VEGAS: The Missing Matplotlib for Scala/Apache Spark with DB Tsai and Roger Menezes

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In this talk, we’ll present techniques for visualizing large scale machine learning systems in Spark. These are techniques that are employed by Netflix to understand and refine the machine learning models behind Netflix’s famous recommender systems that are used to personalize the Netflix experience for their 99 millions members around the world. Essential to these techniques is Vegas, a new OSS Scala library that aims to be the “missing MatPlotLib” for Spark/Scala. We’ll talk about the design of Vegas and its usage in Scala notebooks to visualize Machine Learning Models.

Data & Analytics

Vegas
The Missing Matplotlib for
Scala/Spark
DB Tsai
Roger Menezes

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of the
Experience is
Machine
Learned

Genres: 23 rows/page average
Sims: 10 rows/page average

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Machine Learning at Netflix
● Optimize the Experimentation usecase vs Productionization
● Experimentation
○ Opportunity sizing, Data Exploration
○ Feature Identification and Selection
○ Tweaks to ML algos
○ Model Evaluation

Experimenter’s loop
Problem
Explore
Data
Identify
Features
Produce
Model
Evaluate
Model
Share
Findings

Notebooks
● Optimal for Experimentation
● Sharing reproducible research
○ Facilitates feedback loop with Product Managers
● End to end ML experiment.
○ Interactivity drives productivity

Python Notebooks
● Seamless Experience - ML experimentation
● Well known Scientific computing libraries
● Huge catalog of Visualization plotting libraries
○ Matplotlib, Seaborn, Bokeh, BQPlot, Lightning, etc.

Scala Notebooks
● Zeppelin, Jupyter, Databricks, Spark-Notebooks, ...
● Computing library gap filling up
● Lack of Visualization Libraries
○ Main friction point in adoption
○ End to End ML use case not convincing

Introducing Vegas
● Visualization Library in Scala
● Mainly built for the notebook use case
● Scala wrapper around Vega-Lite
○ Missing MatPlotLib for the Scala/Spark world.

DECLARATIVE
STATISTICAL
VISUALIZATION
GRAMMAR
IN SCALA
You tell it WHAT should be done with the data, and it knows
HOW to do it!
Operations such as filtering, aggregation, faceting are built
into the visualization, rather than putting the burden on the
user to massage the data into shape.
Complex visualizations can be built with a few high level
abstractions:
DATA
TRANS-
FORMS
SCALES
GUIDES MARKS
cf : Altair Talk by Brian Granger in PyData 2016 https://youtu.be/v5mrwq7yJc4

Added Bonus of Declarative
Visualizations:
INTERACTIVITY!
D3JS
VEGAS
VEGAS CODE EXPANDS OUT TO D3JS CODE!

Anatomy of a plot: Channels
X/Y channel
Shape Channel
Size Channel
Color Channel

5. Built-in spark support
Vegas
.withDataFrame(myDataFrame)
.encodeX(“population”)
.encodeY(“age”)
Mapped Columns
Pass In DF.

6. Visual statistics
● Advanced Binning
● Sorting
● Scaling
● Custom Transforms
● Time Series
● Aggregation
● Filtering
● Math functions (log, etc)
● Descriptive Statistics

1. Specify in Scala
2. Embed HTML
(iFrame)
3. Render within
iFrame using JS

VEGA
D3JS
VEGA-LITE*
VEGAS
MOREABSTRACTION SCALA DSL EMITS TYPE-CHECKED
VEGA-LITE JSON
VEGA-LITE CONVERTS INTERNALLY
TO VEGA JSON SPEC
VEGA TRANSLATES JSON TO D3JS
CODE THAT CAN BE VERY VERBOSE
A SCALA DSL FOR VEGA-LITE
* Vega-Lite

Contributors
Aish DB Roger
Sudeep Jeremy

@NetflixResearch
@rogermenezes @dbtsai
The missing MatPlotLib
for Scala/Spark
http://vegas-viz.org

