Kafka Number Of Messages In Topic

Kafka provided a number of utility tools inside the distribution. Specifying Kafka producer properties. Distributed systems and microservices are all the rage these days, and Apache Kafka seems to be getting most of that attention. Producer – In Kafka, Producers issue communications as well as publishes messages to a Kafka topic. Searchable Kafka topic viewer. The major. Choosing the number of partitions affects the scalability of the topic for consumers. Kafka Topics Producers Entities producing streaming data Consumers Applications that read and process messages Kafka Cluster Stores and manages streaming data in a distributed, replicated, fault-tolerant cluster Topic 1 Topic 2 Topic 3 Topic 4 Topic 5 Messages with a common format. Partitions. Here is a sample that reads the last 10 messages from sample-kafka-topic topic, then exit:. 907Z (Statistics of the local KSQL server interaction with the Kafka topic spotify-tracks) The logs were indicating a very high number of failed messages (consumer-failed-messages). Here is a description of a few of the popular use cases for Apache Kafka®. Apache Kafka is a distributed commit log for fast, fault-tolerant communication between producers and consumers using message based topics. We carefully read and essay kafka metamorphos correct essays so that essay kafka metamorphos you will receive a paper that is ready for submission or. Kafka nomenclature recap: a generic queue is called 'topic', and each one of them can be split in multiple partitions that producers and consumers will use to spread the load. Send OffsetRequest to each of the partitions to get the earliest and latest offset of a partition so you get number of messages there. java -classpath hive-kafka-1. Partitions: The data or message is divided into small subparts, known as partitions. Apache Kafka is an open source distributed pub/sub messaging system originally released by the engineering team at LinkedIn. The kafka-console-producer tool can be used to read data from standard output and write it to a Kafka topic. Topic to tables. Next chart shows the Records/second versus message size for writes between Apache Kafka cluster based on NVMe-oF FlashArray and Apache Kafka cluster based on SAS-DAS using the topic hello333. Going back to the “commit log” description, a partition is a single log. As a distributed streaming platform, Kafka replicates a publish-subscribe service. Apache Kafka is a distributed commit log for fast, fault-tolerant communication between producers and consumers using message based topics. The Zookeeper integration does the following jobs: Loads broker metadata from Zookeeper before we can communicate with the Kafka server; Watches broker state, if broker changes, the client will refresh broker and topic metadata stored in the. The default is 1. The Kafka consumer uses the poll method to get N number of records. All CloudKarafka brokers have a very large number of file descriptors. The ProducerRecord has two components: a key and a value. It is a continuation of the Kafka Architecture article. host1 consumed 8 partitions, but host2 consumed 12 partitions, it should be 8 partitions. We’ll call processes that subscribe to topics and process the feed of published messages consumers. New in ROS indigo. One of the nice things about how pipeline_kafka works is that it continuously saves its position (Kafka calls this an offset) in a given Kafka topic durably in the database. You keep scaling out by starting more consumer instances, but this will only be useful until you reach the point where the number of consumer instances is equal to the number of partitions. Verify the state of a topic. The effect was a substantial decrease in the number of bytes transmitted from MirrorMaker to the aggregate cluster. com:9092 --topic t1. json --broker-list broker 1, broker 2--generate Running the command lists the distribution of partition replicas on your current brokers followed by a proposed partition reassignment configuration. 0 on Ubuntu 18. Apache Kafka provides the concept of Partitions in a Topic. The closest analogies for a topic are a database table or a folder in a filesystem. And if you have multiple services, they can all write events to this user event topic. In my case, the ID for the first partition is 0 and for the second partition is 1. Kafka Consumer. Flexibility : Compared to ActiveMQ, Kafka has much more configuration options for consumers and producers. It allows a service to send messages to a queue, where another service can then read those messages. Kafka does not currently support reducing the number of partitions for a topic or changing the replication factor. Using a single partition per topic guarantees I will consume messages in the order they arrive. Kafka topics are divided into a number of partitions, which contains messages in an unchangeable sequence. The default retention time is 168 hours, i. So host3 could only consume 4 partitions. This can be done via the rstring topic attribute in the incoming tuple or you can specify this using the topic parameter in the KafkaProducer (see the highlighted code in the beacon operator below). The Kafka Handler sends instances of the Kafka ProducerRecord class to the Kafka producer API, which in turn publishes the ProducerRecord to a Kafka topic. Kafka has managed SaaS on Azure, AWS, and Confluent. txt