ORC File - Optimizing Your Big Data

ORC File –
Optimizing Your Big Data
Owen O’Malley, Co-founder Hortonworks
Apache Hadoop, Hive, ORC, and
Incubator
@owen_omalley

2 © Hortonworks Inc. 2011 – 2017. All Rights Reserved
Overview

In the Beginning…
 Hadoop applications used text or SequenceFile
– Text is slow and not splittable when compressed
– SequenceFile only supports key and value and user-defined serialization
 Hive added RCFile
– User controls the columns to read and decompress
– No type information and user-defined serialization
– Finding splits was expensive
 Avro files created
– Type information included!
– Had to read and decompress entire row

ORC File Basics
 Columnar format
– Enables user to read & decompress just the bytes they need
 Fast
– See https://www.slideshare.net/HadoopSummit/file-format-benchmark-avro-json-orc-parquet
 Indexed
 Self-describing
– Includes all of the information about types and encoding
 Rich type system
– All of Hive’s types including timestamp, struct, map, list, and union

File Compatibility
 Backwards compatibility
– Automatically detect the version of the file and read it.
 Forward compatibility
– Most changes are made so old readers will read the new files
– Maintain the ability to write old files via orc.write.format
– Always write old version until your last cluster upgrades
 Current file versions
– 0.11 – Original version
– 0.12 – Updated run length encoding (RLE)

File Structure
 File contains a list of stripes, which are sets of rows
– Default size is 64MB
– Large stripe size enables efficient reads
 Footer
– Contains the list of stripe locations
– Type description
– File and stripe statistics
 Postscript
– Compression parameters
– File format version

Stripe Structure
 Indexes
– Offsets to jump to start of row group
– Row group size defaults to 10,000 rows
– Minimum, Maximum, and Count of each column
 Data
– Data for the stripe organized by column
 Footer
– List of stream locations
– Column encoding information

File Layout
Page 8
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
Index Data
Row Data
Stripe Footer
~64MBStripe
Index Data
Row Data
Stripe Footer
~64MBStripe
Index Data
Row Data
Stripe Footer
~64MBStripe
File Footer
Postscript
File Metadata

Schema Evolution
 ORC now supports schema evolution
– Hive 2.1 – append columns or type conversion
– Upcoming Hive 2.3 – map columns or inner structures by name
– User passes desired schema to ORC reader
 Type conversions
– Most types will convert although some are ugly.
– If the value doesn’t fit in the new type, it will become null.
 Cautions
– Name mapping requires ORC files written by Hive ≥ 2.0
– Some of the type conversions are slow

Using ORC

From Hive or Presto
 Modify your table definition:
– create table my_table (
name string,
address string,
) stored as orc;
 Import data:
– insert overwrite table my_table select * from my_staging;
 Use either configuration or table properties
– tblproperties ("orc.compress"="NONE")
– set hive.exec.orc.default.compress=NONE;

From Java
 Use the ORC project rather than Hive’s ORC.
– Hive’s master branch uses it.
– Maven group id: org.apache.orc version: 1.4.0
– nohive classifier avoids interfering with Hive’s packages
 Two levels of access
– orc-core – Faster access, but uses Hive’s vectorized API
– orc-mapreduce – Row by row access, simpler OrcStruct API
 MapReduce API implements WritableComparable
– Can be shuffled
– Need to specify type information in configuration for shuffle or output

From C++
 Pure C++ client library
– No JNI or JDK so client can estimate and control memory
 Combine with pure C++ HDFS client from HDFS-8707
– Work ongoing in feature branch, but should be committed soon.
 Reader is stable and in production use.
 Alibaba has created a writer and is contributing it to Apache ORC.
– Should be in the next release ORC 1.5.0.

