Arrow Arrays and Data Types
Arrow arrays are the foundation of the Apache Arrow data model. An Arrow array is a contiguous, typed, and immutable block of memory that represents a single column of data. This columnar structure means that all values in an array share the same data type and are stored sequentially in memory. By organizing data in columns rather than rows, Arrow arrays support efficient access patterns for analytical workloads and are optimized for modern hardware architectures. If you recall the columnar memory model from the previous section, Arrow arrays are its core building block, providing a powerful abstraction for high-performance, in-memory analytics.
An Arrow array is a one-dimensional, typed, immutable sequence of values stored in contiguous memory. Arrow supports a diverse set of data types, including:
- Primitive types: integers, floating-point numbers, booleans, and fixed-size binaries;
- Temporal types: dates, times, timestamps, and durations;
- String and binary types: variable-length UTF-8 strings and arbitrary byte arrays;
- Nested types: lists, structs (records), maps, and unions, allowing for complex, hierarchical data structures.
The design of Arrow arrays enables efficient computation, both in terms of speed and memory usage. By storing values contiguously and enforcing a strict, consistent data type, Arrow arrays allow for vectorized operations — processing many values at once using low-level hardware instructions. This reduces CPU overhead and improves cache locality, which is especially beneficial for large datasets. The rich typing system, including support for nested and variable-length types, means you can represent both simple and complex data structures without sacrificing performance or interoperability. As you work with Arrow arrays, you will find that this approach provides a robust foundation for scalable, high-performance analytics and seamless data exchange across tools and languages.
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Arrow arrays are the foundation of the Apache Arrow data model. An Arrow array is a contiguous, typed, and immutable block of memory that represents a single column of data. This columnar structure means that all values in an array share the same data type and are stored sequentially in memory. By organizing data in columns rather than rows, Arrow arrays support efficient access patterns for analytical workloads and are optimized for modern hardware architectures. If you recall the columnar memory model from the previous section, Arrow arrays are its core building block, providing a powerful abstraction for high-performance, in-memory analytics.
An Arrow array is a one-dimensional, typed, immutable sequence of values stored in contiguous memory. Arrow supports a diverse set of data types, including:
- Primitive types: integers, floating-point numbers, booleans, and fixed-size binaries;
- Temporal types: dates, times, timestamps, and durations;
- String and binary types: variable-length UTF-8 strings and arbitrary byte arrays;
- Nested types: lists, structs (records), maps, and unions, allowing for complex, hierarchical data structures.
The design of Arrow arrays enables efficient computation, both in terms of speed and memory usage. By storing values contiguously and enforcing a strict, consistent data type, Arrow arrays allow for vectorized operations — processing many values at once using low-level hardware instructions. This reduces CPU overhead and improves cache locality, which is especially beneficial for large datasets. The rich typing system, including support for nested and variable-length types, means you can represent both simple and complex data structures without sacrificing performance or interoperability. As you work with Arrow arrays, you will find that this approach provides a robust foundation for scalable, high-performance analytics and seamless data exchange across tools and languages.
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