Improving Python and Spark Performance and Interoperability with Apache Arrow DevOps и эксплуатация
Li Jin is a distributed systems developer in Two Sigma. Li works on a Mesos framework that runs thousands of computing tasks every day. In his spare time, Li likes to read distributed system papers, hiking and winter sports
Apache Spark has become a popular and successful way for Python programming to parallelize and scale up data processing. In many use cases though, a PySpark job can perform worse than an equivalent job written in Scala. It is also costly to push and pull data between the user’s Python environment and the Spark master.
Apache Arrow-based interconnection between the various big data tools (SQL, UDFs, machine learning, big data frameworks, etc.) enables you to use them together seamlessly and efficiently, without overhead. When collocated on the same processing node, read-only shared memory and IPC avoid communication overhead. When remote, scatter-gather I/O sends the memory representation directly to the socket avoiding serialization costs.
In this talk, we will demonstrate how we improve PySpark performance with Apache Arrow.