Stinger Initiative - Deep Dive

© Hortonworks Inc. 2013
Stinger Initiative: Deep Dive
Interactive Query on Hadoop
Page 1
Chris Harris
E-Mail : charris@hortonworks.com
Twitter : cj_harris5

Agenda
• Key Hive Use Cases
• Brief Refresher on Hive
• The Stinger Initiative: Interactive Query for Hive
Page 2

Key Hive Use Cases
• RDBMS / MPP Offload
–More data under query.
–Database unable to keep up with SLAs.
• Analysis of semi-structured data.
• ETL / Data Refinement
• +++ Increasingly: Business Intelligence and
interactive query
Page 3

BI Use Cases
Page 4
Enterprise Reports Dashboard / Scorecard Parameterized Reports
Visualization Data Mining

Organize Tiers and Process with Metadata
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Work
Tier
Standardize, Cleanse, Transform
MapReduce
Pig
Raw
Tier
Extract & Load
WebHDF
S
Flume
Sqoop
Gold
Tier
Transform, Integrate, Storage
MapReduce
Pig
Conform, Summarize, Access
HiveQL
Pig
Access
Tier
HCat
Provides unified
metadata access
to Pig, Hive &
MapReduce
• Organize data
based on
source/derived
relationships
• Allows for fault
and rebuild
process

Hive Current Focus Area
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• Online systems
• R-T analytics
• CEP
Real-Time Interactive Batch
• Parameterized
Reports
• Drilldown
• Visualization
• Exploration
• Operational batch
processing
• Enterprise
Reports
• Data Mining
Data Size
0-5s 5s – 1m 1m – 1h 1h+
Non-
Interactive
• Data preparation
• Incremental batch
processing
• Dashboards /
Scorecards
Current Hive Sweet Spot

Stinger: Extending Hive‟s Sweetspot
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• Online systems
• R-T analytics
• CEP
Real-Time Interactive Batch
• Parameterized
Reports
• Drilldown
• Visualization
• Exploration
• Operational batch
processing
• Enterprise
Reports
• Data Mining
Data Size
0-5s 5s – 1m 1m – 1h 1h+
Non-
Interactive
• Data preparation
• Incremental batch
processing
• Dashboards /
Scorecards
Improve Latency & Throughput
• Query engine improvements
• New “Optimized RCFile” column store
• Next-gen runtime (elim‟s M/R latency)
Extend Deep Analytical Ability
• Analytics functions
• Improved SQL coverage
• Continued focus on core Hive use cases
Current Hive Sweet SpotFuture Hive
Expansion

The top BI vendors support Hive today
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Agenda
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Brief Refresher on Hive
The State of Hive Today (0.10)
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Hive‟s Origins
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Hive was originally developed at Facebook.
More data than existing RDBMS could handle.
60,000+ Hive queries per day.
More than 1,000 users per day.
100+ PB of data.
15+ TB of data loaded daily.
Hive is a proven solution at extreme scale.

Hive 0.10 Capabilities
• De-facto SQL Interface for Hadoop
• Multiple persistence options:
–Flat text for simple data imports.
–Columnar format (RCFile) for high performance processing.
• Secure and concurrent remote access
• ODBC/JDBC connectivity
• Highly extensible:
–Supports User Defined Functions and User Defined Aggregation
Functions.
–Ships with more than 150 UDF/UDAF.
–Extensible readers/writers can process any persisted data.
• Support from 10+ BI vendors
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HDP 1.2: ODBC Access for Popular BI Tools
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• Seamless integration with BI
tools such as Excel, PowerPivot,
MicroStrategy, and Tableau
• Efficiently maps advanced SQL
functionality into HiveQL
– With configurable pass-through of
HiveQL for Hive-aware apps
• ODBC 3.52 standard compliant
• Supports Linux & Windows
High quality ODBC driver developed in partnership with Simba.
Free to download & use with Hortonworks Data Platform.
Applications &
Spreadsheets
Visualization &
Intelligence
ODBC
Hortonworks
Data Platform

0 to Big Data in 15 Minutes
Page 14
Hands on tutorials
integrated into
Sandbox
HDP environment for
evaluation

