How to sort by column in descending order in Spark SQL?

You can also sort the column by importing the spark sql functions

import org.apache.spark.sql.functions._
df.orderBy(asc("col1"))

Or

import org.apache.spark.sql.functions._
df.sort(desc("col1"))

importing sqlContext.implicits._

import sqlContext.implicits._
df.orderBy($"col1".desc)

Or

import sqlContext.implicits._
df.sort($"col1".desc)

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It's in org.apache.spark.sql.DataFrame for sort method:

df.sort($"col1", $"col2".desc)

Note $ and .desc inside sort for the column to sort the results by.

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PySpark only

I came across this post when looking to do the same in PySpark. The easiest way is to just add the parameter ascending=False:

df.orderBy("col1", ascending=False).show(10)

Reference: http://spark.apache.org/docs/2.1.0/api/python/pyspark.sql.html#pyspark.sql.DataFrame.orderBy

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import org.apache.spark.sql.functions.desc

df.orderBy(desc("columnname1"),desc("columnname2"),asc("columnname3"))

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df.sort($"ColumnName".desc).show()

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In the case of Java:

If we use DataFrames, while applying joins (here Inner join), we can sort (in ASC) after selecting distinct elements in each DF as:

Dataset<Row> d1 = e_data.distinct().join(s_data.distinct(), "e_id").orderBy("salary");

where e_id is the column on which join is applied while sorted by salary in ASC.

Also, we can use Spark SQL as:

SQLContext sqlCtx = spark.sqlContext();
sqlCtx.sql("select * from global_temp.salary order by salary desc").show();

where

spark -> SparkSession

salary -> GlobalTemp View.

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