
Seafowl has supported UDFs [for some time now](https://www.splitgraph.com/blog/seafowl-wasm-udfs), and they continue to offer an exciting way for Seafowl users to significantly extend functionality in a fully sandboxed environment. Recently, in response to user request, we added a Golang example.

<ButtonLink href="https://github.com/splitgraph/seafowl-udf-go/">
  See the code
</ButtonLink>

<br />

[See the UDF docs](https://seafowl.io/docs/guides/custom-udf-wasm).

To learn more about compiling Golang to WASM, read on.

## Handling UDF requirements
There are certain requirements incumbent on the UDF implementer when it comes to running UDFs in Seafowl, and Golang is included. Today we cover a partial list:
- reading data in from the host through linear raw memory
- returning the computed result as a msgpack-encoded buffer
- working around Golang default syntax (e.g. TitleCase function exports)
- compiling Golang into WASM

## Reading data in
As discussed previously, when you register a Seafowl UDF it's required to [specify certain parameters](https://github.com/splitgraph/seafowl-udf-go/blob/2e49dd7a110213a7c49b19845cd537a59164c034/create_udf.sh#L11-L16) including e.g. `wasm_export` (e.g. addi64), `return_type` (e.g. BIGINT), `input_types` (e.g. BIGINT).

This is important because raw memory is the medium for passing data between Seafowl and the UDF. By specifying the parameter types upfront at UDF registration time, both Seafowl host and UDF know how to properly encode/decode the data they share with each other. That's why the function [`addi64`](https://github.com/splitgraph/seafowl-udf-go/blob/master/main.go#L12), expects a _pointer to a buffer_ vs conventional Go function params.

## Writing data out
Conversely, when we return the result to Seafowl, we need to return a buffer that includes the length of the result [first](https://github.com/splitgraph/seafowl-udf-go/blob/2e49dd7a110213a7c49b19845cd537a59164c034/udf.go#L115), with the actual msgpack-encoded result appended [afterwards](https://github.com/splitgraph/seafowl-udf-go/blob/2e49dd7a110213a7c49b19845cd537a59164c034/udf.go#L14).

This allows Seafowl's query engine to return the result of the UDF in the typical way.

## TitleCase function export workaround
Golang conventionally uses TitleCase to indicate when a function should be exported from its module scope. Some may find it [cute](https://youtu.be/M3BM9TB-8yA?t=879) and this convention is presumably fine in traditional Golang, but in certain contexts like UDFs, we are required to provide `alloc`/`dealloc` functions _in lower case_ - which poses a fun hurdle.

Fortunately, while researching how `tinygo` works, I learned it supports annotating functions regardless of casing via
`//export $functionName`

For example:
```go
//export alloc
func alloc(size uintptr) unsafe.Pointer {
	...
}
```

And Seafowl will be able to call this function in the required way.

## Compiling Golang into WASM
In August 2023 the Golang team shipped WASI/WASM [support](https://go.dev/blog/go1.21#a-new-port-to-wasi), so the following approach we used may no longer be strictly necessary these days.

When we tackled this project, Golang did not yet support WASM natively so looking elsewhere, we found [Tinygo](https://tinygo.org/docs/concepts/faq/what-is-tinygo/).

It turns out we can generate a WASM module ready for import into Seafowl via:
`tinygo build -o seafowl-udf-go.wasm -target=wasi`

NOTE: it's necessary to include `-target=wasi`.

## Recap

In this post we reviewed some of the steps taken to port the [Rust-based UDF](https://github.com/splitgraph/seafowl-udf-rust) into Golang. This was my first Golang project and I appreciated the opportunity to work at the lower level, learn about byteslice manipulation, msgpack serialization, WASI and more.

If you build a cool UDF, please consider letting us know!


#### Image credits
<ValeIgnore>[liantomtit](https://unsplash.com/@liantomtit), [evanescentlight](https://unsplash.com/@evanescentlight)</ValeIgnore>


We return to the wide world of WASM. In our previous round we implemented a UDF using Rust; today we're porting it to Golang.

Writing UDFs in Golang


## Introduction

[Seafowl](https://seafowl.io) is a new analytics database based on [DataFusion](https://arrow.apache.org/datafusion/) and [delta-rs](https://github.com/delta-io/delta-rs), designed for running "at the edge" and serving queries over HTTP with cache-optimized responses. Because it separates storage from compute, it's an ideal candidate for running in "serverless" environments. In this post, we'll explore the architecture behind Seafowl and demonstrate how to deploy it to Google Cloud Run (GCR) as a true "scale-to-zero" serverless database that doesn't require any persistent compute resources, and can query data stored in Google Cloud Storage (GCS), while minimizing cold-start times.

## Why do we need a serverless database?

The advantage of serverless applications is that each instance of an application can execute in a region close to the user requesting it, without the need to provision a server in every region ahead of time. But there's a catch: for most use cases, the application is bottlenecked by the database, which _does_ require provisioning a persistent server ahead of time. This can mean sacrificing the main benefit of serverless, as every cache miss needs to query a single master database in an arbitrary region, regardless of how close to the user the HTTP server is executing.

Seafowl solves this problem for analytical workloads, by accepting certain tradeoffs that might be less acceptable for transactional databases, in exchange for fast and cacheable responses to read queries. For example, as an OLAP database, Seafowl assumes that data is loaded into it periodically, and it's generally okay to return stale data while loading new data into it.

## Why Seafowl?

Seafowl separates compute and storage of the data it queries. It makes data in [object storage](https://seafowl.io/docs/guides/scaling-multiple-nodes#postgresql-as-a-catalog-minio-as-s3-storage) queryable by a Docker [image](https://hub.docker.com/r/splitgraph/seafowl) ([binary](https://github.com/splitgraph/seafowl/releases) also available), and maintains its internal catalog by writing to a SQLite file. This makes it easy to deploy as a serverless function to any platform that can run Docker images, like Google Cloud Run[^2]. With Seafowl you can expect a fast initialization time (e.g. 10ms) and configurable dependencies keep you in the driver's seat for deciding which tradeoffs make sense for your situation.

Another perk: because Seafowl offers a [CDN-friendly HTTP story](https://seafowl.io/docs/guides/querying-cache-cdn), if your data is suitable for caching your end users will likely further benefit if you choose to front with e.g. CloudFlare or similar CDN providers.

## What's the challenge?

The challenge is avoiding cold starts, while also avoiding the need for a persistent node for storing metadata. The Seafowl architecture is divided into three parts: compute (e.g. the binary, deployed in e.g. a Docker image); storage (the blob data in Object Storage); and a catalog (the metadata and information schema for the database). Compute and storage map naturally to services like GCR and GCS. But for the catalog, it's less clear. Seafowl supports writing catalog metadata to either a Postgres database, or a SQLite file. A Postgres catalog supports multiple writers, making each Seafowl instance able to act as a reader and a writer. This comes at the cost of requiring a persistent node (options like Neon[^1] exist, but in our testing, the latency was unacceptable as it caused cold starts of multiple seconds). A SQLite catalog can be bundled with the image, but at the cost of being able to actually persist Seafowl writes.


