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Performance troubleshooting of distributed data processing systems is a complex task. Apache Spark comes to rescue with a large set of metrics and instrumentation that you can use to understand and improve the performance of your Spark-based applications. You will learn about the available metric-based instrumentation in Apache Spark: executor task metrics and the Dropwizard-based metrics system. The talk will cover how Hadoop and Spark service at CERN is using Apache Spark metrics for troubleshooting performance and measuring production workloads. Notably, the talk will cover how to deploy a performance dashboard for Spark workloads and will cover the use of sparkMeasure, a tool based on the Spark Listener interface. The speaker will discuss the lessons learned so far and what improvements you can expect in this area in Apache Spark 3.0.

Presentation at Spark Summit 2015

Données & analyses

Spark on YARN:
The Road Ahead
Marcelo Vanzin

What to expect...
-  What is YARN?
-  Why YARN for Spark?
-  Current state and future direction for:
-  Resource Management
-  Security

What is YARN?
Resource management and negotiation
-  Manages vcores and memory
-  Multiple users
-  Multiple apps
-  Advanced features

Why YARN for Spark?
-  Advanced resource management features
-  Dynamic allocation for Spark executors
-  Security

Resource Management
Lots of knobs to turn, lots of trial and error.
yarn.nodemanager.resource.memory-‐mb

Executor
Container

spark.yarn.executor.memoryOverhead
(10%)

spark.executor.memory

spark.shuﬄe.memoryFracBon
(0.4)
spark.storage.memoryFracBon
(0.6)

RM (Cont’d)
Dynamic allocation
-  No need to specify number of executors!
-  Application grows and shrinks based on
outstanding task count
-  Still need to specify everything else…

SparkContext
Job 1
Task Task
Executor 1
Task
Task
Executor 2
Task
Dynamic Allocation

Dynamic Allocation (Cont’d)
How to make dynamic allocation better?
-  Reduce allocation latency
-  Data locality hints
-  Handle cached RDDs

Data Locality
-  Allocate executors
close to data
-  More predictable
performance
-  SPARK-4352

Cached RDDs
-  Short term: avoid discarding cached data
-  Keep executors around.
-  Long term:
-  Cache rebalance
-  Container resizing
-  Off-heap caching

Memory Sizing
How to make application sizing easier?
-  Soft container limits (YARN-3119)
-  Overhead memory
-  Simplified parameters

Overhead Memory
Everything that is not the Java heap. Very
opaque, hard to size correctly.
-  Better metrics to understand usage
-  Better heuristics for automatic sizing

Future: Simplified Allocation
Single parameter: “task size”. Spark figures
out the rest (vcores, heap, overhead).
Container sizes can change to match
available resources, data locality, etc.
task-size: s
Container request:
vcores = x
memory = y

RM Summary
“Dynamic allocation is not for everybody. It
can only meet the needs of 90% of
applications.”

Security: Kerberos
-  User authentication via Kerberos
-  Supported in all services (YARN, HDFS,
HBase, Hive, etc)
-  Uses per-service delegation tokens

Security: Delegation Tokens
Delegation tokens allow for authentication without running
into KDC limitations.

Tokens (Cont’d)
Tokens have a TTL. This is a problem for
long-lived applications
-  Spark Streaming
-  Thrift Server
Solution: re-generate tokens periodically.

Tokens (Cont’d)
Generating tokens requires a kerberos ticket
-  Spark driver handles KDC login
-  Need access to user credentials (keytab)
Secure if coupled with encryption.

Security: Encryption
-  HDFS supports wire encryption
-  YARN supports wire encryption
-  Spark not so much

Encryption (Cont’d)
Where does Spark need encryption?
-  Control plane
-  File distribution
-  Block Manager
-  User UI / REST API
-  Data-at-rest (shuffle files)

Encryption (Cont’d)
Goal: easy to configure encryption.
-  SSL for HTTP endpoints
-  SASL everywhere else
In 1.4: SASL available for shuffle data, SSL
available for Akka / file distribution.

