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Category: Streaming

Message Transformation Within Kafka

Published 2017-09-08 by Kevin Feasel

Robin Moffatt shows how to use Single Message Transforms inside Kafka Connect to reshape messages as you send them downstream:

Single Message Transforms (SMT) is a functionality within Kafka Connect that enables the transformation … of single messages. Clever naming, right?! Anything that’s more complex, such as aggregating or joins streams of data should be done with Kafka Streams — but simple transformations can be done within Kafka Connect itself, without needing a single line of code.

SMTs are applied to messages as they flow through Kafka Connect; inbound it modifies the message before it hits Kafka, outbound and the message in Kafka remains untouched but the data landed downstream is modified.

There’s quite a bit you can do with this, so check it out.

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How The New York Times Uses Apache Kafka

Published 2017-09-07 by Kevin Feasel

Boerge Svingen gives us an architectural overview of how the New York Times uses Apache Kafka to link different services together:

These are all sources of what we call published content. This is content that has been written, edited, and that is considered ready for public consumption.

On the other side we have a wide range of services and applications that need access to this published content — there are search engines, personalization services, feed generators, as well as all the different front-end applications, like the website and the native apps. Whenever an asset is published, it should be made available to all these systems with very low latency — this is news, after all — and without data loss.

This article describes a new approach we developed to solving this problem, based on a log-based architecture powered by Apache KafkaTM. We call it the Publishing Pipeline. The focus of the article will be on back-end systems. Specifically, we will cover how Kafka is used for storing all the articles ever published by The New York Times, and how Kafka and the Streams API is used to feed published content in real-time to the various applications and systems that make it available to our readers.  The new architecture is summarized in the diagram below, and we will deep-dive into the architecture in the remainder of this article.

This is a nice write-up of a real-world use case for Kafka.

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Lambda And Kappa Architectures

Published 2017-08-31 by Kevin Feasel

Michael Verrilli has a post contrasting the Lambda and Kappa data architectures:

Any query may get a complete picture by retrieving data from both the batch views and the real-time views. The queries will get the best of both worlds. The batch views may be processed with more complex or expensive rules and may have better data quality and less skew, while the real-time views give you up to the moment access to the latest possible data. As time goes on, real-time data expires and is replaced with data in the batch views.

One additional benefit to this architecture is that you can replay the same incoming data and produce new views in case code or formula changes.

The biggest detraction to this architecture has been the need to maintain two distinct (and possibly complex) systems to generate both batch and speed layers. Luckily with Spark Streaming (abstraction layer) or Talend (Spark Batch and Streaming code generator), this has become far less of an issue… although the operational burden still exists.

I haven’t seen much on the topic of Big Data architectures this year; it seems like it was a much more popular topic last year.

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KSQL: Streaming SQL For Kafka

Published 2017-08-29 by Kevin Feasel

Neha Narkhende announces KSQL:

I’m really excited to announce KSQL, a streaming SQL engine for Apache KafkaTM. KSQL lowers the entry bar to the world of stream processing, providing a simple and completely interactive SQL interface for processing data in Kafka. You no longer need to write code in a programming language such as Java or Python! KSQL is open-source (Apache 2.0 licensed), distributed, scalable, reliable, and real-time. It supports a wide range of powerful stream processing operations including aggregations, joins, windowing, sessionization, and much more.

Feasel’s Law wins again.  The syntax looks pretty similar to Spark Streaming and Stream Analytics, so if you get those, you’ll get this.

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Learning Spark Structured Streaming

Published 2017-08-28 by Kevin Feasel

Jules Damji has a nice compendium of links and additional resources for people wanting to learn more about Apache Spark’s Structured Streaming:

Structured Streaming In Apache Spark: A new high-level API for streaming

Databricks’ engineers and Apache Spark committers Matei Zaharia, Tathagata Das, Michael Armbrust and Reynold Xin expound on why streaming applications are difficult to write, and how Structured Streaming addresses all the underlying complexities.

