Get Started Using SingleStoreDB for Free

SingleStoreDB can handle many workloads completely for free, and it’s very easy to get started. The free edition of SingleStoreDB can scale up to 128GB of RAM (total) and 32 CPU cores – enough power to process billions of records on just a few virtual machines.

See the video version of this guide.

This guide walks you through setting up a "cluster in a box"/single machine deployment using the SingleStoreDB free license. It’s quick and easy. That said, with a little more work, you can use that same license to deploy on a small group of machines (multi-node cluster) for free. For more information about deploying a multi-node cluster, see this deployment guide.

Overview

This guide shows you how to quickly build a single-instance SingleStoreDB cluster running on Docker Desktop, on a laptop computer, for free. You’ll need a machine with at least 8GB RAM and 4 CPUs. This is ideal for quickly provisioning a system to understand the capabilities of SingleStoreDB’s SQL engine. Everything described in this guide will be running on your computer, and by using a Docker container, you do not need to install or configure much of SingleStoreDB to get it running.

Here’s the process, with each step outlined below:

After following these steps, you’ll have a bare-bones SingleStoreDB cluster running on your machine. After that, check out some possible next steps.

Notice

With specs well below SingleStoreDB’s limits, performance will be atypical, and not suited for a production environment. However, you can use this setup to experience the full features of SingleStoreDB, and understand how it applies to your business challenges.

Cluster-in-a-box

Multi-node Cluster

Hardware

Laptop computer

Many hefty servers

Best use-cases

  • Try out SingleStoreDB

  • Test SingleStoreDB capabilities

  • Prototyping

  • Proof of concept (PoC)

  • Production workloads

  • High availability

Cost

Free up to four nodes with 32GB RAM each, and with community support

Free up to four nodes with 32GB RAM each, and with community support