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Master PostgreSQL 17 from MVCC Snapshot Isolation, WAL Durability, `pg_hba.conf` & Multi-Tenant `search_path` Schemas to Exact `NUMERIC`, `BIGINT GENERATED ALWAYS AS IDENTITY`, `TIMESTAMPTZ`, `INTERVAL`, `UUID`, `JSONB` GIN Indexing, TOAST `TEXT` & Transactional DDL — with runnable SQL scripts, 244 Topic Mock Tests (10,440 MCQs), and Spoken Voice + MockAttempt AI Interviews at ₹2,999 + 18% GST.
Manish Chaudhary
✓ Enrolled📍 Gurugram • SQL Dev → PostgreSQL 16 & MVCC
Enrolled: PostgreSQL 16 & pgvector (PGSQ)
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Deeksha Bhat
✓ Enrolled📍 Bengaluru • AI Backend Dev → pgvector Search
Enrolled: PostgreSQL 16 & pgvector (PGSQ)
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Ritesh Wagh
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Enrolled: PostgreSQL 16 & pgvector (PGSQ)
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PGSQ • Relational Database Internals & SQL Engineering
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Click any module or topic below to expand its learning objectives, plain-language overview, real-world purpose, hands-on code, lab tasks, 50-MCQ mocks, MockAttempt AI interviews, and capstone project.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, PostgreSQL 17 Architecture — Object-Relational Engine, 8 KB Pages, WAL & MVCC Snapshot Isolation teaches you how PostgreSQL applications how PostgreSQL achieves strict ACID compliance using Write-Ahead Logging (WAL), 8 KB slotted heap pages, `shared_buffers`, and `xmin`/`xmax` MVCC tuple snapshots. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply PostgreSQL 17 Architecture — Object-Relational Engine, 8 KB Pages, WAL & MVCC Snapshot Isolation in real PostgreSQL systems — specifically: How PostgreSQL achieves strict ACID compliance using Write-Ahead Logging (WAL), 8 KB slotted heap pages, `shared_buffers`, and `xmin`/`xmax` MVCC tuple snapshots, so your code stays reliable, fast, and easy to maintain in production.
PostgreSQL is a popular relational database management system (RDBMS). Our PostgreSQL tutorial provides basic and advanced concepts of PostgreSQL. In this tutorial, we are going to learn all the topics of PostgreSQL language, such as create a database, drop a database, select database, select table, update a record, create a table, delete record, drop table, triggers, functions, insert the record, procedures, cursors...
PostgreSQL 17 Architecture — Object-Relational Engine, 8 KB Pages, WAL & MVCC Snapshot Isolation — Practical Assignment
Implement a production-ready PostgreSQL 17, MVCC, JSONB & Query Optimization solution demonstrating PostgreSQL 17 Architecture — Object-Relational Engine, 8 KB Pages, WAL & MVCC Snapshot Isolation usi...
PostgreSQL 17 Architecture — Object-Relational Engine, 8 KB Pages, WAL & MVCC Snapshot Isolation — Hands-On Lab Task
CREATE TEMP TABLE course_inventory (
code TEXT PRIMARY KEY,
seats INT NOT NULL CHECK (seats >= 0)
);
INSERT INTO course_inventory (code, seats) VALUES ('PGSQ', 25);
INSERT INTO course_inventory (code, seats) VALUES ('PGSQ', 10)
ON CONFLICT (code) DO UPDATE
SET seats = course_inventory.seats + EXCLUDED.seats
RETURNING code, seats, (xmin IS NOT NULL) AS has_mvcc_xmin;💡 Self-Explanatory Overview: In simple, everyday terms, Advantages of PostgreSQL — Transactional DDL, Partial/GIN Indexes, Rich Types & Extensions teaches you how PostgreSQL applications why PostgreSQL leads modern engineering stacks: atomic Transactional DDL (`BEGIN ... ALTER TABLE ... ROLLBACK`), Partial & Expression indexes, `JSONB`, `pgvector`, and BSD/PostgreSQL open licensing. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Advantages of PostgreSQL — Transactional DDL, Partial/GIN Indexes, Rich Types & Extensions in real PostgreSQL systems — specifically: Why PostgreSQL leads modern engineering stacks: atomic Transactional DDL (`BEGIN ... ALTER TABLE ... ROLLBACK`), Partial & Expression indexes, `JSONB`, `pgvector`, and BSD/PostgreSQL open licensing, so your code stays reliable, fast, and easy to maintain in production.
Introduction PostgreSQL is an open-source relational database software that runs on Linux and interacts with objects as a relational component of the database management system. It accesses data in the database using Structured Query Language (SQL), which is why it is also known as Postgres. Some of the most notable features of this database are its high robustness and reliability, the ease with which it can be recov...
Advantages of PostgreSQL — Transactional DDL, Partial/GIN Indexes, Rich Types & Extensions — Practical Assignment
Implement a production-ready PostgreSQL 17, MVCC, JSONB & Query Optimization solution demonstrating Advantages of PostgreSQL — Transactional DDL, Partial/GIN Indexes, Rich Types & Extensions using BEG...
Advantages of PostgreSQL — Transactional DDL, Partial/GIN Indexes, Rich Types & Extensions — Hands-On Lab Task
CREATE TEMP TABLE jobs (id INT PRIMARY KEY, status TEXT NOT NULL);
INSERT INTO jobs VALUES (1, 'pending'), (2, 'archived');
BEGIN;
DROP TABLE jobs;
ROLLBACK;
CREATE INDEX idx_jobs_pending ON jobs (id) WHERE status = 'pending';
SELECT count(*) AS survived_rows FROM jobs;Write a PostgreSQL 17 SQL script inspecting live vs dead tuple counters (`pg_stat_user_tables`), transaction IDs (`pg_current_xact_id()`), and atomic idempotent upserts via `ON CONFLICT DO UPDATE RETURNING`.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Installing & Tuning PostgreSQL 17 — `initdb --data-checksums`, `$PGDATA` & `postgresql.conf` teaches you how PostgreSQL applications provisioning PostgreSQL 17 with hardware page checksums (`--data-checksums`) and sizing `shared_buffers` (~25% RAM), `work_mem`, and `wal_level = replica`. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Installing & Tuning PostgreSQL 17 — `initdb --data-checksums`, `$PGDATA` & `postgresql.conf` in real PostgreSQL systems — specifically: Provisioning PostgreSQL 17 with hardware page checksums (`--data-checksums`) and sizing `shared_buffers` (~25% RAM), `work_mem`, and `wal_level = replica`, so your code stays reliable, fast, and easy to maintain in production.
PostgreSQL is one of the most popular relational database management software widely used in today's industry. It provides multi-user access support with various storage engines. In this chapter, we are going to learn how the beginners can download and install PostgreSQL on Windows Platform. Following are the System requirements required to download the latest version of PostgreSQL in our computer Systems: Postg...
Installing & Tuning PostgreSQL 17 — `initdb --data-checksums`, `$PGDATA` & `postgresql.conf` — Practical Assignment
Implement a production-ready PostgreSQL 17, MVCC, JSONB & Query Optimization solution demonstrating Installing & Tuning PostgreSQL 17 — `initdb --data-checksums`, `$PGDATA` & `postgresql.conf` using i...
Installing & Tuning PostgreSQL 17 — `initdb --data-checksums`, `$PGDATA` & `postgresql.conf` — Hands-On Lab Task
SET LOCAL statement_timeout = '5s';
SET LOCAL idle_in_transaction_session_timeout = '30s';
SET LOCAL work_mem = '16MB';
SELECT
current_setting('statement_timeout') AS stmt_timeout,
current_setting('idle_in_transaction_session_timeout') AS idle_tx_timeout,
current_setting('work_mem') AS local_work_mem;💡 Self-Explanatory Overview: In simple, everyday terms, Connecting to PostgreSQL — Port 5432, `pg_hba.conf` (`scram-sha-256`), TLS & PgBouncer Pooling teaches you how PostgreSQL applications securing TCP/Unix-socket connections via `pg_hba.conf` (`hostssl ... scram-sha-256`), `psql` connection URIs, `sslmode=verify-full`, and PgBouncer process-per-connection pooling. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Connecting to PostgreSQL — Port 5432, `pg_hba.conf` (`scram-sha-256`), TLS & PgBouncer Pooling in real PostgreSQL systems — specifically: Securing TCP/Unix-socket connections via `pg_hba.conf` (`hostssl ... scram-sha-256`), `psql` connection URIs, `sslmode=verify-full`, and PgBouncer process-per-connection pooling, so your code stays reliable, fast, and easy to maintain in production.
In this section, we are going to understand how to connect the PostgreSQL database server through an interactive terminal software called pgAdmin and the SQL Shell(psql) application. If we installed the PostgreSQL database server, then the PostgreSQL installer will also connect with some beneficial tools to work through the PostgreSQL database server. Here, we are going to discuss how we can connect the PostgreSQL da...
Connecting to PostgreSQL — Port 5432, `pg_hba.conf` (`scram-sha-256`), TLS & PgBouncer Pooling — Practical Assignment
Implement a production-ready PostgreSQL 17, MVCC, JSONB & Query Optimization solution demonstrating Connecting to PostgreSQL — Port 5432, `pg_hba.conf` (`scram-sha-256`), TLS & PgBouncer Pooling using...
Connecting to PostgreSQL — Port 5432, `pg_hba.conf` (`scram-sha-256`), TLS & PgBouncer Pooling — Hands-On Lab Task
BEGIN;
SET LOCAL application_name = 'mockattempt_billing_worker';
SELECT
current_user IS NOT NULL AS has_role,
current_setting('application_name') AS app_name,
pg_backend_pid() > 0 AS valid_backend_pid;
COMMIT;Write a PostgreSQL DBA diagnostic script auditing key runtime settings (`pg_settings`), idle-in-transaction timeouts, and active client backend states (`pg_stat_activity`).
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, PostgreSQL SQL Syntax — Unquoted Lowercase Folding, Single vs Double Quotes, `$$` & Clause Order teaches you how PostgreSQL applications mastering PostgreSQL’s strict ANSI lexical rules: unquoted identifiers fold to lowercase (`UserID` -> `userid`), `'single quotes'` denote values, `"double quotes"` denote identifiers, and `$$` avoids backslash/quote escaping. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply PostgreSQL SQL Syntax — Unquoted Lowercase Folding, Single vs Double Quotes, `$$` & Clause Order in real PostgreSQL systems — specifically: Mastering PostgreSQL’s strict ANSI lexical rules: unquoted identifiers fold to lowercase (`UserID` -> `userid`), `'single quotes'` denote values, `"double quotes"` denote identifiers, and `$$` avoids backslash/quote escaping, so your code stays reliable, fast, and easy to maintain in production.
We can see the syntax of all postgreSQL commands by using help command. Follow the following steps to see the syntax of all commands in postgreSQL. After installing postgreSQL, open the psql as:Program Files > PostgreSQL 9.2 > SQL Shell(psql) Use the following command to see the syntax of a specific command.postgres-# \help &<command_name> All PostgreSQL commands Here, we are providing a list of a...
PostgreSQL SQL Syntax — Unquoted Lowercase Folding, Single vs Double Quotes, `$$` & Clause Order — Practical Assignment
Implement a production-ready PostgreSQL 17, MVCC, JSONB & Query Optimization solution demonstrating PostgreSQL SQL Syntax — Unquoted Lowercase Folding, Single vs Double Quotes, `$$` & Clause Order usi...
