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 three formats: Avro (opens new window), Protobuf (opens new window), and custom encoding. 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. User-defined (3rd-party) coding and decoding services can perform coding and decoding more closely to business needs via HTTP or TCP callbacks.

TIP

Schema Registry maintains Schema text for built-in encoding formats such as Avro and Protobuf, but for custom codec (3rd-party) formats, Schema text needs to be maintained by the codec service itself, if required.

Data Format

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.

Binary format support

Schema Registry data formats include Avro (opens new window) and Protobuf (opens new window). Avro and Protobuf are Schema-dependent data formats and encoded as binary data. The internal data format (Map, explained later) decoded using the Schema Registry can be used directly by rule engines and other plugins. Besides, Schema Registry supports user-defined (3rd-party) coding and decoding services that can perform coding and decoding more closely to business needs via HTTP or TCP callbacks.

Architecture Design

Schema Registry maintains Schema text for built-in encoding formats such as Avro and Protobuf, but for custom codec (3rd-party) formats, Schema text needs to be maintained by the codec service itself, if required. The Schema API provides for add, query, and delete operations via Schema Name.

The Schema Registry can perform both decoding and encoding. Schema Name needs to be specified when encoding and decoding.

Example of an encoding call: parameter is Schema:

schema_encode(SchemaName, Data) -> RawData

Example of a decoding call:

schema_decode(SchemaName, RawData) -> Data

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.

Codec + Rules Engine

The message processing level 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).

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:

SELECT user.id AS my_id

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

JSON Codec

The SQL statements of the rules engine provide support for coding 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.