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Hive’s RCFile has been the standard format for storing Hive data for the last 3 years. However, RCFile has limitations because it treats each column as a binary blob without semantics. The upcoming Hive 0.11 will add a new file format named Optimized Row Columnar (ORC) file that uses and retains the type information from the table definition. ORC uses type specific readers and writers that provide light weight compression techniques such as dictionary encoding, bit packing, delta encoding, and run length encoding — resulting in dramatically smaller files. Additionally, ORC can apply generic compression using zlib, LZO, or Snappy on top of the lightweight compression for even smaller files. However, storage savings are only part of the gain. ORC supports projection, which selects subsets of the columns for reading, so that queries reading only one column read only the required bytes. Furthermore, ORC files include light weight indexes that include the minimum and maximum values for each column in each set of 10,000 rows and the entire file. Using pushdown filters from Hive, the file reader can skip entire sets of rows that aren’t important for this query. Columnar storage formats like ORC reduce I/O and storage use, but it’s just as important to reduce CPU usage. A technical breakthrough called vectorized query execution works nicely with column store formats to do this. Vectorized query execution has proven to give dramatic performance speedups, on the order of 10X to 100X, for structured data processing. We describe how we’re adding vectorized query execution to Hive, coupling it with ORC with a vectorized iterator.
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Copyright 2013 by Hortonworks and Microsoft
ORC File & Vectorization Improving Hive Data Storage and Query Performance
June 2013
Page 1
Owen O’Malley
@owen_omalley
Jitendra Pandey
Eric Hanson
ORC – Optimized RC File
Page 2
History
Page 3
Remaining Challenges
Page 4
Requirements
Page 5
File Structure
Page 6
Stripe Structure
Page 7
File Layout
Page 8
File Footer
Postscript
Index Data
Row Data
Stripe Footer
256 M
B S
trip
e
Index Data
Row Data
Stripe Footer
256
MB
Str
ipe
Index Data
Row Data
Stripe Footer
25
6 M
B S
trip
e
Column 1
Column 2
Column 7
Column 8
Column 3
Column 6
Column 4
Column 5
Column 1
Column 2
Column 7
Column 8
Column 3
Column 6
Column 4
Column 5
Stream 2.1
Stream 2.2
Stream 2.3
Stream 2.4
Compression
Page 9
Integer Column Serialization
Page 10
String Column Serialization
Page 11
Hive Compound Types
Page 12
0Struct
4Struct
3String
1Int
2Map
7Time
5String
6 Double
Compound Type Serialization
Page 13
Generic Compression
Page 14
Column Projection
Page 15
How Do You Use ORC
Page 16
Managing Memory
Page 17
TPC-DS File Sizes
Page 18
ORC Predicate Pushdown
Page 19
Additional Details
Page 20
Current work for Hive 0.12
Page 21
Future Work
Page 22
Comparison
Page 23
RC File Trevni Parquet ORC
Hive Integration Y N N Y
Active Development N N Y Y
Hive Type Model N N N Y
Shred complex columns N Y Y Y
Splits found quickly N Y Y Y
Files per a bucket 1 many 1 or many 1
Versioned metadata N Y Y Y
Run length data encoding N N Y Y
Store strings in dictionary N N Y Y
Store min, max, sum, count N N N Y
Store internal indexes N N N Y
No overhead for non-null N N N Y ≥ 0.12
Predicate Pushdown N N N Y ≥ 0.12
Vectorization
Page 24
Vectorization
Page 25
Why row-at-a-time execution is slow
Page 26
• Hive uses Object Inspectors to work on a row
• Enables level of abstraction
• Costs major performance
• Exacerbated by using lazy serdes
• Inner loop has many method, new(), and if-
then-else calls
• Lots of CPU instructions
• Pipeline stalls Poor instructions/cycle
• Poor cache locality
How the code works (simplified)
Page 27
class LongColumnAddLongScalarExpression {int inputColumn;int outputColumn;long scalar;void evaluate(VectorizedRowBatch batch) {long [] inVector =
((LongColumnVector) batch.columns[inputColumn]).vector;long [] outVector =
((LongColumnVector) batch.columns[outputColumn]).vector;
if (batch.selectedInUse) {for (int j = 0; j < batch.size; j++) {
int i = batch.selected[j];outVector[i] = inVector[i] + scalar;
} } else {for (int i = 0; i < batch.size; i++) {outVector[i] = inVector[i] + scalar;
} }
}}
}
No method calls
Low instruction count
Cache locality to 1024 values
No pipeline stalls
SIMD in Java 8
Vectorization project
Page 28
Preliminary performance results
• NOT a benchmark
• 218 million row fact table of real data, 25 columns
• 18GB raw data
• 6 core, 12 thread workstation, 1 disk, 16GB RAM• select a, b, count(*) from t
where c >= const group by a, b -- 53 row result
Page 29
warm start times RC non-
vectorized
(default, not
compressed)
ORC non-
vectorized
(default,
compressed)
ORC vectorized
(default,
compressed)
Runtime (sec) 261 58 43
Total CPU (sec) 381 159 42
Thanks to contributors!
Page 30
• Microsoft Big Data:
• Eric Hanson, Remus Rusanu, Sarvesh
Sakalanaga, Tony Murphy, Ashit Gosalia
• Hortonworks:
• Jitendra Pandey, Owen O’Malley, Gopal V
• Others:
• Teddy Choi, Tim Chen
Jitendra/Eric are joint leads
