When your SaaS Scaleup can't scale, I can help
This post has been a long time coming - I wrote the idea down in my first list of potential posts, and I wrote a draft way back in 2019!
It is also the first time I can say that the content has been approved by my employer since they published it on their website.
It's a great read, and I hope you enjoy it:
Dropdooms! How Binding an Unbounded Reference Table Can Kill Your UI’s Performance
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.
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.
After an initial infrastructure consolidation the Job Service might look like this:
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.
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
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.
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 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!
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.
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 architecture comes with a few distinct properties:
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.
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.
If you are enjoying this series, consider subscribing to my mailing list (https://shermanonsoftware.com/subscribe/) so that you don’t miss an installment!
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.
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.
Two of the factors, complexity and scalability, are generally invisible to clients. When complexity and scalability are noticed, it is negative:
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.
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.
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.
Please subscribe to my mailing list to make sure you don’t miss out!
Recently, I’ve spent a lot of time discussing the evolution of SaaS company Tenancy Models with my colleague Benjamin. These conversations have revealed that my thinking on the subject is vague and needs focus and sharpening through writing.
This is the first in a series of posts where I will dive deep on the technical aspect of tenancy models, the tradeoffs, which factors go into deciding on appropriate models, and how implementations evolve over time.
There are 2 ideal models, single-tenant and multi-tenant, but most actual implementations are a hybrid mix.
In the computer realm, single-tenant systems are ones where the client is the only user of the servers, databases and other system tiers. Software is installed on the system and it runs for one client. Multi-tenant means that there are multiple clients on the servers and client data is mingled in the databases.
Pre-web software tended to be single-tenant because it ran on the client’s hardware. As software migrated online and the SaaS model took off more complicated models became possible. Moving from Offline to Online to the Cloud was mostly an exercise in who owned the hardware, and how difficult it was to get more.
When the software ran on the client’s hardware, at the client’s site, the hardware was basically unchangeable. As things moved online, software became much easier to update, but hardware considerations were often made years in advance. With cloud services, more hardware is just a click away allowing continuous evolution.
The main factors driving tenancy decisions are complexity, security, scalability, and consistent performance.
Keeping client data mingled on the servers without exposing anything to the wrong client tends to make multi-tenant software more complex than single-tenant. The extra complexity translates to longer development cycles and higher developer costs.
Most SaaS software starts off with a single-tenant design by accident. It isn’t a case of tech debt or cutting corners, Version 1 of the software needs to support a single client. Supporting 10 clients with 10 instances is usually easier than developing 1 instance that supports 10 clients. Being overwhelmed by interested clients is a good problem to have!
Eventually the complexity cost of running massive numbers of single instances outweighs development savings, and the model begins evolving towards a multi-tenant model.
The biggest driver of complexity is the second most pressing factor - security. Ensuring that data doesn’t leak between clients is difficult.
A setup like this looks simple, but is extremely dangerous:
Forgetting to include client_id in any SQL Where clause will result in a data leak.
On the server side, it is also very easy to have a user log in, but lose track of which client an active session belongs to, and which data it can access. This creates a whole collection of bugs around guessing and iterating contact ids.
Single-tenant systems don’t have these types of security problems. No matter how badly a system is secured, each instance can only leak data for a single client. Systems in industries with heavy penalties for leaking data, like Healthcare and Education tend to be more single-tenant. Single tenant models make audits easier and reduce overall company risk.
Scalability concerns come in after complexity and security because they fall into the “good problems to have” category. Scaling problems are a sign of product market fit and paying customers. Being able to go internet scale and process 1 million events a second is nice, but it is meaningless without customers.
Single-tenant systems scale poorly. Each client needs separate servers, databases, caches, and other resources. There are no economies or efficiencies of scale. The smallest, least powered machines are generally way more powerful than any single client. Worse, usage patterns mean that these resources will mostly eat money and sit idle.
Finally, all of those machines have to be maintained. That’s not a big deal with 10 clients, or even 100. With 100,000 clients, completely separate stacks would require teams of people to maintain.
Multi-tenant models scale much better because the clients share resources. Cloud services make it easy to add another server to a pool, and large pools make the impact of adding clients negligible. Adding database nodes is more difficult, but the principle holds - serving dozens to hundreds of clients on a single database allows the SaaS to minimize wasted resources and keeps teams smaller.
Consistent Performance, also known as the Noisy Neighbor Problem, comes up as a negative side effect of multi-tenant systems.
Perfectly even load distribution is impossible. At any given moment, some clients will have greater needs than others. Whichever servers and databases those clients are on will run hotter than others. Other clients will experience worse performance than normal because there are fewer resources available on the server.
Bursty and compute intensive SaaS will feel these problems more than SaaS with a regular cadence. For example a URL shortening service will have a long tail of links that rarely, if ever, get hit. Some links will suddenly go viral and suck up massive amounts of resources. On the other extreme - a company that does End Of Day processing for retail stores knows when the data processing starts, and the amount of sales in any one store is limited by the number of registers.
Single tenant systems don’t have these problems because there are no neighbors sucking up resources. But, due to their higher operating costs, they also don’t have as much extra resources available to handle bursts.
Consistent performance is rarely a driver in initial single vs multi-tenant design because the problems appear as a side effect of scale. By the time the issue comes up, the design has been in production for years. Instead, consistent performance becomes a major factor as designs evolve.
Initial forays into multi-tenant design are especially vulnerable to these problems. Multi-tenant worker pools fed from single-tenant client repositories are ripe for bursty and long running process problems.
Fully multi-tenant systems, with large resource pools, have more resilience. Additionally, processing layers have access to all of the data needed to orchestrate and balance between clients.
In this post I covered the two tenancy models, touched on why most SaaS companies start off with single-tenant models, and the major factors impacting and influencing tenancy design.
Single tenant systems tend to be simpler to develop and more secure, but are more expensive to run on a per client basis and don’t scale well. Multi tenant systems are harder to develop and secure, but have economic and performance advantages as they scale. As a result, SaaS companies usually start with single tenant designs and iterate towards multi-tenancy.
Next up, I will cover the gray dividing line between single and multi-tenant data within a SaaS, The Tenancy Line.
