SQL View vs Table: 6 Key Differences

A table stores data; a view stores a query. That single sentence explains almost every practical difference between the two — storage, freshness, performance, and what you're allowed to do with each. Yet "should this be a view or a table?" comes up constantly in real projects, so it's worth walking through the six differences that actually matter.

What is the difference between a view and a table?

A table is a physical container: rows live in data pages on disk, and inserting a row writes bytes. A view is a named, stored query — a virtual table. When you select from a view, the database substitutes the view's definition into your query and executes it against the real tables underneath. No rows are stored under the view's name.

-- A table: physically stores rows
CREATE TABLE orders (
    order_id   INT PRIMARY KEY,
    customer_id INT,
    amount     DECIMAL(10,2),
    status     VARCHAR(20)
);

-- A view: physically stores only this query text
CREATE VIEW paid_orders AS
SELECT order_id, customer_id, amount
FROM orders
WHERE status = 'paid';

-- Querying the view re-runs the SELECT above each time
SELECT COUNT(*) FROM paid_orders;

Which is faster, a view or a table?

Reading a table is faster, all else equal, because the work is already done — the rows exist. A plain view adds no storage but re-executes its defining query on every read, so a view over an expensive join or aggregation pays that cost every time. The view itself adds almost no overhead; the cost is whatever its query costs.

-- This view hides a heavy aggregation...
CREATE VIEW daily_revenue AS
SELECT order_date, SUM(amount) AS revenue
FROM orders
GROUP BY order_date;

-- ...so every SELECT pays the full GROUP BY cost:
SELECT * FROM daily_revenue WHERE order_date >= '2026-01-01';

Good news: most optimizers push predicates into the view, so the WHERE clause above is applied before the aggregation, not after. But a view cannot make a slow query fast — it only makes it reusable.

The 6 key differences at a glance

#	Dimension	Table	View
1	Storage	Stores rows on disk	Stores only the query definition
2	Freshness	Only as fresh as your last load	Always current — reflects base tables instantly
3	Read performance	Fast; data is precomputed	Pays the defining query's cost per read
4	Permissions	Grants expose all columns/rows	Can expose a filtered, column-limited slice
5	Indexes	Fully indexable	Not directly indexable (uses base-table indexes)
6	DML (INSERT/UPDATE/DELETE)	Always allowed	Only simple single-table views are updatable

On point 5: SQL Server's indexed views and Oracle's materialized views are the exceptions — they physically store results precisely so they can be indexed, which is why they behave more like tables.

When is a view enough?

Reach for a view when you want to name a query, not copy data. Views shine for hiding join complexity behind a clean interface, enforcing row- or column-level security, and keeping report logic in one place so every dashboard uses the same definition of "active customer".

-- Security use case: analysts see the view, never the table
CREATE VIEW customer_public AS
SELECT customer_id, region, signup_date   -- no email, no SSN
FROM customers
WHERE deleted_at IS NULL;

GRANT SELECT ON customer_public TO analyst_role;

Because the view runs against live tables, there is no sync job to build and no staleness to explain. If the underlying query is cheap — filters and simple joins on indexed columns — a view is almost always the right call. For a deeper tour of view patterns, see database views explained.

When do you need a real table instead?

Create a table (or a snapshot table populated by a scheduled job) when the query behind the view is too expensive to run per-read, when you need indexes on the derived result, or when you need a stable point-in-time copy — month-end reporting numbers should not silently change when someone backfills a base table.

-- Snapshot pattern: precompute once, read many times
CREATE TABLE daily_revenue_snapshot AS
SELECT order_date, SUM(amount) AS revenue
FROM orders
GROUP BY order_date;

CREATE INDEX idx_snapshot_date ON daily_revenue_snapshot (order_date);

What about materialized views?

A materialized view is the middle ground: you define it like a view, but the database stores its result set like a table and lets you refresh it on demand or on a schedule. You get table-like read speed with view-like maintainability — at the price of staleness between refreshes and extra storage.

-- PostgreSQL
CREATE MATERIALIZED VIEW daily_revenue_mv AS
SELECT order_date, SUM(amount) AS revenue
FROM orders
GROUP BY order_date;

-- Reads are fast (stored result), but data ages until you:
REFRESH MATERIALIZED VIEW CONCURRENTLY daily_revenue_mv;

PostgreSQL, Oracle, Snowflake, and BigQuery all support materialized views natively; in MySQL you emulate one with a summary table plus a scheduled job. If your view is slow but your data only needs to be minutes or hours fresh, a materialized view usually beats maintaining a hand-rolled snapshot table.