Hitesh Shah, Talk at Hadoop Summit 2012.
Hadoop YARN is the next generation computing platform in Apache Hadoop with support for programming paradigms besides MapReduce. In the world of Big Data, one cannot solve all the problems wholly using the Map Reduce programming model. Typical installations run separate programming models like MR, MPI, graph-processing frameworks on individual clusters. Running fewer larger clusters is cheaper than running more small clusters. Therefore, leveraging YARN to allow both MR and non-MR applications to run on top of a common cluster becomes more important from an economical and operational point of view. This talk will cover the different APIs and RPC protocols that are available for developers to implement new application frameworks on top of YARN. We will also go through a simple application which demonstrates how one can implement their own Application Master, schedule requests to the YARN resource-manager and then subsequently use the allocated resources to run user code on the NodeManagers.
Fedbench - A Benchmark Suite for Federated Semantic Data ProcessingPeter Haase
(1) FedBench is a benchmark suite for evaluating federated semantic data processing systems.
(2) It includes parameterized benchmark drivers, a variety of RDF datasets and SPARQL queries, and an evaluation framework to measure system performance.
(3) An initial evaluation was conducted to demonstrate FedBench's flexibility in comparing centralized and federated query processing using different systems and scenarios.
This document summarizes a talk about Facebook's use of HBase for messaging data. It discusses how Facebook migrated data from MySQL to HBase to store metadata, search indexes, and small messages in HBase for improved scalability. It also outlines performance improvements made to HBase, such as for compactions and reads, and future plans such as cross-datacenter replication and running HBase in a multi-tenant environment.
This document summarizes a company's migration from an on-premise co-located infrastructure to utilizing cloud services from AWS. It describes the business needs driving the move, including rapid data growth and variability in usage. An initial solution using AWS S3 provided cost savings and flexibility. Today a hybrid architecture uses local storage managed by a storage router and AWS services like S3 and CloudFront. Future plans aim for a fully cloud-based architecture by the end of the year.
Apache Spark for RDBMS Practitioners: How I Learned to Stop Worrying and Lov...Databricks
This talk is about sharing experience and lessons learned on setting up and running the Apache Spark service inside the database group at CERN. It covers the many aspects of this change with examples taken from use cases and projects at the CERN Hadoop, Spark, streaming and database services. The talks is aimed at developers, DBAs, service managers and members of the Spark community who are using and/or investigating “Big Data” solutions deployed alongside relational database processing systems. The talk highlights key aspects of Apache Spark that have fuelled its rapid adoption for CERN use cases and for the data processing community at large, including the fact that it provides easy to use APIs that unify, under one large umbrella, many different types of data processing workloads from ETL, to SQL reporting to ML.
Spark can also easily integrate a large variety of data sources, from file-based formats to relational databases and more. Notably, Spark can easily scale up data pipelines and workloads from laptops to large clusters of commodity hardware or on the cloud. The talk also addresses some key points about the adoption process and learning curve around Apache Spark and the related “Big Data” tools for a community of developers and DBAs at CERN with a background in relational database operations.
GCP Meetup #3 - Approaches to Cloud Native Architecturesnine
Talk by Daniel Leahy and Nic Gibson, given at the Google Cloud Meetup on March 3, 2020, hosted by Nine Internet Solutions AG - Your Swiss Managed Cloud Service Provider.
Reimagining Devon Energy’s Data Estate with a Unified Approach to Integration...Databricks
This document discusses Devon Energy's efforts to modernize its data landscape by implementing a data hub architecture. The data hub consolidates various data sources and tools on cloud services like Snowflake, Databricks and Azure. This has improved agility, reduced costs, and allowed various teams to access and analyze data. Devon Energy is working to improve continuous integration/deployment, testing, and monitoring across its data engineering and analytics workflows on the data hub platform.
The Azure Cognitive Services on Spark: Clusters with Embedded Intelligent Ser...Databricks
We present the Azure Cognitive Services on Spark, a simple and easy to use extension of the SparkML Library to all Azure Cognitive Services. This integration allows Spark Users to embed cloud intelligence directly into their spark computations, enabling a new generation of intelligent applications on Spark. Furthermore, we show that with our new Containerized Cognitive Services, one can embed cloud intelligence directly into the Spark cluster for ultra-low latency, on-prem, and offline applications. We show how using our Integration, one can compose these cognitive services with other services, SQL computations, and Deep Networks to create sophisticated and intelligent heterogenous applications. Moreover, we show how to redeploy these compositions as Restful Services with Spark Serving. We will also explore the architecture of these contributions which leverage HTTP on Spark, a novel integration between Spark with the widely used Hypertext Transfer Protocol (HTTP). This library can integrate any framework into the Spark ecosystem that is capable of communicating through HTTP. Finally, we demonstrate how to use these services to create a large class of intelligent applications such as custom search engines, realtime facial recognition systems, and unsupervised object detectors.
