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https://github.com/spiceai/spiceai
A self-hostable CDN for databases. Spice provides a unified SQL query interface and portable runtime to locally materialize, accelerate, and query datasets across databases, data warehouses, and data lakes.
https://github.com/spiceai/spiceai
artificial-intelligence data developers infrastructure machine-learning sql time-series
Last synced: about 10 hours ago
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A self-hostable CDN for databases. Spice provides a unified SQL query interface and portable runtime to locally materialize, accelerate, and query datasets across databases, data warehouses, and data lakes.
- Host: GitHub
- URL: https://github.com/spiceai/spiceai
- Owner: spiceai
- License: apache-2.0
- Created: 2021-08-08T23:26:13.000Z (over 3 years ago)
- Default Branch: trunk
- Last Pushed: 2024-11-25T08:41:33.000Z (17 days ago)
- Last Synced: 2024-11-25T08:45:04.551Z (17 days ago)
- Topics: artificial-intelligence, data, developers, infrastructure, machine-learning, sql, time-series
- Language: Rust
- Homepage: https://docs.spiceai.org
- Size: 10.8 MB
- Stars: 1,925
- Watchers: 21
- Forks: 79
- Open Issues: 194
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Codeowners: CODEOWNERS
- Security: SECURITY.md
- Roadmap: docs/ROADMAP.md
Awesome Lists containing this project
- awesome-repositories - spiceai/spiceai - A self-hostable CDN for databases. Spice provides a unified SQL query interface and portable runtime to locally materialize, accelerate, and query datasets across databases, data warehouses, and data (Rust)
- awesome-duckdb - Spice.ai - A unified SQL query interface and portable runtime to locally materialize (using an embedded DuckDB), accelerate, and query datasets from any database, data warehouse, or data lake. (Tools Powered by DuckDB)
README
**Spice** is a portable runtime written in Rust that offers developers a unified SQL interface to materialize, accelerate, and query data from any database, data warehouse, or data lake.
📣 Read the [Spice.ai OSS announcement blog post](https://blog.spiceai.org/posts/2024/03/28/adding-spice-the-next-generation-of-spice.ai-oss/).
Spice connects, fuses, and delivers data to applications, machine-learning models, and AI-backends, functioning as an application-specific, tier-optimized Database CDN.
Spice is built-with industry leading technologies such as [Apache DataFusion](https://datafusion.apache.org), Apache Arrow, Apache Arrow Flight, SQLite, and DuckDB.
🎓 [Read the MaterializedView interview on Spice.ai](https://materializedview.io/p/building-a-cdn-for-databases-spice-ai)
🎥 [Watch the CMU Databases Accelerating Data and AI with Spice.ai Open-Source](https://www.youtube.com/watch?v=tyM-ec1lKfU)
## Why Spice?
Spice makes it fast and easy to query data from one or more sources using SQL. You can co-locate a managed dataset with your application or machine learning model, and accelerate it with Arrow in-memory, SQLite/DuckDB, or with attached PostgreSQL for fast, high-concurrency, low-latency queries. Accelerated engines give you flexibility and control over query cost and performance.
### How is Spice different?
1. **Application-focused:** Spice is designed to integrate at the application level; 1:1 or 1:N application to Spice mapping, whereas most other data systems are designed for multiple applications to share a single database or data warehouse. It's not uncommon to have many Spice instances, even down to one for each tenant or customer.
2. **Dual-Engine Acceleration:** Spice supports both **OLAP** (Arrow/DuckDB) and **OLTP** (SQLite/PostgreSQL) databases at the dataset level, unlike other systems that only support one type.
3. **Separation of Materialization and Storage/Compute:** Spice separates storage and compute, allowing you to keep data close to its source and bring a materialized working set next to your application, dashboard, or data/ML pipeline.
4. **Edge to Cloud Native**. Spice is designed to be deployed anywhere, from a standalone instance to a Kubernetes container sidecar, microservice, or cluster at the Edge/POP, On-Prem, or in public clouds. You can also chain Spice instances and deploy them across multiple infrastructure tiers.
