apache-avro 0.16.0

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//! 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/master/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:
//!
//! ```
//! 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](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:
//!
//! ```
//! # use apache_avro::Schema;
//! use apache_avro::types::Record;
//! use apache_avro::Writer;
//! #
//! # 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).unwrap();
//! // 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::Schema;
//! # use serde::Serialize;
//! use apache_avro::Writer;
//!
//! #[derive(Debug, Serialize)]
//! struct Test {
//!     a: i64,
//!     b: String,
//! }
//!
//! # 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).unwrap();
//! // 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](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`:
//! ```
//! # use apache_avro::Schema;
//! use apache_avro::Writer;
//! use apache_avro::Codec;
//! #
//! # 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).unwrap();
//! 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;
//! # use apache_avro::Schema;
//! # use apache_avro::types::Record;
//! # use apache_avro::Writer;
//! #
//! # 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).unwrap();
//! # let mut writer = Writer::new(&schema, Vec::new());
//! # let mut record = Record::new(writer.schema()).unwrap();
//! # record.put("a", 27i64);
//! # record.put("b", "foo");
//! # writer.append(record).unwrap();
//! # let input = writer.into_inner().unwrap();
//! // 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;
//! # use apache_avro::types::Record;
//! # use apache_avro::Writer;
//! #
//! # let writer_raw_schema = r#"
//! #     {
//! #         "type": "record",
//! #         "name": "test",
//! #         "fields": [
//! #             {"name": "a", "type": "long", "default": 42},
//! #             {"name": "b", "type": "string"}
//! #         ]
//! #     }
//! # "#;
//! # let writer_schema = Schema::parse_str(writer_raw_schema).unwrap();
//! # let mut writer = Writer::new(&writer_schema, Vec::new());
//! # let mut record = Record::new(writer.schema()).unwrap();
//! # record.put("a", 27i64);
//! # record.put("b", "foo");
//! # writer.append(record).unwrap();
//! # let input = writer.into_inner().unwrap();
//!
//! 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:
//!
//! ```
//! # use apache_avro::Schema;
//! # use apache_avro::types::Record;
//! # use apache_avro::Writer;
//! use apache_avro::Reader;
//! #
//! # 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).unwrap();
//! # let schema = Schema::parse_str(raw_schema).unwrap();
//! # let mut writer = Writer::new(&schema, Vec::new());
//! # let mut record = Record::new(writer.schema()).unwrap();
//! # record.put("a", 27i64);
//! # record.put("b", "foo");
//! # writer.append(record).unwrap();
//! # let input = writer.into_inner().unwrap();
//! 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::Schema;
//! # use apache_avro::Writer;
//! # use serde::{Deserialize, Serialize};
//! use apache_avro::Reader;
//! use apache_avro::from_value;
//!
//! # #[derive(Serialize)]
//! #[derive(Debug, Deserialize)]
//! struct Test {
//!     a: i64,
//!     b: String,
//! }
//!
//! # 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).unwrap();
//! # let mut writer = Writer::new(&schema, Vec::new());
//! # let test = Test {
//! #     a: 27,
//! #     b: "foo".to_owned(),
//! # };
//! # writer.append_ser(test).unwrap();
//! # let input = writer.into_inner().unwrap();
//! 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](https://avro.apache.org/docs/current/spec.html#Logical+Types):
//!
//! 1. `Decimal` using the [`num_bigint`](https://docs.rs/num-bigint/0.2.6/num_bigint) crate
//! 1. UUID using the [`uuid`](https://docs.rs/uuid/1.0.0/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("local_timestamp_millis", Value::LocalTimestampMillis(4));
//!     record.put("local_timestamp_micros", Value::LocalTimestampMicros(5));
//!     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::<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`](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_eq!(true, SchemaCompatibility::can_read(&writers_schema, &readers_schema));
//! ```
//!
//! 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_eq!(false, SchemaCompatibility::can_read(&writers_schema, &readers_schema));
//! ```

mod codec;
mod de;
mod decimal;
mod decode;
mod duration;
mod encode;
mod error;
mod reader;
mod ser;
mod util;
mod writer;

pub mod rabin;
pub mod schema;
pub mod schema_compatibility;
pub mod types;

pub use codec::Codec;
pub use de::from_value;
pub use decimal::Decimal;
pub use duration::{Days, Duration, Millis, Months};
pub use error::Error;
pub use reader::{
    from_avro_datum, from_avro_datum_schemata, read_marker, GenericSingleObjectReader, Reader,
    SpecificSingleObjectReader,
};
pub use schema::{AvroSchema, Schema};
pub use ser::to_value;
pub use util::{max_allocation_bytes, set_serde_human_readable};
pub use writer::{
    to_avro_datum, to_avro_datum_schemata, GenericSingleObjectWriter, SpecificSingleObjectWriter,
    Writer,
};

#[cfg(feature = "derive")]
pub use apache_avro_derive::*;

#[macro_use]
extern crate log;

