Data LibertyAlternatives to the shackles of limited scale in data

solutions

Andy CrossWindows Azure MVP

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Data value at scale requires technology choices;• often prioritising data read traversal over operational

characteristics of create/update/delete

• embracing hybrid data platforms with varied technology partners over homogenous estates

• establishing alternative skillsets, augmented with entrenched languages, trusting cloud over maintenance

• following robust engineering processes to provide rigour in a deterministic world

Bravery leads to rewards;

• the winners will have data which shows them that they’ve won

• the commoditised query turns energy sucking data silos into profit centres

• new data traversal mechanisms lead to new connotative data expression

• everything you already know is relevant and valid; the constraints on how it is applied are not

Most developers have heard of Big Data. I’m going to show how Microsoft are increasingly relevant in this space.

My talk is about architecture and approach. Note we’re talking Big Data and not strictly Data Science.

But it’s always worth context so lets start with the history.

WHAT’S A DATA SCIENTISTS FAVOURITE LANGUAGE?

IBM have been a leader in Big Data for years.

Wikimedia commons

We’re not as great as we’d hope; we’re often still bound by our ability to marshal our IO.

Just as the speed of loading punchcards was historically a limiting factor, we are now limited by our capacity to ingest data on individual machines.

This leads to ideas such as DFS and data locality.

During the evolution of data we eventually moved to client/server and this was a big step up from dBase et al of the time.

Fundamentally however, the tabular structured nature of data poses many changes; not least the long term effects of normalisation which trade off effective storage in the short term with long term offset compute which is required to reconstruct sets.

This eventually leads to such ideas as NoSQL document and entity stores.

Modelling of data provides a consistent challenge. Our world is highly connected and our brains are effective connectors of data. Real world data fits poorly into highly structured data sets.

This leads to semi-structured and unstructured data formats and data queryability through relationship traversal

The technologies shown today are primarily written in non-.net and non-Microsoft languages and frameworks. Every time we do this, I’ll show examples ONLY in the .net and Microsoft stacks.

There are obviously challenges beyond language to running the alternative stacks; but remember in the Cloud you aren’t responsible for tuning a Linux cluster which has been running for 5 years. You should provision for a duration that is bounded by the likelihood of the cluster requiring routine maintenance.

Open Source; Apache Foundation.

Java.

Map Reduce framework for job distribution; Distributed File System for file access.

In Windows Azure this is known as HDInsight.

Hadoop – KEY FACTS

Hadoop is O(n)It exhibits linear performance; when the dataset doubles, the time taken to execute the algorithm doubles.

Lets look at some scary JavaAny children should look away now.

Hadoop SDK

C# integrationRemote Data & JobsHive in C#Serialization

public class SwedishSessionsJob : HadoopJob<SwedishSessionsMapper, SessionsReducer> { public override HadoopJobConfiguration Configure(ExecutorContext context) { var config = new HadoopJobConfiguration() { InputPath = "\"/AllSessions/*.gz\"", OutputFolder = "/SwedishSessions/" }; return config; } }

Jobs

public class SwedishSessionsMapper : MapperBase

{

public override void Map(string inputLine, MapperContext context)

{

if (inputLine.Contains("Country=Sweden")

{

context.IncrementCounter("SwedishSession");

context.EmitKeyValue(“SE", "1");

}

}

}

Mapper

public class SessionsReducer : ReducerCombinerBase

{

public override void Reduce(string key, IEnumerable<string> values, ReducerContext context)

{

context.EmitKeyValue(key, values.Count());

}

}

Reducer

Testing Hadoop Queries

var inputData = "Country=Sweden&Name=Magnus";var result = StreamingUnit.Execute<Jobs.SwedishJob>(new[]{inputData});Assert.AreEqual("SE\t1", result.ReducerResult.First());

Skill reuse

Express elegant solutions in C#Familiar Unit Testing patternsConcise programmatic terseness

Your existing development team can immediately

realise value

The frameworks

facilitate deterministic

testing for highly reliable

queries

Complex logic is best expressed in programmatic

form

Commoditised query

Provision

Execute

De-provision

Valu

e

Action Cost

Value of query

Time

Cost

* Tools are great but not friendly

HDInsight wins.

