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PostgreSQL interface library

Project description

pg8000 is a pure-Python PostgreSQL driver that complies with DB-API 2.0. It is tested on Python versions 3.8+, on CPython and PyPy, and PostgreSQL versions 12+. pg8000’s name comes from the belief that it is probably about the 8000th PostgreSQL interface for Python. pg8000 is distributed under the BSD 3-clause license.

All bug reports, feature requests and contributions are welcome at http://github.com/tlocke/pg8000/.

Build Status

Installation

To install pg8000 using pip type:

pip install pg8000

Native API Interactive Examples

pg8000 comes with two APIs, the native pg8000 API and the DB-API 2.0 standard API. These are the examples for the native API, and the DB-API 2.0 examples follow in the next section.

Basic Example

Import pg8000, connect to the database, create a table, add some rows and then query the table:

>>> import pg8000.native
>>>
>>> # Connect to the database with user name postgres
>>>
>>> con = pg8000.native.Connection("postgres", password="cpsnow")
>>>
>>> # Create a temporary table
>>>
>>> con.run("CREATE TEMPORARY TABLE book (id SERIAL, title TEXT)")
>>>
>>> # Populate the table
>>>
>>> for title in ("Ender's Game", "The Magus"):
...     con.run("INSERT INTO book (title) VALUES (:title)", title=title)
>>>
>>> # Print all the rows in the table
>>>
>>> for row in con.run("SELECT * FROM book"):
...     print(row)
[1, "Ender's Game"]
[2, 'The Magus']
>>>
>>> con.close()

Transactions

Here’s how to run groups of SQL statements in a transaction:

>>> import pg8000.native
>>>
>>> con = pg8000.native.Connection("postgres", password="cpsnow")
>>>
>>> con.run("START TRANSACTION")
>>>
>>> # Create a temporary table
>>> con.run("CREATE TEMPORARY TABLE book (id SERIAL, title TEXT)")
>>>
>>> for title in ("Ender's Game", "The Magus", "Phineas Finn"):
...     con.run("INSERT INTO book (title) VALUES (:title)", title=title)
>>> con.run("COMMIT")
>>> for row in con.run("SELECT * FROM book"):
...     print(row)
[1, "Ender's Game"]
[2, 'The Magus']
[3, 'Phineas Finn']
>>>
>>> con.close()

rolling back a transaction:

>>> import pg8000.native
>>>
>>> con = pg8000.native.Connection("postgres", password="cpsnow")
>>>
>>> # Create a temporary table
>>> con.run("CREATE TEMPORARY TABLE book (id SERIAL, title TEXT)")
>>>
>>> for title in ("Ender's Game", "The Magus", "Phineas Finn"):
...     con.run("INSERT INTO book (title) VALUES (:title)", title=title)
>>>
>>> con.run("START TRANSACTION")
>>> con.run("DELETE FROM book WHERE title = :title", title="Phineas Finn")
>>> con.run("ROLLBACK")
>>> for row in con.run("SELECT * FROM book"):
...     print(row)
[1, "Ender's Game"]
[2, 'The Magus']
[3, 'Phineas Finn']
>>>
>>> con.close()

NB. There is a longstanding bug in the PostgreSQL server whereby if a COMMIT is issued against a failed transaction, the transaction is silently rolled back, rather than an error being returned. pg8000 attempts to detect when this has happened and raise an InterfaceError.

Query Using Functions

Another query, using some PostgreSQL functions:

>>> import pg8000.native
>>>
>>> con = pg8000.native.Connection("postgres", password="cpsnow")
>>>
>>> con.run("SELECT TO_CHAR(TIMESTAMP '2021-10-10', 'YYYY BC')")
[['2021 AD']]
>>>
>>> con.close()

Interval Type

A query that returns the PostgreSQL interval type:

>>> import pg8000.native
>>>
>>> con = pg8000.native.Connection("postgres", password="cpsnow")
>>>
>>> import datetime
>>>
>>> ts = datetime.date(1980, 4, 27)
>>> con.run("SELECT timestamp '2013-12-01 16:06' - :ts", ts=ts)
[[datetime.timedelta(days=12271, seconds=57960)]]
>>>
>>> con.close()

Point Type

A round-trip with a PostgreSQL point type:

>>> import pg8000.native
>>>
>>> con = pg8000.native.Connection("postgres", password="cpsnow")
>>>
>>> con.run("SELECT CAST(:pt as point)", pt=(2.3,1))
[[(2.3, 1.0)]]
>>>
>>> con.close()

Client Encoding

When communicating with the server, pg8000 uses the character set that the server asks it to use (the client encoding). By default the client encoding is the database’s character set (chosen when the database is created), but the client encoding can be changed in a number of ways (eg. setting CLIENT_ENCODING in postgresql.conf). Another way of changing the client encoding is by using an SQL command. For example:

