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A library for working with Apache Avro in Rust.

This documentation is under construction.


This project is licensed under the Apache License, Version 2.0. Please note that this is not an official project maintained by Apache Avro.


Everyone is encouraged to contribute! You can contribute by forking the GitHub repo and making a pull request or opening an issue. All contributions will be licensed under the Apache License, Version 2.0.



Apache Avro is a data serialization system which provides rich data structures and a compact, fast, binary data format.

All data in Avro is schematized, as in the following example:

    "type": "record",
    "name": "test",
    "fields": [
        {"name": "a", "type": "long", "default": 42},
        {"name": "b", "type": "string"}

There are basically two ways of handling Avro data in Rust:

  • as Avro-specialized data types based on an Avro schema;
  • as generic Rust types with custom serialization logic implementing AvroDecode (currently only supports deserialization, not serialization).

avro provides a way to read and write both these data representations easily and efficiently.

Installing the library

Add to your Cargo.toml:

avro = "x.y"

Or in case you want to leverage the Snappy codec:

version = "x.y"
features = ["snappy"]

Defining a schema

Avro data cannot exist without an Avro schema. Schemas must be used both while writing and reading and they carry the information regarding the type of data we are handling. Avro schemas are used for both schema validation and resolution of Avro data.

Avro schemas are defined in JSON format and can just be parsed out of a raw string:

use mz_avro::Schema;

let raw_schema = r#"
        "type": "record",
        "name": "test",
        "fields": [
            {"name": "a", "type": "long", "default": 42},
            {"name": "b", "type": "string"}

// if the schema is not valid, this function will return an error
let schema: Schema = raw_schema.parse().unwrap();

// schemas can be printed for debugging
println!("{:?}", schema);

For more information about schemas and what kind of information you can encapsulate in them, please refer to the appropriate section of the Avro Specification.

Writing data

Once we have defined a schema, we are ready to serialize data in Avro, validating them against the provided schema in the process.

NOTE: The library also provides a low-level interface for encoding a single datum in Avro bytecode without generating markers and headers (for advanced use), but we highly recommend the Writer interface to be totally Avro-compatible. Please read the API reference in case you are interested.

Given that the schema we defined above is that of an Avro Record, we are going to use the associated type provided by the library to specify the data we want to serialize:

use mz_avro::types::Record;
use mz_avro::Writer;
// a writer needs a schema and something to write to
let mut writer = Writer::new(schema.clone(), Vec::new());

// the Record type models our Record schema
let mut record = Record::new(schema.top_node()).unwrap();
record.put("a", 27i64);
record.put("b", "foo");

// schema validation happens here

// flushing makes sure that all data gets encoded

// this is how to get back the resulting avro bytecode
let encoded = writer.into_inner();

The vast majority of the time, schemas tend to define a record as a top-level container encapsulating all the values to convert as fields and providing documentation for them, but in case we want to directly define an Avro value, the library offers that capability via the Value interface.

use mz_avro::types::Value;

let mut value = Value::String("foo".to_string());

Using codecs to compress data

Avro supports three different compression codecs when encoding data:

  • Null: leaves data uncompressed;
  • Deflate: writes the data block using the deflate algorithm as specified in RFC 1951, and typically implemented using the zlib library. Note that this format (unlike the "zlib format" in RFC 1950) does not have a checksum.
  • Snappy: uses Google's Snappy compression library. Each compressed block is followed by the 4-byte, big-endianCRC32 checksum of the uncompressed data in the block. You must enable the snappy feature to use this codec.

To specify a codec to use to compress data, just specify it while creating a Writer:

use mz_avro::Writer;
use mz_avro::Codec;
let mut writer = Writer::with_codec(schema, Vec::new(), Codec::Deflate);

Reading data

As far as reading Avro encoded data goes, we can just use the schema encoded with the data to read them. The library will do it automatically for us, as it already does for the compression codec:

use mz_avro::Reader;
// reader creation can fail in case the input to read from is not Avro-compatible or malformed
let reader = Reader::new(&input[..]).unwrap();

In case, instead, we want to specify a different (but compatible) reader schema from the schema the data has been written with, we can just do as the following:

use mz_avro::Schema;
use mz_avro::Reader;

let reader_raw_schema = r#"
        "type": "record",
        "name": "test",
        "fields": [
            {"name": "a", "type": "long", "default": 42},
            {"name": "b", "type": "string"},
            {"name": "c", "type": "long", "default": 43}

let reader_schema: Schema = reader_raw_schema.parse().unwrap();

// reader creation can fail in case the input to read from is not Avro-compatible or malformed
let reader = Reader::with_schema(&reader_schema, &input[..]).unwrap();

The library will also automatically perform schema resolution while reading the data.

For more information about schema compatibility and resolution, please refer to the Avro Specification.

There are two ways to handle deserializing Avro data in Rust, as you can see below.

NOTE: The library also provides a low-level interface for decoding a single datum in Avro bytecode without markers and header (for advanced use), but we highly recommend the Reader interface to leverage all Avro features. Please read the API reference in case you are interested.

The avro way

We can just read directly instances of Value out of the Reader iterator:

use mz_avro::Reader;
let mut reader = Reader::new(&input[..]).unwrap();

// value is a Result of an Avro Value in case the read operation fails
for value in reader {
    println!("{:?}", value.unwrap());

Custom deserialization (advanced)

It is possible to avoid the intermediate stage of decoding to Value, by implementing AvroDecode for one or more structs that will determine how to decode various schema pieces.

This API is in flux, and more complete documentation is coming soon. For now, Materialize furnishes the most complete example.


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