WSO2 Analytics Platform: The One Stop Shop for All Your Data Needs

Anjana FernandoSenior Technical Lead, WSO2

Sriskandarajah SuhothayanTechnical Lead, WSO2

WSO2 Analytics Platform

WSO2 Analytics Platform uniquely combines simultaneous real-time and interactive, batch with predictive analytics to turn data from IoT, mobile and Web apps into actionable insights

WSO2 Analytics Platform

WSO2 Data Analytics Server

• Fully-open source solution with the ability to build systems and applications that collect and analyze both realtime and persisted data and communicatethe results.

• Part of WSO2 Big Data Analytics Platform

• High performance data capture framework

• Highly available and scalable by design

• Pre-built Data Agents for WSO2 products

WSO2 DAS Architecture

Data Processing Pipeline

Collect Data

• Define scheme for data

• Send events to batch and/or Real time pipeline

•Publish events

Analyze

•Spark SQL for batch analytics

•Siddhi Query Language for real time analytics

•Predictive models for Machine Learning.

Communicate

•Alerts•Dashboards•API

Highly Pluggable Event Receiver Architecture

Data Model

{

'name': 'stream.name',

'version': '1.0.0',

'nickName': 'stream nick name',

'description': 'description of the stream',

'metaData':[

{'name':'meta_data_1','type':'STRING'},

],

'correlationData':[

{'name':'correlation_data_1','type':'STRING'}

],

'payloadData':[

{'name':'payload_data_1','type':'BOOL'},

{'name':'payload_data_2','type':'LONG'}

]

}

● Data published conforming to a strongly typed data stream

Data Persistence

● Data Abstraction Layer to enable pluggable data connectors

○ RDBMS, Cassandra, HBase, custom..

● Analytics Tables

○ The data persistence entity in WSO2 Data Analytics Server

○ Provides a backend data source agnostic way of storing and retrieving data

○ Allows applications to be written in a way, that it does not depend on a specific data source, e.g. JDBC

(RDBMS), Cassandra APIs etc..

○ WSO2 DAS gives a standard REST API in accessing the Analytics Tables

● Analytics Record Stores

○ An Analytics Record Store, stores a specific set of Analytics Tables

○ Event persistence can configure which Analytics Record Store to be used for storing incoming events

○ Single Analytics Table namespace, the target record store only given at the time of table creation

○ Useful in creating Analytics Tables where data will be stored in multiple target databases

● Analytics File System

Interactive Analytics

Interactive Analysis

● Full text data indexing support powered by Apache Lucene

● Drill down search support

● Distributed data indexing

○ Designed to support scalability

● Near real time data indexing and retrieval

○ Data indexed immediately as received

Interactive Analysis

Batch Analytics

Batch Analytics

● Powered by Apache Spark up to 30x higher performance than Hadoop

● Parallel, distributed with optimized in-memory processing

● Scalable script-based analytics written using an easy-to-learn, SQL-like

query language powered by Spark SQL

● Interactive built in web interface for ad-hoc query execution

● HA/FO supported scheduled query script execution

● Run Spark on a single node, Spark embedded Carbon server cluster or

Batch Analytics

Batch Analytics

● Idea is to given the “Overall idea” in a glance

(e.g. car dashboard)

● Support for personalization, you can build

your own dashboard.

● Also the entry point for Drill down

● How to build?

○ Dashboard via Google Gadget and

content via HTML5 + Javascript

○ Use WSO2 User Engagement Server to

build a dashboard (or JSP/PHP)

○ Use charting libraries like Vega or D3

Communicate: Dashboards

• Start with data in tabular format • Map each column to dimension in your plot like X,Y, color,

point size, etc • Also do drill-downs• Create a chart with few clicks

Gadget Generation Wizard

Realtime Analysis

What’s Realtime Analytics?

Realtime Analytics in Complex Event Processing

→

What’s Realtime Analytics?...

Realtime Analytics in Complex Event Processing

→

• Gather data from multiple sources• Correlate data streams over time• Find interesting occurrences • And Notify• All in Realtime !