Is there a way we can get the best of both worlds? With GCS FUSE, we can come close. That's what we'll explore in this post. We're going to deploy Seafowl to GCR, and mount its catalog as a SQLite "file" on the FUSE filesystem backed by GCS. The end result is a serverless database that can respond to read queries from any region, with start times on the order of 10ms.

## Goal
In today's post we show you how to stand up serverless Seafowl on [Cloud Run](https://cloud.google.com/run).

GCP offers an "always free" tier which may cover both your [storage](https://cloud.google.com/free/docs/.free-cloud-features#storage) and [compute](https://cloud.google.com/free/docs/free-cloud-features#cloud-run) needs. This HOWTO seeks to stay within those limits, so you are likely to have low or possibly zero costs. Either way, this guide sequesters all resources we create here into a separate GCP project for easy cleanup.

## Requirements

- Access to [GCP](https://console.cloud.google.com/getting-started) (e.g. a Google account[^3])
- The `gcloud` CLI (aka Cloud SDK. [Need it?](https://cloud.google.com/sdk/docs/install-sdk))
- Choosing a unique-to-GCP
  - project name (`$PROJECT_NAME`)
  - bucket name (`$BUCKET_NAME`)

Ready? Let's begin!

1. After installing `gcloud`, login to your Google account.
```shell
gcloud auth login
```

2. Create a new project (optional, suggested).
```shell
gcloud projects create $PROJECT_NAME
Create in progress for [https://cloudresourcemanager.googleapis.com/v1/projects/seafowl-gcsfuse].
Waiting for [operations/cp.6073745129322302465] to finish...done.
Enabling service [cloudapis.googleapis.com] on project [seafowl-gcsfuse]...
Operation "operations/acat.p2-124549755242-c3e8de2b-a6fa-4a73-aaa1-ad949f26e7cb" finished successfully.
```
ℹ️ In case you get an "already in use" error, please choose a unique name. Consider appending some randomness and use `export PROJECT_NAME=YourName` to refer to it through the rest of the commands).

3. Set your project and region

Creating resources in the same region gives best performance as well as helps avoid cross-regional networking charges.
```shell
gcloud config set project $PROJECT_NAME
Updated property [core/project].
```

```shell
gcloud config set run/region $REGION # e.g. us-east1
Updated property [run/region].
```

4. Create a bucket

This bucket will be mounted as a pseudo-filesystem inside Seafowl's container.
```shell
gsutil mb -l us-east1 gs://$BUCKET_NAME
Creating gs://$BUCKET_NAME/...
```
ℹ️ Cloud Storage bucket names _share a global namespace_. Similarly to the project name, in case you encounter e.g. "The requested bucket name is not available", please pick a [unique name](https://cloud.google.com/storage/docs/buckets#naming) and `export BUCKET_NAME=MyBucket`.

5. Setup up Seafowl's config + credentials
- Seafowl can be configured via `seafowl.toml`. We persist it in GCP's [Secret Manager](https://console.cloud.google.com/security/secret-manager) so it can be mounted in our function.
```
[object_store]
type = "gcs"
bucket = "$BUCKET_NAME"
google_application_credentials = "seafowl-data/seafowl-gcsfuse.json"
[catalog]
type = "sqlite"
dsn = "seafowl-data/seafowl.sqlite"
[frontend.http]
bind_host = "0.0.0.0"
write_access = "efe507120a88bdb1b7a2e0479bdc4c163bc31c2d83787240dbccd8b95115d92a"
```
Replace with your real bucket name, then save the above config as `seafowl.toml`, and then run:

```shell
gcloud secrets create --data-file seafowl.toml seafowl_toml
```
(Allow the API to be enabled, if it asks)

- Upload the credential file into the bucket via:
```shell
gcloud storage cp seafowl-gcsfuse.json gs://$BUCKET_NAME/seafowl-gcsfuse.json
Copying file://seafowl-gcsfuse.json to gs://$BUCKET_NAME/seafowl-gcsfuse.json
  Completed files 1/1 | 2.3kiB/2.3kiB
```
NOTE: The [Secret Manager](https://console.cloud.google.com/security/secret-manager) is also suitable for this file.
In the interest of keeping a lid on costs we only save one secret. By mounting `seafowl-gcsfuse.json` via GCS Fuse, it's provided to Seafowl before it starts.

7. Establish least privilege permissions
We create a service account so that minimum needed permissions are sequestered into their own identity.
```shell
gcloud iam service-accounts create seafowl-gcsfuse-identity
```
Add a binding for object storage and accessing secrets.
```
gcloud projects add-iam-policy-binding $PROJECT_NAME \
     --member "serviceAccount:seafowl-gcsfuse-identity@$PROJECT_NAME.iam.gserviceaccount.com" \
     --role "roles/storage.objectAdmin" \
     --role="roles/secretmanager.secretAccessor"
Updated IAM policy for project [$PROJECT_NAME].
bindings:
- members:
  - user:user@example.com
  role: roles/owner
- members:
  - serviceAccount:seafowl-gcsfuse-identity@$PROJECT_NAME.iam.gserviceaccount.com
  role: roles/storage.objectAdmin
etag: BwX8Ze-ob4Y=
version: 1
```

8. Almost done! Now we can deploy the function:
```shell
gcloud run deploy seafowl-gcsfuse \
  --image splitgraph/seafowl-gcsfuse:latest \
  --execution-environment gen2 \
  --allow-unauthenticated \
  --service-account seafowl-gcsfuse-identity \
  --update-secrets=/app/config/seafowl.toml=seafowl_toml:latest \
  --update-env-vars BUCKET=$BUCKET_NAME

API [run.googleapis.com] not enabled on project [814934642383]. Would you like to enable and retry (this will take a few minutes)? (y/N)?  y

Enabling service [run.googleapis.com] on project [814934642383]...
```
[Dockerfile](https://github.com/splitgraph/seafowl-gcsfuse/blob/master/Dockerfile) available.

8. Check it with `curl`

Just swap your endpoint for the sample one.

