title: Spark 2.2 Illegal pattern component: XXX java.lang.IllegalArgumentException: Illegal pattern component: XXX
body: <p>I'm trying to upgrade from Spark 2.1 to 2.2. When I try to read or write a dataframe to a location (CSV or JSON) I am receiving this error: </p>
  
  <pre><code>Illegal pattern component: XXX
  java.lang.IllegalArgumentException: Illegal pattern component: XXX
  at org.apache.commons.lang3.time.FastDatePrinter.parsePattern(FastDatePrinter.java:282)
  at org.apache.commons.lang3.time.FastDatePrinter.init(FastDatePrinter.java:149)
  at org.apache.commons.lang3.time.FastDatePrinter.&lt;init&gt;(FastDatePrinter.java:142)
  at org.apache.commons.lang3.time.FastDateFormat.&lt;init&gt;(FastDateFormat.java:384)
  at org.apache.commons.lang3.time.FastDateFormat.&lt;init&gt;(FastDateFormat.java:369)
  at org.apache.commons.lang3.time.FastDateFormat$1.createInstance(FastDateFormat.java:91)
  at org.apache.commons.lang3.time.FastDateFormat$1.createInstance(FastDateFormat.java:88)
  at org.apache.commons.lang3.time.FormatCache.getInstance(FormatCache.java:82)
  at org.apache.commons.lang3.time.FastDateFormat.getInstance(FastDateFormat.java:165)
  at org.apache.spark.sql.catalyst.json.JSONOptions.&lt;init&gt;(JSONOptions.scala:81)
  at org.apache.spark.sql.catalyst.json.JSONOptions.&lt;init&gt;(JSONOptions.scala:43)
  at org.apache.spark.sql.execution.datasources.json.JsonFileFormat.inferSchema(JsonFileFormat.scala:53)
  at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$7.apply(DataSource.scala:177)
  at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$7.apply(DataSource.scala:177)
  at scala.Option.orElse(Option.scala:289)
  at org.apache.spark.sql.execution.datasources.DataSource.getOrInferFileFormatSchema(DataSource.scala:176)
  at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:366)
  at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:178)
  at org.apache.spark.sql.DataFrameReader.json(DataFrameReader.scala:333)
  at org.apache.spark.sql.DataFrameReader.json(DataFrameReader.scala:279)
  </code></pre>
  
  <p>I am not setting a default value for dateFormat, so I'm not understanding where it is coming from. </p>
  
  <pre><code>spark.createDataFrame(objects.map((o) =&gt; MyObject(t.source, t.table, o.partition, o.offset, d)))
      .coalesce(1)
      .write
      .mode(SaveMode.Append)
      .partitionBy("source", "table")
      .json(path)
  </code></pre>
  
  <p>I still get the error with this:</p>
  
  <pre><code>import org.apache.spark.sql.{SaveMode, SparkSession}
  val spark = SparkSession.builder.appName("Spark2.2Test").master("local").getOrCreate()
  import spark.implicits._
  val agesRows = List(Person("alice", 35), Person("bob", 10), Person("jill", 24))
  val df = spark.createDataFrame(agesRows).toDF();
  
  df.printSchema
  df.show
  
  df.write.mode(SaveMode.Overwrite).csv("my.csv")
  </code></pre>
  
  <p>Here is the schema:
      root
   |-- name: string (nullable = true)
   |-- age: long (nullable = false)</p>