tang8330

Great question - there's no Debezium under the hood.

Artie deals with TOAST columns using 2 layers: 1. Reader layer: If REPLICA IDENTITY FULL is set on the table, Reader detects any TOASTed columns in the after-image and copies the real value from the before-image. So the message that goes to Kafka has the actual value, not the placeholder. 2. Transfer layer: Artie has a Transfer layer that maintains an in-memory row cache. If REPLICA IDENTITY FULL isn't set and the before-image wasn't in the WAL, Transfer pulls the previous value from the cache so the destination never sees the __debezium_unavailable_value placeholder. Artie Reader does emit the __debezium_unavailable_value string when it encounters a TOASTed column in the WAL because we built Transfer to be compatible with both Debezium pipelines and Artie Reader.

Schema drift: Artie never requires a schema registry. Schema changes are detected live from the source's mechanism (eg WAL for Postgres, DDL for MySQL) via Artie's Reader layer. If Reader sees a column that it doesn't recognize yet, it logs a warning and skips instead of crashing.

Let me know if you have any other questions!

Thanks for the flag. We'll get that fixed!

Artie OSS: https://github.com/artie-labs/transfer Artie Cloud: https://www.artie.so/

Artie Cloud -- https://www.artie.so/

Artie OSS -- https://github.com/artie-labs/transfer

On a single unbounded (CPU + mem) Debezium running on a VM extracting Postgres, I was able to clock in about 7-10m/hr. You could increase the # of tasks, but then it'll hinder your DB perf. Also, this is on your primary DB.

We found it far more efficient and less risky to do CDC streaming and snapshotting w/o read lock in parallel to two different topics. Once snapshot is done and drained, we then move to drain the CDC topic.

Hi! Yes, our next 2 connectors are going to be S3 and DynamoDB.

GitHub Issue: https://github.com/artie-labs/transfer/issues/157

If you'd like to be another design partner for us on this, do reach out. I'm at [email protected].

Definitely. For staging temporary tables to merge for our Redshfit destination, we're uploading it to S3.

We will be creating a S3 destination with TSV, Avro, Parquet format support verily soon.

For Postgres, we have our own custom snapshotter that is capable of doing parallel snapshots against your read replica and not incur WAL growth. More details here: https://news.ycombinator.com/item?id=36855338

For MySQL and MongoDB, we rely on Debezium to perform the initial snapshots.

Thank you so much for the support!

Thanks for the comment!

Your comment regarding DDL is interesting.

Today, this is what happens:

1/ Column doesn't exist in the destination, we'll create it based on our typing inference from the data type (important: not the data value).

2/ Certain tools will handle automatic column data type conversion if a change like this was detected at the source. We do not do this. We will simply hard fail and cause head-of-line blocking reasons being: this is anti-pattern and should be rare, in which case - it's okay to cause an err and require manual intervention for this breaking change.

3/ If the column has been dropped from the source, you as the end user can decide whether this column should be also dropped in the destination, or not. The default is not to drop it.

^ We hear more customers explicitly don't want columns to be dropped because it could cause downstream errors, such as other views / tables not compiling due to referencing a non-existent column.

We haven't heard much from folks that don't even want columns to be added. If there is a need, we can definitely add that as a config option to provide maximum configurability.

> Finally, the biggest issue with CDC always ends up being the seed loads, recoveries and the incremental snapshot strategies.

Yep totally. On the recovery bit, this is exactly why we are leveraging Kafka. If there are any particular issues, we simply don't commit the offset and cause head-of-line blocking.

On the incremental snapshot and recoveries bit, we primarily leverage Debezium's DDD-3 high watermark strategy [1] for MySQL and MongoDB. Postgres has a different issue in that replication slots can grow really fast, esp on AWS! [2]. We ended up writing our own custom snapshotter for Postgres that is Debezium compatible to onboard customers that have a massive dataset and cannot afford to have a read lock on their WAL.

[1] https://github.com/debezium/debezium-design-documents/blob/m... [2] https://www.morling.dev/blog/insatiable-postgres-replication...

Hm, perhaps I wasn't being clear, apologies for that.

What I am proposing above is ways to provide a view to teams that do not want real-time data while keeping your underlying dataset in real-time.

Thanks for your feedback!

> Something McKinley doesn't address is that it's quite advantageous if the values in your data warehouse don't change intra-day because this lets business users reach consensus. Whereas if Bob runs a report and gets $X, and Alice runs the same report 5 minutes later and gets $Y, that creates confusion (much more than you would expect). I recall a particular system I built that refreshed every 6 hours (limited by upstream), that eventually Marketing asked me to dial back to every 24 hours because they couldn't stand things changing in the middle of the day.

If they want to see a consistent view of the report, you could bound this.

1/ SELECT * FROM FOO WHERE DATE_TRUNC('day', updated_at) < DATE_TRUNC('day', DATEADD(day, -1, CURRENT_DATE()));

If your dataset doesn't contain kv, you can turn on include `artie_updated_at` which will provide an additional column with the updated_at field to support incremental ingestion.

2/ If you had stateful data, you could also explore creating a Snowflake task and leveraging the time travel f(x) to create a "snapshot" if your workload depended on it.

3/ Also, if you _did_ want this to be more lagged, you can actually increase the flushIntervalSeconds [1] to 6h, 24h, whichever time interval you fancy. You as the customer should have maximum flexibility when it comes to when to flush to DWH.

> Now of course I see you're targeting more real-time use cases like fraud detection. That's great! But why you would run a fraud detection process out of your data warehouse, which likely doesn't even have a production-grade uptime SLA? Run it out of your production database, that's what it's for!

You can certainly do this in production db (that was our original hypothesis as well!), however, after talking to more companies...it has become more obvious to us that folks that are running fraud algos actually want to join this across various data sets. Further, by using a DWH - it provides a nice visualization layer on top.

Of course, you could go with something even more bespoke by utilizing real-time DBs such as Materialize / Rockset / RisingWave. Just comes with trade offs such as increase in architectural complexity.

There are also plenty of additional use cases this can unlock given that DWH is a platform, any post-DWH application can benefit from less lag, such as reverse ETLs.

[1] https://docs.artie.so/running-transfer/options

For now, we're super focused on databases as sources. We really want to do this well before we move on to other data sources such as APIs.

Thank you!!

Definitely. Do you expect the resulting data to also be in binary / bytes format in your DWH?

I ask because there's a workaround by setting `binary.handling.mode` to a STRING type [1].

Transfer will then automatically pick this up and write this as a B64 string to the DWH.

[1] https://debezium.io/documentation/reference/stable/connector...