GSpread DBMS

This Python module is a Class implementation for using Google Spreadsheet as a pseudo- DataBase Management System (DBMS). It provides all the basic functionalities of DBMS like inserting rows, retrieve rows and columns, updating cells, querying the DB based on conditions etc. Due to the inherent nature and purpose of Google Spreadsheet, it is not possible to operate as a high performance DBMS. However, this is suitable for low number of input/output (I/O) operations requirement (say 100 ops per 100 seconds) and preferably no concurrent write operations. For a Database, building an efficient and feature-rich fronted is a very tedious task, having the option to use the powerful Google Spreadsheet interface is a huge advantage while sacrificing some I/O performance by backend programs.

There are specific features built in this package to overcome some of the I/O limitations:

  1. Implementation of DBMS read caching system - Read rows and columns are cached for small time period, so that repeated read operation can be served quickly and without sending multiple read request to our DB. The cache duration is modifiable cache_duration and also the cache is flush-able by user if needed during runtime _flush_cache(..).
  2. Batch write operation - Function is provided to accumulate multiple write operations in a buffer update_cell_Buffered(..), and then execute all in a single batch update_cell_BufferCommit(). This way we are improving performance and utilizing only one actual write operation.


An database example ><

Let's take an example of the above Google Spreadsheet. Below we show examples of various operations and queries we can perform on this table.

Starting a Database instance

from gspread_dbms import GSpreadDBMS
        ssid='ID',           # Spreadsheet ID
        key='KEY_FILE',      # File name or parsed JSON of the Key for the google service account.  
    # Below are the optional parameters. Default values are shown here
        worksheet=1,         # Workseet name/number.
        headrow=1,           # Row number of column names header
        startrow=2,          # Data start row
        EnableCache=True,    # Caching to be enabled or dsabled
        verbose=False         # Whether to turn on verbosity

ssid - is the ID of the Google Spreadsheet you can find in the URL.
key - This is key you need to obtain from service account in google developer console. Make sure this service account is given edit permission in the google spreadsheet.

Insert rows

Inserting a row where values are given for each column in a dict. You may opt out a column name to leave it blank. Insertion are always at the top of the table by default.

GDB.insert_row({'object':'2020gcy', # 2020gcy inserted in column 'object'
        'survey name': 'Gaia20bsa',  # Similarly for 'survey name' column
        'ra': '18:38:15.540',  # Similarly for 'ra' column
        'dec':'-61:35:01.46'})  # Similarly for 'dec' column

Selecting row

Returns the row number given by a value equal to '2020har' in column 'object'.

Note: row number is always with reference to the data start row specified above.

>>> GDB.find_row('2020har', 'object')

Read column

Reading an entire column specified by column name


Read row

Reading an entire row specified by row number


or you can combine with find_row(...) function to read row by a value

GDB.read_col( GDB.find_row('2020har', 'object') )

Querying based on conditions

It is possibly to do wide range of simple to complex conditional queries

# Function definition
GDB.query(select_keys,          # List of Columns/Keys which should be returned
        query_key_val=None,     # Dict of list of keys and condition to apply
        date_fmt=None)          # Optional - if condtions are based on date in a column, you may want to specify a date format. Default is ISO format.


This will return all rows for selected columns without applying any conditions

GDB.query(['object', 'ra', 'dec'])

This will return all rows based on arithmetic condition on 'Mag' column.

GDB.query(['object', 'ra', 'dec'], {'mag': '<=16.7'})  # mag <=16.7
# or
GDB.query(['object', 'ra', 'dec'], {'mag': '16.7'})  # mag == 16.7
# or
GDB.query(['object', 'ra', 'dec'], {'mag': '=16.7'})  # mag == 16.7
# or
GDB.query(['object', 'ra', 'dec'], {'mag': '>16.7'})   # mag > 16.7
# or
GDB.query(['object', 'ra', 'dec'], {'mag': '!=16.7'})   # mag not equal to 16.7

This will return all rows based on date comparison. Supports all arithmetic inequalities.

GDB.query(['object', 'ra', 'dec'], {'date': '2020-04-12'})
# or
GDB.query(['object', 'ra', 'dec'], {'date': '<2020-04-12'})

This will return all rows based on string comparison.

GDB.query(['object', 'ra', 'dec'], {'object': '2020har'})  # equals to a value
# or 
GDB.query(['object', 'ra', 'dec'], {'object': '!=2020har'}) # not equals to a value

other operators can be used too, like =, <, >, < =, >= etc., which will lead to string comparisons.

Following will return all rows for values found in given list of strings.

GDB.query(['object', 'ra', 'dec'], {'object': ['2020har', '2020hax']})

This will return all rows for values passed onto a function and which returns True

def MyFunc(value):
    # A demo function to check if Hour part of the RA is 16<= Hour <=18
    if Hrs>=16 and Hrs<=18:
        return True
        return False

GDB.query(['object', 'ra', 'dec'], {'ra': MyFunc})

Updating single cell

GDB.update_cell (rownum,  # Row number w.r.t start-row
        colname,         # Column name
        value)           # Value to update in the cell


GDB.update_cell (3, 'ra', '18:33:15.330')

Updating entire row

GDB.update_row (rownum,  # Row number w.r.t start-row
        values)          # A dict of Values to update in the cell


GDB.update_row (1, 
            {'survey name': 'Gaia20bsa', 'ra': '18:38:15.540'})

Updating cells in batch

First we put the cell update operations in buffer, without actually making changes in the database.

# Syntax is same as update_cell( ... ) function.
GDB.update_cell_Buffered (3, 'ra', '18:33:15.330')
GDB.update_cell_Buffered (2, 'object', '2020gpe')
GDB.update_cell_Buffered (4, 'dec', '-37:09:50.78')
GDB.update_cell_Buffered (3, 'object', '2020har')

Then we commit all operation in a single go.


Cache control

Changing read cache duration. Default is 10 seconds.

GDB.cache_duration = 60   # Changing to 60 seoconds

Flush cache for a row and/or column or everything. Syntax is:



# Flush cache for a row number
# Flush cache for a column name
# Flush cache for a row and a column both
# Flush all cache