I understand that the offset is used to determine which messages a consumer wants. But is the offset a hash? Is it a timestamp? Is it simply an integer, where 3 could mean the last 3 messages?
An offset is "a sequential id number [..] that uniquely identifies each record within the partition" (source: Kafka documentation).
It starts at 0, which is the first record ever published in a given partition. It increases monotonically with each record added to the partition.
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I had a few questions from Kafka. Please help me in understanding the problem.
As per official documentation, each partition will have one unique sequential id which called offset.
How does the offset numbers will be generated i.e based on the message arrival into a partition or offset numbers will be generated whenever the partitions are created?
do the same offset ID/number generates/exists in another partition because each partition is independent each other?
If the same offset can be possible in another partition then, How consumer uniquely identifies the message across multiple partitions?
How does consumer know the particular offset belongs to a particular partition? Please let me understand in both situations like a message with key & without a key?
Each partition maintains the messages it has received in a sequential order where they are identified by an offset. This offset is a sequential number and it automatically generated and assigned to messages.
Yes this is correct. Message ordering is guaranteed only on the partition level. This means that if you have a topic with multiple partitions, messages on different partitions might have the same offset. Therefore, an offset has a true meaning only within a single partition (as you can also see in the picture below, which is taken from Kafka Docs).
3/4. The consumers are subscribed to topics, but behind the scenes they are subscribed to particular partitions (well, if you have a single consumer in the consumer group it will subscribe to all of the partitions). Therefore, when the consumer reads messages from a particular partition, it can uniquely identify messages using their unique offsets which are maintained throughout the partition. As I already mentioned, the message order is guaranteed only within a single partition.
Note that messages without key, will be evenly distributed across the partitions of the topic, in a round-robin fashion. On the other hand, messages with the same key will be stored in the same partition and hence, you can use the key to store and order messages having the same key. For example, if you need to process users and you'd like order guarantee for each distinct user, you can use userID as a key, so that all the events of that user are stored in the same partition. Later on, you will be able to consume these user-specific messages, in the order they were originally received.
I have a topic with 10 partitions, 1 consumer group with 4 consumers and worker size is 3.
I could see there is an uneven distribution of messages in the partitions, One partition is having so much data and another one is free.
How can I make my producer to evenly distribute the load into all the partitions, so that all partitions are being utilized properly?
According to the JavaDoc comment in the DefaultPartitioner class itself, the default partitioning strategy is:
If a partition is specified in the record, use it.
If no partition is specified but a key is present choose a partition based on a hash of the key.
If no partition or key is present choose a partition in a round-robin fashion.
https://github.com/apache/kafka/blob/trunk/clients/src/main/java/org/apache/kafka/clients/producer/internals/DefaultPartitioner.java
So here are two possible reasons that may be causing the uneven distribution, depending on whether you are specifying a key while producing the message or not:
If you are specifying a key and you are getting an uneven distribution using the DefaultPartitioner, the most apparent explanation would be that you are specifying the same key multiple times.
If you are not specifying a key and using the DefaultPartitioner, a non-obvious behavior could be happening. According to the above you would expect round-robin distribution of messages, but this is not necessarily the case. An optimization introduced in 0.8.0 could be causing the same partition to be used. Check this link for a more detailed explanation: https://cwiki.apache.org/confluence/display/KAFKA/FAQ#FAQ-Whyisdatanotevenlydistributedamongpartitionswhenapartitioningkeyisnotspecified? .
Instead of going for the default partitioner class you can assign the producer with a partition number so that message directly goes to the specified partition,
ProducerRecord<String, String> record = new ProducerRecord<String, String>(topicName, partitionNumber,key, value);
Seems like your problem is uneven consumption of messages rather than uneven producing of messages to Kafka topic. In other words, your amount of reading threads doesn't match amount of partitions you have (they do not need to match 1:1 though, only be the same amout of partitions to read from per each consumer thread).
See short explanation for more details.
