Forum Discussion
Power BI Dataflow - response too large to return
Hello Community,
I have run into a PowerBI dataflow issue. My source table is in Big Query and is a table that gets updated daily and has data for the last 90 days in a rolling fashion.
What i would like to accomplish is provide this data to another team in the org using dataflows.
Can't use direct connection to BQ for access reasons
Can't use dataset as the team has it's own dataset with other sources
My error is when i try to create the dataflow and run the first refresh it fails on me with response too large to return error.
I tried to filter it to 30 days and that worked but even if i create 3 different dataflows i can't think of a way on how to schedule this as i am manually adding the last 30 days filter on one and the other two has actual dates for the days before accumulating to 90.
I also have made the table as lean as possible and it can't be aggregated (requirement is for that level of granularity)
I am considering bringing in just tiny amount (30 days) of data and then running on an incremental load (loading the data piece by piece until i get 90 days) but am yet to try that out? Before i begin just wanted to find if i had any other options or different approaches.
Thanks
Can't use direct connection to BQ for access reasonsChallenge that assumption. Tell them that 650 GB is too much.
If they don't budge, use Incremental Refresh. Start VERY small, with a day or so. See how many rows you get. Recommended partition size is around 8 to 20 million rows.
Please explain "import does not work" - are you getting an error message?
Direct Query on a dataflow = datamart = lipstick on a pig (Azure SQL db being the lipstick and the dataflow being the pig). Possible, sure, but a travesty nevertheless.
You can consider switching the incremental refresh to the dataset instead and to completely remove the dataflow from the equation.
You can consider dataflow Gen2 and store the results as Delta Lake in Fabric.
36 Replies
requesting a check from parry2k SaiTejaTalasila lbendlin
Please share your opinions
Much appreciate it.Hi Sam_Jain ,
Let's assume If you were able to bring your 90days to dataflow 1 and on the dataflow 2 apply incremental refresh and append 90+new data on it or you can take dataflow 3 and append that dataflow 1 and dataflow 2 on it.
Or
Option 2 using Fabric
You can try this bring your data to datalake and save it as a parquet file.
You can refer it and you can try it-
Loading data from Google Big Query into Microsoft Fabric using Python Notebooks: A Simple Guide
https://www.linkedin.com/pulse/loading-data-from-google-big-query-microsoft-fabric-using-jakub-vala-1nlue?utm_source=share&utm_medium=member_android&utm_campaign=share_viaI hope it will be helpful
Thanks,
Sai Teja
Hello SaiTejaTalasila
Thanks for your repsonse. Both are good approaches, only challenge is i need to maintain that 90 day rolling window. And if i have a consolidated dataflow my source would be the other two dataflows and i would have to somehow make them all run on an incremental refresh.I figured out the solution with another member's help. Start small and then use incremental refresh!
Thanks again for your input.
That's a great question, sorry! I am not a hundred percent sure but the space is itself is premium with SKU:P3 and i just created the dataflow off of the workspace hitting New -> Dataflow and then to big query for the connection. I did not encounter an option for Gen 1 or Gen 2, if there is a different method to get to them i can definitely try.
Thanks
Attempting to try the GEN2 dataflow via creating the data lakehouse method.
Sam_Jain Sorry I'm not fully sure what and how your current data movement process looks like. If data has already landed in the lakehouse, you should have many options for providing rolling 90 days of data to end users. You can create a view on the SQL endpoint of the lakehouse with a 90-day rolling method and let the user use that view in their model, not sure why you want to have another dataflow on top of lakehouse. You have a lot more options available if data is already in the lakehouse.
Oh i wasn't planning on a lake house. I just looked up Gen2 Dataflow and the method it suggested me was to create a lakehouse and then you will find the GEN2 dataflow option.
If there is a different method, i'd like to try that.
