Apache Kylin Introduction

http://kylin.io
Apache Kylin Introduction
韩卿｜Luke Han
Sr. Product Manager | lukehan@apache.org | @lukehq
v2015.3

Agenda
 What’s Apache Kylin?
 Features & Tech Highlights
 Performance
 Roadmap
 Q & A

Extreme OLAP Engine for Big Data
Kylin is an open source Distributed Analytics Engine from eBay
that provides SQL interface and multi-dimensional analysis
(OLAP) on Hadoop supporting extremely large datasets
What’s Kylin
kylin / ˈkiːˈlɪn / 麒麟
--n. (in Chinese art) a mythical animal of composite form
• Open Sourced on Oct 1st, 2014
• Be accepted as Apache Incubator Project on Nov 25th, 2014

Big Data Era
 More and more data becoming available on Hadoop
 Limitations in existing Business Intelligence (BI) Tools
 Limited support for Hadoop
 Data size growing exponentially
 High latency of interactive queries
 Scale-Up architecture
 Challenges to adopt Hadoop as interactive analysis system
 Majority of analyst groups are SQL savvy
 No mature SQL interface on Hadoop
 OLAP capability on Hadoop ecosystem not ready yet

Business Needs for Big Data Analysis
 Sub-second query latency on billions of rows
 ANSI SQL for both analysts and engineers
 Full OLAP capability to offer advanced functionality
 Seamless Integration with BI Tools
 Support of high cardinality and high dimensions
 High concurrency – thousands of end users
 Distributed and scale out architecture for large data volume

http://kylin.io6
Why not
Build an engine from scratch?

Transaction
Operation
Strategy
High Level
Aggregation
•Very High Level, e.g GMV by
site by vertical by weeks
Analysis
Query
•Middle level, e.g GMV by site by vertical,
by category (level x) past 12 weeks
Drill Down
to Detail
•Detail Level (Summary Table)
Low Level
Aggregation
•First Level
Aggragation
Transaction
Level
•Transaction Data
Analytics Query Taxonomy
OLAP
Kylin is designed to accelerate 80+% analytics queries performance on Hadoop
OLTP

 Huge volume data
 Table scan
 Big table joins
 Data shuffling
 Analysis on different granularity
 Runtime aggregation expensive
 Map Reduce job
 Batch processing
Technical Challenges

OLAP Cube – Balance between Space and Time
time, item
time, item, location
time, item, location, supplier
time item location supplier
time, location
Time, supplier
item, location
item, supplier
location, supplier
time, item, supplier
time, location, supplier
item, location, supplier
0-D(apex) cuboid
1-D cuboids
2-D cuboids
3-D cuboids
4-D(base) cuboid
• Base vs. aggregate cells; ancestor vs. descendant cells; parent vs. child cells
1. (9/15, milk, Urbana, Dairy_land) - <time, item, location, supplier>
2. (9/15, milk, Urbana, *) - <time, item, location>
3. (*, milk, Urbana, *) - <item, location>
4. (*, milk, Chicago, *) - <item, location>
5. (*, milk, *, *) - <item>
• Cuboid = one combination of dimensions
• Cube = all combination of dimensions (all cuboids)

From Relational to Key-Value

Kylin Architecture Overview
11
Cube Build Engine
(MapReduce…)
SQL
Low Latency -
Seconds
Mid Latency - Minutes
Routing
3rd Party App
(Web App, Mobile…)
Metadata
SQL-Based Tool
(BI Tools: Tableau…)
Query Engine
Hadoop
Hive
REST API JDBC/ODBC
 Online Analysis Data Flow
 Offline Data Flow
 Clients/Users interactive with
Kylin via SQL
 OLAP Cube is transparent to
users
Star Schema Data Key Value Data
Data
Cube
OLAP
Cube
(HBase)
SQL
REST Server

 Hive
 Input source
 Pre-join star schema during cube building
 MapReduce
 Pre-aggregation metrics during cube building
 HDFS
 Store intermediated files during cube building.
 HBase
 Store data cube.
 Serve query on data cube.
 Coprocessor is used for query processing.
How Does Kylin Utilize Hadoop Components?

 Extremely Fast OLAP Engine at Scale
Kylin is designed to reduce query latency on Hadoop for 10+ billions of rows of data
 ANSI SQL Interface on Hadoop
Kylin offers ANSI SQL on Hadoop and supports most ANSI SQL query functions
 Seamless Integration with BI Tools
Kylin currently offers integration capability with BI Tools like Tableau.
 Interactive Query Capability
Users can interact with Hadoop data via Kylin at sub-second latency, better than Hive
queries for the same dataset
 MOLAP Cube
User can define a data model and pre-build in Kylin with more than 10+ billions of raw
data records
Features Highlights

 Compression and Encoding Support
 Incremental Refresh of Cubes
 Approximate Query Capability for distinct Count (HyperLogLog)
 Leverage HBase Coprocessor for query latency
 Job Management and Monitoring
 Easy Web interface to manage, build, monitor and query cubes
 Security capability to set ACL at Cube/Project Level
 Support LDAP Integration
Features Highlights…

Cube Designer

Job Management

Query and Visualization

Tableau Integration

Data Modeling
Cube: …
Fact Table: …
Dimensions: …
Measures: …
Storage(HBase): …Fact
Dim Dim
Dim
Source
Star Schema
row A
row B
row C
Column Family
Val 1
Val 2
Val 3
Row Key Column
Target
HBase Storage
Mapping
Cube Metadata
End User Cube Modeler Admin