Agenda
Page 15

The Stinger Initiative
Interactive Query on Hadoop
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Stinger Initiative: 2-Pronged Approach
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Tez
• New primitives move beyond map-reduce
and beyond batch
• Avoid unnecessary persistence of
temporary data
• Hive, Pig and others generate Tez plans
for high perf
Query Engine Improvements
• Cost-based optimizer
• In-memory joins
• Caching hot tables
• Vector processing
State-of-the-art Column Store
• “Optimized RCFile” or ORCFile
• Minimizes disk IO and deserialization
Tez Service
• Always-on service for query interactivity
Improve Latency and Throughput
Analytics Functions
• SQL:2003 Compliant
• OVER with PARTITION BY and ORDER
BY
• Wide variety of windowing functions:
• RANK
• LEAD/LAG
• ROW_NUMBER
• FIRST_VALUE
• LAST_VALUE
• Many more
• Aligns well with BI ecosystem
Improved SQL Coverage
• Non-correlated Subqueries using IN in
WHERE
• Expanded SQL types including
DATETIME, VARCHAR, etc.
Extend Deep Analytical Ability
Making Hive Best for Interactive Query

Hive: Performance Improvements
Page 18

Stinger Initiative At A Glance
Page 19

Base Optimizations: Intelligent Optimizer
• Introduction of In-Memory Hash Join:
–For joins where one side fits in memory:
–New in-memory-hash-join algorithm.
–Hive reads the small table into a hash table.
–Scans through the big file to produce the output.
• Introduction of Sort-Merge-Bucket Join:
–Applies when tables are bucketed on the same key.
–Dramatic speed improvements seen in benchmarks.
• Other Improvements:
–Lower the footprint of the fact tables in memory.
–Enable the optimizer to automatically pick map joins.
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Dimensionally Structured Data
• Extremely common pattern in EDW.
• Results in large “fact tables” and small “dimension
tables”.
• Dimension tables often small enough to fit in RAM.
• Sometimes called Star Schema.
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A Query on Dimensional Data
• Derived from TPC-DS Query 27
• Dramatic speedup on Hive 0.11
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SELECT col5, avg(col6)
FROM fact_table
join dim1 on (fact_table.col1 = dim1.col1)
GROUP BY col5
ORDER BY col5
LIMIT 100;

Star Schema Join Improvements in 0.11
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Hive: Bucketing
• Bucketing causes Hive to physically co-locate rows
within files.
• Buckets can be sorted or unsorted.
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CREATE EXTERNAL TABLE IF NOT EXISTS test_table
(
Id INT, name String
)
PARTITIONED BY (dt STRING, hour STRING)
CLUSTERED BY(country,continent) SORTED BY(country,continent) INTO n BUCKETS
ROW FORMAT DELIMITED FIELDS TERMINATED BY '|'
LOCATION '/home/test_dir';

ORCFile - Optimized Column Storage
• Make a better columnar storage file
–Tightly aligned to Hive data model
• Decompose complex row types into primitive fields
–Better compression and projection
• Only read bytes from HDFS for the required columns.
• Store column level aggregates in the files
–Only need to read the file meta information for common queries
–Stored both for file and each section of a file
–Aggregates: min, max, sum, average, count
–Allows fast access by sorted columns
• Ability to add bloom filters for columns
–Enables quick checks for whether a value is present
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Performance Futures - Vectorization
• Operates on blocks of 1K or more records, rather than
one record at a time
• Each block contains an array of Java scalars, one for
each column
• Avoids many function calls, virtual dispatch, CPU pipeline
stalls
• Size to fit in L1 cache, avoid cache misses
• Generate code for operators on the fly to avoid branches
in code, maximize deep pipelines of modern processers
• Up to 30x faster processing of records
• Beta possible in 2H 2013
Page 26

Performance Futures – Cost-Based
Optimizer
• Generate more intelligent DAGs based on properties of
data being queried, e.g. table size, statistics, histograms,
etc.
Page 27

Performance Futures - Buffering
• Query workloads always have hotspots:
–Metadata
–Small dimension tables
• Build into YARN or Tez Service ways of buffering
frequently used data into memory so it is not always read
from disk.
• Part of the “last mile” of latency efforts.
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Yarn
Moving Hive and Hadoop beyond MapReduce
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Hadoop 2.0 Innovations - YARN
• Focus on scale and innovation
– Support 10,000+ computer clusters
– Extensible to encourage innovation
• Next generation execution
– Improves MapReduce performance
• Supports new frameworks beyond
MapReduce
– Low latency, Streaming, Services
– Do more with a single Hadoop cluster
HDFS
MapReduce
Redundant, Reliable Storage
YARN: Cluster Resource Management
Tez
GraphProcessing
Other

