Kafka broker behind load balancer

Kafka clients—specifically the Kafka consumer, Kafka Connect, and Kafka Streams, which are the focus in this post—have used a sophisticated, paradigmatic way of balancing resources since the very beginning.

After reading this blog post, you will walk away with an understanding of how load balancing works in Kafka clients, the challenges of existing load balancing protocols, and how a new approach with Incremental Cooperative Rebalancing allows large-scale deployment of clients.

The ability of these clients to form groups is facilitated by a Kafka broker that acts as coordinator for the clients participating in the group. The actual distribution of load between the clients happens amongst themselves without burdening the Kafka broker with extra responsibility.

Load balancing of the clients depends on the election of a leader client process within the group and the definition of a protocol that only the clients know how to interpret.

The embedded protocols used so far by the consumer, Connect, and Streams applications are rebalance protocols, and their purpose is to distribute resources Kafka partitions to consume records from, connector tasks, etc. Embedding the load balancing algorithm within the group management protocol itself offers some clear advantages:. To keep things simple, all rebalancing protocols so far have been built around the same straightforward principle: a new round of rebalancing starts whenever a load needs to be distributed among clients, during which all the processes release their resources.

Kafka Tutorial - Exactly once processing

In short, this is also known as stop-the-world rebalancinga phrase that can be traced back to garbage collection literature. A load balancing algorithm that stops-the-world in every rebalance presents certain limitations, as seen through these increasingly notable cases:. Despite workarounds to accommodate these use cases, such as splitting clients into smaller groups or increasing rebalancing-related timeouts, which tend to be less flexible, it became clear that stop-the-world rebalancing needed to be replaced with a less disruptive approach.

The proposition that gained traction in the Kafka community and aimed to alleviate the impact of rebalancing that the current Eager Rebalancing protocol exhibits in large clusters of Kafka clients is Incremental Cooperative Rebalancing. Naturally, these principles lend themselves to the name of the proposition behind the improved rebalance protocols in Kafka 240sx zenki hood. The new rebalancing is:.

In Kafka Connect, the resources that are balanced between workers are connectors and their tasks. A connector is a special component that mainly performs coordination and bookkeeping with the external data system, and acts either as a source or a sink of Kafka records. Connect tasks are the constructs that perform the actual data transfers. In some cases—also known as rebalance storms—, it could bring the cluster into a state of consecutive rebalances and the Connect cluster could take several minutes to stabilize.

Before Incremental Cooperative Rebalancing and due to rebalancing delays, the number of Connect tasks that a cluster could host was often capped below the actual capacity, giving the wrong impression that Connect tasks are out-of-the-box heavy weight entities.

With Incremental Cooperative Rebalancing, a Connect task can be what it was always intended for: a runtime thread of execution that is lightweight and can be quickly scheduled globally, anywhere in the Connect cluster. Scheduling these lightweight entities potentially based on information that is specific to Kafka Connect, such as the connector type, owner or task size, etc.

Provisioning and deploying workers, which are the main vehicles of a Connect cluster, is still a responsibility of the orchestrator in use—that being Kubernetes or a similar infrastructure. A new worker joins Figure 1. During the first rebalance, a new global assignment is computed by the leader Worker1 that results in the revocation of one task from each existing worker Worker1 and Worker2. Because this first rebalance round included task revocations, the first rebalance is followed immediately by a second rebalance, during which the revoked tasks are assigned to the new member of the group Worker3.Security is an important aspect today because cyber-attacks have become a common occurrence and the threat of data breaches is a reality for businesses of all sizes.

Before version 0. Although it was possible to lock down access at the network level, that approach was not viable for a large shared multi-tenant cluster used across a large company. There are a number of different ways to secure a Kafka cluster depending on your requirements.

Now let's take a look at how TLS authentication can be applied to Kafka brokers, producers and consumers. Before you start, you need to generate a key and certificate for each broker and client in the cluster. The common name CN of the broker certificate must match the fully qualified domain name FQDN of the server because the client compares the CN with the DNS domain name to ensure that it is connecting to the desired broker, instead of a malicious one.

Now that each broker has a public-private key pair and an unsigned certificate to identify itself, it is important for each certificate to be signed by a certificate authority CA to prevent forged certificates.

As long as the CA is a genuine and trusted authority, the clients have high assurance that they are connecting to authentic brokers. Importing a certificate into a truststore also means trusting all certificates that are signed by that certificate. This attribute is called the chain of trust, and it is particularly useful when deploying TLS on a large Kafka cluster.

You can sign all certificates in the cluster with a single CA, and have all machines share the same truststore that contains the CA certificate. That way all machines can authenticate all other machines. Now Let's see how you can generate your own CA, which is simply a public-private key pair and certificate.

Incremental Cooperative Rebalancing in Apache Kafka: Why Stop the World When You Can Change It?

The following bash script generates the keystore and truststore for brokers kafka. It is difficult to simultaneously upgrade all systems to the new secure clients. Therefore, you can allow supporting a mix of secure and unsecured clients. To support a mix of secure and unsecured clients, you need to add a PLAINTEXT port to listeners, but ensure that you restrict access to this port to trusted clients only.

