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Update doctest code for more consistent runs #3053

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Oct 11, 2024
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Update doctest code for more consistent runs #3053

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8fa7823
Update doctest code for more consistent runs
Swiddis Oct 4, 2024
318b55b
Fix doctest tables
Swiddis Oct 7, 2024
4916938
Fix misaligned prometheus connector docs
Swiddis Oct 7, 2024
7caaff6
Merge remote-tracking branch 'upstream/main' into maintenance/stabili…
Swiddis Oct 7, 2024
259e146
Merge remote-tracking branch 'upstream/main' into maintenance/stabili…
Swiddis Oct 8, 2024
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20 changes: 10 additions & 10 deletions docs/user/beyond/partiql.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -202,11 +202,11 @@ Selecting top level for object fields, object fields of array value and nested f

os> SELECT city, accounts, projects FROM people;
fetched rows / total rows = 1/1
+-----------------------------------------------------+------------+----------------------------------------------------------------------------------------------------------------+
| city | accounts | projects |
|-----------------------------------------------------+------------+----------------------------------------------------------------------------------------------------------------|
| {'name': 'Seattle', 'location': {'latitude': 10.5}} | {'id': 1} | [{'name': 'AWS Redshift Spectrum querying'},{'name': 'AWS Redshift security'},{'name': 'AWS Aurora security'}] |
+-----------------------------------------------------+------------+----------------------------------------------------------------------------------------------------------------+
+-----------------------------------------------------+-----------+----------------------------------------------------------------------------------------------------------------+
| city | accounts | projects |
|-----------------------------------------------------+-----------+----------------------------------------------------------------------------------------------------------------|
| {'name': 'Seattle', 'location': {'latitude': 10.5}} | {'id': 1} | [{'name': 'AWS Redshift Spectrum querying'},{'name': 'AWS Redshift security'},{'name': 'AWS Aurora security'}] |
+-----------------------------------------------------+-----------+----------------------------------------------------------------------------------------------------------------+

Example 2: Selecting Deeper Levels
----------------------------------
Expand All @@ -215,11 +215,11 @@ Selecting at deeper levels for object fields of regular value returns inner fiel

os> SELECT city.location, city.location.latitude FROM people;
fetched rows / total rows = 1/1
+--------------------+--------------------------+
| city.location | city.location.latitude |
|--------------------+--------------------------|
| {'latitude': 10.5} | 10.5 |
+--------------------+--------------------------+
+--------------------+------------------------+
| city.location | city.location.latitude |
|--------------------+------------------------|
| {'latitude': 10.5} | 10.5 |
+--------------------+------------------------+


For selecting second level for nested fields, please read on and find more details in the following sections.
Expand Down
216 changes: 108 additions & 108 deletions docs/user/dql/aggregations.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -34,12 +34,12 @@ The group by expression could be identifier::

os> SELECT gender, sum(age) FROM accounts GROUP BY gender;
fetched rows / total rows = 2/2
+----------+------------+
| gender | sum(age) |
|----------+------------|
| F | 28 |
| M | 101 |
+----------+------------+
+--------+----------+
| gender | sum(age) |
|--------+----------|
| F | 28 |
| M | 101 |
+--------+----------+


Ordinal
Expand All @@ -49,12 +49,12 @@ The group by expression could be ordinal::

os> SELECT gender, sum(age) FROM accounts GROUP BY 1;
fetched rows / total rows = 2/2
+----------+------------+
| gender | sum(age) |
|----------+------------|
| F | 28 |
| M | 101 |
+----------+------------+
+--------+----------+
| gender | sum(age) |
|--------+----------|
| F | 28 |
| M | 101 |
+--------+----------+


Expression
Expand All @@ -64,14 +64,14 @@ The group by expression could be expression::

os> SELECT abs(account_number), sum(age) FROM accounts GROUP BY abs(account_number);
fetched rows / total rows = 4/4
+-----------------------+------------+
| abs(account_number) | sum(age) |
|-----------------------+------------|
| 1 | 32 |
| 13 | 28 |
| 18 | 33 |
| 6 | 36 |
+-----------------------+------------+
+---------------------+----------+
| abs(account_number) | sum(age) |
|---------------------+----------|
| 1 | 32 |
| 13 | 28 |
| 18 | 33 |
| 6 | 36 |
+---------------------+----------+


