Cookie Consent by Free Privacy Policy Generator 📌 Understanding and Mitigating Message Loss in Apache Kafka

✅ Understanding and Mitigating Message Loss in Apache Kafka

💡 Newskategorie: Programmierung
🔗 Quelle:

Apache Kafka, a popular distributed streaming platform, enables the building of scalable and fault-tolerant real-time data pipelines and applications. However, like any distributed system, Kafka is not immune to message loss, which can lead to data inconsistencies and impact system reliability.
In this article, we will explore the potential causes of message loss in Apache Kafka, focusing on producers, brokers, and consumers. We'll discuss scenarios such as improper acknowledgment settings, asynchronous disk flushing, replica synchronization issues, and the pitfalls of auto-commit in consumers.
By understanding these message loss scenarios, we can develop strategies to mitigate risks and ensure the reliability of our Kafka-based applications. We'll cover techniques like proper configuration, manual offset committing, idempotent message processing, and transactional commits.
So, let's dive in and learn how to build robust and fault-tolerant Kafka-based systems that minimize message loss and guarantee data integrity.


When we call producer.send() to send a message, it doesn't get sent to the broker directly. There are two threads and a queue involved in the message-sending process:

  1. Application thread
  2. Record accumulator
  3. Sender thread (I/O thread)

The flow of a message from the producer to the broker can be visualized as follows:


  1. Application Thread:
  • The application thread is responsible for creating the producer record and calling the producer.send() method.
  • It serializes the key and value of the record using the configured serializers.
  • The record is then added to the record accumulator.
  1. Record Accumulator:
  • The record accumulator is a buffer that holds the records waiting to be sent to the Kafka broker.
  • It is essentially a queue that stores the records in memory.
  • The records are grouped into batches based on the topic and partition they belong to.
  • The batch size and the time to wait before sending the batch can be configured using the 'batch.size' and '' properties, respectively.
  1. Sender Thread:
    • The sender thread, also known as the I/O thread, is responsible for sending the batches of records from the record accumulator to the Kafka broker.
    • It periodically checks the record accumulator for any ready batches and sends them to the corresponding broker.
    • The sender thread handles the network communication with the Kafka broker.

To ensure that messages are reliably sent to the broker and to prevent message loss, we need to configure the following properties:

  • 'acks': This property determines the level of acknowledgment required from the broker.

    • 'acks=0': The producer does not wait for any acknowledgment from the broker. Messages may be lost if the broker fails.
    • 'acks=1' (default): The producer waits for the leader to acknowledge the write. Messages may be lost if the leader fails before replicating to followers.
    • 'acks=all' or 'acks=-1': The producer waits for the full set of in-sync replicas to acknowledge the write. This provides the highest level of durability but increases latency.
  • 'retries': This property specifies the number of times the producer will attempt to resend a failed record.

    • By default, 'retries' is set to 0, meaning no retries will be attempted.
    • Setting 'retries' to a higher value, such as 3 or 5, allows the producer to handle transient failures and improve the chances of successful message delivery.


import (

config := sarama.NewConfig()
config.Producer.RequiredAcks = sarama.WaitForAll
config.Producer.Retry.Max = 3

producer, err := sarama.NewSyncProducer([]string{"localhost:9092"}, config)
if err != nil {
    // Handle error
defer producer.Close()

msg := &sarama.ProducerMessage{
    Topic: "my-topic",
    Key:   sarama.StringEncoder("key"),
    Value: sarama.StringEncoder("value"),

partition, offset, err := producer.SendMessage(msg)
if err != nil {
    // Handle error


A broker cluster should not lose messages when it is functioning normally. However, we need to understand which extreme situations might lead to message loss:

  1. Asynchronous Disk Flush:
  • Kafka brokers use an asynchronous disk flush mechanism to improve I/O throughput and performance.
  • When a producer sends a message to a broker, the message is initially stored in memory and not immediately written to disk.
  • The broker periodically flushes the messages from memory to disk in the background.
  • If the broker instance crashes or experiences a sudden shutdown before the flush occurs, the messages that were in memory but not yet flushed to disk will be lost.
  • To mitigate this risk, Kafka provides configurable flush settings, such as 'log.flush.interval.messages' and '', which determine when the broker should flush messages to disk.
  1. Replica Synchronization:
    • Kafka ensures data durability and fault tolerance by maintaining replicas of partitions across multiple broker instances.
    • Each partition has a leader replica and zero or more follower replicas.
    • The leader replica handles all read and write operations for the partition, while the follower replicas passively replicate the data from the leader.
    • If the leader replica fails, one of the follower replicas is elected as the new leader to ensure data availability.
    • However, if the replicas are not properly synchronized or if there is a significant lag between the leader and followers, message loss can occur in certain scenarios:
      • If the leader replica fails and a follower replica with outdated data is elected as the new leader, any messages that were not yet replicated to the follower will be lost.
      • If a producer sends a message with 'acks=1' (only wait for the leader acknowledgment) and the leader fails before replicating the message to followers, the message will be lost.
    • To minimize the risk of message loss due to replica synchronization issues, it is important to configure the replication factor and the 'min.insync.replicas' setting appropriately.
    • The replication factor determines the number of replicas for each partition, providing redundancy.
    • The 'min.insync.replicas' setting specifies the minimum number of in-sync replicas required for a write to be considered successful, ensuring data consistency.

Here's a diagram illustrating the replication process in Kafka:


In this diagram, the producer sends a message to the leader replica, which then replicates the message to the follower replica. Once the follower replica acknowledges the replication, the leader replica sends an acknowledgment back to the producer.

To ensure data durability and minimize message loss, it is recommended to:

  • Configure a sufficient replication factor (e.g., 3) to maintain multiple copies of the data.
  • Set 'min.insync.replicas' to a value greater than 1 to ensure that writes are considered successful only when the message is replicated to multiple replicas.
  • Use 'acks=all' or 'acks=-1' to wait for acknowledgment from all in-sync replicas before considering a write successful.

By following these best practices and configuring Kafka appropriately, the risk of message loss in a broker cluster can be significantly reduced, even in extreme situations.


Kafka offers different ways to commit offsets, which represent the position of the last consumed message in a partition. Offset committing is crucial for tracking the progress of a consumer and ensuring that messages are not processed multiple times or skipped. However, the auto-commit feature in Kafka can sometimes lead to message loss if not used carefully.


  • By default, Kafka consumers have auto-commit enabled, which means the consumer will automatically commit the offsets of the messages it has received at a regular interval.
  • The auto-commit interval is controlled by the '' configuration property, which defaults to 5 seconds.
  • When auto-commit is enabled, the consumer sends an asynchronous commit request to the Kafka broker at the specified interval, acknowledging the messages it has received and processed.
  • However, auto-commit can be problematic in certain scenarios, particularly when the consumer is down or fails in the middle of processing a batch of messages.

Potential Message Loss with Auto-Commit:

  1. Consumer Failure during Message Processing:
  • Consider a scenario where the consumer has received a batch of messages and auto-commit is enabled.
  • The consumer starts processing the messages but encounters an error or crashes before completing the processing of all messages in the batch.
  • Since the offsets were automatically committed at the beginning of the batch, the consumer will resume from the next offset when it restarts, even though some messages in the previous batch were not fully processed.
  • This can lead to message loss because the unprocessed messages will be skipped, and the consumer will move on to the next batch.
  1. Delayed Message Processing:
    • In some cases, message processing might take longer than the auto-commit interval.
    • If the consumer is still processing a message when the auto-commit interval elapses, the offset will be committed before the message processing is complete.
    • If the consumer fails or is shut down after the auto-commit but before completing the message processing, the message will be considered processed even though it was not fully handled.


