score:73

Accepted answer

I had the same issue, and the following syntax worked for me:

df.filter(df("state")==="TX").show()

I'm using Spark 1.6.

score:-1

In Spark 2.4

To compare with one value:

df.filter(lower(trim($"col_name")) === "<value>").show()

To compare with collection of value:

df.filter($"col_name".isInCollection(new HashSet<>(Arrays.asList("value1", "value2")))).show()

score:1

Here is the complete example using spark2.2+ taking data in json...

val myjson = "[{\"name\":\"Alabama\",\"abbreviation\":\"AL\"},{\"name\":\"Alaska\",\"abbreviation\":\"AK\"},{\"name\":\"American Samoa\",\"abbreviation\":\"AS\"},{\"name\":\"Arizona\",\"abbreviation\":\"AZ\"},{\"name\":\"Arkansas\",\"abbreviation\":\"AR\"},{\"name\":\"California\",\"abbreviation\":\"CA\"},{\"name\":\"Colorado\",\"abbreviation\":\"CO\"},{\"name\":\"Connecticut\",\"abbreviation\":\"CT\"},{\"name\":\"Delaware\",\"abbreviation\":\"DE\"},{\"name\":\"District Of Columbia\",\"abbreviation\":\"DC\"},{\"name\":\"Federated States Of Micronesia\",\"abbreviation\":\"FM\"},{\"name\":\"Florida\",\"abbreviation\":\"FL\"},{\"name\":\"Georgia\",\"abbreviation\":\"GA\"},{\"name\":\"Guam\",\"abbreviation\":\"GU\"},{\"name\":\"Hawaii\",\"abbreviation\":\"HI\"},{\"name\":\"Idaho\",\"abbreviation\":\"ID\"},{\"name\":\"Illinois\",\"abbreviation\":\"IL\"},{\"name\":\"Indiana\",\"abbreviation\":\"IN\"},{\"name\":\"Iowa\",\"abbreviation\":\"IA\"},{\"name\":\"Kansas\",\"abbreviation\":\"KS\"},{\"name\":\"Kentucky\",\"abbreviation\":\"KY\"},{\"name\":\"Louisiana\",\"abbreviation\":\"LA\"},{\"name\":\"Maine\",\"abbreviation\":\"ME\"},{\"name\":\"Marshall Islands\",\"abbreviation\":\"MH\"},{\"name\":\"Maryland\",\"abbreviation\":\"MD\"},{\"name\":\"Massachusetts\",\"abbreviation\":\"MA\"},{\"name\":\"Michigan\",\"abbreviation\":\"MI\"},{\"name\":\"Minnesota\",\"abbreviation\":\"MN\"},{\"name\":\"Mississippi\",\"abbreviation\":\"MS\"},{\"name\":\"Missouri\",\"abbreviation\":\"MO\"},{\"name\":\"Montana\",\"abbreviation\":\"MT\"},{\"name\":\"Nebraska\",\"abbreviation\":\"NE\"},{\"name\":\"Nevada\",\"abbreviation\":\"NV\"},{\"name\":\"New Hampshire\",\"abbreviation\":\"NH\"},{\"name\":\"New Jersey\",\"abbreviation\":\"NJ\"},{\"name\":\"New Mexico\",\"abbreviation\":\"NM\"},{\"name\":\"New York\",\"abbreviation\":\"NY\"},{\"name\":\"North Carolina\",\"abbreviation\":\"NC\"},{\"name\":\"North Dakota\",\"abbreviation\":\"ND\"},{\"name\":\"Northern Mariana Islands\",\"abbreviation\":\"MP\"},{\"name\":\"Ohio\",\"abbreviation\":\"OH\"},{\"name\":\"Oklahoma\",\"abbreviation\":\"OK\"},{\"name\":\"Oregon\",\"abbreviation\":\"OR\"},{\"name\":\"Palau\",\"abbreviation\":\"PW\"},{\"name\":\"Pennsylvania\",\"abbreviation\":\"PA\"},{\"name\":\"Puerto Rico\",\"abbreviation\":\"PR\"},{\"name\":\"Rhode Island\",\"abbreviation\":\"RI\"},{\"name\":\"South Carolina\",\"abbreviation\":\"SC\"},{\"name\":\"South Dakota\",\"abbreviation\":\"SD\"},{\"name\":\"Tennessee\",\"abbreviation\":\"TN\"},{\"name\":\"Texas\",\"abbreviation\":\"TX\"},{\"name\":\"Utah\",\"abbreviation\":\"UT\"},{\"name\":\"Vermont\",\"abbreviation\":\"VT\"},{\"name\":\"Virgin Islands\",\"abbreviation\":\"VI\"},{\"name\":\"Virginia\",\"abbreviation\":\"VA\"},{\"name\":\"Washington\",\"abbreviation\":\"WA\"},{\"name\":\"West Virginia\",\"abbreviation\":\"WV\"},{\"name\":\"Wisconsin\",\"abbreviation\":\"WI\"},{\"name\":\"Wyoming\",\"abbreviation\":\"WY\"}]"
import spark.implicits._
val df = spark.read.json(Seq(myjson).toDS)
df.show 
   import spark.implicits._
    val df = spark.read.json(Seq(myjson).toDS)
    df.show

