substitute create temp table for CREATE OR REPLACE temp table

GoogleCloudPlatform · Sep 19, 2023 · 0fead11 · 0fead11
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commit 0fead11
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diff --git a/sql/query/invoke_backfill_customer_lifetime_value_label.sqlx b/sql/query/invoke_backfill_customer_lifetime_value_label.sqlx
@@ -17,7 +17,7 @@ DECLARE min_date DATE;
 SET max_date = (SELECT DATE_SUB(MAX(event_date), INTERVAL {{interval_max_date}} DAY) FROM `{{mds_project_id}}.{{mds_dataset}}.event`); 
 SET min_date = (SELECT DATE_ADD(MIN(event_date), INTERVAL {{interval_min_date}} DAY) FROM `{{mds_project_id}}.{{mds_dataset}}.event`); 
 
-CREATE TEMP TABLE dates_interval as (
+CREATE OR REPLACE TEMP TABLE dates_interval as (
   SELECT DISTINCT 
     event_date as input_date,
     DATE_ADD(event_date, INTERVAL {{interval_end_date}} DAY) as end_date
@@ -28,7 +28,7 @@ CREATE TEMP TABLE dates_interval as (
 
 
 ## All users in the platform
-CREATE TEMP TABLE events_users_days as (
+CREATE OR REPLACE TEMP TABLE events_users_days as (
   SELECT DISTINCT
   Users.user_pseudo_id
   FROM `{{mds_project_id}}.{{mds_dataset}}.event` Users 

diff --git a/sql/query/invoke_backfill_purchase_propensity_label.sqlx b/sql/query/invoke_backfill_purchase_propensity_label.sqlx
@@ -17,7 +17,7 @@ DECLARE min_date DATE;
 SET max_date = (SELECT DATE_SUB(MAX(event_date), INTERVAL {{interval_max_date}} DAY) FROM `{{mds_project_id}}.{{mds_dataset}}.event`); 
 SET min_date = (SELECT DATE_ADD(MIN(event_date), INTERVAL {{interval_min_date}} DAY) FROM `{{mds_project_id}}.{{mds_dataset}}.event`); 
 
-CREATE TEMP TABLE dates_interval as (
+CREATE OR REPLACE TEMP TABLE dates_interval as (
   SELECT DISTINCT 
     event_date as input_date,
     DATE_ADD(event_date, INTERVAL {{interval_end_date}} DAY) as end_date
@@ -28,7 +28,7 @@ CREATE TEMP TABLE dates_interval as (
 );
 
 ## All users in the platform
-CREATE TEMP TABLE all_users_possible_purchases as (
+CREATE OR REPLACE TEMP TABLE all_users_possible_purchases as (
     SELECT DISTINCT
       Users.user_pseudo_id,
       DI.event_date as event_date,
@@ -56,7 +56,7 @@ CREATE TEMP TABLE all_users_possible_purchases as (
   );
 
   ## Future User metrics: 1-7-day future purchases per user, 1-15-day future purchases per user, 1-30-day future purchases per user, 1–90-day future purchases per user
-  CREATE TEMP TABLE future_purchases_per_user AS (
+  CREATE OR REPLACE TEMP TABLE future_purchases_per_user AS (
   SELECT
     user_pseudo_id,
     input_date as feature_date,

diff --git a/sql/query/invoke_backfill_user_dimensions.sqlx b/sql/query/invoke_backfill_user_dimensions.sqlx
@@ -17,7 +17,7 @@ DECLARE min_date DATE;
 SET max_date = (SELECT DATE_SUB(MAX(event_date), INTERVAL {{interval_max_date}} DAY) FROM `{{mds_project_id}}.{{mds_dataset}}.event`); 
 SET min_date = (SELECT DATE_ADD(MIN(event_date), INTERVAL {{interval_min_date}} DAY) FROM `{{mds_project_id}}.{{mds_dataset}}.event`); 
 
