Lecture 11 Hadoop & Spark

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1 Lecture 11 Hadoop & Spark Dr. Wilson Rivera ICOM 6025: High Performance Computing Electrical and Computer Engineering Department University of Puerto Rico

2 Outline Distributed File Systems Hadoop Ecosystem Hadoop Architecture and Features Apache Spark ICOM 6025: High Performance Computing 2

3 How do we get data to the workers? NAS Compute Nodes SAN

4 Distributed File System Don t move data to workers move workers to the data! Store data on the local disks of nodes in the cluster Start up the workers on the node that has the data local Why? Not enough RAM to hold all the data in memory Disk access is slow, but disk throughput is reasonable A distributed file system is the answer GFS (Google File System) HDFS (Hadoop Distributed File System)

5 Apache Hadoop Ecosystem PIG R Hive MapReduce HBase Cassandra Hadoop Distributed File System (HDFS) ICOM 6025: High Performance Computing 5

6 Hadoop Designed to reliably store data using commodity hardware Redundant storage Designed to expect hardware failures Fault tolerance mechanism Intended for large files Not suitable for small data sets Not suitable for low latency data access COP6727 6

7 Hadoop Files are stored as a collection of blocks Blocks are 64 MB chunks of a file (configurable) Blocks are replicated on 3 nodes (configurable) NameNode (NN) Manages metadata about files and blocks DataNodes (DN) store and serve blocks

8 Jobs and Tasks in Hadoop Job: a user-submitted map and reduce implementation to apply to a data set Task: a single mapper or reducer task Failed tasks get retried automatically Tasks run local to their data, ideally JobTracker (JT) manages job submission and task delegation TaskTrackers (TT) ask for work and execute tasks COP6727 8

9 Hadoop Architecture Client Job Tracker Name Node Secondary Name Node Data Node Data Node Data Node Task Tracker Task Tracker Task Tracker 9

10 Typical Hadoop Cluster Switch Name Node Switch Switch Switch Job Tracker Secondary NN DN +TT DN +TT DN +TT DN +TT DN +TT DN +TT DN +TT DN +TT DN +TT Rack 1 Rack 2 Rack 3 Rack N ICOM 6025: High Performance Computing 10

11 Hadoop Fault Tolerance If a Task crashes: Retry on another node: OK for a map because it has no dependencies OK for a reduce because map outputs are on disk If a node crashes: Re-launch its current task on other nodes Re-run any maps the node previously ran to get output data If a task is going slowly (straggler): Launch second copy of task on another node ( speculative execution )

12 Hadoop Fault Tolerance Reactive way Worker failure Heartbeat, Workers are periodically pinged by master NO response = failed worker If the processor of a worker fails, the tasks of that worker are reassigned to another worker. Master failure Master writes periodic checkpoints Another master can be started from the last checkpointed state If eventually the master dies, the job will be aborted 12

13 Hadoop Data Locality Move computation to the data Hadoop tries to schedule tasks on nodes with the data When not possible TT has to fetch data from DN Thousands of machines read input at local disk speed. Without this, rack switches limit read rate and network bandwidth becomes the bottleneck. COP

14 Hadoop Scheduling Fair Sharing conducts fair scheduling using greedy method to maintain data locality Delay uses delay scheduling algorithm to achieve good data locality by slightly compromising fairness restriction LATE (Longest Approximate Time to End) improves MapReduce application performance in heterogenous environment, like virtualized environment, through accurate speculative execution Capacity Supports multiple queues for shared users and guarantees each queue a fraction of the capacity of the cluster 14

15 MPI vs. Map Reduce Programming in communication Explicit MPI Implicit Map Reduce Fault tolerance No Yes Main resources Memory I/O Latency Low High ICOM 6025: High Performance Computing 15

16 Other Frameworks Batch Processing Hadoop Independent tasks GraphLab Dependent tasks Interactive Processing Drill Spark Low latency for Interactive data analysis Resilient distributed datasets Primitives for in memory computing 100x faster than Hadoop!! Stream processing Storm, Apache S4

17 Apache Spark HDFS ICOM 6025: High Performance Computing 17

18 Use Memory Instead of Disk Input HDFS read HDFS read HDFS write HDFS read HDFS write iteration 1 iteration 2... query 1 query 2 result 1 result 2 Input query 3... result 3

19 In-Memory Data Sharing Input HDFS read one-time processing Input iteration 1 iteration 2... Distributed memory query 1 query 2 query x faster than network and disk result 1 result 2 result 3

20 Resilient Distributed Datasets (RDDs) Write programs in terms of operations on distributed datasets Partitioned collections of objects spread across a cluster, stored in memory or on disk RDDs built and manipulated through a diverse set of parallel transformations (map, filter, join) and actions (count, collect, save) RDDs automatically rebuilt on machine failure

21 The Spark Computing Framework Provides programming abstraction and parallel runtime to hide complexities of fault-tolerance and slow machines Here s an operation, run it on all of the data I don t care where it runs (you schedule that) In fact, feel free to run it twice on different nodes

22 Tradeoff Space Fine Granularity of Updates K-V stores, databases, RAMCloud HDFS Network bandwidth Best for transactional workloads RDDs Memory bandwidth Best for batch workloads Coarse Low Write Throughput High

23 Scalability Logistic Regression K-Means Iteration time (s) Hadoop HadoopBinMem Spark Iteration time (s) Hadoop HadoopBinMem Spark Number of machines Number of machines

24 Spark and Map Reduce Differences Hadoop Map Reduce Spark Storage Disk only In-memory or on disk Operations Map and Reduce Map, Reduce, Join, Sample, etc Execution model Batch Batch, interactive, streaming Programming environments Java Scala, Java, R, and Python

25 Summary Distributed File Systems Hadoop Ecosystem Hadoop Architecture and Features Apache Spark ICOM 6025: High Performance Computing 25

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