Crate avro_rs [] [src]

avro-rs

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;

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

Installing the library

Add to your Cargo.toml:

[dependencies]
avro-rs = "0.1"

Or in case you want to leverage the Snappy codec:

[dependencies.avro-rs]
version = "0.1"
features = ["snappy"]

To use the library, just add at the top of the crate:

extern crate avro_rs;

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 avro_rs::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);

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 avro_rs::types::Record;
use avro_rs::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();

// flushing makes sure that all data gets encoded
writer.flush().unwrap();

// this is how to get back the resulting avro bytecode
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, the library offers that capability via the Value interface.

use avro_rs::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:

#[macro_use]
extern crate serde_derive;

use avro_rs::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();

// flushing makes sure that all data gets encoded
writer.flush().unwrap();

// this is how to get back the resulting avro bytecode
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.

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

use avro_rs::Writer;
use avro_rs::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 avro_rs::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 avro_rs::Schema;
use avro_rs::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 avro_rs::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:

#[macro_use]
extern crate serde_derive;

use avro_rs::Reader;
use avro_rs::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:

extern crate avro_rs;

#[macro_use]
extern crate serde_derive;
extern crate failure;

use avro_rs::{Codec, Reader, Schema, Writer, from_value, types::Record};
use failure::Error;

#[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)?;

    writer.flush()?;

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

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

Re-exports

pub use schema::Schema;

Modules

schema

Logic for parsing and interacting with schemas in Avro format.

types

Logic handling the intermediate representation of Avro values.

Structs

Reader

Main interface for reading Avro formatted values.

Writer

Main interface for writing Avro formatted values.

Enums

Codec

The compression codec used to compress blocks.

Functions

from_value
to_avro_datum

Encode a compatible value (implementing the ToAvro trait) into Avro format, also performing schema validation.