Postgresql – Copying CSV file to temp table with dynamic number of columns

You can use csvsql, which is part of csvkit (a suite of utilities for converting to and working with CSV files):

• Linux or Mac OS X
• free and open source
• sudo pip install csvkit
• Example: csvsql --dialect mysql --snifflimit 100000 datatwithheaders.csv > mytabledef.sql
• It creates a CREATE TABLE statement based on the file content. Column names are taken from the first line of the CSV file.

To extend on ivansabik's answer using pandas, see How to insert pandas dataframe via mysqldb into database?.

In the end I coded a Python function import_csv_to_dynamodb(table_name, csv_file_name, colunm_names, column_types) that imports a CSV into a DynamoDB table. Column names and column must be specified. It uses boto, and takes a lot of inspiration from this gist. Below is the function as well as a demo (main()) and the CSV file used. Tested on Windows 7 x64 with Python 2.7.5, but it should work on any OS that has boto and Python.

import boto

MY_ACCESS_KEY_ID = 'copy your access key ID here'
MY_SECRET_ACCESS_KEY = 'copy your secrete access key here'


def do_batch_write(items, table_name, dynamodb_table, dynamodb_conn):
    '''
    From https://gist.github.com/griggheo/2698152#file-gistfile1-py-L31
    '''
    batch_list = dynamodb_conn.new_batch_write_list()
    batch_list.add_batch(dynamodb_table, puts=items)
    while True:
        response = dynamodb_conn.batch_write_item(batch_list)
        unprocessed = response.get('UnprocessedItems', None)
        if not unprocessed:
            break
        batch_list = dynamodb_conn.new_batch_write_list()
        unprocessed_list = unprocessed[table_name]
        items = []
        for u in unprocessed_list:
            item_attr = u['PutRequest']['Item']
            item = dynamodb_table.new_item(
                    attrs=item_attr
            )
            items.append(item)
        batch_list.add_batch(dynamodb_table, puts=items)


def import_csv_to_dynamodb(table_name, csv_file_name, colunm_names, column_types):
    '''
    Import a CSV file to a DynamoDB table
    '''        
    dynamodb_conn = boto.connect_dynamodb(aws_access_key_id=MY_ACCESS_KEY_ID, aws_secret_access_key=MY_SECRET_ACCESS_KEY)
    dynamodb_table = dynamodb_conn.get_table(table_name)     
    BATCH_COUNT = 2 # 25 is the maximum batch size for Amazon DynamoDB

    items = []

    count = 0
    csv_file = open(csv_file_name, 'r')
    for cur_line in csv_file:
        count += 1
        cur_line = cur_line.strip().split(',')

        row = {}
        for colunm_number, colunm_name in enumerate(colunm_names):
            row[colunm_name] = column_types[colunm_number](cur_line[colunm_number])

        item = dynamodb_table.new_item(
                    attrs=row
            )           
        items.append(item)

        if count % BATCH_COUNT == 0:
            print 'batch write start ... ', 
            do_batch_write(items, table_name, dynamodb_table, dynamodb_conn)
            items = []
            print 'batch done! (row number: ' + str(count) + ')'

    # flush remaining items, if any
    if len(items) > 0: 
        do_batch_write(items, table_name, dynamodb_table, dynamodb_conn)


    csv_file.close() 


def main():
    '''
    Demonstration of the use of import_csv_to_dynamodb()
    We assume the existence of a table named `test_persons`, with
    - Last_name as primary hash key (type: string)
    - First_name as primary range key (type: string)
    '''
    colunm_names = 'Last_name First_name'.split()
    table_name = 'test_persons'
    csv_file_name = 'test.csv'
    column_types = [str, str]
    import_csv_to_dynamodb(table_name, csv_file_name, colunm_names, column_types)


if __name__ == "__main__":
    main()
    #cProfile.run('main()') # if you want to do some profiling

test.csv's content (must be located in the same folder as the Python script):

John,Doe
Bob,Smith
Alice,Lee
Foo,Bar
a,b
c,d
e,f
g,h
i,j
j,l