Distributed resource scheduling frameworks like Kubernetes, Mesos, YARN, and Swarm each take different architectural approaches to scheduling resources across a cluster. The document provides an overview of each framework's architecture, key features related to scheduling like priority, isolation, and support for multiple container types. It also compares the frameworks based on functional attributes such as resource granularity, scheduler support, oversubscription, and support for isolation and applications.
This document provides an overview of a Hadoop cluster deployment and configuration best practices from Rohith Sharma, Naganarasimha, and Sunil. It discusses:
1. Examples of YARN resource configurations for high-end node managers with 64GB RAM, 8-16 CPU cores, and 100TB of disk space.
2. Common YARN and MapReduce configuration parameters to tune resources like memory, CPU, and I/O.
3. Anti-patterns related to container memory allocation, long shuffle phases in MapReduce, and RM restarts impacting performance.
4. Best practices for queue configuration, capacity planning, user limits, and application priorities to improve cluster utilization.
This document discusses the timeline server which collects and stores application metrics and event data in YARN. It describes the limitations of the original job history server and application history server, which only supported MapReduce jobs and did not capture YARN-level data. The timeline server versions 1 and 2 are presented as improved solutions, with version 2 focusing on distributed and reliable storage in HBase, a new data model to support arbitrary application types, and online aggregation of metrics.
Anti patterns in Hadoop Cluster deploymentSunil Govindan
Rohith Sharma, Naganarasimha, and Sunil presented on Hadoop cluster configurations and anti-patterns. They discussed sample node manager configurations with high resources, related YARN and MapReduce resource tuning settings, and anti-patterns like not configuring container heap size properly leading to out of memory errors. They also covered YARN capacity scheduler queue planning best practices like queue mapping, preemption, user limits, and application priority to improve cluster utilization.
As part of the recent release of Hadoop 2 by the Apache Software Foundation, YARN and MapReduce 2 deliver significant upgrades to scheduling, resource management, and execution in Hadoop.
At their core, YARN and MapReduce 2’s improvements separate cluster resource management capabilities from MapReduce-specific logic. YARN enables Hadoop to share resources dynamically between multiple parallel processing frameworks such as Cloudera Impala, allows more sensible and finer-grained resource configuration for better cluster utilization, and scales Hadoop to accommodate more and larger jobs.
YARN allows applications to share resources in a cluster by separating resource management from job scheduling and monitoring. It introduces the ResourceManager for resource management and per-application ApplicationMasters for negotiating resources and monitoring applications. The NodeManager runs on slave nodes and manages containers which are allocated by the ResourceManager and used by applications to execute tasks. YARN supports high availability, rolling upgrades, multi-tenancy and fault tolerance.
Reservations Based Scheduling: if you’re late don’t blame us! DataWorks Summit
This document proposes techniques for teaching a resource manager about time-based constraints and deadlines. It summarizes an approach called reservation-based scheduling that allows jobs to declare their resource needs and deadlines. Key aspects include a Reservation Definition Language to specify jobs and their requirements, greedy allocation algorithms, a PlanFollower to dynamically enact the resource plan, and adapting to changing conditions over time. The approach aims to meet production job SLAs while improving latency for best-effort jobs and increasing cluster utilization.
YARN is a framework for job scheduling and cluster resource management. It improves on classic MapReduce by separating resource management from job scheduling and tracking. In YARN, a resource manager allocates containers for tasks from applications and monitors containers. An application master negotiates container resources and coordinates tasks within the application. Tasks execute in containers managed by node managers. The application progress and completion is tracked and reported by the application master.
Resource Aware Scheduling for Hadoop [Final Presentation]Lu Wei
The document describes a resource-aware scheduler for Hadoop that aims to improve task scheduling by considering both job resource demands and node resource availability. It captures job and node profiles, estimates task execution times, and applies scheduling policies like shortest job first. Evaluation on word count and Pi estimation workloads showed the estimated task times closely matched the actual times, demonstrating the accuracy of the scheduler's resource modeling and estimations.
This document provides an overview of a Hadoop cluster deployment and configuration best practices from Rohith Sharma, Naganarasimha, and Sunil. It discusses:
1. Examples of YARN resource configurations for high-end node managers with 64GB RAM, 8-16 CPU cores, and 100TB of disk space.
2. Common YARN and MapReduce configuration parameters to tune resources like memory, CPU, and I/O.
3. Anti-patterns related to container memory allocation, long shuffle phases in MapReduce, and RM restarts impacting performance.
4. Best practices for queue configuration, capacity planning, user limits, and application priorities to improve cluster utilization.
This document discusses the timeline server which collects and stores application metrics and event data in YARN. It describes the limitations of the original job history server and application history server, which only supported MapReduce jobs and did not capture YARN-level data. The timeline server versions 1 and 2 are presented as improved solutions, with version 2 focusing on distributed and reliable storage in HBase, a new data model to support arbitrary application types, and online aggregation of metrics.
Anti patterns in Hadoop Cluster deploymentSunil Govindan
Rohith Sharma, Naganarasimha, and Sunil presented on Hadoop cluster configurations and anti-patterns. They discussed sample node manager configurations with high resources, related YARN and MapReduce resource tuning settings, and anti-patterns like not configuring container heap size properly leading to out of memory errors. They also covered YARN capacity scheduler queue planning best practices like queue mapping, preemption, user limits, and application priority to improve cluster utilization.
As part of the recent release of Hadoop 2 by the Apache Software Foundation, YARN and MapReduce 2 deliver significant upgrades to scheduling, resource management, and execution in Hadoop.
At their core, YARN and MapReduce 2’s improvements separate cluster resource management capabilities from MapReduce-specific logic. YARN enables Hadoop to share resources dynamically between multiple parallel processing frameworks such as Cloudera Impala, allows more sensible and finer-grained resource configuration for better cluster utilization, and scales Hadoop to accommodate more and larger jobs.
YARN allows applications to share resources in a cluster by separating resource management from job scheduling and monitoring. It introduces the ResourceManager for resource management and per-application ApplicationMasters for negotiating resources and monitoring applications. The NodeManager runs on slave nodes and manages containers which are allocated by the ResourceManager and used by applications to execute tasks. YARN supports high availability, rolling upgrades, multi-tenancy and fault tolerance.
