Schema Registry Architecture
We should now have some understanding of the benefits that Schema Registry provides a modern data architecture. Let’s take a closer look at the main components that make up the registry.
Schema Registry has the following main components:
|Registry Web Server||Web Application exposing the REST endpoints you can use to manage schema entities. You can use a web proxy and load balancer with multiple Web Servers to provide HA and scalability.|
|Schema Metadata Storage||Relational store that holds the metadata for the schema entities. In-memory storage and mySQL databases are supported.|
|Serdes Storage||File storage for the serializer and deserializer jars. Local file system and HDFS storage are supported.|
|Schema Registry Client||A java client that HDF components can use to interact with the RESTful services.|
Below is a graphic outlining how the different components come into play when sending and receiving messages affected by a schema.
Schema Registry can be seen as being made up of different type of metadata entities.
|Schema Group||A logical grouping of similar schemas. A Schema Group can be based on any criteria you have for managing schemas. Schema Groups can have multiple Schema Metadata definitions.||The group name trucking_data_truck or trucking_data_traffic|
|Schema Metadata||Metadata associated with a named schema. A metadata definition is applied to all the schema versions that are assigned to it.||Key metadata elements include: Schema Name, Schema Type, Description, Compatibility Policy, Serializers/Deserializers|
|Schema Version||The versioned schema (the actual schema text) associated with a schema metadata definition.||(Schema text example in following sections)|
Integration with HDF
When Schema Registry is paired with other services available as part of the Hortonworks DataFlow (HDF), integration with Schema Registry is baked in.
|Component||Schema Registry Integration|
|NiFi||New processors and controller services in NiFi interact with Schema Registry. This allows creating flows using drag-and-drop processors that grant the benefits mentioned in the previous section without writing any code.|
|Kafka||A Kafka serializer and deserializer that uses Schema Registry is included with Kafka, allowing events to be marshalled and unmarshalled automatically.|
|Streaming Analytics Manager (SAM)||Using a drag-and-drop paradigm to create processing jobs, SAM will automatically infer schema from data sources and sinks, ensuring that data expected by connected services are compatible with one another.|
Next: Using the Web Interface
Now that we have some understanding of what Schema Registry looks like under the hood, let’s take it for a ride and poke around its web interface. The interface makes it easy to create and modify schemas for any application running on our cluster.