Making Hadoop Realtime by Dr. William Bain of Scaleout Software

Enabling Operational Intelligence:
Making Hadoop Real-Time
Copyright © 2014 by ScaleOut Software, Inc.
Los Angeles Big Data Users Group
August 6, 2014
Bill Bain, CEO (wbain@scaleoutsoftware.com)

2 ScaleOut Software, Inc.
• Operational Intelligence vs. Business Intelligence
• Data-Parallel Computation
• Implementing Using an In-Memory Data Grid:
• Distributing the Data Across a Cluster
• Running the Computation using “Parallel Method Invocation”
• An Example in Financial Services
• Implementing In-Memory Hadoop MapReduce
• Video Demo
• Examples of Applications in Operational Intelligence
Agenda

• Develops and markets In-Memory Data Grids,
software middleware for:
• Scaling application performance and
• Providing operational intelligence using
• In-memory data storage and computing
• Dr. William Bain, Founder & CEO
• Career focused on parallel computing – Bell Labs, Intel, Microsoft
• 3 prior start-ups, last acquired by Microsoft and product now ships as
Network Load Balancing in Windows Server
• Eight years in the market; 400 customers, 10,000 servers
• Sample customers:
About ScaleOut Software

Goal: Provide immediate feedback to a system handling live data.
A few examples:
• Equity trading: to minimize risk during a trading day
• Ecommerce: for real-time recommendations
• Reservations systems: to identify issues, reroute, etc.
• Credit cards & wire transfers: to detect fraud in real time
• Smart grids: to optimize power distribution & detect issues
Online Systems Need Operational
Intelligence

Big Data Analytics
Real-Time vs. Batch Analytics
Static data sets
Petabytes
Disk storage
Hours to minutes
Best uses:
• Analyzing
warehoused data
• Mining for long-
term trends
Live data sets
Gigabytes to terabytes
In-memory storage
Minutes to seconds
Best uses:
• Tracking live data
• Immediately
identifying trends
and capturing
opportunities
• Providing immediate
feedback
Analytics
Server
hServer
Hadoop
IBM
Teradata
SAS
SAP
Real-Time Batch
Real-time
“Operational Intelligence”
Batch
“Business Intelligence”

• Operational intelligence can co-exist with business intelligence:
• Processes streaming data close to its sources.
• Provides real-time, “tactical” feedback (e.g., recommendations, alerts).
• Translates data for storage in the data warehouse (ETL).
• Data warehouse provides “strategic” guidance.
• Using the same tool set (e.g., Hadoop MapReduce) lowers TCO:
• Leverages common skill set.
• Simplifies design (e.g., loading data into HDFS).
Integrated View of Analytics

• To keep up with fast
growing “live” workloads &
maintain fast response times:
• Ex.: Handle incoming data
streams in real time.
• Ex. Process updates to data
set based on incoming data.
• To identify and respond to
trends in fast-changing data:
• Ex. Evaluate data set changes in
real time.
• Ex. Respond to identified
patterns within seconds.
Challenges for Operational Intelligence
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“More data has been
created in the past three
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40,000.”

The Solution: Data-Parallel Computing
• Straightforward, well understood model of parallel computation
• An alternative to task-parallel computation (e.g., Storm)
• Simple: runs the same code on multiple, in-memory data items.
• Powerful: maintains a “live,” in-memory model of a real-world system
• Fast: avoids data motion which lowers speedup.
Analyze Data (Eval)
Combine Results
(Merge)
Server Cluster

Track “live” data and analyze in real-time:
Implementing OI
In-Memory
State
NoSQL
Storage
Real-Time
Data Parallel
Analysis

• Storm implements pipelined execution of tasks by “bolts” on
incoming data streams.
• Streams can be distributed to bolts with configurable mappings.
• Developer controls the number of tasks per bolt.
• Storm uses a centralized master node and Zookeeper for fault-
tolerance.
• Key strength: continuous
processing of input
streams
• Issues:
• Complexity / tuning
• Minimizing data motion
• Managing global state
Quick Comparison to Storm