Aggregation
Expand All @@ -91,12 +91,12 @@ The aggregation could be used select::

os> SELECT gender, sum(age) FROM accounts GROUP BY gender;
fetched rows / total rows = 2/2
+----------+------------+
| gender | sum(age) |
|----------+------------|
| F | 28 |
| M | 101 |
+----------+------------+
+--------+----------+
| gender | sum(age) |
|--------+----------|
| F | 28 |
| M | 101 |
+--------+----------+

Expression over Aggregation
---------------------------
Expand All @@ -105,12 +105,12 @@ The aggregation could be used as arguments of expression::

os> SELECT gender, sum(age) * 2 as sum2 FROM accounts GROUP BY gender;
fetched rows / total rows = 2/2
+----------+--------+
| gender | sum2 |
|----------+--------|
| F | 56 |
| M | 202 |
+----------+--------+
+--------+------+
| gender | sum2 |
|--------+------|
| F | 56 |
| M | 202 |
+--------+------+

Expression as argument of Aggregation
-------------------------------------
Expand All @@ -119,12 +119,12 @@ The aggregation could has expression as arguments::

os> SELECT gender, sum(age * 2) as sum2 FROM accounts GROUP BY gender;
fetched rows / total rows = 2/2
+----------+--------+
| gender | sum2 |
|----------+--------|
| F | 56 |
| M | 202 |
+----------+--------+
+--------+------+
| gender | sum2 |
|--------+------|
| F | 56 |
| M | 202 |
+--------+------+

COUNT Aggregations
------------------
Expand All @@ -150,12 +150,12 @@ Example::

os> SELECT gender, count(*) as countV FROM accounts GROUP BY gender;
fetched rows / total rows = 2/2
+----------+----------+
| gender | countV |
|----------+----------|
| F | 1 |
| M | 3 |
+----------+----------+
+--------+--------+
| gender | countV |
|--------+--------|
| F | 1 |
| M | 3 |
+--------+--------+

SUM
---
Expand All @@ -169,12 +169,12 @@ Example::

os> SELECT gender, sum(age) as sumV FROM accounts GROUP BY gender;
fetched rows / total rows = 2/2
+----------+--------+
| gender | sumV |
|----------+--------|
| F | 28 |
| M | 101 |
+----------+--------+
+--------+------+
| gender | sumV |
|--------+------|
| F | 28 |
| M | 101 |
+--------+------+

AVG
---
Expand All @@ -188,12 +188,12 @@ Example::

os> SELECT gender, avg(age) as avgV FROM accounts GROUP BY gender;
fetched rows / total rows = 2/2
+----------+--------------------+
| gender | avgV |
|----------+--------------------|
| F | 28.0 |
| M | 33.666666666666664 |
+----------+--------------------+
+--------+--------------------+
| gender | avgV |
|--------+--------------------|
| F | 28.0 |
| M | 33.666666666666664 |
+--------+--------------------+

MAX
---
Expand All @@ -207,11 +207,11 @@ Example::

os> SELECT max(age) as maxV FROM accounts;
fetched rows / total rows = 1/1
+--------+
| maxV |
|--------|
| 36 |
+--------+
+------+
| maxV |
|------|
| 36 |
+------+

MIN
---
Expand All @@ -225,11 +225,11 @@ Example::

os> SELECT min(age) as minV FROM accounts;
fetched rows / total rows = 1/1
+--------+
| minV |
|--------|
| 28 |
+--------+
+------+
| minV |
|------|
| 28 |
+------+

