apache-avro

A library for working with Apache Avro in Rust.

Please check our documentation for examples, tutorials and API reference.

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 serde-compatible types implementing/deriving Serialize and Deserialize;

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

Installing the library

Add to your Cargo.toml:

[dependencies]
apache-avro = "x.y"

Or in case you want to leverage the Snappy codec:

[dependencies.apache-avro]
version = "x.y"
features = ["snappy"]

Or in case you want to leverage the Zstandard codec:

[dependencies.apache-avro]
version = "x.y"
features = ["zstandard"]

Or in case you want to leverage the Bzip2 codec:

[dependencies.apache-avro]
version = "x.y"
features = ["bzip"]

Or in case you want to leverage the Xz codec:

[dependencies.apache-avro]
version = "x.y"
features = ["xz"]

Upgrading to a newer minor version

The library is still in beta, so there might be backward-incompatible changes between minor versions. If you have troubles upgrading, check the version upgrade guide.

Defining a schema

An Avro data cannot exist without an Avro schema. Schemas must be used while writing and can be used while 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 apache_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::parse_str(raw_schema).unwrap();

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

Additionally, a list of of definitions (which may depend on each other) can be given and all of them will be parsed into the corresponding schemas.

use apache_avro::Schema;

let raw_schema_1 = r#"{
        "name": "A",
        "type": "record",
        "fields": [
            {"name": "field_one", "type": "float"}
        ]
    }"#;

// This definition depends on the definition of A above
let raw_schema_2 = r#"{
        "name": "B",
        "type": "record",
        "fields": [
            {"name": "field_one", "type": "A"}
        ]
    }"#;

// if the schemas are not valid, this function will return an error
let schemas = Schema::parse_list(&[raw_schema_1, raw_schema_2]).unwrap();

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

N.B. It is important to note that the composition of schema definitions requires schemas with names. For this reason, only schemas of type Record, Enum, and Fixed should be input into this function.

The library provides also a programmatic interface to define schemas without encoding them in JSON (for advanced use), but we highly recommend the JSON interface. Please read the API reference in case you are interested.

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. As mentioned before, there are two ways of handling Avro data in Rust.

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.

The avro way

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

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

// schema validation happens here
writer.append(record).unwrap();

// this is how to get back the resulting avro bytecode
// this performs a flush operation to make sure data has been written, so it can fail
// you can also call `writer.flush()` yourself without consuming the writer
let encoded = writer.into_inner().unwrap();

The vast majority of the times, 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 apache_avro::types::Value;

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

The serde way

Given that the schema we defined above is an Avro Record, we can directly use a Rust struct deriving Serialize to model our data:

use apache_avro::Writer;

#[derive(Debug, Serialize)]
struct Test {
    a: i64,
    b: String,
}

// a writer needs a schema and something to write to
let mut writer = Writer::new(&schema, Vec::new());

// the structure models our Record schema
let test = Test {
    a: 27,
    b: "foo".to_owned(),
};

// schema validation happens here
writer.append_ser(test).unwrap();

// this is how to get back the resulting avro bytecode
// this performs a flush operation to make sure data is written, so it can fail
// you can also call `writer.flush()` yourself without consuming the writer
let encoded = writer.into_inner();

The vast majority of the times, 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, any type implementing Serialize should work.

let mut value = "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.
• Zstandard: uses Facebook's Zstandard compression library. You must enable the zstandard feature to use this codec.
• Bzip2: uses BZip2 compression library. You must enable the bzip feature to use this codec.
• Xz: uses xz2 compression library. You must enable the xz feature to use this codec.

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

use apache_avro::Writer;
use apache_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 apache_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 apache_avro::Schema;
use apache_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::parse_str(reader_raw_schema).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.

As usual, there are two ways to handle 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 apache_avro::Reader;
let 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());
}

The serde way

Alternatively, we can use a Rust type implementing Deserialize and representing our schema to read the data into:

use apache_avro::Reader;
use apache_avro::from_value;

#[derive(Debug, Deserialize)]
struct Test {
    a: i64,
    b: String,
}

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

// value is a Result in case the read operation fails
for value in reader {
    println!("{:?}", from_value::<Test>(&value.unwrap()));
}

Putting everything together

The following is an example of how to combine everything showed so far and it is meant to be a quick reference of the library interface:

use apache_avro::{Codec, Reader, Schema, Writer, from_value, types::Record, Error};
use serde::{Deserialize, Serialize};

#[derive(Debug, Deserialize, Serialize)]
struct Test {
    a: i64,
    b: String,
}

fn main() -> Result<(), Error> {
    let raw_schema = r#"
        {
            "type": "record",
            "name": "test",
            "fields": [
                {"name": "a", "type": "long", "default": 42},
                {"name": "b", "type": "string"}
            ]
        }
    "#;

    let schema = Schema::parse_str(raw_schema)?;

    println!("{:?}", schema);

