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.
Related
I want to write data from Kafka to Cassandra with Flink. The messages in Kafka are serialized via Avro schema which is managed by a Confluent schema registry.
I have already got
a Kafka Consumer, which can consume the Avro messages properly
and a CassandraSink which can write data with Flink's datatype Row
I've already tried to use AvroRowDeserializationSchema (see here) but this didn't work due to the Confluent schema registry. Unfortunately, I couldn't find any resources to deserialize Confluent Avro to Row.
But maybe there is a completely different and easier way to achieve this simple goal (read Confluent Avro and write to Cassandra table). I would really appreciate any hint or idea.
I need to receive messages to my BigQuery tables and I want to know what is the best way to consume those messages.
My Kafka servers who are at AWS they produce AVRO messages and from what I saw Dataflow needs receive JSON format messages. So I googled and found an article explaining how to receive messages to PubSub, but on PubSub what I only see in this type of architecture, they create a Kafka VM on GCP to produce the messages.
What I need to know is:
It's possible to receive AVRO messages on PubSub from external Kafka Servers and then deserialize the message using my Schema, sending it to Dataflow and finally send it to BigQuery tables?
Or do I need to create a Kafka VM and use it to consume messages from external servers?
This might seem a bit confusing but it is what I am feeling right now. The main goal here is to get messages from Kafka (AVRO format) at AWS and put them on BigQuery tables. If you have any suggestions they are very welcomed
Thanks a lot in advance
The Kafka Connect BigQuery Connector may be exactly what you need. It is a Kafka sink connector that allows you to export messages from Kafka directly to BigQuery. The README page provides detailed configuration instructions, including how to let the connector recognize your Kafka queue and how to enter the information for the destination BigQuery table. This connector should be able to retrieve the AVRO schema automatically from your Kafka project.
I'm trying to read data from DB2 using Kafka and then to write it to HDFS. I use distributed confluent platform with standard JDBC and HDFS connectors.
As the HDFS connector needs to know the schema, it requires avro data as an input. Thus, I have to specify the following avro converters for the data fed to Kafka (in etc/kafka/connect-distributed.properties):
key.converter=io.confluent.connect.avro.AvroConverter
key.converter.schema.registry.url=http://localhost:8081
value.converter=io.confluent.connect.avro.AvroConverter
value.converter.schema.registry.url=http://localhost:8081
I then run my JDBC connector and check with the console-avro-consumer that I can successfully read the data fetched from the DB2.
However, when I launch the HDFS Connector, it does not work anymore. Instead, it outputs SerializationException:
Error deserializing Avro message for id -1
... Unknown magic byte!
To check if this is a problem with the HDFS connector, I tried to use a simple FileSink connector instead. However, I saw exactly the same exception when using the FileSink (and the file itself was created but stayed empty).
I then carried out the following experiment: Instead of using avro converter for the key and value I used json converters:
key.converter=org.apache.kafka.connect.json.JsonConverter
key.converter.schema.enable=false
value.converter=org.apache.kafka.connect.json.JsonConverter
value.converter.schema.enable=false
This fixed the problem with the FileSink connector, i.e., the whole pipeline from DB2 to the file worked fine. However, for the HDFS connector this solution is infeasible as the connector needs the schema and consequently avro format as an input.
It feels to me that the deserialization of avro format in the sink connectors is not implemented properly as the console-avro-consumer can still successfully read the data.
Does anyone have any idea of what could be the reason of this behavior? I'd also appreciate an idea of a simple fix for this!
check with the console-avro-consumer that I can successfully read the data fetched
I'm guessing you didn't add --property print.key=true --from-beginning when you did that.
Its possible that the latest values are Avro, but connect is clearly failing somewhere on the topic, so you need to scan it to find out where that happens
If using JsonConverter works, and the data is actually readable JSON on disk, then it sounds like the JDBC Connector actually wrote JSON, not Avro
If you are able to pinpoint the offset for the bad message, you can use the regular console consumer with the connector group id set, then add --max-messages along with a partition and offset specified to skip those events
We need to export production data from a Kafka topic to use it for testing purposes: the data is written in Avro and the schema is placed on the Schema registry.