    scala> df.show
    +------------+--------------------+
    |abbreviation|                name|
    +------------+--------------------+
    |          AL|             Alabama|
    |          AK|              Alaska|
    |          AS|      American Samoa|
    |          AZ|             Arizona|
    |          AR|            Arkansas|
    |          CA|          California|
    |          CO|            Colorado|
    |          CT|         Connecticut|
    |          DE|            Delaware|
    |          DC|District Of Columbia|
    |          FM|Federated States ...|
    |          FL|             Florida|
    |          GA|             Georgia|
    |          GU|                Guam|
    |          HI|              Hawaii|
    |          ID|               Idaho|
    |          IL|            Illinois|
    |          IN|             Indiana|
    |          IA|                Iowa|
    |          KS|              Kansas|
    +------------+--------------------+

    // equals matching
    scala> df.filter(df("abbreviation") === "TX").show
    +------------+-----+
    |abbreviation| name|
    +------------+-----+
    |          TX|Texas|
    +------------+-----+
    // or using lit

    scala> df.filter(df("abbreviation") === lit("TX")).show
    +------------+-----+
    |abbreviation| name|
    +------------+-----+
    |          TX|Texas|
    +------------+-----+

    //not expression
    scala> df.filter(not(df("abbreviation") === "TX")).show
    +------------+--------------------+
    |abbreviation|                name|
    +------------+--------------------+
    |          AL|             Alabama|
    |          AK|              Alaska|
    |          AS|      American Samoa|
    |          AZ|             Arizona|
    |          AR|            Arkansas|
    |          CA|          California|
    |          CO|            Colorado|
    |          CT|         Connecticut|
    |          DE|            Delaware|
    |          DC|District Of Columbia|
    |          FM|Federated States ...|
    |          FL|             Florida|
    |          GA|             Georgia|
    |          GU|                Guam|
    |          HI|              Hawaii|
    |          ID|               Idaho|
    |          IL|            Illinois|
    |          IN|             Indiana|
    |          IA|                Iowa|
    |          KS|              Kansas|
    +------------+--------------------+
    only showing top 20 rows

score:1

Let's create a sample dataset and do a deep dive into exactly why OP's code didn't work.