### Read
```shell
curl -i -H "Content-Type: application/json" \
-X POST "https://seafowl-gcsfuse-YourEndpointHere.a.run.app/q" -d@- <<EOF
{"query": "
SELECT now()
"}
EOF
```
Should return something like:
```shell
HTTP/2 200
content-type: application/octet-stream
vary: Content-Type, Origin, X-Seafowl-Query
x-cloud-trace-context: 2f378466d69425e60793db49b4f406a4
date: Fri, 19 May 2023 14:57:37 GMT
server: Google Frontend
content-length: 43

{"now()":"2023-05-24T14:57:37.809652290Z"}
```

### Write
```shell
curl -i -H "Content-Type: application/json" \
-H "Authorization: Bearer Fc8yA8SZ0On70pg7znAu7s6EEHnDgUzP" \
-X POST "https://seafowl-gcsfuse-YourEndpointHere.a.run.app/q" -d@- <<EOF
{"query": "
CREATE TABLE holditright (id int)
"}
EOF
```

Should return
```shell
HTTP/2 200
content-type: application/octet-stream
vary: Content-Type, Origin, X-Seafowl-Query
x-cloud-trace-context: 8f75d4895dac499dbeef7eabd6fdfcba;o=1
date: Fri, 19 May 2023 16:36:09 GMT
server: Google Frontend
content-length: 0
```

### Verify
If you look at your bucket using the web console, you should see something roughly similar to this:
![Bucket](/static-v2/seafowl/blog/deploying-serverless-seafowl/bucket-example.jpg)

### Tear down
The function you just deployed is likely to be within the always free tier, but in case you'd like to clean up the project, run:
```
gcloud run services delete $SERVICE_NAME # e.g. seafowl-gcsfuse
gcloud config set project $PROJECT_NAME
gcloud config set run/region $REGION
```
Here's how to delete the [bucket](https://cloud.google.com/storage/docs/deleting-bucket) and the [service account](https://cloud.google.com/iam/docs/service-accounts-delete-undelete?hl=en#deleting).


## Conclusion
Congrats! 🎉 You have a scale to zero, web (HTTP)-first analytical database, deployed to the edge.
Ready to load up with whatever data you want, and ready to be fronted by a CDN.

![Low cloud bills are tasty](/static-v2/seafowl/blog/deploying-serverless-seafowl/rainbow-unsplash.com-@jorgefdezsalas.jpeg)


## Optional Follow Up
Two bonus ideas for consideration:
- For public & production deployments, a common next step is controlling who can write vs read. Consider deploying two functions: read-only for public access and write-enabled which is only available to trusted parties. Setting read-only can be done by providing [env var](https://seafowl.io/docs/reference/seafowl-toml-configuration#using-environment-variables) `SEAFOWL__FRONTEND__HTTP__WRITE_ACCESS=off` and `RUN_SEAFOWL_READ_ONLY=true` [info](https://github.com/splitgraph/seafowl-gcsfuse/blob/21376efa28c6ab7b7198e5394c3657b9d78002a1/gcsfuse_run.sh#L11).
- If your data is public, and your users would benefit from better latency, consider [fronting the Cloud Run endpoint with CloudFlare](https://seafowl.io/docs/getting-started/tutorial-fly-io/part-4-2-cdn-with-cloudflare). The CloudFlare CDN offers a convenient and powerful way to cache query results.


#### Image credits
[nikkotations](https://unsplash.com/@nikkotations), [jorgefdezsalas](https://unsplash.com/@jorgefdezsalas)

[^1]:
    More details [here](https://neon.tech/blog/quicker-serverless-postgres)

[^2]:
    Not your preferred cloud provider? Let us know if you'd like to see a [Lambda](https://aws.amazon.com/lambda/) or [Container Instance](https://azure.microsoft.com/en-us/products/container-instances) version of this post.

[^3]:
    This is roughly what you should see if you visit the [GCS console](https://console.cloud.google.com/) for the first time.

    <img src="/static-v2/seafowl/blog/deploying-serverless-seafowl/setup-gcs.png" />



Learn how to combine Seafowl with GCS FUSE to achieve true scale to zero. Serve users at the edge with a web (HTTP)-first analytical database that works on GCP Cloud Run, including within the "always free" tier.

Deploying a serverless Seafowl DB to Google Cloud Run using GCS FUSE and SQLite


## Why Seafowl
Seafowl is a database designed to power data-driven Web apps like dashboards and interactive visualizations.
Its [cache-friendly HTTP API](seafowl-sql-cdn) is optimized for clients sending SQL queries directly from the browser.
As a single-binary database (built in Rust with <ValeIgnore>DataFusion</ValeIgnore> and [delta-rs](seafowl-delta-storage-layer)),
it's easily deployable to multi-region cloud functions that run "at the edge" and query columnar data in object storage.

Of course, before Seafowl can answer clients' SQL queries, it must have access to the data in question.
For Parquet or CSV data accessible over HTTP, Seafowl's [external tables](https://seafowl.io/docs/guides/csv-parquet-http-external) feature enables querying without any preceding import step.

For everything else, the data must be uploaded into Seafowl.
In this blog post, we'll demonstrate how to populate a Seafowl table using [Dagster](https://dagster.io), the hip new Pythonic framework that excels at creating declarative and reactive data pipelines.

## Why Dagster

Dagster solves the problem of unmaintainable data pipelines, by providing a framework for defining them with Python functions that you can run from the command line or a web UI. It doesn't require too much ceremony or "activation energy" to start using it, so even the simplest Python scripts or dbt jobs can start life as a Dagster [asset](https://docs.dagster.io/concepts/assets/software-defined-assets) and benefit from features like parallel processing of data on multiple nodes. You can add complexity if and when it becomes required.

If you want to run Dagster on your own, you can: it's open source. But Dagster Cloud is also available, so the same Python codebase can grow from a proof-of-concept on a developer's laptop to a production data pipeline powering applications gradually, without full rewrites.

## Inserting data into Seafowl from a Dagster pipeline

Here's how easy it is to append an "upload to Seafowl" step to the end of the Hacker News data pipeline from the [canonical "Hello Dagster" example](https://docs.dagster.io/getting-started/hello-dagster):

```python
@op
def export_to_seafowl(
    context, config: ExportToSeafowlConfig, data: pd.DataFrame
) -> None:
    context.log.info("exporting to seafowl")
    conn = SeafowlConnectionParams(
        url=config.url,
        secret=config.secret,
        database=None)
    destination = QualifiedTableName(schema="public", table=config.table)
    dataframe_to_seafowl(data, conn, destination)
```

Check out the [demo project repository](https://github.com/splitgraph/seafowl-dagster-demo) to follow along as we load the data used in the official [Hello Dagster](https://docs.dagster.io/getting-started/hello-dagster) example into Seafowl!

### 3-minute setup

#### Seafowl

If you don't already have a running Seafowl instance, head over to the [Seafowl Releases page](https://github.com/splitgraph/seafowl/releases/latest) and download the appropriate build for your platform.
Move the `seafowl` binary into the demo repo's folder prior to execution to use the provided `seafowl.toml` configuration.

#### Python and Dagster setup

You will also need a Python installation containing [Dagster](https://dagster.io/) and the [Python Seafowl client](https://github.com/splitgraph/seafowl/tree/main/examples/clients/python).

