Nice, but I can't find any reason to choose Spark over modern Distributed SQL databases (CockroachDB, CitusDB, TiDB etc. or cloud vendor-specific SQL DBs)
You can use the following options individually or in combination.
Option 1 : Pipeline DB extension (PostgreSQL)
Option 2 : Service broker in commercial SQL databases or building PUSH/PULL queue if not supported. There are many libraries in each programming language which tries to do that. Also see option 4.
Option 3 : Using CDC or Replication for synchronous or asynchronous streamed computation on single or multi node cluster
Option 4 : Transducers. For example, you can compose many sql functions or procedures to act on a single chunk of data instead of always doing async streamed computation after each stage of transformation.
For data cleaning, processing, analytics, ML on decently large datasets? Spark wins out
Dask+Perfect is a much better experience all round including perf, with virtually none of the cluster management hell involved.
Unlike Airflow, this lends itself to microbatching and streaming. Plus a bunch of housekeeping items ticked off that Airflow never got around to. With a bit of devops engineering time, you can have perfect manage the size of your worker cluster on k8s and scale it up/down with ingest demand, etc.
I'll say one thing though. The Perfect website used to be a lot more technical and explicit about what it is and isn't. Now it's mostly sales gobbledegook. Maybe not a good sign. I've seen this happen before with dremio.
Do you run dask on k8s ? I have been concerned that dask does not leverage kubernetes HPA for autoscaling...but instead chooses to run an external scheduler.
How has your experience been ?
Not the most SEO-friendly choice of name. Great product though.
Data cleaning -> PL/SQL and various inbuilt functions for the transformation of data (or new UDF if required at all)
Processing -> PostgreSQL Parallel processing on the local node and Citus DB extension for distributed computing and sharding
Analytics -> Many options here. Materialized views OR Triggers OR Streaming computation with PipelineDB extension OR Using Logical replication for stream computation
ML -> PG support variety of statistics functions. It also supports PL/R and PL/Python extension to interface with ML libraries.
Also, there are various kinds of Foreign Data Wrappers supported by PG - https://wiki.postgresql.org/wiki/Foreign_data_wrappers
PG is great but it's not suitable to be a feature store and sure as hell not suitable to fan out ML workloads. In a modern ML stack, PG might play the role of the slow but reliable master store that the rest of the ML pipeline feeds off.
depends on the scale? Not everyone processes petabytes of data.
> PG might play the role of the slow
You have any benchmark in your hand to support this? I believe highly optimized C code in PG can be significantly faster than Scala inside Spark.
There's no question about this. If you can express your task in terms of PG on a single instance, then you probably should.
When you get to more complex tasks, like running input through GloVe and pushing ngrams to a temporal store, PG offers very little - which is fine, it's not at all what PG is designed for. Inter-node IO eclipses single node perf, which is why Spark is used despite being a terribly inefficient thing (although in the case of Spark, it's so inefficient that for interim sized workloads you'd actually be better off vertically scaling a single node and using something else). PG won't help at all with these tasks.
Also, that smorgasbord of extensions GP listed isn't offered by any cloud vendor as a managed service afaik, meaning you must roll and manage your own. Depending on your needs, that might be a show stopper.
why exactly you think PG will not do this well?
Then you join first and second table.
Also, I think typical scenario is to resolve embeddings in your model code or data input pipeline.
Correct. PG has no place in this workload other than being the final store for the model output. And even then, you'd be using a column store like Redshift or Clickhouse. PG not even suitable for the ngram counters because its ingest rates are way too slow to keep up with a fanned out model spitting out millions of ngrams per second in addition to everything else going on in the pipeline.
You -could- probably do it all in PG. But that'd be a silly esoteric challenge exercise and not something anyone would try on a project. I am sure you recognise that.
But you have a year worth of historical data that you want to work with. If you're able to process 1m ngrams per second, it'll take a couple of days to get through that. You probably want to get closer to 10m/s if you're tweaking your model and want to iterate reasonably quickly. Of course there's ways to optimise all that and batch it and whatnot, but basically any big data tasks with the need to work on historical data and iterate on their models, quickly end up with kafka clusters piping millions of messages per second to keep those iteration times productive.
Ultimately this post is about Spark, and the comment that started this was someone listing PG 'replacements' for traditional ML pipeline components. If you need Spark, you're at scales where PG has no place.
Also in typical ML pipeline as I mentioned you can generate ngrams in input function of your model (Dataset API in TF), you don't need to store it somewhere.
It's also graph analysis and ML models.
ML models - I already mentioned how to uplift R and Python functions to SQL function. even if you are not using PostgreSQL many other databases help you with uplifting and interfacing with existing ML libraries through FFI