Category: Streaming

Kai Waehner demonstrates building a user-defined function for Kafka Streams:

As you can see, the full implementation is just a few lines of Java code. In general, you need to implement the logic between receiving input and returning output of the UDF in the evaluate()method. You also need to implement exception handling (e.g. invalid input arguments) where applicable. The init() method is empty in this case, but could initialise any required object instances.

Note that this UDF has state: dateFormat can be null or already initialized. However, no worries. You do not have to manage the scope as Kafka Streams (and therefore KSQL) threads are independent of each other. So this won’t cause any issues.

Click through for the entire process.

Apurva Mehta announces the release of KSQL 0.4:

The SHOW TOPICS command has been enhanced to include the number of active consumers and also the number of active consumer groups which are reading the topics.

Consumer groups are a feature of Apache Kafka which enable multiple consumer processes to divide the work of consuming Kafka topic. You can learn more about them in the Kafka Consumer JavaDocs, and of course you should read the SHOW TOPICS documentation for more information.

Read on for the full set of changes.

Aleksandar Pejakovic shows some subtle but important changes to an application running Kafka Streams 0.11 versus 1.0:

One would expect that by changing the version, the previous behavior would remain the same. Well, it hasn’t. What has changed?

After each process method, a punctuate method is called. After punctuateInterval is scheduled, punctuate also occurs. This means the following:

• In the first test scenario, each “Arrived: message_<offset>” message in the console is accompanied with “Punctuate call”. Unsurprisingly, we have one: “Processed: 1” message in output topic. After ten messages, we have another: “Punctuate call” and “Processed: 0” pair.
• In the second scenario, we have nine: “Arrived: message_<offset>” and “Punctuate call” pairs on the console, followed with 9: “Processed: 1” in the output topic. After the pause and tenth message we have: “Arrived: message_<offset>” and 3 “Punctuate call”. In the output topic, we see “Processed: 1”, “Processed: 0”, and “Processed 0”.

Read the whole thing.  This sort of behavioral change can be hard to suss out when testing a streaming application.

Randhir Singh shows how to use open-source tools to monitor Kafka streaming pipelines:

The solution uses the following open-source tools. The solution architecture is illustrated below.

• Apache Kafka Connect is a tool to stream data between Apache Kafka and other components.
• InfluxDB which is a time series database from InfluxData. It will be used to store time series data from Kafka input and output topics.
• Influx sink connector from Datamountaineer. It is a connector and sink to write events from Kafka to InfluxDB.
• Chronograf is an open-source monitoring solution from InfluxData.

Click through for the solution.

Andrew Stevenson announces Stream Reactor 1.0.0 for Kafka Connect 1.0:

Stream Reactor is an Apache License, Version 2.0 open source collection of components built on top of Kafka and provides Kafka Connect compatible connectors to move data between Kafka and popular data stores. Stream Reactor provides source connectors to publish data into Kafka and sink connectorsto bring data from Kafka into other systems. The connectors support KCQL (Kafka Connect Query Language), an open source component of Lenses SQL Enginethat provides an elegant and simple SQL like syntax for selecting fields and routing from sources or topics to Kafka or the target system (topic to target entity mapping, field selection, auto creation, auto evolution, error policies).

We hope you find Stream Reactor useful, and want to give it a try! Stream Reactor has over 25 connectors available, tested and documented, supporting both Kafka 0.11 and Kafka 1.0 and you can give it a go by downloading Lenses Development Environment or find the jars on GitHub, or even build the code locally and help us improve and add even more connectors.

Read on for more details, as well as a link to the GitHub repo.

Tim Spann shows off some nice additions to Apache NiFi:

Another cool processor that I will talk about in greater detail in future articles is the much-requested Spark Processor. The ExecuteSparkInteractive processor with its Livy Controller Service gives you a much better alternative to my hacky REST integration to calling Apache Spark batch and machine learning jobs.

There are a number of enhancements, new processors, and upgrades I’m excited about, but the main reason I am writing today is because of a new feature that allows for having an Agile SDLC with Apache NiFi. This is now enabled by Apache NiFi Registry. It’s as simple as a quick git clone or download and then, you’ll use Apache Maven to install Apache NiFi Registry and start it. This process will become even easier with future Ambari integration for a CLI-free install.

To integrate the Registry with Apache NiFi, you need to add a Registry Client. It’s very simple to add the default local one — see below.

There are several new features in the latest release.

Rathnadevi Manivannan shows how to use Kafka SQL to query streaming data:

Kafka SQL, a streaming SQL engine for Apache Kafka by Confluent, is used for real-time data integration, data monitoring, and data anomaly detection. KSQL is used to read, write, and process Citi Bike trip data in real-time, enrich the trip data with other station details, and find the number of trips started and ended in a day for a particular station. It is also used to publish trip data from the source to other destinations for further analysis.

In this article, let’s discuss enriching the Citi Bike trip data and finding the number of trips on a particular day to and from a particular station.

Read on for a nice tutorial.