Efficient Data Storage for Analytics with Parquet 2.0 - Hadoop Summit 2014

1. Efficient Data Storage for Analytics
with Apache Parquet 2.0
Julien Le Dem @J_
Processing tools tech lead, Data Platform at Twitter
Nong Li nong@cloudera.com
Software engineer, Cloudera Impala
@ApacheParquet

2. Outline
2
- Why we need efficiency
- Properties of efficient algorithms
- Enabling efficiency
- Efficiency in Apache Parquet

3. Why we need efficiency

4. Producing a lot of data is easy
4
Producing a lot of derived data is even easier.
Solution: Compress all the things!

5. Scanning a lot of data is easy
5
1% completed
… but not necessarily fast.
Waiting is not productive. We want faster turnaround.
Compression but not at the cost of reading speed.

6. Trying new tools is easy
6
ETL
Storage
ad-hoc
queries
log
collection
automated
dashboard
machine
learning
graph
processing
external
datasources and
schema definition
...
...
We need a storage format interoperable with all the tools we use
and keep our options open for the next big thing.

7. Enter Apache Parquet

8. Parquet design goals
8
- Interoperability
- Space efficiency
- Query efficiency

9. Parquet timeline
9
- Fall 2012: Twitter & Cloudera merge efforts to develop columnar formats
- March 2013: OSS announcement; Criteo signs on for Hive integration
- July 2013: 1.0 release. 18 contributors from more than 5 organizations.
- May 2014: Apache Incubator. 40+ contributors, 18 with 1000+ LOC. 26 incremental releases.
- Parquet 2.0 coming as Apache release

10. Interoperability

11. Interoperable
11
Model agnostic
Language agnostic
Java C++
Avro Thrift
Protocol
Buffer
Pig Tuple Hive SerDe
Assembly/striping
Parquet file format
Object model
parquet-avroConverters parquet-thrift parquet-proto parquet-pig parquet-hive
Column encoding
Impala
...
...
Encoding
Query
execution

12. Frameworks and libraries integrated with Parquet
12
Query engines:
Hive, Impala, HAWQ,
IBM Big SQL, Drill, Tajo,
Pig, Presto
!
Frameworks:
Spark, MapReduce, Cascading,
Crunch, Scalding, Kite
!
Data Models:
Avro, Thrift, ProtocolBuffers,
POJOs

13. Enabling efficiency

14. Columnar storage
14
Logical table
representation
Row layout
Column layout
encoding
Nested schema
a b c
a b c
a1 b1 c1
a2 b2 c2
a3 b3 c3
a4 b4 c4
a5 b5 c5
a1 b1 c1 a2 b2 c2 a3 b3 c3 a4 b4 c4 a5 b5 c5
a1 b1 c1a2 b2 c2a3 b3 c3a4 b4 c4a5 b5 c5
encoded chunk encoded chunk encoded chunk

15. Parquet nested representation
15
Document
DocId Links Name
Backward Forward Language Url
Code Country
Columns:
docid
links.backward
links.forward
name.language.code
name.language.country
name.url
Schema:
Borrowed from the Google Dremel paper
https://blog.twitter.com/2013/dremel-made-simple-with-parquet

16. Statistics for filter and query optimization
16
Vertical partitioning
(projection push down)
Horizontal partitioning
(predicate push down)
Read only the data
you need!
+ =
a b c
a1 b1 c1
a2 b2 c2
a3 b3 c3
a4 b4 c4
a5 b5 c5
a b c
a1 b1 c1
a2 b2 c2
a3 b3 c3
a4 b4 c4
a5 b5 c5
a b c
a1 b1 c1
a2 b2 c2
a3 b3 c3
a4 b4 c4
a5 b5 c5
+ =

17. Properties of efficient algorithms

18. CPU Pipeline
18
pipe
1 a b c d
2 a b c d
3 a b c d
4 a b c
1 2 3 4 5 6
1 a b c d
2 a b c
3 a b
4 a
clock1 2 3 4 5 6
7
d
7 8
b
b
b
b
c d
c d
c d
c d
9 10
clock
pipe
pipeline
time
8 9 10
d
c
b
a
Mis-prediction
(“Bubble”)
Ideal case

19. Optimize for the processor pipeline
19
ifs
“Bubbles” can be caused by:
loops
virtual
calls
data
dependency
cost ~ 12 cycles

20. Minimize CPU cache misses
20
a cache miss costs 10 to 100s cycles depending on the level
RAM
Bus
CPU Cache

21. Encodings in Apache Parquet 2.0

22. The right encoding for the right job
22
- Delta encodings:
for sorted datasets or signals where the variation is less important than the absolute
value. (timestamp, auto-generated ids, metrics, …) Focuses on avoiding branching.
!
- Prefix coding (delta encoding for strings)
When dictionary encoding does not work.
!
- Dictionary encoding:
small (60K) set of values (server IP, experiment id, …)
!
- Run Length Encoding:
repetitive data.

