topic 12: nosql in action

12: NoSQL in Action

Zubair Nabi

[email protected]

April 20, 2013

Zubair Nabi 12: NoSQL in Action April 20, 2013 1 / 33

Outline

1 Amazon’s Dynamo

2 MongoDB

3 Google BigTable

4 Cassandra

Outline

1 Amazon’s Dynamo

2 MongoDB

3 Google BigTable

4 Cassandra

Introduction

At the forefront of the NoSQL movement and has influenced the designof many subsequent systems

Design considerations are two-fold: 1) Infrastructure and 2) Business

Introduction

At the forefront of the NoSQL movement and has influenced the designof many subsequent systems

Design considerations are two-fold: 1) Infrastructure and 2) Business

Infrastructure Considerations

Tens of thousands of servers and network elements distributed acrossthe globe

Commodity off-the-shelf hardwareI Failure is normal

Hundreds of services, all decentralized and loosely coupled

Tens of thousands of servers and network elements distributed acrossthe globeCommodity off-the-shelf hardware

I Failure is normal

Tens of thousands of servers and network elements distributed acrossthe globeCommodity off-the-shelf hardware

I Failure is normal

Business Considerations

Strict, internal SLAs regarding performance, reliability, and efficiency

Reliability is of paramount importance because an outage means lossin revenue and customer trust

The platform needs to be highly scalable, to support continuous growthMost services only store and retrieve data by primary key, such as bestsellers lists, shopping carts, etc.

I No need for complex querying and management afforded by RDBMS

The platform needs to be highly scalable, to support continuous growth

Most services only store and retrieve data by primary key, such as bestsellers lists, shopping carts, etc.

Design

1 Implemented as a partitioned system with replication and consistencywindows

2 Targets applications that require weaker consistency

3 Gives high availability

4 Possibility for write operations even in the presence of partitioningamongst replicas

5 Always writeable so conflict resolution needs to happen during reads

Design

Conflict Resolution

A datastore can only perform simple conflict resolution

Passes the buck to the application

The application is aware of the data schema and hence better suited tochoose a conflict resolution mechanism

If the application does not want to implement conflict resolution, simplemechanisms, such as “last write wins” provided by the framework

Conflict Resolution

Interface

1 Simple key/value interface storing values as BLOBs

2 Operations limited to one key/value pair at a time

3 No support for hierarchichal namespaces (like those in filesystems)

Interface

Node Assignment

Completely decentralized so all nodes have equal responsibilities

As nodes can be heterogeneous, work is distributed proportional to thecapabilities of a node

Node Assignment

Completely decentralized so all nodes have equal responsibilities

As nodes can be heterogeneous, work is distributed proportional to thecapabilities of a node

Operations

Provides two operations:

1 get(key), returns a list of objects and a context2 put(key, context, object)

get can return more than one object if more than one conflictingversions

The context contains system metadata such as the object version

Keys and values are stored as an array of bytes, and only interpretedby the application

Operations

Provides two operations:1 get(key), returns a list of objects and a context

2 put(key, context, object)

Operations

Provides two operations:1 get(key), returns a list of objects and a context2 put(key, context, object)

Operations

Partitioning

MD5 hash of keys determines their storage nodes

Consistent hashing to provide incremental scalability

Partitioning done across virtual nodes instead of physical ones to takehardware heterogeneity into account

Partitioning

Outline

1 Amazon’s Dynamo

2 MongoDB

3 Google BigTable

4 Cassandra

Introduction

Schemaless document database in C++

Used by a large number of organizations including SourceForge.net.foursquare, the New York Times, bit.ly, Craigslist, SAP, MTV, EASports, github, etc.

Databases are distributed over multiple servers

Introduction

Databases and Collections

Databases contain collections (“named groupings”) of documents

Documents within a collection might be heterogeneous

But a good strategy is to create a database collection for each objecttype

A collection is created automatically whenever the first document isinserted into the database

Hierarchical Namespaces

Documents can be organized into a hierarchical structure using adot-notation

I For instance, the collections wiki.articles, wiki.categoriesand wiki.authors exist within the namespace wiki

The collection namespace itself is flat, hierarchical structure only forthe user

Documents

Unit of data storage

Conceptually similar to an XML document, JSON document, etc.

Documents are persisted in Binary JSON (BSON)

Easy to convert between BSON and JSON and between BSON andother programming language structures

Possible to insert (insert), search (find), and update a document(save)

Documents

Datatypes

Scalar: boolean, integer, double

Character sequence: string, code, etc.

