Database Applications (15-415)
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1 Database Applications (15-415) Hadoop Lecture 24, April 23, 2014 Mohammad Hammoud
2 Today Last Session: NoSQL databases Today s Session: Hadoop = HDFS + MapReduce Announcements: Final Exam is on Sunday April 27 th, at 9:00AM in room 2051 (all materials are included- open book, open notes) We will hold a review session (for the final exam) tomorrow during the recitation PS4 grades are out PS5 (the last assignment) is due tomorrow, by midnight
3 Outline A Very Brief Primer and GFS/HDFS MapReduce: Systems and Applications Perspectives MapReduce: Programming, Computation, Architectural and Scheduling Models Fault-Tolerance in MapReduce
4 Hadoop MapReduce MapReduce is one of the most successful realizations of largescale data-parallel distributed analytics engines Hadoop is an open source implementation of MapReduce Hadoop MapReduce uses Hadoop Distributed File System (HDFS) as a distributed storage layer HDFS is an open source implementation of GFS
5 GFS Data Distribution Policy The Google File System (GFS) is a scalable DFS for dataintensive applications GFS divides large files into multiple pieces called chunks or blocks (by default 64MB) and stores them on different data servers This design is referred to as block-based design Each GFS chunk has a unique 64-bit identifier and is stored as a file in the lower-layer local file system on the data server GFS distributes chunks across cluster data servers using a random distribution policy
6 GFS Random Distribution Policy Large File 5 6 Server 0 (Writer) Server 1 Server 2 Server 3 0M 64M 128M 192M 256M 320M 384M
7 GFS Architecture GFS adopts a master-slave architecture File name GFS client Master Contact address Chunk Id, range Chunk Server Chunk Server Chunk Server Chunk data Linux File System Linux File System Linux File System
8 Outline A Very Brief Primer and GFS/HDFS MapReduce: Systems and Applications Perspectives MapReduce: Programming, Computation, Architectural and Scheduling Models Fault-Tolerance in MapReduce
9 The Problem Scope Hadoop MapReduce is used for powerful and efficient analytics over Big Data The power of MapReduce lies in its ability to scale to 100s and even 1000s of machines What amount of work can MapReduce handle? Big Data in the order of 100s of GBs, TBs or PBs It is unlikely that datasets of such sizes can fit on a single machine Hence, a storage layer like HDFS is required! 9
10 Hadoop MapReduce: A System s View Hadoop MapReduce incorporates two phases, Map and Reduce phases, which encompass multiple Map and Reduce tasks HDFS Split BLK 0 HDFS Split BLK 1 Dataset HDFS Split BLK 2 HDFS HDFS Split BLK 3 Map Task Map Task Map Task Map Task Map Phase Shuffle Stage Merge Stage Reduce Phase Reduce Task Reduce Task Reduce Task Reduce Stage To HDFS 10
11 Data Structure: Keys and Values The MapReduce programmer has to specify only two sequential functions, the Map and the Reduce functions These functions will be translated automatically into multiple Map and Reduce tasks In MapReduce, data elements are always structured as key-value (i.e., (K, V)) pairs In particular, the Map and Reduce functions receive and emit (K, V) pairs Input Splits Intermediate Outputs Final Outputs (K, V) Pairs Map Function (K, V ) Pairs Reduce Function (K, V ) Pairs
12 WordCount: An Application View A Map Function Key2 Value2 A Chunk of File Mohammad is delivering a A Text File lecture at CMUQ Mohammad is delivering a lecture at CMUQ CMUQ is a member of QF A Chunk of File CMUQ is a member of QF Key1 Value1 0 Mohammad is 20 delivering a 18 lecture at CMUQ Key1 A Map Function Value1 0 CMUQ is a 17 member of QF Parse & Count Parse & Count Mohammad 1 is 1 delivering 1 a 1 lecture 1 at 1 CMUQ 1 Key2 Value2 CMUQ 1 is 1 a 1 member 1 A Reduce Function Iterate & Sum Key2 Value2 Mohammad 1 is 2 delivering 1 a 2 lecture 1 at 1 CMUQ 2 member 1 of 1 QF 1 of 1 QF 1 12
13 Hadoop MapReduce: A Closer Look Node 1 Node 2 Chunks loaded from a (local) HDFS datanode Chunks loaded from a (local) HDFS datanode InputFormat InputFormat Chunk. Chunk Split Split Split Split Split Split Chunk. Chunk RecordReaders RR RR RR RR RR RR RecordReaders Input (K, V) pairs Input (K, V) pairs Map Map Map Map Map Map Intermediate (K, V) pairs er Shuffling Process er Intermediate (K, V) pairs Sort Reduce Intermediate (K,V) pairs exchanged by all nodes Sort Reduce Final (K, V) pairs Final (K, V) pairs Writeback to HDFS store OutputFormat OutputFormat Writeback to HDFS store
