I'm trying to join two continuous queries, but keep running into the following error:
Rowtime attributes must not be in the input rows of a regular join. As a workaround you can cast the time attributes of input tables to TIMESTAMP before.\nPlease check the documentation for the set of currently supported SQL features.
Here's the table definition:
CREATE TABLE `Combined` (
`machineID` STRING,
`cycleID` BIGINT,
`start` TIMESTAMP(3),
`end` TIMESTAMP(3),
WATERMARK FOR `end` AS `end` - INTERVAL '5' SECOND,
`sensor1` FLOAT,
`sensor2` FLOAT
)
and the insert query
INSERT INTO `Combined`
SELECT
a.`MachineID`,
a.`cycleID`,
MAX(a.`start`) `start`,
MAX(a.`end`) `end`,
MAX(a.`sensor1`) `sensor1`,
MAX(m.`sensor2`) `sensor2`
FROM `Aggregated` a, `MachineStatus` m
WHERE
a.`MachineID` = m.`MachineID` AND
a.`cycleID` = m.`cycleID` AND
a.`start` = m.`timestamp`
GROUP BY a.`MachineID`, a.`cycleID`, SESSION(a.`start`, INTERVAL '1' SECOND)
In the source tables Aggregated and MachineStatus, the start and timestamp columns are time attributes with a watermark.
I've tried casting the input rows of the join to timestamps, but that didn't fix the issue and would mean that I cannot use SESSION, which is supposed to ensure that only one data point gets recorded per cycle.
Any help is greatly appreciated!
I investigated this a little further and noticed that the GROUP BY statement doesn't make sense in that context.
Furthermore, the SESSION can be replaced by a time window, which is the more idiomatic approach.
INSERT INTO `Combined`
SELECT
a.`MachineID`,
a.`cycleID`,
a.`start`,
a.`end`,
a.`sensor1`,
m.`sensor2`
FROM `Aggregated` a, `MachineStatus` m
WHERE
a.`MachineID` = m.`MachineID` AND
a.`cycleID` = m.`cycleID` AND
m.`timestamp` BETWEEN a.`start` AND a.`start` + INTERVAL '0' SECOND
To understand the different ways to join dynamic tables, I found the Ververica SQL training extremely helpful.
Related
We've setup a stream on a table that is continuously loaded via snowpipe.
We're consuming this data with a task that runs every minute where we merge into another table. There is a possibility of duplicate keys so we use a ROW_NUMBER() window function, ordered by the file created timestamp descending where row_num=1. This way we always get the latest insert
Initially we used a standard task with the merge statement but we noticed that in some instances, since snowpipe does not guarantee loading in order of when the files were staged, we were updating rows with older data. As such, on the WHEN MATCHED section we added a condition so only when the file created ts > existing, to update the row
However, since we did that, reconciliation checks show that some new inserts are missing. I don't know for sure why changing the matched clause would interfere with the not matched clause.
My theory was that the extra clause added a bit of time to the task run where some runs were skipped or the next run happened almost immediately after the last one completed. The idea being that the missing rows were caught up in the middle and the offset changed before they could be consumed
As such, we changed the task to call a stored procedure which uses an explicit transaction. We did this because the docs seem to suggest that using a transaction will lock the stream. However even with this we can see that new inserts are still missing. We're talking very small numbers e.g. 8 out of 100,000s
Any ideas what might be happening?
Example task code below (not the sp version)
WAREHOUSE = TASK_WH
SCHEDULE = '1 minute'
WHEN SYSTEM$stream_has_data('my_stream')
AS
MERGE INTO processed_data pd USING (
select
ms.*,
CASE WHEN ms.status IS NULL THEN 1/mv.count ELSE NULL END as pending_count,
CASE WHEN ms.status='COMPLETE' THEN 1/mv.count ELSE NULL END as completed_count
from my_stream ms
JOIN my_view mv ON mv.id = ms.id
qualify
row_number() over (
partition by
id
order by
file_created DESC
) = 1
) ms ON ms.id = pd.id
WHEN NOT MATCHED THEN INSERT (col1, col2, col3,... )
VALUES (ms.col1, ms.col2, ms.col3,...)
