The Kafka component is used for communicating with Apache Kafka message broker. Maven users will need to add the following dependency to their pom. URL of the Kafka brokers to use. The format is host1:port1,host2:port2, and the list can be a subset of brokers or a VIP pointing to a subset of brokers. This option is known as bootstrap. The client id is a user-specified string sent in each request to help trace calls.
It should logically identify the application making the request. Allows to pre-configure the Kafka component with common options that the endpoints will reuse. To use a custom HeaderFilterStrategy to filter header to and from Camel message. The maximum amount of time in milliseconds to wait when reconnecting to a broker that has repeatedly failed to connect. If provided, the backoff per host will increase exponentially for each consecutive connection failure, up to this maximum.
Timeout in milli seconds to wait gracefully for the consumer or producer to shutdown and terminate its worker threads. Whether to allow doing manual commits via KafkaManualCommit.
If this option is enabled then an instance of KafkaManualCommit is stored on the Exchange message header, which allows end users to access this API and perform manual offset commits via the Kafka consumer. If true, periodically commit to ZooKeeper the offset of messages already fetched by the consumer. This committed offset will be used when the process fails as the position from which the new consumer will begin.
Whether to perform an explicit auto commit when the consumer stops to ensure the broker has a commit from the last consumed message. This requires the option autoCommitEnable is turned on. The possible values are: sync, async, or none. And sync is the default value. The value can be one of: sync, async, none. What to do when there is no initial offset in ZooKeeper or if an offset is out of range: earliest : automatically reset the offset to the earliest offset latest : automatically reset the offset to the latest offset fail: throw exception to the consumer.
The value can be one of: latest, earliest, none. This options controls what happens when a consumer is processing an exchange and it fails. If the option is false then the consumer continues to the next message and processes it. If the option is true then the consumer breaks out, and will seek back to offset of the message that caused a failure, and then re-attempt to process this message. However this can lead to endless processing of the same message if its bound to fail every time, eg a poison message.
Allows for bridging the consumer to the Camel routing Error Handler, which mean any exceptions occurred while the consumer is trying to pickup incoming messages, or the likes, will now be processed as a message and handled by the routing Error Handler.
By default the consumer will use the org. Automatically check the CRC32 of the records consumed. This ensures no on-the-wire or on-disk corruption to the messages occurred. This check adds some overhead, so it may be disabled in cases seeking extreme performance. The configuration controls the maximum amount of time the client will wait for the response of a request.
If the response is not received before the timeout elapses the client will resend the request if necessary or fail the request if retries are exhausted. The maximum amount of data the server should return for a fetch request This is not an absolute maximum, if the first message in the first non-empty partition of the fetch is larger than this value, the message will still be returned to ensure that the consumer can make progress.
The maximum message size accepted by the broker is defined via message. Note that the consumer performs multiple fetches in parallel. The minimum amount of data the server should return for a fetch request.It can be used for streaming data into Kafka from numerous places including databases, message queues and flat files, as well as streaming data from Kafka out to targets such as document stores, NoSQL, databases, object storage and so on.
In a perfect world, nothing would go wrong, but when it does we want our pipelines to handle it as gracefully as possible. Since Apache Kafka 2. Sometimes you may want to stop processing as soon as an error occurs. In this example, the connector is configured to read JSON data from a topic, writing it to a flat file. To fix the pipeline, we need to resolve the issue with the message on the source topic. However, if it is indeed a bad record on the topic, we need to find a way to not block the processing of all of the other records that are valid.
Now when we launch the connector against the same source topic as before, in which there is a mix of valid and invalid messagesit runs just fine:. There are no errors written to the Kafka Connect worker output, even with invalid messages on the source topic being read by the connector. Data from the valid messages is written to the output file, as expected:. If you do set errors. The most simplistic approach to determining if messages are being dropped is to tally the number of messages on the source topic with those written to the output:.
A much more solid route to take would be using JMX metrics and actively monitoring and alerting on error message rates:. We can see that there are errors occurring, but we have no idea what and on which messages. This is where the concept of a dead letter queue comes in. Valid messages are processed as normal, and the pipeline keeps on running.
Invalid messages can then be inspected from the dead letter queueand ignored or fixed and reprocessed as required.
Using the same source topic as before—with a mix of good and bad JSON records—the new connector runs successfully:. So our pipeline is intact and continues to run, and now we also have data in the dead letter queue topic. This can be seen from the metrics:.This enables choreographed service collaborations, where many components can subscribe to events stored in the event log and react to them asynchronously.
Making sense of the communication and dataflow patterns inside these choreographies, however, can be a challenge. Unlike orchestration communication—which is based on remote procedure calls RPCsinvolves services communicating directly with each other and has explicit dependencies—choreographies contain implicit dependencies and rely on how the components handle and emit events.
Moreover, these distributed components can be implemented in different programming languages, deployed on different platforms and managed by different teams. Distributed tracing has been key for helping us create a clear understanding of how applications are related to each other.
Most importantly, it has helped us analyze latency issues and debug applications at runtime. This article describes how to instrument Kafka-based applications with distributed tracing capabilities in order to make dataflows between event-based components more visible.
Distributed tracing is a method for recording, collecting and recreating execution traces from distributed components. These traces are represented as a set of recorded steps, where every step is known as a span. Each span contains information about the service and operations being traced, the latency i. This service has a dependency on another service called translation, which emits a response according to the geographical location of the request.
Spring Kafka – Adding Custom Header to Kafka Message Example
Then another span is created to record how long it takes to call the translation service. The translation service, on the other hand, receives a call to the translate operation and records tracing data. For the translation service to correlate its spans with the caller service traces, it receives tracing metadata via context propagation i. A distributed tracing system is designed to collect, process and store tracing data to be queried and visualized later. In order to generate tracing data, applications can explicitly declare where to create spans and measure latency by annotating code with a tracer utility.