PostgreSQL Finland October meetup - PostgreSQL monitoring in ZalandoUri Savelchev
This document discusses PostgreSQL monitoring at Zalando. Zalando migrated their PostgreSQL databases to AWS RDS in 2015 and later began using the PostgreSQL operator to deploy PostgreSQL clusters on Kubernetes. Zalando's monitoring system, ZMON, is used to collect metrics from Kubernetes, AWS, and PostgreSQL internal views to monitor infrastructure and databases. The ZMON workers run in each Kubernetes cluster and use separate credentials to connect to databases and query views and tables while respecting explicit permissions.
Fedbench - A Benchmark Suite for Federated Semantic Data ProcessingPeter Haase
(1) FedBench is a benchmark suite for evaluating federated semantic data processing systems.
(2) It includes parameterized benchmark drivers, a variety of RDF datasets and SPARQL queries, and an evaluation framework to measure system performance.
(3) An initial evaluation was conducted to demonstrate FedBench's flexibility in comparing centralized and federated query processing using different systems and scenarios.
This document summarizes a talk about Facebook's use of HBase for messaging data. It discusses how Facebook migrated data from MySQL to HBase to store metadata, search indexes, and small messages in HBase for improved scalability. It also outlines performance improvements made to HBase, such as for compactions and reads, and future plans such as cross-datacenter replication and running HBase in a multi-tenant environment.
This document summarizes a company's migration from an on-premise co-located infrastructure to utilizing cloud services from AWS. It describes the business needs driving the move, including rapid data growth and variability in usage. An initial solution using AWS S3 provided cost savings and flexibility. Today a hybrid architecture uses local storage managed by a storage router and AWS services like S3 and CloudFront. Future plans aim for a fully cloud-based architecture by the end of the year.
Apache Spark for RDBMS Practitioners: How I Learned to Stop Worrying and Lov...Databricks
This talk is about sharing experience and lessons learned on setting up and running the Apache Spark service inside the database group at CERN. It covers the many aspects of this change with examples taken from use cases and projects at the CERN Hadoop, Spark, streaming and database services. The talks is aimed at developers, DBAs, service managers and members of the Spark community who are using and/or investigating “Big Data” solutions deployed alongside relational database processing systems. The talk highlights key aspects of Apache Spark that have fuelled its rapid adoption for CERN use cases and for the data processing community at large, including the fact that it provides easy to use APIs that unify, under one large umbrella, many different types of data processing workloads from ETL, to SQL reporting to ML.
Spark can also easily integrate a large variety of data sources, from file-based formats to relational databases and more. Notably, Spark can easily scale up data pipelines and workloads from laptops to large clusters of commodity hardware or on the cloud. The talk also addresses some key points about the adoption process and learning curve around Apache Spark and the related “Big Data” tools for a community of developers and DBAs at CERN with a background in relational database operations.
GCP Meetup #3 - Approaches to Cloud Native Architecturesnine
Talk by Daniel Leahy and Nic Gibson, given at the Google Cloud Meetup on March 3, 2020, hosted by Nine Internet Solutions AG - Your Swiss Managed Cloud Service Provider.
Reimagining Devon Energy’s Data Estate with a Unified Approach to Integration...Databricks
This document discusses Devon Energy's efforts to modernize its data landscape by implementing a data hub architecture. The data hub consolidates various data sources and tools on cloud services like Snowflake, Databricks and Azure. This has improved agility, reduced costs, and allowed various teams to access and analyze data. Devon Energy is working to improve continuous integration/deployment, testing, and monitoring across its data engineering and analytics workflows on the data hub platform.
The Azure Cognitive Services on Spark: Clusters with Embedded Intelligent Ser...Databricks
We present the Azure Cognitive Services on Spark, a simple and easy to use extension of the SparkML Library to all Azure Cognitive Services. This integration allows Spark Users to embed cloud intelligence directly into their spark computations, enabling a new generation of intelligent applications on Spark. Furthermore, we show that with our new Containerized Cognitive Services, one can embed cloud intelligence directly into the Spark cluster for ultra-low latency, on-prem, and offline applications. We show how using our Integration, one can compose these cognitive services with other services, SQL computations, and Deep Networks to create sophisticated and intelligent heterogenous applications. Moreover, we show how to redeploy these compositions as Restful Services with Spark Serving. We will also explore the architecture of these contributions which leverage HTTP on Spark, a novel integration between Spark with the widely used Hypertext Transfer Protocol (HTTP). This library can integrate any framework into the Spark ecosystem that is capable of communicating through HTTP. Finally, we demonstrate how to use these services to create a large class of intelligent applications such as custom search engines, realtime facial recognition systems, and unsupervised object detectors.
PostgreSQL Finland October meetup - PostgreSQL monitoring in ZalandoUri Savelchev
This document discusses PostgreSQL monitoring at Zalando. Zalando migrated their PostgreSQL databases to AWS RDS in 2015 and later began using the PostgreSQL operator to deploy PostgreSQL clusters on Kubernetes. Zalando's monitoring system, ZMON, is used to collect metrics from Kubernetes, AWS, and PostgreSQL internal views to monitor infrastructure and databases. The ZMON workers run in each Kubernetes cluster and use separate credentials to connect to databases and query views and tables while respecting explicit permissions.
