For some data processing , we need to reprocess all the messages between 2 timestamps
say between 1st Jan to 15th Jan.
to control upper bound we are planning to create a new topic that will have these messages so that once this task is complete , we can delete the topic too.
The new topic will have data from a particular offsets of source topic
partition 1 - from offset 100
partition 2 - from offset 2400...
and so on
What is the most suitable solution for this ? approx 10lacs messages fall in this.
Create a consumer from the source topic.
Call .assign for the partitions you want to copy
Call .seek for each starting offset of those partitions. You can use offsetsForTimes method to get them for a specific timestamp; then you can pass those on to the seek method.
Create a Producer
Start a poll loop (one thread per partition, ideally,each thread with the reference of the created producer).
As polling, check the timestamp of the record
If record timestamp exceeds the date you're reading to, stop the poll loop / thread
Else, send that data via the producer to your output topic
Related
Let's say, this is a one-partition topic, and while consuming the message I want to read it in a sequence based on one of the attributes (Let's assume attr1) in the message.
Message 1 ('attr1'="01") was posted to that partition at 9:50 pm.
Message 2 ('attr1'="03") was posted to that partition at 9:55 pm.
Message 3 ('attr1'="02") was posted to that partition at 10:55 pm.
I want to consume it in the sequence based on the attr1 value, so Message1, Message3, and Message2 should be my consuming order.
No, that is not possible.
A fundamental thing to remember about Kafka is Offset. When you write a message to a partition - its always gets incremental offset.
In your example, if
message 1 gets offset 1
message 2 will get offset 2
message 3 will get offset 3
On the consumer side as well, message will always be read in sequence of increasing offsets. You can specify your consumer to start reading from a particular offset, but once it starts reading the message, the consumer will always get message in the sequence of increasing offset.
You can use alternative tools such as ksqlDB or Kafka Streams to first read the entire topic, then sort based on custom attributes, or use Punctuator class to delay processing based on time windows.
Otherwise, Kafka Connect can dump to a database, where you can query/sort based on columns/fields/attributes, etc.
I am working on a messaging system where messages are generated by user which have a time parameter.
I have a consumer which runs a job every hour and looks for messages that have time with current time and sends these messages.
I want to parition the topic "message" based on this time/timestamp in groups of one hour per parition, so that my consumer will only process one parition every hour instead of going through all messages every hour.
as of now i have a producer that will produce message in key value pair where key is the time with hour rounded off.
I have two questions :-
I can create 2-3 partitions by specifying in kafka settings but how can I have multiple patitions for every hour slot?
Should I rather create new topic for every hour and ask the consumer to only listen to the topic of the current hour?
example I am creating a topic named say "2020-7-23-10" which will contain all messages that need to be delivered between 10-11AM 23 July 2020. so I can just subscribe to this topic and process them.
or I can create a topic named "messages" and partition it based on the time and force my consumer to only process a particular patition.
I new to Kafka and i have a configuration where i have a source Kafka topic which has messages with a default retention for 7 days. I have 3 brokers with 1 partition and 1 replication.
When i try to consume messages from source Kafka topic and to my target Kafka topic i was able to consume messages in the same order. Now my question is if i am trying to reprocess all the messages from my source Kafka and consume in ,y Target Kafka i see that my Target Kafka is not consuming any messages. I know that duplication should be avoided but lets say i have a scenario where i have 100 messages in my source Kafka and i am expecting 200 messages in my target Kafka after running it twice. But i am just getting 100 messages in my first run and my second run returns nothing.
Can some one please explain why this is happening and what is the functionality behind it ?
Kafka consumer reads data from a partition of a topic. One consumer can read from one partition at one time only.
Once a message has been read by the consumer, it can't be re-read again. Let me first explain the current offset. When we call a poll method, Kafka sends some messages to us. Let us assume we have 100 records in the partition. The initial position of the current offset is 0. We made our first call and received 100 messages. Now Kafka will move the current offset to 100.
The current offset is a pointer to the last record that Kafka has already sent to a consumer in the most recent poll and that has been committed. So, the consumer doesn't get the same record twice because of the current offset. Please go through the following diagram and URL for complete understanding.
https://www.learningjournal.guru/courses/kafka/kafka-foundation-training/offset-management/
I want to read all the messages starting from a specific time in kafka.
Say I want to read all messages between 0600 to 0800
Request messages between two timestamps from Kafka
suggests the solution as the usage of offsetsForTimes.
Problem with that solution is :
If say my consumer is switched on everyday at 1300. The consumer would not have read any messages that day, which effectively means no offset was committed at/after 0600, which means offsetsForTimes(< partitionname > , <0600 for that day in millis>) will return null.
Is there any way I can read a message which was published to kafka queue at a certain time, irrespective of offsets?
offsetsForTimes() returns offsets of messages that were produced for the requested time. It works regardless if offsets were committed or not because the offsets are directly fetched from the partition logs.
So yes you should be using this method to find the first offset produced after 0600, seek to that position and consume messages until you reach 0800.
I would like to consume events from a kafka topic after the time they arrive. The time on which I want the event to be consumed is in the payload of the message. Is it possible to achieve something like that in Kafka? What are the drawbacks of it?
Practical example: a message M is produced at 12:10, arrives to my kafka topic at 12:11 and I want the consumer to poll it at 12:41 (30 minutes after arrival)
Kafka has a default retention period of all topic for 7 days. You can therefore consume up to a week's data at any moment, the drawback being network saturation if you are constantly doing this.
If you want to consume data that is not at the latest offset, then for any new consumer group, you would set auto.offset.reset=earliest. Otherwise for existing groups, you would need to use kafka-consumer-groups --reset command in order to re-consume an already consumed record.
Sometimes you may want to start from beginning of a topic, for example, if you have a compacted topic, in order to rebuild the "deltas" of the data within a topic - lookup the "Stream / Table Duality"
The time on which I want the event to be consumed is in the payload of the message
Since KIP-32 every message has a timestamp outside the payload, by the way
I want the consumer to poll it ... (30 minutes after arrival)
Sure, you can start a consumer whenever, as long as the data is within the retention window, you will get that event.
There isn't a way to finely control when that happens that other than acually making your consumer at that time, for example 30 minutes later. You could play with max.poll.records and max.poll.interval.ms, but I find anything larger than a few seconds really isn't a use-case for Kafka.
For example, you could rather have a TimerTask around a consumer thread, or Spark or MapReduce scheduled with an Oozie/Airflow task that reads a max amount of records.