Avrotize & Structurize

mcp-name: io.github.clemensv/avrotize

[📚 Documentation & Examples](https://clemensv.github.io/avrotize/) | [🎨 Conversion Gallery](https://clemensv.github.io/avrotize/gallery/)

Avrotize is a "Rosetta Stone" for data structure definitions, allowing you to convert between numerous data and database schema formats and to generate code for different programming languages.

It is, for instance, a well-documented and predictable converter and code generator for data structures originally defined in JSON Schema (of arbitrary complexity).

The tool leans on the Apache Avro-derived Avrotize Schema as its schema model.

• Programming languages: Python, C#, Java, TypeScript, JavaScript, Rust, Go, C++
• SQL Databases: MySQL, MariaDB, PostgreSQL, SQL Server, Oracle, SQLite, BigQuery, Snowflake, Redshift, DB2
• Other databases: KQL/Kusto, MongoDB, Cassandra, Redis, Elasticsearch, DynamoDB, CosmosDB
• Data schema formats: Avro, JSON Schema, XML Schema (XSD), Protocol Buffers 2 and 3, ASN.1, Apache Parquet

Installation

You can install Avrotize from PyPI, having installed Python 3.10 or later:

pip install avrotize

For MCP server support (avrotize mcp), install with the MCP extra:

pip install "avrotize[mcp]"

For SQL database support (sql2a command), install the optional database drivers:

# PostgreSQL
pip install avrotize[postgres]

# MySQL
pip install avrotize[mysql]

# SQL Server
pip install avrotize[sqlserver]

# All SQL databases
pip install avrotize[all-sql]

Usage

Avrotize provides several commands for converting schema formats via Avrotize Schema.

Converting to Avrotize Schema:

• avrotize p2a - Convert Protobuf (2 or 3) schema to Avrotize Schema.
• avrotize j2a - Convert JSON schema to Avrotize Schema.
• avrotize x2a - Convert XML schema to Avrotize Schema.
• avrotize asn2a - Convert ASN.1 to Avrotize Schema.
• avrotize k2a - Convert Kusto table definitions to Avrotize Schema.
• avrotize sql2a - Convert SQL database schema to Avrotize Schema.
• avrotize json2a - Infer Avro schema from JSON files.
• avrotize json2s - Infer JSON Structure schema from JSON files.
• avrotize xml2a - Infer Avro schema from XML files.
• avrotize xml2s - Infer JSON Structure schema from XML files.
• avrotize pq2a - Convert Parquet schema to Avrotize Schema.
• avrotize csv2a - Convert CSV file to Avrotize Schema.
• avrotize kstruct2a - Convert Kafka Connect Schema to Avrotize Schema.

Converting from Avrotize Schema:

• avrotize a2p - Convert Avrotize Schema to Protobuf 3 schema.
• avrotize a2j - Convert Avrotize Schema to JSON schema.
• avrotize a2x - Convert Avrotize Schema to XML schema.
• avrotize a2k - Convert Avrotize Schema to Kusto table definition.
• avrotize s2k - Convert JSON Structure Schema to Kusto table definition.
• avrotize a2sql - Convert Avrotize Schema to SQL table definition.
• avrotize s2sql - Convert JSON Structure Schema to SQL table definition.
• avrotize a2pq - Convert Avrotize Schema to Parquet or Iceberg schema.
• avrotize a2ib - Convert Avrotize Schema to Iceberg schema.
• avrotize s2ib - Convert JSON Structure to Iceberg schema.
• avrotize a2mongo - Convert Avrotize Schema to MongoDB schema.
• avrotize a2cassandra - Convert Avrotize Schema to Cassandra schema.
• avrotize s2cassandra - Convert JSON Structure Schema to Cassandra schema.
• avrotize a2es - Convert Avrotize Schema to Elasticsearch schema.
• avrotize a2dynamodb - Convert Avrotize Schema to DynamoDB schema.
• avrotize a2cosmos - Convert Avrotize Schema to CosmosDB schema.
• avrotize a2couchdb - Convert Avrotize Schema to CouchDB schema.
• avrotize a2firebase - Convert Avrotize Schema to Firebase schema.
• avrotize a2hbase - Convert Avrotize Schema to HBase schema.
• avrotize a2neo4j - Convert Avrotize Schema to Neo4j schema.
• avrotize a2dp - Convert Avrotize Schema to Datapackage schema.
• avrotize a2md - Convert Avrotize Schema to Markdown documentation.
• avrotize s2md - Convert JSON Structure schema to Markdown documentation.

Direct conversions (JSON Structure):

• avrotize s2p - Convert JSON Structure to Protocol Buffers (.proto files).
• avrotize oas2s - Convert OpenAPI 3.x document to JSON Structure.

Generate code from Avrotize Schema:

• avrotize a2cs - Generate C# code from Avrotize Schema.
• avrotize a2java - Generate Java code from Avrotize Schema.
• avrotize a2py - Generate Python code from Avrotize Schema.
• avrotize a2ts - Generate TypeScript code from Avrotize Schema.
• avrotize a2js - Generate JavaScript code from Avrotize Schema.
• avrotize a2cpp - Generate C++ code from Avrotize Schema.
• avrotize a2go - Generate Go code from Avrotize Schema.
• avrotize a2rust - Generate Rust code from Avrotize Schema.

Generate code from JSON Structure:

• avrotize s2cpp - Generate C++ code from JSON Structure schema.
• avrotize s2cs - Generate C# code from JSON Structure schema.
• avrotize s2go - Generate Go code from JSON Structure schema.
• avrotize s2java - Generate Java code from JSON Structure schema.
• avrotize s2py - Generate Python code from JSON Structure schema.
• avrotize s2rust - Generate Rust code from JSON Structure schema.
• avrotize s2ts - Generate TypeScript code from JSON Structure schema.

Direct JSON Structure conversions:

• avrotize s2csv - Convert JSON Structure schema to CSV schema.
• avrotize a2csv - Convert Avrotize schema to CSV schema.
• avrotize s2x - Convert JSON Structure to XML Schema (XSD).
• avrotize s2graphql - Convert JSON Structure schema to GraphQL schema.
• avrotize a2graphql - Convert Avrotize schema to GraphQL schema.

Other commands:

• avrotize pcf - Create the Parsing Canonical Form (PCF) of an Avrotize Schema.
• avrotize validate - Validate JSON instances against Avro or JSON Structure schemas.
• avrotize mcp - Run Avrotize as a local MCP server exposing conversion tools to MCP clients.
• avrotize validate-tmsl - Validate TMSL scripts locally against documented object structure.

JSON Structure conversions:

• avrotize s2dp - Convert JSON Structure schema to Datapackage schema.

