
# Introducing SQLite file uploads

Splitgraph already had excellent support for [importing CSV files](csv-to-sql-upload-to-data-delivery-network-ddn), but now we can use the same interface to upload SQLite files as well. Try it out:

<EmbeddedUpload />

# Why use Splitgraph for SQLite data?

[SQLite](https://sqlite.org/index.html) is an incredibly versatile embedded database, used by apps on mobile devices, client code running in web browsers (via WebAssembly), and native applications.
The main difference compared to more traditional relational databases such as PostgreSQL is that an SQLite database is just a file, there's no need for a dedicated database server process.
This is an enormous advantage in resource-constrained environments and makes it possible to build cost-effective data-intensive applications using [Litestream](https://litestream.io/) for example.
At the same time, SQLite's database-as-a-file approach makes concurrent multi user access -and thus collaboration- difficult.

In addition to collaboration, Splitgraph offers countless features beyond what SQLite provides, for example:
- column-based data storage for efficient aggregation queries
- immutable data images
- built-in support for [`dbt` transformations](dbt)
- joining tables from other databases using [foreign data wrappers](foreign-data-wrappers)

# What about Datasette?

[Datasette](https://datasette.io/) is a versatile open source tool for data analysis and publishing built on SQLite.
It's a popular solution for making SQLite data available to the public, but doesn't have built-in support for controlling access to published data.
For those Datasette users who need [fine grained access control](data-sharing-settings-invites-private-repositories), Splitgraph offers a solution.

Similar to Datasette, Splitgraph also makes it possible to directly query database tables [from the browser](select-from-the-browser).
Unlike Datasette, authenticated Splitgraph-backed web apps can even write to database tables.

Datasette documentation includes two demo databases, [`global-power-plants.db`](https://global-power-plants.datasettes.com/global-power-plants.db
) and [`legislators.db`](https://congress-legislators.datasettes.com/legislators.db), both of which can be loaded into Splitgraph. You can also try the SQLite version of the [Chinook](https://github.com/lerocha/chinook-database/blob/master/ChinookDatabase/DataSources/Chinook_Sqlite.sqlite?raw=true) reference database. Try [uploading them here](/upload)!

# Key differences and limitations

SQLite [inherited](https://www.pgcon.org/2014/schedule/events/736.en.html) most of it's syntax from PostgreSQL, so the majority of SQLite queries should work on Splitgraph out of the box.
A major difference is that by default, SQLite allows any type of data to be written to any column, regardless of the column's declared type.
In fact, column type declarations are entirely optional in SQLite!

PostgreSQL is more strict when it comes to enforcing data types. Importing SQLite databases which store different types of data in a column than what was declared in the table schema may fail.
Tables containing columns without a declared type are ignored by the current implementation of SQLite import.

We'd love to hear from you! If you're excited about this new feature or if you're missing something from the current SQLite import functionality, [let us know](mailto:hello@splitgraph.com?subject=sqlite%20import)!


Importing SQLite data into Splitgraph is now as easy as drag-n-drop!

SQLite file uploads


![](/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


## Introduction

**[Seafowl](https://seafowl.io)** is a database for **analytics at the edge**:
data-driven Web visualizations, dashboards and notebooks that require
low-latency execution of analytical queries.

To put it plainly, Seafowl lets you stop building these kinds of custom APIs for
every visualization in your application:

```bash
curl https://custom-api.myproject.io/basic_summary?country_of_import=BRAZIL&commodity=COFFEE&calculate=country_of_production,avg_volume,total_entries
```

and execute plain SQL queries straight from the user's browser instead:

```bash
curl https://demo.seafowl.io/q \
  -XPOST -H "Content-Type: application/json" \
  -d@- <<EOF
{"query": "SELECT \
    country_of_production, \
    AVG(volume) AS avg_volume, \
    COUNT(*) AS total_entries \
FROM supply_chains \
WHERE country_of_import = 'BRAZIL' \
  AND commodity = 'COFFEE' \
GROUP BY 1 ORDER BY 2 DESC"}
EOF
```

The key feature of Seafowl is being **CDN- and HTTP-cache friendly**. Seafowl's
SQL query results can be cached by CDNs like Cloudflare or HTTP caches like
Varnish (as well as the end user's browser cache).

This decreases the latency of applications that are powered by Seafowl, as well
as the resource requirements for running a Seafowl instance.