PostgreSQL SQL Syntax — Unquoted Lowercase Folding, Single vs Double Quotes, `$$` & Clause Order — Hands-On Lab Task
CREATE TEMP TABLE Cohort_Notes (Note_ID INT PRIMARY KEY, Body_Text TEXT NOT NULL);
INSERT INTO cohort_notes (note_id, body_text)
VALUES (1, $doc$PostgreSQL's single-quote & dollar-quote test$doc$);
SELECT attname AS folded_column_name
FROM pg_attribute
WHERE attrelid = 'cohort_notes'::regclass AND attnum > 0
ORDER BY attnum;💡 Self-Explanatory Overview: In simple, everyday terms, PostgreSQL `CREATE DATABASE` — `template0` vs `template1`, `UTF8` ICU Collations & Cross-DB Isolation teaches you how PostgreSQL applications creating physically isolated databases inside a PostgreSQL cluster (`OWNER`, `ENCODING 'UTF8'`, `TEMPLATE template0`, `LOCALE_PROVIDER icu`, `CONNECTION LIMIT`, and PG 13+ `DROP DATABASE ... WITH (FORCE)`). Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply PostgreSQL `CREATE DATABASE` — `template0` vs `template1`, `UTF8` ICU Collations & Cross-DB Isolation in real PostgreSQL systems — specifically: Creating physically isolated databases inside a PostgreSQL cluster (`OWNER`, `ENCODING 'UTF8'`, `TEMPLATE template0`, `LOCALE_PROVIDER icu`, `CONNECTION LIMIT`, and PG 13+ `DROP DATABASE ... WITH (FORCE)`), so your code stays reliable, fast, and easy to maintain in production.
In this section, we are going to discuss how we can create a database in PostgreSQL. In PostgreSQL, we can create a database in two ways: PostgreSQL Create Database using pgAdmin PSQL Create Database Command Line (SQL Shell) Creating Database using pgAdmin To create a database in pgAdmin, we are going to follow the below steps: Firstly, we will open the pgAdmin in our local system and then in the Object tree, we will...
PostgreSQL `CREATE DATABASE` — `template0` vs `template1`, `UTF8` ICU Collations & Cross-DB Isolation — Practical Assignment
Implement a production-ready PostgreSQL 17, MVCC, JSONB & Query Optimization solution demonstrating PostgreSQL `CREATE DATABASE` — `template0` vs `template1`, `UTF8` ICU Collations & Cross-DB Isolatio...
PostgreSQL `CREATE DATABASE` — `template0` vs `template1`, `UTF8` ICU Collations & Cross-DB Isolation — Hands-On Lab Task
WITH template_meta AS (
SELECT encoding, datistemplate, datconnlimit
FROM pg_database
WHERE datname = 'template0'
)
SELECT
pg_encoding_to_char(encoding) AS encoding_name,
datistemplate,
(datconnlimit = -1) AS unlimited_conns
FROM template_meta;Write a clean snake_case PostgreSQL SQL query using CTEs (`WITH`), dollar-quoted strings, and `COALESCE` alongside role/database `CONNECTION LIMIT` and `STATEMENT_TIMEOUT` governance.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, PostgreSQL Schemas & `search_path` — Namespace Resolution, PG 15+ `public` Hardening & Multi-Tenancy teaches you how PostgreSQL applications how `Cluster -> Database -> Schema -> Table` hierarchy allows cross-schema Foreign Keys and joins while `search_path` controls unqualified table/function resolution. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply PostgreSQL Schemas & `search_path` — Namespace Resolution, PG 15+ `public` Hardening & Multi-Tenancy in real PostgreSQL systems — specifically: How `Cluster -> Database -> Schema -> Table` hierarchy allows cross-schema Foreign Keys and joins while `search_path` controls unqualified table/function resolution, so your code stays reliable, fast, and easy to maintain in production.
In this section, we are going to learn PostgreSQL Schema, the public Schema, how PostgreSQL uses the schema search path to resolve objects in Schema, PostgreSQL schema and privileges, and the advantages of using PostgreSQL Schema. Introduction of PostgreSQL schema Each database starts with one schema, and it should be the public schema. And a schema is a named collection of tables. The Schema is a namespace which pro...
PostgreSQL Schemas & `search_path` — Namespace Resolution, PG 15+ `public` Hardening & Multi-Tenancy — Practical Assignment
Implement a production-ready PostgreSQL 17, MVCC, JSONB & Query Optimization solution demonstrating PostgreSQL Schemas & `search_path` — Namespace Resolution, PG 15+ `public` Hardening & Multi-Tenancy...
PostgreSQL Schemas & `search_path` — Namespace Resolution, PG 15+ `public` Hardening & Multi-Tenancy — Hands-On Lab Task
BEGIN;
CREATE SCHEMA catalog_ns;
CREATE SCHEMA billing_ns;
CREATE TABLE catalog_ns.items (sku TEXT PRIMARY KEY, price INT NOT NULL);
CREATE TABLE billing_ns.orders (id INT PRIMARY KEY, sku TEXT NOT NULL REFERENCES catalog_ns.items(sku));
INSERT INTO catalog_ns.items VALUES ('PGSQ-999', 999);
INSERT INTO billing_ns.orders VALUES (101, 'PGSQ-999');
SELECT o.id, i.price FROM billing_ns.orders o JOIN catalog_ns.items i USING (sku);
ROLLBACK;💡 Self-Explanatory Overview: In simple, everyday terms, PostgreSQL `CREATE SCHEMA` — `AUTHORIZATION`, Inline Atomic DDL & Default Privileges teaches you how PostgreSQL applications provisioning role-owned schemas (`CREATE SCHEMA IF NOT EXISTS analytics AUTHORIZATION analytics_owner`) and configuring `ALTER DEFAULT PRIVILEGES` for read-only reporting roles. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply PostgreSQL `CREATE SCHEMA` — `AUTHORIZATION`, Inline Atomic DDL & Default Privileges in real PostgreSQL systems — specifically: Provisioning role-owned schemas (`CREATE SCHEMA IF NOT EXISTS analytics AUTHORIZATION analytics_owner`) and configuring `ALTER DEFAULT PRIVILEGES` for read-only reporting roles, so your code stays reliable, fast, and easy to maintain in production.
In this section, we are going to learn how to create a schema and create a table in schema commands in pgAdmin 4 and SQL shell(psql). In PostgreSQL, the Schema is a namespace which provides various objects such as data types, indexes, tables, operators, views, sequence, and functions. And the create statement provides the exact object name, which helps us create the object in the existing schema. Creating Schema in P...
PostgreSQL `CREATE SCHEMA` — `AUTHORIZATION`, Inline Atomic DDL & Default Privileges — Practical Assignment
Implement a production-ready PostgreSQL 17, MVCC, JSONB & Query Optimization solution demonstrating PostgreSQL `CREATE SCHEMA` — `AUTHORIZATION`, Inline Atomic DDL & Default Privileges using CREATE SC...
PostgreSQL `CREATE SCHEMA` — `AUTHORIZATION`, Inline Atomic DDL & Default Privileges — Hands-On Lab Task
BEGIN;
CREATE SCHEMA IF NOT EXISTS reporting_v1
CREATE TABLE daily_kpis (day DATE PRIMARY KEY, signups INT NOT NULL CHECK (signups >= 0));
INSERT INTO reporting_v1.daily_kpis VALUES (DATE '2026-11-01', 140);
SELECT n.nspname AS schema_name, k.signups
FROM pg_namespace n CROSS JOIN reporting_v1.daily_kpis k
WHERE n.nspname = 'reporting_v1';
ROLLBACK;Create isolated `catalog` and `billing` schemas within a single database, link `billing.invoices` to `catalog.courses` via schema-qualified Foreign Keys, and lock down `search_path`.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, PostgreSQL Data Types Overview — Rich Native Types (`inet`, Ranges, Arrays), Casting (`::`) & Column Alignment teaches you how PostgreSQL applications choosing expressive self-validating PostgreSQL types (`inet` IP subnets, `tstzrange` exclusion constraints, `pg_typeof()`, `CAST` / `::`) and minimizing 8-byte struct alignment padding. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply PostgreSQL Data Types Overview — Rich Native Types (`inet`, Ranges, Arrays), Casting (`::`) & Column Alignment in real PostgreSQL systems — specifically: Choosing expressive self-validating PostgreSQL types (`inet` IP subnets, `tstzrange` exclusion constraints, `pg_typeof()`, `CAST` / `::`) and minimizing 8-byte struct alignment padding, so your code stays reliable, fast, and easy to maintain in production.
A data type specifies what kind of data you want to store in the table field. While creating a table, for each column, you have to use a datatype. It identifies a particular type of data, like integer, Boolean, floating points, and so on. In this section, we are going to discuss the various data types used in PostgreSQL. In PostgreSQL, each database table has many columns and has precise data types for each column. I...
PostgreSQL Data Types Overview — Rich Native Types (`inet`, Ranges, Arrays), Casting (`::`) & Column Alignment — Practical Assignment
Implement a production-ready PostgreSQL 17, MVCC, JSONB & Query Optimization solution demonstrating PostgreSQL Data Types Overview — Rich Native Types (`inet`, Ranges, Arrays), Casting (`::`) & Column...
PostgreSQL Data Types Overview — Rich Native Types (`inet`, Ranges, Arrays), Casting (`::`) & Column Alignment — Hands-On Lab Task
WITH net_sample AS (
SELECT '10.20.4.19'::inet AS host_ip, '10.20.0.0/16'::cidr AS vpc_cidr
)
SELECT
(host_ip << vpc_cidr) AS ip_in_vpc,
pg_column_size(ROW(true, 100::bigint, true, 200::bigint)) AS padded_bytes,
pg_column_size(ROW(100::bigint, 200::bigint, true, true)) AS packed_bytes
FROM net_sample;💡 Self-Explanatory Overview: In simple, everyday terms, PostgreSQL `NUMERIC` / `DECIMAL` — Exact Base-10 Arithmetic vs IEEE-754 Float Drift, Scale & `NaN` teaches you how PostgreSQL applications why financial ledgers, tax engines, and token balances require exact `NUMERIC(precision, scale)` (synonym `DECIMAL`) over inexact binary `DOUBLE PRECISION` (`0.1::float8 + 0.2::float8 <> 0.3`). Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply PostgreSQL `NUMERIC` / `DECIMAL` — Exact Base-10 Arithmetic vs IEEE-754 Float Drift, Scale & `NaN` in real PostgreSQL systems — specifically: Why financial ledgers, tax engines, and token balances require exact `NUMERIC(precision, scale)` (synonym `DECIMAL`) over inexact binary `DOUBLE PRECISION` (`0.1::float8 + 0.2::float8 <> 0.3`), so your code stays reliable, fast, and easy to maintain in production.
In this section, we are going to understand the working of PostgreSQL Numeric data types, which allows us to store the numeric data. And we also see examples of Numeric data type. What is PostgreSQL NUMERIC Data Type? In PostgreSQL, the Numeric data type is used to store the numbers with various significant numbers of digits. In other words, we can say that the PostgreSQL Numeric data type is used to specify the nume...
PostgreSQL `NUMERIC` / `DECIMAL` — Exact Base-10 Arithmetic vs IEEE-754 Float Drift, Scale & `NaN` — Practical Assignment
Implement a production-ready PostgreSQL 17, MVCC, JSONB & Query Optimization solution demonstrating PostgreSQL `NUMERIC` / `DECIMAL` — Exact Base-10 Arithmetic vs IEEE-754 Float Drift, Scale & `NaN` u...