Data-Parallel Enables Linear Speedup
Avoids data motion (network or disk I/O) which limits throughput:

Data-Parallel Computing Is Not New
• 1980’s: Special Purpose Hardware: “SIMD”
Thinking Machines
Connection Machine 5
• 1990’s: General Purpose Parallel Supercomputers:
“Domain Decomposition”, “SPMD”
Intel
IPSC-2
IBM
SP1

Data-Parallel Computing Is Not New
• 1990’s – early 2000’s: HPC on Clusters: “MPI”
• Since 2003: Clusters, the Cloud, and IMDGs: “MapReduce”
HP
Blade
Servers
Amazon EC2,
Windows Azure

• In-memory data grid
(IMDG) holds active
entities undergoing
state changes in
memory.
• Backing store
optionally holds large
population of entities.
• IMDG processes
incoming stream of
state changes.
• Analytics engine
examines entities in real
time and generates
alerts within seconds
as needed.
A Data-Parallel Architecture for
Operational Intelligence

In-Memory Data Grid (IMDG) stores “live” data in a cluster:
• Fits in the business logic layer:
• Follows object-oriented view of data
(vs. relational view).
• Stores collections of Java/.NET
objects shared by multiple clients.
• Uses create/read/update/delete
and query APIs to access data.
• Implemented across a cluster of
servers or VMs:
• Scales storage and throughput
by adding servers.
• Provides high availability
in case a server fails.
In-Memory Data Grid for Live Data

• IMDG’s collections of objects act like in-
memory collections:
• Unstructured, typically instances of a class
(stored as serialized blobs)
• Individually accessible / update-able
• IMDG adds attributes:
• Accessible by global key
• Query-able by properties
• Highly available
• Optional timeouts
• Distributed locking
• Integration with a backing store
• Optional dependency relationships
• Asynchronous event handling
IMDGs Store “Live” Data
Basic “CRUD” APIs:
• Create(key, obj, tout)
• Read(key)
• Update(key, obj)
• Delete(key)
and…
• Lock(key)
• Unlock(key)
Object
key

Spark / Spark Streaming from U.C.
Berkeley amplab:
• In-memory computing to accelerate and
extend Hadoop MapReduce using data-
parallel operators in Scala.
• Stores data as “resilient
distributed datasets” (RDDs):
• Distributed across cluster
• Immutable
• Hold data from/output to HDFS.
• Store data stream as a sequence of RDDs.
• Comparison to IMDG:
• Not designed for “live” data:
• Lacks CRUD on individual objects.
• Lacks high availability.
• Designed for “data parallel” operators.
Quick Comparison to Spark

Data-Parallel Computing Using PMI
“Parallel Method Invocation” (PMI): an object-oriented version of data-
parallel computing from the HPC community:
• Serves as a platform for MapReduce and other data-parallel operators.
• Selects objects using a parallel query on data hosted in the IMDG.
• Runs user-defined methods in parallel across the cluster.
Analyze Data (Eval)
Combine Results
(Merge)
In-Memory Data Grid Runs
Data-Parallel Computation.

Integrate analysis into a stock trading platform:
• The IMDG holds market data and hedging strategies.
• Updates to market data
continuously flow through
the IMDG.
• The IMDG performs
repeated data-parallel
analysis on hedging
strategies and alerts
traders in real time.
• IMDG automatically and dynamically
scales its throughput to handle new
hedging strategies by adding servers.
Example in Financial Services

Selects all relevant objects in a distributed collection.
• Query spec matches data’s object-oriented properties.
• Selected objects are fed to the analysis engine on the local server.
Step 1: Select with Parallel Query