>>> import pg8000.native
>>>
>>> con = pg8000.native.Connection("postgres", password="cpsnow")
>>>
>>> con.run("SET CLIENT_ENCODING TO 'UTF8'")
>>> con.run("SHOW CLIENT_ENCODING")
[['UTF8']]
>>>
>>> con.close()

JSON

JSON always comes back from the server de-serialized. If the JSON you want to send is a dict then you can just do:

>>> import pg8000.native
>>>
>>> con = pg8000.native.Connection("postgres", password="cpsnow")
>>>
>>> val = {'name': 'Apollo 11 Cave', 'zebra': True, 'age': 26.003}
>>> con.run("SELECT CAST(:apollo as jsonb)", apollo=val)
[[{'age': 26.003, 'name': 'Apollo 11 Cave', 'zebra': True}]]
>>>
>>> con.close()

JSON can always be sent in serialized form to the server:

>>> import json
>>> import pg8000.native
>>>
>>> con = pg8000.native.Connection("postgres", password="cpsnow")
>>>
>>>
>>> val = ['Apollo 11 Cave', True, 26.003]
>>> con.run("SELECT CAST(:apollo as jsonb)", apollo=json.dumps(val))
[[['Apollo 11 Cave', True, 26.003]]]
>>>
>>> con.close()

JSON queries can be have parameters:

>>> import pg8000.native
>>>
>>> with pg8000.native.Connection("postgres", password="cpsnow") as con:
...     con.run(""" SELECT CAST('{"a":1, "b":2}' AS jsonb) @> :v """, v={"b": 2})
[[True]]

Retrieve Column Metadata From Results

Find the column metadata returned from a query:

>>> import pg8000.native
>>>
>>> con = pg8000.native.Connection("postgres", password="cpsnow")
>>>
>>> con.run("create temporary table quark (id serial, name text)")
>>> for name in ('Up', 'Down'):
...     con.run("INSERT INTO quark (name) VALUES (:name)", name=name)
>>> # Now execute the query
>>>
>>> con.run("SELECT * FROM quark")
[[1, 'Up'], [2, 'Down']]
>>>
>>> # and retrieve the metadata
>>>
>>> con.columns
[{'table_oid': ..., 'column_attrnum': 1, 'type_oid': 23, 'type_size': 4, 'type_modifier': -1, 'format': 0, 'name': 'id'}, {'table_oid': ..., 'column_attrnum': 2, 'type_oid': 25, 'type_size': -1, 'type_modifier': -1, 'format': 0, 'name': 'name'}]
>>>
>>> # Show just the column names
>>>
>>> [c['name'] for c in con.columns]
['id', 'name']
>>>
>>> con.close()

Notices And Notifications

PostgreSQL notices are stored in a deque called Connection.notices and added using the append() method. Similarly there are Connection.notifications for notifications. Here’s an example:

>>> import pg8000.native
>>>
>>> con = pg8000.native.Connection("postgres", password="cpsnow")
>>>
>>> con.run("LISTEN aliens_landed")
>>> con.run("NOTIFY aliens_landed")
>>> # A notification is a tuple containing (backend_pid, channel, payload)
>>>
>>> con.notifications[0]
(..., 'aliens_landed', '')
>>>
>>> con.close()

Parameter Statuses

Certain parameter values are reported by the server automatically at connection startup or whenever their values change and pg8000 stores the latest values in a dict called Connection.parameter_statuses. Here’s an example where we set the aplication_name parameter and then read it from the parameter_statuses:

>>> import pg8000.native
>>>
>>> con = pg8000.native.Connection(
...     "postgres", password="cpsnow", application_name='AGI')
>>>
>>> con.parameter_statuses['application_name']
'AGI'
>>>
>>> con.close()

LIMIT ALL

You might think that the following would work, but in fact it fails:

>>> import pg8000.native
>>>
>>> con = pg8000.native.Connection("postgres", password="cpsnow")
>>>
>>> con.run("SELECT 'silo 1' LIMIT :lim", lim='ALL')
Traceback (most recent call last):
pg8000.exceptions.DatabaseError: ...
>>>
>>> con.close()

Instead the docs say that you can send null as an alternative to ALL, which does work:

>>> import pg8000.native
>>>
>>> con = pg8000.native.Connection("postgres", password="cpsnow")
>>>
>>> con.run("SELECT 'silo 1' LIMIT :lim", lim=None)
[['silo 1']]
>>>
>>> con.close()

IN and NOT IN

You might think that the following would work, but in fact the server doesn’t like it:

>>> import pg8000.native
>>>
>>> con = pg8000.native.Connection("postgres", password="cpsnow")
>>>
>>> con.run("SELECT 'silo 1' WHERE 'a' IN :v", v=['a', 'b'])
Traceback (most recent call last):
pg8000.exceptions.DatabaseError: ...
>>>
>>> con.close()

instead you can write it using the unnest function:

>>> import pg8000.native
>>>
>>> con = pg8000.native.Connection("postgres", password="cpsnow")
>>>
>>> con.run(
...     "SELECT 'silo 1' WHERE 'a' IN (SELECT unnest(CAST(:v as varchar[])))",
...     v=['a', 'b'])
[['silo 1']]
>>> con.close()

and you can do the same for NOT IN.

