Schema

pytonik database query schema module provides a convenient, fluent interface to creating and running database queries. It can be used to perform most database operations in your application and works on all supported database systems.

Import Module

from pytonik.Driver import Schema

Callable

DB = Schema.Schema()

Tables

TABLES = {}

TABLES['users'] = (
        "CREATE TABLE `users` ("
        "  `users_id` int(11) NOT NULL AUTO_INCREMENT,"
        "  `birth_date` date NOT NULL,"
        "  `first_name` varchar(14) NOT NULL,"
        "  `last_name` varchar(16) NOT NULL,"
        "  `email` varchar(255) NOT NULL,"
        "  `votes` int(11) NOT NULL,"
        "  `create_date` date NOT NULL,"
        "  PRIMARY KEY (`emp_no`)"
        ") ENGINE=InnoDB")

TABLES['banks'] = (
        "CREATE TABLE `banks` ("
        "  `banks_id` int(11) NOT NULL AUTO_INCREMENT,"
        "  `users_id` int(11) NOT NULL,"
        "  `bank_name` varchar(40) NOT NULL,"
        "  `bank_sort` varchar(40) NOT NULL,"
        "  PRIMARY KEY (`banks_id`)"
        ") ENGINE=InnoDB")

TABLES['transactions'] = (
        "CREATE TABLE `transactions` ("
        "  `transaction_id` int(11) NOT NULL,"
        "  `users_id` int(11) NOT NULL,"
        "  `amount` varchar(40) NOT NULL,"
        "  `from_date` date NOT NULL,"
        "  `to_date` date NOT NULL,"
        "  PRIMARY KEY (`transaction_id`,`from_date`), KEY `transaction_id` (`transaction_id`),"
        ") ENGINE=InnoDB")

create

Create table using create method

DB.table(TABLES).create()

drop

If you wish to drop the entire table, you may use the drop method:

Drop All

DB.table('users').drop()

Drop if Exist

DB.table('users').exists().drop()

insert

The query schema also provides an insert method for inserting records into the database table. The insert method accepts an array of column names and values:

insert = DB.table('users').insert(
                [
                dict(email ='info@pytonik.com', name= 'Pytonik MVC', created_at='2020-02-05 09:02:08.26'),
                ])

Multiple Insert

You may even insert several records into the table with a single call to insert by passing an array of arrays. Each dictionary inside list represents a row to be inserted into the table:

insert = DB.table('users').insertGetId(
                [
                dict(email ='dev@pytonik.com', name = 'Pytonik Moduel', created_at='2020-02-05 09:02:08.26'),
                ])

Auto-Incrementing IDs

If the table has an auto-incrementing id, use the insertGetId method to insert a record and then retrieve the ID:

insert = DB.table('users').insert(
                [
                dict(email ='info@pytonik.com', name= 'Pytonik MVC', created_at='2020-02-05 09:02:08.26'),
                dict(email ='dev@pytonik.com', name = 'Pytonik Moduel', created_at='2020-02-05 09:02:08.26'),
                ])

Note

When using PostgreSQL the insertGetId method expects the auto-incrementing column to be named id. If you would like to retrieve the ID from a different sequence, you may pass the column name as the second parameter to the insertGetId method.

update

In addition to inserting records into the database, the query schema can also update existing records using the update method. The update method, like the insert method, accepts an dict of column and value pairs containing the columns to be updated. You may constrain the update query using where clauses:

Update

DB.table('users').where('id', '=', 18).update([dict(email='info@pytonik.com')]))

delete

The query schema may also be used to delete records from the table via the delete method. You may constrain delete statements by adding where clauses before calling the delete method:

DB.table('users').delete()
DB.table('users').where('users_id', 1).delete()

selects

If you don’t even need an entire row, you may extract a single value from a record using the value method. This method will return the value of the column directly:

DB.table('users').value('users_username', 'email').select().get()
DB.table('users').where('users_id', 18).select().get()

The query schema also provides a variety of aggregate methods such as counts, max, min, avg, and sum. You may call any of these methods after constructing your query:

MAX()

DB.table('transactions').max('amount').select().get()

MIN()

DB.table('transactions').min('amount').select().get()

AVG

DB.table('transactions').avg('amount').select().get()

COUNT()

DB.table('transactions').counts().select().get().result

You may combine these methods with other methods:

DB.table('transactions').where('status', 1).min('amount').select().get()

