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
from message queues With Kafka (Connect and Streams) we’d like to continuously transform these messages 10100110111010101 Kafka Connect Kafka Brokers Exercise #1 - breakfast
from message queues 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 Exercise #1 - breakfast
my offsets ▼ Reset my streaming app and set my auto reset to ▽ ▼ Destroy the topic, no message = no poison pill ▽ ▼ My favourite <3 ▽ Don’t Do ▼ Fill an issue and suggest a fix to the tooling team
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 ...
exceptions thrown by deserializers are caught by a 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
deserializers are caught by a 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
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 ...) Sentinel Value Pattern → G H
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 ...) This allows downstream processors to recognize and handle such sentinel values Sentinel Value Pattern → G H G H
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 ...) This allows downstream processors to recognize and handle such sentinel values With Kafka Streams this can be achieved by implementing a Deserializer Sentinel Value Pattern → G H G H null
implementing a custom serde we can create a safe . 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
implementing a custom serde we can create a safe . 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
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
You can provide your own implementation of . 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 . 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
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
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 - Sentinel Value: By extending a
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