Big Data landscape Lecture #2
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1 Big Data landscape Lecture #2
2 Contents 1 1 CORE Technologies 2 3 MapReduce YARN 4 SparK 5 Cassandra
3 Contents 2 16 HBase Accumulo memcached 94 Blur 10 5 Sqoop/Flume
4 Contents MongoDB Hive SparK SQL Shark 14 4 Giraph
5 CORE Technologies 4 Doug Cutting & Mike Cafarella Index of the web, search engine purpose Project Nutch (10,000 node cluster) Rdbms (relational DataBase Management System) + SQL access Issues: Inappropriate Not scalable Costly Not fault tolerant Performance was terrible
6 Google Team technical talk Google File System Programming model on clustered servers MapReduce Renamed Hadoop after a toy elephant Yahoo initial user, later released to Apache
7 Hadoop components: HDFS (a distributed file system) Runs in user space MapReduce Collection of tools to assist in the above Later Add on were several pieces to: Program Manage Configure Manage storage Analytics, etc
8 HDFS Purpose: high capacity, fault tolerant, inexpensive storage for very large data sets Philosophy: WORM style, sequential blocks of data Non-POSIX, non-os kernel managed, 3 copies of each scattered Nodes: name node (meta data) and data node
9 HDFS Examples:
10 Features: Hardware Failure Streaming Data Access Large Data Sets Simple Coherency Model Moving Computation is Cheaper than Moving Data Much more efficient if executed near the data it operates on Especially true when the size of the data set is huge Minimizes network congestion Increases the overall throughput of the system HDFS provides interfaces for applications to move themselves closer to where the data is located. Portability Across Heterogeneous Hardware and Software Platforms
11 HDFS
12 HDFS commands Examples:
13 HDFS commands Examples:
14 MapReduce What does it do? A programming paradigm (model) for processing data
15 MapReduce Example: Wordcount: Goal: produce a list of all the words and the number of times they appear in the data set MapReduce jobs: Java programs called mappers and reducers each of the mappers is given chunks of data to analyze Result: name/value pair word and the value (count) Mappers == programs that extract data Reducers == programs that take the output from the mappers and aggregate results Pig example of MapReduce specific language!
16 MapReduce 15 MapReduce Raw Input: <key, value> MAP <K1, V1> <K2,V2> <K3,V3> REDUCE
17 YARN Processing module May of 2012 version 2.0 of Hadoop was released and with it came an exciting change to the way you can interact with your data. This change came with the introduction of YARN which stands for Yet Another Resource Negotiator. YARN exists in the space between your data and where MapReduce now lives and it allows for many other tools which used to live outside your Hadoop system, such as Spark and Giraph, to now exist natively within a Hadoop cluster. It s important to understand that Yarn does not replace MapReduce; in fact Yarn doesn t do anything at all on its own. What Yarn does do is provide a convenient, uniform way for a variety of tools such as MapReduce, HBase, or any custom utilities you might build to run on your Hadoop cluster.
18 SparK Processing and Storage Spark is designed to provide a more flexible model which supports many of the multipass application that falter in MapReduce. It accomplishes this by taking advantage of memory whenever possible in order to reduce the amount of data that is written to and read from disk. Spark is not a tool for making MapReduce easier to use like Pig or Hive. It is a complete replacement for MapReduce that includes it s own work execution engine. Spark operates with three core ideas 1) Resilient Distributed Dataset (RDD):: RDDs contain data that you want to transform or analyze. They can either be read from an external source such as a file or a database or they can be created by a transformation. 2) Transformation:: A transformation modifies an existing RDD to create a new RDD. For example, a filter that pulls ERROR messages out of a log file would be a transformation. 3) Action:: An action analyzes an RDD and returns a single result. For example, an action would count the number of results identified by our ERROR filter.
19 Cassandra Database (key/value store) o o o o Often times you may need to simply organize some of your big data for easy retrieval. One common way to do this is to use a key/value datastore. This type of database looks like the white pages in a phone book. Your data is organized by a unique key and values are associated with that key. For example, if you want to store information about your customers you may use that customer s user name is their key and associate information such as their transaction history and address as values associated with that key. Key/Value datastores are a common fixture in any big data system because they are easy to scale, quick and straightforward to work with. Cassandra is a distributed key/value database designed with simplicity and scalability in mind. While often compared to HBase, Cassandra differs in a few key ways: o o Cassandra is an all-inclusive system, this means you do not need a Hadoop environment or any other big data tools behind Cassandra. Cassandra is completely masterless, it operates as a peer-to-peer system. This makes it easier to configure and highly resilient.
