5 Things Every Apache Kafka Dev Needs To Know: A Performance and Architectural Deep Dive

Apache Kafka® is an event streaming platform used by more than 30% of the Fortune 500 today. There are numerous features of Kafka that make it the de-facto standard for an event streaming platform, and in this blog post, I explain what I think are the top five things every Kafka developer should know.

Some items in our top five are performance related, while others are about the key architectural concepts that make Kafka tick. I hope that at the end of this blog post, you’ll walk away with a deeper understanding of how Kafka works, as well as with a new trick or two up your sleeve.

Tip #1: Understand message delivery and durability guarantees

For data durability, the

KafkaProducer

acks

acks

• none:

  The producer considers the records successfully delivered once it sends the records to the broker. This is basically “fire and forget.”

• one:

  The producer waits for the lead broker to acknowledge that it has written the record to its log.

• all:

  The producer waits for an acknowledgment from the lead broker and from the following brokers that they have successfully written the record to their logs.

As you can see, there is a trade-off to make here—and that’s by design because different applications have different requirements. You can opt for higher throughput with a chance for data loss, or you may prefer a very high data durability guarantee at the expense of a lower throughput.

Now let’s take a second to talk a little bit about the

acks=all

acks

But there’s some subtlety to using the 

acks=all

For example:

By setting 

acks=all

In a nutshell, having only one in-sync replica follows the “letter of the law” but not the “spirit of the law.” What we need is a guarantee when using the 

acks=all

There just so happens to be one such configuration: 

min.insync.replicas

min.insync.replicas

Note that the 

min.insync.replicas

min.insync.replicas

Let’s revisit our previous example from before and see the difference:

If the number of replicas that are in sync is below the configured amount, the lead broker won’t attempt to append the record to its log. The leader throws either a 

NotEnoughReplicasException

NotEnoughReplicasAfterAppendException

So by setting the 

min.insync.replicas

acks

Now let’s move on to the next items in our list: improvements to the Kafka clients. Over the past year, the Kafka producer and Kafka consumer APIs have added some new features that every Kafka developer should know.

Tip #2: Learn about the new sticky partitioner in the producer API

Kafka uses partitions to increase throughput and spread the load of messages to all brokers in a cluster. Kafka records are in a key/value format, where the keys can be null. Kafka producers don’t immediately send records, instead placing them into partition-specific batches to be sent later. Batches are an effective means of increasing network utilization. There are three ways the partitioner determines into which partition the records should be written.

The partition can be explicitly provided in the ProducerRecord object via the overloaded ProducerRecord constructor. In this case, the producer always uses this partition.

If no partition is provided, and the ProducerRecord has a key, the producer takes the hash of the key modulo the number of partitions. The resulting number from that calculation is the partition that the producer will use.

If there is no key and no partition present in the ProducerRecord, then previously Kafka used a round robin approach to assign messages across partitions. The producer would assign the first record in the batch to partition zero, the second to partition one, and so on, until the end of the partitions. The producer would then start over with partition zero and repeat the entire process for all remaining records.

The following illustration depicts this process:

The round robin approach works well for even distribution of records across partitions. But there’s one drawback. Due to this “fair” round robin approach, you can end up sending multiple sparsely populated batches. It’s more efficient to send fewer batches with more records in each batch. Fewer batches mean less queuing of produce requests, hence less load on the brokers.

Let’s look at a simplified example where you have a topic with three partitions to explain this. For the sake of simplicity, let’s assume that your application produced nine records with no key, all arriving at the same time:

As you can see above, the nine incoming records will result in three batches of three records. But, it would be better if we could send one batch of nine records. As stated before, fewer batches result in less network traffic and less load on the brokers.

Apache Kafka 2.4.0 added the sticky partitioner approach, which now makes this possible. Instead of using a round-robin approach per record, the sticky partitioner assigns records to the same partition until the batch is sent.

Then, after sending a batch, the sticky partitioner increments the partition to use for the next batch. Let’s revisit our illustration from above but updated using the sticky partitioner:

By using the same partition until a batch is full or otherwise completed, we’ll send fewer produce requests, which reduces the load on the request queue and reduces latency of the system as well.

It’s worth noting that the sticky partitioner still results in an even distribution of records. The even distribution occurs over time, as the partitioner sends a batch to each partition. You can think of it as a “per-batch” round-robin or “eventually even” approach.

To learn more about the sticky partitioner, you can read the Apache Kafka Producer Improvements with the Sticky Partitioner blog post and the related KIP-480 design document.

Now let’s move on to the consumer changes.

Tip #3: Avoid “stop-the-world” consumer group rebalances by using cooperative rebalancing

Kafka is a distributed system, and one of the key things distributed systems need to do is deal with failures and disruptions—not just anticipate failures, but fully embrace them.