Overview

MCP server

You can run Avrotize as a local MCP server over stdio:

avrotize mcp

Catalog-ready metadata files are included:

• Official MCP Registry manifest: server.json
• Microsoft/GitHub MCP catalog listing template: catalogs/microsoft-github-mcp.md
• Generic cross-catalog manifest (optional): mcp-server.json
• VS Code + Copilot usage quickstart: copilot-vscode-mcp-quickstart.md

To publish to the official MCP Registry:

mcp-publisher validate server.json
mcp-publisher publish server.json

The MCP server exposes tools to:

• describe server capabilities and routing guidance (describe_capabilities)
• list available conversion commands (list_conversions)
• inspect a conversion command (get_conversion)
• execute conversions (run_conversion)

You can use Avrotize to convert between Avro/Avrotize Schema and other schema formats like JSON Schema, XML Schema (XSD), Protocol Buffers (Protobuf), ASN.1, and database schema formats like Kusto Data Table Definition (KQL) and SQL Table Definition. That means you can also convert from JSON Schema to Protobuf going via Avrotize Schema.

You can also generate C#, Java, TypeScript, JavaScript, and Python code from Avrotize Schema documents. The difference to the native Avro tools is that Avrotize can emit data classes without Avro library dependencies and, optionally, with annotations for JSON serialization libraries like Jackson or System.Text.Json.

The tool does not convert data (instances of schemas), only the data structure definitions.

Mind that the primary objective of the tool is the conversion of schemas that describe data structures used in applications, databases, and message systems. While the project's internal tests do cover a lot of ground, it is nevertheless not a primary goal of the tool to convert every complex document schema like those used for devops pipeline or system configuration files.

Why?

Data structure definitions are an essential part of data exchange, serialization, and storage. They define the shape and type of data, and they are foundational for tooling and libraries for working with the data. Nearly all data schema languages are coupled to a specific data exchange or storage format, locking the definitions to that format.

Avrotize is designed as a tool to "unlock" data definitions from JSON Schema or XML Schema and make them usable in other contexts. The intent is also to lay a foundation for transcoding data from one format to another, by translating the schema definitions as accurately as possible into the schema model of the target format's schema. The transcoding of the data itself requires separate tools that are beyond the scope of this project.

The use of the term "data structure definition" and not "data object definition" is quite intentional. The focus of the tool is on data structures that can be used for messaging and eventing payloads, for data serialization, and for database tables, with the goal that those structures can be mapped cleanly from and to common programming language types.

Therefore, Avrotize intentionally ignores common techniques to model object-oriented inheritance. For instance, when converting from JSON Schema, all content from allOf expressions is merged into a single record type rather than trying to model the inheritance tree in Avro.

Avrotize Schema

Avrotize Schema is a schema model that is a full superset of the popular Apache Avro Schema model. Avrotize Schema is the "pivot point" for this tool. All schemas are converted from and to Avrotize Schema.

Since Avrotize Schema is a superset of Avro Schema and uses its extensibility features, every Avrotize Schema is also a valid Avro Schema and vice versa.

Why did we pick Avro Schema as the foundational schema model?

Avro Schema ...

• provides a simple, clean, and concise way to define data structures. It is quite easy to understand and use.
• is self-contained by design without having or requiring external references. Avro Schema can express complex data structure hierarchies spanning multiple namespace boundaries all in a single file, which neither JSON Schema nor XML Schema nor Protobuf can do.
• can be resolved by code generators and other tools "top-down" since it enforces dependencies to be ordered such that no forward-referencing occurs.
• emerged out of the Apache Hadoop ecosystem and is widely used for serialization and storage of data and for data exchange between systems.
• supports native and logical types that cover the needs of many business and technical use cases.
• can describe the popular JSON data encoding very well and in a way that always maps cleanly to a wide range of programming languages and systems. In contrast, it's quite easy to inadvertently define a JSON Schema that is very difficult to map to a programming language structure.
• is itself expressed as JSON. That makes it easy to parse and generate, which is not the case for Protobuf or ASN.1, which require bespoke parsers.

It needs to be noted here that while Avro Schema is great for defining data structures, and data classes generated from Avro Schema using this tool or other tools can be used to with the most popular JSON serialization libraries, the Apache Avro project's own JSON encoding has fairly grave interoperability issues with common usage of JSON. Avrotize defines an alternate JSON encoding

in avrojson.md.

Avro Schema does not support all the bells and whistles of XML Schema or JSON Schema, but that is a feature, not a bug, as it ensures the portability of the schemas across different systems and infrastructures. Specifically, Avro Schema does not support many of the data validation features found in JSON Schema or XML Schema. There are no pattern, format, minimum, maximum, or required keywords in Avro Schema, and Avro does not support conditional validation.

In a system where data originates as XML or JSON described by a validating XML Schema or JSON Schema, the assumption we make here is that data will be validated using its native schema language first, and then the Avro Schema will be used for transformation or transfer or storage.

Adding CloudEvents columns for database tables

When converting Avrotize Schema to Kusto Data Table Definition (KQL), SQL Table Definition, or Parquet Schema, the tool can add special columns for CloudEvents attributes. CNCF CloudEvents is a specification for describing event data in a common way.

The rationale for adding such columns to database tables is that messages and events commonly separate event metadata from the payload data, while that information is merged when events are projected into a database. The metadata often carries important context information about the event that is not contained in the payload itself. Therefore, the tool can add those columns to the database tables for easy alignment of the message context with the payload when building event stores.

Convert Proto schema to Avrotize Schema

avrotize p2a <path_to_proto_file> [--out <path_to_avro_schema_file>]

Parameters:

• <path_to_proto_file>: The path to the Protobuf schema file to be converted. If omitted, the file is read from stdin.
• --out: The path to the Avrotize Schema file to write the conversion result to. If omitted, the output is directed to stdout.

Conversion notes:

• Proto 2 and Proto 3 syntax are supported.
• Proto package names are mapped to Avro namespaces. The tool does resolve imports and consolidates all imported types into a single Avrotize Schema file.
• The tool embeds all 'well-known' Protobuf 3.0 types in Avro format and injects them as needed when the respective types are imported. Only the Timestamp type is mapped to the Avro logical type 'timestamp-millis'. The rest of the well-known Protobuf types are kept as Avro record types with the same field names and types.
• Protobuf allows any scalar type as key in a map, Avro does not. When converting from Proto to Avro, the type information for the map keys is ignored.
• The field numbers in message types are not mapped to the positions of the fields in Avro records. The fields in Avro are ordered as they appear in the Proto schema. Consequently, the Avrotize Schema also ignores the extensions and reserved keywords in the Proto schema.
• The optional keyword results in an Avro field being nullable (union with the null type), while the required keyword results in a non-nullable field. The repeated keyword results in an Avro field being an array of the field type.
• The oneof keyword in Proto is mapped to an Avro union type.
• All options in the Proto schema are ignored.

