Hacker News Reader

Comment by benbjohnson | Hacker News Reader

I just open-sourced a streaming replication tool for SQLite called Litestream that does physical replication (raw pages) instead of logical replication (SQL commands). Each approach has its pros and cons. Physical replication logs tend to be larger than logical logs but I agree that you avoid a lot of issues if you do physical replication.

https://github.com/benbjohnson/litestream

View on HN

I've been following this, and am anxious for the direct-to-sqlite replication.

One of rqlite's big limitations is that it resyncs the entire DB at startup time. Being able to start with a "snapshot" and then incrementally replicate changes would be a big help.

That can be painful for sure. Litestream will do a snapshot on startup if it detects that it can't pick up from where it left off in the WAL. That can happen if Litestream is shut down and another process perform a checkpoint. But generally a restart will just use the existing snapshot & continue with the WAL replication.

This is tangential, but depending on your sql needs, CouchDB's replication story is amazing, and you can replicate to the browser using PouchDB. There is an optional SQL layer, but obviously the good replication story comes with some trade-offs.

I built an app (https://actualbudget.com/) that uses a local sqlite db and syncs changes, and that's exactly how it works. It takes quite a different approach though, using CRDTs to represent changes and those are synced around. When a fresh client comes into play, it downloads the latest sqlite snapshot from a server and then syncs up.

Consider adding family sharing or just general making / joining of a group with shared expenses and accounts. Me and my wife use such an app currently but it lacks features and ergonomy.

rqlite creator here.

I'm not sure I follow why it's a "big limitation"? Is it causing you long start-up times? I'm definitely interested in improving this, if it's an issue. What are you actually seeing?

Also, rqlite does do log truncation (as per Raft spec), so after a certain amount of log entries (8192 by default) node restarts work exactly like you suggested. The SQLite database is restored from a snapshot, and any remaining Raft Log entries are applied to the database.

Ah, ok that's some nuance I didn't know about!

We're storing a few GB of data in the sqlite DB. Rebuilding those when rqlite restarts is slow and intensive process compared to just using the file on disk over again.

Our particular use case means we'll end up restarting 100+ replica nodes all at once, so the way we're doing things makes it more painful than necessary.

But how do you know it's intensive? Are you watching disk IO? Is there a noticeable delay when the node starts before it's ready to receive requests?

Try setting "-raft-snap" to a lower number, maybe 1024, and see if it helps. You'll have much fewer log entries to apply on startup. However the node will perform a snapshot more often, and writes are blocked during the snapshotting. It's a trade-off.

It might be possible to always restart using some sort of snapshot, independent of Raft, but that would add significant complexity to rqlite. The fact the SQLite database is built from scratch on startup, from the data in Raft log, means rqlite is much more robust.

Oh, we're reading the sqlite files directly. rqlite is really just a mechanism for us to propagate read only data to a bunch of clients.

We need that sqlite file to never go away. Even a few seconds is bad. And since our replicas are spread all over the world, it's not feasible to move 1GB+ data from the "servers" fast enough.

Is there a way for us to use that sqlite file without it ever going away? We've thought about hardlinking it elsewhere and replacing the hardlink when rqlite is up, but haven't built any tooling to do that.

Hmmmm, that's a different issue. :-)

Today the rqlite code deletes the SQLite database (if present) and then rebuilds it from the Raft log. It makes things so simple, and ensures the node can always recover, regardless of the prior state of the SQLite database -- basically the Raft log is the only thing that matters and that is guaranteed to be the same under each node.

The fundamental issue here is that Raft can only guarantee that the Raft log is in consensus, so rqlite can rely on that. It's always possible the one of the copies of SQLite under a single node gets a different state that all other nodes. This is because the change to the Raft log, and corresponding change to SQLite, are not atomic. Blowing away the SQLite database means a restart would fix this.

If this is important -- and what you ask sounds reasonable for the read-only case that rqlite can support -- I guess the code could rebuild the SQLite database in a temporary place, wait until that's done, and then quickly swap any existing SQLite file with the rebuilt copy. That would minimize the time the file is not present. But the file has to go away at some point.

Alternatively rqlite could open any existing SQLite file and DROP all data first. At least that way the file wouldn't disappear, but the data in the database would wink out of existence and then come back. WDYT?

Rebuilding and then moving it in place sounds pretty nice to me.

This may be hopelessly naive - but is the rebuilt sqlite db expected to be identical? Could a sha256 hash of the file be written into the raft log at intervals, and be used as a "valid" starting point?

Ie:

    log1: empty db
    log2..N: changes
    log3: snapshot at N
    log4..M: changes

You start with empty db, and replay up to N; or if you have a db that matches log3 - start with that and only replay log4+?

That's not a bad idea, might have promise. Hashing large databases files after every few log entries might be a performance hit....not sure.

Something to think about -- thanks.

Snapshotting at intervals is how most point-in-time restores scale up.

