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00/08 · ~40 min
GUIDEDECK · for the data behind every app

Databases & SQL
the model, the queries,
and why they go slow.

A 40-minute working session on relational databases — from why we model data as tables, through the SQL you write every day, transactions and ACID, indexes and query plans, which engine to pick, and how to design a schema that won't fight you later.

~40 MINMIXED TEAMSQL · VENDOR-NEUTRAL
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01 · Why relational 4 min

A database is the one place
your data has to stay true.

Code gets rewritten constantly; the data outlives all of it. The relational model wins because it makes one promise nothing else does: a single, trustworthy copy of your data that many programs can read and write at the same time without stepping on each other. Think of a bank's ledger — every teller and ATM works against the same balances, and nobody is allowed to leave it half-updated.

Relational database — data kept in tables of rows and columns, where related tables are linked together (the word "relational" comes from those links). It follows a fixed schema — the agreed list of tables, their columns, and the type of value each column holds — and you ask questions of it in SQL. The engine itself (Postgres, MySQL, SQLite) enforces the rules — correct types, no duplicates, valid links — so every program that connects sees the same consistent picture.

What the engine does for you

  • Persistence — survives restarts, crashes and power loss.
  • Concurrency — hundreds of users read and write at once without corrupting each other.
  • Integrity — constraints reject bad data before it lands, not after.
  • Declarative queries — you say what you want; the engine figures out how to fetch it.
Web app Mobile API Analytics job DATABASE one source of truth

Many programs, one shared truth — the database, not any one app, owns the data.

Why not just keep it in the app?

Data living inside code / files
// a file each service writes to its own way [ { "id": 1, "total": "42.0", "user": "Mara" }, { "id": 1, "total": "oops" } // dup id, bad type ] // no rules · two writers race · who is "Mara"?
Data in a relational engine
CREATE TABLE orders ( id BIGINT PRIMARY KEY, total NUMERIC(10,2) NOT NULL, user_id BIGINT REFERENCES users(id) ); // types, uniqueness & references enforced for every writer

The schema turns "please be careful" into rules the engine cannot let you break.

02 · Tables, keys & relationships 6 min

Rows hold facts; keys connect
them without copying data.

A good schema stores every fact once and links facts together with keys (a key is just a column whose job is to identify or point at a row). Get the keys right and the model explains itself — you can see how everything connects just by reading the tables, because the links live in the data instead of only in your head.

Primary key — the column that uniquely identifies a row. A foreign keyis a column that points at another table's primary key. Together they let one row reference another instead of duplicating its data — and the engine guarantees the reference is always valid.
Primary key

One row, one identity

Unique and never null. Prefer a surrogate key (an auto id) over a natural one like email — natural values change, ids shouldn't.

Foreign key

A pointer with a guarantee

order.user_id → users.id. The engine refusesan order for a user that doesn't exist — no orphan rows.

Constraints

Rules at the data layer

NOT NULL, UNIQUE, CHECK, DEFAULT — invariants enforced for every writer, not just the careful ones.

The three relationship shapes

  • One-to-many — a user has many orders. The foreign key lives on the many side (orders.user_id).
  • One-to-one — a user has one profile. Foreign key plus a UNIQUE constraint.
  • Many-to-many — orders contain many products; products appear in many orders. Needs a junction table.

Like a library: one borrower card links to many loans; each loan points back to one book and one borrower.

users id PK name email orders id PK user_id FK placed_at order_items order_id FK product_id FK products id PK price 1—∞

order_items is the junction table: it turns orders↔products (many-to-many) into two clean one-to-many links.

CREATE TABLE users ( id BIGSERIAL PRIMARY KEY, email TEXT UNIQUE NOT NULL ); CREATE TABLE products ( id BIGSERIAL PRIMARY KEY, price NUMERIC(10,2) CHECK (price >= 0) ); CREATE TABLE orders ( id BIGSERIAL PRIMARY KEY, user_id BIGINT REFERENCES users(id) ); CREATE TABLE order_items ( order_id BIGINT REFERENCES orders(id), product_id BIGINT REFERENCES products(id), qty INT NOT NULL CHECK (qty > 0), PRIMARY KEY (order_id, product_id) // composite key );
03 · The SQL you write 7 min

SELECT what, WHERE filtered,
JOIN related, GROUP to summarize.