Many SQL Statements Can’t Be Parameterized

In PostgreSQL parameters can only be used for data values, not identifiers. Sometimes this might not work as expected, for example the following fails:

>>> import pg8000.native
>>>
>>> con = pg8000.native.Connection("postgres", password="cpsnow")
>>>
>>> channel = 'top_secret'
>>>
>>> con.run("LISTEN :channel", channel=channel)
Traceback (most recent call last):
pg8000.exceptions.DatabaseError: ...
>>>
>>> con.close()

It fails because the PostgreSQL server doesn’t allow this statement to have any parameters. There are many SQL statements that one might think would have parameters, but don’t. For these cases the SQL has to be created manually, being careful to use the identifier() and literal() functions to escape the values to avoid SQL injection attacks:

>>> from pg8000.native import Connection, identifier, literal
>>>
>>> con = Connection("postgres", password="cpsnow")
>>>
>>> channel = 'top_secret'
>>> payload = 'Aliens Landed!'
>>> con.run(f"LISTEN {identifier(channel)}")
>>> con.run(f"NOTIFY {identifier(channel)}, {literal(payload)}")
>>>
>>> con.notifications[0]
(..., 'top_secret', 'Aliens Landed!')
>>>
>>> con.close()

COPY FROM And TO A Stream

The SQL COPY statement can be used to copy from and to a file or file-like object. Here’ an example using the CSV format:

>>> import pg8000.native
>>> from io import StringIO
>>> import csv
>>>
>>> con = pg8000.native.Connection("postgres", password="cpsnow")
>>>
>>> # Create a CSV file in memory
>>>
>>> stream_in = StringIO()
>>> csv_writer = csv.writer(stream_in)
>>> csv_writer.writerow([1, "electron"])
12
>>> csv_writer.writerow([2, "muon"])
8
>>> csv_writer.writerow([3, "tau"])
7
>>> stream_in.seek(0)
0
>>>
>>> # Create a table and then copy the CSV into it
>>>
>>> con.run("CREATE TEMPORARY TABLE lepton (id SERIAL, name TEXT)")
>>> con.run("COPY lepton FROM STDIN WITH (FORMAT CSV)", stream=stream_in)
>>>
>>> # COPY from a table to a stream
>>>
>>> stream_out = StringIO()
>>> con.run("COPY lepton TO STDOUT WITH (FORMAT CSV)", stream=stream_out)
>>> stream_out.seek(0)
0
>>> for row in csv.reader(stream_out):
...     print(row)
['1', 'electron']
['2', 'muon']
['3', 'tau']
>>>
>>> con.close()

It’s also possible to COPY FROM an iterable, which is useful if you’re creating rows programmatically:

>>> import pg8000.native
>>>
>>> con = pg8000.native.Connection("postgres", password="cpsnow")
>>>
>>> # Generator function for creating rows
>>> def row_gen():
...     for i, name in ((1, "electron"), (2, "muon"), (3, "tau")):
...         yield f"{i},{name}\n"
>>>
>>> # Create a table and then copy the CSV into it
>>>
>>> con.run("CREATE TEMPORARY TABLE lepton (id SERIAL, name TEXT)")
>>> con.run("COPY lepton FROM STDIN WITH (FORMAT CSV)", stream=row_gen())
>>>
>>> # COPY from a table to a stream
>>>
>>> stream_out = StringIO()
>>> con.run("COPY lepton TO STDOUT WITH (FORMAT CSV)", stream=stream_out)
>>> stream_out.seek(0)
0
>>> for row in csv.reader(stream_out):
...     print(row)
['1', 'electron']
['2', 'muon']
['3', 'tau']
>>>
>>> con.close()

Execute Multiple SQL Statements

If you want to execute a series of SQL statements (eg. an .sql file), you can run them as expected:

>>> import pg8000.native
>>>
>>> con = pg8000.native.Connection("postgres", password="cpsnow")
>>>
>>> statements = "SELECT 5; SELECT 'Erich Fromm';"
>>>
>>> con.run(statements)
[[5], ['Erich Fromm']]
>>>
>>> con.close()

The only caveat is that when executing multiple statements you can’t have any parameters.

