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In Kafka, I would like to use only a single broker, single topic and a single partition having one producer and multiple consumers (each consumer getting its own copy of data from the broker). Given this, I do not want the overhead of using Zookeeper; Can I not just use the broker only? Why is a Zookeeper must?
Yes, Zookeeper is required for running Kafka. From the Kafka Getting Started documentation:
Step 2: Start the server
Kafka uses zookeeper so you need to first start a zookeeper server if
you don't already have one. You can use the convenience script
packaged with kafka to get a quick-and-dirty single-node zookeeper
instance.
As to why, well people long ago discovered that you need to have some way to coordinating tasks, state management, configuration, etc across a distributed system. Some projects have built their own mechanisms (think of the configuration server in a MongoDB sharded cluster, or a Master node in an Elasticsearch cluster). Others have chosen to take advantage of Zookeeper as a general purpose distributed process coordination system. So Kafka, Storm, HBase, SolrCloud to just name a few all use Zookeeper to help manage and coordinate.
Kafka is a distributed system and is built to use Zookeeper. The fact that you are not using any of the distributed features of Kafka does not change how it was built. In any event there should not be much overhead from using Zookeeper. A bigger question is why you would use this particular design pattern -- a single broker implementation of Kafka misses out on all of the reliability features of a multi-broker cluster along with its ability to scale.
As explained by others, Kafka (even in most recent version) will not work without Zookeeper.
Kafka uses Zookeeper for the following:
Electing a controller. The controller is one of the brokers and is responsible for maintaining the leader/follower relationship for all the partitions. When a node shuts down, it is the controller that tells other replicas to become partition leaders to replace the partition leaders on the node that is going away. Zookeeper is used to elect a controller, make sure there is only one and elect a new one it if it crashes.
Cluster membership - which brokers are alive and part of the cluster? this is also managed through ZooKeeper.
Topic configuration - which topics exist, how many partitions each has, where are the replicas, who is the preferred leader, what configuration overrides are set for each topic
(0.9.0) - Quotas - how much data is each client allowed to read and write
(0.9.0) - ACLs - who is allowed to read and write to which topic
(old high level consumer) - Which consumer groups exist, who are their members and what is the latest offset each group got from each partition.
[from https://www.quora.com/What-is-the-actual-role-of-ZooKeeper-in-Kafka/answer/Gwen-Shapira]
Regarding your scenario, only one broker instance and one producer with multiple consumer, u can use pusher to create a channel, and push event to that channel that consumer can subscribe to and hand those events.
https://pusher.com/
Important update - August 2019:
ZooKeeper dependency will be removed from Apache Kafka. See the high-level discussion in KIP-500: Replace ZooKeeper with a Self-Managed Metadata Quorum.
These efforts will take a few Kafka releases and additional KIPs. Kafka Controllers will take over the tasks of current ZooKeeper tasks. The Controllers will leverage the benefits of the Event Log which is a core concept of Kafka.
Some benefits of the new Kafka architecture are a simpler architecture, ease of operations, and better scalability e.g. allow "unlimited partitions".
Updated on Oct 2022
For new clusters in the 3.3 release you can use Apache Kafka without ZooKeeper (in new mode, called KRaft mode) in production.
Apache Kafka Raft (KRaft) is the consensus protocol that was introduced to remove Apache Kafka’s dependency on ZooKeeper for metadata management. The development progress is tracked in KIP-500.
KRaft mode was released in early access in Kafka 2.8. It was not suitable for production before 3.3 version (see details in KIP-833: Mark KRaft as Production Ready)
1. Benefits of Kafka’s new quorum controller
Enables Kafka clusters to scale to millions of partitions through improved control plane performance with the new metadata management
Improves stability, simplifies the software, and makes it easier to monitor, administer, and support Kafka.
Allows Kafka to have a single security model for the whole system
Provides a lightweight, single process way to get started with Kafka
Makes controller failover near-instantaneous
2. Timeline
Note: this timeline is very rough and subject to change.
2022/10: KRaft mode declared production-ready in Kafka 3.3
2023/02: Upgrade from ZK mode supported in Kafka 3.4 as early access.
2023/04: Kafka 3.5 released with both KRaft and ZK support. Upgrade from ZK goes production. ZooKeeper mode deprecated.
