I have created an AWS Glue Job (pyspark script), which pulls the data from S3 bucket and load the data into RDS (SQL Server). I have to perform few pre-actions (delete selective data) on the destination table before loading the data. For this, i have used data-frames i.e. bring the entire destination table data into data frame first then performing the delete operation and finally appending (UNION) the data with source data (S3) and loading into RDS table.
Looks like this is not a feasible approach, as it has to load entire destination table data into memory first for performing pre-action. Also there are two connections gets established within the script (JDBC and Glue context), what if one commit gets executed successfully but the other gets failed.
Can someone please suggest the better approach for performing these operations along with maintaining proper TRANSACTION properties?
Related
I am creating a data warehouse using Azure Data Factory to extract data from a MySQL table and saving it in parquet format in an ADLS Gen 2 filesystem. From there, I use Synapse notebooks to process and load data into destination tables.
The initial load is fairly easy using spark.write.saveAsTable('orders') however, I am running into some issues doing incremental load following the intial load. In particular, I have not been able to find a way to reliably insert/update information into an existing Synapse table.
Since Spark does not allow DML operations on a table, I have resorted to reading the current table into a Spark DataFrame and inserting/updating records in that DataFrame. However, when I try to save that DataFrame using spark.write.saveAsTable('orders', mode='overwrite', format='parquet'), I run into a Cannot overwrite table 'orders' that is also being read from error.
A solution indicated by this suggests creating a temporary table and then inserting using that but that still resorts in the above error.
Another solution in this post suggests to write the data into a temporary table, drop the target table, and then rename the table but upon doing this, Spark gives me a FileNotFound errors regarding metadata.
I know Delta Tables can fix this issue pretty reliably but our company is not yet ready to move over to DataBricks.
All suggestions are greatly appreciated.
I have a mapping dataflow inside a foreach activity which I'm using to copy several tables into ADLS; in the dataflow source activity, I call a stored procedure from my synapse environment. In the SP, I have a small temp table which I create to store some values which I will later use for processing a query.
When I run the pipeline, I get an error on the mapping dataflow; "SQLServerException: 111212: Operation cannot be performed within a transaction." If I remove the temp table, and just do a simple select * from a small table, it returns the data fine; it's only after I bring back the temp table that I get an issue.
Have you guys ever seen this before, and is there a way around this?
If you go through the official MS docs, this error is very well documented.
Failed with an error: "SQLServerException: 111212; Operation cannot be performed within a transaction."
Symptoms
When you use the Azure SQL Database as a sink in the data flow to
preview data, debug/trigger run and do other activities, you may find
your job fails with following error message:
{"StatusCode":"DFExecutorUserError","Message":"Job failed due to reason: at Sink 'sink': shaded.msdataflow.com.microsoft.sqlserver.jdbc.SQLServerException: 111212;Operation cannot be performed within a transaction.","Details":"at Sink 'sink': shaded.msdataflow.com.microsoft.sqlserver.jdbc.SQLServerException: 111212;Operation cannot be performed within a transaction."}
Cause
The error "111212;Operation cannot be performed within a transaction." only occurs in the Synapse dedicated SQL pool. But you
mistakenly use the Azure SQL Database as the connector instead.
Recommendation
Confirm if your SQL Database is a Synapse dedicated SQL pool. If so,
use Azure Synapse Analytics as a connector shown in the picture below.
So after running some tests around this issue, it seems like Mapping Dataflows do not like Temp Tables when I call my stored procedure.
The way I ended up fixing this was that instead of using a Temp Table, I ended up using a CTE, which believe it or not, runs a bit faster than when I used the Temp Table.
#KarthikBhyresh
I looked at that article before, but it wasn't an issue with the sink, I was using Synapse LS as my source and a Data Lake Storage as my sink, so I knew from the beginning that this did not apply to my issue, even though it was the same error number.
I have flow files with data records in it. I'm able to place it on S3 bucket. From there on I want to run COPY command and update command with joins to achieve MERGE / UPSERT operation. Can anyone suggest ways to solve this as firehose only executes copy command and I can't make UPSERT / MERGE operation as prescribed by AWS docs directly, so has to copy into staging table and update or insert using some conditions.
There are a number of ways to do this but I usually go with a lambda function run every 5 minutes or so that takes the data put in Redshift from firehose and merges it with existing data. Redshift likes to run on larger "chunks" of data and it is most efficient if you build up some size before performing these operations. The best practice is to move the data from the firehose target in an atomic operation like ALTER TABLE APPEND and use this new table as the source for merging. This is so firehose can keep adding data while the merge is in process.
Is there a way to capture only change data in RDS Postgres instance and write those change records to another RDS Postgres DB instance for later use?
I have looked on few option like using Debezium but it doesn't seems to be helpful as I don't have to actually write to streams.
My requirement is that :
1) Load all the existing data to another DB at once
2) Capture the change data (delta) and store in a database (with same replica) and process the change data.
I have followed below steps to implement above scenario:
1) Written a Spring batch job which connects to RDS and does a full load once.
2) Once the full load is completed, we added logic to export WAL logs for CDC data and then put the data to stream to process further.
Not the very best solution but this solution worked well for the above scenario.
I am trying to migrate a huge table from postgres into Redshift.
The size of the table is about 5,697,213,832
tool: pentaho Kettle Table input(from postgres) -> Table output(Redshift)
Connecting with Redshift JDBC4
By observation I found the inserting into Redshift is the bottleneck. only about 500 rows/second.
Is there any ways to accelerate the insertion into Redshift in single machine mode ? like using JDBC parameter?
Have you consider using S3 as mid-layer?
Dump your data to csv files and apply gzip compression. Upload files to the S3 and then use copy command to load the data.
http://docs.aws.amazon.com/redshift/latest/dg/r_COPY.html
The main reason for bottleneck of redshift performance, which i considered is that Redshift treats each and every hit to the cluster as one single query. It executes each query on its cluster and then proceeds to the next stage. Now when i am sending across multiple rows (in this case 10), each row of data is treated a separate query. Redshift executes each query one by one and loading of the data is completed once all the queries are executed. It means if you are having 100 million rows, there would be 100 million queries running on your Redshift cluster. Well the performance goes to dump !!!
Using S3 File Output step in PDI will load your data to S3 Bucket and then apply the COPY command on the redshift cluster to read the same data from S3 to Redshift. This will solve your problem of performance.
You may also read the below blog links :
Loading data to AWS S3 using PDI
Reading Data from S3 to Redshift
Hope this helps :)
Better to export data to S3, then use COPY command to import data into Redshift. In this way, the import process is fast while you don't need to vacuum it.
Export your data to S3 bucket and use the COPY command in Redshift . COPY command is the fastest way to insert data in Redshift .