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‎tests/sqllogictests/suites/mode/standalone/ee/explain_virtual_column.test‎

Lines changed: 94 additions & 96 deletions
Original file line numberDiff line numberDiff line change
@@ -478,108 +478,106 @@ data_aggregation AS (
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)
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SELECT * FROM data_aggregation;
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----
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EvalScalar
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├── output columns: [a.entity_id (#0), a.source_id (#1), event_date (#22), type_code (#26), primary_category (#27), secondary_category (#28)]
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├── expressions: [group_item (#23), group_item (#24), group_item (#25)]
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├── estimated rows: 0.04
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└── AggregateFinal
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├── output columns: [a.entity_id (#0), a.source_id (#1), type_code (#23), primary_category (#24), secondary_category (#25), event_date (#22)]
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├── group by: [entity_id, source_id, type_code, primary_category, secondary_category, event_date]
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├── aggregate functions: []
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Sequence
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├── MaterializedCTE: cte_cse_0
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│ └── TableScan
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│ ├── table: default.test_virtual_db.data_source_a
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│ ├── output columns: [entity_id (#0), source_id (#1), metadata_object['type'] (#5), content_object['category_a'] (#6), content_object['category_b'] (#7), content_object['event_date'] (#8)]
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│ ├── read rows: 1
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│ ├── read size: < 1 KiB
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│ ├── partitions total: 1
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│ ├── partitions scanned: 1
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│ ├── pruning stats: [segments: <range pruning: 1 to 1>, blocks: <range pruning: 1 to 1>]
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│ ├── push downs: [filters: [], limit: NONE]
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│ ├── virtual columns: [content_object['category_a'], content_object['category_b'], content_object['event_date'], metadata_object['type']]
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│ └── estimated rows: 1.00
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└── EvalScalar
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├── output columns: [a.entity_id (#0), a.source_id (#1), event_date (#22), type_code (#26), primary_category (#27), secondary_category (#28)]
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├── expressions: [group_item (#23), group_item (#24), group_item (#25)]
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├── estimated rows: 0.04
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└── AggregatePartial
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└── AggregateFinal
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├── output columns: [a.entity_id (#0), a.source_id (#1), type_code (#23), primary_category (#24), secondary_category (#25), event_date (#22)]
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├── group by: [entity_id, source_id, type_code, primary_category, secondary_category, event_date]
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├── aggregate functions: []
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├── estimated rows: 0.04
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└── EvalScalar
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├── output columns: [a.entity_id (#0), a.source_id (#1), event_date (#22), type_code (#23), primary_category (#24), secondary_category (#25)]
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├── expressions: [if(CAST(is_not_null(CAST(a.metadata_object['type'] (#5) AS String NULL)) AS Boolean NULL), CAST(assume_not_null(CAST(a.metadata_object['type'] (#5) AS String NULL)) AS String NULL), true, 'Unknown', NULL), CAST(a.content_object['category_a'] (#6) AS String NULL), CAST(a.content_object['category_b'] (#7) AS String NULL)]
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└── AggregatePartial
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├── group by: [entity_id, source_id, type_code, primary_category, secondary_category, event_date]
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├── aggregate functions: []
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├── estimated rows: 0.04
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└── HashJoin
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├── output columns: [a.entity_id (#0), a.source_id (#1), a.metadata_object['type'] (#5), a.content_object['category_a'] (#6), a.content_object['category_b'] (#7), event_date (#22)]
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├── join type: INNER
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├── build keys: [p.entity_id (#9), p.source_id (#10), p.event_date (#22)]
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├── probe keys: [a.entity_id (#0), a.source_id (#1), CAST(CAST(CAST(a.content_object['event_date'] (#8) AS Int64 NULL) AS Timestamp NULL) AS Date NULL)]
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├── keys is null equal: [false, false, false]
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├── filters: []