What is WSO2 CEP ?

Event Flow of WSO2 CEP

Realtime Execution

• Process in streaming fashion (one event at a time)

• Execution logic written as Execution Plans

• Execution Plan– An isolated logical execution unit– Includes a set of queries, and relates to multiple input and

output event streams– Executed using dedicated WSO2 Siddhi engine

Realtime Processing Patterns

• Transformation - project, translate, enrich, split

• Filter

• Composition / Aggregation / Analytics

• basic stats, group by, moving averages

• Join multiple streams

• Detect patterns

• Coordinating events over time

• Trends – increasing, decreasing, stable, on-increasing, non-

decreasing, mixed

• Integrate with historical data

Siddhi Query Structure

define stream <event stream>(<attribute> <type>,<attribute> <type>, ...);

from <event stream>select <attribute>,<attribute>, ...insert into <event stream> ;

define stream SoftDrinkSales (region string, brand string, quantity int,

price double);

from SoftDrinkSalesselect brand, quantityinsert into OutputStream ;

define stream OutputStream(brand string, quantity int); Output Streams are inferred

Siddhi Query ...

define stream SoftDrinkSales (region string, brand string, quantity int,

price double);

from SoftDrinkSalesselect brand, avg(price*quantity) as avgCost,‘USD’ as currencyinsert into AvgCostStream

from AvgCostStreamselect brand, toEuro(avgCost) as avgCost,‘EURO’ as currencyinsert into OutputStream ;

Enriching Streams

Using Functions

Siddhi Query ...

define stream SoftDrinkSales (region string, brand string, quantity int,

price double);

from SoftDrinkSales[region == ‘USA’ and quantity > 99]select brand, price, quantityinsert into WholeSales ;

from SoftDrinkSales#window.time(1 hour)select region, brand, avg(quantity) as avgQuantitygroup by region, brandinsert into LastHourSales ;

Filtering

Aggregation over 1 hour

Other supported window types:

timeBatch(), length(), lengthBatch(), etc.

Siddhi Query (Filter & Window) ...

define stream Purchase (price double, cardNo long,place string);

from every (a1 = Purchase[price < 10] ) ->a2 = Purchase[ price >10000 and a1.cardNo == a2.cardNo ]

within 1 dayselect a1.cardNo as cardNo, a2.price as price, a2.place as placeinsert into PotentialFraud ;

Siddhi Query (Pattern) ...

define stream StockStream (symbol string, price double, volume int);

partition by (symbol of StockStream)begin

from t1=StockStream,t2=StockStream [(t2[last] is null and t1.price < price) or

(t2[last].price < price)]+within 5 min

select t1.price as initialPrice, t2[last].price as finalPrice,t1.symbolinsert into IncreaingMyStockPriceStream

end;

Siddhi Query (Trends & Partition)...

define table CardUserTable (name string, cardNum long) ;

@from(eventtable = 'rdbms' , datasource.name = ‘CardDataSource’ , table.name = ‘UserTable’, caching.algorithm’=‘LRU’)define table CardUserTable (name string, cardNum long)

Cache types supported• Basic: A size-based algorithm based on FIFO.• LRU (Least Recently Used): The least recently used event is dropped

when cache is full.• LFU (Least Frequently Used): The least frequently used event is dropped

when cache is full.

Siddhi Query (Table) ...

Supported for RDBMS, In-

Memory, Analytics Table,

Hazelcast

define stream Purchase (price double, cardNo long, place string);define stream CardUserStream (name string, cardNo long) ;

define table CardUserTable (name string, cardNum long) ;

from Purchase#window.length(1) join CardUserTableon Purchase.cardNo == CardUserTable.cardNum

select Purchase.cardNo as cardNo, CardUserTable.name as name, Purchase.price as priceinsert into PurchaseUserStream ;

from CardUserStreamselect name, cardNo as cardNumupdate CardUserTable

on CardUserTable.name == name ;

Similarly insert into and

delete are also supported!

