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A unified SQL query interface and portable runtime to locally materialize, accelerate, and query datasets from any database, data warehouse, or data lake.


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spiceai/spiceai OSS

CodeQL License: Apache-2.0 Discord Follow on X

What is Spice?

Spice is a portable runtime providing developers with a unified SQL interface to materialize, accelerate, and query data sourced from any database, data warehouse, or data lake.

📣 Read the OSS announcement blog post.

Spice connects, fuses, and delivers data to applications and AI, acting as an application-specific, tier-optimized Database CDN.

The Spice runtime is written in Rust and is built-with industry leading technologies like Apache DataFusion, Apache Arrow, Apache Arrow Flight, SQlite, and DuckDB.

Why Spice?

Spice makes querying data by SQL across one or more data sources simple and fast. Easily co-locate a managed working set of data with your application or ML, accelerated with in-memory Arrow, with SQLite/DuckDB, or with attached PostgreSQL for high-performance, low-latency queries. Accelerated engines run tier-native in your infrastructure giving you flexibility and control over cost and performance.

How is Spice different?

  1. Tier-optimized Acceleration with both OLAP (Arrow/DuckDB) and OLTP (SQLite/PostgreSQL) databases at dataset granularity compared to other OLAP only or OLTP only systems.

  2. Separation of materialization and storage/compute compared with monolith data systems and data lakes. Keep compute colocated with source data while bringing a materialized working set next to your application, dashboard, or data/ML pipeline.

  3. Edge to cloud native. Designed to be deployed standalone, as a container sidecar, as a microservice, in a cluster across laptops, the Edge, On-Prem, to a POP, and to all public clouds. Spice instances can also be chained, and deployed distributed across tiers of infrastructure.

Before Spice

Before Spice

With Spice

With Spice

Example Use-Cases

1. Faster applications and frontends. Accelerate and co-locate datasets with applications and frontends, to serve more concurrent queries and users with faster page loads and data updates. Try the CQRS sample app

2. Faster dashboards, analytics, and BI. Faster, more responsive dashboards without massive compute costs. Watch the Apache Superset demo

3. Faster data pipelines, machine learning training and inferencing. Co-locate datasets in pipelines where the data is needed to minimize data-movement and improve query performance. Predict hard drive failure with the SMART data demo

4. Easily query many data sources. Federated SQL query across databases, data warehouses, and data lakes using Data Connectors.


  • Is Spice a cache? No, however you can think of Spice data materialization like an active cache or data prefetcher. A cache would fetch data on a cache-miss while Spice prefetches and materializes filtered data on an interval or as new data becomes available. In addition to materialization Spice supports results caching.

  • Is Spice a CDN for databases? Yes, you can think of Spice like a CDN for different data sources. Using CDN concepts, Spice enables you to ship (load) a working set of your database (or data lake, or data warehouse) where it's most frequently accessed, like from a data application or for AI-inference.

Watch a 30-sec BI dashboard acceleration demo


Supported Data Connectors

Currently supported data connectors for upstream datasets. More coming soon.

Name Description Status Protocol/Format
databricks Databricks Alpha Spark Connect
S3/Delta Lake
postgres PostgreSQL Alpha
spiceai Alpha Arrow Flight
s3 S3 Alpha Parquet, CSV
dremio Dremio Alpha Arrow Flight
mysql MySQL Alpha
duckdb DuckDB Alpha
clickhouse Clickhouse Alpha
odbc ODBC Alpha ODBC
spark Spark Alpha Spark Connect
flightsql Apache Arrow Flight SQL Alpha Arrow Flight SQL
snowflake Snowflake Alpha Arrow
ftp, sftp FTP/SFTP Alpha Parquet, CSV

Supported Data Stores/Accelerators

Currently supported data stores for local materialization/acceleration. More coming soon.

Name Description Status Engine Modes
arrow In-Memory Arrow Records Alpha memory
duckdb Embedded DuckDB Alpha memory, file
sqlite Embedded SQLite Alpha memory, file
postgres Attached PostgreSQL Alpha file

Intelligent Applications

Spice enables developers to build both data and AI-driven applications by co-locating data and ML models with applications. Read more about the vision to enable the development of intelligent AI-driven applications.

⚠️ DEVELOPER PREVIEW Spice is under active alpha stage development and is not intended to be used in production until its 1.0-stable release. If you are interested in running Spice in production, please get in touch so we can support you (See Connect with us below).

⚡️ Quickstart (Local Machine)


Step 1. Install the Spice CLI:

On macOS, Linux, and WSL:

curl | /bin/bash

Or using brew:

brew install spiceai/spiceai/spice

On Windows:

curl -L "" -o Install.ps1 && PowerShell -ExecutionPolicy Bypass -File ./Install.ps1

Step 2. Initialize a new Spice app with the spice init command:

spice init spice_qs

A spicepod.yaml file is created in the spice_qs directory. Change to that directory:

cd spice_qs

Step 3. Start the Spice runtime:

spice run

Example output will be shown as follows: runtime starting...
Using latest 'local' runtime version.
2024-06-03T23:21:26.819978Z  INFO spiced: Metrics listening on
2024-06-03T23:21:26.821863Z  INFO runtime::http: Spice Runtime HTTP listening on
2024-06-03T23:21:26.821898Z  INFO runtime::flight: Spice Runtime Flight listening on
2024-06-03T23:21:26.821958Z  INFO runtime::opentelemetry: Spice Runtime OpenTelemetry listening on
2024-06-03T23:21:26.822128Z  INFO runtime: Initialized results cache; max size: 128.00 MiB, item ttl: 1s

The runtime is now started and ready for queries.

