Pushkar,
Kafka uses the concept of offsets to identify the order of each record
within the log. But this concept is more powerful than it looks like.
Committed offsets are also used to keep track of which records has been
successfully read and which ones are not. When you commit a offset in
the consumer; a message is sent to Kafka that in turn register this
commit into a internal topic called `__committed_offsets`.
Point being: you can elegantly solve this problem by handling properly
the exception in your code but only committing the offset if the record
was deemed fully read -- which means being able to deserialize the
record with no exceptions thrown. In order to do this, you will need to
disable auto commit and manually commit the offsets either in a
per-batch basis or in a per-record basis.
Non-committed offsets will be picked up by the same or another thread
from the consumer group. This is the part where *Gerbrand's* suggestion
might take place. You might want to have another stream processor
specifically handling those outliers and sending them out to a DLQ topic
for manual reprocessing purposes.
Thanks,
-- Ricardo
On 6/18/20 7:45 AM, Pushkar Deole wrote:
Hi Gerbrand,
thanks for the update, however if i dig more into it, the issue is because
of schema registry issue and the schema registry not accessible. So the
error is coming during poll operation itself:
So this is a not a bad event really but the event can't be deserialized
itself due to schema not available. Even if this record is skipped, the
next record will meet the same error.
Exception in thread "Thread-9"
org.apache.kafka.common.errors.SerializationException: Error deserializing
key/value for partition tenant.avro-2 at offset 1. If needed, please seek
past the record to continue consumption.
Caused by: org.apache.kafka.common.errors.SerializationException: Error
deserializing Avro message for id 93
Caused by: java.net.ConnectException: Connection refused (Connection
refused)
at java.base/java.net.PlainSocketImpl.socketConnect(Native Method)
at java.base/java.net.AbstractPlainSocketImpl.doConnect(Unknown Source)
at java.base/java.net.AbstractPlainSocketImpl.connectToAddress(Unknown
Source)
at java.base/java.net.AbstractPlainSocketImpl.connect(Unknown Source)
at java.base/java.net.Socket.connect(Unknown Source)
at java.base/sun.net.NetworkClient.doConnect(Unknown Source)
at java.base/sun.net.www.http.HttpClient.openServer(Unknown Source)
at java.base/sun.net.www.http.HttpClient.openServer(Unknown Source)
at java.base/sun.net.www.http.HttpClient.<init>(Unknown Source)
at java.base/sun.net.www.http.HttpClient.New(Unknown Source)
at java.base/sun.net.www.http.HttpClient.New(Unknown Source)
at
java.base/sun.net.www.protocol.http.HttpURLConnection.getNewHttpClient(Unknown
Source)
at
java.base/sun.net.www.protocol.http.HttpURLConnection.plainConnect0(Unknown
Source)
at
java.base/sun.net.www.protocol.http.HttpURLConnection.plainConnect(Unknown
Source)
at java.base/sun.net.www.protocol.http.HttpURLConnection.connect(Unknown
Source)
at
java.base/sun.net.www.protocol.http.HttpURLConnection.getInputStream0(Unknown
Source)
at
java.base/sun.net.www.protocol.http.HttpURLConnection.getInputStream(Unknown
Source)
at java.base/java.net.HttpURLConnection.getResponseCode(Unknown Source)
at
io.confluent.kafka.schemaregistry.client.rest.RestService.sendHttpRequest(RestService.java:208)
at
io.confluent.kafka.schemaregistry.client.rest.RestService.httpRequest(RestService.java:252)
at
io.confluent.kafka.schemaregistry.client.rest.RestService.getId(RestService.java:482)
at
io.confluent.kafka.schemaregistry.client.rest.RestService.getId(RestService.java:475)
at
io.confluent.kafka.schemaregistry.client.CachedSchemaRegistryClient.getSchemaByIdFromRegistry(CachedSchemaRegistryClient.java:153)
