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SwiddisSwiddis
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Update doctest code for more consistent runs (#3053)
Signed-off-by: Simeon Widdis <[email protected]>
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‎docs/user/beyond/partiql.rst

+10-10
Original file line numberDiff line numberDiff line change
@@ -202,11 +202,11 @@ Selecting top level for object fields, object fields of array value and nested f
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os> SELECT city, accounts, projects FROM people;
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fetched rows / total rows = 1/1
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+-----------------------------------------------------+------------+----------------------------------------------------------------------------------------------------------------+
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| city | accounts | projects |
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|-----------------------------------------------------+------------+----------------------------------------------------------------------------------------------------------------|
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| {'name': 'Seattle', 'location': {'latitude': 10.5}} | {'id': 1} | [{'name': 'AWS Redshift Spectrum querying'},{'name': 'AWS Redshift security'},{'name': 'AWS Aurora security'}] |
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+-----------------------------------------------------+------------+----------------------------------------------------------------------------------------------------------------+
205+
+-----------------------------------------------------+-----------+----------------------------------------------------------------------------------------------------------------+
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| city | accounts | projects |
207+
|-----------------------------------------------------+-----------+----------------------------------------------------------------------------------------------------------------|
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| {'name': 'Seattle', 'location': {'latitude': 10.5}} | {'id': 1} | [{'name': 'AWS Redshift Spectrum querying'},{'name': 'AWS Redshift security'},{'name': 'AWS Aurora security'}] |
209+
+-----------------------------------------------------+-----------+----------------------------------------------------------------------------------------------------------------+
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211211
Example 2: Selecting Deeper Levels
212212
----------------------------------
@@ -215,11 +215,11 @@ Selecting at deeper levels for object fields of regular value returns inner fiel
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os> SELECT city.location, city.location.latitude FROM people;
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fetched rows / total rows = 1/1
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+--------------------+--------------------------+
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| city.location | city.location.latitude |
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|--------------------+--------------------------|
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| {'latitude': 10.5} | 10.5 |
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+--------------------+--------------------------+
218+
+--------------------+------------------------+
219+
| city.location | city.location.latitude |
220+
|--------------------+------------------------|
221+
| {'latitude': 10.5} | 10.5 |
222+
+--------------------+------------------------+
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For selecting second level for nested fields, please read on and find more details in the following sections.

‎docs/user/dql/aggregations.rst

+108-108
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@@ -34,12 +34,12 @@ The group by expression could be identifier::
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os> SELECT gender, sum(age) FROM accounts GROUP BY gender;
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fetched rows / total rows = 2/2
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+----------+------------+
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| gender | sum(age) |
39-
|----------+------------|
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| F | 28 |
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| M | 101 |
42-
+----------+------------+
37+
+--------+----------+
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| gender | sum(age) |
39+
|--------+----------|
40+
| F | 28 |
41+
| M | 101 |
42+
+--------+----------+
4343

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Ordinal
@@ -49,12 +49,12 @@ The group by expression could be ordinal::
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os> SELECT gender, sum(age) FROM accounts GROUP BY 1;
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fetched rows / total rows = 2/2
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+----------+------------+
53-
| gender | sum(age) |
54-
|----------+------------|
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| F | 28 |
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| M | 101 |
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+----------+------------+
52+
+--------+----------+
53+
| gender | sum(age) |
54+
|--------+----------|
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| F | 28 |
56+
| M | 101 |
57+
+--------+----------+
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Expression
@@ -64,14 +64,14 @@ The group by expression could be expression::
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os> SELECT abs(account_number), sum(age) FROM accounts GROUP BY abs(account_number);
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fetched rows / total rows = 4/4
67-
+-----------------------+------------+
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| abs(account_number) | sum(age) |
69-
|-----------------------+------------|
70-
| 1 | 32 |
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| 13 | 28 |
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| 18 | 33 |
73-
| 6 | 36 |
74-
+-----------------------+------------+
67+
+---------------------+----------+
68+
| abs(account_number) | sum(age) |
69+
|---------------------+----------|
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| 1 | 32 |
71+
| 13 | 28 |
72+
| 18 | 33 |
73+
| 6 | 36 |
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+---------------------+----------+
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7777
Aggregation
@@ -91,12 +91,12 @@ The aggregation could be used select::
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os> SELECT gender, sum(age) FROM accounts GROUP BY gender;
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fetched rows / total rows = 2/2
94-
+----------+------------+
95-
| gender | sum(age) |
96-
|----------+------------|
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| F | 28 |
98-
| M | 101 |
99-
+----------+------------+
94+
+--------+----------+
95+
| gender | sum(age) |
96+
|--------+----------|
97+
| F | 28 |
98+
| M | 101 |
99+
+--------+----------+
100100