This document provides an outline and overview of training convolutional neural networks. It discusses update rules like stochastic gradient descent, momentum, and Adam. It also covers techniques like data augmentation, transfer learning, and monitoring the training process. The goal of training a CNN is to optimize its weights and parameters to correctly classify images from the training set by minimizing output error through backpropagation and updating weights.
Hadoop Infrastructure @Uber Past, Present and FutureDataWorks Summit
Uber’s mission is to provide transportation as reliable as running water and for fulfilling that mission data plays a critical role. In Uber, Hadoop plays a critical role in Data Infrastructure. We want to talk about the journey of Hadoop @Uber and our future plans in terms of scaling for billions of trips. We will talk about most unique use case Uber have and how Hadoop and eco system which we built, helped us in this journey. We want to talk about how we scaled from 10 -> 2000 and In future to scale up to 10’s X1000 of Nodes. We will talk about our mistakes, learning and wins and how we process billions of events per day. We will talk about the unique challenges and real world use-cases and how we will co-locate the Uber’s service architecture with batch (e.g data pipelines, machine learning and analytical workloads). Uber have done lot of improvements to current Hadoop eco system and uniquely solved some of the problems in a way which is never been solved in the past. This presentation will help audience to use this as an example and even encourage them to enhance the eco system. This will help to increase the community of these project and overall help the whole big data space. Audience is anybody who is working on Big Data and want to understand how to scale Hadoop and eco system for 10s of thousands of node. This talk will help them understand the Hadoop ecosystem and how to efficiently use that. It will also introduce them to some of the awesome technologies which Uber team is building in big data space.
Solr + Hadoop: Interactive Search for Hadoopgregchanan
This document discusses Cloudera Search, which integrates Apache Solr with Cloudera's distribution of Apache Hadoop (CDH) to provide interactive search capabilities. It describes the architecture of Cloudera Search, including components like Solr, SolrCloud, and Morphlines for extraction and transformation. Methods for indexing data in real-time using Flume or batch using MapReduce are presented. The document also covers querying, security features like Kerberos authentication and collection-level authorization using Sentry, and concludes by describing how to obtain Cloudera Search.
Data Engineering Course Syllabus - WeCloudDataWeCloudData
This document provides information about the Programming for Data Engineers course offered by WeCloudData. The course teaches essential programming skills for data engineering such as Scala, Spark, Linux, and Docker over 10 sessions. Students will learn key topics like Scala programming, Spark fundamentals, and how to build data pipelines. They will also complete hands-on projects and get interview preparation support to help find jobs as a data engineer.
Spark-on-Yarn: The Road Ahead-(Marcelo Vanzin, Cloudera)Spark Summit
Spark on YARN provides resource management and security features through YARN, but still has areas for improvement. Dynamic allocation in YARN allows Spark applications to grow and shrink executors based on task demand, though latency and data locality could be enhanced. Security supports Kerberos authentication and delegation tokens, but long-lived applications face token expiration issues and encryption needs improvement for control plane, shuffle files, and user interfaces. Overall, usability, security, and performance remain areas of focus.
The document discusses Rocana Search, a system built by Rocana to enable large scale real-time collection, processing, and analysis of event data. It aims to provide higher indexing throughput and better horizontal scaling than general purpose search systems like Solr. Key features include fully parallelized ingest and query, dynamic partitioning of data, and assigning partitions to nodes to maximize parallelism and locality. Initial benchmarks show Rocana Search can index over 3 times as many events per second as Solr.
IEEE International Conference on Data Engineering 2015Yousun Jeong
SK Telecom developed a Hadoop data warehouse (DW) solution to address the high costs and limitations of traditional DW systems for handling big data. The Hadoop DW provides a scalable architecture using Hadoop, Tajo and Spark to cost-effectively store and analyze over 30PB of data across 1000+ nodes. It offers SQL analytics through Tajo for faster querying and easier migration from RDBMS systems. The Hadoop DW has helped SK Telecom and other customers such as semiconductor manufacturers to more affordably store and process massive volumes of both structured and unstructured data for advanced analytics.
Monitoring of GPU Usage with Tensorflow Models Using PrometheusDatabricks
Understanding the dynamics of GPU utilization and workloads in containerized systems is critical to creating efficient software systems. We create a set of dashboards to monitor and evaluate GPU performance in the context of TensorFlow. We monitor performance in real time to gain insight into GPU load, GPU memory and temperature metrics in a Kubernetes GPU enabled system. Visualizing TensorFlow training job metrics in real time using Prometheus allows us to tune and optimize GPU usage. Also, because Tensor flow jobs can have both GPU and CPU implementations it is useful to view detailed real time performance data from each implementation and choose the best implementation. To illustrate our system, we will show a live demo gathering and visualizing GPU metrics on a GPU enabled Kubernetes cluster with Prometheus and Grafana.