WHEN MATCHED AND ms.file_created >= pd.file_created THEN UPDATE SET pd.col1 = ms.col1, pd.col2 = ms.col2, pd.col3 = ms.col3, ....
;
I am not fully sure what is going wrong here, but the file created time related recommendation is given by Snowflake somewhere. It suggest that the file created timestamp is calculated in cloud service and it may be bit different than you think. There is another recommendation related to snowpipe and data ingestion. The queue service takes a min to consume the data from pipe and if you have lot of data being flown inside with in a min, you may end up this issue. Look you implementation and simulate if pushing data in 1min interval solve that issue and don't rely on file create time.
The condition "AND ms.file_created >= pd.file_created" seems to be added as a mechanism to avoid updating the same row multiple times.
Alternative approach could be using IS DISTINCT FROM to compare source against target columns(except id):
MERGE INTO processed_data pd USING (
select
ms.*,
CASE WHEN ms.status IS NULL THEN 1/mv.count ELSE NULL END as pending_count,
CASE WHEN ms.status='COMPLETE' THEN 1/mv.count ELSE NULL END as completed_count
from my_stream ms
JOIN my_view mv ON mv.id = ms.id
qualify
row_number() over (
partition by
id
order by
file_created DESC
) = 1
) ms ON ms.id = pd.id
WHEN NOT MATCHED THEN INSERT (col1, col2, col3,... )
VALUES (ms.col1, ms.col2, ms.col3,...)
WHEN MATCHED
AND (pd.col1, pd.col2,..., pd.coln) IS DISTINCT FROM (ms.col1, ms.col2,..., ms.coln)
THEN UPDATE SET pd.col1 = ms.col1, pd.col2 = ms.col2, pd.col3 = ms.col3, ....;
This approach will also prevent updating row when nothing has changed.
(Submitting for a Snowflake User, hoping to receive additional assistance)
Is there another way to perform table insertion using a stored procedure faster?
I started building a usp with the purpose to insert million or so of rows of test data into a table for the purpose of load testing.
I got to this stage show below and set the iteration value to 10,000.
This took over 10 mins to iterate 10,000 times to insert a single integer into a table each iteration
Yes - I am using a XS data warehouse, but even if this is increased to MAX - this is way to slow to be of any use.
--build a test table
CREATE OR REPLACE TABLE myTable
(
myInt NUMERIC(18,0)
);
--testing a js usp using a while statement with the intention to insert multiple rows into a table (Millions) for load testing
CREATE OR REPLACE PROCEDURE usp_LoadTable_test()
RETURNS float
LANGUAGE javascript
EXECUTE AS OWNER
AS
$$
//set the number of iterations
var maxLoops = 10;
//set the row Pointer
var rowPointer = 1;
//set the Insert sql statement
var sql_insert = 'INSERT INTO myTable VALUES(:1);';
//Insert the fist Value
sf_startInt = rowPointer + 1000;
resultSet = snowflake.execute( {sqlText: sql_insert, binds: [sf_startInt] });
//Loop thorugh to insert all other values
while (rowPointer < maxLoops)
{
rowPointer += 1;
sf_startInt = rowPointer + 1000;
resultSet = snowflake.execute( {sqlText: sql_insert, binds: [sf_startInt] });
}
return rowPointer;
$$;
CALL usp_LoadTable_test();
So far, I've received the following recommendations:
Recommendation #1
One thing you can do is to use a "feeder table" containing 1000 or more rows instead of INSERT ... VALUES, eg:
INSERT INTO myTable SELECT <some transformation of columns> FROM "feeder table"
Recommendation #2
When you perform a million single row inserts, you consume one million micropartitions - each 16MB.
