We are working on Confluent Platform and we are still getting to know the internals. But we have implemented generic use cases . We are trying to optimizing our cluster
In my use case, I need to increase the number of partitions of a topic . What is the impact of it ? Can you please share of it
Sure, you can increase partitions.
However,
Increasing partitions does not move existing data. If using Confluent Enterprise, you could use confluent-rebalancer, or if not, then kafka-reassign-partitions CLI tool. So, you'll definitely want to rebalance a topic to "optimize" the cluster.
During the retention period of the topic (read: for the existing data), if you previously had a producer sending data to partition N, and now had N+1 partitions, then you lose ordering of those messages that solely existed in partition N. New messages could be spread across new partitions that a new producer calculates with the DefaultPartitioner. If you don't send keys with messages, then this isn't a problem.
Related
I'm relatively new to working with Kafka, below is a sample of what my current set up is.
Kafka Setup
Multiple topics that all have one partition each. 2 Consumer Groups with each group containing one consumer.
The issue I am seeing is that the Lag is enormous, sometimes upwards of 8-10 hours waiting for consuming, the load is about 100-200 million messages a day
What steps should I look at in order to address this? Is it as simple as reassigning partitions or creating new partitions for the 3 topics that are being consumed by the two consumers? - I've also looked at compressing the contents of the producer with gzip but it doesn't really help in terms of the lag. I've looked at network connections and don't feel that it is anything got to do with this. If anyone could point me in the direction of Kafka and Low Latency documents that would be good also.
Generally the flow is to parallelize your consumption through the increase on the number of partitions and consumers in consumer groups that subscribe to those topics with increased partitions (Nconsumers <= Npartitions).
And distribute your topics with increase on the number of brokers in your cluster.
So from topic considerations:
Less partition per topic result:
in producer and/or consumer lag
starved or overloaded brokers and consumers.
(But take into account) More partition per topic result in:
More broker resources – file handlers and memory.
There is an overhead with each additional partition and a number of partitions a broker can handle is limited.
Overhead of replication load
Then increase the number of consumers in that consumer groups.
Try increasing partition per topic, but by itself it should not help! You also will need to increase the number of consumers in your consumer group. Is that single consumers or consumer groups on your diagram? How many consumers in your consumer group vs partitions on the topic that they are subscibed to.
From this in your message:
I've also looked at compressing the contents of the producer with gzip but it doesn't really help in terms of the lag.
I get an idead that your messages may be huge! Is it so? In case yes, try to keep messages small (for example by excluding BLOBs and keep external links to them)
Still the issue may be somewhere else like bad configs, consumer commit messages (acknowledgment handling), etc.
So, I highly advice you to read article Fine-tune Kafka performance with the Kafka optimization theorem
I also advise you to go through Apache Kafka courses on Confluent web-page
This should be added as a comment, but I haven't had permissions to do so. The provided info is very limited with incorrect diagram, which limits the ability to provide an adequate helpfull answer. If possible please correct your diagram and add more details about your set-up like:
broker configuration, file attached;
consumer set-up (Consumer commit messages);
producer set-up;
topic set-up;
kafka version (the defaults differ with major/minor versions)
The provided diagram is not correct in the notion of topic - partition relationship, so I assume it is a mistype and Partition 0 must be substituded with Broker 0, right?
Kafka's topics are divided into several partitions. While the topic is a logical concept in Kafka, a partition is the smallest storage unit that holds a subset of records owned by a topic...
Then there is an open question on the number of partiotions in each topic and the number of topics in each broker, as well as the number of brokers in your cluster!
In a Kafka deployment a custom topic partitioner logic is used to route all messages that belong to the same root entity (for example all message for particular user) to the same partition.
Can anyone recommend a strategy on how to deal with partitioning logic change in such live system?
One example that affects the partitioning is the obvious change of the partitioner implementation. The other example would be change of the number of partitions for a given topic.
In both cases, we would end up in a situation where some of the messages for user A, that entered the Kafka before the change, will be in partition 1, while after the change in partitioning logic or number of partitions messages for that same user A will go the partition 2.
This can lead to a problem where messages for user A are processed out of order. Consumer reading the messages from partition 2 could process messages before the consumer that reads the messages from partition 1.
Have anyone faced this issue in live system? How did you or would you solve this issue?
This seems like a very common scenario, but I was not able to find anything about it.