Siddhi Query (Table) ...

• Function extension

• Aggregator extension

• Window extension

• Stream Processor extension

define stream SalesStream (brand string, price double, currency string);

from SalesStreamselect brand, custom:toUSD(price, currency) as priceInUSDinsert into OutputStream ;

Referred with namespaces

Siddhi Query (Extension) ...

• geo: Geographical processing

• nlp: Natural language Processing (with Stanford NLP)

• ml: Running machine learning models of WSO2 Machine Lerner

• pmml: Running PMML models learnt by R

• timeseries: Regression and time series

• math: Mathematical operations

• str: String operations

• regex: Regular expression

Siddhi Extensions

Demo on Realtime Analytics

WSO2 CEP (Realtime) High Availability

WSO2 CEP (Realtime) Scalability

Distributed Realtime = Siddhi +

Advantages over Apache Storm

• No need to write Java code (Supports SQL like query language)

• No need to start from basic principles (Supports high level

language)

• Adoption for change is fast

• Govern artifacts using Toolboxes

• etc ...

How we scale ?

Scaling with Storm

Handling Stateless & Stateful Queries

Siddhi QL

define stream StockStream (symbol string, volume int, price double);

@name(‘Filter Query’)from StockStream[price > 75]select *insert into HighPriceStockStream ;

@name(‘Window Query’)from HighPriceStockStream#window.time(10 min)select symbol, sum(volume) as sumVolume insert into ResultStockStream ;

Siddhi QL - with partition

define stream StockStream (symbol string, volume int, price double);

@name(‘Filter Query’)from StockStream[price > 75]select *insert into HighPriceStockStream ;

@name(‘Window Query’)partition with (symbol of HighPriceStockStream)begin

from HighPriceStockStream#window.time(10 min)select symbol, sum(volume) as sumVolume insert into ResultStockStream ;

end;

Siddhi QL - distributed

define stream StockStream (symbol string, volume int, price double);

@name(Filter Query’)@dist(parallel= ‘3')from StockStream[price > 75]select *insert into HightPriceStockStream ;

@name(‘Window Query’)@dist(parallel= ‘2')partition with (symbol of HighPriceStockStream)begin

from HighPriceStockStream#window.time(10 min)select symbol, sum(volume) as sumVolume insert into ResultStockStream ;

end;

Distributed Execution on Storm UI

Notifying Events

Event Publisher

*Supports custom event publishers via its pluggable architecture!

Realtime Dashboard

• Dashboard – Google Gadget – HTML5 + javascripts

• Support gadget generation

– Using D3 and Vega

• Gather data for UI from – Websockets – Polling

• Support Custom Gadgets and Dashboards

Beyond Boundaries

• Expose analytics results as API– Mobile Apps, Third Party

• Provides – Security, Billing, – Throttling, Quotas & SLA

• How ? – Write data to database from DAS – Build Services via WSO2 Data Services Server – Expose them as APIs via WSO2 API Manager

Demo on Notifying Events

Predictive Analysis

What’s Realtime Analytics?...

Predictive Analytics in→

• Extract, pre-process, and explore data• Create models, tune algorithms and make

predictions• Integrate for better intelligence

Predictive Analytics

• Guided UI to build machine learning models

– Via Spark MlLib – Via R and export them as

PMML (from WSO2 ML 1.1)

• Run models using CEP, DAS and ESB

Run R Scripts, Regression and Anomaly Detection on

Realtime

Machine Learning Pipeline

ML Models

ML_Algo(Data) => Model

• Outcome of ML algos are models – E.g. Learning classification generate a model that you can use to classify

data.

• ML Wizard help you create models • These models will be publish to registry or downloaded • Than can be applied in CEP, DAS, ESB etc. for prediction

Data Exploration

Visualizing Results

Upcoming ML features

• Out of the box model generation support for R • Deep learning algorithms• NLP techniques• Data pre-processing techniques

Demo on Predictive Analytics

Iris DataSet

setosa versicolorvirginica

Thank You