BSON-objects: object

Object ID: To identify documents within a collection

Misc: null, array, date

Datatypes

References

No mechanism for foreign keys

References between documents need to be resolved by clientapplications

References

No mechanism for foreign keys

References between documents need to be resolved by clientapplications

Transaction Properties

Atomicity for only update and delete operations

Allows code to be executed locally on database nodes (server-sidecode execution)Three different strategies for server-side execution:

1 Execution of arbitrary code on a single node via eval operator2 Aggregation via count, group, and distinct3 MapReduce code execution on multiple nodes

Allows code to be executed locally on database nodes (server-sidecode execution)

Three different strategies for server-side execution:1 Execution of arbitrary code on a single node via eval operator2 Aggregation via count, group, and distinct3 MapReduce code execution on multiple nodes

1 Execution of arbitrary code on a single node via eval operator

2 Aggregation via count, group, and distinct3 MapReduce code execution on multiple nodes

1 Execution of arbitrary code on a single node via eval operator2 Aggregation via count, group, and distinct

3 MapReduce code execution on multiple nodes

Outline

1 Amazon’s Dynamo

2 MongoDB

3 Google BigTable

4 Cassandra

Introduction

Supports a relaxed relational model that is dynamically controlled bythe clients

Clients can reason about the locality properties of the data

Data indexing can be row-wise as well as column-wise

Data can be delivered either out of memory or from disk

Used internally by Google for more than 60 projects including GoogleEarth, Google Analytics, Orkut, and Google Docs

Introduction

Data Model

Values stored as arrays of bytes which need to be interpreted by theclients

Values are addressed by a 3-tuple (row-key, column-key,timestamp)

Row keys are strings of up to 64KBRows are maintained in lexicographic order and are dynamicallypartitioned into tablets

I The unit of distribution and load balancing

Reads can be made efficient (only having to access a small number ofservers) by wisely choosing row keys

I Row ranges with small lexicographic distances are partitioned into fewertablets

I For instance storing URLs in reverse order: com.cnn.blogs,com.cnn.www, etc.

Data Model

Row keys are strings of up to 64KB

Rows are maintained in lexicographic order and are dynamicallypartitioned into tablets

Data Model

Columns

No limit on the number of columns per table

Columns grouped into sets called column families based on their keyprefix

I Basic unit of access controlI Expected to store the same or similar type of data so that it can be

compressedI Need to be created before data can be stored in a column

Columns

No limit on the number of columns per tableColumns grouped into sets called column families based on their keyprefix

Columns

I Basic unit of access control

I Expected to store the same or similar type of data so that it can becompressed

I Need to be created before data can be stored in a column

Columns

compressed

I Need to be created before data can be stored in a column

Columns

Timestamps

64-bit integers that represent different versions of a cell value

Value assigned by either the datastore or the client

Cells ordered in decreasing order of their timestamp

Automatic garbage collection can be used to remove revisions

Timestamps

Read operations for lookup, selection, etc.

Write operations for creation, update, and deletion of values

Write operations for tables and column families for creation anddeletion

Administrative operations to modify store configuration and metadata

MapReduce hooks

Transactions are atomic at the single-row level

MapReduce hooks

Architecture

Implemented atop GFS

Multiple tablet servers and a single master

Architecture

Implemented atop GFS

Multiple tablet servers and a single master

Open source clone of HBase in Java

Implemented atop HDFS

HBase can be the source and/or the sink of Hadoop jobs

Facebook Chat implemented using HBase

Outline

1 Amazon’s Dynamo

2 MongoDB

3 Google BigTable

4 Cassandra

Introduction

Borrows concepts from both Dynamo and BigTable

Originally developed by Facebook but now an Apache open sourceproject

Designed for Facebook Chat for efficiently storing, indexing, andsearching messages

Introduction

Design Goals

Processing of a large amount of data

Highly scalable

Reliability at a massive scale

High throughput writes without sacrificing read efficiency

Design Goals

Highly scalable

Design Goals

Highly scalable

Design Goals

Highly scalable

Data Model

A table is a distributed multidimensional map indexed by a key

Rows are identified by a string-key and operations over them areatomic per replica regardless of the number of columns

Column families encapsule columns and super columns

Columns have a name and store a number of values per row, each witha timestamp

Super columns are columns with sub columns

Only three operations to get, insert, and delete

Data Model

References

1 NoSQL Databases: https://oak.cs.ucla.edu/cs144/handouts/nosqldbs.pdf

topic 12: nosql in action

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