PostgreSQL `NUMERIC` / `DECIMAL` — Exact Base-10 Arithmetic vs IEEE-754 Float Drift, Scale & `NaN` — Hands-On Lab Task
WITH invoice_calc AS (
SELECT 999.00::numeric(12, 2) AS base_fee, 0.18::numeric(4, 2) AS gst_rate
)
SELECT
(0.1::float8 + 0.2::float8 = 0.3::float8) AS float8_exact,
(0.1::numeric + 0.2::numeric = 0.3::numeric) AS numeric_exact,
ROUND(base_fee * (1 + gst_rate), 2) AS gst_inclusive_total
FROM invoice_calc;Demonstrate binary `FLOAT8` (`0.1 + 0.2`) inaccuracy versus exact `NUMERIC` arithmetic and build a `CHECK (balance >= 0)` wallet ledger calculating inclusive/exclusive GST taxes.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, PostgreSQL `VARCHAR(n)` vs `CHAR(n)` vs `TEXT` — `varlena` Internals, Character Count & Zero-Lock Limits teaches you how PostgreSQL applications why PostgreSQL stores `VARCHAR(n)`, `VARCHAR`, and `TEXT` using the exact same `varlena` C structure (counting Unicode characters, not bytes!), and why `CHAR(n)` wastes space with trailing spaces. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply PostgreSQL `VARCHAR(n)` vs `CHAR(n)` vs `TEXT` — `varlena` Internals, Character Count & Zero-Lock Limits in real PostgreSQL systems — specifically: Why PostgreSQL stores `VARCHAR(n)`, `VARCHAR`, and `TEXT` using the exact same `varlena` C structure (counting Unicode characters, not bytes!), and why `CHAR(n)` wastes space with trailing spaces, so your code stays reliable, fast, and easy to maintain in production.
In this section, we are going to understand the working of PostgreSQL varchar data types, which allows us to store the character of unlimited length. And we also see examples of the Varchar data type and spaces. What is PostgreSQL Varchar datatype? In PostgreSQL, the Varchar data type is used to keep the character of infinite length. And it can hold a string with a maximum length of 65,535 bytes. In other words, we c...
PostgreSQL `VARCHAR(n)` vs `CHAR(n)` vs `TEXT` — `varlena` Internals, Character Count & Zero-Lock Limits — Practical Assignment
Implement a production-ready PostgreSQL 17, MVCC, JSONB & Query Optimization solution demonstrating PostgreSQL `VARCHAR(n)` vs `CHAR(n)` vs `TEXT` — `varlena` Internals, Character Count & Zero-Lock Li...
PostgreSQL `VARCHAR(n)` vs `CHAR(n)` vs `TEXT` — `varlena` Internals, Character Count & Zero-Lock Limits — Hands-On Lab Task
WITH text_samples AS (
SELECT '₹1,999'::varchar(4) AS price_tag, 'PG'::varchar(10) AS vc_val, 'PG'::char(10) AS ch_val
)
SELECT
char_length(price_tag) AS unicode_chars,
octet_length(price_tag) AS utf8_bytes,
octet_length(vc_val) AS varchar_bytes,
octet_length(ch_val) AS padded_char_bytes
FROM text_samples;💡 Self-Explanatory Overview: In simple, everyday terms, PostgreSQL `BOOLEAN` — 1-Byte Storage, Three-Valued Logic (`TRUE`/`FALSE`/`NULL`) & `IS DISTINCT FROM` teaches you how PostgreSQL applications mastering 1-byte SQL `BOOLEAN` columns, `FILTER (WHERE ...)` aggregates, and avoiding silent data-loss bugs from `NULL` in `<>`, `=`, and `NOT IN` clauses. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply PostgreSQL `BOOLEAN` — 1-Byte Storage, Three-Valued Logic (`TRUE`/`FALSE`/`NULL`) & `IS DISTINCT FROM` in real PostgreSQL systems — specifically: Mastering 1-byte SQL `BOOLEAN` columns, `FILTER (WHERE ...)` aggregates, and avoiding silent data-loss bugs from `NULL` in `<>`, `=`, and `NOT IN` clauses, so your code stays reliable, fast, and easy to maintain in production.
In this section, we are going to understand the working of PostgreSQL Boolean data types, which allows us to design the database tables. And we also see examples of the Boolean data type. What is PostgreSQL Boolean Data Type? A Boolean is a commonly used data type in PostgreSQL, which always to return two probable values, such as TRUE and FALSE. The Boolean data type can be abbreviated as BOOL. In other words, we can...
PostgreSQL `BOOLEAN` — 1-Byte Storage, Three-Valued Logic (`TRUE`/`FALSE`/`NULL`) & `IS DISTINCT FROM` — Practical Assignment
Implement a production-ready PostgreSQL 17, MVCC, JSONB & Query Optimization solution demonstrating PostgreSQL `BOOLEAN` — 1-Byte Storage, Three-Valued Logic (`TRUE`/`FALSE`/`NULL`) & `IS DISTINCT FRO...
PostgreSQL `BOOLEAN` — 1-Byte Storage, Three-Valued Logic (`TRUE`/`FALSE`/`NULL`) & `IS DISTINCT FROM` — Hands-On Lab Task
WITH users(id, banned) AS (
VALUES
(1, false),
(2, true),
(3, NULL::boolean)
)
SELECT
COUNT(*) FILTER (WHERE banned <> true) AS naive_not_equal_count,
COUNT(*) FILTER (WHERE banned IS NOT TRUE) AS null_safe_count
FROM users;Demonstrate PostgreSQL’s 3-valued boolean trap where `WHERE is_banned <> true` silently drops `NULL` rows (fixed via `IS NOT TRUE`) and `NOT IN (SELECT nullable_col ...)` returns 0 rows (fixed via `NOT EXISTS`).
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, PostgreSQL `BIGINT` (`int8`) — 64-Bit Range, Preventing `2.14B` `INT4` Exhaustion & FK Type Matching teaches you how PostgreSQL applications why high-growth events, logs, and primary keys standardize on 8-byte signed `BIGINT` (`-9.22e18` to `+9.22e18`) to eliminate catastrophic 32-bit `INTEGER` (`2,147,483,647`) exhaustion. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply PostgreSQL `BIGINT` (`int8`) — 64-Bit Range, Preventing `2.14B` `INT4` Exhaustion & FK Type Matching in real PostgreSQL systems — specifically: Why high-growth events, logs, and primary keys standardize on 8-byte signed `BIGINT` (`-9.22e18` to `+9.22e18`) to eliminate catastrophic 32-bit `INTEGER` (`2,147,483,647`) exhaustion, so your code stays reliable, fast, and easy to maintain in production.
In this section, we are going to understand the working of the PostgreSQL Bigint data type. And we also see examples of the bigint data type. What is PostgreSQL Bigint Data Type? In PostgreSQL, the next integer data type that we are going to understand is BIGINT. The BIGINT data types are large-range integers, which involve 8 bytes of storage size and store integers in the signed range and unsigned range. And the Sig...
PostgreSQL `BIGINT` (`int8`) — 64-Bit Range, Preventing `2.14B` `INT4` Exhaustion & FK Type Matching — Practical Assignment
Implement a production-ready PostgreSQL 17, MVCC, JSONB & Query Optimization solution demonstrating PostgreSQL `BIGINT` (`int8`) — 64-Bit Range, Preventing `2.14B` `INT4` Exhaustion & FK Type Matching...
PostgreSQL `BIGINT` (`int8`) — 64-Bit Range, Preventing `2.14B` `INT4` Exhaustion & FK Type Matching — Hands-On Lab Task
WITH id_bounds AS (
SELECT 2147483647::int4 AS max_int4, 9000000000::bigint AS sample_big
)
SELECT
pg_column_size(max_int4) AS int4_bytes,
pg_column_size(sample_big) AS bigint_bytes,
(max_int4::bigint + 1000000) AS safe_beyond_int4
FROM id_bounds;💡 Self-Explanatory Overview: In simple, everyday terms, PostgreSQL `SERIAL` vs `GENERATED ALWAYS AS IDENTITY` — Sequence Mechanics, Gaps & `setval` Desync teaches you how PostgreSQL applications understanding how legacy `SERIAL` expands into `INTEGER + CREATE SEQUENCE + DEFAULT nextval(...)`, why explicit ID inserts desync `SERIAL` sequences, and why PostgreSQL 10+ recommends `GENERATED ALWAYS AS IDENTITY`. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply PostgreSQL `SERIAL` vs `GENERATED ALWAYS AS IDENTITY` — Sequence Mechanics, Gaps & `setval` Desync in real PostgreSQL systems — specifically: Understanding how legacy `SERIAL` expands into `INTEGER + CREATE SEQUENCE + DEFAULT nextval(...)`, why explicit ID inserts desync `SERIAL` sequences, and why PostgreSQL 10+ recommends `GENERATED ALWAYS AS IDENTITY`, so your code stays reliable, fast, and easy to maintain in production.
In this section, we are going to understand the working of PostgreSQL Serial pseudo-type, which allows us to define auto-increment columns in tables. And we also see examples of the PostgreSQL Serial pseudo-type. What is PostgreSQL Serial pseudo-type? In PostgreSQL, we have one particular kind of database object generator known as Serial, which is used to create a sequence of Integers that are frequently used as a Pr...
PostgreSQL `SERIAL` vs `GENERATED ALWAYS AS IDENTITY` — Sequence Mechanics, Gaps & `setval` Desync — Practical Assignment
Implement a production-ready PostgreSQL 17, MVCC, JSONB & Query Optimization solution demonstrating PostgreSQL `SERIAL` vs `GENERATED ALWAYS AS IDENTITY` — Sequence Mechanics, Gaps & `setval` Desync u...
PostgreSQL `SERIAL` vs `GENERATED ALWAYS AS IDENTITY` — Sequence Mechanics, Gaps & `setval` Desync — Hands-On Lab Task
CREATE TEMP TABLE ledger_entries (
id BIGINT GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
note TEXT NOT NULL
);
INSERT INTO ledger_entries (note) VALUES ('First');
SAVEPOINT sp1;
INSERT INTO ledger_entries (note) VALUES ('RolledBack');
ROLLBACK TO SAVEPOINT sp1;
INSERT INTO ledger_entries (note) VALUES ('AfterRollback');
SELECT id, note FROM ledger_entries ORDER BY id;Build an order ledger using `BIGINT GENERATED ALWAYS AS IDENTITY` that rejects accidental manual ID overrides (unless `OVERRIDING SYSTEM VALUE` is explicit) and demonstrates why rolled-back inserts advance sequence counters.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, PostgreSQL `TIMESTAMPTZ` vs `TIMESTAMP` — 8-Byte UTC Microseconds, `AT TIME ZONE` & SARGable Ranges teaches you how PostgreSQL applications why PostgreSQL’s “Don’t Do This” wiki explicitly warns against naive `TIMESTAMP` (`without time zone`): `TIMESTAMPTZ` takes the exact same 8 bytes, stores pure UTC, and formats per session `TimeZone`. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply PostgreSQL `TIMESTAMPTZ` vs `TIMESTAMP` — 8-Byte UTC Microseconds, `AT TIME ZONE` & SARGable Ranges in real PostgreSQL systems — specifically: Why PostgreSQL’s “Don’t Do This” wiki explicitly warns against naive `TIMESTAMP` (`without time zone`): `TIMESTAMPTZ` takes the exact same 8 bytes, stores pure UTC, and formats per session `TimeZone`, so your code stays reliable, fast, and easy to maintain in production.
In this section, we are going to understand the working of the PostgreSQL Timestamp data type. And we also see examples of the Timestamp data type, and we also see some accessible timestamp functions like NOW(), CURRENT_TIMESTAMP, CURRENT_TIME, TIMEOFDAY(), and timezone(zone, timestamp), which help us to handle time values more efficiently. What is PostgreSQL Timestamp Data Type? In PostgreSQL, the next data type is...
PostgreSQL `TIMESTAMPTZ` vs `TIMESTAMP` — 8-Byte UTC Microseconds, `AT TIME ZONE` & SARGable Ranges — Practical Assignment
Implement a production-ready PostgreSQL 17, MVCC, JSONB & Query Optimization solution demonstrating PostgreSQL `TIMESTAMPTZ` vs `TIMESTAMP` — 8-Byte UTC Microseconds, `AT TIME ZONE` & SARGable Ranges...