The following commands create such a virtualenv for you:

```bash
python3 -m venv venv
# Note: the following command is required before running anything installed in the virtualenv
. venv/bin/activate
pip install -e 'git+https://git@github.com/splitgraph/seafowl.git@main#egg=seafowl[pandas]&subdirectory=examples/clients/python'
pip install dagster dagit
```

### Starting Dagster
To start the Dagster UI (called "Dagit"), run the following:
```bash
dagster dev -f demo.py
```

Once the UI is running, the assets must be materialized before the data can be sent to Seafowl:

<video width="100%" height="240" controls>
  <source src="/static-v2/demo-videos/dagster-materialize.mp4" type="video/mp4" />
</video>


After the assets materialize, it's time to configure and run the `hn_stories_to_seafowl_pipeline` job:

<video width="100%" height="240" controls>
  <source src="/static-v2/demo-videos/dagster-run-job.mp4" type="video/mp4" />
</video>

### Querying Seafowl

The [Python Seafowl Client](https://github.com/splitgraph/seafowl/tree/v0.4.1/examples/clients/python) library can be used from the command line to verify the data has been successfully written to Seafowl by the Dagster job:

```bash
PASSWORD="iHBHflYfEaMpX4j7Du0z6vFNS6w2BMaX" ENDPOINT="http://127.0.0.1:8080/q" python -m seafowl "SELECT * FROM hn"
```

## Other ways to load data into Seafowl

As convenient as Dagster is, you might not need an ETL pipeline if your data is already in a Splitgraph repository.

You can export query results directly from Splitgraph to Seafowl. Just click the button with a database icon in the Splitgraph Console: ![Export to seafowl](/static-v2/seafowl/blog/seafowl-dagster/export-to-seafowl.png)

You can import CSV or Parquet files into Seafowl by [uploading them via HTTP](https://seafowl.io/docs/guides/uploading-csv-parquet).

In the above demo, the Dagster job invokes the [Python Seafowl Client](https://github.com/splitgraph/seafowl/tree/v0.4.1/examples/clients/python) to upload a Pandas DataFrame into Seafowl, which can be used directly from custom Python code.

For TypeScript users, the [madatdata library](https://github.com/splitgraph/madatdata) provides a polished API for querying and importing data into Seafowl.

Seafowl also includes a [Node.js client](https://github.com/splitgraph/seafowl/blob/v0.4.1/examples/clients/node/seafowl-client.js).

Finally, one can simply [`INSERT` data directly](https://seafowl.io/docs/guides/writing-sql-queries) into Seafowl.

The Python Seafowl client's `dataframe_to_seafowl()` function uses the Parquet upload endpoint if the `pyarrow` Python package is installed.
Otherwise, it issues `INSERT` queries for each row individually.

## Conclusion

Building interactive analytical applications like [Open Data Monitor](opendatamonitor) on Seafowl starts with uploading data. Dagster is a powerful tool for creating data pipelines which populate the Seafowl tables queried by users' browsers.


We're looking forward to seeing the incredible analytical applications you build with Seafowl! If you're excited about using Dagster with Seafowl or missing something, [let us know](mailto:hello@splitgraph.com?subject=seafowl%20dagster)!


Import the result of your data pipeline into Seafowl easily using Dagster!

Using Dagster with Seafowl

Announcing the replacement of our custom storage layer with Delta Lake, facilitated by its open-source Rust implementation.

A Lakehouse by the sea: Migrating Seafowl storage layer to delta-rs


![](/static-v2/og/blog/opendatamonitor.png)

## Tracking open government datasets
Say hello to [Open Data Monitor](https://open-data-monitor.splitgraph.io/), an open government dataset diff visualizer. Observe when [Socrata](https://dev.socrata.com/) datasets were added/deleted by day, week, and month, with upstream attribution and easy querying via the respective Splitgraph repository.

In a previous [post](https://www.splitgraph.com/blog/seafowl-sql-cdn) we discussed the benefits of Seafowl's approach to HTTP caching; in particular its effective use of [ETags](https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/ETag) to deliver results on the edge with excellent latency characteristics. Today, we continue on our journey to:
- Ingest Splitgraph's Socrata catalog into Seafowl
- Insert tables that expose added/deleted status ("observe" Socrata)
- Swap Fly.io instances in a cost-effective way
- Build a mini Next.js app to render it all out in the browser, making sure to balance the best of SSR SEO benefits with Seafowl's browser-friendly cache semantics.

## Ingestion
In case you didn't already know, Splitgraph maintains a catalog of Socrata datasets, stored as a [Splitgraph repository at `splitgraph/socrata`](https://www.splitgraph.com/splitgraph/socrata/). Additionally, Splitgraph actually _indexes_ all of the 40,000+ datasets and makes each one queryable at its own URL (like [`cdc-gov/20202021-nationwide-blood-donor-seroprevalence-mtc3-kq6r`](https://www.splitgraph.com.cdc-gov/20202021-nationwide-blood-donor-seroprevalence-mtc3-kq6r)) and Splitgraph repository on the Data Delivery Network (DDN). It's possible to point your favorite Postgres client to the DDN and query arbitrary Socrata datasets in a familiar way. From [traffic accidents](https://neumark.github.io/splitgraph-map-demo/) in Fort Worth, TX [^1], to [taxi rides](https://nyctaxis.surge.sh/) as a proxy for "how's NYC Covid bounce back going"[^2], we've built several integrations that present data in visual and interactive ways without custom plumbing.
We also made [Socrata roulette](https://splitgraph.github.io/socrata-roulette/) for generating random queries against all of the available datasets.

As useful as the DDN is, today we will demonstrate how [Seafowl](https://seafowl.io) can make for an effective alternative that performs with low latency and generally has a smaller resource footprint. First, by starting with this catalog, we [fetch](https://github.com/splitgraph/socrata-to-seafowl/blob/2df59549675136056b096cac7d96f18bd3a8a7ad/src/s2sf/ingest.py#L42) all Socrata images courtesy of Splitgraph's GQL API. (Conveniently we can export directly in [Parquet format](https://github.com/splitgraph/socrata-to-seafowl/blob/master/src/s2sf/ingest.py#L141)!) Then, we [insert](https://github.com/splitgraph/socrata-to-seafowl/blob/2df59549675136056b096cac7d96f18bd3a8a7ad/src/s2sf/ingest.py#L213) the results into Seafowl tables. This whole job is powered by Github [Actions](https://docs.github.com/en/actions), and occurs on a regular basis (currently nightly). After we've ingested the new state of all Socrata datasets, we [pre-compute a diff across time](https://github.com/splitgraph/socrata-to-seafowl/blob/master/src/s2sf/sql/daily_diff.sql) of all datasets that were added or removed on a given day. We also downsample these diffs to week-over-week and month-over-month changes.

Because this involves a self-join of a table with several million rows, this currently requires a large amount of memory as you'll see later.

