Dr. Elephant is a tool for the users of Hadoop to help them understand, analyze and tune their Hadoop/Spark applications easily, thus improving their productivity and the cluster’s efficiency. It analyzes the Hadoop and Spark jobs using a set of pluggable, configurable, rule-based heuristics that provide insights on how a job performed, and then uses the results to make suggestions about how to tune the job to make it perform more efficiently.

1. Dr. Elephant
github.com/linkedin/dr-elephant
Akshay Rai
Hadoop Dev Team

2. Introduction

3. Scaling Hadoop
Infrastructure

4. Scale and Optimize Hardware
● More users, more jobs, more resources
● Large investment in hardware
● Can’t keep upgrading and adding machines to solve problem forever
● Some tuning is needed to get things running

5. Users are more valuable than machines
What do we do?

6. Improve User Productivity

7. User Productivity
● Freedom to experiment and run jobs on the cluster
● Build tools to help developers. (Hadoop DSL, Resolvers for Pig/Hive)
○ Improve developer lifecycle
○ Also reduce unnecessary resource wastage

8. The Tuning Problem

9. How easy is it to tune a job?
● Problems are not obvious
● Critical information is scattered
● Inter-related settings
● Large parameter space

10. Here’s what we learned!

11. Expert Intervention
● Not enough support resources available
● Poor coverage
● Difficult to prioritize efforts
● Delays user development
Random
Suggestions

12. Training is not at all easy
● Too many users
● Diverse backgrounds
● Scope is large and evolving
● Other responsibilities are more important

13. Scaling Productivity is Hard!

14. Dr. Elephant to the Rescue

15. What does Dr. Elephant do?
● Automated performance monitoring and tuning tool
● Help every user get the best performance from their jobs
● Highlights common mistakes
● Indicates best practices and tuning tips
● Provides a platform for other performance related tools
● Analyzes hundred thousand jobs every day

16. Architecture

17. Dashboard

18. Search

19. Job Page

20. MapReduce Report

21. Failed Job

22. Help Page

23. Tuning Tips

24. Awesome Features

25. Simplified analysis of a flow’s historical executions
● Monitoring performance, resource usage and many others
● Comparing flows against previous executions
● Impact of tuning a specific parameter or a changing a line of code

26. Flow History

27. Job History

28. Heuristics

29. How does a Heuristic work?
● Fetch Counters and Task Data
● Some logic to compute a value
● Compare value against threshold levels

30. Heuristic Severity
Severity Color Description
CRITICAL The job is in critical state and must be tuned
SEVERE There is scope for improvement
MODERATE There is scope for further improvement
LOW There is scope for few minor improvements
NONE The job is safe. No tuning necessary

31. Example | Mapper Data Skew

32. Mapper Skew Problem
● Number of Mappers depend on the number of splits
● Varying size of splits can cause skewness in the Mapper Input

33. Solution to Mapper Skewness
● Each Mapper should process the same amount of data
● Combine the small chunks and feed it to a single Mapper

34. Example | Spark Executor Load Balance

35. Spark Driver
Executor
1
Executor
2
Executor
3
RDD
Partition 1
Partition 2
Partition 3

36. Custom Heuristics

37. Adding a New Heuristic
1. Create a new heuristic and test it.
2. Create a new view for the heuristic. For example, helpMapperSpill.scala.html
3. Add the details of the heuristic in the HeuristicConf.xml file.
<heuristic>
<applicationtype>mapreduce</applicationtype>
<heuristicname>Mapper GC</heuristicname>
<classname>com.linkedin.dre.mapreduce.heuristics.MapperGC</classname>
<viewname>views.html.help.mapreduce.helpGC</viewname>
</heuristic>
4. Run Dr. Elephant. It should now include the new heuristics.

38. Configuring Heuristics/Threshold levels
<heuristics>
<heuristic>
<applicationtype>mapreduce</applicationtype>
<heuristicname>Mapper Data Skew</heuristicname>
<classname>com.linkedin.dre.mapreduce.heuristics.MapperDataSkew</classname>
<viewname>views.html.help.mapreduce.helpMapperDataSkew</viewname>
<params>
<num_tasks_severity>10, 50, 100, 200</num_tasks_severity>
<deviation_severity>2, 4, 8, 16</deviation_severity>
<files_severity>1/8, 1/4, 1/2, 1</files_severity>
</params>
</heuristic>
</heuristics>

39. Elephagent

40. Workflow monitoring and reports
● Performance characteristics change
○ Data Growth
○ Data distribution change
○ Hardware change
○ Incremental software change
● Monitor performance on each execution
● Compare behaviour across revisions
● Cost to Serve analysis

41. Production Reviews | JIRA Bot
● Separate cluster for critical workloads
● Audit before deployment
● Improved accuracy
● Faster turnaround
● Higher throughput

42. Future Plans

43. Upcoming
● Job Resource Usage and Wastage
● Job Wait time
● Real time analysis of a job
● Workflow DAG visualization
● Improved Spark heuristics

44. References
Engineering Blog: engineering.linkedin.com/blog/2016/04/dr-elephant-open-
source-self-serve-performance-tuning-hadoop-spark
Open Source Github Link:
github.com/linkedin/dr-elephant
Mailing List:
Dr-elephant-users
Hadoop Summit 2015:
https://www.youtube.com/watch?v=aL3OJ4YoxPA

45. Thank You

46. ©2014 LinkedIn Corporation. All Rights
Reserved.
©2014 LinkedIn Corporation. All Rights
Reserved.
© 2016