I have a database with time series data and this data is sent to Kafka.
Many consumers build aggregations and reporting based on this data.
My Kafka cluster stores data with TTL for 1 day.
But how I can build a new report and run a new consumer from 0th position that does not exist in Kafka but exists in source storage.
For example - some callback for the producer if I request an offset that does not exist in Kafka?
If it is not possible please advise other architectural solutions. I want to use the same codebase to aggregate this data.
For example - some callback for the producer if I request an offset
that does not exist in Kafka?
If the data does not exist in Kafka, you cannot consume it much less do any aggregation on top of it.
Moreover, there is no concept of a consumer requesting a producer. Producer sends data to Kafka broker(s) and consumers consume from those broker(s). There is no direct interaction between a producer and a consumer as such.
Since you say that the data still exists in the source DB, you can fetch your data from there and reproduce it to Kafka.
When you produce that data again, they will be new messages which will be eventually consumed by the consumers as usual.
In case you would like to differentiate between initial consumption and re-consumption, you can produce these messages to a new topic and have your consumers consume from them.
Other way is to increase your TTL (I suppose you mean retention in Kafka when you say TTL) and then you can seek back to a timestamp in the consumers using the offsetsForTimes(Map<TopicPartition,Long> timestampToSearch) and seek(TopicPartition topicPartition, long offset) methods.
Kafka version: 1.0.0
Let's say the stream application uses low level processor API which maintains the state and reads from a topic with 10 partitions. Please clarify if the internal topic is expected to be created with the same number of partitions OR is it per the broker default. If it's the later, if we need to increase the partitions of the internal topic, is there any option?
Kafka Streams will create the topic for you. And yes, it will create it with the same number of partitions as your input topic. During startup, Kafka Streams also checks if the topic has the expected number of partitions and fails if not.
The internal topic is basically a regular topic as any other and you can change the number of partitions via command line tools like for any other topic. However, this should never be required. Also note, that dropping/adding partitions, will mess up your state.
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 have kafka stream application as mentioned at How to evaluate consuming time in kafka stream application
With this application, I able to write the data in one partition of a topic. How can I write the data to multiple partitions of a topic? Please help me.
If you use Kafka Streams and write data into a topic via #to(String topicName) data will be automatically written to "all" partitions. (Ie, each message is written to a single partition but different messages can be written to different partitions.) The partition will be picked via hashing based on the message key. If the key is null a random partition is used.
If all your output data has the same key, it would all go to a single partition.
You can also customize the partitioning by using #to(StreamPartitioner, String) (old API) or #to(String, Produced)` (new API, v1.0+).
I have a server that needs to keep an in-memory cache of all users. So assuming that a list won't be big - couple hundred thousands items, I'd like to use a Kafka topic with keyed messages where key is a userId to keep the current state of that list and the admin application will send new user object to that topic when something changed. So when the server starts it simply needs to read everything from that topic from the beginning and populate it's cache.
The population phase takes about 20-30 seconds depending on a connection to Kafka so the server needs not become online until it reads everything from the topic to have an up-to-date cache (all the messages in the topic at the moment of start is considered up-to-date). But I don't see how to determine if I read everything from Kafka stream to notify other services that cache is populated and the server can start server requests. I've read about high watermark but don't see it exposed in Java consumer API.
So how to find out the latest offset of a Kafka topic to know when my reader is up-to-date?
Assuming you are using High level consumer.
High watermark is not available in High level consumer.
**As you mentioned: all the messages in the topic at the moment of start is considered up-to-date**
when your application starts, you can do the following using SimpleConsumer Api :-
Find the number of partitions in topic by issuing a TopicMetadataRequest to any broker in the kafka cluster.
Create partition to latestOffset map, where key is partition and value is latestOffset available in that partition.
Map<Integer,Integer> offsetMap = new HashMap<>()
For each partition p in Topic:
A. Find the leader of partition p
B. Send an OffsetRequest to the leader
C. Get the latestOffset from the OffsetResponse
D. Add an entry to offsetMap where key is partition p and offset is
latestOffset.
Start reading messages from kafka using High level consumer:
A. For each message you get from KafkaStream:
AA. Get the partition && offset of the message
BB. if( offsetMap.get(partition)<=offset) stop Reading from this steam
Hope this helps.