Creating a KafkaConsumer is very similar to creating a KafkaProducer—you create a Java Properties instance with the properties you want to pass to the consumer. While printing aggregated CDC data is interesting, it is hardly useful. In fact, one of the main design goals in Kafka was to make the data produced to Kafka topics available for many use cases throughout the organization. In the case of a rebalance, this will cause more duplicates. If there was an error in seek() (e.g., the offset does not exist), the exception will be thrown by poll(). In these cases, a single consumer can’t possibly keep up with the rate data flows into a topic, and adding more consumers that share the load by having each consumer own just a subset of the partitions and messages is our main method of scaling. The only new property here is group.id, which is the name of the consumer group this consumer belongs to. Another option is the asynchronous commit API. Overview. In release 0.10.1, the Kafka community introduced a separate heartbeat thread that will send heartbeats in between polls as well. Kafka consumers are typically part of a consumer group. Updates and deletes are not considered. It is common for Kafka consumers to do high-latency operations such as write to a database or a time-consuming computation on the data. In this chapter we discussed the Java KafkaConsumer client that is part of the org.apache.kafka.clients package. With the advent of the Apache MiNiFi sub-project,MiNiFi can bring data from sources directly to a central NiFi instance, which can then deliver data tothe appropriate Kafka topic. Unlike many traditional messaging systems, Kafka scales to a large number of consumers and consumer groups without reducing performance. Reading data from Kafka is a bit different than reading data from other messaging systems, and there are few unique concepts and ideas involved. In the next part of this tutorial, we will install Grafana, Graphite Carbon, and Graphite Web onto an Ubuntu 18.04 EC2 instance to stream and plot the CDC data transformed by Spark. This is the second part in a three-part tutorial describing instructions to create a Microsoft SQL Server CDC (Change Data Capture) data pipeline. The leader receives a list of all consumers in the group from the group coordinator (this will include all consumers that sent a heartbeat recently and which are therefore considered alive) and is responsible for assigning a subset of partitions to each consumer. It is possible to configure the commit interval to commit more frequently and reduce the window in which records will be duplicated, but it is impossible to completely eliminate them. Just like everything else in the consumer, the automatic commits are driven by the poll loop. The default is org.apache.kafka.clients.consumer.RangeAssignor, which implements the Range strategy described above. ./bin/kafka-topics.sh --zookeeper localhost:2181 --delete --topic demo . Here is a skeleton example of how this may work. Check out the talk I did at Kafka Summit in London earlier this year. The same way that sharks must keep moving or they die, consumers must keep polling Kafka or they will be considered dead and the partitions they are consuming will be handed to another consumer in the group to continue consuming. The amount of time a consumer can be out of contact with the brokers while still considered alive defaults to 10 seconds. Once the consumer subscribes to topics, the poll loop handles all details of coordination, partition rebalances, heartbeats, and data fetching, leaving the developer with a clean API that simply returns available data from the assigned partitions. Because the current consumer supports both behaviors and provides much more reliability and control to the developer, we will not discuss the older APIs. When you know exactly which partitions the consumer should read, you don’t subscribe to a topic—instead, you assign yourself a few partitions. It should be obvious that the serializer used to produce events to Kafka must match the deserializer that will be used when consuming events. If the instance sequence number is higher, don’t retry because a newer commit was already sent. Most of the parameters have reasonable defaults and do not require modification, but some have implications on the performance and availability of the consumers. So, let’s discuss how to exit cleanly. For Windows there is an excellent guide by Shahrukh Aslam, and they definitely exist for other OS’s as well.Next install Kafka-Python. partition.fetch.bytes or to increase the session timeout. This is exactly what seek() can be used for. If the committed offset is larger than the offset of the last message the client actually processed, all messages between the last processed offset and the committed offset will be missed by the consumer group. This ability can be used in a variety of ways; for example, to go back a few messages or skip ahead a few messages (perhaps a time-sensitive application that is falling behind will want to skip ahead to more relevant messages). And of course the heartbeats that keep consumers alive are sent from within the poll loop. The expression can match multiple topic names, and if someone creates a new topic with a name that matches, a rebalance will happen almost immediately and the consumers will start consuming from the new topic. Increase the sequence number every time you commit and add the sequence number at the time of the commit to the commitAsync callback. However, when I query the in-memory table, the schema of the dataframe seems to be correct, but all the values are null and I don't really know why. However, this tutorial can work as a standalone tutorial to install Apache Spark 2.4.7 on AWS and use it to read JSON data from a Kafka topic. However, this can be optimized in different ways. It is also possible to call subscribe with a regular expression. We will now look at how to create custom deserializers for your own objects and how to use Avro and its deserializers. The subcribe() method takes a list of topics as a parameter, so it’s pretty simple to use: Here we simply create a list with a single element: the topic name customerCountries. This command will have no effect if in the Kafka server.properties file, if delete.topic.enable is not set to be true. Here is what the exit code will look like if the consumer is running in the main application