To mitigate the risk of message loss with auto-commit, consider the following approaches:

  1. Manual Offset Committing:
  • Instead of relying on auto-commit, you can manually control when offsets are committed.
  • After successfully processing a message or a batch of messages, the consumer can call the commitSync() or commitAsync() method to commit the offsets explicitly.
  • This ensures that offsets are only committed when the messages are fully processed, reducing the chances of message loss.
  1. Idempotent Message Processing:
  • Design your consumer to handle message processing in an idempotent manner.
  • Idempotence means that the processing of the same message multiple times should have the same effect as processing it once.
  • By making the message processing idempotent, even if a message is processed more than once due to offset commit issues, it will not have unintended consequences.
  1. Batch Processing with Transactional Commits:
    • If your consumer processes messages in batches, you can use transactional commits to ensure atomic offset commits.
    • With transactional commits, the offset commit is tied to the successful processing of the entire batch.
    • If the batch processing fails, the transaction is aborted, and the offsets are not committed, allowing the consumer to reprocess the batch from the beginning.

Here's an example of manual offset committing in using the Kafka consumer API:

import (

func main() {
    config := sarama.NewConfig()
    config.Consumer.Return.Errors = true

    // Create a new consumer
    consumer, err := sarama.NewConsumer([]string{"localhost:9092"}, config)
    if err != nil {
    defer func() {
        if err := consumer.Close(); err != nil {

    // Subscribe to the topic
    topic := "my-topic"
    partitionConsumer, err := consumer.ConsumePartition(topic, 0, sarama.OffsetNewest)
    if err != nil {
    defer func() {
        if err := partitionConsumer.Close(); err != nil {

    // Consume messages
    for {
        select {
        case msg := <-partitionConsumer.Messages():
            // Process the message
            fmt.Printf("Received message: %s\n", string(msg.Value))

            // Manually commit the offset after processing the message
            partitionConsumer.MarkOffset(msg.Offset+1, "")
        case err := <-partitionConsumer.Errors():
            fmt.Println("Error:", err)

Another condition is that the consumer may consume the message multiple times.

In the given scenario, the Kafka consumer is configured with auto-commit enabled, which means that the consumer automatically commits the offsets of the messages it has consumed at regular intervals specified by the auto-commit interval.

The consumer starts by polling a batch of messages (batch 1) from the Kafka broker. It then processes messages 1, 2, and 3 from batch 1. During the processing of batch 1, the auto-commit interval elapses, but the consumer does not explicitly commit the offsets for batch 1 to the Kafka broker.

Next, the consumer polls the next batch of messages (batch 2) from the Kafka broker and starts processing messages 4 and 5 from batch 2. However, while processing batch 2, the consumer fails or crashes.

When the consumer restarts, it resumes from the last committed offset, which is the offset before batch 1 (since no explicit offset commit was made). The consumer polls messages from the last committed offset, which includes messages from batch 1 and batch 2. As a result, the consumer reprocesses messages 4 and 5 from batch 2, along with messages 6 and 7.

The issue with this scenario is that messages 4 and 5 are reprocessed because the offsets were not explicitly committed to the Kafka broker before the consumer failure. Reprocessing messages can lead to challenges such as duplicate processing or inconsistent state.



In this article, we explored the potential causes of message loss in Kafka and discussed strategies to mitigate them. We covered message loss scenarios in Kafka producers, brokers, and consumers, highlighting the importance of proper configuration and handling.

For producers, configuring the appropriate acks and retries settings ensures reliable message delivery. In the case of brokers, asynchronous disk flushing and replica synchronization issues can lead to message loss, which can be mitigated by configuring flush settings, replication factors, and min.insync.replicas.

Consumers, on the other hand, need to be cautious when using auto-commit, as it can result in message loss if the consumer fails during message processing. To prevent this, manual offset committing, idempotent message processing, and transactional commits can be employed.

Additionally, we discussed the scenario where a consumer may consume the same message multiple times due to the lack of explicit offset commits before a consumer failure. This can lead to duplicate processing and inconsistent states. To address this, manual offset committing or transactional processing can be used to ensure that offsets are only committed when messages are fully processed.

