I am connected to a database using JDBC and I am trying to run an update query. First I am typing the query, then I am executing it (in the same way I do the SELECT which works perfectly fine).
caseoutputUpdateQuery = "(UPDATE dbo.CASEOUTPUT_TEST SET NOTIFIED = 'YES') alias_output "
spark.read.jdbc(url=jdbcUrl, table=caseoutputUpdateQuery, properties=connectionProperties)
When I run this I have the following error:
A nested INSERT, UPDATE, DELETE, or MERGE statement must have an OUTPUT clause.
I tried to fix this in different ways but there is always another error. For example, I tried to rewrite the query in the following way:
caseoutputUpdateQuery = "(UPDATE dbo.CASEOUTPUT_TEST SET NOTIFIED = 'YES' OUTPUT DELETED.*, INSERTED.* FROM dbo.CASEOUTPUT_TEST) alias_output "
but I encounter this error:
A nested INSERT, UPDATE, DELETE, or MERGE statement is not allowed in a SELECT statement that is not the immediate source of rows for an INSERT statement.
The other way I tried to rewrite it was:
caseoutputUpdateQuery = "(INSERT INTO dbo.UpdateOutput(OldCaseID,NotifiedOld) SELECT * FROM( UPDATE dbo.CASEOUTPUT_TEST SET NOTIFIED = 'YES' OUTPUT deleted.OldCaseID,DELETED.NotifiedOld ) AS tbl) alias_output "
but I've got this error:
A nested INSERT, UPDATE, DELETE, or MERGE statement is not allowed inside another nested INSERT, UPDATE, DELETE, or MERGE statement.
I've literally tried everything I found on the internet but without luck. Do you have any suggestion on how I can fix this and run my update statement?
I think Spark is not designed for that UPDATE statement use case. That's not the scenario where Spark can help to deal with RDBMS. I suggest to use a direct connection using a JDBC from the code you are writing (I mean calling that JDBC directly). If you are using Scala you can use as suggested here (for example, but there are other multiple ways) or from Python as explained here. Those samples reach Oracle engine, but please change the driver/connector if you are using MySQL, SQL Server, Postgres or any other RDMBS
spark.read under the covers does a select * from the source jdbc table. if you pass a query, spark translates it to
select your query
from ( their query select *)
Sql complains because you are trying to do an update on a view "select * from"
Related
I want to create a persistent (global) view in spark sql that gets data from an underlying jdbc database connection. It works fine when I use a temporary (session-scoped) view as shown below but fails when trying to create a regular (persistent and global) view.
I don't understand why the latter should not work but couldn't find any docs/hints as all examples are always done with temporary views. Technically, I cannot see why it shouldn't work as the data is properly retrieved from jdbc source in the temporary view and thus it should not matter if I wanted to "store" the query in a persistent view so that whenever calling the view it would retrieve data directly from jdbc source.
Config.
tbl_in = myjdbctable
tbl_out = myview
db_user = 'myuser'
db_pw = 'mypw'
jdbc_url = 'jdbc:sqlserver://myserver.domain:1433;database=mydb'
This works.
query = f"""
create or replace temporary view {tbl_out}
using jdbc
options(
dbtable '{tbl_in}',
user '{db_user}',
password '{db_pw}',
url '{jdbc_url}'
)
"""
spark.sql(query)
> DataFrame[]
This does not work.
query = f"""
create or replace view {tbl_out}
using jdbc
options(
dbtable '{tbl_in}',
user '{db_user}',
password '{db_pw}',
url '{jdbc_url}'
)
"""
spark.sql(query)
> ParseException:
Error.
ParseException:
mismatched input 'using' expecting {'(', 'UP_TO_DATE', 'AS', 'COMMENT', 'PARTITIONED', 'TBLPROPERTIES'}(line 3, pos 0)
== SQL ==
create or replace view myview
using jdbc
^^^
options(
dbtable 'myjdbctable',
user 'myuser',
password '[REDACTED]',
url 'jdbc:sqlserver://myserver.domain:1433;database=mydb'
)
TL;DR: A spark sql table over jdbc source behaves like a view and so can be used like one.
