SQL from zero: reading and changing rows
Learn to read SELECT, WHERE, INSERT, UPDATE, DELETE, NULL, parameters, sorting, and aggregation before advanced database topics.
Timeline
Understanding: SQL from zero: reading and changing rows
You can read this chapter before running the experiment. Then return to the live trace and match each concept to a real event.
Think of a table as a strict spreadsheet. Columns define allowed fields, rows store individual entities, and a SQL statement asks for a result or mutation. Unlike a local JavaScript array, the database coordinates many processes and enforces declared rules.
SQL is declarative: you describe a desired result and PostgreSQL chooses how to produce it. A statement is made of clauses. SELECT chooses output expressions, FROM supplies input, WHERE filters rows, GROUP BY forms groups, HAVING filters groups, ORDER BY sorts, and LIMIT bounds output. INSERT, UPDATE, and DELETE change data; RETURNING immediately returns changed rows.
Terms used in this experiment
Understand the words first, then the execution order.
Table / row / column
A table stores one entity kind, a row is one record, and a column is a named field with a data type.
Statement
A complete SQL command such as SELECT, INSERT, UPDATE, DELETE, or CREATE TABLE.
Clause
A statement part with one job: FROM supplies input, WHERE filters, and ORDER BY sorts.
Expression
A calculated fragment such as price * stock, lower(email), count(*), or price <= $1.
NULL
A missing or unknown value. It is tested with IS NULL or IS NOT NULL rather than equality.
Parameter $1
A value placeholder sent separately by the driver; its number maps to the values array position.
Alias AS
A temporary name for a column, expression, or table inside a query result.
Result set
The rows returned by a query; node-postgres exposes them through result.rows.
What happens step by step
Each step maps to an observable runtime state.
- 01Define a table
CREATE TABLE declares columns, data types, defaults, and constraints.
- 02Insert rows
INSERT INTO names target columns, VALUES supplies data, and RETURNING shows created rows.
- 03Read rows
SELECT forms output columns, FROM selects the table, and WHERE keeps matching rows.
- 04Order output
ORDER BY sorts, LIMIT bounds count, and OFFSET skips an initial portion.
- 05Mutate safely
UPDATE uses SET and a deliberate WHERE; RETURNING exposes the actual outcome.
- 06Aggregate
Aggregate functions calculate values, GROUP BY forms groups, and HAVING filters groups.
- 07Delete deliberately
DELETE without WHERE affects the whole table, so first verify its predicate with SELECT.
Where the result needs context
These details explain why similar code can sometimes produce a different trace.
Written and logical order differ
SELECT is written first, but FROM and WHERE logically determine input before the SELECT list is produced.
SQL keywords are case-insensitive
PostgreSQL reads select and SELECT alike; uppercase is a readability convention.
NULL creates three-valued logic
Comparison with NULL normally yields UNKNOWN, while WHERE keeps only TRUE.
Parameters protect values, not identifiers
$1 can represent an email or price, not a table name, column, or sort direction. Choose those from an allowlist.
LIMIT without ORDER BY is unstable
Without explicit ordering, any matching rows may be returned; physical table order is not an API contract.
Mutations report their scope
Inspect rowCount and RETURNING. Zero rows can be a business outcome; an unexpectedly large count is a safety signal.
First understand which parts of Node participate in execution.
Then remove instrumentation and focus on the central mechanism.
Finally match the model to the code that produces the live trace.
A minimal model without instrumentation
const result = await db.query(
`SELECT
id,
name AS product_name,
price * stock AS inventory_value
FROM products
WHERE category = $1
AND price <= $2
ORDER BY price DESC
LIMIT $3`,
['books', 3500, 10],
);
console.log(result.rows);The complete code executed by the scenario
This is not an alternative example: these are the functions and files used by the Run button.