You can make use of the key parameter of the producer record. Here is a thing that for a specific key the data goes in to the same partition always now, I don’t know the structure of your producer record but as you said you have 10 partition then you can use simply n%10 as your producer record key.
Where n is 0 to 9 now your for record 0 key will be 0 and then kafka will generate a hash key and put it in some partition say partition 0, and for record 1 it will be one and then it will go into the 1st partition and so on.
This way you will able to apply round robin on your producer record your key will be independent from the fields in your record so you can have a variable n and key as n%10.
Or you can specify the partition in your producer record. So either you use the key or the partition field of the producer record.
If you have defined partitioner from record let's say in Kafka key is string and value is student Pojo.
In student Pojo let's say based on student country field, I want to go in a specific partition. Imagine that there is 10 partitions in a topic and for example, in value, "India" is a country and based on "India" we got partition number 5.
Whenever country is "India", Kafka will allocate the 5 number partition and that record goes to the partition number 5 always (if the partition has not changed).
Let's say that in your pipeline there are lots of records which are coming and have a country "India", all those records will go to partition number 5, and you will see uneven distribution in Kafka partition.
In my case, I used the default partitioner but still had much much more records in one partition than in others. The problem was I unexpectedly had many records with the same key. Check your keys!
As I was unable to resolve this with Faust, the approach I am using is to implement the 'round-robin' distribution myself.
I iterate over my records to produce and do for example:
for index, message in enumerate(messages):
topic.send(message, partition=index % num_partitions)
I.e. bound my index to within the range of partitions I have.
There could still be unevenness - consider you repeatedly run this but your number of records is less than your num_partitions - then your first partitions will keep getting the major share of messages. You can avoid this issue by adding a random offset:
import random
initial_partition = random.randrange(0, num_partitions)
for index, message in enumerate(messages):
topic.send(message, partition=(initial_partition + index) % num_partitions)
I came across the below kafka official statement
For each topic, the Kafka cluster maintains a partitioned log
Each partition is an ordered, immutable sequence of records that is continually appended to—a structured commit log. The records in the partitions are each assigned a sequential id number called the offset that uniquely identifies each record within the partition.
So, Lets say we have a kafka topic called "emprecords" and just assume that it has only one partition for now and in that partition let's say we have 10 offset starting from 0 to 9
My question is
Does each offset has got the ability to store only one record?
Or
Does each offset has got the ability to store more than one records?
For each partition, each offset can only be assigned to one record.
Is kafka offeset value unique per partition or per topic (considering same group id)?
It is unique per partition. start from zero and long data type.
It is a signed long, unique per partition and is incremented for every messages added to the partition log.
Kafka Topic Partition offset position always start from 0 or random value and How to ensure the consumer record is the first record in the partition ? Is there any way to find out ? If any please let me know. Thanks.
Yes and no.
When you start a new topic, the offset start at zero. Depending on the Kafka version you are using, the offsets are
logical – and incremented message by message (since 0.8.0: https://issues.apache.org/jira/browse/KAFKA-506) – or
physical – ie, the offset is increased by the number of bytes for each message.
Furthermore, old log entries are cleared by configurable conditions:
retention time: eg, keep message of the last week only
retention size: eg, use at max 10GB of storage; delete old messages that cannot be stored any more
log-compaction (since 0.8.1): you only preserve the latest value for each key (see https://cwiki.apache.org/confluence/display/KAFKA/Log+Compaction)
Thus, the first offset might not be zero if old messages got deleted. Furthermore, if you turn on log-compaction, some offsets might be missing.
In any case, you can always seek to any offset safely, as Kafka can figure out if the offset is valid or not. For an invalid offset, is automatically advances to the next valid offset. Thus, if you seek to offset zero, you will always get the oldest message that is stored.
Yes, Kafka offset starts from 0 and ends with byte length of the complete record and then next record picks the offset from there onward.
As Kafka is distributed so we can not assure that Consumer will get the data in ordered way.