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OpenMLDB
What it is
OpenMLDB is an open-source machine learning database and feature platform designed to keep features consistent between training and inference. It uses SQL to create feature-engineering scripts, deploy them online, and configure online data sources. Its architecture combines a real-time SQL engine, a batch SQL engine based on a tailored Spark distribution, and a unified execution-plan generator. Documentation says the real-time engine can produce features in a few milliseconds, and SQL extensions include LAST JOIN and WINDOW UNION. OpenMLDB offers a cluster version for large-scale production applications and a lightweight standalone version for evaluation and demonstration. Listed production capabilities include distributed storage and computing, fault recovery, high availability, scale-out, upgrades, monitoring, and heterogeneous memory support. The project provides integrations for importing Apache Pulsar streams and incorporating feature engineering into DolphinScheduler workflows. Kubernetes deployment is described for online and offline engines, but the documented cluster deployment lacks a TaskManager, leaving LOAD DATA, SELECT INTO, and offline-related functions unsupported there. Its Spark distribution includes Scala, Java, Python, and R interfaces.
For and against
For
- Open-source feature platform for training and inference.
- SQL workflow covers feature development and online deployment.
- Offers standalone and cluster deployment options.
- Spark distribution includes Scala, Java, Python, and R interfaces.
- Integrates with Pulsar and DolphinScheduler.
Against
- Documented Kubernetes cluster deployment lacks a TaskManager.
- LOAD DATA, SELECT INTO, and offline-related functions are unsupported in that deployment.
- Kubernetes deployment tool is tested with Kubernetes 1.19 or later and Helm 3.2.0 or later.
The verdict
OpenMLDB combines SQL feature engineering with real-time and batch execution and offers standalone and cluster deployment options. Check the documented Kubernetes TaskManager limitation if that deployment is part of your plan.
Who it suits
It suits teams building machine-learning features with SQL who need both online and offline workflows. The standalone version is aimed at evaluation and demonstration, while the cluster version targets large-scale production applications.
Plans and prices
OpenMLDB
Open-source machine learning database; standalone and cluster versions
Read 3 Oct 2026 · openmldb.ai
On the record
- Batch and real-time engines
- Its architecture includes a real-time SQL engine, a batch SQL engine based on a tailored Spark distribution, and a unified execution plan generator.openmldb.ai · read 3 Oct 2026
- Community support
- The project directs users to GitHub Issues for bug reports and feature requests, GitHub Discussions, Slack, and a developer mailing list.openmldb.ai · read 3 Oct 2026
- Deployment options
- OpenMLDB has a cluster version for large-scale production applications and a lightweight single-node standalone version for evaluation and demonstration.openmldb.ai · read 3 Oct 2026
- DolphinScheduler integration
- OpenMLDB provides a DolphinScheduler task for integrating feature engineering into workflows, including offline import, feature extraction, SQL deployment, and online import.openmldb.ai · read 3 Oct 2026
- Kubernetes deployment
- The deployment guide describes Kubernetes deployment for both OpenMLDB's offline and online engines.openmldb.ai · read 3 Oct 2026
- Kubernetes limitation
- The documented Kubernetes cluster deployment does not include a TaskManager, so LOAD DATA, SELECT INTO, and offline-related functions are unsupported in that deployment.openmldb.ai · read 3 Oct 2026
- Kubernetes requirements
- The Kubernetes deployment tool is tested with Kubernetes 1.19 or later and Helm 3.2.0 or later.openmldb.ai · read 3 Oct 2026
- Production capabilities
- The documentation lists distributed storage and computing, fault recovery, high availability, scale-out, upgrades, monitoring, and heterogeneous memory support.openmldb.ai · read 3 Oct 2026
- Pulsar integration
- The OpenMLDB Pulsar Connector is described as a way to import real-time data streams from Apache Pulsar into OpenMLDB.openmldb.ai · read 3 Oct 2026
- Real-time features
- The documentation says its real-time SQL engine can produce features in a few milliseconds.openmldb.ai · read 3 Oct 2026
- Spark distribution
- The OpenMLDB Spark distribution provides Scala, Java, Python, and R interfaces, and its precompiled AllinOne version supports Linux and macOS.openmldb.ai · read 3 Oct 2026
- SQL extensions
- OpenMLDB extends SQL for feature engineering with syntax including LAST JOIN and WINDOW UNION.openmldb.ai · read 3 Oct 2026
- SQL workflow
- OpenMLDB uses SQL to develop feature engineering scripts, deploy them online, and configure online data sources.openmldb.ai · read 3 Oct 2026
- What it does
- OpenMLDB is an open-source machine learning database and feature platform for consistent features in training and inference.openmldb.ai · read 3 Oct 2026