The source is generated from the real server function. Scenarios using a child process or Worker include every participating file.
import { randomUUID } from 'node:crypto';
import { performance } from 'node:perf_hooks';
import pg from 'pg';
const { Pool } = pg;
const STATEMENT_TIMEOUT_MS = 8_000;
const CONNECTION_TIMEOUT_MS = 1_500;
function databaseUrl() {
return process.env.DATABASE_URL?.trim() || null;
}
function safeIdentifier(prefix) {
return `${prefix}_${randomUUID().replaceAll('-', '').slice(0, 12)}`;
}
function planReport(result) {
const raw = result.rows[0]['QUERY PLAN'];
const report = Array.isArray(raw) ? raw[0] : JSON.parse(raw)[0];
const nodes = [];
function visit(node, depth = 0) {
nodes.push({
depth,
type: node['Node Type'],
relation: node['Relation Name'] ?? null,
index: node['Index Name'] ?? null,
estimatedRows: node['Plan Rows'],
actualRows: node['Actual Rows'],
loops: node['Actual Loops'],
});
for (const child of node.Plans ?? []) visit(child, depth + 1);
}
visit(report.Plan);
return {
nodes,
executionMs: Number(report['Execution Time'] ?? 0),
planningMs: Number(report['Planning Time'] ?? 0),
};
}
function planLine(plan) {
return plan.nodes
.map((node) => {
const target = node.index ?? node.relation;
return `${' '.repeat(node.depth)}${node.type}${target ? ` [${target}]` : ''}`;
})
.join(' → ');
}
async function configureClient(client) {
await client.query(`SET statement_timeout = '${STATEMENT_TIMEOUT_MS}ms'`);
await client.query(
`SET idle_in_transaction_session_timeout = '${STATEMENT_TIMEOUT_MS}ms'`,
);
await client.query("SET lock_timeout = '2500ms'");
}
async function rollbackQuietly(client) {
if (!client) return;
try {
await client.query('ROLLBACK');
} catch {
// The client may not currently be in a transaction.
}
}
async function withDatabaseLab(emit, run) {
const connectionString = databaseUrl();
if (!connectionString) {
emit(
'postgres',
'skip',
'PostgreSQL не подключён: задайте DATABASE_URL или запустите проект через Docker Compose',
);
return false;
}
const pool = new Pool({
connectionString,
max: 4,
connectionTimeoutMillis: CONNECTION_TIMEOUT_MS,
idleTimeoutMillis: 5_000,
allowExitOnIdle: true,
application_name: 'node-loop-lab',
});
const schema = safeIdentifier('node_loop_lab');
try {
const versionResult = await pool.query(
"SELECT current_setting('server_version') AS version",
);
emit(
'postgres',
'connect',
`Подключён PostgreSQL ${versionResult.rows[0].version}; создаём изолированную схему ${schema}`,
);
await pool.query(`CREATE SCHEMA ${schema}`);
await run({ pool, schema });
return true;
} catch (error) {
const safeMessage =
error?.code === 'ECONNREFUSED'
? 'соединение отклонено'
: error?.code
? `SQLSTATE ${error.code}`
: 'ошибка подключения';
emit('postgres', 'error', `Сценарий PostgreSQL остановлен: ${safeMessage}`);
return false;
} finally {
try {
await pool.query(`DROP SCHEMA IF EXISTS ${schema} CASCADE`);
emit('cleanup', 'drop', 'Учебная схема удалена; постоянные данные не создавались');
} catch {
// Connection failures can make cleanup impossible; the schema name is unique
// and contains no user data.
}
await pool.end().catch(() => {});
}
}
export async function databaseSqlBasics(emit) {
await withDatabaseLab(emit, async ({ pool, schema }) => {
const client = await pool.connect();
try {
await configureClient(client);
await client.query(`
CREATE TABLE ${schema}.products (
id bigint GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
name text NOT NULL,
category text NOT NULL,
price numeric(10, 2) NOT NULL CHECK (price >= 0),
stock integer NOT NULL DEFAULT 0 CHECK (stock >= 0),
description text,
active boolean NOT NULL DEFAULT true
)
`);
emit(
'ddl',
'create-table',
'CREATE TABLE описал columns, data types, defaults и constraints таблицы products',
);
const inserted = await client.query(
