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Category: Spark

Text Normalization With Spark

Published 2017-04-14 by Kevin Feasel

Engineers at Treselle Systems have put together a two-part series on text normalization using Apache Spark.  First, they walk through normalizing the text:

We have used Spark shared variable “broadcast” to achieve distributed caching. Broadcast variables are useful when large datasets need to be cached in executors. “stopwords_en.txt” is not a large dataset but we have used in our use case to make use of that feature.

What are Broadcast Variables?
Broadcast variables in Apache Spark is a mechanism for sharing variables across executors that are meant to be read-only. Without broadcast variables, these variables would be shipped to each executor for every transformation and action, which can cause network overhead. However, with broadcast variables, they are shipped once to all executors and are cached for future reference.

From there, they dig into details on what the Spark engine did and why we see what we do:

Note: Stage 2 has both reduceByKey() and sortByKey() operations and as indicated in job summary “saveAsTextFile()” action triggered Job 2. Do you have any guess whether Stage 2 will be further divided into other stages in Job 2? The answer is: yes Job 2 DAG: This job is triggered due to saveAsTextFile() action operation. The job DAG clearly indicates the list of operations used before the saveAsTextFile() operations.Stage 2 in Job 1 is further divided into another stage as Stage 2. In Stage 2 has both reduceByKey() and sortByKey() operations and both operations can shuffle the data so that Stage 2 in Job 1 is broken down into Stage 4 and Stage 5 in Job 2. There are three stages in this job. But, Stage 3 is skipped. The answer for the skipped stage is provided below “What does “Skipped Stages” mean in Spark?” section.

There’s some good information here if you want to become more familiar with how Spark works.

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The Basics Of SparkR

Published 2017-04-13 by Kevin Feasel

Yanbo Liang has an introductory article on what SparkR is and why you might want to use it:

However, data analysis using R is limited by the amount of memory available on a single machine and further as R is single threaded it is often impractical to use R on large datasets. To address R’s scalability issue, the Spark community developed SparkR package which is based on a distributed data frame that enables structured data processing with a syntax familiar to R users. Spark provides distributed processing engine, data source, off-memory data structures. R provides a dynamic environment, interactivity, packages, visualization. SparkR combines the advantages of both Spark and R.

In the following section, we will illustrate how to integrate SparkR with R to solve some typical data science problems from a traditional R users’ perspective.

This is a fairly introductory article, but gives an idea of what SparkR can accomplish.

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Using h2o.ai On HDInsight

Published 2017-04-13 by Kevin Feasel

Xiaoyong Zhu shows how to set up h2o.ai on Azure HDInsight:

H2O Flow is an interactive web-based computational user interface where you can combine code execution, text, mathematics, plots and rich media into a single document, much like Jupyter Notebooks. With H2O Flow, you can capture, rerun, annotate, present, and share your workflow. H2O Flow allows you to use H2O interactively to import files, build models, and iteratively improve them. Based on your models, you can make predictions and add rich text to create vignettes of your work – all within Flow’s browser-based environment. In this blog, we will only focus on its visualization part.

H2O FLOW web service lives in the Spark driver and is routed through the HDInsight gateway, so it can only be accessed when the spark application/Notebook is running

You can click the available link in the Jupyter Notebook, or you can directly access this URL:

https://yourclustername-h2o.apps.azurehdinsight.net/flow/index.html

Setup is pretty easy.

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Probabilistic Record Linking In Spark

Published 2017-04-10 by Kevin Feasel

Tom Lous builds a solution to link similar companies together by address:

Recently a colleague asked me to help her with a data problem, that seemed very straightforward at a glance.
She had purchased a small set of data from the chamber of commerce (Kamer van Koophandel: KvK) that contained roughly 50k small sized companies (5–20FTE), which can be hard to find online.
She noticed that many of those companies share the same address, which makes sense, because a lot of those companies tend to cluster in business complexes.

Read on for the solution.  Like many data problems, it turns out to be a lot more complicated than you’d think at first glance.

