Confluent Cloud -> BigQuery - How to diagnose "Bad records" cause

Question

I could push the data from MSSql Server to Topics on Confluent Cloud,but not from topics to BigQuery, it throws an error "Bad records in the last hour - 65"

I could able to connect the topics to bigQuery but not able to ingest the data.

MSSQL and BigQuery table format are the same first(string) last(string) raj ram

Do I need to add any other columns to ingest data such as timestamp, offset,etc.?

Answer 1

If there are messages that can't be sent to the target they'll get written to a Dead Letter Queue with details of the problem.

From the Connectors screen you can see the ID of your connector

Use that id to locate a topic with the same name and a dlq- prefix.

You can then browse the topic and use the header information to determine the cause of the problem

If you prefer you can use kafkacat to view the headers:

$ docker run --rm edenhill/kafkacat:1.5.0 \
         -X security.protocol=SASL_SSL -X sasl.mechanisms=PLAIN \
         -X ssl.ca.location=./etc/ssl/cert.pem -X api.version.request=true \
         -b ${CCLOUD_BROKER_HOST} \
         -X sasl.username="${CCLOUD_API_KEY}" \
         -X sasl.password="${CCLOUD_API_SECRET}" \
         -t dlq-lcc-emj3x \
         -C -c1 -o beginning \
         -f 'Topic %t[%p], offset: %o, Headers: %h'

Topic dlq-lcc-emj3x[0], offset: 12006, Headers: __connect.errors.topic=mysql-01-asgard.demo.transactions,__connect.errors.partition=5,__connect.errors.offset=90,__connect.errors.connector.name=lcc-emj3x,__connect.errors.task.id=0,__connect.errors.stage=VALUE_CONVERTER,__connect.errors.class.name=org.apache.kafka.connect.json.JsonConverter,__connect.errors.exception.class.name=org.apache.kafka.connect.errors.DataException,__connect.errors.exception.message=Converting byte[] to Kafka Connect data failed due to serialization error: ,__connect.errors.exception.stacktrace=org.apache.kafka.connect.errors.DataException: Converting byte[] to Kafka Connect data failed due to serialization error:
        at org.apache.kafka.connect.json.JsonConverter.toConnectData(JsonConverter.java:344)
        at org.apache.kafka.connect.runtime.WorkerSinkTask.lambda$convertAndTransformRecord$1(WorkerSinkTask.java:487)
        at org.apache.kafka.connect.runtime.errors.RetryWithToleranceOperator.execAndRetry(RetryWithToleranceOperator.java:128)
        at org.apache.kafka.connect.runtime.errors.RetryWithToleranceOperator.execAndHandleError(RetryWithToleranceOperator.java:162)
        at org.apache.kafka.connect.runtime.errors.RetryWithToleranceOperator.execute(RetryWithToleranceOperator.java:104)
        at org.apache.kafka.connect.runtime.WorkerSinkTask.convertAndTransformRecord(WorkerSinkTask.java:487)
        at org.apache.kafka.connect.runtime.WorkerSinkTask.convertMessages(WorkerSinkTask.java:464)
        at org.apache.kafka.connect.runtime.WorkerSinkTask.poll(WorkerSinkTask.java:320)
        at org.apache.kafka.connect.runtime.WorkerSinkTask.iteration(WorkerSinkTask.java:224)
        at org.apache.kafka.connect.runtime.WorkerSinkTask.execute(WorkerSinkTask.java:192)
        at org.apache.kafka.connect.runtime.WorkerTask.doRun(WorkerTask.java:177)
        at org.apache.kafka.connect.runtime.WorkerTask.run(WorkerTask.java:227)
        at java.base/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:515)
        at java.base/java.util.concurrent.FutureTask.run(FutureTask.java:264)
        at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
        at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
        at java.base/java.lang.Thread.run(Thread.java:834)
Caused by: org.apache.kafka.common.errors.SerializationException: com.fasterxml.jackson.core.JsonParseException: Illegal character ((CTRL-CHAR, code 0)): only regular white space (\r, \n, \t) is allowed between tokens
 at [Source: (byte[])"

From there on in it's just a case of understanding the error. A lot of the time it's down to serialisation issues, which you can learn more about here.