Reservations Based Scheduling: if you’re late don’t blame us! DataWorks Summit
This document proposes techniques for teaching a resource manager about time-based constraints and deadlines. It summarizes an approach called reservation-based scheduling that allows jobs to declare their resource needs and deadlines. Key aspects include a Reservation Definition Language to specify jobs and their requirements, greedy allocation algorithms, a PlanFollower to dynamically enact the resource plan, and adapting to changing conditions over time. The approach aims to meet production job SLAs while improving latency for best-effort jobs and increasing cluster utilization.
YARN is a framework for job scheduling and cluster resource management. It improves on classic MapReduce by separating resource management from job scheduling and tracking. In YARN, a resource manager allocates containers for tasks from applications and monitors containers. An application master negotiates container resources and coordinates tasks within the application. Tasks execute in containers managed by node managers. The application progress and completion is tracked and reported by the application master.
Resource Aware Scheduling for Hadoop [Final Presentation]Lu Wei
The document describes a resource-aware scheduler for Hadoop that aims to improve task scheduling by considering both job resource demands and node resource availability. It captures job and node profiles, estimates task execution times, and applies scheduling policies like shortest job first. Evaluation on word count and Pi estimation workloads showed the estimated task times closely matched the actual times, demonstrating the accuracy of the scheduler's resource modeling and estimations.
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.
The document discusses resource scheduling techniques for cloud computing including single processor scheduling algorithms, cloud scheduling approaches for multi-tenant systems like the Dominant Resource Fairness scheduler, and Hadoop schedulers like the fair scheduler and capacity scheduler. It also proposes a management model for analyzing elasticity of cluster capacity and job dependencies to enable data bursting between private and public clouds.
Learning Objectives - In this module, you will understand the newly added features in Hadoop 2.0, namely, YARN, MRv2, NameNode High Availability, HDFS Federation, support for Windows etc.
The new YARN framework promises to make Hadoop a general-purpose platform for Big Data and enterprise data hub applications. In this talk, you'll learn about writing and taking advantage of applications built on YARN.
This document discusses Spark running on YARN and how it can provide benefits over other cluster managers. It covers calculating container sizes to optimize resource usage, isolating containers, label-based scheduling, dynamic resource allocation, resilience to failures, accessing secure environments, and debugging YARN applications. Future work areas discussed are more advanced dynamic allocation algorithms, integrating the YARN timeline server, and making security services pluggable.
Operating multi-tenant clusters requires careful planning of capacity for on-time launch of big data projects and applications within expected budget and with appropriate SLA guarantees. Making such guarantees with a set of standard hardware configurations is key to operate big data platforms as a hosted service for your organization.
This talk highlights the tools, techniques and methodology applied on a per-project or user basis across three primary multi-tenant deployments in the Apache Hadoop ecosystem, namely MapReduce/YARN and HDFS, HBase, and Storm due to the significance of capital investments with increasing scale in data nodes, region servers, and supervisor nodes respectively. We will demo the estimation tools developed for these deployments that can be used for capital planning and forecasting, and cluster resource and SLA management, including making latency and throughput guarantees to individual users and projects.
As we discuss the tools, we will share considerations that got incorporated to come up with the most appropriate calculation across these three primary deployments. We will discuss the data sources for calculations, resource drivers for different use cases, and how to plan for optimum capacity allocation per project with respect to given standard hardware configurations.
This document summarizes a proposal to improve fault tolerance in Hadoop clusters. It proposes adding a "Backup" state to store intermediate MapReduce data, so reducers can continue working even if mappers fail. It also proposes a "supernode" protocol where neighboring slave nodes communicate task information. If one node fails, a neighbor can take over its tasks without involving the JobTracker. This would improve fault tolerance by allowing computation to continue locally between nodes after failures.
we are interested in performing Big Data analytics, we need to
learn Hadoop to perform operations with Hadoop MapReduce. In this Presentation, we
will discuss what MapReduce is, why it is necessary, how MapReduce programs can
be developed through Apache Hadoop, and more.
Extending Spark Streaming to Support Complex Event ProcessingOh Chan Kwon
In this talk, we introduce the extensions of Spark Streaming to support (1) SQL-based query processing and (2) elastic-seamless resource allocation. First, we explain the methods of supporting window queries and query chains. As we know, last year, Grace Huang and Jerry Shao introduced the concept of “StreamSQL” that can process streaming data with SQL-like queries by adapting SparkSQL to Spark Streaming. However, we made advances in supporting complex event processing (CEP) based on their efforts. In detail, we implemented the sliding window concept to support a time-based streaming data processing at the SQL level. Here, to reduce the aggregation time of large windows, we generate an efficient query plan that computes the partial results by evaluating only the data entering or leaving the window and then gets the current result by merging the previous one and the partial ones. Next, to support query chains, we made the result of a query over streaming data be a table by adding the “insert into” query. That is, it allows us to apply stream queries to the results of other ones. Second, we explain the methods of allocating resources to streaming applications dynamically, which enable the applications to meet a given deadline. As the rate of incoming events varies over time, resources allocated to applications need to be adjusted for high resource utilization. However, the current Spark's resource allocation features are not suitable for streaming applications. That is, the resources allocated will not be freed when new data are arriving continuously to the streaming applications even though the quantity of the new ones is very small. In order to resolve the problem, we consider their resource utilization. If the utilization is low, we choose victim nodes to be killed. Then, we do not feed new data into the victims to prevent a useless recovery issuing when they are killed. Accordingly, we can scale-in/-out the resources seamlessly.
This document provides a technical introduction to Hadoop, including:
- Hadoop has been tested on a 4000 node cluster with 32,000 cores and 16 petabytes of storage.
- Key Hadoop concepts are explained, including jobs, tasks, task attempts, mappers, reducers, and the JobTracker and TaskTrackers.
- The process of launching a MapReduce job is described, from the client submitting the job to the JobTracker distributing tasks to TaskTrackers and running the user-defined mapper and reducer classes.
