Faster process for large insert

Faster process for large insert

By : leoworlds
Date : October 18 2020, 08:10 PM
hop of those help? This sounds like a 'standard' summary table for a Data Warehouse application. I'll state a couple of assumptions, then discuss how to do that. The resulting query may take an hour; it may take only minutes.
The ActionLog is huge, but it is only "added" to. You never UPDATE or DELETE data (except perhaps for aging out old data). "an arbitrary eventID" is really something more regular, such as "start of some day".
code :

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LINQ Faster way to insert a large amount of data into SQL?

LINQ Faster way to insert a large amount of data into SQL?

By : Ka Ho Tam
Date : March 29 2020, 07:55 AM
around this issue bulk insert helped, along with a pipeline model, and general code efficiency. It's never going to take less than 30 minutes unless you're on a fast machine with fast internet...
Faster way to process 1.2 million JSON geolocation queries from large dataframe

Faster way to process 1.2 million JSON geolocation queries from large dataframe

By : Alexandre Volpi
Date : March 29 2020, 07:55 AM
This might help you You're going to have issues even if you persevere with time. The service you're querying allows 'an absolute maximum of one request per second', which you're already breaching. It's likely that they will throttle your requests before you reach 1.2m queries. Their website notes similar APIs for larger uses have only around 15k free daily requests.
It'd be much better for you to use an offline option. A quick search shows that there are many freely available datasets of populated places, along with their longitude and latitudes. Here's one we'll use: http://simplemaps.com/resources/world-cities-data
code :
> library(dplyr)

> cities.data <- read.csv("world_cities.csv") %>% tbl_df
> print(cities.data)

Source: local data frame [7,322 x 9]

             city     city_ascii     lat     lng    pop     country   iso2   iso3 province
           (fctr)         (fctr)   (dbl)   (dbl)  (dbl)      (fctr) (fctr) (fctr)   (fctr)
1   Qal eh-ye Now      Qal eh-ye 34.9830 63.1333   2997 Afghanistan     AF    AFG  Badghis
2     Chaghcharan    Chaghcharan 34.5167 65.2500  15000 Afghanistan     AF    AFG     Ghor
3     Lashkar Gah    Lashkar Gah 31.5830 64.3600 201546 Afghanistan     AF    AFG  Hilmand
4          Zaranj         Zaranj 31.1120 61.8870  49851 Afghanistan     AF    AFG   Nimroz
5      Tarin Kowt     Tarin Kowt 32.6333 65.8667  10000 Afghanistan     AF    AFG  Uruzgan
6    Zareh Sharan   Zareh Sharan 32.8500 68.4167  13737 Afghanistan     AF    AFG  Paktika
7        Asadabad       Asadabad 34.8660 71.1500  48400 Afghanistan     AF    AFG    Kunar
8         Taloqan        Taloqan 36.7300 69.5400  64256 Afghanistan     AF    AFG   Takhar
9  Mahmud-E Eraqi Mahmud-E Eraqi 35.0167 69.3333   7407 Afghanistan     AF    AFG   Kapisa
10     Mehtar Lam     Mehtar Lam 34.6500 70.1667  17345 Afghanistan     AF    AFG  Laghman
..            ...            ...     ...     ...    ...         ...    ...    ...      ...
# make up toy data
> candidate.longlat <- data.frame(vid = 1:3, 
                                lat = c(12.53, -16.31, 42.87), 
                                long = c(-70.03, -48.95, 74.59))
> install.packages("geosphere")
> library(geosphere)

# compute distance matrix using geosphere
> distance.matrix <- distm(x = candidate.longlat[,c("long", "lat")], 
                         y = cities.data[,c("lng", "lat")])
# work out which index in the matrix is closest to the data
> closest.index <- apply(distance.matrix, 1, which.min)

# rbind city and country of match with original query
> candidate.longlat <- cbind(candidate.longlat, cities.data[closest.index, c("city", "country")])
> print(candidate.longlat)

  vid    lat   long       city    country
1   1  12.53 -70.03 Oranjestad      Aruba
2   2 -16.31 -48.95   Anapolis     Brazil
3   3  42.87  74.59    Bishkek Kyrgyzstan
Is there any faster way to process pandas dataframe into large csv?

Is there any faster way to process pandas dataframe into large csv?

By : dwarrington
Date : March 29 2020, 07:55 AM
it helps some times This is probably the fastest you can get; I think it should work. It's writing files out as it reads them in, instead of piling up everything in memory until the end and doing concat (which is a bit slow)
code :
import pandas
import glob

rawCSV = glob.glob('C:\RAW\*.csv')

for filename in rawCSV:
    data = pandas.read_csv(filename, delimiter=';')
    date_time = data['DATE_TIME'].str.split(" ", n = 1, expand = True)
    data.drop(columns =["DATE_TIME"], inplace = True)
    data.insert(loc=0, column='DATE_ONLY', value=date_time[0].str.replace('.','-'))
    data.insert(loc=1, column='TIME_ONLY', value=date_time[1])
    with open('C:\CLEAN\__result.csv', 'a') as fh:
        data.to_csv(fh, index=False , header=False)
Faster way to process calculations on very large data frames

Faster way to process calculations on very large data frames

By : user2982928
Date : March 29 2020, 07:55 AM
Does that help I am working with several data frames that are all 61,143 rows and 9,864 columns. That makes just over 600 million values in each data frame. This makes any calculation on the data frame extremely slow - several hours. For example: , It is always easier to talk with some example data
code :
b <- brick(ncol=245, nrow=250, nl=9864)
v <- matrix(rep(1:ncell(b), nlayers(b)), ncol=nlayers(b))
values(b) <- v
bb <- writeRaster(b, "test.nc", overwrite=TRUE)
d <- data.frame(v)
# matrix 
system.time(apply(v, 1, max))
#   user  system elapsed 
#  10.68    0.79   11.46 

# data.frame
system.time(apply(d, 1, max))
#   user  system elapsed 
#  20.48    0.61   21.11 

# RasterBrick (values in memory)
#   user  system elapsed 
#   6.72    0.29    7.00 

system.time(calc(b, max))
#   user  system elapsed 
#  16.76    0.33   17.11 

# RasterBrick (values on disk)
#   user  system elapsed 
#  19.25    8.43   27.70 

system.time(calc(bb, max))
#   user  system elapsed 
#  22.69    5.92   28.62 
system.time( values(bb))
#   user  system elapsed 
#  21.91    5.28   27.18 
Why NSOperationQueue is faster than GCD or performSelectorOnMainThread when they process a large number of tasks on the

Why NSOperationQueue is faster than GCD or performSelectorOnMainThread when they process a large number of tasks on the

By : nono
Date : March 29 2020, 07:55 AM
wish help you to fix your issue For posterity, lest there be any doubt about this, let's consider the following test harness, built in a simple empty application template. It does 1000 operations using each mechanism, and also has a run loop observer, so we can see how our enqueued asynchronous tasks relate to the spinning of the main run loop. It logs to console, but does so asynchronously, so that the cost of NSLog isn't confounding our measurement. It also intentionally blocks the main thread while it enqueues NSOperations/dispatch_asyncs/performSelectors tasks, so that the act of enqueuing also doesn't interfere. Here's the code:
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