This document provides a summary of MapReduce improvements in the MapR Hadoop distribution. It discusses how MapR addresses architectural flaws in HDFS through its scalable container-based filesystem. It also describes how MapR improves MapReduce performance through techniques like direct shuffle and express lanes. The document notes how MapR provides high availability for systems like the container location database and JobTracker. It concludes by discussing MapR's support for other frameworks like HBase and Apache Drill beyond traditional MapReduce.
Architectural Overview of MapR's Apache Hadoop Distributionmcsrivas
Describes the thinking behind MapR's architecture. MapR"s Hadoop achieves better reliability on commodity hardware compared to anything on the planet, including custom, proprietary hardware from other vendors. Apache HDFS and Cassandra replication is also discussed, as are SAN and NAS storage systems like Netapp and EMC.
MapR M7: Providing an enterprise quality Apache HBase APImcsrivas
Provides an overview of M7, which is the first unified data platform for tables and files. Does a deep dive into the MapR architecture, especially containers, and how M7 tables integrates with the rest of MapR architecture, including volumes, management and Hadoop.
Describes some of the problems with Apache HBase, and how M7 from MapR solves many of these issues.
Design, Scale and Performance of MapR's Distribution for Hadoopmcsrivas
Details the first ever Exabyte-scale system that can hold a Trillion large files. Describes MapR's Distributed NameNode (tm) architecture, and how it scales very easily and seamlessly. Shows map-reduce performance across a variety of benchmarks like dfsio, pig-mix, nnbench, terasort and YCSB.
NYC Hadoop Meetup - MapR, Architecture, Philosophy and ApplicationsJason Shao
Slides from: http://www.meetup.com/Hadoop-NYC/events/34411232/
There are a number of assumptions that come with using standard Hadoop that are based on Hadoop's initial architecture. Many of these assumptions can be relaxed with more advanced architectures such as those provided by MapR. These changes in assumptions have ripple effects throughout the system architecture. This is significant because many systems like Mahout provide multiple implementations of various algorithms with very different performance and scaling implications.
I will describe several case studies and use these examples to show how these changes can simplify systems or, in some cases, make certain classes of programs run an order of magnitude faster.
About the speaker: Ted Dunning - Chief Application Architect (MapR)
Ted has held Chief Scientist positions at Veoh Networks, ID Analytics and at MusicMatch, (now Yahoo Music). Ted is responsible for building the most advanced identity theft detection system on the planet, as well as one of the largest peer-assisted video distribution systems and ground-breaking music and video recommendations systems. Ted has 15 issued and 15 pending patents and contributes to several Apache open source projects including Hadoop, Zookeeper and Hbase. He is also a committer for Apache Mahout. Ted earned a BS degree in electrical engineering from the University of Colorado; a MS degree in computer science from New Mexico State University; and a Ph.D. in computing science from Sheffield University in the United Kingdom. Ted also bought the drinks at one of the very first Hadoop User Group meetings.
Architectural Overview of MapR's Apache Hadoop Distributionmcsrivas
Describes the thinking behind MapR's architecture. MapR"s Hadoop achieves better reliability on commodity hardware compared to anything on the planet, including custom, proprietary hardware from other vendors. Apache HDFS and Cassandra replication is also discussed, as are SAN and NAS storage systems like Netapp and EMC.
MapR M7: Providing an enterprise quality Apache HBase APImcsrivas
Provides an overview of M7, which is the first unified data platform for tables and files. Does a deep dive into the MapR architecture, especially containers, and how M7 tables integrates with the rest of MapR architecture, including volumes, management and Hadoop.
Describes some of the problems with Apache HBase, and how M7 from MapR solves many of these issues.
Design, Scale and Performance of MapR's Distribution for Hadoopmcsrivas
Details the first ever Exabyte-scale system that can hold a Trillion large files. Describes MapR's Distributed NameNode (tm) architecture, and how it scales very easily and seamlessly. Shows map-reduce performance across a variety of benchmarks like dfsio, pig-mix, nnbench, terasort and YCSB.
