In my last article, You Won’t Pay Me to Rescue Your Legacy System, I talked about my original attempt at specializing, and why it didn’t work. I bumbled along until I lucked into a client that helped me understand when Legacy System Rescue becomes an Expensive Problem.
Rather than Legacy System Rescue, I was hired to do “keep the lights on” work. The company had a 3 developer team working on a next generation system, all I had to do was to keep things running as smoothly as possible until they delivered.
The legacy system was buckling under the weight of their current customers. Potential customers were waiting in line to give them money, and had to be turned. Active customers were churning because the platform was buckling.
That’s when I realized – Legacy System Rescue may grudgingly get a single developer, but Scaling gets three developers to scale and one to keep the lights on. Scaling is an expensive problem because it involves churning existing customers and turning away new ones.
Over 10 months I iteratively rescued the legacy system by fixing bugs and removing choke points. After investing over 50 developer months, the next generation system was completely scrapped.
The Lesson – Companies won’t pay to rescue a legacy system, but they’d gladly pay 4x to scaleup and meet demand.
When I first started consulting, I tried to specialize in Legacy System Rescue. I quickly learned that this is terrible positioning because Legacy System Rescue isn’t an Expensive Problem. Jonathan Stark defines an Expensive Problem as a problem that someone would like to spend a lot of money on to solve right now.
Legacy System Rescue is certainly a Big Problem. Everyone agrees that a buggy system that makes development slow and painful is bad. Errors in production are bad. Spending time and resources to mitigate production outages are bad. But there is no immediacy. There’s no reason to spend a lot of money right now instead of waiting until the next feature ships, or the next quarter. Letting things go just a little bit longer is usually why the system needs a rescue.
Hiring someone like me to come in, analyze the codebase and find a way to untangle the mess is a lot of work. Fixing bugs and making it easy to add new features is a low leverage situation. It takes a lot of time by highly skilled developers. Highly skilled developers in low leverage situations makes Legacy System Rescue an Expensive Solution. It will probably pay off for the company, but no one department is going to get enough value from fixing the legacy system to cover the costs. The ROI gets worse when you factor in the resentments of the developers. Bringing in an outsider to judge their work and dictate fundamental changes doesn’t fill people with joy.
Combine the two and you have a Tragedy of the Commons – a Big Problem that requires an Expensive Solution. What you don’t have is a business case to spend a lot of money, right now, to fix things.
You won’t pay me to rescue your legacy system because paying a lot, right now, for the solution is worth less to you than living with the problem.
Over the past few months I have been ruminating on SaaS Tenancy Models and how they drive architectural decisions. I hope you’ve enjoyed the series as I’ve scratched my itch.
Here is a roundup of the 7 articles In case you missed any of the parts, or need a handy index to what I’m sure is the most in depth discussion of SaaS Tenancy Models ever written.
3 Signs Your Resource Allocation Model Is Working Against You
After 6 posts on SaaS Tenancy Models, I want to bring it back to some concrete examples. When your SaaS has a Single Tenant model, clients expect to allocate all the resources they need, whenever they want. When every client is entitled to the entire resource pool, no client gets a great customer experience.
Here are 3 signs your Resource Allocation Model is working against you:
Large clients cause small client’s work to stall
You have to rebalance the mix of clients in a cell for stability
Run your job at night for best performance
Large clients cause small client’s work to stall
This is a classic “noisy neighbor” problem. Each client tries to claim all the shared resources needed to do their work. This isn’t much of a problem when none of the clients need a significant percentage of the pool. When a large client comes along, it drains the pool, and leaves your small clients flopping like fish out of water.
You have to rebalance the mix of clients in a cell for stability
When having multiple large clients in a cell affects stability, the short term solution is to migrate some clients to another cell. Large clients can impact performance, but they should not be able to impact stability. Moving clients around buys you time, but it also forces you to focus on smaller, less profitable clients.
Run your job at night for best performance
This is advice that often pops up on SaaS message boards. Don’t try to run your job during the day, schedule it to run in the evening so it is ready for the morning. When clients start posting workarounds to your problems, it’s a clear sign of frustration. Your clients are noticing that performance varies by the time of day. They are building mental models of your platform and deciding you have load and scale issues. By being helpful to each other, your clients are advertising your problems.
The tenancy line is a useful construct for separating SaaS data from client data. When you have few clients, separating the data may not be worth the effort of having multiple data stores. As your system grows, ensuring that client data is separated from the SaaS data becomes as critical as ensuring the clients’ data remains separate from each other.
