Datafusion Query Execution With Rust And Arrow
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DataFusion: Query Execution with Rust and Arrow
"DataFusion: Query Execution with Rust and Arrow" "DataFusion: Query Execution with Rust and Arrow" is a comprehensive exploration into the architecture, execution, and innovation that power modern analytical query engines. This book begins by establishing a solid foundation in advanced Rust programming, data systems engineering, and the transformative role of Apache Arrow’s columnar memory format. Through its in-depth examination of DataFusion’s core architecture, readers gain a clear understanding of how high-performance, safe, and flexible query processing is achieved in cloud-native analytics environments. Delving deeper, the book covers the full spectrum of query lifecycle stages: from SQL parsing and logical planning to physical execution and advanced optimization. It demystifies the interplay between logical and physical plans, highlighting strategies such as predicate pushdown, schema inference, and cost-based optimization. Detailed discussions of parallelism, vectorized execution, memory management, and the seamless integration of diverse data sources position DataFusion at the forefront of modern large-scale analytics. Chapters dedicated to distributed execution with Ballista, resource-adaptive scheduling, and workload profiling provide practical guidance for building scalable and robust analytical platforms. With dedicated sections on observability, debugging, security, and extensibility, "DataFusion: Query Execution with Rust and Arrow" equips both practitioners and architects to tackle real-world challenges in analytical data systems. Coverage of Arrow Flight, custom data connectors, auditability, user-defined functions, and future directions ensures readers are prepared for the rapidly evolving landscape of cloud, stream, and real-time analytics. This work is an essential guide for anyone seeking deep technical mastery of the systems powering next-generation, high-performance data analytics.
In-Memory Analytics with Apache Arrow
Author: Matthew Topol
language: en
Publisher: Packt Publishing Ltd
Release Date: 2022-06-24
Process tabular data and build high-performance query engines on modern CPUs and GPUs using Apache Arrow, a standardized language-independent memory format, for optimal performance Key Features Learn about Apache Arrow's data types and interoperability with pandas and Parquet Work with Apache Arrow Flight RPC, Compute, and Dataset APIs to produce and consume tabular data Reviewed, contributed, and supported by Dremio, the co-creator of Apache Arrow Book DescriptionApache 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.What you will learn Use Apache Arrow libraries to access data files both locally and in the cloud Understand the zero-copy elements of the Apache Arrow format Improve read performance by memory-mapping files with Apache Arrow Produce or consume Apache Arrow data efficiently using a C API Use the Apache Arrow Compute APIs to perform complex operations Create Arrow Flight servers and clients for transferring data quickly Build the Arrow libraries locally and contribute back to the community Who this book is for This book is for developers, data analysts, and data scientists looking to explore the capabilities of Apache Arrow from the ground up. This book will also be useful for any engineers who are working on building utilities for data analytics and query engines, or otherwise working with tabular data, regardless of the programming language. Some familiarity with basic concepts of data analysis will help you to get the most out of this book but isn't required. Code examples are provided in the C++, Go, and Python programming languages.
Scaling Up with R and Apache Arrow
Analyze large datasets directly from R. Scaling Up With R and Arrow provides a guide to working efficiently with larger-than-memory datasets using the arrow R package. As data grows in size and complexity, traditional data analysis methods in R often hit technical limitations. In this book, you'll learn how to overcome these hurdles without needing to set up complex infrastructure. You'll learn about the Apache Arrow project's origins, goals, and its significance in bridging the gap between data science and big data ecosystems. You'll also learn how to leverage the arrow R package to work directly with files in various formats, such as CSV and Parquet, using familiar dplyr syntax. This book explores practical topics like data manipulation, file formats, working with larger datasets, and optimizing workflows for data in cloud storage. Advanced chapters examine user-defined functions, integration with other tools like DuckDB, and extending Arrow's capabilities to work with geospatial data. Written by developers of the Arrow R package, this guide is essential for anyone looking to scale their data processing capabilities in R.