My talk at Hadoop Summit 2015 Video can be found here: https://www.youtube.com/watch?v=jupArYWxoq0

1. L E S S O N S L E A R N E D AT T W I T T E R
H A D O O P P E R F O R M A N C E
O P T I M I Z AT I O N AT S C A L E
A L E X L E V E N S O N |
I A N O ' C O N N E L L |
@ T H I S W I L LW O R K
@ 0 X 1 3 8

2. DATA PLATFORM @TWITTER
Develop, maintain, and support the core data processing
libraries used at Twitter
In a good position to make system-wide performance
improvements
Core Data Libraries Team

3. DATA PLATFORM @TWITTER
Idiomatic functional Scala library for writing Hadoop map reduce
Functional programming is a natural fit for map reduce
Compile time type checked
Core Data Libraries Team
github.com/twitter/scalding

4. DATA PLATFORM @TWITTER
Columnar storage format for the Hadoop ecosystem
Uses the Google Dremel column shredding and assembly
algorithm
Core Data Libraries Team
APACHE PARQUET
github.com/apache/parquet-mr

5. DATA PLATFORM @TWITTER
Streaming map reduce for hybrid realtime / batch topologies
Write once, execute in parallel on Storm / Heron (online) and
Scalding (offline)
Core Data Libraries Team
SUMMINGBIRD
github.com/twitter/summingbird

6. Hadoop at Twitter Scale
H A D O O P AT T W I T T E R

7. 300+PETABYTES OF
DATA

8. 100k MAP REDUCE
JOBS DAILY
MULTIPLES OF

9. 1000+MACHINE
HADOOP
CLUSTERS
MULTIPLE

10. LARGEST
HADOOP
CLUSTERS IN
THE WORLD
AMONG THE

11. At this scale, even small system-wide
improvements can save significant
amounts of compute resources
C O S T AT S C A L E

12. What does your Hadoop cluster
spend most of its time doing?
W H AT T O I M P R O V E ?

13. Profile your cluster, you might be
surprised by what you find
M E A S U R E - D O N ' T G U E S S

14. ENABLE JVM PROFILING WITH -XPROF
Built into the JVM (HotSpot), so there's nothing to install
Xprof: a low overhead profiler built into the jvm
mapreduce.task.profile='true'
mapreduce.task.profile.maps='0-'
mapreduce.task.profile.reduces='0-'
mapreduce.task.profile.params='-Xprof'

15. ENABLE JVM PROFILING WITH -XPROF
Low overhead (uses stack sampling)
Surfaces the most expensive methods
Prints directly to task logs (stdout)
Xprof: a low overhead profiler built into the jvm

16. Flat profile of 412.48 secs (38743 total ticks): SpillThread
Interpreted + native Method
12.5% 0 + 32215 org.apache.hadoop.io.compress.lz4.Lz4Compressor.compressBytesDirect
4.6% 0 + 822 java.io.FileOutputStream.writeBytes
...
19.4% 352 + 3082 Total interpreted (including elided)
Compiled + native Method
50.0% 8549 + 299 java.lang.StringCoding.decode
16.9% 2823 + 158 cascading.tuple.hadoop.io.HadoopTupleInputStream.getNextElement
4.1% 734 + 0 sun.nio.cs.UTF_8$Decoder.decode
2.3% 401 + 0 org.apache.hadoop.mapred.IFileOutputStream.write
2.0% 352 + 0 cascading.tuple.hadoop.util.TupleComparator.compare
1.7% 296 + 0 org.apache.hadoop.mapred.MapTask$MapOutputBuffer.compare
...
79.0% 13514 + 467 Total compiled
Thread-local ticks:
54.3% 21053 Blocked (of total)

17. HADOOP CONFIGURATION OBJECT
Looks and behaves a lot like a HashMap
Surprisingly expensive
Configuration conf = new Configuration()
conf.set("myKey", "myValue")
String value = conf.get("myKey")