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Getting Ready to use Redis with Apache Spark is a technical tutorial designed to address integrating Redis with an Apache Spark deployment to increase the performance of serving complex decision models. To set the context for the session, we start with a quick introduction to Redis and the capabilities Redis provides. We cover the basic data types provided by Redis and cover the module system. Using an ad serving use-case, we look at how Redis can improve the performance and reduce the cost of using complex ML-models in production. Attendees will be guided through the key steps of setting up and integrating Redis with Spark, including how to train a model using Spark then load and serve it using Redis, as well as how to work with the Spark Redis module. The capabilities of the Redis Machine Learning Module (redis-ml) will be discussed focusing primarily on decision trees and regression (linear and logistic) with code examples to demonstrate how to use these feature. At the end of the session, developers should feel confident building a prototype/proof-of-concept application using Redis and Spark. Attendees will understand how Redis complements Spark and how to use Redis to serve complex, ML-models with high performance.

Getting Ready to Use Redis with Apache Spark with Dvir Volk

Spark Summit

Here we present a general supervised framework for record deduplication and author-disambiguation via Spark. This work differentiates itself by – Application of Databricks and AWS makes this a scalable implementation. Compute resources are comparably lower than traditional legacy technology using big boxes 24/7. Scalability is crucial as Elsevier’s Scopus data, the biggest scientific abstract repository, covers roughly 250 million authorships from 70 million abstracts covering a few hundred years. – We create a fingerprint for each content by deep learning and/or word2vec algorithms to expedite pairwise similarity calculation. These encoders substantially reduce compute time while maintaining semantic similarity (unlike traditional TFIDF or predefined taxonomies). We will briefly discuss how to optimize word2vec training with high parallelization. Moreover, we show how these encoders can be used to derive a standard representation for all our entities namely such as documents, authors, users, journals, etc. This standard representation can simplify the recommendation problem into a pairwise similarity search and hence it can offer a basic recommender for cross-product applications where we may not have a dedicate recommender engine designed. – Traditional author-disambiguation or record deduplication algorithms are batch-processing with small to no training data. However, we have roughly 25 million authorships that are manually curated or corrected upon user feedback. Hence, it is crucial to maintain historical profiles and hence we have developed a machine learning implementation to deal with data streams and process them in mini batches or one document at a time. We will discuss how to measure the accuracy of such a system, how to tune it and how to process the raw data of pairwise similarity function into final clusters. Lessons learned from this talk can help all sort of companies where they want to integrate their data or deduplicate their user/customer/product databases.

Deduplication and Author-Disambiguation of Streaming Records via Supervised M...

Spark Summit

The use of large-scale machine learning and data mining methods is becoming ubiquitous in many application domains ranging from business intelligence and bioinformatics to self-driving cars. These methods heavily rely on matrix computations, and it is hence critical to make these computations scalable and efficient. These matrix computations are often complex and involve multiple steps that need to be optimized and sequenced properly for efficient execution. This work presents new efficient and scalable matrix processing and optimization techniques based on Spark. The proposed techniques estimate the sparsity of intermediate matrix-computation results and optimize communication costs. An evaluation plan generator for complex matrix computations is introduced as well as a distributed plan optimizer that exploits dynamic cost-based analysis and rule-based heuristics The result of a matrix operation will often serve as an input to another matrix operation, thus defining the matrix data dependencies within a matrix program. The matrix query plan generator produces query execution plans that minimize memory usage and communication overhead by partitioning the matrix based on the data dependencies in the execution plan. We implemented the proposed matrix techniques inside the Spark SQL, and optimize the matrix execution plan based on Spark SQL Catalyst. We conduct case studies on a series of ML models and matrix computations with special features on different datasets. These are PageRank, GNMF, BFGS, sparse matrix chain multiplications, and a biological data analysis. The open-source library ScaLAPACK and the array-based database SciDB are used for performance evaluation. Our experiments are performed on six real-world datasets are: social network data ( e.g., soc-pokec, cit-Patents, LiveJournal), Twitter2010, Netflix recommendation data, and 1000 Genomes Project sample. Experiments demonstrate that our proposed techniques achieve up to an order-of-magnitude performance.

