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Kernel: Python 3

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Reading PostgreSQL database from TensorFlow IO

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Overview

This tutorial shows how to create tf.data.Dataset from a PostgreSQL database server, so that the created Dataset could be passed to tf.keras for training or inference purposes.

A SQL database is an important source of data for data scientist. As one of the most popular open source SQL database, PostgreSQL is widely used in enterprises for storing critial and transactional data across the board. Creating Dataset from a PostgreSQL database server directly and pass the Dataset to tf.keras for training or inference, could greatly simplify the data pipeline and help data scientist to focus on building machine learning models.

Setup and usage

Install required tensorflow-io packages, and restart runtime

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try:
  %tensorflow_version 2.x
except Exception:
  pass

!pip install tensorflow-io

Install and setup PostgreSQL (optional)

Warning: This notebook is designed to be run in a Google Colab only. It installs packages on the system and requires sudo access. If you want to run it in a local Jupyter notebook, please proceed with caution.

In order to demo the usage on Google Colab you will install PostgreSQL server. The password and an empty database is also needed.

If you are not running this notebook on Google Colab, or you prefer to use an existing database, please skip the following setup and proceed to the next section.

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# Install postgresql server
!sudo apt-get -y -qq update
!sudo apt-get -y -qq install postgresql
!sudo service postgresql start

# Setup a password `postgres` for username `postgres`
!sudo -u postgres psql -U postgres -c "ALTER USER postgres PASSWORD 'postgres';"

# Setup a database with name `tfio_demo` to be used
!sudo -u postgres psql -U postgres -c 'DROP DATABASE IF EXISTS tfio_demo;'
!sudo -u postgres psql -U postgres -c 'CREATE DATABASE tfio_demo;'

Setup necessary environmental variables

The following environmental variables are based on the PostgreSQL setup in the last section. If you have a different setup or you are using an existing database, they should be changed accordingly:

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%env TFIO_DEMO_DATABASE_NAME=tfio_demo
%env TFIO_DEMO_DATABASE_HOST=localhost
%env TFIO_DEMO_DATABASE_PORT=5432
%env TFIO_DEMO_DATABASE_USER=postgres
%env TFIO_DEMO_DATABASE_PASS=postgres

Prepare data in PostgreSQL server

For demo purposes this tutorial will create a database and populate the database with some data. The data used in this tutorial is from Air Quality Data Set, available from UCI Machine Learning Repository.

Below is a sneak preview of a subset of the Air Quality Data Set:

Date	Time	CO(GT)	PT08.S1(CO)	NMHC(GT)	C6H6(GT)	PT08.S2(NMHC)	NOx(GT)	PT08.S3(NOx)	NO2(GT)	PT08.S4(NO2)	PT08.S5(O3)	T	RH	AH
10/03/2004	18.00.00	2,6	1360	150	11,9	1046	166	1056	113	1692	1268	13,6	48,9	0,7578
10/03/2004	19.00.00	2	1292	112	9,4	955	103	1174	92	1559	972	13,3	47,7	0,7255
10/03/2004	20.00.00	2,2	1402	88	9,0	939	131	1140	114	1555	1074	11,9	54,0	0,7502
10/03/2004	21.00.00	2,2	1376	80	9,2	948	172	1092	122	1584	1203	11,0	60,0	0,7867
10/03/2004	22.00.00	1,6	1272	51	6,5	836	131	1205	116	1490	1110	11,2	59,6	0,7888

More information about Air Quality Data Set and UCI Machine Learning Repository are availabel in References section.

To help simplify the data preparation, a sql version of the Air Quality Data Set has been prepared and is available as AirQualityUCI.sql.

The statement to create the table is:

CREATE TABLE AirQualityUCI (
  Date DATE,
  Time TIME,
  CO REAL,
  PT08S1 INT,
  NMHC REAL,
  C6H6 REAL,
  PT08S2 INT,
  NOx REAL,
  PT08S3 INT,
  NO2 REAL,
  PT08S4 INT,
  PT08S5 INT,
  T REAL,
  RH REAL,
  AH REAL
);

The complete commands to create the table in database and populate the data are:

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!curl -s -OL https://github.com/tensorflow/io/raw/master/docs/tutorials/postgresql/AirQualityUCI.sql

!PGPASSWORD=$TFIO_DEMO_DATABASE_PASS psql -q -h $TFIO_DEMO_DATABASE_HOST -p $TFIO_DEMO_DATABASE_PORT -U $TFIO_DEMO_DATABASE_USER -d $TFIO_DEMO_DATABASE_NAME -f AirQualityUCI.sql

Create Dataset from PostgreSQL server and use it in TensorFlow

Create a Dataset from PostgreSQL server is as easy as calling tfio.experimental.IODataset.from_sql with query and endpoint arguments. The query is the SQL query for select columns in tables and the endpoint argument is the address and database name:

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import os
import tensorflow_io as tfio

endpoint="postgresql://{}:{}@{}?port={}&dbname={}".format(
    os.environ['TFIO_DEMO_DATABASE_USER'],
    os.environ['TFIO_DEMO_DATABASE_PASS'],
    os.environ['TFIO_DEMO_DATABASE_HOST'],
    os.environ['TFIO_DEMO_DATABASE_PORT'],
    os.environ['TFIO_DEMO_DATABASE_NAME'],
)

dataset = tfio.experimental.IODataset.from_sql(
    query="SELECT co, pt08s1 FROM AirQualityUCI;",
    endpoint=endpoint)

print(dataset.element_spec)

As you could see from the output of dataset.element_spec above, the element of the created Dataset is a python dict object with column names of the database table as keys. It is quite convenient to apply further operations. For example, you could select both nox and no2 field of the Dataset, and calculate the difference:

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dataset = tfio.experimental.IODataset.from_sql(
    query="SELECT nox, no2 FROM AirQualityUCI;",
    endpoint=endpoint)

dataset = dataset.map(lambda e: (e['nox'] - e['no2']))

# check only the first 20 record
dataset = dataset.take(20)

print("NOx - NO2:")
for difference in dataset:
  print(difference.numpy())

The created Dataset is ready to be passed to tf.keras directly for either training or inference purposes now.

References

Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
S. De Vito, E. Massera, M. Piga, L. Martinotto, G. Di Francia, On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario, Sensors and Actuators B: Chemical, Volume 129, Issue 2, 22 February 2008, Pages 750-757, ISSN 0925-4005