使用Arrow管理数据

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)