Every pipeline is composed of one or more tables. Each table represents a specific set of data. In the simplest situation, a table can contain data entered either manually by a human or automatically by some other piece of software. These Manual tables are similar to a spreadsheet in Excel, for example. A table always belongs to a schema which helps to organize tables into groups. By placing related tables into one schema you can keep your data pipeline well structured.

Note

This tutorial assumes that you already have a database server that you can connect to and that you have installed DataJoint for Matlab. If either of this is not true, be sure to checkout our Getting Started Tutorial first before proceeding with this tutorial!

## Creating a schema¶

Let’s get started by importing DataJoint and creating a new schema to define tables in. Start MATLAB and connect to your database.

Note

If you need a review on how to connect to the database from DataJoint, checkout Configuring DataJoint.

Let’s create our first schema called tutorial! Type dj.createSchema and enter tutorial_db when prompted for the database name.

dj.createSchema
Enter database name >> tutorial


Then a GUI window will appear to prompt you for the package folder. Navigate to your desired directory and enter +tutorial to create a new package folder.

Note

If you are connected to the tutorial database hosted by DataJoint.io, you will have to prefix the the schema name with your username followed by underscore: username_. For example, if your username is johndoe, then you would do the following:

dj.createSchema
Enter database name >> johndoe_tutorial


And that’s it! We have just created a schema in the database, and now we can now begin creating tables inside of this schema.

## Defining the Mouse table class¶

Now we will create a new table. In our hypothetical example, everything starts with a particular mouse. So let’s create a table to enter and track all the mice we will work with. Open a new script called Mouse.m inside your newly crated +tutorial package and copy the following into the file:

%{
# mouse
mouse_id: int                  # unique mouse id
---
dob: date                      # mouse date of birth
sex: enum('M', 'F', 'U')       # sex of mouse - Male, Female, or Unknown/Unclassified
%}

classdef Mouse < dj.Manual
end


and it turns out that this is enough to define a table! There is actually a lot going on here, so let’s walk through this code step by step.

### Table classes¶

In DataJoint, tables are defined and accessed via classes inheriting from one of the table superclasses provided by DataJoint. Since we will be entering data about new mice manually, we want to create a table called “Mouse” as a manual table. You do so by defining a class called Mouse and inheriting from dj.Manual super-class.

### Table definition¶

In addition to specifying the type or “tier” of the table (e.g. dj.Manual), you need to define the columns or attributes of the table. You define these in the header comment of the class using the DataJoint data definition language. Let’s take a closer look a the definition string here.

#### Table comment¶

%{
# mouse
mouse_id: int                  # unique mouse id
---
dob: date                      # mouse date of birth
sex: enum('M', 'F', 'U')       # sex of mouse - Male, Female, or Unknown/Unclassified
%}


The very first line of the definition starts with a # comment that describes what this table is about. Although this is optional, leaving a meaningful comment here can be really helpful when you start defining increasingly complex tables.

#### Attribute (column) definition¶

%{
# mouse
mouse_id: int                  # unique mouse id
---
dob: date                      # mouse date of birth
sex: enum('M', 'F', 'U')       # sex of mouse - Male, Female, or Unknown/Unclassified
%}


In the definition string, you define the table’s attributes (or columns) one at a time, each in a separate line. The attribute definition takes the following format:

attribute_name :  data_type     # comment


As you probably can guess, the attribute_name is the name of the attribute. Separated by :, you then specify the data type of the attribute. This determines what kind of data can go into that attribute.

For mouse_id, we have chosen type int which can hold integers between -2147483648 and 2147483647, with the exact range depending on your database server. Since we don’t expect to have that many mice, int is a safe choice for holding the numerical ID for the mouse.

Note

In the table definition above, we have used date data type to hold dates in the form YYYY-MM-DD (e.g. 2017-01-31) and enum data type to have predefined values the attribute can chose from. enum('M', 'F', 'U') states that sex attribute can take on the value of either 'M', 'F', or 'U'.

At the end of the definition, you can give a comment describing what this attribute stores. Although this is optional, it is strongly recommended that you add a brief comment to help remind everyone (including yourself!) what that field is about. A good combination of a well thought-out attribute name and a good comment can help make your table very readable.