The document discusses the challenges of managing a multi-tenant Hadoop cluster over time as the cluster grows and diversifies. As the cluster scales up in size and number of users, it faces performance problems such as missed SLAs, poor application performance, underutilized resources, and low throughput. Managing such a complex cluster manually is difficult. The document proposes automating resource management and using a new interface that allows administrators to specify performance goals and ask "what-if" questions to optimize configuration settings and improve performance.
Transitioning Compute Models: Hadoop MapReduce to SparkSlim Baltagi
This presentation is an analysis of the observed trends in the transition from the Hadoop ecosystem to the Spark ecosystem. The related talk took place at the Chicago Hadoop User Group (CHUG) meetup held on February 12, 2015.
Hadoop clusters are operated on an ephemeral basis in the cloud by Qubole, processing over 300 petabytes of data per month across over 100 customers. Qubole addresses challenges of ephemeral clusters through auto-scaling of resources using YARN, optimizing performance for cloud storage, and storing job history remotely. Volatile low-cost nodes are leveraged through policies that ensure data replication despite potential node failures.
The document discusses tools and techniques used by Uber's Hadoop team to make their Spark and Hadoop platforms more user-friendly and efficient. It introduces tools like SCBuilder to simplify Spark context creation, Kafka dispersal to distribute RDD results, and SparkPlug to provide templates for common jobs. It also describes a distributed log debugger called SparkChamber to help debug Spark jobs and techniques like building a spatial index to optimize geo-spatial joins. The goal is to abstract out infrastructure complexities and enforce best practices to make the platforms more self-service for users.
Maintaining Low Latency While Maximizing Throughput on a Single ClusterMapR Technologies
The good news: Hadoop has a lot of tools. The bad news: Hadoop has a lot of tools, and conflicting priorities. This talk shows how advances in YARN and Mesos allow you to run multiple distinct workloads together. We show how to use SLA and latency rules along with preemption in YARN to maintain high throughput while guaranteeing latency for applications such as HBase and Drill
Application architectures with Hadoop – Big Data TechCon 2014hadooparchbook
Building applications using Apache Hadoop with a use-case of clickstream analysis. Presented by Mark Grover and Jonathan Seidman at Big Data TechCon, Boston in April 2014
Spark is a fast and general engine for large-scale data processing. It provides APIs in Java, Scala, and Python and an interactive shell. Spark applications operate on resilient distributed datasets (RDDs) that can be cached in memory for faster performance. RDDs are immutable and fault-tolerant via lineage graphs. Transformations create new RDDs from existing ones while actions return values to the driver program. Spark's execution model involves a driver program that coordinates tasks on executor machines. RDD caching and lineage graphs allow Spark to efficiently run jobs across clusters.
Deep Dive into the New Features of Apache Spark 3.1Databricks
Continuing with the objectives to make Spark faster, easier, and smarter, Apache Spark 3.1 extends its scope with more than 1500 resolved JIRAs. We will talk about the exciting new developments in the Apache Spark 3.1 as well as some other major initiatives that are coming in the future. In this talk, we want to share with the community many of the more important changes with the examples and demos.
The following features are covered: the SQL features for ANSI SQL compliance, new streaming features, and Python usability improvements, the performance enhancements and new tuning tricks in query compiler.
Data Engineer's Lunch #55: Get Started in Data EngineeringAnant Corporation
In Data Engineer's Lunch #55, CEO of Anant, Rahul Singh, will cover 10 resources every data engineer needs to get started or master their game.
Accompanying Blog: Coming Soon!
Accompanying YouTube: Coming Soon!
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Data Engineer’s Lunch Weekly at 12 PM EST Every Monday:
https://www.meetup.com/Data-Wranglers-DC/events/
Cassandra.Link:
https://cassandra.link/
Follow Us and Reach Us At:
Anant:
https://www.anant.us/
Awesome Cassandra:
https://github.com/Anant/awesome-cassandra
Email:
solutions@anant.us
LinkedIn:
https://www.linkedin.com/company/anant/
Twitter:
https://twitter.com/anantcorp
Eventbrite:
https://www.eventbrite.com/o/anant-1072927283
Facebook:
https://www.facebook.com/AnantCorp/
Join The Anant Team:
https://www.careers.anant.us
The presentation covers lambda architecture and implementation with spark. In the presentation we will discuss about components of lambda architecture like batch layer, speed layer and serving layer. We will also discuss its advantages and benefits with spark.
Hadoop application architectures - using Customer 360 as an examplehadooparchbook
Hadoop application architectures - using Customer 360 (more generally, Entity 360) as an example. By Ted Malaska, Jonathan Seidman and Mark Grover at Strata + Hadoop World 2016 in NYC.
The NameNode was experiencing high load and instability after being restarted. Graphs showed unknown high load between checkpoints on the NameNode. DataNode logs showed repeated 60000 millisecond timeouts in communication with the NameNode. Thread dumps revealed NameNode server handlers waiting on the same lock, indicating a bottleneck. Source code analysis pointed to repeated block reports from DataNodes to the NameNode as the likely cause of the high load.
