AvroTurf is a library that makes it easier to encode and decode data using the Apache Avro serialization format. It adds a layer on top of the official Avro gem which makes it easier to integrate Avro into your application:
The AvroTurf::SchemaRegistry, AvroTurf::CachedSchemaRegistry,
and FakeSchemaRegistryServer names have been deprecated because the Avro spec recently
introduced an incompatible single-message encoding format.
These classes have been renamed to AvroTurf::ConfluentSchemaRegistry,
AvroTurf::CachedConfluentSchemaRegistry, and FakeConfluentSchemaRegistry.
The aliases for the original names will be removed in a future release.
As of AvroTurf version 1.0.0, only top-level schemas that have their own .avsc file will be loaded and resolvable by the AvroTurf::SchemaStore#find method. This change will likely not affect most users. However, if you use AvroTurf::SchemaStore#load_schemas! to pre-cache all your schemas and then rely on AvroTurf::SchemaStore#find to access nested schemas that are not defined by their own .avsc files, your code may stop working when you upgrade to v1.0.0.
As an example, if you have a person schema (defined in my/schemas/contacts/person.avsc) that defines a nested address schema like this:
{
"name": "person",
"namespace": "contacts",
"type": "record",
"fields": [
{
"name": "address",
"type": {
"name": "address",
"type": "record",
"fields": [
{ "name": "addr1", "type": "string" },
{ "name": "addr2", "type": "string" },
{ "name": "city", "type": "string" },
{ "name": "zip", "type": "string" }
]
}
}
]
}
…this will no longer work in v1.0.0:
store = AvroTurf::SchemaStore.new(path: 'my/schemas')
store.load_schemas!
# Accessing 'person' is correct and works fine.
person = store.find('person', 'contacts') # my/schemas/contacts/person.avsc exists
# Trying to access 'address' raises AvroTurf::SchemaNotFoundError
address = store.find('address', 'contacts') # my/schemas/contacts/address.avsc is not found
For details and context, see this pull request.
Add this line to your application’s Gemfile:
gem 'avro_turf'
And then execute:
$ bundle
Or install it yourself as:
$ gem install avro_turf
Using AvroTurf is quite simple:
# Schemas will be looked up from the specified directory.
avro = AvroTurf.new(schemas_path: "app/schemas/")
# Decode some data using a named schema. The schema file should exist in the
# schemas directory with the file name `<name>.avsc`.
avro.decode(encoded_data, schema_name: "person")
# Encode some data using the named schema.
avro.encode({ "name" => "Jane", "age" => 28 }, schema_name: "person")
# Data can be validated before encoding to get a description of problem through
# Avro::SchemaValidator::ValidationError exception
avro.encode({ "titl" => "hello, world" }, schema_name: "person", validate: true)
# If you do not want to register the schema in case it does not exist, you can pass the register_schemas option as false
avro.encode({ "name" => "Jane", "age" => 28 }, schema_name: "person", register_schemas: false)
Unlike the official Avro library, AvroTurf allows schemas to reference each other. As an example:
// person.avsc
{
"name": "person",
"type": "record",
"fields": [
{
"name": "full_name",
"type": "string"
},
{
"name": "address",
"type": "address"
}
]
}
// address.avsc
{
"name": "address",
"type": "record",
"fields": [
{
"name": "street",
"type": "string"
},
{
"name": "city",
"type": "string"
}
]
}
In the example above, the person schema references the address schema, even though the latter is defined in another file. This makes it possible to share types across schemas, e.g.
// person_list.avsc
{
"type": "array",
"items": "person"
}
There’s no reason to copy-paste the person schema into the person_list schema, as you can reference it directly.
This feature helps avoid subtle errors when the same type is represented using slightly different schemas.
By default, AvroTurf will encode data in the Avro data file format. This means that the schema used to encode the data is prepended to the output. If you want to decrease the size of the output, e.g. when storing data in a log such as Apache Kafka or in a database, you can use the AvroTurf::Messaging API. This top-level API requires the use of Schema Registry, a service which allows registering and fetching Avro schemas.
The Messaging API will automatically register schemas used for encoding data, and will fetch the corresponding schema when decoding. Instead of including the full schema in the output, only a schema id generated by the registry is included. Registering the same schema twice is idempotent, so no coordination is needed.
An optional schema_context parameter allows the registry to be scoped to a
schema context.
If there is a need to access multiple contexts, you will need to use multiple instances of ConfluentSchemaRegistry.
NOTE: The Messaging format is not compatible with the Avro data file API.
The Messaging API is not included by default, so you must require ‘avro_turf/messaging’ explicitly if you want to use it.