23. Delta encoding
23
8 * 64bits values = 64 bytes 8 * 64bits values = 64 bytes
101 100101 105102 107101 11499 116101 102 101 119 120 121
values:
deltas
1 10 51 2-1 7-2 20 1 -1 3 1 1100
100
101 100101 105102 107101 11499 116101 102 101 119 120 121100
reference block 1 block 2

24. Delta encoding
24
3 02 43 11 60 12 3 1 2 0 0100 -2
min
delta
1 10 51 2-1 7-2 20 1 -1 3 1 1100
1
min
delta
make deltas > 0
by subtracting min

25. 3 02 43 11 60 12 3 1 2 0 0
maxbits = 2
11 10 11 01 0010 11 01
1110110110110100
maxbits = 3
8 * 2 bits = 2 bytes
000 100 001 110 001 010 000 000
000100001110001010000000
8 * 3 bits = 3 bytes
2 3
bits bits
100 -2
100 -2
1
1
min
delta
min
delta
reference
packing packing
1110110110110100 0001000011100010100000002 3100 -2 1result:
min
delta
min
delta
Delta encoding
25

26. Delta encoding
26
3 02 43 11 60 12 3 1 2 0 0
maxbits = 2
11 10 11 01 0010 11 01
1110110110110100
8 * 64bits values = 64 bytes 8 * 64bits values = 64 bytes
maxbits = 3
8 * 2 bits = 2 bytes
000 100 001 110 001 010 000 000
000100001110001010000000
8 * 3 bits = 3 bytes
2 3
bits bits
101 100101 105102 107101 11499 116101 102 101 119 120 121
100
values:
-2
min
delta
100 -2
deltas
1 10 51 2-1 7-2 20 1 -1 3 1 1100
1
min
delta
make deltas > 0
by subtracting min
1
min
delta
min
delta
100
101 100101 105102 107101 11499 116101 102 101 119 120 121100
reference block 1 block 2
reference
packing packing
1110110110110100 0001000011100010100000002 3100 -2 1result:

27. Binary packing designed for CPU efficiency
27
better:
orvalues = 0!
for (int i = 0; i<values.length; ++i) {!
orvalues |= values[i]!
}!
max = maxbit(orvalues)!
see paper:
“Decoding billions of integers per second through vectorization”
by Daniel Lemire and Leonid Boytsov
Unpredictable branch! Loop => Very predictable branch
naive maxbit:
max = 0!
for (int i = 0; i<values.length; ++i) {!
current = maxbit(values[i])!
if (current > max) max = current!
}!
even better:
orvalues = 0!
orvalues |= values[0]!
…!
orvalues |= values[32]!
max = maxbit(orvalues)
no branching at all!

28. Binary unpacking designed for CPU efficiency
28
!
int j = 0!
while (int i = 0; i < output.length; i += 32) {!
maxbit = input[j]!
unpack_32_values(values, i, out, j + 1, maxbit);!
j += 1 + maxbit!
}!

29. Compression comparison
29
TPCH: compression of two 64 bits id columns with delta encoding
Primary key
0%
20%
40%
60%
80%
100%
plain delta
no compression + snappy

30. Compression comparison
30
TPCH: compression of two 64 bits id columns with delta encoding
Primary key
0%
20%
40%
60%
80%
100%
plain delta
no compression + snappy
Foreign key
0%
20%
40%
60%
80%
100%
plain delta

31. Decoding time vs Compression
31
decodingspeed:!
Million/second
0
350
700
1050
1400
Compression (percent saved)
0% 25% 50% 75% 100%
Delta
Plain + Snappy
Plain

32. Performance

33. Size comparison
33
TPCDS 100GB scale factor (+ Snappy unless otherwise specified)
Store salesLineitem
Text uncompressed
Seq Avro Text + LZO
RC
Parquet 1
Parquet 2
The area of the circle is proportional to the file size
Text uncompressed
Seq
RC Avro Parquet 1
Parquet 2

34. Impala query performance
34
Seconds
0
75
150
225
300
Interactive Reporting Deep Analytics
Text Seq RC Parquet 1.0 Parquet 2.0
10 machines:
8 cores
48 GB of RAM
12 Disks
OS buffer cache flushed between every query
TPCDS geometric mean per query category

35. Roadmap 2.x

36. Roadmap 2.x
36
C++ library: implementation of encodings
!
Predicate push down:
use statistics to implement filters at the metadata level
!
Decimal, Timestamp logical types

37. Community

38. Thank you to our contributors
38
Open Source announcement
1.0 release

39. Get involved
39
Mailing lists:
- dev@parquet.incubator.apache.org
!
Parquet sync ups:
- Regular meetings on google hangout

40. Questions
40
Questions.foreach( answer(_) )
@ApacheParquet