contains the following data: IBM,100 DEL,200 ABC,120 XYZ,340 AXL,212 This is useful to create a large number of messages at once. Broker: This is the place where the issued messages are stored. Distributed systems and microservices are all the rage these days, and Apache Kafka seems to be getting most of that attention. A modern data platform requires a robust Complex Event Processing (CEP) system, a cornerstone of which is a distributed messaging system. See the rostopic page for more documentation. For more information about using a KafkaProducer node, see Producing messages on Kafka topics. With this test, we went from the Business plan tier to the Premium tier,. Order of messages If you’ve ever used Apache Kafka, you probably know about the guarantees that the official documentation states. The call to rebalance() causes data to be re-partitioned so that all machines receive messages (for example, when the number of Kafka partitions is fewer than the number of Flink parallel instances). 2 Topic The Topic module displays detailed information about the topic creation and the topic. Scenario 2: This is identical to scenario 1, except that in this case the Kafka Streams application outputs all the filtered messages to a new topic called matchTopic, by using the. The brokers do not usually own all the partitions for all the topics. Also, Kafka is able to support message throughput of thousands of messages per second. Although it's possible to increase the number of partitions over time, one has to be careful if messages are produced with keys. So as you can expect, in case of failure when a record is not acknowledged by broker, producer may send records which very likely will be stored in the wrong order and this is normal behaviour of Kafka producer, so by default Kafka doesn’t guarantee that messages sent by a producer to a particular topic partition will be appended in the order. Apache Kafka is a distributed streaming messaging platform. The topic is created by default When sending a message to a non-existing topic, the topic is created by default since auto. sh --describe --bootstrap-server localhost:9092 --topic NewTopic The output will be as follows. 0, the topic is configured to accept messages up to 10 MB in size by default. size measures batch size in total bytes instead of the number of messages. The command for "Get number of messages in a topic ???" will only work if our earliest offsets are zero, correct? If we have a topic, whose message retention period already passed (meaning some messages were discarded and new ones were added), we would have to get the earliest and latest offsets, subtract them for each partition accordingly and then add them, right?. serializers. Get all the partition information of the topic by issuing a topic metadata request. Multiple logstash reading from a single kafka topic. This allowed us to increase retention for some of the higher volume topics. All the Topics are divided into a number of partitions. Before understand the Kafka bench-marking, let me give a quick brief of what Kafka is and a few details about how it works. You may be wondering why Twitter chose to built an in-house messaging system in the first place. get kafka topic message count. Kafka Magic for Apache Kafka® Filter Messages Download. Above command will create a “hello-topic“, with replication-factor = 1 and the number of partitions is 1. sh --zookeeper zk_host:port/chroot --delete --topic my_topic_name Topic deletion option is disabled by default. Kafka is very scalable. Verify the state of a topic. For Kafka, Confluent's Jeremy Custenborder has written a Salesforce source connector for placing notifications from a Salesforce PushTopic to a Kafka topic. Number of Views 83 Number of Likes 0 Number of Comments 3 Build a combined docker image for snowflake-kafka-connector with cp-kafka-connect-base to deploy on kafka connect cluster Knowledge Base holmes September 4, 2019 at 10:55 AM. Kafka optimizes for message batches so this is efficient. The number of messages we processed was on the order of about 3 million per day (yup…pretty low compared to the big boys). kafkabroker. Messages in a. A Kafka cluster is made up of one or more Kafka brokers. Topic Design Minimum number of topics is implied by the minimum different retention, etc settings you require -> you probably don’t want to mix message types with different scalability or latency requirements Maximum number of topics is largely limited by imagination In between is a set of design trade-offs:. “Apache Kafka” Jan 15, 2017. of Apache Kafka on HDInsight, the number of. High-level Consumer ¶ * Decide if you want to read messages and events from the `. Option 1 – Read values (without message keys) from Kafka topic with kafka-console-consumer. Kafka clusters contain topics, that act like a message queue where client applications can write and read their data. In one line to help you out: one could say that a topic in Kafka is the same concept as a table in a database. If all goes well, kafka topics --describe again and you should see the leaders properly balanced. size measures batch size in total bytes instead of the number of messages. Todd Palino talks about the start of Apache Kafka® at LinkedIn, what learning to use Kafka was like, how Kafka has changed, and what he and others in …. Set this as high as possible, without exceeding available memory. The number