Using the Messaging API is simple once you have set up a Schema Registry service:
require 'avro_turf/messaging'
# You need to pass the URL of your Schema Registry.
avro = AvroTurf::Messaging.new(registry_url: "http://my-registry:8081/")
# The API for encoding and decoding data is similar to the default one. Encoding
# data has the side effect of registering the schema. This only happens the first
# time a schema is used.
data = avro.encode({ "title" => "hello, world" }, schema_name: "greeting")
# If you don't want to automatically register new schemas, you can pass explicitly
# both subject and version to specify which schema should be used for encoding.
# It will fetch that schema from the registry and cache it. Subsequent instances
# of the same schema version will be served by the cache.
data = avro.encode({ "title" => "hello, world" }, subject: 'greeting', version: 1)
# If you want to use a specific local schema, but register it with a different name in the
# registry, then provide a subject and a schema_name, but not a version
data = avro.encode({ "title" => "hello, world" }, subject: "greeting-value", schema_name: "greeting")
# You can also pass explicitly schema_id to specify which schema
# should be used for encoding.
# It will fetch that schema from the registry and cache it. Subsequent instances
# of the same schema version will be served by the cache.
data = avro.encode({ "title" => "hello, world" }, schema_id: 2)
# Message can be validated before encoding to get a description of problem through
# Avro::SchemaValidator::ValidationError exception
data = avro.encode({ "titl" => "hello, world" }, schema_name: "greeting", validate: true)
# When decoding, the schema will be fetched from the registry and cached. Subsequent
# instances of the same schema id will be served by the cache.
avro.decode(data) #=> { "title" => "hello, world" }
# If you want to get decoded message as well as the schema used to encode the message,
# you can use `#decode_message` method.
result = avro.decode_message(data)
result.message #=> { "title" => "hello, world" }
result.schema_id #=> 3
result.writer_schema #=> #<Avro::Schema: ...>
result.reader_schema #=> nil
# You can also work with schema through this interface:
# Fetch latest schema for subject from registry
schema, schema_id = avro.fetch_schema(subject: 'greeting')
# Fetch specific version
schema, schema_id = avro.fetch_schema(subject: 'greeting', version: 1)
# Fetch schema by id
schema, schema_id = avro.fetch_schema_by_id(3)
# Register schema fetched from store by name
schema, schema_id = avro.register_schema(schema_name: 'greeting')
# Specify namespace (same as schema_name: 'somewhere.greeting')
schema, schema_id = avro.register_schema(schema_name: 'greeting', namespace: 'somewhere')
# Customize subject under which to register schema
schema, schema_id = avro.register_schema(schema_name: 'greeting', namespace: 'somewhere', subject: 'test')
The ConfluentSchemaRegistry client used by the Messaging API can also be used directly. It can check whether a schema is compatible with a subject in the registry using the Compatibility API:
require 'avro_turf'
require 'avro_turf/confluent_schema_registry'
schema = <<-JSON
{
"name": "person",
"type": "record",
"fields": [
{
"name": "full_name",
"type": "string"
},
{
"name": "address",
"type": "address"
}
]
}
JSON
registry = AvroTurf::ConfluentSchemaRegistry.new("http://my-registry:8081/")
# Returns true if the schema is compatible, nil if the subject or version is not registered, and false if incompatible.
registry.compatible?("person", schema)
# Returns an array of any breaking changes, nil if the subject or version is not registered
registry.compatibility_issues("person", schema)
The ConfluentSchemaRegistry client can also change the global compatibility level or the compatibility level for an individual subject using the Config API:
registry.update_global_config(compatibility: 'FULL')
registry.update_subject_config("person", compatibility: 'NONE')
AvroTurf includes a FakeConfluentSchemaRegistryServer that can be used in tests. The
fake schema registry server depends on Sinatra but it is not listed as a runtime
dependency for AvroTurf. Sinatra must be added to your Gemfile or gemspec in order
to use the fake server.
Given the recent update in sinatra to fix CVE-2024-21510 that included a new HostAuthorization middleware, the FakeConfluentSchemaRegistryServer is provided as a base implementation that has to be inherited into a new class and configured by the user so requests are properly authorised to the test registry host.
Example using RSpec:
require 'avro_turf/test/fake_confluent_schema_registry_server'
require 'webmock/rspec'
class AuthorizedFakeConfluentSchemaRegistryServer < FakeConfluentSchemaRegistryServer
set :host_authentication, permitted_hosts: ['registry.example.com']
end
# within an example
let(:registry_url) { "http://registry.example.com" }
before do
stub_request(:any, /^#{registry_url}/).to_rack(AuthorizedFakeConfluentSchemaRegistryServer)
AuthorizedFakeConfluentSchemaRegistryServer.clear
end
# Messaging objects created with the same registry_url will now use the fake server.