Cube Build Job Flow

How To Store Cube? – HBase Schema

Query Engine – Kylin Explain Plan
SELECT test_cal_dt.week_beg_dt, test_category.category_name, test_category.lvl2_name, test_category.lvl3_name,
test_kylin_fact.lstg_format_name, test_sites.site_name, SUM(test_kylin_fact.price) AS GMV, COUNT(*) AS TRANS_CNT
FROM test_kylin_fact
LEFT JOIN test_cal_dt ON test_kylin_fact.cal_dt = test_cal_dt.cal_dt
LEFT JOIN test_category ON test_kylin_fact.leaf_categ_id = test_category.leaf_categ_id AND test_kylin_fact.lstg_site_id =
test_category.site_id
LEFT JOIN test_sites ON test_kylin_fact.lstg_site_id = test_sites.site_id
WHERE test_kylin_fact.seller_id = 123456OR test_kylin_fact.lstg_format_name = ’New'
GROUP BY test_cal_dt.week_beg_dt, test_category.category_name, test_category.lvl2_name, test_category.lvl3_name,
test_kylin_fact.lstg_format_name,test_sites.site_name
OLAPToEnumerableConverter
OLAPProjectRel(WEEK_BEG_DT=[$0], category_name=[$1], CATEG_LVL2_NAME=[$2], CATEG_LVL3_NAME=[$3],
LSTG_FORMAT_NAME=[$4], SITE_NAME=[$5], GMV=[CASE(=($7, 0), null, $6)], TRANS_CNT=[$8])
OLAPAggregateRel(group=[{0, 1, 2, 3, 4, 5}], agg#0=[$SUM0($6)], agg#1=[COUNT($6)], TRANS_CNT=[COUNT()])
OLAPProjectRel(WEEK_BEG_DT=[$13], category_name=[$21], CATEG_LVL2_NAME=[$15], CATEG_LVL3_NAME=[$14],
LSTG_FORMAT_NAME=[$5], SITE_NAME=[$23], PRICE=[$0])
OLAPFilterRel(condition=[OR(=($3, 123456), =($5, ’New'))])
OLAPJoinRel(condition=[=($2, $25)], joinType=[left])
OLAPJoinRel(condition=[AND(=($6, $22), =($2, $17))], joinType=[left])
OLAPJoinRel(condition=[=($4, $12)], joinType=[left])
OLAPTableScan(table=[[DEFAULT, TEST_KYLIN_FACT]], fields=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]])
OLAPTableScan(table=[[DEFAULT, TEST_CAL_DT]], fields=[[0, 1]])
OLAPTableScan(table=[[DEFAULT, test_category]], fields=[[0, 1, 2, 3, 4, 5, 6, 7, 8]])
OLAPTableScan(table=[[DEFAULT, TEST_SITES]], fields=[[0, 1, 2]])

 Full Cube
 Pre-aggregate all dimension combinations
 “Curse of dimensionality”: N dimension cube has 2N cuboid.
 Partial Cube
 To avoid dimension explosion, we divide the dimensions into
different aggregation groups
 2N+M+L  2N + 2M + 2L
 For cube with 30 dimensions, if we divide these dimensions into 3
group, the cuboid number will reduce from 1 Billion to 3 Thousands
 230  210 + 210 + 210
 Tradeoff between online aggregation and offline pre-aggregation
How To Optimize Cube? – Full Cube vs. Partial
Cube

How To Optimize Cube? – Partial Cube

How To Optimize Cube? – Incremental Building

Kylin vs. Hive
# Query
Type
Return Dataset Query
On Kylin (s)
Query
On Hive (s)
Comments
1 High Level
Aggregation
4 0.129 157.437 1,217 times
2 Analysis Query 22,669 1.615 109.206 68 times
3 Drill Down to
Detail
325,029 12.058 113.123 9 times
4 Drill Down to
Detail
524,780 22.42 6383.21 278 times
5 Data Dump 972,002 49.054 N/A
0
50
100
150
200
SQL #1 SQL #2 SQL #3
Hive
Kylin
High Level
Aggregatio
n
Analysis
Query
Drill Down
to Detail
Low Level
Aggregatio
n
Transactio
n Level
Based on 12+B records case

Performance -- Concurrency
Linear scale out with more nodes

Performance - Query Latency
90%tile queries <5s
Green Line: 90%tile queries
Gray Line: 95%tile queries

Kylin Evolution Roadmap
201520142013
Initial
Prototype
for MOLAP
• Basic end to end
POC
MOLAP
• Incremental
Refresh
• ANSI SQL
• ODBC Driver
• Web GUI
• ACL
• Open Source
HOLAP
• Streaming OLAP
• JDBC Driver
• New UI
• Excel Support
• … more
Next Gen
• Automation
• Capacity
Management
• In-Memory
Analysis (TBD)
• Spark (TBD)
• … more
TBD
Future…
Sep, 2013
Jan, 2014
Sep, 2014
Q1, 2015

 Kylin Core
 Fundamental framework of
Kylin OLAP Engine
 Extension
 Plugins to support for
additional functions and
features
 Integration
 Lifecycle Management
Support to integrate with
other applications
 Interface
 Allows for third party users to
build more features via user-
interface atop Kylin core
 Driver
 ODBC and JDBC Drivers
Kylin OLAP
Core
Extension
 Security
 Redis Storage
 Spark Engine
 Docker
Interface
 Web Console
 Customized BI
 Ambari/Hue Plugin
Integration
 ODBC Driver
 ETL
 Drill
 SparkSQL
Kylin Ecosystem

 Kylin Site:
 http://kylin.io
 Twitter:
 @ApacheKylin
 Github:
 apache/incubator-kylin
 WeChat (微信)
 ApacheKylin
Open Source

If you want to go fast, go alone.
If you want to go far, go together.
--African Proverb

Apache Kylin Introduction

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