VAR_POP
-------
Expand Down Expand Up @@ -364,11 +364,11 @@ To get the count of distinct values of a field, you can add a keyword ``DISTINCT

os> SELECT COUNT(DISTINCT gender), COUNT(gender) FROM accounts;
fetched rows / total rows = 1/1
+--------------------------+-----------------+
| COUNT(DISTINCT gender) | COUNT(gender) |
|--------------------------+-----------------|
| 2 | 4 |
+--------------------------+-----------------+
+------------------------+---------------+
| COUNT(DISTINCT gender) | COUNT(gender) |
|------------------------+---------------|
| 2 | 4 |
+------------------------+---------------+

PERCENTILE or PERCENTILE_APPROX
-------------------------------
Expand All @@ -382,12 +382,12 @@ Example::

os> SELECT gender, percentile(age, 90) as p90 FROM accounts GROUP BY gender;
fetched rows / total rows = 2/2
+----------+-------+
| gender | p90 |
|----------+-------|
| F | 28 |
| M | 36 |
+----------+-------+
+--------+-----+
| gender | p90 |
|--------+-----|
| F | 28 |
| M | 36 |
+--------+-----+

HAVING Clause
=============
Expand All @@ -413,11 +413,11 @@ Here is an example for typical use of ``HAVING`` clause::
... GROUP BY gender
... HAVING sum(age) > 100;
fetched rows / total rows = 1/1
+----------+------------+
| gender | sum(age) |
|----------+------------|
| M | 101 |
+----------+------------+
+--------+----------+
| gender | sum(age) |
|--------+----------|
| M | 101 |
+--------+----------+

Here is another example for using alias in ``HAVING`` condition. Note that if an identifier is ambiguous, for example present both as a select alias and an index field, preference is alias. This means the identifier will be replaced by expression aliased in ``SELECT`` clause::

Expand All @@ -427,11 +427,11 @@ Here is another example for using alias in ``HAVING`` condition. Note that if an
... GROUP BY gender
... HAVING s > 100;
fetched rows / total rows = 1/1
+----------+-----+
| gender | s |
|----------+-----|
| M | 101 |
+----------+-----+
+--------+-----+
| gender | s |
|--------+-----|
| M | 101 |
+--------+-----+

HAVING without GROUP BY
-----------------------
Expand All @@ -443,11 +443,11 @@ Additionally, a ``HAVING`` clause can work without ``GROUP BY`` clause. This is
... FROM accounts
... HAVING sum(age) > 100;
fetched rows / total rows = 1/1
+------------------------+
| 'Total of age > 100' |
|------------------------|
| Total of age > 100 |
+------------------------+
+----------------------+
| 'Total of age > 100' |
|----------------------|
| Total of age > 100 |
+----------------------+


FILTER Clause
Expand All @@ -465,12 +465,12 @@ The group by aggregation with ``FILTER`` clause can set different conditions for

os> SELECT avg(age) FILTER(WHERE balance > 10000) AS filtered, gender FROM accounts GROUP BY gender
fetched rows / total rows = 2/2
+------------+----------+
| filtered | gender |
|------------+----------|
| 28.0 | F |
| 32.0 | M |
+------------+----------+
+----------+--------+
| filtered | gender |
|----------+--------|
| 28.0 | F |
| 32.0 | M |
+----------+--------+

FILTER without GROUP BY
-----------------------
Expand All @@ -482,11 +482,11 @@ The ``FILTER`` clause can be used in aggregation functions without GROUP BY as w
... count(*) FILTER(WHERE age > 34) AS filtered
... FROM accounts
fetched rows / total rows = 1/1
+--------------+------------+
| unfiltered | filtered |
|--------------+------------|
| 4 | 1 |
+--------------+------------+
+------------+----------+
| unfiltered | filtered |
|------------+----------|
| 4 | 1 |
+------------+----------+

Distinct count aggregate with FILTER
------------------------------------
Expand All @@ -495,9 +495,9 @@ The ``FILTER`` clause is also used in distinct count to do the filtering before

os> SELECT COUNT(DISTINCT firstname) FILTER(WHERE age > 30) AS distinct_count FROM accounts
fetched rows / total rows = 1/1
+------------------+
| distinct_count |
|------------------|
| 3 |
+------------------+
+----------------+
| distinct_count |
|----------------|
| 3 |
+----------------+

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