We tried the following strategies:
Using kafka-console-consumer with StringDeserializer or BinaryDeserializer. We were unable to obtain a file which we could parse in Java: we always got exceptions when parsing it, suggesting the file was in the wrong format.
Using kafka-avro-console-consumer: it generates a json which includes also some bytes, for example when deserializing BigDecimal. We didn't even know which parsing option to choose (it is not avro, it is not json)
Other unsuitable strategies:
deploying a special kafka consumer would require us to package and place that code in some production server, since we are talking about our production cluster. It is just too long. After all, isn't kafka console consumer already a consumer with configurable options?
Potentially suitable strategies
Using a kafka connect Sink. We didn't find a simple way to reset the consumer offset since apparently the connector created consumer is still active even when we delete the sink
Isn't there a simply, easy way to dump the content of the value (not the schema) of a Kafka topic containing avro data to a file so that it can be parsed? I expect this to be achievable using kafka-console-consumer with the right options, plus using the correct Java Api of Avro.
for example, using kafka-console-consumer... We were unable to obtain a file which we could parse in Java: we always got exceptions when parsing it, suggesting the file was in the wrong format.
You wouldn't use regular console consumer. You would use kafka-avro-console-consumer which deserializes the binary avro data into json for you to read on the console. You can redirect > topic.txt to the console to read it.
If you did use the console consumer, you can't parse the Avro immediately because you still need to extract the schema ID from the data (4 bytes after the first "magic byte"), then use the schema registry client to retrieve the schema, and only then will you be able to deserialize the messages. Any Avro library you use to read this file as the console consumer writes it expects one entire schema to be placed at the header of the file, not only an ID pointing to anything in the registry at every line. (The basic Avro library doesn't know anything about the registry either)
The only thing configurable about the console consumer is the formatter and the registry. You can add decoders by additionally exporting them into the CLASSPATH
in such a format that you can re-read it from Java?
Why not just write a Kafka consumer in Java? See Schema Registry documentation
package and place that code in some production server
Not entirely sure why this is a problem. If you could SSH proxy or VPN into the production network, then you don't need to deploy anything there.
How do you export this data
Since you're using the Schema Registry, I would suggest using one of the Kafka Connect libraries
Included ones are for Hadoop, S3, Elasticsearch, and JDBC. I think there's a FileSink Connector as well
We didn't find a simple way to reset the consumer offset
The connector name controls if a new consumer group is formed in distributed mode. You only need a single consumer, so I would suggest standalone connector, where you can set offset.storage.file.filename property to control how the offsets are stored.
KIP-199 discusses reseting consumer offsets for Connect, but feature isn't implemented.
However, did you see Kafka 0.11 how to reset offsets?
Alternative options include Apache Nifi or Streamsets, both integrate into the Schema Registry and can parse Avro data to transport it to numerous systems
One option to consider, along with cricket_007's, is to simply replicate data from one cluster to another. You can use Apache Kafka Mirror Maker to do this, or Replicator from Confluent. Both give the option of selecting certain topics to be replicated from one cluster to another- such as a test environment.
I have a system pushing Avro data in to multiple Kafka topics.
I want to push that data to HDFS. I came across confluent but am not sure how can I send data to HDFS without starting kafka-avro-console-producer.
Steps I performed:
I have my own Kafka and ZooKeeper running so i just started schema registry of confluent.
I started kafka-connect-hdfs after changing topic name.
This step is also successful. It's able to connect to HDFS.
After this I started pushing data to Kafka but the messages were not being pushed to HDFS.
Please help. I'm new to Confluent.
You can avoid using the kafka-avro-console-producer and use your own producer to send messages to the topics, but we strongly encourage you to use the Confluent Schema Registry (https://github.com/confluentinc/schema-registry) to manage your schemas and use the Avro serializer that is bundled with the Schema Registry to keep your Avro data consistent. There's a nice writeup on the rationale for why this is a good idea to do here.
If you are able to send messages that were produced with the kafka-avro-console-producer to HDFS, then your problem is likely in the kafka-connect-hdfs connector not being able to deserialize the data. I assume you are going through the quickstart guide. The best results will come from you using the same serializer on both sides (in and out of Kafka) if you are intending to write Avro to HDFS. How this process works is described in this documentation.