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The arrow in-memory format is a powerful way to work with data frame like structures. The surrounding ecosystem includes a rich set of libraries, ranging from data frames such as Polars to query engines such as DataFusion. However, the API of the underlying Rust crates can be at times cumbersome to use due to the statically typed nature of Rust.
serde_arrow, offers a simple way to convert Rust objects into Arrow arrays and
back. serde_arrow relies on the Serde package to
interpret Rust objects. Therefore, adding support for serde_arrow to custom
types is as easy as using Serde's derive macros.
In the Rust ecosystem there are two competing implementations of the arrow
in-memory format. serde_arrow supports both arrow and
arrow2 for schema tracing, serialization from Rust structs to
arrays, and deserialization from arrays to Rust structs.
The following examples assume that serde_arrow is added to the Cargo.toml
file and its features are configured. serde_arrow supports different arrow
and arrow2 versions. The relevant one can be selected by specifying the
correct feature (e.g., arrow-51 to support arrow=51). See
here for more details.
The following examples use the following Rust structure and example records
#[derive(Serialize, Deserialize)]
struct Record {
a: f32,
b: i32,
}
let records = vec![
Record { a: 1.0, b: 1 },
Record { a: 2.0, b: 2 },
Record { a: 3.0, b: 3 },
];use arrow::datatypes::FieldRef;
use serde_arrow::schema::{SchemaLike, TracingOptions};
// Determine Arrow schema
let fields = Vec::<FieldRef>::from_type::<Record>(TracingOptions::default())?;
// Build a record batch
let batch = serde_arrow::to_record_batch(&fields, &records)?;This RecordBatch can now be written to disk using ArrowWriter from the parquet crate.
let file = File::create("example.pq");
let mut writer = ArrowWriter::try_new(file, batch.schema(), None)?;
writer.write(&batch)?;
writer.close()?;use arrow2::datatypes::Field;
use serde_arrow::schema::{SchemaLike, TracingOptions};
let fields = Vec::<Field>::from_type::<Record>(TracingOptions::default())?;
let arrays = serde_arrow::to_arrow2(&fields, &records)?;These arrays can now be written to disk using the helper method defined in the arrow2 guide. For parquet:
use arrow2::{chunk::Chunk, datatypes::Schema};
// see https://jorgecarleitao.github.io/arrow2/io/parquet_write.html
write_chunk(
"example.pq",
Schema::from(fields),
Chunk::new(arrays),
)?;The written files can be read in Python via
# using polars
>>> import polars as pl
>>> pl.read_parquet("example.pq")
shape: (3, 2)
┌─────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ f32 ┆ i32 │
╞═════╪═════╡
│ 1.0 ┆ 1 │
│ 2.0 ┆ 2 │
│ 3.0 ┆ 3 │
└─────┴─────┘
# using pandas
>>> import pandas as pd
>>> pd.read_parquet("example.pq")
a b
0 1.0 1
1 2.0 2
2 3.0 3arrow: the JSON component of the official Arrow package supports serializing objects that support serialize via the Decoder object. It supports primitives types, structs and listsarrow2-convert: adds derive macros to convert objects from and to arrow2 arrays. It supports primitive types, structs, lists, and chrono's date time types. Enum support is experimental according to the Readme. If performance is the main objective,arrow2-convertis a good choice as it has no or minimal overhead over building the arrays manually.
The different implementation have the following performance differences, when compared to arrow2-convert:
The detailed runtimes of the benchmarks are listed below.
| label | time [ms] | arrow2_convert: | serde_arrow::to | serde_arrow::to | arrow_json::Rea |
|---|---|---|---|---|---|
| arrow2_convert::TryIntoArrow | 54.01 | 1.00 | 0.31 | 0.30 | 0.14 |
| serde_arrow::to_arrow2 | 173.84 | 3.22 | 1.00 | 0.98 | 0.46 |
| serde_arrow::to_arrow | 177.92 | 3.29 | 1.02 | 1.00 | 0.47 |
| arrow_json::ReaderBuilder | 378.48 | 7.01 | 2.18 | 2.13 | 1.00 |
| label | time [ms] | arrow2_convert: | serde_arrow::to | serde_arrow::to | arrow_json::Rea |
|---|---|---|---|---|---|
| arrow2_convert::TryIntoArrow | 576.81 | 1.00 | 0.34 | 0.33 | 0.16 |
| serde_arrow::to_arrow2 | 1701.46 | 2.95 | 1.00 | 0.97 | 0.46 |
| serde_arrow::to_arrow | 1748.89 | 3.03 | 1.03 | 1.00 | 0.48 |
| arrow_json::ReaderBuilder | 3676.51 | 6.37 | 2.16 | 2.10 | 1.00 |
| label | time [ms] | arrow2_convert: | serde_arrow::to | serde_arrow::to | arrow_json::Rea |
|---|---|---|---|---|---|
| arrow2_convert::TryIntoArrow | 15.83 | 1.00 | 0.51 | 0.36 | 0.12 |
| serde_arrow::to_arrow2 | 30.90 | 1.95 | 1.00 | 0.70 | 0.23 |
| serde_arrow::to_arrow | 43.96 | 2.78 | 1.42 | 1.00 | 0.33 |
| arrow_json::ReaderBuilder | 133.97 | 8.46 | 4.34 | 3.05 | 1.00 |
| label | time [ms] | arrow2_convert: | serde_arrow::to | serde_arrow::to | arrow_json::Rea |
|---|---|---|---|---|---|
| arrow2_convert::TryIntoArrow | 153.07 | 1.00 | 0.47 | 0.35 | 0.11 |
| serde_arrow::to_arrow2 | 327.32 | 2.14 | 1.00 | 0.74 | 0.23 |
| serde_arrow::to_arrow | 440.39 | 2.88 | 1.35 | 1.00 | 0.31 |
| arrow_json::ReaderBuilder | 1429.31 | 9.34 | 4.37 | 3.25 | 1.00 |
Copyright (c) 2021 - 2024 Christopher Prohm and contributors
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