This document summarizes key aspects of the Hadoop Distributed File System (HDFS). HDFS is designed for storing very large files across commodity hardware. It uses a master/slave architecture with a single NameNode that manages file system metadata and multiple DataNodes that store application data. HDFS allows for streaming access to this distributed data and can provide higher throughput than a single high-end server by parallelizing reads across nodes.
In this session, we will introduce “Knitting Boar”, an open-source Java library for performing distributed online learning on a Hadoop cluster under YARN. We will give an overview of how Woven Wabbit works and examine the lessons learned from YARN application construction.
Featuring a brief overview of fault-tolerant mechanisms across various Big Data systems such as Google File system (GFS), Amazon Dynamo, Bigtable, Hadoop - Map Reduce, Facebook Cassandra along with description of an existing fault tolerant model
This presentation will investigate how using micro-batching for submitting writes to Cassandra can improve throughput and reduce client application CPU load.
Micro-batching combines writes for the same partition key into a single network request and ensures they hit the "fast path" for writes on a Cassandra node.
About the Speaker
Adam Zegelin Technical Co-founder, Instaclustr
As Instaclustrs founding software engineer, Adam provides the foundation knowledge of our capability and engineering environment. He delivers business-focused value to our code-base and overall capability architecture. Adam is also focused on providing Instaclustr's contribution to the broader open source community on which our products and services rely, including Apache Cassandra, Apache Spark and other technologies such as CoreOS and Docker.
Presenter: Chris Lohfink, Engineer at Pythian
This session will cover a walk-through to provide an understanding of key metrics critical to operating a Cassandra cluster effectively. Without context to the metrics, we just have pretty graphs. With context, we have a powerful tool to determine problems before they happen and to debug production issues more quickly.
Presentation from September, 2010 about the RTI proposal to improve the C++ API for the OMG's Data Distribution Service specification (DDS). See also http://code.google.com/p/dds-psm-cxx/.
Running Dataproc At Scale in production - Searce Talk at GDG DelhiSearce Inc
This document provides information about Dataproc, Google Cloud's fully managed Spark and Hadoop service. It discusses how Dataproc allows users to create clusters on-demand to process large datasets in a flexible and cost-effective manner. It also covers how Dataproc integrates with other Google Cloud services and provides open-source tools like Spark, Hadoop, Hive and Pig. Additionally, it summarizes best practices for using Dataproc such as leveraging initialization actions, specifying cluster versions, and using the Jobs API for submissions.
Challenges & Capabilites in Managing a MapR Cluster by David TuckerMapR Technologies
"If you're using Hadoop in production, how do you manage it? Does the distribution you're using provide any tools to make the job easier? What are the pitfalls? Are there parts of the system that are less robust or that have problems more often? Are you running Hadoop on bare metal, or in a cloud environment, and is one easier than the other?"
MapR Senior Solutions Architect David Tucker speaks about the challenges and capabilites in managing a cluster. This talk was given at the SF Bay Area Large Scale Production Engineering Meetup (Sept 19, 2013).
The document discusses distributed resource scheduling frameworks and compares several open source schedulers. It covers the architectural evolution of resource scheduling including monolithic, two-level, shared state, distributed, and hybrid models. Prominent frameworks discussed include Kubernetes, Swarm, Mesos, YARN, and others. The document outlines key aspects of distributed scheduling like resource types, container orchestration, application support, networking, storage, scalability, and security.
Mesos - A Platform for Fine-Grained Resource Sharing in the Data CenterAnkur Chauhan
Papers we Love @ Seattle, 08/14/2015
Abstract
We present Mesos, a platform for sharing commodity clusters between multiple diverse cluster computing frameworks, such as Hadoop and MPI. Sharing improves cluster utilization and avoids per-framework data replication. Mesos shares resources in a fine-grained manner, allowing frameworks to achieve data locality by taking turns reading data stored on each machine. To support the sophisticated schedulers of today's frameworks, Mesos introduces a distributed two-level scheduling mechanism called resource offers. Mesos decides how many resources to offer each framework, while frameworks decide which resources to accept and which computations to run on them. Our results show that Mesos can achieve near-optimal data locality when sharing the cluster among diverse frameworks, can scale to 50,000 (emulated) nodes, and is resilient to failures.
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.
The document discusses resource scheduling techniques for cloud computing including single processor scheduling algorithms, cloud scheduling approaches for multi-tenant systems like the Dominant Resource Fairness scheduler, and Hadoop schedulers like the fair scheduler and capacity scheduler. It also proposes a management model for analyzing elasticity of cluster capacity and job dependencies to enable data bursting between private and public clouds.
Learning Objectives - In this module, you will understand the newly added features in Hadoop 2.0, namely, YARN, MRv2, NameNode High Availability, HDFS Federation, support for Windows etc.
The new YARN framework promises to make Hadoop a general-purpose platform for Big Data and enterprise data hub applications. In this talk, you'll learn about writing and taking advantage of applications built on YARN.
This document discusses Spark running on YARN and how it can provide benefits over other cluster managers. It covers calculating container sizes to optimize resource usage, isolating containers, label-based scheduling, dynamic resource allocation, resilience to failures, accessing secure environments, and debugging YARN applications. Future work areas discussed are more advanced dynamic allocation algorithms, integrating the YARN timeline server, and making security services pluggable.
Operating multi-tenant clusters requires careful planning of capacity for on-time launch of big data projects and applications within expected budget and with appropriate SLA guarantees. Making such guarantees with a set of standard hardware configurations is key to operate big data platforms as a hosted service for your organization.
This talk highlights the tools, techniques and methodology applied on a per-project or user basis across three primary multi-tenant deployments in the Apache Hadoop ecosystem, namely MapReduce/YARN and HDFS, HBase, and Storm due to the significance of capital investments with increasing scale in data nodes, region servers, and supervisor nodes respectively. We will demo the estimation tools developed for these deployments that can be used for capital planning and forecasting, and cluster resource and SLA management, including making latency and throughput guarantees to individual users and projects.
