I am aware, I can load anything into a DataFrame using JDBC, that works well from Oracle sources. Is there an equivalent in Spark SQL, so I can combine datasets as well?
Basically something like so - you get the idea...
select
lt.field1,
rt.field2
from localTable lt
join remoteTable#serverLink rt
on rt.id = lt.id
Thanks
dblink does not exist. You can create two table statements with JDBC sources and then join the two tables. It will be a little more to write, but you'll get the correct table.
In python, you can maybe do it easier with something like:
<!— begin snippet: js hide: false console: true babel: false -->
spark.read.jdbc(config1).join(spark.read.jdbc(config2), "key", "type")
There is an upcoming Query Federation functionality that allows to access tables in other databases by registering them in Databricks SQL.
Related
I am trying to execute the following SQL clause using Databricks SQL:
DELETE FROM prod_gbs_gpdi.bronze_data.sapex_ap_posted AS HISTORICAL_DATA
WHERE
HISTORICAL_DATA._JOB_SOURCE_FILE = (SELECT MAX(NEW_DATA._JOB_SOURCE_FILE) FROM temp_sapex_posted AS NEW_DATA)
The intention of the query is to delete a set of rows in a historical data table based on a value present in a column of new data table.
For reasons that I cannot understand it is raising an error like:
Error in SQL statement: AnalysisException: nondeterministic expressions are only allowed in
Project, Filter, Aggregate, Window, or Generate, but found:
(HISTORICAL_DATA._JOB_SOURCE_FILE IN (listquery()))
in operator DeleteCommandEdge
It seems it is not accepting a subquery inside the where clause. That's odd for me, as in the Databricks documentation Link it is acceptable.
I even tried other types of predicates, like:
(SELECT FIRST(NEW_DATA._JOB_SOURCE_FILE) FROM temp_sapex_posted AS NEW_DATA)
(SELECT DISTINCT NEW_DATA._JOB_SOURCE_FILE FROM temp_sapex_posted AS NEW_DATA)
IN (SELECT NEW_DATA._JOB_SOURCE_FILE FROM temp_sapex_posted AS NEW_DATA)
None of them seems to take effect in executing the query successfully.
What's even odd for me is that I was able to accomplish a similar case with a slightly different query, as it can be seen in this link.
I have created demo_table1 & demo_table2 for querying purpose. I have created the following query carrying the similar purpose. I haven’t considered double aliases and have given straight query using subquery, it also depends on data frame in usage use a normal pandas data frame. it works fine for me.
delete from demo_table1 as t1 where t1.age = (select min(t2.age) from demo_table2 as t2);
I figured out that running the following code will do full scan of the table:
select max(run_id) from database.table
So I switched my code to work with the following syntax:
select max(run_id) from "database"."table$partitions"
This query works great on Athena but when I try to execute it with Spark Sql I get the following error:
mismatched input '"database"' expecting <EOF>(line 1, pos 24)
It seems like spark sql identify the quotes as the end of the query.
Any ideas how to make this query work on spark sql?
Thanks
My solution for this problem was:
sql_context.sql(f'show partitions {table_name}').agg(
f.max(f.regexp_extract('partition', rf'''{partition_name}=([^/]+)''', 1))).collect()[0][0]
The advantage: It's not doing full scan on the table
Disadvantage: It's scan all partitions levels + code isn't elegant.
Anyway that's the best I found
in snowflake it searches data with case sensitiveness while in sql server it used to search with case insensitiveness i changed database level collation with below command
ALTER DATABASE IF EXISTS powerdb SET COLLATION = 'en-ci'
but it did not help is there any other way to achive case insensitiveness
There's a number of ways really.
one of them is using ILIKE for your string comparison: https://docs.snowflake.net/manuals/sql-reference/functions/ilike.html
another one is setting up collation at column level:
https://docs.snowflake.net/manuals/sql-reference/collation.html
- but please note that not all of the string functions are supported on collated columns
also you can use COLLATION functions (also described in the link below) or set it on database level with account-level parameter of DEFAULT_DDL_COLLATION = 'en-ci'
everything depends on what you want to achieve really...
Is there any way to save query result to file using pure SQL form?
I understand that we can do it easily using java or scala api. But I am looking for pure SQL solution which can be executed via Spark-sql CLI directly.
Create table is the closest thing:
CREATE TABLE table
USING format -- some format
LOCATION '/path/to/location'
AS SELECT * FROM some_view -- some query
It is not fully equivalent to simple save methods, as it uses metastore.
Does anyone know of a way in Spark SQL to execute a string variable like the following?
INSERT TableA (Col1,Col2) SELECT Col1,Col2 FROM TableB
I understand that I can obviously write this statement directly. However, I am using a work flow engine where my Insert/Select statement is in String variable. If not, I assume I should use spark_submit. I was looking for other options.
I'm not sure what environment you're in. If this is a Spark application or a Spark shell you always provide queries as strings:
val query = "INSERT TableA (Col1,Col2) SELECT Col1,Col2 FROM TableB"
sqlContext.sql(query)
(See http://spark.apache.org/docs/latest/sql-programming-guide.html#running-sql-queries-programmatically.)
Spark Sql also support hive queries
insert overwrite table usautomobiles select * from sourcedata
Go Through this link