Aggregate Tables

danger

Aggregate Tables are an experimental feature, the API might be changed in any release.

To use this feature, please add the following JVM argument: -Dactiveviam.feature.aggregate_tables.enabled=true.

DirectQuery is using the external database to feed some aggregate providers and answer some queries. This can result in some large queries with heavy load on the database, and if the same cube is started multiple times, the same query can be repeated.

Aggregate tables are a way to cache some aggregated data in the external database to make some queries faster and cheaper.

How does it work

Aggregate Tables are tables added in the external database as a table fed from aggregated data.

Whenever possible, DirectQuery will query the Aggregate Table instead of the base Schema for a more optimized query:

For instance, consider a Sales table containing sales such as this one:

Id	Date	Product	Buyer	Quantity	Price
1	2023-01-01	P1	Buyer_A	100.0	15.5
2	2023-01-01	P1	Buyer_B	100.0	16.5
3	2023-01-01	P2	Buyer_A	100.0	30
4	2023-01-01	P2	Buyer_B	100.0	31
5	2023-01-02	P1	Buyer_A	100.0	15.5
6	2023-01-02	P1	Buyer_B	100.0	15.5
7	2023-01-02	P1	Buyer_A	100.0	18
8	2023-01-03	P1	Buyer_B	100.0	18
9	2023-01-03	P2	Buyer_A	100.0	35
10	2023-01-03	P2	Buyer_B	100.0	40

It is possible to aggregate the sum of the Quantity per Date and Product in the external database into an other table agg_sales:

date	product	sum_of_quantity	count_of_rows
2023-01-01	P1	200.0	2
2023-01-01	P2	200.0	2
2023-01-02	P1	300.0	3
2023-01-03	P1	100.0	1
2023-01-03	P2	200.0	2

If this table is added as an Aggregate Table in DirectQuery, it will be used whenever possible instead of the original Sales table.

For instance when asking for the SUM(Quantity) per Product in the Sales table, DirectQuery will retrieve the SUM(sum_of_quantity) per product in agg_sales. As the agg_sales is smaller and already aggregated the result will be faster to compute and will potentially avoid a huge scan of the database, saving both time and compute costs.

Use cases

There are a few classic use cases for using an Aggregate Table:

• Feeding an aggregate provider faster by pre-aggregating over the provider fields
• Having an "external database aggregate provider", i.e. being able to answer some queries from the pre-aggregated data in the external database but without having to store additional data in-memory.
• Feeding some hierarchies faster

Supported aggregations

The following aggregation function can be used in an Aggregate Table:

• Sum
• Min
• Max
• Average
• Sum product
• Count
• Long
• Short
• Gross
• Square sum

Custom Aggregations

If you have a custom aggregation functions, you must implement an equivalent ISqlReAggregationFunction plugin value and register it in the Registry with the same key as your aggregation function.

Examples

Create a simple Aggregate Table to feed an aggregate provider

Let’s create a new table that will pre-aggregate the quantity (in the tutorial sales table) per Date and Product:

BigQuery

CREATE OR REPLACE TABLE `tutorial`.SALES_BY_PRODUCT (
  PRODUCT STRING NOT NULL,
  QUANTITY_SUM FLOAT64 NOT NULL,
  CONTRIBUTORS_COUNT INT64 NOT NULL
);

We feed it with the data from the other tables:

INSERT INTO `tutorial`.SALES_BY_PRODUCT
  SELECT
    T0.PRODUCT AS PRODUCT,
    sum(T0.QUANTITY) AS QUANTITY_SUM,
    COUNT(*) AS CONTRIBUTORS_COUNT
  FROM
    `tutorial`.SALES AS T0
  GROUP BY
    T0.PRODUCT;

ClickHouse

create table
  TUTORIAL.sales_by_product
(
  `product` String NOT NULL,
  `quantity_sum` Float64 NOT NULL,
  `contributors_count` UInt64 NOT NULL
)
ENGINE = MergeTree()
ORDER BY (product);

We feed it with the data from the other tables:

INSERT INTO TUTORIAL.sales_by_product
  SELECT
    T0.PRODUCT AS `product`,
    sum(T0.QUANTITY) AS `quantity_sum`,
    COUNT(*) AS `contributors_count`
  FROM
    TUTORIAL.SALES AS T0
  GROUP BY
    T0.PRODUCT;

Databricks

CREATE OR REPLACE TABLE
  tutorial.SALES_BY_PRODUCT
(
  PRODUCT String NOT NULL,
  QUANTITY_SUM FLOAT NOT NULL,
  CONTRIBUTORS_COUNT INTEGER NOT NULL
)

We feed it with the data from the other tables:

INSERT INTO tutorial.SALES_BY_PRODUCT
  SELECT
    T0.PRODUCT AS PRODUCT,
    sum(T0.QUANTITY) AS QUANTITY_SUM,
    COUNT(*) AS CONTRIBUTORS_COUNT
  FROM
    tutorial.SALES AS T0
  GROUP BY
    T0.PRODUCT;

MsSql

if not exists (select * from sys.tables where name = 'SALES_BY_PRODUCT')
CREATE TABLE TUTORIAL.SALES_BY_PRODUCT (
  [PRODUCT] VARCHAR(50) NOT NULL,
  [QUANTITY_SUM] FLOAT NOT NULL,
  [CONTRIBUTORS_COUNT] BIGINT NOT NULL
);

We feed it with the data from the other tables:

