The python 4DBI
Project description
Python4DBI
In Memory Of Edson de Sousa (14/04/94 - 16/08/18), i will see you again
Table of contents
General info
- 4D is an incredibly productive development platform that lets you focus on your data model and your business rules.
- The 4D framework takes care of running your application code natively on macOS and Windows.
- The 4DBI is written in pure python and allows a python program to use SQL to access one or more databases from a single application without using the 4D ODBC driver.
- 4D and 4D Server both provide a native SQL server that enables this functionality.
Technologies
Project is created with:
- Python
- texttable - https://github.com/foutaise/texttable/
Setup
To run this project, install it locally using pip:
$ pip install python4DBI
API
Cursor
It consists of pure application logic, which interacts with the database. It includes all the information to represent data to the end user.
Properties
row_number
Return: int or None
This read-only attribute provides the current 0-based index of the cursor in the result set or None if the index cannot be determined
row_count
Return: int
This read-only attribute specifies the number of rows that the last .execute*()
description
Return: List
A Cursor object's description attribute returns information about each of the result columns of a query.
Methods
close
Return: None
Close the current 4D SQL server cursor
prepare_statement
Params: query ( str )
Return: int
Checks if the statement is valid should always be execute before an execute statement returns FOURD_OK or FOURD_ERROR
set_input_sizes
Not implemented!
Params: size ( int )
Return: None
This can be used before a call to .execute*() to predefine memory areas for the operation's parameters.
set_output_size
Not implemented!
Params: size ( int )
Return: None
Set a column buffer size for fetches of large columns (e.g. LONGs, BLOBs, etc.). the column is specified as an index into the result sequence. Not specifying the column will set the default size for all large columns in the cursor.
execute
Params: query (str), params (dict), page_size (int), on_before_execute (funtion), on_executed (funtion), *args, **kwargs
Return: None
Prepare and execute a database operation (query or command).
fetch_one
Return: List or None
Fetch the next row of a query result set, returning a single sequence, or None when no more data is available.
fetch_many
Params: size (int)
Return: Two-dimensional List or None
Fetch the next set of rows of a query result, returning a sequence of sequences (e.g. a list of tuples). An empty sequence is returned when no more rows are available.
fetch_all
Return: Two-dimensional List or None
Fetch all (remaining) rows of a query result. Note that the cursor's array size attribute can affect the performance of this operation.
start_transaction
Return: None
Opens a transaction
cancel_transaction
Return: None
Rollback an open transaction Same operation as rollback method
rollback
Return: None
Rollback an open transaction Same operation as cancel_transaction method
commit
Return: None
Commits an open transaction Same operation as validate_transaction method
validate_transaction
Return: None
Commits an open transaction Same operation as commit method
send_messages_in_base_64
Params: use_b64 (bool)
Return: None
Sets the base 64 mode
set_protocol_version
Params: protocol_version (str)
Return: None
Sets the 4D SQL server protocol version
set_preferred_image_types
Params: preferred_image_types (str)
Return: None
Sets the preferred image type
Currently only supports 'png' pr 'jpg' formats
set_fmt
Params: fmt (str)
Return: None
Prints a 4D SQL server cursor result
Sets the type of binary architecture
Currently supports the following formats:
'<' little-endian for MAC OS X - RVLB
'>' big-endian for Windows - BLVR
print_result
Params: headers (List), rows (List), max_width (int), logger(obj)
Return: None
Database Interface
It acts as an intermediary between view and model
Methods
connect
Params: ** kwargs
Return: None
Opens a socket connection to the 4D SQL Server
kwargs supported are:
- socket_timeout : int (default 10)
- dsn : str (default '')
- host : str (default '127.0.0.1')
- port : int (default 19812)
- user : str (default '')
- password : str (default '')
close
Return: None
Closes the current 4D SQL server socket connection
cursor
Return: _python4DCursor object
Returns a 4D SQL server cursor object
connected
Return: bool
Returns true if the socket is connected to the 4D SQL server and false otherwise
get_socket
Return: self._socket object
Returns the current socket object
get_socket_timeout
Return: int
Returns the current socket timeout
get_host
Return: str
Returns the current to 4D SQL server host
get_port
Return: int
Returns the current to 4D SQL server port
get_user
Return: str
Returns the current to 4D SQL server user
get_password
Return: str
Returns the current to 4D SQL server password
get_logger
Return: str
Returns the logger object
Example
import time
from python4DBI.python4DBI import python4DBI
con = python4DBI()
con.connect(user='theUser', password='thePassword')
cursor = con.cursor()
t0 = time.time()
cursor.execute(query='SELECT * FROM EMPLOYEES')
if cursor.row_count > 0:
result = cursor.fetch_all()
con.print_result(headers=cursor.description, rows=result)
t1 = time.time()
total = t1-t0
+------------+--------+----------+----------+----------+----------+----------+----------+-----------+------+---------+-----------+--------+-------+-------+------+
| EmployeeID | Last | Name | First | Name | Address1 | Address2 | Zip | Code | City | Country | Telephone | Mobile | Phone | Birth | Date |
+------------+--------+----------+----------+----------+----------+----------+----------+-----------+------+---------+-----------+--------+-------+-------+------+
| 1 | Barros | Marciano | Address1 | Address2 | 9000 | FUNCHAL | Portugal | 3.510e+09 | | None | | None | None | | |
+------------+--------+----------+----------+----------+----------+----------+----------+-----------+------+---------+-----------+--------+-------+-------+------+
Execution time : 0.022388219833374023
Template example 1
"""
Learn how to open a connection to the 4D SQL Server.