Convert Avrotize Schema to Proto schema

avrotize a2p <path_to_avro_schema_file> [--out <path_to_proto_directory>] [--naming <naming_mode>] [--allow-optional]

Parameters:

• <path_to_avro_schema_file>: The path to the Avrotize Schema file to be converted. If omitted, the file is read from stdin.
• --out: The path to the Protobuf schema directory to write the conversion result to. If omitted, the output is directed to stdout.
• --naming: (optional) Type naming convention. Choices are snake, camel, pascal.
• --allow-optional: (optional) Enable support for 'optional' fields.

Conversion notes:

• Avro namespaces are resolved into distinct proto package definitions. The tool will create a new .proto file with the package definition and an import statement for each namespace found in the Avrotize Schema.
• Avro type unions [] are converted to oneof expressions in Proto. Avro allows for maps and arrays in the type union, whereas Proto only supports scalar types and message type references. The tool will therefore emit message types containing a single array or map field for any such case and add it to the containing type, and will also recursively resolve further unions in the array and map values.
• The sequence of fields in a message follows the sequence of fields in the Avro record. When type unions need to be resolved into oneof expressions, the alternative fields need to be assigned field numbers, which will shift the field numbers for any subsequent fields.

Convert JSON schema to Avrotize Schema

avrotize j2a <path_to_json_schema_file> [--out <path_to_avro_schema_file>] [--namespace <avro_schema_namespace>] [--split-top-level-records]

Parameters:

• <path_to_json_schema_file>: The path to the JSON schema file to be converted. If omitted, the file is read from stdin.
• --out: The path to the Avrotize Schema file to write the conversion result to. If omitted, the output is directed to stdout.
• --namespace: (optional) The namespace to use in the Avrotize Schema if the JSON schema does not define a namespace.
• --split-top-level-records: (optional) Split top-level records into separate files.

Conversion notes:

• JSON Schema Handling in Avrotize

Convert Avrotize Schema to JSON schema

avrotize a2j <path_to_avro_schema_file> [--out <path_to_json_schema_file>] [--naming <naming_mode>]

Parameters:

• <path_to_avro_schema_file>: The path to the Avrotize Schema file to be converted. If omitted, the file is read from stdin.
• --out: The path to the JSON schema file to write the conversion result to. If omitted, the output is directed to stdout.
• --naming: (optional) Type naming convention. Choices are snake, camel, pascal, default.

Conversion notes:

• JSON Schema Handling in Avrotize

Convert XML Schema (XSD) to Avrotize Schema

avrotize x2a <path_to_xsd_file> [--out <path_to_avro_schema_file>] [--namespace <avro_schema_namespace>]

Parameters:

• <path_to_xsd_file>: The path to the XML schema file to be converted. If omitted, the file is read from stdin.
• --out: The path to the Avrotize Schema file to write the conversion result to. If omitted, the output is directed to stdout.
• --namespace: (optional) The namespace to use in the Avrotize Schema if the XML schema does not define a namespace.

Conversion notes:

• All XML Schema constructs are mapped to Avro record types with fields, whereby both, elements and attributes, become fields in the record. XML is therefore flattened into fields and this aspect of the structure is not preserved.
• Avro does not support xsd:any as Avro does not support arbitrary typing and must always use a named type. The tool will map xsd:any to a field any typed as a union that allows scalar values or two levels of array and/or map nesting.
• simpleType declarations that define enums are mapped to enum types in Avro. All other facets are ignored and simple types are mapped to the corresponding Avro type.
• complexType declarations that have simple content where a base type is augmented with attributes is mapped to a record type in Avro. Any other facets defined on the complex type are ignored.
• If the schema defines a single root element, the tool will emit a single Avro record type. If the schema defines multiple root elements, the tool will emit a union of record types, each corresponding to a root element.
• All fields in the resulting Avrotize Schema are annotated with an xmlkind extension attribute that indicates whether the field was an element or an attribute in the XML schema.

Convert Avrotize Schema to XML schema

avrotize a2x <path_to_avro_schema_file> [--out <path_to_xsd_schema_file>] [--namespace <target_namespace>]

Parameters:

• <path_to_avro_schema_file>: The path to the Avrotize Schema file to be converted. If omitted, the file is read from stdin.
• --out: The path to the XML schema file to write the conversion result to. If omitted, the output is directed to stdout.
• --namespace: (optional) Target namespace for the XSD schema.

Conversion notes:

• Avro record types are mapped to XML Schema complex types with elements.
• Avro enum types are mapped to XML Schema simple types with restrictions.
• Avro logical types are mapped to XML Schema simple types with restrictions where required.
• Avro unions are mapped to standalone XSD simple type definitions with a union restriction if all union types are primitives.
• Avro unions with complex types are resolved into distinct types for each option that are

then joined with a choice.

Convert JSON Structure to XML Schema (XSD)

avrotize s2x <path_to_structure_file> [--out <path_to_xsd_schema_file>] [--namespace <target_namespace>]

Parameters:

• <path_to_structure_file>: The path to the JSON Structure schema file to be converted. If omitted, the file is read from stdin.
• --out: The path to the XML schema file to write the conversion result to. If omitted, the output is directed to stdout.
• --namespace: (optional) Target namespace for the XSD schema.

Conversion notes:

• JSON Structure object types are mapped to XML Schema complex types with elements.
• JSON Structure primitive types (string, int8-128, uint8-128, float/double, boolean, etc.) are mapped to appropriate XSD simple types.
• Extended primitive types are mapped as follows:
• binary/bytes → xs:base64Binary
• date → xs:date
• time → xs:time
• datetime/timestamp → xs:dateTime
• duration → xs:duration
• uuid → xs:string
• uri → xs:anyURI
• decimal → xs:decimal
• Collection types:
• array and set → complex types with sequences of items
• map → complex type with entry elements containing key and value
• tuple → complex type with fixed sequence of typed items
• Union types (choice or type arrays like ["string", "null"]):
• Tagged unions (with discriminator) → xs:choice elements
• Inline unions → abstract base types with concrete extensions
• Nullable types → elements with minOccurs="0"
• Type references ($ref) are resolved to named XSD types
• Type extensions ($extends) are mapped to XSD complex type extensions with xs:complexContent
• Abstract types are marked with abstract="true" in XSD
• Validation constraints (minLength, maxLength, pattern, minimum, maximum) are converted to XSD restrictions/facets
• Required properties become elements with minOccurs="1", optional properties have minOccurs="0"

Convert ASN.1 schema to Avrotize Schema

avrotize asn2a <path_to_asn1_schema_file>[,<path_to_asn1_schema_file>,...] [--out <path_to_avro_schema_file>]

Parameters:

• <path_to_asn1_schema_file>: The path to the ASN.1 schema file to be converted. The tool supports multiple files in a comma-separated list. If omitted, the file is read from stdin.
• --out: The path to the Avrotize Schema file to write the conversion result to. If omitted, the output is directed to stdout.