That 16 TB chunk of storage might be visible on your Snowflake bill ... Normal tables are retained for 7 days minimum after drop.
To optimize storage, you could define a clustering key and load the table in ascending order with each chunk filling up as much of a micropartition as possible.
Recommendation #3
Use data generation functions that work very fast if you need sequential integers: https://docs.snowflake.net/manuals/sql-reference/functions/seq1.html
Any other ideas?
This question was also asked at the Snowflake Lodge some weeks ago.
Given the answers you received, do you still feel unanswered, then maybe hint about why?
If you just want a table with a single column of sequence numbers, use GENERATOR() as in #3 above. Otherwise, if you want more advice, share your specific requirements.
HANA Version: SP12
All,
I've successfully created Calc Views with INPUT_PARAMETERS as described by Lars in many blogs and forums. While these views work without issue when querying directly for single and multi inputs, I'm encountering an issue with performing joins on the Calc View itself within a stored proc or table function.
Example:
"BASE_SCHEMA"."BASE_TABLE_EXAMPLE" - record count(*) ~ 2million records
Keys: Material (20k distinct), Plant (200 distinct)
"_SYS_BIC"."CA_EXAMPLE_PRODUCTIVITY"
Input Parameters: IP_MATNR (nvarchar (5000)), IP_PLANT (nvarchar(5000))
Issue #1: The maximum value for nvarchar is 5000. Unable to utilize multiple inputs within the parameter if the count of distinct characters are 5000+.
Issue #2: How to use PLACEHOLDER logic in the same method of performing an INNER_JOIN in SQL.
base_data =
select
PLANT
,MATERIAL
from "BASE_SCHEMA"."BASE_TABLE_EXAMPLE"
group by PLANT,MATERIAL;
I would think to perform the below but the output would cause issues when concatenating multiple strings for use within input parameter of nvarchar(5000).
select
string_agg(PLANT,''',''') as PLANT
,string_agg(MATERIAL,''',''') as MATERIAL
into var_PLANT, var_MATERIAL
from
(
select
PLANT
,MATERIAL
from :base_data
);
While I'm successful up to this point, once adding the variables into the PLACEHOLDER of the Calc View, it fails stating that I'm passing too many characters to the IP. Any suggestions??? Thanks in advance.
base_calc =
select
PLANT
,MATERIAL
,MATERIAL_BU
,etc....
from "_SYS_BIC"."CA_EXAMPLE_PRODUCTIVITY"
(PLACEHOLDER."IP_MATNR"=> :var_MATERIAL, --<---Fails here. :(
PLACEHOLDER."IP_PLANT"=> :var_PLANT);
Question raised on SAP SCN. Located here!
Did you tried to use WHERE clause instead of PLACEHOLDER?
base_calc =
select
PLANT,
MATERIAL,
MATERIAL_BU,
etc....
from "_SYS_BIC"."CA_EXAMPLE_PRODUCTIVITY"
WHERE MATERIAL = var_MATERIAL AND PLANT = var_PLANT;
I have a two tables in SQL Server, in which one is the source for a MERGE operation into another.
The source table has 30Mil Records
The Target table has 180Mil Records. Both tables have 227 columns.
I do have SSIS, but I'm told in this case, a MERGE statement is the better option. Below is a shortened version of it:
;WITH MySource as (
SELECT * FROM [STAGE].[dbo].[STAGE_TABLE]
)
MERGE [EDW].[dbo].[TARGET_TABLE] AS MyTarget
USING MySource
ON MySource.[ID_FIELD] = MyTarget.[ID_FIELD]
AND MySource.[LoadDate] >= MyTarget.[LoadDate]
WHEN MATCHED THEN UPDATE SET
<<Target Column>> = MySource.<<Source Colums>> --227 columns
WHEN NOT MATCHED THEN INSERT
(
[ID_FIELD],
[LoadDate],
<<225 Other Columns>>
)
VALUES (
MySource.[ID_FIELD],
MySource.[LoadDate],
MySource.<<225 other columns>>
);
The only changes I made to the script above is truncating the list of columns to keep the code block here short.
