Performance tuning your Hadoop/Spark clusters to use cloud storage
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Remote storage provides the ability to separate compute and storage, which ushers in a new world of infinitely scalable and cost-effective storage. Remote storage in the cloud built to the HDFS standard has unique features that make it a great choice for storing and analyzing petabytes of data at a time. Customers can have unlimited storage capacity without any limit to the number or size of the files. With such scale, superior I/O performance becomes an increasingly important consideration when performing analysis on this data. For all workloads, a remote storage in the cloud can provide amazing performance when all the different knobs are tuned correctly... Speaker Stephen Wu, Senior Program Manager, Microsoft
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Disaster Recovery and Cloud Migration for your Apache Hive Warehouse
As Apache Hadoop clusters become central to an organization’s operations, they have clusters in more than one data center. Historically, this has been largely driven by requirements of business continuity planning or geo localization. It has also recently been gaining a lot of interest from a hybrid cloud perspective, i.e. wherein people are trying to augment their traditional on-prem setup with cloud-based additions as well. A robust replication solution is a fundamental requirement in such cases.
The Apache Hive community has been working on new capabilities for efficient and fault tolerant replication of data in the Hive warehouse. In this talk, we will discuss these new capabilities, how it works, what replication at Hive-scale looks like, what challenges it poses, what we have done to solve those issues. We will also focus on what we need to be aware of in our use case that might make replication optimal.
Speaker
Sankar Hariappan, Senior Software Engineer, Hortonworks
A machine learning and data science pipeline for real companies
Comcast is one of the largest cable and telecommunications providers in the country built on decades of mergers, acquisitions, and subscriber growth. The success of our company depends on keeping our customers happy and how quickly we can pivot with changing trends and new technologies. Data abounds within our internal data centers and edge networks as well as both the private and public cloud across multiple vendors.
Within such an environment and given such challenges, how do we get AI, machine learning, and data science platforms built so our company can respond to the market, predict our customers’ needs and create new revenue generating products that delight our customers? If you don’t happen to be our friends and colleagues at Google, Facebook, and Amazon, what are technologies, strategies, and toolkits you can employ to bring together disparate data sets and quickly get them into the hands of your data scientists and then into your own production systems for use by your customers and business partners?
We’ll explore our journey and evolution and look at specific technologies and decisions that have gotten us to where we are today and demo how our platform works.
Speaker
Ray Harrison, Comcast, Enterprise Architect
Prashant Khanolkar, Comcast, Principal Architect Big Data
HPE Keynote Hadoop Summit San Jose 2016
This document discusses enterprise-grade big data solutions from HPE. It outlines HPE's reference architecture for big data workloads including components like data lakes, data warehouses, archival storage, event processing, and in-memory analytics. It also discusses HPE's investments in Hortonworks and collaboration to optimize Hadoop for performance. The document promotes attending an HPE session at the Hadoop Summit on modernizing data warehouses and visiting the HPE booth for demos and a trivia game.
Coexistence and Migration of Vendor HPC based infrastructure to Hadoop Ecosys...
This document discusses integrating an existing HPC infrastructure with the Hadoop ecosystem and YARN. It proposes building a custom YARN Application Master that acts as a "valve" between YARN and the HPC scheduler, allowing HPC applications to run on the shared infrastructure while reusing existing hardware. The advantages are better resource utilization and allowing new systems to leverage existing infrastructure. Potential drawbacks include added complexity from the HPC Application Master and slower performance from gradual changes.
YARN Ready: Apache Spark
http://hortonworks.com/hadoop/spark/
Recording:
https://hortonworks.webex.com/hortonworks/lsr.php?RCID=03debab5ba04b34a033dc5c2f03c7967
As the ratio of memory to processing power rapidly evolves, many within the Hadoop community are gravitating towards Apache Spark for fast, in-memory data processing. And with YARN, they use Spark for machine learning and data science use cases along side other workloads simultaneously. This is a continuation of our YARN Ready Series, aimed at helping developers learn the different ways to integrate to YARN and Hadoop. Tools and applications that are YARN Ready have been verified to work within YARN.
Uncovering an Apache Spark 2 Benchmark - Configuration, Tuning and Test Results
Apache Spark is increasingly adopted as an alternate processing framework to MapReduce, due to its ability to speed up batch, interactive and streaming analytics. Spark enables new analytics use cases like machine learning and graph analysis with its rich and easy to use programming libraries. And, it offers the flexibility to run analytics on data stored in Hadoop, across data across object stores and within traditional databases. This makes Spark an ideal platform for accelerating cross-platform analytics on-premises and in the cloud. Building on the success of Spark 1.x release, Spark 2.x delivers major improvements in the areas of API, Performance, and Structured Streaming. In this paper, we will cover a high-level view of the Apache Spark framework, and then focus on what we consider to be very important improvements made in Apache Spark 2.x. We will then share the results of a real-world benchmark effort and share details on Spark and environment configuration changes made to our lab, discuss the results of the benchmark, and provide a reference architecture example for those interested in taking Spark 2.x for their own test drive. This presentation stresses the value of refreshing the Spark 1 with Spark 2 as performance testing results show 2.3x improvement with SparkSQL workloads similar to TPC Benchmark™ DS (TPC-DS). MARK LOCHBIHLER, Principal Architect, Hortonworks and VIPLAVA MADASU, Big Data Systems Engineer, Hewlett Packard Enterprise
Apache Hadoop 3.0 Community Update
Apache Hadoop 3 is coming! As the next major milestone for hadoop and big data, it attracts everyone's attention as showcase several bleeding-edge technologies and significant features across all components of Apache Hadoop: Erasure Coding in HDFS, Docker container support, Apache Slider integration and Native service support, Application Timeline Service version 2, Hadoop library updates and client-side class path isolation, etc. In this talk, first we will update the status of Hadoop 3.0 releasing work in apache community and the feasible path through alpha, beta towards GA. Then we will go deep diving on each new feature, include: development progress and maturity status in Hadoop 3. Last but not the least, as a new major release, Hadoop 3.0 will contain some incompatible API or CLI changes which could be challengeable for downstream projects and existing Hadoop users for upgrade - we will go through these major changes and explore its impact to other projects and users.
Speaker: Sanjay Radia, Founder and Chief Architect, Hortonworks
Node Labels in YARN
Wangda Tan and Mayank Bansal presented on YARN Node Labels. Node labels allow grouping nodes with similar hardware or software profiles. This allows applications to request specific nodes and improves cluster partitioning and resource management. Key features include exclusive and non-exclusive node partitions, centralized and distributed configuration, and support in projects like Spark, MapReduce, Slider, and Ambari. Future work includes adding node constraints and supporting node labels in other schedulers like FairScheduler. Node labels help optimize cluster resource utilization and isolate workloads.