Conversion notes:

• All ASN.1 types are mapped to Avro record types, enums, and unions. Avro does not support the same level of nesting of types as ASN.1, the tool will map the types to the best fit.
• The tool will map the following ASN.1 types to Avro types:
• SEQUENCE and SET are mapped to Avro record types.
• CHOICE is mapped to an Avro record types with all fields being optional. While the CHOICE type technically corresponds to an Avro union, the ASN.1 type has different named fields for each option, which is not a feature of Avro unions.
• OBJECT IDENTIFIER is mapped to an Avro string type.
• ENUMERATED is mapped to an Avro enum type.
• SEQUENCE OF and SET OF are mapped to Avro array type.
• BIT STRING is mapped to Avro bytes type.
• OCTET STRING is mapped to Avro bytes type.
• INTEGER is mapped to Avro long type.
• REAL is mapped to Avro double type.
• BOOLEAN is mapped to Avro boolean type.
• NULL is mapped to Avro null type.
• UTF8String, PrintableString, IA5String, BMPString, NumericString, TeletexString, VideotexString, GraphicString, VisibleString, GeneralString, UniversalString, CharacterString, T61String are all mapped to Avro string type.
• All other ASN.1 types are mapped to Avro string type.
• The ability to parse ASN.1 schema files is limited and the tool may not be able to parse all ASN.1 files. The tool is based on the Python asn1tools package and is limited to that package's capabilities.

Convert Kusto table definition to Avrotize Schema

avrotize k2a --kusto-uri <kusto_cluster_uri> --kusto-database <kusto_database> [--out <path_to_avro_schema_file>] [--emit-cloudevents-xregistry]

Parameters:

• --kusto-uri: The URI of the Kusto cluster to connect to.
• --kusto-database: The name of the Kusto database to read the table definitions from.
• --out: The path to the Avrotize Schema file to write the conversion result to. If omitted, the output is directed to stdout.
• --emit-cloudevents-xregistry: (optional) See discussion below.

Conversion notes:

• The tool directly connects to the Kusto cluster and reads the table definitions from the specified database. The tool will convert all tables in the database to Avro record types, returned in a top-level type union.
• Connecting to the Kusto cluster leans on the same authentication mechanisms as the Azure CLI. The tool will use the same authentication context as the Azure CLI if it is installed and authenticated.
• The tool will map the Kusto column types to Avro types as follows:
• bool is mapped to Avro boolean type.
• datetime is mapped to Avro long type with logical type timestamp-millis.
• decimal is mapped to a logical Avro type with the logicalType set to decimal and the precision and scale set to the values of the decimal type in Kusto.
• guid is mapped to Avro string type.
• int is mapped to Avro int type.
• long is mapped to Avro long type.
• real is mapped to Avro double type.
• string is mapped to Avro string type.
• timespan is mapped to a logical Avro type with the logicalType set to duration.
• For dynamic columns, the tool will sample the data in the table to determine the structure of the dynamic column. The tool will map the dynamic column to an Avro record type with fields that correspond to the fields found in the dynamic column. If the dynamic column contains nested dynamic columns, the tool will recursively map those to Avro record types. If records with conflicting structures are found in the dynamic column, the tool will emit a union of record types for the dynamic column.
• If the --emit-cloudevents-xregistry option is set, the tool will emit an xRegistry registry manifest file with a CloudEvent message definition for each table in the Kusto database and a separate Avro Schema for each table in the embedded schema registry. If one or more tables are found to contain CloudEvent data (as indicated by the presence of the CloudEvents attribute columns), the tool will inspect the content of the type (or __type or __type) columns to determine which CloudEvent types have been stored in the table and will emit a CloudEvent definition and schema for each unique type.

Convert SQL database schema to Avrotize Schema

avrotize sql2a --connection-string <connection_string> [--username <user>] [--password <pass>] [--dialect <dialect>] [--database <database>] [--table-name <table>] [--out <path_to_avro_schema_file>] [--namespace <namespace>] [--infer-json] [--infer-xml] [--sample-size <n>] [--emit-cloudevents] [--emit-xregistry]

Parameters:

• --connection-string: The database connection string. Supports SSL/TLS and integrated authentication options (see examples below).
• --username: (optional) Database username. Overrides any username in the connection string. Use this to avoid credentials in command history.
• --password: (optional) Database password. Overrides any password in the connection string. Use this to avoid credentials in command history.
• --dialect: (optional) The SQL dialect: postgres (default), mysql, sqlserver, oracle, or sqlite.
• --database: (optional) The database name if not specified in the connection string.
• --table-name: (optional) A specific table to convert. If omitted, all tables are converted.
• --out: The path to the Avrotize Schema file. If omitted, output goes to stdout.
• --namespace: (optional) The Avro namespace for the generated schema.
• --infer-json: (optional, default: true) Infer schema for JSON/JSONB columns by sampling data.
• --infer-xml: (optional, default: true) Infer schema for XML columns by sampling data.
• --sample-size: (optional, default: 100) Number of rows to sample for JSON/XML schema inference.
• --emit-cloudevents: (optional) Detect CloudEvents tables and emit CloudEvents declarations.
• --emit-xregistry: (optional) Emit an xRegistry manifest instead of a single schema file.

Connection string examples:

# PostgreSQL with separate credentials (preferred for security)
avrotize sql2a --connection-string "postgresql://host:5432/mydb?sslmode=require" --username myuser --password mypass --out schema.avsc

# PostgreSQL with SSL (credentials in URL)
avrotize sql2a --connection-string "postgresql://user:pass@host:5432/mydb?sslmode=require" --out schema.avsc

# MySQL with SSL
avrotize sql2a --connection-string "mysql://user:pass@host:3306/mydb?ssl=true" --dialect mysql --out schema.avsc

# SQL Server with Windows Authentication (omit user/password)
avrotize sql2a --connection-string "mssql://@host:1433/mydb" --dialect sqlserver --out schema.avsc

# SQL Server with TLS encryption
avrotize sql2a --connection-string "mssql://user:pass@host:1433/mydb?encrypt=true" --dialect sqlserver --out schema.avsc

# SQLite file
avrotize sql2a --connection-string "/path/to/database.db" --dialect sqlite --out schema.avsc

Conversion notes:

• The tool connects to a live database and reads the schema from the information schema or system catalogs.
• Type mappings for each dialect:
• PostgreSQL: All standard types including uuid, jsonb, xml, arrays, and custom types.
• MySQL: Standard types including json, enum, set, and spatial types.
• SQL Server: Standard types including uniqueidentifier, xml, money, and hierarchyid.
• Oracle: Standard types including number, clob, blob, and Oracle-specific types.
• SQLite: Dynamic typing mapped based on declared type affinity.
• For JSON/JSONB columns (PostgreSQL, MySQL) and XML columns, the tool samples data to infer the structure. Fields that appear in some but not all records are folded together. If field types conflict across records, the tool emits a union of record types.
• For columns with keys that cannot be valid Avro identifiers (UUIDs, URLs, special characters), the tool generates map<string, T> types instead of record types.
• Table and column comments are preserved as Avro doc attributes where available.
• Primary key columns are noted in the schema's unique attribute.