And the answer to your first question so basically the data itself is housed in Big Query and the original dataset runs on a daily referesh and has a data size of about 650 GB and the solution here was to create a subset so i created a new table with just the last 90 days of data and a stored procedure alongside with it to have the oldest day's data deleted and new day's data added from the original table which also runs on a daily basis to maintian that 90 day rolling window.
My only goal here is to somehow provide this data without actually giving direct access to BQ and for the team to be able to use this data on their own datasets/reports as just another source.
Thanks
Update - The GEN2 Dataflow failed as well, also on the same mark of time as the GEN1 at 1:44.
So, back to square one.
Yea, so here's what the error reads - apologies i copy pasted the entire thing
Mashup Exception Data Source Error Couldn't refresh the entity because of an issue with the mashup document MashupException.Error: DataSource.Error: Error in replacing table's content with new data in a version: #{0}., InnerException: #{0}: #{1}, Underlying error: ODBC: ERROR [HY000] [Microsoft][BigQuery] (100) HTTP Error 403: responseTooLarge (Response too large to return. Consider specifying a destination table in your job configuration. For more details, see https://cloud.google.com/bigquery/troubleshooting-errors). Details: Reason = DataSource.Error;Message = ODBC: ERROR [HY000] [Microsoft][BigQuery] (100) HTTP Error 403: responseTooLarge (Response too large to return. Consider specifying a destination table in your job configuration. For more details, see https://cloud.google.com/bigquery/troubleshooting-errors).;Detail = [DataSourceKind = "GoogleBigQuery", DataSourcePath = "GoogleBigQuery", OdbcErrors = error "Microsoft.Mashup.Engine1.Runtime.ValueException: [Expression.Error] Value was not specified.#(cr)#(lf) at Microsoft.Mashup.Engine1.Language.ValueCreator.CreateValueForThrow(IThrowExpression throwExpr)#(cr)#(lf) at Microsoft.Mashup.Engine1.Language.ValueCreator.<>c__DisplayClass23_0.<CreateValueForRecord>b__0(Int32 index)#(cr)#(lf) at Microsoft.Mashup.Engine1.Runtime.RecordValue.DemandRecordValue.get_Item(Int32 index)#(cr)#(lf) at Microsoft.Data.Mashup.ProviderCommon.MashupResource.TryGetValue(Func`1 getValue, IValue& value, String& errorMessage)#(cr)#(lf)Record"];Message.Format = #{0}: #{1};Message.Parameters = {"ODBC", "ERROR [HY000] [Microsoft][BigQuery] (100) HTTP Error 403: responseTooLarge (Response too large to return. Consider specifying a destination table in your job configuration. For more details, see https://cloud.google.com/bigquery/troubleshooting-errors)."};Microsoft.Data.Mashup.Error.Context = User
Alternatively, please let me know if there is any other approach or method i can use to accomplish the task.
ThanksCan't use direct connection to BQ for access reasonsChallenge that assumption. Tell them that 650 GB is too much.
If they don't budge, use Incremental Refresh. Start VERY small, with a day or so. See how many rows you get. Recommended partition size is around 8 to 20 million rows.
Yea, so the 650 GB is for the entire dataset on BQ, which is roughly about a year's worth of data and the proposed solution is to have two datasets one rolled up monthly, which i was able to succesfully create and provide a dataflow access. The second one was a 90 day rolling window but at daily level and i was able to create a stored procedure on BQ that deletes the oldest day's data and add's new day's data from source table , had that schduled as a daily run to comply the 90 day rolling window.
Challenge started when i created a dataflow off of it where the data size on bq is about 200 GB, which kept failing on me so i cut the columns into half to 30 from 50 odd. Still didn't work.
So, i then filtered by date on power query editor - that worked! But now it only has 30 days of data and am not sure how i proceed from here?
Also the dataset on BQ is partitioned by day for effeciency.
From what you are suggesting, it looks like i have done the first bit getting data for 30 days and now i should use incremental refresh for every 15-30 days until i have all 90? Is that the right approach?
Thanks.
