Skip to content

Introduction to Schema Registry

Because of the variety of IoT device terminals and the different coding formats used by various manufacturers, the need for a unified data format arises when accessing the IoT platform for device management by the applications on the platform.

The Schema Registry manages the Schema used for coding and decoding, processes the encoding or decoding requests, and returns the results. The Schema Registry in collaboration with the rule engine can be adapted for device access and rule design in various scenarios.

EMQX Schema Registry currently supports codecs in below formats:

Avro and Protobuf are Schema-dependent data formats. The encoded data is binary and the decoded data is in Map format. The decoded data can be used directly by the rule engine and other plugins. Schema Registry maintains Schema text for built-in encoding formats such as Avro and Protobuf.

JSON schema can be used to validate if input JSON object is following the schema definetions or if the JSON object output from the rule engine is valid before producing the data to downstream.

The diagram below shows an example of a Schema Registry application. Multiple devices report data in different formats, which are decoded by Schema Registry into a uniform internal format and then forwarded to the backend application.


Architecture Design

EMQX can use schema for encoding, decoding, and validating whether the published messages comply with the schema specifications. It maintains schema text for built-in encoding formats, including Avro and Protobuf.

The Schema API provides for add, query, and delete operations via schema name, so the schema name needs to be specified when encoding and decoding.


A common use case is to use the rule engine to call the encoding and decoding interfaces provided by the Schema Registry and then use the encoded or decoded data as input for subsequent actions.

Example of an encoding call:

schema_encode(SchemaName, Map) -> Bytes

Example of a decoding call:

schema_decode(SchemaName, Bytes) -> Map

When encoding data from MQTT messages which are JSON-encoded, you also need to decode it to the Map internal format using the json_decode function before encoding with the schema function. For example:

schema_encode(SchemaName, json_decode(Map)) -> Bytes

When checking if JSON data can be validated against the JSON schema before encoding or after decoding, use the following schema validation example:

schema_check(SchemaName, Map | Bytes) -> Boolean

Schema Registry + Rule Engine

The message processing layer of EMQX can be divided into three parts: Messaging, Rule Engine, and Data Conversion.

EMQX's PUB/SUB system routes messages to specified topics. The rule engine has the flexibility to configure business rules for the data, match messages to the rules and then specify the corresponding action. Data format conversion occurs before the rule matching process, converting the data into a Map format that can participate in rule matching, and then matching it.


Rule Engine Internal Data Format (Map)

The data format used in the internal rule engine is Erlang Map, so if the original data is in binary or other formats, it must be converted to Map using codec functions (such as schema_decode and json_decode as mentioned above). It is very similar to a JSON object.

A Map is a data structure of the form Key-Value, in the form #{key => value}. For example, user = #{id => 1, name => "Steve"} defines a user Map with id of 1 and name of "Steve".

The SQL statement provides the . operator to extract and add Map fields in a nested way. The following is an example of this Map operation using a SQL statement:


The filter result of the SQL statement is #{my_id => 1}.

JSON Codec

The SQL statements of the rule engine provide support for encoding and decoding JSON formatted strings. The SQL functions for converting JSON strings to Map format are json_decode() and json_encode():

SELECT json_decode(payload) AS p FROM "t/#" WHERE p.x = p.y

The SQL statement above will match an MQTT message with the content of the payload as a JSON string: {"x" = 1, "y" = 1}, and the topic as t/a.

json_decode(payload) as p decodes the JSON string into the following Map data structure so that the fields in the Map can be used in the WHERE clause using p.x and p.y.

  p => #{
    x => 1,
    y => 1

Note: The AS clause is required to assign the decoded data to a key so that subsequent operations can be performed on it later.