使用Arrow管理数据，使用Arrow高效管理数据之道

摘要：通过Arrow管理系统，可以高效地管理数据。该系统提供了强大的数据存储和处理能力，支持多种数据类型和大规模数据集的处理。Arrow能够优化数据存储和访问速度，提高数据处理效率。Arrow还提供了易于使用的API和灵活的编程接口，使得开发人员可以轻松地集成和使用该系统，从而更好地管理和分析数据。Arrow是一个可靠的数据管理工具，适用于各种规模的数据处理任务。

在之前的数据挖掘：是时候更新一下TCGA的数据了推文中，保存TCGA的数据就是使用Arrow格式，因为占空间小，读写速度快，多语言支持（我主要使用的3种语言都支持）

Format

https://arrow.apache.org

Apache Arrow defines a language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware like CPUs and GPUs. The Arrow memory format also supports zero-copy reads for lightning-fast data access without serialization overhead.

Language Supported

Arrow's libraries implement the format and provide building blocks for a range of use cases, including high performance analytics. Many popular projects use Arrow to ship columnar data efficiently or as the basis for analytic engines.

Libraries are available for C, C++, C#, Go, Java, JavaScript, Julia, MATLAB, Python, R, Ruby, and Rust.

Ecosystem

Apache Arrow is software created by and for the developer community. We are dedicated to open, kind communication and consensus decisionmaking. Our committers come from a range of organizations and backgrounds, and we welcome all to participate with us.

R

install.packages("arrow")

library(arrow)

# write iris to iris.arrow and compressed by zstd

arrow::write_ipc_file(iris,'iris.arrow', compression = "zstd",compression_level=1)

# read iris.arrow as DataFrame

iris=arrow::read_ipc_file('iris.arrow')

python

# conda install -y pandas pyarrow

import pandas as pd

# read iris.arrow as DataFrame

iris=pd.read_feather('iris.arrow')

# write iris to iris.arrow and compressed by zstd

iris.to_feather('iris.arrow',compression='zstd', compression_level=1)

Julia

using Pkg

Pkg.add(["Arrow","DataFrames"])

using Arrow, DataFrames

# read iris.arrow as DataFrame

iris = Arrow.Table("iris.arrow") |> DataFrame

# write iris to iris.arrow, using 8 threads and compressed by zstd

Arrow.write("iris.arrow",iris,compress=:zstd,ntasks=8)