In event-driven design we strive to find out events that we interested of. Using Kafka we can easily subscribe (a new group.id) to a topic and start consuming events. If retention policy is default one we could consume also one week old messages if specify auto.offset.reset=earliest. Right? But what if we want to start from the very beginning? I guess that KTable should be used but I'm not sure what will happened when a new client has subscribed to a stateful stream. Could you tell me is it true that the new subscriber will receive all aggregated messages?
You can't consume data that has been deleted.
That's why KTables are built on top of compacted topics, which will store the latest keys for each record, and have infinite retention.
If you want to read the "current state" of the table, to get all aggregated messages, then you can use Interactive Queries.
not sure what will happened when a new client has subscribed to a stateful stream
It needs to read the entire compacted topic, starting from the beginning (earliest available offset, not necessarily the first ever produced message) since it cannot easily find where in the topic that each unique key may start.
Related
Reading about log compaction on a topic, I was wondering if there is any way for a consumer to get hold of any of the positions/offsets of the following?
end of the head
start of the tail
compaction cleaner point
Basically the point at which the compacted and non-compacted parts of the log meet?
I've read that there is a cleaner-offset-checkpoint file that sits on the broker at /var/lib/kafka/data/cleaner-offset-checkpoint but is the info in this file available to a consumer?
My use case is a consumer that will consume compacted keys one way and non-compacted keys another way.
thanks for any advice.
UPDATE:
thinking for example of a topic holding various customer events like here https://www.confluent.io/blog/put-several-event-types-kafka-topic/; new customer, customer updates name, customer updates address, etc. Log compaction, I believe, will leave one event per customer in the tail but still many events per customer in the head (assuming compaction is slower than message production..?) A new consumer of this topic would have to treat all compacted messages as CREATES, but then also treat non-compacted message as their more fine grained event? In any case I was wondering if a consumer could tell how far along a topic compaction has got, at any given time?
It's not possible, with the consumer api, no.
If you want to check that checkpoint file on disk, you could use Jssh, for example, to access a broker, and read the file. If it has offset data, you could then use seek methods, but keep in mind that the Log Cleaner thread may be actively running when you seek to or consume that data
A new consumer of this topic would have to treat all compacted messages as CREATES, but then also treat non-compacted message as their more fine grained event?
I don't think this is a valid use case. For a stream of customer updates, you'd just update a new customer model in a table via a streaming reduce function. If any consumer restarts, it'll have to always read from the beginning of the topic to rebuild its local state then continue reading any updates to those stored values, so doesn't make sense to skip past them all, or have two separate consumers
I also don't necessarily think you need different models. Some UUID would be unique, and every event can contain the full model of a "customer". Most fields can remain optional/nullable until they are provided with a new message with all those fields set (or not), and this defines a batch update since you can set/update/remove multiple attributes at once. If you need more granularity, that's also possible to define at the producer level by storing and looping over your attributes and producing individual "customer" objects with each new attribute
In a Kafka Streams app, an instance only gets messages of an input topic for the partitions that have been assigned to that instance. And as the group.id, which is based on the (for all instances identical) application.id, that means that every instance sees only parts of a topic.
This all makes perfect sense of course, and we make use of that with the high-throughput data topic, but we would also like to control the streams application by adding topic-wide "control messages" to the input topic. But as all instances need to get those messages, we would either have to send
one control message per partition (making it necessary for the sender to know about the partitioning scheme, something we would like to avoid)
one control message per key (so every active partition would be getting at least one control message)
Because this is cumbersome for the sender, we are thinking about creating a new topic for control messages that the streams application consumes, in addition to the data topic. But how can we make it so that every partition receives all messages from the control message topic?
According to https://stackoverflow.com/a/55236780/709537, the group id cannot be set for Kafka Streams.
One way to do this would be to create and use a KafkaConsumer in addition to using Kafka Streams, which would allow us to set the group id as we like. However this sounds complex and dirty enough to wonder if there isn't a more straightforward way that we are missing.
Any ideas?
You can use a global store which sources data from all the partitions.
From the documentation,
Adds a global StateStore to the topology. The StateStore sources its
data from all partitions of the provided input topic. There will be
exactly one instance of this StateStore per Kafka Streams instance.
The syntax is as follows:
public StreamsBuilder addGlobalStore(StoreBuilder storeBuilder,
String topic,
Consumed consumed,
ProcessorSupplier stateUpdateSupplier)
The last argument is the ProcessorSupplier which has a get() that returns a Processor that will be executed for every new message. The Processor contains the process() method that will be executed every time there is a new message to the topic.
The global store is per stream instance, so you get all the topic data in every stream instance.
In the process(K key, V value), you can write your processing logic.
A global store can be in-memory or persistent and can be backed by a changelog topic, so that even if the streams instance local data (state) is deleted, the store can be built using the changelog topic.
I am trying to, better, understand what happens in the level of resources when you create a KStream and a KTable. Below, I wil mention some conclusions that I have come to, as I understand them (feel free to correct me).
Firstly, every topic has a number of partitions and all the messages in those partitions are stored in the hard disk(s) in continuous order.
A KStream does not need to store the messages, that are read from a topic, again to another location, because the offset is sufficient to retrieve those messages from the topic which is connected to.
(Is this correct? )
The question regards the KTable. As I have understand, a KTable, in contrast with a KStream, updates every message with the with the same key. In order to do that, you have to either store externally the messages that arrive from the topic to a static table, or read all the message queue, each time a new message arrives. The later does not seem very efficient regarding time performance. Is the first approach I presented correct?
read all the message queue, each time a new message arrives.
All messages are only read at the fresh start of the application. Once the app reads up to the latest offset, it's just updating the table like any other consumer
How disk usage is determined ultimately depends on the state store you've configured for the application, along with its own settings. For example, in-memory vs rocksdb vs an external state store interface that you've written on your own
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.
Should I use the Kafka Consumer API or the Kafka Streams API for this use case? I have a topic with a number of consumer groups consuming off it. This topic contains one type of event which is a JSON message with a type field buried internally. Some messages will be consumed by some consumer groups and not by others, one consumer group will probably not be consuming many messages at all.
My question is:
Should I use the consumer API, then on each event read the type field and drop or process the event based on the type field.
OR, should I filter using the Streams API, filter method and predicate?
After I consume an event, the plan is to process that event (DB delete, update, or other depending on the service) then if there is a failure I will produce to a separate queue which I will re-process later.
Thanks you.
This seems more a matter of opinion. I personally would go with Streams/KSQL, likely smaller code that you would have to maintain. You can have another intermediary topic that contains the cleaned up data that you can then attach a Connect sink, other consumers, or other Stream and KSQL processes. Using streams you can scale a single application on different machines, you can store state, have standby replicas and more, which would be a PITA to do it all yourself.