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Simple form of looping formula that takes a long time


Simple form of looping formula that takes a long time

By : user3100668
Date : January 12 2021, 09:11 PM
I wish this helpful for you Writing to a large number of cells, one cell at a time, is very inefficient. Instead, first create a temporary array, and write the results to the array. Then, transfer the contents of the array to your worksheet all at once.
code :
'set the source worksheet
Dim sourceWorksheet As Worksheet
Set sourceWorksheet = Sheets("PETA")

'set the source range
Dim sourceRange As Range
Set sourceRange = sourceWorksheet.Range("D2:WQ201")

'create a temporary array to hold the results
Dim i As Long
Dim j As Long
Dim tempArray() As Variant
ReDim tempArray(1 To sourceRange.Rows.Count, 1 To sourceRange.Columns.Count \ 6)
With Application
    For i = LBound(tempArray, 1) To UBound(tempArray, 1)
        For j = LBound(tempArray, 2) To UBound(tempArray, 2)
            'Debug.Print sourceRange.Resize(1, 6).Offset(i - 1, j * 6 - 6).Address
            tempArray(i, j) = .CountA(sourceRange.Resize(1, 6).Offset(i - 1, j * 6 - 6))
        Next j
    Next i
End With

'transfer the contents of the temporary array to the active worksheet
Range("E11").Resize(UBound(tempArray, 1), UBound(tempArray, 2)).Value = tempArray


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R: looping over large dataset takes a long time

R: looping over large dataset takes a long time


By : Shyara Hardik
Date : March 29 2020, 07:55 AM
help you fix your problem Here's a very simple solution that splits your dataset into pieces and then applies MannKendall to each piece.
code :
# example dataset
d <- data.frame(a=rnorm(1e6), b=rep(1:1000, each=1000))

mk <- lapply(split(d$a, d$b), MannKendall)
> mk[[1]]
tau = 0.0319, 2-sided pvalue =0.13133
mk <- do.call(rbind, mk)
Simple query takes long time to execute

Simple query takes long time to execute


By : dimtas
Date : March 29 2020, 07:55 AM
Hope this helps If you put a compound index on (title, content, poster), and another index on url, you should see a speedup.
Note well. Each OR clause of a query like this can only use one index. So, it won't help this query to put separate indexes on title, content, and poster.
Why this simple ArangoDB query sometimes takes very long time

Why this simple ArangoDB query sometimes takes very long time


By : Faydee
Date : March 29 2020, 07:55 AM
With these it helps As your Explain shows, your query doesn't utilize indices, but does a full collection scan.
Depending on when it finds the match (at the start or the end of the collection) execution times may vary.
Hibernate Criteria.list() takes a very long time for a very simple query

Hibernate Criteria.list() takes a very long time for a very simple query


By : Ramon Portillo Guisa
Date : March 29 2020, 07:55 AM
This might help you This is what I did. I made the association mandatory (optional = false), it trusts that for every sample record, there is going to be a Deposit record. Therefore, when loading Sample, Hibernate doesn't load Deposit table as well.
This is the underlying code,
code :
    @OneToOne(optional = false, fetch = FetchType.LAZY, mappedBy = "sample",         cascade = CascadeType.ALL)
    public deposit getDeposit() {
        return this.deposit;
    }
Very simple AVG() aggregation query on MySQL server takes ridiculously long time

Very simple AVG() aggregation query on MySQL server takes ridiculously long time


By : Mohammad Tayyab
Date : March 29 2020, 07:55 AM
I wish this helpful for you To count the number of rows with a specific date, MySQL has to locate that value in the index (which is pretty fast, after all that is what indexes are made for) and then read the subsequent entries of the index until it finds the next date. Depending on the datatype of esi, this will sum up to reading some MB of data to count your 700k rows. Reading some MB does not take much time (and that data might even already be cached in the buffer pool, depending on how often you use the index).
To calculate the average for a column that is not included in the index, MySQL will, again, use the index to find all rows for that date (the same as before). But additionally, for every row it finds, it has to read the actual table data for that row, which means to use the primary key to locate the row, read some bytes, and repeat this 700k times. This "random access" is a lot slower than the sequential read in the first case. (This gets worse by the problem that "some bytes" is the innodb_page_size (16KB by default), so you may have to read up to 700k * 16KB = 11GB, compared to "some MB" for count(*); and depending on your memory configuration, some of this data might not be cached and has to be read from disk.)
code :
select * from (select `date`, avg(`01`), ..., avg(`12`) 
               from mytable where `date` = ...) as part1
cross join    (select avg(`13`), ..., avg(`24`) 
               from mytable where `date` = ...) as part2;
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