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Rolling A Log Analytics System

Published 2017-04-03 by Kevin Feasel

Michael Sun and Jeff Shmain put together a log analytics sytem using several technologies:

This is an example of tiered system design. Tiered system is a system design pattern where data is categorized and stored in different data stores that match best to each category. It can both improve performance and lower the cost of a system. One of the most famous tiered system designs is computer memory hierarchy.  In the log analytics use case, analysts mostly search for logs in recent months, but often run batch jobs to get long term trends from logs in recent years. Therefore, recent logs are indexed and stored in Solr for search, while years of logs are stored in HBase for batch processing. As such, the index in Solr is small, which both improves performance and reduces cost, among other benefits.

Although only months of logs are stored in Solr, the logs before that period are stored in HBase and can be indexed on demand for further analysis.

Now that we have covered a high level architecture of a log analytics system, we will dive into more details of individual components.

This looks like a solid architecture for a logging system and can apply to other cases as well.

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Tuning Kafka And Spark Data Pipelines

Published 2017-03-29 by Kevin Feasel

Larry Murdock explains the tuning options available to Kafka and Spark Streams:

Kafka is not the Ferrari of messaging middleware, rather it is the salt flats rocket car. It is fast, but don’t expect to find an AUX jack for your iPhone. Everything is stripped down for speed.

Compared to other messaging middleware, the core is simpler and handles fewer features. It is a transaction log and its job is to take the message you sent asynchronously and write it to disk as soon as possible, returning an acknowledgement once it is committed via an optional callback. You can force a degree of synchronicity by chaining a get to the send call, but that is kind of cheating Kafka’s intention. It does not send it on to a receiver. It only does pub-sub. It does not handle back pressure for you.

I like this as a high-level overview of the different options available.  Definitely gets a More Research Is Required tag, but this post helps you figure out where to go next.

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replyr

Published 2017-03-08 by Kevin Feasel

John Mount shows off replyr, which is dplyr for remote, distributed data sets (think SparkR or sparklyr):

Suppose we had a large data set hosted on a Spark cluster that we wished to work with using dplyr and sparklyr (for this article we will simulate such using data loaded into Spark from the nycflights13 package).

We will work a trivial example: taking a quick peek at your data. The analyst should always be able to and willing to look at the data.

It is easy to look at the top of the data, or any specific set of rows of the data.

Read on for more details.

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Building A Neural Net

Published 2017-03-01 by Kevin Feasel

Shirin Glander has a great post on using Spark + sparklyr + h2o + rsparkling to build a neural net to study arrhythmia of the heart:

The data I am using to demonstrate the building of neural nets is the arrhythmia dataset from UC Irvine’s machine learning database. It contains 279 features from ECG heart rhythm diagnostics and one output column. I am not going to rename the feature columns because they are too many and the descriptions are too complex. Also, we don’t need to know specifically which features we are looking at for building the models. For a description of each feature, see https://archive.ics.uci.edu/ml/machine-learning-databases/arrhythmia/arrhythmia.names. The output column defines 16 classes: class 1 samples are from healthy ECGs, the remaining classes belong to different types of arrhythmia, with class 16 being all remaining arrhythmia cases that didn’t fit into distinct classes.

Very interesting post.

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Broadcast Nested Loop Joins In Spark

Published 2017-03-01 by Kevin Feasel

Reynold Xin, et al, debug an interesting test case:

While we were pretty happy with the improvement, we noticed that one of the test cases in Databricks started failing. To simulate a hanging query, the test case performed a cross join to produce 1 trillion rows.

spark.range(1000 * 1000).crossJoin(spark.range(1000 * 1000)).count()

On a single node, we expected this query would run infinitely or “hang.” To our surprise, we started seeing this test case failing nondeterministically because sometimes it completed on our Jenkins infrastructure in less than one second, the time limit we put on this query.

You’re not going to get this performance against a real data set, but it was interesting reading their troubleshooting notes.

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Analyzing Flight Data With Sparklyr

Published 2017-02-24 by Kevin Feasel

Aki Ariga continues his sparklyr series with some analysis of US flight data:

In this post, we will show you a visualization and build a predictive model of US flights with sparklyr. Flight visualization code is based on this article.

This post assumes you already have the following tables:

You should make these tables available through Apache Hive or Apache Impala (incubating) with Hue.

There’s some setup work to get this going, but getting a handle on sparklyr looks to be a good idea if you’re in the analytics space.

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