I need to know the current slot count value in redshift Queue configuration for a specific redshift user. Is there any system table in redshift which provide this user level information.
You can find a list of the internal tables at "WLM System Tables and Views" http://docs.aws.amazon.com/redshift/latest/dg/cm-c-wlm-system-tables-and-views.html
This query summarizes things:
SELECT
wlm.service_class queue
, TRIM( wlm.name ) queue_name
, LISTAGG( TRIM( cnd.condition ), ', ' ) condition
, wlm.num_query_tasks query_concurrency
, wlm.query_working_mem per_query_memory_mb
, ROUND(((wlm.num_query_tasks * wlm.query_working_mem)::NUMERIC / mem.total_mem::NUMERIC) * 100, 0)::INT cluster_memory_pct
, wlm.max_execution_time
, wlm.user_group_wild_card
, wlm.query_group_wild_card
FROM stv_wlm_service_class_config wlm
JOIN stv_wlm_classification_config cnd ON wlm.service_class = cnd.action_service_class
CROSS JOIN (SELECT SUM( num_query_tasks * query_working_mem ) total_mem
FROM pg_catalog.stv_wlm_service_class_config
WHERE service_class > 5) mem
WHERE wlm.service_class > 5
GROUP BY wlm.service_class, TRIM( wlm.name ), wlm.num_query_tasks, wlm.query_working_mem, mem.total_mem,
wlm.max_execution_time, wlm.user_group_wild_card, wlm.query_group_wild_card
ORDER BY 1
;
Output
queue | queue_name | condition | query_concurrency | per_query_memory_mb | cluster_memory_pct | max_execution_time | user_group_wild_card | query_group_wild_card
-------+------------------+------------------+-------------------+---------------------+--------------------+--------------------+----------------------+-----------------------
6 | Service class #1 | (querytype: any) | 5 | 1208 | 100 | 0 | false | false
(1 row)
Related
I have an unusual problem I'm trying to solve with SQL where I need to generate sequential numbers for partitioned rows but override specific numbers with values from the data, while not breaking the sequence (unless the override causes a number to be used greater than the number of rows present).
I feel I might be able to achieve this by selecting the rows where I need to override the generated sequence value and the rows I don't need to override the value, then unioning them together and somehow using coalesce to get the desired dynamically generated sequence value, or maybe there's some way I can utilise recursive.
I've not been able to solve this problem yet, but I've put together a SQL Fiddle which provides a simplified version:
http://sqlfiddle.com/#!17/236b5/5
The desired_dynamic_number is what I'm trying to generate and the generated_dynamic_number is my current work-in-progress attempt.
Any pointers around the best way to achieve the desired_dynamic_number values dynamically?
Update:
I'm almost there using lag:
http://sqlfiddle.com/#!17/236b5/24
step-by-step demo:db<>fiddle
SELECT
*,
COALESCE( -- 3
first_value(override_as_number) OVER w -- 2
, 1
)
+ row_number() OVER w - 1 -- 4, 5
FROM (
SELECT
*,
SUM( -- 1
CASE WHEN override_as_number IS NOT NULL THEN 1 ELSE 0 END
) OVER (PARTITION BY grouped_by ORDER BY secondary_order_by)
as grouped
FROM sample
) s
WINDOW w AS (PARTITION BY grouped_by, grouped ORDER BY secondary_order_by)
Create a new subpartition within your partitions: This cumulative sum creates a unique group id for every group of records which starts with a override_as_number <> NULL followed by NULL records. So, for instance, your (AAA, d) to (AAA, f) belongs to the same subpartition/group.
first_value() gives the first value of such subpartition.
The COALESCE ensures a non-NULL result from the first_value() function if your partition starts with a NULL record.
row_number() - 1 creates a row count within a subpartition, starting with 0.
Adding the first_value() of a subpartition with the row count creates your result: Beginning with the one non-NULL record of a subpartition (adding the 0 row count), the first following NULL records results in the value +1 and so forth.