`INSERT INTO ${schema}.products
(name, category, price, stock, description)
VALUES
($1, $2, $3, $4, $5),
($6, $7, $8, $9, $10),
($11, $12, $13, $14, $15)
RETURNING id, name, price`,
[
'Node.js Handbook',
'books',
2400,
8,
'Runtime and backend foundations',
'NestJS Patterns',
'books',
3100,
5,
null,
'Mechanical Keyboard',
'hardware',
7800,
2,
'USB keyboard',
],
);
emit(
'insert',
'rows',
`INSERT добавил ${inserted.rowCount} строки; RETURNING вернул generated id без отдельного SELECT`,
);
const selected = await client.query(
`SELECT
id,
name AS product_name,
price,
stock,
price * stock AS inventory_value
FROM ${schema}.products
WHERE category = $1
AND price <= $2
AND active IS TRUE
ORDER BY price DESC
LIMIT $3`,
['books', 3500, 10],
);
emit(
'select',
'filter',
`SELECT → FROM → WHERE → ORDER BY → LIMIT вернул: ${selected.rows
.map((row) => row.product_name)
.join(', ')}`,
);
const nullWrong = await client.query(
`SELECT count(*)::int AS count
FROM ${schema}.products
WHERE description = NULL`,
);
const nullRight = await client.query(
`SELECT count(*)::int AS count
FROM ${schema}.products
WHERE description IS NULL`,
);
emit(
'null',
'comparison',
`description = NULL нашёл ${nullWrong.rows[0].count}; IS NULL нашёл ${nullRight.rows[0].count}`,
);
const updated = await client.query(
`UPDATE ${schema}.products
SET stock = stock - $1
WHERE name = $2
AND stock >= $1
RETURNING id, name, stock`,
[2, 'Node.js Handbook'],
);
emit(
'update',
'returning',
`UPDATE изменил stock и вернул новое значение=${updated.rows[0].stock}`,
);
const grouped = await client.query(
`SELECT
category,
count(*)::int AS product_count,
round(avg(price), 2) AS average_price,
sum(stock)::int AS total_stock
FROM ${schema}.products
GROUP BY category
HAVING count(*) >= $1
ORDER BY category`,
[1],
);
emit(
'aggregate',
'group-by',
`GROUP BY создал ${grouped.rowCount} группы; HAVING фильтрует уже агрегированные группы`,
);
const deleted = await client.query(
`DELETE FROM ${schema}.products
WHERE active IS FALSE
RETURNING id`,
);
emit(
'delete',
'safe-delete',
`DELETE с WHERE удалил строк=${deleted.rowCount}; без WHERE удалились бы все строки`,
);
} finally {
await rollbackQuietly(client);
client.release();
}
});
}
export async function databaseConstraintsAndAcid(emit) {
await withDatabaseLab(emit, async ({ pool, schema }) => {
const client = await pool.connect();
try {
await configureClient(client);
emit(
'ddl',
'schema',
'Создаём PRIMARY KEY, UNIQUE, CHECK и FOREIGN KEY как правила целостности внутри БД',
);
await client.query(`
CREATE TABLE ${schema}.customers (
id bigint GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
email text NOT NULL UNIQUE,
name text NOT NULL CHECK (char_length(name) >= 2)
);
CREATE TABLE ${schema}.orders (
id bigint GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
customer_id bigint NOT NULL
REFERENCES ${schema}.customers(id) ON DELETE RESTRICT,
amount numeric(12, 2) NOT NULL CHECK (amount > 0),
status text NOT NULL DEFAULT 'new'
CHECK (status IN ('new', 'paid', 'cancelled'))
);
`);
const customer = await client.query(
`INSERT INTO ${schema}.customers (email, name)
VALUES ($1, $2)
RETURNING id`,
['learner@example.com', 'Learner'],
);
emit(
'query',
'parameters',
'Параметры $1/$2 переданы отдельно от SQL: значения не становятся частью синтаксиса запроса',
);
try {
await client.query(
`INSERT INTO ${schema}.orders (customer_id, amount)
VALUES ($1, $2)`,
[customer.rows[0].id, -50],
);
} catch (error) {
emit(
'constraint',
'check',
`CHECK отклонил отрицательную сумму: SQLSTATE ${error.code}`,
);
}
await client.query('BEGIN');
await client.query(
`INSERT INTO ${schema}.orders (customer_id, amount, status)
VALUES ($1, $2, $3)`,
[customer.rows[0].id, 1250, 'paid'],
);
const inside = await client.query(
`SELECT count(*)::int AS count FROM ${schema}.orders`,
);
