Hey @simplesteph, @mike, @magaton - lots of interesting thoughts here! I'll discuss them in turn.
Last time I checked, Apache Kafka was schema technology agnostic But in all seriousness, I know what you mean - the Apache Kafka ecosystem is at the moment consolidating around the open-source and commercial technology provided by the VC-backed startup Confluent.
There are two independent questions here:
- Should we use Avro with Snowplow?
- Should we add support for Confluent schema registry alongside Iglu schema registry?
Should we use Avro with Snowplow?
In principle, the answer is yes - it's a great schema technology. But there are a couple of qualifications to this:
1. The collector payloads
Currently these are represented in Thrift with this schema:
There are minimal benefits to moving this to Avro:
- Thrift is already a very compact format
- Avro has better schema evolution capabilities than Thrift, but the
CollectorPayload schema evolves very slowly (no changes since defining it)
If we were starting Snowplow again today then I would use Avro for the collector payloads, but I'm not convinced there's a strong reason to move. Happy to hear a counter-argument.
2. The enriched events
At the moment Snowplow enriched events are an... unusual... TSV+JSON format, comprising two components:
- A set of legacy properties represented as first-class columns in the TSV
- Single/arrays of heterogeneous self-describing JSON records, stored in three columns
This is a very non-standard format, and we plan on migrating away from it over time.
The first phase of this migration is straightforward: moving the legacy TSV properties into JSON, alongside all the other JSON records. Expect an RFC on this soon.
Where we go next in the migration is an open question. There isn't an existing schema technology which really fits the Snowplow enriched event, because it's so heterogenous: a given event can contain 10 or 20 discrete entities, all independently versioned. Complicating things further, we also want to move to a graph structure inside each Snowplow enriched event (think
IP->Location+Time->Weather), which again is difficult to model in a first-class way using existing schema technology.
So it's an open question. However, what is clear is that lots of users want to analyze Snowplow events at scale in a type-safe way using Spark, Athena, Flink, Impala, Presto, Hive etc. To support them, we are planning to add an equivalent of our Redshift load process, which will pre-process our enriched events into S3 in an analytics-friendly way, using Avro plus Parquet:
3. Enriched events in Avro/Parquet/S3 with the Hive metastore
Everything old is new again - it seems like the Hive metastore has "won" in terms of being the place where you register the various tables/folders of events that you are writing to your data lake, ready for subsequent querying.
We need to come up with a process like so:
enriched events -> Avro format -> Parquet + folder structure -> S3 / Azure Data Lake Store
Iglu schema registry Hive metastore
There are plenty of questions still about how to structure all of this. Expect an RFC on this once the atomic.events migration RFC has settled down.
Should we add support for Confluent schema registry alongside Iglu schema registry?
Confluent schema registry is much narrower in scope than Iglu:
- Depends on Kafka and Kafka clients
- Limited to Avro
- Doesn't support schema federation (assumes that all schemas live in a single company-internal schema registry)
- Doesn't support new
MODEL version changes to schemas (so you end up with
However, it does have deep integration with the Confluent ecosystem that's emerging around Kafka.
Where does that leave us? The interesting thing is that Confluent schema registry is effectively a subset of the functionality of Iglu schema registry, so I believe it would be relatively straightforward to maintain a clone/mirror of the Iglu schema registry in Confluent, in much the same way that we will support the Hive metastore.
So potentially we would implement something like this:
enriched events -> Snowplow Avro format -> Confluent Avro format -> Confluent ecosystem
Iglu schema registry ----> Confluent schema registry
Anyway it needs further thought - certainly an interesting idea...