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Materialized Views Cheat Sheet

Materialized Views Cheat Sheet

Materialized views across PostgreSQL, ClickHouse, and Snowflake covering creation syntax, refresh strategies, and indexing.

2 PagesIntermediateApr 2, 2026

PostgreSQL Materialized View

Create, index, and refresh a materialized view without blocking reads.

sql
CREATE MATERIALIZED VIEW daily_revenue ASSELECT    date_trunc('day', created_at) AS day,    sum(amount) AS revenue,    count(*) AS order_countFROM ordersGROUP BY 1WITH DATA;-- Required for CONCURRENTLY refresh: a unique indexCREATE UNIQUE INDEX ON daily_revenue (day);-- Refresh without locking out concurrent readsREFRESH MATERIALIZED VIEW CONCURRENTLY daily_revenue;

ClickHouse & TimescaleDB Equivalents

Incrementally maintained materialized views, rather than full-refresh snapshots.

sql
-- ClickHouse: incrementally populated on every insert to the source tableCREATE MATERIALIZED VIEW daily_revenue_mvENGINE = SummingMergeTreeORDER BY day ASSELECT toDate(created_at) AS day, sum(amount) AS revenueFROM ordersGROUP BY day;-- TimescaleDB: continuous aggregate, refreshed on a scheduleCREATE MATERIALIZED VIEW daily_revenueWITH (timescaledb.continuous) ASSELECT time_bucket('1 day', created_at) AS day, sum(amount) AS revenueFROM ordersGROUP BY day;

Scheduling Refreshes (pg_cron)

Keep a Postgres materialized view fresh on a schedule.

sql
CREATE EXTENSION IF NOT EXISTS pg_cron;SELECT cron.schedule(    'refresh-daily-revenue',    '*/15 * * * *',    $$REFRESH MATERIALIZED VIEW CONCURRENTLY daily_revenue$$);-- Check scheduled jobsSELECT * FROM cron.job;

Materialized vs. Regular Views — Tradeoffs

When to reach for a materialized view instead of a plain view or app-level cache.

  • Regular view- just a stored query, always live, zero storage, but re-executes the full query every time
  • Materialized view- stores results on disk; fast reads, but data is stale until refreshed
  • REFRESH ... CONCURRENTLY- Postgres-only, avoids locking readers out during refresh, requires a unique index
  • Incremental MVs (ClickHouse, Timescale)- update as new rows arrive instead of full recompute, ideal for append-only event data
  • Staleness budget- decide upfront how stale is acceptable; drives your refresh interval/schedule

Indexing a Materialized View

A materialized view is a real relation on disk and accepts the same indexing toolkit as a table.

sql
CREATE INDEX idx_daily_revenue_day ON daily_revenue (day);-- Partial index for a common dashboard filterCREATE INDEX idx_daily_revenue_high_value ON daily_revenue (day)    WHERE revenue > 10000;-- Covering index avoids a heap fetch entirely for this query shapeCREATE INDEX idx_daily_revenue_covering    ON daily_revenue (day) INCLUDE (revenue, order_count);-- Stats go stale after every REFRESH; re-analyze so the planner stays accurateANALYZE daily_revenue;

Chained & Dependent Materialized Views

Layered materialized views must be refreshed in dependency order, leaf to root.

sql
CREATE MATERIALIZED VIEW monthly_revenue ASSELECT date_trunc('month', day) AS month, sum(revenue) AS revenueFROM daily_revenueGROUP BY 1;-- Inspect what depends on daily_revenue before scripting a refresh chainSELECT dependent_ns.nspname, dependent_view.relnameFROM pg_dependJOIN pg_rewrite ON pg_depend.objid = pg_rewrite.oidJOIN pg_class dependent_view ON pg_rewrite.ev_class = dependent_view.oidJOIN pg_class source_table ON pg_depend.refobjid = source_table.oidJOIN pg_namespace dependent_ns ON dependent_ns.oid = dependent_view.relnamespaceWHERE source_table.relname = 'daily_revenue';-- Refresh leaf-to-root or monthly_revenue reads stale dataREFRESH MATERIALIZED VIEW CONCURRENTLY daily_revenue;REFRESH MATERIALIZED VIEW CONCURRENTLY monthly_revenue;

Tracking Refresh Staleness

Postgres doesn't record a materialized view's last-refresh timestamp natively, so log it yourself.

sql
CREATE TABLE mv_refresh_log (    mv_name TEXT PRIMARY KEY,    last_refreshed TIMESTAMPTZ);CREATE OR REPLACE FUNCTION refresh_and_log(mv TEXT) RETURNS void AS $$BEGIN    EXECUTE format('REFRESH MATERIALIZED VIEW CONCURRENTLY %I', mv);    INSERT INTO mv_refresh_log (mv_name, last_refreshed)    VALUES (mv, now())    ON CONFLICT (mv_name) DO UPDATE SET last_refreshed = now();END;$$ LANGUAGE plpgsql;SELECT refresh_and_log('daily_revenue');SELECT mv_name, now() - last_refreshed AS staleness FROM mv_refresh_log;

True Incremental Maintenance with pg_ivm

The pg_ivm extension updates a view's rows on every write instead of recomputing the whole query on a schedule.

sql
CREATE EXTENSION IF NOT EXISTS pg_ivm;SELECT create_immv('daily_revenue_ivm',    'SELECT date_trunc(''day'', created_at) AS day, sum(amount) AS revenue     FROM orders GROUP BY 1');-- No REFRESH call needed — the view updates inside the same transaction-- as the write to ordersINSERT INTO orders (created_at, amount) VALUES (now(), 42.00);SELECT * FROM daily_revenue_ivm WHERE day = date_trunc('day', now());

Refresh Strategy Tradeoffs, Beyond Basic Scheduling

Choosing between eager, lazy, and incremental maintenance once REFRESH ... CONCURRENTLY alone isn't enough.

  • Eager (synchronous)- the view updates inside the write transaction; always consistent, but adds latency to every write (pg_ivm, Oracle ON COMMIT)
  • Lazy (scheduled)- refresh runs on a timer or cron job; cheap writes, but reads see a bounded staleness window
  • Incremental view maintenance- only the rows affected by a change are recomputed, not the entire query (pg_ivm, ClickHouse MVs, TimescaleDB continuous aggregates)
  • Full refresh- recomputes the entire result set from scratch on every run; simplest to reason about but the most expensive at scale
  • Dependency ordering- multi-level materialized views must refresh leaf-to-root, or a downstream view reads a stale upstream snapshot
  • Swap-and-rename pattern- build a new table under a temp name and atomically rename it into place, giving zero-downtime refreshes on engines without a CONCURRENTLY option
Pro Tip

In PostgreSQL, `REFRESH MATERIALIZED VIEW` without `CONCURRENTLY` takes an ACCESS EXCLUSIVE lock and blocks all reads for the duration — always create the required unique index up front and use CONCURRENTLY in production, even though it's slower per refresh.

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