Managing Multi-Tenant SaaS Applications at Scale
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Speaker: Justin LaBreck, Senior Consulting Engineer, Professional Services, MongoDB Level: 200 (Intermediate) Track: Application Architecture Multi-tenant applications face unique challenges at scale. A mix of hardware, software, and developer costs make implementing software-as-a-service (SaaS) and basic application architecture expensive and complex making growth problematic. In this talk, we will explore the strategies, trade-offs, and “gotchas” common in multi-tenant environments. We draw from daily work with MongoDB customers and application development in the SaaS events industry migrating from one multi-tenant strategy to another. We'll discuss several approaches on the shared-to-isolation spectrum with cost and benefit considerations for each. We’ll even offer tips so today’s application and MongoDB design choices don’t affect tomorrow’s bottom line. What You Will Learn: - What strategies exist for storing data from multiple clients? - How can these strategies be compared for cost effectiveness? - How can these strategies be compared for cost effectiveness? How can MongoDB features be leveraged for future scale?
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Managing Multi-Tenant SaaS Applications at Scale
- 1. Managing Multi-tenancy Meaningfully Strategies for planning and deploying multi-tenant environments
- 2. Professional Services (Consulting Engineer)
- 3. What is multi-tenancy? Multiple clients Regional data Permissioned data Regulated data Any set of data that can (or should) be segregated logically for managerial purposes.
- 4. The What? Data partitioning Database partitioning Device partitioning The How? Trade-offs, considerations, and solutions
- 5. 012 - Justin 012 - MongoDB 012 - 949-555-1234 345 - John 345 - Widget Llc. 678 - Jane 678 - Acme Inc. The What? Visualized ClientA.People 012 - Justin 012 - MongoDB 012 - 949-555-1234 ClientB.People 012 - Justin 012 - MongoDB 345 - John 345 - Widget Ltc. ClientC.People 678 - Jane 678 - Acme Inc. People 012 - Justin 012 - MongoDB 012 - 949-555-1234 People 987 - Justin 987 - MongoDB 345 - John 345 - Widget Ltc. People 678 - Jane 678 Acme Inc. ClientC ClientB ClientA Data Partitioning People Database Partitioning Database Partitioning
- 6. All your data in one place (one giant dataset) Provide users access based on permissions Authentication Authorization Auditing }Fundamental, otherwise it’s not multi-tenant Data partitioning shared database, shared collection Data storage Processing Filtering Delivery
- 7. Database partitioning isolated database, shared environment Data is split logically between different databases Options are many … as are consequences of choosing wrong Software level - hard; make the software pick a database Database level - easy; let the database route queries appropriate Single instance, multiple databases vs. multiple instances, single database per instance Which one is better? (more on this later) Data storage Filtering Processing Delivery
- 8. Device partitioning isolated environments Similar to scaling horizontally across many servers Multiple server instances, each with a single database (virtual isolated, containers) Multiple servers, single database on each Allows for customized code solutions ($) Data storageFiltering Processing Delivery
- 9. There are trade-offs for each! Costs to consider: Financial cost ($) Developer cost (man-hours) Infrastructure cost What are infrastructure costs? Physical costs of maintenance Organizational costs Service Level Agreements (SLA)
- 10. Infrastructure Costs = ($ + man-hours) “We had no idea this would turn into a public and global infrastructure.” - Vint Cerf (co-inventor, TCP/IP) ● Physical costs - maintaining hardware ● Organizational costs ○ Data partitioning - how do you test permission based partitioning? ○ Database partitioning - who owns/manages code that connects to multiple environments? ○ Device partitioning - who fields the additional costs? ● Service Level Agreements ($ + man-hours)
- 11. Cost of data partitioning Hardware - low cost ($) Software - extreme cost (man-hours) Infrastructure - medium ($ + man-hours) Physical servers - medium Organizational - low Service Level Agreements - high 012 - Justin 012 - MongoDB 012 - 949-555-1234 345 - John 345 - Widget Llc. 678 - Jane 678 - Acme Inc. ClientC ClientB ClientA
- 12. Cost of database partitioning Hardware - medium ($) Software - medium (man-hours) Infrastructure - medium ($ + man-hours) Physical servers - low Organizational - high Service Level Agreements - medium/high
- 13. Cost of device partitioning Hardware - medium/high ($) Software - low (man-hours) Infrastructure - high ($ + man-hours) Physical servers - high Organizational - high Service Level Agreements - low
- 14. Pick intelligently! Switch later. Adding servers is easy, adding developers is hard Change strategies when costs are projected to rise Software costs are typically the highest Organizational costs increase with complexity Hardware is cheap
- 15. MongoDB Can Help Changing software is expensive Leverage sharding “Shard early and often” Higher initial cost Seamless transition between database partitioning and device partitioning. $redact Aggregation Stage Only useful in a narrow range of cases. Security based entirely in code.
