Latest Posts

I stopped working on the Osm Admin project, for financial reasons. Did it hurt? A lot. Was it necessary? Unfortunately, yes. This post is a short retrospection on this matter.

The goal is to be the most productive and focused on the most important things, both personally, and (later) as a team.

After maintaining the "done" log for several months, I decided to improve my estimation accuracy, and here is how.

Before diving into what should be in the docs, let's define what you should more or less know before using Osm Admin.

There are four types of people who will read the docs: power users, developers, vendors and contributors.

Here are my thoughts on product backlog and roadmap.

Recently, I implemented mass-editing in Osm Admin. It allows you to view and edit multiple objects in a single operation.

This article describes mass-editing of products in a typical e-commerce application.

In the user interface, you can narrow displayed data using grid column filters, or filters displayed in the sidebar. To enable that, apply #[Filter\*] attributes to class properties.

Applied filters appear in the URL query parameters, for example, .../edit?id=5+16+19, and on the page.

You can apply filters not only to a grid page, but also to a form page - to mass edit all matching objects, or to an action URL (edit, delete, or custom) - to perform the action on all matching objects.

In the same way, you can apply filters to the API URLs in order to retrieve or modify matching objects in a script.

After 3 days of intensive work, I've got dot syntax working!

Along the way, I refactored the query internals once again, which reminds me a heuristics saying that you've got to implement something three times to get it right :)

New query implementation uses the concept of formulas - SQL-like expressions used for selecting, filtering and sorting data. Currently, formulas are quite limited, but with time, they should be a really powerful feature.

I decided to support selecting, filtering and ordering by properties of a related object using dot syntax: product.price, parent.level. This syntax will automatically join the table that stored related objects.

I realized that implementing joins requires a better query model than my current naive wrapper around Laravel query. This article describes the new query model.

This article is about running SELECT and INSERT queries on data objects stored in the database.

In short, use the query() function. It runs on top of Laravel Query\Builder. It handles the mapping of data class properties onto table columns, and initiates the computation of indexed properties.

Currently, you can only SELECT and INSERT, other operations are coming soon.

Today, I implemented computation of indexed properties during insert. The implementation dictated some changes in indexed property definitions compare to the initial design, and how indexers are reflected into the data schema.

Also, I implemented a table query class working on the top of Laravel Query\Builder. More about it next time, for now I'll review how indexed properties are computed during INSERT operation.

It's often needed to compute, or index, data in database tables based on data in other tables.

This article is the first sketch of a consistent indexing solution.

I've finished refactoring how Osm Admin stores data objects in the database. Also, in order to support multi-website, multi-vendor, multi-language applications, I've introduced the concept of scopes.

Most reasoning from the first version is still valid, so let's take a fresh look of what's changed.

I developed dehydrate()/hydrate() functions for a very practical need - storing the data class schema:

• in cache - to reduce processing time spent on reflection;
• and in database - to compare current and new database schema, and only add new tables and columns.

This article describes the information stored in the schema, and unit tests that I prepared in order to be sure that schema classes are property hydrated.

As described in Migrations, most data objects of the same class will be stored in a database table.

But what about subclasses? In an e-commerce application, bags, dresses, and digital products, collectively known as subclasses, are all products stored in products table, and they may have bad-specific, dress-specific or digital product specific properties that should also be stored there.

This article describes how subclasses are defined and stored in Osm Admin.

Different data classes have repeating structural patterns. For example, most data classes stored in database tables have the auto-incremented id property. Use PHP traits to effectively introduce the same properties to different data classes over and over again.

I partly implemented data schema migration. It takes data class definitions, and incrementally creates or alters underlying database tables.

The article below describes how schema migration works, and what's not implemented yet, but most probably will be.

Note. This topic is continued in the new article.

The project repository, osmphp/admin, follows a typical Osm Framework-based project structure. However, this repository is going to be a reusable Composer package, and has important structural differences presented in this article.

This article introduces the core project concepts: data object, data class, data query, and data schema.

I'm super-excited to announce that I'm starting a new project codenamed "The Big Red Button". It's about defining application data structures and logic in PHP code, pressing the big red button (hence the codename), and getting fully functioning Admin Panel and API.

This article describes our package release process before v1.0.0. In short, every change is released as soon as it's ready, and all dependent packages are updated at the same time.

This article provides a practical example of contributing changes to Osm Framework and other osmphp/* GitHub repositories. It's based on the Osm_Project application pull request.