of partitions per topic are configurable while creating it. The number of messages we processed was on the order of about 3 million per day (yup…pretty low compared to the big boys). Each broker has a unique Id that contains more than one Topic partition. Above command will create a "hello-topic", with replication-factor = 1 and the number of partitions is 1. Topics: It is a common name or a heading given to represent a similar type of data. Note how each of the colouring tasks has half of the colours and notice also that we now need an additional Kafka topic to hold the repartitioned data. enable=true. Lastly, Kafka, as a distributed system, runs in a cluster. Simple but powerful syntax for mapping Kafka fields to DataStax database table columns. AvroConsoleProducer console hivekafka-1:9092 Note that you'll also need Avro and Kafka libs on the classpath. To delete a topic (associated with a message queue in other systems). Broker: This is the place where the issued messages are stored. If all goes well, kafka topics --describe again and you should see the leaders properly balanced. All CloudKarafka brokers have a very large number of file descriptors. Match and match_not fields help you limit the rule execution to a specific subset of the events in the topic. Topic statistics. Scenario 3: To increase or decrease the number of nodes in a Kafka cluster. The load on Kafka is strictly related to the number of consumers, brokers, partitions and frequency of commits from the consumer. The %{variable_name} nomenclature represents segments that vary based on context. will display Messages published to /topic_name. Top 30 Apache Kafka Interview Questions Q1) Explain what is Kafka? Kafka is a publish-subscribe messaging application which is coded in “Scala”. In those cases, we want each application to get all of the messages, rather than just a subset. Apache Kafka is a publish-subscribe messaging system. However, if you’re needing to distribute a large amount of information across a large number of servers then Kafka can be one hell of a tool to accomplish that. Performance. In Kafka, only one consumer can read messages from specific partition, so number of active consumers per topic is limited by the number of partitions in that topic. For more information about using a KafkaProducer node, see Producing messages on Kafka topics. Total messages. It allows a service to send messages to a queue, where another service can then read those messages. “Apache Kafka” Jan 15, 2017. the topic name to source from Kafka; the project, dataset and Keyfile which are the connection parameters to BigQuery. demoproducer. There are other ways to increase the number of partitions and still maintain the read order but for this use case a topic per message type seemed to make sense. Messages in Kafka are ordered by timestamp and are immutable, with read as the only permitted operation. enable=true Refer to How to setup standalone instance of Apache Kafka 0. After verifying the settings, as per Kafka connect instructions, I had to create the tarball of the connector and extract it's contents. an HTTP proxy) are published to Kafka, back-pressure can be applied easily to the whole pipeline, limiting the number of messages in-flight and controlling memory usage. In those cases, we want each application to get all of the messages, rather than just a subset. Technically no, but practically yes. group_events: Sets the number of events to be published to the same partition, before the partitioner selects a new partition by random. Calling pause with a topic that the consumer is not subscribed to is a no-op, calling resume with a topic that is not paused is also a no-op. To purge the Kafka topic, you need to change the retention time of that topic. Up to a specific time, any number of consumer applications can pull this message any number of times. Here is a description of a few of the popular use cases for Apache Kafka®. 1 on localhost for Mac OS X? for enabling this setting. Increasing the number of threads improves the flush throughput to hide write / network latency. Set consumer. A screenshot of the Dashboard can be found below: 2. 2 and up – that would allow developers to consume Kafka events directly from SQL and PL/SQL and – at a late stage – also publish events from within the database straight to Kafka Topics. A ‘Topic’ is similar to a ‘File’ structure. However, if you’re needing to distribute a large amount of information across a large number of servers then Kafka can be one hell of a tool to accomplish that. However, although the server hands out messages in order, the messages are deliv. It is a continuation of the Kafka Architecture article. You can do the following, use simple consumer, 1. Kafka can be run as a single instance or as a cluster on multiple servers. Will Delay – Allows a message to be published if a client is disconnected for more than a user defined period of time. Topic Alias – Allows topic strings in messages to be replaced with a single number, reducing the number of bytes that need to be transmitted if a publisher repeatedly uses the same topics. This supports copying a limited or all messages from a Kafka Topic. 