await client.query('ROLLBACK');
const after = await client.query(
`SELECT count(*)::int AS count FROM ${schema}.orders`,
);
emit(
'transaction',
'rollback',
`Внутри транзакции строк=${inside.rows[0].count}; после ROLLBACK строк=${after.rows[0].count}`,
);
emit(
'acid',
'model',
'ACID: constraints поддерживают consistency, транзакция даёт atomicity, WAL/disk — durability, а isolation управляет видимостью параллельных изменений',
);
} finally {
await rollbackQuietly(client);
client.release();
}
});
}
export async function databaseIndexesAndExplain(emit) {
await withDatabaseLab(emit, async ({ pool, schema }) => {
const client = await pool.connect();
try {
await configureClient(client);
emit(
'dataset',
'seed',
'Создаём 40 000 событий с коррелированным временем, tenant_id, status и массивом tags',
);
await client.query(`
CREATE TABLE ${schema}.events (
id bigint GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
tenant_id integer NOT NULL,
created_at timestamptz NOT NULL,
status text NOT NULL,
tags text[] NOT NULL
);
INSERT INTO ${schema}.events (tenant_id, created_at, status, tags)
SELECT
(g % 100) + 1,
now() - (g * interval '1 second'),
CASE WHEN g % 5 = 0 THEN 'failed' ELSE 'processed' END,
ARRAY[
CASE WHEN g % 3 = 0 THEN 'api' ELSE 'worker' END,
CASE WHEN g % 7 = 0 THEN 'priority' ELSE 'normal' END
]
FROM generate_series(1, 40000) AS g;
ANALYZE ${schema}.events;
`);
const query = `
SELECT id, created_at, status
FROM ${schema}.events
WHERE tenant_id = 37
AND created_at >= now() - interval '6 hours'
ORDER BY created_at DESC
`;
const before = planReport(
await client.query(`EXPLAIN (ANALYZE, BUFFERS, FORMAT JSON) ${query}`),
);
emit(
'planner',
'before-index',
`До составного индекса: ${planLine(before)}; execution=${before.executionMs.toFixed(2)} ms`,
);
await client.query(`
CREATE INDEX events_tenant_created_btree
ON ${schema}.events USING btree (tenant_id, created_at DESC)
INCLUDE (status);
CREATE INDEX events_status_hash
ON ${schema}.events USING hash (status);
CREATE INDEX events_created_brin
ON ${schema}.events USING brin (created_at);
CREATE INDEX events_tags_gin
ON ${schema}.events USING gin (tags);
ANALYZE ${schema}.events;
`);
const after = planReport(
await client.query(`EXPLAIN (ANALYZE, BUFFERS, FORMAT JSON) ${query}`),
);
emit(
'planner',
'after-index',
`После B-tree: ${planLine(after)}; execution=${after.executionMs.toFixed(2)} ms`,
);
const sizes = await client.query(
`
SELECT c.relname, pg_relation_size(c.oid)::bigint AS bytes
FROM pg_class AS c
JOIN pg_namespace AS n ON n.oid = c.relnamespace
WHERE n.nspname = $1 AND c.relkind = 'i'
ORDER BY bytes DESC
`,
[schema],
);
emit(
'indexes',
'size',
`Размеры индексов: ${sizes.rows
.map((row) => `${row.relname}=${Math.round(Number(row.bytes) / 1024)} KiB`)
.join(', ')}`,
);
emit(
'optimizer',
'decision',
'Индекс не является приказом: planner выбирает Seq Scan, Index Scan или Bitmap Scan по статистике, селективности и стоимости',
);
} finally {
client.release();
}
});
}
export async function databaseTransactionsAndLocks(emit) {
await withDatabaseLab(emit, async ({ pool, schema }) => {
await pool.query(`
CREATE TABLE ${schema}.accounts (
id integer PRIMARY KEY,
balance integer NOT NULL CHECK (balance >= 0),
version integer NOT NULL DEFAULT 0
);
INSERT INTO ${schema}.accounts (id, balance) VALUES (1, 1000);
`);
const first = await pool.connect();
const second = await pool.connect();
try {
await Promise.all([configureClient(first), configureClient(second)]);
await first.query('BEGIN ISOLATION LEVEL READ COMMITTED');
const rcBefore = await first.query(
`SELECT balance FROM ${schema}.accounts WHERE id = 1`,
);
await second.query(
`UPDATE ${schema}.accounts SET balance = 1100 WHERE id = 1`,
);
const rcAfter = await first.query(
`SELECT balance FROM ${schema}.accounts WHERE id = 1`,
);
