For deployments at a more serious scale, it seems they support buffering WAL events into Kafka, similar to Debezium (the current leader for change data capture), to de-couple the replication slot reader on the Postgres side throughput from the event handlers you deliver the events to.
Pgstream seems more batteries-included compared to using Debezium to consume the PG log; Kafka is optional with pgstream and things like webhook delivery and OpenSearch indexing are packaged in, rather than being a “choose your own adventure” game with Kafka Streams middleware ecosystem jungle. If their offerings constraints work for your use-case, why not prefer it over Debezium since it seems easier? I’d rather write Go than Java/JVM-language if I need to plug into the pipeline.
However at any sort of serious scale you’ll need the Kafka in there, and then I’m less sure you’d make use of the other plug and play stuff like the OpenSearch indexer or webhooks at all. Certainly as volume grows webhooks start to feel like a bad fit for CDC events at the level of a single row change; at least in my brief Debezium CDC experience, my consumer pulls batches of 1000+ changes at once from Kafka.
The other thing I don’t see is transaction metadata, maybe I shouldn’t worry much about it (not many people seem to be concerned) but I’d like my downstream consumer to have delayed consistency with my Postgres upstream, which means I need to consume record changes with the same transactional grouping as in Postgres, otherwise I’ll probably never be consistent in practice: https://www.scattered-thoughts.net/writing/internal-consiste...
You're right, at scale, Kafka allows you to parallelise the workload more efficiently, as well as supporting multiple consumers for your replication slot. However, you should still be able to use the plug and play output processors with Kafka just as easily (implementation internally is abstracted, it works the same way with/without Kafka). We currently use the search indexer with Kafka at Xata for example. As long as the webhook server has support for rate limiting, it should be possible to handle a large amount of event notifications.
Regarding the transaction metadata, the current implementation uses the wal2json postgres output plugin (https://github.com/eulerto/wal2json), and we don't include the transactional metadata. However, this is something that would be easy to enable, and integrate into the pgstream pipeline if needed, to ensure transactional consistency on the consumer side.
Thanks for your feedback!
1. Can it handle deliveries to slow on unreliable endpoints? In particular, what happens if the external server is deliberately slow to respond - someone could be trying to crash your system by forcing it to deliver to slow-loading endpoints.
2. How are retries handled? If the server returns a 500, a good webhooks system will queue things up for re-delivery with an exponential backoff and try a few more times before giving up completely.
Point 1. only matters if you are delivering webhooks to untrusted endpoints - systems like GitHub where anyone can sign up for hook deliveries.
2. is more important.
https://github.com/xataio/pgstream/blob/bab0a8e665d37441351c... shows that the HTTP client can be configured with a timeout (which defaults to 10s https://github.com/xataio/pgstream/blob/bab0a8e665d37441351c... )
From looking at https://github.com/xataio/pgstream/blob/bab0a8e665d37441351c... it doesn't look like this system handles retries.
Retrying would definitely be a useful addition. PostgreSQL is a great persistence store for recording failures and retry attempts, so that feature would be a good fit for this system.
Even with a retry scheme, unless you are willing to retry indefinitely, there is always the problem of missed events to handle. If you need to handle this anyway, it might as well use this assumption to simplify the process and not attempt retries at all.
From a reliability perspective, it is hard to monitor whether the delivery is working if there is a variable event rate. Instead, designing it to have a synchronous endpoint the web hook server can call periodically to “catch up” has better reliability properties. Incidentally, this scheme handles at-most-once semantics pretty well, because the server can periodically catch up on all missed events.
Implementing robust retries is a huge favor you can do for your clients though: knowing that they'll get every event delivered eventually provided they return 200 codes for accepted events and error codes for everything else can massively simplify the code they need to write.
The answers are probably either "yes" or "yes, but not yet."
Microsoft's Graph API also requires consumers to re-register webhooks every N days, a kind of "heartbeat" to make sure the webhooks aren't sent to a dead server forever.
It can be annoying to have to re-subscribe every N days when your application is working as expected though. I wonder if one way to work around this could be to "blacklist" servers that have failed consistently for an amount of time. They could be deleted from the subscriptions table if they remain inactive once they've been blacklisted.
I have opened an issue in the repo to track this (https://github.com/xataio/pgstream/issues/68), since it would be a critical feature to avoid denial-of-service attacks.
Thanks for your feedback!
Are there real world use cases being solved with this, or is it just hobby tool?
CDC would enable the processes to focus what's changed instead of continually processing the entire database.
I feel that DB support isn't necessarily the biggest issue for adopting these things, rather it's that it's somewhat more complicated to debug event driven systems for "regular" developers rather than request-response or loop based code.
In that sense delivering the events via webhooks is a good idea since it allows them to work with their regular request-response debugging tools.
