I have two, similar queries. In one of case have additional dummy where conditions (1=1, 0=0, true):
SELECT t1.*
FROM table1 t1
JOIN table2 t2 ON t2.fk_t1 = t1.id
JOIN table3 t3 ON t3.id = t1.fk_t3
WHERE
0 = 0 AND /* with this in 1st case, without this line in 2nd case */
t3.field = 6
AND EXISTS (SELECT 1 FROM table2 x WHERE x.fk2_t2 = t2.id)
All necessary fields are indexed.
For each case, Firebird (both versions 2.1 and 3.0) works differently, and statistics of reads see like this:
1st case (with 0=0):
Query Time
------------------------------------------------
Prepare : 32,00 ms
Execute : 1 046,00 ms
Avg fetch time: 61,53 ms
Operations
------------------------------------------------
Read : 8 342
Writes : 1
Fetches: 1 316 042
Marks : 0
Enhanced Info:
+-------------------------------+-----------+-----------+-------------+---------+---------+---------+----------+----------+----------+
| Table Name | Records | Indexed | Non-Indexed | Updates | Deletes | Inserts | Backouts | Purges | Expunges |
| | Total | reads | reads | | | | | | |
+-------------------------------+-----------+-----------+-------------+---------+---------+---------+----------+----------+----------+
|TABLE2 | 0 | 4804 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
|TABLE1 | 0 | 0 | 96884 | 0 | 0 | 0 | 0 | 0 | 0 |
|TABLE3 | 0 | 387553 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
+-------------------------------+-----------+-----------+-------------+---------+---------+---------+----------+----------+----------+
And in 2nd case (without dummy condition):
Query Time
------------------------------------------------
Prepare : 16,00 ms
Execute : 515,00 ms
Avg fetch time: 30,29 ms
Operations
------------------------------------------------
Read : 7 570
Writes : 1
Fetches: 648 103
Marks : 0
Enhanced Info:
+-------------------------------+-----------+-----------+-------------+---------+---------+---------+----------+----------+----------+
| Table Name | Records | Indexed | Non-Indexed | Updates | Deletes | Inserts | Backouts | Purges | Expunges |
| | Total | reads | reads | | | | | | |
+-------------------------------+-----------+-----------+-------------+---------+---------+---------+----------+----------+----------+
|TABLE2 | 0 | 506 | 152655 | 0 | 0 | 0 | 0 | 0 | 0 |
|TABLE1 | 0 | 467 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
|TABLE3 | 0 | 1885 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
+-------------------------------+-----------+-----------+-------------+---------+---------+---------+----------+----------+----------+
Queries have different execution plans.
PLAN JOIN (T2 NATURAL, T1 INDEX (T1_ID_IDX), T3 INDEX (T3_ID_IDX))
PLAN JOIN (T1 NATURAL, T3 INDEX (T3_ID_IDX1), T2 INDEX (T2_FK_T1_IDX))
It's strange for me. Why query with same sense of conditions works so different? How work FB optimizer and how write quick and optimal queries? How understand this?
P.S. https://github.com/FirebirdSQL/firebird/issues/6941
Related
I am trying to find cases where one type of error causes multiple sequential instances of a second type of error on a vehicle. For example, if there are two vehicles, 'a' and 'b', and vehicle a has an error of type 1 ('error_1') on day 0, it can cause errors of type 2 ('error_2') on days 1, 2, 3, and 4. I want to create a variable named cascading_error that shows every consecutive error_2 following an error_1. Note that in the case of vehicle b, it is possible to have an error_2 without a preceding error_1, in which case the value for cascading_error should be 0.
Here's what I've tried:
vals = [('a',0,1,0),('a',1,0,1),('a',2,0,1),('a',3,0,1),('b',0,0,0),('b',1,0,0),('b',2,0,1), ('b',3,0,1)]
df = spark.createDataFrame(vals, ['vehicle','day','error_1','error_2'])
w = Window.partitionBy('vehicle').orderBy('day')
df = df.withColumn('cascading_error', F.lag(df.error_1).over(w) * df.error_2)
df = df.withColumn('cascading_error', F.when((F.lag(df.cascading_error).over(w)==1) & (df.error_2==1), F.lit(1)).otherwise(df.cascading_error))
df.show()
This is my result
| vehicle | day | error_1 | error_2 | cascading_error |
| ------- | --- | ------- | ------- | --------------- |
| a | 0 | 1 | 0 | null |
| a | 1 | 0 | 1 | 1 |
| a | 2 | 0 | 1 | 1 |
| a | 3 | 0 | 1 | 0 |
| a | 4 | 0 | 1 | 0 |
| b | 0 | 0 | 0 | null |
| b | 1 | 0 | 0 | 0 |
| b | 2 | 0 | 1 | 0 |
| b | 3 | 0 | 1 | 0 |
The code is generating the correct cascading_error value on days 1 and 2 for vehicle a, but not on days 3 and 4, which should also be 1. It seems that the logic of combining cascading_error with error_2 to update cascading_error only works for a single row, not sequential ones.
