Digital transformation is continuously disrupting industries, particularly financial services organizations, in an unprecedented way. Why? Two key reasons: customer demand and business agility. The monolithic databases that enterprise organizations have relied on for decades for core business operations cannot support the requirements of the financial services industry today. To be competitive in the market, FinServ applications must be immediately consistent, continuously available, able to scale out and in on demand, and agile enough to maintain compliance with changing regulations. While legacy database infrastructure powered much of the banking industries’ applications to date, to do so many organizations have accumulated significant technical debt. As forward-thinking enterprises move to the cloud, experts like you are seeking solutions to reduce technical debt while increasing business agility. So what can you do to transition more seamlessly to the cloud? Scale Out. Still SQL.
Join us for this technical webinar and you’ll learn:
How to meet customer demands for application availability and agility
How to migrate to the cloud without rearchitecting every application
How to use distributed SQL databases as your on-ramp to the cloud
How to lower TCO and improve performance within your business-critical applications
Scale Out Not Up with Distributed
SQL: Transition to the Cloud
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Meet the Speakers
Senior Product Manager
Enterprise Benchmarking and Sizing
Temenos – World’s #1 Core Banking Platform
• Over 3,000 banks in 150 countries
• 41 of 50 world’s largest banks
• 330 successful deployments in 2019
• Model bank for local use
Transaction Engine (TE)
Storage Manager (SM)
Traditional RDBMS NuoDB Distributed RDBMS
App App App App App
Why Distributed Matter?
● Need to get bigger machine to
increase capacity scale limit
● Need to have a passive failover
● Need time to failover RTO != 0
● Can get more machines to increase
capacity unlimited scale
● All servers are Active lower cost
● Continuous HA RTO = 0
Scale Up Scale Out
Traditional RDBMS Distributed RDBMS - NuoDB
The database has historically been the least
scalable component in application architectures,
requiring expensive pre-provisioning and disaster
A distributed SQL database provides the
transactional consistency your critical
applications require, while enabling on demand
scale out and continuous availability.
Scale Out Lowers TCO & Improves Performance
Microservices, containers, and Kubernetes enable enterprises to update and
deploy applications rapidly.
Traditional RDBMS cannot take advantage of elasticity and automation
offered by these technologies.
Active-Active Scale Out Automated Ops
Zero failover time
(RTO=0) for always
on-demand scale out
and scale in
NuoDB: The Database To Build Your Future On
Deploy mission-critical applications on a database built for on-premises, cloud, & hybrid environments
Meet end user expectations for
continuously available applications
Scale elastically to meet ever-changing
application workload demands
Choose when, where, and how to
deploy mission-critical applications
Leverage NuoDB to migrate enterprise SQL applications
to cloud-native environments.
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Customers have very different requirements.
Very different transactions
Very different transaction volumes and SLA’s
Retail / Loans / Corporate with large peaks in performance.
Move to the cloud – why NuoDB ?
Built in DR & Resilience
Reduction in costs
Active-active across clouds
The objective of the benchmark was to compare *any* traditional RDBMS with NuoDB
Test case was Oracle but could be any RDBMS both with 8 DB cores.
The transaction mix used for comparison was based on retail transactions.
Customer information query – 16%
Account Transaction query - 10 %
Account Balance query – 34% (showing account overview and balances, not the account
Customer Information update – 0,1%
Account Details query – 0.1% (owners, product details, overdraft limits etc.)
Money Transfer account to account – 10 % (debit and credit in same transaction)
Posting Debit and Credit transactions – 19.8 % (debit and credit separate transactions)
Posting Cover Reservations – 9%
The traditional RDBMS requires 32 cores @95% busy
NuoDB required less than 6 cores @80% busy.
An additional server (TE) was required with good memory for NuoDB
Lower cost (Based on license in the cloud for TCO)
NuoDB is very well suited to a cloud environment with Temenos software.
Mirror Benchmark conclusions
Temenos benchmarked the performance & scalability of its latest Transact R19 and
Infinity product offerings on AWS Cloud and NUODB, and demonstrated :-
Highly Scalable Performance – 50K+ TPS, which is beyond what a typical customer requires
Lower TCO using Auto scaling (Only pay for what you use)
Use of Modern architecture - Transact, Data Event Streaming, Micro-services, Cloud
Cloud native / Agnostic characteristics – Use of cloud native technologies such as Lambda
functions, DynamoDB and Kinesis
Dynamic Deployment – Docker Containers, ECS (Elastic Container Service) and EKS (Elastic
High Water Transact & Infinity Benchmark
51,200 TPS (transactions per second) achieved, with a mix of Transact & Infinity traffic
12.7k TPS on Transact (Reservation, Booking, Transfer & Payments)
38.5k TPS on Infinity (Balance enquiry & Transaction list microservices)
COB with auto-scaling completed in 2hrs 34mins on 100m accounts
Database of 100 million accounts across 38 million customers and 50 branches
Streaming Platform (DES) kept Transact and Infinity in sync, with low latency ( < 1.2 s )
Benchmark executed on AWS cloud with NuoDB database, leveraging native AWS technologies
Run with Native Database Locks
Transaction Workload Profile:
Ramp up: 0 to 50K TPS gradually over 45min
Steady State: At 50K TPS for 30min
Ramp down: From 50K TPS to 0, over 30min
Native Locking v1.0
NuoDB CONSISTENT READ isolation level was used for Transact and READ COMMITTED for Services
Transact & Infinity – Detailed Workload
- Invocations Per Min
Benchmark Results – Infinity (Lambda)
60 / 40 split between
Peak TPS of
NOTE: Invocations Per Min = Transactions Per Min
time < 40ms, as
indicated from API
Response times from
(JMeter) are higher, at
120-150ms, likely to be
down to latency of
High Level Deployment Architecture
DES Source Amazon Kinesis
Customer benchmark example (Traditional deployment)
Customer benchmark example IBM Cloud (2017)
5 x 48 core database
servers = 240 cores
Servers online at all
Response times circa.
Customer benchmark example IBM LinuxOne 2019.
62 cores in total
12 DB cores / Oracle
9999.9 guaranteed up
Response times circa.
IBM LinuxOne Cont.
• RESULTS : 3,000 Transact tps on 62 total LinuxOne cores
• Now on plan to be tested with NuoDB
• Expectations are high due to the way LinuxOne / Mainframe handles its
• Plan for on premise Transact & Infinity deployment.
● Webinar: 3 Things You Need to Know When
Assessing Database Scalability
● White paper: 4 Key Considerations for Maximizing
Database ROI for Banking Applications
● White paper: Visionary Financial Services
Organizations Move Beyond Multi-Cloud to Inter-
● Get NuoDB Community Edition