As we discuss the tools, we will share considerations that got incorporated to come up with the most appropriate calculation across these three primary deployments. We will discuss the data sources for calculations, resource drivers for different use cases, and how to plan for optimum capacity allocation per project with respect to given standard hardware configurations.
This document summarizes a proposal to improve fault tolerance in Hadoop clusters. It proposes adding a "Backup" state to store intermediate MapReduce data, so reducers can continue working even if mappers fail. It also proposes a "supernode" protocol where neighboring slave nodes communicate task information. If one node fails, a neighbor can take over its tasks without involving the JobTracker. This would improve fault tolerance by allowing computation to continue locally between nodes after failures.
we are interested in performing Big Data analytics, we need to
learn Hadoop to perform operations with Hadoop MapReduce. In this Presentation, we
will discuss what MapReduce is, why it is necessary, how MapReduce programs can
be developed through Apache Hadoop, and more.
Extending Spark Streaming to Support Complex Event ProcessingOh Chan Kwon
In this talk, we introduce the extensions of Spark Streaming to support (1) SQL-based query processing and (2) elastic-seamless resource allocation. First, we explain the methods of supporting window queries and query chains. As we know, last year, Grace Huang and Jerry Shao introduced the concept of “StreamSQL” that can process streaming data with SQL-like queries by adapting SparkSQL to Spark Streaming. However, we made advances in supporting complex event processing (CEP) based on their efforts. In detail, we implemented the sliding window concept to support a time-based streaming data processing at the SQL level. Here, to reduce the aggregation time of large windows, we generate an efficient query plan that computes the partial results by evaluating only the data entering or leaving the window and then gets the current result by merging the previous one and the partial ones. Next, to support query chains, we made the result of a query over streaming data be a table by adding the “insert into” query. That is, it allows us to apply stream queries to the results of other ones. Second, we explain the methods of allocating resources to streaming applications dynamically, which enable the applications to meet a given deadline. As the rate of incoming events varies over time, resources allocated to applications need to be adjusted for high resource utilization. However, the current Spark's resource allocation features are not suitable for streaming applications. That is, the resources allocated will not be freed when new data are arriving continuously to the streaming applications even though the quantity of the new ones is very small. In order to resolve the problem, we consider their resource utilization. If the utilization is low, we choose victim nodes to be killed. Then, we do not feed new data into the victims to prevent a useless recovery issuing when they are killed. Accordingly, we can scale-in/-out the resources seamlessly.
This document provides a technical introduction to Hadoop, including:
- Hadoop has been tested on a 4000 node cluster with 32,000 cores and 16 petabytes of storage.
- Key Hadoop concepts are explained, including jobs, tasks, task attempts, mappers, reducers, and the JobTracker and TaskTrackers.
- The process of launching a MapReduce job is described, from the client submitting the job to the JobTracker distributing tasks to TaskTrackers and running the user-defined mapper and reducer classes.
This document summarizes key aspects of the Hadoop Distributed File System (HDFS). HDFS is designed for storing very large files across commodity hardware. It uses a master/slave architecture with a single NameNode that manages file system metadata and multiple DataNodes that store application data. HDFS allows for streaming access to this distributed data and can provide higher throughput than a single high-end server by parallelizing reads across nodes.
In this session, we will introduce “Knitting Boar”, an open-source Java library for performing distributed online learning on a Hadoop cluster under YARN. We will give an overview of how Woven Wabbit works and examine the lessons learned from YARN application construction.
Featuring a brief overview of fault-tolerant mechanisms across various Big Data systems such as Google File system (GFS), Amazon Dynamo, Bigtable, Hadoop - Map Reduce, Facebook Cassandra along with description of an existing fault tolerant model
This presentation will investigate how using micro-batching for submitting writes to Cassandra can improve throughput and reduce client application CPU load.
Micro-batching combines writes for the same partition key into a single network request and ensures they hit the "fast path" for writes on a Cassandra node.
About the Speaker
Adam Zegelin Technical Co-founder, Instaclustr
As Instaclustrs founding software engineer, Adam provides the foundation knowledge of our capability and engineering environment. He delivers business-focused value to our code-base and overall capability architecture. Adam is also focused on providing Instaclustr's contribution to the broader open source community on which our products and services rely, including Apache Cassandra, Apache Spark and other technologies such as CoreOS and Docker.
Presenter: Chris Lohfink, Engineer at Pythian
This session will cover a walk-through to provide an understanding of key metrics critical to operating a Cassandra cluster effectively. Without context to the metrics, we just have pretty graphs. With context, we have a powerful tool to determine problems before they happen and to debug production issues more quickly.
Presentation from September, 2010 about the RTI proposal to improve the C++ API for the OMG's Data Distribution Service specification (DDS). See also http://code.google.com/p/dds-psm-cxx/.
Running Dataproc At Scale in production - Searce Talk at GDG DelhiSearce Inc
This document provides information about Dataproc, Google Cloud's fully managed Spark and Hadoop service. It discusses how Dataproc allows users to create clusters on-demand to process large datasets in a flexible and cost-effective manner. It also covers how Dataproc integrates with other Google Cloud services and provides open-source tools like Spark, Hadoop, Hive and Pig. Additionally, it summarizes best practices for using Dataproc such as leveraging initialization actions, specifying cluster versions, and using the Jobs API for submissions.
Challenges & Capabilites in Managing a MapR Cluster by David TuckerMapR Technologies
"If you're using Hadoop in production, how do you manage it? Does the distribution you're using provide any tools to make the job easier? What are the pitfalls? Are there parts of the system that are less robust or that have problems more often? Are you running Hadoop on bare metal, or in a cloud environment, and is one easier than the other?"
MapR Senior Solutions Architect David Tucker speaks about the challenges and capabilites in managing a cluster. This talk was given at the SF Bay Area Large Scale Production Engineering Meetup (Sept 19, 2013).
The document discusses distributed resource scheduling frameworks and compares several open source schedulers. It covers the architectural evolution of resource scheduling including monolithic, two-level, shared state, distributed, and hybrid models. Prominent frameworks discussed include Kubernetes, Swarm, Mesos, YARN, and others. The document outlines key aspects of distributed scheduling like resource types, container orchestration, application support, networking, storage, scalability, and security.