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├── build join filters:
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│ ├── filter id:2, build key:p.entity_id (#9), probe key:a.entity_id (#0), filter type:inlist,min_max
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│ └── filter id:3, build key:p.source_id (#10), probe key:a.source_id (#1), filter type:inlist,min_max
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└── EvalScalar
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├── output columns: [a.entity_id (#0), a.source_id (#1), a.metadata_object['type'] (#5), a.content_object['category_a'] (#6), a.content_object['category_b'] (#7), event_date (#22), type_code (#23), primary_category (#24), secondary_category (#25)]
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├── expressions: [if(CAST(is_not_null(CAST(a.metadata_object['type'] (#5) AS String NULL)) AS Boolean NULL), CAST(assume_not_null(CAST(a.metadata_object['type'] (#5) AS String NULL)) AS String NULL), true, 'Unknown', NULL), CAST(a.content_object['category_a'] (#6) AS String NULL), CAST(a.content_object['category_b'] (#7) AS String NULL)]
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├── estimated rows: 0.04
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├── EvalScalar(Build)
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│ ├── output columns: [a.entity_id (#9), a.source_id (#10), event_date (#22)]
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│ ├── expressions: [group_item (#21)]
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│ ├── estimated rows: 0.20
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│ └── AggregateFinal
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│ ├── output columns: [a.entity_id (#9), a.source_id (#10), event_date (#21)]
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│ ├── group by: [entity_id, source_id, event_date]
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│ ├── aggregate functions: []
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│ ├── estimated rows: 0.20
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│ └── AggregatePartial
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│ ├── group by: [entity_id, source_id, event_date]
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│ ├── aggregate functions: []
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│ ├── estimated rows: 0.20
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│ └── EvalScalar
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│ ├── output columns: [a.entity_id (#9), a.source_id (#10), event_date (#21)]
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│ ├── expressions: [CAST(CAST(CAST(a.content_object['event_date'] (#17) AS Int64 NULL) AS Timestamp NULL) AS Date NULL)]
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│ ├── estimated rows: 0.20
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│ └── HashJoin
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│ ├── output columns: [a.content_object['event_date'] (#17), a.entity_id (#9), a.source_id (#10)]
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│ ├── join type: INNER
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│ ├── build keys: [a.entity_id (#9), a.source_id (#10)]
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│ ├── probe keys: [c.entity_id (#18), c.source_id (#19)]
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│ ├── keys is null equal: [false, false]
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│ ├── filters: []
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│ ├── build join filters:
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│ │ ├── filter id:0, build key:a.entity_id (#9), probe key:c.entity_id (#18), filter type:inlist,min_max
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│ │ └── filter id:1, build key:a.source_id (#10), probe key:c.source_id (#19), filter type:inlist,min_max
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│ ├── estimated rows: 0.20
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│ ├── Filter(Build)
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│ │ ├── output columns: [a.entity_id (#9), a.source_id (#10), a.content_object['event_date'] (#17)]
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│ │ ├── filters: [is_not_null(CAST(CAST(CAST(a.content_object['event_date'] (#17) AS Int64 NULL) AS Timestamp NULL) AS Date NULL))]
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│ │ ├── estimated rows: 0.20
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│ │ └── TableScan
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│ │ ├── table: default.test_virtual_db.data_source_a
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│ │ ├── output columns: [entity_id (#9), source_id (#10), content_object['event_date'] (#17)]
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│ │ ├── read rows: 1
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│ │ ├── read size: < 1 KiB
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│ │ ├── partitions total: 1
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│ │ ├── partitions scanned: 1
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│ │ ├── pruning stats: [segments: <range pruning: 1 to 1>, blocks: <range pruning: 1 to 1>]
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│ │ ├── push downs: [filters: [is_not_null(CAST(CAST(CAST(a.content_object['event_date'] (#17) AS Int64 NULL) AS Timestamp NULL) AS Date NULL))], limit: NONE]
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│ │ ├── virtual columns: [content_object['event_date']]
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│ │ └── estimated rows: 1.00
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│ └── Filter(Probe)