Quoted Identifiers in SQL

Say you had a column called My Column. Since it’s case sensitive and contains a space, you’d have to surround it by double quotes. But you can’t do:

>>> import pg8000.native
>>>
>>> con = pg8000.native.Connection("postgres", password="cpsnow")
>>>
>>> con.run("select 'hello' as "My Column"")
Traceback (most recent call last):
SyntaxError: invalid syntax...
>>>
>>> con.close()

since Python uses double quotes to delimit string literals, so one solution is to use Python’s triple quotes to delimit the string instead:

>>> import pg8000.native
>>>
>>> con = pg8000.native.Connection("postgres", password="cpsnow")
>>>
>>> con.run('''SELECT 'hello' AS "My Column"''')
[['hello']]
>>>
>>> con.close()

another solution, that’s especially useful if the identifier comes from an untrusted source, is to use the identifier() function, which correctly quotes and escapes the identifier as needed:

>>> from pg8000.native import Connection, identifier
>>>
>>> con = Connection("postgres", password="cpsnow")
>>>
>>> sql = f"SELECT 'hello' as {identifier('My Column')}"
>>> print(sql)
SELECT 'hello' as "My Column"
>>>
>>> con.run(sql)
[['hello']]
>>>
>>> con.close()

this approach guards against SQL injection attacks. One thing to note if you’re using explicit schemas (eg. pg_catalog.pg_language) is that the schema name and table name are both separate identifiers. So to escape them you’d do:

>>> from pg8000.native import Connection, identifier
>>>
>>> con = Connection("postgres", password="cpsnow")
>>>
>>> query = (
...     f"SELECT lanname FROM {identifier('pg_catalog')}.{identifier('pg_language')} "
...     f"WHERE lanname = 'sql'"
... )
>>> print(query)
SELECT lanname FROM pg_catalog.pg_language WHERE lanname = 'sql'
>>>
>>> con.run(query)
[['sql']]
>>>
>>> con.close()

Custom adapter from a Python type to a PostgreSQL type

pg8000 has a mapping from Python types to PostgreSQL types for when it needs to send SQL parameters to the server. The default mapping that comes with pg8000 is designed to work well in most cases, but you might want to add or replace the default mapping.

A Python datetime.timedelta object is sent to the server as a PostgreSQL interval type, which has the oid 1186. But let’s say we wanted to create our own Python class to be sent as an interval type. Then we’d have to register an adapter:

>>> import pg8000.native
>>>
>>> con = pg8000.native.Connection("postgres", password="cpsnow")
>>>
>>> class MyInterval(str):
...     pass
>>>
>>> def my_interval_out(my_interval):
...     return my_interval  # Must return a str
>>>
>>> con.register_out_adapter(MyInterval, my_interval_out)
>>> con.run("SELECT CAST(:interval as interval)", interval=MyInterval("2 hours"))
[[datetime.timedelta(seconds=7200)]]
>>>
>>> con.close()

Note that it still came back as a datetime.timedelta object because we only changed the mapping from Python to PostgreSQL. See below for an example of how to change the mapping from PostgreSQL to Python.

Custom adapter from a PostgreSQL type to a Python type

pg8000 has a mapping from PostgreSQL types to Python types for when it receives SQL results from the server. The default mapping that comes with pg8000 is designed to work well in most cases, but you might want to add or replace the default mapping.

If pg8000 receives PostgreSQL interval type, which has the oid 1186, it converts it into a Python datetime.timedelta object. But let’s say we wanted to create our own Python class to be used instead of datetime.timedelta. Then we’d have to register an adapter:

>>> import pg8000.native
>>>
>>> con = pg8000.native.Connection("postgres", password="cpsnow")
>>>
>>> class MyInterval(str):
...     pass
>>>
>>> def my_interval_in(my_interval_str):  # The parameter is of type str
...     return MyInterval(my_interval)
>>>
>>> con.register_in_adapter(1186, my_interval_in)
>>> con.run("SELECT \'2 years'")
[['2 years']]
>>>
>>> con.close()

Note that registering the ‘in’ adapter only afects the mapping from the PostgreSQL type to the Python type. See above for an example of how to change the mapping from PostgreSQL to Python.

Could Not Determine Data Type Of Parameter

Sometimes you’ll get the ‘could not determine data type of parameter’ error message from the server:

>>> import pg8000.native
>>>
>>> con = pg8000.native.Connection("postgres", password="cpsnow")
>>>
>>> con.run("SELECT :v IS NULL", v=None)
Traceback (most recent call last):
pg8000.exceptions.DatabaseError: {'S': 'ERROR', 'V': 'ERROR', 'C': '42P18', 'M': 'could not determine data type of parameter $1', 'F': 'postgres.c', 'L': '...', 'R': '...'}
>>>
>>> con.close()

One way of solving it is to put a CAST in the SQL:

>>> import pg8000.native
>>>
>>> con = pg8000.native.Connection("postgres", password="cpsnow")
>>>
>>> con.run("SELECT cast(:v as TIMESTAMP) IS NULL", v=None)
[[True]]
>>>
>>> con.close()

Another way is to override the type that pg8000 sends along with each parameter:

>>> import pg8000.native
>>>
>>> con = pg8000.native.Connection("postgres", password="cpsnow")
>>>
>>> con.run("SELECT :v IS NULL", v=None, types={'v': pg8000.native.TIMESTAMP})
[[True]]
>>>
>>> con.close()

Prepared Statements

Prepared statements can be useful in improving performance when you have a statement that’s executed repeatedly. Here’s an example:

>>> import pg8000.native
>>>
>>> con = pg8000.native.Connection("postgres", password="cpsnow")
>>>
>>> # Create the prepared statement
>>> ps = con.prepare("SELECT cast(:v as varchar)")
>>>
>>> # Execute the statement repeatedly
>>> ps.run(v="speedy")
[['speedy']]
>>> ps.run(v="rapid")
[['rapid']]
>>> ps.run(v="swift")
[['swift']]
>>>
>>> # Close the prepared statement, releasing resources on the server
>>> ps.close()
>>>
>>> con.close()

Use Environment Variables As Connection Defaults

You might want to use the current user as the database username for example:

>>> import pg8000.native
>>> import getpass
>>>
>>> # Connect to the database with current user name
>>> username = getpass.getuser()
>>> connection = pg8000.native.Connection(username, password="cpsnow")
>>>
>>> connection.run("SELECT 'pilau'")
[['pilau']]
>>>
>>> connection.close()

or perhaps you may want to use some of the same environment variables that libpg uses:

>>> import pg8000.native
>>> from os import environ
>>>
>>> username = environ.get('PGUSER', 'postgres')
>>> password = environ.get('PGPASSWORD', 'cpsnow')
>>> host = environ.get('PGHOST', 'localhost')
>>> port = environ.get('PGPORT', '5432')
>>> database = environ.get('PGDATABASE')
>>>
>>> connection = pg8000.native.Connection(
...     username, password=password, host=host, port=port, database=database)
>>>
>>> connection.run("SELECT 'Mr Cairo'")
[['Mr Cairo']]
>>>
>>> connection.close()

It might be asked, why doesn’t pg8000 have this behaviour built in? The thinking follows the second aphorism of The Zen of Python:

Explicit is better than implicit.

So we’ve taken the approach of only being able to set connection parameters using the pg8000.native.Connection() constructor.

Connect To PostgreSQL Over SSL

To connect to the server using SSL defaults do:

import pg8000.native
connection = pg8000.native.Connection('postgres', password="cpsnow", ssl_context=True)
connection.run("SELECT 'The game is afoot!'")

To connect over SSL with custom settings, set the ssl_context parameter to an ssl.SSLContext object:

import pg8000.native
import ssl


ssl_context = ssl.create_default_context()
ssl_context.verify_mode = ssl.CERT_REQUIRED
ssl_context.load_verify_locations('root.pem')
connection = pg8000.native.Connection(
  'postgres', password="cpsnow", ssl_context=ssl_context)

It may be that your PostgreSQL server is behind an SSL proxy server in which case you can set a pg8000-specific attribute ssl.SSLContext.request_ssl = False which tells pg8000 to connect using an SSL socket, but not to request SSL from the PostgreSQL server:

import pg8000.native
import ssl

ssl_context = ssl.create_default_context()
ssl_context.request_ssl = False
connection = pg8000.native.Connection(
    'postgres', password="cpsnow", ssl_context=ssl_context)

Server-Side Cursors

You can use the SQL commands DECLARE, FETCH, MOVE and CLOSE to manipulate server-side cursors. For example:

>>> import pg8000.native
>>>
>>> con = pg8000.native.Connection('postgres', password="cpsnow")
>>> con.run("START TRANSACTION")
>>> con.run("DECLARE c SCROLL CURSOR FOR SELECT * FROM generate_series(1, 100)")
>>> con.run("FETCH FORWARD 5 FROM c")
[[1], [2], [3], [4], [5]]
>>> con.run("MOVE FORWARD 50 FROM c")
>>> con.run("FETCH BACKWARD 10 FROM c")
[[54], [53], [52], [51], [50], [49], [48], [47], [46], [45]]
>>> con.run("CLOSE c")
>>> con.run("ROLLBACK")
>>>
>>> con.close()

BLOBs (Binary Large Objects)

There’s a set of SQL functions for manipulating BLOBs. Here’s an example:

>>> import pg8000.native
>>>
>>> con = pg8000.native.Connection('postgres', password="cpsnow")
>>>
>>> # Create a BLOB and get its oid
>>> data = b'hello'
>>> res = con.run("SELECT lo_from_bytea(0, :data)", data=data)
>>> oid = res[0][0]
>>>
>>> # Create a table and store the oid of the BLOB
>>> con.run("CREATE TEMPORARY TABLE image (raster oid)")
>>>
>>> con.run("INSERT INTO image (raster) VALUES (:oid)", oid=oid)
>>> # Retrieve the data using the oid
>>> con.run("SELECT lo_get(:oid)", oid=oid)
[[b'hello']]
>>>
>>> # Add some data to the end of the BLOB
>>> more_data = b' all'
>>> offset = len(data)
>>> con.run(
...     "SELECT lo_put(:oid, :offset, :data)",
...     oid=oid, offset=offset, data=more_data)
[['']]
>>> con.run("SELECT lo_get(:oid)", oid=oid)
[[b'hello all']]
>>>
>>> # Download a part of the data
>>> con.run("SELECT lo_get(:oid, 6, 3)", oid=oid)
[[b'all']]
>>>
>>> con.close()

Replication Protocol

The PostgreSQL Replication Protocol is supported using the replication keyword when creating a connection:

>>> import pg8000.native
>>>
>>> con = pg8000.native.Connection(
...    'postgres', password="cpsnow", replication="database")
>>>
>>> con.run("IDENTIFY_SYSTEM")
[['...', 1, '.../...', 'postgres']]
>>>
>>> con.close()

DB-API 2 Interactive Examples

These examples stick to the DB-API 2.0 standard.

