Apache Arrow

Powering Columnar In-Memory Analytics

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Fast

Apache Arrow™ enables execution engines to take advantage of the latest SIMD (Single input multiple data) operations included in modern processors, for native vectorized optimization of analytical data processing. Columnar layout is optimized for data locality for better performance on modern hardware like CPUs and GPUs.

The Arrow memory format supports zero-copy reads for lightning-fast data access without serialization overhead.

Flexible

Arrow acts as a new high-performance interface between various systems. It is also focused on supporting a wide variety of industry-standard programming languages. Java, C, C++, Python, Ruby, and JavaScript implementations are in progress and more languages are welcome.

Standard

Apache Arrow is backed by key developers of 13 major open source projects, including Calcite, Cassandra, Drill, Hadoop, HBase, Ibis, Impala, Kudu, Pandas, Parquet, Phoenix, Spark, and Storm making it the de-facto standard for columnar in-memory analytics.

Performance Advantage of Columnar In-Memory

SIMD

Advantages of a Common Data Layer

common data layer
  • Each system has its own internal memory format
  • 70-80% computation wasted on serialization and deserialization
  • Similar functionality implemented in multiple projects
common data layer
  • All systems utilize the same memory format
  • No overhead for cross-system communication
  • Projects can share functionality (eg, Parquet-to-Arrow reader)