Function analysis

datafusion-examples/examples/sql_ops/analysis.rs:39–182

Demonstrates how to analyze a SQL query by counting JOINs and identifying join-trees using DataFusion’s `LogicalPlan` and `TreeNode` API.

()

/// Demonstrates how to analyze a SQL query by counting JOINs and identifying
/// join-trees using DataFusion’s `LogicalPlan` and `TreeNode` API.
pub async fn analysis() -> Result<()> {
    // To show how we can count the joins in a sql query we'll be using query 88
    // from the TPC-DS benchmark.
    //
    // q8 has many joins, cross-joins and multiple join-trees, perfect for our
    // example:

    let tpcds_query_88 = "
select  *
from
 (select count(*) h8_30_to_9
 from store_sales, household_demographics , time_dim, store
 where ss_sold_time_sk = time_dim.t_time_sk   
     and ss_hdemo_sk = household_demographics.hd_demo_sk 
     and ss_store_sk = s_store_sk
     and time_dim.t_hour = 8
     and time_dim.t_minute >= 30
     and ((household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2) or
          (household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2) or
          (household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2)) 
     and store.s_store_name = 'ese') s1,
 (select count(*) h9_to_9_30 
 from store_sales, household_demographics , time_dim, store
 where ss_sold_time_sk = time_dim.t_time_sk
     and ss_hdemo_sk = household_demographics.hd_demo_sk
     and ss_store_sk = s_store_sk 
     and time_dim.t_hour = 9 
     and time_dim.t_minute < 30
     and ((household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2) or
          (household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2) or
          (household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2))
     and store.s_store_name = 'ese') s2,
 (select count(*) h9_30_to_10 
 from store_sales, household_demographics , time_dim, store
 where ss_sold_time_sk = time_dim.t_time_sk
     and ss_hdemo_sk = household_demographics.hd_demo_sk
     and ss_store_sk = s_store_sk
     and time_dim.t_hour = 9
     and time_dim.t_minute >= 30
     and ((household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2) or
          (household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2) or
          (household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2))
     and store.s_store_name = 'ese') s3,
 (select count(*) h10_to_10_30
 from store_sales, household_demographics , time_dim, store
 where ss_sold_time_sk = time_dim.t_time_sk
     and ss_hdemo_sk = household_demographics.hd_demo_sk
     and ss_store_sk = s_store_sk
     and time_dim.t_hour = 10 
     and time_dim.t_minute < 30
     and ((household_demographics.hd_dep_count = 3 and household_demographics.hd_vehicle_count<=3+2) or
          (household_demographics.hd_dep_count = 0 and household_demographics.hd_vehicle_count<=0+2) or
          (household_demographics.hd_dep_count = 1 and household_demographics.hd_vehicle_count<=1+2))
     and store.s_store_name = 'ese') s4,
 (select count(*) h10_30_to_11
 from store_sales, household_demographics , time_dim, store
 where ss_sold_time_sk = time_dim.t_time_sk
     and ss_hdemo_sk = household_demographics.hd_demo_sk