##  Dynamically scaling our Fly.io instance for ingestion
Similarly to the [Seafowl tutorial](https://seafowl.io/docs/getting-started/tutorial-fly-io/introduction), we again went with Fly.io for hosting. While the free tier generally offers enough performance for Open Data Monitor's read-heavy workload, when it comes to _ingestion_ and building the day-over-day diff table, this time we ran into memory issues because of a self-join. It is perhaps unsurprising that as Socrata has grown over time, so have the number and size of datasets on offer. That's why we added a step in the GHA to temporarily [scale up](https://github.com/splitgraph/socrata-to-seafowl/blob/master/.github/workflows/sync.yml#L30) the Fly.io instance memory for ingestion purposes. We also include an [insurance policy](https://github.com/splitgraph/socrata-to-seafowl/blob/master/.github/workflows/downsize-insurance.yml) to help reduce risk of job errors causing unexpected cloud costs.

## Rendering in the browser
With a loaded Seafowl instance ready to go, we can now build a [Next.js app](https://github.com/splitgraph/open-data-monitor) so we can fetch from Seafowl and render it in the browser.

By default, we render the current week, but the user can specify a certain day, week, or month.

## Disappearing data examples

<div style={{display: 'flex'}}>
<div>
Because traversing over time is now trivial, we can surface curious moments.
<br /><br />
For example, just a few clicks moving backwards from January 2023 yielded disappearing

- [greenhouse gas emissions](https://open-data-monitor.splitgraph.io/2023-01-13#data.bayareametro.gov)
- [commercial ovens](https://open-data-monitor.splitgraph.io/2023-01-13#data.energystar.gov)
- [hot spots](https://open-data-monitor.splitgraph.io/2023-01-05) 🤔
- [Austin Schools Rated as Improvement Required](https://open-data-monitor.splitgraph.io/2022-12-31#data.austintexas.gov)

</div>
<img src="/static-v2/seafowl/blog/open-data-monitor/opendatamonitor-hero-2.png"
  style={{float: 'right', width: '300px', paddingLeft: '2em'}}
  alt="detective smoking a pipe"
/>
</div>


## SSR by default, but browser talks to Seafowl directly on nav
To strike a balance between SSR-conferred SEO advantages, and Seafowl's HTTP cache friendly defaults, we pre-fetch inside [getInitialProps](https://github.com/splitgraph/open-data-monitor/blob/153e4464eb24994ce5503680ce4b907a8e1faa8d/pages/index.tsx#L26), which means the page renders properly even with JS disabled.
But since we don't want the Vercel server to intermediate subsequent fetches, when the user clicks Previous/Next, their browser requests from Seafowl directly. Because we are using the GET API, we benefit from the nice cache behavior, so subsequent loads are nice and zippy, even on Fly.io's free tier, and even if the actual ingest was a memory hungry job.

<video width="100%" height="240" controls>
  <source src="/static-v2/demo-videos/opendatamonitor-demo.mp4" type="video/mp4" />
</video>

## Read the code

The code for all of this is available on GitHub, in two repositories:

- [splitgraph/socrata-to-seafowl](https://github.com/splitgraph/socrata-to-seafowl): GitHub Actions for syncing from Socrata to Seafowl instance hosted on Fly.io
- [splitgraph/open-data-monitor](https://github.com/splitgraph/open-data-monitor): The Next.js frontend application



## Conclusion

We hope this post has illustrated a pathway to combine Seafowl's "analytics at the edge" benefits with Socrata's bevy of government data, in a cost-effective way.
While today we focused on open government data, regardless of the data you're working with, consider checking out what Seafowl offers.

If you discover any curious dataset mysteries, or build an integration with Seafowl, we'd love to hear from you. Tweet us [@splitgraph](https://twitter.com/intent/tweet?text=@splitgraph).

Happy sleuthing!


[^1]: https://www.splitgraph.com/jumpstart/mapping-geo-data
[^2]: https://github.com/splitgraph/nyctaxis


##### Image credits
[mediamodifier](https://unsplash.com/photos/yx17UuZw1Ck), [sammywilliams](https://unsplash.com/photos/ocvLVIrC7c0), [ayosake](https://unsplash.com/photos/esOA3o92zxE)


How can we track open government datasets over time? Say hello to Open Data Monitor, a Socrata tracking tool powered by Seafowl and Splitgraph.

Open Data Monitor


## Measuring Urbanness

Sometimes a hard-to-measure property has significant impact on performance. For example, it's safe to assume sexier lingerie sells better,
but devising an algorithm to classify the "sexiness" of an article of clothing based on its design is far from trivial.

Another ill-defined quality impacting countless more measurable metrics is "urbanness", an individual's preference for densely or sparsely populated areas.
All sorts of trends, from [voting patterns](https://government.cornell.edu/news/exploring-widening-chasm-between-urban-and-rural-voters),
to life expectancy and religious affiliation all have marked differences in urban versus rural populations.

Statistically, it makes sense to classify someone as urban or rural based on their current residence, regardless of where they may have lived in the past.
On an individual level, it's not always so clear-cut. I currently live in a city with a population of over 1.5 million,
but I've also lived in a small town (less than 4000 residents). Today, a census would classify me as urban, but is that who I really am?

I decided to find out using photo location data. Smartphones usually encode the longitude and latitude in the EXIF data of photographs.
Over the years, I've collected thousands of photos (21808 to be exact), enough to give me a representative sample of where I prefer to spend my time.

## Classifying locations

The geometric center of a city is not always its most densely populated spot. What's a better proxy for "urbanness"?

If urban individuals are those who choose to spend their time in the city, areas offering many places to "hang out" are probably ideal locations for spotting urbanites.
Conversely, if one frequents such areas, chances are they can be labelled as urban. And what could possibly be a more urban place to hang out than the largest coffeehouse chain in the world, Starbucks?

Following in the footsteps of [Golden Arches Theory of Conflict Prevention](https://en.wikipedia.org/wiki/The_Lexus_and_the_Olive_Tree#Golden_Arches_theory),
let's define the equally tongue in cheek **Omnipresent [Siren](https://stories.starbucks.com/stories/2016/who-is-starbucks-siren/) Metric of Urbanness** (OSM) as the number of Starbucks locations within 2 kilometers of any given point on the globe.

Conveniently, there's a dataset of Starbucks locations [which can be downloaded from Kaggle](https://www.kaggle.com/datasets/starbucks/store-locations?resource=download), which I used to classify my photos by this metric.

## Loading photo coordinates

An easy way to extract EXIF metadata from images is to let Google Photos do it for you. For users of Google Photos, [Google Takeout](https://takeout.google.com/) offers a way to download photos along with their EXIF data extracted to separate JSON files.
Although not covered here, it's also possible to read location data (and much more) directly from images using [exiftool](https://exiftool.org/).