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│ ├── output columns: [c.entity_id (#18), c.source_id (#19)]
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│ ├── filters: [is_true(c.process_mode (#20) = 'standard_mode')]
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│ ├── estimated rows: 1.00
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│ └── TableScan
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│ ├── table: default.test_virtual_db.config_table
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│ ├── output columns: [entity_id (#18), source_id (#19), process_mode (#20)]
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│ ├── read rows: 1
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│ ├── read size: < 1 KiB
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│ ├── partitions total: 1
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│ ├── partitions scanned: 1
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│ ├── pruning stats: [segments: <range pruning: 1 to 1>, blocks: <range pruning: 1 to 1, bloom pruning: 1 to 1>]
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│ ├── push downs: [filters: [is_true(config_table.process_mode (#20) = 'standard_mode')], limit: NONE]
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│ ├── apply join filters: [#0, #1]
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│ └── estimated rows: 1.00
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└── Filter(Probe)
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├── output columns: [a.entity_id (#0), a.source_id (#1), a.metadata_object['type'] (#5), a.content_object['category_a'] (#6), a.content_object['category_b'] (#7), a.content_object['event_date'] (#8)]
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├── filters: [is_not_null(CAST(a.content_object['category_a'] (#6) AS String NULL)), is_not_null(CAST(a.content_object['category_b'] (#7) AS String NULL)), is_not_null(CAST(CAST(CAST(a.content_object['event_date'] (#8) AS Int64 NULL) AS Timestamp NULL) AS Date NULL))]
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├── estimated rows: 0.20
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└── TableScan
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├── table: default.test_virtual_db.data_source_a
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├── output columns: [entity_id (#0), source_id (#1), metadata_object['type'] (#5), content_object['category_a'] (#6), content_object['category_b'] (#7), content_object['event_date'] (#8)]
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├── read rows: 1
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├── read size: < 1 KiB
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├── partitions total: 1
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├── partitions scanned: 1
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├── pruning stats: [segments: <range pruning: 1 to 1>, blocks: <range pruning: 1 to 1>]
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├── push downs: [filters: [and_filters(and_filters(is_not_null(CAST(a.content_object['category_a'] (#6) AS String NULL)), is_not_null(CAST(a.content_object['category_b'] (#7) AS String NULL))), is_not_null(CAST(CAST(CAST(a.content_object['event_date'] (#8) AS Int64 NULL) AS Timestamp NULL) AS Date NULL)))], limit: NONE]
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├── apply join filters: [#2, #3]
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├── virtual columns: [content_object['category_a'], content_object['category_b'], content_object['event_date'], metadata_object['type']]
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└── estimated rows: 1.00
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└── HashJoin
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├── output columns: [a.entity_id (#0), a.source_id (#1), a.metadata_object['type'] (#5), a.content_object['category_a'] (#6), a.content_object['category_b'] (#7), event_date (#22)]
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├── join type: INNER
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├── build keys: [p.entity_id (#9), p.source_id (#10), p.event_date (#22)]
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├── probe keys: [a.entity_id (#0), a.source_id (#1), CAST(CAST(CAST(a.content_object['event_date'] (#8) AS Int64 NULL) AS Timestamp NULL) AS Date NULL)]
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├── keys is null equal: [false, false, false]
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├── filters: []
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├── build join filters:
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│ ├── filter id:2, build key:p.entity_id (#9), probe key:a.entity_id (#0), filter type:inlist,min_max
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│ └── filter id:3, build key:p.source_id (#10), probe key:a.source_id (#1), filter type:inlist,min_max
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├── estimated rows: 0.04
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├── EvalScalar(Build)
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│ ├── output columns: [a.entity_id (#9), a.source_id (#10), event_date (#22)]
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│ ├── expressions: [group_item (#21)]
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│ ├── estimated rows: 0.20
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│ └── AggregateFinal
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│ ├── output columns: [a.entity_id (#9), a.source_id (#10), event_date (#21)]
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│ ├── group by: [entity_id, source_id, event_date]
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│ ├── aggregate functions: []
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│ ├── estimated rows: 0.20
