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Category: Spark

Microsoft R Server On Spark

Published 2016-08-10 by Kevin Feasel

Max Kaznady, et al, discuss using Microsoft R Server on Spark to perform rapid prototyping against the NYC Taxi dataset:

Once the cluster is created, you can connect to the edge node where MRS is already pre-installed by SSHing to r-server.YOURCLUSTERNAME-ssh.azurehdinsight.net with the credentials which you supplied during the cluster creation process. In order to do this in MobaXterm, you can go to Sessions, then New Sessions and then SSH.

The default installation of HDI Spark on Linux cluster does not come with RStudio Server installed on the edge node. RStudio Server is a popular open source integrated development environment (IDE) available for R that provides a browser-based IDE for use by remote clients. This tool allows you to benefit from all the power of R, Spark and Microsoft HDInsight cluster through your browser. In order to install RStudio you can follow the steps detailed in the guide, which reduces to running a script on the edge node.

If you’ve been meaning to get further into Spark & R, this is a great article to follow along with on your own.

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Developing Spark Applications In .NET

Published 2016-08-05 by Kevin Feasel

Kaarthik Sivashanmugam talks about Mobius, a Microsoft-driven .NET wrapper for Spark:

The C# language binding to Spark is similar to the Python and R bindings. In fact, Mobius follows the same design pattern and leverages the existing implementation of language binding components in Spark where applicable for consistency and reuse. The following picture shows the dependency between the .NET application and the C# API in Mobius, which internally depends on Spark’s public API in Scala and Java and extends PythonRDD from PySpark to implement CSharpRDD.

Looks like there’s some fuzziness on just how well F# is supported.  Still, this is very exciting as a way of bridging the gap for .NET developers.

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Structured Streaming

Published 2016-08-03 by Kevin Feasel

Matei Zaharia, et al discuss how to use structured streaming in Apache Spark 2.0:

In Structured Streaming, we tackle the issue of semantics head-on by making a strong guarantee about the system: at any time, the output of the application is equivalent to executing a batch job on a prefix of the data. For example, in our monitoring application, the result table in MySQL will always be equivalent to taking a prefix of each phone’s update stream (whatever data made it to the system so far) and running the SQL query we showed above. There will never be “open” events counted faster than “close” events, duplicate updates on failure, etc. Structured Streaming automatically handles consistency and reliability both within the engine and in interactions with external systems (e.g. updating MySQL transactionally).

If you want to learn more about streaming data using Spark, check this out.

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Don’t Use Cron For Scheduling Hadoop Jobs

Published 2016-08-02 by Kevin Feasel

Matthew Rathbone explains why cron is not a great choice for scheduling Hadoop and Spark jobs:

Reason 3: Poor transparency for teammates

Which jobs are running right now? Which are going to run today? How long do these jobs take? How do I schedule my job? What machine should I schedule it on? These are all questions that are impossible to answer without building custom orchestration around your Cron process – time you’d be better off spending on building a better system.

Matthew then gives us four alternative products.

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Structured Streaming

Published 2016-08-02 by Kevin Feasel

Andrew Ray explains streaming solutions using Spark 2.0:

If you are familiar with traditional Spark streaming you may notice that the above example is lacking an explicit batch duration. In structured streaming the equivalent feature is a trigger. By default it will run batches as quickly as possible, starting the next batch as soon as more data is available and the previous batch is complete. You can also set a more traditional fixed batch interval for your trigger. In the future more flexible trigger options will be added.

A related consequence is that windows are no longer forced to be a multiple of the batch duration. Furthermore, windows needn’t be only on processing time anymore, we can rearrange events that may have been delayed or arrived out of order and window by event time. Suppose our input stream had a column event_time that we wanted to do windowed counts on. Then we could do something like the following to get counts of events in a 1 minute window:

Right now, there are some pretty strict limitations on this new streaming, but I imagine they’ll loosen up quite soon.

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Spark 2.0 Out

Published 2016-07-29 by Kevin Feasel

Apache Spark 2.0 has officially been released.  Vinay Shukla gives us some highlights:

Performance
Project Tungsten has completed another major phase and with new completely new stage code generation, significant performance improvements have been delivered. Parquet and ORC file processing have also delivered performance improvements.

Databricks Community Edition offers (tiny) free clusters with Spark 2.0 on top of Scala 2.10 and Scala 2.11.

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Using The Spark-HBase Connector

Published 2016-07-25 by Kevin Feasel

Anunay Tiwari shows how to use the Spark-HBase connector in HDInsight:

The Spark-Hbase Connector provides an easy way to store and access data from HBase clusters with Spark jobs. HBase is really successful for highest level of data scale needs. Thus, existing Spark customers should definitely explore this storage option. Similarly, if the customers are already having HDinsight HBase clusters and they want to access their data by Spark jobs then there is no need to move data to any other storage medium. In both the cases, the connector will be extremely useful.

I’m not the biggest fan of HBase, but if it’s part of your environment, you should definitely look at this Spark connector.

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Clustering With Spark

Published 2016-07-22 by Kevin Feasel

Konur Unyelioglu shows how to implement k-means and Guassian clustering techniques in Apache Spark using MLlib:

Clustering is the task of assigning entities into groups based on similarities among those entities. The goal is to construct clusters in such a way that entities in one cluster are more closely related, i.e. similar to each other than entities in other clusters. As opposed to classification problems where the goal is to learn based on examples, clustering involves learning based on observation. For this reason, it is a form of unsupervised learning task.

There are many different clustering algorithms and a central notion in all of those is the definition of ’similarity’ between the entities that are being grouped. Different clustering algorithms may have different ways of measuring the similarity. In many clustering algorithms, another common notion is the so-called cluster center, which is a basis to represent the cluster. For example, in K-means clustering algorithm, the cluster center is the arithmetic mean position of all the points in that cluster.

This is a fairly lengthy article but if you want to get into machine learning with Spark, it’s a good one.

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SparkR + Zeppelin

Published 2016-07-21 by Kevin Feasel

I take a look at using SparkR and Zeppelin:

My goal is to do some of the things that I did in my Touching on Advanced Topics post.  Originally, I wanted to replicate that analysis in its entirety using Zeppelin, but this proved to be pretty difficult, for reasons that I mention below.  As a result, I was only able to do some—but not all—of the anticipated work.  I think a more seasoned R / SparkR practitioner could do what I wanted, but that’s not me, at least not today.

With that in mind, let’s start messing around.

SparkR is a bit of a mindset change from traditional R.

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Understanding Spark APIs

Published 2016-07-19 by Kevin Feasel

Jules Damji explains when to use RDDs, when to use DataFrames, and when to use Datasets in Spark:

Like an RDD, a DataFrame is an immutable distributed collection of data. Unlike an RDD, data is organized into named columns, like a table in a relational database. Designed to make large data sets processing even easier, DataFrame allows developers to impose a structure onto a distributed collection of data, allowing higher-level abstraction; it provides a domain specific language API to manipulate your distributed data; and makes Spark accessible to a wider audience, beyond specialized data engineers.

With Spark 2.0, the balance moves in favor of the more structured data types.  What’s old is new; what’s unstructured is structured…

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