In-Memory Data Management Goes Mainstream - OpenSlava 2015

©2015 Software AG. All rights reserved. For internal use only1 |
Manish Devgan
Software AG
In-Memory Data Management Goes Mainstream
October 9, 2015

Introduction
Product Manager
Many years in Enterprise Software industry
Lives in Washington DC Metro
Interests – In-Memory, NoSQL, Distributed
Systems, Modern BI, Analytics, Security,
Cloud
www.linkedin.com/in/manishdevgan
@mdevgan
manish@terracotta.orgEmail

1.
2.
3.
4.
Overview – In-Memory Data Management
Software AG Open Source Projects
Use Cases
Q&A
Agenda

Growth of Data
Transactions, Processes, Mobile, M2M, Sensors, Logs, ..

The value of ^ time
+0.1s costs Amazon 1% in sales
+0.5s costs Google 20% search traffic

Uptime, SLAs
Performance and Scale

The shift where data lives and gets analyzed
90% of Data
in Disk-based
Databases
90% of Data
in In-Memory
MEMORY

Why now?
Explosion in
volume &
velocity of
business data
Steep drop in
price of RAM

©2015 Software AG. All rights reserved. For internal use only9 | ©2015 Software AG. All rights reserved.9 |
In-Memory Computing Hits Mainstream
“In-Memory Data Platform
Is No Longer An Option —
It's a Necessity!”
“..In-Memory Computing
Technologies Enable
Digital Business”

In-Memory Data Grids – Forrester Wave

Change how you manage & integrate data, and you
completely change what’s possible for the business.
11

©2015 Software AG. All rights reserved. For internal use only12 | ©2015 Software AG. All rights reserved. For internal use only12 |
Open Source

Terracotta Open Source Footprint

•  Standards in the enterprise Java ecosystem
•  Each with Millions of installs - huge user bases
•  Hundreds-of-thousands of downloads
•  Used in other popular projects/frameworks/products
Existing Open Source Projects

340K+Ehcache 2.x (Maven)
TC Server OS 4.3 – offheap +
clustering (website)
4,000+
Downloads (May to Aug 2015)
Open Source Projects
© 2015 Software AG. All rights reserved.
For internal use only
Frameworks leveraging Ehcache

Ehcache3: Modern, JSR-107 compliant, caching API
API with generics & typing support
cache.put(1L, “a value"));
String myVal = cache.get(1L);
cache.put
(new Element(Long.valueOf(1), “a value"));
String myVal =
(String)cache.get(Long.valueOf(1)).getValue();
v2
v3
www.ehcache.org

Terracotta In-Memory Open Source offering
ü  API
ü  Distributed storage (1 stripe – active
with optional mirror)
ü  Key/Value Offheap Data
Management
ü  Integrations (e.g. Hibernate)

Your Problem: You are
working with data that you want
to use, manage, and harness the
value of instantaneously
Our Solution: Terracotta
BigMemory stores “big” amounts
of data in machine memory,
right where the application
runs, for ultra fast access. It
delivers high speed performance
at any scale

Tiered Memory Store
Tiered Storage
Distributed BigMemory
Server RAM or Flash/SSD
Process Memory
Local BigMemory
2,000,000+
1,000,000
100,000
Micro-
seconds
Micro-
seconds
Milli-
seconds
Speed (TPS)
1,000s
Latency
External Data Source
(e.g., Database, Hadoop, Data Warehouse)
BigMemoryBigMemory
4 GB
32 GB – 12 TB
100s GB – 100s TB
Seconds

Terracotta BigMemory

Terracotta BigMemoryServerArray
Stripe
Commodity Server
Application
Stripe Stripe Stripe Stripe
Commodity Server
Application
Commodity Server
Application
Commodity Server
Application
Ehcache
BigMemory
TCP
Client
aka L1
Highest value data is
stored in-process - right
where it is used
API

Terracotta BigMemoryServerArray
Stripe
§  High Availability
§  Scalability
§  Consistency
§  Durability
§  Monitoring
§  Management
Commodity Server
Application
Stripe Stripe Stripe Stripe
Commodity Server
Application
Commodity Server
Application
TCP
Server on
commodity HW
Transactionally
updated mirror
for high
availability
Stripe
Commodity Server
Active
Server
Commodity Server
Mirror
Server
aka L2
DRAM
FRS
DRAM
FRS

