Project Metamorphosis: Unveiling the next-gen event streaming platformLearn More

Dawn of Kafka DevOps: Managing Kafka Clusters at Scale with Confluent Control Center

When managing Apache Kafka® clusters at scale, tasks that are simple on small clusters turn into significant burdens. To be fair, a lot of things turn into significant burdens at scale, and it’s Confluent Control Center’s job to ease as many of them as possible. In Confluent Platform 5.2, Control Center has grown a couple of new features that make large deployments a little more pleasant to manage: It has become much better at managing configuration changes among a large number of brokers, and it scales to a larger number of managed partitions. Let us explain these two in a bit of detail.

Dynamic broker configuration

Two challenges present themselves when configuring a large number of brokers: visualizing the differences in broker configuration and modifying them without resorting to rolling restarts everywhere. These are problems endemic to any distributed system that uses a large number of the same node type, and they become more costly as the number of nodes grows. Confluent Platform 5.2 makes solving both of these problems a bit easier.

In previous versions of Control Center, you could view and download broker configurations, which was good as far as it went. If you wanted to compare broker configurations (hardly an unusual thing to do when trying to troubleshoot a misbehaving cluster), you were left to do the diffing on your own. As of 5.2, you can now directly view the differences in configurations from within Control Center.

Confluent Control Center

Relatedly, KIP-226 enabled dynamic broker reconfiguration since Apache Kafka 1.1. Put this together with the configuration view, and you now have a powerful way to get misconfigured brokers whipped into shape. Not only can you view their configurations side by side, but you can also make changes to parameters as needed without having to restart the brokers, which is an enormous time savings no matter how you manage your deployment. Of course, not every broker configuration parameter can be changed dynamically. See the documentation (or, if you please, the Apache Kafka wiki) for a complete list of which parameters this applies to.

Confluent Control Center

Dynamic broker configuration is enabled by default. To disable, set in the And remember, you can always consult the documentation for more information.

Improved scalability

Control Center gives you visibility into the behavior of each topic that it manages, which means it has to keep track of the state of each partition in each topic. (No, this is not a terribly surprising claim of software architecture, but work with me here.) This, in turn, implies that Control Center has its own upper bounds on how large a deployment it can manage. Just how many topics Control Center can handle turns out to be somewhat of a complicated question, depending on replication factor and the distribution of topic partition counts. A cluster may have many topics with a small number of partitions, a small number of topics with many partitions or something in between; and a cluster may have a large replication factor or a small one.

Those statistical variations notwithstanding, Control Center has leveled up significantly in Confluent Platform 5.2. It can now comfortably handle around 120,000 individual partition replicas. Assuming a very typical replication factor of three, this means it can now manage around 40,000 individual partitions. If you take a typical topic partition count of six, this translates to a cluster of about 6,700 topics—which, you might be thinking, is a lot. Most users of Control Center don’t manage deployments that large; in fact, our data tells us that this covers more than 90% of existing customer workloads.

Cluster with Partitions

Operating large systems is always a lot of work, and Confluent Platform is singularly focused on making it easier for you. Confluent Platform 5.2 has moved the ball forward in two areas: Control Center’s ability to manage larger clusters, and its ability to help you more easily compare broker configurations and dynamically change them. If you’re running a big cluster, these features will make life that much more pleasant for you.

If you’re still not using Control Center or other parts of the Confluent Platform, you know what to do! Go here, download and check it out.

Other articles in this series:

Tim Berglund is a teacher, author and technology leader with Confluent, where he serves as the Senior Director of Developer Experience. He can frequently be found at speaking at conferences in the U.S. and all over the world. He is the co-presenter of various O’Reilly training videos on topics ranging from Git to distributed systems, and is the author of “Gradle Beyond the Basics.” He tweets as @tlberglund and lives in Littleton, CO, U.S., with the wife of his youth and their youngest child, the other two having mostly grown up.

Viktor Gamov is a developer advocate at Confluent, the company that makes an event streaming platform based on Apache Kafka. Working in the field, Viktor Gamov has developed comprehensive expertise in building enterprise application architectures using open source technologies. Back in his consultancy days, he co-authored O’Reilly’s “Enterprise Web Development.” He is a professional conference speaker on distributed systems, Java and JavaScript topics. Follow Viktor on Twitter @gAmUssA, where he posts there about gym life, food, open source and, of course, Kafka and Confluent!

Did you like this blog post? Share it now

Subscribe to the Confluent blog

More Articles Like This

Project Metamorphosis Month 2: Cost-Effective Apache Kafka for Use Cases Big and Small

In April, we kicked off Project Metamorphosis. Project Metamorphosis is an effort to bring the simplicity of best of breed cloud systems to the world of event streaming. It is […]

Scaling Apache Kafka to 10+ GB Per Second in Confluent Cloud

Apache Kafka® is the de facto standard for event streaming today. The semantics of the partitioned consumer model that Kafka pioneered have enabled scale at a level and at a […]

Stream Processing with IoT Data: Challenges, Best Practices, and Techniques

The rise of IoT devices means that we have to collect, process, and analyze orders of magnitude more data than ever before. As sensors and devices become ever more ubiquitous, […]

Jetzt registrieren

Start your 3-month trial. Get up to $200 off on each of your first 3 Confluent Cloud monthly bills

Nur neue Registrierungen.

Wenn Sie oben auf „registrieren“ klicken, erklären Sie sich damit einverstanden, dass wir Ihre personenbezogenen Daten verarbeiten – gemäß unserer und bin damit einverstanden.

Indem Sie oben auf „Registrieren“ klicken, akzeptieren Sie die Nutzungsbedingungen und den gelegentlichen Erhalt von Marketing-E-Mails von Confluent. Zudem ist Ihnen bekannt, dass wir Ihre personenbezogenen Daten gemäß unserer und bin damit einverstanden.

Auf einem einzigen Kafka Broker unbegrenzt kostenlos verfügbar

Die Software ermöglicht die unbegrenzte Nutzung der kommerziellen Funktionen auf einem einzelnen Kafka Broker. Nach dem Hinzufügen eines zweiten Brokers startet automatisch ein 30-tägiger Timer für die kommerziellen Funktionen, der auch durch ein erneutes Herunterstufen auf einen einzigen Broker nicht zurückgesetzt werden kann.

Wählen Sie den Implementierungstyp aus
Manuelle Implementierung
  • tar
  • zip
  • deb
  • rpm
  • docker
Automatische Implementierung
  • kubernetes
  • ansible

Wenn Sie oben auf „kostenlos herunterladen“ klicken, erklären Sie sich damit einverstanden, dass wir Ihre personenbezogenen Daten verarbeiten – gemäß unserer Datenschutzerklärung zu.

Indem Sie oben auf „kostenlos herunterladen“ klicken, akzeptieren Sie die Confluent-Lizenzvertrag und den gelegentlichen Erhalt von Marketing-E-Mails von Confluent. Zudem erklären Sie sich damit einverstanden, dass wir Ihre personenbezogenen Daten gemäß unserer Datenschutzerklärung zu.

Diese Website verwendet Cookies zwecks Verbesserung der Benutzererfahrung sowie zur Analyse der Leistung und des Datenverkehrs auf unserer Website. Des Weiteren teilen wir Informationen über Ihre Nutzung unserer Website mit unseren Social-Media-, Werbe- und Analytics-Partnern.