0, the topic is configured to accept messages up to 10 MB in size by default. When the topic is name not found , the Field*TopicSelector will write messages into default topic. Display messages to determine the data structure of the topic messages. We’ll call processes that subscribe to topics and process the feed of published messages consumers. We maintain over 100 Kafka clusters with more than 4,000 brokers, which serve more than 100,000 topics and 7 million partitions. Each partition is an ordered queue of messages assigned to a specific consumer. Note that encoding and sending the data to InfluxDB might lower this maximum performance. Events()` channel (set `"go. It is identified by its name, which depends on the user's choice. Normally Spark has a 1-1 mapping of Kafka TopicPartitions to Spark partitions consuming from Kafka. To use Apache Kafka binder, messages sent to the DLQ topic are enhanced with the If a topic already exists with a larger number of partitions than the. This means each consumer will be responsible for processing 50% of the messages, doubling the potential throughput of a single consumer. The load on Kafka is strictly related to the number of consumers, brokers, partitions and frequency of commits from the consumer. You may be wondering why Twitter chose to built an in-house messaging system in the first place. Kafka output broker event partitioning strategy. Note: There is currently no transactional producer for Kafka, so Flink can not guarantee exactly-once delivery into a Kafka topic. Kafka does not use a traditional queuing paradigm, but instead arranges events in the form of an immutable time-ordered log. And Kafka's not gonna do any validation on this data. enable": true`) or by calling `. The server's default configuration for this property is given under the Server Default Property heading, setting this default in the server config allows you to change the default given to. Kafka consumers. Conclusion : In this Apache Kafka Tutorial - Describe Kafka Topic, we have learnt to check Kafka Broker Instance that is acting as leader for a Kafka Topic, and the Broker Instances acting as replicas and in-sync replicas. partitions to the number of partitions that you have set at topic creation for the topic that you want to consume messages from. If you increase the size of your buffer, it might never get full. 20 last-failed: 2019-03-14T10:01:39. Note: There is currently no transactional producer for Kafka, so Flink can not guarantee exactly-once delivery into a Kafka topic. Basic format. This blog post will focus on the latter and demonstrate how to develop a scalable application that responds to messages on a Kafka Topic. Consumer – Kafka Consumers subscribes to a topic(s) and also reads and processes messages from the topic(s). TestTopic1 If you drop the code in SE38 and execute you will be presented with the following options. Currently it is advised for cluster to have a maximum 4000 partitions per brokers. Figure 2 - Global Apache Kafka architecture (with 1 topic, 1 partition, replication factor 4). The effect was a substantial decrease in the number of bytes transmitted from MirrorMaker to the aggregate cluster. You can define multiple topics. Set consumer. Scenario 2: This is identical to scenario 1, except that in this case the Kafka Streams application outputs all the filtered messages to a new topic called matchTopic, by using the. For an overview of a number of these areas in action, see this blog post. Performance. Message Distribution and Topic Partitioning in Kafka When coming over to Apache Kafka from other messaging systems, there’s a conceptual hump that needs to first be crossed, and that is – what is a this topic thing that messages get sent to, and how does message distribution inside it work?. To create messages from a file, type kafka-console-producer. Apache Kafka is an alternative to traditional message queue systems, such as ActiveMQ or RabbitMQ. Now suppose when I am producing any message I want to retrieve the offset that has been assigned to it. Configuring a Batch Listener. The total number of messages handled by LinkedIn’s Kafka deployments recently surpassed 7 trillion per day. It also interacts with the assigned kafka Group Coordinator node to allow multiple consumers to load balance consumption of topics (requires kafka >= 0. Popular formats are: string, JSON, and Avro. Record is having a key-value pair which contains the topic name and partition number to be sent. Micronaut applications built with Kafka can be deployed with or without the presence of an HTTP server. I started with simple non-compressed and non-batched messages with one broker, one partition, one producer and one consumer to understand the relative performance of each aspect of the system. In previous versions, the broker default was used (1 MB by default). Apache Kafka can be used in Knative applications in two ways: firstly, Kafka can be used as the underlying transport for internal Channels; and secondly, it can be used as the source of events. If you need to delete all messages in topic, you can exploit the retention times. Message magic Change requested:KAFKA-2511. With Building Data Streaming Applications with Apache Kafka, build efficient real-time streaming applications in Apache Kafka to process data streams of data. A messaging system let you send messages between processes, applications, and servers. You keep scaling out by starting more consumer instances, but this will only be useful until you reach the point where the number of consumer instances is equal to the number of partitions. Apache