How to optimize the database queries in a Clear Framework application?

When I started building Clear Framework-powered enterprise applications back in 2018, I assumed that the framework’s built-in database abstraction would handle all query optimization heavy lifting. Fast forward to 2024, and I’ve seen firsthand how even the most well-structured Clear applications can grind to a halt when database queries aren’t tuned properly. As a Clear Framework supplier, I work with dozens of clients every quarter who come to us frustrated by slow load times, timeouts during batch jobs, and higher-than-necessary database infrastructure costs—all stemming from avoidable query inefficiencies. Today, I want to break down the actionable, tested steps we teach our customers to optimize their Clear Framework database queries, based on real-world projects we’ve delivered over the last six years. Clear Framework

First, let’s ground this in how Clear Framework works with databases. Clear uses an ORM (Object-Relational Mapper) that maps application entities directly to database tables, which reduces boilerplate code and cuts down on human error. But that convenience comes with a catch: many developers new to Clear let the ORM generate underperforming SQL without digging under the hood to inspect the actual queries being sent to the database. I once worked with a retail client whose Clear application was taking 12 seconds to load a customer dashboard that should have taken 200ms. The culprit? The ORM was generating 17 separate “N+1 queries” to pull in associated data for each order line—checking the product price, the customer’s discount tier, the store location, and so on, one at a time instead of fetching all needed data in a single join. The fix? Enabled Clear’s built-in eager loading, which pulls all related entities in a single query, cutting load time to 800ms almost instantly. That’s why the first rule of Clear query optimization is to be hyper-aware of how the ORM is constructing your queries.

Let’s start with the most common and impactful fix for Clear applications: mastering eager loading and reducing N+1 query patterns. N+1 queries happen when you iterate over a collection of entities (say, 100 order records) and then access a related entity for each order—like order.customer, order.product, or order.location. By default, Clear’s ORM will run one query to fetch all 100 orders, then 100 additional queries to fetch each related customer, resulting in 101 total queries. For small datasets, this is negligible, but for large datasets (10,000+ orders), that becomes 10,001 queries, which floods the database connection pool and causes latency. In Clear, the fix is straightforward: use the .fetch() method with the includes parameter to explicitly load all related entities in advance. For example, instead of writing:

const orders = orm.em.find(Order, { status: ‘shipped’ });
for (const order of orders) {
console.log(order.customer.name); // Triggers an extra query per order
}

You’d write:

const orders = orm.em.find(Order, { status: ‘shipped’ }, { includes: [‘customer’, ‘product’, ‘location’] });
for (const order of orders) {
console.log(order.customer.name); // No extra queries—all data was fetched in one join
}

We tested this on the retail client’s order dashboard: before adding eager loading, the N+1 queries generated 1.2MB of SQL traffic and took 11.2 seconds. After enabling eager loading, that dropped to 0.07MB of SQL traffic and 0.8 seconds. That’s a 14x performance gain, no code rewrite beyond adding a single includes parameter. The key here is to map your query’s data needs to Clear’s entity relationships—if you know you’ll need related data for a specific use case, always include it upfront instead of letting the ORM lazy-load it on demand.

Next, indexing is non-negotiable, even in Clear Framework applications. I can’t tell you how many times I’ve seen a client’s database have zero secondary indexes, even on fields used in frequent WHERE clauses, sort operations, or join conditions. Clear Framework doesn’t automatically create indexes on entity fields (though we do include a tooling add-on that suggests indexes based on query patterns), so it’s up to developers to add them. For example, if you have a User entity with an email field used for login searches and unique constraints, Clear will create a unique index on email by default—but if you have an Order entity with a orderNumber field used 10,000 times a day to look up individual orders, you’ll need to add a regular index on orderNumber manually. We recently worked with a healthcare client whose patient lookup page was taking 3 seconds to load, because they were querying the patient table by birthDate and zipCode with no composite index on those two fields. Adding a composite index on (birth_date, zip_code) cut the lookup time to 180ms. Another common mistake: adding indexes to fields that are rarely used in queries, which increases write time (every INSERT and UPDATE has to update the index too). A good rule of thumb: for any query that runs more than 100 times a day, check if the fields in the WHERE, JOIN, or ORDER BY clauses have corresponding indexes. If you’re unsure, use database tools like PostgreSQL’s EXPLAIN plan or MySQL’s EXPLAIN to see if the query is doing a full table scan—if it is, that’s a sign you need an index.

Third, avoid fetching unnecessary data with partial queries and projection. Clear’s ORM is powerful enough to fetch only the fields you need, instead of pulling every column from a table. A lot of developers use the .find() method to get full entities, even for use cases that only need a couple of fields—like a dropdown list of order IDs and order dates, or a user table showing only names and emails. For example, if you write:

const orders = orm.em.find(Order, {}, { fields: [‘orderNumber’, ‘orderDate’] });

This generates an SQL query that selects only order_number and order_date, instead of SELECT * from orders, which is what you’d get if you omitted the fields parameter. On a table with 10 columns, that reduces the data transferred over the network by 60%, and the database query time by a similar margin. We worked with a SaaS client whose admin panel was loading 200 rows of product data at a time, and each row was pulling 15 fields including large binary image URLs and long product descriptions that weren’t visible in the table view. By adding the fields parameter to only fetch the 4 visible fields, their panel load time dropped from 1.5 seconds to 220ms, and they saw a 15% reduction in their application’s memory usage on the backend. The other part of this is avoiding fetching large text or binary fields (like blobs) unless absolutely necessary. If you need a product’s description for a detail page, load it when the user navigates to the detail, not when they’re browsing the product list. Clear’s lazy loading works here too—you can mark a field as lazy, and it will only be fetched when you explicitly access it, not when you load the main entity.

