Python PostgreSQL adapter to stream results of multi-statement queries without a server-side cursor

Overview

streampq CircleCI Test Coverage

Stream results of multi-statement PostgreSQL queries from Python without server-side cursors. Has benefits over some other Python PostgreSQL libraries:

  • Streams results from complex multi-statement queries even though SQL doesn't allow server-side cursors for such queries - suitable for large amounts of results that don't fit in memory.

  • CTRL+C (SIGINT) by default behaves as expected even during slow queries - a KeyboardInterrupt is raised and quickly bubbles up through streampq code. Unless client code prevents it, the program will exit.

  • Every effort is made to cancel queries on KeyboardInterrupt, SystemExit, or errors - the server doesn't continue needlessly using resources.

Particularly useful when temporary tables are needed to store intermediate results in multi-statement SQL scripts.

Installation

pip install streampq

The libpq binary library is also required. This is typically either already installed, or installed by:

  • macOS + brew: brew install libpq
  • Linux (Debian): apt install libpq5
  • Linux (Red Hat):yum install postgresql-libs

The only runtime dependencies are libpq and Python itself.

Usage

from streampq import streampq_connect

# libpq connection paramters
# https://www.postgresql.org/docs/current/libpq-connect.html#LIBPQ-PARAMKEYWORDS
#
# Any can be ommitted and environment variables will be used instead
# https://www.postgresql.org/docs/current/libpq-envars.html
connection_params = (
    ('host', 'localhost'),
    ('port', '5432'),
    ('dbname', 'postgres'),
    ('user', 'postgres'),
    ('password', 'password'),
)

# SQL statement(s) - if more than one, separate by ;
sql = '''
    SELECT * FROM my_table;
    SELECT * FROM my_other_table;
'''

# Connection and querying is via a context manager
with streampq_connect(connection_params) as query:
    for (columns, rows) in query(sql):
        print(columns)  # Tuple of column names
        for row in rows:
            print(row)  # Tuple of row  values

PostgreSQL types to Python type decoding

There are 164 built-in PostgreSQL data types (including array types), and streampq converts them to Python types. In summary:

PostgreSQL types Python type
null None
text (e.g. varchar), xml, network addresses, and money str
byte (e.g. bytea) bytes
integer (e.g. int4) int
inexact real number (e.g. float4) float
exact real number (e.g. numeric) Decimal
date date
timestamp datetime (without timezone)
timestamptz datetime (with offset timezone)
json and jsonb output of json.loads
interval streampq.Interval
range (e.g. daterange) streampq.Range
multirange (e.g. datemultirange) tuples of streampq.Range
arrays and vectors tuple (of any of the above types, or of nested tuples)

To customise these, override the default value of the get_decoders parameter of the streampq_connect function in streampq.py.

In general, built-in types are preferred over custom types, and immutable types are preferred over mutable.

streampq.Interval

The Python built-in timedelta type is not used for PostgreSQL interval since timedelta does not offer a way to store PostgreSQL intervals of years or months, other than converting to days which would be a loss of information.

Instead, a namedtuple is defined, streampq.Interval, with members:

Member Type
years int
months int
days int
hours int
minutes int
seconds Decimal

streampq.Range

There is no Python built-in type for a PosgreSQL range. So for these, a namedtuple is defined, streampq.Range, with members:

Member Type
lower int, date, datetime (without timezone), or datetime (with offset timezone)
upper int, date, datetime (without timezone), or datetime (with offset timezone)
bounds str - one of (), (], [), or []

Bind parameters - literals

Dynamic SQL literals can be bound using the literals parameter of the query function. It must be an iterable of key-value pairs.

sql = '''
    SELECT * FROM my_table WHERE my_col = {my_col_value};
'''

with streampq_connect(connection_params) as query:
    for (columns, rows) in query(sql, literals=(
        ('my_col_value', 'my-value'),
    )):
        for row in rows:
            pass

Bind parameters - identifiers

Dynamic SQL identifiers, e.g. column names, can be bound using the identifiers parameter of the query function. It must be an iterable of key-value pairs.

sql = '''
    SELECT * FROM my_table WHERE {column_name} = 'my-value';
'''

with streampq_connect(connection_params) as query:
    for (columns, rows) in query(sql, identifiers=(
        ('column_name', 'my_col'),
    )):
        for row in rows:
            pass

Identifiers and literals use different escaping rules - hence the need for 2 different parameters.

Single-statement SQL queries

While this library is specialsed for multi-statement queries, it works fine when there is only one. In this case the iterable returned from the query function yields only a single (columns, rows) pair.

Exceptions

Exceptions derive from streampq.StreamPQError. If there is any more information available on the error, it's added as a string in its args property. This is included in the string representation of the exception by default.

Exception hierarchy

  • StreamPQError

    Base class for all explicitly-thrown exceptions

    • ConnectionError

      An error occurred while attempting to connect to the database.

    • QueryError

      An error occurred while attempting to run a query. Typically this is due to a syntax error or a missing column.

    • CancelError

      An error occurred while attempting to cancel a query.

    • CommunicationError

      An error occurred communicating with the database after successful connection.

