Databases

Overview

Parsons offers support for a variety of popular SQL database dialects. The functionality is focused on the ability to query and upload data to SQL databases. Each database class also includes the ability to infer datatypes and data schemas from a Parsons table and automatically create new tables.

Similar to other classes in Parsons, the query methods for databases all return Parsons Table, which allow them to be easily converted to other data types.

There is also support for synchronization of tables between databases as part of the Database Sync framework.

Google BigQuery

See Google for documentation.

MySQL

MySQL is the world’s most popular open source database. The Parsons class leverages on the MySQLdb1 python package.

Quick Start

Authentication

from parsons import MySQL

# Instantiate MySQL from environmental variables
mysql = MySQL()

# Instantiate MySQL from passed variables
mysql = MySQL(username='me', password='secret', host='mydb.com', db='dev', port=3306)

Quick Start

# Query database
tbl = mysql.query('select * from my_schema.secret_sauce')

# Copy data to database
tbl = Table.from_csv('my_file.csv') # Load from a CSV or other source.
mysql.copy(tbl, 'my_schema.winning_formula')

class parsons.MySQL(host=None, username=None, password=None, db=None, port=3306)[source]

Connect to a MySQL database.

Args:

username: str: Required if env variable MYSQL_USERNAME not populated
password: str: Required if env variable MYSQL_PASSWORD not populated
host: str: Required if env variable MYSQL_HOST not populated
db: str: Required if env variable MYSQL_DB not populated
port: int: Can be set by env variable MYSQL_PORT or argument.

connection()[source]

Generate a MySQL connection. The connection is set up as a python “context manager”, so it will be closed automatically (and all queries committed) when the connection goes out of scope.

When using the connection, make sure to put it in a with block (necessary for any context manager): with mysql.connection() as conn:

Returns:: MySQL connection object

query(sql, parameters=None)[source]

Execute a query against the database. Will return None if the query returns zero rows.

To include python variables in your query, it is recommended to pass them as parameters, following the mysql style. Using the parameters argument ensures that values are escaped properly, and avoids SQL injection attacks.

Parameter Examples

# Note that the name contains a quote, which could break your query if not escaped
# properly.
name = "Beatrice O'Brady"
sql = "SELECT * FROM my_table WHERE name = %s"
mysql.query(sql, parameters=[name])

names = ["Allen Smith", "Beatrice O'Brady", "Cathy Thompson"]
placeholders = ', '.join('%s' for item in names)
sql = f"SELECT * FROM my_table WHERE name IN ({placeholders})"
mysql.query(sql, parameters=names)

Args:

sql: str: A valid SQL statement
parameters: list: A list of python variables to be converted into SQL values in your query

Returns:

Parsons Table: See Parsons Table for output options.

query_with_connection(sql, connection, parameters=None, commit=True)[source]

Execute a query against the database, with an existing connection. Useful for batching queries together. Will return None if the query returns zero rows.

Args:

sql: str: A valid SQL statement
connection: obj: A connection object obtained from mysql.connection()
parameters: list: A list of python variables to be converted into SQL values in your query
commit: boolean: Whether to commit the transaction immediately. If False the transaction will be committed when the connection goes out of scope and is closed (or you can commit manually with connection.commit()).

Returns:

Parsons Table: See Parsons Table for output options.

copy(tbl: Table, table_name: str, if_exists: str = 'fail', chunk_size: int = 1000, strict_length: bool = True)[source]

Copy a Parsons Table to the database.

Note

This method utilizes extended inserts rather LOAD DATA INFILE since many MySQL Database configurations do not allow data files to be loaded. It results in a minor performance hit compared to LOAD DATA.

Args:

tbl: parsons.Table: A Parsons table object
table_name: str: The destination schema and table (e.g. my_schema.my_table)
if_exists: str: If the table already exists, either fail, append, drop or truncate the table.
chunk_size: int: The number of rows to insert per query.
strict_length: bool: If the database table needs to be created, strict_length determines whether the created table’s column sizes will be sized to exactly fit the current data, or if their size will be rounded up to account for future values being larger then the current dataset. defaults to True

table_exists(table_name: str) → bool[source]

Check if a table or view exists in the database.

