"PostgreSQL and Python" Lightning Talk @EuroPython2014
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PL/Python allows users to write PostgreSQL functions and procedures using Python. It enables accessing PostgreSQL data and running Python code from within SQL queries. For example, a function could query a database table, process the results in Python by accessing modules, and return a value to the SQL query. This opens up possibilities to leverage Python's extensive libraries and expressiveness to expose data and perform complex validation from PostgreSQL.
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![PostgreSQL speaks Python!
CREATE FUNCTION greet(name TEXT) RETURNS TEXT AS
$$
import random
attr = random.choice([
'great', 'fabulous', 'bright', 'brave'])
return 'Hello {} {}!'.format(attr, name)
$$
LANGUAGE plpythonu;
SELECT greet('Python fan');](https://image.slidesharecdn.com/europython2014-lightningtalk-postgresql-python-150529143502-lva1-app6892/85/PostgreSQL-and-Python-Lightning-Talk-EuroPython2014-3-320.jpg)
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id (primary key - generated by the database)
cpuname
performance
price
Project.csv contents:CPU NamePerformancePrice (USD)Intel Core i7-3770K @
3.50GHz9,556$560.50Intel Core i7-3770 @ 3.40GHz9,327$335.55Intel Core i7-3820 @
3.60GHz8,990$404.38AMD FX-8350 Eight-Core8,940$149.99Intel Core i7-2600K @
3.40GHz8,501$379.97Intel Core i7-2600 @ 3.40GHz8,242$214.99Intel Core i7-4720HQ @
2.60GHz8,046NAAMD FX-8320 Eight-Core8,008$145.99Intel Core i7-6700HQ @
2.60GHz7,997$1509Intel Core i7-4710HQ @ 2.50GHz7,826NAIntel Core i5-6600K @
3.50GHz7,762$239.99Intel Core i7-4700HQ @ 2.40GHz7,754$383.00Intel Core i7-4700MQ
@ 2.40GHz7,736$467.40Intel Core i5-4690K @ 3.50GHz7,690$239.99AMD FX-8150 Eight-
Core7,619$165.99Intel Core i7-3630QM @ 2.40GHz7,604$304.49Intel Core i5-4670K @
3.40GHz7,598$249.99Intel Core i5-4690 @ 3.50GHz7,542$224.99Intel Core i7-3610QM @
2.30GHz7,460$399.99Intel Core i5-4670 @ 3.40GHz7,342$226.99Intel Core i5-4590 @
3.30GHz7,174$199.99Intel Core i7-4702MQ @ 2.20GHz7,146NAIntel Core i5-3570K @
3.40GHz7,130$477.23
Solution
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.util.Arrays;
public class Main
{
/**
* This the main function that runs at the start
* param args - input arguments from the command line
*/
static public void main(String[] args)
{
CPUList cpuList = new CPUList(); //The CPUList used to retrieve data from the
fiile and store in the db
CPUList cpuListRetrieved = new CPUList(); //The CPUList used to retrieve data from the
database
CpuDb cpuDb = new CpuDb(); //The database object used to move data to and
from the CPU Lists
try
{
//Read in the file and store each line into the CPU objects in a list
Files.lines(Paths.get(\"Project04Data.csv\"))
.map(line -> line.split(\"\ \ \")) // Stream
.flatMap(Arrays::stream) // Stream
.forEach(line -> cpuList.AddCpu(line));
//Clear the list table for the new listing
cpuDb.Clear();
//Insert the Cpu List into the database
cpuDb.SetCpuList(cpuList);
//Retrieve the Cpu List into a different CPU List object from the database
cpuDb.GetCpuList(cpuListRetrieved);
//Show the report from the new list that was retrieved from the database
cpuListRetrieved.ShowReport();
} catch (IOException e)
{
e.printStackTrace();
}
}
}
CPUList.java
import java.util.ArrayList;
import java.util.Collections;
import java.util.Iterator;
public class CPUList
{
ArrayList theList = new ArrayList<>();
/**
* Default constructor for the CPU
*/
public void CPUList()
{
}
/**
* param strInputLine Input line to be used in creating the CPU object
*/
public void AddCpu(String strInputLine)
{
theList.add(new CPU(strInputLine));
}
/**
* param tempCPU - A CPU object to.
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Learn how TypeScript can improve your code with dynamic typing, better tooling, and cleaner architecture. Whether you're a beginner or have some experience with JavaScript, this session will give you a solid foundation in TypeScript and how to integrate it into your projects.
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LinkedIn: https://www.linkedin.com/in/anishkumar/
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This presentation will focus on how FME has supported NG911 transformations to date, and how we are positioning FME to support the enhanced capabilities to come. This session will be of interest to emergency services, municipalities and anyone who may be interested to know more about how emergency services are being improved to provide more accurate, localized information in order to improve the speed and relevance of emergency response and ultimately save more lives and provide better outcomes for those in need.
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Can We Use Rust to Develop Extensions for PostgreSQL? (POSETTE: An Event for ...
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“From Enterprise to Makers: Driving Vision AI Innovation at the Extreme Edge,...Edge AI and Vision Alliance
"PostgreSQL and Python" Lightning Talk @EuroPython2014
- 2. Well known... import psycopg2 conn = psycopg2.connect('dbname=mydb user=myusr') cur = conn.cursor() cur.execute('SELECT * FROM mytable') row = cur.fetchone()
- 3. PostgreSQL speaks Python! CREATE FUNCTION greet(name TEXT) RETURNS TEXT AS $$ import random attr = random.choice([ 'great', 'fabulous', 'bright', 'brave']) return 'Hello {} {}!'.format(attr, name) $$ LANGUAGE plpythonu; SELECT greet('Python fan');
- 4. PostgreSQL speaks Python ● PL/Python – “just” another procedural language ● SQL script that contains a string of Python ● Allows using any Python module
- 5. Real World Examples... mydb=# SELECT * FROM get_server_connection_stats() LIMIT 5; server_addr | server_port | client_addr | client_port ---------------+-------------+--------------+------------- 10.160.26.128 | 5435 | 10.111.74.16 | 40524 10.160.26.128 | 5435 | 10.160.26.1 | 49681 10.160.26.128 | 5435 | 10.160.26.1 | 15456 10.160.26.128 | 5435 | 10.160.26.2 | 64136 10.160.26.128 | 5435 | 10.160.26.1 | 1938 (5 rows)
- 6. Expose Stats via SQL with Python CREATE FUNCTION get_server_connection_stats() RETURNS SETOF server_connection AS $$ # … try: with open('/proc/net/tcp') as fp: lines = fp.read().splitlines() except: plpy.error('Unable to read /proc/net/tcp') # … return result $$ LANGUAGE plpythonu;
- 7. Custom JSON Type Validation CREATE TRIGGER validation AFTER INSERT OR UPDATE ON config_value FOR EACH … CREATE FUNCTION validate(value JSON, type_id INT) RETURNS VOID AS $$ import json # … complex type checking here # … using all of Python's expressiveness :-) raise ValueError('{} not valid'.format(value)) $$ LANGUAGE plpythonu;