2008 Ur Tech Talk Zshao

Facebook and Open Source University of Illinois, Urbana-Champaign ,[object Object],[object Object],[object Object]

Current Status of Facebook ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Architecture ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Architecture (continued) ,[object Object],Web Servers Scribe Servers Network Storage Hive on Hadoop Cluster Oracle RAC MySQL

Open Source Strategy ,[object Object],[object Object],[object Object],[object Object],[object Object]

Open Source at Facebook ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Hadoop and Hive Distributed File System, Map-Reduce, and SQL

Hadoop ,[object Object],[object Object],[object Object]

Why Hadoop? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Hive ,[object Object],[object Object],[object Object],[object Object]

Why Hive? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Hive Architecture HDFS Map Reduce Planner Hive CLI DDL Queries Browsing SerDe Thrift Jute JSON Thrift API MetaStore Web UI Mgmt, etc Hive QL Planner Execution Parser

(Simplified) Map Reduce Review Machine 2 Machine 1 <k1, v1> <k2, v2> <k3, v3> <k4, v4> <k5, v5> <k6, v6> <nk1, nv1> <nk2, nv2> <nk3, nv3> <nk2, nv4> <nk2, nv5> <nk1, nv6> Local Map <nk2, nv4> <nk2, nv5> <nk2, nv2> <nk1, nv1> <nk3, nv3> <nk1, nv6> Global Shuffle <nk1, nv1> <nk1, nv6> <nk3, nv3> <nk2, nv4> <nk2, nv5> <nk2, nv2> Local Sort <nk2, 3> <nk1, 2> <nk3, 1> Local Reduce

Hive QL – Join ,[object Object],[object Object],[object Object],[object Object],X = page_view user pv_users 9:08:14 222 1 9:08:13 111 2 9:08:01 111 1 time userid pageid male 32 222 female 25 111 gender age userid 32 1 25 2 25 1 age pageid

Hive QL – Join in Map Reduce page_view user pv_users Map Shuffle Sort Reduce < 1, 1> 222 < 1, 2> 111 < 1, 1> 111 value key 9:08:14 222 1 9:08:13 111 2 9:08:01 111 1 time userid pageid male 32 222 female 25 111 gender age userid < 2, 32> 222 < 2, 25> 111 value key < 2, 25> 111 < 1, 2> 111 < 1, 1> 111 value key < 2, 32> 222 < 1, 1> 222 value key 25 2 25 1 age pageid 32 1 age pageid

Hive QL – Group By ,[object Object],[object Object],[object Object],[object Object],[object Object],pv_users pageid_age_sum 25 2 32 1 25 2 25 1 age pageid 32 25 25 age 1 2 1 pageid 1 2 1 Count

Hive QL – Group By in Map Reduce pv_users pageid_age_sum Map Shuffle Sort Reduce 25 2 25 1 age pageid 32 25 age 1 1 pageid 1 1 Count 25 2 32 1 age pageid 1 <2,25> 1 <1,25> value key 1 <2,25> 1 <1,32> value key 1 <1,32> 1 <1,25> value key 1 <2,25> 1 <2,25> value key 25 age 2 pageid 2 Count

Hive QL – Group By with Distinct ,[object Object],[object Object],[object Object],page_view result 9:08:20 111 2 9:08:14 222 1 9:08:13 111 2 9:08:01 111 1 time userid pageid 1 2 2 1 count_distinct_userid pageid

Hive QL – Group By with Distinct in Map Reduce ,[object Object]

Hive Optimizations Efficient execution of SQL on Map Reduce

(Simplified) Map Reduce Revisit Machine 2 Machine 1 <k1, v1> <k2, v2> <k3, v3> <k4, v4> <k5, v5> <k6, v6> <nk1, nv1> <nk2, nv2> <nk3, nv3> <nk2, nv4> <nk2, nv5> <nk1, nv6> Local Map <nk2, nv4> <nk2, nv5> <nk2, nv2> <nk1, nv1> <nk3, nv3> <nk1, nv6> Global Shuffle <nk1, nv1> <nk1, nv6> <nk3, nv3> <nk2, nv4> <nk2, nv5> <nk2, nv2> Local Sort <nk2, 3> <nk1, 2> <nk3, 1> Local Reduce

Hive Optimizations – Merge Sequential Map Reduce Jobs ,[object Object],[object Object],A Map Reduce B C AB Map Reduce ABC 111 av 222 bv 1 key 111 1 av key 222 1 bv key 333 1 cv key 222 bv 333 cv 111 av 1 key

Hive Optimizations – Share Common Read Operations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Map Reduce Map Reduce 32 2 25 age 1 pageid 1 2 1 count 1 pageid 32 2 25 age 1 pageid 1 32 1 count 25 age

Hive Optimizations – Load Balance Problem pv_users pageid_age_sum Map-Reduce pageid_age_partial_sum Map-Reduce 25 1 32 2 25 1 25 1 25 1 age pageid 32 25 age 2 1 pageid 1 4 count 2 25 1 1 32 2 25 age 1 pageid 2 count

Future Works ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Credits Data Management at Facebook, Jeff Hammerbacher Hive – Data Warehousing & Analytics on Hadoop, Joydeep Sen Sarma, Ashish Thusoo People Suresh Anthony Zheng Shao Prasad Chakka Pete Wyckoff Namit Jain Raghu Murthy Joydeep Sen Sarma Ashish Thusoo

Dealing with Structured Data ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

MetaStore ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Hive CLI ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Web UI for Hive ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Hive Query Language ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Hive QL – Custom Map/Reduce Scripts ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

2008 Ur Tech Talk Zshao

Empfohlen

Empfohlen

Weitere ähnliche Inhalte

Was ist angesagt?

Was ist angesagt? (20)

Andere mochten auch

Andere mochten auch (20)

Ähnlich wie 2008 Ur Tech Talk Zshao

Ähnlich wie 2008 Ur Tech Talk Zshao (20)

Mehr von Jeff Hammerbacher

Mehr von Jeff Hammerbacher (20)

Kürzlich hochgeladen

Kürzlich hochgeladen (20)

2008 Ur Tech Talk Zshao