NYC Hadoop Meetup - MapR, Architecture, Philosophy and ApplicationsJason Shao
Slides from: http://www.meetup.com/Hadoop-NYC/events/34411232/
There are a number of assumptions that come with using standard Hadoop that are based on Hadoop's initial architecture. Many of these assumptions can be relaxed with more advanced architectures such as those provided by MapR. These changes in assumptions have ripple effects throughout the system architecture. This is significant because many systems like Mahout provide multiple implementations of various algorithms with very different performance and scaling implications.
I will describe several case studies and use these examples to show how these changes can simplify systems or, in some cases, make certain classes of programs run an order of magnitude faster.
About the speaker: Ted Dunning - Chief Application Architect (MapR)
Ted has held Chief Scientist positions at Veoh Networks, ID Analytics and at MusicMatch, (now Yahoo Music). Ted is responsible for building the most advanced identity theft detection system on the planet, as well as one of the largest peer-assisted video distribution systems and ground-breaking music and video recommendations systems. Ted has 15 issued and 15 pending patents and contributes to several Apache open source projects including Hadoop, Zookeeper and Hbase. He is also a committer for Apache Mahout. Ted earned a BS degree in electrical engineering from the University of Colorado; a MS degree in computer science from New Mexico State University; and a Ph.D. in computing science from Sheffield University in the United Kingdom. Ted also bought the drinks at one of the very first Hadoop User Group meetings.
HBaseCon 2015: HBase at Scale in an Online and High-Demand EnvironmentHBaseCon
Pinterest runs 38 different HBase clusters in production, doing a lot of different types of work—with some doing up to 5 million operations per second. In this talk, you'll get details about how we do capacity planning, maintenance tasks such as online automated rolling compaction, configuration management, and monitoring.
Alibaba builds the data infrastructure with Apache Hadoop YARN since 2013, and till now it manages more than 10k nodes. In Alibaba, Hadoop YARN serves various systems such as search, advertising, and recommendation etc. It runs not just batch jobs, also streaming, machine learning, OLAP, and even online services that directly impact Alibaba’s user experience. To extend YARN’s ability to support such complex scenarios, we have done and leveraged a lot of YARN 3.x improvements. In this talk, you will find what are these improvements and how they helped to solve difficult problems in large production clusters.
This includes:
1. Highly improved performance with Capacity Scheduler’s async scheduling framework
2. Better placement decisions with node attributes, placement constraints
3. Better resource utilization with opportunistic containers
4. Introduce a load balancer to balance resource utilization
5. Generic resource types scheduling/isolation to manage new resources such as GPU and FPGA
In the presentation, we will further introduce how we build the entire ecosystem on top of YARN and how we keep evolving YARN’s ability to tackle the challenges brought by continuously increasing data and business in Alibaba.
Speakers
Weiwei Yang, Alibaba, Staff Software Engineer
Ren Chunde, Alibaba Group, Senior Engineer
Hadoop World 2011: Hadoop Network and Compute Architecture Considerations - J...Cloudera, Inc.
Hadoop is a popular framework for web 2.0 and enterprise businesses who are challenged to store, process and analyze large amounts of data as part of their business requirements. Hadoop’s framework brings a new set of challenges related to the compute infrastructure and underlined network architectures. This session reviews the state of Hadoop enterprise environments, discusses fundamental and advanced Hadoop concepts and reviews benchmarking analysis and projection for big data growth as related to Data Center and Cluster designs. The session also discusses network architecture tradeoffs, and the advantages of close integration between compute and networking.
Speakers: Kevin O'Dell, Aleksandr Shulman & Kathleen Ting (Cloudera)
From supporting the 0.90.x, 0.92, 0.94, and 0.96 HBase installations on clusters ranging from tens to hundreds of nodes, Cloudera has seen it all. Having automated the upgrade paths from the different Apache releases, we have developed a smooth path that can help the community with upcoming upgrades. In addition to automation best practices, in this talk you'll also learn proactive configuration tweaks and operational best practices to keep your HBase cluster always up and running. We'll also walk through how to contain an application bug let loose in production, to minimize the impact on HBase posed by faulty hardware, and the direct correlation between inefficient schema design and HBase performance.