-CREATE TEMP TABLE dates_interval as (
+CREATE OR REPLACE TEMP TABLE dates_interval as (
   SELECT DISTINCT 
     event_date as input_date,
     DATE_SUB(event_date, INTERVAL {{interval_end_date}} DAY) as end_date
@@ -26,7 +26,7 @@ CREATE TEMP TABLE dates_interval as (
     ORDER BY input_date DESC)
 ;
 
-CREATE TEMP TABLE user_dimensions_event_scoped as (
+CREATE OR REPLACE TEMP TABLE user_dimensions_event_scoped as (
   SELECT DISTINCT
   event_date as feature_date,
   user_pseudo_id,
@@ -62,7 +62,7 @@ CREATE TEMP TABLE user_dimensions_event_scoped as (
   )
 ;
 
-CREATE TEMP TABLE user_dimensions_session_scoped as (
+CREATE OR REPLACE TEMP TABLE user_dimensions_session_scoped as (
   SELECT DISTINCT
   event_date as feature_date,
   user_pseudo_id,
@@ -106,7 +106,7 @@ CREATE TEMP TABLE user_dimensions_session_scoped as (
 ;
 
 -- All users in the platform
-CREATE TEMP TABLE events_users as (
+CREATE OR REPLACE TEMP TABLE events_users as (
   SELECT DISTINCT
   Users.user_pseudo_id,
   Users.user_id,

diff --git a/sql/query/invoke_backfill_user_lifetime_dimensions.sqlx b/sql/query/invoke_backfill_user_lifetime_dimensions.sqlx
@@ -17,7 +17,7 @@ DECLARE min_date DATE;
 SET max_date = (SELECT DATE_SUB(MAX(event_date), INTERVAL {{interval_max_date}} DAY) FROM `{{mds_project_id}}.{{mds_dataset}}.event`); 
 SET min_date = (SELECT DATE_ADD(MIN(event_date), INTERVAL {{interval_min_date}} DAY) FROM `{{mds_project_id}}.{{mds_dataset}}.event`); 
 
-CREATE TEMP TABLE dates_interval as (
+CREATE OR REPLACE TEMP TABLE dates_interval as (
 SELECT DISTINCT 
   event_date as input_date,
   DATE_SUB(event_date, INTERVAL {{interval_end_date}} DAY) as end_date
@@ -26,7 +26,7 @@ WHERE event_date BETWEEN min_date AND max_date
   ORDER BY input_date DESC
 );
 
-CREATE TEMP TABLE user_dimensions_event_session_scoped as (
+CREATE OR REPLACE TEMP TABLE user_dimensions_event_session_scoped as (
 SELECT DISTINCT
 event_date as feature_date,
 user_pseudo_id,
@@ -74,7 +74,7 @@ AND D.device_os IS NOT NULL
 );
 
 -- All users in the platform
-CREATE TEMP TABLE events_users as (
+CREATE OR REPLACE TEMP TABLE events_users as (
 SELECT DISTINCT
 Users.user_pseudo_id,
 Users.user_id,

diff --git a/sql/query/invoke_backfill_user_lookback_metrics.sqlx b/sql/query/invoke_backfill_user_lookback_metrics.sqlx
@@ -15,7 +15,7 @@
 DECLARE min_date DATE;
 SET min_date = (SELECT DATE_ADD(MIN(event_date), INTERVAL {{interval_min_date}} DAY) FROM `{{mds_project_id}}.{{mds_dataset}}.event`); 
 
-CREATE TEMP TABLE dates_interval as (
+CREATE OR REPLACE TEMP TABLE dates_interval as (
   SELECT DISTINCT 
     event_date as input_date,
     DATE_SUB(event_date, INTERVAL {{interval_end_date}} DAY) as end_date
@@ -25,7 +25,7 @@ CREATE TEMP TABLE dates_interval as (
 );
 
 ##All users metrics: 7-day active users, 8–14-day active users
-CREATE TEMP TABLE rolling_active_users AS (
+CREATE OR REPLACE TEMP TABLE rolling_active_users AS (
 SELECT
   user_pseudo_id,
   input_date as feature_date,
@@ -39,7 +39,7 @@ GROUP BY user_pseudo_id, feature_date
 );
 