This document discusses writing application frameworks on Apache Hadoop YARN. It provides an overview of the YARN architecture including the resource manager, node manager, and application master. It then describes the key components needed to write a new framework, including an application submission client, application master, and optional application history and auxiliary services. Finally, it provides a code example for a distributed shell use case and discusses registering with the resource manager and defining containers.
Running Non-MapReduce Big Data Applications on Apache Hadoophitesh1892
Apache Hadoop has become popular from its specialization in the execution of MapReduce programs. However, it has been hard to leverage existing Hadoop infrastructure for various other processing paradigms such as real-time streaming, graph processing and message-passing. That was true until the introduction of Apache Hadoop YARN in Apache Hadoop 2.0. YARN supports running arbitrary processing paradigms on the same Hadoop cluster. This allows for development of newer frameworks as well as more efficient implementations of existing frameworks that can all run on and share the resources of a single multi-tenant YARN cluster. This talk gives a brief introduction to YARN. We will illustrate how to create applications and how to best make use of YARN. We will show examples of different applications such as Apache Tez and Apache Samza that can leverage YARN and present best practices/guidelines on building applications on top of Apache Hadoop YARN.
This document provides an outline and overview of training convolutional neural networks. It discusses update rules like stochastic gradient descent, momentum, and Adam. It also covers techniques like data augmentation, transfer learning, and monitoring the training process. The goal of training a CNN is to optimize its weights and parameters to correctly classify images from the training set by minimizing output error through backpropagation and updating weights.
Hadoop Infrastructure @Uber Past, Present and FutureDataWorks Summit
Uber’s mission is to provide transportation as reliable as running water and for fulfilling that mission data plays a critical role. In Uber, Hadoop plays a critical role in Data Infrastructure. We want to talk about the journey of Hadoop @Uber and our future plans in terms of scaling for billions of trips. We will talk about most unique use case Uber have and how Hadoop and eco system which we built, helped us in this journey. We want to talk about how we scaled from 10 -> 2000 and In future to scale up to 10’s X1000 of Nodes. We will talk about our mistakes, learning and wins and how we process billions of events per day. We will talk about the unique challenges and real world use-cases and how we will co-locate the Uber’s service architecture with batch (e.g data pipelines, machine learning and analytical workloads). Uber have done lot of improvements to current Hadoop eco system and uniquely solved some of the problems in a way which is never been solved in the past. This presentation will help audience to use this as an example and even encourage them to enhance the eco system. This will help to increase the community of these project and overall help the whole big data space. Audience is anybody who is working on Big Data and want to understand how to scale Hadoop and eco system for 10s of thousands of node. This talk will help them understand the Hadoop ecosystem and how to efficiently use that. It will also introduce them to some of the awesome technologies which Uber team is building in big data space.
Solr + Hadoop: Interactive Search for Hadoopgregchanan
This document discusses Cloudera Search, which integrates Apache Solr with Cloudera's distribution of Apache Hadoop (CDH) to provide interactive search capabilities. It describes the architecture of Cloudera Search, including components like Solr, SolrCloud, and Morphlines for extraction and transformation. Methods for indexing data in real-time using Flume or batch using MapReduce are presented. The document also covers querying, security features like Kerberos authentication and collection-level authorization using Sentry, and concludes by describing how to obtain Cloudera Search.
Data Engineering Course Syllabus - WeCloudDataWeCloudData
This document provides information about the Programming for Data Engineers course offered by WeCloudData. The course teaches essential programming skills for data engineering such as Scala, Spark, Linux, and Docker over 10 sessions. Students will learn key topics like Scala programming, Spark fundamentals, and how to build data pipelines. They will also complete hands-on projects and get interview preparation support to help find jobs as a data engineer.
Spark-on-Yarn: The Road Ahead-(Marcelo Vanzin, Cloudera)Spark Summit
Spark on YARN provides resource management and security features through YARN, but still has areas for improvement. Dynamic allocation in YARN allows Spark applications to grow and shrink executors based on task demand, though latency and data locality could be enhanced. Security supports Kerberos authentication and delegation tokens, but long-lived applications face token expiration issues and encryption needs improvement for control plane, shuffle files, and user interfaces. Overall, usability, security, and performance remain areas of focus.
The document discusses Rocana Search, a system built by Rocana to enable large scale real-time collection, processing, and analysis of event data. It aims to provide higher indexing throughput and better horizontal scaling than general purpose search systems like Solr. Key features include fully parallelized ingest and query, dynamic partitioning of data, and assigning partitions to nodes to maximize parallelism and locality. Initial benchmarks show Rocana Search can index over 3 times as many events per second as Solr.
IEEE International Conference on Data Engineering 2015Yousun Jeong
SK Telecom developed a Hadoop data warehouse (DW) solution to address the high costs and limitations of traditional DW systems for handling big data. The Hadoop DW provides a scalable architecture using Hadoop, Tajo and Spark to cost-effectively store and analyze over 30PB of data across 1000+ nodes. It offers SQL analytics through Tajo for faster querying and easier migration from RDBMS systems. The Hadoop DW has helped SK Telecom and other customers such as semiconductor manufacturers to more affordably store and process massive volumes of both structured and unstructured data for advanced analytics.