It seems my assumptions about jdbc tables in spark sql were flawed. It turns out that a sql table with a jdbc source (i.e. created via using jdbc) is actually a live query against the jdbc source (and not a one-off jdbc query during table creation as I assumed). In my mind it actually behaves like a view then. That means if the underlying jdbc source changes (e.g. new entries in a column) this is reflected in the spark sql table on read (e.g. select from) without having to re-create the table.
It follows that the spark sql table over jdbc source satisfies my requirements of having an always up2date reflection of the underlying table/sql object in the jdbc source. Usually, I would use a view for that. Maybe this is the reason why there is no persistent view over a jdbc source but only temporary views (which of course still make sense as they are session-scoped). It should be noted that the spark sql jdbc table behaves like a view which may be surprising, in particular:
if you add a column in underlying jdbc table, it will not show up in spark sql table
if you remove a column from underlying jdbc table, an error will occur when spark sql table is accessed (assuming the removed column was present during spark sql table creation)
if you remove the underlying jdbc table, an error will occur when spark sql table is accessed
The input of spark.sql should be DML (Data Manipulation Language). Its output is a dataframe.
In terms of best practices, you should avoid using DDL (Data Definition Language) with spark.sql. Even if some statements may work, that's not meant to be used this way.
If you want to use DDL, simply connect to your DB using python packages.
If you want to create a temp view in spark, do it using spark syntaxe createTempView
I'm trying to do a simple incremental update from an on-prem database as source to Azure SQL database based on a varchar column called "RP" in On-Prem database that contains "date+staticdescription" for example: "20210314MetroFactory"
1- I've created a Lookup activity called Lookup1 using a table created in Azure SQL Database and uses this Query
"Select RP from SubsetwatermarkTable"
2- I've created a Copy data activity where the source settings have this Query
"Select * from SourceDevSubsetTable WHERE RP NOT IN '#{activity('Lookup1').output.value}'"
When debugging -- I'm getting the error:
Failure type: User configuration issue
Details: Failure happened on 'Source' side.
'Type=System.Data.SqlClient.SqlException,Message=Incorrect syntax near
'[{"RP":"20210307_1Plant
1KAO"},{"RP":"20210314MetroFactory"},{"RP":"20210312MetroFactory"},{"RP":"20210312MetroFactory"},{"RP":"2'.,Source=.Net
SqlClient Data
Provider,SqlErrorNumber=102,Class=15,ErrorCode=-2146232060,State=1,Errors=[{Class=15,Number=102,State=1,Message=Incorrect
syntax near
'[{"RP":"20210311MetroFactory"},{"RP":"20210311MetroFactory"},{"RP":"202103140MetroFactory"},{"RP":"20210308MetroFactory"},{"RP":"2'.,},],'
Can anyone tell me what I am doing wrong and how to fix it even if it requires creating more activities.
Note: There is no LastModifiedDate column in the table. Also I haven't yet created the StoredProcedure that will update the Lookup table when it is done with the incremental copy.
Steve is right as to why it is failling and the query you need in the Copy Data.
As he says, you want a comma-separated list of quoted values to use in your IN clause.
You can get this more easily though - from your Lookup directly using this query:-
select stuff(
(
select ','''+rp+''''
from subsetwatermarktable
for xml path('')
)
, 1, 1, ''
) as in_clause
The sub-query gets the comma separated list with quotes around each rp-value, but has a spurious comma at the start - the outer query with stuff removes this.
Now tick the First Row Only box on the Lookup and change your Copy Data source query to:
select *
from SourceDevSubsetTable
where rp not in (#{activity('lookup').output.firstRow.in_clause})
The result of #activity('Lookup1').output.value is an array like your error shows
[{"RP":"20210307_1Plant
1KAO"},{"RP":"20210314MetroFactory"},{"RP":"20210312MetroFactory"},{"RP":"20210312MetroFactory"},{"RP":"2'.,Source=.Net
SqlClient Data
Provider,SqlErrorNumber=102,Class=15,ErrorCode=-2146232060,State=1,Errors=[{Class=15,Number=102,State=1,Message=Incorrect
syntax near
'[{"RP":"20210311MetroFactory"},{"RP":"20210311MetroFactory"},{"RP":"202103140MetroFactory"},{"RP":"20210308MetroFactory"},{"RP":"2'.,},]
However, your SQL should be like this:Select * from SourceDevSubsetTable WHERE RP NOT IN ('20210307_1Plant 1KAO','20210314MetroFactory',...).
