# apache-avro [](https://crates.io/crates/apache-avro) [](https://github.com/apache/avro/actions/workflows/test-lang-rust-ci.yml) [](https://docs.rs/apache-avro) [](https://github.com/apache/avro/blob/main/LICENSE.txt) A library for working with [Apache Avro](https://avro.apache.org/) in Rust. Please check our [documentation](https://docs.rs/apache-avro) for examples, tutorials and API reference. **[Apache Avro](https://avro.apache.org/)** 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: ```json { "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`: ```toml [dependencies] apache-avro = "x.y" ``` Or in case you want to leverage the **Snappy** codec: ```toml [dependencies.apache-avro] version = "x.y" features = ["snappy"] ``` Or in case you want to leverage the **Zstandard** codec: ```toml [dependencies.apache-avro] version = "x.y" features = ["zstandard"] ``` Or in case you want to leverage the **Bzip2** codec: ```toml [dependencies.apache-avro] version = "x.y" features = ["bzip"] ``` Or in case you want to leverage the **Xz** codec: ```toml [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](https://github.com/apache/avro/blob/main/lang/rust/migration_guide.md). ## 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: ```rust 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. ```rust 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](https://avro.apache.org/docs/current/spec.html#schemas). ## 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: ```rust 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. ```rust 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: ```rust 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. ```rust 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](http://google.github.io/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](https://facebook.github.io/zstd/) compression library. You must enable the `zstandard` feature to use this codec. * **Bzip2**: uses [BZip2](https://sourceware.org/bzip2/) compression library. You must enable the `bzip` feature to use this codec. * **Xz**: uses [xz2](https://github.com/alexcrichton/xz2-rs) 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`: ```rust 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: ```rust 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: ```rust 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](https://avro.apache.org/docs/current/spec.html#schemas). 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: ```rust 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: ```rust 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::(&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: ```rust 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::(&record?)); } Ok(()) } ``` `apache-avro` also supports the logical types listed in the [Avro specification](https://avro.apache.org/docs/current/spec.html#Logical+Types): 1. `Decimal` using the [`num_bigint`](https://docs.rs/num-bigint/latest/num_bigint) crate 1. UUID using the [`uuid`](https://docs.rs/uuid/latest/uuid) crate 1. Date, Time (milli) as `i32` and Time (micro) as `i64` 1. Timestamp (milli and micro) as `i64` 1. Local timestamp (milli and micro) as `i64` 1. 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 ```rust 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: ```rust 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::()); println!("{}", schema.fingerprint::()); println!("{}", schema.fingerprint::()); 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`](https://doc.rust-lang.org/std/sync/struct.Once.html) 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). ```rust 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) ```rust 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) ```rust 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](https://avro.apache.org/docs/1.11.1/specification/#names)! 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. ```rust 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](https://avro.apache.org/docs/1.11.1/specification/#parsing-canonical-form-for-schemas) 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: ```rust 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](https://github.com/apache/avro/blob/main/LICENSE.txt). ## 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](https://github.com/apache/avro/blob/main/LICENSE.txt). Please consider adding documentation and tests! If you introduce a backward-incompatible change, please consider adding instruction to migrate in the [Migration Guide](migration_guide.md) If you modify the crate documentation in `lib.rs`, run `make readme` to sync the README file.