Streaming Apps and Poison Pills: handle the unexpected with Kafka Streams (Loic Divad, Xebia France) Kafka Summit SF 2019

1@loicmdivad @XebiaFr@loicmdivad @XebiaFr
Streaming Apps and Poison Pills:
handle the unexpected with Kafka Streams

Loïc DIVAD
Data Engineer @XebiaFr
@loicmdivad
dataxday.fr organizer

3@loicmdivad @XebiaFr
Processor API:
The dark side of Kafka Streams

4@loicmdivad @XebiaFr 4@loicmdivad @XebiaFr
> println(sommaire)
Incoming records may be corrupted, or cannot be
handled by the serializer / deserializer. These
records are referred to as “poison pills”
1. Log and Crash
2. Skip the Corrupted
3. Sentinel Value Pattern
4. Dead Letter Queue Pattern

Ratatouille app, a delicious use case
Streaming
APP

Streaming App Poison Pills
1. Log and Crash - Breakfast
2. Skip the Corrupted - Lunch
3. Sentinel Value Pattern - Drink
4. Dead Letter Queue Pattern - Dinner

Apache Kafka Brokers / Clients

Log and Crash
Exercise #1 - breakfast

Really old systems receive raw bytes
directly from message queues
10100110111010101

With Kafka (Connect and Streams)
we’d like to continuously transform
these messages
10100110111010101
Kafka Connect
Kafka Brokers

With Kafka (Connect and Streams)
we’d like to continuously transform
these messages
But we need a deserializer with
special decoder to understand each
event
What happens if we get a buggy
implementation of the deserializer?
10100110111010101
Kafka Connect
Kafka Brokers
Kafka Streams

The Tooling Team
They will provide an appropriate deserializer

// Exercise #1: Breakfast
sealed trait FoodOrder
case class Breakfast(lang: Lang,
fruit: Fruit,
liquid: Liquid,
pastries: Vector[Pastry] = Vector.empty) extends FoodOrder

fruit: Fruit,
liquid: Liquid,
implicit lazy val BreakfastCodec: Codec[Breakfast] = new Codec[Breakfast] = ???

fruit: Fruit,
liquid: Liquid,
class FoodOrderSerializer extends Serializer[FoodOrder] = ???
class FoodOrderDeserializer extends Deserializer[FoodOrder] = ???

fruit: Fruit,
liquid: Liquid,
class FoodOrderSerializer extends Serializer[FoodOrder] = ???
org.apache.kafka.common.serialization
Take
Away

Log and Crash
2019-04-17 03:43:12 macbook-de-lolo [ERROR] (LogAndFailExceptionHandler.java:39) - Exception caught during
Deserialization, taskId: 0_0, topic: input-food-order, partition: 0, offset: 109
Exception in thread "answer-one-breakfast-0d808ce7-0ef1-44c6-808a-f594bc7fceae-StreamThread-1"
org.apache.kafka.streams.errors.StreamsException: Deserialization exception handler is set to fail upon a
deserialization error. If you would rather have the streaming pipeline continue after a deserialization error, please
set the default.deserialization.exception.handler appropriately.
at org.apache.kafka.streams.processor.internals.RecordDeserializer.deserialize(RecordDeserializer.java:80)
at org.apache.kafka.streams.processor.internals.RecordQueue.addRawRecords(RecordQueue.java:101)
at org.apache.kafka.streams.processor.internals.PartitionGroup.addRawRecords(PartitionGroup.java:124)
...
at org.apache.kafka.streams.processor.internals.StreamTask.addRecords(StreamTask.java:711)
at org.apache.kafka.streams.processor.internals.StreamThread.run(StreamThread.java:747)
Caused by: java.lang.IllegalArgumentException: dishes: Insufficient number of elements: decoded 0 but should have
decoded 268435712
at scodec.Attempt$Failure.require(Attempt.scala:108)
at fr.xebia.ldi.ratatouille.serde.BreakfastDeserializer.deserialize(BreakfastDeserializer.scala:22)
at org.apache.kafka.common.serialization.Deserializer.deserialize(Deserializer.java:58)
at org.apache.kafka.streams.processor.internals.SourceNode.deserializeValue(SourceNode.java:60)

Log and Crash
Deserialization, taskId: 0_0, topic: exercise-breakfast, partition: 0, offset: 109
deserialization error. If you would rather have the streaming pipeline continue after a deserialization error, please
set the default.deserialization.exception.handler appropriately.
...
Caused by: java.lang.IllegalArgumentException: dishes: Insufficient number of
elements: decoded 0 but should have decoded 268435712

|val frame1: Array[Byte] = Array(0x33, 0xd4, 0xfc, 0x00, 0x00, 0x00, 0x01, 0xa5)
|val frame2: Array[Byte] = Array(0x44, 0xd2, 0xfe, 0x10, 0x02, 0x03, 0x01)

|val frame1: Array[Byte] = Array( , 0xd4, 0xfc, 0x00, 0x00, 0x00, 0x01, 0xa5)
|val frame2: Array[Byte] = Array( , 0xd2, 0xfe, 0x10, 0x02, 0x03, 0x01)

|val frame1: Array[Byte] = Array( , 0xd4, 0xfc, 0x00, 0x00, 0x00, 0x01, 0xa5)
|val frame2: Array[Byte] = Array( , 0xd2, 0xfe, 0x10, x2, 0x03, 0x01)
|case class Meat(sausages: Int, bacons: Int, . . . )

