Today, every company is a software company. If your organization is like most of them, you’re thinking about digital transformation and the cloud. To move to the cloud and embrace that transformation, you’ll need to think about how to scale your database, and indeed, what database scalability really means. Every new cloud application is built on the expectation that it will scale. The trick is to improve availability and performance while planning for rapidly changing demands. New offerings, including distributed SQL, eliminate many of the availability and scalability issues that legacy databases had when moving to the cloud. Join us for this technical webinar as we look at the ways that databases can be scaled and examine the benefits and drawbacks of each method. Watch this webinar to learn: + Scaling options for databases + What it means to trade off consistency for scalability + Differences between strongly consistent scaling strategies

1. Ariff Kassam | Chief Technology Officer
3 Things You Need to Know When
Assessing Database Scalability
November 12th, 2019

2. Copyright © 2019 NuoDB, Inc. 2 |
Meet the Speaker
Ariff Kassam
Chief Technology Officer
NuoDB

3. Copyright © 2019 NuoDB, Inc.
+ Database Availability & Scalability
+ Scaling Options for Databases
+ Trade Offs
+ About NuoDB
+ Q&A
3 |
Agenda

4. Copyright © 2019 NuoDB, Inc.
+ Database availability, performance, and scalability have
always been challenging for organizations
+ Many have accepted the fact that it requires expensive
hardware and software to deliver a robust database
solution
+ However, recent innovations in database technologies have
expanded the options available to organizations
+ These innovations can provide better availability and
scalability at lower TCO
+ Need to understand the tradeoffs that need to be made
4 |
Holy Grail: Database Availability & Scalability

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+ For stateless
processing, scale out
architectures are
standard practices
▪ Costs increase with
performance
+ For databases, scale up
architecture is the norm
▪ Costs increase faster
than performance
+ Kubernetes adds additional
complexity 5 |
Scale Up vs Scale Out
Source: Paris Technology

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+ As data volumes and usage grows, the only approach to
address database performance issues is to throw more
hardware at the problem
+ Add more CPUs, memory, and/or disk to improve performance
+ Server upgrades require maintenance windows, so servers are
sized for peak performance requirements in the future
+ Hardware needs to be replicated for any HA or DR
environments
+ Results in significant costs
6 |
Scale Up

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+ Newer database solutions provide scale out capabilities
▪ Dynamic provisioning
▪ Lower TCO
▪ Better availability
+ But databases are stateful systems
+ Scale out requires the ability to distribute the data and
workloads across multiple systems
+ To enable scale out, certain trade offs need to be made
7 |
Scale Out

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+ NoSQL
+ Traditional SQL architectures
▪ Read replicas
▪ Database sharding
+ Storage focused SQL architectures
▪ Clustering / shared disk
▪ Distributed storage
+ Distributed SQL
▪ Automatic partitioning
▪ Durable distributed cache
8 |
Database Scale Out Options

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+ Innovation driven by the need to address the
limitations with scalability and availability of
traditional SQL databases
+ CAP theorem
▪ For distributed systems, when a
Partition occurs, the system must
choose to save Consistency or
provide Availability
+ Different types of NoSQL models
depending on use cases
9 |
NoSQL

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+ Works well for certain types of applications and use
cases
▪ Event logging, content management, metadata
management, social data, IoT, real-time analytics, etc.
+ But for existing applications written to use traditional
SQL databases needing ACID transactions
▪ High risk to convert to NoSQL
▪ Need to re-develop consistency and transactional
assumptions, data model, performance, and operational
management, etc.
10 |
NoSQL

11. Copyright © 2019 NuoDB, Inc.
+ Originally developed to provide faster
failover in case of failure of master
+ Database copies that are kept in sync
with the master database and used for
read only queries
+ Need a load balancer to auto
route traffic or segregate
workload
+ Does not address write scale out and
requires an outage on failover
11 |
Traditional SQL: Read Replicas
App
Reads &
Writes
BI
Reads
Only
Master Read
Replicas

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+ Standard technique to improve performance for large SQL
databases
+ A single large database is split up into multiple smaller
independent databases
▪ All databases have the same schema
▪ Data is split across the smaller
databases
+ Improve performance
+ Application needs to be shardable
and harder to manage/operate
+ Does not improve availability
+ Hard to add shards dynamically
12 |
Traditional SQL: Database Sharding

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+ Originally developed to remove the need for an outage
on node failure
+ This architecture supports multiple read/write database
nodes, all storing the data on the same shared disk
+ Works well for read scale out
+ Has limitations for write scale out
+ Also requires specialized hardware and
networking not available in the cloud
+ Examples: Oracle RAC, IBM PureScale
13 |
Storage: Clustered/Shared Storage
App App App