await first.query('ROLLBACK');
emit(
'isolation',
'read-committed',
`READ COMMITTED: первый SELECT=${rcBefore.rows[0].balance}, второй SELECT=${rcAfter.rows[0].balance}`,
);
await pool.query(
`UPDATE ${schema}.accounts SET balance = 1000, version = 0 WHERE id = 1`,
);
await first.query('BEGIN ISOLATION LEVEL REPEATABLE READ');
const rrBefore = await first.query(
`SELECT balance FROM ${schema}.accounts WHERE id = 1`,
);
await second.query(
`UPDATE ${schema}.accounts SET balance = 1100 WHERE id = 1`,
);
const rrAfter = await first.query(
`SELECT balance FROM ${schema}.accounts WHERE id = 1`,
);
await first.query('ROLLBACK');
emit(
'isolation',
'repeatable-read',
`REPEATABLE READ: первый SELECT=${rrBefore.rows[0].balance}, второй SELECT=${rrAfter.rows[0].balance}`,
);
await pool.query(
`UPDATE ${schema}.accounts SET balance = 1000, version = 0 WHERE id = 1`,
);
await first.query('BEGIN');
await first.query(
`SELECT balance FROM ${schema}.accounts WHERE id = 1 FOR UPDATE`,
);
await second.query('BEGIN');
let secondAcquired = false;
const waitStarted = performance.now();
const secondLock = second
.query(
`SELECT balance FROM ${schema}.accounts WHERE id = 1 FOR UPDATE`,
)
.then((result) => {
secondAcquired = true;
return result;
});
await new Promise((resolve) => setTimeout(resolve, 120));
emit(
'lock',
'wait',
`SELECT FOR UPDATE: вторая транзакция ждёт блокировку=${!secondAcquired}`,
);
await first.query(
`UPDATE ${schema}.accounts SET balance = balance - 100 WHERE id = 1`,
);
await first.query('COMMIT');
await secondLock;
const waitedMs = performance.now() - waitStarted;
await second.query(
`UPDATE ${schema}.accounts SET balance = balance - 200 WHERE id = 1`,
);
await second.query('COMMIT');
const pessimistic = await pool.query(
`SELECT balance FROM ${schema}.accounts WHERE id = 1`,
);
emit(
'lock',
'pessimistic',
`Пессимистичная блокировка ждала ${waitedMs.toFixed(0)} ms; итоговый balance=${pessimistic.rows[0].balance}`,
);
await pool.query(
`UPDATE ${schema}.accounts SET balance = 1000, version = 0 WHERE id = 1`,
);
const snapshotA = await first.query(
`SELECT balance, version FROM ${schema}.accounts WHERE id = 1`,
);
const snapshotB = await second.query(
`SELECT balance, version FROM ${schema}.accounts WHERE id = 1`,
);
const updateA = await first.query(
`UPDATE ${schema}.accounts
SET balance = $1, version = version + 1
WHERE id = 1 AND version = $2`,
[snapshotA.rows[0].balance - 100, snapshotA.rows[0].version],
);
const updateB = await second.query(
`UPDATE ${schema}.accounts
SET balance = $1, version = version + 1
WHERE id = 1 AND version = $2`,
[snapshotB.rows[0].balance - 200, snapshotB.rows[0].version],
);
emit(
'lock',
'optimistic',
`Оптимистичная версия: update A=${updateA.rowCount}, stale update B=${updateB.rowCount}; 0 означает конфликт`,
);
} finally {
await Promise.all([rollbackQuietly(first), rollbackQuietly(second)]);
first.release();
second.release();
}
});
}
export async function databaseJoinsAndMaterializedViews(emit) {
await withDatabaseLab(emit, async ({ pool, schema }) => {
const client = await pool.connect();
try {
await configureClient(client);
emit(
'dataset',
'seed',
'Создаём 2 000 клиентов и 30 000 заказов для JOIN и агрегирования',
);
await client.query(`
CREATE TABLE ${schema}.customers (
id integer PRIMARY KEY,
name text NOT NULL,
active boolean NOT NULL
);
CREATE TABLE ${schema}.orders (
id bigint GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
customer_id integer NOT NULL REFERENCES ${schema}.customers(id),
amount numeric(12, 2) NOT NULL,
created_at timestamptz NOT NULL DEFAULT now()
);
INSERT INTO ${schema}.customers (id, name, active)
SELECT g, 'customer-' || g, g % 5 <> 0
FROM generate_series(1, 2000) AS g;
INSERT INTO ${schema}.orders (customer_id, amount, created_at)
SELECT
(g % 2000) + 1,
