PostgreSQL is becoming a catch-all solution for simple scenario, and this is a trend, not an accident. A new article full of evidence enforced the ideas I wrote down in the 2019 about Kafka vs PostgreSQL: lets dig into it.

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Some co-workers started using Apache Kafka con a bunch of our Customers.

Apache Kafka is a community distributed event streaming platform capable of handling trillions of events a day. Initially conceived as a messaging queue, Kafka is based on an abstraction of a distributed commit log[*].

To get this goal, Apache Kafka needs a complex servers setup, even more complex if you want the certification for the producing company (Confluent).  Now, if you are planning to use Kafka like a simple JavaMessaeSystem (JMS) implementation, think twice before going on this route.

PostgreSQL 12 offers a fair (and open source) partition implementation, whereas if money are not a problem, Oracle 12c can happy scale on billions of record before running into troubles (and ExaData can scale even more).

PostgreSQL and Oracle offer optimizations for partitioned data, called “Partition Pruning” in PostreSQL teminology:

With partition pruning enabled, the planner will examine the definition of each partition and prove that the partition need not be scanned because it could not contain any rows meeting the query's WHERE clause. When the planner can prove this, it excludes (prunes) the partition from the query plan.

This feature is quite brand new (popped in PostreSQL 11) but it is essential to a successful partition strategy. Before these feature, partitioning was a black magic art. Now it is simpler to manage.
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