It is intented to be a Spark ThriftServer alternative with an important distinction that unlike ThriftServer which streams query results back to the client through a single server,. Apache Arrow is integrated with Spark since version 2.3. conda install -c conda-forge pyarrow or pip install pyarrow. Enable PyArrow — Its usage is not automatic and it will require some minor changes to configuration or code to take full advantage and ensure compatibility.
The service uses a simple producer with an InMemoryStore from the Arrow Flight examples. This allows clients to put/get Arrow streams to an in-memory store. The Spark client maps.
An arrow numeric array contains a Data buffer and a Validity buffer. The role of the data buffer is to store the data inserted in the array of types f32, u64, etc (shown in orange). The use of validity buffer is a bit array, used to indicate missing data. Since the missing data is represented by bits, there is minimal memory overhead.
The cross-platform, cross-language aspect supports polyglot microservice architectures and allows for easy integration with the existing Big Data landscape. The built-in RPC framework called.
To the time of writing the latest release is 0.11.0 - 8th of October 2018. Apache Arrow has a bright future ahead, and it's one of its kind in its field. It can be coupled with Parquet and ORC makes a great Big Data ecosystem. The adaption of Apache Arrow has been on rising since its first release in and adopted by Spark.