Tez
Moving Hive and Hadoop beyond MapReduce
Page 31

Tez
• Low level data-processing execution engine
• Use it for the base of
MapReduce, Hive, Pig, Cascading etc.
• Enables pipelining of jobs
• Removes task and job launch times
• Hive and Pig jobs no longer need to move to the end
of the queue between steps in the pipeline
• Does not write intermediate output to HDFS
–Much lighter disk and network usage
• Built on YARN
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Tez - Core Idea
Task with pluggable Input, Processor & Output
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YARN ApplicationMaster to run DAG of Tez Tasks
Input Processor
Task
Output
Tez Task - <Input, Processor, Output>

Tez – Blocks for building tasks
MapReduce „Map‟
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MapReduce „Reduce‟
HDFS
Input
Map
Processor
MapReduce „Map‟ Task
Sorted
Output
Shuffle
Input
Reduce
Processor
HDFS
Output
Intermediate „Reduce‟
for
Map-Reduce-Reduce
Shuffle
Input
Reduce
Processor
Intermediate „Reduce‟ for Map-
Reduce-Reduce
Sorted
Output
MapReduce „Reduce‟ Task

Tez – More tasks
Special Pig/Hive „Map‟
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In-memory Map
HDFS
Input
Map
Processor
Tez Task
Pipeline
Sorter
Output
HDFSIn
put
Map
Processor
Tez Task
In-
memory
Sorted
Output
Special Pig/Hive
„Reduce‟
Shuffle
Skip-
merge
Input
Reduce
Processor
Tez Task
Sorted
Output

Pig/Hive-MR versus Pig/Hive-Tez
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SELECT a.state, COUNT(*), AVERAGE(c.price)
FROM a
JOIN b ON (a.id = b.id)
JOIN c ON (a.itemId = c.itemId)
GROUP BY a.state
Pig/Hive - MR Pig/Hive - Tez
I/O Synchronization
Barrier
I/O Synchronization
Barrier
Job 1
Job 2
Job 3
Single Job

FastQuery: Beyond Batch with YARN
Page 37
Tez Generalizes Map-Reduce
Simplified execution plans process
data more efficiently
Always-On Tez Service
Low latency processing for
all Hadoop data processing

Tez Service
• MR Query Startup Expensive
–Job launch & task-launch latencies are fatal for short queries (in
order of 5s to 30s)
• Solution
–Tez Service
– Removes task-launch overhead
– Removes job-launch overhead
–Hive/Pig
– Submit query-plan to Tez Service
–Native Hadoop service, not ad-hoc
Page 38

Tez Service Delivers Low Latency
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SELECT a.state, COUNT(*), AVERAGE(c.price)
FROM a
JOIN b ON (a.id = b.id)
JOIN c ON (a.itemId = c.itemId)
GROUP BY a.state
Existing Hive
Parse Query 0.5s
Create Plan 0.5s
Launch Map-Reduce 20s
Process Map-
Reduce
10s
Total 31s
Hive/Tez
Parse Query 0.5s
Create Plan 0.5s
Launch Map-Reduce 20s
Process Map-
Reduce
2s
Total 23s
Tez and Tez Service
Parse Query 0.5s
Create Plan 0.5s
Submit to Tez Service 0.5s
Process Map-Reduce 2s
Total 3.5s
* Numbers for illustration only

Recap and Questions: Hive Performance
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Improving Hive‟s SQL Support
Page 41

Stinger: Deep Analytical Capabilities
• SQL:2003 Window Functions
–OVER clauses
– Multiple PARTITION BY and ORDER BY supported
– Windowing supported (ROWS PRECEDING/FOLLOWING)
– Large variety of aggregates
– RANK
– FIRST_VALUE
– LAST_VALUE
– LEAD / LAG
– Distrubutions
Page 42

Hive Data Type Conformance
• Data Types:
–Add fixed point NUMERIC and DECIMAL type (in progress)
–Add VARCHAR and CHAR types with limited field size
–Add DATETIME
–Add size ranges from 1 to 53 for FLOAT
–Add synonyms for compatibility
– BLOB for BINARY
– TEXT for STRING
– REAL for FLOAT
• SQL Semantics:
–Sub-queries in IN, NOT IN, HAVING.
–EXISTS and NOT EXISTS
Page 43

Questions?
Page 44

Thank You!
Questions & Answers
Page 45

Stinger Initiative - Deep Dive

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