By understanding the potential pitfalls and applying the appropriate strategies, developers can build reliable and fault-tolerant Kafka-based systems. It is crucial to carefully consider the configuration settings, offset commit strategies, and error handling mechanisms to ensure data integrity and prevent message loss.

As with any distributed system, testing and monitoring play a vital role in identifying and resolving issues related to message loss. Regular monitoring of Kafka metrics, logs, and consumer lag can help detect anomalies and take corrective actions promptly.

In conclusion, while Kafka provides a robust and scalable platform for real-time data streaming, it is important to be aware of the potential message loss scenarios and employ the necessary measures to mitigate them. By following best practices and implementing the appropriate strategies discussed in this article, developers can build reliable and resilient Kafka-based applications that ensure data integrity and minimize the risk of message loss.


✅ Understanding and Mitigating Message Loss in Apache Kafka

📈 66.55 Punkte

✅ The Apache Kafka Handbook – How to Get Started Using Kafka

📈 35.14 Punkte

✅ What is Apache Kafka? explain architecture of Kafka

📈 35.14 Punkte

✅ Apache Kafka bis Broker Request Data Loss Denial of Service

📈 32.8 Punkte

✅ Apache Kafka up to Broker Request Data Loss denial of service

📈 32.8 Punkte

✅ Understanding the Need for Apache Kafka

📈 29.13 Punkte

✅ Learning Kafka Part One: What is Kafka?

📈 28.78 Punkte

✅ Livro de Kafka Connect e Kafka Streams em Português

📈 28.78 Punkte

✅ [Kafka] 1.Cài Đặt Kafka Server Trong 1 Phút

📈 28.78 Punkte

✅ Why Apache Kafka and Apache Flink Work Well Together to Boost Real-Time Data Analytics

📈 28.62 Punkte

✅ Is Apache Kafka Providing Real Message Ordering?

📈 28.54 Punkte

✅ Mitigating Memorization in Language Models: The Goldfish Loss Approach

📈 28.13 Punkte

✅ Understanding and mitigating AI and ML risks when integrated into common household IoT

📈 27.48 Punkte

✅ Apache Software Foundation gibt Apache Kafka 1.0.0 frei

📈 27.11 Punkte

✅ Integrating Apache Kafka with Apache AGE for Real-Time Graph Processing

📈 27.11 Punkte

✅ Understanding and Mitigating Single Sign-on Risk

📈 25.97 Punkte

✅ Blockchain Security: Understanding vulnerabilities and mitigating risks

📈 25.97 Punkte

✅ Understanding and Mitigating LLM Hallucinations

📈 25.97 Punkte

✅ Understanding and Mitigating the Potential AI Risks in Business

📈 25.97 Punkte

✅ Understanding and Mitigating IP Spoofing Attacks

📈 25.97 Punkte

✅ Understanding Kappa Architecture and Kafka: Empowering Real-Time Data Processing

📈 24.28 Punkte

✅ Understanding and Resolving Infinite Consumer Lag Growth on Compacted Kafka Topics

📈 24.28 Punkte

✅ Real-Time Logistics, Shipping, and Transportation With Apache Kafka

📈 22.26 Punkte

✅ How To Use Change Data Capture With Apache Kafka and ScyllaDB

📈 22.26 Punkte

✅ Data Stream Using Apache Kafka and Camel Application

📈 22.26 Punkte

✅ Processing Time Series Data With QuestDB and Apache Kafka

📈 22.26 Punkte

✅ Apache Kafka in Java [Video Tutorials]: Architecture and Simple Consumer/Producer

📈 22.26 Punkte

✅ How To Install CMAK, Apache Kafka, Java 18, and Java 19 [Video Tutorials]

📈 22.26 Punkte

✅ Building a Real-Time Data Architecture With Apache Kafka, Flink, and Druid

📈 22.26 Punkte

✅ Modernizing SCADA Systems and OT/IT Integration with Apache Kafka

📈 22.26 Punkte


Datei nicht gefunden!