Dead Letter Queue (DLQ) in Kafka

Introduction to Kafka Dead Letter Queue (DLQ) and its implementation in Python

Dead Letter Queue is a secondary Kafka topic which receives the messages for which the Kafka Consumer failed to process due to certain errors like improper deserialization of message, improper message format, etc.

Installation

There are various libraries in Python which can be used to connect to Kafka Cluster. Some of them are:

1. kafka-python
2. confluent-kafka
3. PyKafka

I’ll be using kafka-python to connect to Kafka Cluster and to create Kafka Producer and Consumer Clients.

#programming #python #kafka

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Dead Letter Queue (DLQ) in Kafka

Introduction to Kafka Dead Letter Queue (DLQ) and its implementation in Python

Dead Letter Queue is a secondary Kafka topic which receives the messages for which the Kafka Consumer failed to process due to certain errors like improper deserialization of message, improper message format, etc.

Installation

There are various libraries in Python which can be used to connect to Kafka Cluster. Some of them are:

1. kafka-python
2. confluent-kafka
3. PyKafka

I’ll be using kafka-python to connect to Kafka Cluster and to create Kafka Producer and Consumer Clients.

#programming #python #kafka

Kafka Spark Streaming | Kafka Tutorial

In this kafka spark streaming tutorial you will learn what is apache kafka, architecture of apache kafka & how to setup a kafka cluster, what is spark & it’s features, components of spark and hands on demo on integrating spark streaming with apache kafka and integrating spark flume with apache kafka.

# Kafka Spark Streaming #Kafka Tutorial #Kafka Training #Kafka Course #Intellipaat

Kafka Connect 101: Error Handling and Dead Letter Queues

Kafka Connect supports various options for handling errors in the pipeline, including sending failed messages to a dead letter queue topic. This video explains the different configuration options and illustrates their effect using a common error scenario, that of a mismatched converter causing the infamous “SerializationException: Unknown magic byte!” error.

#kafka

Handling failed SQS Events using AWS Dead Letter Queue(DLQ)

Amazon SQS is an amazing simple queuing service which offers us a secure, durable hosted queue which lets us integrate and decouple distributed software component. One of the exciting features that SQS provides us with is the support of dead letter queue. Whenever we are using SQS for queuing messages for our services there may be times that our message gets corrupted or for some reason the application is not able to consume the message. This is where DLQ comes in.

How does dead letter Queue Works?

Sometimes, messages can’t be processed because of a variety of possible issues, such as erroneous conditions within the producer or consumer application or an unexpected state change that causes an issue with your application code. Sometimes, producers and consumers might fail to interpret aspects of the protocol that they use to communicate, causing message corruption or loss. Also, the consumer’s hardware errors might corrupt message payload.

The redrive policy specifies the source queue, the dead-letter queue, and the conditions under which Amazon SQS moves messages from the former to the latter if the consumer of the source queue fails to process a message a specified number of times. Some important points to remember are:

• To specify a dead-letter queue, you can use the console or the AWS SDK for Java. You must do this for each queue that sends messages to a dead-letter queue. Multiple queues of the same type can target a single dead-letter queue.
• The dead-letter queue of a FIFO queue must also be a FIFO queue. Similarly, the dead-letter queue of a standard queue must also be a standard queue.
• You must use the same AWS account to create the dead-letter queue and the other queues that send messages to the dead-letter queue.

#aws #sqs #dlq

Diving Deep into Kafka

The objective of this blog is to build some more understanding of Apache Kafka concepts such as Topics, Partitions, Consumer, and Consumer Groups. Kafka’s basic concepts have been covered in my previous article.

Kafka Topic & Partitions

As we know, messages in Kafka are categorized or stored inside Topics. In simple terms, Topic can be construed as a Database table. Kafka Topics inside is broken down into partitions. Partitions allow us to parallelize a topic by splitting the data of a topic across multiple brokers, thus adding an essence of parallelism to the ecosystem.

Behind the scenes

Messages are written to a partition in an append-only manner, and messages are read from a partition from beginning to end, FIFO mannerism. Each message within a partition is identified by an integer value called _offset. _An offset is an immutable sequential ordering of messages, maintained by Kafka. Anatomy of a Topic with multiple partitions:

Partitioned Topic

Sequential number in array fashion is the offset value maintained by Kafka

Some key points:

1. Ordering of messages is maintained at the partition level, not across the topic.
2. Data written to partition is immutable and can’t be updated.
3. Each message in the Kafka broker is a collection of message topics, partition, offset, key, and value.
4. Each partition will have a leader that will take care of Read/Write operations in the partition.

#kafka-python #kafka #streaming #apache-kafka