As Twitter's use of mapreduce rapidly expands, tracking usage on our clusters grows correspondingly more difficult. With an ever increasing job load, and a reliance on higher level abstractions such as Pig and Scalding, the utility of existing tools for viewing job history decreases rapidly, and extracting insights becomes a challenge. At Twitter, we created hRaven to fill this gap. hRaven archives the full history and metrics from all mapreduce jobs on our clusters, and strings together each job from a Pig or Scalding script execution into a combined flow. From this archive, we can easily derive aggregate resource utilization by user, pool, or application. While the historical trending of an individual application allows us to perform runtime optimization of resource scheduling. We will cover how hRaven provides a rich historical archive of mapreduce job execution, and how the data is structured into higher level flows representing the job sequence for frameworks such as Pig, Scalding, and Hive. We will then explore how we mine hRaven data to account for Hadoop resource utilization, to optimize runtime scheduling, and to identify common anti-patterns in user jobs. Finally, we will look at the end user experience, including Ambrose integration for flow visualization.

1. A Bird’s-Eye View of Pig and Scalding
with hRaven
a tale by @gario and @joep
Hadoop Summit 2013
v1.2

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Apache HBase PMC member and
Committer
Software Engineer @ Twitter
Core Storage Team - Hadoop/HBase
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About the authors
Software Engineer @ Twitter
Engineering Manager Hadoop/HBase
team @ Twitter
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Chapter 1: The Problem
Chapter 2: Why hRaven?
Chapter 3: How Does it Work?
3a: Loading
3b: Table structure / querying
Chapter 4: Current Uses
Appendix: Future Work
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Table of Contents

4. Chapter 1: The Problem
Illustration by Sirxlem (CC BY-NC-ND
3.0)

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Most users run Pig and Scalding scripts, not straight map reduce
JobTracker UI shows jobs, not DAGs of jobs generated by Pig and Scalding
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Chapter 1: Mismatched Abstractions

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Chapter 1: A Problem of Scale
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How many Pig versus Scalding jobs do we run ?
What cluster capacity do jobs in my pool take ?
How many jobs do we run each day ?
What % of jobs have > 30k tasks ?
Why do I need to hand-tune these (hundreds) of jobs, can’t the cluster learn ?
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Chapter 1: Questions

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How many Pig versus Scalding jobs do we run ?
What cluster capacity do jobs in my pool take ?
How many jobs do we run each day ?
What % of jobs have > 30k tasks ?
Why do I need to hand-tune these (hundreds) of jobs, can’t the cluster learn ?
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Chapter 1: Questions
#Nevermore

9. Chapter 2: Why hRaven?
Photo by DAVID ILIFF. License: CC-BY-SA
3.0

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Stores stats, configuration and timing for every map reduce job on every
cluster
Structured around the full DAG of jobs from a Pig or Scalding application
Easily queryable for historical trending
Allows for Pig reducer optimization based on historical run stats
Keep data online forever (12.6M jobs, 4.5B tasks + attempts)
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Chapter 2: Why hRaven?

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cluster - each cluster has a unique name mapping to the Job Tracker
user - map reduce jobs are run as a given user
application - a Pig or Scalding script (or plain map reduce job)
flow - the combined DAG of jobs executed from a single run of an
application
version - changes impacting the DAG are recorded as a new version of the
same application
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Chapter 2: Key Concepts

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Chapter 2: Application Flows
Edgar

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Chapter 2: Application Flows
Edgar

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All jobs in a flow are ordered together•
Chapter 2: Flow Storage

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Most recent flow is ordered first•
Chapter 2: Flow Storage

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All jobs in a flow are ordered together
Per-job metrics stored
Total map and reduce tasks
HDFS bytes read / written
File bytes read / written
Total map and reduce slot milliseconds
Easy to aggregate stats for an entire flow
Easy to scan the timeseries of each application’s flows
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Chapter 2: Key Features

17. Chapter 3: How Does it Work?

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Chapter 3: ETL - Step 1: JobFilePreprocessor

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Chapter 3: ETL - Step 2: JobFileRawLoader

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Chapter 3: ETL - Step 3: JobFileProcessor

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Chapter 3: ETL - Step 3: JobFileProcessor
Jobs finish out of order with respect to job_id

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job_history_raw
job_history
job_history_task
job_history_app_version
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Chapter 3: Tables

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Row key: cluster!jobID
Columns:
jobconf - stores serialized raw job_*_conf.xml file
jobhistory - stored serialized raw job history log file
job_processed_success - indicates whether job has been processed
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Chapter 3: job_history_raw

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Row key: cluster!user!application!timestamp!jobID
cluster - unique cluster name (ie. “cluster1@dc1”)
user - user running the application (“edgar”)
application - application ID derived from job configuration:
uses “batch.desc” property if set
otherwise parses a consistent ID from “mapred.job.name”
timestamp - inverted (Long.MAX_VALUE - value) value of submission time
jobID - stored as Job Tracker start time (long), concatenated with job sequence number
job_201306271100_0001 -> [1372352073732L][1L]
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Chapter 3: job_history

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Row key: cluster!user!application!timestamp!jobID!taskID
same components as job_history key (same ordering)
taskID - (ie. “m_00001”) uniquely identifies individual task/attempt in job
Two row types:
Task - “meta” row
cluster1@dc1!edgar!wordcount!9654...!...[00001]!m_00001
Task Attempt - individual execution on a Task Tracker
cluster1@dc1!edgar!wordcount!9654...!...[00001]!m_00001_1
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Chapter 3: job_history_task

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Row key: cluster!user!application
Example: cluster1@dc1!edgar!wordcount
Columns:
v1=1369585634000
v2=1372263813000
Chapter 3: job_history_app_version

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Using Pig’s HBaseStorage (or direct HBase APIs)
Through Client API
Through REST API
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Chapter 3: Querying hRaven

28. Chapter 4: Current Uses

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Pig reducer optimizations
Cluster utilization / capacity planning
Application performance trending over time
Identifying common job anti-patterns
Ad-hoc analysis troubleshooting cluster problems
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Chapter 4: Current Uses

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Chapter 4: Cluster reads-writes

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Chapter 4: Pool / Application reads/writes
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Pool view
Spike in File size read
Indicates jobs spilling
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Application view
Spike in HDFS size
read
Indicates spiking input
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Chapter 4: Pool usage: Used vs. Allocated
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Chapter 4: Compute cost

34. Appendix: Future Work

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Real-time data loading from Job Tracker / Application Master
Full flow-centric UI (Job Tracker UI replacement)
Hadoop 2.0 compatibility (in-progress)
Ambrose integration
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Appendix: Future Work

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hRaven on Github
https://github.com/twitter/hraven
hRaven Mailing Lists
hraven-user@googlegroups.com
hraven-dev@googlegroups.com
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Additional Resources

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Afterword
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Now will thou drop your job data on the floor ?
Quoth the hRaven, 'Nevermore.'

38. #TheEnd
@gario and @joep
Come visit us at booth #26 to continue the story

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Desired order
job_201306271100_9999
job_201306271100_10000
...
job_201306271100_99999
job_201306271100_100000
...
job_201306271100_999999
job_201306271100_1000000
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Sort order Variable length job_id
Lexical order
job_201306271100_10000
job_201306271100_100000
job_201306271100_1000000
job_201306271100_9999
job_201306271100_99999
job_201306271100_999999
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