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https://github.com/splitgraph/seafowl
Analytical database for data-driven Web applications đĒļ
https://github.com/splitgraph/seafowl
api database datafusion delta-lake delta-rs edge http rust serverless sql visualization
Last synced: 2 days ago
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Analytical database for data-driven Web applications đĒļ
- Host: GitHub
- URL: https://github.com/splitgraph/seafowl
- Owner: splitgraph
- License: apache-2.0
- Created: 2022-07-04T13:41:52.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-01-22T08:56:46.000Z (9 days ago)
- Last Synced: 2025-01-28T03:13:21.132Z (3 days ago)
- Topics: api, database, datafusion, delta-lake, delta-rs, edge, http, rust, serverless, sql, visualization
- Language: Rust
- Homepage: https://seafowl.io
- Size: 4.47 MB
- Stars: 461
- Watchers: 11
- Forks: 14
- Open Issues: 46
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE.md
Awesome Lists containing this project
README
[](https://hub.docker.com/r/splitgraph/seafowl)
[](https://hub.docker.com/r/splitgraph/seafowl)
[](https://github.com/splitgraph/seafowl/releases)
[](https://github.com/splitgraph/seafowl/releases)
**[Home page](https://seafowl.io) |
[Docs](https://www.splitgraph.com/docs/seafowl/getting-started/introduction) |
[Benchmarks](https://observablehq.com/@seafowl/benchmarks) |
[Demo](https://observablehq.com/@seafowl/interactive-visualization-demo) |
[Nightly builds](https://nightly.link/splitgraph/seafowl/workflows/nightly/main) |
[Download](https://github.com/splitgraph/seafowl/releases)**
[**â¨â¨â¨ Seafowl now proudly powers the EDB Postgres Lakehouse â¨â¨â¨**](https://www.enterprisedb.com/workload/rapid-analytics-for-postgres)
Seafowl is an analytical database for modern data-driven Web applications.
Its CDN and HTTP cache-friendly query execution API lets you deliver data to your visualizations,
dashboards and notebooks by running SQL straight from the user's browser.
## Features
### Fast analytics...
Seafowl is built around
[Apache DataFusion](https://arrow.apache.org/datafusion/user-guide/introduction.html), a fast and
extensible query execution framework. It uses [Apache Parquet](https://parquet.apache.org/) columnar
storage, adhering to the [Delta Lake](https://delta.io/) protocol, making it perfect for analytical
workloads.
For `SELECT` queries, Seafowl supports a large subset of the PostgreSQL dialect. If there's
something missing, you can
[write a user-defined function](https://splitgraph.com/docs/seafowl/guides/custom-udf-wasm) for
Seafowl in anything that compiles to WebAssembly.
In addition, you can write data to Seafowl by:
- [uploading a CSV or a Parquet file](https://splitgraph.com/docs/seafowl/guides/uploading-csv-parquet)...
- pointing a table to a local or an
[externally hosted CSV or a Parquet file](https://seafowl.io/docs/guides/csv-parquet-http-external)...
- pointing a table to a [remote database](https://seafowl.io/docs/guides/remote-tables)...
- or using
[standard SQL DML statements](https://splitgraph.com/docs/seafowl/guides/writing-sql-queries).
### ...at the edge
Seafowl is designed to be deployed to modern serverless environments. It ships as a single binary,
making it simple to run anywhere.
Seafowl's architecture is inspired by modern cloud data warehouses like Snowflake or BigQuery,
letting you separate storage and compute. You can store Seafowl data in an object storage like S3 or
Minio and scale to zero. Or, you can
[build a self-contained Docker image](https://splitgraph.com/docs/seafowl/guides/baking-dataset-docker-image)
with Seafowl and your data, letting you deploy your data to any platform that supports Docker.
Seafowl's query execution API follows HTTP cache semantics. This means you can
[put Seafowl behind a CDN](https://splitgraph.com/docs/seafowl/guides/querying-cache-cdn) like
Cloudflare or a cache like Varnish and have query results cached and delivered to your users in
milliseconds. Even without a cache, you can get the benefits of caching query results in your user's
browser.
