test collapsible sections

scripts/optimization/README.md

@@ -25,6 +25,10 @@ shown below:
 # Project Analysis
 Project level analysis enables us to understand key metrics such as slot_time, bytes_scanned, bytes_shuffled  and bytes_spilled on a daily basis within a project. The metrics are examined as averages, medians and p80s. This enables us to understand at a high level what jobs within a project consume 80% of the time and 50% of the time daily.
 
+<details>
+
+<summary>Daily project metrics</summary>
+
 ## :mag: Daily project metrics
 
 The [daily_project_analysis.sql](daily_project_analysis.sql) script creates a table called,
@@ -41,9 +45,11 @@ The [daily_project_analysis.sql](daily_project_analysis.sql) script creates a ta
     LIMIT 100
     ```
 
+</details>
+
 # Table Analysis
 
-## BigQuery Clustering/Partitioning Recommender Tool
+## :mag: BigQuery Clustering/Partitioning Recommender Tool
 
 The BigQuery partitioning and clustering recommender analyzes workflows on your BigQuery tables and offers recommendations to better optimize your workflows and query costs using either table partitioning or table clustering. The recommender uses your BigQuery's workload execution data from the past 30 days to analyze each BigQuery table for suboptimal partitioning and clustering configurations.
 
@@ -75,7 +81,7 @@ resource "google_project_service" "recommender_service" {
 }
 ```
 
-## Tables with high slot consumption 
+## :mag: Tables with high slot consumption 
 
 The [table_read_patterns.sql](table_read_patterns.sql) script creates a table called, `table_read_patterns`, and populate it with usage data to help you determine:
 * Which tables (when queried) are resulting in high slot consumption.
@@ -101,7 +107,7 @@ The [table_read_patterns.sql](table_read_patterns.sql) script creates a table ca
     LIMIT 100
     ```
 
-## Tables without partitioning or clustering
+## :mag: Tables without partitioning or clustering
 
 The [tables_without_partitioning_or_clustering.sql](tables_without_partitioning_or_clustering.sql) script creates a table named, `tables_without_part_clust`,
 that contains a list of tables which meet any of the following conditions:
@@ -120,7 +126,7 @@ that contains a list of tables which meet any of the following conditions:
     LIMIT 100
     ```
 
-## Frequently read tables without partitioning or clustering
+## :mag: Frequently read tables without partitioning or clustering
 
 **Note:** The [freq_read_tables_without_partitioning_or_clustering.sql](freq_read_tables_without_partitioning_or_clustering.sql) script depends on the `table_read_patterns` table so you must first run the [table_read_patterns.sql](table_read_patterns.sql) script.
 
@@ -141,7 +147,7 @@ that contains a list of the most frequently read tables which meet any of the fo
     LIMIT 100
     ```
 
-## Tables receiving high quantity of daily DML statements
+## :mag: Tables receiving high quantity of daily DML statements
 
 The [frequent_daily_table_dml.sql](frequent_daily_table_dml.sql) script creates a table named, `frequent_daily_table_dml`, that contains tables that have had more than 24 daily DML statements run against them in the past 30 days.
 
@@ -164,7 +170,7 @@ The [frequent_daily_table_dml.sql](frequent_daily_table_dml.sql) script creates
 
 # Query Analysis
 
-## Queries grouped by hash
+## :mag: Queries grouped by hash
 
 The [queries_grouped_by_hash.sql](queries_grouped_by_hash.sql) script creates a table named, 
 `queries_grouped_by_hash`. This table groups queries by their normalized query pattern, which ignores
@@ -200,7 +206,7 @@ SELECT * FROM my_table WHERE date = '2020-01-03';
   LIMIT 100
   ```
 
-## Queries grouped by script
+## :mag: Queries grouped by script
 
 The [queries_grouped_by_script.sql](queries_grouped_by_script.sql) script creates a table named, 
 `queries_grouped_by_script`. This table groups queries by their parent job id which means that any queries that
@@ -241,7 +247,7 @@ ORDER BY total_slots DESC
 LIMIT 100
 ```
 
-## Queries with performance insights
+## :mag: Queries with performance insights
 
 The [query_performance_insights.sql](query_performance_insights.sql) script creates a table named, `query_performance_insights` retrieves all queries that have had performance insights
 generated for them in the past 30 days.