PostgreSQL `TIMESTAMPTZ` vs `TIMESTAMP` — 8-Byte UTC Microseconds, `AT TIME ZONE` & SARGable Ranges — Hands-On Lab Task
SET LOCAL TimeZone = 'UTC';
WITH evt AS (
SELECT TIMESTAMPTZ '2026-11-01 19:00:00+05:30' AS class_utc
)
SELECT
class_utc,
class_utc AT TIME ZONE 'Asia/Kolkata' AS ist_wall_clock,
pg_column_size(class_utc) AS bytes
FROM evt;💡 Self-Explanatory Overview: In simple, everyday terms, PostgreSQL `INTERVAL` — 16-Byte Calendar/Day/Microsecond Triplet, DST Awareness & `justify_interval` teaches you how PostgreSQL applications performing precise SLA, retention, and billing-cycle date math using PostgreSQL’s 16-byte `INTERVAL` (`months` + `days` + `microseconds`), `AGE()`, `EXTRACT(EPOCH FROM ...)`, and `make_interval()`. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply PostgreSQL `INTERVAL` — 16-Byte Calendar/Day/Microsecond Triplet, DST Awareness & `justify_interval` in real PostgreSQL systems — specifically: Performing precise SLA, retention, and billing-cycle date math using PostgreSQL’s 16-byte `INTERVAL` (`months` + `days` + `microseconds`), `AGE()`, `EXTRACT(EPOCH FROM ...)`, and `make_interval()`, so your code stays reliable, fast, and easy to maintain in production.
In this section, we are going to understand the working of the PostgreSQL Interval data type, and we also see examples of the Interval data type. And mostly used Interval functions, for example, NOW(), TO_CHAR(), EXTRACT(), justify_days(), justify_hours(), justify_interval(). The Input and output format for PostgreSQL interval values. What is PostgreSQL Interval Data Type? In PostgreSQL, the Interval is another type...
PostgreSQL `INTERVAL` — 16-Byte Calendar/Day/Microsecond Triplet, DST Awareness & `justify_interval` — Practical Assignment
Implement a production-ready PostgreSQL 17, MVCC, JSONB & Query Optimization solution demonstrating PostgreSQL `INTERVAL` — 16-Byte Calendar/Day/Microsecond Triplet, DST Awareness & `justify_interval`...
PostgreSQL `INTERVAL` — 16-Byte Calendar/Day/Microsecond Triplet, DST Awareness & `justify_interval` — Hands-On Lab Task
WITH schedule_anchor AS (
SELECT DATE '2026-01-31' AS jan_end, DATE '2026-11-01' AS cohort_start
)
SELECT
(jan_end + INTERVAL '1 month')::date AS feb_clamped,
(cohort_start + make_interval(weeks => 10))::date AS cohort_end,
EXTRACT(EPOCH FROM INTERVAL '2 hours 30 minutes')::bigint AS total_seconds
FROM schedule_anchor;Write a timezone-safe PostgreSQL billing query computing `Asia/Kolkata` and `America/New_York` views over UTC `TIMESTAMPTZ` instants using SARGable range bounds and calendar `INTERVAL` renewals.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, PostgreSQL `UUID` — 16-Byte Binary Storage, `gen_random_uuid()`, & `UUIDv4` B-Tree Fragmentation vs `UUIDv7` teaches you how PostgreSQL applications generating collision-free distributed identifiers without leaking sequential row counts, storing them in 16 binary bytes (not 36 ASCII chars), and avoiding random `UUIDv4` B-Tree page-split write amplification on massive tables. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply PostgreSQL `UUID` — 16-Byte Binary Storage, `gen_random_uuid()`, & `UUIDv4` B-Tree Fragmentation vs `UUIDv7` in real PostgreSQL systems — specifically: Generating collision-free distributed identifiers without leaking sequential row counts, storing them in 16 binary bytes (not 36 ASCII chars), and avoiding random `UUIDv4` B-Tree page-split write amplification on massive tables, so your code stays reliable, fast, and easy to maintain in production.
In this section, we are going to understand the working of the PostgreSQL UUID data type. And we also see examples of the UUID data type, and we also see some how to create UUID values with the help of a supplied module. What is PostgreSQL UUID Data Type? In PostgreSQL, the next data type is a Universally Unique Identifier, which is also known as UUID specified by RFC 4122 and another connected standard. The UUID val...
PostgreSQL `UUID` — 16-Byte Binary Storage, `gen_random_uuid()`, & `UUIDv4` B-Tree Fragmentation vs `UUIDv7` — Practical Assignment
Implement a production-ready PostgreSQL 17, MVCC, JSONB & Query Optimization solution demonstrating PostgreSQL `UUID` — 16-Byte Binary Storage, `gen_random_uuid()`, & `UUIDv4` B-Tree Fragmentation vs...
PostgreSQL `UUID` — 16-Byte Binary Storage, `gen_random_uuid()`, & `UUIDv4` B-Tree Fragmentation vs `UUIDv7` — Hands-On Lab Task
WITH sample AS (
SELECT gen_random_uuid() AS uid
)
SELECT
pg_column_size(uid) AS native_uuid_bytes,
pg_column_size(uid::text) AS text_uuid_bytes,
substring(uid::text FROM 15 FOR 1) AS uuid_version
FROM sample;💡 Self-Explanatory Overview: In simple, everyday terms, PostgreSQL `JSON` vs `JSONB` — Decomposed Binary Storage, `->` vs `->>`, Containment `@>` & GIN Indexes teaches you how PostgreSQL applications combining relational ACID rigor with schemaless flexibility using binary `JSONB`, containment (`@>`), key-existence (`?`), `jsonb_set()`, SQL/JSON path (`@?`), and `GIN (col jsonb_path_ops)` indexing. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply PostgreSQL `JSON` vs `JSONB` — Decomposed Binary Storage, `->` vs `->>`, Containment `@>` & GIN Indexes in real PostgreSQL systems — specifically: Combining relational ACID rigor with schemaless flexibility using binary `JSONB`, containment (`@>`), key-existence (`?`), `jsonb_set()`, SQL/JSON path (`@?`), and `GIN (col jsonb_path_ops)` indexing, so your code stays reliable, fast, and easy to maintain in production.
In this section, we are going to understand the working of the PostgreSQL JSON data type, examples of the JSON data type, and some accessible functions json_each(), json_object_keys (), json_typeof(), etc. We also see JSON operator's example with WHERE clause, which helps us to handle JSON data values more resourcefully, and we will use some aggregate function (SUM, MIN, AVG, MAX) to get the JSON data. What is P...
PostgreSQL `JSON` vs `JSONB` — Decomposed Binary Storage, `->` vs `->>`, Containment `@>` & GIN Indexes — Practical Assignment
Implement a production-ready PostgreSQL 17, MVCC, JSONB & Query Optimization solution demonstrating PostgreSQL `JSON` vs `JSONB` — Decomposed Binary Storage, `->` vs `->>`, Containment `@>` & GIN Inde...
PostgreSQL `JSON` vs `JSONB` — Decomposed Binary Storage, `->` vs `->>`, Containment `@>` & GIN Indexes — Hands-On Lab Task
CREATE TEMP TABLE learner_profiles (id INT PRIMARY KEY, attrs JSONB NOT NULL);
CREATE INDEX idx_profiles_attrs ON learner_profiles USING gin (attrs jsonb_path_ops);
INSERT INTO learner_profiles VALUES
(1, '{"track": "backend", "skills": ["postgresql", "fastapi"]}'),
(2, '{"track": "frontend", "skills": ["angular"]}');
UPDATE learner_profiles SET attrs = jsonb_set(attrs, '{certified}', 'true'::jsonb) WHERE id = 1;
SELECT id, attrs->>'track' AS track, (attrs->>'certified')::boolean AS cert
FROM learner_prof...Create an event store keyed by native 16-byte `UUID` (`DEFAULT gen_random_uuid()`) with a `JSONB` metadata payload indexed via `GIN (meta jsonb_path_ops)` and queried cleanly using `@>` and `jsonb_set`.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, PostgreSQL `TEXT` — TOAST Internals (`lz4` Compression, Out-of-Line Chunks), `SELECT *` Cost & Full-Text Search teaches you how PostgreSQL applications how PostgreSQL stores `TEXT` strings up to 1 GB transparently across 8 KB heap pages via TOAST (`COMPRESSION lz4`), why `SELECT id, title` avoids detoasting out-of-line chunks, and indexing text via `tsvector` / `pg_trgm`. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply PostgreSQL `TEXT` — TOAST Internals (`lz4` Compression, Out-of-Line Chunks), `SELECT *` Cost & Full-Text Search in real PostgreSQL systems — specifically: How PostgreSQL stores `TEXT` strings up to 1 GB transparently across 8 KB heap pages via TOAST (`COMPRESSION lz4`), why `SELECT id, title` avoids detoasting out-of-line chunks, and indexing text via `tsvector` / `pg_trgm`, so your code stays reliable, fast, and easy to maintain in production.
In this section, we are going to understand the working of PostgreSQL Text data types, which allows us to store the character of unlimited length. And we also see examples of the text data type. What is PostgreSQL Text datatype? In PostgreSQL, the text data type is used to keep the character of infinite length. And the text data type can hold a string with a maximum length of 65,535 bytes. In other words, we can say...
PostgreSQL `TEXT` — TOAST Internals (`lz4` Compression, Out-of-Line Chunks), `SELECT *` Cost & Full-Text Search — Practical Assignment
Implement a production-ready PostgreSQL 17, MVCC, JSONB & Query Optimization solution demonstrating PostgreSQL `TEXT` — TOAST Internals (`lz4` Compression, Out-of-Line Chunks), `SELECT *` Cost & Full-...
PostgreSQL `TEXT` — TOAST Internals (`lz4` Compression, Out-of-Line Chunks), `SELECT *` Cost & Full-Text Search — Hands-On Lab Task
CREATE TEMP TABLE docs (
id INT PRIMARY KEY,
content TEXT NOT NULL,
tsv TSVECTOR GENERATED ALWAYS AS (to_tsvector('english', content)) STORED
);
CREATE INDEX idx_docs_tsv ON docs USING gin (tsv);
INSERT INTO docs (id, content) VALUES
(1, 'PostgreSQL MVCC snapshot isolation prevents read locks'),
(2, 'Static CSS flexbox layout alignment guide');
SELECT id, tsv @@ websearch_to_tsquery('english', 'postgresql lock') AS matched
FROM docs WHERE tsv @@ websearch_to_tsquery('english', 'postgresql lock'...💡 Self-Explanatory Overview: In simple, everyday terms, PostgreSQL `ALTER SCHEMA` — `RENAME TO`, `OWNER TO`, OID Dependency Tracking & Zero-Downtime Cutovers teaches you how PostgreSQL applications managing schema lifecycle changes (`ALTER SCHEMA staging RENAME TO v2_live; ALTER SCHEMA v2_live OWNER TO ...`) safely inside atomic transactions while understanding catalog OID binding vs string-based client references. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply PostgreSQL `ALTER SCHEMA` — `RENAME TO`, `OWNER TO`, OID Dependency Tracking & Zero-Downtime Cutovers in real PostgreSQL systems — specifically: Managing schema lifecycle changes (`ALTER SCHEMA staging RENAME TO v2_live; ALTER SCHEMA v2_live OWNER TO ...`) safely inside atomic transactions while understanding catalog OID binding vs string-based client references, so your code stays reliable, fast, and easy to maintain in production.
In this section, we are going to learn PostgreSQL ALTER SCHEMA command, which is used to change the description of a schema. ALTER SCHEMA command The alter schema command will allow us to modify the schema's definition. For example, we can also rename a schema with the help of the below alter schema command. The syntax of altering schema command is as follows: ALTER SCHEMA schema_name RENAME TO new_name; We have...