Mesos - A Platform for Fine-Grained Resource Sharing in the Data CenterAnkur Chauhan
Papers we Love @ Seattle, 08/14/2015
Abstract
We present Mesos, a platform for sharing commodity clusters between multiple diverse cluster computing frameworks, such as Hadoop and MPI. Sharing improves cluster utilization and avoids per-framework data replication. Mesos shares resources in a fine-grained manner, allowing frameworks to achieve data locality by taking turns reading data stored on each machine. To support the sophisticated schedulers of today's frameworks, Mesos introduces a distributed two-level scheduling mechanism called resource offers. Mesos decides how many resources to offer each framework, while frameworks decide which resources to accept and which computations to run on them. Our results show that Mesos can achieve near-optimal data locality when sharing the cluster among diverse frameworks, can scale to 50,000 (emulated) nodes, and is resilient to failures.
In Apache Cassandra Lunch #41: Apache Cassandra Lunch #41: Cassandra on Kubernetes - Docker/Kubernetes/Helm Part 1, we discuss Cassandra on Kubernetes and give an introduction to Docker, Kubernetes, and Helm.
Accompanying Blog: https://blog.anant.us/apache-cassandra-lunch-41-cassandra-on-kubernetes-docker-kubernetes-helm-part-1/
Accompanying YouTube: https://youtu.be/-I8cKQO_Qr0
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Mesos is a cluster manager that provides efficient resource sharing across distributed applications. It sits between applications and the operating system to make deploying and managing applications in large clusters more efficient. Mesos introduces distributed two-level scheduling where it decides how to allocate resources to frameworks, which then decide how to use those resources. It provides features like fault tolerance, scalability, resource isolation, and APIs for building distributed apps. Common uses include running Hadoop, Spark, Storm, Jenkins, and Docker on Mesos clusters.
PostgreSQL High Availability in a Containerized WorldJignesh Shah
This document discusses PostgreSQL high availability in a containerized environment. It begins with an overview of containers and their advantages like lower footprint and density. It then covers enterprise needs for high availability like recovery time objectives. Common approaches to PostgreSQL high availability are discussed like replication, shared storage, and using projects like Patroni and Stolon. Modern trends with containers are highlighted like separating data and binaries. Kubernetes is presented as a production-grade orchestrator that can provide horizontal scaling and self-healing capabilities. The discussion concludes with challenges of multi-region deployments and how service discovery with Consul can help address those challenges.
Stop Worrying and Keep Querying, Using Automated Multi-Region Disaster RecoveryDoKC
Stop Worrying and Keep Querying, Using Automated Multi-Region Disaster Recovery - Shivani Gupta, Elotl & Sergey Pronin, Percona
Disaster Recovery(DR) is critical for business continuity in the face of widespread outages taking down entire data centers or cloud provider regions. DR relies on deployment to multiple locations, data replication, monitoring for failure and failover. The process is typically manual involving several moving parts, and, even in the best case, involves some downtime for end-users. A multi-cluster K8s control plane presents the opportunity to automate the DR setup as well as the failure detection and failover. Such automation can dramatically reduce RTO and improve availability for end-users. This talk (and demo) describes one such setup using the open source Percona Operator for PostgreSQL and a multi-cluster K8s orchestrator. The orchestrator will use policy driven placement to replicate the entire workload on multiple clusters (in different regions), detect failure using pluggable logic, and do failover processing by promoting the standby as well as redirecting application traffic
Mesos is a cluster manager that provides efficient resource isolation and sharing across distributed applications running on a shared pool of nodes. It provides APIs for resource management and scheduling and can run applications like Hadoop, Spark, and Kafka across large compute clusters. Mesos was created at Berkeley and adopted by companies like Twitter, Airbnb, and Hubspot to improve resource utilization, allow dynamic scaling of services, and reduce the operational complexity of managing large fleets of servers. Mesos also integrates with Docker and Kubernetes to enable containerized workloads and services to be deployed and orchestrated on Mesos clusters.
Solving k8s persistent workloads using k8s DevOps styleMayaData
Solving k8s persistent workloads
using k8s DevOps style. Presented at Container_stack-Zurich-2019
-How Hardware trends enforce a change in the way we do things
-Storage limitations bubble up
-Infrastructure as code
Openstack on Fedora, Fedora on Openstack: An Introduction to cloud IaaSSadique Puthen
Openstack is an open source cloud operating system that provides infrastructure as a service capabilities. It includes components for compute (Nova), storage (Cinder, Swift, Manila), networking (Neutron), orchestration (Heat), metering (Ceilometer), and dashboard (Horizon). The document discusses these components in depth and how they provide infrastructure services. It also covers deployment options like Packstack, TripleO, and Ironic as well as other Openstack projects. The presentation introduces Openstack and its capabilities and components.
Cluster schedulers allocate computing resources across nodes in a distributed system and manage the lifecycle of tasks running on those resources. There are different architectures for cluster schedulers, including monolithic, two-level, shared-state, and distributed. Specific schedulers discussed include YARN, which uses a two-level architecture to separate resource management from task lifecycle management, Kubernetes which provides container orchestration and services, Mesos which uses a two-level architecture and is flexible in the resources it can allocate, and Nomad which uses a shared-state architecture across schedulers.
Intro to cluster scheduler for Linux containersKumar Gaurav
Cluster schedulers help manage resources and schedule jobs across multiple nodes in a container cluster. The main types are monolithic, two-level, and shared-state schedulers. Popular cluster schedulers for containers include Mesos, Docker Swarm, and Kubernetes. Each takes a different approach to scheduling but aim to provide high resource utilization, placement constraints, rapid scheduling, and fairness.
[WSO2Con Asia 2018] Architecting for Container-native EnvironmentsWSO2
Cloud native applications take advantage of cloud characteristics by using microservices architectures and containers. Microservices allow each service to have a single focus, be loosely coupled, lightweight, and highly scalable. Containers enable fast, immutable deployments and optimize resource usage. Orchestration tools like Kubernetes manage containers and provide additional capabilities like networking and scaling. Observability tools provide monitoring, logging, and distributed tracing to gain insights into application performance and issues.