Spark UI
Support SSL for HTTP UI / API
-  SPARK-2750
-  Outstanding issues:
-  Certificate distribution (cluster mode)
-  Shared certificates

Spark RPC
-  Control plane encryption
-  Current: Akka-over-SSL
-  Future: RPC-over-SASL
-  SPARK-6028, SPARK-6230
-  File distribution
-  Replace HTTP channel with RPC

Data-at-Rest
Local shuffle files, mostly.
-  Now: configure “local dirs” on encrypted
volumes
-  Later: Spark itself encrypts shuffle files
(SPARK-5682).

Summary
-  YARN provides lots of useful features
-  Not always easy to use
-  Focus on:
-  Usability
-  Security
-  Performance

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This presentation introduces how we design and implement a real-time processing platform using latest Spark Structured Streaming framework to intelligently transform the production lines in the manufacturing industry. In the traditional production line there are a variety of isolated structured, semi-structured and unstructured data, such as sensor data, machine screen output, log output, database records etc. There are two main data scenarios: 1) Picture and video data with low frequency but a large amount; 2) Continuous data with high frequency. They are not a large amount of data per unit. However the total amount of them is very large, such as vibration data used to detect the quality of the equipment. These data have the characteristics of streaming data: real-time, volatile, burst, disorder and infinity. Making effective real-time decisions to retrieve values from these data is critical to smart manufacturing. The latest Spark Structured Streaming framework greatly lowers the bar for building highly scalable and fault-tolerant streaming applications. Thanks to the Spark we are able to build a low-latency, high-throughput and reliable operation system involving data acquisition, transmission, analysis and storage. The actual user case proved that the system meets the needs of real-time decision-making. The system greatly enhance the production process of predictive fault repair and production line material tracking efficiency, and can reduce about half of the labor force for the production lines.

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As common sense would suggest, weather has a definite impact on traffic. But how much? And under what circumstances? Can we improve traffic (congestion) prediction given weather data? Predictive traffic is envisioned to significantly impact how driver’s plan their day by alerting users before they travel, find the best times to travel, and over time, learn from new IoT data such as road conditions, incidents, etc. This talk will cover the traffic prediction work conducted jointly by IBM and the traffic data provider. As a part of this work, we conducted a case study over five large metropolitans in the US, 2.58 billion traffic records and 262 million weather records, to quantify the boost in accuracy of traffic prediction using weather data. We will provide an overview of our lambda architecture with Apache Spark being used to build prediction models with weather and traffic data, and Spark Streaming used to score the model and provide real-time traffic predictions. This talk will also cover a suite of extensions to Spark to analyze geospatial and temporal patterns in traffic and weather data, as well as the suite of machine learning algorithms that were used with Spark framework. Initial results of this work were presented at the National Association of Broadcasters meeting in Las Vegas in April 2017, and there is work to scale the system to provide predictions in over a 100 cities. Audience will learn about our experience scaling using Spark in offline and streaming mode, building statistical and deep-learning pipelines with Spark, and techniques to work with geospatial and time-series data.

Improving Traffic Prediction Using Weather Data with Ramya Raghavendra

Graph is on the rise and it’s time to start learning about scalable graph analytics! In this session we will go over two Spark-based Graph Analytics frameworks: Tinkerpop and GraphFrames. While both frameworks can express very similar traversals, they have different performance characteristics and APIs. In this Deep-Dive by example presentation, we will demonstrate some common traversals and explain how, at a Spark level, each traversal is actually computed under the hood! Learn both the fluent Gremlin API as well as the powerful GraphFrame Motif api as we show examples of both simultaneously. No need to be familiar with Graphs or Spark for this presentation as we’ll be explaining everything from the ground up!

