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What is Spice?

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

Spice makes it easy to build data-driven and data-intensive applications by streamlining the use of data and machine learning (ML) in software.

The Spice runtime is written in Rust and leverages industry leading technologies like Apache DataFusion, Apache Arrow, Apache Arrow Flight, 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 your data with your application or ML, locally accelerated in-memory, with DuckDB, or with an attached database like PostgreSQL for high-performance, low-latency queries.

Before Spice


With Spice


Example Use-Cases

1. Faster frontends. Accelerate and co-locate data views with your frontend application, to serve more concurrent users with faster page loads and data updates.

2. Faster analytics and BI.

3. Faster machine learning training and inferencing.

⚠️ DEVELOPER PREVIEW Spice is under active alpha stage development and is not intended to be used in production until its 1.0-stable release.


Step 1. Install the Spice CLI:

curl | /bin/bash

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

spice init spice_app

A Spicepod.yaml file is created in the working directory.

Step 3. Connect to the sample Dremio instance to access the sample data:

spice login dremio -u demo -p demo1234

Step 4. Start the Spice runtime:

spice run

Example output will be shown as follows: runtime starting...
Using latest 'local' runtime version.
2024-02-21T06:11:56.381793Z  INFO runtime::http: Spice Runtime HTTP listening on
2024-02-21T06:11:56.381853Z  INFO runtime::flight: Spice Runtime Flight listening on
2024-02-21T06:11:56.382038Z  INFO runtime::opentelemetry: Spice Runtime OpenTelemetry listening on

The runtime is now started and ready for queries.

Step 5. 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.

2024-02-22T05:53:48.222952Z  INFO runtime: Loaded dataset: taxi_trips
2024-02-22T05:53:48.223101Z  INFO runtime::dataconnector: Refreshing data for taxi_trips

Step 6. Start the Spice SQL REPL:

spice sql

The SQL REPL inferface will be shown:

Welcome to the interactive SQL Query Utility! 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 |
| datafusion    | public             | taxi_trips  | BASE TABLE |
| datafusion    | information_schema | tables      | VIEW       |
| datafusion    | information_schema | views       | VIEW       |
| datafusion    | information_schema | columns     | VIEW       |
| datafusion    | information_schema | df_settings | VIEW       |

Query took: 0.004728897 seconds

Enter a query to display the most expensive tax trips:

sql> SELECT trip_distance_mi, fare_amount FROM taxi_trips ORDER BY fare_amount LIMIT 10;


| trip_distance_mi | fare_amount |
| 1.1              | 7.5         |
| 6.1              | 23.0        |
| 0.6              | 4.5         |
| 16.7             | 52.0        |
| 11.3             | 37.5        |
| 1.1              | 6.0         |
| 5.3              | 18.5        |
| 1.3              | 7.0         |
| 1.0              | 7.0         |
| 3.5              | 17.5        |

Query took: 0.002458976 seconds

Next Steps

You can use any number of predefined datasets available from in the Spice Runtime.

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

In order to access public datasets from Spice, 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. In a new directory, 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 if you haven't already done so. Then, start the runtime:

spice init my_spiceai_project
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

What is the dataset name? eth_recent_blocks

Enter the location of the dataset:

Where is your dataset located?

Select y when prompted whether to accelerate the data:

Locally accelerate this dataset (y/n)? y

You should see the following output from your runtime terminal:

2024-02-21T22:49:10.038461Z  INFO runtime: Loaded dataset: eth_recent_blocks

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 |

Query took: 0.004057791 seconds

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.

Importing dataset from Dremio

Step 1. If you have a dataset hosted in Dremio, you can load it into the Spice Runtime as follows:

spice login dremio -u <USERNAME> -p <PASSWORD>

Step 2. If you haven't already initialized a new project, you need to do so. Then, start the Spice Runtime.

spice init dremio-demo-project
spice run

Step 3. We now configure the dataset from Dremio:

spice dataset configure

Enter the name of the dataset:

What is the dataset name? my_dataset

Specify the location of the dataset:

Where is your dataset located? dremio/datasets.my_dataset

Select "y" when prompted whether to locally accelerate the dataset:

Locally accelerate this dataset (y/n)? y

We should now see the following output:

Dataset settings written to `datasets/my_dataset/dataset.yaml`!

If the login credentials were entered correctly, your dataset will have loaded into the runtime. You should see the following in the Spice runtime terminal :

2024-02-14T18:34:15.174564Z  INFO spiced: Loaded dataset: my_dataset
2024-02-14T18:34:15.175189Z  INFO runtime::datasource: Refreshing data for my_dataset

Step 4. Run queries against the dataset using the Spice SQL REPL.

In a new terminal, start the Spice SQL REPL

spice sql

You can now now query my_dataset in the runtime.

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! 🙏

For a more comprehensive guide, see the full online documentation.