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Maintaining Low Latency While Maximizing Throughput on a Single Cluster
The document discusses the integration of Apache Myriad, an open-source project that allows YARN and Mesos to cooperate for improved resource management in data centers. It emphasizes the need for a datacenter-wide resource manager to enhance resource utilization, manage compute frameworks alongside Hadoop applications, and optimize workloads through dynamic resource allocation. The architecture and functionality of Myriad are highlighted, demonstrating its capability to support various compute frameworks while maintaining efficiency and low latency.
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Maintaining Low Latency While Maximizing Throughput on a Single Cluster
- 1. ® © 2015 MapRTechnologies 1 ® © 2014 MapR Technologies Maintaining Low Latency while Maximizing Throughput Yuliya Feldman February 19, 2015
- 2. ® © 2015 MapRTechnologies 2 Top-Ranked NoSQL Top-Ranked Hadoop Distribution Top-Ranked SQL-on-Hadoop Solution ®
- 3. ® © 2015 MapRTechnologies 3 What We Have – Cluster per Use Case YARN cluster Web ServersYARN cluster Too much isolation and poor resource utilization
- 4. ® © 2015 MapRTechnologies 4 Need Datacenter-wide Resource Manager What choices do we have? • YARN (capacity/fair scheduler) • Omega • Mesos • Others (e.g. Quasar)
- 5. ® © 2015 MapRTechnologies 5 YARN • Motivated by Mesos, but is a Hadoop resource manager • Manages Hadoop resources well – “retail” • Pluggable schedulers for Hadoop • Started handling long-lived tasks • Can pre-empt tasks • YARN-1051 - YARN Admission Control/Planner: enhancing the resource allocation model with time
- 6. ® © 2015 MapRTechnologies 6 Mesos • Data-center wide resource manager – negotiator between frameworks • Manages all resources for frameworks well, not particular framework (e.g. Hadoop) – “wholesale” • Doing two-level scheduling • Excellent Docker support • Schedules, allocates, and isolates cpu, mem, disk, network, and arbitrary custom resource types
- 7. ® © 2015 MapRTechnologies 7 Can we…. – Continue leveraging YARN resource scheduling capabilities for YARN-based applications? – Treat YARN as “yet another” framework within Mesos? – Let YARN not bother about non-YARN applications coexistence?
- 8. ® © 2015 MapRTechnologies 8 Introducing Myriad
- 9. ® © 2015 MapRTechnologies 9 Apache Myriad: True Multi-tenancy • Open-source project launched Oct `14 – MapR, eBay, Mesosphere, others participating • Allows Mesos and YARN to cooperate with each other • Mesos: datacenter-wide resource manager – Dockerized containers and/or cgroups used for isolation • Hadoop is launched inside cgroup containers • Myriad manages conversation between RM and Mesos master and between NM and Mesos slaves
- 10. ® © 2015 MapRTechnologies 10 Why Myriad • Run many types of compute frameworks side-by-side – Hadoop family, etc. (YARN, Spark, Kafka, Storm) – Web-server farm – MPP databases (e.g., Vertica) – Other services: SOA web-services, Jenkins/build-farm, cron-jobs, shell scripts, Kubernetes, Cassandra, ElasticSearch, etc. – Each compute framework is a cluster in itself • Need to break up a physical cluster into many virtual clusters – Using Docker (containers) for good isolation – But most schedulers can only manage individual nodes inside a cluster • Move resources between virtual clusters on-demand
- 11. ® © 2015 MapRTechnologies 11 Utilize Excess Capacity for Analytics DC Server Farm Hadoop Analytics Utilizatiion Long lived excess capacity situations • “Scale up” Hadoop during long periods of low utilization • “Scale down” Hadoop ahead of anticipated high utilization
- 12. ® © 2015 MapRTechnologies 12 Myriad Again • Mesos creates virtual clusters • YARN uses resources provided by Mesos • Myriad can ask YARN to release some resources • Or give it more Mesos YARN cluster YARN cluster Web Servers
- 13. ® © 2015 MapRTechnologies 13 Myriad Services Architecture Node ManagerResource Manager Executor Mesos Scheduler Mesos Container Container App YARN Scheduler (fairshare) Offers Launch Tasks Launch Tasks Task Status Launch containers via HB Submit Map<Node, Capacity>
- 14. ® © 2015 MapRTechnologies 14 REST API Framework + Master 2. Mesos Resource Manager YARN Mesos Slave Mesos Node Node Manager YARN Launch Node Manager 2.5 CPU, 2.5 GB Advertise Resources 2 CPU, 2 GB How it works Mesos scheduler
- 15. ® © 2015 MapRTechnologies 15 REST API Framework + Master 2. Mesos Resource Manager YARN Mesos Slave Mesos Node Node Manager YARN Launch Containers C1 C2 Mesos scheduler
- 16. ® © 2015 MapRTechnologies 16 2. Slave Mesos Node1 Node Manager YARN 8 CPU, 8 GB 2. Slave Mesos Node2 Node Manager YARN 8 CPU,8 GB REST API Framework + Master Mesos Resource Manager YARN Web Traffic spike Resize NodeManager(s) 6 CPU, 6 GB 6 CPU, 6 GBWebService 2 CPU, 2 GB 2 CPU, 2 GB WebService Use Case – Web Traffic spikes Mesos scheduler
- 17. ® © 2015 MapRTechnologies 17 2. Slave Mesos Node1 Node Manager YARN 8 CPU, 8 GB 2. Slave Mesos Node2 Node Manager YARN 8 CPU,8 GB REST API Framework + Master Mesos Resource Manager YARN Web Traffic spike over Resize NodeManager(s) 6 CPU, 6 GB 6 CPU, 6 GBWebService 2 CPU, 2 GB 2 CPU, 2 GB WebService Mesos scheduler
- 18. ® © 2015 MapRTechnologies 18 Myriad Demo At MapR booth 1009
- 19. ® © 2015 MapRTechnologies 19 Maintaining Low Latency while Maximizing Throughput on a single cluster
- 20. ® © 2015 MapRTechnologies 20 Batch and Real-time Analytics Together Compute Cluster NM DrillBit NM DrillBit NM DrillBit NM DrillBit NM DrillBit NM DrillBit NM DrillBit NM DrillBit Cluster/DC Scheduler
- 21. ® © 2015 MapRTechnologies 21 Sharing Resources between Batch and Real-Time • Real-time services resource usage pattern can be unpredictable – Analysts use services during the day – Analysts on the other side of the globe work during the night – There are steady states, spikes and dips in the workloads • Batch resource usage – more or less predictable – Running same jobs all over again with some occasional spikes and dips
- 22. ® © 2015 MapRTechnologies 22 Real-time Services Resource Utilization/Provisioning Aggressive resource provisioning. < 10% utilization Moderate resource provisioning < 60% utilization Conservative resource provisioning > 80% utilization
- 23. ® © 2015 MapRTechnologies 23 What Can We Do To Provision Conservatively? Compute Cluster NM DrillBit NM DrillBit NM DrillBit NM DrillBit Cluster/DC Resource Manager Drill Service Watcher Monitors Drill Performance Latency decrease Accept Offers (Mesos) Need additional Containers (YARN) Allocate Resources (Preempt if needed) C1 C2 C3 Dummy containers Latency increase
- 24. ® © 2015 MapRTechnologies 24 SHOWTIME
- 25. ® © 2015 MapRTechnologies 25
- 26. ® © 2015 MapRTechnologies 26 Q&A @mapr maprtech yfeldman@mapr.com Engage with us! MapR maprtech mapr-technologies