Any way to do this? Basically trying to do a SQL UPDATE SET function if matching record for one or more key fields exists in another dataset.
Tried using Joins and Merge. Joins seems like more steps and the Merge appends records instead of updating the correlating rows.
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I want to utilise the ADF Copy function, to carry out incremental table extracts from one Azure DB to another. Every table in the database that I need all have the same 2 relevant fields i.e. date1, date2. For Watermark comparison purposes, I need to use isnull(date1,date2), but unsure how to do this, i.e. I am not sure how I can add this consistent derived value to the Source as an additional field that can perhaps be added via the Query or Stored Procedure Option on the source, to utilise the #item().source.schema and #item().source.table values that have already been generated as parameters..?
You can use the query option in the Copy data activity source and add a new column in the query itself to get the results of isnull(date1,date2) and include the parameter values to get the table name instead of hardcoding them as shown below.
In source, select Query option under Use query and add dynamic content to concat() select statement with parameter values.
#concat('select *, isnull(date1,date2) as final_dt from ',pipeline().parameters.schema,'.',pipeline().parameters.table)
Sink table data output:
I am querying data from server to update fields in my local DB.
But, I have more than 100 columns hence I just want to know which column has new value(different than previous) so that I can use only those fields to prepare/make/build update command to my local DB.
EXAMPLE: I have 100 columns already in my DB. Same row in server has been update. Now that I have fetched all those 100 columns from server.(Stored in list object to make prepare statement in Java). Only 10 from 100 columns have updated. I want to which 10 columns have been updated.
How to do it with triggers?
Or there is any other way than triggers like, in Cassandra, inserting using same PK will act as an update for a row.
If you have to do the update you can add a RETURN statement for show updated rows.
You cn see more on the documentation of Postgres: https://www.postgresql.org/docs/current/dml-returning.html
I am working in Microsoft Azure Databricks environment using sparksql and pyspark.
So I have a delta table on a lake where data is partitioned by say, file_date. Every partition contains files storing millions of records per day with no primary/unique key. All these records have a "status" column which can either contain values NULL (if everything looks good on that specific record) or Not null (say if a particular lookup mapping for a particular column is not found). Additionally, my process contains another folder called "mapping" which gets refreshed on a periodic basis, lets say nightly to make it simple, from where mappings are found.
On a daily basis, there is a good chance that about 100~200 rows get errored out (status column containing not null values). From these files, on a daily basis, (hence is the partition by file_date) , a downstream job pulls all the valid records and sends it for further processing ignoring those 100-200 errored records, waiting for the correct mapping file to be received. The downstream job, in addition to the valid status records, should also try and see if a mapping is found for the errored records and if present, take it down further as well (after of course, updating the data lake with the appropriate mapping and status).
What is the best way to go? The best way is to directly first update the delta table/lake with the correct mapping and update the status column to say "available_for_reprocessing" and my downstream job, pull the valid data for the day + pull the "available_for_reprocessing" data and after processing, update back with the status as "processed". But this seems to be super difficult using delta.
I was looking at "https://docs.databricks.com/delta/delta-update.html" and the update example there is just giving an example for a simple update with constants to update, not for updates from multiple tables.
The other but the most inefficient is, say pull ALL the data (both processed and errored) for the last say 30 days , get the mapping for the errored records and write the dataframe back into the delta lake using the replaceWhere option. This is super inefficient as we are reading everything (hunderds of millions of records) and writing everything back just to process say a 1000 records at the most. If you search for deltaTable = DeltaTable.forPath(spark, "/data/events/") at "https://docs.databricks.com/delta/delta-update.html", the example provided is for very simple updates. Without a unique key, it is impossible to update specific records as well. Can someone please help?
I use pyspark or can use sparksql but I am lost
If you want to update 1 column ('status') on the condition that all lookups are now correct for rows where they weren't correct before (where 'status' is currently incorrect), I think UPDATE command along with EXISTS can help you solve this. It isn't mentioned in the update documentation, but it works both for delete and update operations, effectively allowing you to update/delete records on joins.
