I have 6 partitions for some topic and created a consumer group with 6 consumers. So, Here each consumer reading data from 1 partition. Here the problem is one partition having more data and other partitions having minimal data, due to data skew in one partition one consumer performing slow. How to handle this situation.
You need to know which keys you are sending into the topic. If the keys are null, then this shouldn't be possible.
If there is a distribution of non-null keys that is skewed towards a few values, then those partitions will be greater.
You are welcome to write your own Partitioner interface, otherwise.
Related
We want to put messages/records of a different customers on different partitions of a kafka topic.
But number of customers is not known in prior. So how can we set partition count for kafka topic in this case? Do we need any other way where partition count changes at runtime based on keys (customer_id in this case). Thanks in advance.
need to know number of partitions
Assuming Java, use AdminClient.describeTopics() method call and get partitions of each response object.
Regarding the rest of the question, consumer instances automatically distribute partition assignment when subscribing to topics.
Producers should not know about consumers, so you don't "put records on partitions" based on any factor of (possible) consumers.
partition count changes at runtime based on keys (customer_id)
Unclear what this means. Partition count can only increase, and if you do increase it, then your partitions will become unordered, so you should consider how large your keyspace is before creating the topic. For example, if you have a numeric ID, and use the first two digits as the partition value, then you could create a topic up to 100 partitions.
Most of the information I find relates to primary key joins. I understand foreign key joins are a relatively new feature for Kafka Streams. I'm interested in how this will scale. I understand that Kafka Streams parallelism is capped by the number of partitions on each topic, however I have a few questions around what it means to increase the input topic partitions.
Does the foreign key join have the same requirement to co-partition input topics? That is, do both topics need to have the same number of partitions?
How does one add a partitions later after the application has been running in production for months or years? The changelog topics backing each KTable store data from certain input topic partitions. If one is to increase the partitions in the input topics, how does this impact our KTables' state stores and changelogs? Presumably, we cannot just start over and lose that data since it has accumulated over months and years and is essential to performing the join. It may not be quickly replaced by upstream data. Do we need to blow away our state stores, create new input topics, and re-send all KTable changelog topic data to them?
How about the other internal "subscription" topics?
Does the foreign key join have the same requirement to co-partition input topics? That is, do both topics need to have the same number of partitions?
No. For more details check out https://www.confluent.io/blog/data-enrichment-with-kafka-streams-foreign-key-joins/
How does one add a partitions later after the application has been running in production for months or years?
You cannot really do this, even if you don't use Kafka Streams. The issue is, that your input data is partitioned by key, and if you add a partition the partitioning in your input topic breaks. -- The recommended pattern is to create a new topic with different number of partitions.
The changelog topics backing each KTable store data from certain input topic partitions. If one is to increase the partitions in the input topics, how does this impact our KTables' state stores and changelogs?
It would break the application. In fact, Kafka Streams will check and will raise an exception if it detect that the number of input topic partitions does not match the number of changelog topic partitions.
Recently, I started to study Kafka and have been thinking how to adopt it into my service. Some of my messages should be processed in strict order, so I chose to use a key for partitioning on producer. However, even though we just need one partition right now, we might increase the number of partitions in the near future. So, in Kafka, if I increase the number of partitions in a topic then will consumers get messages in order?
Thanks in advance.
If you increase partitions, there's no guarantee that future, equal keys will land in their prior partition, so you'll experience a temporary period, based on topic retention, where you'll have keys spanning more than one partition (by default)
One workaround is to ensure you've consumed all messages, stop all clients interacting with the topic, then empty the topic and increase the count
Or you can start with an increased count to begin with and continue having all equal keys distributed over multiple partitions
Say, I have an application that reads a batch of data from KAFKA, it uses the keys of the incoming messages and makes a query to HBase (reads the current data from HBase for those keys), does some computation and writes data back to HBase for the same set of keys. For e.g.
{K1, V1}, {K2, V2}, {K3, V3} (incoming messages from KAFKA) --> My Application (Reads the current value of K1, K2 and K3 from HBase, uses the incoming value V1, V2 and V3 does some compute and writes the new values for K1 (V1+x), K2 (V2+y) and K3(V3+z) back to HBase after the processing is complete.