Hadoop World 2011: Next Generation Apache Hadoop MapReduce - Mohadev Konar, H...Cloudera, Inc.
The Apache Hadoop MapReduce framework has hit a scalability limit around 4,000 machines. In this session, we will be presenting the architecture and design of the next generation of MapReduce and will delve into the details of the architecture that makes it much easier to innovate. The architecture will have built in HA, security and multi-tenancy to support many users on the larger clusters. It will also increase innovation, agility and hardware utilization. We will also be presenting large scale and small scale comparisons on some benchmarks with MRV1.
Impetus provides expert consulting services around Hadoop implementations, including R&D, assessment, deployment (on private and public clouds), optimizations for enhanced static shared data implementations.
This presentation speaks about Advanced Hadoop Tuning and Optimisation.
Did you like it? Check out our blog to stay up to date: https://getindata.com/blog
We share our slides about Apache Tez delivered as a lightening talk given at Warsaw Hadoop User Group http://www.meetup.com/warsaw-hug/events/218579675
Speakers: Jesse Yates (Salesforce.com), Demai Ni, Richard Ding & Jing Chen He (IBM)
This talk provides an overview of enterprise-scale backup strategies for HBase: Jesse Yates will describe how Salesforce.com runs backup and recovery on its multi-tenant, enterprise scale HBase deploys; Demai Ni, Songqinq Ding, and Jing Chen of the IBM InfoSphere BigInsights development team will then follow with a description of IBM's recently open-sourced disaster/recovery solution based on HBase snapshots and replication.
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).
Adobe has packaged HBase in Docker containers and uses Marathon and Mesos to schedule them—allowing us to decouple the RegionServer from the host, express resource requirements declaratively, and open the door for unassisted real-time deployments, elastic (up and down) real-time scalability, and more. In this talk, you'll hear what we've learned and explain why this approach could fundamentally change HBase operations.
With Hadoop-3.0.0-alpha2 being released in January 2017, it's time to have a closer look at the features and fixes of Hadoop 3.0.
We will have a look at Core Hadoop, HDFS and YARN, and answer the emerging question whether Hadoop 3.0 will be an architectural revolution like Hadoop 2 was with YARN & Co. or will it be more of an evolution adapting to new use cases like IoT, Machine Learning and Deep Learning (TensorFlow)?
HBaseCon 2015: HBase at Scale in an Online and High-Demand EnvironmentHBaseCon
Pinterest runs 38 different HBase clusters in production, doing a lot of different types of work—with some doing up to 5 million operations per second. In this talk, you'll get details about how we do capacity planning, maintenance tasks such as online automated rolling compaction, configuration management, and monitoring.
Alibaba builds the data infrastructure with Apache Hadoop YARN since 2013, and till now it manages more than 10k nodes. In Alibaba, Hadoop YARN serves various systems such as search, advertising, and recommendation etc. It runs not just batch jobs, also streaming, machine learning, OLAP, and even online services that directly impact Alibaba’s user experience. To extend YARN’s ability to support such complex scenarios, we have done and leveraged a lot of YARN 3.x improvements. In this talk, you will find what are these improvements and how they helped to solve difficult problems in large production clusters.
This includes:
1. Highly improved performance with Capacity Scheduler’s async scheduling framework
2. Better placement decisions with node attributes, placement constraints
3. Better resource utilization with opportunistic containers
4. Introduce a load balancer to balance resource utilization
5. Generic resource types scheduling/isolation to manage new resources such as GPU and FPGA
In the presentation, we will further introduce how we build the entire ecosystem on top of YARN and how we keep evolving YARN’s ability to tackle the challenges brought by continuously increasing data and business in Alibaba.
Speakers
Weiwei Yang, Alibaba, Staff Software Engineer
Ren Chunde, Alibaba Group, Senior Engineer
Hadoop World 2011: Hadoop Network and Compute Architecture Considerations - J...Cloudera, Inc.
