Vjylku

With the introduction of the Data Access module in arcpy (30x faster search cursors), I want to know if counting features matching sql criteria is faster than the traditional MakeTableView + GetCount methodology?

I am using an example with 1 million randomly generated points inside of a filegeodatabase. Attached here.

Here is some code to get us started:

import time

import arcpy



arcpy.env.workspace = "C:CountTest.gdb"



time.sleep(5) # Let the cpu/ram calm before proceeding!



"""Method 1"""

StartTime = time.clock()

with arcpy.da.SearchCursor("RandomPoints", ["OBJECTID"]) as cursor:

    rows = {row[0] for row in cursor}



count = 0

for row in rows:

    count += 1



EndTime = time.clock()

print "Finished in %s seconds" % (EndTime - StartTime)

print "%s features" % count



time.sleep(5) # Let the cpu/ram calm before proceeding!



"""Method 2"""

StartTime2 = time.clock()

arcpy.MakeTableView_management("RandomPoints", "myTableView")

count = int(arcpy.GetCount_management("myTableView").getOutput(0))



EndTime2 = time.clock()

print "Finished in %s seconds" % (EndTime2 - StartTime2)

print "%s features" % count

And some initial results:

>>> 

Finished in 6.75540050237 seconds

1000000 features

Finished in 0.801474780332 seconds

1000000 features

>>> =============================== RESTART ===============================

>>> 

Finished in 6.56968596918 seconds

1000000 features

Finished in 0.812731769756 seconds

1000000 features

>>> =============================== RESTART ===============================

>>> 

Finished in 6.58207512487 seconds

1000000 features

Finished in 0.841122157314 seconds

1000000 features

Imagine larger, more complex datasets. The SearchCursor will indefinitely crawl.

I am not at all dissatisfied with the results, however, the DataAccess module is being used extensively in our GIS development circle. I am looking to rebuild some of our function definitions with this module as it is more flexible than a MakeTableView + GetCount methodology.

I have tested solution from answer above and on my real world data the difference is negligible. Opposite to results in other answer, my times for arcpy.MakeTableView_management and arcpy.da.SearchCursor within ArcMap are same same.

I have tested variations with and without query, please see the code for query version, and final measured results below:

@staticmethod

def query_features(feature_class, query):



    # Method 1

    time.sleep(5)  # Let the cpu/ram calm before proceeding!

    start_time = time.clock()

    count = len(list(i for i in arcpy.da.SearchCursor(feature_class, ["OBJECTID"], query)))

    end_time = time.clock()

    arcpy.AddMessage("Method 1 finished in {} seconds".format((end_time - start_time)))

    arcpy.AddMessage("{} features".format(count))



    # Method 2

    time.sleep(5)  # Let the cpu/ram calm before proceeding!

    start_time = time.clock()

    arcpy.MakeTableView_management(feature_class, "myTableView", query)

    count = int(arcpy.GetCount_management("myTableView").getOutput(0))



    end_time = time.clock()

    arcpy.AddMessage("Method 2 in {} seconds".format((end_time - start_time)))

    arcpy.AddMessage("{} features".format(count))

The results below:

    No query:

    Method 1 finished in 5.3616442 seconds

    804140 features

    Method 2 in 4.2843138 seconds

    804140 features



    Many results query:

    Method 1 finished in 12.7124766 seconds

    518852 features

    Method 2 in 12.1396602 seconds

    518852 features



    Few results query:

    Method 1 finished in 11.1421476 seconds

    8 features

    Method 2 in 11.2232503 seconds

    8 features