As your systems become more reliable, gradually increase both the magnitude (the intensity of the experiment) and the blast radius (the number of systems impacted by the experiment) so that youre testing your entire deployment holistically. From this we can conclude that our cluster can withstand temporary majority failures. Since Kafka provides a critical pipeline between applications, reliability is paramount. We showed failover for Kafka brokers by shutting one down, then using the producer console to send two more messages. To prevent multiple producers from writing to a single partition, each partition has one broker that acts as the leader, and zero or more brokers that act as followers. The idea is that it is more important to maintain the performance of the overall system, as opposed to processing every single event. Well create a Status Check that fetches a list of healthy brokers from the Kafka Monitoring API to verify that all three brokers are up and running. This also allows multiple consumers (more specifically, consumer groups) to process a topic simultaneously. With Confluent Platform, we can use Control Center to visually observe cluster performance in real-time from a web browser. Note that you can only apply this strategy if the ordering is not essential to the application.
This reduces the risk of lost messages between the producer and broker, but it does not prevent lost messages between brokers. We also demonstrate load balancing Kafka consumers. Now run all three in separate terminals/shells. The throttle will notice and reduce the throughput by waiting a few milliseconds between each event. Performance will drop significantly, and the cluster will need to elect new leaders, reassign partitions, and replicate data among the remaining brokers, but we wont end up with a split brain scenario. Partial failures occur when the system fails to process only a small portion of events. This eventually led to data corruption and teams had to manually restore backup data to servers. These can impact our entire deployment in unexpected ways, and if they happen in production, may require extensive troubleshooting and firefighting. Well use the topic endpoint to retrieve the status of our brokers and parse the JSON response to determine if theyre currently in-sync. Pro tip: Do not consider malformed events as failures, as this can lead to unwanted side effects, especially when throttling, which leads to our next topic: Because our systems need to process all of the messages in near real time, we have to match the consumer throughput with the rate of events created by the producers. In writing our code to address each of these Kafka consumer failure scenarios, we have managed to maintain performance and avoid data loss. For example, if your experiment is to test your ability to withstand a broker outage, your hypothesis might state that if a broker node fails, messages are automatically routed to other brokers with no loss in data.. Notice that the messages are spread evenly among the consumers. Similarly to successful acknowledgment, negative acknowledgment can be triggered manually (using the nack method) or handled automatically. The built-in concurrency mechanism that Go provides is particularly useful when the event process is I/O bound.
But as said earlier, failures are inevitable. In addition, well create a Status Check that uses the Kafka Monitoring API to check the health of our brokers between each stage. As soon as a message is nacked, the connector reports the failure, and the application stops. Each of our consumers has a series of concurrent workers. Gremlin lets you run experiments on your applications and infrastructure in a simple, safe, and secure way. Improving the reliability of a Kafka cluster necessarily involves improving the reliability of its associated ZooKeeper cluster. Then we sent more messages. Confluent Platform builds on Kafka by adding enterprise features such as a web-based GUI, comprehensive security controls, and the ability to easily deploy multi-region clusters. When a message is nacked, it ignores the failure and continues the processing: The log indicates the failure, but it continues the processing with the next one. Brokers constantly read and write data to and from local storage, and bandwidth usage can become a limiting factor as message rate and cluster size increase. Now send seven messages from the Kafka producer console. The first strategy is the simplest, but not sure we can qualify it with "smoothly."
If we start to notice disk I/O increasing and throughput decreasing, we should consider: To ensure messages are delivered successfully, producers and brokers use an acknowledgement mechanism. In this tutorial, we are going to run many Kafka Nodes on our development laptop so that you will need at least 16 GB of RAM for local dev machine. Does it confirm or reject your hypothesis? It is important to note that not all solutions apply to all use cases. In this case, our consumer will automatically fail to process the events. In our demo, its dead-letter-topic-movies. This lets us find and fix problems with our systems before they can cause issues for our users, while also teaching us more about how our systems behave under various conditions. Here is our fully configured Scenario showing the Status Check and shutdown Gremlin: Kafka will experience a temporary stop in throughput, but both broker nodes will rejoin the cluster without issue. The best example would be a database rejecting requests for several minutes. Now that weve shown you four different chaos experiments for Kafka, try running these and other experiments yourself by requesting a free Gremlin trial. Keeping Kafka running reliably requires planning, continuous monitoring, and proactive failure testing. For example, you may have a misbehaving component throwing exceptions, or the outbound connector cannot send the messages because the remote broker is unavailable. You can stop the first broker by hitting CTRL-C in the broker terminal or by running the above command. Control Center still lists three brokers, but shows that two of them are out-of-sync and have under-replicated partitions. Give the servers a minute to startup and connect to ZooKeeper. Notice the second consumer gets messages m2 and m6. We are going to lists which broker owns (leader of) which partition, and list replicas and ISRs of each partition. Both metrics remained stable, none of our brokers became out-of-sync or under-replicated, and no messages were lost or corrupted.
