Understanding Message Loss in Kafka

Over the years, many individuals have formed their interpretations of message loss in Kafka, resulting in various proposed solutions. Before diving into specific strategies, it's essential to define what message loss means within the context of Kafka and the conditions under which it can ensure message retention. This understanding is vital, as it helps clarify the lines of accountability. If we fail to grasp who is responsible, we won’t know who should be tasked with providing a solution.

When does Kafka ensure that messages are not lost? In essence, Kafka offers limited guarantees regarding the persistence of "committed" messages.

Let’s break this down into two main components.

The first component is "committed messages." But what does this mean? When multiple Kafka brokers successfully receive a message and log it, they notify the producer that the message has been successfully committed. At this point, Kafka recognizes this message as "committed." The reason for mentioning multiple brokers is that your definition of "committed" can vary. You might consider a message committed if just one broker saves it, or you might require that all brokers save it. Regardless, Kafka only provides persistence assurances for messages that are deemed committed.

The second component involves "limited persistence guarantees." This indicates that Kafka cannot ensure that messages will never be lost under every circumstance. Consider an extreme scenario: if the Earth were to cease existing, would Kafka still retain any messages? Clearly, the answer is no. To retain messages in such an unlikely event, one would have to deploy Kafka broker servers on another planet.

Therefore, the term "limited" highlights that Kafka's message retention capabilities are conditional. If your messages are stored across N Kafka brokers, at least one of those brokers must be operational for Kafka to guarantee that your message will not be lost.

In summary, while Kafka can help prevent message loss, it only does so for committed messages and under specific conditions. This clarification is not meant to shift blame away from Kafka but to delineate the responsibility when issues arise.

Exploring Common Message Loss Scenarios

Now that we have established how Kafka ensures message durability, let’s examine some typical scenarios where Kafka is often incorrectly blamed for message loss.

Case Study 1: Loss of Data from the Producer Program

Producer-side message loss is one of the most frequently cited complaints. Picture this scenario: you create a producer application to send messages to Kafka, only to discover later that Kafka did not save them. You might exclaim, "Kafka is awful! How can it lose messages without notifying me?!" If this sounds familiar, take a moment to reflect.

Currently, Kafka Producers send messages asynchronously. This means that when you invoke the producer.send(msg) API, it generally returns immediately, but this does not guarantee that the message has been successfully sent. This method is often referred to as "fire and forget." This term, originally from missile guidance, has been adapted in computing to signify executing an operation without concern for its success. Therefore, if messages are lost, there’s no way to be informed about it. Although this method may seem unreliable, some organizations still use it to send messages.

What factors might cause messages to fail during this process? There are several potential reasons, such as network issues preventing messages from reaching the broker, or the broker rejecting the messages if they exceed size limits. Given these possibilities, it's unfair to solely blame Kafka. As stated earlier, if Kafka does not recognize messages as committed, it cannot be held responsible for their loss.

However, we still need a solution. The remedy is straightforward: producers should utilize the sending API with callback notifications. Instead of using producer.send(msg), one should implement producer.send(msg, callback). This callback is essential, as it confirms whether the message was successfully sent. If a submission fails, you can respond appropriately.

For instance, if the failure is due to transient issues, a simple retry might suffice. If the message is invalid, you can correct its format and resend it. Thus, the responsibility for handling send failures lies with the producer, not the broker.

You may wonder if broker-side issues could lead to send failures. Certainly! If all your brokers crash, no amount of retries from the producer will succeed. In such cases, addressing broker issues should be your immediate priority. Nonetheless, the fundamental argument remains: Kafka does not consider that message as committed, so it provides no persistence guarantee.

Case Study 2: Consumer Program Data Loss

Loss of data on the consumer side often appears as messages that the consumer program fails to retrieve. In the consumer program, there is a concept called "offset," which indicates the current position of the consumer in the topic partition it is reading from.