Kafka is a distributed streaming messaging platform. It is a continuation of the Kafka Architecture article. Topic statistics. His simplified instructions in the GitHub README assume usage of Confluent's wrap of Kafka including the Confluent-only Schema Registry with Avro-formatted messages. When a producer publishes a message, the Kafka server appends it to the end of the log file for its given topic. Comparing Pulsar and Kafka: unified queuing and streaming Sijie Guo In previous blog posts , we described several reasons why Apache Pulsar is an enterprise-grade streaming and messaging system that you should consider for your real-time use cases. We will publish some data onto this topic in a few minutes. Though using some variant of a message queue is common when building event/log analytics pipeliines, Kafka is uniquely suited to Parse. size measures batch size in total bytes instead of the number of messages. If the Kafka client sees more than one topic+partition on the same Kafka Node, it can send messages for both topic+partitions in a single message. Everything on the Kafka bus is related to topics. Scenario 3: To increase or decrease the number of nodes in a Kafka cluster. Kafka broker allow the fetching of messages for consumers, it’s known as Kafka server and Kafka node. Messages consist of a payload of raw bytes, with topic and partition info encoded. You created a Kafka Consumer that uses the topic to receive messages. Note that partitions are based on a persistent ordered data structure which only supports appends: you cannot delete nor modify messages which have already been sent to the broker, you can only. Performance. Will Delay – Allows a message to be published if a client is disconnected for more than a user defined period of time. kafka-topics --zookeeper localhost:2181 --topic test --delete. Apache Kafka - Basic Operations - First let us start implementing single node-single broker configuration and we will then migrate our setup to single node-multiple brokers configuration. In Kafka, a topic is a category, similar to a JMS destination or both an AMQP exchange and queue. The number of Kafka partitions when using compression/batching Early on in my Kafka performance testing, I started as simple as possible. It was originally developed at LinkedIn Corporation and later on became a part of Apache project. In Kafka, we can create n number of topics as we want. You can set up this step to collect monitored events, track user consumption of data streams, and monitor alerts. Topics are comprised of some number of partitions. One of the session at CodeOne 2018 discussed an upcoming feature for Oracle Database – supported in Release 12. Eg on Windows in a command prompt from the Kafka directory we can use:. Note that the replication-factor controls how many servers will replicate each message that is written; therefore, it should be less than or equal the number of Kafka servers/brokers. The KafkaConsumer node then receives messages that are published on the Kafka topic, as input to the message flow. 7 consumer and 0. Match and match_not fields help you limit the rule execution to a specific subset of the events in the topic. For an overview of a number of these areas in action, see this blog post. You can think of the consumer assignments as a map-like data structure with consumers as the key and a set of partitions as the value. We’ll call processes that publish messages to a Kafka topic producers. Apache Kafka Apache Kafka Concepts Kafka appends new messages to a partition in an ordered, immutable sequence. So, basically it can be used as a message bus or distribution platform or collection system or…. Display messages to determine the data structure of the topic messages. We could also see that the additional 4 partitions consumed by host2 have many messages lagged behind. You may still receive messages for the topic within the current batch. Brokers – While it comes to manage storage of messages in the topic(s) we use. This is because, although every message in a Kafka topic is persisted by default, no mechanism is available as of yet that allows for fast lookups of a specific message in a topic. The API has a Reactive Stream to an unconfirmed-transactions topic on the Kafka cluster. 7 consumer and 0. All Kafka messages are organized into topics. TopicRecordNameStrategy: The subject name is -, where is the Kafka topic name, and is the fully-qualified name of the Avro record type of the message. Distributed systems and microservices are all the rage these days, and Apache Kafka seems to be getting most of that attention. Each record is routed and stored in a specific partition based on a partitioner. Each partition will contain a discrete subset of the events (or messages, in Kafka parlance) belonging to a given topic. With Building Data Streaming Applications with Apache Kafka, build efficient real-time streaming applications in Apache Kafka to process data streams of data. A consumer pulls messages off of a Kafka topic while producers push messages into a Kafka topic. topics= # max historical time that will be pulled from each partition based on event timestamp # Note: max. It holds the messages that have been written by the producer before being consumed by the ‘consumer’. As a result, Kafka aims to be highly scalable. However, kafka-streams provides higher-level operations on the data, allowing much easier creation of derivative streams. Kafka persist messages on disk, so hard disk space is crucial. …We've heard maybe the term before,…it's called a Kafka cluster,…and cluster means that it's comprised,…composed of multiple brokers,…and each broker is basically a server. 