Bug/postgres performance (#126)

* patch/update-macro-readme

* bug/postgres-performance

* remove int model

* remove int model

* switch to jsonb

* add limit for test

* update changelog and regen docs

* Update README.md

fixed links

* Update CHANGELOG.md

* Update CHANGELOG.md

* Update CHANGELOG.md

* Update CHANGELOG.md

* Update CHANGELOG.md

---------

Co-authored-by: Alex Ilyichov <alexander.ilyichov@fivetran.com>

CHANGELOG.md

@@ -1,3 +1,16 @@
+# dbt_fivetran_log v1.7.3
+[PR #126](https://github.com/fivetran/dbt_fivetran_log/pull/126) includes the following updates:
+
+## Performance Improvements
+- Updated the sequence of JSON parsing for model `fivetran_platform__audit_table` to reduce runtime. 
+
+## Bug Fixes
+- Updated model `fivetran_platform__audit_user_activity` to correct the JSON parsing used to determine column `email`. This fixes an issue introduced in v1.5.0 where `fivetran_platform__audit_user_activity` could potentially have 0 rows.
+
+## Under the hood
+- Updated logic for macro `fivetran_log_lookback` to align with logic used in similar macros in other packages. 
+- Updated logic for the postgres dispatch of macro `fivetran_log_json_parse` to utilize `jsonb` instead of `json` for performance.
+
 # dbt_fivetran_log v1.7.2
 [PR #123](https://github.com/fivetran/dbt_fivetran_log/pull/123) includes the following updates:
 

README.md

@@ -16,7 +16,7 @@
 # Fivetran Platform dbt Package ([Docs](https://fivetran.github.io/dbt_fivetran_log/))
 # 📣 What does this dbt package do?
 - Generates a comprehensive data dictionary of your Fivetran Platform connector (previously called Fivetran Log) data via the [dbt docs site](https://fivetran.github.io/dbt_fivetran_log/)
-- Produces staging models in the format described by [this ERD](https://fivetran.com/docs/logs/fivetran-log#schemainformation) which clean, test, and prepare your Fivetran data from [Fivetran's free connector](https://fivetran.com/docs/applications/fivetran-log) and generates analysis ready end models.
+- Produces staging models in the format described by [this ERD](https://fivetran.com/docs/logs/fivetran-platform#schemainformation) which clean, test, and prepare your Fivetran data from [Fivetran's free connector](https://fivetran.com/docs/logs/fivetran-platform)) and generates analysis ready end models.
 - The above mentioned models enable you to better understand how you are spending money in Fivetran according to our [consumption-based pricing model](https://fivetran.com/docs/getting-started/consumption-based-pricing) as well as providing details about the performance and status of your Fivetran connectors. This is achieved by:
     - Displaying consumption data at the table, connector, destination, and account levels
     - Providing a history of measured free and paid monthly active rows (MAR), credit consumption, and the relationship between the two

dbt_project.yml

@@ -1,6 +1,6 @@
 config-version: 2
 name: 'fivetran_log'
-version: '1.7.2'
+version: '1.7.3'
 require-dbt-version: [">=1.3.0", "<2.0.0"]
 
 models:

integration_tests/ci/sample.profiles.yml

@@ -49,15 +49,15 @@ integration_tests:
       host: "{{ env_var('CI_DATABRICKS_DBT_HOST') }}"
       http_path: "{{ env_var('CI_DATABRICKS_DBT_HTTP_PATH') }}"
       schema: fivetran_platform_integration_tests 
-      threads: 2
+      threads: 8
       token: "{{ env_var('CI_DATABRICKS_DBT_TOKEN') }}"
       type: databricks
     databricks-sql:
       catalog: "{{ env_var('CI_DATABRICKS_DBT_CATALOG') }}"
       host: "{{ env_var('CI_DATABRICKS_DBT_HOST') }}"
       http_path: "{{ env_var('CI_DATABRICKS_SQL_DBT_HTTP_PATH') }}"
       schema: sqlw_tests 
-      threads: 2
+      threads: 8
       token: "{{ env_var('CI_DATABRICKS_SQL_DBT_TOKEN') }}"
       type: databricks
     sqlserver:

integration_tests/dbt_project.yml

@@ -1,5 +1,5 @@
 name: 'fivetran_log_integration_tests'
-version: '1.7.2'
+version: '1.7.3'
 
 config-version: 2
 profile: 'integration_tests'

macros/fivetran_log_json_parse.sql

@@ -28,7 +28,7 @@
 {% macro postgres__fivetran_log_json_parse(string, string_path) %}
 
   case when {{ string }} ~ '^\s*[\{].*[\}]?\s*$' -- Postgres has no native json check, so this will check the string for indicators of a JSON object
-    then {{ string }}::json #>> '{ {%- for s in string_path -%}{{ s }}{%- if not loop.last -%},{%- endif -%}{%- endfor -%} }'
+    then {{ string }}::jsonb #>> '{ {%- for s in string_path -%}{{ s }}{%- if not loop.last -%},{%- endif -%}{%- endfor -%} }'
     else null end
 
 {% endmacro %}

macros/fivetran_log_lookback.sql

@@ -1,35 +1,18 @@
-{% macro fivetran_log_lookback(from_date, datepart='day', interval=7, default_start_date='2010-01-01') %}
+{% macro fivetran_log_lookback(from_date, datepart='day', interval=7, safety_date='2010-01-01') %}
 