"""
# Instantiate a connection object
con = python4DBI()
# Authentication
con.connect(user='theUser', password='thePassword')
# Check if the socket is connected
if con.connected() is True:
# Do stuff
pass
# Close the socket connection
con.close()
Template example 2
"""
Learn to open a connection to the 4D SQL Server and handle the exceptions.
To learn more about the exceptions raised at a particular method please read the doc strings.
For example:
- help(python4DBI) will print the documentation for the 4DBI interface
- help(python4DBI.__init) will print the documentation for the 4DBI constructor.
"""
try:
# Instantiate a connection object
con = python4DBI()
except OperationalError:
# Handle error
con = None
except Exception as e:
# Handle error
con = None
if con:
try:
# Authentication
con.connect(user='theUser', password='thePassword')
except ProgrammingError:
# Handle error
pass
except OperationalError:
# Handle error
pass
except Exception as e:
# Handle error
pass
if con.connected():
# Do stuff
pass
try:
# Close the socket connection
con.close()
except Exception as e:
# Handle error
pass
Template example 3
"""
As we have seen in previous examples handling errors will exponentiate the number of try/except cases
because every function on the DBI will have the potential of raising errors in this example I will show
how to simplify the 'try catch' with the usage of superclass's.
4DBI Error Classes List
StandardError
|__Warning
|__Error
|__InterfaceError
|__DatabaseError
|__DataError
|__OperationalError
|__IntegrityError
|__InternalError
|__ProgrammingError
|__NotSupportedError
"""
try:
# Instantiate a connection object
con = python4DBI()
# Authentication
con.connect(user='theUser', password='thePassword')
if con.connected():
# Do stuff
pass
# Close the socket connection
con.close()
except Warning as e:
# Handle warning
pass
except Error as e:
# Handle error
pass
except Exception as e:
# Handle error
pass
Template example 4
"""
I will show how to change the standard logging file and level.
Note: All exception raised are logged before being raised.
The default logging file is "python4DBI.log" and the default level is CRITICAL.
The 'logging_file' should be the absolute file path by default is the same path as the module.
Be careful enabling DEBUG level logging, will make the DBI slower, because every iteration with
the DB will be logged and such configuration is not recommended for production environments.
"""
try:
# Instantiate a connection object with custom logging level and log location
con = python4DBI(logging_level=logging.DEBUG, logging_file='../python4DBI.log')
# Authentication
con.connect(user='theUser', password='thePassword')
if con.connected():
# Do stuff
pass
# Close the socket connection
con.close()
except Warning as e:
# Handle error
pass
except Error as e:
# Handle error
pass
except Exception as e:
# Handle error
pass
Template example 5
"""
Getting a 4D cursor.
The 4D cursor database cursor is a control structure that enables traversal over the records in a database.
Cursors facilitate subsequent processing in conjunction with the traversal, such as retrieval, addition
and removal of database records.
Cursors are used by database programmers to process individual rows returned by database system queries.
Cursors enable manipulation of whole result sets at once.
Note: Normally a cursor can be viewed as a pointer to one row in a set of rows.
The cursor can only reference one row at a time, but can move to other rows of the result set as needed.
In this DBI was not possible to implement such behavior so every time you execute an SQL command you will
receive the result set in a Python list, if there is a need to update or operate directly at the DB
level you need to make use of the 'SELECT FOR UPDATE' clause on the specific row/record.