SQL is declarative: you describe the result set you want, not the loops to build it. Four clauses cover the vast majority of reads — and they run in an order that surprises most newcomers.

SQL — Structured Query Language — the standard language for relational data. A SELECT reads rows; WHERE filters them; JOIN combines tables by matching keys; GROUP BY collapses many rows into per-group summaries.

Written order ≠ run order

You write SELECT … FROM … WHERE … GROUP BY …, but the engine evaluates FROM first and SELECT nearly last. That's why you can't use a SELECT alias inside WHERE— it doesn't exist yet.

1 · FROM / JOIN 2 · WHERE 3 · GROUP BY 4 · HAVING 5 · SELECT build the row source, filter rows, fold into groups, filter groups, THEN shape columns.

The logical evaluation order — internalize it and confusing errors start to make sense.

Read and filter the rows you want

SELECT id, email, created_at FROM users WHERE country = 'PT' AND created_at >= '2026-01-01' ORDER BY created_at DESC LIMIT 50; // never SELECT * for big reads
Mental model
Take the table, keep rows matching WHERE, sort, then cut to a page.
Watch for
NULL isn't equal to anything — use IS NULL, never = NULL.

Combine tables by matching keys

SELECT u.name, o.id, o.placed_at FROM users u JOIN orders o ON o.user_id = u.id // inner: matches only WHERE o.placed_at >= '2026-06-01'; // LEFT JOIN keeps every user, NULLs where no order exists
INNER vs LEFT
INNER = rows present in both. LEFT = all of the left, padded with NULL.
Watch for
A missing or wrong ON turns a join into a Cartesian product — rows explode.

Collapse rows into per-group answers

SELECT u.country, COUNT(*) AS orders, SUM(o.total) AS revenue FROM orders o JOIN users u ON u.id = o.user_id GROUP BY u.country HAVING SUM(o.total) > 10000; // filter the GROUPS
WHERE vs HAVING
WHERE filters rows before grouping; HAVING filters groups after.
Rule
Every non-aggregated SELECT column must appear in GROUP BY.

Name a step, then build on it

WITH big_spenders AS ( SELECT user_id, SUM(total) AS spent FROM orders GROUP BY user_id HAVING SUM(total) > 5000 ) SELECT u.email, b.spent FROM big_spenders b JOIN users u ON u.id = b.user_id;
CTE
A WITH block is a named, readable subquery — chain them instead of nesting five deep.
It's really
Refactoring for SQL: small named steps beat one unreadable monster query.
04 · Writing data · transactions · ACID 6 min

A transaction turns many writes
into one all-or-nothing step.

Reads are forgiving; writes are where data gets corrupted. The fix is the transaction — a boundary that says "all of these succeed, or none of them do". ACID is the four guarantees it gives you.

Transaction — a group of statements committed as a single unit. Wrap related writes in BEGIN … COMMIT; if anything fails, ROLLBACK undoes the whole batch as if it never ran.
A
Atomicity
all writes or none
C
Consistency
constraints always hold
I
Isolation
concurrent txns don't collide
D
Durability
committed = survives a crash

The classic example: moving money

Two separate writes
UPDATE accounts SET bal = bal - 100 WHERE id = 1; // ── crash here ── UPDATE accounts SET bal = bal + 100 WHERE id = 2; // $100 vanished. account 1 charged, account 2 never paid.
One atomic transaction
BEGIN; UPDATE accounts SET bal = bal - 100 WHERE id = 1; UPDATE accounts SET bal = bal + 100 WHERE id = 2; COMMIT; // crash before COMMIT → both roll back
BEGIN UPDATE account 1 UPDATE account 2 COMMIT ✓ both ROLLBACK ✕ none

Atomicity in one picture: the transaction lands as a whole or disappears as a whole.

Isolation, briefly

When transactions run at once, the isolation leveldecides what each one can see of the others' in-flight work. Looser = faster but allows anomalies; stricter = safer but more contention.

  • Read Committed — you only see committed data (the common default).
  • Repeatable Read — re-reading a row gives the same answer within the txn.
  • Serializable — as if transactions ran one at a time. Safest, slowest.

Like editing a shared doc: isolation is how much of someone else's half-finished edit you're allowed to see.

05 · Indexes & query plans 6 min

Slow queries are usually
a full scan that wants an index.