My Problem is that I am getting hung on the execution. The profile screen shows a CXPACKET suspension with the error: cwaitpipenewrow, node=2.
How do I troubleshoot this? Thank you.
Seems like CXPACKET and suspended state means that some threads which have completed are logging that other thread's state which have not completed yet.
Please check here. The query need to update upto 1 Billion values in the table. hence it would be slow running queries.
https://dba.stackexchange.com/questions/96346/cxpacket-suspended-and-null-wait-type
https://www.sqlshack.com/troubleshooting-the-cxpacket-wait-type-in-sql-server/
Hope these articles might help you debug.
I have a pair of databases (one mssql and one oracle), ran by different teams. Some data are now being synchronized regularily by a stored procedure in the mssql table. This stored procedure is calling a very large
MERGE [mssqltable].[Mytable] as s
USING THEORACLETABLE.BLA as t
ON t.[R_ID] = s.[R_ID]
WHEN MATCHED THEN UPDATE SET [Field1] = s.[Field1], ..., [Brokenfield] = s.[BrokenField]
WHEN NOT MATCHED BY TARGET THEN
... another big statement
Field Brokenfield was a numeric one until today, and could take value NULL, 0, 1, .., 24
Now, the oracle team introduced a breaking change today for some reason, changed the type of the column to string and now has values NULL, "", "ALFA", "BRAVO"... in the column. Of course, the sync got broken.
What is the easiest way to fix the sync here? I (Mysql team lead, frontend expert but not so in databases) would usually apply one of our database expert guys here, but all of them are now ill, and the fix must go online today....
I thought of a stored procedure like CONVERT_BROKENFIELD_INT_TO_STRING or so, based on some switch-case, which could be called in that merge statement, but not sure how to do that.
Edit/Clarification:
What I need is a way to make a chunk of SQL code (stored procedure), taking an input of "ALFA" and returning 1, "BRAVO" -> 2, etc. and which can be reused, to avoid writing huge ifs in more then one place.
If you can not simplify the logic for correct values the way #RichardHansell desribed, you can create a crosswalk table for BrokenField to the correct values. Then you can use a common table expression or subquery with a left join to that crosswalk to use in the merge.
create table dbo.BrokenField_Crosswalk (
BrokenField varchar(32) not null primary key
, CorrectedValue int
);
insert into dbo.BrokenField_Crosswalk (BrokenField,CorrectedValue) values
('ALFA', 1)
, ('ALPHA', 1)
, ('BRAVO', 2)
...
go
And your code for the merge would look something like this:
;with cte as (
select o.R_ID
, o.Field1
, BrokenField = cast(isnull(c.CorrectedValue,o.BrokenField) as int)
....
from oracle_table.bla as o
left join dbo.BrokenField_Crosswalk as c
)
merge into [mssqltable].[Mytable] t
using cte as s
on t.[R_ID] = s.[R_ID]
when matched
then update set
[Field1] = s.[Field1]
, ...
, [Brokenfield] = s.[BrokenField]
when not matched by target
then
If they are using names with a letter at the start that goes in a sequence:
A = 1
B = 2
C = 3
etc.
Then you could do something like this:
MERGE [mssqltable].[Mytable] as s
USING THEORACLETABLE.BLA as t
ON t.[R_ID], 1)) - ASCII('A') + 1 = s.[R_ID]
WHEN MATCHED THEN UPDATE SET [Field1] = s.[Field1], ..., [Brokenfield] = s.[BrokenField]
WHEN NOT MATCHED BY TARGET THEN
... another big statement
Edit: but actually I re-read your question and you are talking about [Brokenfield] being the problem column, so my solution wouldn't work.
I don't really understand now, as it seems as though the MERGE statement is updating the oracle table with numbers, so surely you need the mapping to work the other way, i.e. 1 -> ALFA, 2 -> BETA, etc.?