18. HADOOP CONFIGURATION OBJECT
Constructor reads + unzips + parses an XML file from disk
Surprisingly expensive
public class KryoSerialization {
public KryoSerialization() {
this(new Configuration())
}
}

19. HADOOP CONFIGURATION OBJECT
get() method involves regular expressions, variable substitution
Surprisingly expensive
String value = conf.get("myKey")

20. HADOOP CONFIGURATION OBJECT
Calling these methods in a loop, or once per record, is
expensive
Some (non trivial) jobs were spending 30% of their time in
Configuration methods
Surprisingly expensive

21. It's hard to predict what needs to
be optimized without a profiler
L E S S O N L E A R N E D

22. If you don't profile, you could be
missing easy wins
L E S S O N L E A R N E D

23. Measure whether IO or CPU is your
biggest cost
L E S S O N L E A R N E D

24. INTERMEDIATE COMPRESSION
Xprof surfaced that compression + decompression in the spill
thread was taking a lot of time
Intermediate outputs are temporary
We now use lz4 instead of lzo level 3, which produces 30%
larger intermediate data that's faster to read
Made some large jobs 1.5X faster
Find the right balance

25. Record Serialization + Deserialization
can be the most expensive part of
your job
L E S S O N L E A R N E D

26. Record Serialization is CPU intensive,
and may overshadow IO
L E S S O N L E A R N E D

27. How to reduce costs due to record
serialization?
L E S S O N L E A R N E D

28. USE HADOOP'S RAW COMPARATOR API
Hadoop MR deserializes the map output keys in order to sort
them between the map and reduce phases
Don't make sorting more expensive than it already is
deserialize(keyBytes1).compare(deserialize(keyBytes2))

29. USE HADOOP'S RAW COMPARATOR API
This can cost a lot, especially for complex non-primitive keys,
which is fairly common
Don't make sorting more expensive than it already is
requests.groupBy { req => (req.country, req.client) }

30. USE HADOOP'S RAW COMPARATOR API
This can cost a lot, especially for complex non-primitive keys,
which is fairly common
Don't make sorting more expensive than it already is
Complex object
that requires sorting
requests.groupBy { req => (req.country, req.client) }

31. Flat profile of 412.48 secs (38743 total ticks): SpillThread
Interpreted + native Method
12.5% 0 + 32215 org.apache.hadoop.io.compress.lz4.Lz4Compressor.compressBytesDirect
4.6% 0 + 822 java.io.FileOutputStream.writeBytes
...
19.4% 352 + 3082 Total interpreted (including elided)
Compiled + native Method
50.0% 8549 + 299 java.lang.StringCoding.decode
16.9% 2823 + 158 cascading.tuple.hadoop.io.HadoopTupleInputStream.getNextElement
4.1% 734 + 0 sun.nio.cs.UTF_8$Decoder.decode
2.3% 401 + 0 org.apache.hadoop.mapred.IFileOutputStream.write
2.0% 352 + 0 cascading.tuple.hadoop.util.TupleComparator.compare
1.7% 296 + 0 org.apache.hadoop.mapred.MapTask$MapOutputBuffer.compare
...
79.0% 13514 + 467 Total compiled
Thread-local ticks:
54.3% 21053 Blocked (of total)

32. USE HADOOP'S RAW COMPARATOR API
Hadoop comes with a RawComparator API for comparing
records in their serialized (raw) form
Don't make sorting more expensive than it already is
deserialize(keyBytes1).compare(deserialize(keyBytes2))
compare(keyBytes1, keyBytes2)

33. USE HADOOP'S RAW COMPARATOR API
Hadoop comes with a RawComparator API for comparing
records in their serialized (raw) form
Don't make sorting more expensive than it already is
public interface RawComparator<T> {
public int compare(byte[] b1, int s1, int l1,
byte[] b2, int s2, int l2);
}

34. USE HADOOP'S RAW COMPARATOR API
Unfortunately, this requires you to write a custom comparator
by hand
And assumes that your data is actually easy to compare in its
serialized form
Don't make sorting more expensive than it already is
public interface RawComparator<T> {
public int compare(byte[] b1, int s1, int l1, byte[] b2, int s2, int l2);
}

35. SCALA MACROS FOR RAW COMPARATORS
Macros to the rescue!
A slightly more hipster API for Raw Comparators in Scala
And a handful of macros to generate implementations of this
API for tuples, case classes, thrift objects, primitives, Strings,
etc.