Company data is everything operational. Was an action successful? Does it need to be retried? How many jobs are running right now? This data is extremely important to make sure that client jobs are run efficiently, but it’s not relevant to clients. Clients care about how long a job takes to complete, not about your concurrency, load shaping, or retry rate.
While the data is nearly meaningless to your clients, it is useful to you. It becomes more useful in aggregate. It has synergy. A random failure for one client becomes a pattern when you can see across all clients. When operational data is stored in logically separated databases you quickly lose the ability to check the data. This is when it becomes important to separate operational data from clients.
Pull the operational data from a single client into a multi-tenant repository for the SaaS, and suddenly you can see what’s happening system wide. Instead of only seeing what’s happening to a single client, you see the system.
If visibility isn’t enough, extracting operational data is usually its own reward.
Operational data is usually high velocity – tracking a job’s progress involves updating the status with every state change. If your operational store is the same as the client store, tracking progress conflicts with the actual work.
A Jobs Service is a very common service for SaaS companies. It provides a way to run work on a schedule, on demand, and independent of human activity. Often, everything that isn’t done through the website is done by a Job Service.
I have never worked at a SaaS without some version of a Job Service, usually homegrown and built off a database instead of a queue. They usually have descriptive and funny names – Task Processor, Crons, Crontabulous, Maestro, Batch Processor and of course Polite Batch Jobs.
Starting early in the SaaS’s life, they also evolve and grow with the SaaS, creating problems as they migrate from Single Tenant to a logically shared environment.
Single Tenant Job Service
In a single tenant model, provisioning a Job Service with a pool of workers is fairly straightforward. Jobs are generated and put onto a queue (and not a database!)
The Job Service takes jobs off of the queue and fans them out to the worker pool. This is simple and works well because the Queue handles the complexities of tracking and retrying jobs.
Multiple clients exist on a single database cluster, each with their own logically separate schema.
The CRUD service has become a pool of servers that can act on behalf of any client.
There is still only 1 queue and 1 Job Service; Workers can act on behalf of any client, just like the CRUD servers.
Jobs get added haphazardly, and processed in a FIFO manner.
This model is much more resource efficient – sharing workers allows you to size the pool to keep things busy.
But this design is a disaster from a Noisy Neighbor standpoint.
Because the Queue is FIFO, the Job Service has no visibility into the client composition of the pending jobs, and a large client can easily starve a small one of resources by adding hundreds or thousands of jobs to the queue. The large client will see progress as the jobs are processed, but nothing happens for the small client until the large job finishes.
Things get even worse if the Queue and Job Service are Global instead of Cell based. A global queue feeding a global worker pool that works on clients spread across multiple database clusters will naturally cause database cluster hot spots. Performance will degrade for everyone on the cluster while the workers do massive jobs for a few large clients.
You can add bandaids like limiting the number of jobs per client and moving excess work onto overflow queues. This will help smaller clients somewhat, but natural hotspots will still occur.
Cross The Tenancy Line – Become Multi-Tenant
The Job Service needs to evolve from being Logically Separated into a Multi-Tenant service.
It needs to know how many jobs each client has pending, how long the jobs are taking, and how hot the database clusters are running so that it can operate a priority queue instead of FIFO.
The Jobs Service needs to move across the Tenancy Line
What is the Tenancy Line?
With Logically Separate infrastructure the clients share infrastructure, but the data and services all behave as if there is only one client at a time. As a result each client can regulate its own behavior, but has no visibility into the infrastructure as a whole.
To stop acting like a Single Tenant service, the Jobs Service needs to cross the line into Multi-Tenancy.
This change is conceptually simple, but has a lot of subtle implications.
The Service can control load across clients
In the original model work loads are random based on when jobs are added to the queue. When a hotspot emerges, there’s not much that the service can do without manual intervention. When there’s a noisy neighbor you can’t do much to stop them from starving smaller clients because you don’t know where those clients are in the queue.
With a Multi-Tenant job service, you can control resources across cells and the entire platform. Small clients can be protected by moving jobs up in priority based on how many recent jobs they have completed.
Jobs will finish faster as worker loads can be managed across cells, preventing hotspots.
Overall throughput will rise, smaller client performance will improve dramatically, and large clients will see more consistent execution times.
The Job Service Becomes a Queue
The original design used a single simple queue. Every client adds jobs directly to the queue, and the Job Service’s responsibility is to take work, pass it to a worker, and mark the job as complete. If there’s a failure, the queue will time the job out and put the work back on the queue.
A FIFO queue prioritizes by insertion order and doesn’t have any mechanism for reordering. The Job Service will have to build prioritization logic and find a way to integrate into a queuing mechanism. Do not give in to temptation and turn your database into a queue!
