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Hello everyone! I'm really happy that you made it here. I had my doubts about whether I was going to make it, but in the end, cooler heads prevailed, and here we are.
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Let me just go through the formalities. My name is Karol Szuster, and I am a software engineer working at Upside.
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Today, I want to talk to you about nightmare neighbors. This might sound confusing without the subtitle, but I don’t mean the guy that lives next door to you.
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Although those kinds of neighbors can also cause you to lose quite a bit of sleep.
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To clarify what I mean by neighbors, I am referring to the multi-tenant application pattern. Let's fill the waters here.
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By a show of hands, could you tell me who here has previous experience with such an architecture? It's quite a few of you, which is great. I hope you will serve as fact-checkers, and I also hope you will learn something today.
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This presentation assumes very little previous knowledge about this pattern, so I’ll begin with a small introduction.
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Multi-tenancy is a software development pattern where a single instance of an application serves multiple clients. We can visualize it in a diagram.
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In this case, we should probably name something different. By 'users' on the slide, I mean customers—businesses that use applications.
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These are the tenants for whom we will separate data. We can contrast this pattern with the traditional single-tenant approach.
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In a single-tenant model, each customer or client has a separate instance of our application.
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As opposed to multi-tenancy, which aims to create the illusion for the users that they are the only ones using the application.
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Quickly, I will go over why we would even want to implement multi-tenancy.
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First of all, the setup is much quicker. Spinning up a new instance for each client is complicated and costly.
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Maintenance becomes unscalable if we rely on the single-tenant application pattern.
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Additionally, the costs of using a single-tenant application might lead to underutilization of resources.
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Multi-tenancy allows us to optimize our resource usage effectively.
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The pivotal concern in this pattern is how to partition the data. How do we actually isolate the tenants?
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This is critical because our reputation as a company relies on maintaining this isolation without data leaks.
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It's especially important if our clients are enterprises—leaks in this context are unacceptable.
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There are multiple levels at which we can separate the data, and I will go through these methods one by one.
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The first method is row-level partitioning, which is a common approach in relational databases. The idea is simple: each record in the database has a tenant assigned.
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For implementation, you could have a tenant table in the database that links to users.
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To do this effectively, I advocate for a normalized schema whenever possible.
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This normalization makes it easier to work with the data without constantly having to refer to separate relationships.
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As for implementing this partitioning, you can use default scopes to avoid having to repeat the same where clause in each query.
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This ensures that the application injects the correct tenant information into all relevant queries.
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Extracting the tenant can depend on your particular use case, each with its advantages and disadvantages.
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You could extract it from the host, subdomain, or request headers.
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To actually set the current tenant, there's no standard method; it generally hinges on the thread current object.
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You could use request store, or something in ActiveSupport called current attributes.
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Having a current tenant per request allows you to populate this for each request.
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This method allows you to avoid special infrastructure and maintains minimal overhead.
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Creating new tenants using this approach is straightforward. It can be done simply by adding records into the database.
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However, the application holds full responsibility for separation. We must ensure that all requests are scoped properly, including validations, to maintain isolation.
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To assist with this, gems like ActsAsTenant or ActiveRecord MultiTenant are available.
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Despite having these tools at our disposal, we should remain vigilant about data integrity, especially when our focus is on maintaining it.
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Should we forget our workloads, this could lead to undesired behaviors like leaking data.
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One approach to mitigate these risks is using row-level security in the database.
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Row-level security allows us to restrict which rows can be returned based on which tenant is making the request.
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To enable this, we define security policies for each table that require tenant identification for record access.
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Note that not all users are subject to these restrictions. Certain roles, such as super users, may bypass this security.
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It's essential not to run production databases as super users.
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With the database session parameter handling, we can keep projects simpler by ensuring the correct tenant is set once per request.
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However, we must also reset session parameters in Rails to prevent connection pool issues.
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Utilizing middleware is a common method to extract the tenant and manage connection pooling.
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This approach has both benefits and potential pitfalls.
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If we forget to explicitly set the tenant, we may not retrieve the expected records, which can lead to application errors.
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This failsafe allows us to restrict data returns more securely than traditional where clauses.
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Despite its strengths, this introduces implicit state management issues, which we must be aware of.
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The connection pool mechanism must ensure that we track the correct tenant through requests.
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Scaling applications requires diligence when managing connection pool operations.
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When utilizing multiple replicas, it would be wise to consider external connection pooling.
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For example, PgBouncer can effectively manage database connections, especially in transaction pooling scenarios.
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We can define which databases to connect when performing queries and transactions.
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Another method to achieve multi-tenancy is through schema-level partitioning, where each tenant has its own schema.
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Each schema contains tables representing different tenants, helping further isolate data.
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Again, using the search path session parameter is crucial for managing schemas effectively.
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While this method offers low effort in migration processes, we face drawbacks as well.
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Migrations will need to be custom-built for every tenant, which can lead to complexity and increased scope.
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Creating new tenants becomes a more involved task, transferring data to the new schema.
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The Heroku platform cautions against this approach due to backup challenges involved.
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Shared schema management adds another layer of complexity that can affect use cases.
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Eventually, we get to database-level partitioning—where each tenant is served by a completely separate database.
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While it offers clear benefits, it may also require re-establishing connections frequently.
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Some tools manage this by retaining previous connections when switching databases.
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Although easy to implement, hidden issues can arise as the application scales.
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Database connections must be handled with caution to avoid leaking data across threads.
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Horizontal sharding, where we maintain multiple databases with the same schema, offers a viable solution.
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This became natively supported in Rails, showcasing a modern approach to multi-tenancy.
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As with so many architecture choices, weighing pros and cons is essential.
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You must consider the unique demands of your business to select an appropriate approach.
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Plan early to avoid complications arising from locking yourself into a decision.
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I appreciate your time and hope this discussion has been valuable.
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I managed to wrap it up in time, and I'll now take any questions.
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So, which of these solutions would you recommend if you need to have aggregated computations from multiple tenants?
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Both the schema-level and row-level approaches allow for aggregation since they enable table referencing across schemas.
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If I had to choose one, the row-level approach is the most battle-tested solution for this problem.
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Many successful multi-tenant architectures like Shopify and Salesforce utilize this strategy.
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Thank you for your question!
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I understand the concerns about row-level multi-tenancy, particularly regarding where clause handling.
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If a model is scoped to a tenant, trying to access the data without setting the tenant will result in an error.
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It’s essential to remain vigilant about managing these access rules.
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Yes, it's a valid concern about accessing the database not just through the application but other services.
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This reinforces how important it is to mind tenant scoping in various contexts.
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Thank you for your insightful remarks!
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Regarding your question about database performance and indexing per tenant:
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Indexing can indeed improve performance, especially when using multi-column indexes.
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Partitioning inside the database could also improve query speed by reducing the search scope.
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It’s an important aspect that requires long-term planning.
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Additionally, using read models in a multi-tenant context could enhance data separation.
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Using materialized views might structure your data to improve access.
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Thank you all for engaging! If there's nothing else, I’ll wrap it up here.
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Thank you again. Have a great day!