at
io.confluent.kafka.schemaregistry.client.CachedSchemaRegistryClient.getBySubjectAndId(CachedSchemaRegistryClient.java:232)
at
io.confluent.kafka.schemaregistry.client.CachedSchemaRegistryClient.getById(CachedSchemaRegistryClient.java:211)
at
io.confluent.kafka.serializers.AbstractKafkaAvroDeserializer.deserialize(AbstractKafkaAvroDeserializer.java:116)
at
io.confluent.kafka.serializers.AbstractKafkaAvroDeserializer.deserialize(AbstractKafkaAvroDeserializer.java:88)
at
io.confluent.kafka.serializers.KafkaAvroDeserializer.deserialize(KafkaAvroDeserializer.java:55)
at
org.apache.kafka.common.serialization.Deserializer.deserialize(Deserializer.java:60)
at
org.apache.kafka.clients.consumer.internals.Fetcher.parseRecord(Fetcher.java:1268)
at
org.apache.kafka.clients.consumer.internals.Fetcher.access$3600(Fetcher.java:124)
at
org.apache.kafka.clients.consumer.internals.Fetcher$PartitionRecords.fetchRecords(Fetcher.java:1492)
at
org.apache.kafka.clients.consumer.internals.Fetcher$PartitionRecords.access$1600(Fetcher.java:1332)
at
org.apache.kafka.clients.consumer.internals.Fetcher.fetchRecords(Fetcher.java:645)
at
org.apache.kafka.clients.consumer.internals.Fetcher.fetchedRecords(Fetcher.java:606)
at
org.apache.kafka.clients.consumer.KafkaConsumer.pollForFetches(KafkaConsumer.java:1294)
at
org.apache.kafka.clients.consumer.KafkaConsumer.poll(KafkaConsumer.java:1225)
at
org.apache.kafka.clients.consumer.KafkaConsumer.poll(KafkaConsumer.java:1201)
at
com.avaya.analytics.dsi.DsiConsumer.runAdminConsumer(DsiConsumer.java:797)
at java.base/java.lang.Thread.run(Unknown Source)
On Thu, Jun 18, 2020 at 3:17 PM Gerbrand van Dieijen <gerbr...@vandieijen.nl>
wrote:
Hello Pushkar,
I'd split records/events in categories based on the error:
- Events that can be parsed or otherwise handled correctly, e.g. good
events
- Fatal error, like parsing error, empty or incorrect values, etc., e.g.
bad events
- Non-fatal, like database-connection failure, io-failure, out of memory,
and others
that could be retried
Best to avoid doing something blocking while handling the error, so create
a separate stream for each. That way 'good' events don't have to wait for
the handling of 'bad' events.
Any fatal can events you could store in a separate topic, or send to some
monitoring/logging system. As a simple start you could sent the erroneous
events to a separate topic named something like 'errorevents'.
Any non-fatal errors could be retried. Last time I used Akka for that (
https://doc.akka.io/docs/alpakka-kafka/current/errorhandling.html) but
afaik KStreams has mechanism for that as well. You could also store
records that you want to retry into a separate topic 'retry'.
Do not store records that that you want to retry back into the original
topic! If you do that you're have a great risk that overload you're whole
kafka-cluster.
Op 18-06-2020 09:55 heeft Pushkar Deole <pdeole2...@gmail.com>
geschreven:
Hi All,
This is what I am observing: we have a consumer which polls data from
topic, does the processing, again polls data which keeps happening
continuously.
At one time, there was some bad data on the topic which could not be
consumed by consumer, probably because it couldn't deserialize the
event
due to incompatible avro schema or something similar,
and consumer got error deserializing event. Since the exception wasn't
handled, it crashed the consumer thread which then stopped consuming
data.
The question here is how these kind of scenarios can be handled:
1. Even if I catch the exception and log it, the consumer will i think
process the next event. So the bad event will be lost
2. When consumer goes for another poll, it would commit offsets of
previous
poll which includes bad event, So the event will be lost
How can this scenario be handled in best possible way?