Extreme Scale Up on Intel® Xeon® Processor E7 v2-based Servers
•  Enterprises increasingly need to take
immediate action on big data
intelligence, which is driving changes to
the traditional enterprise architecture
•  Desire to keep data instantly available in
high-speed machine memory, rather than
locking it away in slow, disk-bound
databases
•  Intel Xeon processor E7 v2-based servers
have the memory capacity and the
compute power to deliver new innovative
solutions
•  Increasing data volumes do not deter
throughput or latency of Intel Xeon
processor E7-4890 v2 using BigMemory
MAX* software
•  From 2 TB using 1 billion elements
to 5.5 TB using 3 billion elements,
the throughput and latency
remained fairly constant
23
6TB
3x data fidelity, consistent
performance

Use Cases

SPEED
FORTUNE 500 ONLINE
PAYMENTS PROCESSOR
Before
•  Lost 30 cents on every $100 to fraud
•  With traditional data management solutions, failed to meet
800 ms SLA around 10% of time
•  Limited to 50 rules, even though each new rule generated
$12 million in profit
Radically Improving Profitability
With Better, Faster Fraud Detection

SPEED
After
ü  Savings of tens of millions of dollars in reduced costs
from missed SLAs and fraudulent charges
ü  Meeting stricter 250-millisecond SLA 99% of time
ü  Savings of $1 million annually in reduced database
licenses
Radically Improving Profitability
With Better, Faster Fraud Detection

Raising Profitability by Shifting Customers
from Call Centers to e-service SCALE
$26 BILLION TELCO
Before
•  Retrieving customer data took as long as 30 seconds
•  Inadequate performance meant company could
not promote e-service
•  Other In-Memory solutions rejected due to cost and performance
concerns at terabyte scale

SCALE
After
ü  Supports more than 10,000 customer interactions daily,
ready to scale to 10x
ü  Slashed access times to only 200 milliseconds with 100%
data availability
ü  Tens of millions of dollars saved in support costs
ü  Latency and capacity constraints eliminated
Raising Profitability by Shifting Customers
from Call Centers to e-service

“The team began almost immediately to
cache the data. The result was
encouraging: the site's overall response
time--the time it took a page to load--
dropped on the evening of Oct. 22 from
eight seconds to two. That was still
terrible, of course, but it represented
such an improvement that it cheered the
engineers. They could see that
HealthCare.gov could be saved instead
of scrapped.”
http://content.time.com/time/magazine/article/0,9171,2166770,00.html

What’s next?

Storage – typed & loosely schema’ed
•  Opaque, non-structured entries
•  Disconnected search indexes (On-
disk)
•  Support for Store/Retrieve
operations only
§  Structured, typed records
§  Integrated search indexes (in-
memory)
§  Purpose built storage for in-place
compute
Storage
Off-heap, FRS, Hybrid
Search
Indexes
Storage, Search, & Compute
Off-heap, FRS, Hybrid, Indexes
Key Value (byte[]) Key “name”:
“John”
“age”:
21
“pic”:
byte[]

Intelligent Storage & Simple APIs
Synchronous – “awaiting results” Asynchronous – “fire & forget”
p1
“num”:
123
“age”:
21
“name”:
“Marcus
”
“hired”
: true
dataset.applyMutation(WS,
myCounterKey,
write(myCounterCell,
longValueOf(myCounterCell).increm
ent()));
Operation result =
dataset.async().applyMutation(WS,
myCounterKey, ..);
persons.add(WS, “p1”, FIRST_NAME.newCell("Marcus”));
Simple CRUD API
Mutation - without the need to bring data back to client

•  Well-known
“functions”
•  E.g. “average”,
“map”, “reduce”
•  Supports Java 8
Streams
•  Support for functional
expressions
(“Lambdas”)
Compute pipeline on storage
•  Fluent DSL (Domain Specific Language)
persons.compute().filter(boolValueOf(HIRED)).count();
persons.compute().map( … )
persons.compute().filter( … ).map( … ).reduce(…).sorted(…).forEach(…);

FINE grained Data consistency & Durability
•  More fine-grained than simple
“Strong-Eventual”
•  “Pay-as-you-go” model
•  Pay performance costs warranted
for given need
•  Read & Write settings per operation
•  Write operations can specify range
of visibility and durability
requirements
persons.compute(STANDARD). …
persons.get(PARANOID). …
Persons.add(WS, … )
Persons.applyMutation(WS, … )
WriteSettings WS =
WriteVisibiltiy.IMMEDIATE.withDurabi
lity(maximal());
or…
WriteVisibiltiy.EVENTUAL.withDurabil
ity(onAtLeastNPassives(1));

Q&A
www.terracotta.org
We are hiring!
jobs.softwareag.com

In-Memory Data Management Goes Mainstream - OpenSlava 2015

In-Memory Data Management Goes Mainstream - OpenSlava 2015

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In-Memory Data Management Goes Mainstream - OpenSlava 2015