14 Outline A Very Brief Primer and GFS/HDFS MapReduce: Systems and Applications Perspectives MapReduce: Programming, Computation, Architectural and Scheduling Models Fault-Tolerance in MapReduce
15 The Programming Model Hadoop MapReduce employs a shared-memory programming model This entails two main issues: Developers need not explicitly encode functions that send/receive messages within their MapReduce programs HDFS provides a shared abstraction to all tasks A Shared-Memory Storage Address Space (Provided by HDFS) MT1 MT2 MT3 MT4 MT5 MT6 Implicit communication (Provided by the MapReduce Engine) RT1 RT2 RT3 A Shared-Memory Storage Address Space (Provided by HDFS)
16 The Computation Model Hadoop MapReduce adopts a synchronous computation model A distributed program is said to be synchronous if and only if the tasks operate in a lock-step mode MT1 0 1 Shuffle, Merge and Sort start ONLY after 5% of Map Tasks commit! MT RT1 Reduce starts ONLY after ALL partitions are shuffled merged and sorted! MT3 4 5 Map Phase Shuffle Stage Merge & Sort Stage Reduce Phase Reduce Stage
17 The Architectural and Scheduling Models Hadoop MapReduce employs a master-slave architecture
18 The Architectural and Scheduling Models Hadoop MapReduce employs a master-slave architecture Core Switch Rack Switch 1 Rack Switch 2 TaskTracker1 TaskTracker2 TaskTracker3 TaskTracker4 TaskTracker5 JobTracker MT2 MT3 Request a Map Task Schedule a Map Task at an Empty Map Slot on TaskTracker1 MT1 MT2 MT3 A pull-based task scheduling strategy is used, whereby: Map tasks are scheduled nearby HDFS blocks Reduce tasks are scheduled anywhere
19 Job Scheduling in MapReduce An application is represented by one or many jobs A job consists of one or many Map and Reduce tasks Hadoop MapReduce comes with various choices of job schedulers: FIFO Scheduler: schedules jobs in order of submission Fair Scheduler: aims at giving every user a fair share of the cluster capacity over time Capacity Scheduler: Similar to Fair Scheduler but does not apply job preemption 19
20 Summary Aspect Parallelism Model Programming Model Computation Model Architectural Model Scheduling Model Application Suitability Hadoop MapReduce Data-Parallel Shared-Memory Synchronous Master-Slave Pull-Based Loosely-Connected/Embarrassingly-Parallel Applications 20
21 Outline A Very Brief Primer and GFS/HDFS MapReduce: Systems and Applications Perspectives MapReduce: Programming, Computation, Architectural and Scheduling Models Fault-Tolerance in MapReduce
22 Fault Tolerance in Hadoop: Node Failures MapReduce can guide jobs toward a successful completion even when jobs are run on large clusters (where probability of failures increases) Hadoop MapReduce achieves fault-tolerance through restarting tasks If a TT fails to communicate with JT for a period of time (by default, 1 minute), JT will assume that TT in question has crashed If the job is still in the Map phase, JT asks another TT to reexecute all Map tasks that previously ran at the failed TT If the job is in the Reduce phase, JT asks another TT to reexecute all Reduce tasks that were in-progress on the failed TT 22
23 Fault Tolerance in Hadoop: Speculative Execution A MapReduce job is dominated by the slowest task MapReduce attempts to locate slow tasks (or stragglers) and run replicated (or speculative) tasks that will optimistically commit before the stragglers In general, this strategy is known as task resiliency or task replication (as opposed to data replication), but in Hadoop it is referred to as speculative execution Only one copy of a straggler is allowed to be replicated Whichever copy (among the two copies) of a task commits first, it becomes the definitive copy, and the other one is killed by JT
24 But, How to Locate Stragglers? Hadoop monitors each task progress using a progress score between 0 and 1 If a task s progress score is less than (average 0.2), and the task has run for at least 1 minute, it is marked as a straggler T1 Not a straggler PS= 2/3 T2 A straggler PS= 1/12 Time
25 Next Class A Review Session
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