Connect consumers jobs are getting deleted when restarting the cluster - apache-kafka

I am facing the below issue on changing some properties related to kafka and re-starting the cluster.
In kafka Consumer, there were 5 consumer jobs are running .
If we make some important property change , and on restarting cluster some/all the existing consumer jobs are not able to start.
Ideally all the consumer jobs should start ,
since it will take the meta-data info from the below System-topics .
config.storage.topic
offset.storage.topic
status.storage.topic

First, a bit of background. Kafka stores all of its data in topics, but those topics (or rather the partitions that make up a topic) are append-only logs that would grow forever unless something is done. To prevent this, Kafka has the ability to clean up topics in two ways: retention and compaction. Topics configured to use retention will retain data for a configurable length of time: the broker is free to remove any log messages that are older than this. Topics configured to use compaction require every message have a key, and the broker will always retain the last known message for every distinct key. Compaction is extremely handy when each message (i.e., key/value pair) represents the last known state for the key; since consumers are reading the topic to get the last known state for each key, they will eventually get to that last state a bit faster if older states are removed.
Which cleanup policy a broker will use for a topic depends on several things. Every topic created implicitly or explicitly will use retention by default, though you can change a couple of ways:
change the globally log.cleanup.policy broker setting, affecting only topics created after that point; or
specify the cleanup.policy topic-specific setting when you create or modify a topic
Now, Kafka Connect uses several internal topics to store connector configurations, offsets, and status information. These internal topics must be compacted topics so that (at least) the last configuration, offset, and status for each connector are always available. Since Kafka Connect never uses older configurations, offsets, and status, it's actually a good thing for the broker to remove them from the internal topics.
Before Kafka 0.11.0.0, the recommended process is to manually create these internal topics using the correct topic-specific settings. You could rely upon the broker to auto-create them, but that is problematic for several reasons, not the least of which is that the three internal topics should have different numbers of partitions.
If these internal topics are not compacted, the configurations, offsets, and status info will be cleaned up and removed after the retention period has elapsed. By default this retention period is 24 hours! That means that if you restart Kafka Connect more than 24 hours after deploying / updating a connector configuration, that connector's configuration may have been purged and it will appear as if the connector configuration never existed.
So, if you didn't create these internal topics correctly, simply use the topic admin tool to update the topic's settings as described in the documentation.
BTW, not properly creating these internal topics is a very common problem, so much so that Kafka Connect 0.11.0.0 will be able to automatically create these internal topics using the correct settings without relying upon broker auto-creation of topics.
In 0.11.0 you will still have to rely upon manual creation or broker auto-creation for topics that source connectors write to. This is not ideal, and so there's a proposal to change Kafka Connect to automatically create the topics for the source connectors while giving the source connectors control over the settings. Hopefully that improvement makes it into 0.11.1.0 so that Kafka Connect is even easier to use.

Related

Kafka Connect best practices for topic compaction

I am using Debezium which makes of Kafka Connect.
Kafka Connect exposes a couple of topics that need to be created:
OFFSET_STORAGE_TOPIC
This environment variable is required when running the Kafka Connect service. Set this to the name of the Kafka topic where the Kafka Connect services in the group store connector offsets. The topic should have many partitions, be highly replicated (e.g., 3x or more) and should be configured for compaction.
STATUS_STORAGE_TOPIC
This environment variable should be provided when running the Kafka Connect service. Set this to the name of the Kafka topic where the Kafka Connect services in the group store connector status. The topic can have multiple partitions, should be highly replicated (e.g., 3x or more) and should be configured for compaction.
Does anyone have any specific recommended compaction configs for these topics?
e.g.
is it enough to set just:
cleanup.policy: compact
unclean.leader.election.enable: true
or also:
min.compaction.lag.ms: 60000
segment.ms: 1800000
min.cleanable.dirty.ratio: 0.01
delete.retention.ms: 100
The defaults should be fine, and Connect will create/configure those topics on its own unless you preconfigure those topics with those settings.
These are the only cases when I can think of when to adjust the compaction settings
a connect-group lingering on the topic longer than you want it to be. For example, a source connector doesn't start immediately after a long downtime because it's processing the offsets topic
your Connect cluster doesn't accurately report its state, or the tasks do not rebalance appropriately (because the status topic is in a bad state)
The __consumer_offsets (compacted) topic is what is used for Sink connectors, and would be configured separately for all consumers, not only Connect

Which Kafka broker configuration gets precedence when there's a conflict?