INSERT INTO TUTORIAL.SALES_BY_PRODUCT
  SELECT
    T0.PRODUCT AS PRODUCT,
    sum(T0.QUANTITY) AS QUANTITY_SUM,
    COUNT(*) AS CONTRIBUTORS_COUNT
  FROM
    TUTORIAL.SALES AS T0
  GROUP BY
    T0.PRODUCT;

Redshift

CREATE TABLE IF NOT EXISTS
  tutorial."SALES_BY_PRODUCT"
(
  "PRODUCT" VARCHAR NOT NULL,
  "QUANTITY_SUM" FLOAT NOT NULL,
  "CONTRIBUTORS_COUNT" BIGINT NOT NULL
);

We feed it with the data from the other tables:

INSERT INTO tutorial."SALES_BY_PRODUCT"
  SELECT
    T0."PRODUCT" AS "PRODUCT",
    sum(T0."QUANTITY") AS "QUANTITY_SUM",
    COUNT(*) AS "CONTRIBUTORS_COUNT"
  FROM
    tutorial."SALES" AS T0
  GROUP BY
    T0."PRODUCT";

Snowflake

CREATE OR REPLACE TABLE
  TUTORIAL.SALES_BY_PRODUCT
(
  PRODUCT String NOT NULL,
  QUANTITY_SUM FLOAT NOT NULL,
  CONTRIBUTORS_COUNT INTEGER NOT NULL
)

We feed it with the data from the other tables:

INSERT INTO TUTORIAL.SALES_BY_PRODUCT
  SELECT
    T0.PRODUCT AS PRODUCT,
    sum(T0.QUANTITY) AS QUANTITY_SUM,
    COUNT(*) AS CONTRIBUTORS_COUNT
  FROM
    TUTORIAL.SALES AS T0
  GROUP BY
    T0.PRODUCT;

Synapse

CREATE VIEW TUTORIAL.SALES_BY_PRODUCT_VIEW AS
  SELECT
    T0.PRODUCT AS PRODUCT,
    sum(T0.QUANTITY) AS QUANTITY_SUM,
    COUNT(*) AS CONTRIBUTORS_COUNT
  FROM
    TUTORIAL.SALES AS T0
  GROUP BY
    T0.PRODUCT;

note

To simplify this example on Synapse we create a view containing the Aggregated data. However, views means that the result will be recomputed for each query so it is not a wise choice for an Aggregate Table. We recommend to use a materialized table.

The pre-aggregated data looks like this:

PRODUCT	QUANTITY_SUM	CONTRIBUTORS_COUNT
BED_2	6	4
HOO_5	3	3
TAB_0	4	4
TAB_1	4	3
TSH_3	3	3
TSH_4	5	3

Let's first discover the SQL table corresponding to the Aggregate Table:

BigQuery

final Table salesTable = session.discoverTable(TableId.of(PROJECT_ID, "tutorial", "SALES"));
final Table table = session.discoverTable(TableId.of(PROJECT_ID, "tutorial", "SALES_BY_PRODUCT"));

ClickHouse

final Table salesTable = session.discoverTable("TUTORIAL", "SALES");
final Table table = session.discoverTable("TUTORIAL", "sales_by_product");

Databricks

final Table salesTable =
        session.discoverTable(new SqlTableId(TEST_CATALOG_NAME, TUTORIAL_SCHEMA_NAME, "SALES"));
final Table table =
        session.discoverTable(new SqlTableId(TEST_CATALOG_NAME, TUTORIAL_SCHEMA_NAME, "SALES_BY_PRODUCT"));

MsSql

final Table salesTable = session.discoverTable(new SqlTableId(DATABASE_NAME, SCHEMA_NAME, "SALES"));
final Table table = session.discoverTable(new SqlTableId(DATABASE_NAME, SCHEMA_NAME, "SALES_BY_PRODUCT"));

Redshift

final Table salesTable =
        session.discoverTable(new SqlTableId(TEST_DATABASE_NAME, TUTORIAL_SCHEMA_NAME, "SALES"));
final Table table = session
        .discoverTable(new SqlTableId(TEST_DATABASE_NAME, TUTORIAL_SCHEMA_NAME, "SALES_BY_PRODUCT"));

Snowflake

final Table salesTable =
        session.discoverTable(new SqlTableId(TEST_DATABASE_NAME, TUTORIAL_SCHEMA_NAME, "SALES"));
final Table table = session
        .discoverTable(new SqlTableId(TEST_DATABASE_NAME, TUTORIAL_SCHEMA_NAME, "SALES_BY_PRODUCT"));

Synapse

final Table salesTable = session.discoverTable(new SqlTableId(TEST_DATABASE_NAME, "TUTORIAL", "SALES"));
final Table table =
        session.discoverTable(new SqlTableId(TEST_DATABASE_NAME, "TUTORIAL", "SALES_BY_PRODUCT_VIEW"));

We will map the PRODUCT field of the sales table to the product field of the Aggregate Table:

BigQuery

final Map<FieldPath, String> fieldMapping = Map.of(FieldPath.of("PRODUCT"), "PRODUCT");

ClickHouse

final Map<FieldPath, String> fieldMapping = Map.of(FieldPath.of("PRODUCT"), "product");

Databricks

final Map<FieldPath, String> fieldMapping = Map.of(FieldPath.of("PRODUCT"), "PRODUCT");

MsSql

final Map<FieldPath, String> fieldMapping = Map.of(FieldPath.of("PRODUCT"), "PRODUCT");

Redshift

final Map<FieldPath, String> fieldMapping = Map.of(FieldPath.of("PRODUCT"), "PRODUCT");

Snowflake

final Map<FieldPath, String> fieldMapping = Map.of(FieldPath.of("PRODUCT"), "PRODUCT");