Thanks
By partitioning logic, if you meant partitioning algorithm, I do not understand how that would just change like that. As for increasing partitions, it is in theory not possible to achieve increasing of partitions while guaranteeing the order of messages. -- there is a KIP for that, but its status is still "under discussion".
What I do usually when I increase partitions is to accept a small downtime.
The playbook is like this:
Stop the producer
Monitor the lag for the consumer group
Once lag is zero, shut down the consumers
Increase the number of partitions
Start the consumers
Start the producers
This way, you can be sure that there are no message losses and no out of order message consumption.
If you want to avoid a downtime, you may have to rely on an external system which can temporarily hold the data per partition in order and publish, but that solution depends on a few things
The best way to change how records are partitioned is to use the default Apache Kafka® partitioner, and change the record keys. If all records from a user need to go to the same topic then make sure they all have the same key.
If you'd like to change the keys for a whole set you can use KSQL to re-key (republish to a new topic with new keys) the data using the PARTITION BY function.
We have a business process/workflow that is being started when initial event message is received and closed when the last message is processed. We have up to 100,000 processes executed each day. My problem is that the order of the messages that come to specific process has to be processed by the same order messages were received. If one of the messages fails, the process has to freeze until the problem is fixed, despite that all other processes has to continue. For this kind of situation i am thinking of using Kafka. first solution that came to my mind was to use Topic partitioning by message key. The key of the message would be the ProcessId. This way i could be sure that all process messages would be partitioned and kafka would guarantee the order. As i am new to Kafka what i managed to figure out that partitions has to be created in advance and that makes everything to difficult. so my questions are:
1) when i produce message to kafka's topic that does not exist, the topic is created on runtime. Is it possible to have same behavior for topic partitions?
2) there can be more than 100,000 active partitions on the topic, is that a problem?
3) can partition be deleted after all messages from that topic were read?
4) maybe you can suggest other approaches to my problem?
When i produce message to kafka's topic that does not exist, the topic is created on runtime. Is it possible to have same behavior for topic partitions?
You need to specify number of partitions while creating topic. New Partitions won't be create automatically(as is the case with topic creation), you have to change number of partitions using topic tool.
More Info: https://kafka.apache.org/documentation/#basic_ops_modify_topi
As soon as you increase number of partitions, producer and consumer will be notified of new paritions, thereby leading them to rebalance. Once rebalanced, producer and consumer will start producing and consuming from new partition.
there can be more than 100,000 active partitions on the topic, is that a problem?
Yes, having this much partitions will increase overall latency.
Go through how-choose-number-topics-partitions-kafka-cluster on how to decide number of partitions.
can partition be deleted after all messages from that topic were read?
Deleting a partition would lead to data loss and also the remaining data's keys would not be distributed correctly so new messages would not get directed to the same partitions as old existing messages with the same key. That's why Kafka does not support decreasing partition count on topic.
Also, Kafka doc states that
Kafka does not currently support reducing the number of partitions for a topic.
I suppose you choose wrong feature to solve you task.
In general, partitioning is used for load balancing.
Incoming messages will be distributed on given number of partition according to the partitioning strategy which defined at broker start. In short, default strategy just calculate i=key_hash mod number_of_partitions and put message to ith partition. More about strategies you could read here
Message ordering is guaranteed only within partition. With two messages from different partitions you have no guarantees which come first to the consumer.
Probably you would use group instead. It's option for consumer
Each group consumes all messages from topic independently.
Group could consist of one consumer or more if you need it.
You could assign many groups and add new group (in fact, add new consumer with new groupId) dynamically.
As you could stop/pause any consumer, you could manually stop all consumers related to specified group. I suppose there is no single command to do that but I'm not sure. Anyway, if you have single consumer in each group you could stop it easily.
If you want to remove the group you just shutdown and drop out related consumers. No actions on broker side is needed.
As a drawback you'll get 100,000 consumers which read (single) topic. It's heavy network load at least.
I am investigating on Kafka to assess its suitability for our use case. Can you please help me understand how flexible is Kafka with changing the number of partitions for an existing Topic?
Specifically,
Is it possible to change the number of partitions without tearing down the cluster?
And is it possible to do that without bringing down the topic?
Will adding/removing partitions automatically take care of redistributing messages across the new partitions?
Ideally, I would want the change to be transparent to the producers and consumers. Does Kafka ensure this?