Java Example: Parallel Query
public class Portfolio {
private long id;
private Set<Stock> longPositions;
private Set<Stock> shortPositions;
private double totalValue;
private Region region;
private boolean alerted; // alert for trading
@SossIndexAttribute // query-able property
public double getTotalValue() {…}
@SossIndexAttribute // query-able property
public Region getRegion() {…}
public Set<Long> evalPositions(MarketSnapshot ms) {…};
}
NamedCache pset = CacheFactory.getCache(“portfolios");
Set<Portfolio> res = pset.queryObjects(Portfolio.class,
and(greaterThan(“totalValue”, 1000000),
equals(“region”, Region.US)));

• Create method to analyze a queried portfolio and another method to
pair-wise merge the result sets of alerted portfolios:
Java Example: Parallel Method Invocation
public class PortfolioAnalysis implements
Invokable<Portfolio, MarketSnapshot, Set<Long>>
{
public Set<Long> eval(Portfolio p, MarketSnapshot ms)
throws InvokeException {
// update portfolio and return id if alerted:
return p.evalPositions(ms);
}
public Set<Long> merge(Set<Long> set1, Set<Long> set2)
throws InvokeException {
set1.addAll(set2);
return set1; // merged set of alerted portfolio ids
}}

• Run a parallel method invocation on a queried set of portfolios and
return set of ids for alerted portfolios:
Java Example: Parallel Method Invocation
NamedCache pset = CacheFactory.getCache(“portfolios");
InvokeResult alertedPortolios = pset.invoke(
PortfolioAnalysis.class,
Portfolio.class,
and(greaterThan(“totalValue”, 1000000), // query spec
equals(“region”, Region.US)),
marketSnapshot, // parameters
...
);
System.out.println("The alerted portfolios are" +
alertedPortfolios.getResult());

• IMDG ships user’s code and libraries to its servers.
• IMDG automatically schedules analysis operations across all grid
servers and cores:
• The analysis runs on all objects selected
by the parallel query.
• Each grid server analyzes its locally stored
objects to minimize data motion.
• Parallel execution ensures fast
completion time:
• IMDG automatically distributes
workload across servers/cores.
• Scaling the IMDG automatically
handles larger data sets.
Running the Analysis

• The IMDG automatically merges all analysis results:
• The IMDG first merges all results within each grid server in parallel.
• It then merges results across all grid servers to create one combined
result.
• Efficient parallel merge
minimizes the delay in
combining all results.
• The IMDG delivers the
combined result to the
invoking application as
one object.
Merging the Results

• Measured a similar financial services application (back testing stock
trading strategies on stock histories)
• Hosted IMDG in Amazon EC2 using 75 servers holding 1 TB of stock
history data in memory
• IMDG handled a continuous stream of updates (1.1 GB/s)
• Results: analyzed 1 TB in 4.1 seconds (250 GB/s) with linear scaling
Sample Performance Results for PMI

Benefits:
• Enables use of Hadoop MapReduce for operational intelligence.
• Accelerates data access by holding data in memory.
• Analyzes and updates “live” data.
• Reduces overheads of standard
Hadoop distributions:
• Batch scheduling
• Disk access
• Data shuffling
• Mandatory key sorting
• Enables new features, e.g.:
• Global combining, optional sorting
Using PMI to Implement
“In-Memory” Hadoop MapReduce

• A Hadoop distribution does not have to be installed unless HDFS is used.
• The developer starts MapReduce applications from a remote workstation.
• The IMDG automatically builds a reusable “invocation grid” of JVMs on the
grid’s servers for PMI and ships the application’s jars.
• Results are stored in the IMDG, HDFS, or optionally globally merged and
returned to the remote workstation.
Running MapReduce on the IMDG

Run In-Memory MR with YARN
• YARN, transparently integrates batch and in-memory MapReduce into
a single execution framework with shared access to HDFS.
• For example, hServer can transparently run Apache Hive in-memory.
Example of ScaleOut hServer with Hortonworks
Example of Hive
Running on hServer