After downloading and extracting the Google Takeout archives, I ran the following to get a CSV file with the coordinates of each photo:

```bash
# enter Google Photos directory within Takeout directory
cd 'Takeout/Google Photos'
# extract image metadata from JSON files
find 'Photos from'* -name '*.json' -exec jq -c '{latitude: .geoData.latitude, longitude: .geoData.longitude, timestamp: .photoTakenTime.timestamp, filename: input_filename}' {} >> unfiltered_image_attributes.ndjson \;
# only keep image metadata with GPS data (non-smartphone cameras don't write location data)
grep -v '{"latitude":0,' unfiltered_image_attributes.ndjson  > filtered_image_attributes.ndjson
# write CSV header
echo "latitude,longitude,timestamp,filename" > photo_attributes.csv
# write CSV data
jq -r '([.latitude, .longitude, .timestamp, .filename]) | @csv' < filtered_image_attributes.ndjson >> photo_attributes.csv
```

## Loading data into Seafowl

Seafowl has great CSV support. To see it in action, [install Seafowl](https://seafowl.io/docs/guides/installing) if you haven't yet, then run:

```bash
curl -v -F "data=@starbucks_directory.csv" "http://localhost:8080/upload/public/starbucks_directory"
curl -v -F "data=@photo_attributes.csv" "http://localhost:8080/upload/public/photo_attributes"
```

Note that I changed the CSV header of the [Kaggle Starbucks dataset](https://www.kaggle.com/datasets/starbucks/store-locations?resource=download) for more convenient querying to:
```
brand,store_number,store_name,ownership_type,street_address,city,state,country,post_code,phone_number,timezone,longitude,latitude
```

## Calculating distances

Seafowl doesn't come with built-in functions for calculating the distance between two points on the planet,
but fortunately there is an excellent [Rust library](https://docs.rs/geoutils/latest/geoutils/) we can use.

To call it from Seafowl queries, we must create a user-defined function (UDF) and compile it to [WebAssembly](/blog/seafowl-wasm-udfs).
The code for the UDF is available [on GitHub](https://github.com/splitgraph/seafowl-udf-geo).

After running the [`create_udf.sh`](https://github.com/splitgraph/seafowl-udf-geo/blob/main/create_udf.sh) script, the previously unavailable `distance()` function may be used in queries
like so:

```sql
-- calculate the distance between Berlin and Paris in meters
-- distance(longitude_1, latitude_1, longitude_2, latitude_2) -> distance in meters
SELECT distance(52.5200, 13.4050, 48.8566, 2.3522)
```
We're ready to start classifying our photo collection by the Omnipresent Siren Metric!

## Calculating proximity: the naive approach

A simple way to calculate nearby Starbucks count for each photo is to find the distance to each Starbucks, then filter out distant Starbucks in the `WHERE` clause:

```sql
SELECT
    filename,
    COUNT(0) AS nearby_starbucks_count
FROM (
    SELECT
        filename,
        photo_lat,
        photo_lon,
        starbucks_lat,
        starbucks_lon,
        distance(photo_lat, photo_lon, starbucks_lat, starbucks_lon) as dist
    FROM (
        SELECT
            filename,
            latitude as photo_lat,
            longitude as photo_lon
        FROM photo_attributes
    ) p
    JOIN (
        SELECT
            latitude as starbucks_lat,
            longitude as starbucks_lon
        FROM starbucks_directory
    ) s ON true
) t
WHERE t.dist < 2000 -- distance is in meters, in this case a 2km radius
GROUP BY 1
ORDER BY 2
```

One of the great advantages of using an SQL database is that we can ask how a query will be executed with the `EXPLAIN SELECT ...` statement.
The logical plan for this query is:

```
Sort: nearby_starbucks_count ASC NULLS LAST
  Projection: t.filename, COUNT(Int64(0)) AS nearby_starbucks_count
    Aggregate: groupBy=[[t.filename]], aggr=[[COUNT(Int64(0))]]
      Projection: t.filename, alias=t
        Projection: p.filename, alias=t
          Filter: distance(p.photo_lat, p.photo_lon, s.starbucks_lat, s.starbucks_lon) < Float64(2000)
            CrossJoin:
              Projection: p.filename, p.photo_lat, p.photo_lon, alias=p
                Projection: photo_attributes.filename, photo_attributes.latitude AS photo_lat, photo_attributes.longitude AS photo_lon, alias=p
                  TableScan: photo_attributes projection=[filename, latitude, longitude]
              Projection: s.starbucks_lat, s.starbucks_lon, alias=s
                Projection: starbucks_directory.latitude AS starbucks_lat, starbucks_directory.longitude AS starbucks_lon, alias=s
                  TableScan: starbucks_directory projection=[latitude, longitude]
```

Notice the `CrossJoin` (also called Cartesian product)! It's the type of join between two tables where every row in table A is joined with every row in table B.
The resulting table will have a total row count of `rows in A * rows in B`.
The `distance()` function is pretty fast: a single call returns in 5-6 microseconds on my laptop. Even so, calculating all `25600 * 21808 = 558284800` distances took me about an hour.
Can we do better?

## Calculating proximity: using pre-filtered pairs

Calculating the precise distance between two points on a globe is more complex than on a plane.
Still, we can use a rough heuristic to filter out photo - Starbucks pairs which are obviously much farther away than 2 km.
At the equator, one degree latitude / longitude corresponds to 111km. I've never been so close to the poles that 2 degrees would be less than 2km apart, so any Starbucks
more than two degrees latitude or longitude away from the photo's location will surely be out of the 2km range.

### Pre-filtering pairs
First, I created a table of candidate pairs based on differences in latitude and longitude:

```sql
CREATE TABLE filtered_pairs_less_than_2_degrees_apart AS WITH
    all_pairings AS (
        SELECT
                filename,
                photo_lat,
                photo_lon,
                starbucks_lat,
                starbucks_lon,
                ABS(photo_lat - starbucks_lat) AS diff_lat,
                ABS(photo_lon - starbucks_lon) AS diff_lon
        FROM (
            SELECT
                filename,
                photo_lat,
                photo_lon,
                starbucks_lat,
                starbucks_lon
            FROM (
                SELECT
                    filename,
                    latitude as photo_lat,
                    longitude as photo_lon
                FROM photo_attributes
            ) p
            JOIN (
                SELECT
                    latitude as starbucks_lat,
                    longitude as starbucks_lon
                FROM starbucks_directory
            ) s ON true) r
    )
SELECT *
FROM all_pairings
-- A difference of 2 degrees or more latitude or longitude will definitely
-- result in more than 2 kilometers between the points in the range we're
-- working with.
WHERE diff_lat < 2.0 AND diff_lon < 2.0;
```

The `filtered_pairs_less_than_2_degrees_apart` table has 687911 rows, less than 1% of the rows resulting from the cross join in the previous query.