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

Building accurate machine learning models has been an art of data scientists, i.e., algorithm selection, hyper parameter tuning, feature selection and so on. Recently, challenges to breakthrough this “black-arts” have got started. In cooperation with our partner, NEC Laboratories America, we have developed a Spark-based automatic predictive modeling system. The system automatically searches the best algorithm, parameters and features without any manual work. In this talk, we will share how the automation system is designed to exploit attractive advantages of Spark. The evaluation with real open data demonstrates that our system can explore hundreds of predictive models and discovers the most accurate ones in minutes on a Ultra High Density Server, which employs 272 CPU cores, 2TB memory and 17TB SSD in 3U chassis. We will also share open challenges to learn such a massive amount of models on Spark, particularly from reliability and stability standpoints. This talk will cover the presentation already shown on Spark Summit SF’17 (#SFds5) but from more technical perspective.

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

In Sweden, from the Rise ICE Data Center at www.hops.site, we are providing to reseachers both Spark-as-a-Service and, more recently, Tensorflow-as-a-Service as part of the Hops platform. In this talk, we examine the different ways in which Tensorflow can be included in Spark workflows, from batch to streaming to structured streaming applications. We will analyse the different frameworks for integrating Spark with Tensorflow, from Tensorframes to TensorflowOnSpark to Databrick’s Deep Learning Pipelines. We introduce the different programming models supported and highlight the importance of cluster support for managing different versions of python libraries on behalf of users. We will also present cluster management support for sharing GPUs, including Mesos and YARN (in Hops Hadoop). Finally, we will perform a live demonstration of training and inference for a TensorflowOnSpark application written on Jupyter that can read data from either HDFS or Kafka, transform the data in Spark, and train a deep neural network on Tensorflow. We will show how to debug the application using both Spark UI and Tensorboard, and how to examine logs and monitor training.

Apache Spark and Tensorflow as a Service with Jim Dowling

Apache Spark and Tensorflow as a Service with Jim Dowling

With the rapid growth of available datasets, it is imperative to have good tools for extracting insight from big data. The Spark ML library has excellent support for performing at-scale data processing and machine learning experiments, but more often than not, Data Scientists find themselves struggling with issues such as: low level data manipulation, lack of support for image processing, text analytics and deep learning, as well as the inability to use Spark alongside other popular machine learning libraries. To address these pain points, Microsoft recently released The Microsoft Machine Learning Library for Apache Spark (MMLSpark), an open-source machine learning library built on top of SparkML that seeks to simplify the data science process and integrate SparkML Pipelines with deep learning and computer vision libraries such as the Microsoft Cognitive Toolkit (CNTK) and OpenCV. With MMLSpark, Data Scientists can build models with 1/10th of the code through Pipeline objects that compose seamlessly with other parts of the SparkML ecosystem. In this session, we explore some of the main lessons learned from building MMLSpark. Join us if you would like to know how to extend Pipelines to ensure seamless integration with SparkML, how to auto-generate Python and R wrappers from Scala Transformers and Estimators, how to integrate and use previously non-distributed libraries in a distributed manner and how to efficiently deploy a Spark library across multiple platforms.

MMLSpark: Lessons from Building a SparkML-Compatible Machine Learning Library...

The Next Accelerator Logging Service (NXCALS) is a new Big Data project at CERN aiming to replace the existing Oracle-based service. The main purpose of the system is to store and present Controls/Infrastructure related data gathered from thousands of devices in the whole accelerator complex. The data is used to operate the machines, improve their performance and conduct studies for new beam types or future experiments. During this talk, Jakub will speak about NXCALS requirements and design choices that lead to the selected architecture based on Hadoop and Spark. He will present the Ingestion API, the abstractions behind the Meta-data Service and the Spark-based Extraction API where simple changes to the schema handling greatly improved the overall usability of the system. The system itself is not CERN specific and can be of interest to other companies or institutes confronted with similar Big Data problems.