Infer Avro schema from JSON files

avrotize json2a <json_files...> [--out <path>] [--type-name <name>] [--namespace <namespace>] [--sample-size <n>] [--infer-choices] [--choice-depth <n>]

Parameters:

• <json_files...>: One or more JSON files to analyze. Supports JSON arrays, single objects, and JSONL (JSON Lines) format. Use @filelist.txt to read file paths from a response file.
• --out: The path to the Avro schema file. If omitted, output goes to stdout.
• --type-name: (optional) Name for the root type (default: "Document").
• --namespace: (optional) Avro namespace for generated types.
• --sample-size: (optional) Maximum number of records to sample (0 = all, default: 0).
• --infer-choices: (optional) Detect discriminated unions and emit as Avro unions with discriminator field defaults.
• --choice-depth: (optional) Maximum nesting depth for choice inference (1 = root only, 2+ = nested objects, default: 1).

Example:

# Infer schema from multiple JSON files
avrotize json2a data1.json data2.json --out schema.avsc --type-name Event --namespace com.example

# Infer schema from JSONL file with discriminated union detection
avrotize json2a events.jsonl --out events.avsc --type-name LogEntry --infer-choices

# Use response file for many input files
avrotize json2a @file_list.txt --out schema.avsc --infer-choices --choice-depth 2

Infer JSON Structure schema from JSON files

avrotize json2s <json_files...> [--out <path>] [--type-name <name>] [--base-id <uri>] [--sample-size <n>] [--infer-choices] [--choice-depth <n>] [--infer-enums]

Parameters:

• <json_files...>: One or more JSON files to analyze. Use @filelist.txt to read file paths from a response file.
• --out: The path to the JSON Structure schema file. If omitted, output goes to stdout.
• --type-name: (optional) Name for the root type (default: "Document").
• --base-id: (optional) Base URI for $id generation (default: "https://example.com/").
• --sample-size: (optional) Maximum number of records to sample (0 = all, default: 0).
• --infer-choices: (optional) Detect discriminated unions and emit as choice types with discriminator field defaults.
• --choice-depth: (optional) Maximum nesting depth for choice inference (1 = root only, 2+ = nested objects, default: 1).
• --infer-enums: (optional) Detect enum types from repeated string values with low cardinality.

The inferrer also automatically detects:

• Datetime patterns: ISO 8601 timestamps, dates, and times are typed as datetime, date, or time.
• Required vs optional fields: Fields present in all records are marked required; sparse fields are optional.

Example:

# Basic inference
avrotize json2s data.json --out schema.jstruct.json --type-name Person --base-id https://myapi.example.com/schemas/

# Full inference with choices and enums
avrotize json2s events/*.json --out events.jstruct.json --type-name Event --infer-choices --choice-depth 2 --infer-enums

# Process many files via response file
avrotize json2s @file_list.txt --out schema.jstruct.json --infer-choices --infer-enums

Infer Avro schema from XML files

avrotize xml2a <xml_files...> [--out <path>] [--type-name <name>] [--namespace <namespace>] [--sample-size <n>]

Parameters:

• <xml_files...>: One or more XML files to analyze. Use @filelist.txt to read file paths from a response file.
• --out: The path to the Avro schema file. If omitted, output goes to stdout.
• --type-name: (optional) Name for the root type (default: "Document").
• --namespace: (optional) Avro namespace for generated types.
• --sample-size: (optional) Maximum number of documents to sample (0 = all, default: 0).

Example:

avrotize xml2a config.xml --out config.avsc --type-name Configuration --namespace com.example.config

Infer JSON Structure schema from XML files

avrotize xml2s <xml_files...> [--out <path>] [--type-name <name>] [--base-id <uri>] [--sample-size <n>]

Parameters:

• <xml_files...>: One or more XML files to analyze. Use @filelist.txt to read file paths from a response file.
• --out: The path to the JSON Structure schema file. If omitted, output goes to stdout.
• --type-name: (optional) Name for the root type (default: "Document").
• --base-id: (optional) Base URI for $id generation (default: "https://example.com/").
• --sample-size: (optional) Maximum number of documents to sample (0 = all, default: 0).

Conversion notes (applies to all inference commands):

• XML attributes are converted to fields prefixed with @ (normalized to valid identifiers).
• Text content in mixed-content elements becomes a #text field.
• Repeated elements are inferred as arrays.
• Multiple files with different structures are merged into a unified schema.
• Sparse data (fields that appear in some but not all records) is folded into a single type.

Convert Avrotize Schema to Kusto table declaration

avrotize a2k <path_to_avro_schema_file> [--out <path_to_kusto_kql_file>] [--record-type <record_type>] [--emit-cloudevents-columns] [--emit-cloudevents-dispatch]

Parameters:

• <path_to_avro_schema_file>: The path to the Avrotize Schema file to be converted. If omitted, the file is read from stdin.
• --out: The path to the Kusto KQL file to write the conversion result to. If omitted, the output is directed to stdout.
• --record-type: (optional) The name of the Avro record type to convert to a Kusto table.
• --emit-cloudevents-columns: (optional) If set, the tool will add CloudEvents attribute columns to the table: ___id, ___source, ___subject, ___type, and ___time.
• --emit-cloudevents-dispatch: (optional) If set, the tool will add a table named _cloudevents_dispatch to the script or database, which serves as an ingestion and dispatch table for CloudEvents. The table has columns for the core CloudEvents attributes and a data column that holds the CloudEvents data. For each table in the Avrotize Schema, the tool will create an update policy that maps events whose type attribute matches the Avro type name to the respective table.

Conversion notes:

• Only the Avro record type can be mapped to a Kusto table. If the Avrotize Schema contains other types (like enum or array), the tool will ignore them.
• Only the first record type in the Avrotize Schema is converted to a Kusto table. If the Avrotize Schema contains other record types, they will be ignored. The --record-type option can be used to specify which record type to convert.
• The fields of the record are mapped to columns in the Kusto table. Fields that are records or arrays or maps are mapped to columns of type dynamic in the Kusto table.

