[0]: https://github.com/psanford/sqlite3vfs

> Ever wanted to do a quick query against a prod dataset, but didn’t want to shell into a prod server and fumble with the sqlite3 terminal command like a hacker in an 80s movie? Or needed to do a quick sanity check against yesterday’s data, but without doing a full database restore? Litestream VFS makes that easy. I’m so psyched about how it turned out.

Man this is cool. I love the unix ethos of Litestream's design. SQLite works as normal and Litestream operates transparently on that process.

  export LITESTREAM_REPLICA_URL="s3://my-bucket/my.db"
  export AWS_ACCESS_KEY_ID="your-access-key"
  export AWS_SECRET_ACCESS_KEY="your-secret-key"

  sqlite3

  .load litestream.so
  .open file:///my.db?vfs=litestream
  PRAGMA litestream_time = '5 minutes ago'; 
  select * from sandwich_ratings limit 3;

  import { Database } from "bun:sqlite";
  Database.setCustomSQLite("/opt/homebrew/opt/sqlite/lib/libsqlite3.dylib");

  // Load extension first with a temp db
  const temp = new Database(":memory:");
  temp.loadExtension("/path/to/litestream.dylib", "sqlite3_litestreamvfs_init");

  // Now open with litestream VFS
  const db = new Database("file:my.db?vfs=litestream");

  const fruits = db.query("SELECT * FROM fruits;").all();
  console.log(fruits);

Currently on this app, I have the Python/flask app just refreshing the sqlite db from a Google spreadsheet as the auth source (via dataframe then convert to sqlite) for the sqlite db on a daily scheduled basis done within the app.

For reference this is the current app: (yes the app is kinda shite but I’m just a sysadmin trying to learn Python!) https://github.com/jgbrwn/my-upc/blob/main/app.py

Slightly different API (programmatic, no env variables, works with as many databases as you may want), but otherwise, everything should work.

Note that PRAGMA litestream_time is per connection, so some care is necessary when using a connection pool.

DuckDB has a lakehouse extension called "DuckLake" which generates "snapshots" for every transaction and lets you "time travel" through your database. Feels kind of analogous to LiteStream VFS PITR - but it's fascinating to see the nomenclature used for similar features. The OLTP world calls it Point In Time Recovery, while in the OLAP/data lake world, they call it Time Travel and it feels like a first-class feature.

In SQLite Litestream VFS, you use `PRAGMA litestream_time = ‘5 minutes ago’` ( or a timestamp ) - and in DuckLake, you use `SELECT * FROM tbl AT (VERSION => 3);` ( or a time stamp ).

DuckDB (unlike SQLite) doesn't allow other processes to read while one process is writing to the same file - all processes get locked out during writes. DuckLake solves this by using an external catalog database (PostgreSQL, MySQL, or SQLite) to coordinate concurrent access across multiple processes, while storing the actual data as Parquet files. It's a clever architecture for "multiplayer DuckDB.” - deliciously dependent on an OLTP to manage their distributed multiple user OLAP. Delta Lake uses uploaded JSON files to manage the metadata skipping the OLTP.

Another interesting comparison is the Parquet files used in the OLAP world - they’re immutable, column oriented and contain summaries of the content in the footers. LTX seems analogous - they’re immutable, stored on shared storage s3, allowing multiple database readers. No doubt they’re row oriented, being from the OLTP world.

Parquet files (in DuckLake) can be "merged" together - with DuckLake tracking this in its PostgreSQL/SQLite catalog - and in SQLite Litestream, the LTX files get “compacted” by the Litestream daemon, and read by the LitestreamVFS client. They both use range requests on s3 to retrieve the headers so they can efficiently download only the needed pages.

Both worlds are converging on immutable files hosted on shared storage + metadata + compaction for handling versioned data.

I'd love to see more cross-pollination between these projects!

Litestream continues to work as always, making continuous backups to S3.

Like always, I can restore from those backups to my local system.

But now I have the option of doing “virtual restores” where I can query a database backup directly on S3.

Different use case, but makes me think of sqlite Rewrite-it-it-Rust Turso announcing AgentFS. Here the roles are flipped, sqlite is acting as a file store to back FUSE, to allow watching/transaction-managing the filesystem/what agents are doing. Turso also has a sick CDC system built in, that just writes all changes to a cdc table. Which is related to this whole meta question, of what is happening to my sqlite DB. https://turso.tech/blog/agentfs

SQLite VFS is really cool tech, and pretty easy to work with (IMO easier than FUSE).

I had made a _somewhat similar_ VFS [1] (with a totally different set of guarantees), and it felt pretty magical how it "just worked" with normal SQLite

[1] https://github.com/danthegoodman1/gRPSQLite

ALSO I'm thinking about mixing this with object store caching... maybe combining memfs with remote metadata; would love to see more details on performance.

BUT I might be overthinking it... just excited to see SQLite exploring beyond local files...

If you are not familiar with data systems, havea read DDIA(Designing Data Intensive Applications) Chapter 3. Especially the part on building a database from the ground up — It almost starts with sthing like "Whats the simplest key value store?": `echo`(O(1) write to end of file, super fast) and `grep`(O(n) read, slow) — and then build up all the way to LSMTrees and BTrees. It will all make a lot more sense why this preserves so many of those ideas.

I am going to integrate Litestream into the thing I am going to building[1]. I experimented with a lot of ways, but it turns out there is WebDAV support recently merged, not in the docs.

[1]: https://github.com/blue-monads/potatoverse