Run MapReduce as two PMI
phases:
• Data can be input from either the
IMDG or an external data source.
• Works with any input/output format
compatible with the Apache
distribution.
• IMDG uses its data-parallel
execution engine (PMI) to invoke
the mappers and the reducers.
• Eliminates batch scheduling
overhead.
• Intermediate results are stored
within the IMDG.
• Minimizes data motion between the
mappers and reducers.
• Allows optional sorting.
• Output of a single reducer/combiner
optionally can be globally merged.
Implementing MapReduce

• IMDG adds grid input format for
accessing key/value pairs held in
the IMDG.
• MapReduce programs optionally
can output results to IMDG with
grid output format.
• Grid Record Reader optimizes
access to key/value pairs to
eliminate network overhead.
• Applications can access and
update key/value pairs as
operational data during analysis.
Accessing IMDG Data for M/R

• IMDG adds Dataset Record Reader (wrapper) to cache HDFS
data during program execution.
• Hadoop automatically retrieves data from ScaleOut IMDG on
subsequent runs.
• Dataset Record Reader
stores and retrieves data
with minimum network
and memory overheads.
• Tests with Terasort
benchmark have
demonstrated 11X
faster access latency
over HDFS without IMDG.
Optional Caching of HDFS Data

IMDG needs multiple in-memory
storage models:
• Named cache, optimized for
rich semantics on large
objects:
• Property-based query
• Distributed locking
• Access from remote grids
• Named map, optimized for
efficient storage and bulk
analysis (e.g., MapReduce):
• Highly efficient object storage
• Pipelined, bulk-access
mechanisms
Optimized In-Memory Storage

In-Memory Named Map:
• Stores key/value pairs in chunks.
• Allows CRUD operations on kvps.
• Automatically organizes chunks into
splits.
• Uses per-split hash table to access
keys and manage multi-valued
keys.
• Stores shuffled data set between
mappers and reducers.
• Pipelines chunks to mappers and
from reducers.
• Optionally uses memory mapped
files to reduce access latency.
• Provides support for sorting keys.
Named Map Optimizations

• Measured performance:
• Startup times reduced to a few milliseconds
• Word count benchmark shows 20X speedup.
• Real-world example shows >40X speedup.
• MapReduce optimizations:
• Optional sorting
• Optional multicast of parameters to mappers
• Optional O(logN) global combining (avoids
single reducer)
• Optional HDFS caching
• Optional reuse of JVMs across jobs
• Current limitations:
• No specific security for multi-tenancy
• Intermediate data must fit in the IMDG
Performance & Optimizations

• Invocation grids can be re-used across MapReduce jobs:
Accelerating Start-Up Times
public static void main(String argv[]) throws Exception {
//Configure and load the invocation grid
InvocationGrid grid = HServerJob.getInvocationGridBuilder("myGrid").
// Add JAR files as IG dependencies
addJar("main-job.jar"). addJar("first-library.jar").
// Add classes as IG dependencies
addClass(MyMapper.class). addClass(MyReducer.class).
// Define custom JVM parameters
setJVMParameters("-Xms512M -Xmx1024M").
load();
//Run 10 jobs on the same invocation grid
for(int i=0; i<10; i++) {
Configuration conf = new Configuration();
//The preloaded invocation grid is passed as the parameter to the job
Job job = new HServerJob(conf, "Job number "+i, false, grid);
//......Configure the job here.........
//Run the job
job.waitForCompletion(true);
}
//Unload the invocation grid when we are done
grid.unload();
}

• IMDG can run Apache Hive distribution
unchanged.
• Accelerates queries for datasets hosted in
HDFS or the IMDG:
• Intermediate data must fit within the IMDG.
• Challenges we faced:
• Requires YARN to transparently invoke
MapReduce on IMDG.
• IMDG must use multiple JVMs per server
since Hive tasks are not thread-safe.
• IMDG must support Hadoop’s distributed
cache (required by Hive).
Running Hive on In-Memory Data