To achieve this in ADF, you need to do something like this:
create three variables like the following screenshot:
loop your result of #activity('Lookup1').output.value and append 'item().RP' to arrayvalues:
expression:#activity('Lookup1').output.value
expression:#concat(variables('apostrophe'),item().RP,variables('apostrophe'))
3.cast arrayvalues to string and add parentheses by Set variable activity
expression:#concat('(',join(variables('arrayvalues'),','),')')
4.copy to your Azure SQL database
expression:Select * from SourceDevSubsetTable WHERE RP NOT IN #{variables('stringvalues')}
The database is in Azure cloud and not being used in production currently. There are 80.000 rows and a uprn is a VARCHAR(100);
I'm already using JOI to validate each UPRN as well;
I'm using KNEX with a SQL Server database with the following whereIn query:
knex(LOCATIONS.table).whereIn(LOCATIONS.uprn, req.body.uprns)
but this takes 8-12s to complete and sometimes timesout. if I use .toQuery() on the same thing, SSMS will return the result within 1-2.
If I do a raw query, the resulting .toQuery() or toString() works in SSMS and returns results. But if I try to use the raw directly, it will return 0 results.
I'm looking to either fix what's making whereIn so slow or get the raw query working.
EDIT 1:
After much debugging and trying -- it seems that the bug is due to how knex deals with arrays, so I made a for-of loop to add ? ? ? for each array element and then inputed the array for all params.
This led me to realizing the performance issue is due to SQL server way of parameterising.
I ended up building a raw query string with all of the parameters and validating the input with Joi string/regex config:
Joi.string()
.min(1)
.max(35)
.regex(/^[a-z\d\-_\s]+$/i)
allowing only for alphanumeric, dashes and spaces which should prevent sql injection.
I'm going to look deeper into security issues with this and might make a separate login that can only SELECT data from that table and nothing more to run with these queries.
Needed to just handle it raw and validate separately.
So I have a NodeJS+KnexJS setup on a PostgreSQL DB, and am using the .whereRaw() method so I can use a CASE statement in my WHERE clause.
The query was tested in my CLI before migrating to code. Here is the code that is being used.
var qry = knex.select(....); // ignore the select, not important.
qry.with('daspecs', function(qy) {
qy.select('spec_id').from('drawings').where('uid', query.d);
}).whereRaw('CASE WHEN (select "spec_id" from "daspecs") IS NULL THEN true ELSE c.spec_id = (select "spec_id" from "daspecs") END');
The SQL that KnexJS is generating (output using qry.toString()) is correct, and I can even copy and paste this to my psql CLI and it returns the results I want (12 records), but for some wierd reason the KnexJS query seems to return a completely different set of results (1106 records).
Not sure where to go next, since KnexJS is giving me the right SQL, but seems like it's executing something else, and not sure how else to diagnose what it is actually doing (I've tried the knex.on('query'...) event).
Any alteration on the final SQL would result in an error (i've tested), so at the point of ruling out missing pieces.
Has anyone had any experience or issues with KnexJS saying one thing, but doing another, in particular, with the whereRaw command?
USE users_tracking;
SELECT user_name FROM visits
where port_name IN
(SELECT port_name FROM ports where location = 'NY' )//as temp;
It gives an error
mismatched input 'SELECT' expecting RULE_T_R_PAREN
Is there any way I can store the inner query in a variable and then use that?
I tried using set#varname := query but it does not recognize the set command.
Nested queries are not allowed in Cassandra CQL. For this kind of complex querying feature you'll need to use Hive or SparkSQL.
Here is a full CQL reference,
http://cassandra.apache.org/doc/cql3/CQL.html