2023/10: Kafka 4.0 released with only KRaft mode supported.
References:
KIP-500: Replace ZooKeeper with a Self-Managed Metadata Quorum
Apache Kafka Needs No Keeper: Removing the Apache ZooKeeper Dependency
Preparing Your Clients and Tools for KIP-500: ZooKeeper Removal from Apache Kafka
KRaft: Apache Kafka Without ZooKeeper
Kafka is built to use Zookeeper. There is no escaping from that.
Kafka is a distributed system and uses Zookeeper to track status of kafka cluster nodes. It also keeps track of Kafka topics, partitions etc.
Looking at your question, it seems you do not need Kafka. You can use any application that supports pub-sub such as Redis, Rabbit MQ or hosted solutions such as Pub-nub.
IMHO Zookeeper is not an overhead but makes your life a lot easier.
It is basically used to maintain co-ordination between different nodes in a cluster. One of the most important things for Kafka is it uses zookeeper to periodically commit offsets so that in case of node failure it can resume from the previously committed offset (imagine yourself taking care of all this by your own).
Zookeeper also plays a vital role for serving many other purposes, such as leader detection, configuration management, synchronization, detecting when a new node joins or leaves the cluster, etc.
Future Kafka releases are planning to remove the zookeeper dependency but as of now it is an integral part of it.
Here are a few lines taken from their FAQ page:
Once the Zookeeper quorum is down, brokers could result in a bad state and could not normally serve client requests, etc. Although when Zookeeper quorum recovers, the Kafka brokers should be able to resume to normal state automatically, there are still a few corner cases the they cannot and a hard kill-and-recovery is required to bring it back to normal. Hence it is recommended to closely monitor your zookeeper cluster and provision it so that it is performant.
For more details check here
Zookeeper is centralizing and management system for any kind of distributed systems. Distributed system is different software modules running on different nodes/clusters (might be on geographically distant locations) but running as one system. Zookeeper facilitates communication between the nodes, sharing configurations among the nodes, it keeps track of which node is leader, which node joins/leaves, etc. Zookeeper is the one who keeps distributed systems sane and maintains consistency. Zookeeper basically is an orchestration platform.
Kafka is a distributed system. And hence it needs some kind of orchestration for its nodes that might be geographically distant (or not).
Apache Kafka v2.8.0 gives you early access to KIP-500 that removes the Zookeeper dependency on Kafka which means it no longer requires Apache Zookeeper.
Instead, Kafka can now run in Kafka Raft metadata mode (KRaft mode) which enables an internal Raft quorum. When Kafka runs in KRaft mode its metadata is no longer stored on ZooKeeper but on this internal quorum of controller nodes instead. This means that you don't even have to run ZooKeeper at all any longer.
Note however that v2.8.0 is currently early access and you should not use Zookeeper-less Kafka in production for the time being.
A few benefits of removing ZooKeeper dependency and replacing it with an internal quorum:
More efficient as controllers no longer need to communicate with ZooKeeper to fetch cluster state metadata every time the cluster is starting up or when a controller election is being made
More scalable as the new implementation will be able to support many more topics and partitions in KRaft mode
Easier cluster management and configuration as you don't have to manage two distinct services any longer
Single process Kafka Cluster
For more details you can read the article Kafka No Longer Requires ZooKeeper
Yes, Zookeeper is must by design for Kafka. Because Zookeeper has the responsibility a kind of managing Kafka cluster. It has list of all Kafka brokers with it. It notifies Kafka, if any broker goes down, or partition goes down or new broker is up or partition is up. In short ZK keeps every Kafka broker updated about current state of the Kafka cluster.
Then every Kafka client(producer/consumer) all need to do is connect with any single broker and that broker has all metadata updated by Zookeeper, so client need not to bother about broker discovery headache.
Other than the usual payload message transfer, there are many other communications that happens in kafka, like
Events related to brokers requesting the cluster membership.
Events related to Brokers becoming available.
Getting bootstrap config setups.
Events related to controller and leader updates.
Help status updates like Heartbeat updates.
Zookeeper itself is a distributed system consisting of multiple nodes in an ensemble. Zookeeper is centralised service for maintaining such metadata.