"""
try:
# Instantiate a connection object with custom logging level and log location
con = python4DBI(logging_level=logging.DEBUG, logging_file='../python4DBI.log')
# Authentication
con.connect(user='theUser', password='thePassword')
if con.connected():
# Get cursor
cursor = con.cursor()
# Do stuff
# Close cursor
cursor.close()
# Close the socket connection
con.close()
except Warning as e:
# Handle error
pass
except Error as e:
# Handle error
pass
except Exception as e:
# Handle error
pass
Template example 6
"""
Prepare an SQL statement.
It is a good practice before executing an SQL statement to make a validation.
This command will return false if the SQL statement is not valid and true otherwise.
Note: If the programmer does not validate the SQL statement the DBI always validates the the query before
executing the difference is that on the execute the DBI will raise an error.
"""
try:
# Instantiate a connection object with custom logging level and log location
con = python4DBI(logging_level=logging.DEBUG, logging_file='../python4DBI.log')
# Authentication
con.connect(user='theUser', password='thePassword')
if con.connected():
# Get cursor
cursor = con.cursor()
# Prepare the statement i.e check if it OK for execution
query = 'SELECT * FROM EMPLOYEES'
result = cursor.prepare_statement(query=query)
if result is FOURD_OK:
# Do stuff
pass
else:
# Handle wrong statement
pass
# Close cursor
cursor.close()
# Close the socket connection
con.close()
except Warning as e:
# Handle error
pass
except Error as e:
# Handle error
pass
except Exception as e:
# Handle error
pass
Template example 7
"""
Execute an SQL statement.
Note: After executing an SQL statement the programmer can use the row_count to check the
number of result rows. The 'fetch_all' is not always the best approach since it can result
in a slower fetch imagine that the size of the result set is 100000 rows, that means that the
return list will be a list of size 100000!
In such cases the programmer can use the 'fetch_one' will return only the first row or the
'fetch_many'.
The are some caveats regarding the way that the DBI works internally since the 4D SQL protocol
puts the result page in a buffer every time you execute an SQL statement the DBI will fetch by default
a page size of 100 rows i.e. imagine if you use execute a statement then a 'fetch_one' internally the
DBI will fetch the first page i.e. at least 100 rows, if the programmer knows before hand that it only needs
the first N records than it is more efficient to execute the SQL statement with a page size of N.
"""
try:
# Instantiate a connection object with custom logging level and log location
con = python4DBI(logging_level=logging.DEBUG, logging_file='../python4DBI.log')
# Authentication
con.connect(user='theUser', password='thePassword')
if con.connected():
# Get cursor
cursor = con.cursor()
# Prepare the statement i.e check if it OK for execution
query = 'SELECT * FROM EMPLOYEES'
result = cursor.prepare_statement(query=query)
if result is FOURD_OK:
# Execute the query
cursor.execute(query=query)
# Check the results
if cursor.row_count > 0:
# Fetch all the results
result_rows = cursor.fetch_all()
else:
# Handle wrong statement
pass
# Close cursor
cursor.close()
# Close the socket connection
con.close()
except Warning as e:
# Handle error
pass
except Error as e:
# Handle error
pass
except Exception as e:
# Handle error
pass
Template example 8
"""
Printing results to the console.
Note: Every time you execute the an SQL statement the DBI will create a list of the row headers with tha name
of the DB columns, the programmer can use 'description' for such purposes.
The 'print_result' is implemented on the DBI and will provide a 'terminal/console' way to see the results formatted
into a table, and can be usefully for debugging purposes.
"""
try:
con = python4DBI(logging_level=logging.DEBUG, logging_file='../python4DBI.log')
# Authentication
con.connect(user='theUser', password='thePassword')
if con.connected():
# Get cursor
cursor = con.cursor()
# Prepare the statement i.e check if it OK for execution
query = 'SELECT * FROM EMPLOYEES'
result = cursor.prepare_statement(query=query)
if result is FOURD_OK:
# Execute the query
cursor.execute(query=query)
# Check the results
if cursor.row_count > 0:
# Fetch all the results
result_rows = cursor.fetch_all()
# Print result page to console
cursor.print_result(headers=cursor.description, rows=result_rows)
else:
# Handle wrong statement
pass
# Close cursor
cursor.close()
# Close the socket connection
con.close()
except Warning as e:
# Handle error
pass
except Error as e:
# Handle error
pass
except Exception as e:
# Handle error
pass
Template example 9
"""
Using params in SQL statements.
Using transactions.
