authors brian f. cooper, raghu ramakrishnan, utkarsh srivastava, adam silberstein, philip bohannon,...

Authors

Brian F. Cooper, Raghu Ramakrishnan, Utkarsh Srivastava, Adam Silberstein, Philip Bohannon, Hans-Arno Jacobsen, Nick Puz, Daniel

Weaver, Ramana Yerneni

PresentersDaniel Burgener, Gautam Bhawsar

Road MapIntroduction

Background

Requirements

PNUTS Overview & Functionality

System Architecture & Applications

Experimental Results

Comparison to Competitors

Conclusion & Future Work

IntroductionPNUTS is

Massively parallel

Geographically distributed database system

Designed Yahoo!

Used by their web application

Shared between several applications


Background

Requirements






Background

* taken from http://msdn.microsoft.com/en-us/library/ms978603.aspx

Pub/Sub ModelSending Applications (Publishers)Receiving Applications (Subscribes)Communicate through asynchronous

messaging paradigm


Background

Requirements






RequirementsPNUTS is designed to meet the following

requirements:

Scalability

Response Time and Geographic Scope

High Availability & Fault Tolerance

Relaxed Consistency Grantees


Background

Requirements






PNUTS Overview & FunctionalityData Model & Features

Fault Tolerance

Pub-Sub Message System

Record-level Mastering

Hosting

PNUTS Overview & Functionality (Cont’d)Functionality

Data & Query Model

Consistency Model

Data & Query ModelSimplified relational data model

Organizes data into tables of records with attributes

Allows arbitrary structure inside a record – “blob”

Schema are flexible

New attribute is added without halting query or update

activity

Allow to have empty attribute in the record

Query language

Supports selection and projection in single table

Updates & deletes with primary key only

Consistency ModelHide the complexity of replication

Considered between general serializability & eventual consistency

Per-record timeline consistency“All replica of given record apply all updates to the

record in the same order”

Consistency Model (Cont’d)Support range of API calls with different

levels of consistency

Read-any

Read-critical(required_version)

Read-latest

Write

Test-and-set-write(required_version)


Background

Requirements






System Arch. & App.Data tables are horizontally partitioned into

groups of records called tablets

System Arch. & App. (Cont’d)Data Storage & Retrieval

Replication & Consistency

PNUTS Applications

Data Storage & RetrievalOrdered table

Primary-key space of a table is divided into

intervals

Each interval corresponds to one tablet

The router stores interval mapping

For a given PMK, binary search is used to find

the tablet

Data Storage & Retrieval (Cont’d)Hash-organized table

n-bit hash function H(), 0 ≤ H() < 2n [0... 2n) is divided into intervals Each interval corresponds to single tabletTo map a key to a tablet,

1. Hash the key2. Search set of interval using binary search

Replication & ConsistencyNo redo log

The system uses asynchronous replication

To ensure low-latency updates

Yahoo! Message Broker (YMB)

Used for replication & logging because:

1. Multiple steps are applied before committed to DB

2. YMB is designed for wide-area replication

Replication & Consistency (Cont’d)Consistency via YMB & mastership

Per-record timeline consistency

One copy of a record considered as master

Direct all updates to the master copy

This is called Record-level mechanism

Mastership is assigned on a record-by-record basis

Different master records in the same table can be in

different clusters

All updates are propagated to non-master replicas by

publishing them to YMB and delivered as commit order

Replication & Consistency (Cont’d)Recovery from failure (3 Steps)

1. the tablet controller requests a copy from the source tablet3. the source tablet is copied to the destination region2. “checkpoint message” is published to YMB

PNUTS ApplicationsUser Database

Social Applications

Content Meta-Data

Listings Management

Session Data


Background

Requirements






Experimental Results3 regions PNUTS cluster

2 on the west coast and 1 on the east coast

Storage engine for hash table “Yahoo! propriety disk-based hashtable”

Storage engine for ordered tablesMySQL using InnoDB

Written primarily in C++ Some components written in PHP & Perl

Experimental Results (Cont’d)Experimental parameters:

The coming experiments showThe impact of several factors on the average

latency for request

Varying Load

Varying Read/Write Ratio

Varying Skew

Varying Number of Storage Units

Varying Size of Range Scan


Background

Requirements






Comparison to CompetitorsGoogle BigTable

Geographic replication

Secondary indexes

Materialized views

Create multiple tables

Hash organized tables

Comparison to CompetitorsAmazon Dynamo

Eventual consistency too weak

No support for ordered tables

Comparison to CompetitorsSharding

No automated data migration

No shard splitting

Comparison to CompetitorsDFS

Hard to scale

Less rich database functionality


Background

Requirements






ConclusionPNUTS is

Massively parallel

Geographically distributed database system

Designed Yahoo! to be used by their web

application

Yahoo!s Hosted Data Serving Platform

Architecture of PNUTS is based on record-level

Consistency model

Delivers the data management as hosted service

Future WorkImproving query functionality

Enforce Constraints such as referential integrity

Complex ad hoc queries such as join & group-by

Query optimization techniques

Provide better technique than simple incremental

scanning

Add more API calls in consistency model:

Bundled Update

Relaxed Consistency

ThankYou

authors brian f. cooper, raghu ramakrishnan, utkarsh srivastava, adam silberstein, philip bohannon,...

Documents

version slide

order slide

introduction pnuts

gautam bhawsar slide

relational data model

applications publishers

record timeline consistency

data tables