Basic Example

Import pg8000, connect to the database, create a table, add some rows and then query the table:

>>> import pg8000.dbapi
>>>
>>> conn = pg8000.dbapi.connect(user="postgres", password="cpsnow")
>>> cursor = conn.cursor()
>>> cursor.execute("CREATE TEMPORARY TABLE book (id SERIAL, title TEXT)")
>>> cursor.execute(
...     "INSERT INTO book (title) VALUES (%s), (%s) RETURNING id, title",
...     ("Ender's Game", "Speaker for the Dead"))
>>> results = cursor.fetchall()
>>> for row in results:
...     id, title = row
...     print("id = %s, title = %s" % (id, title))
id = 1, title = Ender's Game
id = 2, title = Speaker for the Dead
>>> conn.commit()
>>>
>>> conn.close()

Query Using Functions

Another query, using some PostgreSQL functions:

>>> import pg8000.dbapi
>>>
>>> con = pg8000.dbapi.connect(user="postgres", password="cpsnow")
>>> cursor = con.cursor()
>>>
>>> cursor.execute("SELECT TO_CHAR(TIMESTAMP '2021-10-10', 'YYYY BC')")
>>> cursor.fetchone()
['2021 AD']
>>>
>>> con.close()

Interval Type

A query that returns the PostgreSQL interval type:

>>> import datetime
>>> import pg8000.dbapi
>>>
>>> con = pg8000.dbapi.connect(user="postgres", password="cpsnow")
>>> cursor = con.cursor()
>>>
>>> cursor.execute("SELECT timestamp '2013-12-01 16:06' - %s",
... (datetime.date(1980, 4, 27),))
>>> cursor.fetchone()
[datetime.timedelta(days=12271, seconds=57960)]
>>>
>>> con.close()

Point Type

A round-trip with a PostgreSQL point type:

>>> import pg8000.dbapi
>>>
>>> con = pg8000.dbapi.connect(user="postgres", password="cpsnow")
>>> cursor = con.cursor()
>>>
>>> cursor.execute("SELECT cast(%s as point)", ((2.3,1),))
>>> cursor.fetchone()
[(2.3, 1.0)]
>>>
>>> con.close()

Numeric Parameter Style

pg8000 supports all the DB-API parameter styles. Here’s an example of using the ‘numeric’ parameter style:

>>> import pg8000.dbapi
>>>
>>> pg8000.dbapi.paramstyle = "numeric"
>>> con = pg8000.dbapi.connect(user="postgres", password="cpsnow")
>>> cursor = con.cursor()
>>>
>>> cursor.execute("SELECT array_prepend(:1, CAST(:2 AS int[]))", (500, [1, 2, 3, 4],))
>>> cursor.fetchone()
[[500, 1, 2, 3, 4]]
>>> pg8000.dbapi.paramstyle = "format"
>>>
>>> con.close()

Autocommit

Following the DB-API specification, autocommit is off by default. It can be turned on by using the autocommit property of the connection:

>>> import pg8000.dbapi
>>>
>>> con = pg8000.dbapi.connect(user="postgres", password="cpsnow")
>>> con.autocommit = True
>>>
>>> cur = con.cursor()
>>> cur.execute("vacuum")
>>> conn.autocommit = False
>>> cur.close()
>>>
>>> con.close()

Client Encoding

When communicating with the server, pg8000 uses the character set that the server asks it to use (the client encoding). By default the client encoding is the database’s character set (chosen when the database is created), but the client encoding can be changed in a number of ways (eg. setting CLIENT_ENCODING in postgresql.conf). Another way of changing the client encoding is by using an SQL command. For example:

>>> import pg8000.dbapi
>>>
>>> con = pg8000.dbapi.connect(user="postgres", password="cpsnow")
>>> cur = con.cursor()
>>> cur.execute("SET CLIENT_ENCODING TO 'UTF8'")
>>> cur.execute("SHOW CLIENT_ENCODING")
>>> cur.fetchone()
['UTF8']
>>> cur.close()
>>>
>>> con.close()

JSON

JSON is sent to the server serialized, and returned de-serialized. Here’s an example:

>>> import json
>>> import pg8000.dbapi
>>>
>>> con = pg8000.dbapi.connect(user="postgres", password="cpsnow")
>>> cur = con.cursor()
>>> val = ['Apollo 11 Cave', True, 26.003]
>>> cur.execute("SELECT cast(%s as json)", (json.dumps(val),))
>>> cur.fetchone()
[['Apollo 11 Cave', True, 26.003]]
>>> cur.close()
>>>
>>> con.close()

