Private: Mastering SQL using Postgresql

Getting Started
9 Topics
Connecting to Database
Using psql
Setup Postgres using Docker
Setup SQL Workbench
SQL Workbench and Postgres
SQL Workbench Features
Data Loading Utilities
Loading Data – Docker
Exercise – Loading Data
DML or CRUD Operations
10 Topics
Normalization Principles
Tables as Relations
Database Operations – Overview
CRUD Operations
Creating Table
Inserting Data
Updating Data
Deleting Data
Overview of Transactions
Exercises – Database Operations
Writing Basic SQL Queries
12 Topics
Standard Transformations
Overview of Data Model
Define Problem Statement – Daily Product Revenue
Preparing Tables
Selecting or Projecting Data
Filtering Data
Joining Tables – Inner
Joining Tables – Outer
Performing Aggregations
Sorting Data
Solution – Daily Product Revenue
Exercises – Basic SQL Queries
Creating Tables and Indexes
11 Topics
DDL – Data Definition Language
Overview of Data Types
Adding or Modifying Columns
Different Types of Constraints
Managing Constraints
Indexes on Tables
Indexes for Constraints
Overview of Sequences
Truncating Tables
Dropping Tables
Exercises – Managing Database Objects
Partitioning Tables and Indexes
12 Topics
Overview of Partitioning
List Partitioning
Managing Partitions – List
Manipulating Data
Range Partitioning
Managing Partitions – Range
Repartitioning – Range
Hash Partitioning
Managing Partitions – Hash
Usage Scenarios
Sub Partitioning
Exercises – Partitioning Tables
Predefined Functions
8 Topics
Overview of Pre-Defined Functions
String Manipulation Functions
Date Manipulation Functions
Overview of Numeric Functions
Data Type Conversion
Handling NULL Values
Using CASE and WHEN
Exercises – Pre-Defined Functions
Writing Advanced SQL Queries
15 Topics
Overview of Views
Named Queries – Using WITH Clause
Overview of Sub Queries
CTAS – Create Table as Select
Advanced DML Operations
Merging or Upserting Data
Pivoting Rows into Columns
Overview of Analytic Functions
Analytic Functions – Aggregations
Cumulative or Moving Aggregations
Analytic Functions – Windowing
Analytic Functions – Ranking
Analytic Functions – Filtering
Ranking and Filtering – Recap
Exercises – Analytics Functions
Query Performance Tuning
9 Topics
Preparing Database
Interpreting Explain Plans
Overview of Cost Based Optimizer
Performance Tuning using Indexes
Criteria for Indexing
Criteria for Partitioning
Writing Queries – Partition Pruning
Overview of Query Hints
Exercises – Tuning Queries
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Overview of Cost Based Optimizer

Private: Mastering SQL using Postgresql Query Performance Tuning Overview of Cost Based Optimizer

Let us get an overview of cost-based optimizer.

  • Databases use cost-based optimizer to generate explain plans. In the earlier days, they used to use rule based optimizer.

  • For cost based optimizer to generate optimal explain plan, we need to ensure statistics of our data in tables are collected at regular intervals.

  • We can analyze tables to collect statistics. Typically DBAs schedule to collect statistics at regular intervals.

  • In some cases we might have to compute statistics on the tables that are used in the query which we are trying to tune. The database user need to have permissions to compute statistics.

  • Here are some of the basic statistics typically collected.

    • Approximate number of records at table level.

    • Approximate number of unique records at index level.

  • When explain plans are generated, these statistics will be used by cost based optimizer to provide us with the most optimal plan for our query.

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