## Try it out

You can run Seafowl on any infrastructure provider. For this example, we have a
sample Seafowl deployment running on [Fly.io](https://fly.io) behind Cloudflare:

![](/static-v2/seafowl/blog/seafowl-sql-cdn/cache-hit.png)

This is an Observable notebook backed by a 3M row dataset served by this
deployment (thanks to [Trase](https://trase.earth) for the data):

<iframe
  width="100%"
  height="900"
  frameBorder="0"
  src={`https://observablehq.com/embed/@seafowl/interactive-visualization-demo?cells=viewof+productionCountry,viewof+importCountry,viewof+commodity,topExportersL,topExportersP,volumeByYearL,volumeByYearP,allImportsL,allImportsP`}
/>

Because you loaded this page, the result for the aggregation queries that
produced these visualizations is already cached in your browser. On first load,
it should have also been served from Cloudflare's edge cache.

Press F12 to go to your browser's Developer Tools and go to the Network tab. If
you refresh this page now, you should see HTTP requests to
`https://demo.seafowl.io` being cached by your browser.

![](/static-v2/seafowl/tutorial/observable/observable-network-cached.png)

You can also check "Disable Cache" and refresh the page again: you should see
requests going directly to the source. If you inspect one of them, it should
have a `CF-Cache-Status: HIT` header, meaning it was cached by Cloudflare.

![](/static-v2/seafowl/tutorial/observable/observable-cdn-cached.png)

So, how does this work? Let's dive in![^1]

## Query results are static assets

HTTP has provisions for caching **static assets**. If we can make our query
results behave like other assets (images, JavaScript, documents), we can benefit
from caching as well.

How do caches handle static assets? They:

- use the URL of the asset as part of the cache key
- usually only cache GET requests
- cache based on the MIME type or the
  [file extension (Cloudflare)](https://developers.cloudflare.com/cache/about/default-cache-behavior/)

Seafowl is designed to power Web applications backed by slowly-changing data.
This means we can make some assumptions that make this easier to build.

In particular, we can assume that the result of a single SQL query is
**identified by the text of the SQL query** (what if the query result changes
because the source data changed? More on that later).

This means also assuming that the original SQL query doesn't contain
**volatile** functions (like `random()`).

## Let's put the query in the URL!

**Update (May 2023): As of Seafowl `0.4.1` this approach, due to it being the simplest one,
 [is also supported](/seafowl/docs/guides/querying-cache-cdn#query-in-the-url-path),
 despite some of the potential shortcomings discussed in this paragraph. **

A first approximation of getting this to work is putting the query text into the
URL. We can URI-encode special characters in the query to make it a valid URL.
However, this has a couple of drawbacks.

First, we can hit URL length limits. These vary between browsers and HTTP
intermediaries (like CDNs). The limit can be anywhere from 2KB to 64KB
([source](https://stackoverflow.com/a/417184)), depending on the exact
implementation.

It also looks suspicious. A SQL query inside of the URL looks too much like an
attempt at SQL injection. We haven't explicitly confirmed it, but it could be
something a Web application firewall or an intrusion detection system would
flag.

## Let's put something else in the URL?

How about we use a string that changes with the query but isn't as long as the
query itself? Something like a SHA-256 hash of the query?

This raises another question: how does the server now recover the original query
to actually execute it? There are two ways we can do this.

We can pass the original query as a **GET request body**. This is unusual but is
sometimes used by servers like ElasticSearch.
[RFC7231](https://www.rfc-editor.org/rfc/rfc7231#section-4.3.1) doesn't
explicitly forbid it, but does warn against using it:

> A payload within a GET request message has no defined semantics; sending a
> payload body on a GET request might cause some existing implementations to
> reject the request.

Unfortunately, it's also unsupported by some clients that can reject or quietly
drop the GET body, to the point where the Fetch standard explicitly disallows it
(see the discussion in the
[GitHub issue](https://github.com/whatwg/fetch/issues/551)).

This leaves us with the other option: **using an HTTP header**. We can pass the
original query as the header, percent-encoding it as well. HTTP headers support
some characters that would normally have to be encoded in a URL, like spaces.
This means that we only need to encode non-ASCII characters and newlines,
decreasing the length of the query (HTTP headers have length limits too!).