### Calculating the distance of candidate pairs
The following query filters the pairs further based on the precise distance:

```sql
CREATE TABLE filtered_pairs_less_than_2_km_apart AS SELECT * FROM (
    SELECT
        filename,
        photo_lat,
        photo_lon,
        starbucks_lat,
        starbucks_lon,
        distance(photo_lat, photo_lon, starbucks_lat, starbucks_lon) as dist
    FROM filtered_pairs_less_than_2_degrees_apart) t
-- Filter the list according to the actual distance in meters, so
-- < 2000 results in a 2km radius.
WHERE t.dist < 2000;
```

This second filter throws out over 94% of the rows in the `filtered_pairs_less_than_2_degrees_apart` table, leaving only
Starbucks locations within the 2km radius of each photograph.

The `filtered_pairs_less_than_2_km_apart` table contains 39715 rows, the same number we got with the original hour-long calculation, but the two
`CREATE TABLE` queries take a total of 15.1 seconds, less than 1% of the original runtime.

## My most urban photograph

There's a clear winner among the 21808 contestants for "most urban" photo, with an OSM of 68:

![Sheep in Shanghai](/static-v2/images/blog/20221209-seafowl-geo-distance-udf/IMG_6092.JPG)

Directing the [Starbucks Store Locator](https://www.starbucks.com/store-locator?map=31.232686,121.460572,15z) to the coordinates where the photo was captured helps explain why.
Considering Shanghai has the most Starbucks locations of any city on Earth, more than double that of Seoul at second place, it's not surprising that my "winning" photo was taken there.

## How urban am I

To find out, I needed the number of photographs for each OSM score:

```sql
-- query.sql
SELECT
    nearby_starbucks_count,
    COUNT(0) as num_photos
FROM (
    SELECT
        filename,
        COUNT(0) AS nearby_starbucks_count
    FROM filtered_pairs_less_than_2_km_apart
    GROUP BY 1
) t
GROUP BY 1
ORDER BY 1 ASC;
```

I used the following bash command to get the results of the query as CSV with the help of `jq`:

```bash
curl  -H "Content-Type: application/json" http://localhost:8080/q -d@- << END |
{"query": $(cat "query.sql" | jq -Rsa .)}
END
jq -r '[.nearby_starbucks_count, .num_photos] | @csv' > photos_by_osm.csv
```

Using the [Node.js Seafowl client](https://github.com/splitgraph/seafowl/blob/main/examples/clients/node/seafowl-client.js) is also an option:

```bash
seafowl/examples/clients/node/seafowl-client.js -f filtered_step3_simplified_osm_histogram.sql | tail -n +2 | jq -r '.[] | [.nearby_starbucks_count, .num_photos]' | @csv > photos_by_osm.csv'
```

With most of my photos having an OSM of 0, the answer seems to be that I'm not very urban, especially when compared to someone who feels at home in Shanghai:

![Number of photos by OSM](/static-v2/images/blog/20221209-seafowl-geo-distance-udf/number_of_photos_by_osm.svg)

## Urbanness over time

If "urbanness" is an innate quality unlikely to change much once our adult personality has developed, any reliable indicator should be pretty stable over time.
Looking at the number of photos I've taken with OSM ≥ 1 each month makes it seem like that is indeed the case.

![OSM ≥ 1 photo count by month](/static-v2/images/blog/20221209-seafowl-geo-distance-udf/osm_gte_1_photo_count_by_month.svg)

The exception is a trip to Shanghai in October of 2015 when I took the picture with the plastic sheep shown above.

## Conclusion

It was fun analyzing where I've taken photos over the last decade. Seafowl has been a convenient tool throughout the process:
- CSV import made it easy to work with data from [Kaggle](https://www.kaggle.com/datasets/starbucks/store-locations?resource=download) and Google Photos.
- We can easily extend Seafowl's builtin functions with [our own UDFs](https://github.com/splitgraph/seafowl-udf-geo).
- JSON output is convenient in most cases and can easily be converted to CSV using `jq` when necessary.

I look forward to all the cool stuff people build with this fast new database!


I used Seafowl to analyze how much of a city slicker I am—with geographic user-defined WASM functions and caffeine addiction!

Deciding if I'm urban with WebAssembly and Seafowl


## Do we really need User-Defined Functions?

User-defined functions (UDFs) are bits of application-specific logic which can be invoked within database queries, extending the builtin capabilities of the database.
You might be wondering why not keep application-specific logic in the _application_ layer? Are user-defined functions (UDFs) the relic of a past when databases were designed to be directly queried by end-users?

Far from it! Placing domain-specific logic in the database is often the best (sometimes only) way to implement functionality such as:
- Security policies such as PostgreSQL's [RLS feature](https://www.postgresql.org/docs/current/ddl-rowsecurity.html), which rely on UDFs to enforce application-specific security policies.
- Custom aggregation functions similar to `MIN`, `MAX` and `AVG` which must run within the database to avoid shipping the entire set of input rows to the client to calculate output.
- General, application-independent domain specific capabilities such as working with JSON fields, calculating geographical distances or full text search, extending the core DB's capabilities without having to modify the database itself.
- Finally, if raw and cleaned data live in the same database, it makes sense to use the database to transform that data with tools like [dbt](https://www.getdbt.com/) instead of passing it through a separate process -- and UDFs might be needed to implement some special functionality for that. [^1]

UDFs are an essential building block supported by the majority of databases one way or another, and in this regard Seafowl is no exception. What makes Seafowl special is its commitment to running "at the edge": as close to the user as possible, which means pretty much anywhere.
But if Seafowl is to run _anywhere_, UDFs referenced by Seafowl queries should be just as portable.
How can Seafowl offer UDF authors a platform which makes this possible?

## Platforms

Like any program, a UDF targets a specific platform. This platform should be hardware-independent so UDFs keep working if the database is moved to a different machine, but also efficient enough to process large datasets in acceptable time.

Mature databases often have a custom language for defining UDFs such as [PostgreSQL's PL/PGSQL](https://www.postgresql.org/docs/current/plpgsql.html) or [PL/SQL](https://www.oracle.com/database/technologies/appdev/plsql.html).
For simple functions just beyond the capabilities of an SQL statement, these languages offer an ideal solution.
For more complex functionality or code with lots of dependencies, many developers choose a general programming language, preferably one used in the application layer.

PostgreSQL and Redis both support authoring UDFs in Lua, for example. As a high-level language,
Lua abstracts away the lower layers of the stack such as the CPU's instruction set or the flavor of the C standard library.
Instead, the platform becomes the Lua virtual machine and standard library.
Lua is designed to be embedded in the host process, allowing scripts to efficiently invoke native functions exposed by the host.
It isn't the only scripting language embedded in databases. PostgreSQL for example supports many scripting languages including javascript and python.
While the source of the UDF may be portable, Lua, Javascript, Python, Ruby rely on native modules typically written in C or C++ for performance-intensive code such as crypto libraries.
These modules are co-located with the database and must be recompiled for each architecture supported by the database, limiting the portability of UDFs using these functions.

Custom logic written in compiled languages such as C++, Rust or Java is more difficult to invoke from a native database process, requiring
either running the UDF in a separate process or compiling against the C ABI of the database and executing it with full access to the database.
The security implications of the second option rule out this approach for UDFs.

