CPS 216: Advanced Database Systems

CPS 216: Data-intensive Computing Systems Shivnath Babu A Brief History Relational database management systems Time 19751985 19851995 19952005 20052010 2020 Let us first see what a relational database system is User/Application Data Management Query Query Query

Data DataBase Management System (DBMS) Example: At a Company Query 1: Is there an employee named Nemo? Query 2: What is Nemos salary? Query 3: How many departments are there in the company? Query 4: What is the name of Nemos department? Query 5: How many employees are there in the Accounts department? Employee Department ID Name DeptID Salary ID Name

10 Nemo 12 120K 12 IT 20 Dory 156 79K

34 Accounts 40 Gill 89 76K 89 HR 52 Ray 34 85K

156 Marketing DataBase Management System (DBMS) High-level Query Q

Answer DBMS Data Translates Q into best execution plan for current conditions, runs plan Example: Store that Sells Cars Make Model OwnerID ID Name Owners of 12 12 Nemo Honda Accords Honda Accord who are <= Honda Accord 156 156 Dory 23 years old Join (Cars.OwnerID = Owners.ID) Filter (Make = Honda and Model = Accord) Cars

Age 22 21 Filter (Age <= 23) Owners Make Model OwnerID ID Name Age Honda Accord 12 12

Nemo 22 Toyota Camry 34 34 Ray 42 Mini Cooper 89 89 Gill 36 Honda

Accord 156 156 Dory 21 DataBase Management System (DBMS) High-level Query Q Answer

DBMS Keeps data safe and correct despite failures, concurrent updates, online processing, etc. Data Translates Q into best execution plan for current conditions, runs plan A Brief History Relational database management systems Time 19751985 19851995 19952005 20052010 2020 Assumptions and

requirements changed over time Semi-structured and unstructured data (Web) Hardware developments Developments in system software Changes in data sizes Big Data: How much data? Google processes 20 PB a day (2008) Wayback Machine has 3 PB + 100 TB/month (3/2009) eBay has 6.5 PB of user data + 50 TB/day (5/2009) Facebook has 36 PB of user data + 80-90 TB/day (6/2010)

CERNs LHC: 15 PB a year (any day now) LSST: 6-10 PB a year (~2015) 640K ought to be enough for anybody. From http://www.umiacs.umd.edu/~jimmylin/ From: http://www.cs.duke.edu/smdb10/ NEW REALITIES The quest for knowledge TBto disks < $100 used begin with grand theories. Everything is data Rise of data-driven culture Now it begins with massive amounts

of data. Very publicly espoused by Google, Wired, etc. Welcome to the Petabyte Age. Sloan Digital Sky Survey, Terraserver, etc. From: http://db.cs.berkeley.edu/jmh/ FOX AUDIENCE NETWORK Greenplum parallel DB 42 Sun X4500s (Thumper) each with: 48 500GB drives 16GB RAM

2 dual-core Opterons Big and growing 200 TB data (mirrored) Fact table of 1.5 trillion rows Growing 5TB per day 4-7 Billion rows per day From: http://db.cs.berkeley.edu/jmh/ Also extensive use of R and Hadoop Yahoo! runs a 4000 node Hadoop cluster (probably the largest). Overall, there are 38,000 nodes running Hadoop at Yahoo!

As reported by FAN, Feb, 2009 A SCENARIO FROM FAN How many female WWF fans under the age of 30 visited the Toyota community over the last 4 days and saw a Class A ad? How are these people similar to those that visited Nissan? Open-ended question about statistical densities (distributions) From: http://db.cs.berkeley.edu/jmh/ MULTILINGUAL DEVELOPMENT SQL or MapReduce Sequential code in a variety of languages Perl Python Java R Mix and Match! From: http://db.cs.berkeley.edu/jmh/

SE HABLA MAPREDUCE SQL SPOKEN HERE QUI SI PARLA PYTHON HIER JAVA GESPROCKEN R PARL ICI From: http://outsideinnovation.blogs.com/pseybold/2009/03/-sun-will-shine-in-blue-cloud.html What we will cover Principles of query processing (35%) Indexes Query execution plans and operators Query optimization Data storage (15%) Databases Vs. Filesystems (Google/Hadoop Distributed FileSystem) Data layouts (row-stores, column-stores, partitioning, compression) Scalable data processing (40%) Parallel query plans and operators Systems based on MapReduce Scalable key-value stores Processing rapid, high-speed data streams

Concurrency control and recovery (10%) Consistency models for data (ACID, BASE, Serializability) Write-ahead logging Course Logistics Web: http://www.cs.duke.edu/courses/fall11/cps216 TA: Rozemary Scarlat Books: (Recommended) Hadoop: The Definitive Guide, by Tom White Cassandra: The Definitive Guide, by Eben Hewitt Database Systems: The Complete Book, by H. Garcia-Molina, J. D. Ullman, and J. Widom Grading: Project 25% (Hopefully, on Amazon Cloud!) Homeworks 25% Midterm 25% Final 25% Projects + Homeworks (50%) Project 1 (Sept to late Nov): 1. Processing collections of records: Systems like Pig, Hive, Jaql, Cascading, Cascalog, HadoopDB 2. Matrix and graph computations: Systems like Rhipe, Ricardo, SystemML, Mahout, Pregel, Hama 3. Data stream processing: Systems like Flume, FlumeJava, S4, STREAM, Scribe, STORM 4. Data serving systems: Systems like BigTable/HBase, Dynamo/Cassandra, CouchDB, MongoDB, Riak, VoltDB

Project 1 will have regular milestones. The final report will include: 1. What are properties of the data encountered? 2. What are concrete examples of workloads that are run? Develop a benchmark workload that you will implement and use in Step 5. 3. What are typical goals and requirements? 4. What are typical systems used, and how do they compare with each other? 5. Install some of these systems and do an experimental evaluation of 1, 2, 3, & 4 Project 2 (Late Nov to end of class). Of your own choosing. Could be a significant new feature added to Project 1 Programming assignment 1 (Due third week of class ~Sept 16) Programming assignment 2 (Due fifth week of class ~Sept 30) Written assignments for major topics

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