Stinger Initiative - Deep Dive
The document discusses the Stinger Initiative from Hortonworks to improve the performance and capabilities of interactive queries in Hive. The initiative takes a two-pronged approach, focusing on improvements to the query engine and the introduction of a new optimized column store file format called ORCFile. A new Tez execution engine is also introduced to avoid bottlenecks in MapReduce and enable lower latency queries. The goal is to extend Hive's ability to handle interactive queries with response times measured in seconds rather than minutes.
Practice of large Hadoop cluster in China Mobile
China Mobile Limited is the leading telecommunications services provider in China, with more than 800 million active users. In China Mobile, distributed big data clusters are built by branch companies in each province for their unique requirements. Meanwhile, we have built a centralized Hadoop cluster with scale more than 1600 nodes, on which we collect data from dozens of distributed clusters and make analysis for our business.
In this session, we will introduce the architecture of the centralized Hadoop cluster and experience of constructing and tuning this large scale Hadoop cluster. Key points are as follows:
1. About Ambari: we improve Ambari with features like supporting HDFS Federation and Ambari HA , improving its performance and enabling it to support up to 1600 nodes.
2. About HDFS: we build a large HDFS cluster with data up to 60PB, using federation, ViewFS, FairCallQueue. Our best practice of cluster operation and management will also be included.
3. About Flume: We use the reformed Flume to collect data as much as 200TB per day.
Speakers
Yuxuan Pan, Software Engineer, China Mobile Software Technology
Duan Yunfeng, Chief Designer of China Mobile's big data system, China Mobile Communications Corporation
To The Cloud and Back: A Look At Hybrid Analytics
This document discusses hybrid analytics and the movement of data between on-premise and cloud environments. It notes that IT infrastructures now require both traditional and cloud-native approaches. Moving forward, hybrid analytics using both on-premise and cloud resources will become more common. Specifically, automatically moving data between on-premise storage and the cloud based on policies will help break down data silos and enable greater analysis. The document also explores using Dell EMC's OneFS software to enable this type of policy-based data movement to and from the cloud.
What's new in apache hive
Apache Hive is a rapidly evolving project which continues to enjoy great adoption in big data ecosystem. Although, Hive started primarily as batch ingestion and reporting tool, community is hard at work in improving it along many different dimensions and use cases. This talk will provide an overview of latest and greatest features and optimizations which have landed in project over last year. Materialized view, micro managed tables and workload management are some noteworthy features.
I will deep dive into some optimizations which promise to provide major performance gains. Support for ACID tables has also improved considerably. Although some of these features and enhancements are not novel but have existed for years in other DB systems, implementing them on Hive poses some unique challenges and results in lessons which are generally applicable in many other contexts. I will also provide a glimpse of what is expected to come in near future.
Speaker: Ashutosh Chauhan, Engineering Manager, Hortonworks
Startup Case Study: Leveraging the Broad Hadoop Ecosystem to Develop World-Fi...
Back in 2014, our team set out to change the way the world exchanges and collaborates with data. Our vision was to build a single tenant environment for multiple organisations to securely share and consume data. And we did just that, leveraging multiple Hadoop technologies to help our infrastructure scale quickly and securely.
Today Data Republic’s technology delivers a trusted platform for hundreds of enterprise level companies to securely exchange, commercialise and collaborate with large datasets.
Join Head of Engineering, Juan Delard de Rigoulières and Senior Solutions Architect, Amin Abbaspour as they share key lessons from their team’s journey with Hadoop:
* How a startup leveraged a clever combination of Hadoop technologies to build a secure data exchange platform
* How Hadoop technologies helped us deliver key solutions around governance, security and controls of data and metadata
* An evaluation on the maturity and usefulness of some Hadoop technologies in our environment: Hive, HDFS, Spark, Ranger, Atlas, Knox, Kylin: we've use them all extensively.
* Our bold approach to expose APIs directly to end users; as well as the challenges, learning and code we created in the process
* Learnings from the front-line: How our team coped with code changes, performance tuning, issues and solutions while building our data exchange
Whether you’re an enterprise level business or a start-up looking to scale - this case study discussion offers behind-the-scenes lessons and key tips when using Hadoop technologies to manage data governance and collaboration in the cloud.
Speakers:
Juan Delard De Rigoulieres, Head of Engineering, Data Republic Pty Ltd
Amin Abbaspour, Senior Solutions Architect, Data Republic
Deep Learning using Spark and DL4J for fun and profit
This document discusses deep learning using Spark and DL4J. It introduces the speakers, Adam Gibson and Dhruv Kumar, and outlines the topics to be covered: an overview of deep learning, architectures, implementation and libraries for real-life applications, and a demonstration. Deep learning is described as one technique in data science that excels at tasks like image recognition, speech translation, and voice recognition by being loosely inspired by human brain models. The document then discusses using these techniques for enterprise use cases and realizing modern data applications in a Hadoop-centric world.
HDFS tiered storage
Most users know HDFS as the reliable store of record for big data analytics. HDFS is also used to store transient and operational data when working with cloud object stores, such as Microsoft Azure or Amazon S3, and on-premises object stores, such as Western Digital’s ActiveScale. In these settings, applications often manage data stored in multiple storage systems or clusters, requiring a complex workflow for synchronizing data between filesystems for business continuity planning (BCP) and/or supporting hybrid cloud architectures to achieve the required business goals for durability, performance, and coordination.
To resolve this complexity, HDFS-9806 has added a PROVIDED storage tier to mount external storage systems in the HDFS NameNode. Building on this functionality, we can now allow remote namespaces to be synchronized with HDFS, enabling asynchronous writes to the remote storage and the possibility to synchronously and transparently read data back to a local application wanting to access file data which is stored remotely. In this talk, which corresponds to the work in progress under HDFS-12090, we will present how the Hadoop admin can manage storage tiering between clusters and how that is then handled inside HDFS through the snapshotting mechanism and asynchronously satisfying the storage policy.
Speakers
Chris Douglas, Microsoft, Principal Research Software Engineer
Thomas Denmoor, Western Digital, Object Storage Architect
Lessons learned from running Spark on Docker
Today, most any application can be “Dockerized.” However, there are special challenges when deploying a distributed application such as Spark on containers. This session will describe how to overcome these challenges in deploying Spark on Docker containers, with many practical tips and techniques for running Spark in a container environment.
Containers are typically used to run stateless applications on a single host. There are significant real-world enterprise requirements that need to be addressed when running a stateful, distributed application in a secure multi-host container environment.
There are decisions that need to be made concerning which tools and infrastructure to use. There are many choices with respect to container managers, orchestration frameworks, and resource schedulers that are readily available today and some that may be available tomorrow including:]
• Mesos
• Kubernetes
• Docker Swarm
Each has its own strengths and weaknesses; each has unique characteristics that may make it suitable, or unsuitable, for Spark. Understanding these differences is critical to the successful deployment of Spark on Docker containers.
