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Over the past few decades, databases and data analysis have changed dramatically.

Businesses have increasingly complex requirements for analyzing and using data – and increasingly high standards for query performance.

Memory has become inexpensive, enabling a replacement set of performance strategies supported in-memory analysis.

CPUs and GPUs have increased in performance, but have also evolved to optimize processing data in parallel

New sorts of databases have emerged for various use cases, each with its own way of storing and indexing data. for instance , because real-world objects are easier to represent as hierarchical and nested data structures, JSON and document databases became popular.

New disciplines have emerged, including data engineering and data science, both with dozens of latest tools to realize specific analytical goals.

Columnar data representations became mainstream for analytical workloads because they supply dramatic advantages in terms of speed and efficiency.

With these trends in mind, a transparent opportunity emerged for a typical in-memory representation that each engine can use; one that’s modern, which takes advantage of all the new performance strategies that are now available; and one that creates sharing of knowledge across platforms seamless and efficient. this is often the goal of Apache Arrow. Learn more about the origins and history of Apache Arrow.

To use an analogy, consider traveling to Europe on vacation before the EU. to go to 5 countries in 7 days, you’ll calculate the very fact that you simply were getting to spend a couple of hours at the border for passport control, and you were getting to lose a number of your money within the currency exchange. this is often how working with data in-memory works without Arrow: enormous inefficiencies exist to serialize and de-serialize data structures, and a replica is formed within the process, wasting your memory and CPU resources. In contrast, Arrow is like visiting Europe after the EU and therefore the Euro: you do not wait at the border, and there are one currency is employed everywhere.

Arrow combines the advantages of columnar data structures with in-memory computing. It provides the performance benefits of those modern techniques while also providing the pliability of complex data and dynamic schemas. And it does all of this in an open source and standardized way.

Apache Arrow Core Technologies

Apache Arrow itself isn’t a storage or execution engine. it’s designed to function a shared foundation for the subsequent sorts of systems:

SQL execution engines (such as Drill and Impala)

Data analysis systems (such as Pandas and Spark)

Streaming and queueing systems (such as Kafka and Storm)

Storage systems (such as Parquet, Kudu, Cassandra, and HBase)

Arrow consists of variety of connected technologies designed to be integrated into storage and execution engines. The key components of Arrow include:

Defined Data Type Sets including both SQL and JSON types, like Int, BigInt, Decimal, VarChar, Map, Struct and Array.

Canonical Representations: Columnar in-memory representations of knowledge to support an arbitrarily complex record structure built on top of the defined data types.

Common Data Structures: Arrow-aware companion data structures including pick-lists, hash tables, and queues.

Inter-Process Communication achieved within shared memory, TCP/IP and RDMA.

Data Libraries for reading and writing columnar data in multiple languages, including Java, C++, Python, Ruby, Rust, Go, and JavaScript.

Pipeline and SIMD Algorithms for various operations including bitmap selection, hashing, filtering, bucketing, sorting and matching.

Columnar In-Memory Compression including a set of techniques to extend memory efficiency.

Memory Persistence Tools for short-term persistence through non-volatile memory, SSD or HDD.

As such, Arrow doesn’t compete with any of those projects. Its core goal is to figure within each of them to supply increased performance and stronger interoperability. In fact, Arrow is being built by the lead developers of the many of those projects.


The faster a user can get to the solution , the faster they will ask other questions. High performance leads to more analysis, more use cases and further innovation. As CPUs become faster and more sophisticated, one among the key challenges is ensuring processing technology uses CPUs efficiently.

Arrow is specifically designed to maximize:

Cache Locality: Memory buffers are compact representations of knowledge designed for contemporary CPUs. The structures are defined linearly, matching typical read patterns. meaning that data of comparable type is co-located in memory. This makes cache prefetching simpler , minimizing CPU stalls resulting from cache misses and main memory accesses. These CPU-efficient data structures and access patterns reach both traditional flat relational structures and modern complex data structures.

Pipelining: Execution patterns are designed to require advantage of the superscalar and pipelined nature of recent processors. this is often done by minimizing in-loop instruction count and loop complexity. These tight loops cause better performance and fewer branch-prediction failures.

SIMD Instructions: Single Instruction Multiple Data (SIMD) instructions allow execution algorithms to work more efficiently by executing multiple operations during a single clock cycle. Arrow organizes data to be fitted to SIMD operations.

Cache locality, pipelining and superscalar operations frequently provide 10-100x faster execution performance. Since many analytical workloads are CPU-bound, these benefits translate into dramatic end-user performance gains. Here are some examples:

PySpark: IBM measured a 53x speedup in processing by Python and Spark after adding support for Arrow in PySpark

Parquet and C++: Reading data into Parquet from C++ at up to 4GB/s

Pandas: Reading into pandas up to 10GB/s

Arrow also promotes zero-copy data sharing. As Arrow is adopted because the representation in each system, one system can hand data on to the opposite system for consumption. And when these systems are located on an equivalent node, the copy described above also can be avoided through the utilization of shared memory. this suggests that in many cases, moving data between two systems will haven’t any overhead.

Memory Efficiency

In-memory performance is great, but memory are often scarce. Arrow is meant to figure albeit the info doesn’t fit entirely in memory. The core arrangement includes vectors of knowledge and collections of those vectors (also called record batches). Record batches are typically 64KB-1MB, counting on the workload, and are usually bounded at 2^16 records. This not only improves cache locality, but also makes in-memory computing possible even in low-memory situations.

With many Big Data clusters starting from 100’s to 1000’s of servers, systems must be ready to cash in of the mixture memory of a cluster. Arrow is meant to attenuate the value of moving data on the network. It utilizes scatter/gather reads and writes and features a zero serialization/ deserialization design, allowing low-cost data movement between nodes. Arrow also works directly with RDMA-capable interconnects to supply one memory grid for larger in-memory workloads.

Programming Language Support

Another major advantage of adopting Apache Arrow, besides stronger performance and interoperability, may be a level-playing field among different programming languages. Traditional data sharing is predicated on IPC and API-level integrations. While this is often often simple, it hurts performance when the user’s language is different from the underlying system’s language. Counting on the language and therefore the set of algorithms implemented, the language transformation causes the bulk of the time interval.


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