SparkR: Enabling Interactive Data Science at Scale

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"SparkR" presentation by Shivaram Venkataraman and Zongheng Yang

Software

SparkR: Enabling Interactive
Data Science at Scale
Shivaram Venkataraman
Zongheng Yang

Fast !
Scalable
Flexible
Statistics !
Plots
Packages

Outline
SparkR API
Live Demo
Design Details

RDD
Parallel Collection
Transformations
map
filter
groupBy
…
Actions
count
collect
saveAsTextFile
…

R + RDD =
RRDD
lapply
lapplyPartition
groupByKey
reduceByKey
sampleRDD
collect
cache
filter
…
broadcast
includePackage
textFile
parallelize

SparkR – R package for Spark
R
RRDD
Spark

$Example: word_count.R library(SparkR) lines <-‐ textFile(sc, “hdfs://my_text_file”) words <-‐ flatMap(lines, function(line) { strsplit(line, " ")[[1]] }) wordCount <-‐ lapply(words, function(word) { list(word, 1L) })$

A
b
|| Ax − b ||2 Minimize
x = (ATA)−1ATb

Dataflow
Local
R Spark
Context
Java
Spark
Context
JNI
Worker
Worker

Dataflow
Local Worker
R Spark
Context
Worker Java
Spark
Context
JNI
Spark
Executor exec R
Spark
Executor exec R

From http://obeautifulcode.com/R/How-R-Searches-And-Finds-Stuff/

Pipelined RDD
words
<-‐
flatMap(lines,…)
wordCount
<-‐
lapply(words,…)
Spark
Executor exec R Spark
Executor R exec

Pipelined RDD
Spark
Executor exec R Spark
Executor R exec
Spark
Executor exec R R Spark
Executor

Alpha developer release
One line install !
install_github("amplab-‐extras/SparkR-‐pkg",
subdir="pkg")

SparkR Implementation
Very similar to PySpark
Spark is easy to extend
329 lines of Scala code
2079 lines of R code
693 lines of test code in R

EC2 setup scripts
All Spark examples
MNIST demo
YARN, Windows support
Also on github

Developer Community
13 contributors
(10 from outside AMPLab)
Collaboration with Alteryx

On the Roadmap
High level DataFrame API
Integrating Spark’s MLLib from R
Merge with Apache Spark

SparkR
RDD à distributed lists
Run R on clusters
Re-use existing packages
Combine scalability & utility

SparkR
https://github.com/amplab-extras/SparkR-pkg
Shivaram Venkataraman shivaram@cs.berkeley.edu
Zongheng Yang zongheng.y@gmail.com
SparkR mailing list sparkr-dev@googlegroups.com

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R has evolved to become an ideal environment for exploratory data analysis. The language is highly flexible - there is an R package for almost any algorithm and the environment comes with integrated help and visualization. SparkR brings distributed computing and the ability to handle very large data to this list. SparkR is an R package distributed within Apache Spark. It exposes Spark DataFrames, which was inspired by R data.frames, to R. With Spark DataFrames, and Spark’s in-memory computing engine, R users can interactively analyze and explore terabyte size data sets. In this webinar, Hossein will introduce SparkR and how it integrates the two worlds of Spark and R. He will demonstrate one of the most important use cases of SparkR: the exploratory analysis of very large data. Specifically, he will show how Spark’s features and capabilities, such as caching distributed data and integrated SQL execution, complement R’s great tools such as visualization and diverse packages in a real world data analysis project with big data.

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We are a company driven by inquisitive data scientists, having developed a pragmatic and interdisciplinary approach, which has evolved over the decades working with over 100 clients across multiple industries. Combining several Data Science techniques from statistics, machine learning, deep learning, decision science, cognitive science, and business intelligence, with our ecosystem of technology platforms, we have produced unprecedented solutions. Welcome to the Data Science Analytics team that can do it all, from architecture to algorithms. Our practice delivers data driven solutions, including Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, and Prescriptive Analytics. We employ a number of technologies in the area of Big Data and Advanced Analytics such as DataStax (Cassandra), Databricks (Spark), Cloudera, Hortonworks, MapR, R, SAS, Matlab, SPSS and Advanced Data Visualizations. This presentation is designed for Spark Enthusiasts to get started and details of the course are below. 1. Introduction to Apache Spark 2. Functional Programming + Scala 3. Spark Core 4. Spark SQL + Parquet 5. Advanced Libraries 6. Tips & Tricks 7. Where do I go from here?

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Conference: Engage2024 in Antwerp Type: Workshop Speakers: Florian Vogler, Henning Kunz, Christoph Adler Title: Navigating the Future with The Hitchhiker's Guide to Notes and Domino 14 Abstract: Embark on an exhilarating journey with industry trailblazers Florian Vogler, Henning Kunz, and Christoph Adler in this not-to-be-missed workshop at the forefront of the tech universe. Get ready for a thrilling kick-off as we navigate the current state of the HCL universe, setting the stage for an exploration of the groundbreaking Notes and Domino 14. Discover the latest enhancements and revolutionary features that will redefine your experience. In this interactive session, unlock a treasure trove of tips and tricks to elevate your utilization of version 14, both with and without the game-changing panagenda MarvelClient. Brace yourself for also diving into Nomad, Nomad Web, and VoltMX, expanding your horizons in the expansive HCL landscape. Be a part of this exclusive opportunity to stay ahead in the ever-evolving world of HCL technologies. Your journey to mastering Notes and Domino 14 begins here. And remember, in the spirit of intergalactic exploration, don't forget to bring your towel!