Synapse

final Map<FieldPath, String> fieldMapping = Map.of(FieldPath.of("PRODUCT"), "PRODUCT");

We will also map the sum of the sales quantity column into a column called quantity_sum:

BigQuery

final List<MeasureMapping> measureMapping = List.of(
        MeasureMapping.sum(FieldPath.of("QUANTITY"), "QUANTITY_SUM"),
        MeasureMapping.count("CONTRIBUTORS_COUNT"));

ClickHouse

final List<MeasureMapping> measureMapping = List.of(
        MeasureMapping.sum(FieldPath.of("QUANTITY"), "quantity_sum"),
        MeasureMapping.count("contributors_count"));

Databricks

final List<MeasureMapping> measureMapping = List.of(
        MeasureMapping.sum(FieldPath.of("QUANTITY"), "QUANTITY_SUM"),
        MeasureMapping.count("CONTRIBUTORS_COUNT"));

MsSql

final List<MeasureMapping> measureMapping = List.of(
        MeasureMapping.sum(FieldPath.of("QUANTITY"), "QUANTITY_SUM"),
        MeasureMapping.count("CONTRIBUTORS_COUNT"));

Redshift

final List<MeasureMapping> measureMapping = List.of(
        MeasureMapping.sum(FieldPath.of("QUANTITY"), "QUANTITY_SUM"),
        MeasureMapping.count("CONTRIBUTORS_COUNT"));

Snowflake

final List<MeasureMapping> measureMapping = List.of(
        MeasureMapping.sum(FieldPath.of("QUANTITY"), "QUANTITY_SUM"),
        MeasureMapping.count("CONTRIBUTORS_COUNT"));

Synapse

final List<MeasureMapping> measureMapping = List.of(
        MeasureMapping.sum(FieldPath.of("QUANTITY"), "QUANTITY_SUM"),
        MeasureMapping.count("CONTRIBUTORS_COUNT"));

We can now build the AggregateTable:

BigQuery

AggregateTable aggregateTable = AggregateTable.builder()
        .fromTable(table)
        .asAggregationOf(salesTable)
        .withGroupByFieldsMapping(fieldMapping)
        .withMeasuresMappings(measureMapping)
        .build();

ClickHouse

AggregateTable aggregateTable = AggregateTable.builder()
        .fromTable(table)
        .asAggregationOf(salesTable)
        .withGroupByFieldsMapping(fieldMapping)
        .withMeasuresMappings(measureMapping)
        .build();

Databricks

AggregateTable aggregateTable = AggregateTable.builder()
        .fromTable(table)
        .asAggregationOf(salesTable)
        .withGroupByFieldsMapping(fieldMapping)
        .withMeasuresMappings(measureMapping)
        .build();

MsSql

AggregateTable aggregateTable = AggregateTable.builder()
        .fromTable(table)
        .asAggregationOf(salesTable)
        .withGroupByFieldsMapping(fieldMapping)
        .withMeasuresMappings(measureMapping)
        .build();

Redshift

AggregateTable aggregateTable = AggregateTable.builder()
        .fromTable(table)
        .asAggregationOf(salesTable)
        .withGroupByFieldsMapping(fieldMapping)
        .withMeasuresMappings(measureMapping)
        .build();

Snowflake

AggregateTable aggregateTable = AggregateTable.builder()
        .fromTable(table)
        .asAggregationOf(salesTable)
        .withGroupByFieldsMapping(fieldMapping)
        .withMeasuresMappings(measureMapping)
        .build();

Synapse

AggregateTable aggregateTable = AggregateTable.builder()
        .fromTable(table)
        .asAggregationOf(salesTable)
        .withGroupByFieldsMapping(fieldMapping)
        .withMeasuresMappings(measureMapping)
        .build();

And add it to the schema:

BigQuery

final Schema schema = Schema.builder()
        .withExternalTables(List.of(salesTable), List.of())
        .withExternalAggregateTables(List.of(aggregateTable))
        .build();

ClickHouse

final Schema schema = Schema.builder()
        .withExternalTables(List.of(salesTable), List.of())
        .withExternalAggregateTables(List.of(aggregateTable))
        .build();

Databricks

final Schema schema = Schema.builder()
        .withExternalTables(List.of(salesTable), List.of())
        .withExternalAggregateTables(List.of(aggregateTable))
        .build();

MsSql

final Schema schema = Schema.builder()
        .withExternalTables(List.of(salesTable), List.of())
        .withExternalAggregateTables(List.of(aggregateTable))
        .build();

Redshift

final Schema schema = Schema.builder()
        .withExternalTables(List.of(salesTable), List.of())
        .withExternalAggregateTables(List.of(aggregateTable))
        .build();

Snowflake

final Schema schema = Schema.builder()
        .withExternalTables(List.of(salesTable), List.of())
        .withExternalAggregateTables(List.of(aggregateTable))
        .build();

Synapse

final Schema schema = Schema.builder()
        .withExternalTables(List.of(salesTable), List.of())
        .withExternalAggregateTables(List.of(aggregateTable))
        .build();

Let's now define a bitmap aggregate provider on the sum of quantity per Product, the feeding of the in-memory provider will be done directly from the Aggregate Table:

