We are streaming messages to a Kafka topic at a rate of a few hundred per second. Each message has a timestamp and a payload. Ultimately, we would like aggregate one hour worth of data - based on the timestamp of the message - into parquet files and upload them to a cheap remote storage (object-store).
A naive approach would be to have the consumer simply read the messages from the topic and do the aggregation/roll-up in memory, and once there is one hour worth of data, generate and upload the parquet file.
However, in case the consumer crashes or needs to be restarted, we would lose all data since the beginning of the current hour - if we use enable.auto.commit=true or enable.auto.commit=false and manually commit after a batch of messages.
A simple solution for the Consumer could be to keep reading until one hour worth of data is in memory, do the parquet file generation (and upload it), and only then call commitAsync() or commitSync() (using enable.auto.commit=false and use an external store to keep track of the offsets).
But this would lead to millions of messages not being committed for at least one hour. I am wondering if Kafka does even allow to "delay" the commit of messages for so many messages / so long time (I seem to remember to have read about this somewhere but for the life of me I cannot find it again).
Actual questions:
a) is there a limit to the number of messages (or duration) not being committed before Kafka possibly considers the Consumer to be broken or stops giving additional messages to the consumer? this seems counter-intuitive though, since what would be the purpose of enable.auto.commit=false and managing the offsets in the Consumer (with e.g. the help of an external database).
b) in terms of robustness/redundancy and scalability, it would be great to have more than one Consumer in the consumer group; if I understand correctly, it is never possible to have more than one Consumer per partition. If we then run more than one Consumer and configure multiple partitions per topic we cannot do this kind of aggregation/roll-up, since now messages will be distributed across Consumers. The only way to work-around this issue would be to have an additional (external) temporary storage for all those messages belonging to such one-hour group, correct?
You can configure Kafka Streams with a TimestampExtractor to aggregate data into different types of time-windows
into parquet files and upload them to a cheap remote storage (object-store).
Kafka Connect S3 sink, or Pinterest Secor tool, already do this
The use case is to flush records from partitions which did not receive new data in kafka streams as we are using suppress which requires stream time.
So We have a Window Store with tumbling window of 1 minute with reduce operation attached with suppress. The design of suppress is dependent on stream time. So if any partition does not receive new consumer record then suppress will not move ahead for the pending consumer record in that partition.
It is worth noting that retention period of state store is set to 65 seconds.
So, to do a explicit flush from window state store decided to go with tranform api and used in the DSL topology.
In transform node we are using context.schedule to schedule punctuator to get access to state store and run a windowed query i.e. fetchall(startTimeInstant,endTimeInstant) to get old keys which are still not flushed out.
It is worth noting from the documentation that retention period is the minimum amount of time the data will stay in window store. Only if all the records in the window are old enough then only it is flushed.
Now the idea is the successful records should not be there in state store when we run fetchall (as starttime is (utc-3minutes). But till 6 minutes old data which was flushed out is still there in the window store.
The PROBLEM here is i do not want to see old records in window store as then payload has to be seen/parsed to make a choice whether to flush the data or not which is performance intensive.
i also checked the changelog store topic compact/delete policy. It also has 65 seconds.
I know classic approach is to send keep alive packet on all the partitions of the input topic but that is not feasible in our case as input topic is used by multiple clients. They all will have to change.
We have a Kafka producer that produces keyed messages in a very high frequency to topics whose retention time = 10 hours. These messages are real-time updates and the used key is the ID of the element whose value has changed. So the topic is acting as a changelog and will have many duplicate keys.
Now, what we're trying to achieve is that when a Kafka consumer launches, regardless of the last known state (new consumer, crashed, restart, etc..), it will somehow construct a table with the latest values of all the keys in a topic, and then keeps listening for new updates as normal, keeping the minimum load on Kafka server and letting the consumer do most of the job. We tried many ways and none of them seems the best.
What we tried:
1 changelog topic + 1 compact topic:
The producer sends the same message to both topics wrapped in a transaction to assure successful send.
Consumer launches and requests the latest offset of the changelog topic.
Consumes the compacted topic from beginning to construct the table.
Continues consuming the changelog since the requested offset.
Cons:
Having duplicates in compacted topic is a very high possibility even with setting the log compaction frequency the highest possible.
x2 number of topics on Kakfa server.
KSQL:
With KSQL we either have to rewrite a KTable as a topic so that consumer can see it (Extra topics), or we will need consumers to execute KSQL SELECT using to KSQL Rest Server and query the table (Not as fast and performant as Kafka APIs).
Kafka Consumer API:
Consumer starts and consumes the topic from beginning. This worked perfectly, but the consumer has to consume the 10 hours change log to construct the last values table.
Kafka Streams:
By using KTables as following:
KTable<Integer, MarketData> tableFromTopic = streamsBuilder.table("topic_name", Consumed.with(Serdes.Integer(), customSerde));
KTable<Integer, MarketData> filteredTable = tableFromTopic.filter((key, value) -> keys.contains(value.getRiskFactorId()));
Kafka Streams will create 1 topic on Kafka server per KTable (named {consumer_app_id}-{topic_name}-STATE-STORE-0000000000-changelog), which will result in a huge number of topics since we a big number of consumers.