101101
Expression over Aggregation
102102
---------------------------
@@ -105,12 +105,12 @@ The aggregation could be used as arguments of expression::
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os> SELECT gender, sum(age) * 2 as sum2 FROM accounts GROUP BY gender;
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fetched rows / total rows = 2/2
108-
+----------+--------+
109-
| gender | sum2 |
110-
|----------+--------|
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| F | 56 |
112-
| M | 202 |
113-
+----------+--------+
108+
+--------+------+
109+
| gender | sum2 |
110+
|--------+------|
111+
| F | 56 |
112+
| M | 202 |
113+
+--------+------+
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Expression as argument of Aggregation
116116
-------------------------------------
@@ -119,12 +119,12 @@ The aggregation could has expression as arguments::
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os> SELECT gender, sum(age * 2) as sum2 FROM accounts GROUP BY gender;
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fetched rows / total rows = 2/2
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+----------+--------+
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| gender | sum2 |
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|----------+--------|
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| F | 56 |
126-
| M | 202 |
127-
+----------+--------+
122+
+--------+------+
123+
| gender | sum2 |
124+
|--------+------|
125+
| F | 56 |
126+
| M | 202 |
127+
+--------+------+
128128

129129
COUNT Aggregations
130130
------------------
@@ -150,12 +150,12 @@ Example::
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os> SELECT gender, count(*) as countV FROM accounts GROUP BY gender;
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fetched rows / total rows = 2/2
153-
+----------+----------+
154-
| gender | countV |
155-
|----------+----------|
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| F | 1 |
157-
| M | 3 |
158-
+----------+----------+
153+
+--------+--------+
154+
| gender | countV |
155+
|--------+--------|
156+
| F | 1 |
157+
| M | 3 |
158+
+--------+--------+
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160160
SUM
161161
---
@@ -169,12 +169,12 @@ Example::
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os> SELECT gender, sum(age) as sumV FROM accounts GROUP BY gender;
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fetched rows / total rows = 2/2
172-
+----------+--------+
173-
| gender | sumV |
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|----------+--------|
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| F | 28 |
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| M | 101 |
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+----------+--------+
172+
+--------+------+
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| gender | sumV |
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|--------+------|
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| F | 28 |
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| M | 101 |
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+--------+------+
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179179
AVG
180180
---
@@ -188,12 +188,12 @@ Example::
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os> SELECT gender, avg(age) as avgV FROM accounts GROUP BY gender;
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fetched rows / total rows = 2/2
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+----------+--------------------+
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| gender | avgV |
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|----------+--------------------|
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| F | 28.0 |
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| M | 33.666666666666664 |
196-
+----------+--------------------+
191+
+--------+--------------------+
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| gender | avgV |
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|--------+--------------------|
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| F | 28.0 |
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| M | 33.666666666666664 |
196+
+--------+--------------------+
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198198
MAX
199199
---
@@ -207,11 +207,11 @@ Example::
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os> SELECT max(age) as maxV FROM accounts;
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fetched rows / total rows = 1/1
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+--------+
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| maxV |
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|--------|
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| 36 |
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+--------+
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+------+
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| maxV |
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|------|
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| 36 |
214+
+------+
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MIN
217217
---
@@ -225,11 +225,11 @@ Example::
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os> SELECT min(age) as minV FROM accounts;
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fetched rows / total rows = 1/1
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+--------+
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| minV |
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|--------|
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| 28 |
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+--------+
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+------+
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| minV |
230+
|------|
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| 28 |
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+------+
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VAR_POP
235235
-------
@@ -364,11 +364,11 @@ To get the count of distinct values of a field, you can add a keyword ``DISTINCT
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os> SELECT COUNT(DISTINCT gender), COUNT(gender) FROM accounts;
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fetched rows / total rows = 1/1
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+--------------------------+-----------------+
368-
| COUNT(DISTINCT gender) | COUNT(gender) |
369-
|--------------------------+-----------------|
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| 2 | 4 |
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+--------------------------+-----------------+
367+
+------------------------+---------------+
368+
| COUNT(DISTINCT gender) | COUNT(gender) |
369+
|------------------------+---------------|
370+
| 2 | 4 |
371+
+------------------------+---------------+
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373373
PERCENTILE or PERCENTILE_APPROX
374374
-------------------------------
@@ -382,12 +382,12 @@ Example::
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383383
os> SELECT gender, percentile(age, 90) as p90 FROM accounts GROUP BY gender;
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fetched rows / total rows = 2/2
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+----------+-------+
386-
| gender | p90 |
387-
|----------+-------|
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| F | 28 |
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| M | 36 |
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+----------+-------+
385+
+--------+-----+
386+
| gender | p90 |
387+
|--------+-----|
388+
| F | 28 |
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| M | 36 |
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+--------+-----+
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392392
HAVING Clause
393393
=============
@@ -413,11 +413,11 @@ Here is an example for typical use of ``HAVING`` clause::
413413
... GROUP BY gender
414414
... HAVING sum(age) > 100;
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fetched rows / total rows = 1/1
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+----------+------------+
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| gender | sum(age) |
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|----------+------------|
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| M | 101 |
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+----------+------------+
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+--------+----------+
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| gender | sum(age) |
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|--------+----------|
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| M | 101 |
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+--------+----------+
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422422
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::
423423