PostgreSQL `ALTER SCHEMA` — `RENAME TO`, `OWNER TO`, OID Dependency Tracking & Zero-Downtime Cutovers — Practical Assignment
Implement a production-ready PostgreSQL 17, MVCC, JSONB & Query Optimization solution demonstrating PostgreSQL `ALTER SCHEMA` — `RENAME TO`, `OWNER TO`, OID Dependency Tracking & Zero-Downtime Cutover...
PostgreSQL `ALTER SCHEMA` — `RENAME TO`, `OWNER TO`, OID Dependency Tracking & Zero-Downtime Cutovers — Hands-On Lab Task
BEGIN;
CREATE SCHEMA blue_ns;
CREATE TABLE blue_ns.rates (plan TEXT PRIMARY KEY, fee INT NOT NULL);
INSERT INTO blue_ns.rates VALUES ('PRO', 999);
CREATE TEMP VIEW v_rates AS SELECT plan, fee FROM blue_ns.rates;
ALTER SCHEMA blue_ns RENAME TO live_ns;
SELECT (SELECT fee FROM live_ns.rates) AS direct_fee, (SELECT fee FROM v_rates) AS view_oid_fee;
ROLLBACK;Build a PostgreSQL 17 documentation search table using a stored generated `tsvector` column (`GENERATED ALWAYS AS (to_tsvector('english', ...)) STORED`) and execute an atomic transactional `ALTER SCHEMA ... RENAME TO` verification.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Tuple Visibility (xmin/xmax), Dead Tuples, HOT Updates & Table Bloat teaches you how PostgreSQL applications design update patterns and fillfactor settings that keep PostgreSQL heaps lean and fast. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Tuple Visibility (xmin/xmax), Dead Tuples, HOT Updates & Table Bloat in real PostgreSQL systems — specifically: Design update patterns and fillfactor settings that keep PostgreSQL heaps lean and fast, so your code stays reliable, fast, and easy to maintain in production.
What Is Tuple Visibility (xmin/xmax), Dead Tuples, HOT Updates & Table Bloat & Why Industry Teams Use ItIn PostgreSQL Training, Tuple Visibility (xmin/xmax), Dead Tuples, HOT Updates & Table Bloat exists for one clear real-world purpose: Design update patterns and fillfactor settings that keep PostgreSQL heaps lean and fast. Even if you come from a non-technical background, we start with the everyday real...
Hands-On Implementation: Tuple Visibility (xmin/xmax), Dead Tuples, HOT Updates & Table Bloat
Build and verify a clean working module demonstrating Tuple Visibility (xmin/xmax), Dead Tuples, HOT Updates & Table Bloat (Design update patterns and fillfactor settings that keep PostgreSQL heaps le...
Live Lab Sprint: Tuple Visibility (xmin/xmax), Dead Tuples, HOT Updates & Table Bloat
-- Tuple Visibility (xmin/xmax), Dead Tuples, HOT Updates & Table Bloat
-- Purpose: Design update patterns and fillfactor settings that keep PostgreSQL heaps lean and fast.
EXPLAIN
SELECT id, status, created_at
FROM production_records
WHERE status = 'ACTIVE'
ORDER BY created_at DESC
LIMIT 25;💡 Self-Explanatory Overview: In simple, everyday terms, Tuning Per-Table Autovacuum & Preventing Transaction ID Wraparound teaches you how PostgreSQL applications ensure high-churn queue/session tables vacuum continuously without emergency downtime. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Tuning Per-Table Autovacuum & Preventing Transaction ID Wraparound in real PostgreSQL systems — specifically: Ensure high-churn queue/session tables vacuum continuously without emergency downtime, so your code stays reliable, fast, and easy to maintain in production.
What Is Tuning Per-Table Autovacuum & Preventing Transaction ID Wraparound & Why Industry Teams Use ItIn PostgreSQL Training, Tuning Per-Table Autovacuum & Preventing Transaction ID Wraparound exists for one clear real-world purpose: Ensure high-churn queue/session tables vacuum continuously without emergency downtime. Even if you come from a non-technical background, we start with the everyday real-life...
Hands-On Implementation: Tuning Per-Table Autovacuum & Preventing Transaction ID Wraparound
Build and verify a clean working module demonstrating Tuning Per-Table Autovacuum & Preventing Transaction ID Wraparound (Ensure high-churn queue/session tables vacuum continuously without emergency d...
Live Lab Sprint: Tuning Per-Table Autovacuum & Preventing Transaction ID Wraparound
-- Tuning Per-Table Autovacuum & Preventing Transaction ID Wraparound
-- Purpose: Ensure high-churn queue/session tables vacuum continuously without emergency downtime.
EXPLAIN
SELECT id, status, created_at
FROM production_records
WHERE status = 'ACTIVE'
ORDER BY created_at DESC
LIMIT 25;💡 Self-Explanatory Overview: In simple, everyday terms, Lock Modes, lock_timeout Guards & Zero-Downtime DDL Habits teaches you how PostgreSQL applications add columns, NOT NULL check constraints, and CREATE INDEX CONCURRENTLY without locking traffic!. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Lock Modes, lock_timeout Guards & Zero-Downtime DDL Habits in real PostgreSQL systems — specifically: Add columns, NOT NULL check constraints, and CREATE INDEX CONCURRENTLY without locking traffic!, so your code stays reliable, fast, and easy to maintain in production.
What Is Lock Modes, lock_timeout Guards & Zero-Downtime DDL Habits & Why Industry Teams Use ItIn PostgreSQL Training, Lock Modes, lock_timeout Guards & Zero-Downtime DDL Habits exists for one clear real-world purpose: Add columns, NOT NULL check constraints, and CREATE INDEX CONCURRENTLY without locking traffic!. Even if you come from a non-technical background, we start with the everyday real-life proble...
Hands-On Implementation: Lock Modes, lock_timeout Guards & Zero-Downtime DDL Habits
Build and verify a clean working module demonstrating Lock Modes, lock_timeout Guards & Zero-Downtime DDL Habits (Add columns, NOT NULL check constraints, and CREATE INDEX CONCURRENTLY without locking...
Live Lab Sprint: Lock Modes, lock_timeout Guards & Zero-Downtime DDL Habits
-- Lock Modes, lock_timeout Guards & Zero-Downtime DDL Habits
-- Purpose: Add columns, NOT NULL check constraints, and CREATE INDEX CONCURRENTLY without locking traffic!
EXPLAIN
SELECT id, status, created_at
FROM production_records
WHERE status = 'ACTIVE'
ORDER BY created_at DESC
LIMIT 25;💡 Self-Explanatory Overview: In simple, everyday terms, SKIP LOCKED Transactional Queues & Advisory Locks teaches you how PostgreSQL applications build rock-solid concurrent job claimers right inside PostgreSQL. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply SKIP LOCKED Transactional Queues & Advisory Locks in real PostgreSQL systems — specifically: Build rock-solid concurrent job claimers right inside PostgreSQL, so your code stays reliable, fast, and easy to maintain in production.
What Is SKIP LOCKED Transactional Queues & Advisory Locks & Why Industry Teams Use ItIn PostgreSQL Training, SKIP LOCKED Transactional Queues & Advisory Locks exists for one clear real-world purpose: Build rock-solid concurrent job claimers right inside PostgreSQL. Even if you come from a non-technical background, we start with the everyday real-life problem first before writing production syntax.Sub-Topi...
Hands-On Implementation: SKIP LOCKED Transactional Queues & Advisory Locks
Build and verify a clean working module demonstrating SKIP LOCKED Transactional Queues & Advisory Locks (Build rock-solid concurrent job claimers right inside PostgreSQL.). Handle invalid inputs and b...
Live Lab Sprint: SKIP LOCKED Transactional Queues & Advisory Locks
-- SKIP LOCKED Transactional Queues & Advisory Locks
-- Purpose: Build rock-solid concurrent job claimers right inside PostgreSQL.
EXPLAIN
SELECT id, status, created_at
FROM production_records
WHERE status = 'ACTIVE'
ORDER BY created_at DESC
LIMIT 25;Design, implement, test, and document 'Zero-Bloat High-Write Financial Ledger on PostgreSQL 16' applying every production concept from Module 11 (MVCC Heap Internals, VACUUM/Autovacuum Tuning & Lock Contention) of PostgreSQL Training.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Reading EXPLAIN (ANALYZE, BUFFERS): Seq/Bitmap/Index-Only Scans & Sort Spills teaches you how PostgreSQL applications spot disk work_mem spills and stale statistics immediately from execution plans. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Reading EXPLAIN (ANALYZE, BUFFERS): Seq/Bitmap/Index-Only Scans & Sort Spills in real PostgreSQL systems — specifically: Spot disk work_mem spills and stale statistics immediately from execution plans, so your code stays reliable, fast, and easy to maintain in production.
What Is Reading EXPLAIN (ANALYZE, BUFFERS): Seq/Bitmap/Index-Only Scans & Sort Spills & Why Industry Teams Use ItIn PostgreSQL Training, Reading EXPLAIN (ANALYZE, BUFFERS): Seq/Bitmap/Index-Only Scans & Sort Spills exists for one clear real-world purpose: Spot disk work_mem spills and stale statistics immediately from execution plans. Even if you come from a non-technical background, we start with the eve...
Hands-On Implementation: Reading EXPLAIN (ANALYZE, BUFFERS): Seq/Bitmap/Index-Only Scans & Sort Spills
Build and verify a clean working module demonstrating Reading EXPLAIN (ANALYZE, BUFFERS): Seq/Bitmap/Index-Only Scans & Sort Spills (Spot disk work_mem spills and stale statistics immediately from exe...
Live Lab Sprint: Reading EXPLAIN (ANALYZE, BUFFERS): Seq/Bitmap/Index-Only Scans & Sort Spills
-- Reading EXPLAIN (ANALYZE, BUFFERS): Seq/Bitmap/Index-Only Scans & Sort Spills
-- Purpose: Spot disk work_mem spills and stale statistics immediately from execution plans.
EXPLAIN
SELECT id, status, created_at
FROM production_records
WHERE status = 'ACTIVE'
ORDER BY created_at DESC
LIMIT 25;💡 Self-Explanatory Overview: In simple, everyday terms, Partial Indexes, Expression Indexes & Covering INCLUDE Columns teaches you how PostgreSQL applications index only the 2% active unpaid orders for tiny RAM-resident sub-millisecond lookups. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Partial Indexes, Expression Indexes & Covering INCLUDE Columns in real PostgreSQL systems — specifically: Index only the 2% active unpaid orders for tiny RAM-resident sub-millisecond lookups, so your code stays reliable, fast, and easy to maintain in production.
What Is Partial Indexes, Expression Indexes & Covering INCLUDE Columns & Why Industry Teams Use ItIn PostgreSQL Training, Partial Indexes, Expression Indexes & Covering INCLUDE Columns exists for one clear real-world purpose: Index only the 2% active unpaid orders for tiny RAM-resident sub-millisecond lookups. Even if you come from a non-technical background, we start with the everyday real-life problem f...
Hands-On Implementation: Partial Indexes, Expression Indexes & Covering INCLUDE Columns
Build and verify a clean working module demonstrating Partial Indexes, Expression Indexes & Covering INCLUDE Columns (Index only the 2% active unpaid orders for tiny RAM-resident sub-millisecond looku...
Live Lab Sprint: Partial Indexes, Expression Indexes & Covering INCLUDE Columns
-- Partial Indexes, Expression Indexes & Covering INCLUDE Columns
-- Purpose: Index only the 2% active unpaid orders for tiny RAM-resident sub-millisecond lookups.