In Apache Kafka, each broker has its own configuration file. Some of the config entries, such as broker ID, are evidently unique to each node.
However, others such as topic retention time or maximum message size should be global to the entire cluster.
In case two brokers have conflicting configurations, which value gets precedence? Or am I wrong to assume that some config entries should be global?
Kafka does not check that each broker has exactly the same configuration.
That said, as you've pointed, some settings could conflict and if it's the case my guess is that at best a crash or worse undefined behaviour !
There is KIP-226 in progress that addresses some of these issues but if you're to deploy many brokers it's recommended you use some automation (K8s, Mesos) to ensure configuration is consistent across all of them.

Kafka Connect: Connectors Disappear when Worker shutsdown [duplicate]

I am facing the below issue on changing some properties related to kafka and re-starting the cluster.
In kafka Consumer, there were 5 consumer jobs are running .
If we make some important property change , and on restarting cluster some/all the existing consumer jobs are not able to start.
Ideally all the consumer jobs should start ,
since it will take the meta-data info from the below System-topics .
config.storage.topic
offset.storage.topic
status.storage.topic
First, a bit of background. Kafka stores all of its data in topics, but those topics (or rather the partitions that make up a topic) are append-only logs that would grow forever unless something is done. To prevent this, Kafka has the ability to clean up topics in two ways: retention and compaction. Topics configured to use retention will retain data for a configurable length of time: the broker is free to remove any log messages that are older than this. Topics configured to use compaction require every message have a key, and the broker will always retain the last known message for every distinct key. Compaction is extremely handy when each message (i.e., key/value pair) represents the last known state for the key; since consumers are reading the topic to get the last known state for each key, they will eventually get to that last state a bit faster if older states are removed.
Which cleanup policy a broker will use for a topic depends on several things. Every topic created implicitly or explicitly will use retention by default, though you can change a couple of ways:
change the globally log.cleanup.policy broker setting, affecting only topics created after that point; or
specify the cleanup.policy topic-specific setting when you create or modify a topic
Now, Kafka Connect uses several internal topics to store connector configurations, offsets, and status information. These internal topics must be compacted topics so that (at least) the last configuration, offset, and status for each connector are always available. Since Kafka Connect never uses older configurations, offsets, and status, it's actually a good thing for the broker to remove them from the internal topics.
Before Kafka 0.11.0.0, the recommended process is to manually create these internal topics using the correct topic-specific settings. You could rely upon the broker to auto-create them, but that is problematic for several reasons, not the least of which is that the three internal topics should have different numbers of partitions.
If these internal topics are not compacted, the configurations, offsets, and status info will be cleaned up and removed after the retention period has elapsed. By default this retention period is 24 hours! That means that if you restart Kafka Connect more than 24 hours after deploying / updating a connector configuration, that connector's configuration may have been purged and it will appear as if the connector configuration never existed.
So, if you didn't create these internal topics correctly, simply use the topic admin tool to update the topic's settings as described in the documentation.
BTW, not properly creating these internal topics is a very common problem, so much so that Kafka Connect 0.11.0.0 will be able to automatically create these internal topics using the correct settings without relying upon broker auto-creation of topics.
In 0.11.0 you will still have to rely upon manual creation or broker auto-creation for topics that source connectors write to. This is not ideal, and so there's a proposal to change Kafka Connect to automatically create the topics for the source connectors while giving the source connectors control over the settings. Hopefully that improvement makes it into 0.11.1.0 so that Kafka Connect is even easier to use.

Kafka 0.10.0.1 partition reassignment after broker failure

I'm testing kafka's partition reassignment as a precursor to launching a production system. I have several topics with 9 partitions each and a replication factor of 3. I've killed one of the brokers to simulate a failure condition and verified that some topics became under replicated (verification done via a fork of yahoo's kafka manager modified to allow adding a version 0.10.0.1 cluster).
I then started a new broker with a different id. I would now like to distribute partitions to this new broker. I attempted to use kafka manager's reassign partitions functionality however that did not work (possibly due to an improperly modified fork).
I saw that kafka comes with a bin/kafka-reassign-partitions.sh script but the docs say that I have to manually write out the partition reassignments for each topic in json format. Is there a way to handle this without manually deciding on which brokers partitions must go?
Hmm what a coincidence that I was doing exactly the same thing today. I don't have an answer you're probably going to like but I achieved what I wanted in the end.
Ultimately, what I did was executed the kafka-reassign-partitions command with what the same tool proposed for a reassignment. But whatever it generated I just replaced the new broker id with the old failed broker id. For some reason the generated json moved everything around.
This will fail (or rather never complete) because the old broker has passed on. I then had to delete the reassignment operation in zookeeper (znode: admin/reassign_partitions or something).
Then I restarted kafka on the new broker and it magically picked up as leader of the partition that was looking for a new replacement leader.
I'll let you know if everything is still working tomorrow and if I still have a job ;-)

In Storm, how to migrate offsets to store in Kafka?

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.