[apache-spark] How to show full column content in a Spark Dataframe?

I am using spark-csv to load data into a DataFrame. I want to do a simple query and display the content:

val df = sqlContext.read.format("com.databricks.spark.csv").option("header", "true").load("my.csv")
df.registerTempTable("tasks")
results = sqlContext.sql("select col from tasks");
results.show()

The col seems truncated:

scala> results.show();
+--------------------+
|                 col|
+--------------------+
|2015-11-16 07:15:...|
|2015-11-16 07:15:...|
|2015-11-16 07:15:...|
|2015-11-16 07:15:...|
|2015-11-16 07:15:...|
|2015-11-16 07:15:...|
|2015-11-16 07:15:...|
|2015-11-16 07:15:...|
|2015-11-16 07:15:...|
|2015-11-16 07:15:...|
|2015-11-16 07:15:...|
|2015-11-16 07:15:...|
|2015-11-16 07:15:...|
|2015-11-16 07:15:...|
|2015-11-16 07:15:...|
|2015-11-06 07:15:...|
|2015-11-16 07:15:...|
|2015-11-16 07:21:...|
|2015-11-16 07:21:...|
|2015-11-16 07:21:...|
+--------------------+

How do I show the full content of the column?

Below code would help to view all rows without truncation in each column

df.show(df.count(), False)

results.show(false) will show you the full column content.

Show method by default limit to 20, and adding a number before false will show more rows.

results.show(20,false) did the trick for me in Scala.

PYSPARK

In the below code, df is the name of dataframe. 1st parameter is to show all rows in the dataframe dynamically rather than hardcoding a numeric value. The 2nd parameter will take care of displaying full column contents since the value is set as False.

df.show(df.count(),False)

SCALA

In the below code, df is the name of dataframe. 1st parameter is to show all rows in the dataframe dynamically rather than hardcoding a numeric value. The 2nd parameter will take care of displaying full column contents since the value is set as false.

df.show(df.count().toInt,false)

try this command :

df.show(df.count())

If you put results.show(false) , results will not be truncated

In c# Option("truncate", false) does not truncate data in the output.

StreamingQuery query = spark
                    .Sql("SELECT * FROM Messages")
                    .WriteStream()
                    .OutputMode("append")
                    .Format("console")
                    .Option("truncate", false)
                    .Start();

I use the plugin Chrome extension works pretty well:

[https://userstyles.org/styles/157357/jupyter-notebook-wide][1]

The following answer applies to a Spark Streaming application.

By setting the "truncate" option to false, you can tell the output sink to display the full column.

val query = out.writeStream
          .outputMode(OutputMode.Update())
          .format("console")
          .option("truncate", false)
          .trigger(Trigger.ProcessingTime("5 seconds"))
          .start()

Within Databricks you can visualize the dataframe in a tabular format. With the command:

display(results)

It will look like

Tried this in pyspark

df.show(truncate=0)

results.show(20, False) or results.show(20, false) depending on whether you are running it on Java/Scala/Python

Try this in scala:

df.show(df.count.toInt, false)

The show method accepts an integer and a Boolean value but df.count returns Long...so type casting is required

The other solutions are good. If these are your goals:

1. No truncation of columns,
2. No loss of rows,
3. Fast and
4. Efficient

These two lines are useful ...

    df.persist
    df.show(df.count, false) // in Scala or 'False' in Python

By persisting, the 2 executor actions, count and show, are faster & more efficient when using persist or cache to maintain the interim underlying dataframe structure within the executors. See more about persist and cache.