The Hadoop Distributed File System, by Dhyuba Borthakur and Related Work Presented by Mohit Goenka

The Hadoop Distributed File System: Architecture and Design

Requirement SECTION TITLE Need to process Multi Petabyte Datasets Expensive to build reliability in each application. Nodes fail every day Need common infrastructure

Introduction SECTION TITLE HDFS, Hadoop Distributed File System is designed to run on commodity hardware Built out by brilliant engineers and contributors from Yahoo, and Facebook and Cloudera and other companies Has grown into really large project at Apache with significant ecosystem

Typically in 2 level architecture – Nodes are commodity PCs – nodes/rack – Uplink from rack is 3-4 gigabit – Rack-internal is 1 gigabit Commodity Hardware SECTION TITLE

Goals SECTION TITLE Very Large Distributed File System – 10K nodes, 100 million files, 10 PB Assumes Commodity Hardware – Files are replicated to handle hardware failure – Detect failures and recovers from them Optimized for Batch Processing – Data locations exposed so that computations can move to where data resides – Provides very high aggregate bandwidth User Space, runs on heterogeneous OS

HDFS Basic Architecture SECTION TITLE Secondary NameNode Client NameNode DataNodes 1. filename 2. BlckId, DataNodes o 3.Read data Cluster Membership NameNode : Maps a file to a file-id and list of MapNodes DataNode : Maps a block-id to a physical location on disk SecondaryNameNode: Periodic merge of Transaction log

Distributed File System SECTION TITLE Single Namespace for entire cluster Data Coherency – Write-once-read-many access model – Client can only append to existing files Files are broken up into blocks – Typically 128 MB block size – Each block replicated on multiple DataNodes Intelligent Client – Client can find location of blocks – Client accesses data directly from DataNode

HDFS Core Architecture SECTION TITLE

NameNode Metadata SECTION TITLE Meta-data in Memory – The entire metadata is in main memory – No demand paging of meta-data Types of Metadata – List of files – List of Blocks for each file – List of DataNodes for each block – File attributes, e.g creation time, replication factor A Transaction Log – Records file creations, file deletions. etc

Data Node SECTION TITLE A Block Server – Stores data in the local file system (e.g. ext3) – Stores meta-data of a block (e.g. CRC) – Serves data and meta-data to Clients Block Report – Periodically sends a report of all existing blocks to the NameNode Facilitates Pipelining of Data – Forwards data to other specified DataNodes

Block Placement SECTION TITLE Current Strategy - One replica on local node - Second replica on a remote rack - Third replica on same remote rack - Additional replicas are randomly placed Clients read from nearest replica Would like to make this policy pluggable

Data Correctness SECTION TITLE Use Checksums to validate data – Use CRC32 File Creation – Client computes checksum per 512 byte – DataNode stores the checksum File access – Client retrieves the data and checksum from DataNode – If Validation fails, Client tries other replicas

NameNode Failure SECTION TITLE A single point of failure Transaction Log stored in multiple directories - A directory on the local file system - A directory on a remote file system (NFS/CIFS) Need to develop a real HA solution

Data Pipelining SECTION TITLE Client retrieves a list of DataNodes on which to place replicas of a block Client writes block to the first DataNode The first DataNode forwards the data to the next DataNode in the Pipeline When all replicas are written, the Client moves on to write the next block in file

Rebalancer SECTION TITLE Goal: % disk full on DataNodes should be similar –Usually run when new DataNodes are added –Cluster is online when Rebalancer is active –Rebalancer is throttled to avoid network congestion –Command line tool

Hadoop Map / Reduce SECTION TITLE The Map-Reduce programming model – Framework for distributed processing of large data sets – Pluggable user code runs in generic framework Common design pattern in data processing cat * | grep | sort | unique -c | cat > file input | map | shuffle | reduce | output Natural for: – Log processing – Web search indexing – Ad-hoc queries

Data Flow SECTION TITLE Web ServersScribe Servers Network Storage Hadoop Cluster Oracle RAC MySQL

Basic Operations SECTION TITLE Listing files -./bin/hadoop fs –ls Writing files -./bin/hadoop fs –put Running Map Reduce Jobs - mkdir input - cp conf/*.xml input - cat output/*

Hadoop Ecosystem Projects SECTION TITLE HBase - Big Table HIVE - Built on Facebook, provides SQL interface Chukwa - Log Processing Pig - Scientific data analysis language Zookeeper - Distributed Systems management

Limitatons SECTION TITLE The gigabytes to terabytes of data this system handles can only be scaled down to limited threshold Due to this threshold being very high, the system is limited in a lot of ways It hampers the efficiency of the system during large computations or parallel data exchange

JSON Interface to Control HDFS An Open Source Project by Mohit Goenka

JSON Interface to Control HDFS An Open Source Project by Mohit Goenka

JSON SECTION TITLE JSON (JavaScript Object Notation) is a lightweight data-interchange format Can be easily read and written by humans Can be easily parsed by machines Written in text format Similar conventions as existing programming languages

JSON Data SECTION TITLE It is based on two structures: - A collection of name/value pairs - An ordered list of values Concept: Use the light-weighted nature of JSON data to automate command execution on HDFS interface

Goal SECTION TITLE Designing a JSON interface to control HDFS Development of two modules: - For writing into the system - For reading from the system

Outcome SECTION TITLE User can specify execution commands directly in the JSON file along with data Only data gets stored into the system Commands are deleted from the file after execution

Sources and Acknowledgements

Sources SECTION TITLE Dhurba Borthakur, Apache Hadoop Developer, Facebook Data Infrastructure Matei Zaharia, Cloudera / Facebook / UC Berkeley RAD Lab Devaraj Das, Yahoo! Inc. Bangalore and Apache Software Foundation HDFS Java API: - HDFS source code: -

Acknowledgements SECTION TITLE Professor Chris Mattmann for guidance as and when reqired Hossein (Farshad) Tajalli for his continued support and help throughout the project All my classmates for providing valuable inputs throughout the work, especially through their presentations

That’s All Folks! SECTION TITLE