It is a fact that writing database documentation is a burden. It can be the final item in a developer’s to-do list, just behind “arrange icons on desktop” and defrag hard drive. You begin well but then you get a feature request and a bug report and before you know it, you have six months old documentation which is being told to you.
If you are left with a bewildering set of tables, mysterious column names and relationships you can no longer remember. Such vagueness is a genuine issue:
- Delays in Onboarding: It takes team members days to learn how everything works together and not hours.
- Reduced Accuracy: Developers write queries on assumptions and therefore generate wrong reports and bugs in the application.
- Wasted Time: Wastes of precious time are spent by all developing artificial keys one by one, or by enquiring of colleagues, what does this column mean again?
Doing this manually is a pain. What, however, should you do to reverse the process? Rather than you writing documentation to describe your database, what happens when your database can create its own documentation?
This isn’t a futuristic dream. It is an effective fact which can be established within minutes.
The Wizardry of Documentation Automation.
Automated documentation tools are competitive because they are linked with your database. They smartly read the schema, tables, columns, data types and indexes and foreign keys and convert that technical structure into a readable, clean and always up to date document.
Consider it a hyper-charged mapper of your data world. It will chart automatically all the tables (city), the column (street), and the relationship (road), and nobody will get lost. The best part?
It’s a set-and-forget solution. You can write your documentation with a single click or a single command every time you push your schema change to your database. The documentation always changes under your code.
Three-step tutorial to the Automatic SQL Docs.
It is quite easy to begin. This is the way you can have documented databases within a short time.
Step 1: Choose Your Tool
This job can be done with some fine tools. They are mostly divided into two types:
- Open-Source Command-Line Tools: such programs as the CLI of SchemaCrawler or CLI of DBDiagram are potent and free. You run them at your terminal to create documentation in different formats (HTML, PDF, Markdown).
- GUI-Based Applications: Many applications such as DbVisualizer or MySQL Workbench include report functionality native to the application which can produce schema documentation.
- To create a balance of power and convenience, most of current-day teams employ specialized platforms that exist to fulfill this goal, which frequently have web-based audience and cooperation abilities.
Step 2: Connect and Scan
After selecting a tool, the next thing that you will do is to provide it with safe access to your database. This generally requires an entry of a connection string that contains:
- Database host/port
- Database name
- username and password (not-writable, due to security)
The tool will then do the scanning of your database. It works its way through the system tables to create an entire inventory of your schema. This is a non-writable process, which has no effect on the performance of your database.
Step 3: Generate and Review
Click “Generate.” You can be having a full documentation within seconds. The result is usually a set of interactive HTML site or a PDF that can be distributed among your team.
But still you have not finished your job. This tool is able to record what is there but not what is in your mind.
Human Touch: Meaning Addition.
This is the most crucial step. Automated tools can tell you that there is a column called user_fk, and that it is an INTEGER but not why this column exists.
Spend ten minutes to look at the created docs and include context:
- Column Descriptions: What is this status column? What do the values (1, 2, 3) represent?
- Business Logic: Record down the rules that are not implicated by the schema. As an example: this table is updated with a nightly cron job or orders in this table are incomplete purchases.
- Examples: Give sample data of complicated fields.
This is an automated power combined with human context resulting in some invaluable documentation.
What You Gain
The advantages of this automatic method are instantaneous:
- Precision: Your records are always at par with your real database scheme.
- Time Savings: Recapture time each week that would have been spent making manual updates.
- Improved Cooperation: There is one source of truth regarding how data is organized and it includes everyone: developers, analysts, product managers.
- Simpler Maintenance: It becomes so simple to know your own data model months after you constructed it.
Quit making database documentation an arduous manual load. You would have been able to come up with a full set of project documents in the period during which this article was read. It is one of the best payback investments that you can deliver to the productivity and sanity of your team.