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Category: Spark

The Power Of Resilient Distributed Datasets

Published 2018-08-27 by Kevin Feasel

Ramandeep Kaur explains just how powerful Resilient Distributed Datasets are:

A fault-tolerant collection of elements that can be operated on in parallel:  “Resilient Distributed Dataset” a.k.a. RDD

RDD (Resilient Distributed Dataset) is the fundamental data structure of Apache Spark which are an immutable collection of objects which computes on the different node of the cluster. Each and every dataset in Spark RDD is logically partitioned across many servers so that they can be computed on different nodes of the cluster.

RDDs provide a restricted form of shared memory, based on coarse-grained transformations rather than fine-grained updates to shared state.

Coarse-grained transformations are those that are applied over an entire dataset. On the other hand, a fine grained transaction is one applied on smaller set, may be a single row. But with fine grained transactions you have to save the updates which can be costlier but it is flexible than a coarse grained one.

Read on for more about the fundamental data structure in Spark.

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Faster User-Defined Functions In SparkR

Published 2018-08-20 by Kevin Feasel

Liang Zhang and Hossein Falaki note a major performance improvement for functions in SparkR using the latest version of the Databricks Runtime:

SparkR offers four APIs that run a user-defined function in R to a SparkDataFrame

  • dapply()
  • dapplyCollect()
  • gapply()
  • gapplyCollect()

dapply() allows you to run an R function on each partition of the SparkDataFrame and returns the result as a new SparkDataFrame, on which you may apply other transformations or actions. gapply() allows you to apply a function to each grouped partition consisting of a key and the corresponding rows in a SparkDataFrame. dapplyCollect() and gapplyCollect()are shortcuts if you want to call collect() on the result.

The following diagram illustrates the serialization and deserialization performed during the execution of the UDF. The data gets serialized twice and deserialized twice in total, all of which are row-wise.

By vectorizing data serialization and deserialization in Databricks Runtime 4.3, we encode and decode all the values of a column at once. This eliminates the primary bottleneck which row-wise serialization, and significantly improves SparkR’s UDF performance. Also, the benefit from the vectorization is more drastic for larger datasets.

It looks like they get some pretty serious gains from this change.

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Last-Click Attribution With Databricks Delta

Published 2018-08-16 by Kevin Feasel

Caryl Yuhas and Denny Lee give us an example of building a last-click digital marketing attribution model with Databricks Delta:

The first thing we will need to do is to establish the impression and conversion data streams.   The impression data stream provides us a real-time view of the attributes associated with those customers who were served the digital ad (impression) while the conversion stream denotes customers who have performed an action (e.g. click the ad, purchased an item, etc.) based on that ad.

With Structured Streaming in Databricks, you can quickly plug into the stream as Databricks supports direct connectivity to Kafka (Apache Kafka, Apache Kafka on AWS, Apache Kafka on HDInsight) and Kinesis as noted in the following code snippet (this is for impressions, repeat this step for conversions)

This is definitely an interesting approach to the problem.  Check it out.

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Getting Started With Azure Databricks

Published 2018-08-15 by Kevin Feasel

David Peter Hansen has a quick walkthrough of Azure Databricks:

RUN MACHINE LEARNING JOBS ON A SINGLE NODE

A Databricks cluster has one driver node and one or more worker nodes. The Databricks runtime includes common used Python libraries, such as scikit-learn. However, they do not distribute their algorithms.

Running a ML job only on the driver might not be what we are looking for. It is not distributed and we could as well run it on our computer or in a Data Science Virtual Machine. However, some machine learning tasks can still take advantage of distributed computation and it a good way to take an existing single-node workflow and transition it to a distributed workflow.

This great example notebooks that uses scikit-learn shows how this is done.

Read the whole thing.

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Databricks Delta: Data Skipping And ZORDER Clustering

Published 2018-08-08 by Kevin Feasel

Adrian Ionescu explains a couple of concepts which can help make selective queries with Databricks much faster:

The general use-case for these features is to improve the performance of needle-in-the-haystack kind of queries against huge data sets. The typical RDBMS solution, namely secondary indexes, is not practical in a big data context due to scalability reasons.

If you’re familiar with big data systems (be it Apache Spark, Hive, Impala, Vertica, etc.), you might already be thinking: (horizontal) partitioning.