Martín Baez presented on limiting process resources in Linux using chroot, cgroups, and namespaces. Some key points:
1. chroot allows changing the apparent root directory for a process and its children, isolating them from files and commands outside the new directory tree.
2. cgroups allow allocating CPU time, memory, bandwidth and other resources among user-defined groups of processes. Each cgroup subsystem controls a different resource.
3. Namespaces provide isolation of global system resources like PID numbers between independent processes running on the same host.
4. Containers leverage namespaces and cgroups to provide lightweight isolation of applications and their dependencies compared to virtual machines.
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2. Who we are !
Naganarasimha G R
❖ System Architect @ Huawei
❖ Apache Hadoop Committer
❖ Working in Hadoop YARN team.
❖ Hobbies :
➢ Chess, Cycling
Varun Saxena
❖ Senior Technical Lead @ Huawei
❖ Apache Hadoop Committer
❖ Working in Hadoop YARN team.
❖ Hobbies :
➢ Photography
4. Agenda
❑Aspects of Distributed Scheduling Framework
❑Architectural evolution of resource scheduling
❑Overview of prominent open source schedulers
❑Functional comparison between prominent schedulers
❑Conclusion
5. Aspects of Distributed Scheduling Framework
❏ Ability to support varied resources types and ensuring isolation
❏ Support of multiple resource type (CPU, Mem, Disk, Network, GPU etc...)
❏ Pluggable resource type
❏ Hierarchical/nested resource types
❏ Macro(logical partition) and Micro(cgroups) isolation
❏ labelling of nodes
❏ Ability to orchestrate Containers
❏ Support for multiple container types(Docker, Rocket )
❏ Manage life cycle of Containers
❏ Support repository Management of Container Images
6. Aspects of Distributed Scheduling Framework
❏ Ability to support wide variety of applications
➢ Big Data (stateful, DAG, ad hoc, batch)
➢ Long running services (stateless, stateful apps)
➢ Support of DevOps and MicroServices Model
❏ Networking support
➢ Network proxy/wiring of containers
➢ DNS support
➢ Service discoverability
❏ Disk Volumes (Persistence storage)
➢ Ability to mounting of multiple Types of Persistent volumes
● local Block Storage (SSD/SATA)
● Raid based persistent disks (SSD/SATA).
● Software based storages : NFS
● Elastic storage for Files/ Objects ( GlusterFS , AWS)
➢ Dynamic mounting
7. Aspects of Distributed Scheduling Framework
❏ Scalability and Reliability
➢ Daemon Services reliability and scalability
➢ Application reliability.
➢ Application recoverability
➢ Integrated Load Balancer
❏ Security
➢ Namespaces
➢ RBAC
➢ Pluggable authentication for the enterprise. LDAP integrations ...
➢ enforce secure communication in all layers, App - Service , Clients - Service, Clients - Apps
❏ Others
➢ Automatable : Deploy and Build
➢ DevOps Collaboration
8. Agenda
❑Aspects of Distributed Scheduling Framework
❑Architectural evolution of resource scheduling
❑Overview of prominent open source schedulers
❑Functional comparison between prominent schedulers
❑Conclusion
9. Architectural evolution of resource scheduling
Monolithic Scheduling ❏ Many of the cluster schedulers are Monolithic. Enterprise- IBM
HPC, Open source - Kubernetes, JobTracker in Hadoop v1
❏ A single scheduler process runs on one machine and assigns
tasks to machines and it alone handles all different kinds of
workloads. All tasks run through the same scheduling logic .
❏ Pro’s
➢ Sophisticated optimizations to avoid negative interference
between workloads competing for resources can be
achieved using ML tech. Ex- Yarn, Paragon and Quasar
❏ Con’s
➢ Support different applications with different needs,
Increases the complexity of its logic and implementation,
which eventually leads to scheduling latency
➢ Queueing effects (e.g., head-of-line blocking) and backlog
of tasks unless the scheduler is carefully designed.
➢ Theoretically might not be scalable for very large cluster.
Ex. Hadoop MRV1
10. Architectural evolution of resource scheduling
Two Level Scheduling ❏ Separates the concerns of resource allocation and App’s task
placement.
❏ Task placement logic to be tailored towards specific applications, but
also maintains the ability to share the cluster between them.
❏ Cluster RM can offer the resources to app level scheduler (pioneered
by Mesos) or application-level schedulers to request resources.
❏ Pro’s
➢ Easy to carve out a dynamic partition out of cluster and get the
application executed in isolation
➢ A very flexible approach that allows for custom, workload-specific
scheduling policies.
❏ Con’s
➢ Information hiding: Cluster RM will not be aware of the App’s task
and will not be able(/complicates) to optimize the resource usage
(preemption)
➢ Interface become complex in request based model.
➢ Resource can get underutlized.
11. Architectural evolution of resource scheduling
Shared State Scheduling
❏ Multiple replicas of cluster state are independently updated by application-level schedulers.
❏ Task placement logic to be tailored towards specific applications, but also maintains the
ability to share the cluster between them.
❏ Local scheduler issues an optimistically concurrent transaction to update local changes to
the shared cluster state.
❏ In the event of transaction failure(another scheduler may have made a conflicting change)
local scheduler retries.
❏ Prominent examples : google’s omega, Microsoft’s Apollo, Hashicorp’s Nomad, of late
Kubernetes something similar.
❏ In general shared cluster state is in single location but it can be designed to achieve
"logical" shared-state materialising the full cluster state anywhere. ex Apollo
❏ Pro’s
➢ Partially distributed and hence faster.
❏ Con’s
➢ Scheduler works with stale information and may experience degraded scheduler
performance under high contention.
➢ Need to deal with lot of split brain scenarios to maintain the state. (although this can
apply to other architectures as well)
12. Architectural evolution of resource scheduling
Fully Distributed Scheduling
❏ Based on hypothesis that the tasks run on clusters are becoming ever shorter in duration
and multiple shorter jobs even large batch jobs can be split into small tasks that finish
quickly.