- 16. Thank you! Justin LaBreck Senior Consulting Engineer, MongoDB justin.labreck@mongodb.com
Editor's Notes
- Who am I? (3 min; 3 min total) MongoDB Sr. Consulting Engineer Work in multi-tenant environment (Events and entertainment industry) How am I qualified to discuss topic?
- Before we dive in, what is multi-tenancy? Fundamentally, any data that can be segregated can be multi-tenant. Most common is multiple client or permissioned data. What is Multi-tenancy? (2 min; 5 min total) Briefly explain idea of multi-tenancy Provide several examples of multi-tenant environment Highlight a single example to be used throughout talk Event entertainment industry
- Introduce what we will be learning: Covering three types of data segregation or “partitioning” Will discuss business considerations and use-cases for each What strategies exist for multi-tenancy? (7 min; 12 min total)
- Shared database + shared collection (data partitioning) Requires explicit storage of permissions - permissions are hard
- Isolated database + shared environment (database partitioning) Multiple options for interacting with different databases Code level - hard, how does the software know where to connect Database level - easy, leverage sharding (more on this later) Single instance, multiple databases vs. multiple instances, single database
- Isolated environments (device partitioning) Akin to scaling horizontally Multiple server instances, single database (virtual isolated environments) vs. multiple servers, single database Allows for custom code per customer
- Don’t over-project; it’s easier to add more servers than more developers. How do the three strategies compare for cost? (20 min; 30 min total) There are trade-offs; business must decide based on growth projections Don’t over project, easier than it seems to switch
- Each of these costs involves both dollars and man-hours. Costs increase exponentially Usually ends up being more than expected What happens if your product is a success?? Infrastructure cost Physical costs of managing hardware (($ + man hours) * expansiveness) Organizational costs Data partition - how do you test permission-based partitioning? Database partitioning - Who owns code that partitions based on shared environment? Isolated environment - who fields the additional cost? Service Level Agreements (SLA) + points of failure Recovery Point Objective (RPO) Recovery Time Objective (RTO)
- Hardware: large servers needed but fewer of them Software: all that code and testing requires a lot of complexity and time Infrastructure Physical: lots of work for fewer servers Organizational: fewer servers to manage means fewer windows to open by the admin, fewer hard drive replacements SLA: one outage means all customers are offline
- Hardware: more servers Software: code less complex, but still issues (how do you route queries?) Infrastructure Physical: larger servers but not much more work Organizational: memory contention, resource contention SLA: all customers go down if software fails, single customers go down if database instance fails
- Hardware: more servers Software: can be transparent to software Infrastructure Physical: instance per server per hardware, additional automation costs Organizational: many databases need management (indexes, cleanup) SLA: one database down means only one customer down
- “You don’t have to pay benefits to hardware and they work more hours than employees!” Change when costs projected to increase Software cost usually the most expensive Organizational cost always increase with complexity Hardware is cheapest “You don’t pay benefits to hardware and they work more hours than employees!”
- MongoDB can help (5 min; 35 min total) Views as aggregation pipelines (data partitioning) Benefit over SQL views (complex aggregations, $redact) Sharding (database partitioning, device partitioning) Best for future-proofing deployment Transparent to software, brings cost of software down Can partition data by database; by instance; or by hardware without changing software at all Migrate data from one set of hardware to another Premium tier vs. non-premium tier Store churned customers online with low cost Automatic balancing Flexible cache sizing for multi-instance environments Micro-sharding (beware)
- Questions (3 minutes; 40 min total) MongoDB Professional Services available