TLS configuration in Broker ssl. Since you need to store passwords in the broker configuration, it is important to restrict access to the broker configuration via filesystem permission. Enabling TLS authentication for Kafka producers and consumers can be done by configuring a set of parameters. It does not require any code changes.

Note : Kafka versions 0. The parameters you need to specify to support TLS is the same for both producers and consumers. It is required to specify the security protocol as well as the truststore and keystore information since you are using mutual authentication:. Follow the sample scenario in Kafka configuration documentationand send the following message to the Kafka broker:. Note : Be sure to include the following configuration in the proxy service when you are building the sample:.Apache Kafka: Broker: Posted by 2 years ago.

A preconfigured hardware or software load balancer forwarding requests to HEC endpoints. Strimzi provides a way to run an Apache Kafka cluster on Kubernetes in various DoctorKafka is a service for Kafka cluster auto healing and workload balancing. This solves part of the "No Downtime" and "Unlimited Scaling" goals from the ideal publish-subscribe system. So 3 loadbalancer definitions in total. Kafka is maintained as clusters where each node within a cluster is called a Broker.

kafka broker behind load balancer

This is achieved by assigning the partitions in the topic to the consumers in the consumer group so that each partition is consumed by exactly one consumer in the group.

Load Balance and Kafka Cluster. Kafka brokers are stateless, so they use ZooKeeper for maintaining their cluster state. Only subscribers who had an active subscription at the time the broker receives the message will get a copy of the message. If Apache Kafka has more than one broker, that is what we call a Kafka cluster. Load Balancer. The more brokers we add, more data we can store in Kafka.

It depends on what do you mean by "one of the brokers is heavily loaded". It is used to read data from standard input or command line and write it to a Kafka topic place holder of messages. Node ports are a very low-level solution. I imagine a need to adjust what data are stored on each server, so as to balance loads between the servers.

For some environments, where the broker hostnames are mapped by the DNS to multiple IPs, it is desirable that clients, on failing to connect to one of the IPs, try the other ones before giving up the connection.

FAQs A keepalive is required because a load balancer cannot differentiate between idle or dead connections as no packets are sent in either case. Better availability than systems using a load balancer.

kafka broker behind load balancer

And Kafka clients need more CPU if they are using encryption, but this does not impact the brokers. Philter responds with the filtered text which is then published onto a separate Kafka As you can see there is a load balancer that balances the traffic across all the Kafka brokers.

At the beginning of this post we defined load balancer as something that distributes incoming traffic across multiple targets. By default, load balancers are disabled for all Confluent Platform components. I have two brokers in the cluster, 4 topics and each topic has 4 partitions. With the load balancer the client might fail to connect when the broker tries to redirect the connections to another listener.

In high throughput environments, configure an external load balancer for your IBM Cloud Private cluster. Since Nginx doesn't require much from the system resources so we have used a server with 4 Cores.

Accessing Kafka: Part 4 - Load Balancers

For producers, also, you can't really use a load-balancer to connect to brokers you can use zk, or you can use a broker list, in 0. The problem i have is with the metadata. A health check can be a ping or a telnet. Currently, in our own usage we publish from a large number of heterogeneous machines and so it is desirable that the publisher not need any explicit knowledge of the cluster topology. Apache Kafka is a distributed, partitioned and replicated commit log service, which enables handling of terabytes of data without any performance impact on brokers.

Data Broker: Kafka. Amazon MSK is a fully managed service for Apache Kafka that makes it easy to provision Kafka clusters with just a few clicks without the need to provision servers, manage storage, or configure Apache Zookeeper manually.We need to create data flow between two kafka clusters cluster1 and cluster2.

We are using Nifi sitting at cluster1 to send the messages from cluster1 kafka to cluster2 kafka. Here we wanted to use the external load balancer in front of cluster2 Kafka. However, it is not working properly.

The Nifi is unable to send the data to cluster2 kafka if we use LB url in the kafka brokers section. If we put the actual kafka brokers, it is working properly.

Can any one help me on this asap. Is there any specific reason why you need a load balancer? Kafka is supposed to work without a separated load balancer, and handle the load on the cluster. With the load balancer the client might fail to connect when the broker tries to redirect the connections to another listener.

The brokers section in the config for a client is used to try getting the actual connect parameter listeners from the cluster. You typically provide multiple brokers to avoid having an issue if the initial broker is down while connecting.

When you try to change the listener configs in the kafka brokers or zookeeperi think you actually disable the cluster, as the broker are communicating to each other as well. Load balancers would help in the case where you want a more friendly name than some DNS records or the case where IP's are dynamic.

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All forum topics Previous Next. Kafka behind an external Load Balancer. Hi, We need to create data flow between two kafka clusters cluster1 and cluster2. Reply 4, Views. Tags 6. Tags: hadoop. Re: Kafka behind an external Load Balancer.

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Reply 2, Views. Besides that, remembering one address is easier than a long list of servers. Is there any way to do so? Reply 1, Views. Already a User? Sign In. Don't have an account? Coming from Hortonworks? Activate your account here.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I am new to Apache Kafka and was playing around with it. If I have 2 brokers and one topic with 4 partitions and assume one of my broker is heavily loaded, will kafka takes care of balancing the incoming traffic from producers to the other free broker?