Convert JSON Structure Schema to Kusto table declaration

avrotize s2k <path_to_structure_schema_file> [--out <path_to_kusto_kql_file>] [--record-type <record_type>] [--emit-cloudevents-columns] [--emit-cloudevents-dispatch]

Parameters:

• <path_to_structure_schema_file>: The path to the JSON Structure Schema file to be converted. If omitted, the file is read from stdin.
• --out: The path to the Kusto KQL file to write the conversion result to. If omitted, the output is directed to stdout.
• --record-type: (optional) The name of the record type to convert to a Kusto table.
• --emit-cloudevents-columns: (optional) If set, the tool will add CloudEvents attribute columns to the table: ___id, ___source, ___subject, ___type, and ___time.
• --emit-cloudevents-dispatch: (optional) If set, the tool will add a table named _cloudevents_dispatch to the script or database, which serves as an ingestion and dispatch table for CloudEvents. The table has columns for the core CloudEvents attributes and a data column that holds the CloudEvents data. For each table in the JSON Structure Schema, the tool will create an update policy that maps events whose type attribute matches the type name to the respective table.

Conversion notes:

• Only JSON Structure object types can be mapped to a Kusto table. Other types (like enum, array, choice) are not directly convertible to tables.
• The tool converts the first object type found in the schema, or uses the type specified with --record-type.
• Object properties are mapped to columns in the Kusto table. Complex types (objects, arrays, maps, sets, tuples, choices) are mapped to columns of type dynamic.
• JSON Structure primitive types are mapped to appropriate Kusto scalar types:
• string, uri, jsonpointer → string
• boolean → bool
• integer, int8, uint8, int16, uint16, int32 → int
• uint32, int64, uint64 → long
• int128, uint128, decimal → decimal
• number, float, double, float8, binary32, binary64 → real
• date, datetime, timestamp → datetime
• time, duration → timespan
• uuid → guid
• binary → dynamic

Convert Avrotize Schema to SQL Schema

avrotize a2sql [input] --out <path_to_sql_script> --dialect <dialect>

Parameters:

• input: The path to the Avrotize schema file to be converted (or read from stdin if omitted).
• --out: The path to the SQL script file to write the conversion result to.
• --dialect: The SQL dialect (database type) to target. Supported dialects include:
• mysql, mariadb, postgres, sqlserver, oracle, sqlite, bigquery, snowflake, redshift, db2
• --emit-cloudevents-columns: (Optional) Add CloudEvents columns to the SQL table.

For detailed conversion rules and type mappings for each SQL dialect, refer to the SQL Conversion Notes document.

Convert JSON Structure Schema to SQL Schema

avrotize s2sql [input] --out <path_to_sql_script> --dialect <dialect> [--emit-cloudevents-columns]

Parameters:

• input: The path to the JSON Structure schema file to be converted (or read from stdin if omitted).
• --out: The path to the SQL script file to write the conversion result to.
• --dialect: The SQL dialect (database type) to target. Supported dialects include:
• mysql, mariadb, postgres, sqlserver, oracle, sqlite, bigquery, snowflake, redshift, db2
• --emit-cloudevents-columns: (Optional) Add CloudEvents columns to the SQL table.

Conversion notes:

• The tool converts JSON Structure schemas to SQL DDL statements for various database dialects.
• JSON Structure primitive types (string, int8-128, uint8-128, float, double, decimal, binary, date, datetime, uuid, etc.) are mapped to appropriate SQL types for each dialect.
• Compound types (array, set, map, object, choice, tuple) are typically mapped to JSON/JSONB columns or equivalent in the target database.
• Required properties from the JSON Structure schema become non-nullable columns and are used for primary keys.
• The namespace and name properties from the JSON Structure schema are used to construct table names.
• Type annotations like maxLength, precision, and scale are preserved in column comments.

For detailed conversion rules and type mappings for each SQL dialect when converting from JSON Structure, refer to the SQL Conversion Notes document.

Convert Avrotize Schema to MongoDB schema

avrotize a2mongo <path_to_avro_schema_file> [--out <path_to_mongodb_schema>] [--emit-cloudevents-columns]

Parameters:

• <path_to_avro_schema_file>: The path to the Avrotize Schema file to be converted. If omitted, the file is read from stdin.
• --out: The path to the MongoDB schema file to write the conversion result to.
• --emit-cloudevents-columns: (optional) If set, the tool will add CloudEvents attribute columns to the MongoDB schema.

Conversion notes:

• The fields of the Avro record type are mapped to fields in the MongoDB schema. Fields that are records or arrays or maps are mapped to fields of type object.
• The emitted MongoDB schema file is a JSON file that can be used with MongoDB's mongoimport tool to create a collection with the specified schema.

Here are the "Convert ..." sections for the newly added commands:

Convert Avrotize schema to Cassandra schema

avrotize a2cassandra [input] --out <output_directory> [--emit-cloudevents-columns]

• input: Path to the Avrotize schema file (or read from stdin if omitted).
• --out: Output path for the Cassandra schema (required).
• --emit-cloudevents-columns: Add CloudEvents columns to the Cassandra schema (optional, default: false).

Refer to the detailed conversion notes for Cassandra in the NoSQL Conversion Notes.

Convert JSON Structure Schema to Cassandra Schema

avrotize s2cassandra [input] --out <output_file> [--emit-cloudevents-columns]

Parameters:

• input: Path to the JSON Structure schema file (or read from stdin if omitted).
• --out: Output path for the Cassandra CQL schema file (required).
• --emit-cloudevents-columns: Add CloudEvents columns to the Cassandra schema (optional, default: false).

Conversion notes:

• The tool converts JSON Structure schemas to Cassandra CQL DDL statements.
• JSON Structure primitive types are mapped to appropriate Cassandra types (int32 → int, string → text, uuid → uuid, etc.).
• Required properties are used to construct the PRIMARY KEY for the table.
• Complex types (array, map, object) are stored as text columns in Cassandra.

Refer to the detailed conversion notes for Cassandra in the NoSQL Conversion Notes.

Convert Avrotize schema to DynamoDB schema

avrotize a2dynamodb [input] --out <output_directory> [--emit-cloudevents-columns]

• input: Path to the Avrotize schema file (or read from stdin if omitted).
• --out: Output path for the DynamoDB schema (required).
• --emit-cloudevents-columns: Add CloudEvents columns to the DynamoDB schema (optional, default: false).

Refer to the detailed conversion notes for DynamoDB in the NoSQL Conversion Notes.

Convert Avrotize schema to Elasticsearch schema

avrotize a2es [input] --out <output_directory> [--emit-cloudevents-columns]

• input: Path to the Avrotize schema file (or read from stdin if omitted).
• --out: Output path for the Elasticsearch schema (required).
• --emit-cloudevents-columns: Add CloudEvents columns to the Elasticsearch schema (optional, default: false).

Refer to the detailed conversion notes for Elasticsearch in the NoSQL Conversion Notes.

Convert Avrotize schema to CouchDB schema

avrotize a2couchdb [input] --out <output_directory> [--emit-cloudevents-columns]

• input: Path to the Avrotize schema file (or read from stdin if omitted).
• --out: Output path for the CouchDB schema (required).
• --emit-cloudevents-columns: Add CloudEvents columns to the CouchDB schema (optional, default: false).

