计算要素类中要素数量的最快方法？

随着arcpy中数据访问模块的引入（搜索光标速度提高了30倍），我想知道与sql条件匹配的功能计数是否比传统的MakeTableView + GetCount方法更快？

我已经从上面的答案中测试了解决方案，并且在我的真实世界数据上，差异可以忽略不计。与其他答案的结果相反，我在ArcMap中使用arcpy.MakeTableView_management和arcpy.da.SearchCursor的时间相同。

我已经测试了有无查询的变体形式，请在下面查看查询版本的代码以及最终的测量结果：

@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))

结果如下：

    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

我正在使用一个示例，该示例在filegeodatabase内部具有一百万个随机生成的点。附在这里。

这是一些使我们入门的代码：

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

和一些初步结果：

>>> 
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

想象更大，更复杂的数据集。SearchCursor将无限期爬网。

我对结果并不满意，但是，DataAccess模块​​已在我们的GIS开发圈中广泛使用。我正在尝试使用此模块重建一些函数定义，因为它比MakeTableView + GetCount方法更灵活。