MatFast: In-Memory Distributed Matrix Computation Processing and Optimization...

Spark Summit

Kapil Malik and Arvind Heda will discuss a solution for interactive querying of large scale structured data, stored in a distributed file system (HDFS / S3), in a scalable and reliable manner using a unique combination of Spark SQL, Apache Zeppelin and Spark Job-server (SJS) on Yarn. The solution is production tested and can cater to thousands of queries processing terabytes of data every day. It contains following components – 1. Zeppelin server : A custom interpreter is deployed, which de-couples spark context from the user notebooks. It connects to the remote spark context on Spark Job-server. A rich set of APIs are exposed for the users. The user input is parsed, validated and executed remotely on SJS. 2. Spark job-server : A custom application is deployed, which implements the set of APIs exposed on Zeppelin custom interpreter, as one or more spark jobs. 3. Context router : It routes different user queries from custom interpreter to one of many Spark Job-servers / contexts. The solution has following characteristics – * Multi-tenancy There are hundreds of users, each having one or more Zeppelin notebooks. All these notebooks connect to same set of Spark contexts for running a job. * Fault tolerance The notebooks do not use Spark interpreter, but a custom interpreter, connecting to a remote context. If one spark context fails, the context router sends user queries to another context. * Load balancing Context router identifies which contexts are under heavy load / responding slowly, and selects the most optimal context for serving a user query. * Efficiency We use Alluxio for caching common datasets. * Elastic resource usage We use spark dynamic allocation for the contexts. This ensures that cluster resources are blocked by this application only when it’s doing some actual work.

Indicium: Interactive Querying at Scale Using Apache Spark, Zeppelin, and Spa...

Spark Summit

spark-bench is an open-source benchmarking tool, and it’s also so much more. spark-bench is a flexible system for simulating, comparing, testing, and benchmarking Spark applications and Spark itself. spark-bench originally began as a benchmarking suite to get timing numbers on very specific algorithms mostly in the machine learning domain. Since then it has morphed into a highly configurable and flexible framework suitable for many use cases. This talk will discuss the high level design and capabilities of spark-bench before walking through some major, practical use cases. Use cases include, but are certainly not limited to: regression testing changes to Spark; comparing performance of different hardware and Spark tuning options; simulating multiple notebook users hitting a cluster at the same time; comparing parameters of a machine learning algorithm on the same set of data; providing insight into bottlenecks through use of compute-intensive and i/o-intensive workloads; and, yes, even benchmarking. In particular this talk will address the use of spark-bench in developing new features features for Spark core.

Apache Spark-Bench: Simulate, Test, Compare, Exercise, and Yes, Benchmark wit...

Spark Summit

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A Tale of Two Graph Frameworks on Spark: GraphFrames and Tinkerpop OLAP Artem...

No More Cumbersomeness: Automatic Predictive Modeling on Apache Spark Marcin ...

Apache Spark and Tensorflow as a Service with Jim Dowling

Next CERN Accelerator Logging Service with Jakub Wozniak

Powering a Startup with Apache Spark with Kevin Kim

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Hiding Apache Spark Complexity for Fast Prototyping of Big Data Applications—...

How Nielsen Utilized Databricks for Large-Scale Research and Development with...

Spline: Apache Spark Lineage not Only for the Banking Industry with Marek Nov...

Goal Based Data Production with Sim Simeonov

Preventing Revenue Leakage and Monitoring Distributed Systems with Machine Le...

Getting Ready to Use Redis with Apache Spark with Dvir Volk

Deduplication and Author-Disambiguation of Streaming Records via Supervised M...

MatFast: In-Memory Distributed Matrix Computation Processing and Optimization...

Indicium: Interactive Querying at Scale Using Apache Spark, Zeppelin, and Spa...

Apache Spark-Bench: Simulate, Test, Compare, Exercise, and Yes, Benchmark wit...

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