Middle Tier Scalability - Present and Future
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Transcript of Middle Tier Scalability - Present and Future
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What are we here to discuss?
Making Sense of the Exploding Data World
The role of middleware to address scalability challenges
The role of middleware to address integration challenges
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Making Sense of The Exploding Data World
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GB
TB
PB
Dat
a Vo
lum
e
Yr Mo Day Hr Min Sec MS µS
Data MiningMachine Learning
Data Velocity
Data Warehouse High Throughput OLTP
Operational Intelligence
Exploratory Analytics
OLTP
Business Intelligence
Streaming
Capacity and Performance Drives New Data Management Technologies
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Let’s Look at Tradeoffs of
Some Selected Solutions
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SQL Queries
• Query: SQL • Semantics:
• CRUD• Aggregation• Projection• Partial update
• Performance: 100’s/Sec • Consistency: Transactional• Scaling: Mostly Scale-UP• Availability: Disk Based
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NoSQL• Query: Proprietary but rich• Semantics:
• CRUD• Limited Aggregation
(Map/Reduce)• No Projection*• No Partial update*
• Performance: 1000s/Sec • Consistency: Eventual* • Scaling: Mostly Scale-Out• Availability: Based on replication
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IMDG • Query: Propriety but rich• Semantics:
• CRUD• Aggregation API +
Map/Reduce• Projection (GigaSpaces)• Partial Update
(GigaSpaces)• Performance: 100k/sec• Consistency: Transactional • Scaling: Mostly Scale-Out• Availability: Replication
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Key/Value
• Query: Key, Value• Semantics:
• Mostly Read• No Aggregation• No Projection• No Partial update
• Performance: 1M’s/sec • Consistency: Atomic*• Scaling: Mostly Scale-Out• Availability: Limited (varies quite substantially between implementations)
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Stream Processing (Storm)
• Semantics– Event driven data processing
• Used for continuous updates– No need for a costly “SELECT
FOR UPDATE”
• Performance: 10’sM/sec updates
Spouts
Bolt
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Common Assumption
Disk is the bottleneck
2010
Perf
orm
ance
1̂0
2000 2020
CPU Perform
ance = 100X PER DECADE
HDD Latency (Seek & Rotate) = Little Improvement
100X
10,000X
Source: GigaOM Research
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Capacity and Performance Drives New Data Management Technologies
(Source: IDC, 2013)
Big Data (Hadoop)
NoSQL
In Memory, Stream Processing
RDBMS
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There’s No One Size Fits All
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A Typical App Looks Like This..
Front End Analytics
RT
Batch
STORM
The Data Flow Complexity
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What if Disk Was no Longer the Bottleneck?
FLASH Closes the CPU to Storage Gap
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Our Application Cloud Look Like This..
Front End
High Speed Data Store
(Using Flash/NVM)
Key/Value
SQL
Document
Graph
Transactional
Map/Reduce
Disk Becomes the new Tape
StreamBase
Common Data Store servingMultiple Semantics/API
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We're not there yet ..
But..
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We can use High Speed Data Bus for Integrating All of our Data Sources
Front End Analytics
RT
Batch
STORM
High Speed Data Bus(Built-In
Caching)
RT Transactional Data Access
Direct Access
RT Streaming
Hadoop Synch
MySQL Synch
Mongo Synch
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High Speed Data Bus (Zoom In)
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Data Grid Ideal Integration Nexus
• Transactional• HA – Self Healing• Horizontally scalable• FIFO (and partial FIFO) support• Queryable• Ultra high performance read/write
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Designed for Transactional and Analytics Scenarios..
Homeland Security
Real Time Search
Social
eCommerce
User Tracking & Engagement
Financial Services
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Typical NoSQL Integration
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Storm Integration
http://ec2-54-89-152-83.compute-1.amazonaws.com:8090/web/
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Many API’s – Same Data
Key/Value SQL Document Graph TransactionalMap/Reduce
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Let’s take a closer look..
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Nested Queries & Projections
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Aggregations.
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Fast Update …
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Fifo/messaging support
@SpaceClass(fifoSupport=FifoSupport.OPERATION) public class Person { ... }
@EventDriven @Polling
public class SimpleListener {
@SpaceDataEvent public Data eventListener(Data event) { //process Data here }
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Transactions support
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So what?
• Data access not tied to store implementation.
• Middle tier grows as source of truth.• Simplifies data access as it grows• Can support strong consistency as
needed.• Provides HA platform for integration.
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- 1KB object size and uniform distribution- 2 sockets 2.8GHz CPU with total 24 cores, CentOS 5.8, 2 FusionIO SLC PCIe cards RAID- YCSB measurements performed by SanDisk
No Read / 100% Write 100 % Read / No Write0
20
40
60
80
100
120
140
160
62
121
17
56
FDF-GigaSpaces on SSDs Stock GigaSpaces in DRAM
Assumptions: 1TB Flash = $2K; 1TB RAM = $20K
The Performance of RAM at a Cost/Capacity Closer to Disk
ZetaScale-GigaSpaces on SSDsStock GigaSpaces in DRAM
ZetaScale-GigaSpaces
Provides 2x – 3.6x Better TPS/$ 1:50 More Capacity
ZetaScale™ – XAP MemoryXtend
Capacity0
200
400
600
800
1000
1200
20
1000
XAP XAP Extend
1:50
242k Read/Sec
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Take Aways
• Explosion of data has created an explosion of targeted technologies
• Many architected on “disk is slow”• Flash changing the equation.• In-memory tech best suited to take
advantage of flash• Continued blurring of in-memory
middleware and data storage.
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Real World Example #1: Fraud Detection
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Real World Example #2: Banking
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Real World Example #3: Clinical Surveillance
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Nati Shalom
Check out the slides on http://www.slideshare.net/dfilppi