avro-preprocessor 0.1.16

A preprocessor for Avro Schemata

Avro-Preprocessor

This Python project provides a preprocessor for Avro resources.

The preprocessor is made up of a list of modules. Each module, in the specified order, performs an operation over the entire list of Avro resources.

The input is a directory tree (namespace-like) in which each file is either a domain_messages or a type.

The output is another directory tree, with the same structure as input, in which Avro resources underwent the list of preprocessing operations.

Input Avro resources (domain_message or type) have exavsc as file extension. NEW: they can also be yaml files with the same Avro extensions as exavsc! You can even mix exavsc and yaml files if passed -ie exavsc,yexavsc. Output Avro resources have avsc as file extension.

Schema definition

Two different kinds of data structures are used in this project: domain messages and types.

Domain messages

Domain messages are Avro schemas that can be sent over a Kafka topic. In general, multiple messages can be associated to each Kafka topic. Domain messages are placed in

schema/com/example/message_schema/domain/<domain_context>/<record_name>

Types

Types are reusable structures that can be referenced in a domain message. They are placed in

schema/com/example/message_schema/type/<typename>

Domain messages cannot be reused (i.e. referenced by other domain messages).

Naming rules

• Directory structure must match the namespaces used inside the message / type definitions
• Records are defined in camel case
• Properties are defined in snake case

Kafka topics and Avro subjects

Each directory <domain_context> under schema/com/example/message_schema/domain/ represents a Kafka topic. All schemas under a specific topic directory (and only them) will be available to be sent on the corresponding Kafka topic, i.e. they will be registered on the schema registry.

For subject naming we follow the guidelines defined by Confluent to put several event types in the same Kafka topic; in particular we use the strategy io.confluent.kafka.serializers.subject.TopicRecordNameStrategy.

This behaviour is defined in avro_naming.py.

Topic name

domain.<domain_context>

Avro subject names

domain.<domain_context>-<fully_qualified_record_name>

Example

For schema directory:

schema/com/example/message_schema/domain/user/

Topic name:

domain.user

List of Avro subjects names:

domain.user-com.example.message_schema.domain.user.UserCreated
domain.user-com.example.message_schema.domain.user.UserUpdated
domain.user-com.example.message_schema.domain.user.UserDeleted

List of available preprocessing modules

The following modules should be applied in the order shown as follows. However, they are all optional.

• DocumentationCondenser: JSON strings don't support multi lines inside an editor. This makes it inconvenient to write long documentation for some fields. This module adds support for lists as doc field in Avro schemas (they will be joined in the output).
• NamespaceChecker: Checks if a resource namespace corresponds to the directory tree structure.
• DocumentationChecker: Checks if the documentation corresponds to specific rules:
  1. (sub) schemas with a "name" property MUST have a "doc" property.
  2. (sub) schemas without a "name" property MUST NOT have a "doc" property.
  3. If two (sub) schemas have the same "doc", they must be identical.
• DefaultValueNullChecker: Makes sure no default values other than null are used.
• NamespaceChecker: Checks if a resource namespace corresponds to the directory tree structure. properties in snake_case.
• AvroOrderChecker: Check if the input schemas are sorted according to a fixed order (see avro_sorter.py). Fails if they are not.
• MetadataAdder: Adds the Metadata type (specified as argument on the command line) as first field of every domain schema. Use the --metadata-exclude option to exclude a comma-separated list of namespaces.
• SchemaDependenciesChecker: Calculates all resources dependencies and assert no cycles are present.
• NullableOptionalExpander: Allows for a compact definition of nullable_optionals for "partial entity updates", see its specific section below.
• ReferencesExpander: While Apache's avro-tools is able to deal with references to other resources in different files, Confluent's schema registry can only take one JSON as input, for each schema. Therefore, references to types in a domain_message have to be expanded. But with a caveat: only the first reference ("depth-first", NOT "breadth-first"!) must be expanded. All subsequent references must not be expanded, i.e. they have to remain references.
• AvroSorter: For the sake of readability and clearness, JSON fields of Avro resources are rearranged according to a fixed order (see avro_sorter.py).
• KeysGenerator: Generates Avro Schemas for Kafka Keys automatically for every topic based on the partition-field property of schema fields.
• SchemaMappingGenerator: Outputs a JSON map fully qualified schema -> kafka topic to the file schema-mapping.json. It also gathers the field names having custom property partition-field and writes them in schema-mapping.json. This is done so that Kafka producers know which set of fields to hash to decide which kafka partition to send a message to. This module also checks that partition keys are consistent within a given Kafka topic. Furthermore, gathers the field names of logicalType == user-id-fields and stores them in schema-mapping.json. These are meant to be those fields related to users so that they can be indexed separately (e.g. for GDPR.)
• DeprecationMappingGenerator: Outputs a JSON map with info about deprecated schemas/fields to the file deprecation-mapping.json. This is later used for code generation. Also removes "deprecated" and "deprecated-symbols" properties from the final schemas.
• JavaClassesCreator: Creates Java classes using Apache's avro-tools. It is also a very convenient way to check correctness of Avro resources.
• SchemaRegistryChecker: Check if a schema is compatible with the latest version of the same schema in the Schema registry.
• SchemaRegistrar: Register an Avro resources (both domain_messages AND types!) for the appropriate Kafka topic in the schema registry. types are registered for a dummy topic so that we can check compatibility for them as well (it would not be possible otherwise). See the documentation in schema_registrar.py for further documentation of subject naming and topics. Autogenerated keys or the generic key for all schemas (-k option from the command line) are registered as well.

