Anti Slow Application

Anti Slow Application – How not to set fire to the database. When your code is slow, you might have the N+1 problem.

Software engineering is a complex field with many problems, one of which is the n+1 problem. This is a performance issue that occurs when an application retrieves a set of data from the data store but then must make a separate query for each element in that set. This leads to an excessive number of requests, resulting in a significant delay in response time.

Anti Slow Application

Anti Slow Application

I personally find that this problem often occurs too late in the implementation or testing process, rather than early in the design stage, when the options for fixing the problem are not fully understood.

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In this blog post, we will discuss the n+1 problem in detail, how it affects the performance of your code, and how it can be solved.

For example, suppose an application needs to get a list of orders along with their associated products. If the application receives a list of orders and then queries the products associated with each order, this will result in n + 1 requests, where n is the number of orders. Thus, the more orders, the more requests for partner products. This becomes especially problematic for large datasets, but the effect is already present for smaller datasets.

As such, it is an inefficient way to retrieve data from a data store and can cause serious performance issues.

An n+1 problem can have a big impact on the performance of an application. When an application makes a separate request and a request for each element in the collection, this can result in a large number of database requests, which can significantly slow down the application’s response time and can burn your database as each request adds additional load and overhead. . Additional memory usage, network traffic, connection processing, etc. Also, if the application is working with a large data set, an excessive number of requests can lead to memory overflow when many open connections are waiting to be processed. It can even crash and monitor the entire system when your database just can’t handle the heavy load.

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The best way to deal with the problem, of course, is during the planning phase. Actively anticipating how objects are stored and their relationship to each other. Here we can greatly optimize data storage in terms of read efficiency. We can discuss some denormalization techniques in another blog post, but sometimes there are no easy “data object design” solutions to apply, so the n+1 problem cannot be avoided simply by using a concise data model.

So in such situations there are different methods to solve the n + 1 problem, some of the more popular methods are:

Eager loading is a technique that involves retrieving all the required data, including related objects, in a single database query. In the example we discussed earlier, instead of fetching a list of orders and then querying the database of products associated with each order, we can use active loading to get all the data we need in a single request.

Anti Slow Application

Let’s take a look at the code-behind – since many object relational maps (ORMs) support this out of the box, like here for example with typeORM for NodeJS in Typescript for some reason, it works the same in Python, Java, Python and others

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This works great if your dataset doesn’t have too many connections or tends to be small. With relational databases, you simply connect related data and load all the relationships together.

Most of the computer’s time ⏳ is spent inside the database collecting all related objects. These larger responses must fit in the memory of each processing service and must also be processed by the client, but sometimes this results in performance issues at the database level. This is especially a problem in arrays with many relationships. For example, where to load a chain of related objects. (A → B → C → D or A → B, A → C, A → D). In the example discussed earlier, this could be “Products” → “Orders” → “Customers” → “Addresses” and an attempt to list all products with all their children. eagerly loaded objects can result in a massive dataset that will be loaded over and over again 🔥.

Rapid loading of huge datasets with many relationships will lead to performance issues at the database level and should be avoided.

Lazy loading is a design pattern that involves delaying the loading of data until it is actually needed for processing. This template can be applied on the back, but also directly on the front. Let’s look at some forward currents.

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For example, in an e-commerce application, we need to display an order (shopping cart) on every web page. We can design the interface in such a way that we don’t have to display products related to it directly on every page. But only show related products on the checkout page or overlay that opens when the user clicks. So the download is delayed until the user clicks. So we can find many ways to get around the N+1 problem by designing a UX that takes it into account.

Another scenario where lazy loading is a good use case is when working with large lists of data that need to be displayed. For example, to display a product catalog as a list in a mobile application, where there are hundreds or thousands of products in the catalog. However, we cannot display the entire product catalog and all related data on one small mobile screen without scrolling. Thus, some products will be outside the visible area. We can take advantage of this and load them not directly when the user opens the page, but later. With lazy loading, we can only load the first 10 products the first time the page is accessed, and then gradually load more as the user scrolls down the page. This is great, especially in situations where a quick first response with low latency is required. So the initial load is fast and then more data is loaded over time.

This is useful for cases where loading can be delayed. But this set of deferred data also has disadvantages. This can negatively impact usability and overall experience if not well designed. It takes careful planning and good design to get it right. To prevent users from being constantly interrupted by lazy loading while using the app. Nobody likes spinners constantly loading.

Anti Slow Application

Lazy loading can also mean poor performance at the database level in a high traffic situation. For the product catalog use case we discussed earlier, we now have multiple calls to the back-end database for each visiting user as they scroll through the list view.

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Assuming each user scrolls an average of 5 pages per 10 seconds, that means 0.5 requests per second (RPS). This can lead to database performance issues because the number of concurrent connections a database can handle is limited, and this limit can be quickly reached when scrolling through a large number of users.

The MySQL database, for example, can typically handle around 3,000 requests per second. So if you only use many of them to showcase products, you will have fewer for actual purchase transactions, which will have higher business value for the app 💰.

So lazy loading can help solve the N+1 problem when dealing with a large dataset, but there may be some UI and connectivity issues that need to be overcome.

Batch download is a method that groups data into batches and retrieves all the necessary data for each batch in a single request. This means that only one query is executed, regardless of the number of objects retrieved. This can greatly improve performance and reduce the risk of the N+1 problem. This allows you to reduce the number of queries against the data source by combining multiple queries into a single query. In the example we discussed earlier, an application can use batch upload to get the products associated with a group of orders in a single request.

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So instead of downloading only products from one order, we collect requests and download all products from all orders at the same time. Batch loading can cause other problems when complex queries put a lot of load on the database.

While the batch upload design pattern can be an effective tool for improving performance and overcoming the N+1 problem, it’s important to use it correctly to avoid introducing new problems.

Dear all… Caching is a widely used technique in modern software systems that involves storing frequently accessed data in memory to reduce the number of database queries. In the example we discussed earlier, an application could use a cache to store the products associated with each order in memory the first time they are accessed so that they

Anti Slow Application

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