▼ Change consumer group
▼ Manually update my offsets
▼ Reset my streaming app and set my auto reset to
LATEST
▽ $ kafka-streams-application-reset ...
▼ Destroy the topic, no message = no poison pill
▽ $ kafka-topics --delete --topic ...
▼ My favourite <3
▽ $ confluent destroy && confluent start
Don’t Do
▼ Fill an issue and suggest a ﬁx to the tooling team

Log and Crash
Like all consumers, Kafka Streams applications
deserialize messages from the broker.
The deserialization process can fail. It raises an
exception that cannot be caught by our code.
Buggy deserializers have to be ﬁxed before the
application restarts, by default ...

Skip the Corrupted
Exercise #2 - lunch

// Exercise #2: Lunch
case class Lunch(name: String, price: Double, `type`: LunchType) extends FoodOrder

// Exercise #2: Lunch
case class Lunch(name: String, price: Double, `type`: LunchType) extends FoodOrder
● starter
● main
● dessert

Skip the Corrupted
Deserialization, taskId: 0_0, topic: exercise-breakfast, partition: 0, offset: 109
deserialization error. If you would rather have the streaming pipeline continue after a
deserialization error, please set the default.deserialization.exception.handler
appropriately.
...
Caused by: java.lang.IllegalArgumentException: ... decoded 0 but should have decoded 268435712

public class LogAndFailExceptionHandler implements DeserializationExceptionHandler
/* ... */
public class LogAndContinueExceptionHandler implements DeserializationExceptionHandler
/* ... */

/* ... */
/* ... */
public interface DeserializationExceptionHandler extends Configurable {
DeserializationHandlerResponse handle(final ProcessorContext context,
final ConsumerRecord<byte[], byte[]> record,
final Exception exception);
enum DeserializationHandlerResponse {
CONTINUE(0, "CONTINUE"),
FAIL(1, "FAIL");
/* ... */
}
}
}

/* ... */
/* ... */
public interface DeserializationExceptionHandler extends Configurable {
DeserializationHandlerResponse handle(final ProcessorContext context,
final ConsumerRecord<byte[], byte[]> record,
final Exception exception);
enum DeserializationHandlerResponse {
CONTINUE(0, "CONTINUE"),
FAIL(1, "FAIL");
/* ... */
}
}
}
Take
Away

The Exception Handler in the call stack
Powered by the Flow intelliJ plugin ➞ ﬁndtheﬂow.io

Skip the Corrupted
All exceptions thrown by deserializers are caught by
a DeserializationExceptionHandler
A handler returns Fail or Continue
You can implement your own Handler
But the two handlers provided by the library are
really basic… let’s explore other methods

All exceptions thrown by deserializers are caught by
a DeserializationExceptionHandler
A handler returns Fail or Continue
You can implement your own Handler
But the two handlers provided by the library are
really basic… let’s explore other methods
Skip the Corrupted
Take
Away

Sentinel Value Pattern
Exercise #3 - drinks

// Exercise #3: Drink
case class Drink(name: String,
quantity: Int,
`type`: DrinkType,
alcohol: Option[Double]) extends FoodOrder

// Exercise #3: Drink
case class Drink(name: String,
quantity: Int,
`type`: DrinkType,
alcohol: Option[Double]) extends FoodOrder
● wine
● rhum
● beer
● champagne
● ...

We need to turn the deserialization process into a
pure transformation that cannot crash
To do so, we will replace corrupted message by a
sentinel value. It’s a special-purpose record (e.g: null,
None, Json.Null, etc ...)
f: G → H
G H

This allows downstream processors to recognize and
handle such sentinel values
f: G → H
G H
G H

This allows downstream processors to recognize and
handle such sentinel values
With Kafka Streams this can be achieved by
implementing a Deserializer
f: G → H
G H
G H
null

case object FoodOrderError extends FoodOrder

case object FoodOrderError extends FoodOrder
class SentinelValueDeserializer extends FoodOrderDeserializer {
override def deserialize(topic: String, data: Array[Byte]): FoodOrder =
Try(super.deserialize(topic, data)).getOrElse(FoodOrderErr)
}

class FoodOrderSentinelValueProcessor extends ValueTransformer[Json, Unit] {
var sensor: Sensor = _
var context: ProcessorContext = _
def metricName(stat: String): MetricName = ???
override def init(context: ProcessorContext): Unit = {
this.context = context
this.sensor = this.context.metrics.addSensor("sentinel-value", INFO)
sensor.add(metricName("total"), new Total())
sensor.add(metricName("rate"), new Rate(TimeUnit.SECONDS, new Count()))
}
override def transform(value: Json): Unit = sensor.record()
}

