pg-database-utils 0.5

A suite of utilities for PostgreSQL database queries and operations built on sqlalchemy

pg-database-utils

A suite of utilities for PostgreSQL database queries and operations built on sqlalchemy.

This library includes support for:

1. TSVECTOR, JSON and JSONB indexes (for PostgreSQL versions 9.5+)
2. Generated columns (for PostgreSQL versions 12+)
3. Optional Django database configuration for Django projects

It also includes:

1. Helpers to make most common DDL queries more readable
2. Performant functions for querying JSON and TSVECTOR columns
3. Support for SELECT INTO queries from existing tables and/or VALUES clauses
4. Support for UPDATE queries that require application logic

Installation

Install with:

pip install pg_database_utils

Configuration

This project is designed to make configuration easy. If you already have database connections defined in Django, then you can reuse them; otherwise, you can configure your own without having Django as a dependency.

To configure with Django

If you want to use the default database, there is nothing to do; otherwise:

1. Create a JSON configuration file:

{
    "django-db-key": "not_default"
}

2. Set the DATABASE_CONFIG_JSON environment variable to point to the location of the file

Note: "django-db-key" takes precedence over all other database connection settings in the JSON file. If you specify a Django database, those database connection settings will be used.

To configure without Django

1. Create a JSON configuration file with at least the required settings (i.e. database-name):

{
    "database-name": "required",     # Name of the database to query
    "database-engine": "optional",   # Defaults to postgres
    "database-host": "optional",     # Defaults to 127.0.0.1
    "database-port": "optional",     # Defaults to 5432
    "database-user": "optional",     # Defaults to postgres
    "database-password": "optional"  # For trusted users like postgres
}

2. Set the DATABASE_CONFIG_JSON environment variable to point to the location of the file

Regardless of the above

Additional configuration options include:

{
    "date-format": "optional",      # Defaults to "%Y-%m-%d"
    "timestamp-format": "optional"  # Defaults to "%Y-%m-%d %H:%M:%S"
}

Note: "date-format" and "timestamp-format" must be compatible with the formatting configured in PostgreSQL.

Usage

This library is designed to make common database operations easy and readable, so most of the utility functions are designed to work with either strings or sqlalchemy objects as parameters.

Schema utilities

• Creating and relating tables

from pg_database import schema

my_table = schema.create_table(
    "my_table",
    dropfirst=True,
    index_cols={"id": "unique"},
    id="int", name="int", addr="text", geom="bytea", deleted="bool"
)
schema.create_index(my_table, "name", index_op="unique")

schema.create_table("other_table", id="int", my_table_id="int", val="text")
schema.create_foreign_key("other_table", "my_table_id", "my_table.id")

• Altering tables

from pg_database import schema

schema.alter_column_type("my_table", "name", "text")
schema.create_index("my_table", "name", index_op="to_tsvector")

schema.create_column("my_table", "json_col", "jsonb", checkfirst=True)
schema.create_index("my_table", "json_col", index_op="json_full")

# These steps require the postgis extension
schema.alter_column_type("my_table", "geom", "geometry", using="geometry(Polygon,4326)")
schema.create_index("my_table", "geom", index_op="spatial")

• Dropping database objects

from pg_database import schema

all_tables = schema.get_metadata().tables
other_table = all_tables["other_table"]

schema.drop_foreign_key(other_table, "other_table_my_table_id_fkey")
schema.drop_index("my_table", index_name="my_table_json_col_json_full_idx")
schema.drop_table("my_table")
schema.drop_table(other_table)

SQL utilities

• Inserting rows

import json
from datetime import datetime, timedelta
from pg_database import sql

create_date = datetime.now()

sql.select_into(
    "new_table",
    [
        (1, "one", {}, create_date),
        (2, "two", {}, create_date),
        (3, "three", {}, create_date)
    ],
    "id,val,json,created",
    "int,text,jsonb,date"
)

• Updating rows

from pg_database import sql

def update_row(row):
    row = list(row)

    pk, val, created, jval = row[0], row[1], row[2], row[3]

    row[1] = f"{pk} {val} first batch"
    row[2] = created + timedelta(days=1)
    row[3] = {"id": pk, "val": val, "batch": "first"}

    return row

sql.update_rows("new_table", "id", "val,created,json", update_row, batch_size=3)

• Querying rows

from pg_database import sql, schema

# Reduce database queries by sending a sqlalchemy table
all_tables = schema.get_metadata().tables
new_table = all_tables["new_table"]

schema.create_index(new_table, "json", index_op="json_path")
schema.create_index(new_table, "val", index_op="to_tsvector")

sql.query_json_keys(new_table, "json", {"batch": "first"})
sql.query_tsvector_columns("new_table", "val", "batch first")