Scaling Customer Reports: Why SQL Views and Secure Permissions Outperform Custom Query Languages

As products mature, the demand for highly customized customer reports often grows, pushing beyond the capabilities of no-code GUIs. While the idea of developing a custom SQL-like query language (à la Salesforce SOQL, ShopifyQL, or Stripe Sigma) might seem appealing, it's a remarkably complex undertaking that most teams should avoid.

The Case Against Building Your Own Query Language

Creating a query language and its processor from scratch is an incredibly difficult task to execute well. It involves parsing, query optimization, security, and a myriad of edge cases that can quickly overwhelm resources. Instead of reinventing the wheel, look to existing, mature solutions.

Embracing SQL: The Path to Scalable Reporting

For most data structures, standard SQL offers a powerful and universally understood language for data querying. Consider putting a robust database like DuckDB in front of your data, allowing users to write SQL directly. Alternatively, you could build a compiler that translates queries from a more human-friendly or application-specific language into SQL, offering a simplified interface while still leveraging SQL's power under the hood.

Even users unfamiliar with SQL can benefit; tools like ChatGPT can readily generate SQL queries from plain English descriptions, making SQL more accessible than ever.

Secure and Efficient Multi-Tenant Reporting with Database Views

The primary challenge in enabling direct SQL access for customers, especially in a multi-tenant environment, is ensuring data isolation, security, and performance. A highly effective strategy involves leveraging your database's built-in capabilities:

1. Define Baseline Views: Start by creating a set of standard database views that expose the necessary data in a clean, business-friendly schema, abstracting away internal complexities.

2. Customer-Specific Views: For each customer, dynamically create a replica of these baseline views, specifically filtered by their customer_id. This ensures that a customer's view will only show data relevant to them. For example:

   sql CREATE VIEW customer_xyz_data AS SELECT * FROM data_stuff WHERE customer_id=x;

3. Dedicated Read-Only Accounts: Crucially, create a dedicated database user account for each customer. Grant this user SELECT permissions only on their specific views. This enforces a rigorous security sandbox at the database level:

   sql CREATE USER customer_xyz WITH PASSWORD 'foo'; GRANT SELECT ON customer_xyz_data TO customer_xyz;

   Running customer queries through these sandboxed accounts guarantees that they can only read data from their designated views and cannot access, modify, or impact other customer data or your core database tables.

Performance and Scalability Considerations

• View Efficiency: A database view is essentially a stored query that acts like a virtual table. Querying a view means combining the user's query with the view's underlying query. Modern database optimizers are highly efficient, so the performance largely depends on the complexity of the view's definition and the user's query, as well as proper indexing on the base tables.
• Resource Isolation: While views are efficient, a single shared database instance can still face performance challenges if many customers run complex, unoptimized queries simultaneously. For very high-demand scenarios, provisioning a separate read-only database instance per customer could offer better resource isolation, though this adds operational overhead.
• Dynamic Management: To manage a large number of customers, consider creating these customer-specific views and user accounts on demand when a user logs in, and then dropping them after a session or period of inactivity. This can help keep the database schema cleaner and manage resources more effectively.

By adopting this SQL-centric approach with robust database-level security and careful schema design, organizations can provide powerful, custom reporting capabilities to their customers without the immense burden of building and maintaining a bespoke query language.