BigQuery

final IActivePivotInstanceDescription cubeDescription = StartBuilding.cube("MyCube")
        .withMeasures(
                superMeasureBuilder -> superMeasureBuilder
                        .withCalculations(c -> Copper.sum("QUANTITY").as("Quantity.SUM").publish(c)))
        .withDimensions(builder -> builder.withSingleLevelDimensions(List.of("DATE", "PRODUCT", "SHOP")))
        .withAggregateProvider()
        .withPartialProvider()
        .withName("MyProvider")
        .bitmap()
        .includingOnlyHierarchies(HierarchyIdentifier.simple("PRODUCT"))
        .includingOnlyMeasures("Quantity.SUM")
        .build();
final ISelectionDescription selection =
        StartBuilding.selection(schema).fromBaseStore("SALES").withAllReachableFields().build();
final IActivePivotManagerDescription managerDescription = StartBuilding.managerDescription()
        .withName("MyManager")
        .withSchema()
        .withSelection(selection)
        .withCube(cubeDescription)
        .build();
final Application app =
        session.applicationBuilder().schema(schema).managerDescription(managerDescription).build();

ClickHouse

final IActivePivotInstanceDescription cubeDescription = StartBuilding.cube("MyCube")
        .withMeasures(
                superMeasureBuilder -> superMeasureBuilder
                        .withCalculations(c -> Copper.sum("QUANTITY").as("Quantity.SUM").publish(c)))
        .withDimensions(builder -> builder.withSingleLevelDimensions(List.of("DATE", "PRODUCT", "SHOP")))
        .withAggregateProvider()
        .withPartialProvider()
        .withName("MyProvider")
        .bitmap()
        .includingOnlyHierarchies(HierarchyIdentifier.simple("PRODUCT"))
        .includingOnlyMeasures("Quantity.SUM")
        .build();
final ISelectionDescription selection =
        StartBuilding.selection(schema).fromBaseStore("SALES").withAllReachableFields().build();
final IActivePivotManagerDescription managerDescription = StartBuilding.managerDescription()
        .withName("MyManager")
        .withSchema()
        .withSelection(selection)
        .withCube(cubeDescription)
        .build();
final Application app =
        session.applicationBuilder().schema(schema).managerDescription(managerDescription).build();

Databricks

final IActivePivotInstanceDescription cubeDescription = StartBuilding.cube("MyCube")
        .withMeasures(
                superMeasureBuilder -> superMeasureBuilder
                        .withCalculations(c -> Copper.sum("QUANTITY").as("Quantity.SUM").publish(c)))
        .withDimensions(builder -> builder.withSingleLevelDimensions(List.of("DATE", "PRODUCT", "SHOP")))
        .withAggregateProvider()
        .withPartialProvider()
        .withName("MyProvider")
        .bitmap()
        .includingOnlyHierarchies(HierarchyIdentifier.simple("PRODUCT"))
        .includingOnlyMeasures("Quantity.SUM")
        .build();
final ISelectionDescription selection =
        StartBuilding.selection(schema).fromBaseStore("SALES").withAllReachableFields().build();
final IActivePivotManagerDescription managerDescription = StartBuilding.managerDescription()
        .withName("MyManager")
        .withSchema()
        .withSelection(selection)
        .withCube(cubeDescription)
        .build();
final Application app =
        session.applicationBuilder().schema(schema).managerDescription(managerDescription).build();

MsSql

final IActivePivotInstanceDescription cubeDescription = StartBuilding.cube("MyCube")
        .withMeasures(
                superMeasureBuilder -> superMeasureBuilder
                        .withCalculations(c -> Copper.sum("QUANTITY").as("Quantity.SUM").publish(c)))
        .withDimensions(builder -> builder.withSingleLevelDimensions(List.of("DATE", "PRODUCT", "SHOP")))
        .withAggregateProvider()
        .withPartialProvider()
        .withName("MyProvider")
        .bitmap()
        .includingOnlyHierarchies(HierarchyIdentifier.simple("PRODUCT"))
        .includingOnlyMeasures("Quantity.SUM")
        .build();
final ISelectionDescription selection =
        StartBuilding.selection(schema).fromBaseStore("SALES").withAllReachableFields().build();
final IActivePivotManagerDescription managerDescription = StartBuilding.managerDescription()
        .withName("MyManager")
        .withSchema()
        .withSelection(selection)
        .withCube(cubeDescription)
        .build();
final Application app =
        session.applicationBuilder().schema(schema).managerDescription(managerDescription).build();

Redshift

final IActivePivotInstanceDescription cubeDescription = StartBuilding.cube("MyCube")
        .withMeasures(
                superMeasureBuilder -> superMeasureBuilder
                        .withCalculations(c -> Copper.sum("QUANTITY").as("Quantity.SUM").publish(c)))
        .withDimensions(builder -> builder.withSingleLevelDimensions(List.of("DATE", "PRODUCT", "SHOP")))
        .withAggregateProvider()
        .withPartialProvider()
        .withName("MyProvider")
        .bitmap()
        .includingOnlyHierarchies(HierarchyIdentifier.simple("PRODUCT"))
        .includingOnlyMeasures("Quantity.SUM")
        .build();
final ISelectionDescription selection =
        StartBuilding.selection(schema).fromBaseStore("SALES").withAllReachableFields().build();
final IActivePivotManagerDescription managerDescription = StartBuilding.managerDescription()
        .withName("MyManager")
        .withSchema()
        .withSelection(selection)
        .withCube(cubeDescription)
        .build();
final Application app =
        session.applicationBuilder().schema(schema).managerDescription(managerDescription).build();