Now, let’s say I have one partition for the KAFKA topic and 1 consumer. My application has one consumer thread that is processing the data.
The problem is that say HBase goes down, at which point my application stops processing messages, and a huge lag builds into KAFKA. Even, though I have the ability to increase the number of partitions and correspondingly the consumers, I cannot increase either of them because of RACE conditions in HBase. HBase doesn’t support row level locking so now if I increase the number of partitions the same key could go to two different partitions and correspondingly to two different consumers who may end up in a RACE condition and whoever writes last is the winner. I will have to wait till all the messages gets processed before I can increase the number of partitions.
For e.g.
HBase goes down --> Initially I have one partition for the topic and there is unprocessed message --> {K3, V3} in partition 0 --> now I increase the number of partitions and message with key K3 is now present let’s say in partition 0 and 1 --> then consumer consuming from partition 0 and another consumer consuming from partition 1 will end up competing to write to HBase.
Is there a solution to the problem? Of course locking the key K3 by the consumer processing the message is not the solution since we are dealing with Big Data.
When you increase a number of partitions only new messages come to the newly added partitions. Kafka takes responsibility for processing one message exactly once
A message will only appear in one and only one kafka partition. It is using a hash function on the message modulo the number of partitions. I believe this guarantee solves your problem.
But bear in mind that if you change the number of partitions the same message key could be allocated to a different partition. That may matter if you care about the ordering of messages that is only guaranteed per partition. If you care about the ordering of messages repartitioning (e.g. increasing the number of partitions) is not an option.
As Vassilis mentioned, Kafka guarantee that single key will be only in one partition.
There are different strategies how to distribute keys on partitions.
When you increase partition number or change partitioning strategy, a rebalance process could occur which may affect to working consumers. If you stop consumers for a while, you could avoid possibility of processing the same key by two consumers.
I am trying to develop a better understanding of how Kafka works. To keep things simple, currently I am running Kafka on one Zookeeper with 3 brokers and one partition with duplication factor of 3. I learned that, in general, it's better to have number of partitions ~= number of consumers.
Question 1: Do topics share offsets in the same partition?
I have multiple topics (e.g. dogs, cats, dinosaurs) on one partition (e.g. partition 0). Now my producers have produced a message to each of the topics. "msg: bark" to dogs, "msg: meow" to cats and "msg: rawr" to dinosaurs. I noticed that if I specify dogs[0][0], I get back bark and if I do the same on cats and dinosaurs, I do get back each message respectively. This is an awesome feature but it contradicts with my understanding. I thought offset is specific to a partition. If I have pushed three messages into a partition sequentially. Shouldn't the messages be indexed with 0, 1, and 2? Now it seems me that offset is specific to a topic.
This is how I imagined it
['bark', 'meow', 'rawr']
In reality, it looks like this
['bark']
['meow']
['rawr']
But that can't be it. There must be something keeping track of offset and the actual physical location of where the message is in the log file.
Question 2: How do you manage your messages if you were to have multiple partitions for one topic?
In question 1, I have multiple topics in one partition, now let's say I have multiple partitions for one topic. For example, I have 4 partitions for the dogs topic and I have 100 messages to push to my Kafka cluster. Do I distribute the messages evenly across partitions like 25 goes in partition 1, 25 goes in partition 2 and so on...?
If a consumer wants to consume all those 100 messages at once, he/she needs to hit all four partitions. How is this different from hitting 1 partition with 100 messages? Does network bandwidth impose a bottleneck?
Thank you in advance
For your question 1: It is impossible to have multiple topics on one partition. Partition is part of topic conceptually. You can have 3 topics and each of them has only one partition. So you have 3 partitions in total. That explains the behavior that you observed.
For your question 2: AT the producer side, if a valid partition number is specified that partition will be used when sending the record. If no partition is specified but a key is present, a partition will be chosen using a hash of the key. If neither key nor partition is present a partition will be assigned in a round-robin fashion. Now the number of partitions decides the max parallelism. There is a concept called consumer group, which can have multiple consumers in the same group consuming the same topic. In the example you gave, if your topic has only one partition, the max parallelism is one and only one consumer in the consumer group will receive messages (100 of them). But if you have 4 partitions, you can have up to 4 consumers, one for each partition and each receives 25 messages.