The engine doesn't run your SQL literally — it builds a query plan. Most performance work is reading that plan and giving it a faster path. The single biggest lever is the index.

Index — a sorted lookup structure (usually a B-tree) over one or more columns. It lets the engine jump straight to matching rows instead of reading the whole table — turning an O(n) scan into an O(log n) seek.

Scan vs. seek

  • Sequential scan — read every row, throw most away. Fine for tiny tables; brutal on millions.
  • Index seek — walk a sorted tree to the rows you want, touching a handful of pages.
  • Index the columns you WHERE, JOIN and ORDER BY on — not every column.
SEQ SCAN reads all rows INDEX SEEK root < key ≥ key row ✓ few hops to the row

Left: the engine reads everything. Right: the B-tree walks a few nodes straight to the match.

Read the plan, then fix it

No index — Seq Scan
EXPLAIN ANALYZE SELECT * FROM orders WHERE user_id = 42; Seq Scan on orders (rows=2,000,000) Filter: user_id = 42 actual time: 480 ms // reads every row
With index — Index Scan
CREATE INDEX ON orders (user_id); EXPLAIN ANALYZE SELECT * FROM orders WHERE user_id = 42; Index Scan using orders_user_id_idx actual time: 0.3 ms // ~1500x faster
  • Every index must be updated on every write — more indexes = slower INSERT/UPDATE.
  • Indexes take disk and memory; unused ones are pure cost.
  • Order matters in composite indexes: (user_id, placed_at) helps a filter on user_id alone, but not on placed_at alone.
  • Index for your real query patterns — measure with EXPLAIN, don't guess.
06 · Normalization vs denormalization 4 min

Store each fact once —
then break the rule on purpose.

Normalize by default: one fact, one place, no contradictions. Denormalize deliberately — and only when a measured read problem justifies the duplication you take on.

Normalization — structuring tables so each fact is stored exactly once, removing redundancy. Denormalization is the deliberate reverse: copying or pre-joining data to make reads faster, accepting that you must now keep the copies in sync.
Denormalized by accident
// user details copied into every order row orders(id, user_name, user_email, user_city, total) // Mara changes her email → update 4,000 rows. // miss one → the same user has two emails. truth forks.
Normalized — one home per fact
users(id, name, email, city) // the fact lives here orders(id, user_id, total) // just a reference // email changes in ONE place. every order sees it.
Normalize when

Writes & truth matter

Transactional systems — orders, accounts, inventory. Correctness and cheap updates beat raw read speed. This is the default.

Denormalize when

Reads dominate & hurt

A hot query joins five tables on every page load. Pre-compute or cache the shape — afteryou've proven the join is the bottleneck.

The cost

You own consistency now

Every duplicated value is a future bug waiting for an update to miss a copy. Denormalization trades write-pain for read-speed — never for free.

Rule of thumb: normalize until it hurts, then denormalize until it works — with numbers, not vibes.

07 · The tooling landscape 4 min

Which engine should
you actually reach for?

Almost everything so far is vendor-neutral — it works the same across the popular databases. The main fork in the road is relational vs document: do you split data into linked tables, or store each record as one self-contained blob? Here are the leading engines, what each is good and bad at, and a rule for picking.

Relational vs document — a relational database splits data into linked tables with a fixed schema (PostgreSQL, MySQL, SQLite). A document database stores each record as one self-contained, JSON-like document with no fixed shape (MongoDB). Relational keeps the relationships and rules for you; document hands you flexibility but makes your application responsible for keeping things consistent.

The two shapes, side by side

  • Relational — a user lives in users; their orders live in orders and point back with a key. Nothing is duplicated, and a join stitches them together when you need them.
  • Document— the user and their orders are nested inside one document, so a single read returns the whole thing — but the same user's data can drift out of sync if it's copied around.
  • Neither is "better". Relational fits data full of relationships; document fits self-contained records you mostly read whole.
RELATIONAL · linked tables users id PK name orders id PK user_id FK total link DOCUMENT · one nested record { name: "Mara", orders: [ { id: 1, total: 42 }, { id: 2, total: 19 } ] } read it all in one hit

Left: facts kept once and linked by a key. Right: the same facts nested inside a single document.