Snowflake

final IActivePivotInstanceDescription cubeDescription = StartBuilding.cube("MyCube")
        .withMeasures(
                superMeasureBuilder -> superMeasureBuilder
                        .withCalculations(c -> Copper.sum("QUANTITY").as("Quantity.SUM").publish(c)))
        .withDimensions(builder -> builder.withSingleLevelDimensions(List.of("DATE", "PRODUCT", "SHOP")))
        .withAggregateProvider()
        .withPartialProvider()
        .withName("MyProvider")
        .bitmap()
        .includingOnlyHierarchies(HierarchyIdentifier.simple("PRODUCT"))
        .includingOnlyMeasures("Quantity.SUM")
        .build();
final ISelectionDescription selection =
        StartBuilding.selection(schema).fromBaseStore("SALES").withAllReachableFields().build();
final IActivePivotManagerDescription managerDescription = StartBuilding.managerDescription()
        .withName("MyManager")
        .withSchema()
        .withSelection(selection)
        .withCube(cubeDescription)
        .build();
final Application app =
        session.applicationBuilder().schema(schema).managerDescription(managerDescription).build();

Synapse

final IActivePivotInstanceDescription cubeDescription = StartBuilding.cube("MyCube")
        .withMeasures(
                superMeasureBuilder -> superMeasureBuilder
                        .withCalculations(c -> Copper.sum("QUANTITY").as("Quantity.SUM").publish(c)))
        .withDimensions(builder -> builder.withSingleLevelDimensions(List.of("DATE", "PRODUCT", "SHOP")))
        .withAggregateProvider()
        .withPartialProvider()
        .withName("MyProvider")
        .bitmap()
        .includingOnlyHierarchies(HierarchyIdentifier.simple("PRODUCT"))
        .includingOnlyMeasures("Quantity.SUM")
        .build();
final ISelectionDescription selection =
        StartBuilding.selection(schema).fromBaseStore("SALES").withAllReachableFields().build();
final IActivePivotManagerDescription managerDescription = StartBuilding.managerDescription()
        .withName("MyManager")
        .withSchema()
        .withSelection(selection)
        .withCube(cubeDescription)
        .build();
final Application app =
        session.applicationBuilder().schema(schema).managerDescription(managerDescription).build();

Note that it is possible to make sure that a provider will be fed by a given Aggregate Table by adding the provider coordinate to this Aggregate Table. This will simply trigger a check when starting the application to make sure that they are compatible.

BigQuery

aggregateTable = AggregateTable.builder()
        .fromTable(table)
        .asAggregationOf(salesTable)
        .withGroupByFieldsMapping(fieldMapping)
        .withMeasuresMappings(measureMapping)
        .forProviders(List.of(ProviderCoordinate.at("MyCube", "MyProvider")))
        .build();

ClickHouse

aggregateTable = AggregateTable.builder()
        .fromTable(table)
        .asAggregationOf(salesTable)
        .withGroupByFieldsMapping(fieldMapping)
        .withMeasuresMappings(measureMapping)
        .forProviders(List.of(ProviderCoordinate.at("MyCube", "MyProvider")))
        .build();

Databricks

aggregateTable = AggregateTable.builder()
        .fromTable(table)
        .asAggregationOf(salesTable)
        .withGroupByFieldsMapping(fieldMapping)
        .withMeasuresMappings(measureMapping)
        .forProviders(List.of(ProviderCoordinate.at("MyCube", "MyProvider")))
        .build();

MsSql

aggregateTable = AggregateTable.builder()
        .fromTable(table)
        .asAggregationOf(salesTable)
        .withGroupByFieldsMapping(fieldMapping)
        .withMeasuresMappings(measureMapping)
        .forProviders(List.of(ProviderCoordinate.at("MyCube", "MyProvider")))
        .build();

Redshift

aggregateTable = AggregateTable.builder()
        .fromTable(table)
        .asAggregationOf(salesTable)
        .withGroupByFieldsMapping(fieldMapping)
        .withMeasuresMappings(measureMapping)
        .forProviders(List.of(ProviderCoordinate.at("MyCube", "MyProvider")))
        .build();

Snowflake

aggregateTable = AggregateTable.builder()
        .fromTable(table)
        .asAggregationOf(salesTable)
        .withGroupByFieldsMapping(fieldMapping)
        .withMeasuresMappings(measureMapping)
        .forProviders(List.of(ProviderCoordinate.at("MyCube", "MyProvider")))
        .build();

Synapse

aggregateTable = AggregateTable.builder()
        .fromTable(table)
        .asAggregationOf(salesTable)
        .withGroupByFieldsMapping(fieldMapping)
        .withMeasuresMappings(measureMapping)
        .forProviders(List.of(ProviderCoordinate.at("MyCube", "MyProvider")))
        .build();

More complex aggregations such as average

In the previous example, SUM was based on a single input and pre-aggregated into a single column, but there are some more complex cases. For instance:

• count has no input and produces 1 pre-aggregated column
• sumProduct can takes N input columns and produces 1 pre-aggregated column
• average takes a single input column but requires 2 pre-aggregated column in order to be able to re-aggregate: one column for the sum and one for the count

BigQuery

final AggregateTable aggregateTable = AggregateTable.builder()
        .fromTable(table)
        .asAggregationOf(salesTable)
        .withGroupByFieldsMapping(Map.of(FieldPath.of("PRODUCT"), "PRODUCT"))
        .withMeasuresMappings(
                List.of(
                        MeasureMapping.sum(FieldPath.of("QUANTITY"), "QUANTITY_SUM"),
                        MeasureMapping.avg(FieldPath.of("QUANTITY"), "QUANTITY_SUM", "CONTRIBUTORS_COUNT"),
                        MeasureMapping.count("CONTRIBUTORS_COUNT")))
        .build();

