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Category: Spark

Using Kafka And Elasticsearch For IoT Data

Published 2018-03-02 by Kevin Feasel

Angelos Petheriotis talks about building an IoT structure which handles ten billion messages per day:

We splitted the pipeline into 2 main units: The aggregator job and the persisting job. The aggregator has one and only one responsibility. To read from the input kafka topic, process the messages and finally emit them to a new kafka topic. The persisting job then takes over and whenever a message is received from topic temperatures.aggregated it persists to elasticsearch.

The above approach might seem to be an overkill at first but it provides a lot of benefits (but also some drawbacks). Having two units means that each unit’s health won’t directly affect each other. If the processing job fails due OOM, the persisting job will still be healthy.

One major benefit we’ve seen using this approach is the replay capabilities this approach offers. For example, if at some point we need to persist the messages from temperatures.aggregated to Cassandra, it’s just a matter of wiring a new pipeline and start consuming the kafka topic. If we had one job for processing and persisting, we would have to reprocess every record from the thermostat.data, which comes with a great computational and time cost.

Angelos also discusses some issues he and his team had with Spark Streaming on this data set, so it’s an interesting comparison.

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Launching A Sparklyr Cluster

Published 2018-02-27 by Kevin Feasel

David Smith shows how to launch a sparklyr cluster in Azure:

When you’re finished, shut down your cluster using the aztk spark cluster delete command. (While you can delete the nodes from the Pools view in the Azure portal, the command does some additional cleanup for you.) You’ll be charged for each node in the cluster at the usual VM rates for as long as the cluster is provisioned. (One cost-saving option is to use low-priority VMs for the nodes, for savings of up to 90% compared to the usual rates.)

That’s it! Once you get used to it, it’s all quick and easy — the longest part is waiting for the cluster to spin up in Step 5. This is just a summary, but the full details see the guide SparklyR on Azure with AZTK.

It’ll take a bit more than five minutes to get started, but it is a good sight easier than building the servers yourself.

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Installing Apache Mesos On EC2

Published 2018-02-08 by Kevin Feasel

Anubhav Tarar has a guide for setting up Apache Mesos along with Spark and Hadoop on EC2:

Apache Mesos is open source project for managing computer clusters originally developed at the University Of California. It sits between the application layer and operating system to manage the application works efficiently on the large-scale distributed environment.

In this blog, we will see how to setup mesos client and master on ec2 from scratch.

Read on for the step-by-step guide.

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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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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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Leveraging Hive In Pyspark

Published 2018-01-23 by Kevin Feasel

Fisseha Berhane shows how to use Spark to connect Python to Hive:

If we are using earlier Spark versions, we have to use HiveContext which is variant of Spark SQL that integrates with data stored in Hive. Even when we do not have an existing Hive deployment, we can still enable Hive support.
In this tutorial, I am using standalone Spark. When not configured by the Hive-site.xml, the context automatically creates metastore_db in the current directory.

As shown below, initially, we do not have metastore_db but after we instantiate SparkSession with Hive support, we see that metastore_db has been created. Further, when we execute create database command, spark-warehouse is created.

Click through for a bunch of examples.

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Unit Testing Spark Streaming DStreams

Published 2018-01-22 by Kevin Feasel

Anuj Saxena shows how to create unit tests for DStreams in Spark Streaming:

The method ‘ testOperation ‘ takes the output of the operation performed on the ‘inputPair’ and check whether it is equal to the ‘outputPair’ and just like this, we can test our business logic.

This short snippet lets you test your business logic without forcing you to create even a Spark session. You can mock the whole streaming environment and test your business logic easily.

This was a simple example of unary operations on DStreams. Similarly, we can test binary operations and window operations on DStreams.

Click through for an example with code.

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Set Operations In Spark

Published 2018-01-18 by Kevin Feasel

Fisseha Berhane compares SparkSQL, DataFrames, and classic RDDs when performing certain set-based operations:

In this fourth part, we will see set operators in Spark the RDD way, the DataFrame way and the SparkSQL way.
Also, check out my other recent blog posts on Spark on Analyzing the Bible and the Quran using Spark and Spark DataFrames: Exploring Chicago Crimes.

The data and the notebooks can be downloaded from my GitHub repository.
The three types of set operators in RDD, DataFrame and SQL approach are shown below.

This is where SparkSQL (and SQL in general) shines, although the DataFrame approach is also compact.

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Setting Up SparklyR In Azure

Published 2018-01-17 by Kevin Feasel

David Smith shows how you can spin up a Spark cluster in Azure and install SparklyR on top of it:

The SparklyR package from RStudio provides a high-level interface to Spark from R. This means you can create R objects that point to data frames stored in the Spark cluster and apply some familiar R paradigms (like dplyr) to the data, all the while leveraging Spark’s distributed architecture without having to worry about memory limitations in R. You can also access the distributed machine-learning algorithms included in Spark directly from R functions.

If you don’t happen to have a cluster of Spark-enabled machines set up in a nearby well-ventilated closet, you can easily set one up in your favorite cloud service. For Azure, one option is to launch a Spark cluster in HDInsight, which also includes the extensions of Microsoft ML Server. While this service recently had a significant price reduction, it’s still more expensive than running a “vanilla” Spark-and-R cluster. If you’d like to take the vanilla route, a new guide details how to set up Spark cluster on Azure for use with SparklyR.

Read on for more details.

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