Quick reminder: In Spark, just like Hive, partitioning works by having one subdirectory for every distinct value of the partition column(s). Queries with filters on the partition column(s) can then benefit from partition pruning, i.e., avoid scanning any partition that doesn’t satisfy those filters.

The main question is: What columns do you partition by?
And the typical answer is: The ones you’re most likely to filter by in time-sensitive queries.
But… What if there are multiple (say 4+), equally relevant columns?

Read the whole thing.

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The Basics Of RDDs In Apache Spark

Published 2018-07-30 by Kevin Feasel

Anmol Sarna walks us through some of the basics of Resilient Distributed Datasets in Apache Spark:

  • Resilient, i.e. fault-tolerant with the help of RDD lineage graph and so able to recompute missing or damaged partitions due to node failures.

  • Distributed with data residing on multiple nodes in a cluster.

  • Dataset is a collection of partitioned data.

Now we know what RDD stands for. Now let’s try to understand it.

It’s a nice intro to the topic.  And even though there are other data models which sit on top of RDDs to make life easier for developers, it’s still important to understand the core model in Spark.

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Using rquery On Databricks

Published 2018-07-27 by Kevin Feasel

Nina Zumel and John Mount talk about rquery, a relational data transformation engine for R which runs on Spark:

rquery is based on an appreciation of Codds’ relational algebra. Codd’s relational algebra is a formal algebra that describes the semantics of data transformations and queries. Previous, hierarchical, databases required associations to be represented as functions or maps. Codd relaxed this requirement from functions to relations, allowing tables that represent more powerful associations (allowing, for instance, two-way multimaps).

Codd’s work allows most significant data transformations to be decomposed into sequences made up from a smaller set of fundamental operations:

  • select (row selection)
  • project (column selection/aggregation)
  • Cartesian product (table joins, row binding, and set difference)
  • extend (derived columns, keyword was in Tutorial-D).

One of the earliest and still most common implementation of Codd’s algebra is SQL. Formally Codd’s algebra assumes that all rows in a table are unique; SQL further relaxes this restriction to allow multisets.

rquery is another realization of the Codd algebra that implements the above operators, some higher-order operators, and emphasizes a right to left pipe notation. This gives the Spark user an additional way to work effectively.

They include a fairly lengthy example and give a great introduction to the tool.  It’s now officially on my list of stuff to try out.

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Analyzing Clickstream Data With Spark

Published 2018-07-25 by Kevin Feasel

Tony Cruz and Denny Lee analyze advertising data in Spark and predict click counts given certain input features:

Let’s look at a concrete example with the Click-Through Rate Prediction dataset of ad impressions and clicks from the data science website Kaggle.  The goal of this workflow is to create a machine learning model that, given a new ad impression, predicts whether or not there will be a click.

To build our advanced analytics workflow, let’s focus on the three main steps:

  • ETL

  • Data Exploration, for example, using SQL

  • Advanced Analytics / Machine Learning

The Databricks blog has a couple other examples, but this was the most interesting one for me.

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Building TensorFlow Neural Networks On Spark With Keras

Published 2018-07-18 by Kevin Feasel

Jules Damji has an example of using the PyCharm IDE to use Keras to build TensorFlow neural network models on the Databricks MLflow library:

Our example in the video is a simple Keras network, modified from Keras Model Examples, that creates a simple multi-layer binary classification model with a couple of hidden and dropout layers and respective activation functions. Binary classification is a common machine learning task applied widely to classify images or text into two classes. For example, an image is a cat or dog; or a tweet is positive or negative in sentiment; and whether mail is spam or not spam.

But the point here is not so much to demonstrate a complex neural network model as to show the ease with which you can develop with Keras and TensorFlow, log an MLflow run, and experiment—all within PyCharm on your laptop.

Click through for the video and explanation of the process.

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Sharing R Notebooks

Published 2018-07-10 by Kevin Feasel

Hanyu Cui and Hossein Falaki show how to share a notebook using RMarkdown:

RMarkdown is the dynamic document format RStudio uses. It is normal Markdown plus embedded R (or any other language) code that can be executed to produce outputs, including tables and charts, within the document. Hence, after changing your R code, you can just rerun all code in the RMarkdown file rather than redo the whole run-copy-paste cycle. And an RMarkdown file can be directly exported into multiple formats, including HTML, PDF,  and Word.

Click through for the demo.

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