ClickHouse

final AggregateTable aggregateTable = AggregateTable.builder()
        .fromTable(table)
        .asAggregationOf(salesTable)
        .withGroupByFieldsMapping(Map.of(FieldPath.of("PRODUCT"), "product"))
        .withMeasuresMappings(
                List.of(
                        MeasureMapping.sum(FieldPath.of("QUANTITY"), "quantity_sum"),
                        MeasureMapping.avg(FieldPath.of("QUANTITY"), "quantity_sum", "contributors_count"),
                        MeasureMapping.count("contributors_count")))
        .build();

Databricks

final AggregateTable aggregateTable = AggregateTable.builder()
        .fromTable(table)
        .asAggregationOf(salesTable)
        .withGroupByFieldsMapping(Map.of(FieldPath.of("PRODUCT"), "PRODUCT"))
        .withMeasuresMappings(
                List.of(
                        MeasureMapping.sum(FieldPath.of("QUANTITY"), "QUANTITY_SUM"),
                        MeasureMapping.avg(FieldPath.of("QUANTITY"), "QUANTITY_SUM", "CONTRIBUTORS_COUNT"),
                        MeasureMapping.count("CONTRIBUTORS_COUNT")))
        .build();

MsSql

final AggregateTable aggregateTable = AggregateTable.builder()
        .fromTable(table)
        .asAggregationOf(salesTable)
        .withGroupByFieldsMapping(Map.of(FieldPath.of("PRODUCT"), "PRODUCT"))
        .withMeasuresMappings(
                List.of(
                        MeasureMapping.sum(FieldPath.of("QUANTITY"), "QUANTITY_SUM"),
                        MeasureMapping.avg(FieldPath.of("QUANTITY"), "QUANTITY_SUM", "CONTRIBUTORS_COUNT"),
                        MeasureMapping.count("CONTRIBUTORS_COUNT")))
        .build();

Redshift

final AggregateTable aggregateTable = AggregateTable.builder()
        .fromTable(table)
        .asAggregationOf(salesTable)
        .withGroupByFieldsMapping(Map.of(FieldPath.of("PRODUCT"), "PRODUCT"))
        .withMeasuresMappings(
                List.of(
                        MeasureMapping.sum(FieldPath.of("QUANTITY"), "QUANTITY_SUM"),
                        MeasureMapping.avg(FieldPath.of("QUANTITY"), "QUANTITY_SUM", "CONTRIBUTORS_COUNT"),
                        MeasureMapping.count("CONTRIBUTORS_COUNT")))
        .build();

Snowflake

final AggregateTable aggregateTable = AggregateTable.builder()
        .fromTable(table)
        .asAggregationOf(salesTable)
        .withGroupByFieldsMapping(Map.of(FieldPath.of("PRODUCT"), "PRODUCT"))
        .withMeasuresMappings(
                List.of(
                        MeasureMapping.sum(FieldPath.of("QUANTITY"), "QUANTITY_SUM"),
                        MeasureMapping.avg(FieldPath.of("QUANTITY"), "QUANTITY_SUM", "CONTRIBUTORS_COUNT"),
                        MeasureMapping.count("CONTRIBUTORS_COUNT")))
        .build();

Synapse

final AggregateTable aggregateTable = AggregateTable.builder()
        .fromTable(table)
        .asAggregationOf(salesTable)
        .withGroupByFieldsMapping(Map.of(FieldPath.of("PRODUCT"), "PRODUCT"))
        .withMeasuresMappings(
                List.of(
                        MeasureMapping.sum(FieldPath.of("QUANTITY"), "QUANTITY_SUM"),
                        MeasureMapping.avg(FieldPath.of("QUANTITY"), "QUANTITY_SUM", "CONTRIBUTORS_COUNT"),
                        MeasureMapping.count("CONTRIBUTORS_COUNT")))
        .build();

Count

The count is measure automatically added to any query sent to the database. When adding an aggregate table, a check is therefore performed to ensure it contains a column with a count. (otherwise it would never be used).

Slicing hierarchies

Slicing hierarchies are always expressed in the queries sent to the database. When a field expressed in a query is not part of the Aggregate Table, the query will not use the Aggregate Table. Therefore it is recommended to include the slicing hierarchies in Aggregate Table definition.

Aggregate tables with vectors

Arrays can be aggregated in an aggregate table but they need to have a specific format depending on the type of original array being aggregated:

Original Array	Aggregate Table Array
Native	Native
Multi Column Array	Multi Column Array
Multi Row Array	Native

See the DirectQuery vector documentation for more information about array types.

Generate SQL for Aggregate Table matching an Aggregate Provider

Aggregate Tables are a good tool to feed Aggregate Providers quickly because it can use the aggregated data instead of running a new aggregation. DirectQuery provides a bootstrapper to generate the SQL to create and feed an Aggregate Table matching an Aggregate Provider.

Let's imagine we have the following Aggregate Provider defined:

BigQuery

.withPartialProvider()
.withName("MyProvider")
.bitmap()
.includingOnlyHierarchies(HierarchyIdentifier.simple("PRODUCT"))
.includingOnlyMeasures("Quantity.SUM")

ClickHouse

.withPartialProvider()
.withName("MyProvider")
.bitmap()
.includingOnlyHierarchies(HierarchyIdentifier.simple("PRODUCT"))
.includingOnlyMeasures("Quantity.SUM")

Databricks

.withPartialProvider()
.withName("MyProvider")
.bitmap()
.includingOnlyHierarchies(HierarchyIdentifier.simple("PRODUCT"))
.includingOnlyMeasures("Quantity.SUM")

MsSql

.withPartialProvider()
.withName("MyProvider")
.bitmap()
.includingOnlyHierarchies(HierarchyIdentifier.simple("PRODUCT"))
.includingOnlyMeasures("Quantity.SUM")