• Assume we have a named map called “customers” of
customer objects:
Example: Querying a Named Map
public class Customer implements Serializable
{
private int customerId;
private String firstName;
private String lastName;
private String login;
public int getCustomerId() {
return customerId;}
public String getFirstName() {
return firstName;}
...
}

• Create a table view of a named map:
• Associates class properties with columns.
• Allows properties to be omitted.
• Allows use of custom serialization.
public hive> CREATE TABLE
customers (customerid int, firstname string, lastname
string, login string)
STORED BY
'com.scaleoutsoftware.soss.hserver.hive.HServerHiveStorage
Handler'
TBLPROPERTIES ("hserver.map.name" = "customers");
OK
Time taken: 0.508 seconds

• Now query the named map:
hive> SELECT * FROM customers;
..............................
1 Eduardo Hazelrigg ehazelrigg
13 Serena Sadberry ssadberry
9 Ermelinda Manganaro emanganaro
5 Edda Speir espeir
17 Tomeka Stovall tstovall
21 Luciano Perkinson lperkinson
25 Jacob Garrow jgarrow
33 Quincy Kreutzer qkreutzer
37 Iona Speir ispeir
41 Ermelinda Thielen ethielen
Time taken: 0.475 seconds, Fetched: 100 row(s)

The Challenge: Operational intelligence to quickly evaluate
and respond to sub-second market changes:
• Hedge fund tracks a set of hedging strategies:
• Strategies can cover various market
sectors, such as high-tech, automotive,
energy, consumer, real estate, etc.
• Each strategy contains list of holdings
and rules for managing the holdings
(such as target allocations).
• Updates to market data
continuously arrive during
the trading day.
• Challenge: The hedge fund must be able to quickly update and
analyze its hedging strategies and provide alerts to traders.
Demo of the Finserv Application

• Delivers a stream of alerts to traders
within a few seconds.
• Enables the trader to examine strategy details in real time:
Output: Real-Time Alerts

• Video Link
Video

Fast map/reduce reconciles inventory and order systems
for an online retailer:
• Challenge: Inventory and online
order management are handled
by different applications.
• Reconciled once per day.
• Inaccurate orders reduces margins.
• Solution:
• Host SKUs in IMDG updated in real
time by order & inventory systems.
• Use MapReduce to reconcile in two minutes.
• Results: Real-time reconciliation ensures accurate orders.
Example in Ecommerce: Inventory
Management

• IMDG holds customer
information for active
Web users.
• IMDG saves/retrieves
customer information
from backing store.
• Web browsers send
activity information to
analytics engine.
• IMDG updates customer history and
preferences.
• Analytics engine identifies browsing and
buying patterns.
• Analytics engine makes suggestions in
real-time. Also sends email follow-ups.
Example: Web Shopping

• Track
connectivity
issues.
• Obtain time-
sensitive
business data.
• Offer enhanced
services.
• Increase
security.
Example: Telecommunications
Optimize Operations
Customer Experience
Historical queries
for real-time data
enrichment
Stream
persistence for
future analysis
Network
Elements

• Online systems need operational
intelligence on “live” data for
immediate feedback.
• Operational intelligence can be
implemented using standard
data-parallel computing
techniques, such as M/R.
• In-memory data grids provide
an excellent platform for
operational intelligence:
• Host and update “live” data.
• Implement high availability.
• Offer fast, data-parallel
computation for immediate
feedback.
Recap

• ScaleOut StateServer®
• In-Memory Data Grid for Windows and
Linux
• Scales application performance.
• Industry-leading performance and ease of use
• ScaleOut GeoServer® adds
• WAN based data replication for DR
• Breakthrough technology for global
data access
• ScaleOut Analytics Server® adds
• Real-time data analysis for “live” data
• Comprehensive management tools
• ScaleOut hServer®
• Full Hadoop Map/Reduce engine (>20X faster*)
• Hadoop Map/Reduce on live, in-memory data
ScaleOut Software Products
ScaleOut StateServer In-Memory Data Grid
Grid
Service
Grid
Service
Grid
Service
Grid
Service
*in benchmark testing