Determining If Records Exist

Instead of using the count method to determine if any records exist that match your query’s constraints, you may use the exists and notExist methods:

Example: Exist

DB.table('orders').where('finalized', 1).exists()

Example: notExist

DB.table('orders')->where('finalized', 1).notExist()

Retrieving A Single Row / Column From A Table

If you just need to retrieve a single row from the database table, you may use the first method. This method will return a single dictionary object {}:

user = DB::table('users')->where('status', 1)->first()

print(user["name"])

Retrieving A List Of Column Values

If you would like to retrieve a Collection containing the values of a single column, you may use the pluck method. In this example, we’ll retrieve a Collection of role titles:

titles = DB::table('roles').pluck('title');

for  title in titles:
     print(title)

You may also specify a custom key column for the returned Collection:

roles = DB.table('roles').pluck('title', 'name');

for title in roles:

    print(title["name"])

If you need to work with thousands of database records, consider using the chunk method. This method retrieves a small chunk of the results at a time and feeds each chunk into a Closure for processing. This method is very useful for writing Artisan commands that process thousands of records. For example, let’s work with the entire users table in chunks of 100 records at a time:

DB.table('users').orderBy('id').chunk(100)

If you are updating database records while chunking results, your chunk results could change in unexpected ways. So, when updating records while chunking, it is always best to use the chunkById method instead. This method will automatically paginate the results based on the record’s primary key:

users = DB.table('users').where('status ', 'PENDING').orderBy('users_id').chunk(100,
DB.table('countries').where('users_id', '{users_id}').updates([dict(vote='200', create_at='20/01/2020')])
    )

Note

When updating or deleting records inside the chunk callback, any changes to the primary key or foreign keys could affect the chunk query. This could potentially result in records not being included in the chunked results.

Select Value

If you don’t even need an entire row, you may extract a single value from a record using the value method. This method will return the value of the column directly

Example 1.0:

DB.table('users').select('users_username', 'email').get()

Example 1.1:

DB.table('users').value('users_username', 'email').select().get()

Select Where with custom column

Example 1.0:

DB.table('users').where('users_id', '=', 1).select('users_username','users_email').get()

Example 1.1:

DB.table('users').where('users_id', '=', 1).value('users_username','users_email').select().get()

Select groupBy

DB.table('users').groupBy('users_id').select().get()

Select groupBy/having

The groupBy and having methods may be used to group the query results. The having method’s signature is similar to that of the where method:

DB.table('users').groupBy('users_id').having('permission', '>', '100').select().get()

You may pass multiple arguments to the groupBy method to group by multiple columns:

DB.table('users')->groupBy('first_name', 'status')->having('permission', '>', '100').select().get()

orderBy

The orderBy method allows you to sort the result of the query by a given column. The first argument to the orderBy method should be the column you wish to sort by, while the second argument controls the direction of the sort and may be either asc or desc:

DB.table('users').orderBy('users_id', 'desc').select().get()
DB.table('users').groupBy('users_id').orderBy('users_id', 'desc').select().get()

limit

To limit the number of results returned from the query, or to skip a given number of results in the query, you may use the skip and take methods:

Example 1.0:

DB.table('users').skip(1).take(2).select().get()

limit with offset

Example 1.1:

DB.table('users').offset(1).limit(2).select().get()

Alternatively, you may use the limit and offset methods:

DB.table('users').limit(1).select().get()

Select limit with offset

Example 1.1:

DB.table('users').offset(1).limit(2).select().get()

where

Where having

DB.table('users').where('status', 1).having('permission', '>', 2).select().get()

You may use the where method on a query schema instance to add where clauses to the query.

The most basic call to where requires three arguments. The first argument is the name of the column. The second argument is an operator, which can be any of the database’s supported operators. Finally, the third argument is the value to evaluate against the column.