For instance, Consumer A may have an offset value of 9, while Consumer B’s offset is 11. This "offset" serves as a bookmark in a book, showing how much has been read so that one can quickly return to that page later.

Using bookmarks correctly involves two steps: reading and updating the bookmark. If these steps are reversed, you might find yourself in a situation where, for example, you move the bookmark to page 100 before reading and later stop on page 95. The next time you try to continue reading, you might miss content from pages 96 to 99, resulting in lost messages.

Likewise, on the consumer side in Kafka, if the consumer program processes messages incorrectly, message loss can occur. To mitigate this, the solution is simple: ensure that messages are consumed (read) before updating the offset (bookmark). This order maximizes the chances of avoiding message loss.

Of course, this approach might lead to duplicate processing, akin to reading the same page multiple times, but that does not equate to message loss. In future discussions, I will address how to manage duplicate consumption effectively.

Besides the previously mentioned scenarios, there exists another less obvious cause of message loss. Let’s continue with our reading analogy. Imagine renting an e-book that consists of 10 chapters, with a reading window of one day. After the time expires, access to the e-book is revoked, but if you finish reading within that day, you get a refund.

To speed up the reading process, you assign each chapter to a different friend, asking them to summarize their sections. When the e-book rental period nears its end, all ten friends inform you that they have completed their reading. You confidently return the book, only to realize one friend lied and didn’t read their assigned chapter. As a result, you lose access to that chapter’s content.

In Kafka, this scenario is akin to a consumer program retrieving messages and processing them asynchronously with multiple threads while automatically updating the offset. If one thread fails to process its messages but the offset has already been updated, those messages are effectively lost to the consumer.

The root cause here is the automatic offset commit by the consumer, which is like returning the book without verifying if all chapters were read. You update the offset without confirming whether the messages were consumed.

The solution is straightforward: if your consumer program uses multiple threads for asynchronous processing, disable automatic offset commits. Instead, opt for manual offset commits. However, I must emphasize that while implementing this can seem easy, doing it correctly in code is quite challenging, as managing offset updates accurately is complex. In other words, while it’s simple to avoid message loss due to non-consumption, it is also easy to end up with messages that are processed multiple times.

Best Practices for Ensuring Message Durability

After examining the above cases, here are some configurations to achieve message durability in Kafka, each addressing the issues discussed.

1. Avoid using `producer.send(msg)`; instead, leverage producer.send(msg, callback). Always utilize the sending method with callback notifications.
2. Set `acks = all`. This parameter in the Producer defines how committed messages are defined. By setting it to all, you ensure that all replica brokers must receive the message for it to be considered committed, reflecting the highest level of commitment.
3. Adjust `retries` to a high value. This parameter correlates with automatic retries. In the event of a temporary network issue, enabling retries > 0 allows the producer to automatically attempt message transmission again, thus minimizing the risk of loss.
4. Set `unclean.leader.election.enable = false`. This broker-side parameter controls which brokers can compete for leadership of partitions. If a broker falls significantly behind the original leader, becoming the new leader may result in message loss. Therefore, it’s advisable to set this to false to prevent such situations.
5. Ensure `replication.factor >= 3`. This broker-side parameter encourages keeping multiple copies of messages, as redundancy is a primary means of preventing message loss.
6. Set `min.insync.replicas > 1`. This parameter specifies how many replicas must write the message for it to be deemed committed. A value greater than 1 enhances message durability. In real-world scenarios, avoid using the default value of 1.
7. Ensure that `replication.factor > min.insync.replicas`. If these values are equal, then if one replica fails, the entire partition may become non-functional. To enhance durability without sacrificing availability, set replication.factor = min.insync.replicas + 1 to ensure messages are consumed before submission.
8. On the consumer side, set `enable.auto.commit` to false and adopt manual offset submission. This is crucial for single consumer multi-threaded processing scenarios.

Understanding common mistakes made with Apache Kafka can help prevent issues related to message loss.

Maintaining the order of events during retries in Kafka is crucial for ensuring message integrity.