improve discoverability of materialized views (#206)

Author: jerrinot

documentation/operations/design-for-performance.md

@@ -20,6 +20,38 @@ considerations.
 The following section describes the underlying aspects to consider when
 formulating queries.
 
+### Materialized views
+
+[Materialized views](/docs/guides/mat-views/) are a powerful feature for optimizing query performance, especially for
+complex aggregate queries. They pre-compute and store query results, providing significant performance improvements for
+frequently executed queries.
+
+Consider using materialized views when:
+
+- You have complex aggregate queries with `SAMPLE BY`, `GROUP BY`, or window functions that are executed frequently
+- Query latency is critical and you can accept slightly stale data
+- You need to optimize dashboard queries or reporting workloads
+
+Example of creating a materialized view for a common aggregate query:
+
+```questdb-sql
+CREATE MATERIALIZED VIEW hourly_metrics AS
+SELECT
+    timestamp_floor('h', timestamp) as hour,
+    symbol,
+    avg(price) as avg_price,
+    sum(volume) as total_volume,
+    count() as trade_count
+FROM trades
+SAMPLE BY 1h;
+```
+
+This view automatically updates as new rows are added to the `trades` table. Queries to `hourly_metrics` are fast because
+they access these pre-computed results instead of scanning the full trades table.
+
+For more details on implementing and managing materialized views, see
+the [materialized views guide](/docs/guides/mat-views/).
+
 ### Row serialization
 
 Row serialization and deserialization has a cost on both client and server. The

documentation/reference/sql/overview.md

@@ -428,6 +428,8 @@ And to learn about some of our favourite, most powerful syntax:
   multiple series within a table
 - [`ASOF JOIN`](/docs/reference/sql/asof-join/) to associate timestamps between
   a series based on proximity; no extra indices required
+- [Materialized Views](/docs/guides/mat-views/) to pre-compute complex queries
+  for optimal performance
 
 Looking for visuals?
 

documentation/reference/sql/sample-by.md

@@ -573,10 +573,25 @@ ALIGN TO CALENDAR;
 | 2021-05-31T00:00:00.000000Z | 1000.5 |
 | 2021-06-01T00:00:00.000000Z | 8007.2 |
 
+## Performance optimization
+
+For frequently executed `SAMPLE BY` queries, consider using [materialized views](/docs/guides/mat-views/) to pre-compute aggregates. This can significantly improve query performance, especially for complex sampling operations on large datasets.
+
+```questdb-sql
+CREATE MATERIALIZED VIEW hourly_metrics AS
+SELECT 
+    timestamp_floor('h', ts) as hour,
+    symbol,
+    avg(price) as avg_price,
+    sum(volume) as total_volume
+FROM trades
+SAMPLE BY 1h;
+```
+
 ## See also
 
 This section includes links to additional information such as tutorials:
 
+- [Materialized Views Guide](/docs/guides/mat-views/) - Pre-compute SAMPLE BY queries for better performance
 - [SQL Extensions for Time-Series Data in QuestDB](/blog/2022/11/23/sql-extensions-time-series-data-questdb-part-ii/)
-
 - [Three SQL Keywords for Finding Missing Data](/blog/three-sql-keywords-for-finding-missing-data/)

documentation/why-questdb.md

@@ -58,6 +58,8 @@ Greatest hits include:
   multiple series within a table
 - [`ASOF JOIN`](/docs/reference/sql/asof-join/) to associate timestamps between
   a series based on proximity; no extra indices required
+- [Materialized Views](/docs/guides/mat-views/) for pre-computing and automatically
+  refreshing complex query results to optimize performance
 
 ## Benefits of QuestDB {#benefits}