Step 4. In a new terminal window, add the spiceai/quickstart Spicepod. A Spicepod is a package of configuration defining datasets and ML models.

spice add spiceai/quickstart

The spicepod.yaml file will be updated with the spiceai/quickstart dependency.

version: v1beta1
kind: Spicepod
  - spiceai/quickstart

The spiceai/quickstart Spicepod will add a taxi_trips data table to the runtime which is now available to query by SQL.

2024-06-03T23:21:29.721705Z  INFO runtime: Dataset taxi_trips registered (s3://spiceai-demo-datasets/taxi_trips/2024/), acceleration (arrow, 10s refresh), results cache enabled.
2024-06-03T23:21:29.722839Z  INFO runtime::accelerated_table::refresh: Loading data for dataset taxi_trips
2024-06-03T23:21:50.813510Z  INFO runtime::accelerated_table::refresh: Loaded 2,964,624 rows (421.71 MiB) for dataset taxi_trips in 21s 90ms.

Step 5. Start the Spice SQL REPL:

spice sql

The SQL REPL inferface will be shown:

Welcome to the SQL REPL! Type 'help' for help.

show tables; -- list available tables

Enter show tables; to display the available tables for query:

sql> show tables
| table_catalog | table_schema | table_name    | table_type |
| spice         | public       | taxi_trips    | BASE TABLE |
| spice         | runtime      | metrics       | BASE TABLE |
| spice         | runtime      | query_history | BASE TABLE |

Time: 0.007505084 seconds. 3 rows.

Enter a query to display the longest taxi trips:

sql> SELECT trip_distance, total_amount FROM taxi_trips ORDER BY trip_distance DESC LIMIT 10;


| trip_distance | total_amount |
| 312722.3      | 22.15        |
| 97793.92      | 36.31        |
| 82015.45      | 21.56        |
| 72975.97      | 20.04        |
| 71752.26      | 49.57        |
| 59282.45      | 33.52        |
| 59076.43      | 23.17        |
| 58298.51      | 18.63        |
| 51619.36      | 24.2         |
| 44018.64      | 52.43        |

Time: 0.015596458 seconds. 10 rows.

⚙️ Runtime Container Deployment

Using the Docker image locally:

docker pull spiceai/spiceai

In a Dockerfile:

from spiceai/spiceai:latest

Using Helm:

helm repo add spiceai
helm install spiceai spiceai/spiceai

🏎️ Next Steps

You can use any number of predefined datasets available from the Cloud Platform in the Spice runtime.

A list of publicly available datasets from can be found here:

In order to access public datasets from, you will first need to create an account with by selecting the free tier membership.

Navigate to and create a new account by clicking on Try for Free.


After creating an account, you will need to create an app in order to create to an API key.


You will now be able to access datasets from For this demonstration, we will be using the dataset.

Step 1. Log in and authenticate from the command line using the spice login command. A pop up browser window will prompt you to authenticate:

spice login

Step 2. Initialize a new project and start the runtime:

# Initialize a new Spice app
spice init spice_app

# Change to app directory
cd spice_app

# Start the runtime
spice run

Step 3. Configure the dataset:

In a new terminal window, configure a new dataset using the spice dataset configure command:

spice dataset configure

You will be prompted to enter a name. Enter a name that represents the contents of the dataset

dataset name: (spice_app) eth_recent_blocks

Enter the description of the dataset:

description: eth recent blocks

Enter the location of the dataset:


Select y when prompted whether to accelerate the data:

Locally accelerate (y/n)? y

You should see the following output from your runtime terminal:

2024-06-03T23:25:59.514395Z  INFO runtime: Dataset eth_recent_blocks registered (, acceleration (arrow, 10s refresh), results cache enabled.
2024-06-03T23:25:59.514624Z  INFO runtime::accelerated_table::refresh: Loading data for dataset eth_recent_blocks
2024-06-03T23:26:00.758813Z  INFO runtime::accelerated_table::refresh: Loaded 143 rows (6.22 MiB) for dataset eth_recent_blocks in 1s 244ms.

Step 4. In a new terminal window, use the Spice SQL REPL to query the dataset

spice sql
SELECT number, size, gas_used from eth_recent_blocks LIMIT 10;

The output displays the results of the query along with the query execution time:

| number   | size   | gas_used |
| 19281345 | 400378 | 16150051 |
| 19281344 | 200501 | 16480224 |
| 19281343 | 97758  | 12605531 |
| 19281342 | 89629  | 12035385 |
| 19281341 | 133649 | 13335719 |
| 19281340 | 307584 | 18389159 |
| 19281339 | 89233  | 13391332 |
| 19281338 | 75250  | 12806684 |
| 19281337 | 100721 | 11823522 |
| 19281336 | 150137 | 13418403 |

Time: 0.004057791 seconds. 10 rows.

You can experiment with the time it takes to generate queries when using non-accelerated datasets. You can change the acceleration setting from true to false in the datasets.yaml file.

📄 Documentation

Comprehensive documentation is available at

🔨 Upcoming Features

🚀 See the Roadmap to v1.0-stable for upcoming features.

🤝 Connect with us

We greatly appreciate and value your support! You can help Spice in a number of ways:

⭐️ star this repo! Thank you for your support! 🙏