There’s quite a bit of reading material on the other side.

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Kafka Connect To Elasticsearch

Published 2017-08-24 by Kevin Feasel

Robin Moffatt shows how to take data from Kafka Connect and feed it into Elasticsearch:

Whilst Kafka Connect is part of Apache Kafka itself, if you want to stream data from Kafka to Elasticsearch you’ll want the Confluent Open Source distribution (or at least, the Elasticsearch connector).

The configuration is pretty simple. As before, see inline comments for details

It’s worth noting that if you’re using the same convertor throughout your pipelines (Avro, in this case) you’d actually put this in the Connect worker config itself rather than repeating it for each connector configuration.

This is a simple example which shows just how easy it can be.

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A Simple Example With Spark And Kafka

Published 2017-08-18 by Kevin Feasel

Gary Dusbabek has a nice example showing how to build a simple application with Spark and Kafka:

This is a hands-on tutorial that can be followed along by anyone with programming experience. If your programming skills are rusty, or you are technically minded but new to programming, we have done our best to make this tutorial approachable. Still, there are a few prerequisites in terms of knowledge and tools.

The following tools will be used:

  • Git—to manage and clone source code

  • Docker—to run some services in containers

  • Java 8 (Oracle JDK)—programming language and a runtime (execution) environment used by Maven and Scala

  • Maven 3—to compile the code we write

  • Some kind of code editor or IDE—we used the community edition of IntelliJ while creating this tutorial

  • Scala—programming language that uses the Java runtime. All examples are written using Scala 2.12. Note: You do not need to download Scala.

The Hello World of streaming apps is a Twitter client.

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Kafka Connect Done Easy

Published 2017-08-14 by Kevin Feasel

Robin Moffatt shows how to build a simple Kafka Connect flow:

This is pretty cool – the update_ts column is managed automagically by MySQL (other RDBMS have similar functionality), and Kafka Connect’s JDBC connector is using this to pick out new and updated rows from the database.

As a side note here, Kafka Connect tracks the offset of the data that its read using the connect-offsets topic. Even if you delete and recreate the connector, if the connector has the same name it will retain the same offsets previously stored. So if you want to start from scratch, you’ll want to change the connector name – for example, use an incrementing suffix for each test version you work with. You can actually check the content of the connect-offsets topic easily:

This is part 1 of a mini-series, but does show you how to build connections to stream data from MySQL into Kafka and then into a flat file.

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Analyzing Twitter Data With Storm In HDInsight

Published 2017-08-11 by Kevin Feasel

Nischal S shows how to configure an HDInsight cluster to process tweets, followed by loading them into a Power BI dashboard:

When we need to process streams of real-time data, Storm is a great contender. Examples of streaming data are the number of consumer clicks and navigations on a website, IIS or user logs, IoT data, and social network information. In all these scenarios, we use real-time data processing. Apache Storm can process real-time unbounded streams of data.

The term “unbounded” defines streams of data with no start or end. Here, the processing of data is continuous and in real-time. Twitter is a good example. Twitter data is continuous, has no start or end time, and is provided in real-time by millions of Twitter users around the world.

Storm wouldn’t rank in my top three technologies for doing this, but it certainly does the job.

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Unit Testing Kafka Streams

Published 2017-08-07 by Kevin Feasel

Anuj Saxena shows us how to build mocks for streams in Kafka Streams:

Here, we are using Kafka streams in our applications. We are done with the implementation but again, the most important thing left is testing. This blog is about how to test the application we have created. For this, I’ll be taking the sample app I created in my previous blog for both high-level DSL and low-level processor API.

Traditionally, we test our Kafka application with an integration test for which we need to create a ZooKeeper and a real Kafka broker. After that, we need a mock producer and mock consumer for our app to produce the inputs and receive the outputs. That makes it such a big hassle just to test our app. Testing it for real scenarios and for the actual integration test, this is needed without a doubt.

Click through for an example.

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