 ## Past User metrics: 1-day purchase per user, 7-day purchases per user, 8-14-day purchases per user
-CREATE TEMP TABLE rolling_purchases_past_days AS (
+CREATE OR REPLACE TEMP TABLE rolling_purchases_past_days AS (
 SELECT
   user_pseudo_id,
   input_date as feature_date,
@@ -56,7 +56,7 @@ GROUP BY user_pseudo_id, feature_date
 );
 
 ## Past User metrics: 1-day visits per user, 7-day visits per user, 8-14-day visits per user
-CREATE TEMP TABLE rolling_visits_past_days AS (
+CREATE OR REPLACE TEMP TABLE rolling_visits_past_days AS (
 SELECT
   user_pseudo_id,
   input_date as feature_date,
@@ -70,7 +70,7 @@ GROUP BY user_pseudo_id, feature_date
 );
 
 ## Past User metrics: 1-day view_item per user, 7-day view_item per user, 8-14-day view_item per user
-CREATE TEMP TABLE rolling_view_item_past_days AS (
+CREATE OR REPLACE TEMP TABLE rolling_view_item_past_days AS (
 SELECT
   user_pseudo_id,
   input_date as feature_date,
@@ -85,7 +85,7 @@ GROUP BY user_pseudo_id, feature_date
 );
 
 ## Past User metrics: 1-day add_to_cart per user, 7-day add_to_cart per user, 8-14-day add_to_cart per user
-CREATE TEMP TABLE rolling_add_to_cart_past_days AS (
+CREATE OR REPLACE TEMP TABLE rolling_add_to_cart_past_days AS (
 SELECT
   user_pseudo_id,
   input_date as feature_date,
@@ -100,7 +100,7 @@ GROUP BY user_pseudo_id, feature_date
 );
 
 ## Past User metrics: 1-day checkout per user, 7-day checkout per user, 8-14-day checkout per user
-CREATE TEMP TABLE rolling_checkout_past_days AS (
+CREATE OR REPLACE TEMP TABLE rolling_checkout_past_days AS (
 SELECT
   user_pseudo_id,
   input_date as feature_date,
@@ -115,7 +115,7 @@ GROUP BY user_pseudo_id, feature_date
 );
 
 ## Past revenue metrics
-CREATE TEMP TABLE rolling_revenue_per_user AS (
+CREATE OR REPLACE TEMP TABLE rolling_revenue_per_user AS (
 SELECT DISTINCT
   user_pseudo_id,
   input_date as feature_date,
@@ -144,7 +144,7 @@ GROUP BY user_pseudo_id, feature_date
 );
 
 -- All users in the platform
-CREATE TEMP TABLE events_users as (
+CREATE OR REPLACE TEMP TABLE events_users as (
 SELECT DISTINCT
   Users.user_pseudo_id,
   DI.input_date as feature_date

diff --git a/sql/query/invoke_backfill_user_rolling_window_lifetime_metrics.sqlx b/sql/query/invoke_backfill_user_rolling_window_lifetime_metrics.sqlx
@@ -17,7 +17,7 @@ DECLARE min_date DATE;
 SET max_date = (SELECT DATE_SUB(MAX(event_date), INTERVAL {{interval_max_date}} DAY) FROM `{{mds_project_id}}.{{mds_dataset}}.event`); 
 SET min_date = (SELECT DATE_ADD(MIN(event_date), INTERVAL {{interval_min_date}} DAY) FROM `{{mds_project_id}}.{{mds_dataset}}.event`); 
 
-CREATE TEMP TABLE dates_interval as (
+CREATE OR REPLACE TEMP TABLE dates_interval as (
 SELECT DISTINCT 
   event_date as input_date,
   DATE_SUB(event_date, INTERVAL {{interval_end_date}} DAY) as end_date
@@ -28,7 +28,7 @@ WHERE event_date BETWEEN min_date AND max_date
 
