In-Memory Analytics with Apache Arrow
To Nha Notes | Sept. 2, 2022, 2:04 p.m.
Apache Arrow is a development platform for in-memory analytics. It contains a set of technologies that enable big data systems to process and move data fast. It specifies a standardized language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware.
- Using the same high-performance internal format across components allows much more code reuse in libraries instead of reimplementing common workflows.
- The Arrow libraries provide mechanisms to directly share memory buffers to reduce copying between processes by using the same internal representation regardless of the language. This is what is being referred to whenever you see the term zero-copy.
- The wire format is the same as the in-memory format to eliminate serialization and deserialization costs when sending data across networks between components of a system.
The sample of use cases where using Arrow as the internal/intermediate data format can be very beneficial:
- SQL execution engines (such as Dremio Sonar, Apache Drill, or Impala)
- Data analysis utilities and pipelines (such as pandas or Spark)
- Streaming and message queue systems (such as Apache Kafka or Storm)
- Storage systems and formats (such as Apache Parquet, Cassandra, and Kudu)
The following are a few different roles that work with data and show how using Arrow could potentially be beneficial
- If you're a data scientist:
- You can utilize Arrow via pandas and NumPy integration to significantly improve the performance of your data manipulations.
- If the tools you use integrate Arrow support, you can gain significant speed-ups to your queries and computations by using Arrow directly yourself to reduce copies and/or serialization costs.
- If you are a data engineer specializing in extract, transform, and load (ETL):
- The higher adoption of Arrow as an internal and externally-facing format can make it easier to integrate with many different utilities.
- By using Arrow, data can be shared between processes and tools with shared memory increasing the tools available to you for building pipelines, regardless of the language you're operating in. You could take data from Python and use it in Spark and then pass it directly to the Java Virtual Machine (JVM) without paying the cost of copying between them.
- If you are a software engineer or ML specialist building computation tools and utilities for data analysis:
- Arrow as an internal format can be used to improve your memory usage and performance by reducing serialization and deserialization between components.
- Understanding how to best utilize the data transfer protocols can improve the ability to parallelize queries and access your data, wherever it might be.
- Because Arrow can be used for any sort of tabular data, it can be integrated into many different areas of data analysis and computation pipelines, and is versatile enough to be beneficial as an internal and data transfer format, regardless of the shape of your data.
References
The book In-Memory Analytics with Apache Arrow