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In-Memory Analytics with Apache Arrow

In-Memory Analytics with Apache Arrow

By : Matthew Topol
4.9 (15)
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In-Memory Analytics with Apache Arrow

In-Memory Analytics with Apache Arrow

4.9 (15)
By: Matthew Topol

Overview of this book

Apache Arrow is designed to accelerate analytics and allow the exchange of data across big data systems easily. In-Memory Analytics with Apache Arrow begins with a quick overview of the Apache Arrow format, before moving on to helping you to understand Arrow’s versatility and benefits as you walk through a variety of real-world use cases. You'll cover key tasks such as enhancing data science workflows with Arrow, using Arrow and Apache Parquet with Apache Spark and Jupyter for better performance and hassle-free data translation, as well as working with Perspective, an open source interactive graphical and tabular analysis tool for browsers. As you advance, you'll explore the different data interchange and storage formats and become well-versed with the relationships between Arrow, Parquet, Feather, Protobuf, Flatbuffers, JSON, and CSV. In addition to understanding the basic structure of the Arrow Flight and Flight SQL protocols, you'll learn about Dremio’s usage of Apache Arrow to enhance SQL analytics and discover how Arrow can be used in web-based browser apps. Finally, you'll get to grips with the upcoming features of Arrow to help you stay ahead of the curve. By the end of this book, you will have all the building blocks to create useful, efficient, and powerful analytical services and utilities with Apache Arrow.
Table of Contents (16 chapters)
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1
Section 1: Overview of What Arrow Is, its Capabilities, Benefits, and Goals
5
Section 2: Interoperability with Arrow: pandas, Parquet, Flight, and Datasets
11
Section 3: Real-World Examples, Use Cases, and Future Development

Streaming results

You'll recall from the beginning of this chapter, in the Querying multifile datasets section, that I mentioned this was the solution for when you had multiple files and the dataset was potentially too large to fit in memory all at one time. So far, the examples we've seen used the ToTable function to completely materialize the results in memory as a single Arrow table. If your results are too large to fit into memory all at one time, this obviously won't work. Even if your results could fit into memory, it's not the most efficient way to perform the query anyway. In addition to the ToTable (C++) or to_table (Python) function we've been calling, the scanner also exposes functions that return iterators for streaming record batches from the query.

To demonstrate the streaming, let's use a public AWS S3 bucket hosted by Ursa Labs, which contains about 10 years of NYC taxi trip record data in Parquet format. The URI for the dataset is s3...

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