Inter-process communication (IPC) with a separate JVM process provides a portable solution supported by [Oracle](https://docs.oracle.com/cd/B19306_01/java.102/b14187/chfive.htm) and [IBM DB2](https://www.ibm.com/docs/en/db2-for-zos/11?topic=java-stored-procedures-user-defined-functions),
but introduces a performance penalty for each UDF invocation along with additional corner cases arising from the need to synchronize a JVM process with the database.
For example, should updating the Java bytecode for a UDF impact running transactions?
If not, how many versions of the UDF must be kept in memory and how should the database keep track of which is the current version in each transaction?
When is it safe to remove classes upon which a previous version of the UDF depends?
If a transaction defining a UDF is rolled back, which classes' bytecode needs to be ejected from the JVM?
The list goes on and on, and it's mostly independent of Java, stemming instead from the marriage of a transactional database and another process which dynamically loads UDF code.

To summarize, there's hardly a silver bullet: each solution for providing UDFs has its pros and cons.
Worse, none of the solutions considered so far are truly portable in the sense that they can be used in the browser, on dedicated servers and desktops without serious limitations.

## Enter WebAssembly

Predating Seafowl and Splitgraph by decades, developers of large native applications faced a similar conundrum.
As new devices and platforms appeared, customers wanted to run their applications in previously unsupported environments.
Porting existing applications to new platforms was the traditional remedy, a slow and expensive process.

In the 90s, Java tried to address this problem, promising developers they could "write once, run everywhere".
As Java eventually found success in the very different niches of backend services and Android devices,
its early focus on "running everywhere" understandably changed to working best where it's used.

With the explosion of web apps, the browser appeared as yet another platform for applications to support, albeit a highly unusual one:
instead of a CPU instruction set, developers were to target a high level language, JavaScript.
Garbage collection came with the package, but integers were out (JavaScript has only floating point numbers).
Being so different from other platforms, porting existing applications to the web seemed more difficult than a complete rewrite in many cases.

Even green field projects struggled to find a stack sufficiently portable and efficient to ensure the application's long-term growth in desktop, mobile and browser environments.

As the performance of computers in general and JavaScript engines in particular improved, it became feasible to emulate a CPU in JavaScript.
A single C/C++ codebase could now be compiled to run natively on desktops or mobile devices, but also run in the browser with [emscripten](https://emscripten.org/), which targeted the javascript-powered CPU emulator.
It was hacky, but it worked and the world started to [take notice](https://kripken.github.io/mloc_emscripten_talk/#/).

With growing demand for efficiently running compiled code in the browser, Chrome, Firefox and Safari started to include new virtual machine called WebAssembly (WASM), very different from the high-level javascript VM:
- It wasn't optimized for a specific programming language like V8 or the JVM.
- It doesn't support sophisticated just-in-time compilation.
- It doesn't even have a garbage collector.

Instead, the WebAssembly specification provides a limited set of low-level instructions and linear memory model.
Thanks to the relative ease of implementation, today there are [many](https://wasmer.io/) [high](https://wasmedge.org/) [quality](https://github.com/wasm3/wasm3) [implementations](https://wasmtime.dev/), allowing code targeting WebAssembly to run
anywhere from embedded devices to [browsers](https://developer.mozilla.org/en-US/docs/WebAssembly), [CDNs](https://blog.cloudflare.com/announcing-wasi-on-workers/), and yes- in [databases](https://seafowl.io/docs/guides/custom-udf-wasm) as well.

## WebAssembly as a platform for UDFs

How does WebAssembly as a platform for implementing UDFs compare to scripting languages like Lua or more traditional compiled solutions like Java or C?
Since WebAssembly is designed to be used in very diverse environments, it cannot afford to make many assumptions:
- Memory is a sequence of bytes: there is no support for typed memory.
- As mentioned earlier, there is no garbage collector.
- WebAssembly code has no access to host process resources such as the filesystem, `stdout` or environment variables unless explicitly provided via functions that the WASM module can call at runtime.
- Catering to multi-tenant scenarios, hosts have find-grained control over how much computational power they grant a WASM function.
- The builtin types are limited to 32 and 64 bit floating point and integer numbers, bytes and booleans.

That last point presents a significant challenge when using WebAssembly for authoring UDFs. Many of the [types](https://seafowl.io/docs/reference/types) supported by Seafowl such as `TEXT` and `DECIMAL`
cannot be simply represented solely by scalar numeric values.

One option is to limit UDFs to consuming and returning types natively supported by WebAssembly (this is [ScyllaDB's approach](https://www.scylladb.com/2022/04/14/wasmtime/)).

For Seafowl's UDFs, we wanted users to be able to work with any datatype including strings, decimal numbers and dates.

In WebAssembly, memory is just a sequence of bytes and pointers are just 32 bit integers, similar to an array index.
Any object which can be serialized to a byte stream can be passed by the host to a WebAssembly function by first writing the serialized bytes to WASM memory,
then invoking the function with a pointer to the freshly written memory address as its argument.
Similarly, the WASM function can serialize it's output as a byte stream to which it returns a pointer.

Seafowl uses [MessagePack](https://msgpack.org/index.html) for serialization - think of it like a faster binary version of JSON.

## We're just getting started

Today, Seafowl supports creating UDFs in any programming language which compiles to WASM. Check out our [Rust example UDF repository](https://github.com/splitgraph/seafowl-udf-rust)! Any Seafowl type may be used for input values and return type.

One of the biggest advantages of using UDFs is aggregated records _within the database_. As of 2022 December, Seafowl does not yet support user-defined aggregation functions, but this support is planned. When it does arrive, you can be sure it will also use WebAssembly!

[^1]: There's an interesting discussion on this approach in this [Hacker News thread](https://news.ycombinator.com/item?id=33874842).


How can Seafowl users extend the database's builtin capabilities in a safe, portable and efficient way? The answer is WebAssembly, read on to learn how!

Splitgraph has been acquired by EDB! Read the blog post.

seafowl

Writing UDFs in Golang

Deploying a serverless Seafowl DB to Google Cloud Run using GCS FUSE and SQLite

Using Dagster with Seafowl

A Lakehouse by the sea: Migrating Seafowl storage layer to delta-rs

Open Data Monitor

Deciding if I'm urban with WebAssembly and Seafowl

Extending Seafowl with WebAssembly

Product

Support

Company

Splitgraph

Splitgraph has been acquired by EDB! Read the blog post.

seafowl

Writing UDFs in Golang

Deploying a serverless Seafowl DB to Google Cloud Run using GCS FUSE and SQLite

Using Dagster with Seafowl

A Lakehouse by the sea: Migrating Seafowl storage layer to delta-rs

Open Data Monitor

Deciding if I'm urban with WebAssembly and Seafowl

Extending Seafowl with WebAssembly

Product

Support

Company

Community

Splitgraph