I am trying to apply WHERE clause on DIMENSION of the AWS Timestream records. However, I got the error: Column does not exist
Here is my table schema:
The table schema
The table measure
First, I will show all the sample data I put in the table
SELECT username, time, manual_usage
FROM "meter-reading"."meter-metrics"
ORDER BY time DESC
LIMIT 4
The result:
Result
What I wanted to do is to query and filter the records by the Dimension ("username" specifically).
SELECT *
FROM "meter-reading"."meter-metrics"
WHERE measure_name = "OnceADay"
ORDER BY time DESC LIMIT 10
Then I got the Error: Column 'OnceADay' does not exist
I tried to search for any quotas for Dimensions name and check for error in my schema:
https://docs.aws.amazon.com/timestream/latest/developerguide/ts-limits.html#limits.naming
https://docs.aws.amazon.com/timestream/latest/developerguide/ts-limits.html#limits.system_identifier
But I didn't find that my "username" for the dimension violate any of the above rules.
I checked for some other queries by AWS Blog, the author used the WHERE clause for the Dimension filter normally:
https://aws.amazon.com/blogs/database/effective-queries-for-common-query-patterns-in-amazon-timestream/
I figured it out after I tried with the sample code. Turn out it was a silly mistake I believe.
Using apostrophe (') instead of single quotation marks ("") solved my problem.
SELECT *
FROM "meter-reading"."meter-metrics"
WHERE username = 'OnceADay'
ORDER BY time DESC LIMIT 10
I have a very large (3.5B records) table that I want to update/insert (upsert) using the MERGE statement in BigQuery. The source table is a staging table that contains only the new data, and I need to check if the record with a corresponding ID is in the target table, updating the row if so or inserting if not.
The target table is partitioned by an integer field called IdParent, and the matching is done on IdParent and another integer field called IdChild. My merge statement/script looks like this:
declare parentList array<int64>;
set parentList = array(select distinct IdParent from dataset.Staging);
merge into dataset.Target t
using dataset.Staging s
on
-- target is partitioned by IdParent, do this for partition pruning
t.IdParent in unnest(parentList)
and t.IdParent = s.IdParent
and t.IdChild = s.IdChild
when matched and t.IdParent in unnest(parentList) then
update
set t.Column1 = s.Column1,
t.Column2 = s.Column2,
...<more columns>
when not matched and IdParent in unnest(parentList) then
insert (<all the fields>)
values (<all the fields)
;
So I:
Pull the IdParent list from the staging table to know which partitions to prune
limit the partitions of the target table in the join predicate
also limit the partitions of the target table in the match/not matched conditions
The total size of dataset.Target is ~250GB. If I put this script in my BQ editor and remove all the IdParent in unnest(parentList) then it shows ~250GB to bill in the editor (as expected since there's no partition pruning). If I add the IdParent in unnest(parentList) back in so the script is exactly like you see it above i.e. attempting to partition prune, the editor shows ~97MB to bill. However, when I look at the query results, I see that it actually billed ~180GB:
The target table is also clustered on the two fields being matched, and I'm aware that the benefits of clustering are typically not shown in the editor's estimate. However, my understanding is that that should only make the bytes billed smaller... I can't think of any reason why this would happen.
Is this a BQ bug, or am I just missing something? BigQuery doesn't even say "the script is estimated to process XX MB", it says "This will process XX MB" and then it processes way more.
That's very interesting. What you did seems totally correct.
It seems BQ query planner could interpret your SQL correctly and know the partition pruning is provided, but when it executes. it failed to do so.
try removing t.IdParent in unnest(parentList) from both when matched clauses to see if the issue still happens, that is,
declare parentList array<int64>;
set parentList = array(select distinct IdParent from dataset.Staging);
merge into dataset.Target t
using dataset.Staging s
on
-- target is partitioned by IdParent, do this for partition pruning
t.IdParent in unnest(parentList)
and t.IdParent = s.IdParent
and t.IdChild = s.IdChild
when matched then
update
set t.Column1 = s.Column1,
t.Column2 = s.Column2,
...<more columns>
when not matched then
insert (<all the fields>)
values (<all the fields)
;
It would be a good idea to submit a bug to BigQuery if it couldn't be resolved.
I have cloudwatch entries that may be group with respect to a certain field. To be clear assume that field is city. I would like to count the entries with respect to cities. This is the easy part.
fields city
|stats count(*) by city
However I also want to get maximum minimum and average of this count, but I can not. Is it possible to have such queries i.e:
fields city
|stats avg(count(*) by city)
The console return an error for such query : mismatched input 'by' expecting {SYM_COMMA, SYM_RParen}
Here's how you'd do it: You'd first get the count (that you already figured) and then get the metrics you want by calling the relevant functions like so:
fields city
|stats count(*) as cityCount, avg(cityCount), max(cityCount), min(cityCount)
We are trying to implement a dashboard that displays various tables, metrics and a map where the dataset is a list of customers. The primary filter condition is the disjunction of two numeric fields. We want to the user to be able to select a threshold for [field 1] and a separate threshold for [field 2] and then impose the condition [field 1] >= <threshold> OR [field 2] >= <threshold>.
After that, we want to also allow various other interactive slicers so the user can restrict the data further, e.g. by country or account manager.
Power BI naturally imposes AND between all filters and doesn't have a neat way to specify OR. Can you suggest a way to define a calculation using the two numeric fields that is then applied as a filter within the same interactive dashboard screen? Alternatively, is there a way to first prompt the user for the two threshold values before the dashboard is displayed -- so when they click Submit on that parameter-setting screen they are then taken to the main dashboard screen with the disjunction already applied?