Refer to the detailed conversion notes for CouchDB in the NoSQL Conversion Notes.

Convert Avrotize schema to Neo4j schema

avrotize a2neo4j [input] --out <output_directory> [--emit-cloudevents-columns]

• input: Path to the Avrotize schema file (or read from stdin if omitted).
• --out: Output path for the Neo4j schema (required).
• --emit-cloudevents-columns: Add CloudEvents columns to the Neo4j schema (optional, default: false).

Refer to the detailed conversion notes for Neo4j in the NoSQL Conversion Notes.

Convert Avrotize schema to Firebase schema

avrotize a2firebase [input] --out <output_directory> [--emit-cloudevents-columns]

• input: Path to the Avrotize schema file (or read from stdin if omitted).
• --out: Output path for the Firebase schema (required).
• --emit-cloudevents-columns: Add CloudEvents columns to the Firebase schema (optional, default: false).

Refer to the detailed conversion notes for Firebase in the NoSQL Conversion Notes.

Convert Avrotize schema to CosmosDB schema

avrotize a2cosmos [input] --out <output_directory> [--emit-cloudevents-columns]

• input: Path to the Avrotize schema file (or read from stdin if omitted).
• --out: Output path for the CosmosDB schema (required).
• --emit-cloudevents-columns: Add CloudEvents columns to the CosmosDB schema (optional, default: false).

Refer to the detailed conversion notes for CosmosDB in the NoSQL Conversion Notes.

Convert Avrotize schema to HBase schema

avrotize a2hbase [input] --out <output_directory> [--emit-cloudevents-columns]

• input: Path to the Avrotize schema file (or read from stdin if omitted).
• --out: Output path for the HBase schema (required).
• --emit-cloudevents-columns: Add CloudEvents columns to the HBase schema (optional, default: false).

Refer to the detailed conversion notes for HBase in the NoSQL Conversion Notes.

Convert Avrotize Schema to empty Parquet file

avrotize a2pq <path_to_avro_schema_file> [--out <path_to_parquet_schema_file>] [--record-type <record-type-from-avro>] [--emit-cloudevents-columns]

Parameters:

• <path_to_avro_schema_file>: The path to the Avrotize Schema file to be converted. If omitted, the file is read from stdin.
• --out: The path to the Parquet schema file to write the conversion result to. If omitted, the output is directed to stdout.
• --record-type: (optional) The name of the Avro record type to convert to a Parquet schema.
• --emit-cloudevents-columns: (optional) If set, the tool will add CloudEvents attribute columns to the Parquet schema: __id, __source, __subject, __type, and __time.

Conversion notes:

• The emitted Parquet file contains only the schema, no data rows.
• The tool only supports writing Parquet files for Avrotize Schema that describe a single record type. If the Avrotize Schema contains a top-level union, the --record-type option must be used to specify which record type to emit.
• The fields of the record are mapped to columns in the Parquet file. Array and record fields are mapped to Parquet nested types. Avro type unions are mapped to structures, not to Parquet unions since those are not supported by the PyArrow library used here.

Convert Avrotize Schema to Iceberg schema

avrotize a2ib <path_to_avro_schema_file> [--out <path_to_iceberg_schema_file>] [--record-type <record-type-from-avro>] [--emit-cloudevents-columns] [--format schema|arrow]

Parameters:

• <path_to_avro_schema_file>: The path to the Avrotize Schema file to be converted. If omitted, the file is read from stdin.
• --out: The path to the Iceberg schema file to write the conversion result to. If omitted, the output is directed to stdout.
• --record-type: (optional) The name of the Avro record type to convert to an Iceberg schema.
• --emit-cloudevents-columns: (optional) If set, the tool will add CloudEvents attribute columns to the Iceberg schema: __id, __source, __subject, __type, and __time.
• --format: (optional) Output format. schema (default) outputs JSON per the Iceberg Table Spec Appendix C. arrow outputs a binary Arrow IPC serialized schema.

Conversion notes:

• The emitted Iceberg file contains only the schema, no data rows.
• The tool only supports writing Iceberg files for Avrotize Schema that describe a single record type. If the Avrotize Schema contains a top-level union, the --record-type option must be used to specify which record type to emit.
• The fields of the record are mapped to columns in the Iceberg file. Array and record fields are mapped to Iceberg nested types. Avro type unions are mapped to structures, not to Iceberg unions since those are not supported by the PyArrow library used here.

Convert JSON Structure to Iceberg schema

avrotize s2ib <path_to_structure_schema_file> [--out <path_to_iceberg_schema_file>] [--record-type <record-type-from-structure>] [--emit-cloudevents-columns] [--format schema|arrow]

Parameters:

• <path_to_structure_schema_file>: The path to the JSON Structure schema file to be converted. If omitted, the file is read from stdin.
• --out: The path to the Iceberg schema file to write the conversion result to. If omitted, the output is directed to stdout.
• --record-type: (optional) The name of the record type in definitions to convert to an Iceberg schema.
• --emit-cloudevents-columns: (optional) If set, the tool will add CloudEvents attribute columns to the Iceberg schema: ___id, ___source, ___subject, ___type, and ___time.
• --format: (optional) Output format. schema (default) outputs JSON per the Iceberg Table Spec Appendix C. arrow outputs a binary Arrow IPC serialized schema.

Conversion notes:

• The emitted Iceberg file contains only the schema, no data rows.
• The tool supports JSON Structure schemas with type: "object" at the top level. If the schema contains a $ref or the record type is in definitions, the --record-type option can be used to specify which type to emit.
• JSON Structure types are mapped to Iceberg types as follows:
• Primitive types: string → StringType, boolean → BooleanType, numeric types (int8-128, uint8-128, float, double) → appropriate IntegerType/LongType/FloatType/DoubleType
• Extended types: binary/bytes → BinaryType, date → DateType, time → TimeType, datetime/timestamp → TimestampType, duration → LongType (microseconds), decimal → DecimalType (with precision/scale), uuid/uri/jsonpointer → StringType
• Compound types: object → StructType, array/set → ListType, map → MapType, tuple → StructType with indexed fields
• Choice types: Mapped to StructType with alternative fields (Iceberg doesn't have native union support)
• Type annotations such as precision, scale, and validation constraints are preserved where applicable.
• The $extends feature is supported - base type properties are included in the conversion.
• Required and optional properties are handled via Iceberg's required field flag.

Convert Parquet schema to Avrotize Schema

avrotize pq2a <path_to_parquet_file> [--out <path_to_avro_schema_file>] [--namespace <avro_schema_namespace>]

Parameters:

• <path_to_parquet_file>: The path to the Parquet file to be converted. If omitted, the file is read from stdin.
• --out: The path to the Avrotize Schema file to write the conversion result to. If omitted, the output is directed to stdout.
• --namespace: (optional) The namespace to use in the Avrotize Schema if the Parquet file does not define a namespace.