 # Run these windows aggregations every day. For each date in training and inference date ranges.
 ##All users metrics: 7-day active users, 8–30-day active users, 31-90-day active users 
-CREATE TEMP TABLE rolling_active_users AS (
+CREATE OR REPLACE TEMP TABLE rolling_active_users AS (
 SELECT
   user_pseudo_id,
   input_date as feature_date,
@@ -46,7 +46,7 @@ GROUP BY user_pseudo_id, feature_date
 );
 
 ## Past User metrics: 1-day purchase per user, 2-7-day purchases per user, 8-14-day purchases per user, 15-30-day purchases per user, 31–90-day purchases per user
-CREATE TEMP TABLE rolling_purchases_per_user AS (
+CREATE OR REPLACE TEMP TABLE rolling_purchases_per_user AS (
 SELECT
   user_pseudo_id,
   input_date as feature_date,
@@ -67,7 +67,7 @@ GROUP BY user_pseudo_id, feature_date
 );
 
 ## Past User metrics: 1-day visits per user, 2-7-day visits per user, 7-14-day visits per user, 15-30-day visits per user, 31–90-day visits per user
-CREATE TEMP TABLE rolling_visits_per_user AS (
+CREATE OR REPLACE TEMP TABLE rolling_visits_per_user AS (
 SELECT
   user_pseudo_id,
   input_date as feature_date,
@@ -85,7 +85,7 @@ GROUP BY user_pseudo_id, feature_date
 );
 
 ## Past User metrics: 1-day view_item per user, 2-7-day view_item per user, 7-14-day view_item per user, 15-30-day view_item per user, 31–90-day view_item per user
-CREATE TEMP TABLE rolling_view_item_per_user AS (
+CREATE OR REPLACE TEMP TABLE rolling_view_item_per_user AS (
 SELECT
   user_pseudo_id,
   input_date as feature_date,
@@ -104,7 +104,7 @@ GROUP BY user_pseudo_id, feature_date
 );
 
 ## Past User metrics: 1-day add_to_cart per user, 2-7-day add_to_cart per user, 7-14-day add_to_cart per user, 15-30-day add_to_cart per user, 31–90-day add_to_cart per user, 1-day checkout per user, 2-7-day checkout per user, 7-14-day checkout per user, 15-30-day checkout per user, 31–90-day checkout per user
-CREATE TEMP TABLE rolling_add_to_cart_per_user AS (
+CREATE OR REPLACE TEMP TABLE rolling_add_to_cart_per_user AS (
 SELECT
   user_pseudo_id,
   input_date as feature_date,
@@ -123,7 +123,7 @@ GROUP BY user_pseudo_id, feature_date
 );
 
 ## Past User metrics: 1-day visits per user, 2-7-day visits per user, 7-14-day visits per user, 15-30-day visits per user, 31–90-day visits per user, 1-day view_item per user, 2-7-day view_item per user, 7-14-day view_item per user, 15-30-day view_item per user, 31–90-day view_item per user, 1-day add_to_cart per user, 2-7-day add_to_cart per user, 7-14-day add_to_cart per user, 15-30-day add_to_cart per user, 31–90-day add_to_cart per user, 1-day checkout per user, 2-7-day checkout per user, 7-14-day checkout per user, 15-30-day checkout per user, 31–90-day checkout per user
-CREATE TEMP TABLE rolling_checkout_per_user AS (
+CREATE OR REPLACE TEMP TABLE rolling_checkout_per_user AS (
 SELECT
   user_pseudo_id,
   input_date as feature_date,
@@ -141,7 +141,7 @@ AND ga_session_id IS NOT NULL
 GROUP BY user_pseudo_id, feature_date
 );
 
-CREATE TEMP TABLE rolling_revenue_per_user AS (
+CREATE OR REPLACE TEMP TABLE rolling_revenue_per_user AS (
 SELECT DISTINCT
   user_pseudo_id,
   input_date as feature_date,
@@ -175,7 +175,7 @@ GROUP BY user_pseudo_id, feature_date
 );
 
