Book Image

In-Memory Analytics with Apache Arrow

By : Matthew Topol
Book Image

In-Memory Analytics with Apache Arrow

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)
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

Picking the right tools

The Arrow compute libraries provide an extremely easy-to-use interface, but what about performance? Do they exist just for ease of use? Let's try it out and compare!

Adding a constant value to an array

For our first test, let's try adding a constant value to a sample array we construct. It doesn't need to be anything extravagant, so we can create a simple 32-bit integer Arrow array and then add 2 to each element and create a new array. We're going to create arrays of various sizes and then see how long it takes to add a constant value of 2 to the Arrow array using different methods.

Remember!

Semantically, an Arrow array is supposed to be immutable, so adding a constant produces a new array. This property of immutability is often used to create optimizations and reusability of memory depending on the particular Arrow implementation. While it is possible to potentially achieve greater performance by modifying a buffer in place...