I have a table with records that look like this:
| id | coord-x | coord-y | time |
---------------------------------
| 1 | 0 | 0 | 123 |
| 1 | 0 | 1 | 124 |
| 1 | 0 | 3 | 125 |
The time column represents a time in milliseconds. What I want to do is find all coord-x, coord-y as a set of points for a given timeframe for a given id. For any given id there is a unique coord-x, coord-y, and time.
What I need to do however is group these points as long as they're n milliseconds apart. So if I have this:
| id | coord-x | coord-y | time |
---------------------------------
| 1 | 0 | 0 | 123 |
| 1 | 0 | 1 | 124 |
| 1 | 0 | 3 | 125 |
| 1 | 0 | 6 | 140 |
| 1 | 0 | 7 | 141 |
I would want a result similar to this:
| id | points | start-time | end-time |
| 1 | (0,0), (0,1), (0,3) | 123 | 125 |
| 1 | (0,140), (0,141) | 140 | 141 |
I do have PostGIS installed on my database, the times I posted above are not representative but I kept them small just as a sample, the time is just a millisecond timestamp.
The tricky part is picking the expression inside your GROUP BY. If n = 5, you can do something like time / 5. To match the example exactly, the query below uses (time - 3) / 5. Once you group it, you can aggregate them into an array with array_agg.
SELECT
array_agg(("coord-x", "coord-y")) as points,
min(time) AS time_start,
max(time) AS time_end
FROM "<your_table>"
WHERE id = 1
GROUP BY (time - 3) / 5
Here is the output
+---------------------------+--------------+------------+
| points | time_start | time_end |
|---------------------------+--------------+------------|
| {"(0,0)","(0,1)","(0,3)"} | 123 | 125 |
| {"(0,6)","(0,7)"} | 140 | 141 |
+---------------------------+--------------+------------+
I have a large single table of sent emails with dates and outcomes and I'd like to be able to match each row with the last time that email was sent and a specific outcome occurred (here that open=1). This needs to be done with PostgreSQL. For example:
Initial table:
id | sent_dt | bounced | open ` | clicked | unsubscribe
1 | 2015-01-01 | 1 | 0 | 0 | 0
1 | 2015-01-02 | 0 | 1 | 1 | 0
1 | 2015-01-03 | 0 | 1 | 1 | 0
2 | 2015-01-01 | 0 | 1 | 0 | 0
2 | 2015-01-02 | 1 | 0 | 0 | 0
2 | 2015-01-03 | 0 | 1 | 0 | 0
2 | 2015-01-04 | 0 | 1 | 0 | 1
Result table:
id | sent_dt | bounced| open | clicked | unsubscribe| previous_time
1 | 2015-01-01 | 1 | 0 | 0 | 0 | NULL
1 | 2015-01-02 | 0 | 1 | 1 | 0 | NULL
1 | 2015-01-03 | 0 | 1 | 1 | 0 | 2015-01-02
2 | 2015-01-01 | 0 | 1 | 0 | 0 | NULL
2 | 2015-01-02 | 1 | 0 | 0 | 0 | 2015-01-01
2 | 2015-01-03 | 0 | 1 | 0 | 0 | 2015-01-01
2 | 2015-01-04 | 0 | 1 | 0 | 1 | 2015-01-03
I have tried using Lag but I don't know how to go about that with the conditional that open needs to equal 1 while still returning all rows. I also tried doing a many to many Join on id then finding the minimum Datediff but that is going to essentially square the size of my table and takes entirely too long to compute (>7hrs). There are several answers which would work for SQL but none that I see work for PostgreSQL.
Thanks for any help guys!
You can use ROW_NUMBER() to achieve this desired result, connect each one to the one that occurred before if it has open = 1.
SELECT t.*,s.sent_dt
FROM
(SELECT p.*,
ROW_NUMBER() OVER(PARTITION BY ID ORDER BY sent_dt DESC) rnk
FROM YourTable p) t
LEFT OUTER JOIN
(SELECT p.*,
ROW_NUMBER() OVER(PARTITION BY ID ORDER BY sent_dt DESC) rnk
FROM YourTable p) s
ON(t.rnk = s.rnk-1 AND s.open = 1)
First I create a cte openFilter for the dates where the mail are open.
Then I join the table mail with those filter and get the dates previous to that email. Finally filter everyone execpt the latest open mail.