Hadoop is a popular framework for web 2.0 and enterprise businesses who are challenged to store, process and analyze large amounts of data as part of their business requirements. Hadoop’s framework brings a new set of challenges related to the compute infrastructure and underlined network architectures. This session reviews the state of Hadoop enterprise environments, discusses fundamental and advanced Hadoop concepts and reviews benchmarking analysis and projection for big data growth as related to Data Center and Cluster designs. The session also discusses network architecture tradeoffs, and the advantages of close integration between compute and networking.
Speakers: Kevin O'Dell, Aleksandr Shulman & Kathleen Ting (Cloudera)
From supporting the 0.90.x, 0.92, 0.94, and 0.96 HBase installations on clusters ranging from tens to hundreds of nodes, Cloudera has seen it all. Having automated the upgrade paths from the different Apache releases, we have developed a smooth path that can help the community with upcoming upgrades. In addition to automation best practices, in this talk you'll also learn proactive configuration tweaks and operational best practices to keep your HBase cluster always up and running. We'll also walk through how to contain an application bug let loose in production, to minimize the impact on HBase posed by faulty hardware, and the direct correlation between inefficient schema design and HBase performance.
Hadoop World 2011: Next Generation Apache Hadoop MapReduce - Mohadev Konar, H...Cloudera, Inc.
The Apache Hadoop MapReduce framework has hit a scalability limit around 4,000 machines. In this session, we will be presenting the architecture and design of the next generation of MapReduce and will delve into the details of the architecture that makes it much easier to innovate. The architecture will have built in HA, security and multi-tenancy to support many users on the larger clusters. It will also increase innovation, agility and hardware utilization. We will also be presenting large scale and small scale comparisons on some benchmarks with MRV1.
Impetus provides expert consulting services around Hadoop implementations, including R&D, assessment, deployment (on private and public clouds), optimizations for enhanced static shared data implementations.
This presentation speaks about Advanced Hadoop Tuning and Optimisation.
Did you like it? Check out our blog to stay up to date: https://getindata.com/blog
We share our slides about Apache Tez delivered as a lightening talk given at Warsaw Hadoop User Group http://www.meetup.com/warsaw-hug/events/218579675
Speakers: Jesse Yates (Salesforce.com), Demai Ni, Richard Ding & Jing Chen He (IBM)
This talk provides an overview of enterprise-scale backup strategies for HBase: Jesse Yates will describe how Salesforce.com runs backup and recovery on its multi-tenant, enterprise scale HBase deploys; Demai Ni, Songqinq Ding, and Jing Chen of the IBM InfoSphere BigInsights development team will then follow with a description of IBM's recently open-sourced disaster/recovery solution based on HBase snapshots and replication.
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).
Adobe has packaged HBase in Docker containers and uses Marathon and Mesos to schedule them—allowing us to decouple the RegionServer from the host, express resource requirements declaratively, and open the door for unassisted real-time deployments, elastic (up and down) real-time scalability, and more. In this talk, you'll hear what we've learned and explain why this approach could fundamentally change HBase operations.
With Hadoop-3.0.0-alpha2 being released in January 2017, it's time to have a closer look at the features and fixes of Hadoop 3.0.
We will have a look at Core Hadoop, HDFS and YARN, and answer the emerging question whether Hadoop 3.0 will be an architectural revolution like Hadoop 2 was with YARN & Co. or will it be more of an evolution adapting to new use cases like IoT, Machine Learning and Deep Learning (TensorFlow)?
The current major release, Hadoop 2.0 offers several significant HDFS improvements including new append-pipeline, federation, wire compatibility, NameNode HA, Snapshots, and performance improvements. We describe how to take advantages of these new features and their benefits. We cover some architectural improvements in detail such as HA, Federation and Snapshots. The second half of the talk describes the current features that are under development for the next HDFS release. This includes much needed data management features such as backup and Disaster Recovery. We add support for different classes of storage devices such as SSDs and open interfaces such as NFS; together these extend HDFS as a more general storage system. Hadoop has recently been extended to run first-class on Windows which expands its enterprise reach and allows integration with the rich tool-set available on Windows. As with every release we will continue improvements to performance, diagnosability and manageability of HDFS. To conclude, we discuss the reliability, the state of HDFS adoption, and some of the misconceptions and myths about HDFS.