Seafowl supports both of these methods. For example, with the HTTP header:

```bash
curl -H "X-Seafowl-Query: SELECT 'Hello, Seafowl'" \
https://demo.seafowl.io/q/7468d08fa2c3a2dc59905511dc478784a550e2d4ad67d101802a6411099d690f

{"Utf8(\"Hello, Seafowl\")":"Hello, Seafowl"}
```

You can also bypass the cached GET API altogether and execute queries with POST
requests instead:

```bash
curl https://demo.seafowl.io/q \
  -XPOST -H "Content-Type: application/json" \
  -d@- <<EOF
{"query": "SELECT 'Hello, Seafowl'"}
EOF
```

### Client code

Instead of making a plain HTTP request, the client does have to do extra work by
hashing the query and URL-encoding it. This, however, is very straightforward to
do.

We have a sample
[Observable client](https://observablehq.com/@seafowl/client#querySeafowl) that
implements the
[DatabaseClient specification](https://observablehq.com/@observablehq/database-client-specification).

You can also use `curl` and Bash:

```bash
#!/bin/bash -e
SEAFOWL_HOST = "http://localhost:8080"
query=$1
hash=$(echo -n "$query" | sha256sum | cut -f 1 -d " ")

curl -v\
 -H "X-Seafowl-Query: ${query}" \
 "$SEAFOWL_HOST/q/$hash"
```

Or
[Node.js](https://github.com/splitgraph/seafowl/blob/main/examples/clients/node/seafowl-client.js),
or
[run JavaScript from the browser](https://seafowl.io/docs/guides/querying-cache-cdn#querying-from-the-browser-using-the-fetch-api).

### Do we need to normalize the query?

You'll notice that multiple queries with the same semantic meaning but different
text (for example, with different formatting) will hash to different values,
resulting in a cache miss.

This is not that big of a deal. If you control the application that queries
Seafowl, you also control the shape of SQL queries it will make and will be able
to minimize cache misses. You can also use the changes in the formatting (or add
comments to your SQL query) as an "escape hatch" to bust the cache and force a
query to re-execute.

## What about invalidation?

What happens when the source dataset changes and we need to update the query
result? Luckily, the HTTP standard has provisions for that: it supports
["Entity tags", or ETags](https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/ETag).

An ETag is something an HTTP server can attach to a response, denoting the
version of the resource that has been served. Clients (including HTTP caches and
CDNs) can then use this ETag to make a **conditional request**:

- give the ETag back to the server as an `If-None-Match` header
- the server will return the resource only if the version provided by the server
  is newer than the provided
- otherwise, it will return a `304 Not Modified` HTTP response ETag.

Seafowl keeps track of the latest version of each table. When you write data to
Seafowl, it bumps the table version. It then uses the version numbers of all
tables participating in a query to compute the ETag, which it attaches to all
responses to its cached GET API.

## Putting it all together

Let's now take a look at how this works with Seafowl running behind a CDN. In
this case, we're using our demo deployment running on Fly.io behind Cloudflare,
but you can use any other CDN or an
[HTTP cache like Varnish](https://github.com/splitgraph/seafowl/tree/main/examples/varnish),
or no CDN at all and rely on your user's browser cache.

### Request 1: cache miss

![](/static-v2/seafowl/blog/seafowl-sql-cdn/cache-initial-miss.png)

When a browser requests a query from Seafowl's GET API for the first time, it's
uncached by Cloudflare.

Cloudflare forwards the query to the origin (Seafowl). Seafowl executes it and
computes an **ETag** for the latest version of all tables that participated in
the query.

Cloudflare caches the ETag and the response and forwards it to the client. The
client's browser caches the response as well.

We do a small hack here: instead of requesting the query hash, we also append a
`.csv` extension to the URL. This is because by default, Cloudflare only
[caches certain extensions](https://developers.cloudflare.com/cache/about/default-cache-behavior/)
and this tricks it into caching our response. The extension is discarded by
Seafowl (it always returns responses in the JSON Lines format).

### Request 2: cache hit

![](/static-v2/seafowl/blog/seafowl-sql-cdn/cache-hit.png)

If another browser (or the same browser with the browser cache disabled)
requests the same query within a certain time window (a few hours), Cloudflare
won't forward it to the Seafowl origin. Instead, it serves it from its own
cache. We can see this happened because Cloudflare returns a
`CF-Cache-Status: HIT` header.