#### Primary vs non-primary key attributes¶

%{
# mouse
mouse_id: int                  # unique mouse id
---
dob: date                      # mouse date of birth
sex: enum('M', 'F', 'U')       # sex of mouse - Male, Female, or Unknown/Unclassified
%}


The --- separator separates two types of attributes in the table. Above the line are your primary-key attributes. These attributes are used to uniquely identify entries in the table. Within a table, the combination of the primary-key attributes values must be unique. In this case, we only have one attribute in the primary key (mouse_id) and thus every entry in the table must have a distinct mouse_id, corresponding to an actual mouse.

Below the --- separator are non-primary-key attributes. As you would guess, these are attributes that are not used to identify the mouse. Typically, these attributes hold values that describe the entry (in this case a mouse) identified by the primary-key (mouse_id).

### Defining a table in a schema¶

Save your new class as Mouse.m in the +tutorial package folder. You may notice that there is a new function getSchema in that folder that was created by dj.createSchema. This function returns the schema object that links the Matlab package +tutorial with the tutorial_db schema in the database.

## Creating the table in the data pipeline¶

Calling the Mouse class for the first time creates the corresponding table in the database server. DataJoint displays the SQL code used to create the table.

ans =

<SQL>
CREATE TABLE tutorial.mouse2 (
mouse_id int                   NOT NULL COMMENT "unique
mouse id",
dob date                       NOT NULL COMMENT "mouse date
of birth",
sex enum('M', 'F', 'U') NOT NULL COMMENT "sex of  mouse -
Male, Female, or Unknown/Unclassified",
PRIMARY KEY (mouse_id)
) ENGINE = InnoDB, COMMENT "mouse"
</SQL>


You can check the contents of the table in the database by typing tutorial.Mouse:

Object tutorial.Mouse

:: mouse ::

0 tuples (0.00769 s)


Of course at this point there are no entries in the mouse table.

Note

If this is not the first time going through this section of the tutorial, chances are you already have the table Mouse defined in the schema tutorial. This is completely fine! The table is only created the first time you instantiate the class.

## What if I make a mistake?¶

As you work through this tutorial, you might occasionally create a table with some errors. Most commonly, you might create a table before you are completely done with the table definition. Although there are ways to update the table definition, it is usually best to simply delete or drop the table with error and redefine the table after correcting your mistakes.

For example, you might have made a spelling error in your definition:

%{
# mouse
mose_id: int                  # unique mouse id
---
dob: date                     # mouse date of birth
sx: enum('M', 'F', 'U')       # sex of mouse - Male, Female, or  Unknown/Unclassified
%}

classdef Mouse < dj.Manual
end


Notice that both mouse_id and sex attributes are spelled incorrectly! If you don’t notice the error before you instantiated your table class:

tutorial.Mouse   % instantiating table with errors in definition


Then your table will be defined in the data pipeline containing these mistakes. Unfortunately, changing the table definition (the definition property) of the class after the table has been created in the data pipeline does not change the definition of the already-existing table.

The best way to deal with this error, especially this early in the design process, is to drop the table alltogether. You can do so as follows:

>>drop(tutorial.Mouse)
tutorial.mouse (manual,    0 tuples)
Proceed? (y/N)  y
Dropped table tutorial.mouse


Now the table is dropped, you can fix errors in your class definition and recreate the table.

## Where is my data pipeline stored?¶

When you create tables in DataJoint, there are actually two things getting created: the table Matlab classes (e.g. Mouse class) and the actual table in the database. As you saw above, you define a class in Matlab to define and access a table in the database.

Therefore, your data pipeline consists of two parts. One is the actual tables in the database server you created using DataJoint. These tables (and schemas) persists across sessions, and all the data you inserted are stored in the database server. Another part is the code you wrote to define and manipulate the tables - the schemas and classes!

So, in order for you or anyone else to access the content of the table in the database server, not only do they need access to the database server (and the right permissions) but also the code for the schema and classes that defines what tables exist. For one schema, these are all stored in the same Matlab package folder (in this case, +tutorial`).

## What’s next?¶

Congratulations again! You have successfully created your first table in your data pipeline. In the next section, we will give the table some substance by inserting data into it!