This session will describe the work done by the BlueData engineering team to run Spark inside containers, on a distributed platform, including the evaluation of various orchestration frameworks and lessons learned. You will learn how to apply practical networking and storage techniques to achieve high performance and agility in a distributed, container environment.
Speaker
Thomas Phelan, Chief Architect, Blue Data, Inc
Efficient Data Formats for Analytics with Parquet and Arrow
Hadoop makes it relatively easy to store petabytes of data. However, storing data is not enough; columnar layouts for storage and in-memory execution allow the analysis of large amounts of data very quickly and efficiently. It provides the ability for multiple applications to share a common data representation and perform operations at full CPU throughput using SIMD and Vectorization. For interoperability, row based encodings (CSV, Thrift, Avro) combined with general purpose compression algorithms (GZip, LZO, Snappy) are common but inefficient. As discussed extensively in the database literature, a columnar layout with statistics and sorting provides vertical and horizontal partitioning, thus keeping IO to a minimum. Additionally a number of key big data technologies have or will soon have in-memory columnar capabilities. This includes Kudu, Ibis and Drill. Sharing a common in-memory columnar representation allows interoperability without the usual cost of serialization.
Understanding modern CPU architecture is critical to maximizing processing throughput. We’ll discuss the advantages of columnar layouts in Parquet and Arrow for in-memory processing and data encodings used for storage (dictionary, bit-packing, prefix coding). We’ll dissect and explain the design choices that enable us to achieve all three goals of interoperability, space and query efficiency. In addition, we’ll provide an overview of what’s coming in Parquet and Arrow in the next year.
Hadoop 3 @ Hadoop Summit San Jose 2017
Hadoop 3.0 will include major new features like HDFS erasure coding for improved storage efficiency and YARN support for long running services and Docker containers to improve resource utilization. However, it will maintain backwards compatibility and a focus on testing given the importance of compatibility for existing Hadoop users. The release is targeted for late 2017 after several alpha and beta stages.
Interactive SQL-on-Hadoop and JethroData
Meetup presentation 21/7/2014
Part 1 - Overview of SQL-on-Hadoop- context and current state (Impala vs. Hive etc)
Part 2 - Introduction to JethroData
Sherlock: an anomaly detection service on top of Druid
Sherlock is an anomaly detection service built on top of Druid. It leverages EGADS (Extensible Generic Anomaly Detection System; github.com/yahoo/egads) to detect anomalies in time-series data. Users can schedule jobs on an hourly, daily, weekly, or monthly basis, view anomaly reports from Sherlock's interface, or receive them via email.
Sherlock has four major components: timeseries generation, EGADS anomaly detection, Redis backend and Spark Java UI. Timeseries generation involves building, validating, querying, parsing the Druid query. Parsed Druid response is then fed to EGADS anomaly detection component which detects and generates the anomaly reports for each input time-series data. Sherlock uses Redis backend to store jobs metadata, generated anomaly reports and persistent job queue for scheduling jobs, etc. Users can choose to have a clustered Redis or standalone Redis. Sherlock provides user interface built with Spark Java. The UI enables users to submit instant anomaly analysis, create, and launch detection jobs, view anomalies on a heatmap and on a graph. Jigarkumar Patel, Software Development Engineer I, Oath Inc. and, David Servose, Software Systems Engineer, Oath
What's hot (20)
Disaster Recovery and Cloud Migration for your Apache Hive Warehouse
Disaster Recovery and Cloud Migration for your Apache Hive Warehouse
A machine learning and data science pipeline for real companies
A machine learning and data science pipeline for real companies
Coexistence and Migration of Vendor HPC based infrastructure to Hadoop Ecosys...
Coexistence and Migration of Vendor HPC based infrastructure to Hadoop Ecosys...
Uncovering an Apache Spark 2 Benchmark - Configuration, Tuning and Test Results
Uncovering an Apache Spark 2 Benchmark - Configuration, Tuning and Test Results
Startup Case Study: Leveraging the Broad Hadoop Ecosystem to Develop World-Fi...
Startup Case Study: Leveraging the Broad Hadoop Ecosystem to Develop World-Fi...
Deep Learning using Spark and DL4J for fun and profit
Deep Learning using Spark and DL4J for fun and profit
Efficient Data Formats for Analytics with Parquet and Arrow
Efficient Data Formats for Analytics with Parquet and Arrow
Sherlock: an anomaly detection service on top of Druid
Sherlock: an anomaly detection service on top of Druid
Similar to Performance tuning your Hadoop/Spark clusters to use cloud storage
August 2016 HUG: Better together: Fast Data with Apache Spark™ and Apache Ign...
August 2016 HUG: Better together: Fast Data with Apache Spark™ and Apache Ign...Yahoo Developer Network
Spark and Ignite are two of the most popular open source projects in the area of high-performance Big Data and Fast Data. But did you know that one of the best ways to boost performance for your next generation real-time applications is to use them together? In this session, Dmitriy Setrakyan, Apache Ignite Project Management Committee Chairman and co-founder and CPO at GridGain will explain in detail how IgniteRDD — an implementation of native Spark RDD and DataFrame APIs — shares the state of the RDD across other Spark jobs, applications and workers. Dmitriy will also demonstrate how IgniteRDD, with its advanced in-memory indexing capabilities, allows execution of SQL queries many times faster than native Spark RDDs or Data Frames. Don't miss this opportunity to learn from one of the experts how to use Spark and Ignite better together in your projects.
Speakers:
Dmitriy Setrakyan, is a founder and CPO at GridGain Systems. Dmitriy has been working with distributed architectures for over 15 years and has expertise in the development of various middleware platforms, financial trading systems, CRM applications and similar systems. Prior to GridGain, Dmitriy worked at eBay where he was responsible for the architecture of an add-serving system processing several billion hits a day. Currently Dmitriy also acts as PMC chair of Apache Ignite project.
IMCSummite 2016 Breakout - Nikita Ivanov - Apache Ignite 2.0 Towards a Conver...
IMCSummite 2016 Breakout - Nikita Ivanov - Apache Ignite 2.0 Towards a Conver...In-Memory Computing Summit
In this presentation we look at the roadmap for Apache Ignite 2.0 towards becoming one of the first convergent data platform that would combine cross-channel tiered storage model (DRAM, Flash, HDD) and multi-paradigm access pattern (K/V, SQL, MapReduce, MPP) into one highly integrated and easy to use data platform.[Rakuten TechConf2014] [C-6] Leveraging Spark for Cluster Computing
Apache Spark is an open-source cluster computing framework that provides faster analytics than Hadoop by keeping data in memory as much as possible. It uses Resilient Distributed Datasets (RDDs) that can be operated on in parallel across a cluster. Spark also offers easier development than Hadoop through APIs in Scala, Java, Python and an interactive shell. It provides unified analytics capabilities including SQL, streaming, machine learning and graph processing. Spark can scale to clusters of over 1,000 nodes and has a large community of over 171 contributors.