Owner
Department for International Trade
Department for International Trade
Motor - the async Python driver for MongoDB and Tornado or asyncio

Motor Info: Motor is a full-featured, non-blocking MongoDB driver for Python Tornado and asyncio applications. Documentation: Available at motor.readt

mongodb 2.1k Dec 26, 2022
Creating a python package to convert /transfer excelsheet data to a mysql Database Table

Creating a python package to convert /transfer excelsheet data to a mysql Database Table

Odiwuor Lameck 1 Jan 07, 2022
Google Sheets Python API v4

pygsheets - Google Spreadsheets Python API v4 A simple, intuitive library for google sheets which gets your work done. Features: Open, create, delete

Nithin Murali 1.4k Dec 31, 2022
Python interface to Oracle Database conforming to the Python DB API 2.0 specification.

cx_Oracle version 8.2 (Development) cx_Oracle is a Python extension module that enables access to Oracle Database. It conforms to the Python database

Oracle 841 Dec 21, 2022
SpyQL - SQL with Python in the middle

SpyQL SQL with Python in the middle Concept SpyQL is a query language that combines: the simplicity and structure of SQL with the power and readabilit

Daniel Moura 853 Dec 30, 2022
Pandas on AWS - Easy integration with Athena, Glue, Redshift, Timestream, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).

AWS Data Wrangler Pandas on AWS Easy integration with Athena, Glue, Redshift, Timestream, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretMana

Amazon Web Services - Labs 3.3k Dec 31, 2022
Example Python codes that works with MySQL and Excel files (.xlsx)

Python x MySQL x Excel by Zinglecode Example Python codes that do the processes between MySQL database and Excel spreadsheet files. YouTube videos MyS

Potchara Puttawanchai 1 Feb 07, 2022
A database migrations tool for SQLAlchemy.

Alembic is a database migrations tool written by the author of SQLAlchemy. A migrations tool offers the following functionality: Can emit ALTER statem

SQLAlchemy 1.7k Jan 01, 2023
Baserow is an open source no-code database tool and Airtable alternative

Baserow is an open source no-code database tool and Airtable alternative

1.3k Jan 01, 2023
Python PostgreSQL database performance insights. Locks, index usage, buffer cache hit ratios, vacuum stats and more.

Python PG Extras Python port of Heroku PG Extras with several additions and improvements. The goal of this project is to provide powerful insights int

Paweł Urbanek 35 Nov 01, 2022
python-bigquery Apache-2python-bigquery (🥈34 · ⭐ 3.5K · 📈) - Google BigQuery API client library. Apache-2

Python Client for Google BigQuery Querying massive datasets can be time consuming and expensive without the right hardware and infrastructure. Google

Google APIs 550 Jan 01, 2023
A fast MySQL driver written in pure C/C++ for Python. Compatible with gevent through monkey patching.

:: Description :: A fast MySQL driver written in pure C/C++ for Python. Compatible with gevent through monkey patching :: Requirements :: Requires P

ESN Social Software 549 Nov 18, 2022
PostgreSQL database access simplified

Queries: PostgreSQL Simplified Queries is a BSD licensed opinionated wrapper of the psycopg2 library for interacting with PostgreSQL. The popular psyc

Gavin M. Roy 251 Oct 25, 2022
edaSQL is a library to link SQL to Exploratory Data Analysis and further more in the Data Engineering.

edaSQL is a python library to bridge the SQL with Exploratory Data Analysis where you can connect to the Database and insert the queries. The query results can be passed to the EDA tool which can giv

Tamil Selvan 8 Dec 12, 2022
Redis Python Client

redis-py The Python interface to the Redis key-value store. Python 2 Compatibility Note redis-py 3.5.x will be the last version of redis-py that suppo

Andy McCurdy 11k Dec 29, 2022
Simple DDL Parser to parse SQL (HQL, TSQL, AWS Redshift, Snowflake and other dialects) ddl files to json/python dict with full information about columns: types, defaults, primary keys, etc.

Simple DDL Parser Build with ply (lex & yacc in python). A lot of samples in 'tests/. Is it Stable? Yes, library already has about 5000+ usage per day

Iuliia Volkova 95 Jan 05, 2023
pandas-gbq is a package providing an interface to the Google BigQuery API from pandas

pandas-gbq pandas-gbq is a package providing an interface to the Google BigQuery API from pandas Installation Install latest release version via conda

Google APIs 348 Jan 03, 2023
dbd is a database prototyping tool that enables data analysts and engineers to quickly load and transform data in SQL databases.

dbd: database prototyping tool dbd is a database prototyping tool that enables data analysts and engineers to quickly load and transform data in SQL d

Zdenek Svoboda 47 Dec 07, 2022
Making it easy to query APIs via SQL

Shillelagh Shillelagh (ʃɪˈleɪlɪ) is an implementation of the Python DB API 2.0 based on SQLite (using the APSW library): from shillelagh.backends.apsw

Beto Dealmeida 207 Dec 30, 2022
Amazon S3 Transfer Manager for Python

s3transfer - An Amazon S3 Transfer Manager for Python S3transfer is a Python library for managing Amazon S3 transfers. Note This project is not curren

the boto project 158 Jan 07, 2023