Debugging Spark In HDInsight

Sajib Mahmood gives various methods for debugging Spark applications running on an HDInsight cluster:

Spark Application Master

To access Spark UI for the running application and get more detailed information on its execution use the Application Master link and navigate through different tabs containing more information on jobs, stages, executors and so on.

These methods also apply for on-prem Spark clusters, although the resource locations might be a little different.

Partition Handling In Spark 2.1

Eric Liang, et al, discuss a change to Spark 2.1 which will make certain partitioned table access faster:

In Spark 2.1, we drastically improve the initial latency of queries that touch a small fraction of table partitions. In some cases, queries that took tens of minutes on a fresh Spark cluster now execute in seconds. Our improvements cut down on table memory overheads, and make the SQL experience starting cold comparable to that on a “hot” cluster with table metadata fully cached in memory.

This looks like a nice improvement in Spark.

Analyzing Taxi Data With Microsoft R Server

Kevin Feasel

2016-12-15

R, Spark

Ali Zaidi builds a Spark cluster to analyze 1.1 billion taxi cab rides using Microsoft R Server:

In a similar spirit to how sparklyr allowed us to reuse our functions from the dplyr package to manipulate Spark DataFrames, the RxSpark API allows a data scientist to develop code that can be deployed in a multitude of environments. This allows the developer to shift their focus from writing code that’s specific to a certain environment, and instead focus on the complex analysis of their data science problem. We call this flexibility Write Once, Deploy Anywhere, or WODA for the acronym lovers.

For a deeper dive into the RevoScaleR package, I recommend you take a look at the online course, Analyzing Big Data with Microsoft R Server. Much of this blogpost follows along the last section of the course, on deployment to Spark.

R isn’t just for small, one-off jobs anymore.

ETL With Spark

Eric Maynard demonstrates that moving data across Hadoop clusters can be sped up by using Spark:

By leveraging Spark for distribution, we can achieve the same results much more quickly and with the same amount of code. By keeping data in HDFS throughout the process, we were able to ingest the same data as before in about 36 seconds. Let’s take a look at Spark code which produced equivalent results as the bash script shown above — note that a more parameterized version of this code code and of all code referenced in this article can be found down below in the Resources section.

Read the whole thing.

Installing Zeppelin On Windows 10

Paul Hernandez shows how to install Apache Zeppelin on Windows 10:

There are several settings you can adjust. Basically, there are two main files in the ZEPPELIN_DIR\conf :

  • zeppelin-env
  • zeppelin-site.xml

In the first one you can configure some interpreter settings. In the second more aspects related to the Website, like for instance, the Zeppelin server port (I am using the 8080 but most probably yours is already used by another application)

This is a very clear walkthrough.  Jupyter is still easier to install, but Paul’s blog post lowers that Zeppelin installation learning curve.

Using Spark MLlib For Categorization

Taras Matyashovskyy uses Apache Spark MLlib to categorize songs in different genres:

The roadmap for implementation was pretty straightforward:

  • Collect the raw data set of the lyrics (~65k sentences in total):

    • Black Sabbath, In Flames, Iron Maiden, Metallica, Moonspell, Nightwish, Sentenced, etc.
    • Abba, Ace of Base, Backstreet Boys, Britney Spears, Christina Aguilera, Madonna, etc.
  • Create training set, i.e. label (0 for metal | 1 for pop) + features (represented as double vectors)

  • Train logistic regression that is the obvious selection for the classification

This is a supervised learning problem, and is pretty fun to walk through.

Spark Clusters On Spot Pricing

Sameer Farooqui explains spot pricing with respect to AWS servers:

The idea behind Spot instances is to allow you to bid on spare Amazon EC2 compute capacity. You choose the max price you’re willing to pay per EC2 instance hour. If your bid meets or exceeds the Spot market price, you win the Spot instances. However, unlike traditional bidding, when your Spot instances start running, you pay the live Spot market price (not your bid amount). Spot prices fluctuate based on the supply and demand of available EC2 compute capacity and are specific to different regions and availability zones.

So, although you may have bid 0.55 cents per hour for a r3.2xlarge instance, you’ll end up paying only 0.10 cents an hour if that’s what the going rate is for the region and availability zone.

Databricks uses spot pricing for Community Edition clusters to control costs.  Click through for a very interesting discussion of spot pricing and how they take advantage of it.

Maximum Temperatures With Spark Languages

Kevin Feasel

2016-11-04

Spark

Praveen Sripati has a two-part series on getting aggregates by year in various Spark languages.  In part one, he looks at Python:

Hadoop – The Definitive Guide revolves around the example of finding the maximum temperature for a particular year from the weather data set. The code for the same is here and the data here. Below is the Spark code implemented in Python for the same.

In part 2, he looks at Spark SQL:

In the previous blog, we looked at how find out the maximum temperature of each year from the weather dataset. Below is the code for the same using Spark SQL which is a layer on top of Spark. SQL on Spark was supported using Shark which is being replaced by Spark SQL.Here is a nice blog from DataBricks on the future of SQL on Spark.

There’s no Scala example here, but it’s pretty straightforward as well.

Spark And .NET

Kevin Feasel

2016-11-04

Spark

Bharath Venkatesh shows how to make Spark calls using the .NET ODBC driver:

Prerequisite

Before you begin, you must have the following:

Check it out.  Using Spark on .NET is pretty easy.

Basics Of Spark

Kevin Feasel

2016-11-01

Spark

Jen Underwood gives a quick explanation of Spark as well as an introduction to SparkSQL and PySpark:

Spark’s distributed data-sharing concept is called “Resilient Distributed Datasets,” or RDD. RDDs are fault-tolerant collections of objects partitioned across a cluster that can be queried in parallel and used in a variety of workload types. RDDs are created by applying operations called “transformations” with map, filter, and groupBy clauses. They can persist in memory for rapid reuse. If an RDD data does not fit in memory, Spark will overflow it to disk.

If you’re not familiar with Spark, now’s as good a time as any to learn.

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