If so how it is done? Producers publish data to the topics of their choice. The producer is responsible for choosing which message to assign to which partition within the topic. This can be done in a round-robin fashion simply to balance load or it can be done according to some semantic partition function say based on some key in the message.

In Kafka producer, a partition key can be specified to indicate the destination partition of the message. By default, a hashing-based partitioner is used to determine the partition id given the key, and people can use customized partitioners also. To reduce of open sockets, in 0. If you specify which partition you want the data to go into, it will always go into that specific partition. If you don't specify, the producer could send it to any partition. I believe this decision is to provide certain guarantees for the ordering of messages in a Kafka partition.

Kafka producer tends to distribute messages equally among all partitions unless you override this behaviorthen you need to have a look if the four partitions is distributed evenly among brokers.

It depends on what do you mean by "one of the brokers is heavily loaded". You can choose the brokers in which partition resides with a cli tools with Kafka or with some kind of UI like yahoo kafka-manager. Learn more. Load balancing in apache kafka Ask Question. Asked 4 years, 7 months ago. Active 4 years, 7 months ago. Viewed 8k times. Karthick S Karthick S 89 1 1 silver badge 6 6 bronze badges.

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Sign up using Facebook.In the fourth part of this blog post series we will look at exposing Kafka using load balancers. This post will explain how to use load balancers in public cloud environments and how they can be used with Kafka. This post is part of a bigger series about different ways to access a Kafka cluster powered by Strimzi.

The other parts published so far are:. Load balancers automatically distribute incoming traffic across multiple targets. Different implementations do the traffic distribution on different levels:. Load balancers are available in most public and private clouds. Load balancing services are also available in OpenStack. If you run your Kubernetes or OpenShift cluster on bare metal, you might not have load balancers available on demand. In that case, using node ports, OpenShift Routes or Ingress might be a better option for you.

Most of them are very well integrated with Kubernetes. When the Kubernetes Service is configured with the type LoadbalancerKubernetes will automatically create the load balancer through the cloud provider, which understands the different services offered by given cloud.

Thanks to that, Kubernetes applications — including Strimzi — do not need to understand the differences and should work everywhere where the cloud infrastructure and Kubernetes are properly integrated. Since none of the common load balancing services supports the Kafka protocol, Strimzi always uses the Layer 4 load balancing.

That has some advantages — you can, for example, decide whether TLS encryption should be enabled or disabled. At the beginning of this post we defined load balancer as something that distributes incoming traffic across multiple targets. However, as you can set from the diagram above, the per-broker load balancers have only one target and are technically not load balancing. That is true, but in most cases the actual implementation is a bit more complicated. When Kubernetes creates the load balancer, they usually target it to all nodes of your Kubernetes cluster and not only to the nodes where your application is actually running.

That means that although the TCP connections will always end on the same node in the same broker, they might be routed through the other nodes of your cluster.See links to previous articles at end.

This article will explain how to use load balancers in public cloud environments and how they can be used with Apache Kafka. Load balancers automatically distribute incoming traffic across multiple targets. Different implementations do traffic distribution on different levels:. Load balancers are available in most public and private clouds.

Load balancing services are also available in OpenStack. If you run your Kubernetes or Red Hat OpenShift cluster on bare metal, you might not have load balancers available on demand.

In that case, using node portsOpenShift routesor Ingress might be a better option for you. Most of them are well integrated with Kubernetes. Thanks to that, Kubernetes applications—including Strimzi—do not need to understand the differences and should work everywhere where the cloud infrastructure and Kubernetes are properly integrated.

With your free Red Hat Developer program membership, unlock our library of cheat sheets and ebooks on next-generation application development. None of the common load balancing services supports the Kafka protocol, so Strimzi always uses the Layer 4 load balancing. Because Layer 4 works on the TCP level, the load balancer will always take the whole TCP connection and direct it to one of the targets.

This approach has some advantages; you can, for example, decide whether TLS encryption should be enabled or disabled. As a result, each broker will get a separate load balancer.

kafka broker behind load balancer

At the beginning of this article, we defined a load balancer as something that distributes incoming traffic across multiple targets. However, as you can set from the diagram above, the per-broker load balancers have only one target and are technically not load balancing. That is true, but, in most cases, the actual implementation is a bit more complicated.

When Kubernetes creates the load balancer, they usually target it to all nodes of your Kubernetes cluster, not just to the nodes where your application is actually running. Thus, although the TCP connections will always end on the same node in the same broker, they might be routed through the other nodes of your cluster. When the connection is sent by the load balancer to the node that does not host the Kafka broker, the kube-proxy component of Kubernetes will forward it to the right node where the broker runs.

This can lead to delays because some connections might be routed through more hops than absolutely necessary. The only exception is the bootstrap load balancer that is distributing the connections to all brokers in your Kafka cluster. Load balancers, in common with the node port external listener, have TLS enabled by default. After Strimzi creates the load balancer type Kubernetes services, the load balancers will be automatically created.