Design patterns and best practices for data analytics with amazon emr (ABD305)
Amazon EMR is one of the largest Hadoop operators in the world, enabling customers to run ETL, machine learning, real-time processing, data science, and low-latency SQL at petabyte scale. In this session, we introduce you to Amazon EMR design patterns such as using Amazon S3 instead of HDFS, taking advantage of both long and short-lived clusters, and other Amazon EMR architectural best practices. We talk about lowering cost with Auto Scaling and Spot Instances, and security best practices for encryption and fine-grained access control. Finally, we dive into some of our recent launches to keep you current on our latest features.
Microservices Architectures With Apache Ignite
This webinar discusses how in-memory computing using Apache® Ignite™ can overcome the performance limitations common to microservices architectures built using traditional database architectures.
Apache Spark and Apache Ignite: Where Fast Data Meets the IoT with Denis Magda
It’s not enough to build a mesh of sensors or embedded devices to get more insights about the surrounding environment and optimize your production. Usually, your IoT solution needs to be capable of transferring enormous amounts of data to a storage or cloud where the data has to be processed further. Quite often, the processing of the endless streams of data has to be done almost in real-time so that you can react on the IoT subsystem’s state accordingly, and in time.
During this session, see how to build a Fast Data solution that will receive endless streams from the IoT side and will be capable of processing the streams in real-time using Apache Ignite’s cluster resources. In particular, learn about data streaming to an Apache Ignite cluster from embedded devices and real-time data processing with Apache Spark.
IMCSummit 2015 - Day 2 Developer Track - Anatomy of an In-Memory Data Fabric:...
IMCSummit 2015 - Day 2 Developer Track - Anatomy of an In-Memory Data Fabric:...In-Memory Computing Summit
In this presentation, Dmitriy will describe the strategy and architecture behind the Apache Ignite(TM) (incubating) In-Memory Data Fabric, a high-performance, distributed in-memory data management software layer that boosts application performance and scale by orders of magnitude. We will dive into the technical details of distributed clusters and compute grids as well as distributed data grids, and provide code samples for each. As integral parts of an In-Memory Data Fabric, Dmitriy will also cover distributed streaming, CEP and Hadoop acceleration. This presentation is particularly relevant for software developers and architects who work on the front lines of high-speed, low-latency Fast Data systems, high-performance transactional systems and real-time analytics applications.Spark_Part 1
This document provides an overview of Apache Spark, including:
- Spark is an open source cluster computing framework built for speed and active use. It can access data from HDFS and other sources.
- Key features include simplicity, speed (both in memory and disk-based), streaming, machine learning, and support for multiple languages.
- Spark's architecture includes its core engine and additional modules for SQL, streaming, machine learning, graphs, and R integration. It can run on standalone, YARN, or Mesos clusters.
- Example uses of Spark include ETL, online data enrichment, fraud detection, and recommender systems using streaming, and customer segmentation using machine learning.
Konsolidace Oracle DB na systémech s procesory M7
Webinář "Konsolidace Oracle DB na systémech s procesory M7, včetně migrace z konkurenčních serverových platforem"
Prezentuje Josef Šlahůnek, Oracle
9.3.2016
Apache Spark in Scientific Applications
This document provides an overview of Apache Spark, including:
- Apache Spark is a next generation data processing engine for Hadoop that allows for fast in-memory processing of huge distributed and heterogeneous datasets.
- Spark offers tools for data science and components for data products and can be used for tasks like machine learning, graph processing, and streaming data analysis.
- Spark improves on MapReduce by being faster, allowing parallel processing, and supporting interactive queries. It works on both standalone clusters and Hadoop clusters.
Apache Spark in Scientific Applciations
This document provides an overview of Apache Spark, including:
- Apache Spark is a next generation data processing engine for Hadoop that allows for fast in-memory processing of huge distributed and heterogeneous datasets.
- Spark offers tools for data science and components for data products and can be used for tasks like machine learning, graph processing, and streaming data analysis.
- Spark improves on MapReduce by being faster, allowing parallel processing, and supporting interactive queries. It works on both standalone clusters and Hadoop clusters.
Using pySpark with Google Colab & Spark 3.0 preview
Spark is an open source engine for large-scale data processing. It provides APIs in multiple languages including Python. Spark includes libraries for SQL, streaming, machine learning and runs on single machines or clusters. The document discusses Spark's architecture, RDD API, transformations and actions, shuffle operations, persistence, and structured APIs like DataFrames and SQL.
Big Data Processing with Hadoop-MapReduce in Cloud Systems
Youtube link : https://www.youtube.com/watch?v=cmZz2eHYarM
Intellipaat Big Data on AWS training: https://intellipaat.com/aws-big-data-certification-training/
Read AWS tutorial here: https://intellipaat.com/blog/tutorial/amazon-web-services-aws-tutorial/
(BDT309) Data Science & Best Practices for Apache Spark on Amazon EMR
Organizations need to perform increasingly complex analysis on their data — streaming analytics, ad-hoc querying and predictive analytics — in order to get better customer insights and actionable business intelligence. However, the growing data volume, speed, and complexity of diverse data formats make current tools inadequate or difficult to use. Apache Spark has recently emerged as the framework of choice to address these challenges. Spark is a general-purpose processing framework that follows a DAG model and also provides high-level APIs, making it more flexible and easier to use than MapReduce. Thanks to its use of in-memory datasets (RDDs), embedded libraries, fault-tolerance, and support for a variety of programming languages, Apache Spark enables developers to implement and scale far more complex big data use cases, including real-time data processing, interactive querying, graph computations and predictive analytics. In this session, we present a technical deep dive on Spark running on Amazon EMR. You learn why Spark is great for ad-hoc interactive analysis and real-time stream processing, how to deploy and tune scalable clusters running Spark on Amazon EMR, how to use EMRFS with Spark to query data directly in Amazon S3, and best practices and patterns for Spark on Amazon EMR.
Using Data Lakes
by Avijit Goswami, Sr. Solutions Architect, AWS
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
Detailed guide to the Apache Spark Framework
Apache Spark is an open-source framework for large-scale data processing. It provides interactive processing, real-time stream processing, batch processing, and in-memory processing at very fast speeds. Spark's key feature is its in-memory cluster computing, which increases data processing speeds. Spark is widely used for big data analysis across industries like security, gaming, travel, finance, e-commerce, and healthcare.
