bennychow commented on code in PR #11041:
URL: https://github.com/apache/iceberg/pull/11041#discussion_r3320636746
##########
format/view-spec.md:
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@@ -322,3 +449,215 @@
s3://bucket/warehouse/default.db/event_agg/metadata/00002-(uuid).metadata.json
} ]
}
```
+
+### Materialized View Example
+
+Imagine the following operation, which creates a materialized view that
precomputes daily event counts:
+
+```sql
+USE prod.default
+```
+```sql
+CREATE MATERIALIZED VIEW event_agg_mv (
+ event_count COMMENT 'Count of events',
+ event_date)
+COMMENT 'Precomputed daily event counts'
+AS
+SELECT
+ COUNT(1), CAST(event_ts AS DATE)
+FROM events
+GROUP BY 2
+```
+
+The materialized view metadata JSON file looks as follows:
+
+```
+s3://bucket/warehouse/default.db/event_agg_mv/metadata/00001-(uuid).metadata.json
+```
+```json
+{
+ "view-uuid": "b2a12651-3038-4a72-8a31-5027ab84da35",
+ "format-version" : 1,
+ "location" : "s3://bucket/warehouse/default.db/event_agg_mv",
+ "current-version-id" : 1,
+ "properties" : {
+ "comment" : "Precomputed daily event counts"
+ },
+ "versions" : [ {
+ "version-id" : 1,
+ "timestamp-ms" : 1573518431292,
+ "schema-id" : 1,
+ "default-catalog" : "prod",
+ "default-namespace" : [ "default" ],
+ "summary" : {
+ "engine-name" : "Spark",
+ "engine-version" : "3.4.1"
+ },
+ "representations" : [ {
+ "type" : "sql",
+ "sql" : "SELECT\n COUNT(1), CAST(event_ts AS DATE)\nFROM
events\nGROUP BY 2",
+ "dialect" : "spark"
+ } ],
+ "storage-table" : {
+ "namespace" : [ "default" ],
+ "name" : "event_agg_mv__storage"
+ }
+ } ],
+ "schemas": [ {
+ "schema-id": 1,
+ "type" : "struct",
+ "fields" : [ {
+ "id" : 1,
+ "name" : "event_count",
+ "required" : false,
+ "type" : "int",
+ "doc" : "Count of events"
+ }, {
+ "id" : 2,
+ "name" : "event_date",
+ "required" : false,
+ "type" : "date"
+ } ]
+ } ],
+ "version-log" : [ {
+ "timestamp-ms" : 1573518431292,
+ "version-id" : 1
+ } ]
+}
+```
+
+After a refresh operation, the storage table's snapshot summary contains the
`refresh-state` property.
+The following is an example of the `refresh-state` JSON value stored in the
snapshot summary of the storage table:
+
+```json
+{
+ "view-version-id" : 1,
+ "refresh-start-timestamp-ms" : 1573518435000,
+ "source-states" : [ {
+ "type" : "table",
+ "namespace" : [ "default" ],
+ "name" : "events",
+ "uuid" : "d4a10b5c-1e8a-4b72-9d67-3f4a8c9e1b2d",
+ "snapshot-id" : 6148331192489823102
+ } ]
+}
+```
+
+## Appendix B: Example strategies for selecting dependencies
+
+Producers may select different sets of dependencies to record in the refresh
state. The strategies below illustrate common choices against the same shared
query.
+
+### Shared query
+
+- `A` (the materialized view being refreshed): `SELECT ... FROM B JOIN C ON
...`
+- `B` (regular view): `SELECT ... FROM E JOIN D ON ...`
+- `C` (regular view or materialized view, varies by strategy): `SELECT ...
FROM F JOIN G ON ...`
+- `D` (regular view or materialized view, varies by strategy): `SELECT ...
FROM H WHERE ...`
+- `E`, `F`, `G`, `H`: base Iceberg tables
+
+### Strategy 1: Track all nested tables and views (no nested MVs)
+
+The view query reads only base tables and regular views. The refresh state
tracks snapshot IDs of all deeply nested base tables and version IDs of all
views traversed. Reuse of the storage table is sensitive to changes in any of
them.
+
+`C` and `D` are regular views.
+
+```
+A [MV — being refreshed]
+├── B [VIEW] <-- recorded in A: version-id: 5
+│ ├── E [TABLE] <-- recorded in A: snapshot-id: 101
+│ └── D [VIEW] <-- recorded in A: version-id: 9
+│ └── H [TABLE] <-- recorded in A: snapshot-id: 104
+└── C [VIEW] <-- recorded in A: version-id: 7
+ ├── F [TABLE] <-- recorded in A: snapshot-id: 102
+ └── G [TABLE] <-- recorded in A: snapshot-id: 103
+```
+
+### Strategy 2: Treat nested materialized views as tables
+
+Same as Strategy 1, but the query reads from materialized views. The producer
stops at each MV boundary and records the MV's storage table snapshot ID. No
expansion beyond the MV.
+
+`C` and `D` are materialized views, treated as tables.
+
+```
+A [MV — being refreshed]
+├── B [VIEW] <-- recorded in A: version-id: 5
+│ ├── E [TABLE] <-- recorded in A: snapshot-id: 101
+│ └── D [MV] <-- recorded in A: storage-table
snapshot-id: 14
+│ ┄┄┄┄┄┄ recursive boundary ┄┄┄┄┄┄
+│ └── H [TABLE] (D's dependency; verified via D's
refresh-state)
+└── C [MV] <-- recorded in A: storage-table
snapshot-id: 12
+ ┄┄┄┄┄┄ recursive boundary ┄┄┄┄┄┄
+ ├── F [TABLE] (C's dependency; verified via C's
refresh-state)
+ └── G [TABLE] (C's dependency; verified via C's
refresh-state)
+```
+
+`F`, `G`, and `H` do not appear in `A`'s `source-states`; they belong to `C`
and `D`'s dependency graphs.
+
+### Strategy 3: Treat nested materialized views as views
+
+Same as Strategy 1, but the query reads from materialized views. The producer
treats each materialized view as a regular view: expand through the MV's view
definition and record the underlying tables and views. The MV's storage table
snapshot ID is **not** recorded.
+
+`C` and `D` are materialized views, treated as views (expanded).
+
+```
+A [MV — being refreshed]
+├── B [VIEW] <-- recorded in A: version-id: 5
+│ ├── E [TABLE] <-- recorded in A: snapshot-id: 101
+│ └── D [MV — expanded as view] <-- recorded in A: version-id: 9
+│ └── H [TABLE] <-- recorded in A: snapshot-id: 104
+└── C [MV — expanded as view] <-- recorded in A: version-id: 7
Review Comment:
This example would be more interesting if the storage table for C was stale
so that this example didn't have the same refresh state as Strategy 1.
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