This post was originally published on the Confluent blog.
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 ofthis 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.
What we'll cover
- Tip #1: Understand message delivery and durability guarantees
- Tip #2: Learn about the new sticky partitioner in the producer API
- Tip #3: Avoid “stop-the-world” consumer group rebalances by using cooperative rebalancing
- Tip #4: Master the command line tools
- Tip #5: Use the powerof record
headers - Recap
Tip #1: Understand message delivery and durability guarantees
For data durability, the KafkaProducer has the configuration setting
acks. The acks configuration specifies how many acknowledgments the
producer receives to consider a record delivered to the broker. The
options to choose from are:
-
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=allscenario. If you produce records with acks set to all to a
cluster of three Kafka brokers, it means that under ideal conditions,
Kafka contains three replicas of your data—one for the lead broker and
one each for two followers. When the logs of each of these replicas all
have the same record offsets, they are considered to be in sync. In
other words, these in-sync replicas have the same content for a
given topic partition. Take a look at the following illustration to
clearly picture what’s going
on:
But there’s some subtlety to using the acks=all configuration. What it
doesn’t specify is how many replicas need to be in sync. The lead
broker will always be in sync with itself. But you could have a
situation where the two following brokers can’t keep up due to network
partitions, record load, etc. So when a producer has a successful send,
the actual number of acknowledgments could have come from only one
broker! If the two followers are not in sync, the producer still
receives the required number of acks, but it’s only the leader in this
case. For example:
By setting acks=all, you are placing a premium on the durability of your
data. So if the replicas aren’t keeping up, it stands to reason that you
want to raise an exception for new records until the replicas are caught
up. 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 setting. A successful send involves at least a
majority of the available in-sync brokers.There just so happens to be
one such configuration: min.insync.replicas. The min.insync.replicas
configuration enforces the number of replicas that must be in sync for
the write to proceed. Note that the min.insync.replicas configuration
is set at the broker or topic level and is not a producer configuration.
The default value for min.insync.replicas is one. So to avoid the
scenario described above, in a three-broker cluster, you’d want to
increase the value to two. 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 or
NotEnoughReplicasAfterAppendException, forcing the producer to retry
the write. Having replicas out of sync with the leader is considered a
retryable error, so the producer will continue to retry and send the
records up to the configured delivery
timeout.So by setting the min.insync.replicas and producer acks configurations
to work together in this way, you’ve increased the durability of your
data. 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 expecteddisruption is the consumer group protocol,
which manages multiple instancesof 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 moredetail 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 consumerin 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 used, consumers are simply
reassigned the same topic partitions that they owned prior to the
rebalance, the net effect being that there is no need to pause work on
those partitions.This implementation of the rebalance protocol is called
eager rebalancing
because it prioritizes the importance of ensuring that no consumers in
the samp group claim ownership over the same topic partitions. Ownership
of the same topic partition by two consumers in the same group would
result in undefined behavior.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 producingan entirely new
assignment from scratch.Now a second rebalance is issued, but this time,
only the topic partitions thatneed 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 costlyrebalances, 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 makes the trade-off of having a second
rebalance but with the benefit of a faster return to normal
operations.To enable this new rebalance protocol, you need to set the
partition.assignment.strategy to use the new
CooperativeStickyAssignor. Also, note that this change is entirely on
the client side. 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 directory. While you’ll find several tools in that directory,I
want to show you the four tools that I think will have the most impact
on your day-to-day work. I’m referring to the console-consumer,
console-producer, dump-log, and 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 an 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 property is arbitrary. You can use any
character. And now, you can send full key/value pairs from the command
line! If you are using Confluent Schema
Registry,
there are command line
producers
available to send records in Avro, Protobuf, and JSON Schemaformats.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
Kafkatopic 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 flag to the command, and you’ll see all records
produced to that topic.
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 and --value-deserializer with the fully
qualified class names of the respective deserializers.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 command is your friend. Here’sa
command used to view the log of an example topic aptly named example:
kafka-dump-log \
--print-data-log \
--files ./var/lib/kafka/data/example-0/00000000000000000000.log
- The
--print-data-logflag specifies to print the datain the log. - The
--filesflag is required. This could also be a comma-separated list of files.
For a full list of options and a description of what each option does,
run kafka-dump-log with the --help flag.Running the command above
yields something like this:
Dumping ./var/lib/kafka/data/example-0/00000000000000000000.logStarting offset: 0baseOffset: 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: -1headerKeys: [] key: 887 payload: -2.1510235baseOffset: 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: -1headerKeys: [] 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 command. You
can clearly see the key, payload (value), offset, and timestamp for
each record. Keep in mind that this data is from a demo topic that
contains only 10 messages, so with a real topic, there will
besubstantially more data. Also note that in this example, the keys and
values for the topic are strings. To run the dump-log tool with key or
value types other than strings, you’ll need to use either the
--key-decoder-class or the --value-decoder-class flags.
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
donot 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.bytescontrolshow large segments can grow before they are eligible for deletion
However, the default setting for log.retention.bytes is -1, which
allows the log segment size to be unlimited. If you’re not careful and
haven’t configured the retention size as well as the retention time,
you could have a situation where you will run out of disk space.
Remember, you never want to go into the filesystem and manually delete
files. Instead, we want a controlled and supported way to delete records
from a topic in order to free up space. Fortunately, Kafka ships with a
tool that deletes data as required. The kafka-delete-records has two
mandatory parameters:
-
--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
For this example, I’m reusing the same topic from the dump-log tool, so
it’s a very simple JSON file. If you had more partitions or topics, you
would simply expand on the JSON config file above. I want to discuss how
to choose the offset in the JSON config file. Because the example topic
contains only 10 records, you could easily calculate the starting offset
to start the deletion process. 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, then the offset of the high watermark is used,
which means you will delete all the data currently in the topic. The
high watermark is the highest available offset for consumption (the
offset of the last successfully replicated message, plus one). 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 operationRecords 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. The low_watermark value of 10 indicates
the lowest offset available to consumers. Because there were only 10
records in the example topic, we know that the offsets ranged from 0
to 9 and no consumer can read those records again. For more background
on how deletes are implemented, you can read
KIP-107 and KIP-204.
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.
- 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.
- 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 toKafka 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);
- Create an instance of the
ProducerRecordclass - Call the
ProducerRecord.headers()method and add the key and value for the header - Adding another header
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 constructors that
accept an Iterable<Header>. You could create your own concrete class
that implements the Header interface and passes in a collection that
implements the Iterable interface. However, in practice, the simple
method shown here should suffice. 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()));
}
}
- Iterating over the
ConsumerRecords - For each
ConsumerRecord, iterating over the headers - Header processing
From the code above, you can see that to process the headers, simply use
the ConsumerRecord.headers()
method to return the headers. In our example above, we’re printing the
headers out to the console for demonstration purposes. Once you have
access to the headers, you can process them as needed. For reading
headers from the command line, KIP-431
adds support for optionally printing headers from the ConsoleConsumer,
which will be available in the Apache Kafka 2.7.0 release.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:
- Message durability and its relationship with delivery guarantees
- The sticky partitioner in the producer API
- The command line tools
- 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.
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