Scale SQL queries across distributed nodes
Extend from a single-node engine to a distributed cluster. Parallelize scans, joins, and inference across executors for high-performance, fault-tolerant processing.
Do more with your data
Consolidate operational, analytical, and object store data into a governed and accelerated SQL query engine.
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up to 100x faster queries
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up to 80% cost savings on data lakehouse spend
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increase in data reliability for critical workloads
Declarative cluster configuration
Define distributed clusters directly in your Spice environment. Designate a master node and executor pool that register automatically with the scheduler-no external orchestration or complex setup required.
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Parallel execution engine
Built on Apache Ballista and DataFusion, Spice distributes query stages across nodes for concurrent reads, joins, and aggregations. Each executor processes partitions independently and merges results in real time.
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Automatic dependency management
Spice executors automatically detect and load dependencies-like embedding models or UDFs-from the master node. Runtime optimizers ensure each executor fetches only what's required, reducing overhead.
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Optimized query planning
A custom optimizer decomposes large scans into shuffle partitions and pushes projections closer to the data source. Less data movement, faster results, and lower network cost.
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Integrations across all your data sources
Accelerate your data stack with a library of 40+ prebuilt connectors for the most common databases, warehouses, and file stores-from Databricks and S3 to MySQL and PostgreSQL.
Deployed in production
Run data-intensive workloads on a high-performance engine trusted by teams building real-time systems at scale.
“Spice opened the door to take these critical control-plane datasets and move them next to our services in the runtime path.”
Peter Janovsky
Software Architect, Twilio
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Faster queries
“It just spins up and works, which is really nice. The responsiveness is amazing, which is a huge gain for the customer.”
Darin Douglass
Principal Software Engineer, Barracuda
“Partnering with Spice AI has transformed how NRC Health delivers AI-driven insights. By unifying siloed data across systems, we accelerated AI feature development, reducing time-to-market from months to weeks - and sometimes days. With predictable costs and faster innovation, Spice isn't just solving some of our data and AI challenges - it's helping us redefine personalized healthcare.”
Tim Ottersburg
VP of Technology, NRC Health
FAQs
Answers to common questions about distributed SQL queries in Spice
How does Spice execute a SQL query across multiple nodes?
Spice distributes query stages across a cluster built on Apache Ballista and DataFusion. A master node schedules work across an executor pool, and each executor processes partitions independently for concurrent reads, joins, and aggregations, merging results in real time.
When should I use distributed query instead of a single node?
Use distributed query when datasets or workloads exceed single-node limits, such as petabyte-scale scans, large joins, or inference over big tables. Single-node Spice with SQL federation and acceleration covers many workloads, and a distributed cluster extends the same engine when you need to parallelize across executors.
Do I need an external orchestrator to run a Spice cluster?
No. Clusters are defined declaratively in your Spice environment: you designate a master node and an executor pool, and executors register automatically with the scheduler. No external orchestration or complex setup is required.
How does Spice reduce data movement during distributed execution?
A custom optimizer decomposes large scans into shuffle partitions and pushes projections closer to the data source, so executors fetch only the data a query needs. Executors also load dependencies such as embedding models or UDFs automatically from the master node, reducing overhead.
Which data sources can a distributed query read from?
Distributed queries use the same connector library as the rest of Spice, with 40+ prebuilt connectors for databases, warehouses, and file stores, including Databricks, S3, MySQL, and PostgreSQL. This makes clusters well suited for data lake acceleration at scale.
See Spice in action
Walk through your use case with an engineer and see how Spice handles federation, acceleration, and AI integration for production workloads.
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