((g % 5000) + 100)::numeric / 10,
now() - (g * interval '1 minute')
FROM generate_series(1, 30000) AS g;
CREATE INDEX orders_customer_id_idx
ON ${schema}.orders (customer_id);
ANALYZE ${schema}.customers;
ANALYZE ${schema}.orders;
`);
const joinPlan = planReport(
await client.query(`
EXPLAIN (ANALYZE, BUFFERS, FORMAT JSON)
SELECT c.id, c.name, sum(o.amount) AS total
FROM ${schema}.customers AS c
JOIN ${schema}.orders AS o ON o.customer_id = c.id
WHERE c.active
GROUP BY c.id, c.name
ORDER BY total DESC
LIMIT 20
`),
);
emit(
'join',
'plan',
`JOIN plan: ${planLine(joinPlan)}; execution=${joinPlan.executionMs.toFixed(2)} ms`,
);
const sampleCustomers = await client.query(
`SELECT id FROM ${schema}.customers ORDER BY id LIMIT 20`,
);
const nPlusOneStarted = performance.now();
for (const customer of sampleCustomers.rows) {
await client.query(
`SELECT count(*) FROM ${schema}.orders WHERE customer_id = $1`,
[customer.id],
);
}
const nPlusOneMs = performance.now() - nPlusOneStarted;
const oneQueryStarted = performance.now();
await client.query(`
SELECT c.id, count(o.id)
FROM ${schema}.customers AS c
LEFT JOIN ${schema}.orders AS o ON o.customer_id = c.id
WHERE c.id <= 20
GROUP BY c.id
`);
const oneQueryMs = performance.now() - oneQueryStarted;
emit(
'query-shape',
'n-plus-one',
`N+1: 21 round trips=${nPlusOneMs.toFixed(2)} ms; один JOIN=1 round trip=${oneQueryMs.toFixed(2)} ms`,
);
await client.query(`
CREATE MATERIALIZED VIEW ${schema}.customer_totals AS
SELECT customer_id, count(*)::int AS orders_count, sum(amount) AS total
FROM ${schema}.orders
GROUP BY customer_id;
CREATE UNIQUE INDEX customer_totals_customer_id_idx
ON ${schema}.customer_totals (customer_id);
`);
const before = await client.query(
`SELECT total FROM ${schema}.customer_totals WHERE customer_id = $1`,
[42],
);
await client.query(
`INSERT INTO ${schema}.orders (customer_id, amount) VALUES ($1, $2)`,
[42, 999],
);
const stale = await client.query(
`SELECT total FROM ${schema}.customer_totals WHERE customer_id = $1`,
[42],
);
await client.query(`REFRESH MATERIALIZED VIEW ${schema}.customer_totals`);
const refreshed = await client.query(
`SELECT total FROM ${schema}.customer_totals WHERE customer_id = $1`,
[42],
);
emit(
'materialized-view',
'refresh',
`Materialized View: было=${before.rows[0].total}, до REFRESH=${stale.rows[0].total}, после=${refreshed.rows[0].total}`,
);
emit(
'sql',
'control',
'Raw SQL здесь параметризован и видим; ORM полезен, пока команда проверяет сгенерированный SQL, планы, N+1 и границы транзакций',
);
} finally {
client.release();
}
});
}
The emit(...) calls become live-trace rows. await and Promise keep the HTTP stream open until the scenario completes.
Practical patterns worth keeping nearby
Compare the goal, code, and caveats instead of memorizing syntax without a model.
SELECT output columns
Return id, name, and a calculated inventory value.
SELECT
id,
name,
price * stock AS inventory_value
FROM products;- Commas separate SELECT expressions.
- FROM supplies the table.
- AS names the calculated result field.
WHERE filters rows
Find active books no more expensive than a supplied value.
SELECT id, name, price
FROM products
WHERE category = $1
AND price <= $2
AND active IS TRUE;- $1 and $2 come from the driver values array.
- AND requires every condition to be true.
INSERT a row
Create a product and receive its generated id.
INSERT INTO products (name, price, stock)
VALUES ($1, $2, $3)
RETURNING id, name, price, stock;- VALUES order follows the column list.
- RETURNING exposes the stored row.
UPDATE matching rows
Atomically reduce stock only when enough remains.
UPDATE products
SET stock = stock - $1
WHERE id = $2
AND stock >= $1
RETURNING id, stock;- SET defines the new value.
- The right-hand stock is the current row value.
- Zero rowCount means the predicate failed.