 ## All users in the platform
-CREATE TEMP TABLE events_users as (
+CREATE OR REPLACE TEMP TABLE events_users as (
 SELECT DISTINCT
   Users.user_pseudo_id,
   DI.input_date as feature_date

diff --git a/sql/query/invoke_backfill_user_rolling_window_metrics.sqlx b/sql/query/invoke_backfill_user_rolling_window_metrics.sqlx
@@ -17,7 +17,7 @@ DECLARE min_date DATE;
 SET max_date = (SELECT DATE_SUB(MAX(event_date), INTERVAL {{interval_max_date}} DAY) FROM `{{mds_project_id}}.{{mds_dataset}}.event`); 
 SET min_date = (SELECT DATE_ADD(MIN(event_date), INTERVAL {{interval_min_date}} DAY) FROM `{{mds_project_id}}.{{mds_dataset}}.event`); 
 
-CREATE TEMP TABLE dates_interval as (
+CREATE OR REPLACE TEMP TABLE dates_interval as (
   SELECT DISTINCT 
     event_date as input_date,
     DATE_SUB(event_date, INTERVAL {{interval_end_date}} DAY) as end_date
@@ -28,7 +28,7 @@ CREATE TEMP TABLE dates_interval as (
 
 # Run these windows aggregations every day. For each date in training and inference date ranges.
 ##All users metrics: 7-day active users, 8–30-day active users, 31-90-day active users 
-CREATE TEMP TABLE rolling_active_users AS (
+CREATE OR REPLACE TEMP TABLE rolling_active_users AS (
   SELECT
     user_pseudo_id,
     input_date as feature_date,
@@ -49,7 +49,7 @@ CREATE TEMP TABLE rolling_active_users AS (
 );
 
 ## Past User metrics: 1-day purchase per user, 2-7-day purchases per user, 8-14-day purchases per user, 15-30-day purchases per user, 31–90-day purchases per user
-CREATE TEMP TABLE rolling_purchases_per_user AS (
+CREATE OR REPLACE TEMP TABLE rolling_purchases_per_user AS (
   SELECT
     user_pseudo_id,
     input_date as feature_date,
@@ -73,7 +73,7 @@ CREATE TEMP TABLE rolling_purchases_per_user AS (
 );
 
 ## Past User metrics: 1-day visits per user, 2-7-day visits per user, 7-14-day visits per user, 15-30-day visits per user, 31–90-day visits per user
-CREATE TEMP TABLE rolling_visits_per_user AS (
+CREATE OR REPLACE TEMP TABLE rolling_visits_per_user AS (
   SELECT
     user_pseudo_id,
     input_date as feature_date,
@@ -94,7 +94,7 @@ CREATE TEMP TABLE rolling_visits_per_user AS (
 );
 
 ## Past User metrics: 1-day view_item per user, 2-7-day view_item per user, 7-14-day view_item per user, 15-30-day view_item per user, 31–90-day view_item per user
-CREATE TEMP TABLE rolling_view_item_per_user AS (
+CREATE OR REPLACE TEMP TABLE rolling_view_item_per_user AS (
   SELECT
     user_pseudo_id,
     input_date as feature_date,
@@ -116,7 +116,7 @@ CREATE TEMP TABLE rolling_view_item_per_user AS (
 );
 
 ## Past User metrics: 1-day add_to_cart per user, 2-7-day add_to_cart per user, 7-14-day add_to_cart per user, 15-30-day add_to_cart per user, 31–90-day add_to_cart per user, 1-day checkout per user, 2-7-day checkout per user, 7-14-day checkout per user, 15-30-day checkout per user, 31–90-day checkout per user
-CREATE TEMP TABLE rolling_add_to_cart_per_user AS (
+CREATE OR REPLACE TEMP TABLE rolling_add_to_cart_per_user AS (
   SELECT
     user_pseudo_id,
     input_date as feature_date,
@@ -138,7 +138,7 @@ CREATE TEMP TABLE rolling_add_to_cart_per_user AS (
 );
 