As this is not a trivial task in most codebases where tracing is an afterthought, tracing libraries offer instrumentation in order to collect most of the interactions between components e. Up next, our focus will be on how to instrument Kafka-based applications using Zipkin as our distributed tracing infrastructure.
Zipkin is one of the most popular distributed tracing projects available. It was open sourced by Twitter inand now it is in the process of becoming an Apache Software Foundation project. The transport and database components are pluggable. Figure 1.The messages to send may be individual FlowFiles or may be delimited, using a user-specified delimiter, such as a new-line.
In the list below, the names of required properties appear in bold.[Distributed Tracing - NYC] Tracing a Kafka Pipeline -- Mahmoud Saada Agolo
Any other properties not in bold are considered optional. The table also indicates any default values, and whether a property supports the NiFi Expression Language. Dynamic Properties allow the user to specify both the name and value of a property. Name Value Description The name of a Kafka configuration property. The value of a given Kafka configuration property. These properties will be added on the Kafka configuration after loading any provided configuration properties.
In the event a dynamic property represents a property that was already set, its value will be ignored and WARN message logged. Supports Expression Language: false. Protocol used to communicate with brokers. Corresponds to Kafka's 'security. The Kerberos principal name that Kafka runs as. Supports Expression Language: true will be evaluated using variable registry only. Specifies the Kerberos Credentials Controller Service that should be used for authenticating with Kerberos.
The Kerberos principal that will be used to connect to brokers. This principal will be set into 'sasl. The Kerberos keytab that will be used to connect to brokers. The name of the Kafka Topic to publish to. Supports Expression Language: true will be evaluated using flow file attributes and variable registry. Specifies the requirement for guaranteeing that a message is sent to Kafka. Corresponds to Kafka's 'acks' property. Specifies whether or not NiFi should provide Transactional guarantees when communicating with Kafka.
If there is a problem sending data to Kafka, and this property is set to false, then the messages that have already been sent to Kafka will continue on and be delivered to consumers. If this is set to true, then the Kafka transaction will be rolled back so that those messages are not available to consumers. A Regular Expression that is matched against all FlowFile attribute names. Any attribute whose name matches the regex will be added to the Kafka messages as a Header.
If not specified, no FlowFile attributes will be added as headers. The Key to use for the Message. If not specified, the flow file attribute 'kafka. Beware that setting Kafka key and demarcating at the same time may potentially lead to many Kafka messages with the same key.To publish messages to Kafka you have to create a producer. Simply call the producer function of the client to create it:. Kafka v0. To send headers with your message, include the key headers with the values.
To produce to multiple topics at the same time, use sendBatch. This can be useful, for example, when migrating between two topics.
It's possible to assign a custom partitioner to the producer. A partitioner is a function which returns another function responsible for the partition selection, something like this:. To use your custom partitioner, use the option createPartitioner when creating the producer.
The JavaCompatiblePartitioner should be compatible with the default partitioner that ships with the Java Kafka client. This can be important to meet the co-partitioning requirement when joining multiple topics.
Use the JavaCompatiblePartitioner by importing it and providing it to the Producer constructor:. Since KafkaJS aims to have as small footprint and as few dependencies as possible, only the GZIP codec is part of the core functionality. Providing plugins supporting other codecs might be considered in the future. Take a look at the official readme for more information. A codec is an object with two async functions: compress and decompress.
Import the libraries and define the codec object:. If this value is larger than the transaction. If enabled producer will ensure each message is written exactly once. Acks must be set to -1 "all". If falsey then no limit.
None By default, the producer is configured to distribute the messages with the following logic: If a partition is specified in the message, use it If no partition is specified but a key is present choose a partition based on a hash murmur2 of the key If no partition or key is present choose a partition in a round-robin fashion Message Headers Kafka v0. Example: await producer. Custom partitioner It's possible to assign a custom partitioner to the producer.
Take a look at Retry for more information. The period of time in milliseconds after which we force a refresh of metadata even if we haven't seen any partition leadership changes to proactively discover any new brokers or partitions.
I also need to add some headers for the message, for example file name, timestamps etc so at the consumer end I can process the message based on file name and other headers. What I am currently doing is creating a object and wrapping the raw message and headers in it and sending the object in a byte array as a message. I would like to know if there is a way by which I can add custom headers while publishing the message?
Kafka is agnostic to the message content and doesn't provide any special means to enrich it so this is something you need to do yourself. A common way of dealing with these things is to use a structured format such as json, avro or similar where you are free to define the necessary fields and could easily add metadata to your message and ship it off to the Kafka brokers.
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For now contains an Avro based implementation but it can be extended to use any other serialization framework of your choice. You just have to create a de serializer for the objects you want to have on the stream Avro based or not and let AvroMessageSerializer work its magic.
You can create your own small java application to send the message with headers to kafka. Write the following code in intellij or any supporting IDE To verify your data you can check the topic in the kafka control center and verify the headers sent. Learn more. Asked 5 years, 4 months ago. Active 7 months ago. Viewed 30k times. I am sending a file as a message by converting it to a byte array using kafka producer. DavidPostill 6, 8 8 gold badges 32 32 silver badges 49 49 bronze badges. Active Oldest Votes.
Lundahl Lundahl 5, 1 1 gold badge 32 32 silver badges 33 33 bronze badges. Kafka v0. You can add them when creating a ProducerRecord like this: new ProducerRecord key, value, headersMichal Borowiecki Michal Borowiecki 3, 1 1 gold badge 7 7 silver badges 17 17 bronze badges. Please demonstrate how this solves the problem in the answer itself. This is still a very young library but I feel it can save many people a lot of time!
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