Monitoring of GPU Usage with Tensorflow Models Using PrometheusDatabricks
Understanding the dynamics of GPU utilization and workloads in containerized systems is critical to creating efficient software systems. We create a set of dashboards to monitor and evaluate GPU performance in the context of TensorFlow. We monitor performance in real time to gain insight into GPU load, GPU memory and temperature metrics in a Kubernetes GPU enabled system. Visualizing TensorFlow training job metrics in real time using Prometheus allows us to tune and optimize GPU usage. Also, because Tensor flow jobs can have both GPU and CPU implementations it is useful to view detailed real time performance data from each implementation and choose the best implementation. To illustrate our system, we will show a live demo gathering and visualizing GPU metrics on a GPU enabled Kubernetes cluster with Prometheus and Grafana.
The document discusses the challenges of managing a multi-tenant Hadoop cluster over time as the cluster grows and diversifies. As the cluster scales up in size and number of users, it faces performance problems such as missed SLAs, poor application performance, underutilized resources, and low throughput. Managing such a complex cluster manually is difficult. The document proposes automating resource management and using a new interface that allows administrators to specify performance goals and ask "what-if" questions to optimize configuration settings and improve performance.
Transitioning Compute Models: Hadoop MapReduce to SparkSlim Baltagi
This presentation is an analysis of the observed trends in the transition from the Hadoop ecosystem to the Spark ecosystem. The related talk took place at the Chicago Hadoop User Group (CHUG) meetup held on February 12, 2015.
Hadoop clusters are operated on an ephemeral basis in the cloud by Qubole, processing over 300 petabytes of data per month across over 100 customers. Qubole addresses challenges of ephemeral clusters through auto-scaling of resources using YARN, optimizing performance for cloud storage, and storing job history remotely. Volatile low-cost nodes are leveraged through policies that ensure data replication despite potential node failures.
The document discusses tools and techniques used by Uber's Hadoop team to make their Spark and Hadoop platforms more user-friendly and efficient. It introduces tools like SCBuilder to simplify Spark context creation, Kafka dispersal to distribute RDD results, and SparkPlug to provide templates for common jobs. It also describes a distributed log debugger called SparkChamber to help debug Spark jobs and techniques like building a spatial index to optimize geo-spatial joins. The goal is to abstract out infrastructure complexities and enforce best practices to make the platforms more self-service for users.
Maintaining Low Latency While Maximizing Throughput on a Single ClusterMapR Technologies
The good news: Hadoop has a lot of tools. The bad news: Hadoop has a lot of tools, and conflicting priorities. This talk shows how advances in YARN and Mesos allow you to run multiple distinct workloads together. We show how to use SLA and latency rules along with preemption in YARN to maintain high throughput while guaranteeing latency for applications such as HBase and Drill
Application architectures with Hadoop – Big Data TechCon 2014hadooparchbook
Building applications using Apache Hadoop with a use-case of clickstream analysis. Presented by Mark Grover and Jonathan Seidman at Big Data TechCon, Boston in April 2014
Spark is a fast and general engine for large-scale data processing. It provides APIs in Java, Scala, and Python and an interactive shell. Spark applications operate on resilient distributed datasets (RDDs) that can be cached in memory for faster performance. RDDs are immutable and fault-tolerant via lineage graphs. Transformations create new RDDs from existing ones while actions return values to the driver program. Spark's execution model involves a driver program that coordinates tasks on executor machines. RDD caching and lineage graphs allow Spark to efficiently run jobs across clusters.
Deep Dive into the New Features of Apache Spark 3.1Databricks
Continuing with the objectives to make Spark faster, easier, and smarter, Apache Spark 3.1 extends its scope with more than 1500 resolved JIRAs. We will talk about the exciting new developments in the Apache Spark 3.1 as well as some other major initiatives that are coming in the future. In this talk, we want to share with the community many of the more important changes with the examples and demos.
The following features are covered: the SQL features for ANSI SQL compliance, new streaming features, and Python usability improvements, the performance enhancements and new tuning tricks in query compiler.
Data Engineer's Lunch #55: Get Started in Data EngineeringAnant Corporation
In Data Engineer's Lunch #55, CEO of Anant, Rahul Singh, will cover 10 resources every data engineer needs to get started or master their game.
Accompanying Blog: Coming Soon!
Accompanying YouTube: Coming Soon!
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Data Engineer’s Lunch Weekly at 12 PM EST Every Monday:
https://www.meetup.com/Data-Wranglers-DC/events/
Cassandra.Link:
https://cassandra.link/
Follow Us and Reach Us At:
Anant:
https://www.anant.us/
Awesome Cassandra:
https://github.com/Anant/awesome-cassandra
Email:
solutions@anant.us
LinkedIn:
https://www.linkedin.com/company/anant/
Twitter:
https://twitter.com/anantcorp
Eventbrite:
https://www.eventbrite.com/o/anant-1072927283
Facebook:
https://www.facebook.com/AnantCorp/
Join The Anant Team:
https://www.careers.anant.us
The presentation covers lambda architecture and implementation with spark. In the presentation we will discuss about components of lambda architecture like batch layer, speed layer and serving layer. We will also discuss its advantages and benefits with spark.