❏ Workflow :
➢ Multiple Independent schedulers servicing the incoming workload
➢ Each of these schedulers works with its local or partial (subset) of the cluster. No
cluster state to be maintained by schedulers.
➢ Based on a simple "slot" concept that chops each machine into n uniform slots, and
places up to n parallel tasks.
➢ Worker-side queues with configurable policies (e.g., FIFO in Sparrow),
➢ Scheduler can choose at which machine to enqueue a task which has available slots
satisfying the request.
➢ If not available locally then will try to get the slot for other scheduler.
❏ Earliest implementers was sparrow.
❏ Federated clusters can be visualized similar to Distributed Scheduling albeit if there is no
central state maintained.
❏ Pro’s
➢ Higher decision throughput supported by the scheduler.
➢ Spread the load across multiple schedulers.
❏ Con’s
➢ Difficult to enforce global invariants (fairness policies, strict priority precedence)
➢ Cannot support application-specific scheduling policies. For example Avoiding
interference between tasks (as its queued),, becomes tricky.
13. Architectural evolution of resource scheduling
❏ Considered mostly academic.
❏ Combines monolithic and Distributed scheduling.
❏ Two scheduling paths:
➢ A distributed one for part of the workload (e.g., very short tasks, or low-priority batch
workloads).
➢ Centralized one for the rest.
❏ Priority will be given to the centralized scheduler in the event of the conflict.
❏ Incorporated in Tarcil, Mercury, and Hawk.
❏ Is also available as part of YARN, More in next slides.
Hybrid architectures
14. Agenda
❑Aspects of Distributed Scheduling Framework
❑Architectural evolution of resource scheduling
❑Overview of prominent open source schedulers
❑Functional comparison between prominent schedulers
❑Conclusion
15. Overview of Kubernetes
Kubernetes Overview
❏ Basic abstraction is POD : Co-locating helper processes,
❏ Everything App/task is a Container
❏ Supports multiple container types: Rocket, Docker
❏ Mounting storage systems and dynamic mount of volumes
❏ Simple interface for application Developer : YAML
❏ Multiple templates /views for the end application
❏ POD
❏ Deployment
❏ ReplicationSet
❏ DaemonServices
❏ Supports Multiple Schedulers and let's application to choose containers
❏ Default scheduler tries to optimize scheduling by bin packing. And scheduling
tries to pick up the node will less load
❏ Supports Horizontal POD scaling for a running app
Kubernetes YAML file
16. Overview of Kubernetes
Kubernetes Architecture
1. Master – Cluster controlling unit
2. etcd – HA Key/value store
3. API Server - Observing the state of the cluster
4. Controller Manager – runs multiple controllers
5. Scheduler Server – assigns workloads to nodes
6. Kubelet - server/slave node that runs pods
7. Proxy Service – host subnetting to external parties
8. Pods – One or more containers
9. Services – load balancer for containers
10. Replication Controller – For horizontally-s
17. Overview of Swarm
Swarm Scheduling Overview
❏ Main job of scheduler is to decide which node to use
when running docker container/service.
❏ Swarm Filters (Labels and Constraints)
■ Label : attribute of the node
E.g. environment = test, storage = ssd
■ Constraints
Restrictions applied by Operator while creating
a service.
E.g. docker service create --constraint
node.labels.storage==ssd …
■ Affinity and Anti Affinity
● Affinity: two containers should be together
● Anti Affinity : two containers should not be
together
❏ Strategy : policy to pick the nodes
■ Spread strategy : schedule tasks on the least
loaded nodes, provided they meet the
constraints and resource requirements.
■ Bin pack
■ Random strategy
❏
18. Overview of Mesos
❏ Master – Enable sharing of resource
❏ Slave – Execute the task in it
❏ Cluster – a group of machines
❏ ZooKeeper – distributed
synchronization/configuration
❏ Framework – scheduler + executor
❏ Scheduler – Accept resources
❏ Executor – Run the Framework task
❏ Task – a job to run
❏ Containerizer – run & monitor executors
Mesos Architecture
19. Overview of Mesos
❏ Works on Offer based model.
❏ Mesos has two levels of scheduling;
● one intra-Frameworks level
● Inter-Framework level application
specific.
❏ Supports Pools’ and ACLs’
Mesos Scheduling Overview
20. Overview of Mesos
❏ Dev -OPS : VAMP
❏ Long Running services :
● Marathon : Mesosphere’s solution which automatically handles hardware or software failures and
ensures that an app is “always on”.
● Aurora : Apache’s project.
● Singularity : for one off tasks and scheduled jobs
❏ Bigdata processing:
● Hadoop Running Hadoop on Mesos distributes MapReduce jobs efficiently across an entire cluster.
● Spark is a fast and general-purpose cluster computing system which makes parallel jobs easy to
write.
● Storm is a distributed realtime computation system.
❏ Batch Scheduling :
● Chronos is a distributed job scheduler that supports complex job topologies. It can be used as a
more fault-tolerant replacement for Cron.
❏ Data Storage : Alluxio, Cassandra, Ceph
Mesos Frameworks :
21. Overview of Apollo
Microsoft’s Research paper on
scalable and coordinated scheduler
for cloud scale computing
incorporating following features
■ Distributed and coordinated
architecture
■ Estimation-based
scheduling
■ Conflict resolution
■ Opportunistic scheduling
22. Overview of YARN
YARN Architecture overview :
❏ Core philosophy
➢ Allocate resources very close to the data.
● supports each RR to specify locality information
● Supports delayed scheduling to ensure locality of
data
➢ Containers are primarily considered as
non-preemptable.
● During all kind of failovers priority is given to
ensure that running containers continue to finish
● Even during preemption we try to provide
opportunity(time window) for the app to finish or
checkpoint the containers state.
23. Overview of YARN
YARN Key features : Distributed Scheduling : (YARN-2877)
❏ Distributed + Centralized = achieves faster scheduling for
small tasks without obstructing the application/queue/tenant
related guarantees.