JSON queries can be have parameters:

>>> import pg8000.dbapi
>>>
>>> with pg8000.dbapi.connect("postgres", password="cpsnow") as con:
...     cur = con.cursor()
...     cur.execute(""" SELECT CAST('{"a":1, "b":2}' AS jsonb) @> %s """, ({"b": 2},))
...     for row in cur.fetchall():
...         print(row)
[True]

Retrieve Column Names From Results

Use the columns names retrieved from a query:

>>> import pg8000
>>> conn = pg8000.dbapi.connect(user="postgres", password="cpsnow")
>>> c = conn.cursor()
>>> c.execute("create temporary table quark (id serial, name text)")
>>> c.executemany("INSERT INTO quark (name) VALUES (%s)", (("Up",), ("Down",)))
>>> #
>>> # Now retrieve the results
>>> #
>>> c.execute("select * from quark")
>>> rows = c.fetchall()
>>> keys = [k[0] for k in c.description]
>>> results = [dict(zip(keys, row)) for row in rows]
>>> assert results == [{'id': 1, 'name': 'Up'}, {'id': 2, 'name': 'Down'}]
>>>
>>> conn.close()

COPY from and to a file

The SQL COPY statement can be used to copy from and to a file or file-like object:

>>> from io import StringIO
>>> import pg8000.dbapi
>>>
>>> con = pg8000.dbapi.connect(user="postgres", password="cpsnow")
>>> cur = con.cursor()
>>> #
>>> # COPY from a stream to a table
>>> #
>>> stream_in = StringIO('1\telectron\n2\tmuon\n3\ttau\n')
>>> cur = con.cursor()
>>> cur.execute("create temporary table lepton (id serial, name text)")
>>> cur.execute("COPY lepton FROM stdin", stream=stream_in)
>>> #
>>> # Now COPY from a table to a stream
>>> #
>>> stream_out = StringIO()
>>> cur.execute("copy lepton to stdout", stream=stream_out)
>>> stream_out.getvalue()
'1\telectron\n2\tmuon\n3\ttau\n'
>>>
>>> con.close()

Server-Side Cursors

You can use the SQL commands DECLARE, FETCH, MOVE and CLOSE to manipulate server-side cursors. For example:

>>> import pg8000.dbapi
>>>
>>> con = pg8000.dbapi.connect(user="postgres", password="cpsnow")
>>> cur = con.cursor()
>>> cur.execute("START TRANSACTION")
>>> cur.execute(
...    "DECLARE c SCROLL CURSOR FOR SELECT * FROM generate_series(1, 100)")
>>> cur.execute("FETCH FORWARD 5 FROM c")
>>> cur.fetchall()
([1], [2], [3], [4], [5])
>>> cur.execute("MOVE FORWARD 50 FROM c")
>>> cur.execute("FETCH BACKWARD 10 FROM c")
>>> cur.fetchall()
([54], [53], [52], [51], [50], [49], [48], [47], [46], [45])
>>> cur.execute("CLOSE c")
>>> cur.execute("ROLLBACK")
>>>
>>> con.close()

BLOBs (Binary Large Objects)

There’s a set of SQL functions for manipulating BLOBs. Here’s an example:

>>> import pg8000.dbapi
>>>
>>> con = pg8000.dbapi.connect(user="postgres", password="cpsnow")
>>> cur = con.cursor()
>>>
>>> # Create a BLOB and get its oid
>>> data = b'hello'
>>> cur = con.cursor()
>>> cur.execute("SELECT lo_from_bytea(0, %s)", [data])
>>> oid = cur.fetchone()[0]
>>>
>>> # Create a table and store the oid of the BLOB
>>> cur.execute("CREATE TEMPORARY TABLE image (raster oid)")
>>> cur.execute("INSERT INTO image (raster) VALUES (%s)", [oid])
>>>
>>> # Retrieve the data using the oid
>>> cur.execute("SELECT lo_get(%s)", [oid])
>>> cur.fetchall()
([b'hello'],)
>>>
>>> # Add some data to the end of the BLOB
>>> more_data = b' all'
>>> offset = len(data)
>>> cur.execute("SELECT lo_put(%s, %s, %s)", [oid, offset, more_data])
>>> cur.execute("SELECT lo_get(%s)", [oid])
>>> cur.fetchall()
([b'hello all'],)
>>>
>>> # Download a part of the data
>>> cur.execute("SELECT lo_get(%s, 6, 3)", [oid])
>>> cur.fetchall()
([b'all'],)
>>>
>>> con.close()

Type Mapping

The following table shows the default mapping between Python types and PostgreSQL types, and vice versa.

If pg8000 doesn’t recognize a type that it receives from PostgreSQL, it will return it as a str type. This is how pg8000 handles PostgreSQL enum and XML types. It’s possible to change the default mapping using adapters (see the examples).

