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In my previous post I described how to load complex data objects from the persistent storage using the “auto-load” technique. Using that approach a developer can statically identify specific (child) properties which should be returned along with the parent object(s) when a client app sends a request to load them. That approach works well for object relations which must be unconditionally returned with the parent object. However, there are many scenarios when the client app must control which relations should be preloaded and returned along with the parent.  The API described below allows to accomplish that.

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Previously I wrote a post on how to retrieve data objects from Backendless. The code in the article loads a collection of the Restaurant objects and although it does not show it, the related collection of the Location objects arrives un-initialized. That is the default behavior of Backendless when it comes to loading related objects. The code below demonstrates that the collection is indeed empty (null). (The code in these examples is from the article describing how to generate client-side code based on data tables).

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In a Backendless backend you can restrict access to API operations and/or application data. A restriction may apply either to specific users or to roles. When a restriction applies to a role, it automatically applies to the users in that role. For example, suppose you have two roles in a job-searching application – employer and job-candidate. Each role will have a certain set of permissions, for instance an employer can see all the candidates who applied for a job.

Backendless supports two types of roles – system-defined and developer-defined roles. System roles automatically come with the backend, Backendless assigns them based on how user logs in or accesses the app. For example, the AuthenticatedUser role is assigned to any users who successfully logs in.

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It is a common use case when two or more users try (or need) to login to an application using the same user ID and password combination. Some applications allow it (for example, Netflix supports concurrent logins with the same credentials from different devices) and other apps restrict it. With Backendless you can easily configure your backend to support either way of handling multiple logins without writing a single line of code. To configure the multiple login policy:

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Since today is a saturday let’s review a fun feature – ROI (return on investment) calculator. That many sound like a boring subject, but we sure tried to make it fun. Indeed, if you are a developer and are tasked to figure out how a product or a service can save money, it may be a daunting task. Certainly not with Backendless. First of all you can start with our service at no cost all – the Backendless free plan is very generous with unlimited API calls and no request per second throttling. On top of this, Backendless will tell you how much money you’re saving just by using it. Here’s how you can find out:

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Data paging is the process of breaking up a larger set of objects into smaller chunks, commonly referred to as pages. Typically paging applies to the results of search queries when the server may return too many objects at once. Since smaller pages of data can be returned to the client much faster, the user of the client application does not need to wait. As a result, the user experience of the app is significantly improved, which is the primary advantage of paging.
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Loading data objects from the Backendless persistent storage is a fundamental operation a large majority of the online/mobile applications require. Backendless data retrieval API is simple, yet very powerful. As you will learn in the course of this series, the API provides the following capabilities:

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In this post we continue our mission to build a restaurant to-go order app. So far we have put together UI mockups for the future Backendless application, and designed data schema for all application’s data entities. At this point we got very close to the coding part. As the title of this article suggests, we will be generating some code, but before we do it, let me describe a core principle of the client-server integration with Backendless.

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As soon as you have data in a persistent storage, the question of searching would be one of the first to come up. Indeed, how can you query the backend for data? We considered multiple options and settled on the most popular one – SQL. However, we had to take a few shortcuts to allow SQL searches – the most notable is you can use only the “where” part of an SQL query to search for your data. Consider the following data table:restaurant table data - Feature 14: SQL-based search for data objects using console

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Backendless can create tables when you store object hierarchies from a client application. Also, I described how to manually create data tables using Backendless console. Finally, there is one more approach which makes table creation as simple as it gets. The approach is by uploading a file which contains schema definition for every table. The schema definition may include data types for all columns, including the ones for relations. As a part of my quest to build a restaurant to-go order app, I created all the tables in my Backendless backend. I used the schema export feature (to be discussed in the future) which generated for me a ZIP file with the schema definitions for all the tables used by the app.

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