❏ Each NM is considered to have resource slots and resource
requests are queued up.
❏ NM proxies the AM-RM communication and decorates the
request and sends to RM.
❏ Distributed scheduling co-ordinator of RM sends and
appends cluster stats information (all NM queued up
resource requests information) to the AM-RM
communication response (allocate call).
❏ NM on receiving the stats can schedule the opportunistic
containers requested by the app based on policy
❏ Pluggable policy to pick the node effectively
❏ At NM priority is given to start the containers allocated by RM
and if free picks from the opportunistic containers queue
24. Overview of YARN
YARN Key features : Federated Scheduling : (YARN-2915)
❏ A large YARN cluster is broken up into multiple small
subclusters with a few thousand nodes each. Sub clusters
can be added or removed.
❏ Router Service
● Exposes ApplicationClientProtocol. Transparently hides
existence of multiple RMs’ in subclusters.
● Application is submitted to Router.
● Stateless, scalable service.
❏ AM-RM Proxy Service
● Implements ApplicationMasterProtocol. Acts as a
proxy to YARN RM.
● Allows application to span across multiple sub-clusters
● Runs in NodeManager.
❏ Policy and State store
● Zookeeper/DB.
25. Overview of YARN
YARN Key features : YARN supports Docker ! (YARN-3611)
❏ Limited support in the released version(2.8) but 2.9 more features are expected to come
❏ supports cgroups resource isolation for docker containers.
❏ Supports multiple networks while launching but port Mapping to host port is yet to be done.
❏ Supports individual task/request to select to be run in docker container environment.
❏ By design can support other Container runtime environments but current support is only for docker
❏ Does not support launching of docker containers in Secured environment yet.
❏ Does not support mounting of external volumes yet.
26. Overview of YARN
YARN Key features : Rich Placement Constraints in YARN (YARN-6592)
{
Priority: 1,
Sizing: {Resource: <8G, 4vcores>, NumAllocations: 1 },
AllocationTags: ["hbase-rs"],
PlacementConstraintExpression: {
AND: [ // Anti-affinity between RegionServers
{Target: allocation-tag NOT_IN “hbase-rs”, Scope: host },
// Allow at most 2 RegionServers per failure-domain/rack
{ MaxCardinality: 2, Scope: failure_domain }
]
}
},
➢ AllocationRequestID
➢ Priority
➢ AllocationTags: tags to be associated with all allocations
returned by this SchedulingRequest
➢ ResourceSizing
○ Number of allocations
○ Size of each allocation
➢ Placement Constraint Expression
Scheduling Request Sample scheduling Request
27. Overview of YARN
YARN Key features : Simplified API layer for services (YARN-4793)
❏ Create and manage the lifecycle of YARN
services by new services API layer backed by
REST interfaces.
❏ Supports for both simple single component and
complex multi-component assemblies
❏ Other important complementing features :
➢ Resource-profile management
(YARN-3926),
➢ service-discovery (YARN-913/YARN-4757).
➢ REST APIs for application-submission and
management (YARN-1695).
➢ Support of System(daemon) services.
YARN-1593
POST URL - http://host.mycompany.com:8088/services/v1/applications
GET URL - http://host.mycompany.com:8088/services/v1/applications/hello-world
28. Agenda
❑Aspects of Distributed Scheduling Framework
❑Architectural evolution of resource scheduling
❑Overview of prominent open source schedulers
❑Functional comparison between prominent schedulers
❑Conclusion
29. Functional comparison between prominent schedulers
Feature / Framework K8s Mesos YARN Swarm
Architecture Monolithic ( shared state
on support of multi
scheduler)
two-level monolithic/
two-level / hybrid
monolithic
Resource granularity Multi dimensional Multi dimensional RAM/CPU (Multi
dimensional after resource
profile)
Multi dimensional
Multiple Scheduler support on going Yes - frameworks can
further schedule
Partial (fair / capacity) not
at the same time but apps
can have their logic.
No
Priority preemption Yes Ongoing Yes (further optimizations
are on going YARN-2009)
No
Over subscription Yes Yes Ongoing (YARN-1011) No
Resource Estimation No No Solutions being devloped
as external components
but supports reservation
queues
No
Resource Isolation Partial (but pluggable) Partial (but pluggable) Partial (but pluggable) No
30. Functional comparison between prominent schedulers
Feature / Framework K8s Mesos YARN
Support for Coarse grained
isolation (partitions / pools)
N (Namespaces : logical
partitions)
N (supports logical pools) Supports partitions
Support multiple Container
runtimes
Yes Predominantly dockers Partial Dockers (will be
available in 2.9) but pluggable
interface
Support Variety of applications Yes but stateful application
support is on going.
Supports concept of
PODS,Daemon services ..
Framework level support
Support pods aka task groups.
Ongoing support for simplifying
services.
Pod concept not supported
Security Supports pluggable
authentication and SSL.
Supports Kerberos
Supports CRAM-MD5
authentication using Cyrus
SASL library. Pluggable
authentication work is ongoing
Supports SSL, Kerberos
Disk Volumes provisioning Yes Yes No
31. Functional comparison between prominent schedulers
Feature / Framework K8s Mesos YARN
Disk Volumes provisioning Yes Yes No
Scalability and Reliability SPOC as there is single
process which holds the
whole state, And possible
load on ETCD as cluster
size increases
Good as the state is
distributed across multiple
frameworks
Good, separation between
app and resource data
Suitable for Cloud Yes Yes Fairly
Suitable for standalone
BigData
Ongoing Yes Yes
32. Agenda
❑Aspects of Distributed Scheduling Framework
❑Architectural evolution of resource scheduling
❑Overview of prominent open source schedulers
❑Functional comparison between prominent schedulers
❑Conclusion
33. Conclusion
❏ All schedulers are in fact trying to solve the same set of problems, duplicating
effort building various shapes and sizes of resource managers, container managers
or long-running service schedulers.
❏ It will lead to a fragmented experience with different terminology and concepts for
very similar things, different interfaces or APIs, different troubleshooting
procedures , documentation and so on, which will only be driving up operations
costs.