11 and newer. It was originally developed at LinkedIn Corporation and later on became a part of Apache project. Kafka does not use a traditional queuing paradigm, but instead arranges events in the form of an immutable time-ordered log. As an example, let’s say we have two topics (t0 and t1) each with two partitions each (p0 and p1). We used the replicated Kafka topic from producer lab. (3 replies) Hi, Please let me know how to find the total number of messages in a particular topic. Unlike Rabbit’s architecture, in which messages from a queue are delivered to a pool of workers, Kafka’s topics (queues) are pre-split into partitions. Consumer – Kafka Consumers subscribes to a topic(s) and also reads and processes messages from the topic(s). I would like to focus on this specific one: Messages sent by a producer to a particular topic partition will be appended in the order they are sent. Partitions: The data or message is divided into small subparts, known as partitions. Kafka topics are divided into a number of partitions, which contains messages in an unchangeable sequence. consumer-failed-messages: 6417 consumer-failed-messages-per-sec: 64. Normally Spark has a 1-1 mapping of Kafka TopicPartitions to Spark partitions consuming from Kafka. Searchable Kafka topic viewer. You can use a KafkaProducer node to publish messages from your message flow to a topic that is hosted on a Kafka server. TopicRecordNameStrategy: The subject name is {topic}-{type}, where {topic} is the Kafka topic name, and {type} is the fully-qualified name of the Avro record type of the message. Note that pausing a topic means that it won't be fetched in the next cycle. This allowed us to increase retention for some of the higher volume topics. Leave number of replicas to one (since there is only one broker) and number of partitions to one. And finally deleting a topic: > bin/kafka-topics. With the cluster selected, click "Topics" in the left side bar, then the "Add a Topic" button in the top right corner. Listing messages from a topic bin/kafka-console-consumer. size measures batch size in total bytes instead of the number of messages. Apache Kafka is the source, and IBM MQ is the target. Kafka consumers. However, if you’re needing to distribute a large amount of information across a large number of servers then Kafka can be one hell of a tool to accomplish that. With Spark 2. Kafka Topic Architecture - Replication, Failover and Parallel Processing. I started with simple non-compressed and non-batched messages with one broker, one partition, one producer and one consumer to understand the relative performance of each aspect of the system. Specifying Kafka producer properties. As a distributed streaming platform, Kafka replicates a publish-subscribe service. A Kafka topic is like a container for a group of related messages. Obtaining Kafka consumer offsets. Kafka has stronger ordering guarantees than a traditional messaging system, too. In Kafka, only one consumer can read messages from specific partition, so number of active consumers per topic is limited by the number of partitions in that topic. A topic is a named instance of a message log on the bus. Currently it is advised for cluster to have a maximum 4000 partitions per brokers. However, to copy messages from one Kafka Topic to the other in the same Broker cluster, the Replay Log Producer, a Kafka tool can be used. Hiya uses Kafka for a number of critical use cases, such as asynchronous data processing, cross-region replication, storing service logs, and more. will list the current topics and $ rostopic echo /topic_name. Now you should be ready to deploy the app on Studio’s embedded Mule runtime ( Run As > Mule Application ). “Apache Kafka” Jan 15, 2017. \bin\windows\kafka-console-consumer. In those cases, we want each application to get all of the messages, rather than just a subset. Set row-level TTL from Kafka fields. Kafka has message producers which send messages (events) to kafka which kafka stores in an entity called a topic which will deliver the messages to one or more consumers in a reliable manner. In that case, I would recommend that you open a case with Snowflake Support so that they can assist you with this issue. “Apache Kafka” Jan 15, 2017. To purge the Kafka topic, you need to change the retention time of that topic. Apache Kafka is a distributed publish-subscribe messaging system. In Apache Kafka, there can be multiple topics in a cluster. Kafka uses a paradigm called publish/subscribe with which you publish messages to a topic and subscribe to it to receive them. This config can be changed so that topics are not created if they do not exist. Consumers read messages from topics. This makes it tricky to track, on a granular level, how long messages are actually stored on the broker. > bin/kafka-topics. This is great—it’s a major feature of Kafka. Now Kafka Producers may send messages to the Kafka topic, my-topic and Kafka Consumers may subscribe to the Kafka Topic. It controls how many bytes of data to collect before sending messages to the Kafka broker. to send and receive messages using Apache Kafka.