### How does Spice compare?
| | Spice | Trino/Presto | Dremio | Clickhouse |
| -------------------------- | ---------------------------------- | -------------------------------- | -------------------------------- | ----------------------- |
| Primary Use-Case | Data & AI Applications | Big Data Analytics | Interactive Analytics | Real-Time Analytics |
| Typical Deployment | Colocated with application | Cloud Cluster | Cloud Cluster | On-Prem/Cloud Cluster |
| Application-to-Data System | One-to-One/Many | Many-to-One | Many-to-One | Many-to-One |
| Query Federation | Native with query push-down | Supported with push-down | Supported with limited push-down | Limited |
| Materialization | Arrow/SQLite/DuckDB/PostgreSQL | Intermediate Storage | Reflections (Iceberg) | Views & MergeTree |
| Query Result Caching | Supported | Supported | Supported | Supported |
| Typical Configuration | Single-Binary/Sidecar/Microservice | Coodinator+Executor w/ Zookeeper | Coodinator+Executor w/ Zookeeper | Clickhouse Keeper+Nodes |
### 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](https://github.com/spiceai/samples/tree/trunk/acceleration#local-materialization-and-acceleration-cqrs-sample)
**2. Faster dashboards, analytics, and BI.** Faster, more responsive dashboards without massive compute costs. [Watch the Apache Superset demo](https://github.com/spiceai/samples/blob/trunk/sales-bi/README.md)
**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](https://github.com/spiceai/demos/tree/trunk/smart-demo#spiceai-smart-demo)
**4. Easily query many data sources.** Federated SQL query across databases, data warehouses, and data lakes using [Data Connectors](https://docs.spiceai.org/components/data-connectors).
### FAQ
- **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](https://docs.spiceai.org/features/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.
- **Where is the AI?** Spice provides a unified API for both data _and_ AI/ML with a high-performance bus between the two. However, because the first step in AI-readiness is data-readiness, the Getting Started content is focused on data. Spice has [endpoints and APIs](https://docs.spiceai.org/api/http) for model deployment and inference including LLMs, accelerated embeddings, and an AI-gateway for providers like OpenAI and Anthropic. Read more about the vision to enable development of [intelligent AI-driven applications](https://docs.spiceai.org/intelligent-applications).
### 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 |
| ---------------------------------- | ------------------------------------- | ----------------- | ---------------------------- |
| `duckdb` | DuckDB | Release Candidate | |
| `github` | GitHub | Release Candidate | |
| `graphql` | GraphQL | Release Candidate | JSON |
| `mysql` | MySQL | Release Candidate | |
| `postgres` | PostgreSQL | Release Candidate | |
| `s3` | [S3][s3] | Release Candidate | Parquet, CSV |
| `databricks (mode: delta_lake)` | [Databricks][databricks] | Release Candidate | S3/Delta Lake |
| `databricks (mode: spark_connect)` | [Databricks][databricks] | Beta | [Spark Connect][spark] |
| `delta_lake` | Delta Lake | Beta | Delta Lake |
| `file` | File | Beta | Parquet, CSV |
| `flightsql` | FlightSQL | Beta | Arrow Flight SQL |
| `odbc` | ODBC | Beta | ODBC |
| `spiceai` | [Spice.ai][spiceai] | Beta | Arrow Flight |
| `abfs` | Azure BlobFS | Alpha | Parquet, CSV |
| `clickhouse` | Clickhouse | Alpha | |
| `debezium` | Debezium CDC | Alpha | Kafka + JSON |
| `dremio` | [Dremio][dremio] | Alpha | Arrow Flight |
| `ftp`, `sftp` | FTP/SFTP | Alpha | Parquet, CSV |
| `http`, `https` | HTTP(s) | Alpha | Parquet, CSV |
| `localpod` | [Local dataset replication][localpod] | Alpha | |
| `mssql` | Microsoft SQL Server | Alpha | Tabular Data Stream (TDS) |
| `sharepoint` | Microsoft SharePoint | Alpha | Unstructured UTF-8 documents |
| `snowflake` | Snowflake | Alpha | Arrow |
| `spark` | Spark | Alpha | [Spark Connect][spark] |
[databricks]: https://github.com/spiceai/quickstarts/tree/trunk/databricks#spice-on-databricks
[spark]: https://spark.apache.org/docs/latest/spark-connect-overview.html
[s3]: https://github.com/spiceai/quickstarts/tree/trunk/s3#readme
