使用Arrow管理数据
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Format
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
Libraries are available for C, C++, C#, Go, Java, JavaScript, Julia, MATLAB, Python, R, Ruby, and Rust.
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)原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。
如有侵权，请联系 cloudcommunity@tencent.com 删除。
原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。
如有侵权，请联系 cloudcommunity@tencent.com 删除。
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