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

    let mut record = Record::new(writer.schema()).unwrap();
    record.put("a", 27i64);
    record.put("b", "foo");

    writer.append(record)?;

    let test = Test {
        a: 27,
        b: "foo".to_owned(),
    };

    writer.append_ser(test)?;

    let input = writer.into_inner()?;
    let reader = Reader::with_schema(&schema, &input[..])?;

    for record in reader {
        println!("{:?}", from_value::<Test>(&record?));
    }
    Ok(())
}

apache-avro also supports the logical types listed in the Avro specification:

1. Decimal using the num_bigint crate
2. UUID using the uuid crate
3. Date, Time (milli) as i32 and Time (micro) as i64
4. Timestamp (milli and micro) as i64
5. Local timestamp (milli and micro) as i64
6. Duration as a custom type with months, days and millis accessor methods each of which returns an i32

Note that the on-disk representation is identical to the underlying primitive/complex type.

Read and write logical types

use apache_avro::{
    types::Record, types::Value, Codec, Days, Decimal, Duration, Millis, Months, Reader, Schema,
    Writer, Error,
};
use num_bigint::ToBigInt;

fn main() -> Result<(), Error> {
    let raw_schema = r#"
    {
      "type": "record",
      "name": "test",
      "fields": [
        {
          "name": "decimal_fixed",
          "type": {
            "type": "fixed",
            "size": 2,
            "name": "decimal"
          },
          "logicalType": "decimal",
          "precision": 4,
          "scale": 2
        },
        {
          "name": "decimal_var",
          "type": "bytes",
          "logicalType": "decimal",
          "precision": 10,
          "scale": 3
        },
        {
          "name": "uuid",
          "type": "string",
          "logicalType": "uuid"
        },
        {
          "name": "date",
          "type": "int",
          "logicalType": "date"
        },
        {
          "name": "time_millis",
          "type": "int",
          "logicalType": "time-millis"
        },
        {
          "name": "time_micros",
          "type": "long",
          "logicalType": "time-micros"
        },
        {
          "name": "timestamp_millis",
          "type": "long",
          "logicalType": "timestamp-millis"
        },
        {
          "name": "timestamp_micros",
          "type": "long",
          "logicalType": "timestamp-micros"
        },
        {
          "name": "local_timestamp_millis",
          "type": "long",
          "logicalType": "local-timestamp-millis"
        },
        {
          "name": "local_timestamp_micros",
          "type": "long",
          "logicalType": "local-timestamp-micros"
        },
        {
          "name": "duration",
          "type": {
            "type": "fixed",
            "size": 12,
            "name": "duration"
          },
          "logicalType": "duration"
        }
      ]
    }
    "#;

    let schema = Schema::parse_str(raw_schema)?;

    println!("{:?}", schema);

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

    let mut record = Record::new(writer.schema()).unwrap();
    record.put("decimal_fixed", Decimal::from(9936.to_bigint().unwrap().to_signed_bytes_be()));
    record.put("decimal_var", Decimal::from((-32442.to_bigint().unwrap()).to_signed_bytes_be()));
    record.put("uuid", uuid::Uuid::parse_str("550e8400-e29b-41d4-a716-446655440000").unwrap());
    record.put("date", Value::Date(1));
    record.put("time_millis", Value::TimeMillis(2));
    record.put("time_micros", Value::TimeMicros(3));
    record.put("timestamp_millis", Value::TimestampMillis(4));
    record.put("timestamp_micros", Value::TimestampMicros(5));
    record.put("timestamp_nanos", Value::TimestampNanos(6));
    record.put("local_timestamp_millis", Value::LocalTimestampMillis(4));
    record.put("local_timestamp_micros", Value::LocalTimestampMicros(5));
    record.put("local_timestamp_nanos", Value::LocalTimestampMicros(6));
    record.put("duration", Duration::new(Months::new(6), Days::new(7), Millis::new(8)));

    writer.append(record)?;

    let input = writer.into_inner()?;
    let reader = Reader::with_schema(&schema, &input[..])?;

    for record in reader {
        println!("{:?}", record?);
    }
    Ok(())
}

Calculate Avro schema fingerprint

This library supports calculating the following fingerprints:

• SHA-256
• MD5
• Rabin

An example of fingerprinting for the supported fingerprints:

use apache_avro::rabin::Rabin;
use apache_avro::{Schema, Error};
use md5::Md5;
use sha2::Sha256;

fn main() -> Result<(), Error> {
    let raw_schema = r#"
        {
            "type": "record",
            "name": "test",
            "fields": [
                {"name": "a", "type": "long", "default": 42},
                {"name": "b", "type": "string"}
            ]
        }
    "#;
    let schema = Schema::parse_str(raw_schema)?;
    println!("{}", schema.fingerprint::<Sha256>());
    println!("{}", schema.fingerprint::<Md5>());
    println!("{}", schema.fingerprint::<Rabin>());
    Ok(())
}

Ill-formed data

In order to ease decoding, the Binary Encoding specification of Avro data requires some fields to have their length encoded alongside the data.

