security features
- They diverge on capability: DuckDB covers In-process Execution, QuestDB covers High Throughput Ingestion.
Where they differ
Only the attributes on which DuckDB and QuestDB actually diverge.
Identical on both: starting price (Free), pricing model (open-source), free tier (Yes), user rating (Not yet rated), category (Unknown).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in DuckDB
- In-process Execution
- Columnar Storage
- Vectorized Execution
- Rich SQL Support
- Parquet Support
- CSV/JSON Import
- Zero Dependencies
- Python
Only in QuestDB
- High Throughput Ingestion
- SQL Support
- Time-series Optimization
- SIMD Vectorization
- Column-oriented Storage
- Built-in Web Console
- InfluxDB Line Protocol
- PostgreSQL
Both cover
- Pandas
- Linux support
- Windows support
- Mac support
What people use each for
The jobs each tool is most often brought in to do.
DuckDB
- Analytics and data warehousingnot QuestDB
- OLAP queries and data explorationnot QuestDB
- Data science and machine learning workflowsnot QuestDB
- Multi-format data ingestion and processingnot QuestDB
QuestDB
- Time-series analytics ingesting up to 20M rows/second from IoT sensors or financial data feedsnot DuckDB
- Real-time dashboarding with 32ms time-to-first-row latency for minute-level analyticsnot DuckDB
- Applications requiring multi-tier storage (hot ingest, real-time SQL, cold Parquet archive)not DuckDB
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
DuckDB
- Client-server setup remains in beta and not recommended for production distributed scenarios
QuestDB
- Open-source edition lacks high-availability, distributed architecture, and enterprise security features
- Enterprise edition pricing not published; requires contacting sales for custom quote
- Ingestion limit of 20M rows/sec platform-dependent; may not scale to extreme throughput requirements
Pricing, plan by plan
DuckDB
FreeNo published plan breakdown. See the DuckDB review.
QuestDB
FreeNo published plan breakdown. See the QuestDB review.
Which should you pick?
Choose DuckDB if
- You need in-process execution.
- You want to start without paying.
- You work on Linux, macOS, Windows, WebAssembly.
- You also want columnar storage.
Choose QuestDB if
- You need high throughput ingestion.
- You want to start without paying.
- You work on Docker, Kubernetes, Cloud (AWS, Azure, GCP).
- You also want sql support.
Questions people ask
- Is DuckDB or QuestDB better?
- Neither clearly leads. DuckDB starts at Free and QuestDB at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, DuckDB or QuestDB?
- DuckDB starts at Free and QuestDB at Free.
- Does DuckDB or QuestDB run on more platforms?
- DuckDB runs on Linux, macOS, Windows, WebAssembly. QuestDB runs on Docker, Kubernetes, Cloud (AWS, Azure, GCP).
- Can I use DuckDB for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is DuckDB best used for?
- DuckDB is most often used for analytics and data warehousing, olap queries and data exploration, data science and machine learning workflows, multi-format data ingestion and processing. Of those, analytics and data warehousing and olap queries and data exploration are not what QuestDB is typically brought in for.
- What can DuckDB do that QuestDB cannot?
- DuckDB covers In-process Execution, Columnar Storage, Vectorized Execution, Rich SQL Support. QuestDB covers High Throughput Ingestion, SQL Support, Time-series Optimization, SIMD Vectorization. Both handle Pandas, Linux support, Windows support, Mac support.