Next CERN Accelerator Logging Service with Jakub Wozniak

In Between (A mobile App for couples, downloaded 20M in Global), from daily batch for extracting metrics, analysis and dashboard. Spark is widely used by engineers and data analysts in Between, thanks to the performance and expendability of Spark, data operating has become extremely efficient. Entire team including Biz Dev, Global Operation, Designers are enjoying data results so Spark is empowering entire company for data driven operation and thinking. Kevin, Co-founder and Data Team leader of Between will be presenting how things are going in Between. Listeners will know how small and agile team is living with data (how we build organization, culture and technical base) after this presentation.

Powering a Startup with Apache Spark with Kevin Kim

Improving Traffic Prediction Using Weather Datawith Ramya Raghavendra

In many cases, Big Data becomes just another buzzword because of the lack of tools that can support both the technological requirements for developing and deploying of the projects and/or the fluency of communication between the different profiles of people involved in the projects. In this talk, we will present Moriarty, a set of tools for fast prototyping of Big Data applications that can be deployed in an Apache Spark environment. These tools support the creation of Big Data workflows using the already existing functional blocks or supporting the creation of new functional blocks. The created workflow can then be deployed in a Spark infrastructure and used through a REST API. For better understanding of Moriarty, the prototyping process and the way it hides the Spark environment to the Big Data users and developers, we will present it together with a couple of examples based on a Industry 4.0 success cases and other on a logistic success case.

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

Large-scale testing of new data products or enhancements to existing products in a research and development environment can be a technical challenge for data scientists. In some cases, tools available to data scientists lack production-level capacity, whereas other tools do not provide the algorithms needed to run the methodology. At Nielsen, the Databricks platform provided a solution to both of these challenges. This breakout session will cover a specific Nielsen business case where two methodology enhancements were developed and tested at large-scale using the Databricks platform. Development and large-scale testing of these enhancements would not have been possible using standard database tools.

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

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

Since the invention of SQL and relational databases, data production has been about specifying how data is transformed through queries. While Apache Spark can certainly be used as a general distributed query engine, the power and granularity of Spark’s APIs enables a revolutionary increase in data engineering productivity: goal-based data production. Goal-based data production concerns itself with specifying WHAT the desired result is, leaving the details of HOW the result is achieved to a smart data warehouse running on top of Spark. That not only substantially increases productivity, but also significantly expands the audience that can work directly with Spark: from developers and data scientists to technical business users. With specific data and architecture patterns spanning the range from ETL to machine learning data prep and with live demos, this session will demonstrate how Spark users can gain the benefits of goal-based data production.

Goal Based Data Production with Sim Simeonov

Have you imagined a simple machine learning solution able to prevent revenue leakage and monitor your distributed application? To answer this question, we offer a practical and a simple machine learning solution to create an intelligent monitoring application based on simple data analysis using Apache Spark MLlib. Our application uses linear regression models to make predictions and check if the platform is experiencing any operational problems that can impact in revenue losses. The application monitor distributed systems and provides notifications stating the problem detected, that way users can operate quickly to avoid serious problems which directly impact the company’s revenue and reduce the time for action. We will present an architecture for not only a monitoring system, but also an active actor for our outages recoveries. At the end of the presentation you will have access to our training program source code and you will be able to adapt and implement in your company. This solution already helped to prevent about US$3mi in losses last year.

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

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

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...

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...

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Plus de Spark Summit (20)

FPGA-Based Acceleration Architecture for Spark SQL Qi Xie and Quanfu Wang

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

Apache Spark Structured Streaming Helps Smart Manufacturing with Xiaochang Wu

Improving Traffic Prediction Using Weather Data with Ramya Raghavendra

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

MMLSpark: Lessons from Building a SparkML-Compatible Machine Learning Library...

Next CERN Accelerator Logging Service with Jakub Wozniak

Powering a Startup with Apache Spark with Kevin Kim

Improving Traffic Prediction Using Weather Datawith Ramya Raghavendra

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...

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