Redshift

.withPartialProvider()
.withName("MyProvider")
.bitmap()
.includingOnlyHierarchies(HierarchyIdentifier.simple("PRODUCT"))
.includingOnlyMeasures("Quantity.SUM")

Snowflake

.withPartialProvider()
.withName("MyProvider")
.bitmap()
.includingOnlyHierarchies(HierarchyIdentifier.simple("PRODUCT"))
.includingOnlyMeasures("Quantity.SUM")

We can create a bootstrapper for it:

BigQuery

final ProviderCoordinate provider = ProviderCoordinate.at("MyCube", "MyProvider");
final AggregateTableBootstrapper bootstrapper = session.getAggregateTableBootstrapper(
        schema,
        provider,
        managerDescription,
        aggregateTableName,
        null,
        AGGREGATE_TABLE_TEMP_DATASET);

ClickHouse

final ProviderCoordinate provider = ProviderCoordinate.at("MyCube", "MyProvider");
final AggregateTableBootstrapper bootstrapper =
        session.getAggregateTableBootstrapper(schema, provider, managerDescription, null, null);

Databricks

final ProviderCoordinate provider = ProviderCoordinate.at("MyCube", "MyProvider");
final AggregateTableBootstrapper bootstrapper = session.getAggregateTableBootstrapper(
        schema,
        provider,
        managerDescription,
        aggregateTableName,
        null,
        AGGREGATE_TABLE_TEMP_SCHEMA_NAME);

MsSql

final ProviderCoordinate provider = ProviderCoordinate.at("MyCube", "MyProvider");
final AggregateTableBootstrapper bootstrapper =
        session.getAggregateTableBootstrapper(schema, provider, managerDescription, null, null, null);

Redshift

final ProviderCoordinate provider = ProviderCoordinate.at("MyCube", "MyProvider");
final AggregateTableBootstrapper bootstrapper = session.getAggregateTableBootstrapper(
        schema,
        provider,
        managerDescription,
        aggregateTableName,
        null,
        AGGREGATE_TABLE_TEMP_SCHEMA_NAME);

Snowflake

final ProviderCoordinate provider = ProviderCoordinate.at("MyCube", "MyProvider");
final AggregateTableBootstrapper bootstrapper = session.getAggregateTableBootstrapper(
        schema,
        provider,
        managerDescription,
        aggregateTableName,
        TEST_DATABASE_NAME,
        AGGREGATE_TABLE_TEMP_SCHEMA_NAME);

This bootstrapper can provide the SQL to create and feed the provider:

BigQuery

System.out.println(bootstrapper.getSqlForCreation());

CREATE TABLE `cubulus-tests`.`test_aggregate_table_temp`.`Aggregate_Table_MyProvider` (
`PRODUCT` String NOT NULL,
`Quantity__PERIOD__SUM` INT64,
`contributors__PERIOD__COUNT` INT64
)

System.out.println(bootstrapper.getSqlForFeeding());

INSERT INTO `cubulus-tests`.`test_aggregate_table_temp`.`Aggregate_Table_MyProvider` SELECT
  `T0`.`PRODUCT` AS `PRODUCT_0`,
  SUM(COALESCE(`T0`.`QUANTITY`, 0)) AS `Quantity__PERIOD__SUM_1`,
  COUNT(*) AS `contributors__PERIOD__COUNT_2`
FROM
  `cubulus-tests`.`tutorial`.`SALES` AS `T0`
GROUP BY
  `T0`.`PRODUCT`

ClickHouse

System.out.println(bootstrapper.getSqlForCreation());

CREATE TABLE `TUTORIAL`.`Aggregate_Table_MyProvider` (
`PRODUCT` String,
`Quantity.SUM` Nullable(Float64),
`contributors.COUNT` Nullable(Int64)
)
ENGINE = MergeTree()
ORDER BY `PRODUCT`

System.out.println(bootstrapper.getSqlForFeeding());

INSERT INTO `TUTORIAL`.`Aggregate_Table_MyProvider` SELECT
  `T0`.`PRODUCT` AS `PRODUCT_0`,
  SUM(COALESCE(`T0`.`QUANTITY`, 0.0)) AS `Quantity.SUM_1`,
  COUNT(*) AS `contributors.COUNT_2`
FROM
  `TUTORIAL`.`SALES` AS `T0`
GROUP BY
  `T0`.`PRODUCT`

Databricks

System.out.println(bootstrapper.getSqlForCreation());

CREATE TABLE `hive_metastore`.`aggregate_table_temp_schema`.`Aggregate_Table_MyProvider` (
`PRODUCT` String NOT NULL,
`Quantity.SUM` DOUBLE,
`contributors.COUNT` BIGINT
)

System.out.println(bootstrapper.getSqlForFeeding());

INSERT INTO `hive_metastore`.`aggregate_table_temp_schema`.`Aggregate_Table_MyProvider` SELECT
  `T0`.`PRODUCT` AS `PRODUCT_0`,
  SUM(`T0`.`QUANTITY`) AS `Quantity.SUM_1`,
  COUNT(*) AS `contributors.COUNT_2`
FROM
  `hive_metastore`.`tutorial`.`SALES` AS `T0`
GROUP BY
  `T0`.`PRODUCT`

MsSql

System.out.println(bootstrapper.getSqlForCreation());

CREATE TABLE "dev"."TUTORIAL"."Aggregate_Table_MyProvider" (
"PRODUCT" nvarchar(4000) NOT NULL,
"Quantity.SUM" BigInt,
"contributors.COUNT" BigInt
)

System.out.println(bootstrapper.getSqlForFeeding());

