At Spotify we collect huge volumes of data for many purposes. Reporting to labels, powering our product features, and analyzing user growth are some of our most common ones. Additionally, we collect many operational metrics related to the responsiveness, utilization and capacity of our servers. To store and process this data, we use scalable and fault-tolerant multi-system infrastructure, and Apache Hadoop is a key part of it. Surprisingly or not, Apache Hadoop generates large amounts of data in the form of logs and metrics that describe its behaviour and performance. To process this data in a scalable and performant manner we use … also Hadoop! During this presentation, I will talk about how we analyze various logs generated by Apache Hadoop using custom scripts (written in Pig or Java/Python MapReduce) and available open-source tools to get data-driven answers to many questions related to the behaviour of our 690-node Hadoop cluster. At Spotify we frequently leverage these tools to learn how fast we are growing, when to buy new nodes, how to calculate the empirical retention policy for each dataset, optimize the scheduler, benchmark the cluster, find its biggest offenders (both people and datasets) and more.

1. Adam Kawa
Data Engineer @ Spotify
Hadoop Operations
Powered By …
Hadoop

2. 1. How many times has Coldplay been streamed this
month?
2. How many times was “Get Lucky” streamed during
first 24h?
3. Who was the most popular artist in NYC last week?
Labels, Advertisers, Partners

3. 1. What song to recommend Jay-Z when he wakes
up?
2. Is Adam Kawa bored with Coldplay today?
3. How to get Arun to subscribe to Spotify
Premium?
Data Scientists

5. (Big) Data At Spotify
■ Data generated by +24M monthly active users
and for users!
- 2.2 TB of compressed data from users per day
- 64 TB of data generated in Hadoop each day
(triplicated)

6. Data Infrastructure At Spotify
■ Apache Hadoop YARN
■ Many other systems including
- Kafka, Cassandra, Storm, Luigi in production
- Giraph, Tez, Spark in the evaluation mode

7. ■ Probably the largest commercial Hadoop cluster in
Europe!
- 694 heterogeneous nodes
- 14.25 PB of data consumed
- ~12.000 jobs each day
Apache Hadoop

8. March 2013
Tricky questions were asked!

9. 1. How many servers do you need to buy to survive
one year?
2. What will you do to use them efficiently?
3. If we agree, don’t come back to us this year! OK?
Finance Department

10. ■ One of Data Engineers responsible for answering
these questions!
Adam Kawa

11. ■ Examples of how to analyze various metrics, logs
and files
- generated by Hadoop
- using Hadoop
- to understand Hadoop
- to avoid guesstimates!
The Topic Of This Talk

12. ■ This knowledge can be useful to
- measure how fast HDFS is growing
- define an empirical retention policy
- measure the performance of jobs
- optimize the scheduler
- and more
What To Use It For

13. 1. Analyzing HDFS
2. Analyzing MapReduce and YARN
Agenda

14. HDFS
Garbage Collection On
The NameNode

15. “ We don’t have any full GC pauses on the NN.
Our GC stops the NN for less than 100 msec,
on average!
:) ”
Adam Kawa @ Hadoop User Mailing List
December 16th, 2013

16. “ Today, between 12:05 and 13:00
we had 5 full GC pauses on the NN.
They stopped the NN for 34min47sec in total!
:( ”
Adam Kawa @ Spotify office, Stockholm
January 13th, 2014

17. What happened
between 12:05 and 13:00?

18. The NameNode was receiving the block reports from
all the DataNodes
Quick Answer!

19. 1. We started the NN when the DNs were running
Detailed Answer

20. 1. We started the NN when the DNs were running
2. 502 DNs immediately registered to the NN
■ Within 1.2 sec (based on logs from the DNs)
Detailed Answer

21. 1. We started the NN when the DNs were running
2. 502 DNs immediately registered to the NN
■ Within 1.2 sec (based on logs from the DNs)
3. 502 DNs started sending the block reports
■ dfs.blockreport.initialDelay = 30 minutes
■ 17 block reports per minute (on average)
■ +831K blocks in each block report (on average)
Detailed Answer