/// A convenience type alias for `Result`s with `Error`s.
pub type AvroResult<T> = Result<T, Error>;

#[cfg(test)]
mod tests {
    use crate::{
        from_avro_datum,
        types::{Record, Value},
        Codec, Reader, Schema, Writer,
    };
    use pretty_assertions::assert_eq;

    //TODO: move where it fits better
    #[test]
    fn test_enum_default() {
        let writer_raw_schema = r#"
            {
                "type": "record",
                "name": "test",
                "fields": [
                    {"name": "a", "type": "long", "default": 42},
                    {"name": "b", "type": "string"}
                ]
            }
        "#;
        let reader_raw_schema = r#"
            {
                "type": "record",
                "name": "test",
                "fields": [
                    {"name": "a", "type": "long", "default": 42},
                    {"name": "b", "type": "string"},
                    {
                        "name": "c",
                        "type": {
                            "type": "enum",
                            "name": "suit",
                            "symbols": ["diamonds", "spades", "clubs", "hearts"]
                        },
                        "default": "spades"
                    }
                ]
            }
        "#;
        let writer_schema = Schema::parse_str(writer_raw_schema).unwrap();
        let reader_schema = Schema::parse_str(reader_raw_schema).unwrap();
        let mut writer = Writer::with_codec(&writer_schema, Vec::new(), Codec::Null);
        let mut record = Record::new(writer.schema()).unwrap();
        record.put("a", 27i64);
        record.put("b", "foo");
        writer.append(record).unwrap();
        let input = writer.into_inner().unwrap();
        let mut reader = Reader::with_schema(&reader_schema, &input[..]).unwrap();
        assert_eq!(
            reader.next().unwrap().unwrap(),
            Value::Record(vec![
                ("a".to_string(), Value::Long(27)),
                ("b".to_string(), Value::String("foo".to_string())),
                ("c".to_string(), Value::Enum(1, "spades".to_string())),
            ])
        );
        assert!(reader.next().is_none());
    }

    //TODO: move where it fits better
    #[test]
    fn test_enum_string_value() {
        let raw_schema = r#"
            {
                "type": "record",
                "name": "test",
                "fields": [
                    {"name": "a", "type": "long", "default": 42},
                    {"name": "b", "type": "string"},
                    {
                        "name": "c",
                        "type": {
                            "type": "enum",
                            "name": "suit",
                            "symbols": ["diamonds", "spades", "clubs", "hearts"]
                        },
                        "default": "spades"
                    }
                ]
            }
        "#;
        let schema = Schema::parse_str(raw_schema).unwrap();
        let mut writer = Writer::with_codec(&schema, Vec::new(), Codec::Null);
        let mut record = Record::new(writer.schema()).unwrap();
        record.put("a", 27i64);
        record.put("b", "foo");
        record.put("c", "clubs");
        writer.append(record).unwrap();
        let input = writer.into_inner().unwrap();
        let mut reader = Reader::with_schema(&schema, &input[..]).unwrap();
        assert_eq!(
            reader.next().unwrap().unwrap(),
            Value::Record(vec![
                ("a".to_string(), Value::Long(27)),
                ("b".to_string(), Value::String("foo".to_string())),
                ("c".to_string(), Value::Enum(2, "clubs".to_string())),
            ])
        );
        assert!(reader.next().is_none());
    }

    //TODO: move where it fits better
    #[test]
    fn test_enum_no_reader_schema() {
        let writer_raw_schema = r#"
            {
                "type": "record",
                "name": "test",
                "fields": [
                    {"name": "a", "type": "long", "default": 42},
                    {"name": "b", "type": "string"},
                    {
                        "name": "c",
                        "type": {
                            "type": "enum",
                            "name": "suit",
                            "symbols": ["diamonds", "spades", "clubs", "hearts"]
                        },
                        "default": "spades"
                    }
                ]
            }
        "#;
        let writer_schema = Schema::parse_str(writer_raw_schema).unwrap();
        let mut writer = Writer::with_codec(&writer_schema, Vec::new(), Codec::Null);
        let mut record = Record::new(writer.schema()).unwrap();
        record.put("a", 27i64);
        record.put("b", "foo");
        record.put("c", "clubs");
        writer.append(record).unwrap();
        let input = writer.into_inner().unwrap();
        let mut reader = Reader::new(&input[..]).unwrap();
        assert_eq!(
            reader.next().unwrap().unwrap(),
            Value::Record(vec![
                ("a".to_string(), Value::Long(27)),
                ("b".to_string(), Value::String("foo".to_string())),
                ("c".to_string(), Value::Enum(2, "clubs".to_string())),
            ])
        );
    }

    #[test]
    fn test_illformed_length() {
        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).unwrap();

        // Would allocated 18446744073709551605 bytes
        let illformed: &[u8] = &[0x3e, 0x15, 0xff, 0x1f, 0x15, 0xff];

        let value = from_avro_datum(&schema, &mut &*illformed, None);
        assert!(value.is_err());
    }
}