Let's Talk Operations! (Hadoop Summit 2014)

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These are the introductory slides I used (in some form or another) for the Let's Talk Operations! sessions for the 2014 Hadoop Summits. No video for this one!

Technology

Let’s Talk Operations!
Allen Wittenauer!

Twitter: @_a__w_
Email: aw @ apache.org!

How many individual
grids should I have?

One big
grid
Grid per
project
• Pros!
• Lower ops overhead!
• One location for all data!
• Cons !
• Dev and Prod on one
system
• Pros!
• Capacity planning per project!
• Cons !
• More headcount to maintain!
• Multiple copies of data!
• Data ingress is a mess

ETL
Dev Prod
Base ETL Pull
Event Feeds
Database Feeds
Base ETL Pull
Base ETL Pull
Post-Processed
Data

How do I solve some
common distcp issues?

• Common issues!
• Version incompatibilities!
• Network bandwidth consumption!
!
• Some tricks!
• Use WebHDFS!
• All modern versions support it!
• Read and write in both directions!
• Create a separate queue with hard limits!
• Pull from larger, push from smaller

Q&A
Allen
Wittenauer

Twitter:
@_a__w_
Email:
aw
@
apache.org

20 GB /, ... 200 GB task space (rest) HDFS
• root partitioning
!
!
!
!
!
• non-root partitioning
5 GB
swap
200 GB task space (rest) HDFS

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Have you ever thought that your SSD storage just doesn't seem anywhere near as fast as advertised? What if I told you that a couple of small changes to your Linux servers had the potential to more than double the amount of throughput your MySQL servers could handle while simultaneously reducing query response time by around 50 percent? It's not a dream, Neo. If you're still in a traditional data center and/or your MySQL footprint lives on spinning rust, then you might want to take the blue pill, attend a different session, and believe whatever you want to believe. However, if your server footprint runs inside Amazon Web Services or if you use SSDs in any fashion, I invite you to take the red pill and come with me on a journey down the rabbit hole, where we'll discuss such things as the Linux block IO driver, CPU starvation, and interrupt handling, using real-world query performance data from the primary MySQL data stores at Pinterest to illustrate the effect.

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The next major version - 0.96- of Apache HBase have several new features. The "Singularity", because you will have to start and stop your cluster to upgrade to 0.96. 0.96 requires Apache Hadoop 1.0.0 at least, and supported on Hadoop 2.0.0 as well. 0.96 uses protobufs all the time. All of its serializations to ZooKeeper, to the filesystem, and over rpc are protobufs. It runs on JDK7. Metrics have been edited and converted to use Hadoop Metrics2. It has HBase Snapshots and PrefixTreeCompression, etc. This presentation captures a high-level overview of what's new in HBase 0.96.

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The quickest way to learn and evolve infrastructure is by encountering obstacles and being forced to overcome limitations that keep you inches away from project goals. At Spotify, we’ve encountered many of these obstacles and frustrations as we grew our Hadoop cluster from a few machines in an office closet aggregating played song events for financial reports, to our current 900 node cluster that plays a large role in many features that you see in our application today. Two members of Spotify’s Hadoop ‘squad’ will weave in war stories, failures, frustrations and lessons learned to describe the Hadoop/Big Data architecture at Spotify and talk about how that architecture has evolved. We’ll talk about how and why we use a number of tools, including Apache Falcon and Apache Bigtop to test changes; Apache Crunch, Scalding and Hive w/ Tez to build features and provide analytics; and Snakebite and Luigi, two in-house tools created to overcome common frustrations.

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An over-ambitious introduction to Spark programming, test and deployment. This slide tries to cover most core technologies and design patterns used in SpookyStuff, the fastest query engine for data collection/mashup from the deep web. For more information please follow: https://github.com/tribbloid/spookystuff A bug in PowerPoint used to cause transparent background color not being rendered properly. This has been fixed in a recent upload.

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Spark is by its nature very fault tolerant. However, faults, and application failures, can and do happen, in production at scale. In this talk, we’ll discuss the nuts and bolts of fault tolerance in Spark. We will begin with a brief overview of the sorts of fault tolerance offered, and lead into a deep dive of the internals of fault tolerance. This will include a discussion of Spark on YARN, scheduling, and resource allocation. We will then spend some time on a case study and discussing some tools used to find and verify fault tolerance issues. Our case study comes from a customer who experienced an application outage that was root caused to a scheduler bug. We discuss the analysis we did to reach this conclusion and the work that we did to reproduce it locally. We highlight some of the techniques used to simulate faults and find bugs. At the end, we’ll discuss some future directions for fault tolerance improvements in Spark, such as scheduler and checkpointing changes.

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Fault Tolerance in Spark: Lessons Learned from Production: Spark Summit East ...

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