thread. The other old API is called high-level consumer or ZookeeperConsumerConnector. In order to understand how to read data from Kafka, you first need to understand its consumers and consumer groups. poll() returns a list of records. To make sure an application gets all the messages in a topic, ensure the application has its own consumer group. The way consumers maintain membership in a consumer group and ownership of the partitions assigned to them is by sending heartbeats to a Kafka broker designated as the group coordinator (this broker can be different for different consumer groups). The same thing happens when a consumer shuts down or crashes; it leaves the group, and the partitions it used to consume will be consumed by one of the remaining consumers. In addition, when partitions are moved from one consumer to another, the consumer loses its current state; if it was caching any data, it will need to refresh its caches—slowing down the application until the consumer sets up its state again. If C1 and C2 described previously used RoundRobin assignment, C1 would have partitions 0 and 2 from topic T1 and partition 1 from topic T2. Similarly, Kafka consumers require deserializers to convert byte arrays received from Kafka into Java objects. So far we’ve seen how to use poll() to start consuming messages from the last committed offset in each partition and to proceed in processing all messages in sequence. Because each topic has an uneven number of partitions and the assignment is done for each topic independently, the first consumer ends up with more partitions than the second. sending demo json data to the kafka topic. 4. In general, if all consumers are subscribed to the same topics (a very common scenario), RoundRobin assignment will end up with all consumers having the same number of partitions (or at most 1 partition difference). But what if you want to commit more frequently than that? It will also trigger a rebalance immediately rather than wait for the group coordinator to discover that the consumer stopped sending heartbeats and is likely dead, which will take longer and therefore result in a longer period of time in which consumers can’t consume messages from a subset of the partitions. In the next section we will show a more involved example that also demonstrates the use of onPartitionsAssigned(): We start by implementing a ConsumerRebalanceListener. The first time you call poll() with a new consumer, it is responsible for finding the GroupCoordinator, joining the consumer group, and receiving a partition assignment. We’ll discuss the different options for committing offsets later in this chapter. After defining the listener class, we have to register an instance of it. Understanding how consumers commit offsets is critical when writing reliable consumers, so we took time to explain the different ways this can be done. Camus with JSON. Kafka Connect’s Elasticsearch sink connector has been improved in 5.3.1 to fully support Elasticsearch 7. We call the action of updating the current position in the partition a commit. However, there is still a chance that our application will crash after the record was stored in the database but before we committed offsets, causing the record to be processed again and the database to contain duplicates. The Spark Python job from this tutorial will also be edited to use StatsD to interface with Graphite Carbon. Typically, this behavior is just what you want, but in some cases you want something much simpler. Kafka with AVRO vs., Kafka with Protobuf vs., Kafka with JSON Schema Protobuf is especially cool, and offers up some neat opportunities beyond what was possible in Avro. Now that you know how to produce and consume events with Kafka, the next chapter explains some of the internals of a Kafka implementation. This is less relevant to readers running Apache Kafka 0.10.1 or later. You can’t just call commitSync() or commitAsync()—this will commit the last offset returned, which you didn’t get to process yet. A better solution would be to use a standard message format such as JSON, Thrift, Protobuf, or Avro. We use ConsumerRebalanceLister and seek() to make sure we start processing at the offsets stored in the database: We use an imaginary method here to commit the transaction in the database. In this example, you load JSON format data from a Kafka topic named topic_json into a single column Greenplum Database table named single_json_column.You perform the load as the Greenplum role gpadmin.The table single_json_column resides in the public schema in a Greenplum database named testdb. Basic data streaming applications move data from a source bucket to a destination bucket. This configuration is used to prevent a livelock, where the application did not crash but fails to make progress for some reason. Note that consumer.wakeup() is the only consumer method that is safe to call from a different thread. Fortunately, the consumer API allows you to call commitSync() and commitAsync() and pass a map of partitions and offsets that you wish to commit. You do this by passing a ConsumerRebalanceListener when calling the subscribe() method we discussed previously. It is used in logging and metrics, and for quotas. During those seconds, no messages will be processed from the partitions owned by the dead consumer. A link will be added HERE when Part 3 is available. Another important consideration when setting max.partition.fetch.bytes is the amount of time it takes the consumer to process data. So far we have focused on learning the consumer API, but we’ve only looked at a few of the configuration properties—just the mandatory bootstrap.servers, group.id, key.deserializer, and value.deserializer. At the time of writing, Apache Kafka still has two older clients written in Scala that are part of the kafka.consumer package, which is part of the core Kafka module. partition.fetch.bytes must be larger than the largest message a broker will accept (determined by the max.message.bytes property in the broker configuration), or the broker may have messages that the consumer will be unable to consume, in which case the consumer will hang trying to read them. (Just like poll(), close() also commits offsets automatically.) Of course, when committing specific offsets you still need to perform all the error handling we’ve seen in previous sections. Data from the MQTT server is written into the