Here's our sample data:

val df = Seq(
  ("Rockets", 2, "TX"),
  ("Warriors", 6, "CA"),
  ("Spurs", 5, "TX"),
  ("Knicks", 2, "NY")
).toDF("team_name", "num_championships", "state")

We can pretty print our dataset with the show() method:

+---------+-----------------+-----+
|team_name|num_championships|state|
+---------+-----------------+-----+
|  Rockets|                2|   TX|
| Warriors|                6|   CA|
|    Spurs|                5|   TX|
|   Knicks|                2|   NY|
+---------+-----------------+-----+

Let's examine the results of df.select(df("state")==="TX").show():

+------------+
|(state = TX)|
+------------+
|        true|
|       false|
|        true|
|       false|
+------------+

It's easier to understand this result by simply appending a column - df.withColumn("is_state_tx", df("state")==="TX").show():

+---------+-----------------+-----+-----------+
|team_name|num_championships|state|is_state_tx|
+---------+-----------------+-----+-----------+
|  Rockets|                2|   TX|       true|
| Warriors|                6|   CA|      false|
|    Spurs|                5|   TX|       true|
|   Knicks|                2|   NY|      false|
+---------+-----------------+-----+-----------+

The other code OP tried (df.select(df("state")=="TX").show()) returns this error:

<console>:27: error: overloaded method value select with alternatives:
  [U1](c1: org.apache.spark.sql.TypedColumn[org.apache.spark.sql.Row,U1])org.apache.spark.sql.Dataset[U1] <and>
  (col: String,cols: String*)org.apache.spark.sql.DataFrame <and>
  (cols: org.apache.spark.sql.Column*)org.apache.spark.sql.DataFrame
 cannot be applied to (Boolean)
       df.select(df("state")=="TX").show()
          ^

The === operator is defined in the Column class. The Column class doesn't define a == operator and that's why this code is erroring out.

Here's the accepted answer that works:

df.filter(df("state")==="TX").show()

+---------+-----------------+-----+
|team_name|num_championships|state|
+---------+-----------------+-----+
|  Rockets|                2|   TX|
|    Spurs|                5|   TX|
+---------+-----------------+-----+

As other posters have mentioned, the === method takes an argument with an Any type, so this isn't the only solution that works. This works too for example:

df.filter(df("state") === lit("TX")).show

+---------+-----------------+-----+
|team_name|num_championships|state|
+---------+-----------------+-----+
|  Rockets|                2|   TX|
|    Spurs|                5|   TX|
+---------+-----------------+-----+

The Column equalTo method can also be used:

df.filter(df("state").equalTo("TX")).show()

+---------+-----------------+-----+
|team_name|num_championships|state|
+---------+-----------------+-----+
|  Rockets|                2|   TX|
|    Spurs|                5|   TX|
+---------+-----------------+-----+

It worthwhile studying this example in detail. Scala's syntax seems magical at times, especially when method are invoked without dot notation. It's hard for the untrained eye to see that === is a method defined in the Column class!

score:5

We can write multiple Filter/where conditions in Dataframe.

For example:

table1_df
.filter($"Col_1_name" === "buddy")  // check for equal to string
.filter($"Col_2_name" === "A")
.filter(not($"Col_2_name".contains(" .sql")))  // filter a string which is    not relevent
.filter("Col_2_name is not null")   // no null filter
.take(5).foreach(println)

score:6

Worked on Spark V2.*

import sqlContext.implicits._
df.filter($"state" === "TX")

if needs to be compared against a variable (e.g., var):

import sqlContext.implicits._
df.filter($"state" === var)

Note : import sqlContext.implicits._

score:9

df.filter($"state" like "T%%") for pattern matching

df.filter($"state" === "TX") or df.filter("state = 'TX'") for equality

score:10

To get the negation, do this ...

df.filter(not( ..expression.. ))

eg

df.filter(not($"state" === "TX"))

score:16

You should be using where, select is a projection that returns the output of the statement, thus why you get boolean values. where is a filter that keeps the structure of the dataframe, but only keeps data where the filter works.

Along the same line though, per the documentation, you can write this in 3 different ways

// The following are equivalent:
peopleDf.filter($"age" > 15)
peopleDf.where($"age" > 15)
peopleDf($"age" > 15)

score:29

There is another simple sql like option. With Spark 1.6 below also should work.

df.filter("state = 'TX'")

This is a new way of specifying sql like filters. For a full list of supported operators, check out this class.


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