Conclusion
Pushing the Jobs Service across the Tenancy Line is a major coming of age step in the evolution of a SaaS company.
It trades significant development resources and complexity for consistent execution and a solution to the Noisy Neighbor Problem. The SaaS benefits from the synergy this creates with better resource utilization and reduced database hotspotting.
Once a SaaS has enough clients to warrant the change, making the Jobs Processor Multi-Tenant is a major step forward.
This is part 4 in a series on SaaS Tenancy Models. Parts 1 , 2 , and 3.
SaaS companies are often approached by potential clients who want their instance to be completely separate from any other client. Sometimes the request is driven by legal requirements (primarily healthcare and defense), sometimes it is a desire for enhanced security.
Often, running a Multi-Tenant service with a single client will satisfy the client’s needs. Clients are often willing to pay for the privilege of their account run Single Tenant, making it a potentially lucrative option for a SaaS.
What is a Cell?
A Cell is an independent instance of a SaaS’ software setup. This is different from having software running in multiple datacenters or even multiple continents. If the services talk to each other, they are in the same cell regardless of physical location.
Cells can differ with the number and power of servers and databases. Cells can even have entirely different caching options depending on need.
The 3 most common Cell setups are Production, Staging (or Test), and Local.
Cell Properties
Cell architecture comes with a few distinct properties:
Cell structures allow SaaS to grow internationally and offer clients low latency and localized data policies (think GDPR). Latency from the US to Europe, Asia and South America is noticeable and degrades the client experience.
Clients exist in 1 cell at a time. They can migrate, but they can’t exist in multiple cells.
Generally speaking, Cells can not be part of a disaster recovery plan. Switching clients between Cells usually involves copying the database, and can’t be done if the client’s original Cell is down.
Cell Isolation as a Single Tenant Option
In part 3 I covered the difficulties in operating in a true Single Tenant model at scale. A Cell with a single client effectively recreates the Single Tenancy experience.
Few clients want this level of isolation, but those that need it are prepared to pay for the extra infrastructure costs of an additional Cell.
Conclusion
For SaaS without global services, a Cell model enables a mix of clients on logically separated Multi-Tenant infrastructure and clients with effectively Single Tenant infrastructure. This allows the company to pursue clients with Single Tenant needs, and the higher price point they offer.
The catch is that Single Tenant Cells can’t exist in an architecture with global services. If there is a single service that must have access to all client data, Single Tenant Cells are out.
For SaaS with a pure Single Tenant model, infrastructure consolidation usually drives the first two, nearly simultaneous, steps towards a Multi-Tenant model. The two steps convert the front end servers to be Multi-Tenant and switch the client databases from physical to logical isolation. These two steps are usually done nearly simultaneously as a SaaS grows beyond a handful of clients, infrastructure costs skyrocket and things become unmanageable.
Considering the 5 factors laid out in the introduction and addendum – complexity, security, scalability, consistent performance, and synergy this move greatly increases scalability, at the cost of increased complexity, decreased security, and opening the door to consistent performance problems. Synergy is not immediately impacted, but these changes make adding Synergy at a later date much easier.
Why is this such an early move when it has 3 negative factors and only 1 positive? Because pure Single Tenant designs have nearly insurmountable scalability problems, and these two changes are the fastest, most obvious and most cost effective solution.
Complexity
Shifting from Single Tenant servers and databases to Multi-Tenant slightly increases software complexity in exchange for massively decreasing platform complexity.
The web servers need to be able to understand which client a request is for, usually through sub domains like client.mySaaS.com, and use that knowledge to validate the user and connect to the correct database to retrieve data.
The difficult and risky part here is making sure that valid sessions stay associated with the correct account.
Database server consolidation tends to be less tricky. Most database servers support multiple schemas with their own credentials and logical isolation. Logical separation provides unique connection settings for the web servers. Individual client logins are restricted to the client’s schema and the SaaS developers do not need to treat logical and physical separation any differently.
Migrations and Versioning Become Expensive
The biggest database problems with a many-to-many design crop up during migrations. Inevitably, web and database changes will be incomparable between versions. Some SaaS models require all clients on the same version, which limits comparability issues to the release window (which itself can take days), while other models allow clients to be on different versions for years.
The general solution to the problem of long lived versions is to stand up a pool of web and database servers on the new version, migrate clients to the new pool, and update request routing.
Security
The biggest risk around these changes is database secret handling; every server can now connect to every database. Compromising a single server becomes a vector for exposing data from multiple clients. This risk can be limited by proxy layers that keep database connections away from public facing web servers. Still a compromised server is now a risk to multiple clients.