INSERT INTO "dev"."TUTORIAL"."Aggregate_Table_MyProvider" SELECT
  "T0"."PRODUCT" AS "PRODUCT_0",
  SUM("T0"."QUANTITY") AS "Quantity.SUM_1",
  COUNT(*) AS "contributors.COUNT_2"
FROM
  "dev"."TUTORIAL"."SALES" AS "T0"
GROUP BY
  "T0"."PRODUCT"

Redshift

System.out.println(bootstrapper.getSqlForCreation());

SET enable_case_sensitive_identifier TO true;
CREATE TABLE "dev"."test_aggregate_table_temp"."Aggregate_Table_MyProvider" (
"PRODUCT" VARCHAR NOT NULL,
"Quantity.SUM" FLOAT8,
"contributors.COUNT" BIGINT
)

System.out.println(bootstrapper.getSqlForFeeding());

SET enable_case_sensitive_identifier TO true;
INSERT INTO "dev"."test_aggregate_table_temp"."Aggregate_Table_MyProvider" SELECT
  "T0"."PRODUCT" AS "PRODUCT_0",
  SUM(COALESCE("T0"."QUANTITY", 0.0)) AS "Quantity.SUM_1",
  COUNT(*) AS "contributors.COUNT_2"
FROM
  "dev"."tutorial"."SALES" AS "T0"
GROUP BY
  "T0"."PRODUCT"

Snowflake

System.out.println(bootstrapper.getSqlForCreation());

CREATE TRANSIENT TABLE "TEST_RESOURCES"."TEST_AGGREGATE_TABLE_TEMP"."Aggregate_Table_MyProvider" (
"PRODUCT" VARCHAR NOT NULL,
"Quantity.SUM" DOUBLE,
"contributors.COUNT" NUMBER
)

System.out.println(bootstrapper.getSqlForFeeding());

INSERT INTO "TEST_RESOURCES"."TEST_AGGREGATE_TABLE_TEMP"."Aggregate_Table_MyProvider" SELECT
  "T0"."PRODUCT" AS "PRODUCT_0",
  SUM(COALESCE("T0"."QUANTITY", 0.0)) AS "Quantity.SUM_1",
  COUNT(*) AS "contributors.COUNT_2"
FROM
  "TEST_RESOURCES"."TUTORIAL"."SALES" AS "T0"
GROUP BY
  "T0"."PRODUCT"

You will need to execute this SQL to create and feed the table. Creating and feeding the SQL table only need to be done once: remember that the purpose of using an Aggregate Table is to avoid re-running the same queries at every start up. A good way to do that is to run the queries manually.

When the table exists, it is possible to use the Aggregate Table in the application, the Aggregate Provider will use it for feeding:

BigQuery

final AggregateTable aggregateTable = bootstrapper.toAggregateTable();
final Application app = session.applicationBuilder()
        .schema(schema)
        .managerDescription(managerDescription)
        .additionalAggregateTables(List.of(aggregateTable))
        .build();
app.start();

ClickHouse

final AggregateTable aggregateTable = bootstrapper.toAggregateTable();
final Application app = session.applicationBuilder()
        .schema(schema)
        .managerDescription(managerDescription)
        .additionalAggregateTables(List.of(aggregateTable))
        .build();
app.start();

Databricks

final AggregateTable aggregateTable = bootstrapper.toAggregateTable();
final Application app = session.applicationBuilder()
        .schema(schema)
        .managerDescription(managerDescription)
        .additionalAggregateTables(List.of(aggregateTable))
        .build();
app.start();

MsSql

final AggregateTable aggregateTable = bootstrapper.toAggregateTable();
final Application app = session.applicationBuilder()
        .schema(schema)
        .managerDescription(managerDescription)
        .additionalAggregateTables(List.of(aggregateTable))
        .build();
app.start();

Redshift

final AggregateTable aggregateTable = bootstrapper.toAggregateTable();
final Application app = session.applicationBuilder()
        .schema(schema)
        .managerDescription(managerDescription)
        .additionalAggregateTables(List.of(aggregateTable))
        .build();
app.start();

Snowflake

final AggregateTable aggregateTable = bootstrapper.toAggregateTable();
final Application app = session.applicationBuilder()
        .schema(schema)
        .managerDescription(managerDescription)
        .additionalAggregateTables(List.of(aggregateTable))
        .build();
app.start();

Is my query using the Aggregate Table ?

The simplest solution to check whether the query is using an aggregate table is to look at the query in the external database. If that is the case, then the aggregate table name should appear in the SQL query as the base table (the first table in the FROM clause). If it using an aggregate table, the aggregate table name should appears as the base table (the first table in FROM clause) in the SQL query.

Additionally, for databases supporting tags such as Snowflake, the tag aggregate_table_name will be added to the query with the name of the aggregate table used.

While building a project, to have more information about why a query is compatible or not with aggregate tables, it is possible to add additional logs by setting the logger com.activeviam.database.sql.internal.query.AggregateTableQueryOptimizer to FINE. The additional logs look like this:

FINE: Trying to match query with 1 aggregate tables. Query is [...]
Aug 21, 2023 4:46:37 PM com.activeviam.database.sql.internal.query.AggregateTableQueryOptimizer isCompatibleAggregateTable
FINE: The query did not match the Aggregate Table AggregateTable-0. Compatible base table: true, compatible group by fields: true, compatible selection fields: true, compatible aggregations: true, compatible condition: false. Query was [...]
Aug 21, 2023 4:46:37 PM com.activeviam.database.sql.internal.query.AggregateTableQueryOptimizer findMatchingAggregateTable
FINE: No matching Aggregate Table for query [...]