Businesses consume and produce massive data in the form of messages and events. Ordering is thus essential. They need a guarantee of order to relay and receive messages in the same order. A lack of event ordering can impair communication, resulting in the failure of operations, which can be very costly. Luckily, the Kafka system solves this challenge by guaranteeing order. In this article, we will learn how Kafka guarantees delivery orders, message ordering, and its overall reliability. Let’s dive into some questions you would ask yourself whether Kafka has the ability to guarantee messages order.
Does Kafka guarantee delivery orders?
One of the reasons we would choose Kafka to manage different apache Kafka use cases data is its guarantee of delivery orders. The Kafka system partitions would allow us to create an intelligent structure that allows recording events as they come. That way, customers will read the events in the same order as they were intended.
How does Kafka guarantee delivery orders?
The partitioning system in Apache Kafka is critical in creating a system that facilitates ordering events. The keys that Kafka uses allow us to order all the events by relying on the same key. This is very convenient in the delivery order. The delivery order will remain the same if you do not alter the existing partition number. In ordering the events, we have to consider if we want our data system ordered globally or do we have to partition the order into smaller bits each to take a different part.
For instance, if we are running an apache Kafka use case that sells products online, we need a cart that lets an individual add the goods before buying them. But since all the carts in the system may carry unrelated products, we can partition the system to allow individuals to have carts for each area. For instance, having different carts for electronic products, food, and services. Kafka facilitates all this through its partitioning system.
Can Kafka deliver the same message twice?
The quick answer to this question is no. Kafka has an “exactly once guarantee” to ensure a message gets processed only once. Apache Kafka further introduced the Kafka transactions feature to reinforce this need to avoid repeatability. The Kafka transactions feature becomes essential when the data we are processing is written to another Kafka topic.
Here is a basic example. If we are writing one or multiple messages on Kafka topics, the transaction is spread across all topics. Upon writing, the transaction ends only if the producer writes a commit marker. The consumer needs a commit marker to access and read the messages. These messages only become processed when the commit marker appears. Thus, putting the “exactly once guarantee” enables the transaction feature on the consumer side while activating the idempotent producer feature on my side. These features ensure that the consumer can only read messages with a commit marker, reducing duplicity.
Can Kafka’s message be lost?
Despite Kafka’s high reliability, it is not immune to data loss. But many of the data loss challenges come down to the implementation. If we set up my Kafka system poorly, it will not offer the maximum benefits of safeguarding us from data loss. Some of the variables that might make a system lose data despite using Apache Kafka include data offsets and consumer misconfiguration.
Data offsets essentially refer to the location of a given piece of data compared to another. They are major reasons why we can lose data when using Apache Kafka. This problem occurs if we send current data to a broker while consuming the data. In this case, say we have data Y and Z, both undergoing parallel processing. Say the processing of Z is successful, and the offset is committed, but Y’s processing fails, and an error occurs. Because Z has a larger offset, Kafka saves the latest offset, but Y’s result never returns. Just like that, we will lose my dataset for Y.
The second reason we can lose data in the Kafka system is down to our errors. Failure to configure the system appropriately is high when the execution of the system is poorly done. This situation can occur if we do not structure the system properly or because of technical issues with the applications we use to access and process the Kafka data.
Does Kafka guarantee that it will preserve the order of produced events?
Yes, it does. One thing we love about Kafka is that it can ensure all our events are arranged in a strict order. If we must ensure that the order of the produced events that Kafka uses is retained, we will only need to partition the system appropriately. For instance, we must have all the events relating to one topic ordered together; we will use one partition. But for higher throughput, Kafka requires the use of several partitions. When using Kafka, the order of events is important. We need records to be ordered in a given manner to avoid data inconsistencies.
Does Kafka guarantee the order of messages?
Yes, Kafka guarantees the order of messages. The only thing is that for this to happen, the messages involved should be within the same partition. We find the need to preserve the message order to be a critical aspect of entities and apache Kafka use cases. If, for instance, we are working on a given commercial project where communication relies on Kafka, the correct order messaging becomes important. It becomes easier to track the communication and order of occurrence of events. If there is no order in the messages, then the execution of the project would be at risk of failure. Consuming messages in the wrong order may result in carrying out project tasks at the wrong times and in the wrong order of operations. But due to Kafka’s partitioning feature, it becomes easy to ensure that the messages sent from the source in a particular order will reach me in the same order and vice versa.
But it is important that the ordering of messages becomes easy and doable when dealing with a single partition. The same guarantee becomes more challenging if, say, we are using three or more partitions. Multiple partitions still allow for message ordering but pose a greater challenge in configuring Kafka guarantee compared to single partitions.
Is Kafka 100% reliable?
Kafka has shown to be a highly reliable system but achieving 100% is unlikely. Kafka displays its reliability through its high availability and fault tolerance. Kafka replicates data at the partition level and stores them on various brokers. When one broker goes offline or fails, data remains accessible to the consumer because there are many copies of it. That is why we would avoid using a one-broker cluster.
Kafka’s resilience is another feature that makes it reliable, especially for businesses. Unplanned downtimes and data breaches are some of the issues that firms cannot afford to experience. The amount of revenue loss and reputation damage is too costly for such firms. So, Kafka’s resilience makes it crucial. Data stored on these systems are protected from corruption.
Even so, the Kafka design cannot efficiently support a highly reliable data streaming service in the cloud. Deploying Kafka in the cloud using best practices may not enhance its reliability. The system may be hampered by infrastructure failures, network issues, and errors arising from maintenance activities, such as bug fixing.
If we rely on the “at most once guarantee” where a message is processed once or never, both the receiver of the data and we must be configured. Upon sending the message, the consumer must commit it before processing begins. If the consumer fails to do so, the system loses all the unprocessed messages because they were already committed from the source.
How does Kafka handle failures in delivery?
There are three primary ways that Kafka uses to handle its failures in delivery:
- Fail-fast – this is the default method of handling delivery failures. Apache Kafka stops and marks the application as unhealthy. When a message’s transmission is held up, the system automatically detects a failure. The fail-fast allows the application to stop and restart the process.
- Ignore – this strategy of handling failure is different from fail-fast in that it allows the processing to continue despite failure. It is as if the system closes ‘its eyes’ to the error and assumes there is no failure. So, when a message is not acknowledged, the system continues to the next one as if the error were not there. I cannot advise you to use this strategy unless you do not need to handle all the messages or your application can handle the process internally.
- Dead-letter-queue – this is another pattern that helps a handle message processing failure on Kafka. For this strategy, the system will send the failing events to another Kafka topic for re-processing. Eventually, a long line of failed events is stored in a given destination. Then the administrator reviews the dead events and chooses to skip or retry processing them.
Is Kafka FIFO or LIFO?
Apache Kafka system handles mass message streaming. The message streams are stored in the first-in-first-out (FIFO) format. But we should know that FIFO is only assured if there is partitioning.
Conclusion
We cannot admit enough that ordering events in a distributed environment is a challenge that no person or entity likes. Apache Kafka, through partitioning, makes this a doable task. The partitions allow for writing events and messages in the same order they were sent. This strategy ensures the receiver will consume the messages in the same order. Kafka thus solves the communication problem that would arise if there is tampering with the order of events.
Furthermore, we note that despite Kafka’s high reliability, it cannot assure 100% protection against data loss. Many of the technicalities that cause data loss occur due to misconfiguration issues. Even so, the system handles its delivery failures using the fail-fast, ignore, and dead-letter-queue methods.