Open-source KX kdb+ alternatives

ClickHouse is an open-source database management system built to generate analytical reports in real time. Its column-oriented storage makes scans and aggregations over very large tables efficient, which is why it is widely used for event data, logs, metrics and product analytics. It is written in C++ and queried with SQL.

The project's topics describe it as an OLAP, massively parallel and distributed system, and it is used for big data workloads where query latency matters. You can install it on Linux, macOS or FreeBSD, follow a tutorial that sets up a small cluster, or skip installation by trying ClickHouse Cloud, a managed service built by its creators. Releases arrive monthly, with a community call for each one, and there are Slack and Telegram channels for help.

ClickHouse is Apache-2.0 licensed. It is a good fit for analytics engineers and platform teams who need fast aggregations over billions of rows and are comfortable operating a database, or who prefer to hand that operation to the vendor's cloud.

InfluxDB is an open-source time series database for collecting, processing, transforming and storing event and metrics data. The current Core edition of InfluxDB 3 is written in Rust and builds on Apache Arrow, DataFusion and Parquet, and it targets workloads where data must be ingested continuously and queried quickly enough to drive dashboards, interactive interfaces and automation.

The README lists typical uses such as sensor monitoring, server and network monitoring, application performance monitoring, financial market analytics and behavioral analytics. Feature highlights include a diskless architecture that can store data in object storage or on local disk with no extra dependencies, an embedded Python virtual machine for plugins and triggers, and persistence in Parquet files. The project quotes very low query latency for last-value lookups, though real numbers depend on your hardware and data.

InfluxDB is Apache-2.0 licensed and can be run on your own servers. InfluxData, the company behind it, also sells hosted and commercial editions. The topics list monitoring, metrics and time series, and it commonly sits behind visualization tools such as Grafana in observability stacks.

TDengine is an open-source time-series database designed for Internet of Things, connected car and Industrial IoT scenarios. It is built for efficient, real-time ingestion, processing and analysis of very large volumes of sensor data, and the maintainers describe it as cloud native and AI powered.

Rather than assembling a time-series store, a cache, a message queue and a stream processor separately, TDengine includes caching, stream processing, data subscription and AI agent capabilities in a single system, which aims to reduce design complexity and operating cost. It uses a natively distributed design with sharding and partitioning, is queried with SQL, and targets workloads where billions of data collection points report continuously.

TDengine is written in C and licensed under AGPL-3.0, and it can be self-hosted or used through TDengine Cloud, the managed service. Documentation is available in English, Chinese and Japanese. It fits manufacturers, energy and vehicle platforms and monitoring teams that collect high-frequency telemetry and need compact storage and fast queries.

TimescaleDB is a PostgreSQL extension that turns Postgres into a database for time-series and event data, aimed at real-time analytics. Because it lives inside PostgreSQL, you keep SQL, existing tools and the broader Postgres ecosystem while gaining storage and query features designed for data that arrives continuously with timestamps.

The quick start walks through running it locally with a one-line install script or Docker, creating a hypertable with the columnstore enabled, inserting data directly into the columnstore and running analytical queries. It suggests Docker, around 8 GB of RAM and a psql or other PostgreSQL client, and warns that the install script is meant for local development and testing, not production. Topics list uses such as IoT, financial analysis and historian data.

The code is written in C, and its license is listed as 'Other' on GitHub because some features sit under Timescale's own license rather than a standard open-source one, so check which apply to you. The company, now branded TigerData, also runs a hosted cloud service. It suits teams who want time-series features without leaving PostgreSQL.

QuestDB is an open-source time-series database aimed at high-speed ingestion and fast analytical queries, with a focus on use cases such as capital markets, tick data and real-time analytics. One SQL engine covers ingestion, stream processing and long-term storage, so teams query live data and years of history the same way.

The engine is written in Java that avoids garbage collection, with C++ and Rust used on performance-critical paths. Data is held in memory-mapped, time-partitioned columns, and queries run across all CPU cores using SIMD and JIT-compiled filters. No third-party libraries sit on the data path. Data moves through three storage tiers: Apache Parquet files, native columnar partitions, and a write-ahead log that is written in parallel. In the open-source edition you convert partitions to Parquet manually, while the Enterprise edition automates tiering to object storage. Because the data stays in open formats (Parquet, Iceberg and Arrow), other tools can read it, and the README publishes benchmark figures for ingestion and query streaming.

QuestDB is Apache-2.0 licensed and runs on your own servers, with documentation also available in Chinese. It suits teams building financial, industrial or telemetry platforms who need very fast time-series ingestion.

Apache IoTDB manages time series data produced by Internet of Things devices. It provides services for collecting, storing and analyzing measurements, and is designed for the industrial IoT field, where datasets are very large and data arrives at high throughput.

The project highlights a lightweight structure, high performance and usable features, along with integration with the Hadoop and Spark ecosystems for complex analysis. It relies on TsFile, a columnar storage file format created for time series data, which is developed as a related project. The system is written in Java and is categorized as a NoSQL time series database.

IoTDB is an Apache Software Foundation project under the Apache-2.0 license and can be deployed on your own servers. The README is available in English and Chinese, and it suits teams that need to store and analyze large volumes of sensor readings.