This article explains the role of Zookeeper in Kafka. It explains how kafka is stateless and how zookeper plays an important role in distributed nature of kafka (and many more distributed systems).
The request to run Kafka without Zookeeper seems to be quite common. The library Charlatan addresses this.
According to the description is Charlatan more or less a mock for Zookeeper, providing the Zookeeper services either backed up by other tools or by a database.
I encountered that library when dealing with the main product of the authors for the Charlatan library; there it works fine …
Firstly
Apache ZooKeeper is a distributed store which is used to provide configuration and synchronization services in a high available way.
In more recent versions of Kafka, work was done in order for the client consumers to not store information about how far it had consumed messages (called offsets) into ZooKeeper.This reduced usage did not get rid of the need for consensus and coordination in distributed systems however. While Kafka provides fault-tolerance and resilience, something is needed in order to provide the coordination needed and ZooKeeper enables that piece of the overall system.
Secondly
Agreeing on who the leader of a partition is, is one example of the practical application of ZooKeeper within the Kafka ecosystem.
Zookeeper would work if there was even a single broker.
These are from Kafka In Action book.
Image is from this course
Related
We have a on-premise setup of the free version of the ELK stack. Actually Elasticsearch cluster and some Kibana nodes (no Logstash).
On the application servers we have installed filebeat 7.9.0 which ships the logs to the Elasticsearch ingest nodes, and there is very minimal processing done by the filebeat on the log events before sending (e.g. multiline=true, dissect, drop_fields and json_decode).
As of today, there are only 3 application servers on the production set-up, but it might scale to more number of machines (application servers) going forward.
I understand that, the central management of the filebeat configuration is possible (which is also coming to its end of life) with a license version of ELK stack.
I want to know what are the alternatives available to manage the filebeat configuration apart from the central management through Kibana.
The goal is in future if number of application servers grow to lets say 20, and the filebeat configuration has to undergo a change, changing the configuration on each of the servers shall be manual activity with its own risks associated. i.e. change the configuration at one location and somehow it is updated on filebeat on all application servers.
Please let me know, if this can be achieved ..
Any pointers / thoughts towards the solution let me know
Note: We do not have infrastructure as a code in the organization yet, so this may not be a suitable solution.
Thanks in advance ..
The replacement of Central Management is Elastic Fleet: Installing a single agent on a server, the rest can be done from Kibana. https://www.elastic.co/blog/introducing-elastic-agent-and-ingest-manager gives a better overview of the features and current screenshots.
Most parts of Fleet are also available for free.
I have created a kafka producer that reads website click data streams from MySQL database and it works well. I found out that I can also just connect kafka to MySQL datasource using kafka connect or debezium. My target is to ingest the data using kafka and send it to Storm to consume and do analysis. It looks like both ways can achieve my target but using kafka producer may require me to build a kafka service that keeps reading the datasource.
Which of the two approaches would be more efficient for my data pipe line?
I'd advice to not re-invent the wheel and use Debezium (disclaimer: I'm its project lead).
It's feature-rich (supported data types, configuration options, can do initial snapshotting etc.) and well tested in production. Another key aspect to keep in mind is that Debezium is based on reading the DB's log instead of polling (you might do the same in your producer, it's not clear from the question). This provides many advantages over polling:
no delay as with low-frequency polls, no CPU load as with high-frequency polls
can capture all changes without missing some between two polls
can capture DELETEs
no impact on schema (doesn't need a column to identify altered rows)
State: We are sharing zookeeper with kafka and several different services, which are using zookeeper for coordination. They are nicely operating on zookeeper subcontext. Looks like this:
/
/service1/...
/service2/...
/brokers/...
/consumers/...
My question is.. Is it possible to setup kafka to use subcontext? So, the other services can't eventually modify other services subcontext. It would be:
/
/service1/...
/service2/...
/kafka/brokers/...
/kafka/consumers/...
I saw this syntax in other projects:
zk://10.0.0.1,10.0.0.2/kafka
lets say. So, kafka would see only the brokers and consumers paths and there would be no way to mess up with other subcontext.
I'm afraid kafka is just not supported this format at the time. Other question is, is there a workaround? Like wrap up zookeeper somehow? Any ideas? Or kafka is supposed to use zookeeper exclusively. Is it best practice and we should spawn zookeeper for each project, which is overkill thus zookeeper need ensemble consists atleast of 3 nodes.