A message can be acked or nacked. Notice that the messages are spread evenly among the remaining live consumers. Chaos Engineering allows us to uncover reliability problems in our Kafka deployments in a safe and effective way. Next, lets demonstrate consumer failover by killing one of the consumers and sending seven more messages. Using this approach, we achieve two basic advantages: 1. See how you can harness chaos to build resilient systems by requesting a demo of Gremlin. This leaves us with plenty of overhead for now, but as we scale up our applications, our throughput requirements will likely increase. If you check the health of the application (using http://localhost:8080/health), everything is fine: The dead-letter queue is a well-known pattern to handle message processing failure. Increasing disk I/O will cause a corresponding drop in throughput. You can only use this strategy if you dont need to manage all the messages or if your application is handling the failure internally. Well create a Scenario and gradually increase the magnitude of the attack over four stages. Achieving a Resilient and Fault-tolerant Processing Pipeline During a Kafka Consumer Implementation. To maximize efficiency and avoid hot spotting, or overloading one partition with events, we typically use a round-robin assignment approach, which distributes events to a pool of workers. We want to put all of the consumers in the sameconsumer group. There is nothing we can do about that, and that also applies to Kafka applications. It uses the dead-letter-topic failure strategy and contains a component reading the dead-letter topic (dead-letter-topic-movies).
If the system hits a certain threshold of failures in a limited amount of time, it presumes that the system is losing too many events and simply shuts down. Form a conclusion from your results. This alone isnt likely to cause a failure, but it can lead to another problem: cascading failures. Notice how Kafka spreads the leadership over the 2nd and 3rd Kafka brokers. When the previous controller (broker 1) went offline, ZooKeeper immediately elected the remaining broker (broker 3) as the new controller. While this was an unusual scenario that HubSpot resolved, it underscores the importance of testing ZooKeeper and Kafka both as individual services and as a holistic service. dead-letter-queue sends failing messages to another Kafka topic for further investigation. Two of the most important metrics to consider when optimizing Kafka are network latency and disk I/O. The side effect of not having rogue events is a predictable latency. Typically speaking, our team experiences two basic failure types: partial failures and outages. Now we just run the producer and three consumers. A simple solution can be to limit the number of instances and workers per instance when the database is partially unavailable. Running the experiment had no result on message throughput or broker availability. How to effectively manage client-side partial failures, avoid data loss and process errors. We believe it will be a useful tool to many in the coding community. In this system, each result carries a different weight on the throttle. Kafka streams data using a publisher/subscriber (or pub/sub) messaging model. In the context of Kafka, there are various commit strategies. Here is an example of our output: If we wanted to guarantee that every message is saved, we can set acks=all on our producer configuration. Kafka consumer failover works! Partitions can be mirrored across multiple brokers to provide replication. For example, a broker failure will cause our producers to shift their load to the remaining brokers. Next time, we will talk about the commit strategies because failures are inevitable, but successful processing happens sometimes! In practice, the runtime retries cover 99% of the failures. You can, later, review the failing messages: To enable this strategy, you need to add the following attribute to your configuration: By default, it writes to the dead-letter-topic-$topic-name topic. In writing the code for our internal systems that require a high throughput, we created a set of rules that will keep the events moving despite mass flow. At CrowdStrike, we use Kafka to manage more than 500 billion events a day that come from sensors all over the world. If you want to make it better, fork the website and show us what youve got. fail-fast (default) stops the application and marks it unhealthy. 
