Materialized view
A materialized view is a persistently stored result of a defining query that can be read without recomputing it and is refreshed or maintained from underlying data.
ScyllaDB in Action
A materialized view is a stored, automatically maintained representation of base-table data organized according to a different key or query access path.
- How it is used
- Enables new query access paths while reducing manual synchronization of denormalized tables.
- How deep it goes
- Explains view creation, key restrictions, replication behavior, and partition-key update costs without detailing internal storage mechanics.
- Examples
- Uses article summaries indexed by date and score, including CREATE MATERIALIZED VIEW, INSERT, and SELECT commands.
- What you can do after
- Apply it via ScyllaDB CQL to create and query automatically maintained denormalized access paths.
MongoDB 8.0 in Action, Third Edition
A materialized view is a persistently stored result of a query or transformation that can be refreshed as source data changes.
- How it is used
- Uses stored aggregation results to improve repeated reads when recalculating a standard view would be costly.
- How deep it goes
- Defines disk-stored results and manual refresh, but defers the details of $merge and $out.
- Examples
- Contrasts stored results with a routes view that uses $match to filter by airline ID; gives no materialized-view command.
- What you can do after
- Operate/configure it by rerunning the aggregation pipeline when changes to source data must appear in stored results.
Read how MongoDB 8.0 in Action, Third Edition explains materialized view →
Kafka for Architects
A materialized view is a stored, continuously maintained representation of query results that can be read without recomputing the underlying query.
- How it is used
- Provides immediate current-state reads for streaming applications without recomputing or rescanning query inputs.
- How deep it goes
- Explains local storage, indexing, RocksDB backing, and Kafka replication, but not view-maintenance internals.
- Examples
- Demonstrates a maintained order-count table grouped by country.
- What you can do after
- Evaluate current-state query trade-offs between indexed views and direct topic scans.
Snowflake Data Engineering
A materialized view is a database object that stores the results of a query and maintains them for subsequent access.
- How it is used
- Speeds queries over external tables by persisting their results in Snowflake for downstream consumers.
- How deep it goes
- Explains persistence, automatic maintenance, refresh dependencies, and cost trade-offs, but does not detail internal maintenance algorithms.
- Examples
- Uses the ORDERS_BISTRO_EXT external table and ORDERS_BISTRO_MV materialized view with executable creation and SELECT statements.
- What you can do after
- Operate/configure a Snowflake materialized view over an external table and assess refresh behavior, performance benefits, edition requirements, and costs.
Read how Snowflake Data Engineering explains materialized view →
Just Use Postgres!
A materialized view is a query-defined relation whose result is persisted and remains unchanged until explicitly refreshed.
- How it is used
- Caches expensive report queries when periodically refreshed results are acceptable.
- How deep it goes
- Explains persistence, query behavior, creation, querying, stale results, and explicit refreshing through a complete sales example.
- Examples
- Uses an e-commerce monthly revenue and order-count report that changes only after a refresh.
- What you can do after
- Apply it via SQL to create, query, and refresh a persisted aggregate report.
Related concepts
- continuous aggregateincludes · A continuous aggregate is a TimescaleDB query-result object that stores precomputed aggregate data and refreshes it incrementally according to a defined policy.
- databasepart of · A database is an organized collection of persistent data managed by software for storage, retrieval, querying, and modification.
- indexingincludes · Indexing is the organization or use of positions, keys, or lookup structures to identify and retrieve selected data or elements.
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