time-series database / real-time analytics
QuestDB is a high-performance open-source time-series database with low-latency SQL, columnar storage, and multi-tier storage (WAL, native, Parquet).
Same problem, same approach. Swapping one for another is a config change, not a rewrite.
Choose ClickHouse for a more mature, distributed columnar SQL database with a broader analytics ecosystem; trade off a heavier cluster deployment and less time-series-specific SQL extensions.
ClickHouse® is a real-time analytics database management system
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
Choose Databend when you want a cloud-native analytics warehouse on object storage with separate compute and storage; it lacks QuestDB's dedicated time-series features and has a younger ecosystem.
Data Agent Ready Warehouse : One for Analytics, Search, AI, Python Sandbox. — rebuilt from scratch. Unified architecture on your S3.
Choose pg_mooncake to add real-time columnar analytics to an existing PostgreSQL deployment instead of standing up a separate time-series server; trade off Postgres coupling and less time-series-specific support.
Real-time analytics on Postgres tables
Choose GigAPI for a lightweight lakehouse-style time-series engine on DuckDB/Parquet over object storage; trade off much lower maturity and a smaller community.
GigAPI is a Timeseries lakehouse for real-time data and sub-second queries, powered by DuckDB OLAP + Parquet Query Engine, Compactor w/ Cloud-Native Storage. Drop-in FDAP alternative ⭐
Choose InfluxDB when you rely on the Telegraf/Grafana metrics ecosystem or a Rust-based TSDB; its different query language (Flux/InfluxQL) and data model mean a non-trivial migration.
Scalable datastore for metrics, events, and real-time analytics
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
Choose Arctic if you need a Python-native tick-data store for pandas workflows and don't want to run a separate server; it is unmaintained (2+ years) and limited to Python.
High performance datastore for time series and tick data
These projects were analysed and named questdb among their alternatives. The relationship is not symmetric — how questdb rates them is a separate judgement, made when questdb is analysed in its own right.
calls questdb “Choose if you need extremely fast single-node SQL queries for time series and do not require distributed scaling in the OSS edition.”
High-performance, scalable time-series database designed for Industrial IoT (IIoT) scenarios
calls questdb “Choose it when you want a Java TSDB with SQL and low-latency ingestion but can trade away IoTDB's industrial edge features.”
Apache IoTDB
calls questdb “When you need the highest raw ingest/query performance in a single binary with SQL and zero dependencies, and can accept a Java-based deployment.”
Scalable datastore for metrics, events, and real-time analytics
calls questdb “Choose QuestDB when you need high-throughput time-series ingestion with SQL and zero commitment to the PostgreSQL ecosystem.”
A time-series database for high-performance real-time analytics packaged as a Postgres extension
calls questdb “Choose when you need a lightweight, high-throughput Java/SQL TSDB for tick/event data and do not need Prometheus compatibility.”
VictoriaMetrics: fast, cost-effective monitoring solution and time series database
calls questdb “Choose it if your analytics are predominantly time-series and you need high ingestion rates.”
The world's fastest open query engine for sub-second analytics both on and off the data lakehouse. With the flexibility to support nearly any scenario, StarRocks provides best-in-class performance for multi-dimensional analytics, real-time analytics, and ad-hoc queries. A Linux Foundation project.
calls questdb “Pick for time-series analytic workloads with high ingestion rates, but you lose Databend's general warehouse and AI search capabilities.”
Data Agent Ready Warehouse : One for Analytics, Search, AI, Python Sandbox. — rebuilt from scratch. Unified architecture on your S3.
calls questdb “Java time-series DB for fast SQL over large metric/event streams; choose it for metrics-only workloads when you don't need GreptimeDB's object-storage log/trace model.”
The open-source observability database. One columnar engine for metrics, logs, and traces, on object storage.