22. 1. We started the NN when the DNs were running
2. 502 DNs immediately registered to the NN
■ Within 1.2 sec (based on logs from the DNs)
3. 502 DNs started sending the block reports
■ dfs.blockreport.initialDelay = 30 minutes
■ 17 block reports per minute (on average)
■ +831K blocks in each block report (on average)
4. This generated a high memory pressure on the NN
■ The NN ran into Full GC !!!
Detailed Answer

23. Hadoop told us everything!

24. ■ Enable GC logging for the NameNode
■ Visualize e.g. GCViewer
■ Analyze memory usage patterns, GC pauses,
misconfiguration
Collecting The GC Stats

25. Time

26. This blue line
shows the heap
used by the NN

27. Loading
FsImage

28. Start replaying
Edit logs

29. First block report
processed

30. 25 block reports
processed

31. 131 block reports
processed

32. 5min 39sec of
Full GC

33. 40 block reports
processed

34. Next Full GC

35. Next Full GC
!!!

36. CMS collector starts
at 98.5% of heap…
We fixed that !

37. What happened in HDFS
between mid-December 2013
and mid-January 2014?

38. HDFS
HDFS Metadata

39. ■ A persistent checkpoint of HDFS metadata
■ It contains information about files + directories
■ A binary file
HDFS FsImage File

40. ■ Converts the content of FsImage to text formats
- e.g. a tab-separated file or XML
■ Output is easily analyzed by any tools
- e.g. Pig, Hive
HDFS Offline Image Viewer

41. 50% of the data
created during last 3
months

43. Anything interesting?

44. 1. NO data added that day
2. Many more files added after

45. The migration to YARN

47. Where
did
the small files
come from?

48. ■ An interactive
visualization of
data in HDFS
Twitter's
HDFS-DU
/app-logs
avg. file size = 253 KB
no. of dirs = 595K
no. of files = 60.6M

49. ■ Statistics broken down by user/group name
■ Candidates for duplicate datasets
■ Inefficient MapReduce jobs
- Small files
- Skewed files
More Uses Of FsImage File

50. ■ You can analyze FsImage to learn how fast HDFS
grows
■ You can combine it with “external” datasets
- number of daily/monthly active users
- total size of logs generated by users
- number of queries / day run by data analysts
Advanced HDFS Capacity Planning

51. ■ You can also use ''trend button'' in Ganglia
Simplified HDFS Capacity Planning
If we do
NOTHING,
we might fill
the cluster
in
September
...

52. What will we do
to survive longer
than September?

53. HDFS
Retention

54. Question
How many days after creation, a dataset is not
accessed anymore?
Retention Policy

55. Question
How many days after creation, a dataset is not
accessed anymore?
Possible Solution
■ You can use modification_time and access_time
from FsImage
Empirical Retention Policy

56. ■ Logs and core datasets are accessed even many
years after creation
■ Many reports are not accessed even a hour after
creation
■ Most intermediate datasets needed less than a
week
■ 10% of data has not been accessed for a year
Our Retention Facts

57. HDFS
Hot Datasets

58. ■ Some files/directories will be accessed more often
than others e.g.:
- fresh logs, core datasets, dictionary files
Idea
■ To process it faster, increase
its replication factor while it’s “hot”
■ To save disk space, decrease
its replication factor when it becomes “cold”
Hot Dataset

59. How to find them?

60. ■ Logs all filesystem access requests sent to the NN
■ Easy to parse and aggregate
- a tab-separated line for each request
HDFS Audit Log
2014-01-18 15:16:12,023
INFO FSNamesystem.audit: allowed=true
ugi=kawaa (auth:SIMPLE) ip=/10.254.28.4
cmd=open
src=/metadata/artist/2013-11-27/part-00061.avro
dst=null perm=null

61. ■ JAR files stored in HDFS and used by Pig scripts
■ A dictionary file with metadata about log messages
■ Core datasets: playlists, users, top tracks
Our Hot Datasets

62. YARN
MapReduce Jobs
Autotuning

63. ■ There are jobs that we schedule regularly
- e.g. top lists for each country
Idea
■ Before submitting it next time, use statistics from the
previous executions of a job
- To learn about its historical performance
- To tweak its configuration settings
Recurring MapReduce Jobs