-{{ adapter.dispatch('fivetran_log_lookback', 'fivetran_log') (from_date, datepart='day', interval=7, default_start_date='2010-01-01') }}
+{{ adapter.dispatch('fivetran_log_lookback', 'fivetran_log') (from_date, datepart='day', interval=7, safety_date='2010-01-01') }}
 
 {%- endmacro %}
 
-{% macro default__fivetran_log_lookback(from_date, datepart='day', interval=7, default_start_date='2010-01-01')  %}
+{% macro default__fivetran_log_lookback(from_date, datepart='day', interval=7, safety_date='2010-01-01')  %}
 
-    coalesce(
-        (select {{ dbt.dateadd(datepart=datepart, interval=-interval, from_date_or_timestamp=from_date) }} 
-            from {{ this }}), 
-        {{ "'" ~ default_start_date ~ "'" }}
-        )
+    {% set sql_statement %}
+        select coalesce({{ from_date }}, {{ "'" ~ safety_date ~ "'" }})
+        from {{ this }}
+    {%- endset -%}
 
-{% endmacro %}
+    {%- set result = dbt_utils.get_single_value(sql_statement) %}
 
-{% macro bigquery__fivetran_log_lookback(from_date, datepart='day', interval=7, default_start_date='2010-01-01')  %}
-
-    -- Capture the latest timestamp in a call statement instead of a subquery for optimizing BQ costs on incremental runs
-    {%- call statement('date_agg', fetch_result=True) -%}
-        select {{ from_date }} from {{ this }}
-    {%- endcall -%}
-
-    -- load the result from the above query into a new variable
-    {%- set query_result = load_result('date_agg') -%}
-
-    -- the query_result is stored as a dataframe. Therefore, we want to now store it as a singular value.
-    {%- set date_agg = query_result['data'][0][0] %}
-
-    coalesce(
-        {{ dbt.dateadd(datepart='day', interval=-7, from_date_or_timestamp="'" ~ date_agg ~ "'") }}, 
-        {{ "'" ~ default_start_date ~ "'" }}
-        )
+    {{ dbt.dateadd(datepart=datepart, interval=-interval, from_date_or_timestamp="cast('" ~ result ~ "' as date)") }}
 
 {% endmacro %}

models/fivetran_platform__audit_table.sql

@@ -10,21 +10,39 @@
     file_format='delta' if is_databricks_sql_warehouse(target) else 'parquet'
 ) }}
 
-with sync_log as (
+with base as (
 
-    select 
-        *,
-        {{ fivetran_log.fivetran_log_json_parse(string='message_data', string_path=['table']) }} as table_name
+    select *
     from {{ ref('stg_fivetran_platform__log') }}
     where event_subtype in ('sync_start', 'sync_end', 'write_to_table_start', 'write_to_table_end', 'records_modified')
 
     {% if is_incremental() %}
-
     and cast(created_at as date) > {{ fivetran_log.fivetran_log_lookback(from_date='max(sync_start_day)', interval=7) }}
-
     {% endif %}
 ),
 
+sync_log as (
+    select 
+        *,
+        {{ fivetran_log.fivetran_log_json_parse(string='message_data', string_path=['table']) }} as table_name,
+        cast(null as {{ dbt.type_string() }}) as schema_name,
+        cast(null as {{ dbt.type_string() }}) as operation_type,
+        cast(null as {{ dbt.type_bigint() }}) as row_count
+    from base
+    where event_subtype in ('sync_start', 'sync_end', 'write_to_table_start', 'write_to_table_end')
+
+    union all
+
+    select 
+        *,
+        {{ fivetran_log.fivetran_log_json_parse(string='message_data', string_path=['table']) }} as table_name,
+        {{ fivetran_log.fivetran_log_json_parse(string='message_data', string_path=['schema']) }} as schema_name,
+        {{ fivetran_log.fivetran_log_json_parse(string='message_data', string_path=['operationType']) }} as operation_type,
+        cast ({{ fivetran_log.fivetran_log_json_parse(string='message_data', string_path=['count']) }} as {{ dbt.type_bigint() }}) as row_count
+    from base
+    where event_subtype = 'records_modified'
+),
+
 connector as (
 
     select *
@@ -80,13 +98,12 @@ records_modified_log as (
     select 
         connector_id,
         created_at,
-        {{ fivetran_log.fivetran_log_json_parse(string='message_data', string_path=['table']) }} as table_name,
-        {{ fivetran_log.fivetran_log_json_parse(string='message_data', string_path=['schema']) }} as schema_name,
-        {{ fivetran_log.fivetran_log_json_parse(string='message_data', string_path=['operationType']) }} as operation_type,
-        cast ({{ fivetran_log.fivetran_log_json_parse(string='message_data', string_path=['count']) }} as {{ dbt.type_bigint() }}) as row_count
+        table_name,
+        schema_name,
+        operation_type,
+        row_count
     from sync_log 
     where event_subtype = 'records_modified'
-
 ),
 
 sum_records_modified as (

models/fivetran_platform__audit_user_activity.sql

@@ -2,7 +2,7 @@ with logs as (
 
     select 
         *,
-        {{ fivetran_log.fivetran_log_json_parse(string='message_data', string_path='actor') }} as actor_email
+        {{ fivetran_log.fivetran_log_json_parse(string='message_data', string_path=['actor']) }} as actor_email
     from {{ ref('stg_fivetran_platform__log') }}
     where lower(message_data) like '%actor%'
 ),

models/staging/stg_fivetran_platform__log.sql

@@ -34,4 +34,4 @@ final as (
 )
 
 select * 
-from final 
+from final