Update:
From my understanding so far, it looks like Kafka's design cannot allow this because it mapping of consumer groups to partitions will have to be altered. Is that correct?
1.Is it possible to change the number of partitions without tearing down the cluster?
Yes kafka supports increasing the number of partitions at runtime but doesn't support decreasing number of partitions due to its design
2.And is it possible to do that without bringing down the topic?
Yes provided you are increasing partitions.
3.Will adding/removing partitions automatically take care of redistributing messages across the new partitions?
As mentioned earlier removing partitions is not supported .
When you increase the number of partitions, the existing messages will remain in the same partitions as before only the new messages will be considered for new partitions (also depending on you partitioner logic). Increasing the partitions for a topic will trigger a cluster rebalance , where in the consumers and producers will get notified with the updated metadata of the topics. Producers will start sending messages to new partitions after receiving updated metadata and consumer rebalancer will redistribute the partitions among the consumers groups and resume consumption from the last committed offset.All this will happen under the hood , so you wont have to do any changes at client side
Yes, it it perfectly possible. You just execute the following command against the topic of your choice: bin/kafka-topics.sh --zookeeper zk_host:port --alter --topic <your_topic_name> --partitions <new_partition_count>. Remember, Kafka only allows increasing the number of partitions, because decreasing it would cause in data loss.
There's a catch here. Kafka doc says the following:
Be aware that one use case for partitions is to semantically partition
data, and adding partitions doesn't change the partitioning of
existing data so this may disturb consumers if they rely on that
partition. That is if data is partitioned by hash(key) %
number_of_partitions then this partitioning will potentially be
shuffled by adding partitions but Kafka will not attempt to
automatically redistribute data in any way.
Yes, if by bringing down the topic you mean deleting the topic.
Once you've increased the partition count, Kafka would trigger a rebalance, for consumers who are subscribing to that topic, and on subsequent polls, the partitions would get distributed across the consumers. It's transparent to the client code, you don't have to worry about it.
NOTE: As I mentioned before, you can only add partitions, removing is not possible.
+one more thing, if you are using stateful operations in clients like aggregations(making use of statestore), change in partition will kill all the streams thread in consumer. This is expected as increase in partition may corrupt stateful applications. So beware changing partition size, it may break stateful consumers connected to the topic.
Good read: Why does kafka streams threads die when the source topic partitions changes ? Can anyone point to reading material around this?
What is maximum limit of topics can a consumer subscribe to in Kafka. Am not able to find this value documented anywhere.
If consumer subscribes 500000 or more topics, will there be downgrade in performance.
500,000 or more topics in a single Kafka cluster would be a bad design from the broker point of view. You typically want to keep the number of topic partitions down to the low tens of thousands.
If you find yourself thinking you need that many topics in Kafka you might instead want to consider creating a smaller number of topics and having 500,000 or more keys instead. The number of keys in Kafka is unlimited.
To be technical the "maximum" number of topics you could be subscribed to would be constrained by the available memory space for your consumer process (if your topics are listed explicitly then a very large portion of the Java String pool will be your topics). This seems the less likely limiting factor (listing that many topics explicitly is prohibitive).
Another consideration is how the Topic assignment data structures are setup at Group Coordinator Brokers. They could run out of space to record the topic assignment depending on how they do it.
Lastly, which is the most plausible, is the available memory on your Apache Zookeeper node. ZK keeps ALL data in memory for fast retrieval. ZK is also not sharded, meaning all data MUST fit onto one node. This means there is a limit to the number of topics you can create, which is constrained by the available memory on a ZK node.
Consumption is initiated by the consumers. The act of subscribing to a topic does not mean the consumer will start receiving messages for that topic. So as long as the consumer can poll and process data for that many topics, Kafka should be fine as well.
Consumer is fairly independent entity than Kafka cluster, unless you are talking about build in command line consumer that is shipped with Kafka
That said logic of subscribing to a kafka topic, how many to subscribe to and how to handle that data is upto the consumer. So scalability issue here lies with consumer logic
Last but not the least, I am not sure it is a good idea to consumer too many topics within a single consumer. The vary purpose of pub sub mechanism that Kafka provides through the segregation of messages into various topics is to facilitate the handling of specific category of messages using separate consumers. So I think if you want to consume many topics like few 1000s of them using a single consumer, why divide the data into separate topics first using Kafka.