Below query gives exact result, but you need to verify with all combinations
select c.*,COALESCE(c.override_as_number,c.act) as final FROM
(
select b.*, dense_rank() over(partition by grouped_by order by grouped_by, actual) as act from
(
select a.*,COALESCE(override_as_number,row_num) as actual FROM
(
select grouped_by , secondary_order_by ,
dense_rank() over ( partition by grouped_by order by grouped_by, secondary_order_by ) as row_num
,override_as_number,desired_dynamic_number from fiddle
) a
) b
) c ;
column "final" is the result
grouped_by | secondary_order_by | row_num | override_as_number | desired_dynamic_number | actual | act | final
------------+--------------------+---------+--------------------+------------------------+--------+-----+-------
AAA | a | 1 | 1 | 1 | 1 | 1 | 1
AAA | b | 2 | | 2 | 2 | 2 | 2
AAA | c | 3 | 3 | 3 | 3 | 3 | 3
AAA | d | 4 | 3 | 3 | 3 | 3 | 3
AAA | e | 5 | | 4 | 5 | 4 | 4
AAA | f | 6 | | 5 | 6 | 5 | 5
AAA | g | 7 | 999 | 999 | 999 | 6 | 999
XYZ | a | 1 | | 1 | 1 | 1 | 1
ZZZ | a | 1 | | 1 | 1 | 1 | 1
ZZZ | b | 2 | | 2 | 2 | 2 | 2
(10 rows)
Hope this helps!
The real world problem I was trying to solve did not have a nicely ordered secondary_order_by column, instead it would be something a bit more randomised (a created timestamp).
For the benefit of people who stumble across this question with a similar problem to solve, a colleague solved this problem using a cartesian join, who's solution I'm posting below. The solution is Snowflake SQL which should be possible to adapt to Postgres. It does fall down on higher override_as_number values though unless the from table(generator(rowcount => 1000)) 1000 value is not increased to something suitably high.
The SQL:
with tally_table as (
select row_number() over (order by seq4()) as gen_list
from table(generator(rowcount => 1000))
),
base as (
select *,
IFF(override_as_number IS NULL, row_number() OVER(PARTITION BY grouped_by, override_as_number order by random),override_as_number) as rownum
from "SANDPIT"."TEST"."SAMPLEDATA" order by grouped_by,override_as_number,random
) --select * from base order by grouped_by,random;
,
cart_product as (
select *
from tally_table cross join (Select distinct grouped_by from base ) as distinct_grouped_by
) --select * from cart_product;
,
filter_product as (
select *,
row_number() OVER(partition by cart_product.grouped_by order by cart_product.grouped_by,gen_list) as seq_order
from cart_product
where CONCAT(grouped_by,'~',gen_list) NOT IN (select concat(grouped_by,'~',override_as_number) from base where override_as_number is not null)
) --select * from try2 order by 2,3 ;
select base.grouped_by,
base.random,
base.override_as_number,
base.answer, -- This is hard coded as test data
IFF(override_as_number is null, gen_list, seq_order) as computed_answer
from base inner join filter_product on base.rownum = filter_product.seq_order and base.grouped_by = filter_product.grouped_by
order by base.grouped_by,
random;
In the end I went for a simpler solution using a temporary table and cursor to inject override_as_number values and shuffle other numbers.
I have the following query code
query = """
with double_entry_book as (
SELECT to_address as address, value as value
FROM `bigquery-public-data.crypto_ethereum.traces`
WHERE to_address is not null
AND block_timestamp < '2022-01-01 00:00:00'
AND status = 1
AND (call_type not in ('delegatecall', 'callcode', 'staticcall') or call_type is null)
union all
-- credits
SELECT from_address as address, -value as value
FROM `bigquery-public-data.crypto_ethereum.traces`
WHERE from_address is not null
AND block_timestamp < '2022-01-01 00:00:00'
AND status = 1
AND (call_type not in ('delegatecall', 'callcode', 'staticcall') or call_type is null)
union all
)
SELECT address,
sum(value) / 1000000000000000000 as balance
from double_entry_book
group by address
order by balance desc
LIMIT 15000000
"""
In the last part, I want to drop rows where "balance" is less than, let's say, 0.02 and then group, order, etc. I imagine this should be a simple code. Any help will be appreciated!
We can delete on a CTE and use returning to get the id's of the rows being deleted, but they still exist until the transaction is comitted.
CREATE TABLE t (
id serial,
variale int);
insert into t (variale) values
(1),(2),(3),(4),(5);
✓
5 rows affected
with del as
(delete from t
where variale < 3
returning id)
select
t.id,
t.variale,
del.id ids_being_deleted
from t
left join del
on t.id = del.id;
id | variale | ids_being_deleted
-: | ------: | ----------------:
1 | 1 | 1
2 | 2 | 2
3 | 3 | null
4 | 4 | null
5 | 5 | null
select * from t;
id | variale
-: | ------:
3 | 3
4 | 4
5 | 5
db<>fiddle here
I need to calculate value of some column X based on some other columns of the current record and the value of X for the previous record (using some partition and order). Basically I need to implement query in the form
SELECT <some fields>,
<some expression using LAG(X) OVER(PARTITION BY ... ORDER BY ...) AS X
FROM <table>
This is not possible because only existing columns can be used in window function so I'm looking way how to overcome this.