A great example of how Kafka handles this expected disruption is the consumer group protocol, which manages multiple instances of a consumer for a single logical application.

If an instance of a consumer stops, by design or otherwise, Kafka will rebalance and make sure another instance of the consumer takes over the work.

As of version 2.4, Kafka introduced a new rebalance protocol: cooperative rebalancing. But before we dive into the new protocol, let’s look in a bit more detail at the consumer group basics.

Let’s assume you have a distributed application with several consumers subscribed to a topic. Any set of consumers configured with the same group.id form one logical consumer called a consumer group.

Each consumer in the group is responsible for consuming from one or more partitions of the subscribed topic(s). These partitions are assigned by the leader of the consumer group.

Here’s an illustration demonstrating this concept:

From the above illustration, you can see that under optimal conditions, all three consumers are processing records from two partitions each. But what happens if one of the applications suffers an error or can’t connect to the network anymore?

Does processing for those topic partitions stop until you can restore the application in question? Fortunately, the answer is no, thanks to the consumer rebalancing protocol.

Here’s another illustration showing the consumer group protocol in action:

As you can see, Consumer 2 fails for some reason and either misses a poll or triggers a session timeout. The group coordinator removes it from the group and triggers what is known as a rebalance.

A rebalance is a mechanism that attempts to evenly distribute (balance) the workload across all available members of a consumer group. In this case, since Consumer 2 left the group, the rebalance assigns its previously owned partitions to the other active members of the group.

So as you can see, losing a consumer application for a particular group ID doesn’t result in a loss of processing on those topic partitions.

There is, however, a drawback of the default rebalancing approach. Each consumer gives up its entire assignment of topic partitions, and no processing takes place until the topic partitions are reassigned—sometimes referred to as a “stop-the-world” rebalance.

To compound the issue, depending on the instance of the 

ConsumerPartitionAssignor

This implementation of the rebalance protocol is called 

eager rebalancing

While it is critical to keep any two consumers from claiming ownership over the same topic partition, it turns out that there is a better approach that provides safety without compromising on time spent not processing: incremental cooperative rebalancing.

First introduced to Kafka Connect in Apache Kafka 2.3, this has now been implemented for the consumer group protocol too. With the cooperative approach, consumers don’t automatically give up ownership of all topic partitions at the start of the rebalance.

Instead, all members encode their current assignment and forward the information to the group leader. The group leader then determines which partitions need to change ownership—instead of producing an entirely new assignment from scratch.

Now a second rebalance is issued, but this time, only the topic partitions that need to change ownership are involved. It could be revoking topic partitions that are no longer assigned or adding new topic partitions.

For the topic partitions that are in both the new and old assignment, nothing has to change, which means continued processing for topic partitions that aren’t moving.

The bottom line is that eliminating the “stop-the-world” approach to rebalancing and only stopping the topic partitions involved means less costly rebalances, thus reducing the total time to complete the rebalance.

Even long rebalances are less painful now that processing can continue throughout them. This positive change in rebalancing is made possible by using the 

CooperativeStickyAssignor

The 

CooperativeStickyAssignor

To enable this new rebalance protocol, you need to set the 

partition.assignment.strategy

CooperativeStickyAssignor

To take advantage of the new rebalance protocol, you only need to update your client version. If you’re a Kafka Streams user, there is even better news. Kafka Streams enables the cooperative rebalance protocol by default, so there is nothing else to do.

Tip #4: Master the command line tools

The Apache Kafka binary installation includes several tools located in the 

bin

console-consumer

console-producer

dump-log

delete-records

Kafka console producer

The console producer allows you to produce records to a topic directly from the command line. Producing from the command line is a great way to quickly test new consumer applications when you aren’t producing data to the topics yet. To start the console producer, run this command:

kafka-console-producer --topic  \
                                                     --broker-list <broker-host:port>

After you execute the command, there’s empty prompt waiting for your input—just type in some characters and hit enter to produce a message.

Using the command line producer in this way does not send any keys, only values. Luckily, there is a way to send keys as well. You just have to update the command to include the necessary flags:

kafka-console-producer --topic  \
                                                     --broker-list <broker-host:port> \
                                                     --property parse.key=true \
                                                     --property key.separator=":"

The choice of the 

key.separator

Now let’s take a look at the other side of the coin: consuming records from the command line.

