Best way to get the max value in a Spark dataframe column

Question

I'm trying to figure out the best way to get the largest value in a Spark dataframe column.

Consider the following example:

df = spark.createDataFrame([(1., 4.), (2., 5.), (3., 6.)], ["A", "B"])
df.show()

Which creates:

+---+---+
|  A|  B|
+---+---+
|1.0|4.0|
|2.0|5.0|
|3.0|6.0|
+---+---+

My goal is to find the largest value in column A (by inspection, this is 3.0). Using PySpark, here are four approaches I can think of:

# Method 1: Use describe()
float(df.describe("A").filter("summary = 'max'").select("A").first().asDict()['A'])

# Method 2: Use SQL
df.registerTempTable("df_table")
spark.sql("SELECT MAX(A) as maxval FROM df_table").first().asDict()['maxval']

# Method 3: Use groupby()
df.groupby().max('A').first().asDict()['max(A)']

# Method 4: Convert to RDD
df.select("A").rdd.max()[0]

Each of the above gives the right answer, but in the absence of a Spark profiling tool I can't tell which is best.

Any ideas from either intuition or empiricism on which of the above methods is most efficient in terms of Spark runtime or resource usage, or whether there is a more direct method than the ones above?

Answer 1

>df1.show()
+-----+--------------------+--------+----------+-----------+
|floor|           timestamp|     uid|         x|          y|
+-----+--------------------+--------+----------+-----------+
|    1|2014-07-19T16:00:...|600dfbe2| 103.79211|71.50419418|
|    1|2014-07-19T16:00:...|5e7b40e1| 110.33613|100.6828393|
|    1|2014-07-19T16:00:...|285d22e4|110.066315|86.48873585|
|    1|2014-07-19T16:00:...|74d917a1| 103.78499|71.45633073|

>row1 = df1.agg({"x": "max"}).collect()[0]
>print row1
Row(max(x)=110.33613)
>print row1["max(x)"]
110.33613

The answer is almost the same as method3. but seems the "asDict()" in method3 can be removed