20 HBase No-SQL database with random access HBase is a No-SQL database system included in the standard Hadoop distributions. It is a key-value store logically. This means that rows are defined by a key, and have associated with them a number of bins (or columns) where the associated values are stored. The only data type is the byte string. Physically, groups of similar columns are stored together in column families. Most often, HBase is accessed via Java code, but APIs exist for using HBase with Pig, Thrift, jython (Python based), and others. HBase is not normally accessed in a MapReduce fashion. It does have a shell interface for interactive use
21 Accumulo Name-value database with cell level security Developed by NSA
22 Memcached In-memory cache demon (server program in the background)
23 Blur/Solr Document Warehouse Sometimes you just want to search through a big stack of documents. Not all tasks require big, complex analysis jobs spanning petabytes of data. For many common use cases you may find that you have too much data for a simple unix grep or windows search but not quite enough to warrant a team of data scientists. Solr fits comfortably in that middle ground, providing an easy to use means to quickly index and search the contents of many documents. Solr supports a distributed architecture that provides many of the benefits you expect from a big data systems such as linear scalability, data replication and failover. It is based on Lucene, a popular framework for indexing and searching documents and implements that framework by providing a set of tools for building indexes and querying data. But it is not Hadoop centric, so Blur does that job
24 Hive Data Interaction The goal of Hive is to allow SQL access to data in the HDFS The Apache Hive data warehouse software facilitates querying and managing large datasets residing in HDFS. Hive defines a simple SQL-like query language, called QL, that enables users familiar with SQL to query the data. Queries written in QL are converted into MapReduce code by Hive and executed by Hadoop. But QL is not full ANSI standard SQL. While the basics are covered, some features are missing. Here s a partial list : there are no correlated sub-queries update and delete statements are not supported transactions are not supported outer joins are not possible
25 MongoDB JSON document oriented database If you have a large number of (to come) documents in your Hadoop cluster and need some data management tool to effectively use them, we can use MongoDB, an open-source, big data, document oriented database whose documents are JSON objects
26 Oozie Is a workflow scheduler. It manages data processing jobs (e.g. load data, storing data, analyze data, cleaning data, running map reduce jobs, etc.) for Hadoop. Users create Directed Acyclical Graphs (DAG) to model their workflow. Oozie at runtime manages the dependencies and execute the actions when the dependencies identified in DAG are satisfied. Supports all types of Hadoop jobs and is integrated with the Hadoop stack. Supports data and time triggers, users can specify execution frequency and can wait for data arrival to trigger an action in the workflow
27 Zookeeper Zookeeper is a stripped down filesystem that exposes a few simple operations and abstractions that enable you to build distributed queues, configuration service, distributed locks, and leader election among a group of peers. Configuration Service store and allows applications to retrieve or update configuration files Distributed Lock is a mechanism for providing mutual exclusion between a collection of processes. At any one time, only a single process may hold the lock. They can be utilized for leader election, where the leader is the process the holds the lock at any point of time. Zookeeper is highly available running across a collection of machines.
28 Sqoop A suite of tools that connect and database systems Imports tables from databases into HDFS for analysis Export MapReduce results back to a database for user to end-users Ability to import from SQL into the HIVE data warehouse
29 Mahout Data Mining, Machine Learning Requires Hadoop ML libraries not well developed Little documentation Little Scalability If you want to do research, ripe for that
30 Implementation of Big Data 25 MapReduce Parallel DBMS technologies Overview: Data-parallel programming model An associated parallel and distributed implementation for commodity clusters Pioneered by Google Processes 20 PB of data per day Popularized by open-source Hadoop Used by Yahoo!, Facebook, Amazon, and the list is growing Popularly used for more than two decades Research Projects: Gamma, Grace, Commercial: Multi-billion dollar industry but access to only a privileged few Relational Data Model Indexing Familiar SQL interface Advanced query optimization Well understood and studied
31 Implementation of Big Data 27 MapReduce Advantages Automatic Parallelization: Depending on the size of RAW INPUT DATA instantiate multiple MAP tasks Similarly, depending upon the number of intermediate <key, value> partitions instantiate multiple REDUCE tasks Run-time: Data partitioning Task scheduling Handling machine failures Managing inter-machine communication Completely transparent to the programmer/analyst/user
32 Implementation of Big Data 28 Map Reduce vs Parallel DBMS Parallel DBMS MapReduce Schema Support Not out of the box Indexing Not out of the box Programming Model Optimizations (Compression, Query Optimization) Declarative (SQL) Imperative (C/C++, Java, ) Extensions through Pig and Hive Not out of the box Flexibility Not out of the box Fault Tolerance Coarse grained techniques
33 References 24 * 2006 Bigtable: A Distributed Storage System for Structured Data from Google Lab. The actual paper can be found h.google.com/en/us/archive/bigtable-osdi06.pdf * 2004 MapReduce: Simplified Data Processing on Large Clusters by Jeffrey Dean and Sanjay Ghemawat from Google Lab. The actual paper can be found at:
34 big picture BluR/SolR C A S S A N D R A SHARK M O N G O D B Spark
35 Example Program - Wordcount map() Receives a chunk of text Outputs a set of word/count pairs reduce() Receives a key and all its associated values Outputs the key and the sum of the values package org.myorg; import java.io.ioexception; import java.util.*; import org.apache.hadoop.fs.path; import org.apache.hadoop.conf.*; import org.apache.hadoop.io.*; import org.apache.hadoop.mapred.*; import org.apache.hadoop.util.*; public class WordCount {
36 Wordcount main( ) public static void main(string[] args) throws Exception { JobConf conf = new JobConf(WordCount.class); conf.setjobname("wordcount"); conf.setoutputkeyclass(text.class); conf.setoutputvalueclass(intwritable.class); conf.setmapperclass(map.class); conf.setreducerclass(reduce.class); conf.setinputformat(textinputformat.class); conf.setoutputformat(textoutputformat.class); FileInputFormat.setInputPaths(conf, new Path(args[0])); FileOutputFormat.setOutputPath(conf, new Path(args[1])); } JobClient.runJob(conf);
37 Wordcount map( ) public static class Map extends MapReduceBase { private final static IntWritable one = new IntWritable(1); private Text word = new Text(); } public void map(longwritable key, Text value, OutputCollector<Text, IntWritable> output, ) { String line = value.tostring(); StringTokenizer tokenizer = new StringTokenizer(line); while (tokenizer.hasmoretokens()) { word.set(tokenizer.nexttoken()); output.collect(word, one); } }
38 Wordcount reduce( ) public static class Reduce extends MapReduceBase { public void reduce(text key, Iterator<IntWritable> values, OutputCollector<Text, IntWritable> output, ) { int sum = 0; while (values.hasnext()) { sum += values.next().get(); } output.collect(key, new IntWritable(sum)); } } }
39
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