For your scenario I believe the sql command would look something like this:
UPDATE your_db.table_name AS a
SET staus = 'correct'
WHERE EXISTS
(
SELECT *
FROM your_db.table_name AS b
JOIN lookup_table_1 AS t1 ON t1.lookup_column_a = b.lookup_column_a
JOIN lookup_table_2 AS t2 ON t2.lookup_column_b = b.lookup_column_b
-- ... add further lookups if needed
WHERE
b.staus = 'incorrect' AND
a.lookup_column_a = b.lookup_column_a AND
a.lookup_column_b = b.lookup_column_b
)
Merge did the trick...
MERGE INTO deptdelta AS maindept
USING updated_dept_location AS upddept
ON upddept.dno = maindept.dno
WHEN MATCHED THEN UPDATE SET maindept.dname = upddept.updated_name, maindept.location = upddept.updated_location
I am asking for help on the following topic. I am trying to create an ETL process with two Excel data sources (S1 ~300 rows and S2 ~7000 rows). S1 contains project information and employee details and S2 contains the amount of hours, which each employee worked in which project at a timestamp.
I want to insert the amount of hours, which each employee worked in each project at a timestamp, into the fact table by referencing to the existing primary keys in the dimension tables. If an entry is not present in the dimension tables already, i want to add a new entry first and use the newly generated id. The destination table structure looks as follows (Data Warehouse, Star Schema):Destination Table Structure
In SSIS, i created three Data Flow tasks for filling the Dimension Tables (project, employee and time) with distinct values (using group by, as S1 and S2 contain a lot of duplicate rows)first, and a fourth data flow task (see image below) to insert the FactTable data, and this is where I'm running into problems:
Data Flow Task FactTable
I am using three LookUp functions to retrieve the foreignKeys project_id, employee_id and time_id from the Dimension tables (using project name, employee number and timestamp). If the id is found, it is passed on all the way to Merge Join 1, if not, a new Dimension Entry is created (lets say project) and the generated project_id passed on instead. Same goes for employee and time respectively.
There is two issues with this:
1) The "amount of hours" (passed by Multicast four, see image above) is not matched in the final result (No Match)
2) The amount of rows being inserted keeps increasing forever (Endless Join, I belive due to the Merge joins).
What I've tried:
I have used one UNION instead of three Merge Joins before, but this resulted in the foreign keys being in seperate rows each, instead of merged together.
I used Merge (instead of Merge Join) and combined the join as well as sort conditions in as I fell all possible ways.
I understand that this scenario might be confusing for everybody else, but thank your for taking time looking at it! Any help is greatly appreciated.
Solved it
For anybody having similar issues:
Seperate Data Flows for filling Dimension Tables with those filling Fact Tables will do the trick.
Its a clean solution and easier to debug.
Also: Dont run the LookUp Functions in parallel, but rather one after each other and pass on the attributes. Saves unnecessary Merges as well.
So as a Sum Up:
Four Data Flow Tasks, three for filling dimension tables ONLY and one for filling fact tables ONLY.
Loading Multiple Tables using SSIS keeping foreign key relationships
The answer posted by onupdatecascade is basically it.
Good luck!
Our cluster has Spark 1.3 and Hive
There is a large Hive table that I need to add randomly selected rows to.
There is a smaller table that I read and check a condition, if that condition is true, then I grab the variables I need to then query for the random rows to fill. What I did was do a query on that condition, table.where(value<number), then make it an array by using take(num rows). Then since all of these rows contain the information I need on which random rows are needed from the large hive table, I iterate through the array.
When I do the query I use ORDER BY RAND() in the query (using sqlContext). I created a var Hive table ( to be mutable) adding a column from the larger table. In the loop, I do a unionAll newHiveTable = newHiveTable.unionAll(random_rows)
I have tried many different ways to do this, but am not sure what is the best way to avoid CPU and temp disk use. I know that Dataframes aren't intended for incremental adds.
One thing I have though now to try is to create a cvs file, write the random rows to that file incrementally in the loop, then when the loop is finished, load the cvs file as a table, and do one unionAll to get my final table.
Any feedback would be great. Thanks
I would recommend that you create an external table with hive, defining the location, and then let spark write the output as csv to that directory:
in Hive:
create external table test(key string, value string)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ';'
LOCATION '/SOME/HDFS/LOCATION'
And then from spark with the aide of https://github.com/databricks/spark-csv , write the dataframe to csv files and appending to the existing ones:
df.write.format("com.databricks.spark.csv").save("/SOME/HDFS/LOCATION/", SaveMode.Append)