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
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.
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.
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'm reading the Kafka documentation and noticed the following line:
Note however that there cannot be more consumer instances in a consumer group than partitions.
Hmm. How can I auto-scale this?
For example let's say I have a messaging system with hi/lo priorities, so I create a topic for messages and partitions for hi and lo priority messages.
If this was RabbitMQ, I'd have an auto-scalable group of consumers assigned to each partition, like this:
If I understand the Kafka model I can't have >1 consumer per partition in a consumer group, so that picture doesn't work for Kafka, right?
Ok, so what about >1 consumer groups like this:
That get's around Kafka's limitation but... If I understand how this works both consumer groups would be pulling from a partition, for example msg.hi, with their own offsets so neither would know about the other--meaning messages would likely be delivered twice!
How can I achieve the capability I had in the Rabbit design w/Kafka and still maintain the "queue-ness" of the behavior (I don't want to send a message twice)? What am I missing?
TL;DR
Topic is made up of partitions. Partitions decide the max number of consumers you can have in a group.
Scenario 1:
When we have only one consumer, It can read all the messages from all the partitions.
Scenario 2:
In the above set up, when you increase the number of consumers in the group, partition reassignment happens and instead of consumer 1 reading all the messages from all the partitions, consumer 2 could share some of the load with consumer 1 as shown below.
Scenario 3:
What happens If I have more number of consumers than the number of partitions.? Each consumer would be assigned 1 partition. Any additional consumers in the group will be sitting idle unless you increase the number of partitions for a Topic.
Summary:
We need to choose the partitions accordingly. That decides the max number of consumers in the group. Changing the partition for an existing topic is really NOT recommended as It could cause issues.
That is, Let's assume a producer producing names into a topic where we have 3 partitions. All the names starting with A-I go to Partition 1, J-R in partition 2 and S-Z in partition 3. Let's also assume that we have already produced 1 million messages. Now if you suddenly increase the number of partitions to 5 from 3, It will create a different A-Z range now. That is, A-F in Partition 1, G-K in partition 2, L-Q in partition 3, R-U in partition 4 and V-Z in partition 5. Do you get it? It kind of affects the order of the messages we had before! So you need to be aware of this. If this could be a problem, then we need to choose the partition accordingly upfront.
More info is here - http://www.vinsguru.com/kafka-scaling-consumers-out-for-a-consumer-group/
Your assumption about messages being consumed twice is correct (since each group consumes 100% of messages from a topic).
I agree with David. Moreover, I suggest that you create more partitions than you really need, which would leave you some headroom to increase the number of threads in the group when such a need arises.
You can always increase the number of partitions later (and/or add additional brokers), but it's nice to have that already done, so that you can only increase number of threads and be done with it (those situations usually require a quick response, so you should do all the prep. that you can do in advance).
Just create a bunch of partitions for hi and lo. 12 is a good number. So is 60. Just pick a number of partitions that matches how much maximum parallelization you want.
Honestly, although I personally would makemsg.hi and msg.lo be different topics entirely, that's not a requirement -- you can do custom parititoning to divide messages between partitions.
You can also use an AI based auto scaler like this https://www.confluent.io/events/kafka-summit-americas-2021/intelligent-auto-scaling-of-kafka-consumers-with-workload-prediction/
This scaler calculates the right number of consumer PODs based on workload prediciton and target KPI metrics
As per Apache Kafka documentation, the order of the messages can be achieved within the partition or one partition in a topic. In this case, what is the parallelism benefit we are getting and it is equivalent to traditional MQs, isn't it?
In Kafka the parallelism is equal to the number of partitions for a topic.
For example, assume that your messages are partitioned based on user_id and consider 4 messages having user_ids 1,2,3 and 4. Assume that you have an "users" topic with 4 partitions.
Since partitioning is based on user_id, assume that message having user_id 1 will go to partition 1, message having user_id 2 will go to partition 2 and so on..