Scaling PHP apps
Matteo Moretti discusses scaling PHP applications. He covers scaling the web server, sessions, database, filesystem, asynchronous tasks, and logging. The key aspects are decoupling services, using caching, moving to external services like Redis, S3, and RabbitMQ, and allowing those services to scale automatically using techniques like auto-scaling. Sharding the database is difficult to implement and should only be done if really needed.
SAS on Kubernetes
This paper summarizes the work of orchestrating SAS Viya analytical workloads. We containerize the workloads and use Kubernetes to orchestrate. For SAS on Cloud related whitepapers and offerings go to https://corecompete.com/sas-on-cloud/
What Is Apache Spark? | Introduction To Apache Spark | Apache Spark Tutorial ...
This presentation about Apache Spark covers all the basics that a beginner needs to know to get started with Spark. It covers the history of Apache Spark, what is Spark, the difference between Hadoop and Spark. You will learn the different components in Spark, and how Spark works with the help of architecture. You will understand the different cluster managers on which Spark can run. Finally, you will see the various applications of Spark and a use case on Conviva. Now, let's get started with what is Apache Spark.
Below topics are explained in this Spark presentation:
1. History of Spark
2. What is Spark
3. Hadoop vs Spark
4. Components of Apache Spark
5. Spark architecture
6. Applications of Spark
7. Spark usecase
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
Simplilearn’s Apache Spark and Scala certification training are designed to:
1. Advance your expertise in the Big Data Hadoop Ecosystem
2. Help you master essential Apache and Spark skills, such as Spark Streaming, Spark SQL, machine learning programming, GraphX programming and Shell Scripting Spark
3. Help you land a Hadoop developer job requiring Apache Spark expertise by giving you a real-life industry project coupled with 30 demos
What skills will you learn?
By completing this Apache Spark and Scala course you will be able to:
1. Understand the limitations of MapReduce and the role of Spark in overcoming these limitations
2. Understand the fundamentals of the Scala programming language and its features
3. Explain and master the process of installing Spark as a standalone cluster
4. Develop expertise in using Resilient Distributed Datasets (RDD) for creating applications in Spark
5. Master Structured Query Language (SQL) using SparkSQL
6. Gain a thorough understanding of Spark streaming features
7. Master and describe the features of Spark ML programming and GraphX programming
Who should take this Scala course?
1. Professionals aspiring for a career in the field of real-time big data analytics
2. Analytics professionals
3. Research professionals
4. IT developers and testers
5. Data scientists
6. BI and reporting professionals
7. Students who wish to gain a thorough understanding of Apache Spark
Learn more at https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training
Hadoop in Practice (SDN Conference, Dec 2014)
You sit on a big pile of data and want to know how to leverage it in your company? Interested in use-cases, examples and practical demos about the full Hadoop stack? Looking for big-data inspiration?
In this talk we will cover:
- Use-cases how implementing a Hadoop stack in TheNewMotion drastically helped us, software engineers, with our everyday challenges. And how Hadoop enables our management team, marketing and operations to become more data-driven.
- Practical introduction into our data warehouse, analytical and visualization stack: Apache Pig, Impala, Hue, Apache Spark, IPython notebook and Angular with D3.js.
- Easy deployment of the Hadoop stack to the cloud.
- Hermes - our homegrown command-line tool which helps us automate data-related tasks.
- Examples of exciting machine learning challenges that we are currently tackling
- Hadoop with Azure and Microsoft stack.
Similar to Performance tuning your Hadoop/Spark clusters to use cloud storage (20)
August 2016 HUG: Better together: Fast Data with Apache Spark™ and Apache Ign...
August 2016 HUG: Better together: Fast Data with Apache Spark™ and Apache Ign...
IMCSummite 2016 Breakout - Nikita Ivanov - Apache Ignite 2.0 Towards a Conver...
IMCSummite 2016 Breakout - Nikita Ivanov - Apache Ignite 2.0 Towards a Conver...
[Rakuten TechConf2014] [C-6] Leveraging Spark for Cluster Computing
[Rakuten TechConf2014] [C-6] Leveraging Spark for Cluster Computing
Design patterns and best practices for data analytics with amazon emr (ABD305)
Design patterns and best practices for data analytics with amazon emr (ABD305)
Apache Spark and Apache Ignite: Where Fast Data Meets the IoT with Denis Magda
Apache Spark and Apache Ignite: Where Fast Data Meets the IoT with Denis Magda
IMCSummit 2015 - Day 2 Developer Track - Anatomy of an In-Memory Data Fabric:...
IMCSummit 2015 - Day 2 Developer Track - Anatomy of an In-Memory Data Fabric:...
Using pySpark with Google Colab & Spark 3.0 preview
Using pySpark with Google Colab & Spark 3.0 preview
Big Data Processing with Hadoop-MapReduce in Cloud Systems
Big Data Processing with Hadoop-MapReduce in Cloud Systems
(BDT309) Data Science & Best Practices for Apache Spark on Amazon EMR
(BDT309) Data Science & Best Practices for Apache Spark on Amazon EMR
What Is Apache Spark? | Introduction To Apache Spark | Apache Spark Tutorial ...
What Is Apache Spark? | Introduction To Apache Spark | Apache Spark Tutorial ...
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Data Science Crash Course
Introduction: This workshop will provide a hands-on introduction to Machine Learning (ML) with an overview of Deep Learning (DL).
Format: An introductory lecture on several supervised and unsupervised ML techniques followed by light introduction to DL and short discussion what is current state-of-the-art. Several python code samples using the scikit-learn library will be introduced that users will be able to run in the Cloudera Data Science Workbench (CDSW).
Objective: To provide a quick and short hands-on introduction to ML with python’s scikit-learn library. The environment in CDSW is interactive and the step-by-step guide will walk you through setting up your environment, to exploring datasets, training and evaluating models on popular datasets. By the end of the crash course, attendees will have a high-level understanding of popular ML algorithms and the current state of DL, what problems they can solve, and walk away with basic hands-on experience training and evaluating ML models.
Prerequisites: For the hands-on portion, registrants must bring a laptop with a Chrome or Firefox web browser. These labs will be done in the cloud, no installation needed. Everyone will be able to register and start using CDSW after the introductory lecture concludes (about 1hr in). Basic knowledge of python highly recommended.
Floating on a RAFT: HBase Durability with Apache Ratis
In a world with a myriad of distributed storage systems to choose from, the majority of Apache HBase clusters still rely on Apache HDFS. Theoretically, any distributed file system could be used by HBase. One major reason HDFS is predominantly used are the specific durability requirements of HBase's write-ahead log (WAL) and HDFS providing that guarantee correctly. However, HBase's use of HDFS for WALs can be replaced with sufficient effort.