Thanks for your answer!
You can use the zk chroot syntax with Kafka, as detailed in the Kafka configuration documentation.
Zookeeper also allows you to add a "chroot" path which will make all kafka data for this cluster appear under a particular path. This is a way to setup multiple Kafka clusters or other applications on the same zookeeper cluster. To do this give a connection string in the form hostname1:port1,hostname2:port2,hostname3:port3/chroot/path which would put all this cluster's data under the path /chroot/path. Note that you must create this path yourself prior to starting the broker and consumers must use the same connection string.
The best practice is to maintain a single ZooKeeper cluster (at least that is what I've seen). Otherwise you are creating more operational workload for maintaining a good ZK ensemble.
The Kafka documentation on operationalizing ZK sort of recommends having multiple ZKs though:
Application segregation: Unless you really understand the application patterns of other apps that you want to install on the same box, it can be a good idea to run ZooKeeper in isolation (though this can be a balancing act with the capabilities of the hardware).
We have a really simple Kafka 0.8.1.1 set up in our development lab. It's just one node. Periodically, we run into this error:
[2015-08-10 13:45:52,405] ERROR Controller 0 epoch 488 initiated state change for partition [test-data,1] from OfflinePartition to OnlinePartition failed (state.change.logger)
kafka.common.NoReplicaOnlineException: No replica for partition [test-data,1] is alive. Live brokers are: [Set()], Assigned replicas are: [List(0)]
at kafka.controller.OfflinePartitionLeaderSelector.selectLeader(PartitionLeaderSelector.scala:61)
at kafka.controller.PartitionStateMachine.electLeaderForPartition(PartitionStateMachine.scala:336)
at kafka.controller.PartitionStateMachine.kafka$controller$PartitionStateMachine$$handleStateChange(PartitionStateMachine.scala:185)
at kafka.controller.PartitionStateMachine$$anonfun$triggerOnlinePartitionStateChange$3.apply(PartitionStateMachine.scala:99)
at kafka.controller.PartitionStateMachine$$anonfun$triggerOnlinePartitionStateChange$3.apply(PartitionStateMachine.scala:96)
at scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:743)
Can anyone recommend a strategy for recovering from this? Is there such a thing or do we need to build out another node or two and set up the replication factor on our topics to cover all of the nodes that we put into the cluster?
We have 3 zookeeper nodes that respond very well for other applications like Storm and HBase, so we're pretty confident that ZooKeeper isn't to blame here. Any ideas?
This question is about Kafka 0.8 which should be out of support if I am not mistaken. However, for future readers the following guidelines should be relevant:
If you care about stability, uptime, reliability or anything in this general direction, make sure you have at least 3 kafka Nodes.
If you have a problem in an old kafka version, seriously consider upgrading to the latest kafka version. At time of writing we are already at Kafka 2
I have a requirement to build a distributed Comet-based server for a large number of clients (over 500K concurrent) with high throughput. I'm currently investigating the possibility of using Tornado for it's high efficiency in dealing with high number of long-polling requests.
My concern is whether a single Tornado server could handle such a large number of long polling clients. As an experiment, I would like to expand Tornado Chat demo (https://github.com/facebook/tornado/tree/master/demos/chat) to a distributed environment. I.e. have a bunch of Tornado chat servers running in parallel, each responsible for a changing set of clients.
I would appreciate any ideas/thoughts you have with regard to implementing such a scheme, or any references to relevant resources.
Thanks!
In general to make the basic chat distributed across several Tornado instances you need to create a distributed message passing mechanism, the most straightforward implementation will be to just use some kind of message queue like RabbitMQ (or it's competitor) and send fanout messages when user types something, while all connections are listening.
My initial thought about this is to have an Nginx server/reverse proxy in the front-end, while have multiple instances of Tornado in the back, this could be a Tornado instance per process, try to do some bench-marking to your machine to see how many running Tornado instances on different process a machine can handle, when you notice degradation in performance, start doing the same thing on another machine.
Nginx will round robin all the servers you have to distribute the load over the long-polling/Tornado servers/instances.
Not really sure how the rabbitmq will be useful in this case.