Note: Normally a DBI will implement only on or tow ways of using param styles in SQL queries, on
this DBI is available qmark, name, format and pyformat.
qmark - Question mark style, e.g. ...WHERE name=?
named - Named style, e.g. ...WHERE name=:name
format - ANSI C printf format codes, e.g. ...WHERE name=%s
pyformat - Python extended format codes, e.g. ...WHERE name=%(name)s
It can be used only one type of param style per query!
"""
try:
# Instantiate a connection object
con = python4DBI()
# Authentication
con.connect(user='theUser', password='thePassword')
# Check if the socket is connected
if con.connected() is True:
# Get cursor
cursor = con.cursor()
# Prepare the statement i.e check if it OK for execution
query = 'INSERT INTO EMPLOYEES (EmployeeID, City) VALUES(:id, :city)'
result = cursor.prepare_statement(query=query)
if result is FOURD_OK:
# Start a transaction
cursor.start_transaction()
for i in range(1, 100):
params = {'id': str(i),
'city': fake.city(),
'Date': fake.date()
}
cursor.execute(query=query, params=params)
# Commit the transaction
cursor.commit()
else:
# Handle wrong statement
pass
# Close cursor
cursor.close()
# Close the socket connection
con.close()
except Warning as e:
# Handle error
pass
except Error as e:
# Handle error
pass
except Exception as e:
# Handle error
pass
Example 1
"""
Time of execution for multiple results.
Note: This examples use the 4D DB that is available at the project level if you wish
to do the same tests please use this DB project.
If you need to seed the DB with data please use the 'Example 3'.
"""
try:
# Instantiate a connection object
con = python4DBI()
# Authentication
con.connect(user='theUser', password='thePassword')
# Check if the socket is connected
if con.connected() is True:
# Get cursor
cursor = con.cursor()
# Prepare the statement i.e check if it OK for execution
query = 'SELECT * FROM EMPLOYEES'
result = cursor.prepare_statement(query=query)
if result is FOURD_OK:
t0 = time.time()
# Execute the query
cursor.execute(query=query)
# Check the results
if cursor.row_count > 0:
# Fetch all the results
result = cursor.fetch_all()
t1 = time.time()
total = round(t1 - t0, 4)
print('Execution time : {} seconds'.format(total))
else:
# Handle wrong statement
pass
# Close cursor
cursor.close()
# Close the socket connection
con.close()
except Exception as e:
print("Can not continue - reason : {}".format(e))
Example 2
"""
Time of execution for single result.
"""
try:
# Instantiate a connection object
con = python4DBI()
# Authentication
con.connect(user='theUser', password='thePassword')
# Check if the socket is connected
if con.connected() is True:
# Get cursor
cursor = con.cursor()
# Prepare the statement i.e check if it OK for execution
query = 'SELECT EmployeeID FROM EMPLOYEES WHERE EmployeeID = :id'
result = cursor.prepare_statement(query=query)
if result is FOURD_OK:
t0 = time.time()
# Execute the query
cursor.execute(query=query, params={'id': '1'})
# Check the results
if cursor.row_count > 0:
# Fetch all the results
result = cursor.fetch_all()
t1 = time.time()
total = round(t1 - t0, 4)
print('Execution time : {} seconds'.format(total))
else:
# Handle wrong statement
pass
# Close cursor
cursor.close()
# Close the socket connection
con.close()
except Exception as e:
print("Can not continue - reason : {}".format(e))
Example 3
"""
DB data seed.
"""
try:
# Instantiate a connection object
con = python4DBI()
# Authentication
con.connect(user='theUser', password='thePassword')
# Check if the socket is connected
if con.connected() is True:
# Get cursor
cursor = con.cursor()
# Prepare the statement i.e check if it OK for execution
query = 'INSERT INTO EMPLOYEES (EmployeeID, Address1, Address2, City) ' \
'VALUES (:EmployeeID, :Address1, :Address2, :City)'
result = cursor.prepare_statement(query=query)
if result is FOURD_OK:
for i in range(0, 1000):
_id = str(i+1)
_name = fake.name()
_first_name = _name.split()[:1]
_last_name = _name.split()[-1:]
_address1 = fake.address()
_address2 = fake.address()
_zipcode = fake.zipcode()
_city = fake.city()
_country = fake.country()
_phone = fake.phone_number()
# Execute the query
params = {'EmployeeID': _id,
'Address1': _address1,
'Address2': _address2,
'City': _city,
}
cursor.execute(query=query, params=params)
else:
# Handle wrong statement
pass
# Close cursor
cursor.close()
# Close the socket connection
con.close()
except Exception as e:
print("Can not continue - reason : {}".format(e))
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