Here is an example. I have a table with events. Each event has type and time_stamp.
create table event (id serial, type integer, time_stamp integer);
I wan't to find "duplicate" events (to skip them). By duplicate I mean the following. Let's order all events for given type by time_stamp ascending. Then
the first event is not a duplicate
all events that follow non duplicate and are within some time frame after it (that is their time_stamp is not greater then time_stamp of the previous non duplicate plus some constant TIMEFRAME) are duplicates
the next event which time_stamp is greater than previous non duplicate by more than TIMEFRAME is not duplicate
and so on
For this data
insert into event (type, time_stamp)
values
(1, 1), (1, 2), (2, 2), (1,3), (1, 10), (2,10),
(1,15), (1, 21), (2,13),
(1, 40);
and TIMEFRAME=10 result should be
time_stamp | type | duplicate
-----------------------------
1 | 1 | false
2 | 1 | true
3 | 1 | true
10 | 1 | true
15 | 1 | false
21 | 1 | true
40 | 1 | false
2 | 2 | false
10 | 2 | true
13 | 2 | false
I could calculate the value of duplicate field based on current time_stamp and time_stamp of the previous non-duplicate event like this:
WITH evt AS (
SELECT
time_stamp,
CASE WHEN
time_stamp - LAG(current_non_dupl_time_stamp) OVER w >= TIMEFRAME
THEN
time_stamp
ELSE
LAG(current_non_dupl_time_stamp) OVER w
END AS current_non_dupl_time_stamp
FROM event
WINDOW w AS (PARTITION BY type ORDER BY time_stamp ASC)
)
SELECT time_stamp, time_stamp != current_non_dupl_time_stamp AS duplicate
But this does not work because the field which is calculated cannot be referenced in LAG:
ERROR: column "current_non_dupl_time_stamp" does not exist.
So the question: can I rewrite this query to achieve the effect I need?
Naive recursive chain knitter:
-- temp view to avoid nested CTE
CREATE TEMP VIEW drag AS
SELECT e.type,e.time_stamp
, ROW_NUMBER() OVER www as rn -- number the records
, FIRST_VALUE(e.time_stamp) OVER www as fst -- the "group leader"
, EXISTS (SELECT * FROM event x
WHERE x.type = e.type
AND x.time_stamp < e.time_stamp) AS is_dup
FROM event e
WINDOW www AS (PARTITION BY type ORDER BY time_stamp)
;
WITH RECURSIVE ttt AS (
SELECT d0.*
FROM drag d0 WHERE d0.is_dup = False -- only the "group leaders"
UNION ALL
SELECT d1.type, d1.time_stamp, d1.rn
, CASE WHEN d1.time_stamp - ttt.fst > 20 THEN d1.time_stamp
ELSE ttt.fst END AS fst -- new "group leader"
, CASE WHEN d1.time_stamp - ttt.fst > 20 THEN False
ELSE True END AS is_dup
FROM drag d1
JOIN ttt ON d1.type = ttt.type AND d1.rn = ttt.rn+1
)
SELECT * FROM ttt
ORDER BY type, time_stamp
;
Results:
CREATE TABLE
INSERT 0 10
CREATE VIEW
type | time_stamp | rn | fst | is_dup
------+------------+----+-----+--------
1 | 1 | 1 | 1 | f
1 | 2 | 2 | 1 | t
1 | 3 | 3 | 1 | t
1 | 10 | 4 | 1 | t
1 | 15 | 5 | 1 | t
1 | 21 | 6 | 1 | t
1 | 40 | 7 | 40 | f
2 | 2 | 1 | 2 | f
2 | 10 | 2 | 2 | t
2 | 13 | 3 | 2 | t
(10 rows)
An alternative to a recursive approach is a custom aggregate. Once you master the technique of writing your own aggregates, creating transition and final functions is easy and logical.
