How to auto-save Avro schema in Confluent Schema Registry from Apache NiFi flow?
That's basically the question.
I am not finding the way of automatically storing the Avro schema of the record in the Confluent Schema Registry from a NiFi flow. It is possible to flexibly read and populate message with the reference to the schema in the Confluent Schema-Registry, but there should be a way of auto-creating one in the registry instead of demanding Confluent Schema-Registry to be initialized upfront before NiFi flow starts.
Update
Here is my current Flow:
I'm reading from a Postgres table using QueryDatabaseTableRecord processor (version 1.10) and publishing [new] records to a Kafka topic using PublishKafkaRecord_2_0 (version 1.10.0).
I want to publish to Kafka in Avro format storing (and passing around) the Avro schema in the Confluent Schema Registry (that works well in other places of my NiFi setup).
For that, I am using AvroRecordSetWriter in the "Record Writer" property on the QueryDatabaseTableRecord processor with the following properties:
PublishKafkaRecord processor is configured to read Avro schema from the input message (using the Confluent schema registry, the schema is not embedded into each FlowFile) and uses same AvroRecordSetWriter as QueryDatabaseTableRecord processor to write to Kafka.
That's basically it.
Trying to replace the first AvroRecordSetWriter with one that embeds the schema with the hope that the second AvroRecordSetWriter could auto generate schema in the Confluent Schema Registry on publish, since I don't want to bloat each message with my embedded Avro schema.
Update
I've tried to follow the advice from the comment as follows
With that I was trying to make first access to the Confluent Schema Registry the last step in the chain. Unfortunately, my attempts were unsuccessful. The only option that worked was my initial described in this question that required a schema in the registry upfront/in advance to work.
Both other cases that I tried ended up with the exception:
org.apache.nifi.schema.access.SchemaNotFoundException: Cannot write Confluent Schema Registry Reference because the Schema Identifier is not known
Please note, that I cannot use "Inherit Schema from record" in the last writer's schema access, since I'm getting an invalid combination and the NiFi config validation doesn't pass such combination through.
Related
The difference between vanilla apache Avro and Avro with confluent schema registry is that when using apache avro , we send schema+message in kafka topic whereas in confluent schema registry , we send schemaID+message in kafka topic ? So here , schema registry helps in performance improvement via schema look up in registry. Is there any other benefit of using confluent schema registry ? Also , does apache avro supports compatabilty rules of schema evolution like schema registry ?
Note: There are other implementations of a "Schema Registry" that can use used with Kafka.
Here are a list of reasons
Clients can discover schemas without interacting with Kafka. For example, Apache Hive / Presto / Spark can download schemas from the Registry to perform analytics.
The registry is centrally responsible for compatibility checks rather than pushing each client to operate that on their own (to answer your second question)
The same applies to any serialization format, as well, not only Avro
I have an app that produces an array of messages in raw JSON periodically. I was able to convert that to Avro using the avro-tools. I did that because I needed the messages to include schema due to the limitations of Kafka-Connect JDBC sink. I can open this file on notepad++ and see that it includes the schema and a few lines of data.
Now I would like to send this to my central Kafka Broker and then use Kafka Connect JDBC sink to put the data in a database. I am having a hard time understanding how I should be sending these Avro files I have to my Kafka Broker. Do I need a schema registry for my purposes? I believe Kafkacat does not support Avro so I suppose I will have to stick with the kafka-producer.sh that comes with the Kafka installation (please correct me if I am wrong).
Question is: Can someone please share the steps to produce my Avro file to a Kafka broker without getting Confluent getting involved.
Thanks,
To use the Kafka Connect JDBC Sink, your data needs an explicit schema. The converter that you specify in your connector configuration determines where the schema is held. This can either be embedded within the JSON message (org.apache.kafka.connect.json.JsonConverter with schemas.enabled=true) or held in the Schema Registry (one of io.confluent.connect.avro.AvroConverter, io.confluent.connect.protobuf.ProtobufConverter, or io.confluent.connect.json.JsonSchemaConverter).
To learn more about this see https://www.confluent.io/blog/kafka-connect-deep-dive-converters-serialization-explained
To write an Avro message to Kafka you should serialise it as Avro and store the schema in the Schema Registry. There is a Go client library to use with examples
without getting Confluent getting involved.