Args:

table_name: str: The table name

Returns:

boolean: True if the table exists and False if it does not.

create_table(table_object, table_name): Create a table based on table object data.

detect_data_type(value, cmp_type=None)

Detect the higher of value’s type cmp_type.

check if it’s a string
check if it’s a number

check if it’s a float

check if it’s an int

Args:

value: str: The value to inspect.
cmp_type: str: The string representation of a type to compare with value’s type.

Returns:

str: The string representation of the higher of the two types.

format_column(col, index='', replace_chars=None, col_prefix='_')

Format the column to meet database contraints.

Formats the columns as follows:

Coverts to lowercase (if case insensitive)
Strips leading and trailing whitespace
Replaces invalid characters
Renames if in reserved words

Args:

col: str: The column to format.
index: int: (Optional) The index of the column. Used if the column is empty.
replace_chars: dict: A dictionary of invalid characters and their replacements. If None uses {” “: “_”}
col_prefix: str: The prefix to use when the column is empty or starts with an invalid character.

Returns:

str: The formatted column.

format_columns(cols, **kwargs)

Format the columns to meet database contraints.

This method relies on format_column to handle most changes. It only handles duplicated columns. Options to format_column can be passed through kwargs.

Args:

cols: list: The columns to format.
kwargs: dicts: Keyword arguments to pass to format_column.

Returns:

list: The formatted columns.

generate_alchemy_url(): Generate a SQL Alchemy engine https://docs.sqlalchemy.org/en/14/core/engines.html#

generate_engine(): Generate a SQL Alchemy engine.

get_bigger_int(int1, int2)

Return the bigger of the two ints.

Args:

int1: str: The string representation if an int type.
int2: str: The string representation if an int type.

Returns:

str: A string representation of the higher of the two int types.

get_table_object(table_name): Get a SQL Alchemy table object.

is_valid_sql_num(val)

Check whether val is a valid sql number.

Args:

val: any: The values to check.

Returns:

bool: Whether or not the value is a valid sql number.

static split_table_name(full_table_name): Utility method to parse the schema and table name.

Postgres

Postgres is popular open source SQL database dialect. The Parsons class leverages the mysql python package.

Quick Start

Authentication

from parsons import Postgres

# Instantiate Postgres from environmental variables
pg = Postgres()

# Instantiate Postgres from passed variables
pg = Postgres(username='me', password='secret', host='mydb.com', db='dev', port=3306)

# Instantiate Postgres from a ~/.pgpass file
pg = Postgres()

Quick Start

# Query database
tbl = pg.query('select * from my_schema.secret_sauce')

# Copy data to database
tbl = Table.from_csv('my_file.csv') # Load from a CSV or other source.
pg.copy(tbl, 'my_schema.winning_formula')

class parsons.Postgres(username=None, password=None, host=None, db=None, port=5432, timeout=10)[source]

A Postgres class to connect to database. Credentials can be passed from a .pgpass file stored in your home directory or with environmental variables.

Args:

username: str: Required if env variable PGUSER not populated
password: str: Required if env variable PGPASSWORD not populated
host: str: Required if env variable PGHOST not populated
db: str: Required if env variable PGDATABASE not populated
port: int: Required if env variable PGPORT not populated.
timeout: int: Seconds to timeout if connection not established.

copy(tbl: Table, table_name: str, if_exists: str = 'fail', strict_length: bool = False)[source]

Copy a Parsons Table to Postgres.

Args:

tbl: parsons.Table: A Parsons table object
table_name: str: The destination schema and table (e.g. my_schema.my_table)
if_exists: str: If the table already exists, either fail, append, drop or truncate the table.
strict_length: bool: If the database table needs to be created, strict_length determines whether the created table’s column sizes will be sized to exactly fit the current data, or if their size will be rounded up to account for future values being larger then the current dataset. Defaults to False.

connection()

Generate a Postgres connection. The connection is set up as a python “context manager”, so it will be closed automatically (and all queries committed) when the connection goes out of scope.

