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Open-source alternatives to KX kdb+

Compare community-driven replacements for KX kdb+ in time-series databases workflows. We curate active, self-hostable options with transparent licensing so you can evaluate the right fit quickly.

KX kdb+ logo

KX kdb+

kdb+ is a columnar time-series database with in-memory/vectorized processing and a built-in q language. It ingests high-volume streaming data, supports time-series joins and windowed analytics, and powers sub-millisecond queries for workloads like market data, IoT, and operational telemetry.Read more
Time-Series Databases
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Key stats

  • 10Alternatives
  • 1Support self-hosting

    Run on infrastructure you control

  • 9Active development

    Recent commits in the last 6 months

  • 7Permissive licenses

    MIT, Apache, and similar licenses

Counts reflect projects currently indexed as alternatives to KX kdb+.

All open-source alternatives

CnosDB logo

CnosDB

High-performance distributed time-series database for IoT and observability

Active developmentIntegration-friendlyRust

Why teams choose it

  • Unlimited time-series scalability with advanced timeline aggregation and configurable caching
  • Standard SQL interface with schema-less writes and out-of-order data support
  • Cloud-native distributed architecture with storage-compute separation and horizontal scaling

Watch for

Requires Rust toolchain, CMake, FlatBuffers, and Protobuf for source builds

Migration highlight

IoT Sensor Network Monitoring

Ingest millions of sensor readings per second with automatic compression, query historical trends via SQL, and cache recent values for real-time dashboards.

CrateDB logo

CrateDB

Distributed SQL database for real-time analytics at scale

Active developmentPermissive licenseIntegration-friendlyJava

Why teams choose it

  • Standard SQL with PostgreSQL wire protocol and HTTP API support
  • Horizontal scalability with auto-sharding, auto-replication, and self-healing
  • Native time-series, full-text search, and geospatial capabilities

Watch for

Requires understanding of distributed database concepts for optimal deployment

Migration highlight

IoT Sensor Data Analytics

Ingest thousands of sensor readings per second and run real-time SQL queries for anomaly detection and trend analysis across distributed clusters.

GreptimeDB logo

GreptimeDB

Unified observability database for metrics, logs, and traces

Active developmentPermissive licenseIntegration-friendlyRust

Why teams choose it

  • Unified storage for metrics, logs, and traces as timestamped wide events
  • Sub-second query performance at petabyte scale with distributed Rust engine
  • Cloud-native architecture with compute/storage separation and object storage support

Watch for

Currently in beta status with GA planned for mid-2025

Migration highlight

Unified Observability Platform

Replace separate Prometheus, Elasticsearch, and tracing backends with one database, reducing operational complexity and infrastructure costs

InfluxDB logo

InfluxDB

Real-time time series database for events and metrics

Self-host friendlyActive developmentPermissive licenseRust

Why teams choose it

  • Sub-10ms last-value queries with diskless object storage architecture
  • SQL and InfluxQL query engines with FlightSQL and HTTP API support
  • Embedded Python VM for custom plugins, triggers, and data transformations

Watch for

Relatively new GA release (April 2025) with evolving feature set

Migration highlight

Server Infrastructure Monitoring

Operations teams visualize CPU, memory, and disk metrics across thousands of servers with real-time dashboards updating every second.

tstorage logo

tstorage

Fast, goroutine-safe on-disk time-series storage for Go

Permissive licenseIntegration-friendlyAI-powered workflowsGo

Why teams choose it

  • Goroutine-safe ingestion with nanosecond-level performance
  • Automatic time-partitioning keeps recent data in memory and older data on disk
  • Simple API supporting labeled metrics and configurable timestamp precision

Watch for

Limited to the Go ecosystem; no native bindings for other languages

Migration highlight

Load-testing metrics collection

Capture millions of latency points with sub-microsecond write latency, keeping heap usage stable.

openGemini logo

openGemini

Scalable, high-performance time-series DB for massive telemetry

Active developmentPermissive licenseFast to deployGo

Why teams choose it

  • High performance with LSM storage and automatic partitioning
  • MPP architecture for linear horizontal scalability
  • High‑cardinality engine with up to 15:1 columnar compression

Watch for

Maturing project as a CNCF sandbox, ecosystem still growing

Migration highlight

Industrial sensor data collection

Ingest millions of sensor readings per second with sub-10 ms query latency, enabling real-time monitoring and anomaly detection.

Prometheus logo

Prometheus

Flexible monitoring system with powerful queries and autonomous servers

Active developmentPermissive licenseFast to deployGo

Why teams choose it

  • Multi‑dimensional time‑series data model
  • PromQL – powerful query language
  • Autonomous single‑server operation

Watch for

Scaling beyond a single node requires federation

Migration highlight

Cluster health monitoring

Continuously scrapes node and pod metrics, alerts on resource exhaustion, and visualizes trends via Grafana.

QuestDB logo

QuestDB

Blazing‑fast time‑series database for real‑time analytics with low‑latency SQL

Active developmentPermissive licenseFast to deployJava

Why teams choose it

  • Sub‑millisecond ingestion of millions of rows per second
  • Vectorized, SIMD‑accelerated SQL engine with time‑series extensions
  • Multi‑tier storage: WAL → native columnar → Parquet on object storage

Watch for

Enterprise features require paid license

Migration highlight

Real‑time crypto trade analytics

Ingests millions of trade events per second and serves sub‑second queries for live price charts via Grafana.

TDengine logo

TDengine

High‑performance, cloud‑native time‑series database for IoT scale

Active developmentIntegration-friendlyAI-powered workflowsC

Why teams choose it

  • High‑performance ingestion and columnar compression
  • Built‑in AI agent (TDgpt) for forecasting and anomaly detection
  • Cloud‑native distributed architecture with RAFT and Kubernetes support

Watch for

Windows support limited to enterprise edition

Migration highlight

Real‑time fleet telemetry processing

Ingests millions of vehicle metrics per minute, runs on‑edge compute, and provides predictive alerts via TDgpt.

TimescaleDB logo

TimescaleDB

PostgreSQL extension delivering high-performance real-time analytics on time-series data

Active developmentPrivacy-firstFast to deployC

Why teams choose it

  • Hypertables with automatic time‑based partitioning
  • Hybrid row‑columnar storage delivering >90 % compression
  • Continuous aggregates for incremental materialized views

Watch for

Learning curve for hypertable design and policies

Migration highlight

IoT Device Telemetry Monitoring

Ingest millions of sensor readings per day, query recent trends instantly, and retain historic data at 90 % reduced storage.

Choosing a time-series databases alternative

Teams replacing KX kdb+ in time-series databases workflows typically weigh self-hosting needs, integration coverage, and licensing obligations.

  • 1 project let you self-host and keep customer data on infrastructure you control.
  • 9 options are actively maintained with recent commits.

Tip: shortlist one hosted and one self-hosted option so stakeholders can compare trade-offs before migrating away from KX kdb+.