Automated provisioning and job execution services.

Transient clusters limit exposure to poorly tooled* java estate.

Persistence with Windows Azure Blob Storage as HDFS proxy known as Azure Storage Vault (ASV).

Persistence in Windows Azure SQL Database for Hive Metastore.

Javascript console.

NoSQL Document and Entity Stores

Examples in MongoDB

Entity stores are similar; you can find a great example in Windows Azure Table Storage

What is a document database?

Relational Database Document Database

{ "_id" : ObjectId("51fccc57f82352d76653bdae"), "Name" : { "FirstName" : "Owen", "LastName" : "Grzegorek" }, "Company" : "Howard Miller Co", "Address" : { "Line1" : "15410 Minnetonka Industrial Rd", "Line2" : "Minnetonka", "Line3" : "Hennepin", "Line4" : "MN", "Line5" : "55345" }, "ContactDetails" : { "Phone" : "952-939-2973", "Fax" : "952-939-4663", "Email" : "owen@grzegorek.com", "Web" : "http://www.owengrzegorek.com" }}

{ "_id" : ObjectId("51fccc57f82352d76653bdae"), "Name" : { "FirstName" : "Owen", "LastName" : "Grzegorek" }, "Company" : "Howard Miller Co", "Address" : { "Line1" : "15410 Minnetonka Industrial Rd", "Line2" : "Minnetonka", "Line3" : "Hennepin", "Line4" : "MN", "Line5" : "55345" }, "ContactDetails" : { "Phone" : "952-939-2973", "Fax" : "952-939-4663", "Email" : "owen@grzegorek.com", "Web" : "http://www.owengrzegorek.com" }}

{ "_id" : ObjectId("51fccc57f82352d76653bdae"), "Name" : { "FirstName" : "Owen", "LastName" : "Grzegorek" }, "Company" : "Howard Miller Co", "Address" : { "Line1" : "15410 Minnetonka Industrial Rd", "Line2" : "Minnetonka", "Line3" : "Hennepin", "Line4" : "MN", "Line5" : "55345" }, "ContactDetails" : { "Phone" : "952-939-2973", "Fax" : "952-939-4663", "Email" : "owen@grzegorek.com", "Web" : "http://www.owengrzegorek.com" }}

{ "_id" : ObjectId("51fccc57f82352d76653bdae"), "Name" : { "FirstName" : "Owen", "LastName" : "Grzegorek" }, "Company" : "Howard Miller Co", "Address" : { "Line1" : "15410 Minnetonka Industrial Rd", "Line2" : "Minnetonka", "Line3" : "Hennepin", "Line4" : "MN", "Line5" : "55345" }, "ContactDetails" : { "Phone" : "952-939-2973", "Fax" : "952-939-4663", "Email" : "owen@grzegorek.com", "Web" : "http://www.owengrzegorek.com" }}

{ "Name" : { "FirstName" : "Owen", "LastName" : "Grzegorek" }, "Company" : "Howard Miller Co", "Address" : { "Line1" : "15410 Minnetonka Industrial Rd", "Line2" : "Minnetonka", "Line3" : "Hennepin", "Line4" : "MN", "Line5" : "55345" }, "ContactDetails" : { "Phone" : "952-939-2973", "Fax" : "952-939-4663", "Email" : "owen@grzegorek.com", "Web" : "http://www.owengrzegorek.com" }}

{ "Name" : { "FirstName" : "Owen", "LastName" : "Grzegorek" }, "Company" : "Howard Miller Co", "Address" : { "Line1" : "15410 Minnetonka Industrial Rd", "Line2" : "Minnetonka", "Line3" : "Hennepin", "Line4" : "MN", "Line5" : "55345" }, "ContactDetails" : { "Phone" : "952-939-2973", "Fax" : "952-939-4663", "Email" : "owen@grzegorek.com", "Web" : "http://www.owengrzegorek.com" }}

{ "Name" : “Richard Conway", “Books Published” : “12”, “Specialises in” : “Data Science”}

{ "Name" : “Andy Cross", “Hometown" : “Blackpool“}

{ "Name" : “Isaac Abraham", “Age" : “33“ “Football Team” : “Tottenham” “Icon” : }