Command Line
 Using hive –orcfiledump from Hive
– -j -p – pretty prints the metadata as JSON
– -d – prints data as JSON
 Using java -jar orc-tools-1.4.0-uber.jar from ORC
– meta – print the metadata as JSON
– data – print data as JSON
– convert – convert JSON to ORC
– json-schema – scan a set of JSON documents to find the matching schema

Optimization

Stripe Size
 Makes a huge difference in performance
– orc.stripe.size or hive.exec.orc.default.stripe.size
– Controls the amount of buffer in writer. Default is 64MB
– Trade off
• Large stripes = Large more efficient reads
• Small stripes = Less memory and more granular processing splits
 Multiple files written at the same time will shrink stripes
– Use Hive’s hive.optimize.sort.dynamic.partition
– Sorting dynamic partitions means a one writer at a time

HDFS Block Padding
 The stripes don’t align exactly with HDFS blocks
 HDFS scatters blocks around cluster
 Often want to pad to block boundaries
– Costs space, but improves performance
– hive.exec.orc.default.block.padding – true
– hive.exec.orc.block.padding.tolerance – 0.05
Index Data
Row Data
Stripe Footer
~64MBStripe
Index Data
Row Data
Stripe Footer
~64MBStripe
Index Data
Row Data
Stripe Footer
~64MBStripe
HDFS Block
HDFS Block
Padding
File Footer
Postscript
File Metadata

Predicate Push Down
 Reader is given a SearchArg
– Limited set predicates over column and literal value
– Reader will skip over any parts of file that can’t contain valid rows
 ORC indexes at three levels:
– File
– Stripe
– Row Group (10k rows)
 Reader still needs to apply predicate to filter out single rows

Row Pruning
 Every primitive column has minimum and maximum at each level
– Sorting your data within a file helps a lot
– Consider sorting instead of making lots of partitions
 Writer can optionally include bloomfilters
– Provides a probabilistic bitmap of hashcodes
– Only works with equality predicates at the row group level
– Requires significant space in the file
– Manually enabled by using orc.bloom.filter.columns
– Use orc.bloom.filter.fpp to set the false positive rate (default 0.05)
– Set the default charset in JVM via -Dfile.encoding=UTF-8

Row Pruning Example
 TPC-DS
– from tpch1000.lineitem where l_orderkey = 1212000001;
 Rows Read
– Nothing – 5,999,989,709
– Min/Max – 540,000
– BloomFilter – 10,000
 Time Taken
– Nothing – 74 sec
– Min/Max – 4.5 sec
– BloomFilter – 1.3 sec

Split Calculation
 Hive’s OrcInputFormat has three strategies for split calculation
– BI
• Small fast queries
• Splits based on HDFS blocks
– ETL
• Large queries
• Read file footer and apply SearchArg to stripes
• Can include footer in splits (hive.orc.splits.include.file.footer)
– Hybrid
• If small files or lots of files, use BI

LLAP – Live Long & Process
 Provides a persistent service to speed up Hive
– Caches ORC and text data
– Saves costs of Yarn container & JVM spin up
– JIT finishes after first few seconds
 Cache uses ORC’s RLE
– Decompresses zlib or Snappy
– RLE is fast and saves memory
– Automatically caches hot columns and partitions
 Allows Spark to use Hive’s column and row security

Current Work In Progress

Speed Improvements for ACID
 Hive supports ACID transactions on ORC tables
– Uses delta files in HDFS to store changes to each partition
– Delta files store insert/update/delete operations
– Used to support SQL insert commands
 Unfortunately, update operations don’t allow predicate push down
on the deltas
 In the upcoming Hive 2.3, we added a new ACID layout
– It change updates to an insert and delete
– Allows predicate pushdown even on the delta files
 Also added SQL merge command in Hive 2.2

Column Encryption (ORC-14)
 Allows users to encrypt some of the columns of the file
– Provides column level security even with access to raw files
– Uses Key Management Server from Ranger or Hadoop
– Includes both the data and the index
– Daily key rolling can anonymize data after 90 days
 User specifies how data is masked if user doesn’t have access
– Nullify
– Redact
– SHA256

Thank You
@owen_omalley
owen@hortonworks.com

ORC File - Optimizing Your Big Data

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