Hadoop application architectures - using Customer 360 as an examplehadooparchbook
Hadoop application architectures - using Customer 360 (more generally, Entity 360) as an example. By Ted Malaska, Jonathan Seidman and Mark Grover at Strata + Hadoop World 2016 in NYC.
The NameNode was experiencing high load and instability after being restarted. Graphs showed unknown high load between checkpoints on the NameNode. DataNode logs showed repeated 60000 millisecond timeouts in communication with the NameNode. Thread dumps revealed NameNode server handlers waiting on the same lock, indicating a bottleneck. Source code analysis pointed to repeated block reports from DataNodes to the NameNode as the likely cause of the high load.
This document discusses writing application frameworks on Apache Hadoop YARN. It provides an overview of the YARN architecture including the resource manager, node manager, and application master. It then describes the key components needed to write a new framework, including an application submission client, application master, and optional application history and auxiliary services. Finally, it provides a code example for a distributed shell use case and discusses registering with the resource manager and defining containers.
Running Non-MapReduce Big Data Applications on Apache Hadoophitesh1892
Apache Hadoop has become popular from its specialization in the execution of MapReduce programs. However, it has been hard to leverage existing Hadoop infrastructure for various other processing paradigms such as real-time streaming, graph processing and message-passing. That was true until the introduction of Apache Hadoop YARN in Apache Hadoop 2.0. YARN supports running arbitrary processing paradigms on the same Hadoop cluster. This allows for development of newer frameworks as well as more efficient implementations of existing frameworks that can all run on and share the resources of a single multi-tenant YARN cluster. This talk gives a brief introduction to YARN. We will illustrate how to create applications and how to best make use of YARN. We will show examples of different applications such as Apache Tez and Apache Samza that can leverage YARN and present best practices/guidelines on building applications on top of Apache Hadoop YARN.
Developing YARN Applications - Integrating natively to YARN July 24 2014Hortonworks
This document provides an overview of developing applications for YARN, the resource management framework in Hadoop 2.0. It describes YARN concepts like containers and the ApplicationMaster, the APIs used to develop YARN applications, and walks through building a simple distributed shell application. It also discusses the Application Timeline Server for application metrics and monitoring.
This document provides best practices for YARN administrators and application developers. For administrators, it discusses YARN configuration, enabling ResourceManager high availability, configuring schedulers like Capacity Scheduler and Fair Scheduler, sizing containers, configuring NodeManagers, log aggregation, and metrics. For application developers, it discusses whether to use an existing framework or develop a native application, understanding YARN components, writing the client, and writing the ApplicationMaster.
YARN: Future of Data Processing with Apache HadoopHortonworks
Vinod Kumar Vavilapalli presented on the future of data processing with Apache Hadoop. He discussed limitations of the classic MapReduce architecture including scalability, single point of failure, and low resource utilization. He then introduced the new YARN architecture which splits up the JobTracker into a ResourceManager and per-application ApplicationMasters for improved fault tolerance, utilization, and scalability. Benchmarks show performance gains of up to 2x compared to classic MapReduce. Hadoop 2.0 alpha is available for testing and feedback.
The document discusses Hortonworks' Slider project, which aims to simplify deploying and managing distributed applications on YARN. Slider provides a packaging format for applications, launches application components as YARN containers via an Application Master, and handles service registration and configuration management. It addresses limitations of earlier frameworks by supporting dynamic configurations, embedded usage, and integration with service discovery in Zookeeper.
ApacheCon North America 2014 - Apache Hadoop YARN: The Next-generation Distri...Zhijie Shen
For diverse organizations, Apache Hadoop has become the de-facto place where data & computational resources are shared. This broad usage has stretched its design beyond its intended target. To address this, Apache Hadoop community has come up with next generation of Hadoop’s compute platform: YARN.
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Hortonworks Yarn Code Walk Through January 2014Hortonworks
This slide deck accompanies the Webinar recording YARN Code Walk through on Jan. 22, 2014, on Hortonworks.com/webinars under Past Webinars, or
https://hortonworks.webex.com/hortonworks/lsr.php?AT=pb&SP=EC&rID=129468197&rKey=b645044305775657
YARN is a resource management framework for distributed applications. It separates compute resources from application execution and includes a global resource manager and per-node node managers. The resource manager arbitrates resources among applications and enforces allocation fairness. Node managers run on slave nodes and manage containers, which are allocated resources. Applications communicate resource needs to the resource manager and are given containers to run in.
The document summarizes the agenda and presentations for a YARN Meet Up in September 2013. Key topics included Hadoop 2.0 beta testing with YARN, a new Application History Server, improving RM reliability through restartability and high availability, Apache Tez and other YARN applications like Samza and Giraph, using YARN at LinkedIn, and a Go programming language YARN application demo. Individual presentations provided details on YARN APIs, existing application compatibility, the Application History Server design and implementation, RM restartability work, RM high availability architecture, and using Tez as a YARN application.