For example, here is a query that verifies the value of the “votes” column is equal to 100:

DB.table('users').where('votes', '=', 1).select().get()

For convenience, if you want to verify that a column is equal to a given value, you may pass the value directly as the second argument to the where method:

DB.table('users').where('votes', 1).select().get()

You may use a variety of other operators when writing a where clause:

DB.table('users').where('votes', '>=', 100).select().get()

DB.table('users').where('votes', '<>', 100).select().get()

DB.table('users').where('votes', 'like', 'T%').select().get()

Or Statements

You may chain where constraints together as well as add or clauses to the query. The orWhere method accepts the same arguments as the where method:

DB.table('users').where('user_id', 15).orWhere('user_id', 15).select('email')

AND

DB.table('users').where('user_id', 18).where('email', 'info@pytonik.com').select().get()

OR

DB.table('users').where('user_id', 18).orWhere('user_id', 15).select().get()

AND/OR

DB.table('users').where('user_id', 18).where('email', 'info@pytonik.com').orWhere('user_id', 15).select().get()

Where Column

The whereColumn method may be used to verify that two columns are equal:

DB.table('users').whereColumn('first_name', '>', 'last_name')

Multiple Where Column

DB.table('users').whereColumn(('first_name', '=', 'last_name'),('updated_at', '>', 'created_at'))

Additional Where Clauses

Where Between

The whereBetween method verifies that a column’s value is between two values:

DB.table('transactions').whereBetween('votes', ['30']).select()

Where Between

The whereNotBetween method verifies that a column’s value lies outside of two values:

DB.table('transactions').whereNotBetween('votes', ['1', '100', '30']).select()

whereIn / whereNotIn

The whereIn method verifies that a given column’s value is contained within the given list:

DB.table('users').whereIn('id', [1, 2, 3]).select().get()

The whereNotIn method verifies that the given column’s value is not contained in the given list:

DB.table('users').whereNotIn('id', [1, 2, 3]).select().get()

whereNull / whereNotNull

The whereNull method verifies that the value of the given column is NULL:

DB.table('users').whereNull('updated_at').select().get()

The whereNotNull method verifies that the column’s value is not NULL:

DB.table('users').whereNotNull('updated_at').select().get()

The query schema also provides a quick way to union two queries together. For example, you may create an initial query and use the union method to union it with a second query:

first = DB.table('username').select().set()

users = DB.table('countries').orderBy('country_id').select().union(first).get()

Note

The unionAll method is also available and has the same method signature as union.

join

The query schema may also be used to write join statements. To perform a basic inner join, you may use the join method on a query schema instance. The first argument passed to the join method is the name of the table you need to join to, while the remaining arguments specify the column constraints for the join. You can even join to multiple tables in a single query:

Join

DB.table('users').join('contacts', 'users.id', '=', 'contacts.user_id').select().get()

Inner Join

DB.table('users').join('contacts', 'users.id', '=', 'contacts.user_id').join('orders', 'users.id', '=', 'orders.user_id').select('users.*', 'contacts.phone', 'orders.price').get()

Left Join / Right Join

If you would like to perform a “left join” or “right join” instead of an “inner join”, use the leftJoin or rightJoin methods. These methods have the same signature as the join method:

left Join

DB.table('users').leftJoin('bank', 'bank.users_id, '=', 'users.users_id').select().get()

left Join with Join

DB.table('users').leftJoin('bank', 'bank.users_id, '=', 'users.users_id').join('message', 'message.users_id', '=', 'users.users_id').select().get()

right Join

DB.table('users').rightJoin('bank', 'bank.users_id', '=', 'users.users_id').select().get()

right Join with

DB.table('users').rightJoin('bank', 'bank.users_id', '=', 'users.users_id').join('message', 'message.users_id', '=', 'users.users_id').select().get()

left outer Join

DB.table('comment').where('comment_status', '=', 1).fromTable('comment').outerJoin(DB.raw("(SELECT parent_id, COUNT(*) AS comment FROM parent GROUP BY parent_id) as sub"), 'comment_id', 'sub.parent_id ').select('a.comment_id', 'a.comment_name', 'a.comment_status', 'a.comment_link', 'sub.Count')

Advanced Join

You may also specify more advanced join. To get started, pass a Closure as the second argument into the join method. The Closure will receive a JoinClause object which allows you to specify constraints on the join clause:

DB.table('users').join('bank', 'bank.users_id', '=', 'users.users_id').where('bank.status', '>', 5).select().get()

If you would like to use a “where” style clause on your joins, you may use the where and orWhere methods on a join. Instead of comparing two columns, these methods will compare the column against a value:

Raw Expressions

Sometimes you may need to use a raw expression in a query. To create a raw expression, you may use the DB.raw method:

DB.raw

DB.table('users').where('status', '>', 1).groupBy('status').select(DB.raw('count(*) as user_count, status')).get()