It's not entirely clear what you mean by this. The Kafka Connect JDBC Sink is written by Confluent. The best way to manage schemas is with the Schema Registry. If you don't want to use the Schema Registry then you can embed the schema in your JSON message but it's a suboptimal way of doing things.
I am new to both NiFi and Avro. So, according to my understanding if we use schema registry the schema won't be added to Avro content that is being published to Kafka, only schema ID will be sent is that correct??
How can I publish and consume through Kafka using Horton works Schema Registry, using Avro serialization and deserialization?
In Nifi ConvertJsonToAvro schema will be embedded while sending.SO, is there any other processor which will use schema registry and won't send schema while publishing.
On publishing side you would use PublishKafkaRecord (with the version corresponding to your Kafka broker) and you would configure it with a JsonTreeReader and an AvroRecordSetWriter. In the record writer you would configure the Schema Write Strategy as Hortonworks Content Encoded.
On consuming side your would ConsumeKafkaRecord (same version as publish) and you would configure it with an AvroRecordReader and a JsonRecordWriter. In the reader you would configure the Schema Access Strategy as Hortonworks Content Encoded.
Lets say we have a setup as follows.
Schema evolution compatibility is set to BACKWARD.
JDBC Source Connector polls data from DB writing to Kafka topic.HDFS Sink Connector read message from Kafka topic and write to HDFS in Avro format.
Following the the flow as I understood.
JDBC Source Connector query DB and generate the Schema V1 from JDBC Metadata from ResultSet.V1 has col1,col2,col3.Schema V1 is registered in Schema Registry.
Source connector polls data from DB and write messages to the Kafka topic in V1 schema.
(Question 1) When HDFS Sink connector read messages from the topic ,does it validate the messages against the V1 schema from the Schema Registry?
Next DB schema is changed. Column "col3" is removed from the table.
Next time JDBC Source polls DB it sees that the schema has changed, generate new Schema V2 (col1,col2) and register V2 is Schema Registry.
Source Connect continue polling data and write to Kafka topic in V2 schema.
Now the Kafka Topic can have messages in both V1 and V2 schema.
(Question 2) When HDFS Sink connector read message does it now validate messages against Schema V2 ?
This this the case addressed in the Confluent documentation under the Backward Compatibility ? :
[https://docs.confluent.io/current/schema-registry/avro.html#schema-evolution-and-compatibility]
An example of a backward compatible change is a removal of a field. A
consumer that was developed to process events without this field will
be able to process events written with the old schema and contain the
field – the consumer will just ignore that field.
The registry only validates when a new schema is registered.
Therefore, it's if/when the source connector detects a change, then validation occurs at the registry side
As for HDFS connector, there is a separate schema.compatibility property that applies a projection over records held in memory and any new records. When you get a record with a new schema, and have a backwards compatible update, then all messages not yet flushed will be updated to hold the new schema when an Avro container file is written.
Aside: just because the registry thinks it's backwards, doesn't guarantee the sink connector does... The validation within the source code is different, and we've had multiple issues with it :/
I have configured Kafka Connect HDFS to work on Azure Datalake, however I just noticed that the keys (Strings) are not being persisted in anyway, only the Avro values.
When I think about this I suppose this makes sense as the partitioning I want to apply in the data lake is not related to the key and I have not specified some new Avro Schema which incorporates the key String into the existing Avro value Schema.
Now within the configurations I supply when running the connect-distributed.sh script, I have (among other configurations)
...
key.converter=org.apache.kafka.connect.storage.StringConverter
value.converter=io.confluent.connect.avro.AvroConverter
value.converter.schema.registry.url=http://<ip>:<port>
...
But within the actual sink connector that I set up using curl I simply specify the output format as
...
"format.class": "io.confluent.connect.hdfs.avro.AvroFormat"
...
so the connector just assumes that the Avro value is to be written.
So I have two questions. How do I tell the connector that it should save the key along with the value as part of a new Avro schema, and where do I define this schema?
Note that this is an Azure HDInsight cluster and so is not a Confluent Kafka solution (though I would have access to open source Confluent code such as Kafka Connect HDFS)