### Request 3: cache revalidation

![](/static-v2/seafowl/blog/seafowl-sql-cdn/cache-revalidation.png)

Let's now say some time has passed since the original query and the CDN needs to
**revalidate** the response to make sure it's still up to date.

Cloudflare makes a **conditional** HTTP request with the `If-None-Match` header
set to its stored ETag. Seafowl inspects the inbound ETag in the `If-None-Match`
header, calculates the current ETag for a given query and compares them.

This operation is much more lightweight than actually executing the query (where
Seafowl would need to potentially download and scan through all
[partitions](/seafowl/docs/learning/storage)).

In this case, the source dataset hasn't changed and Seafowl responds with a
`304 Not Modified` HTTP response (without the query result).

Cloudflare returns the cached response back to the user with a
`CF-Cache-Status: REVALIDATED` header to note that it has revalidated the
response and it's still up to date.

### Request 4: cache invalidation

![](/static-v2/seafowl/blog/seafowl-sql-cdn/cache-invalidation.png)

Finally, let's say the source data has been updated. The user's browser makes
the same request to Cloudflare and Cloudflare makes the same conditional request
to Seafowl with a stale ETag.

In this case, Seafowl's computed ETag will be different from the one passed to
it by Cloudflare. That is, the response has **expired**. Seafowl re-executes the
query and returns the new result to Cloudflare, with a new ETag.

Cloudflare caches the new response and the new ETag. Finally, it forwards the
response to the user's browser with a `CF-Cache-Status: EXPIRED` header to note
that the response was stale and got reloaded from the origin.

## Conclusion

And that's it! With a few tricks we made our HTTP API cache-friendly. Any HTTP
cache or a CDN can now cache Seafowl query results and invalidate them when the
source data changes. This makes applications powered by Seafowl respond faster
and require less resources.

If you're interested in learning more about Seafowl, feel free to check out our
[Observable demo](https://observablehq.com/@seafowl/interactive-visualization-demo),
go through our [quickstart](/seafowl/docs/getting-started/quickstart) or through
a longer [tutorial](/seafowl/docs/getting-started/tutorial-fly-io/introduction)
in which you'll deploy Seafowl to Fly.io, put it behind Cloudflare or Varnish
and reproduce our Observable demo notebook.

## Other questions

### Bringing the data or the query results to the edge?

You might ask, why go through all this trouble to cache query results when we
could deploy our application at the edge instead? The key insight lies in how
analytical queries work:

- They usually reduce a big (millions/billons of rows, a few GB of data) dataset
  into a small (hundreds/thousands of rows, a few KB) summarized response
- They take hundreds of milliseconds or seconds instead of milliseconds or
  microseconds (like transactional queries would)

Let's say:

- an analytical query normally takes 500ms
- the latency to the origin is 100ms
- the latency to the edge location is 10ms

In that case, executing the query closer to the user would turn a 600ms
end-to-end latency to 510ms, which is not a noticeable improvement. It also has
to come at the expense of replicating the data and running the query engine at
the edge location[^2].

However, if a query result is cacheable, caching it at the edge instead of at
the origin would bring a 100ms response time down to 10ms.

Simply put, analytical queries normally take too much time to benefit from
moving the execution closer to the user.

### Using a non-HTTP cache?

An alternative to using HTTP for this would be implementing caching in Seafowl
itself or deploying a cache like Redis or Memcached. However, this would also
mean either requiring some scratch disk space for a Seafowl deployment to store
the query result cache or requiring Seafowl users to run caches at edge
locations.

We'd also need to write a more sophisticated client for querying Seafowl in
order to get query results cached in your application user's browser.

Using HTTP caches gives us all of this for free, including the ability to rely
on existing HTTP infrastructure.

### How does this break?

There are a few known issues with this kind of caching as we implemented it:

- Updating the definition of a
  [user-defined WASM function](/seafowl/docs/guides/custom-udf-wasm) doesn't
  invalidate the cache for queries that use those functions
  ([GitHub issue](https://github.com/splitgraph/seafowl/issues/57))
- Updating a table and then applying an opposite update still changes the table
  version ID, even though the content of the table is effectively unchanged
  ([GitHub issue](https://github.com/splitgraph/seafowl/issues/57))
- Queries that reference
  [external tables](/seafowl/docs/guides/csv-parquet-http-external) don't
  support the invalidation mechanism
- You can't currently change the Cache-Control headers sent out by Seafowl
  ([GitHub issue](https://github.com/splitgraph/seafowl/issues/56))

[^1]:
    This is a longer-form version of
    [our tutorial](/seafowl/docs/getting-started/tutorial-fly-io/part-3-cached-get-api)
    and the [documentation](/seafowl/docs/guides/querying-cache-cdn) for the
    cached GET API feature.