State transition function:
create or replace function is_duplicate(st int[], time_stamp int, timeframe int)
returns int[] language plpgsql as $$
begin
if st is null or st[1] + timeframe <= time_stamp
then
st[1] := time_stamp;
end if;
st[2] := time_stamp;
return st;
end $$;
Final function:
create or replace function is_duplicate_final(st int[])
returns boolean language sql as $$
select st[1] <> st[2];
$$;
Aggregate:
create aggregate is_duplicate_agg(time_stamp int, timeframe int)
(
sfunc = is_duplicate,
stype = int[],
finalfunc = is_duplicate_final
);
Query:
select *, is_duplicate_agg(time_stamp, 10) over w
from event
window w as (partition by type order by time_stamp asc)
order by type, time_stamp;
id | type | time_stamp | is_duplicate_agg
----+------+------------+------------------
1 | 1 | 1 | f
2 | 1 | 2 | t
4 | 1 | 3 | t
5 | 1 | 10 | t
7 | 1 | 15 | f
8 | 1 | 21 | t
10 | 1 | 40 | f
3 | 2 | 2 | f
6 | 2 | 10 | t
9 | 2 | 13 | f
(10 rows)
Read in the documentation: 37.10. User-defined Aggregates and CREATE AGGREGATE.
This feels more like a recursive problem than windowing function. The following query obtained the desired results:
WITH RECURSIVE base(type, time_stamp) AS (
-- 3. base of recursive query
SELECT x.type, x.time_stamp, y.next_time_stamp
FROM
-- 1. start with the initial records of each type
( SELECT type, min(time_stamp) AS time_stamp
FROM event
GROUP BY type
) x
LEFT JOIN LATERAL
-- 2. for each of the initial records, find the next TIMEFRAME (10) in the future
( SELECT MIN(time_stamp) next_time_stamp
FROM event
WHERE type = x.type
AND time_stamp > (x.time_stamp + 10)
) y ON true
UNION ALL
-- 4. recursive join, same logic as base
SELECT e.type, e.time_stamp, z.next_time_stamp
FROM event e
JOIN base b ON (e.type = b.type AND e.time_stamp = b.next_time_stamp)
LEFT JOIN LATERAL
( SELECT MIN(time_stamp) next_time_stamp
FROM event
WHERE type = e.type
AND time_stamp > (e.time_stamp + 10)
) z ON true
)
-- The actual query:
-- 5a. All records from base are not duplicates
SELECT time_stamp, type, false
FROM base
UNION
-- 5b. All records from event that are not in base are duplicates
SELECT time_stamp, type, true
FROM event
WHERE (type, time_stamp) NOT IN (SELECT type, time_stamp FROM base)
ORDER BY type, time_stamp
There are a lot of caveats with this. It assumes no duplicate time_stamp for a given type. Really the joins should be based on a unique id rather than type and time_stamp. I didn't test this much, but it may at least suggest an approach.
This is my first time to try a LATERAL join. So there may be a way to simplify that moe. Really what I wanted to do was a recursive CTE with the recursive part using MIN(time_stamp) based on time_stamp > (x.time_stamp + 10), but aggregate functions are not allowed in CTEs in that manner. But it seems the lateral join can be used in the CTE.
I have a range of data on search queries across diffrent merchants.
I have a python script that 1st creates the head, torso & tail query sets from the main table in qsql, based on count(query) instances as 1000, 100 etc.
Since the number of merchants I my script runs of could have/not have queries that meet that threshold, the script does not log the "head.csv" "torso.csv" .. tail.csv always being produced.
How can I break the queries into head, torso & tail groups by respecting the logic above.
I also tried ntile to break the groups by percentiles(33, 33, 33), but that skews both the head & torso, if a merchant has a very long tail.
Current :
# head
select trim(query) as query, count(*)
from my_merchant_table
-- other conditions & date range
GROUP BY trim(query)
having count(*) >=1000
#torso
select trim(query) as query, count(*)
from my_merchant_table
-- other conditions & date range
GROUP BY trim(query)
having count(*) <1000 and count(*) >=100
#tail
select trim(query) as query, count(*)
from my_merchant_table
-- other conditions & date range
GROUP BY trim(query)
having count(*) <100
# using ntile - but note that I have percentiles of "3" , 33.#% each, which introduces the skew
select trim(query), count(*) as query_count,
ntile(3) over(order by query_count desc) AS group_ntile
from my_merchant_table
group by trim(query)
order by query_count desc limit 100;
Ideally the solution can build on top of this -:
select trim(query), count(*) as query_count,
ntile(100) over(order by query_count desc) AS group_ntile
from my_merchant_table
-- other conditions & date range
group by trim(query)
order by query_count desc
This gives,
btrim query_count group_ntile
q0 1277 1
q1 495 1
q2 357 1
q3 246 1
# so on till group_ntile =100 , while the query_count reduces.
Question :
What is the best way for the logic, to make the overall logic merchant agnostic/no hard-coding the configs ?