SQL Fiddle Demo
WITH openFilter as (
SELECT m."id", m."sent_dt"
FROM mail m
WHERE "open" = 1
)
SELECT m."id",
to_char(m."sent_dt", 'YYYY-MM-DD'),
"bounced", "open", "clicked", "unsubscribe",
to_char(o."sent_dt", 'YYYY-MM-DD') previous_time
FROM mail m
LEFT JOIN openFilter o
ON m."id" = o."id"
AND m."sent_dt" > o."sent_dt"
WHERE o."sent_dt" = (SELECT MAX(t."sent_dt")
FROM openFilter t
WHERE t."id" = m."id"
AND t."sent_dt" < m."sent_dt")
OR o."sent_dt" IS NULL
Output
| id | to_char | bounced | open | clicked | unsubscribe | previous_time |
|----|------------|---------|------|---------|-------------|---------------|
| 1 | 2015-01-01 | 1 | 0 | 0 | 0 | (null) |
| 1 | 2015-01-02 | 0 | 1 | 1 | 0 | (null) |
| 1 | 2015-01-03 | 0 | 1 | 1 | 0 | 2015-01-02 |
| 2 | 2015-01-01 | 0 | 1 | 0 | 0 | (null) |
| 2 | 2015-01-02 | 1 | 0 | 0 | 0 | 2015-01-01 |
| 2 | 2015-01-03 | 0 | 1 | 0 | 0 | 2015-01-01 |
| 2 | 2015-01-04 | 0 | 1 | 0 | 1 | 2015-01-03 |
I want to count the occurrences of particular values in a certain field for an ID. So what I have is this:
| Location ID | Group |
|:----------- |:---------|
| 1 | Group A |
| 2 | Group B |
| 3 | Group C |
| 4 | Group A |
| 4 | Group B |
| 4 | Group C |
| 3 | Group A |
| 2 | Group B |
| 1 | Group C |
| 2 | Group A |
And what I would hope to yield through some computer magic is this:
| Location ID | Group A Count | Group B Count | Group C count|
|:----------- |:--------------|:--------------|:-------------|
| 1 | 1 | 0 | 1 |
| 2 | 1 | 2 | 0 |
| 3 | 1 | 0 | 1 |
| 4 | 1 | 1 | 1 |
Is there some sort of pivoting function I can use in Redshift to achieve this?
This will require the usage of the CASE function and GROUP clause, as in example.
SELECT l_id,
SUM(CASE WHEN l_group = 'Group A' THEN 1 ELSE 0 END) AS a,
SUM(CASE WHEN l_group = 'Group B' THEN 1 ELSE 0 END) AS b-- and so on
FROM location
GROUP BY l_id;
This should give you such result:
| l_id | a | b |
|------|---|---|
| 4 | 1 | 1 |
| 1 | 1 | 0 |
| 3 | 1 | 0 |
| 2 | 1 | 2 |
You can play with it on this SQL Fiddle.
I have a very weird thing happening, where I noticed that a group by (word) wasn't always grouping by word if that word is a UTF-8 string. In the same query, I get cases where it's been grouped correctly, and cases where it hasn't. I wonder if anybody knows what's up with that?
select *,count(*) over (partition by md5(word)) as k
from (
select word,count(*) as n
from :tmpwl
group by 1
) a order by 1,2 limit 12;
/* gives:
word | n | k
------+---+---
いい | 1 | 1
くず | 1 | 1
ごみ | 1 | 1
さま | 1 | 1
さん | 1 | 1
へま | 1 | 1
まめ | 1 | 1
よく | 1 | 1
ろく | 1 | 1
ネガ | 1 | 2 -- what the heck?
ネガ | 1 | 2
パス | 1 | 1
*/
Note that the following workaround works fine:
select word,n,count(*) over (partition by md5(word)) as k
from (
select md5(word),max(word) as word,count(*) as n
from :tmpwl
group by 1
) a order by 1,2 limit 12;
/* gives:
word | n | k
------+---+---
いい | 1 | 1
くず | 1 | 1
ごみ | 1 | 1
さま | 1 | 1
さん | 1 | 1
へま | 1 | 1
まめ | 1 | 1
よく | 1 | 1
ろく | 1 | 1
ネガ | 2 | 1
パス | 1 | 1
プア | 1 | 1
*/
The version is PostgreSQL 8.2.14 (Greenplum Database 4.0.4.0 build 3 Single-Node Edition) on x86_64-unknown-linux-gnu, compiled by GCC gcc.exe (GCC) 4.1.1 compiled on Nov 30 2010 17:20:26.
The source table :tmpwl:
\d :tmpwl
Table "pg_temp_25149.pdtmp_foo706453357357532"
Column | Type | Modifiers
----------+---------+-----------
baseword | text |
word | text |
value | integer |
lexicon | text |
nalts | bigint |
Distributed by: (word)