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3 Things You Need to Know When Assessing Database Scalability


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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

Published in: Technology
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3 Things You Need to Know When Assessing Database Scalability

  1. 1. Ariff Kassam | Chief Technology Officer 3 Things You Need to Know When Assessing Database Scalability November 12th, 2019
  2. 2. Copyright © 2019 NuoDB, Inc. 2 | Meet the Speaker Ariff Kassam Chief Technology Officer NuoDB
  3. 3. Copyright © 2019 NuoDB, Inc. + Database Availability & Scalability + Scaling Options for Databases + Trade Offs + About NuoDB + Q&A 3 | Agenda
  4. 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
  5. 5. Copyright © 2019 NuoDB, Inc. + 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
  6. 6. Copyright © 2019 NuoDB, Inc. + 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
  7. 7. Copyright © 2019 NuoDB, Inc. + 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
  8. 8. Copyright © 2019 NuoDB, Inc. + 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
  9. 9. Copyright © 2019 NuoDB, Inc. + 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
  10. 10. Copyright © 2019 NuoDB, Inc. + 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. 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
  12. 12. Copyright © 2019 NuoDB, Inc. + 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
  13. 13. Copyright © 2019 NuoDB, Inc. + 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
  14. 14. Copyright © 2019 NuoDB, Inc. + 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
  15. 15. Copyright © 2019 NuoDB, Inc. + 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
  16. 16. Copyright © 2019 NuoDB, Inc. 16 | 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
  17. 17. Copyright © 2019 NuoDB, Inc. + 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
  18. 18. Copyright © 2019 NuoDB, Inc. 18 | 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
  19. 19. Copyright © 2019 NuoDB, Inc. 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
  20. 20. Copyright © 2019 NuoDB 20 | 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. 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 |
  22. 22. Copyright © 2019 NuoDB 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. 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. 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. 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. 26. Questions?