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│ └── AggregatePartial
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│ ├── group by: [entity_id, source_id, event_date]
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│ ├── aggregate functions: []
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│ ├── estimated rows: 0.20
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│ └── EvalScalar
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│ ├── output columns: [a.content_object['event_date'] (#17), a.entity_id (#9), a.source_id (#10), event_date (#21)]
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│ ├── expressions: [CAST(CAST(CAST(a.content_object['event_date'] (#17) AS Int64 NULL) AS Timestamp NULL) AS Date NULL)]
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│ ├── estimated rows: 0.20
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│ └── HashJoin
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│ ├── output columns: [a.content_object['event_date'] (#17), a.entity_id (#9), a.source_id (#10)]
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│ ├── join type: INNER
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│ ├── build keys: [a.entity_id (#9), a.source_id (#10)]
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│ ├── probe keys: [c.entity_id (#18), c.source_id (#19)]
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│ ├── keys is null equal: [false, false]
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│ ├── filters: []
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│ ├── build join filters:
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│ │ ├── filter id:0, build key:a.entity_id (#9), probe key:c.entity_id (#18), filter type:inlist,min_max
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│ │ └── filter id:1, build key:a.source_id (#10), probe key:c.source_id (#19), filter type:inlist,min_max
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│ ├── estimated rows: 0.20
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│ ├── Filter(Build)
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│ │ ├── output columns: [a.entity_id (#9), a.source_id (#10), a.content_object['event_date'] (#17)]
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│ │ ├── filters: [is_not_null(CAST(CAST(CAST(a.content_object['event_date'] (#17) AS Int64 NULL) AS Timestamp NULL) AS Date NULL))]
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│ │ ├── estimated rows: 0.20
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│ │ └── MaterializeCTERef
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│ │ ├── cte_name: cte_cse_0
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│ │ ├── cte_schema: [entity_id (#9), source_id (#10), metadata_object['type'] (#14), content_object['category_a'] (#15), content_object['category_b'] (#16), content_object['event_date'] (#17)]
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│ │ └── estimated rows: 1.00
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│ └── Filter(Probe)
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│ ├── output columns: [c.entity_id (#18), c.source_id (#19)]
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│ ├── filters: [is_true(c.process_mode (#20) = 'standard_mode')]
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│ ├── estimated rows: 1.00
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│ └── TableScan
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│ ├── table: default.test_virtual_db.config_table
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│ ├── output columns: [entity_id (#18), source_id (#19), process_mode (#20)]
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│ ├── read rows: 1
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│ ├── read size: < 1 KiB
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│ ├── partitions total: 1
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│ ├── partitions scanned: 1
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│ ├── pruning stats: [segments: <range pruning: 1 to 1>, blocks: <range pruning: 1 to 1, bloom pruning: 1 to 1>]
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│ ├── push downs: [filters: [is_true(config_table.process_mode (#20) = 'standard_mode')], limit: NONE]
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│ ├── apply join filters: [#0, #1]
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│ └── estimated rows: 1.00
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└── Filter(Probe)
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├── output columns: [a.entity_id (#0), a.source_id (#1), a.metadata_object['type'] (#5), a.content_object['category_a'] (#6), a.content_object['category_b'] (#7), a.content_object['event_date'] (#8)]
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├── filters: [is_not_null(CAST(a.content_object['category_a'] (#6) AS String NULL)), is_not_null(CAST(a.content_object['category_b'] (#7) AS String NULL)), is_not_null(CAST(CAST(CAST(a.content_object['event_date'] (#8) AS Int64 NULL) AS Timestamp NULL) AS Date NULL))]
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├── estimated rows: 0.20
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└── MaterializeCTERef
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├── cte_name: cte_cse_0
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├── cte_schema: [entity_id (#0), source_id (#1), metadata_object['type'] (#5), content_object['category_a'] (#6), content_object['category_b'] (#7), content_object['event_date'] (#8)]
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└── estimated rows: 1.00
583581

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query TTTTTT
585583
WITH processed_dates AS (

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