QuestDB Lab
An independent lab for QuestDB time-series in production · Tutorials · Benchmarks · Deployment guides
Independent technical community — not affiliated with questdb.io, not an official QuestDB resource
🔍 What is Time-Series Lab?
Time-Series Lab is an independent technical community focused on QuestDB — a high-performance, open-source time-series database. We provide hands-on tutorials covering IoT sensor data, financial tick data, observability, and performance benchmarks. Every example is runnable, and every performance claim comes with its test conditions.
QuestDB is designed for high-throughput ingestion and sub-millisecond analytical queries, using a distributed columnar storage engine. It's ideal for time-series workloads: sensors/IoT, financial tick/OHLC, and observability metrics. Whether you're writing millions of rows per second or aggregating terabytes of historical data, QuestDB handles it with ease.
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Tutorials
Learn QuestDB from scratch — IoT, financial tick, and observability with real-world scenarios
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Benchmarks
QuestDB vs InfluxDB/TimescaleDB/ClickHouse — data-driven comparisons for tool selection
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Advanced Techniques
SAMPLE BY aggregation, ASOF JOIN, SYMBOL types, and partitioning strategies — deep dives with runnable SQL
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Best Practices
Production deployment, high-throughput ILP ingestion, Grafana dashboard integration — make QuestDB work for you
🧭 Learning Path
1
Basics
Install QuestDB and write your first time-series data via the ILP line protocol
2
Model
Design timestamps, SYMBOL dimensions, and PARTITION BY partitioning strategies
3
Analyze
Use SAMPLE BY, ASOF JOIN, and FIRST/LATEST for time-series aggregation and alignment
4
Production
High-throughput ILP ingestion, Grafana visualization, and distributed deployment
🔥 Featured Posts
QuestDB Installation & Quick Start: From Docker to Your First ILP Write (2026)
The most practical QuestDB installation guide: start QuestDB with one Docker command across macOS/Linux/Windows. From ports, ILP line-protocol writes, to your first time-series query — run the full loop by hand.
QuestDB vs DuckDB vs TimescaleDB: In-Depth Time-Series Selection Guide with Benchmarks
How to choose between QuestDB, DuckDB, and TimescaleDB? This guide compares positioning, write throughput, query latency, deployment complexity, and ecosystem — with 2026 measured numbers and a decision tree to pick right in 10 minutes.
❓ QuestDB FAQ
What is QuestDB best for?
Time-series workloads — IoT sensor data, financial tick/OHLC data, observability metrics, and logs. It excels at high-throughput ingestion and sub-millisecond analytical queries, but is not designed for general OLTP (high-concurrency transactions) or frequent low-latency point updates — that's PostgreSQL territory.
QuestDB vs InfluxDB: which is better?
Both target time-series, but with different emphases. QuestDB is typically stronger in high-concurrency ingestion + low-latency analytical queries, with native standard SQL and Grafana support; InfluxDB has a more mature cloud ecosystem and managed services. Choose based on your write rate, query latency requirements, and ops cost.
Does QuestDB replace ClickHouse?
In some scenarios, yes. QuestDB is purpose-built for time-series, with faster write throughput and time aggregation that's easier to use; ClickHouse is more flexible for general OLAP and complex multi-dimensional analytics. If your data is primarily time-series, QuestDB is often the more convenient choice.
How much data can QuestDB handle?
A single node supports millions of rows per second of high-throughput writes. With PARTITION BY DAY/MONTH partitioning and compression, it efficiently manages TB-scale historical data. For larger scale, use QuestDB's distributed mode to scale horizontally.