(Berkeley CS186 guest lecture) Big Data Analytics Systems: What Goes Around C...Reynold Xin
(Berkeley CS186 guest lecture)
Big Data Analytics Systems: What Goes Around Comes Around
Introduction to MapReduce, GFS, HDFS, Spark, and differences between "Big Data" and database systems.
This talk takes a technological deep dive into MapR M7 including information on some of the key challenges that were solved during the implementation of M7. MapR's M7 is a clean room replication of the HBase API written in C++ and fully integrated into the MapR platform.
In the process of implementing M7, we learned some lessons and solved some interesting challenges. Ted Dunning shares some of these experiences and lessons. Many of these lessons apply across the board to high performance query systems in general and can be applied much more widely. Some of the resulting techniques have already been adopted by the Apache Drill project, but there are lots more places that these techniques can be used.
WANdisco Non-Stop Hadoop: PHXDataConference Presentation Oct 2014 Chris Almond
Hadoop has quickly evolved into the system of choice for storing and processing Big Data, and is now widely used to support mission-critical applications that operate within a ‘data lake’ style infrastructures. A critical requirement of such applications is the need for continuous operation even in the event of various system failures. This requirement has driven adoption of multi-data center Hadoop architectures, a.k.a geo-distributed or global Hadoop. In this session we will provide a brief introduction to WANdisco, then dig into how our Non-Stop Hadoop solution addresses real world use cases, and also a show live demonstration of Non-Stop namenode operation across two WAN connected hadoop clusters.
Improving Apache Spark by Taking Advantage of Disaggregated ArchitectureDatabricks
Shuffle in Apache Spark is an intermediate phrase redistributing data across computing units, which has one important primitive that the shuffle data is persisted on local disks. This architecture suffers from some scalability and reliability issues. Moreover, the assumptions of collocated storage do not always hold in today’s data centers. The hardware trend is moving to disaggregated storage and compute architecture for better cost efficiency and scalability.
To address the issues of Spark shuffle and support disaggregated storage and compute architecture, we implemented a new remote Spark shuffle manager. This new architecture writes shuffle data to a remote cluster with different Hadoop-compatible filesystem backends.
Firstly, the failure of compute nodes will no longer cause shuffle data recomputation. Spark executors can also be allocated and recycled dynamically which results in better resource utilization.
Secondly, for most customers currently running Spark with collocated storage, it is usually challenging for them to upgrade the disks on every node to latest hardware like NVMe SSD and persistent memory because of cost consideration and system compatibility. With this new shuffle manager, they are free to build a separated cluster storing and serving the shuffle data, leveraging the latest hardware to improve the performance and reliability.
Thirdly, in HPC world, more customers are trying Spark as their high performance data analytics tools, while storage and compute in HPC clusters are typically disaggregated. This work will make their life easier.
In this talk, we will present an overview of the issues of the current Spark shuffle implementation, the design of new remote shuffle manager, and a performance study of the work.
Redundancy for Big Hadoop Clusters is hard - Stuart PookEvention
Criteo had an Hadoop cluster with 39 PB raw stockage, 13404 CPUs, 105 TB RAM, 40 TB data imported per day and over 100000 jobs per day. This cluster was critical in both stockage and compute but without backups. After many efforts to increase our redundancy, we now have two clusters that, combined, have more than 2000 nodes, 130 PB, two different versions of Hadoop and 200000 jobs per day but these clusters do not yet provide a redundant solution to our all storage and compute needs. This talk discusses the choices and issues we solved in creating a 1200 node cluster with new hardware in a new data centre. Some of the challenges involved in running two different clusters in parallel will be presented. We will also analyse what went right (and wrong) in our attempt to achieve redundancy and our plans to improve our capacity to handle the loss of a data centre.
Cloud infrastructure. Google File System and MapReduce - Andrii VozniukAndrii Vozniuk
My presentation for the Cloud Data Management course at EPFL by Anastasia Ailamaki and Christoph Koch.