[^2]:
    In the extreme case, the "edge location" is the user's browser itself (see
    [DataStation](https://datastation.multiprocess.io/) or
    [this blog post](https://phiresky.github.io/blog/2021/hosting-sqlite-databases-on-github-pages/)),
    which works pretty well if the query engine doesn't need to load more than a
    few MB of data (because it's on the user's machine already or because it's
    small enough to begin with).


A deep dive into how we designed Seafowl's REST API to be HTTP cache and CDN friendly, including some discussion of ETags and other HTTP cache mechanics.

(Ab)using CDNs for SQL queries


For the past few months, we've been hard at work on something interesting and
it's finally ready for people to start experimenting with.

**[Seafowl](https://seafowl.io)** is our new standalone project, inspired by the
design of modern cloud data warehouses like Snowflake and BigQuery, as well as
the lessons we learned over the past four years of building Splitgraph and the
Splitgraph DDN.

It's a **CDN and HTTP-cache friendly** database designed for **analytics at the
edge**: dashboards, notebooks, visualizations and other data-driven
read-intensive Web applications that need to be responsive while operating on
large datasets.

```bash
curl https://demo.seafowl.io/q/30dcf5da00f94e3fbd28cf98242f7de3b164a440cadfaa6f7de74369ffc2177d.csv \
  -i -XGET -H "Content-Type: application/json" \
  -d@- <<EOF
{"query": "SELECT country_of_production, COUNT(*) AS count \
FROM supply_chains \
GROUP BY 1 ORDER BY 2 DESC"}
EOF

HTTP/2 200
...
cf-cache-status: HIT

{"country_of_production":"BRAZIL","count":2386600}
{"country_of_production":"ARGENTINA","count":260293}
{"country_of_production":"INDONESIA","count":155751}
{"country_of_production":"ECUADOR","count":96842}
{"country_of_production":"PARAGUAY","count":29848}
...
```

## Fast analytics...

Seafowl is written in Rust and relies on
[Apache DataFusion](https://github.com/apache/arrow-datafusion) to plan and
execute queries. For analytical queries, it's:

- up to 10x faster than vanilla PostgreSQL
- ...or up to 4x faster than PostgreSQL with the `parquet_fdw` extension
- ...or around the level of DuckDB in performance, sometimes beating it (apart
  from some pathological queries where it has memory issues, as well as queries that use a lot of subqueries where the optimizer might not be able to build an optimal query plan)

We have some [benchmarks](https://observablehq.com/@seafowl/benchmarks) which
aren't as in-depth as TPC-DS, but do show that Seafowl makes a lot of seemingly
heavy SQL queries now feasible for powering interactive applications.

<iframe
  width="100%"
  height="400"
  frameBorder="0"
  src="https://observablehq.com/embed/@seafowl/benchmarks?cells=viewof+benchmarkRun,viewof+databases,viewof+queries,benchmarkPlot"
/>

For `SELECT` queries, Seafowl supports a large subset of the PostgreSQL dialect.
If there's something missing, you can
[write a user-defined function](/seafowl/docs/guides/custom-udf-wasm)
for Seafowl in anything that compiles to WebAssembly.

You can write data to Seafowl with:

- [standard SQL statements](/seafowl/docs/guides/writing-sql-queries)
  (including `CREATE TABLE AS` statements for future dbt support!)
- by
  [uploading a CSV or a Parquet file](/seafowl/docs/guides/uploading-csv-parquet)
- by
  [setting up a "live" table](/seafowl/docs/guides/querying-http)
  that points to a file on an HTTP server

You can also
["bake" a dataset into a Docker image](/seafowl/docs/guides/baking-dataset-docker-image)
by bundling Seafowl together with its data. This is a nifty scaling strategy for
smaller (well, up to 1GB, which can be as big as the 55M row subset of the New
York Taxi dataset we use for our benchmarks) and read-only data.