64. We implemented
■ A pre-execution hook that automatically sets
- Maximum size of an input split
- Number of Reduce tasks
■ More settings can be tweaked
- Memory
- Combiner
Jobs Autotuning

65. ■ Here, the goal is that a task runs approx. 10 min, on
average
- Inspired by LinkedIn at Hadoop Summit 2013
- Helpful in extreme cases (short/long running tasks)
A Small PoC ;)

66. Another Example - Job Optimized Over Time

67. Even perfect manual settings
may become outdated
when an input dataset grows!

68. YARN
MapReduce Statistics

69. ■ Extracts the statistics from historical MapReduce jobs
- Supports MRv1 and YARN
■ Stores them as Avro files
- Enables easy analysis using e.g. Pig and Hive
■ Similar projects
- Replephant, hRaven
Zlatanitor = Zlatan + Monitor
Zlatanitor

70. Low Medium High

71. A Slow Node
- 40% lower throughput than the average
Low Medium High

72. NIC negotiated 100MbE
instead of 1GbE
Low Medium High

73. According to Facebook
■ ”Small percentage of machines are responsible for
large percentage of failures”
- Worse performance
- More alerts
- More manual intervention
Repeat Offenders

74. Adding nodes to the cluster
increases performance.
Sometimes, removing (crappy) nodes
does too !

75. Fixing
slow and failing
tasks as well !

76. YARN
Application Logs

77. ■ YARN - can be moved to HDFS
- They are stored as TFiles … :(
- Small and many of them!
Location Of Application Logs

78. ■ Frequent exceptions and bugs
- Just looking at the last line of stderr shows a lot!
■ Possible optimizations
- Memory and size of map input buffer
What Might Be Checked
a) AttributeError: 'int' object has no attribute 'iteritems'
b) ValueError: invalid literal for int() with base 10: 'spotify'
c) ValueError: Expecting , delimiter: line 1 column 3257 (char 3257)
d) ImportError: No module named db_statistics

79. YARN
The Capacity Scheduler

80. ■ We specified capacities and elasticity based on a
combination of
- “some” data
- intuition
- desire to shape future usage (!)
Our Initial Capacities

81. ■ Basic information available on the Scheduler Web UI
■ Take print-screens!
- Otherwise, you will lose the history of what you saw :(
Overutilization And Underutilization

82. ■ Capacity Scheduler exposes these metrics via JMX
■ Ganglia does NOT display the metrics related to
utilization of queues (by default)
Visualizing Utilization Of Queue

83. ■ It collects JMX metrics from Java processes
■ It can send metrics to multiple destinations
- Graphite, cacti/rrdtool, Ganglia
- tab-separated text file
- STDOUT
- and more
Jmxtrans

84. ■ Our Production queue often borrows resources
- Usually from the Queue3 and Queue4 queues
Overutilization And Underutilization

86. The Best Time For The Downtime?

87. Three Crowns

88. Three Crowns = Sweden

89. BONUS
Some Cool Stuff
From The Community

90. ■ Aggregates and visualizes Hadoop cluster
utilization across users
LinkedIn's White Elephant

91. ■ Collects run-time statistics from MR jobs
- Stores them in HBase
■ Does not provide built-in visualization layer
- The picture below comes from Twitter's blog
Twitter's hRaven

92. That’s all!

93. ■ Analyzing Hadoop is also a “business” problem
- Save money
- Iterate faster
- Avoid downtimes
Summary

94. Thank you!

95. ■ To my awesome colleagues for great technical
review:
Piotr Krewski, Josh Baer, Rafal Wojdyla,
Anna Dackiewicz, Magnus Runesson, Gustav Landén,
Guido Urdaneta, Uldis Barbans
More Thanks

96. Section name
Questions?

97. Check out spotify.com/jobs or
@Spotifyjobs for more information
kawaa@spotify.com
Check out my blog: HakunaMapData.com
Want to join the band?

98. Backup

99. ■ Tricky question!
■ Use production jobs that represent your workload
■ Use a metric that is independent from size of data
that you process
■ Optimize one setting at the time
Benchmarking

100. Benchmarking

101. Benchmarking