Kafka console consumer

The console consumer gives you the ability to consume records from a Kafka topic directly from the command line. Being able to quickly start a consumer can be an invaluable tool in prototyping or debugging. Consider building a new microservice. To quickly confirm that your producer application is sending messages, you can simply run this command:

kafka-console-consumer --topic  \
                                                     --bootstrap-server <broker-host:port>

After you run this command, you’ll start seeing records scrolling across your screen (so long as data is currently being produced to the topic). If you want to see all the records from the start, you can add a 

--from-beginning

kafka-console-consumer --topic <topic-name> \
                                                            --bootstrap-server <broker-host:port> \
                                                            --from-beginning

If you are using Schema Registry, there are command line consumers available for Avro, Protobuf, and JSON Schema encoded records. The Schema Registry command line consumers are intended for working with records in the Avro, Protobuf or JSON formats, while the plain consumers work with records of primitive Java types: String, Long, Double, Integer, etc. The default format expected for keys and values by the plain console consumer is the String type.

If the keys or values are not strings, you’ll need to provide the deserializers via the command line flags 

--key-deserializer

--value-deserializer

You may well have noticed that by default, the console consumer only prints the value component of the messages to the screen. If you want to see the keys as well, you can do so by including the necessary flags:

kafka-console-consumer --topic
                          --bootstrap-server <broker-host:port> \
    --property print.key=true
    --property key.separator=":"

As with the producer, the value used for the key separator is arbitrary, so you can choose any character you want to use.

Dump log

Sometimes when you’re working with Kafka, you may find yourself needing to manually inspect the underlying logs of a topic. Whether you’re just curious about Kafka internals or you need to debug an issue and verify the content, the 

kafka-dump-log

example

  kafka-dump-log \
  --print-data-log \ 
  --files  ./var/lib/kafka/data/example-0/00000000000000000000.log 

--print-data-log

--files

For a full list of options and a description of what each option does, run 

kafka-dump-log

--help

Running the command above yields something like this:

Dumping ./var/lib/kafka/data/example-0/00000000000000000000.log
Starting offset: 0
baseOffset: 0 lastOffset: 0 count: 1 baseSequence: -1 lastSequence: -1 producerId: -1 producerEpoch: -1 partitionLeaderEpoch: 0 isTransactional: false isControl: false position: 0 CreateTime: 1599775774460 size: 81 magic: 2 compresscodec: NONE crc: 3162584294 isvalid: true
| offset: 0 CreateTime: 1599775774460 keysize: 3 valuesize: 10 sequence: -1 headerKeys: [] key: 887 payload: -2.1510235
baseOffset: 1 lastOffset: 9 count: 9 baseSequence: -1 lastSequence: -1 producerId: -1 producerEpoch: -1 partitionLeaderEpoch: 0 isTransactional: false isControl: false position: 81 CreateTime: 1599775774468 size: 252 magic: 2 compresscodec: NONE crc: 2796351311 isvalid: true
| offset: 1 CreateTime: 1599775774463 keysize: 1 valuesize: 9 sequence: -1 headerKeys: [] key: 5 payload: 33.440664
| offset: 2 CreateTime: 1599775774463 keysize: 8 valuesize: 9 sequence: -1 headerKeys: [] key: 60024247 payload: 9.1408728
| offset: 3 CreateTime: 1599775774463 keysize: 1 valuesize: 9 sequence: -1 headerKeys: [] key: 1 payload: 45.348946
| offset: 4 CreateTime: 1599775774464 keysize: 6 valuesize: 10 sequence: -1 headerKeys: [] key: 241795 payload: -63.786373
| offset: 5 CreateTime: 1599775774465 keysize: 8 valuesize: 9 sequence: -1 headerKeys: [] key: 53596698 payload: 69.431393
| offset: 6 CreateTime: 1599775774465 keysize: 8 valuesize: 9 sequence: -1 headerKeys: [] key: 33219463 payload: 88.307875
| offset: 7 CreateTime: 1599775774466 keysize: 1 valuesize: 9 sequence: -1 headerKeys: [] key: 0 payload: 39.940350
| offset: 8 CreateTime: 1599775774467 keysize: 5 valuesize: 9 sequence: -1 headerKeys: [] key: 78496 payload: 74.180098
| offset: 9 CreateTime: 1599775774468 keysize: 8 valuesize: 9 sequence: -1 headerKeys: [] key: 89866187 payload: 79.459314

There’s lots of information available from the 

dump-log

Also note that in this example, the keys and values for the topic are strings. To run the

dump-log

--key-decoder-class

--value-decoder-class

Delete records

Kafka stores records for topics on disk and retains that data even once consumers have read it. However, records aren’t stored in one big file but are broken up into segments by partition where the offset order is continuous across segments for the same topic partition. Because servers do not have infinite amounts of storage, Kafka provides settings to control how much data is retained, based on time and size:

