sqlutilpy

Python module to query SQL databases and return numpy arrays, upload tables and run join queries involving local arrays and the tables in the DB. This module is optimized to deal efficiently with query results with millions of rows. The module works with PostgreSQL, SQLite and DuckDB databases.

The full documentation is available here

Author: Sergey Koposov (Uni of Cambridge/CMU/Uni of Edinburgh)

Installation

To install the package you just need to do pip install.

pip install sqlutilpy

Authentication

Throughout this readme, I will assume that if you are using PostgreSQL, then the .pgpass file ( https://www.postgresql.org/docs/11/libpq-pgpass.html ) has been created with your login/password details for Postgresql. If that is not the case, many of the commands given below will also need user='....' and password='...' options.

Connection information

Most of the sqlutilpy commands require hostname, database name.
If you don't want to always type it, you can use standard PostgreSQL environment variables like PGPORT, PGDATABASE, PGUSER, PGHOST for the port, database name, user name and hostname of the connection.

Querying the database and retrieving the results

This command will run the query and put the columns into variables ra,dec:

import sqlutilpy
ra,dec = squtilpy.get('select ra,dec from mytable', 
                 host='HOST_NAME_OF_MY_PG_SERVER', 
                 db='THE_NAME_OF_MY_DB')

By default sqlutilpy.get executes the query and returns the tuple with arrays. One array for each column in the query result. You can return the results as dictionary using asDict option.

Uploading your arrays as column in a table

You can use sqlutilpy.upload to upload your arrays as columns in a table.

x = np.arange(10)                                                   
y = x**.5                                                           
sqlutilpy.upload('mytable',(x,y),('xcol','ycol'))    

This will create a table called mytable with columns xcol and ycol

Join query involving your local data and the database table

Sometimes it is beneficial to run a join query involving your local data and the data in the database.

Imagine you have arrays myid and y and you want to extract all the information from somebigtable for objects with id=myid. In principle, you could upload the arrays in the DB and run a query, but local_join function does that for you.

myid = np.arange(10)
y = np.random.uniform(size=10)

R=sqlutilpy.local_join('''select * from mytmptable as m, 
           somebigtable as s where s.id=m.myid order by m.myid''',                                              
           'mytmptable',(myid, y),('myid','ycol'))

It executes a query as if your arrays were in mytmptable. What happens behind the scenes is that it uploads the data to the database and runs a query against it.

Keeping the connection open.

Often it is beneficial to preserve an open connection to the database. You can do that if you first obtain the connection using sqlutilpy.getConnection() and then provide it directly to sqlutil.get() and similar commands using conn= keyword: conn = sqlutilpy.getConnection(db='mydb', user='meuser', password='something', host='hostname') R= sqlutilpy.get('select 1', conn=conn) R1= sqlutilpy.get('select 1', conn=conn)

# How to cite the software

If you use this package, please cite it through Zenodo https://doi.org/10.5281/zenodo.5160118