36. SCALA MACROS FOR RAW COMPARATORS
1 3 f o o 0 1 17 1 88 ...
Macros to the rescue!
First, creates a custom dense serialization format that's easy to
compare
1 3 f o o 0 1 22 0 ... ...
non-null String
null value
non-null int non-null int
null value

37. SCALA MACROS FOR RAW COMPARATORS
1 3 f o o 0 1 17 1 88 ...
Macros to the rescue!
Then, creates a compare method that takes advantage of this
format
1 3 f 0 o 0 1 22 0 ... ...

38. SCALA MACROS FOR RAW COMPARATORS
Macros to the rescue!
TotalComputeTime
Default Raw Comparators
1.5X
FASTER

39. How to reduce costs due to record
serialization?
L E S S O N L E A R N E D

40. COLUMN PROJECTION
Don't read or deserialize data that you don't need
struct User {
1: i64 id
2: Address address
3: string name
4 list<Interest> interests
}

41. COLUMN PROJECTION
Columnar file formats like Apache Parquet support this directly
Specialized record deserializers can skip over unwanted fields
in row oriented storage
Don't read or deserialize data that you don't need

42. APACHE PARQUET
Columnar storage for the people
In traditional row-oriented storage layout, an entire record is
stored sequentially
R1.A R1.B R1.C R2.A R2.B R2.C R3.A R3.B R3.C

43. APACHE PARQUET
Columnar storage for the people
In traditional row-oriented storage layout, an entire record is
stored sequentially
9903489083
"123 elm street"
"alice"
"columnar file formats"
9903489084
"333 oak street"
"bob"
"Hadoop"
Compressed with lzo / gzip / snappy

44. APACHE PARQUET
Columnar storage for the people
In columnar storage layout, an entire column is stored
sequentially
R1.A R2.A R3.A R1.B R2.B R3.B R1.C R2.C R3.C

45. APACHE PARQUET
Columnar storage for the people
All user ids stored together
In columnar storage layout, an entire column is stored
sequentially
9903489083
9903489084
9903489085
9903489075
9903489088
9903489087
"123 elm street"
"333 oak street"
"827 maple drive"

46. APACHE PARQUET
Columnar storage for the people
Schema aware storage can use specialized encodings
9903489083
9903489084
9903489085
9903489075
9903489088
9903489087
9903489083
+1
+1
-10
+3
-1
delta
"twitter.com/foo/bar"
"blog.twitter.com"
"twitter.com/foo/bar"
"twitter.com/foo/bar"
"blog.twitter.com"
"blog.twitter.com"
"blog.twitter.com"
"blog.twitter.com/123"
"twitter.com/foo/bar": 0
"blog.twitter.com": 1
"blog.twitter.com/123": 2
0
1
0
0
1
1
1
2
dictionary

47. FILE SIZE COMPARISON
SizeinGB
B64 Lzo Thrift Block Lzo Thrift Gzipped Json Lzo Parquet
2X
SMALLER
B64 Lzo Thrift Block Lzo Thrift Gzipped Json Lzo Parquet

48. APACHE PARQUET
Columnar storage for the people
Collocating entire columns allows for efficient
column projection
Read off disk only the columns you need
Possibly more importantly: deserialize only
the columns you need

49. TotalComputeTime
1 column 10 columns 40 columns
Parquet Lzo Thrift
COLUMN PROJECTION WITH PARQUET
3X
FASTER
1.5X
FASTER
1.15X
FASTER