• The time configuration controlling data retention is 

  log.retention.hours

  , which defaults to 168 hours (one week)
• The size configuration 

  log.retention.bytes

   controls how large segments can grow before they are eligible for deletion

However, the default setting for 

log.retention.bytes

The 

kafka-delete-records

• --bootstrap-server:

   the broker(s) to connect to for bootstrapping

• --offset-json-file:

   a JSON file containing the deletion settings

Here’s an example of the JSON file:

{
   "partitions": [
                                     {"topic": "example", "partition": 0, "offset": -1}
                                   ],
                                   "version":1
 }

As you can see, the format of the JSON is simple. It’s an array of JSON objects. Each JSON object has three properties:

• Topic: the topic to delete from
• Partition: the partition to delete from
• Offset: the offset you want the delete to start from, moving backward to lower offsets

I want to discuss how to choose the 

offset

But in practice, you most likely won’t know off the top of your head what offset to use. Also bear in mind that offset != message number, so you can’t just delete from “message 42.”

If you supply a 

-1

high watermark

high watermark

Now to run the command, just enter this on the command line:

kafka-delete-records --bootstrap-server <broker-host:port> \
                                                --offset-json-file offsets.json

After running this command, you should see something like this on the console:

Executing records delete operation
Records delete operation completed:
partition: example-0  low_watermark: 10

The results of the command show that Kafka deleted all records from the topic partition

example-0

low_watermark

example topic

Tip #5: Use the power of record headers

Apache Kafka 0.11 introduced the concept of record headers. Record headers give you the ability to add some metadata about the Kafka record, without adding any extra information to the key/value pair of the record itself. Consider if you wanted to embed some information in a message, such as an identifier for the system from which the data originated. Perhaps you want this for lineage and audit purposes and in order to facilitate routing of the data downstream.

Why not just append this information to the key? Then you could extract the part needed and you would be able to route data accordingly. But adding artificial data to the key poses two potential problems.

1. First, if you are using a compacted topic, adding information to the key would make the record incorrectly appear as unique. Thus, compaction would not function as intended.
2. For the second issue, consider the effect if one particular system identifier dominates in the records sent. You now have a situation where you could have significant key skew. Depending on how you are consuming from the partitions, the uneven distribution of keys could have an impact on processing by increasing latency.

These are two situations where you might want to use headers. The original KIP proposing headers provides some additional cases as well:

• Automated routing of messages based on header information between clusters
• Enterprise APM tools (e.g., Appdynamics or Dynatrace) need to stitch in “magic” transaction IDs for them to provide end-to-end transaction flow monitoring.
• Audit metadata is recorded with the message, for example, the client-id that produced the record.
• Business payload needs to be encrypted end to end and signed without tamper, but ecosystem components need access to metadata to achieve tasks.

Now that I’ve made a case for using headers, let’s walk through how you can add headers to your Kafka records.

Adding headers to Kafka records

Here’s the Java code to add headers to a 

ProducerRecord

ProducerRecord<String, String> producerRecord = new ProducerRecord<>("bizops", "value"); 

producerRecord.headers().add("client-id", "2334".getBytes(StandardCharsets.UTF_8)); producerRecord.headers().add("data-file", "incoming-data.txt".getBytes(StandardCharsets.UTF_8)); 

// Details left out for clarity
producer.send(producerRecord);

ProducerRecord

ProducerRecord.headers()

There’s a few things we need to point out with the code example here. The header interface expects a String key and the value as a byte array.

Even though you provide a key, you can add as many headers with the same key if needed. Duplicate keys will not overwrite previous entries with the same key.

Also, there are overloaded 

ProducerRecord

Iterable<Header>

Header

Iterable

Now that you know how to add headers, let’s take a look at how you can access headers from the consumer side of things.

Retrieving headers

This is how you can access headers when consuming records:

//Details left out for clarity

ConsumerRecords<String, String> consumerRecords = consumer.poll(Duration.ofSeconds(1));

for (ConsumerRecord<String, String> consumerRecord : consumerRecords) { 
          for (Header header : consumerRecord.headers()) {  
                    System.out.println("header key " + header.key() + "header value " + new String(header.value())); 
         }
}

ConsumerRecords

ConsumerRecord

From the code above, you can see that to process the headers, simply use the 

ConsumerRecord.headers()

You can also use kafkacat to view headers from the command line. Here’s an example command:

kafkacat -b kafka-broker:9092 -t my_topic_name -C \
  -f '\nKey (%K bytes): %k
  Value (%S bytes): %s
  Timestamp: %T
  Partition: %p
  Offset: %o
  Headers: %h\n'

Recap

You now have read the top five tips for working with Apache Kafka. To recap, we understand:

1. Message durability and its relationship with delivery guarantees
2. The sticky partitioner in the producer API
3. The command line tools
4. The power of record headers

And yet, there is still so much more to learn! Head over to Confluent Developer and Kafka Tutorials to see what’s going on.