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Category: Hadoop

KSQL 0.4 Released

Published 2018-02-02 by Kevin Feasel

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.

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Understanding Kafka Consumers And Offsets

Published 2018-02-02 by Kevin Feasel

Simarpreet Kaur Monga builds a simple Kafka consumer in Scala to demonstrate how offsets work:

The method endOffsets accepts a collection of TopicPartition, for which you want to find the endOffsets.

As I want to find the endOffsets of the partitions that are assigned to my topic, I have passed the value of consumer.assignment() in the parameter of endOffsets. consumer.assignment gives the set of TopicPartitions that the Consumer has been assigned.

Note: You should call the method assignment only after calling poll on the consumer; otherwise, it will give null as the result. Additionally, the method endOffsets doesn’t change the position of the consumer, unlike seek methods, which do change the consumer position/offset.

Read the whole thing.

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Yarn Service Framework Coming

Published 2018-02-01 by Kevin Feasel

Jian He, et al, announce the Yarn Service Framework:

Apache Hadoop YARN is well known as the general resource-management platform for big-data applications such as MapReduce, Hive / Tez and Spark. It abstracts the complicated cluster resource management and scheduling from higher level applications and enables them to focus solely on their own application specific logic.

In addition to big-data apps, another broad spectrum of workloads we see today are long running services such as HBase, Hive/LLAP and container (e.g. Docker) based services. In the past year, the YARN community has been working hard to build first-class support for long running services on YARN.

This is going to ship with Hadoop 3.1.

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PySpark DataFrame Transformations

Published 2018-02-01 by Kevin Feasel

Vincent-Philippe Lauzon shows how to perform data frame transformations using PySpark:

We wanted to look at some more Data Frames, with a bigger data set, more precisely some transformation techniques.  We often say that most of the leg work in Machine learning in data cleansing.  Similarly we can affirm that the clever & insightful aggregation query performed on a large dataset can only be executed after a considerable amount of work has been done into formatting, filtering & massaging data:  data wrangling.

Here, we’ll look at an interesting dataset, the H-1B Visa Petitions 2011-2016 (from Kaggle) and find some good insights with just a few queries, but also some data wrangling.

It is important to note that about everything in this article isn’t specific to Azure Databricks and would work with any distribution of Apache Spark.

The notebook used for this article is persisted on GitHub.

Read on for explanation, or check out the notebook to work on it at your own pace.

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Installing Spark On Windows

Published 2018-01-30 by Kevin Feasel

Nigel Meakins is starting a new series on Spark and his first post involves installing Spark on Windows:

WinUtils provides a number of HDFS-emulating utilities that allow us to run Spark as though it were talking to an HDFS storage system (at least to a certain degree). Without this you will get all manner of file system-related issues wit Spark and won’t get off the launchpad.

Within the WinUtils archive you may have a number of Hortonworks Data Platform versioned folders. For the version of Spark I’m using, being 2.2.1, I have chosen hadoop-2,7,1\bin for my files. Unzip and copy the contents of the bin directory to a directory of your choice. It must however be called ‘bin’ in order to be located by the calling programs. I actually placed mine in the C:\Spark\bin directory together with the other executables that Spark uses but this is not essential.

Once done, you will need to set the following environment variable:

HADOOP_HOME = <your winutils ‘bin’ parent directory>

Note we don’t include the \bin, so for my example this is C:\Spark.

I have a post on installing Spark on Windows that might help if you get stuck on the WinUtils part.

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Subtle Changes In Application Behavior Across Kafka Streams Versions

Published 2018-01-29 by Kevin Feasel

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.

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Azure And The Kappa Architecture

Published 2018-01-26 by Kevin Feasel

Jared Zagelbaum describes the Kappa architecture and points out how there’s limited built-in support in Azure for it:

You can’t support kappa architecture using native cloud services. Cloud providers, including Azure, didn’t design streaming services with kappa in mind. The cost of running streams with TTL greater than 24 hours is more expensive, and generally, the max TTL tops out around 7 days. If you want to run kappa, you’re going to have to run Platform as a Service (PaaS) or Infrastructure as a Service (IaaS), which adds more administration to your architecture. So, what might this look like in Azure?

Read the whole thing.

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Monitoring Kafka Streaming Pipelines

Published 2018-01-25 by Kevin Feasel

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.

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Functions In Spark

Published 2018-01-25 by Kevin Feasel

Fisseha Berhane continues his RDDs vs DataFrames vs SparkSQL series, this time looking at functions:

Let’s use Spark SQL and DataFrame APIs ro retrieve companies ranked by sales totals from the SalesOrderHeader and SalesLTCustomer tables. We will display the first 10 rows from the solution using each method to just compare our answers to make sure we are doing it right.

All three approaches give the same results, though the SQL approach seems to me to be the easiest.

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Lambda Architecture In Azure

Published 2018-01-24 by Kevin Feasel

Jared Zagelbaum describes the Lambda architecture pattern and explains how you can use tooling in Azure to implement it:

Lambda is an organic result of the limitations of existing tools. Distributed systems architects and developers commonly criticize its complexity – and rightly so. Those of us that have worked extensively in Extract-Transform-Load and symmetric multiprocessing systems see red flags when code is replicated in multiple services. Ensuring data quality and code conformity across multiple systems, whether massively parallel processing (MPP) or symmetrically parallel system (SMP), has the same best practice: the least amount of times you reproduce code is always the correct number of times.

We reproduce code in lambda because different services in MPP systems are better at different tasks. The maturity of tools historically hasn’t allowed us to process streams and batch in a single tool. This is starting to change, with Apache Spark emerging as a single preferred compute service for stream and batch querying, hence the timing of Azure Databricks. However, on the storage side, what was meant to be an immutable store that is the data lake in practice, can become the dreaded swamp when governance or testing fails; which is not uncommon. A fundamentally different assumption to how we process data is required to combat this degradation. Enter: the kappa architecture, which we’ll examine in the next post of this series.

Interesting reading.

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