[spiceai]: https://github.com/spiceai/quickstarts/tree/trunk/spiceai#readme
[dremio]: https://github.com/spiceai/quickstarts/tree/trunk/dremio#readme
[localpod]: https://github.com/spiceai/quickstarts/blob/trunk/localpod/README.md
### 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 | Release Candidate | `memory` |
| `duckdb` | Embedded [DuckDB][duckdb] | Release Candidate | `memory`, `file` |
| `postgres` | Attached [PostgreSQL][postgres] | Beta | |
| `sqlite` | Embedded [SQLite][sqlite] | Beta | `memory`, `file` |
[duckdb]: https://docs.spiceai.org/data-accelerators/duckdb
[postgres]: https://github.com/spiceai/quickstarts/tree/trunk/postgres#postgresql-data-accelerator
[sqlite]: https://docs.spiceai.org/data-accelerators/sqlite
## ⚡️ Quickstart (Local Machine)
**Step 1.** Install the Spice CLI:
On **macOS, Linux, and WSL**:
```bash
curl https://install.spiceai.org | /bin/bash
```
Or using `brew`:
```bash
brew install spiceai/spiceai/spice
```
On **Windows**:
```bash
curl -L "https://install.spiceai.org/Install.ps1" -o Install.ps1 && PowerShell -ExecutionPolicy Bypass -File ./Install.ps1
```
**Step 2.** Initialize a new Spice app with the `spice init` command:
```bash
spice init spice_qs
```
A `spicepod.yaml` file is created in the `spice_qs` directory. Change to that directory:
```bash
cd spice_qs
```
**Step 3.** Start the Spice runtime:
```bash
spice run
```
Example output will be shown as follows:
```bash
Spice.ai runtime starting...
2024-08-05T13:02:40.247484Z INFO runtime::flight: Spice Runtime Flight listening on 127.0.0.1:50051
2024-08-05T13:02:40.247490Z INFO runtime::metrics_server: Spice Runtime Metrics listening on 127.0.0.1:9090
2024-08-05T13:02:40.247949Z INFO runtime: Initialized results cache; max size: 128.00 MiB, item ttl: 1s
2024-08-05T13:02:40.248611Z INFO runtime::http: Spice Runtime HTTP listening on 127.0.0.1:8090
2024-08-05T13:02:40.252356Z INFO runtime::opentelemetry: Spice Runtime OpenTelemetry listening on 127.0.0.1:50052
```
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.
```bash
spice add spiceai/quickstart
```
The `spicepod.yaml` file will be updated with the `spiceai/quickstart` dependency.
```yaml
version: v1beta1
kind: Spicepod
name: spice_qs
dependencies:
- spiceai/quickstart
```
The `spiceai/quickstart` Spicepod will add a `taxi_trips` data table to the runtime which is now available to query by SQL.
```bash
2024-08-05T13:04:56.742779Z INFO runtime: Dataset taxi_trips registered (s3://spiceai-demo-datasets/taxi_trips/2024/), acceleration (arrow, 10s refresh), results cache enabled.
2024-08-05T13:04:56.744062Z INFO runtime::accelerated_table::refresh_task: Loading data for dataset taxi_trips
2024-08-05T13:05:03.556169Z INFO runtime::accelerated_table::refresh_task: Loaded 2,964,624 rows (421.71 MiB) for dataset taxi_trips in 6s 812ms.
```
**Step 5.** Start the Spice SQL REPL:
```bash
spice sql
```
The SQL REPL inferface will be shown:
```bash
Welcome to the Spice.ai SQL REPL! Type 'help' for help.
show tables; -- list available tables
sql>
```
Enter `show tables;` to display the available tables for query:
```bash
sql> show tables;
+---------------+--------------+---------------+------------+
| table_catalog | table_schema | table_name | table_type |
+---------------+--------------+---------------+------------+
| spice | public | taxi_trips | BASE TABLE |
| spice | runtime | query_history | BASE TABLE |
| spice | runtime | metrics | BASE TABLE |
+---------------+--------------+---------------+------------+
Time: 0.022671708 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;
```
Output:
```bash
+---------------+--------------+
| 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: 0.045150667 seconds. 10 rows.
```
## ⚙️ Runtime Container Deployment
Using the [Docker image](https://hub.docker.com/r/spiceai/spiceai) locally:
```bash
docker pull spiceai/spiceai
```
In a Dockerfile:
```dockerfile
from spiceai/spiceai:latest
```
Using Helm:
```bash
helm repo add spiceai https://helm.spiceai.org
helm install spiceai spiceai/spiceai
```
## 🏎️ Next Steps
You can use any number of predefined datasets available from the Spice.ai Cloud Platform in the Spice runtime.