Note : I am fetching the data in Redshift, the solution should be compatible to postgres 8.0 & redshift in particular.
I imagine that you from some programming language invokes its queries to process information. My recommendation in this regard is get all the records and apply a filter over they. Consider that if you queries the database where there are several operations over the data this would result that the response time of the application is affected.
Assuming that the main challenge is to create the 'tiles' from a list of values, here is some sample code. It takes the 13 provinces of Canada and breaks it into a requested number of groups. It uses the province names, but numbers would work just as well.
SELECT * FROM Provinces ORDER BY province; -- To see what we are working with
+---------------------------+
| province |
+---------------------------+
| Alberta |
| British Columbia |
| Manitoba |
| New Brunswick |
| Newfoundland and Labrador |
| Northwest Territories |
| Nova Scotia |
| Nunavut |
| Ontario |
| Prince Edward Island |
| Quebec |
| Saskatchewan |
| Yukon |
+---------------------------+
13 rows in set (0.00 sec)
Now for the code:
SELECT #n := COUNT(*), -- Find total count (13)
#j := 0.5, -- 'trust me'
#tiles := 3 -- The number of groupings
FROM Provinces;
SELECT group_start
FROM (
SELECT
IF((#j * #tiles) % #n < #tiles, province, NULL) AS group_start,
#j := #j + 1
FROM Provinces
ORDER BY province
) x
WHERE group_start IS NOT NULL;
+---------------------------+
| group_start |
+---------------------------+
| Alberta |
| Newfoundland and Labrador |
| Prince Edward Island |
+---------------------------+
3 rows in set (0.00 sec)
With #tiles set to 4:
+---------------+
| group_start |
+---------------+
| Alberta |
| New Brunswick |
| Nova Scotia |
| Quebec |
+---------------+
4 rows in set (0.00 sec)
It is reasonably efficient: 1 pass to count the number of rows, 1 pass to do the computation, 1 pass to filter out the non-break values.
i have only one table "tbl_test"
Which have table filed given below
tbl_test table
trx_id | proj_num | parent_num|
1 | 14 | 0 |
2 | 14 | 1 |
3 | 14 | 2 |
4 | 14 | 0 |
5 | 14 | 3 |
6 | 15 | 0 |
Result i want is : when trx_id value 5 is fetched
it's a parent child relationship. so,
trx_id -> parent_num
5 -> 3
3 -> 2
2 -> 1
That means output value:
3
2
1
Getting all parent chain
Query i used :
SELECT * FROM (
WITH RECURSIVE tree_data(project_num, task_num, parent_task_num) AS(
SELECT project_num, task_num, parent_task_num
FROM tb_task
WHERE project_num = 14 and task_num = 5
UNION ALL
SELECT child.project_num, child.task_num, child.parent_task_num
FROM tree_data parent Join tb_task child
ON parent.task_num = child.task_num AND parent.task_num = child.parent_task_num
)
SELECT project_num, task_num, parent_task_num
FROM tree_data
) AS tree_list ;
Can anybody help me ?
There's no need to do this with pl/pgsql. You can do it straight in SQL. Consider:
WITH RECURSIVE my_tree AS (
SELECT trx_id as id, parent_id as parent, trx_id::text as path, 1 as level
FROM tbl_test
WHERE trx_id = 5 -- start value
UNION ALL
SELECT t.trx_id, t.parent_id, p.path || ',' || t.trx_id::text, p.level + 1
FROM my_tree p
JOIN tbl_text t ON t.trx_id = p.parent
)
select * from my_tree;
If you are using PostgresSQL, try using a WITH clause:
WITH regional_sales AS (
SELECT region, SUM(amount) AS total_sales
FROM orders
GROUP BY region
), top_regions AS (
SELECT region
FROM regional_sales
WHERE total_sales > (SELECT SUM(total_sales)/10 FROM regional_sales)
)
SELECT region,
product,
SUM(quantity) AS product_units,
SUM(amount) AS product_sales
FROM orders
WHERE region IN (SELECT region FROM top_regions)
GROUP BY region, product;