Update to secondary indexes doc

use-timescale/hypercore/secondary-indexes.md

@@ -60,44 +60,51 @@ at the right time. When you segment your data to access specific columns, your q
 For example, to access information about a single device with a specific `device_id`, you segment on the `device_id` column. 
 This enables you to run analytical queries on compressed data in the $COLUMNSTORE much faster.
 
-For example for the following $HYPERTABLE:
+To illustrate, let's create a $HYPERTABLE and then run the same query on it with and without optimizations:
 
-```sql
-CREATE TABLE metrics (
-  time TIMESTAMPTZ,
-  user_id INT,
-  device_id INT,
-  data JSONB
-) WITH (
-  tsdb.hypertable
-);
-```
+<Procedure>
 
-<CreateHypertablePolicyNote />
+1. **Create a $HYPERTABLE**
 
-<Procedure>
+   Create a `metrics` $HYPERTABLE with the following command:  
+
+    ```sql
+    CREATE TABLE metrics (
+      time TIMESTAMPTZ,
+      user_id INT,
+      device_id INT,
+      data JSONB
+    ) WITH (
+      tsdb.hypertable
+    );
+    ```
+
+   <CreateHypertablePolicyNote />
+
+1. **Execute a query on the $HYPERTABLE without optimizations**
 
-1. **Execute a query on a regular $HYPERTABLE**
    1. Query your data
       ```sql
-      SELECT device_id, AVG(cpu) AS avg_cpu, AVG(disk_io) AS avg_disk_io 
+      SELECT device_id, AVG(cpu) AS avg_cpu, AVG(disk_io) AS avg_disk_io
       FROM metrics
-      WHERE device_id = 5
+      WHERE time >= '2024-03-01 00:00:00+01'
+        AND time < '2024-03-02 00:00:00+01'
+        AND device_id = 5
       GROUP BY device_id;
       ```
       Gives the following result:
       ```sql
-      device_id |      avg_cpu       |     avg_disk_io     
+      device_id |      avg_cpu       |     avg_disk_io
       -----------+--------------------+---------------------
-      5 | 0.4972598866221261 | 0.49820356730280524
+      5 | 0.4954351575883885 | 0.49725603413909114
       (1 row)
-      Time: 177,399 ms
+      Time: 29.216 ms
       ```
 
 1. **Execute a query on the same data segmented and ordered in the $COLUMNSTORE**
 
-   1. Control the way your data is ordered in the $COLUMNSTORE:
-  
+   1. Control the way your data is ordered and segmented in the $COLUMNSTORE:
+
       ```sql
       ALTER TABLE metrics SET (
         timescaledb.enable_columnstore = true,
@@ -108,24 +115,30 @@ CREATE TABLE metrics (
 
    1. Query your data
       ```sql
-       select avg(cpu) from metrics where time >= '2024-03-01 00:00:00+01' and time < '2024-03-02 00:00:00+01';
-       ```
+      SELECT device_id, AVG(cpu) AS avg_cpu, AVG(disk_io) AS avg_disk_io
+      FROM metrics
+      WHERE time >= '2024-03-01 00:00:00+01'
+        AND time < '2024-03-02 00:00:00+01'
+        AND device_id = 5
+      GROUP BY device_id;
+      ```
       Gives the following result:
+
       ```sql
-      device_id |      avg_cpu      |     avg_disk_io     
-      -----------+-------------------+---------------------
-      5 | 0.497259886622126 | 0.49820356730280535
+      device_id |      avg_cpu       |     avg_disk_io
+      -----------+--------------------+---------------------
+      5 | 0.4954351575883885 | 0.49725603413909114
       (1 row)
-      Time: 42,139 ms
+      Time: 1.828 ms
       ```
 
-   As you see, using `orderby` and `segmentby` not only reduces the amount of space taken by your data, but also 
+   As you see, using `orderby` and `segmentby` not only reduces the amount of space taken by your data, but also
    vastly improves query speed.  
 
 </Procedure>
 
 The number of rows that are compressed together in a single batch (like the ones we see above) is 1000.
-If your chunk does not contain enough data to create big enough batches, your compression ratio will be reduced.
+If your $CHUNK does not contain enough data to create big enough batches, your compression ratio will be reduced.
 This needs to be taken into account when you define your $COLUMNSTORE settings.
 
 [hypercore]: /use-timescale/:currentVersion:/hypercore/