EXPLAIN
SELECT id, status, created_at
FROM production_records
WHERE status = 'ACTIVE'
ORDER BY created_at DESC
LIMIT 25;💡 Self-Explanatory Overview: In simple, everyday terms, JSONB Containment (@>), jsonb_path_ops GIN Indexes & Full-Text tsvector teaches you how PostgreSQL applications query flexible attributes and ranked full-text search without adding separate search infra. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply JSONB Containment (@>), jsonb_path_ops GIN Indexes & Full-Text tsvector in real PostgreSQL systems — specifically: Query flexible attributes and ranked full-text search without adding separate search infra, so your code stays reliable, fast, and easy to maintain in production.
What Is JSONB Containment (@>), jsonb_path_ops GIN Indexes & Full-Text tsvector & Why Industry Teams Use ItIn PostgreSQL Training, JSONB Containment (@>), jsonb_path_ops GIN Indexes & Full-Text tsvector exists for one clear real-world purpose: Query flexible attributes and ranked full-text search without adding separate search infra. Even if you come from a non-technical background, we start with th...
Hands-On Implementation: JSONB Containment (@>), jsonb_path_ops GIN Indexes & Full-Text tsvector
Build and verify a clean working module demonstrating JSONB Containment (@>), jsonb_path_ops GIN Indexes & Full-Text tsvector (Query flexible attributes and ranked full-text search without adding sepa...
Live Lab Sprint: JSONB Containment (@>), jsonb_path_ops GIN Indexes & Full-Text tsvector
-- JSONB Containment (@>), jsonb_path_ops GIN Indexes & Full-Text tsvector
-- Purpose: Query flexible attributes and ranked full-text search without adding separate search infra.
EXPLAIN
SELECT id, status, created_at
FROM production_records
WHERE status = 'ACTIVE'
ORDER BY created_at DESC
LIMIT 25;💡 Self-Explanatory Overview: In simple, everyday terms, BRIN Indexes for Billion-Row Append-Only Logs & pg_stat_statements Audits teaches you how PostgreSQL applications rank the top 10 DB CPU-consuming queries in production and fix them systematically. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply BRIN Indexes for Billion-Row Append-Only Logs & pg_stat_statements Audits in real PostgreSQL systems — specifically: Rank the top 10 DB CPU-consuming queries in production and fix them systematically, so your code stays reliable, fast, and easy to maintain in production.
What Is BRIN Indexes for Billion-Row Append-Only Logs & pg_stat_statements Audits & Why Industry Teams Use ItIn PostgreSQL Training, BRIN Indexes for Billion-Row Append-Only Logs & pg_stat_statements Audits exists for one clear real-world purpose: Rank the top 10 DB CPU-consuming queries in production and fix them systematically. Even if you come from a non-technical background, we start with the everyday...
Hands-On Implementation: BRIN Indexes for Billion-Row Append-Only Logs & pg_stat_statements Audits
Build and verify a clean working module demonstrating BRIN Indexes for Billion-Row Append-Only Logs & pg_stat_statements Audits (Rank the top 10 DB CPU-consuming queries in production and fix them sys...
Live Lab Sprint: BRIN Indexes for Billion-Row Append-Only Logs & pg_stat_statements Audits
-- BRIN Indexes for Billion-Row Append-Only Logs & pg_stat_statements Audits
-- Purpose: Rank the top 10 DB CPU-consuming queries in production and fix them systematically.
EXPLAIN
SELECT id, status, created_at
FROM production_records
WHERE status = 'ACTIVE'
ORDER BY created_at DESC
LIMIT 25;Design, implement, test, and document '100x Query Speedup Lab on 10M-Row JSONB + Relational Data' applying every production concept from Module 12 (Advanced Indexing (B-Tree, GIN, GiST, BRIN) & EXPLAIN BUFFERS Mastery) of PostgreSQL Training.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Declarative Range/Hash Partitioning, Partition Pruning & Instant Detach teaches you how PostgreSQL applications manage 500GB+ time-series tables cleanly and drop old monthly partitions in 5ms. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Declarative Range/Hash Partitioning, Partition Pruning & Instant Detach in real PostgreSQL systems — specifically: Manage 500GB+ time-series tables cleanly and drop old monthly partitions in 5ms, so your code stays reliable, fast, and easy to maintain in production.
What Is Declarative Range/Hash Partitioning, Partition Pruning & Instant Detach & Why Industry Teams Use ItIn PostgreSQL Training, Declarative Range/Hash Partitioning, Partition Pruning & Instant Detach exists for one clear real-world purpose: Manage 500GB+ time-series tables cleanly and drop old monthly partitions in 5ms. Even if you come from a non-technical background, we start with the everyday real-l...
Hands-On Implementation: Declarative Range/Hash Partitioning, Partition Pruning & Instant Detach
Build and verify a clean working module demonstrating Declarative Range/Hash Partitioning, Partition Pruning & Instant Detach (Manage 500GB+ time-series tables cleanly and drop old monthly partitions...
Live Lab Sprint: Declarative Range/Hash Partitioning, Partition Pruning & Instant Detach
-- Declarative Range/Hash Partitioning, Partition Pruning & Instant Detach
-- Purpose: Manage 500GB+ time-series tables cleanly and drop old monthly partitions in 5ms.
EXPLAIN
SELECT id, status, created_at
FROM production_records
WHERE status = 'ACTIVE'
ORDER BY created_at DESC
LIMIT 25;💡 Self-Explanatory Overview: In simple, everyday terms, Multi-Tenant Row-Level Security (RLS) Policies with Session Context teaches you how PostgreSQL applications enforce tenant isolation inside the PostgreSQL kernel so app bugs never leak cross-tenant rows!. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Multi-Tenant Row-Level Security (RLS) Policies with Session Context in real PostgreSQL systems — specifically: Enforce tenant isolation inside the PostgreSQL kernel so app bugs never leak cross-tenant rows!, so your code stays reliable, fast, and easy to maintain in production.
What Is Multi-Tenant Row-Level Security (RLS) Policies with Session Context & Why Industry Teams Use ItIn PostgreSQL Training, Multi-Tenant Row-Level Security (RLS) Policies with Session Context exists for one clear real-world purpose: Enforce tenant isolation inside the PostgreSQL kernel so app bugs never leak cross-tenant rows!. Even if you come from a non-technical background, we start with the everyday real-l...
Hands-On Implementation: Multi-Tenant Row-Level Security (RLS) Policies with Session Context
Build and verify a clean working module demonstrating Multi-Tenant Row-Level Security (RLS) Policies with Session Context (Enforce tenant isolation inside the PostgreSQL kernel so app bugs never leak...
Live Lab Sprint: Multi-Tenant Row-Level Security (RLS) Policies with Session Context
-- Multi-Tenant Row-Level Security (RLS) Policies with Session Context
-- Purpose: Enforce tenant isolation inside the PostgreSQL kernel so app bugs never leak cross-tenant rows!
EXPLAIN
SELECT id, status, created_at
FROM production_records
WHERE status = 'ACTIVE'
ORDER BY created_at DESC
LIMIT 25;💡 Self-Explanatory Overview: In simple, everyday terms, Lateral Joins, Grouping Sets, Window Frames & PL/pgSQL Audit Triggers teaches you how PostgreSQL applications compute rich analytical rollups and tamper-evident compliance audit trails. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Lateral Joins, Grouping Sets, Window Frames & PL/pgSQL Audit Triggers in real PostgreSQL systems — specifically: Compute rich analytical rollups and tamper-evident compliance audit trails, so your code stays reliable, fast, and easy to maintain in production.
What Is Lateral Joins, Grouping Sets, Window Frames & PL/pgSQL Audit Triggers & Why Industry Teams Use ItIn PostgreSQL Training, Lateral Joins, Grouping Sets, Window Frames & PL/pgSQL Audit Triggers exists for one clear real-world purpose: Compute rich analytical rollups and tamper-evident compliance audit trails. Even if you come from a non-technical background, we start with the everyday real-life probl...
Hands-On Implementation: Lateral Joins, Grouping Sets, Window Frames & PL/pgSQL Audit Triggers
Build and verify a clean working module demonstrating Lateral Joins, Grouping Sets, Window Frames & PL/pgSQL Audit Triggers (Compute rich analytical rollups and tamper-evident compliance audit trails....
Live Lab Sprint: Lateral Joins, Grouping Sets, Window Frames & PL/pgSQL Audit Triggers
-- Lateral Joins, Grouping Sets, Window Frames & PL/pgSQL Audit Triggers
-- Purpose: Compute rich analytical rollups and tamper-evident compliance audit trails.
EXPLAIN
SELECT id, status, created_at
FROM production_records
WHERE status = 'ACTIVE'
ORDER BY created_at DESC
LIMIT 25;💡 Self-Explanatory Overview: In simple, everyday terms, pgvector Embeddings, HNSW Indexes & Hybrid Relational + Semantic Queries teaches you how PostgreSQL applications power modern AI RAG retrieval directly inside ACID PostgreSQL transactions. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply pgvector Embeddings, HNSW Indexes & Hybrid Relational + Semantic Queries in real PostgreSQL systems — specifically: Power modern AI RAG retrieval directly inside ACID PostgreSQL transactions, so your code stays reliable, fast, and easy to maintain in production.
What Is pgvector Embeddings, HNSW Indexes & Hybrid Relational + Semantic Queries & Why Industry Teams Use ItIn PostgreSQL Training, pgvector Embeddings, HNSW Indexes & Hybrid Relational + Semantic Queries exists for one clear real-world purpose: Power modern AI RAG retrieval directly inside ACID PostgreSQL transactions. Even if you come from a non-technical background, we start with the everyday real-life...
Hands-On Implementation: pgvector Embeddings, HNSW Indexes & Hybrid Relational + Semantic Queries
Build and verify a clean working module demonstrating pgvector Embeddings, HNSW Indexes & Hybrid Relational + Semantic Queries (Power modern AI RAG retrieval directly inside ACID PostgreSQL transactio...
Live Lab Sprint: pgvector Embeddings, HNSW Indexes & Hybrid Relational + Semantic Queries
-- pgvector Embeddings, HNSW Indexes & Hybrid Relational + Semantic Queries
-- Purpose: Power modern AI RAG retrieval directly inside ACID PostgreSQL transactions.
EXPLAIN
SELECT id, status, created_at
FROM production_records
WHERE status = 'ACTIVE'
ORDER BY created_at DESC
LIMIT 25;Design, implement, test, and document 'Multi-Tenant RLS-Protected SaaS & Vector Search Database' applying every production concept from Module 13 (Declarative Partitioning, Row-Level Security (RLS) & pgvector AI Search) of PostgreSQL Training.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Transaction-Mode Connection Pooling with PgBouncer teaches you how PostgreSQL applications multiplex 2,000 microservice pods smoothly onto 80 physical PostgreSQL backend processes. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Transaction-Mode Connection Pooling with PgBouncer in real PostgreSQL systems — specifically: Multiplex 2,000 microservice pods smoothly onto 80 physical PostgreSQL backend processes, so your code stays reliable, fast, and easy to maintain in production.
What Is Transaction-Mode Connection Pooling with PgBouncer & Why Industry Teams Use ItIn PostgreSQL Training, Transaction-Mode Connection Pooling with PgBouncer exists for one clear real-world purpose: Multiplex 2,000 microservice pods smoothly onto 80 physical PostgreSQL backend processes. Even if you come from a non-technical background, we start with the everyday real-life problem first before writing producti...
Hands-On Implementation: Transaction-Mode Connection Pooling with PgBouncer
Build and verify a clean working module demonstrating Transaction-Mode Connection Pooling with PgBouncer (Multiplex 2,000 microservice pods smoothly onto 80 physical PostgreSQL backend processes.). Ha...
Live Lab Sprint: Transaction-Mode Connection Pooling with PgBouncer
-- Transaction-Mode Connection Pooling with PgBouncer
-- Purpose: Multiplex 2,000 microservice pods smoothly onto 80 physical PostgreSQL backend processes.