W01_panagenda_Navigating-the-Future-with-The-Hitchhikers-Guide-to-Notes-and-D...

panagenda

In the realm of real-time applications, Large Language Models (LLMs) have long dominated language-centric tasks, while tools like OpenCV have excelled in the visual domain. However, the future (maybe) lies in the fusion of LLMs and deep learning, giving birth to the revolutionary concept of Large Action Models (LAMs). Imagine a world where AI not only comprehends language but mimics human actions on technology interfaces. For example, the Rabbit r1 device presented at CES 2024, driven by an AI operating system and LAM, brings this vision to life. It executes complex commands, leveraging GUIs with unprecedented ease. In this presentation, join me on a journey as a software engineer tinkering with WebRTC, Janus, and LLM/LAMs. Together, we’ll evaluate the current state of these AI technologies, unraveling the potential they hold for shaping the future of real-time applications.

Unveiling the Tech Salsa of LAMs with Janus in Real-Time Applications

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Data spaces in distributed environments should be allowed to evolve in agile ways providing data space owners with large flexibility about which data they store. Agility and heterogeneity, however, jeopardize data exchanges because representations may build on varying ontologies and data consumers may not rely on the semantic correctness of their queries in the context of semantically heterogeneous, evolving data spaces. Graph data spaces are one example of a powerful model for representing and querying data whose semantics may change over time. To assert and enforce conditions on individual graph data spaces, shape languages (e.g SHACL) have been developed. We investigate the question of how querying and programming can be guarded by reasoning over SHACL constraints in a distributed setting and we sketch a picture of how a future landscape based on semantically heterogeneous data spaces might look like.

Shapes for Sharing between Graph Data Spaces - and Epistemic Querying of RDF-...

Steffen Staab

Unlocking the Future of AI Agents with Large Language Models

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SparkR: Enabling Interactive Data Science at Scale

1. SparkR: Enabling Interactive Data Science at Scale Shivaram Venkataraman Zongheng Yang

2. Fast ! Scalable Flexible

3. Statistics ! Packages Plots

4. Fast ! Scalable Flexible Statistics ! Plots Packages

5. Outline SparkR API Live Demo Design Details

6. RDD Parallel Collection Transformations map filter groupBy … Actions count collect saveAsTextFile …

7. R + RDD = R2D2

8. R + RDD = RRDD lapply lapplyPartition groupByKey reduceByKey sampleRDD collect cache filter … broadcast includePackage textFile parallelize

9. SparkR – R package for Spark R RRDD Spark

10. Example: word_count.R library(SparkR) lines <-‐ textFile(sc, “hdfs://my_text_file”)

11. Example: word_count.R library(SparkR) lines <-‐ textFile(sc, “hdfs://my_text_file”) words <-‐ flatMap(lines, function(line) { strsplit(line, " ")[[1]] }) wordCount <-‐ lapply(words, function(word) { list(word, 1L) })

12. Example: word_count.R library(SparkR) lines <-‐ textFile(sc, “hdfs://my_text_file”) words <-‐ flatMap(lines, function(line) { strsplit(line, " ")[[1]] }) wordCount <-‐ lapply(words, function(word) { list(word, 1L) }) counts <-‐ reduceByKey(wordCount, "+", 2L) output <-‐ collect(counts)

13. Demo: Digit Classification

14. MNIST

15. A b || Ax − b ||2 Minimize x = (ATA)−1ATb

16. How does this work ?

17. Dataflow Local Worker Worker

18. Dataflow Local R Worker Worker

19. Dataflow Local R Spark Context Java Spark Context JNI Worker Worker

20. Dataflow Local Worker R Spark Context Worker Java Spark Context JNI Spark Executor exec R Spark Executor exec R

21.

22. From http://obeautifulcode.com/R/How-R-Searches-And-Finds-Stuff/

23.

24. Dataflow Local Worker R Spark Context Worker Java Spark Context JNI Spark Executor exec R Spark Executor exec R

25. Pipelined RDD words <-‐ flatMap(lines,…) wordCount <-‐ lapply(words,…) Spark Executor exec R Spark Executor R exec

26. Pipelined RDD Spark Executor exec R Spark Executor R exec Spark Executor exec R R Spark Executor

27. Alpha developer release One line install ! install_github("amplab-‐extras/SparkR-‐pkg", subdir="pkg")

28. SparkR Implementation Very similar to PySpark Spark is easy to extend 329 lines of Scala code 2079 lines of R code 693 lines of test code in R

29. EC2 setup scripts All Spark examples MNIST demo YARN, Windows support Also on github

30. Developer Community 13 contributors (10 from outside AMPLab) Collaboration with Alteryx

31. On the Roadmap High level DataFrame API Integrating Spark’s MLLib from R Merge with Apache Spark

32. SparkR RDD à distributed lists Run R on clusters Re-use existing packages Combine scalability & utility

33. SparkR https://github.com/amplab-extras/SparkR-pkg Shivaram Venkataraman shivaram@cs.berkeley.edu Zongheng Yang zongheng.y@gmail.com SparkR mailing list sparkr-dev@googlegroups.com

SparkR: Enabling Interactive Data Science at Scale

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