Apache Kylin-Hadoop上的大规模联机分析平台


http://kylin.io Apache Kylin Introduction Dec 14, 2014 韩卿|Luke Han Co-creator of Apache Kylin | lukehan@apache.org Sr. Product Manager, eBay CCOE http://kylin.io Agenda  What’s Apache Kylin?  Feature & Tech Highlights  Performance  Open Source & Roadmap  Q & A http://kylin.io 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 http://kylin.io 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 http://kylin.io 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.io 6 Why not Build an engine from scratch? http://kylin.io 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 http://kylin.io  Huge volume data  Table scan  Big table joins  Data shuffling  Analysis on different granularity  Runtime aggregation expensive  Map Reduce job  Batch processing Technical Challenges http://kylin.io 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) - 2. (9/15, milk, Urbana, *) - 3. (*, milk, Urbana, *) - 4. (*, milk, Chicago, *) - 5. (*, milk, *, *) - • Cuboid = one combination of dimensions • Cube = all combination of dimensions (all cuboids) http://kylin.io From Relational to Key-Value http://kylin.io 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 http://kylin.io  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? http://kylin.io Agenda  What’s Apache Kylin?  Feature & Tech Highlights  Performance  Open Source & Roadmap  Q & A http://kylin.io  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 http://kylin.io  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… http://kylin.io Cube Designer http://kylin.io Job Management http://kylin.io Query and Visualization http://kylin.io Tableau Integration http://kylin.io 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 http://kylin.io Cube Build Job Flow http://kylin.io How To Store Cube? – HBase Schema http://kylin.io Query Engine – Calcite (Optiq)  Dynamic data management framework.  Formerly known as Optiq, Calcite is an Apache incubator project, used by Apache Drill and Apache Hive, among others.  http://optiq.incubator.apache.org http://kylin.io 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]]) http://kylin.io  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 http://kylin.io How To Optimize Cube? – Partial Cube http://kylin.io How To Optimize Cube? – Incremental Building http://kylin.io Inverted Index  Challenge  Has no raw data records  Slow table scan on high cardinality dimensions  Inverted Index Storage (an ongoing effort)  Persist the raw table  Bitmap inverted index  Time range partition  In-memory (block cache)  Parallel scan (endpoint coprocessor) http://kylin.io Agenda  What’s Apache Kylin?  Feature & Tech Highlights  Performance  Open Source & Roadmap  Q & A http://kylin.io 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 http://kylin.io Performance -- Concurrency Linear scale out with more nodes http://kylin.io Performance - Query Latency 90%tile queries <5s Green Line: 90%tile queries Gray Line: 95%tile queries http://kylin.io Agenda  What’s Apache Kylin?  Feature & Tech Highlights  Performance  Open Source & Roadmap  Q & A http://kylin.io  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 Kylin Ecosystem http://kylin.io Kylin Evolution Roadmap 2015 2014 2013 Initial Prototype for MOLAP • Basic end to end POC MOLAP • Incremental Refresh • ANSI SQL • ODBC Driver • Web GUI • ACL • Open Source HOLAP • InvertedIndex • JDBC Driver • Automation • Capacity Management • Support Spark •… more Next Gen • In-Memory Analysis • Real Time & Streaming (TBD) •… more TBD Future… Sep, 2013 Jan, 2014 Sep, 2014 Q1, 2015 http://kylin.io  Kylin Site:  http://kylin.io  Twitter:  @ApacheKylin  Source Code Repo:  https://github.com/KylinOLAP  Google Group:  Kylin OLAP Open Source http://kylin.io  时间:  2014-12-14 6:30 PM – 9:00 PM  地点:  3W咖啡 Apache Kylin 北京线下交流会 http://kylin.io Thanks http://kylin.io lukehan@apache.org
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