This talk will cover the design of a "Log Service" which can be embedded inside of HBase that provides a sufficient level of durability that HBase requires for WALs. Apache Ratis (incubating) is a library-implementation of the RAFT consensus protocol in Java and is used to build this Log Service. We will cover the design choices of the Ratis Log Service, comparing and contrasting it to other log-based systems that exist today. Next, we'll cover how the Log Service "fits" into HBase and the necessary changes to HBase which enable this. Finally, we'll discuss how the Log Service can simplify the operational burden of HBase.
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFi
Utilizing Apache NiFi we read various open data REST APIs and camera feeds to ingest crime and related data real-time streaming it into HBase and Phoenix tables. HBase makes an excellent storage option for our real-time time series data sources. We can immediately query our data utilizing Apache Zeppelin against Phoenix tables as well as Hive external tables to HBase.
Apache Phoenix tables also make a great option since we can easily put microservices on top of them for application usage. I have an example Spring Boot application that reads from our Philadelphia crime table for front-end web applications as well as RESTful APIs.
Apache NiFi makes it easy to push records with schemas to HBase and insert into Phoenix SQL tables.
Resources:
https://community.hortonworks.com/articles/54947/reading-opendata-json-and-storing-into-phoenix-tab.html
https://community.hortonworks.com/articles/56642/creating-a-spring-boot-java-8-microservice-to-read.html
https://community.hortonworks.com/articles/64122/incrementally-streaming-rdbms-data-to-your-hadoop.html
HBase Tales From the Trenches - Short stories about most common HBase operati...
Whilst HBase is the most logical answer for use cases requiring random, realtime read/write access to Big Data, it may not be so trivial to design applications that make most of its use, neither the most simple to operate. As it depends/integrates with other components from Hadoop ecosystem (Zookeeper, HDFS, Spark, Hive, etc) or external systems ( Kerberos, LDAP), and its distributed nature requires a "Swiss clockwork" infrastructure, many variables are to be considered when observing anomalies or even outages. Adding to the equation there's also the fact that HBase is still an evolving product, with different release versions being used currently, some of those can carry genuine software bugs. On this presentation, we'll go through the most common HBase issues faced by different organisations, describing identified cause and resolution action over my last 5 years supporting HBase to our heterogeneous customer base.
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...
LocationTech GeoMesa enables spatial and spatiotemporal indexing and queries for HBase and Accumulo. In this talk, after an overview of GeoMesa’s capabilities in the Cloudera ecosystem, we will dive into how GeoMesa leverages Accumulo’s Iterator interface and HBase’s Filter and Coprocessor interfaces. The goal will be to discuss both what spatial operations can be pushed down into the distributed database and also how the GeoMesa codebase is organized to allow for consistent use across the two database systems.
Managing the Dewey Decimal System
OCLC has been using HBase since 2012 to enable single-search-box access to over a billion items from your library and the world’s library collection. This talk will provide an overview of how HBase is structured to provide this information and some of the challenges they have encountered to scale to support the world catalog and how they have overcome them.
Practical NoSQL: Accumulo's dirlist Example
Many individuals/organizations have a desire to utilize NoSQL technology, but often lack an understanding of how the underlying functional bits can be utilized to enable their use case. This situation can result in drastic increases in the desire to put the SQL back in NoSQL.
Since the initial commit, Apache Accumulo has provided a number of examples to help jumpstart comprehension of how some of these bits function as well as potentially help tease out an understanding of how they might be applied to a NoSQL friendly use case. One very relatable example demonstrates how Accumulo could be used to emulate a filesystem (dirlist).
In this session we will walk through the dirlist implementation. Attendees should come away with an understanding of the supporting table designs, a simple text search supporting a single wildcard (on file/directory names), and how the dirlist elements work together to accomplish its feature set. Attendees should (hopefully) also come away with a justification for sometimes keeping the SQL out of NoSQL.
HBase Global Indexing to support large-scale data ingestion at Uber
Danny Chen presented on Uber's use of HBase for global indexing to support large-scale data ingestion. Uber uses HBase to provide a global view of datasets ingested from Kafka and other data sources. To generate indexes, Spark jobs are used to transform data into HFiles, which are loaded into HBase tables. Given the large volumes of data, techniques like throttling HBase access and explicit serialization are used. The global indexing solution supports requirements for high throughput, strong consistency and horizontal scalability across Uber's data lake.
Scaling Cloud-Scale Translytics Workloads with Omid and Phoenix
Recently, Apache Phoenix has been integrated with Apache (incubator) Omid transaction processing service, to provide ultra-high system throughput with ultra-low latency overhead. Phoenix has been shown to scale beyond 0.5M transactions per second with sub-5ms latency for short transactions on industry-standard hardware. On the other hand, Omid has been extended to support secondary indexes, multi-snapshot SQL queries, and massive-write transactions.
These innovative features make Phoenix an excellent choice for translytics applications, which allow converged transaction processing and analytics. We share the story of building the next-gen data tier for advertising platforms at Verizon Media that exploits Phoenix and Omid to support multi-feed real-time ingestion and AI pipelines in one place, and discuss the lessons learned.
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFi
This document discusses using Apache NiFi to build a high-speed cyber security data pipeline. It outlines the challenges of ingesting, transforming, and routing large volumes of security data from various sources to stakeholders like security operations centers, data scientists, and executives. It proposes using NiFi as a centralized data gateway to ingest data from multiple sources using a single entry point, transform the data according to destination needs, and reliably deliver the data while avoiding issues like network traffic and data duplication. The document provides an example NiFi flow and discusses metrics from processing over 20 billion events through 100+ production flows and 1000+ transformations.
Supporting Apache HBase : Troubleshooting and Supportability Improvements
This document discusses supporting Apache HBase and improving troubleshooting and supportability. It introduces two Cloudera employees who work on HBase support and provides an overview of typical troubleshooting scenarios for HBase like performance degradation, process crashes, and inconsistencies. The agenda covers using existing tools like logs and metrics to troubleshoot HBase performance issues with a general approach, and introduces htop as a real-time monitoring tool for HBase.
Security Framework for Multitenant Architecture
In the healthcare sector, data security, governance, and quality are crucial for maintaining patient privacy and ensuring the highest standards of care. At Florida Blue, the leading health insurer of Florida serving over five million members, there is a multifaceted network of care providers, business users, sales agents, and other divisions relying on the same datasets to derive critical information for multiple applications across the enterprise. However, maintaining consistent data governance and security for protected health information and other extended data attributes has always been a complex challenge that did not easily accommodate the wide range of needs for Florida Blue’s many business units. Using Apache Ranger, we developed a federated Identity & Access Management (IAM) approach that allows each tenant to have their own IAM mechanism. All user groups and roles are propagated across the federation in order to determine users’ data entitlement and access authorization; this applies to all stages of the system, from the broadest tenant levels down to specific data rows and columns. We also enabled audit attributes to ensure data quality by documenting data sources, reasons for data collection, date and time of data collection, and more. In this discussion, we will outline our implementation approach, review the results, and highlight our “lessons learned.”
