CODEWITHSUNDEEP
scaladataframeapache-sparkapache-spark-sql
Bharat
Bharat

Reputation: 1082

How to filter spark dataframe entries based on a column value which is a map

I have a dataframe like this

+-------+------------------------+
|key    |                    data|
+-------+------------------------+
|     61|[a -> b, c -> d, e -> f]|
|     71|[a -> 1, c -> d, e -> f]|
|     81|[c -> d, e -> f]        |
|     91|[x -> b, y -> d, e -> f]|
|     11|[a -> a, c -> b, e -> f]|
|     21|[a -> a, c -> x, e -> f]|
+-------+------------------------+

I want to filter rows whose data column map contains the key 'a' and the value of key 'a' is 'a'. So the following dataframe is the desired output.

+-------+------------------------+
|key    |                    data|
+-------+------------------------+
|     11|[a -> a, c -> b, e -> f]|
|     21|[a -> a, c -> x, e -> f]|
+-------+------------------------+

I tried casting the value to a map but I am getting this error

== SQL ==
Map
^^^

  at org.apache.spark.sql.catalyst.parser.AstBuilder$$anonfun$visitPrimitiveDataType$1.apply(AstBuilder.scala:1673)
  at org.apache.spark.sql.catalyst.parser.AstBuilder$$anonfun$visitPrimitiveDataType$1.apply(AstBuilder.scala:1651)
  at org.apache.spark.sql.catalyst.parser.ParserUtils$.withOrigin(ParserUtils.scala:108)
  at org.apache.spark.sql.catalyst.parser.AstBuilder.visitPrimitiveDataType(AstBuilder.scala:1651)
  at org.apache.spark.sql.catalyst.parser.AstBuilder.visitPrimitiveDataType(AstBuilder.scala:49)
  at org.apache.spark.sql.catalyst.parser.SqlBaseParser$PrimitiveDataTypeContext.accept(SqlBaseParser.java:13779)
  at org.apache.spark.sql.catalyst.parser.AstBuilder.typedVisit(AstBuilder.scala:55)
  at org.apache.spark.sql.catalyst.parser.AstBuilder.org$apache$spark$sql$catalyst$parser$AstBuilder$$visitSparkDataType(AstBuilder.scala:1645)
  at org.apache.spark.sql.catalyst.parser.AstBuilder$$anonfun$visitSingleDataType$1.apply(AstBuilder.scala:90)
  at org.apache.spark.sql.catalyst.parser.AstBuilder$$anonfun$visitSingleDataType$1.apply(AstBuilder.scala:90)
  at org.apache.spark.sql.catalyst.parser.ParserUtils$.withOrigin(ParserUtils.scala:108)
  at org.apache.spark.sql.catalyst.parser.AstBuilder.visitSingleDataType(AstBuilder.scala:89)
  at org.apache.spark.sql.catalyst.parser.AbstractSqlParser$$anonfun$parseDataType$1.apply(ParseDriver.scala:40)
  at org.apache.spark.sql.catalyst.parser.AbstractSqlParser$$anonfun$parseDataType$1.apply(ParseDriver.scala:39)
  at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:98)
  at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parseDataType(ParseDriver.scala:39)
  at org.apache.spark.sql.Column.cast(Column.scala:1017)
  ... 49 elided

If I just want to filter based on the column 'key' I can just go by doing df.filter(col("key") === 61). But the problem is, the value is a Map.

Is there any thing like df.filter(col("data").toMap.contains("a") && col("data").toMap.get("a") === "a")

Upvotes: 2

Views: 6117

Answers (1)

rkabhishek
rkabhishek

Reputation: 946

You can filter like this df.filter(col("data.x") === "a") where x is the nested column inside data.

Upvotes: 2

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