Hadoop Futures

Aug 12, 20095 likes1,801 views

Tom White presented on the future of Hadoop at a user group meeting. Key goals for Hadoop include modularity, support for multiple languages, and integration with other systems. The Hadoop project was split into core, HDFS, and MapReduce repositories. Upcoming releases include 0.20.1 and 0.21, with 1.0 to establish versioning rules. Interesting projects include using Avro for RPC, distributed configuration, and improving MapReduce performance.

Hadoop Futures
What to watch

Tom White, Cloudera
Hadoop User Group UK, Bristol
10 August 2009

About me
▪ Apache Hadoop Committer, PMC
Member, Apache Member
▪ Employed by Cloudera
▪ Author of “Hadoop: The Deﬁnitive
Guide”
▪ http://hadoopbook.com

Goals
▪ Modular
▪ E.g. pluggable block placement algorithm
▪ Multiple languages
▪ E.g. not just Java for MapReduce
▪ Integration with other systems
▪ E.g. JMX monitoring hooks

The Project Split
▪ Core -> Common, HDFS, MapReduce
▪ New repositories
▪ New mailing lists
▪ {common,hdfs,mapreduce}-{user,dev,issues}@hadoop.apache.org
▪ New directory layouts
▪ New conﬁguration
▪ hadoop-site.xml -> {core,hdfs,mapreduce}-site.xml
▪ More information at
▪ http://www.cloudera.com/blog/2009/07/17/the-project-split/
▪ general@hadoop.apache.org

Releases
▪ 0.18.3 - 29 Jan 2009
▪ Official “stable” release
▪ Probably the most commonly used
▪ Basis for ﬁrst Cloudera distribution
▪ 0.19.2 - 23 July 2009
▪ 0.19 series is not widely used
▪ 0.20.0 - 22 April 2009
▪ Expect large adoption with 0.20.1 release in coming weeks
▪ Basis for second Cloudera distribution, ﬁrst Yahoo! distribution
▪ 0.21 series - feature freeze end of August 2009

Hadoop 1.0
▪ After 0.21 release
▪ Need to establish rules about version evolution
▪ Hadoop 1.0 Interface Classiﬁcation - HADOOP-5073
▪ API, Data, wire protocol compatibility - HADOOP-5071

Interesting Projects/JIRAs
▪ Common
▪ Avro for Hadoop RPC - HADOOP-6170
▪ Service lifecycle - HDFS-326
▪ Distributed conﬁguration - HADOOP-5670
▪ 10 minute patch builds - HADOOP-5628, HDFS-458,
MAPREDUCE-670
▪ Ivy/Maven integration - HADOOP-5107
▪ Eclipse plugin

Interesting Projects/JIRAs (continued)
▪ MapReduce
▪ Metadata in Serialization - HADOOP-6165
▪ Compute splits on the cluster - MAPREDUCE-207
▪ Context Objects - ongoing migration of libraries/examples
▪ Security - HADOOP-4487
▪ Schedulers
▪ Fair share scheduler - global scheduling, FIFO - MAPREDUCE-548,
MAPREDUCE-706
▪ Capacity - high RAM jobs - HADOOP-5884
▪ Speed: new shuffle
▪ See http://sortbenchmark.org/Yahoo2009.pdf

Popular JIRAs
▪ http://community.cloudera.com/

Questions?
▪ tom@cloudera.com

▪ Cloudera’s Distribution for Hadoop
▪ http://www.cloudera.com/hadoop

(c) 2009 Cloudera, Inc. or its licensors. "Cloudera" is a registered trademark of Cloudera, Inc.. All rights reserved. 1.0

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The document discusses how storage models need to evolve as the underlying technologies change. Object stores like S3 provide scale and high availability but lack semantics and performance of file systems. Non-volatile memory also challenges current models. The POSIX file system metaphor is ill-suited for object stores and NVM. SQL provides an alternative that abstracts away the underlying complexities, leaving just object-relational mapping and transaction isolation to address. The document examines renaming operations, asynchronous I/O, and persistent in-memory data structures as examples of areas where new models may be needed.

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This document outlines the development history of the Dissident bot from its creation in January 2017 to June 2018. It discusses improvements made over time including adding conversation mode, a TODO item to develop a Chomsky-Type-1 Grammar AI, and fixing a bug where conversation mode would spam the bot's username. It also provides details on the bot's configuration settings and methods used to detect spam, bots, and politicans spreading misinformation.

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Extreme Programming DeployedSteve Loughran

This document discusses the principles and practices of Extreme Programming (XP), an agile software development process. It describes XP as an intense, test-centric programming process focused on projects with high rates of change. Key practices include pair programming, test-driven development, planning with user stories and tasks, doing the simplest thing that could work, and refactoring code aggressively. Problems may include short-term "hill-climbing" solutions and risks of fundamental design errors. The document provides additional resources on XP and notes that the day's session will involve practicing XP techniques through pair programming.

TestingSteve Loughran

I hate mockingSteve Loughran

Steve Loughran expresses dislike for mocking in tests because mock code reflects assumptions rather than reality. Any changes to the real code can break the tests, leading to false positives. Test failures are often "fixed" by editing the test or mock code, which could hide real problems. He proposes avoiding mock tests and instead adding functional tests against real infrastructure with fault injection for integration testing.

What does rename() do?Steve Loughran

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Cloud deployments of Apache Hadoop are becoming more commonplace. Yet Hadoop and it's applications don't integrate that well —something which starts right down at the file IO operations. This talk looks at how to make use of cloud object stores in Hadoop applications, including Hive and Spark. It will go from the foundational "what's an object store?" to the practical "what should I avoid" and the timely "what's new in Hadoop?" — the latter covering the improved S3 support in Hadoop 2.8+. I'll explore the details of benchmarking and improving object store IO in Hive and Spark, showing what developers can do in order to gain performance improvements in their own code —and equally, what they must avoid. Finally, I'll look at ongoing work, especially "S3Guard" and what its fast and consistent file metadata operations promise.

Apache Spark and Object StoresSteve Loughran

This document discusses using Apache Spark with object stores like Amazon S3 and Microsoft Azure Blob Storage. It covers challenges around classpath configuration, credentials, code examples, and performance commitments when using these storage systems. Key points include using Hadoop connectors like S3A and WASB, configuring credentials through properties or environment variables, and tuning Spark for object store performance and consistency.

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This document discusses household information security risks in the post-Sony era. It identifies key risks like data integrity, privacy, and availability issues. It provides examples of vulnerabilities across different devices and platforms like LG TVs, iPads, iPhones, and PS4s. It also discusses vulnerabilities in software like Firefox, Chrome, Internet Explorer, Flash, and SparkContext. It recommends approaches to address these risks like using containers for isolation, validating packages with PGP to ensure authentication, and enabling audit logs.

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Slider: Applications on YARNSteve Loughran

This document discusses Apache Slider, which allows applications to be deployed and managed on Apache Hadoop YARN. Slider uses an Application Master, agents, and scripts to deploy applications defined in an XML package. The Application Master keeps applications in a desired state across YARN containers and handles lifecycle commands like start, stop, and scaling. Slider integrates with Apache Ambari for graphical management and configuration of applications on YARN.

YARN ServicesSteve Loughran

This document discusses YARN services in Hadoop, which allow long-lived applications to run within a Hadoop cluster. YARN (Yet Another Resource Negotiator) provides an operating system-like platform for data processing by allowing various applications to share cluster resources. The document outlines features for long-lived services in YARN, including log aggregation, Kerberos token renewal, and service registration/discovery. It also discusses how Hadoop 2.6 and later versions implement these features to enable long-running applications that can withstand failures.

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