Kafka topic. Being JSON the most common way to intercomunicate, and having the schema with the data, we will explore how we build a producer in scala to start populating our system. To feed data, just copy one line at a time from person.json file and paste it on the console where Kafka Producer shell is running. bin/kafka-console-producer.sh \ --broker-list localhost:9092 --topic json_topic 2. Setting session.timeout.ms higher will reduce the chance of accidental rebalance, but also means it will take longer to detect a real failure. Here I’m going to demonstrate how to send Java objects as JSON and map any incoming JSON string into Java object. Camus needs to be told how to read messages from Kafka, and in what format they should be written to HDFS. Closing the consumer will commit offsets if needed and will send the group coordinator a message that the consumer is leaving the group. Then we’ll know that either we are done with the record and the offset is committed or we are not and the record will be reprocessed. Exercise your consumer rights by contacting us at [email protected]. This controls the maximum number of records that a single call to poll() will return. According to direction of the data moved, the connector is classified as: Apache Kafka often serves as a central component in the overall data architecture with other systems pumping data into it. One drawback of manual commit is that the application is blocked until the broker responds to the commit request. The rest of the chapter will discuss some of the challenges with older behaviors and how the programmer can handle them. Most developers exercise more control over the time at which offsets are committed—both to eliminate the possibility of missing messages and to reduce the number of messages duplicated during rebalancing. JSON should be serialized and produced to Kafka as UTF-8 byte strings, one JSON object per Kafka message. The first step to start consuming records is to create a KafkaConsumer instance. You add consumers to an existing consumer group to scale the reading and processing of messages from the topics, so each additional consumer in a group will only get a subset of the messages. This will be based on the “op” parameter found at the end of each JSON data string. Here, we decide to commit current offsets every 1,000 records. By default, Kafka has two assignment strategies: Assigns to each consumer a consecutive subset of partitions from each topic it subscribes to. You may need to edit the Spark transformation to filter specific kinds of CDC data based on the “op” parameter in CDC data. Photo credit: Adli Wahid Every now and then I get a request from my colleagues who would like to delete some or all the records from a Kafka topic. The other two properties, key.deserializer and value.deserializer, are similar to the serializers defined for the producer, but rather than specifying classes that turn Java objects to byte arrays, you need to specify classes that can take a byte array and turn it into a Java object. A man with a passion for information technology. Reassignment of partitions to consumers also happen when the topics the consumer group is consuming are modified (e.g., if an administrator adds new partitions). The job will save the Kafka … By default, Kafka will wait up to 500 ms. One consumer per thread is the rule. Let’s take topic T1 with four partitions. parsed_recipes:- As the name suggests, this will be parsed data of each recipe in JSON format. Consumers are usually long-running applications that continuously poll Kafka for more data. This property is closely related to heartbeat.interval.ms. You’ll want to catch the exception to make sure your application doesn’t exit unexpectedly, but there is no need to do anything with it. There is a temporary communication problem, so the broker never gets the request and therefore never responds. As we saw in the previous section, consumers in a consumer group share ownership of the partitions in the topics they subscribe to. If more than session.timeout.ms passes without the consumer sending a heartbeat to the group coordinator, it is considered dead and the group coordinator will trigger a rebalance of the consumer group to allocate partitions from the dead consumer to the other consumers in the group. If this is set to 0, poll() will return immediately; otherwise, it will wait for the specified number of milliseconds for data to arrive from the broker. All the consumer configuration is documented in Apache Kafka documentation. cp /etc/spark/conf/spark-env.sh.template /etc/spark/conf/spark-env.sh, cp /etc/spark/conf/log4j.properties.template /etc/spark/conf/log4j.properties, /etc/kafka/bin/zookeeper-server-start.sh /etc/kafka/config/zookeeper.properties &> zookeeper_log &, /etc/kafka/bin/kafka-server-start.sh /etc/kafka/config/server.properties &> broker_log &, /etc/kafka/bin/connect-distributed.sh /etc/kafka/config/connect-distributed.properties &> connect_log &. It is common to use the callback to log commit errors or to count them in a metric, but if you want to use the callback for retries, you need to be aware of the problem with commit order: We send the commit and carry on, but if the commit fails, the failure and the offsets will be logged. Rebalances are important because they provide the consumer group with high availability and scalability (allowing us to easily and safely add and remove consumers), but in the normal course of events they are fairly undesirable. Reading data from Kafka is a bit different than reading data from other messaging systems, and there are few unique concepts and ideas involved. max. Let’s take a look at some of the more important properties. A PartitionAssignor is a class that, given consumers and topics they subscribed to, decides which partitions will be assigned to which consumer. This can be any string, and will be used by the brokers to identify messages sent from the client. Once we are done “processing” all the records in the current batch, we call commitSync to commit the last offset in the batch, before polling for additional messages. There are many different ways to implement exactly-once semantics by storing offsets and data in an external store, but all of them will need to use the ConsumerRebalanceListener and seek() to make sure offsets are stored in time and that the consumer starts reading messages from the correct location.