Integrating Cassandra, Spark, PostgreSQL and Hadoop as a Hybrid Data Platform – Part 2

In the previous post, we provided a business and architectural
background for the Postgres FDWs that we are developing for Spark,
Hadoop and Cassandra. In particular, we highlighted the key benefits of
bringing Cassandra and PostgreSQL together.

With this post, we will start taking a more technical look at the
Cassandra FDW.

The C* FDW speaks natively with Cassandra on two levels; it:

  • uses the binary CQL protocol instead of the legacy Thrift protocol.
  • directly relies on the DataStax Native C++ driver for Cassandra.

The DataStax C++ driver is performant and feature-rich; various load
balancing and routing options are available and configurable. We are
already making use of some of these features and plan to provide more of
these to our users.

While exploring Cassandra as a Postgres user, the defaults such as
automatic inclusion of the ALLOW FILTERING clause are useful as they
allow gradual familiarity; especially useful in small development
environments. Our intent is to support tuning for large environments
but to default to a configuration geared toward existing PostgreSQL

At this point, let us consider whether we are introducing a new SPOF by
using PostgreSQL with a Cassandra system. We believe not; a PostgreSQL
node at the edge of a Cassandra cluster – as a transactional or open-SQL
end point – is not at all the same as a central master node critical to
the operation of an entire cluster. We see some trade-offs but mostly
we see benefits of bringing PostgreSQL to Cassandra in this way as we
intend to elucidate through this series.

In the next post, we will show you how to get started with the Cassandra

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