DELETE deliberately
Delete only inactive products and see their ids.
DELETE FROM products
WHERE active IS FALSE
RETURNING id;- Run SELECT with the same WHERE first.
- Without WHERE, every row is deleted.
ORDER BY, LIMIT, and OFFSET
Return the second page of expensive products.
SELECT id, name, price
FROM products
ORDER BY price DESC, id ASC
LIMIT $1
OFFSET $2;- DESC decreases; ASC increases.
- id is a stable tie-breaker.
- Large OFFSET eventually becomes expensive.
GROUP BY and aggregates
Calculate product count and average price per category.
SELECT
category,
count(*) AS product_count,
round(avg(price), 2) AS average_price
FROM products
GROUP BY category
HAVING count(*) >= $1
ORDER BY category;- count and avg produce one value per group.
- HAVING filters groups; WHERE filters rows before aggregation.
NULL means missing
Find products without a description.
SELECT id, name
FROM products
WHERE description IS NULL;- Do not use description = NULL.
- IS NOT NULL performs the opposite check.
- NULL differs from an empty string.
How a learning mistake becomes an incident
A realistic service: the original code, observable failure, corrected implementation, and why the correction works.
A catalog filter concatenates user input into SQL
A Nest repository builds a product list from query parameters. Category and sort look like ordinary strings, so they are inserted directly into a template literal.
A category value can change query syntax, while an arbitrary sort value becomes an uncontrolled identifier. SELECT * also changes the API contract when columns are added.
@Injectable()
export class ProductsRepository {
async find(query: ProductQueryDto) {
const sql = `
SELECT *
FROM products
WHERE category = '${query.category}'
ORDER BY ${query.sort}
LIMIT ${query.limit}
`;
return (await this.db.query(sql)).rows;
}
}The template literal mixes SQL grammar with untrusted values. The driver receives one finished string and cannot distinguish data from operators, quotes, or identifiers.
const SORT_COLUMNS = {
price: 'price',
name: 'name',
newest: 'created_at',
} as const;
@Injectable()
export class ProductsRepository {
async find(query: ProductQueryDto) {
const sortColumn =
SORT_COLUMNS[query.sort] ?? SORT_COLUMNS.newest;
const limit = Math.min(query.limit ?? 20, 100);
const result = await this.db.query(
`SELECT id, name, category, price, stock
FROM products
WHERE category = $1
ORDER BY ${sortColumn} DESC, id DESC
LIMIT $2`,
[query.category, limit],
);
return result.rows;
}
}Category and limit travel as protocol parameters. A column name cannot use $1, so it is selected only from a local allowlist. The explicit SELECT list fixes result shape.
What the unfamiliar calls from both code samples actually do.
SELECT id, name, ...- An explicit SELECT list determines columns and the shape of every object in result.rows.
template literal ${value}- JavaScript substitutes text before PostgreSQL sees it, turning untrusted input into SQL grammar.
$1 / $2- Protocol placeholders for values, bound by node-postgres to elements of a separate values array.
SORT_COLUMNS allowlist- A local mapping from allowed API values to real column identifiers; arbitrary input never enters SQL.
Math.min(limit, 100)- Applies an upper response-size bound even when a DTO contains a very large positive number.
result.rows- The row array returned by pg; object keys follow names or aliases from the SELECT list.
Common misconceptions
The myth is on the left; the accurate model is on the right.
SQL executes literally from top to bottom.
The parser builds a statement, clauses have a logical order, and the planner chooses a physical plan.
SELECT * is ideal for production APIs.
An explicit column list stabilizes contracts, reduces transfer, and documents dependencies.
String interpolation is safe after manual escaping.
Send values through driver parameters and choose identifiers from a fixed allowlist.
description = NULL finds missing values.
Use IS NULL; NULL is unknown or absent, not a normal value.
DELETE removes one row.
DELETE affects every row matching WHERE, or the whole table when WHERE is absent.
Explain it in your own words
If you can explain the answer without quoting documentation, your mental model is starting to take shape.
- What separate jobs do SELECT, FROM, and WHERE perform?
- Why should $1 not be wrapped in quotes inside SQL?
- How do WHERE and HAVING differ?
- Why should LIMIT normally be paired with ORDER BY?
- What does UPDATE return when WHERE matches no rows?
- Why is NULL tested with IS NULL?
- What happens when DELETE has no WHERE?