If encoded data passed to a Reader has been ill-formed, it can happen that the bytes meant to contain the length of data are bogus and could result in extravagant memory allocation.

To shield users from ill-formed data, apache-avro sets a limit (default: 512MB) to any allocation it will perform when decoding data.

If you expect some of your data fields to be larger than this limit, be sure to make use of the max_allocation_bytes function before reading any data (we leverage Rust's std::sync::Once mechanism to initialize this value, if any call to decode is made before a call to max_allocation_bytes, the limit will be 512MB throughout the lifetime of the program).

use apache_avro::max_allocation_bytes;

max_allocation_bytes(2 * 1024 * 1024 * 1024);  // 2GB

// ... happily decode large data

Check schemas compatibility

This library supports checking for schemas compatibility.

Examples of checking for compatibility:

1. Compatible schemas

Explanation: an int array schema can be read by a long array schema- an int (32bit signed integer) fits into a long (64bit signed integer)

use apache_avro::{Schema, schema_compatibility::SchemaCompatibility};

let writers_schema = Schema::parse_str(r#"{"type": "array", "items":"int"}"#).unwrap();
let readers_schema = Schema::parse_str(r#"{"type": "array", "items":"long"}"#).unwrap();
assert!(SchemaCompatibility::can_read(&writers_schema, &readers_schema).is_ok());

2. Incompatible schemas (a long array schema cannot be read by an int array schema)

Explanation: a long array schema cannot be read by an int array schema- a long (64bit signed integer) does not fit into an int (32bit signed integer)

use apache_avro::{Schema, schema_compatibility::SchemaCompatibility};

let writers_schema = Schema::parse_str(r#"{"type": "array", "items":"long"}"#).unwrap();
let readers_schema = Schema::parse_str(r#"{"type": "array", "items":"int"}"#).unwrap();
assert!(SchemaCompatibility::can_read(&writers_schema, &readers_schema).is_err());

Custom names validators

By default the library follows the rules by the Avro specification!

Some of the other Apache Avro language SDKs are not that strict and allow more characters in names. For interoperability with those SDKs, the library provides a way to customize the names validation.

use apache_avro::AvroResult;
use apache_avro::schema::Namespace;
use apache_avro::validator::{SchemaNameValidator, set_schema_name_validator};

struct MyCustomValidator;

impl SchemaNameValidator for MyCustomValidator {
    fn validate(&self, name: &str) -> AvroResult<(String, Namespace)> {
        todo!()
    }
}

// don't parse any schema before registering the custom validator(s) !

set_schema_name_validator(Box::new(MyCustomValidator));

// ... use the library

Similar logic could be applied to the schema namespace, enum symbols and field names validation.

Note: the library allows to set a validator only once per the application lifetime! If the application parses schemas before setting a validator, the default validator will be registered and used!

Custom schema equality comparators

The library provides two implementations of schema equality comparators:

1. SpecificationEq - a comparator that serializes the schemas to their canonical forms (i.e. JSON) and compares them as strings. It is the only implementation until apache_avro 0.16.0. See the Avro specification for more information!
2. StructFieldEq - a comparator that compares the schemas structurally. It is faster than the SpecificationEq because it returns false as soon as a difference is found and is recommended for use! It is the default comparator since apache_avro 0.17.0.

To use a custom comparator, you need to implement the SchemataEq trait and set it using the set_schemata_equality_comparator function:

use apache_avro::{AvroResult, Schema};
use apache_avro::schema::Namespace;
use apache_avro::schema_equality::{SchemataEq, set_schemata_equality_comparator};

#[derive(Debug)]
struct MyCustomSchemataEq;

impl SchemataEq for MyCustomSchemataEq {
    fn compare(&self, schema_one: &Schema, schema_two: &Schema) -> bool {
        todo!()
    }
}

// don't parse any schema before registering the custom comparator !

set_schemata_equality_comparator(Box::new(MyCustomSchemataEq));

// ... use the library

Note: the library allows to set a comparator only once per the application lifetime! If the application parses schemas before setting a comparator, the default comparator will be registered and used!

Minimal supported Rust version

1.73.0

License

This project is licensed under Apache License 2.0.

Contributing

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 Apache License 2.0.

Please consider adding documentation and tests! If you introduce a backward-incompatible change, please consider adding instruction to migrate in the Migration Guide If you modify the crate documentation in lib.rs, run make readme to sync the README file.