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+ Improvement on the Read Replica model
+ Newer database storage architectures to improve I/O
performance and data availability
+ Used by AWS Aurora,
Azure SQL Hyperscale
14 |
Storage: Distributed Storage

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+ Newer systems automatically partition all of the data
across multiple nodes
+ One node is the “master” for a data partition
+ All reads/writes are must be processed by the
master
+ Replicas of that data are distributed to
other nodes for availability
+ Need to be careful of master locality and
Workload
+ Adding new nodes may require re-partitioning
15 |
Distributed SQL: Auto Partitioning

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Distributed SQL: Durable Distributed Cache
+ Modern cloud architecture with split query processing
(compute) and storage management
+ Each layer can be scaled out independently
+ Distributed cache does not require partitioning of the
workload
+ Data is automatically
replicated at the
storage layer
Database Process
Storage Management
Query Processing
Query Processing
Storage Management

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+ All architectures that support scale out make certain
tradeoffs
+ Each have advantages and disadvantages
+ Need to review your use case and requirements to
determine which approach would be best for you
17 |
Scale Out Tradeoffs

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Trade Off Summary
Traditional SQL Storage Focused Distributed SQL
Read
Replicas
Database
Sharding
Clustered Distributed
Storage
Auto
Partitioning
Durable
Distributed Cache
Read Scale Out Y Y Y Y Y Y
Write Scale Out N Y N N Y Y
Workload Independent Y N Y Y N Y
Low Latency Y N Y Y N Y
Availability N N Y Y Y Y
Commodity Hardware Y Y N N Y/N Y
Complexity Low High Medium Low Medium Medium

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NuoDB
19 |
Modern cloud-native, cloud-agnostic
distributed SQL database for high
throughput transactional workloads
+ On-demand scale out and scale in
+ Continuous availability protection
against planned and unplanned
outages for HA & DR
+ Hybrid or multi-cloud deployments in
any public or private cloud and on
physical, virtual, and containerized
environments
+ ANSI SQL interface
Storage
Mgmt
Query
Processing
App App
Traditional
RDBMS
Architecture
NuoDB Architecture
Durability
Connection Mgmt
SQL Parser
SQL Optimizer
Transaction Mgmt
Transaction Engine (TE)
Storage Manager (SM)
TE
SM SM
App App
TE

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TE and/or SM Failure
1. Working Database
SMSM
TE TE TE
App App App
2. TE and SM failure
SMSM
TE TE TE
App App App
3. Application reconnects to surviving TE
SMSM
TE TE TE
App App App
Database Availability
Storage Manager (SM)SMTransaction Engine (TE)TEApplicationApp
SM SM

21. Copyright © 2019 NuoDB
Multi-Tier Scalability
Web/Mobile Application
+ Read-dominated
+ Cache-bound
+ High concurrency
+ Many simple queries
+ Solution: Scale-out Compute Tier
OLTP Application
+ Mixed read/insert/update/delete
+ Compute & IO bound
+ Medium/High concurrency
+ Stable working set
+ Solution: Scale-out Compute & Storage Tiers
TE TE TE TE TE
SM SM
TE TE TE TE TE
SM SMSM SM
21 |

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Multi-Tier Scalability
Logging Application
+ Insert dominated
+ IO bound
+ Large data size
+ Solution: Scale-out Storage Tier
HTAP Application
+ Mixed read/insert/update/delete
+ Compute & IO bound
+ Medium/High concurrency
+ Resource contention
+ Solution: Dedicated Analytical Nodes
TE TE TE TE TE
SM SM
TE TE
SM SMSM SM
22 |

23. Copyright © 2019 NuoDB, Inc.
Distributed
Architecture
Active-Active
Scale Out
Automated Ops
Dynamic
Caching
23 |
Benefits
Modern
architecture
separating
compute and
storage
Zero failover time
(RTO=0) for always
on protection
Automated
deployment and
operations using
Kubernetes
Operators
Address dynamic
performance
requirements with
on-demand scale
out and scale in
Optimize
performance/cost trade
offs with dynamic
memory caching

24. Copyright © 2019 NuoDB, Inc.
+ Various options available to address database
scalability limitations of the past
+ Each has different trade offs with different advantages
or disadvantages
+ Match solutions to your requirements, use cases, and
workloads
+ NuoDB is a Distributed SQL solution for existing low
latency enterprise critical applications moving to the
cloud
24 |
Summary

25. Copyright © 2019 NuoDB, Inc.
Next Steps
Useful resources:
+ The Pros and Cons of Database Scaling
Options
+ The Rising Need for Database Scalability
+ What do we mean by Database Scalability?
+ What are the Database Scalability
methods?
+ What is the Elastic SQL approach?
25 |

26. Questions?