Changing from physical to logical database separation is less risky. Each client will still be logically separated with their own schema, and permissioning should make it impossible to do queries across multiple clients.
Scalability
Scalability is the goal of Multi-Tenant Infrastructure Consolidation.
In addition to helping the SaaS, the consolidation will also help clients. Shared server pools will increase stability and uptime by providing access to a much larger group of active servers. The client also benefits from having more servers and more slack, making it much easier for the SaaS to absorb bursts in client activity.
Likewise, running multiple clients on larger database clusters generally increases uptime and provides slack for bursts and spikes.
These changes only impact response times when the single tenant setup would have been overwhelmed. The minimum response times don’t change, but the maximum response times get lower and occur less frequently.
Consistent Performance
The flip side to the tenancy change is the introduction of the Noisy Neighbor problem. This mostly impacts the database layer and occurs when large clients overwhelm the database servers and drown out resources for smaller clients.
This can be especially frustrating to clients because it can happen at any time, last for an unknown period, and there’s no warning or notification. Things “get slow” and there are no guarantees about how often clients are impacted, notice, or complain.
Synergy
There is no direct Synergy impact from changing the web and database servers.
A SaaS starting from a pure Single Tenant model is not pursuing Synergy, otherwise the initial model would have been Multi-Tenant.
Placing distinct client schemas onto a single server does open the door to future Synergy work. Working with data in SQL across different schemas on the same server is much easier than working across physical servers. The work would still require changing the security model and writing quite a bit of code. There is now a doorway if the SaaS has a reason to walk through.
Conclusion
As discussed in the introduction, a SaaS may begin with a purely Single Tenant model for several reasons. High infrastructure bills and poor resource utilization will quickly drive an Infrastructure Consolidation to Multi-Tenant servers and logically separated databases.
The exceptions to this rule are SaaS that have few very large clients or clients with high security requirements. These SaaS will have to price and market themselves accordingly.
Infrastructure Consolidation is an early driver away from a pure Single Tenant model to Multi-Tenancy. The change is mostly positive for clients, but does add additional security and client satisfaction risks.
In the first post on Saas Tenancy Models, I introduced the two idealized models – Single and Multi-Tenant. Many SaaS companies start off as Single Tenant by default, rather than strategy, and migrate towards increasingly multi-tenant models under the influence of 4 main factors – complexity, security, scalability, and consistent performance.
After publishing, I realized that I left out an important fifth factor, synergy.
Synergy
In the context of this series, synergy is the increased value to the client as a result of mixing the client’s data with other clients. A SaaS may even become a platform if the synergies become more valuable to the clients than the original service.
Another aspect of synergy is that the clients only gain the extra value so long as they remain customers of the SaaS. When clients churn, the SaaS usually retains the extra value, even after deleting the client’s data. This organically strengthens client lock in and increases the SaaS value over time. The existing data set becomes ever more valuable, making it increasingly difficult for clients to leave.
Some types of businesses, like retargeting ad buyers, create a lot of value for their clients by mixing client data. Ad buyers increase effectiveness of their ad purchases by building larger consumer profiles. This makes the ad purchases more effective for all clients.
On the other hand, a traditional CRM, or a codeless service like Zapier, would be very hard pressed to increase client value by mixing client data. Having the same physical person in multiple client instances in a CRM doesn’t open a lot of avenues; what could you offer – track which clients a contact responds to? No code services may mix client data as part of bulk operations, but that doesn’t add value to the clients.
Sometimes there might be potential synergy, like in Healthcare and Education, but it would be unethical and illegal to mix the data.
Not All Factors Are Client Facing
Two of the factors, complexity and scalability, are generally invisible to clients. When complexity and scalability are noticed, it is negative:
Why do new features take so long to develop?
Why are bugs so difficult to resolve?
Why does the client experience get worse as usage grows?
A SaaS never wants a client asking these questions.
Security, Consistent Performance and Synergy are discussion points with clients.
Many SaaS companies can adjust Security concerns and Consistent Performance through configuration isolation.
Synergy is a highly marketable service differentiator and generally not negotiable.
Simplified Drawings
As much as possible I’m going to treat and draw things as 2-tier systems rather than N-tier. As long as the principles are similar, I’ll default to simplified 2-tier diagrams over N-tier or microservice diagrams.
Next Time
Coming up I’ll be breaking down single to multi-tenant transformations.
Why a SaaS would want the transformation, what are the tradeoffs, and what are the potential pitfalls.
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