@@ -427,11 +427,11 @@ Here is another example for using alias in ``HAVING`` condition. Note that if an
427427
... GROUP BY gender
428428
... HAVING s > 100;
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fetched rows / total rows = 1/1
430-
+----------+-----+
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| gender | s |
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|----------+-----|
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| M | 101 |
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+----------+-----+
430+
+--------+-----+
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| gender | s |
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|--------+-----|
433+
| M | 101 |
434+
+--------+-----+
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436436
HAVING without GROUP BY
437437
-----------------------
@@ -443,11 +443,11 @@ Additionally, a ``HAVING`` clause can work without ``GROUP BY`` clause. This is
443443
... FROM accounts
444444
... HAVING sum(age) > 100;
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fetched rows / total rows = 1/1
446-
+------------------------+
447-
| 'Total of age > 100' |
448-
|------------------------|
449-
| Total of age > 100 |
450-
+------------------------+
446+
+----------------------+
447+
| 'Total of age > 100' |
448+
|----------------------|
449+
| Total of age > 100 |
450+
+----------------------+
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452452

453453
FILTER Clause
@@ -465,12 +465,12 @@ The group by aggregation with ``FILTER`` clause can set different conditions for
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466466
os> SELECT avg(age) FILTER(WHERE balance > 10000) AS filtered, gender FROM accounts GROUP BY gender
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fetched rows / total rows = 2/2
468-
+------------+----------+
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| filtered | gender |
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|------------+----------|
471-
| 28.0 | F |
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| 32.0 | M |
473-
+------------+----------+
468+
+----------+--------+
469+
| filtered | gender |
470+
|----------+--------|
471+
| 28.0 | F |
472+
| 32.0 | M |
473+
+----------+--------+
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475475
FILTER without GROUP BY
476476
-----------------------
@@ -482,11 +482,11 @@ The ``FILTER`` clause can be used in aggregation functions without GROUP BY as w
482482
... count(*) FILTER(WHERE age > 34) AS filtered
483483
... FROM accounts
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fetched rows / total rows = 1/1
485-
+--------------+------------+
486-
| unfiltered | filtered |
487-
|--------------+------------|
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| 4 | 1 |
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+--------------+------------+
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+------------+----------+
486+
| unfiltered | filtered |
487+
|------------+----------|
488+
| 4 | 1 |
489+
+------------+----------+
490490

491491
Distinct count aggregate with FILTER
492492
------------------------------------
@@ -495,9 +495,9 @@ The ``FILTER`` clause is also used in distinct count to do the filtering before
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496496
os> SELECT COUNT(DISTINCT firstname) FILTER(WHERE age > 30) AS distinct_count FROM accounts
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fetched rows / total rows = 1/1
498-
+------------------+
499-
| distinct_count |
500-
|------------------|
501-
| 3 |
502-
+------------------+
498+
+----------------+
499+
| distinct_count |
500+
|----------------|
501+
| 3 |
502+
+----------------+
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