It is mainly based on the following two papers:
1) S. Ghemawat, H. Gobioff, S. Leung. The Google File System. SOSP, 2003
2) J. Dean, S. Ghemawat. MapReduce: Simplified Data Processing on Large Clusters. OSDI, 2004
Updated version of my talk about Hadoop 3.0 with the newest community updates.
Talk given at the codecentric Meetup Berlin on 31.08.2017 and on Data2Day Meetup on 28.09.2017 in Heidelberg.
Similar to MapReduce Improvements in MapR Hadoop (20)
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
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Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
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LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
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- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
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- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
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Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
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4. 4
Big Data: Distributed FileSystems
Volume, Variety, Velocity:
Can't have big data without a scalable filesystem
http://www.lbisoftware.com/blog/wp-content/uploads/2013/06/data_mountain1.jpg
6. 6
HDFS Architectural Flaws
● Created for storing crawled web-page data
● Files cannot be modified once written/closed.
– Write-once; append-only
● Files cannot be read before they are closed.
– Must batch-load data
● NameNode stores (in memory)
– Directory/file tree, file->block mapping
– Block replica locations
● NameNode only scales to ~100 Million files
– Some users run jobs to concatenate small files
● Written in Java, slows during GC.
7. 7
Solution: MapR FileSystem
● Visionary CTO/Co-Founder: M.C. Srivas
– Ran Google search infrastructure team
– Chief Storage Architect at Spinnaker Networks
● Take a step back: What kind of DFS do we need in
Hadoop/Distributed-Computer?
– Easy, Scalable, Reliable
● Want traditional apps to work with DFS
– Support random Read/Write
– Standard FS interface (NFS)
● HDFS compatible
– Drop-in replacement, no recompile
9. 9
Easy: MapR Volumes
Groups related files/directories
into a single tree structure so
they can be easily organized,
managed, and secured.
●
Replication factor
●
Scheduled snapshots, mirroring
●
Data placement control
– By device-type, rack, or
geographic location
●
Quotas and usage tracking
●
Administrative permissions
100K+ Volumes are okay
10. 10
Each container contains
Directories & files
Data blocks
Replicated on servers
No need to manage directly
Use MapR Volumes
Scalable: Containers
Files/directories are sharded into
blocks, which are placed into mini-NNs
(containers) on disks
Containers are
16-32 GB disk
segments,
placed on
nodes
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CLDB
Scalable: Container Location DB
N1, N2
N3, N2
N1, N2
N1, N3
N3, N2
N1
N2
N3
Container location
database (CLDB) keeps
track of nodes hosting
each container and
replication chain order
Each container has a replication chain
Updates are transactional
Failures are handled by rearranging replication
Clients cache container locations
12. 12
Scalability Statistics
Containers represent 16 - 32GB of data
Each can hold up to 1 Billion files and directories
100M containers = ~ 2 Exabytes (a very large cluster)
250 bytes DRAM to cache a container
25GB to cache all containers for 2EB cluster
− But not necessary, can page to disk
Typical large 10PB cluster needs 2GB
Container-reports are 100x - 1000x < HDFS
block-reports
Serve 100x more data-nodes
Increase container size to 64G to serve 4EB cluster
MapReduce performance not affected
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Reliable: CLDB High Availability
● As easy as installing CLDB role on more nodes
– Writes go to CLDB master, replicated to slaves
– CLDB slaves can serve reads
● Distributed container metadata, so CLDB only
stores/recovers container locations
– Instant restart (<2 seconds), no single POF
● Shared nothing architecture
● (NFS Multinode HA too)
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vs. Federated NN, NN HA
● Federated NameNodes
– Statically partition namespaces (like Volumes)
– Need additional NN (plus a standby) for each namespace
– Federated NN only in Hadoop-2.x (beta)
● NameNode HA
– NameNode responsible for both fs-namespace (metadata) info and block
locations; more data to checkpoint/recover.
– Starting standby NN from cold state can take tens-of-minutes for metadata,
an hour for block-locations. Need a hot standby.
– Metadata state
● All name space edits logged to shared (NFS/NAS) R/W storage, which must
also be HA; Standby polls edit log for changes.