Presto: Optimizing Performance of SQL-on-Anything Engine
Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Airbnb, Bloomberg, Comcast, Facebook, FINRA, LinkedIn, Lyft, Netflix, Twitter, and Uber, in the last few years Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments over Object Stores, HDFS, NoSQL and RDBMS data stores.
With the ever-growing list of connectors to new data sources such as Azure Blob Storage, Elasticsearch, Netflix Iceberg, Apache Kudu, and Apache Pulsar, recently introduced Cost-Based Optimizer in Presto must account for heterogeneous inputs with differing and often incomplete data statistics. This talk will explore this topic in detail as well as discuss best use cases for Presto across several industries. In addition, we will present recent Presto advancements such as Geospatial analytics at scale and the project roadmap going forward.
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Specialized tools for machine learning development and model governance are becoming essential. MlFlow is an open source platform for managing the machine learning lifecycle. Just by adding a few lines of code in the function or script that trains their model, data scientists can log parameters, metrics, artifacts (plots, miscellaneous files, etc.) and a deployable packaging of the ML model. Every time that function or script is run, the results will be logged automatically as a byproduct of those lines of code being added, even if the party doing the training run makes no special effort to record the results. MLflow application programming interfaces (APIs) are available for the Python, R and Java programming languages, and MLflow sports a language-agnostic REST API as well. Over a relatively short time period, MLflow has garnered more than 3,300 stars on GitHub , almost 500,000 monthly downloads and 80 contributors from more than 40 companies. Most significantly, more than 200 companies are now using MLflow. We will demo MlFlow Tracking , Project and Model components with Azure Machine Learning (AML) Services and show you how easy it is to get started with MlFlow on-prem or in the cloud.
Extending Twitter's Data Platform to Google Cloud
Twitter's Data Platform is built using multiple complex open source and in house projects to support Data Analytics on hundreds of petabytes of data. Our platform support storage, compute, data ingestion, discovery and management and various tools and libraries to help users for both batch and realtime analytics. Our DataPlatform operates on multiple clusters across different data centers to help thousands of users discover valuable insights. As we were scaling our Data Platform to multiple clusters, we also evaluated various cloud vendors to support use cases outside of our data centers. In this talk we share our architecture and how we extend our data platform to use cloud as another datacenter. We walk through our evaluation process, challenges we faced supporting data analytics at Twitter scale on cloud and present our current solution. Extending Twitter's Data platform to cloud was complex task which we deep dive in this presentation.
Event-Driven Messaging and Actions using Apache Flink and Apache NiFi
At Comcast, our team has been architecting a customer experience platform which is able to react to near-real-time events and interactions and deliver appropriate and timely communications to customers. By combining the low latency capabilities of Apache Flink and the dataflow capabilities of Apache NiFi we are able to process events at high volume to trigger, enrich, filter, and act/communicate to enhance customer experiences. Apache Flink and Apache NiFi complement each other with their strengths in event streaming and correlation, state management, command-and-control, parallelism, development methodology, and interoperability with surrounding technologies. We will trace our journey from starting with Apache NiFi over three years ago and our more recent introduction of Apache Flink into our platform stack to handle more complex scenarios. In this presentation we will compare and contrast which business and technical use cases are best suited to which platform and explore different ways to integrate the two platforms into a single solution.
Securing Data in Hybrid on-premise and Cloud Environments using Apache Ranger
Companies are increasingly moving to the cloud to store and process data. One of the challenges companies have is in securing data across hybrid environments with easy way to centrally manage policies. In this session, we will talk through how companies can use Apache Ranger to protect access to data both in on-premise as well as in cloud environments. We will go into details into the challenges of hybrid environment and how Ranger can solve it. We will also talk through how companies can further enhance the security by leveraging Ranger to anonymize or tokenize data while moving into the cloud and de-anonymize dynamically using Apache Hive, Apache Spark or when accessing data from cloud storage systems. We will also deep dive into the Ranger’s integration with AWS S3, AWS Redshift and other cloud native systems. We will wrap it up with an end to end demo showing how policies can be created in Ranger and used to manage access to data in different systems, anonymize or de-anonymize data and track where data is flowing.
Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...
Advanced Big Data Processing frameworks have been proposed to harness the fast data transmission capability of Remote Direct Memory Access (RDMA) over high-speed networks such as InfiniBand, RoCEv1, RoCEv2, iWARP, and OmniPath. However, with the introduction of the Non-Volatile Memory (NVM) and NVM express (NVMe) based SSD, these designs along with the default Big Data processing models need to be re-assessed to discover the possibilities of further enhanced performance. In this talk, we will present, NRCIO, a high-performance communication runtime for non-volatile memory over modern network interconnects that can be leveraged by existing Big Data processing middleware. We will show the performance of non-volatile memory-aware RDMA communication protocols using our proposed runtime and demonstrate its benefits by incorporating it into a high-performance in-memory key-value store, Apache Hadoop, Tez, Spark, and TensorFlow. Evaluation results illustrate that NRCIO can achieve up to 3.65x performance improvement for representative Big Data processing workloads on modern data centers.
Computer Vision: Coming to a Store Near You
Background: Some early applications of Computer Vision in Retail arose from e-commerce use cases - but increasingly, it is being used in physical stores in a variety of new and exciting ways, such as:
● Optimizing merchandising execution, in-stocks and sell-thru
● Enhancing operational efficiencies, enable real-time customer engagement
● Enhancing loss prevention capabilities, response time
● Creating frictionless experiences for shoppers
Abstract: This talk will cover the use of Computer Vision in Retail, the implications to the broader Consumer Goods industry and share business drivers, use cases and benefits that are unfolding as an integral component in the remaking of an age-old industry.
We will also take a ‘peek under the hood’ of Computer Vision and Deep Learning, sharing technology design principles and skill set profiles to consider before starting your CV journey.
Deep learning has matured considerably in the past few years to produce human or superhuman abilities in a variety of computer vision paradigms. We will discuss ways to recognize these paradigms in retail settings, collect and organize data to create actionable outcomes with the new insights and applications that deep learning enables.
We will cover the basics of object detection, then move into the advanced processing of images describing the possible ways that a retail store of the near future could operate. Identifying various storefront situations by having a deep learning system attached to a camera stream. Such things as; identifying item stocks on shelves, a shelf in need of organization, or perhaps a wandering customer in need of assistance.
We will also cover how to use a computer vision system to automatically track customer purchases to enable a streamlined checkout process, and how deep learning can power plausible wardrobe suggestions based on what a customer is currently wearing or purchasing.
