We will also define a basic PostgresWrapper class to hold all of our features and supply some default initialization values. This factory will let us easily serialize query results directly to a Dict type and then subsequently dump that to JSON. Next, we bring in the psycopg2 library and the RealDictCursor factory. We can just call this wrapper.py for now and add the following: #!/usr/bin/env python3 import simplejson as json import psycopg2 from psycopg2.extras import RealDictCursor class PostgresWrapper: def _init_(self, **args): er = args.get('user', 'postgres') self.port = args.get('port', 5432) self.dbname = args.get('dbname', 'world') self.host = args.get('host', 'localhost') nnection = Noneįirst, we’re pulling in simplejson instead of the standard json library because when we query for Decimal values they will only be serialized correctly using a more updated library with support for dumping this datatype. Let’s open up a new Python file and start editing. Now that we’ve got the database up and running with some test data to look at we can begin setting up our wrapper in Python. Try out a test query on the world database: \c world SELECT * FROM country LIMIT 5 The wrapper If you don’t have psql installed you can reference the guide available here to install. Now you should be able to connect to Postgres using psql from the host machine by running (ensure you exit the container first): psql -U postgres -h localhost The last command reloads Postgres so that it picks up the new configuration. Under no circumstances should you ever add this configuration to a production instance of Postgres. This is strictly for testing to make things easier and not deal with passwords and authentication from the host. The first line will allow all TCP/IP connections to Postgres and remove all other authentication. While connected to the container, run the following: echo "host all all 0.0.0.0/0 trust" > /var/lib/postgresql/data/pg_hba.conf pg_ctl reload
We will need to make one tweak to allow access from the host machine into the container’s PG instance. Give the database a few minutes to populate with the test data and then go ahead and attach to the container using the following command: docker exec -it pg-ds-fake /bin/bash docker pull aa8y/postgres-dataset docker run -d -p 5432:5432 -name pg-ds-fake aa8y/postgres-dataset:latest You’ll want to change the first port from 5432 to something else if you’re already running a Postgres instance on your machine. If you’ve never used Docker before or need a quick refresher Docker - Beginner’s Guide - Part 1: Images & Containers is a fantastic guide by Sebastian Eschweiler to help you get up and running.Ĭheck out postgres-dataset on Docker Hub (shout out to aa8y for creating this incredibly handy tool) to pull the latest image and deploy it using the commands below. We’ll be using Docker to setup the database container so we don’t pollute our host operating system with test installations. This gives us a bunch of sample data to query against.
#Json query in python3 full
The databaseįor our example Postgres server we’re going to use a pre-populated database full of test data.
#Json query in python3 how to
Let’s explore how to setup a database wrapper in Python using this module to make executing queries fast and easy. This low-level database connection module for Python allows you to hook up to Postgres, execute SQL and perform other database functions. Getting JSON results out of PG is super easy using the extremely popular psycopg2 module. If you’re prototyping a tiny application or working with a highly constrained embedded system then there can be great benefit in writing a light wrapper. Making a simple wrapper for different queries and commonly used functions is straightforward and effective in languages like Python. You can connect via so many different programming languages and there are a number of top-notch ORMs available too.ĭepending on your specific needs and project size, you might not need to setup a ton of extra abstraction layers or frameworks in order to handle queries into Postgres. One reason Postgres is so popular is because there are a ton of extensions, plugins and connectors for it. This database is fast, efficient and used by a wide range of companies ranging from startups to international conglomerates. It has been around for a while and is well established with great support. Here is what a direct debug or vmk_out.host_vmk_info.values() looks like from my tests:Īnsible 2.9.1 - python 3.Postgres is a legendary database. I did not take time to dig into the root of the problem, but there is clearly a difference in the return of the values() function between python 2.7 and 3.x.
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