I'm a new user of Apache Kafka and I'm still getting to know the internals.
In my use case, I need to increase the number of partitions of a topic dynamically from the Kafka Producer client.
I found other similar questions regarding increasing the partition size, but they utilize the zookeeper configuration. But my kafkaProducer has only the Kafka broker config, but not the zookeeper config.
Is there any way I can increase the number of partitions of a topic from the Producer side? I'm running Kafka version 0.10.0.0.
As of Kafka 0.10.0.1 (latest release): As Manav said it is not possible to increase the number of partitions from the Producer client.
Looking ahead (next releases): In an upcoming version of Kafka, clients will be able to perform some topic management actions, as outlined in KIP-4. A lot of the KIP-4 functionality is already completed and available in Kafka's trunk; the code in trunk as of today allows client to create and to delete topics. But unfortunately, for your use case, increasing the number of partitions is still not possible yet -- this is in scope for KIP-4 (see Alter Topics Request) but is not completed yet.
TL;DR: The next versions of Kafka will allow you to increase the number of partitions of a Kafka topic, but this functionality is not yet available.
It is not possible to increase the number of partitions from the Producer client.
Any specific use case use why you cannot use the broker to achieve this ?
But my kafkaProducer has only the Kafka broker config, but not the
zookeeper config.
I don't think any client will let you change the broker config. You can only access (read) the server side config at max.
Your producer can provide different keys for ProducerRecord's. The broker would place them in different partitions. For example, if you need two partitions, use keys "abc" and "xyz".
This can be done in version 0.9 as well.
Related
I run a system comprising an InfluxDB, a Kafka Broker and data sources (sensors) producing time series data. The purpose of the broker is to protect the database from inbound event overload and as a format-agnostic platform for ingesting data. The data is transferred from Kafka to InfluxDB via Apache Camel routes.
I would like to use Kafka a intermediate message buffer in case a Camel route crashes or becomes unavailable - which is the most often error in the system. Up to now, I didn’t achieve to configure Kafka in a manner that inbound messages remain available for later consumption.
How do I configure it properly?
The messages will retain in Kafka topics based on its retention policies (you can choose between time or byte size limits) as described in the Topic Configurations. With
cleanup.policy=delete
Retention.ms=-1
the messages will in a Kafka topic will never be deleted.
Then your camel consumer will be able to re-read all messages (offsets) if you select a new consumer group or reset the offsets of the existing consumer group. Otherwise, your camel consumer might auto commit the messages (check corresponding consumer configuration) and it will not be possible to re-read offsets again for the same consumer group.
To limit the consumption rate of the camel consumer you may adjust configurations like maxPollRecords or fetchMaxBytes which are described in the docs.
I am using Apache Nifi version 1.10.0. I have put some data into Kafka from Nifi using the PublishKafka_2_0 processor. I have three Kafka brokers running along side with Kafka. I am getting the data from Nifi but the topic that is created in Nifi have a replication-factor of 1 and partitions of 1.
How can I change the default value of replication-factor and partitions when creating a new topic in PublishKafka? In other words, I want the processor to create new topics with partitions=3 and replication-factors=3 instead of 1.
I understand that this can be changed after the topic is created but I would like it to be done dynamically at creation.
If I understand your setup correctly, you are relying on the client side for topic creation, i.e. topics are created when NiFi attempts to produce/consume/fetch metadata for a non-existent topic. In this scenario, Kafka will use num.partitions and default.replication.factor settings for a new topic that are defined in broker config. (Kafka defaults to 1 for both.) Currently, updating these values in server.properties is the only way to control auto-created topics' configuration.
KIP-487 is being worked on to allow producers to control topic creation (as opposed to being server-side, one-for-all verdict), but even in that implementation there is no plan for a client to control number of partitions or replication factor.
I've been having all sorts of instabilities related to Kafka and offsets. Things like workers crashing on startup with exceptions related to invalidate offsets, and other things I don't understand.
I read that it is recommended to migrate offsets to be stored in Kafka instead of Zookeeper. I found the below in the Kafka documentation:
Migrating offsets from ZooKeeper to Kafka Kafka consumers in
earlier releases store their offsets by default in ZooKeeper. It is
possible to migrate these consumers to commit offsets into Kafka by
following these steps: 1. Set offsets.storage=kafka and
dual.commit.enabled=true in your consumer config. 2. Do a rolling
bounce of your consumers and then verify that your consumers are
healthy. 3. Set dual.commit.enabled=false in your consumer config. 4. Do
a rolling bounce of your consumers and then verify that your consumers
are healthy.
A roll-back (i.e., migrating from Kafka back to ZooKeeper) can also
be performed using the above steps if you set
offsets.storage=zookeeper.
http://kafka.apache.org/documentation.html#offsetmigration
But, again, I don't understand what this is instructing me to do. I don't see anywhere in my topology config where I configure where offsets are stored. Is it buried in the cluster yaml?