Many Use Cases:
• Authorizations / Payment
Processing / Mobile Payments
• Service Activation
• Inventory Management
• Sensor Data / SCADA
• Real Time Tracking
• Fraud Detection
• Situational Awareness
• Churn Management
• Market Feed / Event Handlers
• Execution Rules
• Financial: Risk, P&L, Pricing
• Operational Risk Compliance
The Need for Real-Time Analytics
Across Key Industries:
• CPG
• Financial
• Telco
• Retail
• Utilities
• Manufacturing
• Logistics
• IC / DoD
• Life Sciences
• Government
• Health Care
• Law enforcement

• Brick and mortar stores need to compete with online experience.
• Point-of-sale identifies opt-in customers to analytics engine.
• RFID tags identify product selection and availability in showroom.
• Analytics engine sends real-time advisories to sales staff via tablet.
Example: Retail Shopping

• Typically used for very large, static, offline datasets
• Data must be copied from disk-based storage (e.g., HDFS) into
memory for analysis.
• Hadoop Map/Reduce adds lengthy batch scheduling and data
shuffling overhead.
Problem: Hadoop Cannot Efficiently
Perform Real-Time Analytics

// This job will run using the Hadoop
// job tracker:
public static void main(String[] args)
throws Exception {
Job job = new Job(conf, "wordcount");
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
job.setMapperClass(Map.class);
job.setReducerClass(Reduce.class);
job.setInputFormatClass(
TextInputFormat.class);
job.setOutputFormatClass(
TextOutputFormat.class);
FileInputFormat.addInputPath(
job, new Path(args[0]));
FileOutputFormat.setOutputPath(
}
// This job will run using ScaleOut hServer:
public static void main(String[] args)
throws Exception {
Job job = new HServerJob(conf, "wordcount");
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
job.setMapperClass(Map.class);
job.setReducerClass(Reduce.class);
job.setInputFormatClass(
TextInputFormat.class);
job.setOutputFormatClass(
TextOutputFormat.class);
FileInputFormat.addInputPath(
FileOutputFormat.setOutputPath(
}
Configuring MapReduce for the IMDG
• Without YARN, subclass the Hadoop Job class with a one-line change (below).
• With YARN, just replace the MapReduce execution framework.

• Mark class properties as indexes for query:
• Define a query using these properties:
Parallel Query Example (C#)
class Stock {
[SossIndex]
public string Ticker { get; set; }
public decimal TotalShares { get; set; }
public decimal Price { get; set; }}
NamedCache cache = CacheFactory.GetCache("Stocks");
var q = from s in cache.QueryObjects<Stock>()
where s.Ticker == "GOOG" || s.Ticker == "ORCL"
select s;
Console.WriteLine("{0} Stocks found", q.Count());

• Create method to analyze each queried stock object:
• Create method to pair-wise merge the results:
Example of Analysis Code (C#)
static decimal eval(Stock stock, StockCalcParams params)
{
return stock.Price * stock.TotalShares;
}
static decimal merge(decimal r1, decimal r2)
{
return r1 + r2;
}

• Run a parallel method invocation:
Invoking the Parallel Analysis (C#)
NamedCache cache = CacheFactory.GetCache("Stocks");
decimal valueOfSelectedStocks =
(from s in cache.QueryObjects<Stock>()
where s.Ticker == "GOOG" || s.Ticker == "ORCL"
select s)
.Invoke(new StockCalcParams(…),
new Func<Stock, StockCalcParams, decimal>(eval))
.Merge(new Func<decimal, decimal, decimal>(merge));
Console.WriteLine(“The value of selected stocks is {0}",
valueOfSelectedStocks);

Making Hadoop Realtime by Dr. William Bain of Scaleout Software

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