## Quickstart
Start Seafowl:
```bash
docker run --rm -p 8080:8080 \
-e SEAFOWL__FRONTEND__HTTP__WRITE_ACCESS=any \
splitgraph/seafowl:nightly
```
Or download it from [the releases page](https://github.com/splitgraph/seafowl/releases) and run it
without Docker:
```bash
SEAFOWL__FRONTEND__HTTP__WRITE_ACCESS=any ./seafowl
```
Add a Parquet dataset from HTTP:
```bash
curl -i -H "Content-Type: application/json" localhost:8080/q -d@- < CREATE TABLE t
AS VALUES
(1, 'one'),
(2, 'two');
Time: 0.021s
default> SELECT * FROM t;
+---------+---------+
| column1 | column2 |
+---------+---------+
| 1 | one |
| 2 | two |
+---------+---------+
Time: 0.009s
default> \d t
+---------------+--------------+------------+-------------+-----------+-------------+
| table_catalog | table_schema | table_name | column_name | data_type | is_nullable |
+---------------+--------------+------------+-------------+-----------+-------------+
| default | public | t | column1 | Int64 | YES |
| default | public | t | column2 | Utf8 | YES |
+---------------+--------------+------------+-------------+-----------+-------------+
Time: 0.005s
default> \q
$
```
It does so by circumventing Seafowl's primary HTTP interface, which involves properly formatted HTTP
requests with queries, authentication, as well as dealing with potentially faulty networking setups,
and can sometimes be too tedious for a quick manual interactive session.
## Documentation
See the [documentation](https://www.splitgraph.com/docs/seafowl/getting-started/introduction) for
more guides and examples. This includes a longer
[tutorial](https://www.splitgraph.com/docs/seafowl/getting-started/tutorial-fly-io/introduction),
following which you will:
- Deploy Seafowl to [Fly.io](https://fly.io)
- Put it behind Cloudflare CDN or Varnish
- Build an interactive [Observable](https://observablehq.com) notebook querying data on it, just
like [this one](https://observablehq.com/@seafowl/interactive-visualization-demo)
## Tests
Please consult the dedicated [README](./tests/README.md) for more info on how to run the Seafowl
test suite locally.
## Pre-built binaries and Docker images
We do not yet provide full build instructions, but we do produce binaries and Docker images as
prebuilt artifacts.
### Release builds
You can find release binaries on our [releases page](https://github.com/splitgraph/seafowl/releases)
### Nightly builds
We produce nightly binaries after every merge to `main`. You can find them in GitHub Actions
artifacts (only if you're logged in, see
[this issue](https://github.com/actions/upload-artifact/issues/51)) or **via
[nightly.link](https://nightly.link/splitgraph/seafowl/workflows/nightly/main)**:
- [Linux (x86_64-unknown-linux-gnu)](https://nightly.link/splitgraph/seafowl/workflows/nightly/main/seafowl-nightly-x86_64-unknown-linux-gnu.zip)
- [OSX (x86_64-apple-darwin)](https://nightly.link/splitgraph/seafowl/workflows/nightly/main/seafowl-nightly-x86_64-apple-darwin.zip)
- [Windows (x86_64-pc-windows-msvc)](https://nightly.link/splitgraph/seafowl/workflows/nightly/main/seafowl-nightly-x86_64-pc-windows-msvc.zip)
### Docker images
We produce [Docker images](https://hub.docker.com/r/splitgraph/seafowl/tags) on every merge to
`main`.
- Release builds are tagged according to their version, e.g. `v0.1.0` results in
`splitgraph/seafowl:0.1.0` and `0.1`.
- Nightly builds are tagged as `splitgraph/seafowl:nightly`
## Long-term feature roadmap
There are many features we're planning for Seafowl. Where appropriate, we'll also aim to upstream
these changes into DataFusion itself.
### Support for JSON functions and storage
We're planning on adding the JSON datatype to Seafowl, as well as a suite of functions to
manipulate/access JSON data, similar to the
[functions supported by PostgreSQL](https://www.postgresql.org/docs/current/functions-json.html) .
### PostgreSQL-compatible endpoint
This will make Seafowl queryable by existing BI tools like Metabase/Superset/Looker.