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In-Memory Analytics with Apache Arrow

You're reading from   In-Memory Analytics with Apache Arrow Perform fast and efficient data analytics on both flat and hierarchical structured data

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Product type Paperback
Published in Jun 2022
Publisher Packt
ISBN-13 9781801071031
Length 392 pages
Edition 1st Edition
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Author (1):
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Matthew Topol Matthew Topol
Author Profile Icon Matthew Topol
Matthew Topol
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Table of Contents (16) Chapters Close

Preface 1. Section 1: Overview of What Arrow Is, its Capabilities, Benefits, and Goals
2. Chapter 1: Getting Started with Apache Arrow FREE CHAPTER 3. Chapter 2: Working with Key Arrow Specifications 4. Chapter 3: Data Science with Apache Arrow 5. Section 2: Interoperability with Arrow: pandas, Parquet, Flight, and Datasets
6. Chapter 4: Format and Memory Handling 7. Chapter 5: Crossing the Language Barrier with the Arrow C Data API 8. Chapter 6: Leveraging the Arrow Compute APIs 9. Chapter 7: Using the Arrow Datasets API 10. Chapter 8: Exploring Apache Arrow Flight RPC 11. Section 3: Real-World Examples, Use Cases, and Future Development
12. Chapter 9: Powered by Apache Arrow 13. Chapter 10: How to Leave Your Mark on Arrow 14. Chapter 11: Future Development and Plans 15. Other Books You May Enjoy

Executing compute functions

The Arrow compute library has a global FunctionRegistry, which allows looking up functions by name and listing what is available to call. The list of available compute functions can also be found in the Arrow documentation at https://arrow.apache.org/docs/cpp/compute.html#available-functions. Let's see how to execute these functions now!

Using the C++ compute library

The compute library is managed as a separate module in the base Arrow package. If you've compiled the library yourself from source, make sure that you've used the ARROW_COMPUTE=ON option during configuration.

Example 1 – adding a scalar value to an array

Our first example is going to be a simple scalar function call on an array of data, using the same Parquet file as we did previously in the C Data API examples:

  1. First things first, we need to read the column we want from the Parquet file. We can use the Parquet C++ library to open the file and it provides...
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