Any advice on if storing offsets in Kafka, rather than Zookeeper, is a good idea? And how I can perform this change?
At the time of this writing Storm's Kafka spout (see documentation/README at https://github.com/apache/storm/tree/master/external/storm-kafka) only supports managing consumer offsets in ZooKeeper. That is, all current Storm versions (up to 0.9.x and including 0.10.0 Beta) still rely on ZooKeeper for storing such offsets. Hence you should not perform the ZK->Kafka offset migration you referenced above because Storm isn't compatible yet.
You will need to wait until the Storm project -- specifically, its Kafka spout -- supports managing consumer offsets via Kafka (instead of ZooKeeper). And yes, in general it is better to store consumer offsets in Kafka rather than ZooKeeper, but alas Storm isn't there yet.
Update November 2016:
The situation in Storm has improved in the meantime. There's now a new, second Kafka spout that is based on Kafka's new 0.10 consumer client, which stores consumer offsets in Kafka (and not in ZooKeeper): https://github.com/apache/storm/tree/master/external/storm-kafka-client.
However, at the time I am writing this, there are still several issues being reported by the users in the storm-user mailing list (such as Urgent help! kafka-spout stops fetching data after running for a while), so I'd use this new Kafka spout with care, and only after thorough testing.
I've been following the high level consumer example - but it seems these for are consuming from kafka. I want to connect to zookeeper (zookeperhost:2181) and get a list of kafka brokers associated. Is there a way to do this with HLC?
Also, is there a way to use SimpleConsumer to find a list of kafka brokers, given zk?
As you can see in the link you gave, you don't pass a broker list to the HLC, but
props.put("zookeeper.connect", a_zookeeper);
So it's already linked to zookeeper, and from there it will discover kafka brokers.
For you second question, you have the option of using ZkClient to get /brokers data in ZooKeeper, but I wouldn't do it since it depends on Kafka implementation details, which may or may not be stable.
If I have multiple brokers, which broker should my producer use? Do I need to manually switch the broker to balance the load? Also why does the consumer only need a zookeeper endpoint instead of a broker endpoint?
quick example from tutorial:
> bin/kafka-console-producer.sh --broker-list localhost:9092 --topic test
> bin/kafka-console-consumer.sh --zookeeper localhost:2181 --topic test --from-beginning
which broker should my producer use? Do I need to manually switch the broker to balance the load?
Kafka runs on cluster, meaning set of nodes, so while producing anything you need to tell him the LIST of brokers that you've configured for your application, below is a small note taken from their documentation.
“metadata.broker.list” defines where the Producer can find a one or more Brokers to determine the Leader for each topic. This does not need to be the full set of Brokers in your cluster but should include at least two in case the first Broker is not available. No need to worry about figuring out which Broker is the leader for the topic (and partition), the Producer knows how to connect to the Broker and ask for the meta data then connect to the correct Broker.
Hope this clear some of your confusion
Also why does the consumer only need a zookeeper endpoint instead of a
broker endpoint
This is not technically correct, as there are two types of APIs available, High level and Low level consumer.
The high level consumer basically takes care of most of the thing like leader detection, threading issue, etc. but does not provide much control over messages which exactly the purpose of using the other alternatives Simple or Low level consumer, in which you will see that you need to provide the brokers, partition related details.
So Consumer need zookeeper end point only when you are going with the high level API, in case of using Simple you do need to provide other information
Kafka sets a single broker as the leader for each partition of each topic. The leader is responsible for handling both reads and writes to that partition. You cannot decide to read or write from a non-Leader broker.
So, what does it mean to provide a broker or list of brokers to the kafka-console-producer ? Well, the broker or brokers you provide on the command-line are just the first contact point for your producer. If the broker you list is not the leader for the topic/partition you need, your producer will get the current leader info (called "topic metadata" in kafka-speak) and reconnect to other brokers as necessary before sending writes. In fact, if your topic has multiple partitions it may even connect to several brokers in parallel (if the partition leaders are different brokers).
Second q: why does the consumer require a zookeeper list for connections instead of a broker list? The answer to that is that kafka consumers can operate in "groups" and zookeeper is used to coordinate those groups (how groups work is a larger issue, beyond the scope of this Q). Zookeeper also stores broker lists for topics, so the consumer can pull broker lists directly from zookeeper, making an additional --broker-list a bit redundant.
Kafka Producer API does not interact directly with Zookeeper. However, the High Level Consumer API connects to Zookeeper to fetch/update the partition offset information for each consumer. So, the consumer API would fail if it cannot connect to Zookeeper.
All above answers are correct in older versions of Kafka, but things have changed with arrival of Kafka 0.9.
Now there is no longer any direct interaction with zookeeper from either the producer or consumer. Another interesting things is with 0.9, Kafka has removed the dissimilarity between High-level and Low-level APIs, since both follows a uniformed consumer API.