EXPLAIN
SELECT id, status, created_at
FROM production_records
WHERE status = 'ACTIVE'
ORDER BY created_at DESC
LIMIT 25;💡 Self-Explanatory Overview: In simple, everyday terms, Physical Streaming Replication, Replication Slots & Logical CDC teaches you how PostgreSQL applications scale read replicas safely while monitoring slot lag to prevent WAL disk overflow. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Physical Streaming Replication, Replication Slots & Logical CDC in real PostgreSQL systems — specifically: Scale read replicas safely while monitoring slot lag to prevent WAL disk overflow, so your code stays reliable, fast, and easy to maintain in production.
What Is Physical Streaming Replication, Replication Slots & Logical CDC & Why Industry Teams Use ItIn PostgreSQL Training, Physical Streaming Replication, Replication Slots & Logical CDC exists for one clear real-world purpose: Scale read replicas safely while monitoring slot lag to prevent WAL disk overflow. Even if you come from a non-technical background, we start with the everyday real-life problem fi...
Hands-On Implementation: Physical Streaming Replication, Replication Slots & Logical CDC
Build and verify a clean working module demonstrating Physical Streaming Replication, Replication Slots & Logical CDC (Scale read replicas safely while monitoring slot lag to prevent WAL disk overflow...
Live Lab Sprint: Physical Streaming Replication, Replication Slots & Logical CDC
-- Physical Streaming Replication, Replication Slots & Logical CDC
-- Purpose: Scale read replicas safely while monitoring slot lag to prevent WAL disk overflow.
EXPLAIN
SELECT id, status, created_at
FROM production_records
WHERE status = 'ACTIVE'
ORDER BY created_at DESC
LIMIT 25;💡 Self-Explanatory Overview: In simple, everyday terms, Continuous WAL Archiving (pgBackRest) & Point-in-Time Recovery (PITR) teaches you how PostgreSQL applications practice restoring a corrupted cluster to 14:32:10 UTC with verified integrity. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Continuous WAL Archiving (pgBackRest) & Point-in-Time Recovery (PITR) in real PostgreSQL systems — specifically: Practice restoring a corrupted cluster to 14:32:10 UTC with verified integrity, so your code stays reliable, fast, and easy to maintain in production.
What Is Continuous WAL Archiving (pgBackRest) & Point-in-Time Recovery (PITR) & Why Industry Teams Use ItIn PostgreSQL Training, Continuous WAL Archiving (pgBackRest) & Point-in-Time Recovery (PITR) exists for one clear real-world purpose: Practice restoring a corrupted cluster to 14:32:10 UTC with verified integrity. Even if you come from a non-technical background, we start with the everyday real-life p...
Hands-On Implementation: Continuous WAL Archiving (pgBackRest) & Point-in-Time Recovery (PITR)
Build and verify a clean working module demonstrating Continuous WAL Archiving (pgBackRest) & Point-in-Time Recovery (PITR) (Practice restoring a corrupted cluster to 14:32:10 UTC with verified integr...
Live Lab Sprint: Continuous WAL Archiving (pgBackRest) & Point-in-Time Recovery (PITR)
-- Continuous WAL Archiving (pgBackRest) & Point-in-Time Recovery (PITR)
-- Purpose: Practice restoring a corrupted cluster to 14:32:10 UTC with verified integrity.
EXPLAIN
SELECT id, status, created_at
FROM production_records
WHERE status = 'ACTIVE'
ORDER BY created_at DESC
LIMIT 25;💡 Self-Explanatory Overview: In simple, everyday terms, SCRAM-SHA-256, SSL Enforcement, Role Hierarchies & Patroni HA Failover teaches you how PostgreSQL applications automate sub-15-second primary failover and pass enterprise DB security audits. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply SCRAM-SHA-256, SSL Enforcement, Role Hierarchies & Patroni HA Failover in real PostgreSQL systems — specifically: Automate sub-15-second primary failover and pass enterprise DB security audits, so your code stays reliable, fast, and easy to maintain in production.
What Is SCRAM-SHA-256, SSL Enforcement, Role Hierarchies & Patroni HA Failover & Why Industry Teams Use ItIn PostgreSQL Training, SCRAM-SHA-256, SSL Enforcement, Role Hierarchies & Patroni HA Failover exists for one clear real-world purpose: Automate sub-15-second primary failover and pass enterprise DB security audits. Even if you come from a non-technical background, we start with the everyday real-life...
Hands-On Implementation: SCRAM-SHA-256, SSL Enforcement, Role Hierarchies & Patroni HA Failover
Build and verify a clean working module demonstrating SCRAM-SHA-256, SSL Enforcement, Role Hierarchies & Patroni HA Failover (Automate sub-15-second primary failover and pass enterprise DB security au...
Live Lab Sprint: SCRAM-SHA-256, SSL Enforcement, Role Hierarchies & Patroni HA Failover
-- SCRAM-SHA-256, SSL Enforcement, Role Hierarchies & Patroni HA Failover
-- Purpose: Automate sub-15-second primary failover and pass enterprise DB security audits.
EXPLAIN
SELECT id, status, created_at
FROM production_records
WHERE status = 'ACTIVE'
ORDER BY created_at DESC
LIMIT 25;Design, implement, test, and document 'HA Primary–Standby Cluster with PgBouncer & PITR Drills' applying every production concept from Module 14 (Streaming Replication, PgBouncer Pooling, WAL PITR & High Availability) of PostgreSQL Training.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, MVCC Tuple Visibility, Dead Tuples, Autovacuum Tuning & Transaction ID Wraparound Safety teaches you how PostgreSQL applications keep high-write PostgreSQL tables free from table/index bloat and vacuum stalls. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply MVCC Tuple Visibility, Dead Tuples, Autovacuum Tuning & Transaction ID Wraparound Safety in real PostgreSQL systems — specifically: Keep high-write PostgreSQL tables free from table/index bloat and vacuum stalls, so your code stays reliable, fast, and easy to maintain in production.
Self-Explanatory Guide: MVCC Tuple Visibility, Dead Tuples, Autovacuum Tuning & Transaction ID Wraparound Safety (Plain-English & Industry Purpose)In simple, everyday terms, MVCC Tuple Visibility, Dead Tuples, Autovacuum Tuning & Transaction ID Wraparound Safety teaches you how PostgreSQL applications keep high-write PostgreSQL tables free from table/index bloat and vacuum stalls. Even if you have never s...
Practical Implementation: MVCC Tuple Visibility, Dead Tuples, Autovacuum Tuning & Transaction ID Wraparound Safety
Implement and verify MVCC Tuple Visibility, Dead Tuples, Autovacuum Tuning & Transaction ID Wraparound Safety (Keep high-write PostgreSQL tables free from table/index bloat and vacuum stalls) with exp...
Guided Live Lab Checklist: MVCC Tuple Visibility, Dead Tuples, Autovacuum Tuning & Transaction ID Wraparound Safety
-- MVCC Tuple Visibility, Dead Tuples, Autovacuum Tuning & Transaction ID Wraparound Safety
-- Purpose: Keep high-write PostgreSQL tables free from table/index bloat and vacuum stalls
EXPLAIN
SELECT account_id, COUNT(*) AS event_total, MAX(created_at) AS latest_event_at
FROM production_audit_events
WHERE status = 'VERIFIED'
GROUP BY account_id
ORDER BY latest_event_at DESC
LIMIT 25;💡 Self-Explanatory Overview: In simple, everyday terms, PostgreSQL Row-Level Security (RLS) Policies & Zero-Downtime CREATE INDEX CONCURRENTLY teaches you how PostgreSQL applications enforce tenant isolation directly inside Postgres and evolve live schemas without write locks. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply PostgreSQL Row-Level Security (RLS) Policies & Zero-Downtime CREATE INDEX CONCURRENTLY in real PostgreSQL systems — specifically: Enforce tenant isolation directly inside Postgres and evolve live schemas without write locks, so your code stays reliable, fast, and easy to maintain in production.
Self-Explanatory Guide: PostgreSQL Row-Level Security (RLS) Policies & Zero-Downtime CREATE INDEX CONCURRENTLY (Plain-English & Industry Purpose)In simple, everyday terms, PostgreSQL Row-Level Security (RLS) Policies & Zero-Downtime CREATE INDEX CONCURRENTLY teaches you how PostgreSQL applications enforce tenant isolation directly inside Postgres and evolve live schemas without write locks. Even if you ha...
Practical Implementation: PostgreSQL Row-Level Security (RLS) Policies & Zero-Downtime CREATE INDEX CONCURRENTLY
Implement and verify PostgreSQL Row-Level Security (RLS) Policies & Zero-Downtime CREATE INDEX CONCURRENTLY (Enforce tenant isolation directly inside Postgres and evolve live schemas without write loc...
Guided Live Lab Checklist: PostgreSQL Row-Level Security (RLS) Policies & Zero-Downtime CREATE INDEX CONCURRENTLY
-- PostgreSQL Row-Level Security (RLS) Policies & Zero-Downtime CREATE INDEX CONCURRENTLY
-- Purpose: Enforce tenant isolation directly inside Postgres and evolve live schemas without write locks
EXPLAIN
SELECT account_id, COUNT(*) AS event_total, MAX(created_at) AS latest_event_at
FROM production_audit_events
WHERE status = 'VERIFIED'
GROUP BY account_id
ORDER BY latest_event_at DESC
LIMIT 25;💡 Self-Explanatory Overview: In simple, everyday terms, Declarative Range/Hash Partitioning, pg_stat_statements & Connection Pooling (PgBouncer) teaches you how PostgreSQL applications scale to thousands of concurrent app connections and isolate slow queries scientifically. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Declarative Range/Hash Partitioning, pg_stat_statements & Connection Pooling (PgBouncer) in real PostgreSQL systems — specifically: Scale to thousands of concurrent app connections and isolate slow queries scientifically, so your code stays reliable, fast, and easy to maintain in production.
Self-Explanatory Guide: Declarative Range/Hash Partitioning, pg_stat_statements & Connection Pooling (PgBouncer) (Plain-English & Industry Purpose)In simple, everyday terms, Declarative Range/Hash Partitioning, pg_stat_statements & Connection Pooling (PgBouncer) teaches you how PostgreSQL applications scale to thousands of concurrent app connections and isolate slow queries scientifically. Even if you hav...
Practical Implementation: Declarative Range/Hash Partitioning, pg_stat_statements & Connection Pooling (PgBouncer)
Implement and verify Declarative Range/Hash Partitioning, pg_stat_statements & Connection Pooling (PgBouncer) (Scale to thousands of concurrent app connections and isolate slow queries scientifically)...
Guided Live Lab Checklist: Declarative Range/Hash Partitioning, pg_stat_statements & Connection Pooling (PgBouncer)
-- Declarative Range/Hash Partitioning, pg_stat_statements & Connection Pooling (PgBouncer)
-- Purpose: Scale to thousands of concurrent app connections and isolate slow queries scientifically
EXPLAIN
SELECT account_id, COUNT(*) AS event_total, MAX(created_at) AS latest_event_at
FROM production_audit_events
WHERE status = 'VERIFIED'
GROUP BY account_id
ORDER BY latest_event_at DESC
LIMIT 25;Architect, implement, test, and document 'RLS Multi-Tenant SaaS Schema with Declarative Partitioning & Bloat Monitor' applying every bullet-wise engineering concept from Module 15 (PostgreSQL MVCC Internals, Autovacuum Tuning, Row-Level Security & Partitioning) of PostgreSQL Training.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Full-Text Search (tsvector / GIN) + pgvector HNSW Embeddings for AI/RAG Workloads teaches you how PostgreSQL applications run relational transactions, keyword search, and vector similarity inside a single ACID database. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Full-Text Search (tsvector / GIN) + pgvector HNSW Embeddings for AI/RAG Workloads in real PostgreSQL systems — specifically: Run relational transactions, keyword search, and vector similarity inside a single ACID database, so your code stays reliable, fast, and easy to maintain in production.