Added in response to a comment:
The data can be quite simple: no complexity there. The complexity is in getting the user interface to enable a disjunction.
Suppose the data was a list of customers with customer id, country, gender, total value of transactions in the last 12 months, and number of purchases in last 12 months. I want the end-user (with no technical skills) to specify a minimum threshold for total value (e.g. $1,000) and number of purchases (e.g. 10) and then restrict the data set to those where total value of transactions in the last 12 months > $1,000 OR number of purchases in last 12 months > 10.
After doing that, I want to allow the user to see the data set on a dashboard (e.g. with a table and a graph) and from there select other filters (e.g. gender=male, country=Australia).
The key here is to create separate parameter tables and combine conditions using a measure.
Suppose we have the following Sales table:
Customer Value Number
-----------------------
A 568 2
B 2451 12
C 1352 9
D 876 6
E 993 11
F 2208 20
G 1612 4
Then we'll create two new tables to use as parameters. You could do a calculated table like
Number = VALUES(Sales[Number])
Or something more complex like
Value = GENERATESERIES(0, ROUNDUP(MAX(Sales[Value]),-2), ROUNDUP(MAX(Sales[Value]),-2)/10)
Or define the table manually using Enter Data or some other way.
In any case, once you have these tables, name their columns what you want (I used MinNumber and MinValue) and write your filtering measure
Filter = IF(MAX(Sales[Number]) > MIN(Number[MinCount]) ||
MAX(Sales[Value]) > MIN('Value'[MinValue]),
1, 0)
Then put your Filter measure as a visual level filter where Filter is not 0 and use MinCount and MinValues column as slicers.
If you select 10 for MinCount and 1000 for MinValue then your table should look like this:
Notice that E and G only exceed one of the thresholds and tha A and D are excluded.
To my knowledge, there is no such built-in slicer feature in Power BI at the time being. There is however a suggestion in the Power BI forum that requests a functionality like this. If you'd be willing to use the Power Query Editor, it's easy to obtain the values you're looking for, but only for hard-coded values for your limits or thresh-holds.
Let me show you how for a synthetic dataset that should fit the structure of your description:
Dataset:
CustomerID,Country,Gender,TransactionValue12,NPurchases12
51,USA,M,3516,1
58,USA,M,3308,12
57,USA,M,7360,19
54,USA,M,2052,6
51,USA,M,4889,5
57,USA,M,4746,6
50,USA,M,3803,3
58,USA,M,4113,24
57,USA,M,7421,17
58,USA,M,1774,24
50,USA,F,8984,5
52,USA,F,1436,22
52,USA,F,2137,9
58,USA,F,9933,25
50,Canada,F,7050,16
56,Canada,F,7202,5
54,Canada,F,2096,19
59,Canada,F,4639,9
58,Canada,F,5724,25
56,Canada,F,4885,5
57,Canada,F,6212,4
54,Canada,F,5016,16
55,Canada,F,7340,21
60,Canada,F,7883,6
55,Canada,M,5884,12
60,UK,M,2328,12
52,UK,M,7826,1
58,UK,M,2542,11
56,UK,M,9304,3
54,UK,M,3685,16
58,UK,M,6440,16
50,UK,M,2469,13
57,UK,M,7827,6
Desktop table:
Here you see an Input table and a subset table using two Slicers. If the forum suggestion gets implemented, it should hopefully be easy to change a subset like below to an "OR" scenario:
Transaction Value > 1000 OR Number or purchases > 10 using Power Query:
If you use Edit Queries > Advanced filter you can set it up like this:
The last step under Applied Steps will then contain this formula:
= Table.SelectRows(#"Changed Type2", each [NPurchases12] > 10 or [TransactionValue12] > 1000
Now your original Input table will look like this:
Now, if only we were able to replace the hardcoded 10 and 1000 with a dynamic value, for example from a slicer, we would be fine! But no...
I know this is not what you were looking for, but it was the best 'negative answer' I could find. I guess I'm hoping for a better solution just as much as you are!
I have inventory_history table that contains 2 millions of data, on which I am performing uncached lookup.
From source table, I am retrieving last 3 months data, which is around 300 thousand rows.
My mapping contains single lookup and is uncached (inventory_history). Lookup overide is used to retrieve data from inventory_history table, the condition columns are indexed and not using any unwanted columns.
But I see the t/m busy percentage is 100% and is like below 100. Lookup override query is executing well in database. This mapping is taking forever time. How can I tune the performance.
Don't know where the problem exists... Any suggestions ?
SELECT
SUM(CASE WHEN UPPER(GM) = 'B' and UNITS> 100 THEN A.QTY/B.UNITS ELSE QTY END) AS QTY,
A.TDATE as TDATE,
A.TDATE_ID as TDATE_ID,
A.DIST_ID as DIST_ID,
A.PRODID as PROD_ID
FROM
HUSA_ODS.INVENTORY_HISTORY A,
HUSA_ODS.PRODUCT B
WHERE
A.PROD_ID = B.PROD_ID
AND TCODE = '10' AND
DISTID = ?DISTID_IN?
AND A.PROD_ID = ?PROD_ID_IN?
AND TDATE <= ?PERIOD_DATE_IN?
GROUP BY
TDATE,
TDATE_ID,
DIST_ID,
A.PROD_ID
ORDER BY
TDATE DESC,
DIST_ID,
A.PROD_ID , TDATE--
Here output columns are QTY and TDATE
Uncached lookup would hit the database for each of your 300 thousand rows coming from source. Use a cached lookup and see if you can filter out some of the data in your lookup query.