Installation

pip install avro-preprocessor

Example Usage

Download Avro tools JAR

sh download_avro-tools.sh

For help:

avropreprocessor.py -h

It is possible to define the key_subject_name_strategy with the -ksns switch. Possible values areRecordNameStrategy and TopicRecordNameStrategy (default). Only relevant if the SchemaRegistryChecker and/or SchemaRegistrar modules are active as well.

All modules usage

avropreprocessor.py -i schema/ -o extended-schema/ -v -s 'http://localhost:8081' -t com.jaumo.message_schema.type -d com.jaumo.message_schema.type.Metadata -p schema-mapping.json -ie 'exavsc' -a ./avro-tools-1.9.0.jar

No java classes, no schema registry

avropreprocessor.py -i schema/ -o build/schema/ -v -s 'http://localhost:8081' -t com.jaumo.message_schema.type -d com.jaumo.message_schema.type.Metadata -p build/schema-mapping.json -m DocumentationCondenser NamespaceChecker DocumentationChecker DefaultValueNullChecker NamesChecker AvroOrderChecker MetadataAdder SchemaDependenciesChecker NullableOptionalExpander ReferencesExpander AvroSorter SchemaMappingGenerator DeprecationMappingGenerator SchemaRegistryChecker

Only register to the schema registry

avropreprocessor.py -i build/schema/ -o /tmp/ -v -s 'http://localhost:8081' -t com.jaumo.message_schema.type -ie 'avsc' -m SchemaRegistryChecker SchemaRegistrar

Reorder the input schemas according to the order defined in sort_avro.py

avropreprocessor.py -i schema/ -o schema/ -v -t com.jaumo.message_schema.type -ie 'exavsc' -oe 'exavsc' -m AvroSorter

Convert input schemas from exavsc to yaml

avropreprocessor.py -i schema/ -o schema_yaml/ -v -t com.jaumo.message_schema.type -ie 'exavsc' -oe 'yexavsc' -if 'json' -of 'yaml' -m DocumentationCondenser

Then use the yaml schemas as input

avropreprocessor.py -i schema_yaml/ -o build/schema/ -ie 'yexavsc' -if 'yaml' ...

nullable_optionals: a convenient solution to the Set-as-null and null-because-not-present issue

As protobuf, avro does not distinguish between these two cases, unless some sort of wrapper is used. The simple solution is to send the entire content of an event every time (i.e. always complete updates, never partial updates).

However, since partial updates can still be useful, the module NullableOptionalExpander automatises the following process. Let's consider a simple Avro schema for user updates:

{
    "namespace": "com.jaumo",
    "type": "record",
    "name": "UserUpdate",
    "doc": "Update user event",
    "fields": [
        {
            "name": "id",
            "doc": "The id",
            "type": "int"
        },
        {
            "name": "name",
            "doc": "The name of the user",
            "type": "string"
        },
        {
            "name": "age",
            "doc": "The age of the user",
            "type": "int"
        },
        {
            "name": "email",
            "doc": "The email of the user",
            "type": "string"
        }
    ]
}

This schema does not support null values at all. In avro, they can be supported as follows:

{
    "namespace": "com.jaumo",
    "type": "record",
    "name": "UserUpdate",
    "doc": "Update user event",
    "fields": [
        {
            "name": "id",
            "doc": "The id",
            "type": "int"
        },
        {
            "name": "name",
            "doc": "The name of the user",
            "type": ["null", "string"],
            "default": null
        },
        {
            "name": "age",
            "doc": "The age of the user",
            "type": ["null", "int"],
            "default": null
        },
        {
            "name": "email",
            "doc": "The email of the user",
            "type": ["null", "string"],
            "default": null
        }
    ]
}

With the schema above, both set-as-null and null-because-not-present are just null, so they are still ambiguous. To distinguish them, we can wrap around a record type (which in Java will be a specific class). Showing the email property only, this would be

{
    "name": "email",
    "doc": "The email of the user",
    "default": null,
    "type": [
        "null",
        {
            "name": "OptionalString",
            "type": "record",
            "fields": [
                {
                    "name": "value",
                    "default": null,
                    "type": [
                        "null",
                        "string"
                    ]
                }
            ]
        }
    ]
}

Note how the name of the wrapper record ("OptionalString", above) comes from the prefix Optional and the capitalised version of the Avro type field (string). The name of the inner property is always "value" (this is done automatically, see below).

Now we have three cases

• email set to null means null-because-not-present
• email.value set to null means set-as-null
• email.value set to string

Since there's a lot of boilerplate and thus this is error-prone, the nullable_optional is simple extension to the Avro syntax that adds the property "nullable_optional": true. If a field has this property set to true, the field is expanded with its wrapper. The schema above then becomes:

{
    "name": "email",
    "doc": "The email of the user",
    "nullable_optional": true,
    "type": "string"
}

and it is saved to a .exavsc file.

Note how this step is completely language independent: its output is a completely standard Avro schema that can be used by any library or framework.