## ...at the edge

"The edge" is a handwavy and marketing-y term, but it describes what Seafowl is
all about well enough. You can deploy Seafowl to the edge or cache query results
at the edge.

Seafowl ships as a single 50MB binary that starts in 10ms, making it easily
deployable to modern serverless environments, anywhere in the world. Our own
[tutorial](/seafowl/docs/getting-started/tutorial-fly-io/introduction) uses
Fly.io as an example.

Seafowl's query execution API follows HTTP cache semantics. This means you can
[put Seafowl behind a CDN](/seafowl/docs/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 dedicated cache, you can get the benefits of caching query
results in your user's browser (to see it in action, go to our
[demo](https://observablehq.com/@seafowl/interactive-visualization-demo), press
F12, go to the Network tab and refresh the page).

## Try it out!

Seafowl is now available to download and experiment with, though, like with all
early stage projects, it likely has plenty of bugs and missing features. As we
continue its development, we might break APIs or change storage formats in a
backwards-incompatible way.

Feel free to check out our
[Observable demo](https://observablehq.com/@seafowl/interactive-visualization-demo),
go through our
[quickstart](/seafowl/docs/getting-started/quickstart)
or through a longer
[tutorial](/seafowl/docs/getting-started/tutorial-fly-io/introduction)
in which you'll deploy Seafowl to Fly.io, put it behind Cloudflare or Varnish
and reproduce our Observable demo notebook.

When something breaks, please do
[raise a GitHub issue](https://github.com/splitgraph/seafowl/issues/). And, if
it doesn't (or doesn't break badly enough), **give us a
[GitHub star](https://github.com/splitgraph/seafowl)**!

## Future features

There are many features we're planning for Seafowl. Where appropriate, we'll
also aim to upstream these changes into DataFusion itself.

### JSON support

Seafowl currently supports a limited subset of PostgreSQL operators and
functions. The most notable missing feature is the JSON data type for storage of
arbitrary semi-structured objects, as well as helper functions like
`json_extract_path` / `json_agg`.

See the [GitHub issue](https://github.com/splitgraph/seafowl/issues/137) for more details.

### Live querying

You can currently query Parquet/CSV files over HTTP from Seafowl, but you can't
query any other databases "live", like you could with Splitgraph. It's always
been difficult for us to write PostgreSQL foreign data wrappers in C, since we'd
have to do a lot of work to correctly push down various parts of the PostgreSQL
query tree. With the extensibility of DataFusion and the memory safety of Rust,
we expect manipulating the query plan to be much easier.

See the [GitHub issue](https://github.com/splitgraph/seafowl/issues/136) for more details.

### Advanced PostgreSQL client support

Splitgraph has always been mostly PostgreSQL-first, with great support for
PostgreSQL clients (including ones that run complex introspection queries, like
Metabase).

With Seafowl, we flipped this upside down: it's HTTP-first, with basic
PostgreSQL wire protocol support as an afterthought. Perhaps it would be
interesting to polish that more, add more `information_schema` tables and make
Seafowl queryable by existing PostgreSQL clients, dashboarding and BI tools.

This would also open up more data sources, for example, using
[Airbyte](https://airbyte.com/) to load data into Seafowl.

### ...Seafowl Cloud?

No open-source project announcement nowadays is complete without teasing a
managed version of it.

In our case, we already have [Splitgraph](https://www.splitgraph.com) and we're
currently working on a better UI for it, one that moves us from obscure concepts
of "data images" and "repositories" to more familiar ones like "tables" and
"databases". Over time, we're also planning on migrating Splitgraph's query
execution engine from PostgreSQL to Seafowl.

With that in mind, we could provide a management interface for your Seafowl
instance or, later on, a true multi-tenant pay-for-what-you-use analytical query
execution API, powered by Seafowl.

In the words of a certain company, "we're just getting started".


Our new project: a CDN-friendly analytical database that's up to 10x faster than PostgreSQL and up to 5x faster than Splitgraph.

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

ddn

SQLite file uploads

Open Data Monitor

(Ab)using CDNs for SQL queries

Seafowl: a database for analytics at the edge

Product

Support

Company

Splitgraph

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

ddn

SQLite file uploads

Open Data Monitor

(Ab)using CDNs for SQL queries

Seafowl: a database for analytics at the edge

Product

Support

Company

Community

Splitgraph