The engines worth knowing

PostgreSQL

The capable default

Pro — extremely feature-rich and strict about correctness; handles JSON, full-text search and more without add-ons.
Con — more features means more to learn, and the out-of-the-box settings are tuned cautiously.

MySQL / MariaDB

Everywhere & simple

Pro — runs on practically every host, is easy to get started with, and is fast for simple read-heavy sites.
Con — historically looser on correctness and has a thinner feature set than PostgreSQL.

MongoDB · document

Flexible JSON store

Pro — store nested, JSON-shaped records with no fixed schema; quick to start when your data is still changing shape.
Con — no real joins and no engine guarding your relationships, so your app now owns consistency.

SQLite

A database in a file

Pro — zero setup, runs right inside your app, and is perfect for local apps, prototypes and tests.
Con— only one writer at a time, so it's the wrong tool for a busy multi-user server.

How to choose: reach for PostgreSQL for almost any app with real relationships; pick MySQL if your platform already standardizes on it; choose MongoDB only when your data is genuinely document-shaped and rarely joined; and use SQLite for local apps, prototypes and test suites.

When the job is analytics — OLAP & columnar

Everything above is OLTP — online transaction processing: lots of small reads and writes, a row at a time (running the app). Analytics is the opposite — OLAP, online analytical processing: scanning and aggregating millions to billionsof rows over a few columns ("revenue by country last quarter"). For that you want a columnar database that stores each column together and reads only the ones a query actually touches.
ROW STORE · OLTP id · name · total id · name · total id · name · total grab one whole row fast COLUMN STORE · OLAP id name total sum ONE column over billions

Row stores keep a record together (great for "get this order"); column stores keep each field together, so an aggregate reads only the column it needs.

The analytics engines

  • ClickHouse — blazing-fast open-source columnar OLAP; ingests huge event/log streams and aggregates them in milliseconds. The go-to for self-hosted, real-time analytics.
  • Cloud warehouses — BigQuery, Snowflake, Redshift: fully managed, separate storage from compute, scale to petabytes.
  • DuckDB— "SQLite for analytics": an in-process columnar engine for crunching data files locally.
  • Vector databases — pgvector, Qdrant, Pinecone, Weaviate, Chroma: store embeddings(numeric meaning) for semantic / similarity search — the backbone of AI retrieval (see the RAG & Vector Search deck).

Rule of thumb:OLTP (Postgres / MySQL) runs the app; OLAP (ClickHouse or a warehouse) answers questions about it. Teams often run both — feeding the warehouse from the app database (that's the ETL/ELT deck).

08 · Modeling a real schema · recap 3 min

From a spreadsheet smell
to a schema that scales.

Put it together: take a flat "everything in one table" design and turn it into keys, relationships and constraints — then walk out with the rules.

One wide table — the spreadsheet trap
CREATE TABLE signups ( email TEXT, // no key — dups allowed course TEXT, // "SQL", "sql ", "Sql" teacher TEXT, // repeated on every row price TEXT // "42", "free", "$42" ); // rename a course → hunt every row. no integrity at all.
Modeled — keys, refs, types, checks
CREATE TABLE students ( id BIGSERIAL PRIMARY KEY, email TEXT UNIQUE ); CREATE TABLE courses ( id BIGSERIAL PRIMARY KEY, name TEXT, price NUMERIC(8,2) ); CREATE TABLE enrollments ( student_id BIGINT REFERENCES students(id), course_id BIGINT REFERENCES courses(id), PRIMARY KEY (student_id, course_id) // no double-enroll );

Five rules to walk out with

1The schema is the contract. Push rules — keys, types, NOT NULL, CHECK — into the engine, not just the app.
2One fact, one place. Normalize by default; let foreign keys do the linking.
3Wrap related writes in a transaction. All-or-nothing is the difference between a bug and lost money.
4Slow query? Read the plan. EXPLAIN first; index the columns you filter and join on.
5Denormalize with numbers, not vibes. Trade write-pain for read-speed only when a measurement demands it.

Keep going

  • Use The Index, Luke! — Markus Winand (indexing, free online)
  • Designing Data-Intensive Applications — Martin Kleppmann
  • SQL Performance Explained — Markus Winand
  • Your engine's docs on EXPLAIN — Postgres and MySQL both have excellent ones

One sentence to remember

"Model the truth once, let the engine guard it, and ask it questions in SQL."

— the whole talk, compressed

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