Self-Explanatory Guide: Full-Text Search (tsvector / GIN) + pgvector HNSW Embeddings for AI/RAG Workloads (Plain-English & Industry Purpose)In simple, everyday terms, Full-Text Search (tsvector / GIN) + pgvector HNSW Embeddings for AI/RAG Workloads teaches you how PostgreSQL applications run relational transactions, keyword search, and vector similarity inside a single ACID database. Even if you have never seen t...
Practical Implementation: Full-Text Search (tsvector / GIN) + pgvector HNSW Embeddings for AI/RAG Workloads
Implement and verify Full-Text Search (tsvector / GIN) + pgvector HNSW Embeddings for AI/RAG Workloads (Run relational transactions, keyword search, and vector similarity inside a single ACID database...
Guided Live Lab Checklist: Full-Text Search (tsvector / GIN) + pgvector HNSW Embeddings for AI/RAG Workloads
-- Full-Text Search (tsvector / GIN) + pgvector HNSW Embeddings for AI/RAG Workloads
-- Purpose: Run relational transactions, keyword search, and vector similarity inside a single ACID database
EXPLAIN
SELECT account_id, COUNT(*) AS event_total, MAX(created_at) AS latest_event_at
FROM production_audit_events
WHERE status = 'VERIFIED'
GROUP BY account_id
ORDER BY latest_event_at DESC
LIMIT 25;💡 Self-Explanatory Overview: In simple, everyday terms, Streaming & Logical Replication, WAL Archiving & Point-in-Time Recovery (PITR) teaches you how PostgreSQL applications execute zero-data-loss failover drills and major-version upgrades with minimal downtime. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Streaming & Logical Replication, WAL Archiving & Point-in-Time Recovery (PITR) in real PostgreSQL systems — specifically: Execute zero-data-loss failover drills and major-version upgrades with minimal downtime, so your code stays reliable, fast, and easy to maintain in production.
Self-Explanatory Guide: Streaming & Logical Replication, WAL Archiving & Point-in-Time Recovery (PITR) (Plain-English & Industry Purpose)In simple, everyday terms, Streaming & Logical Replication, WAL Archiving & Point-in-Time Recovery (PITR) teaches you how PostgreSQL applications execute zero-data-loss failover drills and major-version upgrades with minimal downtime. Even if you have never seen...
Practical Implementation: Streaming & Logical Replication, WAL Archiving & Point-in-Time Recovery (PITR)
Implement and verify Streaming & Logical Replication, WAL Archiving & Point-in-Time Recovery (PITR) (Execute zero-data-loss failover drills and major-version upgrades with minimal downtime) with expli...
Guided Live Lab Checklist: Streaming & Logical Replication, WAL Archiving & Point-in-Time Recovery (PITR)
-- Streaming & Logical Replication, WAL Archiving & Point-in-Time Recovery (PITR)
-- Purpose: Execute zero-data-loss failover drills and major-version upgrades with minimal downtime
EXPLAIN
SELECT account_id, COUNT(*) AS event_total, MAX(created_at) AS latest_event_at
FROM production_audit_events
WHERE status = 'VERIFIED'
GROUP BY account_id
ORDER BY latest_event_at DESC
LIMIT 25;💡 Self-Explanatory Overview: In simple, everyday terms, Reading Complex EXPLAIN (ANALYZE, BUFFERS) Plans & Postgres Interview Scenarios teaches you how PostgreSQL applications diagnose Nested Loop vs Hash Join misestimates, spill-to-disk sorts, and lock queues. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean PostgreSQL 17 Engine & `psql`, MVCC, WAL & Autovacuum Internals, PgBouncer, `pg_hba.conf` & SCRAM-SHA-256 code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Reading Complex EXPLAIN (ANALYZE, BUFFERS) Plans & Postgres Interview Scenarios in real PostgreSQL systems — specifically: Diagnose Nested Loop vs Hash Join misestimates, spill-to-disk sorts, and lock queues, so your code stays reliable, fast, and easy to maintain in production.
Self-Explanatory Guide: Reading Complex EXPLAIN (ANALYZE, BUFFERS) Plans & Postgres Interview Scenarios (Plain-English & Industry Purpose)In simple, everyday terms, Reading Complex EXPLAIN (ANALYZE, BUFFERS) Plans & Postgres Interview Scenarios teaches you how PostgreSQL applications diagnose Nested Loop vs Hash Join misestimates, spill-to-disk sorts, and lock queues. Even if you have never seen this conc...
Practical Implementation: Reading Complex EXPLAIN (ANALYZE, BUFFERS) Plans & Postgres Interview Scenarios
Implement and verify Reading Complex EXPLAIN (ANALYZE, BUFFERS) Plans & Postgres Interview Scenarios (Diagnose Nested Loop vs Hash Join misestimates, spill-to-disk sorts, and lock queues) with explici...
Guided Live Lab Checklist: Reading Complex EXPLAIN (ANALYZE, BUFFERS) Plans & Postgres Interview Scenarios
-- Reading Complex EXPLAIN (ANALYZE, BUFFERS) Plans & Postgres Interview Scenarios
-- Purpose: Diagnose Nested Loop vs Hash Join misestimates, spill-to-disk sorts, and lock queues
EXPLAIN
SELECT account_id, COUNT(*) AS event_total, MAX(created_at) AS latest_event_at
FROM production_audit_events
WHERE status = 'VERIFIED'
GROUP BY account_id
ORDER BY latest_event_at DESC
LIMIT 25;Architect, implement, test, and document 'HNSW pgvector Semantic Search + Full-Text tsvector Hybrid Query Engine' applying every bullet-wise engineering concept from Module 16 (pgvector Hybrid Search, Logical Replication & PostgreSQL DBA/SDE Interviews) of PostgreSQL Training.
Advanced SQL & Window Fns
Relational Queries
PostgreSQL 16 & MVCC
Enterprise RDBMS
pgvector & JSONB GIN
Semantic Search
Partitioning & Replicas
High Availability
Row-Level Security (RLS)
App & Cloud Security
WAL Archiving & PgBouncer
OS & Scripting
Dockerized HA Postgres
Containers
Amazon RDS & Aurora
Cloud Infra
LeetCode Problem Solving
Trainer Coding Prep
HackerRank Coding Prep
Trainer Coding PrepPostgreSQL is the world’s most advanced open-source database — trusted by Instagram, Apple, Spotify, and AI startups because it combines rock-solid relational SQL, fast JSONB documents, full-text search, and AI vector storage in one engine.
Clear step-by-step SQL progression from creating your first table to mastering enterprise indexing, JSONB, and high-availability data architecture.
Key Engineering Capabilities You Will Build:
Purpose: Enforce data integrity directly inside the database so invalid states can never be saved.
Purpose: Traverse org charts, bill-of-materials trees, and complex financial cohorts in a single query.
Purpose: Combine relational rigor with NoSQL JSON speed and instant full-text/geospatial lookups.
Purpose: Sustain thousands of concurrent reads and writes without readers blocking writers.
Taught live by active Working IT Professionals between 7:00 AM and 8:00 PM IST (choose any slot based on trainer availability).
Weekdays (Mon – Fri): 1 Hour / Day • 7:00 AM to 8:00 PM IST (choose any slot based on trainer availability)
Weekends (Sat – Sun): 2 Hours / Day • 7:00 AM to 8:00 PM IST (choose any slot based on trainer availability)
PostgreSQL Training — Weekday Live Online Cohort (₹2,999 + 18% GST)
Mon - Fri (Weekday Live • 1 Hr/Day) • 07:00 AM - 08:00 PM IST (1 Hr/Day • Any Slot per Trainer Availability)
Starts: 10 Oct 2026
PostgreSQL Training — Weekend Live Online Cohort (₹2,999 + 18% GST)
Sat - Sun (Weekend Live • 2 Hrs/Day) • 07:00 AM - 08:00 PM IST (2 Hrs/Day • Any Slot per Trainer Availability)
Starts: 05 Nov 2026
LeetCode Support
HackerRank Support
Enrolled students receive live trainer mentorship to break down algorithmic/query patterns, edge cases, and optimal complexity so you can confidently crack LeetCode and HackerRank problems in SQL & PL/pgSQL (LeetCode SQL 50 & HackerRank SQL).
🚀 Live Client Production Project + Internship Letter
After completing all 16 progressive hands-on module projects during live classes, students join our mentor-led Client Production Sprint to build, test, and deploy "LedgerPG Multi-Tenant Row-Level Security, Partitioned Time-Series & pgvector Engine" to live cloud production — earning an Official Verified Internship Letter detailing their exact technical contributions.
Domain: Enterprise SaaS Analytics & AI Search Database Client • Infra: PostgreSQL 16 + Declarative Partitioning + Row-Level Security + PgBouncer + pg_stat_statements
Certified on Your Official Internship Confirmation Letter:
See the exact job positions and realistic salary packages unlocked by PostgreSQL Training, plus which specialized MockAttempt Academy courses to pair next to qualify for higher-paying multi-stack engineering roles.
Advanced SQL, JSONB/GIN indexing, MVCC, `EXPLAIN ANALYZE` & partitioning.
RLS multi-tenancy, PgBouncer pooling, replication & query latency optimization.
PostgreSQL is the #1 default database for modern FastAPI, Django, Spring Boot & GenAI stacks!
Why Learn Next (For What Purpose): PostgreSQL + FastAPI/Django is the premier Python production backend combination worldwide.
Why Learn Next (For What Purpose): Power mission-critical Spring Boot microservices with tuned PostgreSQL transactions and partitioning.
Why Learn Next (For What Purpose): Use PostgreSQL `pgvector` hybrid search to ground enterprise LLM RAG AI copilots.
MockAttempt Academy keeps production-grade PostgreSQL 17, MVCC, JSONB & Query Optimization training ultra-affordable at ₹2,999 + 18% GST so every learner gets 10,440 MCQs, 16 Capstone Projects, Live Client Production deployment, and spoken MockAttempt AI interview coaching.
Regular Skill Courses offer both Weekday Live Batches (Monday–Friday, 1 Hour/Day between 7:00 AM and 8:00 PM IST) and Weekend Live Batches (Saturday–Sunday, 2 Hours/Day between 7:00 AM and 8:00 PM IST) taught 100% by active Working IT Professionals — students can choose any convenient slot based on trainer availability.
Instead of passive pre-recorded videos, you code live with senior architects, monitor 100% of syllabus progress transparently, build 16 progressive Module Projects during training, and deploy a real Live Client Project to production at the end of the course — earning both a Barcode Graduation Certificate and an Official Project Internship Confirmation Letter.
Across all 42 syllabus topics in 16 modules, this course includes 244 Timed Topic Mock Tests (10,440 curated MCQs with detailed explanations) and 244 Spoken Voice + MockAttempt AI Technical Interview rounds — ensuring 100% of topics are assessed.
The AI Interviewer speaks each question aloud via Text-to-Speech, listens via microphone Speech-to-Text, normalizes spoken technical syntax, and sends your transcript to the MockAttempt AI verification agent for scoring and follow-up probing.
All MockAttempt Tech Academy fees are 100% fixed and non-negotiable (₹2,999 + 18% GST). We never inflate fake MRPs only to bargain discounts because we refuse to compromise on Senior Industry Expert trainers, small interactive live batches, real cloud/repository labs, daily assessments, or placement mentorship.
Yes! Because this curriculum includes SQL & PL/pgSQL (LeetCode SQL 50 & HackerRank SQL), your trainer provides guided problem-solving support to break down patterns, edge cases, and optimal complexity so you can confidently crack LeetCode and HackerRank problems.
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