Also assume that you have 4 consumers for the topic. Since you have 4 consumers, Kafka will assign each consumer to one partition. So in this case as soon as 4 messages are pushed, they are immediately consumed by the consumers.
If you had 2 consumers for the topic instead of 4, then each consumer will be handling 2 partitions and the consuming throughput will be almost half.
To completely answer your question,
Kafka only provides a total order over messages within a partition, not between different partitions in a topic.
ie, if consumption is very slow in partition 2 and very fast in partition 4, then message with user_id 4 will be consumed before message with user_id 2. This is how Kafka is designed.
I decided to move my comment to a separate answer as I think it makes sense to do so.
While John is 100% right about what he wrote, you may consider rethinking your problem. Do you really need ALL messages to stay in order? Or do you need all messages for specific user_id (or whatever) to stay in order?
If the first, then there's no much you can do, you should use 1 partition and lose all the parallelism ability.
But if the second case, you might consider partitioning your messages by some key and thus all messages for that key will arrive to one partition (they actually might go to another partition if you resize topic, but that's a different case) and thus will guarantee that all messages for that key are in order.
In kafka Messages with the same key, from the same Producer, are delivered to the Consumer in order
another thing on top of that is, Data within a Partition will be stored in the order in which it is written therefore, data read from a Partition will be read in order for that partition
So if you want to get your messages in order across multi partitions, then you really need to group your messages with a key, so that messages with same key goes to same partition and with in that partition the messages are ordered.
In a nutshell, you will need to design a two level solution like above logically to get the messages ordered across multi partition.
You may consider having a field which has the Timestamp/Date at the time of creation of the dataset at the source.
Once, the data is consumed you can load the data into database. The data needs to be sorted at the database level before using the dataset for any usecase. Well, this is an attempt to help you think in multiple ways.
Let's consider we have a message key as the timestamp which is generated at the time of creation of the data and the value is the actual message string.
As and when a message is picked up by the consumer, the message is written into HBase with the RowKey as the kafka key and value as the kafka value.
Since, HBase is a sorted map having timestamp as a key will automatically sorts the data in order. Then you can serve the data from HBase for the downstream apps.
In this way you are not loosing the parallelism of kafka. You also have the privilege of processing sorting and performing multiple processing logics on the data at the database level.
Note: Any distributed message broker does not guarantee overall ordering. If you are insisting for that you may need to rethink using another message broker or you need to have single partition in kafka which is not a good idea. Kafka is all about parallelism by increasing partitions or increasing consumer groups.
Traditional MQ works in a way such that once a message has been processed, it gets removed from the queue. A message queue allows a bunch of subscribers to pull a message, or a batch of messages, from the end of the queue. Queues usually allow for some level of transaction when pulling a message off, to ensure that the desired action was executed, before the message gets removed, but once a message has been processed, it gets removed from the queue.
With Kafka on the other hand, you publish messages/events to topics, and they get persisted. They don’t get removed when consumers receive them. This allows you to replay messages, but more importantly, it allows a multitude of consumers to process logic based on the same messages/events.
You can still scale out to get parallel processing in the same domain, but more importantly, you can add different types of consumers that execute different logic based on the same event. In other words, with Kafka, you can adopt a reactive pub/sub architecture.
ref: https://hackernoon.com/a-super-quick-comparison-between-kafka-and-message-queues-e69742d855a8
Well, this is an old thread, but still relevant, hence decided to share my view.
I think this question is a bit confusing.
If you need strict ordering of messages, then the same strict ordering should be maintained while consuming the messages. There is absolutely no point in ordering message in queue, but not while consuming it. Kafka allows best of both worlds. It allows ordering the message within a partition right from the generation till consumption while allowing parallelism between multiple partition. Hence, if you need
Absolute ordering of all events published on a topic, use single partition. You will not have parallelism, nor do you need (again parallel and strict ordering don't go together).
Go for multiple partition and consumer, use consistent hashing to ensure all messages which need to follow relative order goes to a single partition.