Vinod Kumar Vavilapalli and Jian He presented on Apache Hadoop YARN, the next generation architecture for Hadoop. They discussed YARN's role as a data operating system and resource management platform. They outlined YARN's current capabilities and highlighted several features in development, including resource manager high availability, the YARN timeline server, and improved scheduling. They also discussed how YARN enables new applications beyond MapReduce and the growing ecosystem of projects supported by YARN.
Apache Hadoop YARN is the modern distributed operating system for big data applications. It morphed the Hadoop compute layer to be a common resource management platform that can host a wide variety of applications. Many organizations leverage YARN in building their applications on top of Hadoop without themselves repeatedly worrying about resource management, isolation, multi-tenancy issues, etc.
In this talk, we’ll start with the current status of Apache Hadoop YARN—how it is used today in deployments large and small. We'll then move on to the exciting present and future of YARN—features that are further strengthening YARN as the first-class resource management platform for data centers running enterprise Hadoop.
We’ll discuss the current status as well as the future promise of features and initiatives like: powerful container placement, global scheduling, support for machine learning and deep learning workloads through GPU and FPGA support, extreme scale with YARN federation, containerized apps on YARN, support for long running services (alongside applications) natively without any changes, seamless application upgrades, powerful scheduling features like application priorities, intra-queue preemption across applications, and operational enhancements including insights through Timeline Service V2, a new web UI, and better queue management.
Speakers
Wangda Tan, Staff Software Engineer, Hortonworks
Billie Rinaldi, Principal Software Engineer I, Hortonworks
Dataworks Berlin Summit 18' - Apache hadoop YARN State Of The UnionWangda Tan
This document summarizes the state of Apache Hadoop YARN and its evolution over time. It discusses how YARN started as a sub-project of Hadoop to support multiple applications and long-running services. It then outlines recent initiatives like containerization, GPU/FPGA support, federation, and improved scheduling algorithms to handle larger clusters with tens of thousands of nodes. The document also previews upcoming features in YARN 3.2 and beyond such as node attributes, container overcommit, and auto-spawning of system services.
Virtualizing Latency Sensitive Workloads and vFabric GemFireCarter Shanklin
This presentation was made by Emad Benjamin of VMware Technical Marketing. Normally I wouldn't upload someone else's preso but I really insisted this get posted and he asked me to help him out.
This deck covers tips and best practices for virtualizing latency sensitive apps on vSphere in general, and takes a deep dive into virtualizing vFabric GemFire, which is a high-performance distributed and memory-optimized key/value store.
Best practices include how to configure the virtual machines and how to tune them appropriately to the hardware the application runs on.
Slider is an open source project that allows for easy deployment, management, and monitoring of long-running applications on Hadoop YARN clusters. It provides a simpler platform than coding applications directly for YARN, handling application packaging, resource management, and lifecycle operations. Key features of Slider include application packaging standards, commands for starting, stopping, scaling applications, and integration with cluster management tools like Ambari for monitoring applications.
Developing Applications with Hadoop 2.0 and YARN by Abhijit Lele Hakka Labs
Hadoop 2.0 is approaching. A defining characteristic of Hadoop 2.0 is its next generation resource management framework called YARN. YARN enables Hadoop to grow beyond its MapReduce origins to embrace multiple workloads spanning interactive queries, batch processing, streaming & more.
Hadoop World 2011: Next Generation Apache Hadoop MapReduce - Mohadev Konar, H...Cloudera, Inc.
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Apache Hadoop YARN is the modern distributed operating system for big data applications. It morphed the Hadoop compute layer to be a common resource management platform that can host a wide variety of applications. Many organizations leverage YARN in building their applications on top of Hadoop without themselves repeatedly worrying about resource management, isolation, multi-tenancy issues, etc.
In this talk, we’ll start with the current status of Apache Hadoop YARN—how it is used today in deployments large and small. We'll then move on to the exciting present and future of YARN—features that are further strengthening YARN as the first class resource management platform for data centers running enterprise Hadoop.
We’ll discuss the current status as well as the future promise of features and initiatives like: powerful container placement, global scheduling, support for machine learning and deep learning workloads through GPU and FPGA support, extreme scale with YARN federation, containerized apps on YARN, support for long-running services (alongside applications) natively without any changes, seamless application upgrades, powerful scheduling features like application priorities, intra-queue preemption across applications, and operational enhancements including insights through Timeline Service V2, a new web UI, and better queue management.
Speaker: Sanjay Radia, Chief Architect, Founder, Hortonworks
Taming YARN @ Hadoop Conference Japan 2014Tsuyoshi OZAWA
The document discusses YARN (Yet Another Resource Negotiator), a resource management framework for Hadoop. It describes YARN components like the ResourceManager, NodeManager, and ApplicationMaster. It covers YARN configuration, capacity planning, health checks, thread tuning, and enabling high availability of the ResourceManager through ZooKeeper.
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