Finally, we will cover the various technologies that are powering these applications today. Deep learning tools for research and development. Production tools to distribute that intelligence to an entire inventory of all the cameras situation around a retail location. Tools for exploring and understanding the new data streams produced by the computer vision systems.
By the end of this talk, attendees should understand the impact Computer Vision and Deep Learning are having in the Consumer Goods industry, key use cases, techniques and key considerations leaders are exploring and implementing today.
Big Data Genomics: Clustering Billions of DNA Sequences with Apache Spark
Whole genome shotgun based next generation transcriptomics and metagenomics studies often generate 100 to 1000 gigabytes (GB) sequence data derived from tens of thousands of different genes or microbial species. De novo assembling these data requires an ideal solution that both scales with data size and optimizes for individual gene or genomes. Here we developed an Apache Spark-based scalable sequence clustering application, SparkReadClust (SpaRC), that partitions the reads based on their molecule of origin to enable downstream assembly optimization. SpaRC produces high clustering performance on transcriptomics and metagenomics test datasets from both short read and long read sequencing technologies. It achieved a near linear scalability with respect to input data size and number of compute nodes. SpaRC can run on different cloud computing environments without modifications while delivering similar performance. In summary, our results suggest SpaRC provides a scalable solution for clustering billions of reads from the next-generation sequencing experiments, and Apache Spark represents a cost-effective solution with rapid development/deployment cycles for similar big data genomics problems.
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Floating on a RAFT: HBase Durability with Apache Ratis
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HBase Global Indexing to support large-scale data ingestion at Uber
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Performance tuning your Hadoop/Spark clusters to use cloud storage
- 1. Apache SparkTM and Apache Hadoop® are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries.
- 2. stewu@microsoft.com SparkTM and Hadoop® are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries.
- 3. SparkTM and HiveTM are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries.
- 4. Cloud elasticity Scale compute and storage independently No expensive data centers Less management Global presence Availability SLAs SparkTM and Hadoop® are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries.
- 5. Azure Data Lake/ Azure Storage Blob Azure Databricks/ Azure HDInsight/ Azure VM Storage Compute Amazon Elastic MapReduce/ Amazon Elastic Compute Cloud Amazon S3 Google Dataproc/ Google Compute Engine TM Google Cloud Storage TM Spark TM Hive TM MapReduce Storm TM Kafka ® R Server Azure AWS Google Cloud TM Amazon Web Services, the “Powered by AWS” logo, AWS, Amazon Elastic Compute Cloud, Amazon S3 are trademarks of Amazon.com, Inc. or its affiliates in the United States and/or other countries.©2017 Google LLC All rights reserved. Google and the Google Logo are registered trademarks of Google LLC. SparkTM and Hadoop® are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. All other trademarks are the property of their respective owners.
- 7. CPU Intensive Memory Intensive I/O Intensive • Machine Learning • Natural Language Processing • PageRank • Real-time analytics • Copy • Data preparation
- 8. Choose the right infrastructure Use right YARN settings Right size your application
- 11. # of VMs Application Layer Memory Cores Memory Cores Network Physical Layer Compute Compute Memory GPU VM Type VM Size # of containers Container size Tasks RM (YARN) Layer
- 12. # of VMs RM (YARN) Layer Application Layer Memory Cores Memory Cores Network Physical Layer Compute Compute Memory GPU VM Type VM Size # of containers Container size Tasks
- 13. Increase # of VMs
- 14. Use VMs with more memory
- 15. Use VMs with more network bandwidth Low High
- 17. # of VMs RM (YARN) Layer Application Layer Memory Cores Memory Cores Network Physical Layer Compute Compute Memory GPU VM Type VM Size # of containers Container size Tasks
- 18. Use smaller YARN containers
- 19. Increase cores per container
- 21. # of VMs RM (YARN) Layer Application Layer Memory Cores Memory Cores Network Physical Layer Compute Compute Memory GPU VM Type VM Size # of containers Container size Tasks
- 22. Use all available containers
- 24. SparkTM HiveTM SparkTM and HiveTM are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries.
- 26. Default – Four apps Total resources 64 cores 192 GB Your App 16 cores 48 GB SparkTM is either a registered trademark or trademark of the Apache Software Foundation in the United States and/or other countries.
- 27. Two apps Total resources 64 cores 192 GB App #1 32 cores 96 GB App #2 32 cores 96 GB
- 28. Your app size count Two apps Total resources 64 cores 192 GB App #1 32 cores 96 GB App #2 32 cores 96 GB
- 30. 16 executors More executors Fewer executorsOut of memory 8 executors 32 executors Best practice: set each executor no more than 64GB App resources 32 cores 96GB
- 31. More executors Fewer executors 8 executors16 executors 6 executors Best practice: set cores between 2 and 5 App resources 32 cores 96GB
- 32. 8 4 cores 6GB 8 executors 16 executors 16 2 cores 6GB 8 2 cores 12GB Memory underutilized Both memory and CPU fully utilized CPU underutilized App resources 32 cores 96GB 16 executors 8 executors 16 executors 16 executors
- 34. SparkTM HiveTM SparkTM and HiveTM are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries.
- 35. Map Stage Map tasks Reducer Stage Input Data Reduce tasks Output HiveTM is either a registered trademark or trademark of the Apache Software Foundation in the United States and/or other countries.
- 36. 6GB 16 containers More containers Fewer containersOut of memory YARN resources Memory: 96GB CPU: 32 cores 12GB 8 containers 3GB 32 containers Best practice: set to minimum YARN container size
- 37. 32 containers Mappers Reducers Read Write At least as large as # of containers At least as large as # of containers
- 38. 1.6 waves (best effort) # of containers # of map tasks 10MB Input Data: 80MB Tez.grouping.min-size = 20MB
- 39. 1.6 waves (best effort) Tez.grouping.min-size = 20MB # of containers 20MB # of map tasks Input Data: 80MB Apache Tez® is either a registered trademark or trademark of the Apache Software Foundation in the United States and/or other countries.
- 40. 1.6 waves (best effort) Set tez.grouping.min-size = 5MB # of containers 20MB # of map tasks Input Data: 80MB Apache Tez® is either a registered trademark or trademark of the Apache Software Foundation in the United States and/or other countries.
- 41. 1.6 waves (best effort) Set tez.grouping.min-size = 5MB # of containers 10MB # of map tasks Input Data: 80MB
- 43. ReducerStage estimate Hive.exec.reducers. bytes.per.reducer hive.tez.max. partition.factor Reduce tasks 2512MB Set hive.exec.reducers.bytes.per.reducer = 128MB 5 containers: 256MB
- 44. Set hive.exec.reducers.bytes.per.reducer = 128MB ReducerStage estimate Hive.exec.reducers. bytes.per.reducer hive.tez.max. partition.factor Reduce tasks 5 containers: 2512MB 128MB