MongoDB Key Facts

• General Purpose Operational Database• Real-time updates, ad-hoc queries and batch processing• Maps nicely with popular programming models e.g. .NET• Schema-free documents – lightweight and quick to get up and running

• High Performance• Embedding documents – no expensive joins across tables• Indexes allow query optimization• High-speed saving of data (writes)

• High Availability• Built in replication• Built in failover

• Easy Scalability• “Sharding” allows easily spreading data across multiple databases• Replicated data can be spread throughout the cluster

MongoDB is O(log n)It exhibits logarithmic performance; when the dataset doubles, the time taken to execute the algorithm increases by a fixed amount

Strengths of MongoDB

Low barrier to entry

Uses well-known .NET technologies e.g. LINQ

Good migration path from SQL-style development

Good fit for .NET developers

Works well as operational data store

Batch processing capability for map reduceFlexible

Massively scalable with well-defined replication model

Self-managing – easily add new nodes

High performance writes and eventually consistent reads

Designed for scalability

Database is free to use (tooling is not!)

Popular, so a relatively large communityLow cost

Mongo SDK

There are many different way to connect with MongoDB from a .net project.

Official

Wrapper

Alternative

Tool

C# implementations

If your data is regularly structured, you can use domain classes:

public class Book { public string Author { get; set; } public string Title { get; set; } }// "entities" is the name of the collection

var books = database.GetCollection<Entity>("books");

Book book = new Book { Author = "Ernest Hemingway", Title = "For Whom the Bell Tolls" }; books.Insert(book);

C# implementations

If your data is irregularly structured or semi-structured, you can use a BSON object model:

BsonDocument person = new BsonDocument { { "name", "John Doe" }, { "address", new BsonDocument { { "street", "123 Main St." }, { "city", "Centerville" }, { "state", "PA" }, { "zip", 12345} }}};

var people = database.GetCollection<BsonDocument>("people");

people.Insert(person);

NoSQL Document Wins

Semi-structured data first class citizen

Built in MapReduce

Operational and interactive

Massively scalable

Graph Databases, Neo4j KEY FACTS

Open Source; Neotechnologies

Java

Runs equally well on Windows or Linux. In Windows Azure there are VMDepot images able to be deployed in a few simple steps. Additionally the Azure Linux VMs are a good fit for this database engine.

There is an Open Source .net SDK available through Nuget and actively maintained primarily by an Australian company, Readify.

Neo4j is O(1)It exhibits constant-time performance; that is, the algorithm takes the same time to execute irrespective of the size of the dataset.

How O(1)?

• Graphs don’t have tables. They don’t have collections.• They have nodes and relationships.

• Rather than having to select out a whole table, we can identify a point on the graph• A start point

• Follow the traversal of relationships from that point.

http://www.apcjones.com/arrows/#

Things we can do

• Find all the things formed in Sweden START sweden = node:countryIdx(“country=Sweden”)MATCH Sweden<-[:FORMED_IN]-somethingRETURN something;• Find friends of friendsSTART magnus = node:peopleIdx(“name=magnus”)MATCH magnus-[:FRIENDS]->friend-[:FRIENDS]->friendoffriendRETURN friendoffriend;

NEO4J Client

Open source Neo4j Client

C# examples

var query = neo4Jclient.Cypher .Start(new { sweden = Node.ByIndexLookup("countryIdx", "country", "sweden") }) .Match("sweden-[:FRIENDS]->friend-[:FRIENDS]->friendoffriend") .Return<Node<Friend>>("friendoffriend");

Graph Database Wins

• Modelled domains match cognitive processes

• Optimised for traversal of relationships allow complex and “social” queries to emerge• LIKES of FRIENDS of COLLEAGUES

• O(1) performance characteristics due to ability to START queries at arbitrary graph points.

Summary

•HDInsight brings Hadoop to Azure•Suited to Data Volume, Variety, Variability etc•MongoDB brings Document stores•Suited to Data Volume, Operational concerns•Neo4j brings Graph database•Suited to data relationship traversal

THANKS

Info@elastacloud.com