By implementing a custom serde we can create a safe
Deserializer.
Downstreams now receive a sentinel value
indicating a deserialization error.
Errors can then be treated correctly, example:
monitoring the number of deserialization
errors with a custom metric
But we lost a lot of information about the error…
let’s see a last method

By implementing a custom serde we can create a safe
Deserializer.
Downstreams now receive a sentinel value
indicating a deserialization error.
Errors can then be treated correctly, example:
monitoring the number of deserialization
errors with a custom metric
But we lost a lot of information about the error…
let’s see a last method
Take
Away

Dead Letter Queue Pattern
Exercise #4 - dinner

// Exercise #4: Dinner
case class Dinner(dish: Command,
zone: String,
moment: Moment,
maybeClient: Option[Client]) extends FoodOrder

In this method we will let the deserializer fail.
For each failure we will send a message to a topic
containing corrupted messages.
Each message will have the original content of the
input message (for reprocessing) and additional
meta data about the failure.
With Kafka Streams this can be achieved by
implementing a DeserializationExceptionHandler
Streaming
APP
dead letter queue
input topic output topic

class DeadLetterQueueFoodExceptionHandler() extends DeserializationExceptionHandler {
override def handle(context: ProcessorContext,
record: ConsumerRecord[Array[Byte], Array[Byte]],
exception: Exception): DeserializationHandlerResponse = {
}

val producerRecord = new ProducerRecord(topic, /*same key, value and ts,*/ headers.asJava)
producer.send(producerRecord, /* Producer Callback */ )
DeserializationHandlerResponse.CONTINUE
}

var topic: String = _
var producer: KafkaProducer[Array[Byte], Array[Byte]] = _
override def configure(configs: util.Map[String, _]): Unit = ???
}

val headers = record.headers().toArray ++ Array[Header](
new RecordHeader("processing-time", ???),
new RecordHeader("hexa-datetime", ???),
new RecordHeader("error-message", ???),
...
)
}

Fill the headers with some meta data
01061696e0016536f6d6500000005736f6d65206f
Value message to hexa
Restaurant
description
Event date and time
Food order category

...
)
}

...
)
}
Take
Away

414554=AET=
Australia/Sydney

You can provide your own implementation of
DeserializationExceptionHandler.
This lets you use the Producer API to write a
corrupted record directly to a quarantine topic.
Then you can manually analyse your corrupted
records
⚠Warning: This approach have side effects that are
invisible to the Kafka Streams runtime.

You can provide your own implementation of
DeserializationExceptionHandler.
This lets you use the Producer API to write a
corrupted record directly to a quarantine topic.
Then you can manually analyse your corrupted
records
⚠Warning: This approach have side effects that are
invisible to the Kafka Streams runtime.
Take
Away

Conclusion
Exercise #NaN - take aways

Links
XKE-RATATOUILLE
CONFLUENT FAQ

Related Post
Kafka Connect Deep Dive – Error Handling and
Dead Letter Queues - by Robin Moffatt
Building Reliable Reprocessing and Dead Letter
Queues with Apache Kafka - by Ning Xia
Handling bad messages using Kafka's Streams API -
answer by Matthias J. Sax

Conclusion
When using Kafka, deserialization is the
responsibility of the clients.
These internal errors are not easy to catch
When it’s possible, use Avro + Schema Registry
When it’s not possible, Kafka Streams applies
techniques to deal with serde errors:
- DLQ: By extending a ExceptionHandler
- Sentinel Value: By extending a Deserializer

MERCI

Images
Photo by rawpixel on Unsplash
Photo by João Marcelo Martins on Unsplash
Photo by Jordane Mathieu on Unsplash
Photo by Brooke Lark on Unsplash
Photo by Jakub Kapusnak on Unsplash
Photo by Melissa Walker Horn on Unsplash
Photo by Aneta Pawlik on Unsplash

With special thanks to
Robin R.
Sylvain L.
Giulia B.

How the generator works?

Pure HTML
Akka Http Server
Akka Actor System
Kafka Topic
Exercise1
Exercise2
Me, clicking
everywhere
Akka Stream
Kafka

Streaming Apps and Poison Pills: handle the unexpected with Kafka Streams (Loic Divad, Xebia France) Kafka Summit SF 2019

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Streaming Apps and Poison Pills: handle the unexpected with Kafka Streams (Loic Divad, Xebia France) Kafka Summit SF 2019

Similar to Streaming Apps and Poison Pills: handle the unexpected with Kafka Streams (Loic Divad, Xebia France) Kafka Summit SF 2019 (20)

More from confluent

More from confluent (20)

Recently uploaded

Recently uploaded (20)

Streaming Apps and Poison Pills: handle the unexpected with Kafka Streams (Loic Divad, Xebia France) Kafka Summit SF 2019