50. TotalComputeTime
1 column 10 columns 40 columns
Parquet Lzo Thrift
COLUMN PROJECTION WITH PARQUET
3X
FASTER
1.5X
FASTER
1.15X
FASTER

51. APACHE PARQUET
Columnar storage for the people
Parquet is often slower to read all columns than row oriented
storage
Parquet is a dense format, read performance scales with the
number of columns in the schema -- nulls take time read
Sparse, row oriented formats (thrift) scale with the number of
columns present in the data -- nulls take no time read

52. COLUMN PROJECTION FOR ROW ORIENTED DATA
Row oriented is a very common way to store Thrift, Avro,
Protocol Buffers, etc.
Specialized record deserializers can skip over unwanted fields
in these row oriented storage formats
Prototype implemented as a Scala macro that creates a custom
deserializer at compile time
Don't deserialize data that you don't need

53. COLUMN PROJECTION FOR ROW ORIENTED DATA
Don't deserialize data that you don't need
198 111 121 054 e l m _ s t r ... a l i c e ...
Decode User Id to Long
Skip over unwanted address field
Decode Name to String

54. COLUMN PROJECTION FOR ROW ORIENTED DATA
No IO savings
But only decodes the fields you care about into objects
CPU time spent decoding Strings can be huge compared to
time it takes to load + ignore the encoded bytes
Don't deserialize data that you don't need

55. TotalComputeTime
Number of Columns Selected
1 7 10 13 48
Parquet Thrift
Parquet Pig
Lzo Thrift + Projection
COLUMN PROJECTION: THRIFT VS. PARQUET
Parquet Thrift has a lot
of room for
improvement
Parquet faster than row
oriented until 13 columns
This schema is relatively
flat, and most columns
populated

56. APACHE PARQUET
Columnar storage for the people
Predicate push-down also allows parquet to
skip over records that don't match your filter
criteria
Parquet stores statistics about chunks of
records, so in some cases entire chunks of
data can be skipped after examining these
statistics

57. APACHE PARQUET
Columnar storage for the people
Combining both column projection and predicate push
down is a powerful combination

58. TotalComputeTime
Lzo Thrift Parquet + Filter Parquet + Filter + Project
FILTER PUSH DOWN WITH PARQUET
4.3X
FASTER

59. APACHE PARQUET
Columnar storage for the people
Predicate push-down performance depends on the nature
of the filter
Searching for rare records is the best case, entire chunks of
records are likely to not contain the records you are looking
for

60. Key take aways
I N S U M M A R Y

61. IN SUMMARY
Key takeaways
Profile!
Serialization is expensive, and Hadoop does a lot of it
Choose a storage format that fits your access patterns
Use column projection
Sorting is expensive -- use Raw Comparators
IO may not be your bottleneck -- more IO for less CPU may be
a good tradeoff

62. ACKNOWLEDGEMENTS
Thanks to everyone involved!
Dmitriy Ryaboy @squarecog
Gera Shegalov @gerashegalov
Julien Le Dem @J_
Katya Gonina @katyagonina
Mansur Ashraf @mansur_ashraf
Oscar Boykin @posco
Sriram Krishnan @krishnansriram
Tianshuo Deng @tsdeng
Zak Taylor @zakattacktaylor
And many more!

63. GET INVOLVED
Contributions always welcome!
github.com/twitter/scalding
github.com/twitter/algebird
github.com/twitter/chill
github.com/apache/parquet-mr

64. JOIN THE FLOCK
We're Hiring!
Work on data processing challenges at scale
Strong commitment to open source
jobs.twitter.com
Data Platform: (https://about.twitter.com/careers/positions?jvi=oipMYfwb,Job)

65. Q U E S T I O N S ?
A L E X L E V E N S O N |
I A N O ' C O N N E L L |
@ T H I S W I L LW O R K
@ 0 X 1 3 8