A list of publicly available datasets from Spice.ai can be found here: [https://docs.spice.ai/building-blocks/datasets](https://docs.spice.ai/building-blocks/datasets).
In order to access public datasets from Spice.ai, you will first need to create an account with Spice.ai by selecting the free tier membership.
Navigate to [spice.ai](https://spice.ai/) 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 Spice.ai. For this demonstration, we will be using the `spice.ai/eth.recent_blocks` dataset.
**Step 1.** Initialize a new project.
```bash
# Initialize a new Spice app
spice init spice_app
# Change to app directory
cd spice_app
```
**Step 2.** Log in and authenticate from the command line using the `spice login` command. A pop up browser window will prompt you to authenticate:
```bash
spice login
```
**Step 3.** Start the runtime:
```bash
# Start the runtime
spice run
```
**Step 4.** Configure the dataset:
In a new terminal window, configure a new dataset using the `spice dataset configure` command:
```bash
spice dataset configure
```
You will be prompted to enter a name. Enter a name that represents the contents of the dataset
```bash
dataset name: (spice_app) eth_recent_blocks
```
Enter the description of the dataset:
```bash
description: eth recent blocks
```
Enter the location of the dataset:
```bash
from: spice.ai/eth.recent_blocks
```
Select `y` when prompted whether to accelerate the data:
```bash
Locally accelerate (y/n)? y
```
You should see the following output from your runtime terminal:
```bash
2024-08-05T13:09:08.342450Z INFO runtime: Dataset eth_recent_blocks registered (spice.ai/eth.recent_blocks), acceleration (arrow, 10s refresh), results cache enabled.
2024-08-05T13:09:08.343641Z INFO runtime::accelerated_table::refresh_task: Loading data for dataset eth_recent_blocks
2024-08-05T13:09:09.575822Z INFO runtime::accelerated_table::refresh_task: Loaded 146 rows (6.36 MiB) for dataset eth_recent_blocks in 1s 232ms.
```
**Step 5.** In a new terminal window, use the Spice SQL REPL to query the dataset
```bash
spice sql
```
```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:
```bash
+----------+--------+----------+
| number | size | gas_used |
+----------+--------+----------+
| 20462425 | 32466 | 6705045 |
| 20462435 | 262114 | 29985196 |
| 20462427 | 138376 | 29989452 |
| 20462444 | 40541 | 9480363 |
| 20462431 | 78505 | 16994166 |
| 20462461 | 110372 | 21987571 |
| 20462441 | 51089 | 11136440 |
| 20462428 | 327660 | 29998593 |
| 20462429 | 133518 | 20159194 |
| 20462422 | 61461 | 13389415 |
+----------+--------+----------+
Time: 0.008562625 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 [docs.spiceai.org](https://docs.spiceai.org/).
### 🔌 Extensibility
Spice.ai is designed to be extensible with extension points documented at [EXTENSIBILITY.md](./docs/EXTENSIBILITY.md). Build custom [Data Connectors](https://docs.spiceai.org/components/data-connectors), [Data Accelerators](https://docs.spiceai.org/components/data-accelerators), [Catalog Connectors](https://docs.spiceai.org/components/catalogs), [Secret Stores](https://docs.spiceai.org/components/secret-stores), [Models](https://docs.spiceai.org/components/models), or [Embeddings](https://docs.spiceai.org/components/embeddings).
### 🔨 Upcoming Features
🚀 See the [Roadmap to v1.0-stable](https://github.com/spiceai/spiceai/blob/trunk/docs/ROADMAP.md) for upcoming features.
### 🤝 Connect with us
We greatly appreciate and value your support! You can help Spice in a number of ways:
- Build an app with Spice.ai and send us feedback and suggestions at [[email protected]](mailto:[email protected]) or on [Discord](https://discord.gg/kZnTfneP5u), [X](https://twitter.com/spice_ai), or [LinkedIn](https://www.linkedin.com/company/74148478).
- [File an issue](https://github.com/spiceai/spiceai/issues/new) if you see something not quite working correctly.
- Join our team ([We’re hiring!](https://spice.ai/careers))
- Contribute code or documentation to the project (see [CONTRIBUTING.md](CONTRIBUTING.md)).
- Follow our blog at [blog.spiceai.org](https://blog.spiceai.org)
⭐️ star this repo! Thank you for your support! 🙏