Tables

At the bottom of the DAG of cubeQuerySteps, the measures are measures evaluated by an external table, applying aggregation functions for given GROUP BY and WHERE clauses.

Here is an example of such a DAG:

graph TB
    cubeQuery[kpiA, kpiB on X by L]
    subgraph user query
    cubeQuery
    end
    cubeQuery --> measureA_cubeContext
    cubeQuery --> measureE_cubeContext
    subgraph cube DAG
    measureA_cubeContext[kpiA on Xby L]
    measureB_cubeContext[kpiB on X by L]
    measureC_cubeContext_v2[kpiC on X by L&M]
    measureD_cubeContext_v3[kpiD on X by L]
    measureE_cubeContext[kpiD on Y by L]
    measureF_cubeContext_v3[kpiD on X&Y by L&M]
    end
    measureA_cubeContext --> measureB_cubeContext
    measureB_cubeContext --> measureC_cubeContext_v2
    measureB_cubeContext --> measureD_cubeContext_v3
    measureE_cubeContext --> measureC_cubeContext_v2
    measureE_cubeContext --> measureF_cubeContext_v3
    subgraph table queries
    tableQuery_tableContext_v2[kpiD on X by L]
    tableQuery_tableContext_v3["kpiC, kpiD(on Y) on X by L&M"]
    end
    measureC_cubeContext_v2 --> tableQuery_tableContext_v3
    measureD_cubeContext_v3 --> tableQuery_tableContext_v2
    measureF_cubeContext_v3 --> tableQuery_tableContext_v3

SQL with JooQ

SQL integration is provided with the help of JooQ. To query a complex star/snowflake schema (i.e. with many/deep joins), one should provide a TableLike expressing these JOINs.

For instance:

Table<Record> fromClause = DSL.table(DSL.name(factTable))
        .as("f")
        .join(DSL.table(DSL.name(productTable)).as("p"))
        .using(DSL.field("productId"))
        .join(DSL.table(DSL.name(countryTable)).as("c"))
        .using(DSL.field("countryId"));

Such snowflake schema can be build more easily with the help of JooqTableSupplierBuilder.

Handling null (e.g. from failed JOINs)

See eu.solven.adhoc.column.IMissingColumnManager.onMissingColumn(String)