Forum Discussion
Power Query engine performance issue
Without seeing your query code, it's a bit difficult to diagnose for sure, but given the size of the data, I'd recommend buffering the table after loading and before any transformations like pivoting. Scroll down to the Buffering section in this article for some more detail about the Table.Buffer function.
Other posts related to buffering:
https://community.powerbi.com/t5/Desktop/Using-Table-Buffer/td-p/1535407
ImkeF has a nice list of various recommendations for improving query performance.
I'd consider anything that can comfortably fit into your RAM to not be a large table.
As far as buffering, I suggest buffering right after the API stuff so that any further transformations don't attempt to trigger API calls again. You could do this at this step:
rt_table = Table.Buffer(Table.AddColumn(type_change0, "RT_DATA", each getRTData([handleID], token), type record)),If this table fits in memory nicely, then any subsequent basic transformations should be pretty fast.
Hi AlexisOlson - I am wary of this suggestion because Table.Buffer may not like nested Tables or Binary objects. I have seen this happen with Dataflows.
Element115 - it will help to buffer before running the very expensive Table.Pivot function.I glad it starting to help. One thing that can slow the performance is the API Throttling Limits. You should check how many send and receives you can make per second or minute.
There are two other things would try, but this will depend on whether original data and API data must fully updated each time.
- try an incremental load using the LastReportTime to avoid reload all records each week.
- if ID_External and Handle_ID are not unique then try running them in a distinct batch to avoid using the API to call the same data more than once.
Buffering loads the table to memory as it exists at that particular step. Deciding when and where to do this is more of an experimental art rather than an exact set of rules to follow, especially without a deep understanding of exactly how the query optimization engine works.
Just because you have some version of the table loaded into memory doesn't mean that there's never a need buffer again after that point. If you do expensive calculations or extensive transformations on a table, sometimes it's worth buffering those intermediate results before doing any further steps so that you have those calculations/transformations stored in a format that can be referenced efficiently.
A rather extreme example is this function I wrote here. As ImkeF points out, after I've done some initial transformations to set up some chunks to loop through, buffering them to memory helps a lot since it's doing nested iterations on those chunks. Only buffering the initial input wouldn't be nearly as fast.
It's possible that buffering both before and after the pivot is the fastest but that's something that needs to be tested in your specific situation. Don't go too crazy with buffers though. Take them out anywhere they don't help.
Element115 -
For 1 - it depends, as AlexisOlson says it is experimental. However there is one firm rule that you should follow. If you are connecting to foldable datasource like a database, don't buffer until after Query Folding breaks. Buffering at the very start would break folding and effectively load the entire table to temporary memory.
For 2 - Firstly, I believe Pivot is the more expensive transformation. Second, I would want Power Query to full complete all the steps before starting the Pivot transformation. Hence, my strategy would be to place it before Pivot. However, testing might show that there is very little impact from using either approach.
Buffering loads the table to memory as it exists at that particular step. Deciding when and where to do this is more of an experimental art rather than an exact set of rules to follow, especially without a deep understanding of exactly how the query optimization engine works.
Just because you have some version of the table loaded into memory doesn't mean that there's never a need buffer again after that point. If you do expensive calculations or extensive transformations on a table, sometimes it's worth buffering those intermediate results before doing any further steps so that you have those calculations/transformations stored in a format that can be referenced efficiently.
A rather extreme example is this function I wrote here. As ImkeF points out, after I've done some initial transformations to set up some chunks to loop through, buffering them to memory helps a lot since it's doing nested iterations on those chunks. Only buffering the initial input wouldn't be nearly as fast.
It's possible that buffering both before and after the pivot is the fastest but that's something that needs to be tested in your specific situation. Don't go too crazy with buffers though. Take them out anywhere they don't help.
Anonymous AlexisOlson
I removed one buffer from one intermediated steps and removed the buffer also from both Table.AddColumn calls, and instead of buffering Table.Pivot, moved the buffer to the step immediately prior. That gained 2 minutes. Actually, the refresh time in the Service is 9-10 mins now vs 10-12 mins before these changes. (15 mins when running on the Desktop). Interesting and .... strange.
1__One last thing I am still not clear on is this:
By 'breaking folding', do you mean something like this? Using Table.StopFolding immediately as the step after all the DB related calls are done? I currently have it like this:
let
Source0 = Sql.Databases("DB"),
DB = Source{ [Name="DB_name"] }[Data],
dbo_vOLC = DB{ [Schema="dbo", Item="vOLC"] }[Data],
stop_folding = Table.StopFolding(dbo_vOLC),
type_change0 = Table.TransformColumnTypes(stop_folding, { {"ID_External", Int64.Type} }),
#"Renamed Columns" = Table.RenameColumns(type_change0, { ...,
remove_cols = Table.Buffer(
Table.RemoveColumns(
#"Renamed Columns",
2__Finally, I am confused about this buffering business. For instance, let's say you use buffer once at the top. Would this not mean that all subsequent steps will be performed on an in-memory table anyway? I thought that that is what the PQ engine would do and hence I do not quite understand why multiple buffering is required. I mean, how else are the transformations computed if not in memory?
3__which makes me wonder... if some transformations can be done at the source, then PQ instead of doing them on a in-memory table will fold them to the source, say as native SQL?
4__but then, if the source table is huge (> 1 million rows), and can't all fit in memory, is it not dangerous to use Table.StopFolding as I did in the code snippet above? Wouldn't that prevent some transformations and thus never getting a final result that could be used in the model?
- Breaking folding means that any further steps cannot be pushed back (folded) to be evaluated at the source. This is most relevant when you query something like a SQL database that is better/faster at handling queries than PowerQuery.
- Yes, once you buffer a table, the following steps are done in memory. As explained above, even if a table is in memory doesn't mean it's stored in the most efficient way to do further transformations, which is why additional buffering is sometimes useful.
- It's usually best to try to fold as much of the query as possible to the native source and only use buffering or other things that break query folding when needed.
- If the table is too large to fit into available memory, then Power Query has to process it in chunks. If it can't figure out how to do this, you can definitely get an out-of-memory error that causes the query to fail.