When using the connection, make sure to put it in a with block (necessary for any context manager): with pg.connection() as conn:

Returns:: Psycopg2 connection object

create_table(table_object, table_name): Create a table based on table object data.

detect_data_type(value, cmp_type=None)

Detect the higher of value’s type cmp_type.

check if it’s a string
check if it’s a number

check if it’s a float

check if it’s an int

Args:

value: str: The value to inspect.
cmp_type: str: The string representation of a type to compare with value’s type.

Returns:

str: The string representation of the higher of the two types.

format_column(col, index='', replace_chars=None, col_prefix='_')

Format the column to meet database contraints.

Formats the columns as follows:

Coverts to lowercase (if case insensitive)
Strips leading and trailing whitespace
Replaces invalid characters
Renames if in reserved words

Args:

col: str: The column to format.
index: int: (Optional) The index of the column. Used if the column is empty.
replace_chars: dict: A dictionary of invalid characters and their replacements. If None uses {” “: “_”}
col_prefix: str: The prefix to use when the column is empty or starts with an invalid character.

Returns:

str: The formatted column.

format_columns(cols, **kwargs)

Format the columns to meet database contraints.

This method relies on format_column to handle most changes. It only handles duplicated columns. Options to format_column can be passed through kwargs.

Args:

cols: list: The columns to format.
kwargs: dicts: Keyword arguments to pass to format_column.

Returns:

list: The formatted columns.

generate_alchemy_url(): Generate a SQL Alchemy engine https://docs.sqlalchemy.org/en/14/core/engines.html#

generate_engine(): Generate a SQL Alchemy engine.

get_bigger_int(int1, int2)

Return the bigger of the two ints.

Args:

int1: str: The string representation if an int type.
int2: str: The string representation if an int type.

Returns:

str: A string representation of the higher of the two int types.

get_table_object(table_name): Get a SQL Alchemy table object.

is_valid_sql_num(val)

Check whether val is a valid sql number.

Args:

val: any: The values to check.

Returns:

bool: Whether or not the value is a valid sql number.

query(sql: str, parameters: list | None = None) → Table | None

Execute a query against the database. Will return None if the query returns zero rows.

To include python variables in your query, it is recommended to pass them as parameters, following the psycopg style. Using the parameters argument ensures that values are escaped properly, and avoids SQL injection attacks.

Parameter Examples

# Note that the name contains a quote, which could break your query if not escaped
# properly.
name = "Beatrice O'Brady"
sql = "SELECT * FROM my_table WHERE name = %s"
rs.query(sql, parameters=[name])

names = ["Allen Smith", "Beatrice O'Brady", "Cathy Thompson"]
placeholders = ', '.join('%s' for item in names)
sql = f"SELECT * FROM my_table WHERE name IN ({placeholders})"
rs.query(sql, parameters=names)

Args:

sql: str: A valid SQL statement
parameters: list: A list of python variables to be converted into SQL values in your query

Returns:

Parsons Table: See Parsons Table for output options.

query_with_connection(sql, connection, parameters=None, commit=True)

Execute a query against the database, with an existing connection. Useful for batching queries together. Will return None if the query returns zero rows.

Args:

sql: str: A valid SQL statement
connection: obj: A connection object obtained from redshift.connection()
parameters: list: A list of python variables to be converted into SQL values in your query
commit: boolean: Whether to commit the transaction immediately. If False the transaction will be committed when the connection goes out of scope and is closed (or you can commit manually with connection.commit()).

Returns:

Parsons Table: See Parsons Table for output options.

static split_table_name(full_table_name): Utility method to parse the schema and table name.

table_exists(table_name: str, view: bool = True) → bool

Check if a table or view exists in the database.

Args:

table_name: str: The table name and schema (e.g. myschema.mytable).
view: boolean: Check to see if a view exists by the same name. Defaults to True.

Returns:

boolean: True if the table exists and False if it does not.

Redshift

See Redshift for documentation.