Fourth, optimize query logic and use Clear’s query builder for complex operations. For simple find operations, Clear’s ORM is great, but for complex filters, aggregations, or joins across multiple tables, it’s often better to use Clear’s Query Builder instead of the entity manager’s find method. The find method is designed for fetching full entities, but the Query Builder lets you write more precise SQL, use aggregations (like SUM, COUNT, GROUP BY) without loading all entities into memory, and avoid unnecessary joins. For example, if you need to calculate the total revenue from all shipped orders in the last 30 days, using the entity manager to fetch all orders and then sum them in code would load thousands of entities into memory, which is slow and uses a lot of resources. Instead, using the Query Builder:

const totalRevenue = await orm.qb(Order, ‘o’)
.select(‘SUM(o.amount) as total_revenue’)
.where(‘o.status = :status’, { status: ‘shipped’ })
.andWhere(‘o.orderDate >= :startDate’, { startDate: new Date(Date.now() – 30 * 24 * 60 * 60 * 1000) })
.getSingleScalarResult();

This runs a single aggregation query in the database, which is optimized for these operations, and returns a single number instead of loading all order entities. We’ve seen batch job times drop by 70% for clients that switched from entity-based aggregations to the Query Builder. Another tip for complex queries: avoid subqueries where possible, or use Clear’s support for CTEs (Common Table Expressions) to make queries more readable and efficient for the database engine to optimize.

Fifth, use pagination to avoid fetching large datasets. When building lists or tables, never fetch all records at once. Clear Framework integrates seamlessly with database-level pagination via the limit and offset parameters, which tell the database to return only a subset of results instead of all matching records. For example, if you’re building a customer table that shows 20 customers per page:

const customers = await orm.em.find(Customer, {}, { limit: 20, offset: (page – 1) * 20 });

This is far more efficient than fetching 10,000 customers every time the page loads, even if the user only sees the first 20. A common pitfall with pagination is using offset for very large pages—offset: 10000 means the database has to scan the first 10,000 rows to return rows 10,001 to 10,020, which becomes slower as the offset increases. For that, we recommend using keyset pagination (also called cursor-based pagination) with Clear’s Query Builder, which uses a unique, ordered field (like customer ID) to fetch the next set of results, avoiding the full table scan. For example:

This is significantly faster for large datasets, because the database can jump directly to the starting point using an index on the id field, instead of scanning thousands of rows.

Now, as a Clear Framework supplier, I’ll be the first to say that many of these optimizations are easy to overlook, even for experienced developers. That’s why we’ve built a free, optional tooling add-on for Clear Framework applications that automatically audits query patterns, flags N+1 queries, suggests missing indexes, and highlights over-fetched fields. We tested this tool with 50 small to mid-sized Clear applications last year, and on average, it helped cut query execution time by 42% and reduce database resource usage by 28%. The tool doesn’t require any changes to your existing code—it integrates with your Clear ORM and database driver to log and analyze queries without affecting application performance. For enterprises running Clear applications with 10,000+ daily active users, we also offer dedicated performance audits, where our engineering team will review your query logs, explain bottlenecks, and provide tailored optimizations based on your specific use case.

Before I wrap up, a few final best practices that we’ve found to make a big difference long-term. First, always inspect your actual queries in development. Clear Framework has a debug mode that logs all SQL queries executed by the ORM, so you can see exactly what’s being sent to the database. We recommend turning this on in development and reviewing query logs regularly—look for queries that take longer than 100ms, or queries that run multiple times for the same data. Second, profile your database regularly. Tools like pg_stat_statements for PostgreSQL or MySQL’s slow query log can show you the top slow queries in your application, which is a great starting point for optimization. Third, test changes in staging before production. Every index or query change can have unintended consequences—adding an index that’s not needed can slow down writes, so always test optimizations in a staging environment that mirrors your production database size and traffic.

Metal Framework If you’re running a Clear Framework application and noticing slow queries, high database costs, or timeouts, our team can help. We work with teams of all sizes, from small startups to large enterprises, to optimize their Clear application’s database performance. We’ll start with a free initial review of your query patterns, walk you through bottlenecks, and provide a clear plan to improve performance. We don’t do one-size-fits-all solutions—every application’s query needs are different, so our optimizations are tailored to how you use Clear Framework and your database.

References

Clear Framework Official Documentation: Database Query Optimization
PostgreSQL Documentation: Indexing and Performance Tuning
MySQL Documentation: Slow Query Log and Query Optimization
Enterprise Software Performance Report: Clear Framework Application Best Practices

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