From what we have tried, it looks like we need to either increase the server load, or the consumer launch time. Isn't there a "perfect" way to achieve what we're trying to do?
Thanks in advance.
By using KTables, Kafka Streams will create 1 topic on Kafka server per KTable, which will result in a huge number of topics since we a big number of consumers.
If you are just reading an existing topic into a KTable (via StreamsBuilder#table()), then no extra topics are being created by Kafka Streams. Same for KSQL.
It would help if you could clarify what exactly you want to do with the KTable(s). Apparently you are doing something that does result in additional topics being created?
1 changelog topic + 1 compact topic:
Why were you thinking about having two separate topics? Normally, changelog topics should always be compacted. And given your use case description, I don't see a reason why it should not be:
Now, what we're trying to achieve is that when a Kafka consumer launches, regardless of the last known state (new consumer, crashed, restart, etc..), it will somehow construct a table with the latest values of all the keys in a topic, and then keeps listening for new updates as normal [...]
Hence compaction would be very useful for your use case. It would also prevent this problem you described:
Consumer starts and consumes the topic from beginning. This worked perfectly, but the consumer has to consume the 10 hours change log to construct the last values table.
Note that, to reconstruct the latest table values, all three of Kafka Streams, KSQL, and the Kafka Consumer must read the table's underlying topic completely (from beginning to end). If that topic is NOT compacted, this might indeed take a long time depending on the data volume, topic retention settings, etc.
From what we have tried, it looks like we need to either increase the server load, or the consumer launch time. Isn't there a "perfect" way to achieve what we're trying to do?
Without knowing more about your use case, particularly what you want to do with the KTable(s) once they are populated, my answer would be:
Make sure the "changelog topic" is also compacted.
Try KSQL first. If this doesn't satisfy your needs, try Kafka Streams. If this doesn't satisfy your needs, try the Kafka Consumer.
For example, I wouldn't use the Kafka Consumer if it is supposed to do any stateful processing with the "table" data, because the Kafka Consumer lacks built-in functionality for fault-tolerant stateful processing.
Consumer starts and consumes the topic from beginning. This worked
perfectly, but the consumer has to consume the 10 hours change log to
construct the last values table.
During the first time your application starts up, what you said is correct.
To avoid this during every restart, store the key-value data in a file.
For example, you might want to use a persistent map (like MapDB).
Since you give the consumer group.id and you commit the offset either periodically or after each record is stored in the map, the next time your application restarts it will read it from the last comitted offset for that group.id.
So the problem of taking a lot of time occurs only initially (during first time). So long as you have the file, you don't need to consume from beginning.
In case, if the file is not there or is deleted, just seekToBeginning in the KafkaConsumer and build it again.
Somewhere, you need to store this key-values for retrieval and why cannot it be a persistent store?
In case if you want to use Kafka streams for whatever reason, then an alternative (not as simple as the above) is to use a persistent backed store.
For example, a persistent global store.
streamsBuilder.addGlobalStore(Stores.keyValueStoreBuilder(Stores.persistentKeyValueStore(topic), keySerde, valueSerde), topic, Consumed.with(keySerde, valueSerde), this::updateValue);
P.S: There will be a file called .checkpoint in the directory which stores the offsets. In case if the topic is deleted in the middle you get OffsetOutOfRangeException. You may want to avoid this, perhaps by using UncaughtExceptionHandler
Refer to https://stackoverflow.com/a/57301986/2534090 for more.
Finally,
It is better to use Consumer with persistent file rather than Streams for this, because of simplicity it offers.
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.
I have an events topic with full retention, so I can reprocess at any point. I am using KafkaStreams to process this data (includes sessioning). There are many output topics that are sent to a database.
I have a TimestampExtractor which sets the timestamp of the Kafka record to that of the original event, so as to perform windowing over the data among other things.
However, in the output topics of the processing, I have set up weeks-long retention policies (so they are deleted after they are consumed).
If I reprocess this data from the original topic, the timestamps generated in the output topics may be older than the threshold of the retention policy - so they may be marked for deletion.
Since when they are published they are eligible for retention, how could I prevent them from deletion? How to separate different timestamps for data retention from data processing? Is it almost-mandatory to use "wall clock time" timestamps on output topics subject to retention?
The "right" solution would be to set higher retention time for the output topics. If your downstream applications consume this data, you might want to use "purge data" request (https://cwiki.apache.org/confluence/display/KAFKA/KIP-107%3A+Add+deleteRecordsBefore%28%29+API+in+AdminClient) to delete old data manually.
As an alternative, you could manipulate the timestamps for the output records only. You will need to upgrade to Kafka 2.0 (will be released soon): https://cwiki.apache.org/confluence/display/KAFKA/KIP-251%3A+Allow+timestamp+manipulation+in+Processor+API