The db is snapshotted at a preset time (easy way: lock db for writes whilst snap is in progress, preferably on read replicas) and then restored again (preferably, elsewhere) to see if restore did indeed work as expected (and isn't corrupt).

The latest log sequence number could also be stored in the same db (in a separate table) so that it is captured along with the db-snapshot, atomically.

> Today the rqlite code deletes the SQLite database (if present) and then rebuilds it from the Raft log.

Neat! This is reminiscent of the replay of event log in Smalltalk.

> I just open-sourced a streaming replication tool for SQLite called Litestream that does physical replication (raw pages) instead of logical replication (SQL commands). Each approach has its pros and cons. Physical replication logs tend to be larger than logical logs but I agree that you avoid a lot of issues if you do physical replication.

Hmm. Not having dug into your solution much, is it safe to say that the physical replication logs have something like logical checkpoints? If so, would it make sense to only keep physical logs on a relatively short rolling window, and logical logs (ie. only the interleaved logical checkpoints) longer?

I suppose you could save both the physical and logical logs if you really needed log term retention. SQLite databases (and b-trees in general) tend to compress well so the physical logging isn't as bad as it sounds. You could also store a binary diff of the physical page which would shrink it even smaller.

One benefit to using physical logs is that you end up with a byte-for-byte copy of the original data so it makes it easy to validate that your recovery is correct. You'd need to iterate all the records in your database to validate a logical log.

However, all that being said, Litestream runs as a separate daemon process so it actually doesn't have access to the SQL commands from the application.

So, no logical checkpoints, logical logging would have to be a separate process.

The main reason I would want both is to be certain that I was restoring the DB to a consistent state, even if the application is doing things stupidly (eg. not using transactions correctly. Or at all).

I suppose timestamps would be enough to view the logs interleaved or side-by-side in some fashion.

FYI in Postgresql (Pgpool anyway) and MySQL that would be query replication. Logical replication often means the row data. Binary replication often uses block level data.

Here's another approach to the problem [0]:

This package is part of Gazette [1], and uses a gazette journal (known as a "recovery log") to power raw bytestream replication & persistence.

On top of journals, there's a recovery log "hinting" mechanism [2] that is aware of file layouts on disk, and keeps metadata around the portions of the journal which must be read to recover a particular on-disk state (e.x. what are the current live files, and which segments of the log hold them?). You can read and even live-tail a recovery log to "play back" / maintain the on-disk file state of a database that's processing somewhere else.

Then, there's a package providing RocksDB with an Rocks environment that's configured to transparently replicate all database file writes into a recovery log [3]. Because RocksDB is a a continuously compacted LSM-tree and we're tracking live files, it's regularly deleting files which allow for "dropping" chunks of the recovery log journal which must be read or stored in order to recover the full database.

For the SQLite implementation, SQLite journals and WAL's are well-suited to recovery logs & their live file tracking, because they're short-lived ephemeral files. The SQLite page DB is another matter, however, because it's a super-long lived and randomly written file. Naively tracking the page DB means you must re-play the _entire history_ of page mutations which have occurred.

This implementation solves this by using a SQLite VFS which actually uses RocksDB under the hood for the SQLite page DB, and regular files (recorded to the same recovery log) for SQLite journals / WALs. In effect, we're leveraging RocksDB's regular compaction mechanisms to remove old versions of SQLite pages which must be tracked / read & replayed.

[0] https://godoc.org/go.gazette.dev/core/consumer/store-sqlite

[1] https://gazette.readthedocs.io/en/latest/

[2] https://gazette.readthedocs.io/en/latest/consumers-concepts....

[3] https://godoc.org/go.gazette.dev/core/consumer/store-rocksdb

Do you use compression? And if so, how that affects the relative amount of network traffic vs logical.

Yes, Litestream uses LZ4 compression. I originally used gzip but the compression speed was pretty slow. B-tree pages tend compress well because they tend to be 50-75% full because they need space to insert new records and because pages split when they get full.

I'm seeing files shrink down to 14% of their size (1.7MB WAL compressed to 264KB). However, your exact compression will vary depending on your data.

Ah, that makes sense. Most inserts don't split pages, so are around n worst case pages, but once in a while you get 2n updates where most of them are half full, and so compress better.

So how does that compare to logical replication? (Also I imagine packet size plays a role, since you have to flush the stream quite frequently, right? 1000 bytes isn't much more expensive than 431)

Litestream defaults to flushing out to S3 every 10 seconds but that's mainly because of PUT costs. Each request costs $0.00005 so it costs about $1.30 per month. If you flushed every second then it'd cost you $13/month.

Logical replication would have significantly smaller sizes although the size cost isn't a huge deal on S3. Data transfer in to S3 is free and so are DELETE requests. The data only stays on S3 for as long as your Litestream retention specifies. So if you're retaining for a day then you're just keeping one day's worth of WAL changes on the S3 at any given time.

Reply on news.ycombinator.com