 ## Past User metrics: 1-day visits per user, 2-7-day visits per user, 7-14-day visits per user, 15-30-day visits per user, 31–90-day visits per user, 1-day view_item per user, 2-7-day view_item per user, 7-14-day view_item per user, 15-30-day view_item per user, 31–90-day view_item per user, 1-day add_to_cart per user, 2-7-day add_to_cart per user, 7-14-day add_to_cart per user, 15-30-day add_to_cart per user, 31–90-day add_to_cart per user, 1-day checkout per user, 2-7-day checkout per user, 7-14-day checkout per user, 15-30-day checkout per user, 31–90-day checkout per user
-CREATE TEMP TABLE rolling_checkout_per_user AS (
+CREATE OR REPLACE TEMP TABLE rolling_checkout_per_user AS (
   SELECT
     user_pseudo_id,
     input_date as feature_date,
@@ -160,7 +160,7 @@ CREATE TEMP TABLE rolling_checkout_per_user AS (
 );
 
 ## All users in the platform
-CREATE TEMP TABLE events_users as (
+CREATE OR REPLACE TEMP TABLE events_users as (
   SELECT DISTINCT
     Users.user_pseudo_id,
     DI.input_date as feature_date

diff --git a/sql/query/invoke_backfill_user_scoped_lifetime_metrics.sqlx b/sql/query/invoke_backfill_user_scoped_lifetime_metrics.sqlx
@@ -17,7 +17,7 @@ DECLARE min_date DATE;
 SET max_date = (SELECT DATE_SUB(MAX(event_date), INTERVAL {{interval_max_date}} DAY) FROM `{{mds_project_id}}.{{mds_dataset}}.event`); 
 SET min_date = (SELECT DATE_ADD(MIN(event_date), INTERVAL {{interval_min_date}} DAY) FROM `{{mds_project_id}}.{{mds_dataset}}.event`); 
 
-CREATE TEMP TABLE dates_interval as (
+CREATE OR REPLACE TEMP TABLE dates_interval as (
   SELECT DISTINCT 
     event_date as input_date,
     DATE_SUB(event_date, INTERVAL {{interval_end_date}} DAY) as end_date
@@ -28,7 +28,7 @@ CREATE TEMP TABLE dates_interval as (
 
 # Run these once each day.
 ## Active users, Average daily purchasers, Average engagement time, Average engagement time per session, DAU / MAU, DAU / WAU, First time purchasers, First-time purchaser conversion, First-time purchasers per new user, Max daily purchasers, Min daily purchasers, New users, Returning users, Total purchasers, Total users, User conversion rate, User engagement, WAU / MAU 
-CREATE TEMP TABLE engagement as (
+CREATE OR REPLACE TEMP TABLE engagement as (
   SELECT 
     user_pseudo_id,
     input_date as feature_date,
@@ -47,7 +47,7 @@ CREATE TEMP TABLE engagement as (
   GROUP BY user_pseudo_id, feature_date, session_id
 );
 
-CREATE TEMP TABLE revenue_users as (
+CREATE OR REPLACE TEMP TABLE revenue_users as (
   SELECT 
   input_date as feature_date,
   SUM(ecommerce.purchase_revenue_in_usd) as sum_revenue_per_day,
@@ -62,7 +62,7 @@ CREATE TEMP TABLE revenue_users as (
   GROUP BY feature_date
 );
 
-CREATE TEMP TABLE first_purchasers as (
+CREATE OR REPLACE TEMP TABLE first_purchasers as (
   SELECT
   input_date as feature_date,
   COUNT(first_time_purchasers) as first_time_purchasers
@@ -89,7 +89,7 @@ CREATE TEMP TABLE first_purchasers as (
   GROUP BY feature_date
   );
 
-  CREATE TEMP TABLE new_users_ as (
+  CREATE OR REPLACE TEMP TABLE new_users_ as (
   SELECT
   input_date as feature_date,
   COUNT(distinct case when event_name="first_visit" then user_pseudo_id end) as new_users