Conversion notes:

• The tool reads the schema from the Parquet file and converts it to Avrotize Schema. The data in the Parquet file is not read or converted.
• The fields of the Parquet schema are mapped to fields in the Avrotize Schema. Nested fields are mapped to nested records in the Avrotize Schema.

Convert CSV file to Avrotize Schema

avrotize csv2a <path_to_csv_file> [--out <path_to_avro_schema_file>] [--namespace <avro_schema_namespace>]

Parameters:

• <path_to_csv_file>: The path to the CSV file to be converted. If omitted, the file is read from stdin.
• --out: The path to the Avrotize Schema file to write the conversion result to. If omitted, the output is directed to stdout.
• --namespace: (optional) The namespace to use in the Avrotize Schema if the CSV file does not define a namespace.

Conversion notes:

• The tool reads the CSV file and converts it to Avrotize Schema. The first row of the CSV file is assumed to be the header row, containing the field names.
• The fields of the CSV file are mapped to fields in the Avrotize Schema. The tool infers the types of the fields from the data in the CSV file.

Convert Kafka Connect Schema to Avrotize Schema

avrotize kstruct2a [input] --out <path_to_avro_schema_file>

Parameters:

• input: The path to the Kafka Struct file to be converted (or read from stdin if omitted).
• --out: The path to the Avrotize Schema file to write the conversion result to.
• --kstruct: Deprecated: The path to the Kafka Struct file (for backward compatibility).

Conversion notes:

• The tool converts the Kafka Struct definition to an Avrotize Schema, mapping Kafka data types to their Avro equivalents.
• Kafka Structs are typically used to define data structures for Kafka Connect and other Kafka-based applications. This command facilitates interoperability by enabling the conversion of these definitions into Avro, which can be further used with various serialization and schema registry tools.

Convert Avrotize Schema to C# classes

avrotize a2cs <path_to_avro_schema_file> [--out <path_to_csharp_dir>] [--namespace <csharp_namespace>] [--avro-annotation] [--system_text_json_annotation] [--newtonsoft-json-annotation] [--pascal-properties]

Parameters:

• <path_to_avro_schema_file>: The path to the Avrotize Schema file to be converted. If omitted, the file is read from stdin.
• --out: The path to the directory to write the C# classes to. Required.
• --namespace: (optional) The namespace to use in the C# classes.
• --avro-annotation: (optional) Use Avro annotations.
• --system_text_json_annotation: (optional) Use System.Text.Json annotations.
• --newtonsoft-json-annotation: (optional) Use Newtonsoft.Json annotations.
• --pascal-properties: (optional) Use PascalCase properties.

Conversion notes:

• The tool generates C# classes from the Avrotize Schema. Each record type in the Avrotize Schema is converted to a C# class.
• The fields of the record are mapped to properties in the C# class. Nested records are mapped to nested classes in the C# class.
• The tool supports adding annotations to the properties in the C# class. The --avro-annotation option adds Avro annotations, the --system_text_json_annotation option adds System.Text.Json annotations, and the --newtonsoft-json-annotation option adds Newtonsoft.Json annotations.
• The --pascal-properties option changes the naming convention of the properties to PascalCase.

Convert Avrotize Schema to Java classes

avrotize a2java <path_to_avro_schema_file> [--out <path_to_java_dir>] [--package <java_package>] [--avro-annotation] [--jackson-annotation] [--pascal-properties]

Parameters:

• <path_to_avro_schema_file>: The path to the Avrotize Schema file to be converted. If omitted, the file is read from stdin.
• --out: The path to the directory to write the Java classes to. Required.
• --package: (optional) The package to use in the Java classes.
• --avro-annotation: (optional) Use Avro annotations.
• --jackson-annotation: (optional) Use Jackson annotations.
• --pascal-properties: (optional) Use PascalCase properties.

Conversion notes:

• The tool generates Java classes from the Avrotize Schema. Each record type in the Avrotize Schema is converted to a Java class.
• The fields of the record are mapped to properties in the Java class. Nested records are mapped to nested classes in the Java class.
• The tool supports adding annotations to the properties in the Java class. The --avro-annotation option adds Avro annotations, and the --jackson-annotation option adds Jackson annotations.
• The --pascal-properties option changes the naming convention of the properties to PascalCase.

Convert Avrotize Schema to Python classes

avrotize a2py <path_to_avro_schema_file> [--out <path_to_python_dir>] [--package <python_package>] [--dataclasses-json-annotation] [--avro-annotation]

Parameters:

• <path_to_avro_schema_file>: The path to the Avrotize Schema file to be converted. If omitted, the file is read from stdin.
• --out: The path to the directory to write the Python classes to. Required.
• --package: (optional) The package to use in the Python classes.
• --dataclasses-json-annotation: (optional) Use dataclasses-json annotations.
• --avro-annotation: (optional) Use Avro annotations.

Conversion notes:

• The tool generates Python classes from the Avrotize Schema. Each record type in the Avrotize Schema is converted to a Python class.
• The fields of the record are mapped to properties in the Python class. Nested records are mapped to nested classes in the Python class.
• The tool supports adding annotations to the properties in the Python class. The --dataclasses-json-annotation option adds dataclasses-json annotations, and the --avro-annotation option adds Avro annotations.

Convert Avrotize Schema to TypeScript classes

avrotize a2ts <path_to_avro_schema_file> [--out <path_to_typescript_dir>] [--package <typescript_package>] [--avro-annotation] [--typedjson-annotation]

Parameters:

• <path_to_avro_schema_file>: The path to the Avrotize Schema file to be converted. If omitted, the file is read from stdin.
• --out: The path to the directory to write the TypeScript classes to. Required.
• --package: (optional) The package to use in the TypeScript classes.
• --avro-annotation: (optional) Use Avro annotations.
• --typedjson-annotation: (optional) Use TypedJSON annotations.

Conversion notes:

• The tool generates TypeScript classes from the Avrotize Schema. Each record type in the Avrotize Schema is converted to a TypeScript class.
• The fields of the record are mapped to properties in the TypeScript class. Nested records are mapped to nested classes in the TypeScript class.
• TypeScript contextual keywords such as type, namespace, module, and readonly are preserved when used as field names.
• The tool supports adding annotations to the properties in the TypeScript class. The --avro-annotation option adds Avro annotations, and the --typedjson-annotation option adds TypedJSON annotations.

Convert JSON Structure to TypeScript classes

avrotize s2ts <path_to_structure_schema_file> [--out <path_to_typescript_dir>] [--package <typescript_package>] [--typedjson-annotation] [--avro-annotation]

Parameters:

• <path_to_structure_schema_file>: The path to the JSON Structure schema file to be converted. If omitted, the file is read from stdin.
• --out: The path to the d