● Or use Quorum Journal Manager, separate service/nodes
– Block locations
● Data nodes send block reports, location updates, heartbeats to both NNs
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Reliable: Consistent Snapshots
● Automatic
de-duplication
● Saves space by
sharing blocks
● Lightning fast
● Zero performance loss
on writing to original
● Scheduled,
or on-demand
● Easy recovery with
drag and drop
20. 20
Fast: Direct Shuffle
● Apache Shuffle
– Write map-outputs/spills to local file system
– Merge partitions for a map output into one file, index into it
– Reducers request partitions from Mappers' Http servlets
● MapR Direct Shuffle
– Write to Local Volume in MapR FS (rebalancing)
– Map-output file per reducer (no index file)
– Send shuffleRootFid with MapTaskCompletion on heartbeat
– Direct RPC from Reducer to Mapper using Fid
– Copy is just a file-system copy; no Http overhead
– More copy threads, wider merges
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Fast: Express Lane
● Long-running jobs shouldn't hog all the slots in the
cluster and starve small, fast jobs (e.g. Hive queries)
● One or more small slots reserved on each node for
running small jobs
● Small jobs: <10 maps/reds, small input, time limit
23. 23
Easy: Label-based Scheduling
● Assign labels to nodes or regex/glob expressions for nodes
– perfnode1* → “production”
– /.*ssd[0-9]*/ → “fast_ssd”
● Create label expressions for jobs/queues
– Queue “fast_prod” → “production && fast_ss”
● Tasks from these jobs/queues will only be assigned to nodes whose
labels match the expression.
● Combine with Data Placement policies for data and compute locality
● No static partitioning necessary
– Frequent labels file refresh
– New nodes automatically fall into appropriate regex/glob labels
– New jobs can specify label expression or use queue's or both
● http://www.mapr.com/doc/display/MapR/Placing+Jobs+on+Specified+Nodes
24. 24
Other Improvements
● Parallel Split Computations in JobClient
– Might as well multi-thread it!
● Runaway Job Protection
– One user's fork-bomb shouldn't degrade others' performance
– CPU/memory firewalls protect system processes
● Map-side join locality
– Files in same directory/container follow same replication chain
– Same key ranges likely to be co-located on same node.
● Zero-config XML
– XML parsing takes too much time
25. 25
MapR MapReduce Summary
● Fast
– Direct Shuffle
– Express Lane
– Parallel Split Computation
– Map-side Join Locality
– Zero-config XML
● Reliable
– JobTracker HA
– Runaway Job Protection
● Easy
– Label-based Scheduling
27. 27
M7: Enterprise-Grade HBase
Disks
ext3
JVM
DFS
JVM
HBase
Other
Distributions
Disks
Unified
Easy Dependable Fast
No RegionServers No compactions Consistent low latency
Seamless splits Instant recovery
from node failure
Real-time in-memory
configuration
Automatic merges Snapshots Disk and network
compression
In-memory column families Mirroring Reduced I/O to disk
Unified Data Platform
Increased Performance
Simplified Administration
28. 28
Apache Drill
Interactive analysis of Big Data using standard SQL
Based on Google Dremel
Interactive queries
Data analyst
Reporting
100 ms-20 min
Data mining
Modeling
Large ETL
20 min-20 hr
MapReduce
Hive
Pig
Fas
t
• Low latency queries
• Columnar execution
• Complement native interfaces
and MapReduce/Hive/Pig
Op
en
• Community driven open source project
• Under Apache Software Foundation
Mo
der
n
• Standard ANSI SQL:2003 (select/into)
• Nested/hierarchical data support
• Schema is optional
• Supports RDBMS, Hadoop and NoSQL
31. 31
Contact Us!
I'm not in Sales, so go to mapr.com to learn more:
– Integrations with AWS, GCE, Ubuntu, Lucidworks
– Partnerships, Customers
– Support, Training, Pricing
– Ecosystem Components
We're hiring!
University of Wisconsin-Madison Career Fair tomorrow
Email me at: abordelon@maprtech.com
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