Python to PostgreSQL Type Mapping

Python Type

PostgreSQL Type

Notes

bool

bool

int

int4

str

text

float

float8

decimal.Decimal

numeric

bytes

bytea

datetime.datetime (without tzinfo)

timestamp without timezone

+/-infinity PostgreSQL values are represented as Python str values. If a timestamp is too big for datetime.datetime then a str is used.

datetime.datetime (with tzinfo)

timestamp with timezone

+/-infinity PostgreSQL values are represented as Python str values. If a timestamptz is too big for datetime.datetime then a str is used.

datetime.date

date

+/-infinity PostgreSQL values are represented as Python str values. If a date is too big for a datetime.date then a str is used.

datetime.time

time without time zone

datetime.timedelta

interval

If an interval is too big for datetime.timedelta then a PGInterval is used.

None

NULL

uuid.UUID

uuid

ipaddress.IPv4Address

inet

ipaddress.IPv6Address

inet

ipaddress.IPv4Network

inet

ipaddress.IPv6Network

inet

int

xid

list of int

INT4[]

list of float

FLOAT8[]

list of bool

BOOL[]

list of str

TEXT[]

int

int2vector

Only from PostgreSQL to Python

JSON

json, jsonb

The Python JSON is provided as a Python serialized string. Results returned as de-serialized JSON.

pg8000.Range

*range

PostgreSQL multirange types are represented in Python as a list of range types.

tuple

composite type

Only from Python to PostgreSQL

Theory Of Operation

A concept is tolerated inside the microkernel only if moving it outside the kernel, i.e., permitting competing implementations, would prevent the implementation of the system’s required functionality.

—Jochen Liedtke, Liedtke’s minimality principle

pg8000 is designed to be used with one thread per connection.

Pg8000 communicates with the database using the PostgreSQL Frontend/Backend Protocol (FEBE). If a query has no parameters, pg8000 uses the ‘simple query protocol’. If a query does have parameters, pg8000 uses the ‘extended query protocol’ with unnamed prepared statements. The steps for a query with parameters are:

  1. Query comes in.

  2. Send a PARSE message to the server to create an unnamed prepared statement.

  3. Send a BIND message to run against the unnamed prepared statement, resulting in an unnamed portal on the server.

  4. Send an EXECUTE message to read all the results from the portal.

It’s also possible to use named prepared statements. In which case the prepared statement persists on the server, and represented in pg8000 using a PreparedStatement object. This means that the PARSE step gets executed once up front, and then only the BIND and EXECUTE steps are repeated subsequently.

There are a lot of PostgreSQL data types, but few primitive data types in Python. By default, pg8000 doesn’t send PostgreSQL data type information in the PARSE step, in which case PostgreSQL assumes the types implied by the SQL statement. In some cases PostgreSQL can’t work out a parameter type and so an explicit cast can be used in the SQL.

In the FEBE protocol, each query parameter can be sent to the server either as binary or text according to the format code. In pg8000 the parameters are always sent as text.

Occasionally, the network connection between pg8000 and the server may go down. If pg8000 encounters a network problem it’ll raise an InterfaceError with the message network error and with the original exception set as the cause.

Native API Docs

Native API Docs

DB-API 2 Docs

DB-API 2 Docs

Design Decisions

For the Range type, the constructor follows the PostgreSQL range constructor functions which makes [closed, open) the easiest to express:

>>> from pg8000.types import Range
>>>
>>> pg_range = Range(2, 6)

Tests

  • Install tox: pip install tox

  • Enable the PostgreSQL hstore extension by running the SQL command: create extension hstore;

  • Add a line to pg_hba.conf for the various authentication options:

host    pg8000_md5           all        127.0.0.1/32            md5
host    pg8000_gss           all        127.0.0.1/32            gss
host    pg8000_password      all        127.0.0.1/32            password
host    pg8000_scram_sha_256 all        127.0.0.1/32            scram-sha-256
host    all                  all        127.0.0.1/32            trust
  • Set password encryption to scram-sha-256 in postgresql.conf: password_encryption = 'scram-sha-256'

  • Set the password for the postgres user: ALTER USER postgresql WITH PASSWORD 'pw';

  • Run tox from the pg8000 directory: tox

This will run the tests against the Python version of the virtual environment, on the machine, and the installed PostgreSQL version listening on port 5432, or the PGPORT environment variable if set.

Benchmarks are run as part of the test suite at tests/test_benchmarks.py.

README.rst

This file is written in the reStructuredText format. To generate an HTML page from it, do:

  • Activate the virtual environment: source venv/bin/activate

  • Install Sphinx: pip install Sphinx

  • Run rst2html.py: rst2html.py README.rst README.html

Doing A Release Of pg8000

Run tox to make sure all tests pass, then update the release notes, then do:

git tag -a x.y.z -m "version x.y.z"
rm -r dist
python -m build
twine upload dist/*

Release Notes

Release Notes

Release history Release notifications | RSS feed

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