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layout: global title: Data Types displayTitle: Data Types license: | Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to You under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

 http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Spark SQL and DataFrames support the following data types:

• Numeric types
  • ByteType: Represents 1-byte signed integer numbers. The range of numbers is from -128 to 127.
  • ShortType: Represents 2-byte signed integer numbers. The range of numbers is from -32768 to 32767.
  • IntegerType: Represents 4-byte signed integer numbers. The range of numbers is from -2147483648 to 2147483647.
  • LongType: Represents 8-byte signed integer numbers. The range of numbers is from -9223372036854775808 to 9223372036854775807.
  • FloatType: Represents 4-byte single-precision floating point numbers.
  • DoubleType: Represents 8-byte double-precision floating point numbers.
  • DecimalType: Represents arbitrary-precision signed decimal numbers. Backed internally by java.math.BigDecimal. A BigDecimal consists of an arbitrary precision integer unscaled value and a 32-bit integer scale.
• String type
  • StringType: Represents character string values.
• Binary type
  • BinaryType: Represents byte sequence values.
• Boolean type
  • BooleanType: Represents boolean values.
• Datetime type
  • TimestampType: Represents values comprising values of fields year, month, day, hour, minute, and second, with the session local time-zone. The timestamp value represents an absolute point in time.
  • DateType: Represents values comprising values of fields year, month and day, without a time-zone.
• Complex types
  • ArrayType(elementType, containsNull): Represents values comprising a sequence of elements with the type of elementType. containsNull is used to indicate if elements in a ArrayType value can have null values.
  • MapType(keyType, valueType, valueContainsNull): Represents values comprising a set of key-value pairs. The data type of keys is described by keyType and the data type of values is described by valueType. For a MapType value, keys are not allowed to have null values. valueContainsNull is used to indicate if values of a MapType value can have null values.
  • StructType(fields): Represents values with the structure described by a sequence of StructFields (fields).
    • StructField(name, dataType, nullable): Represents a field in a StructType. The name of a field is indicated by name. The data type of a field is indicated by dataType. nullable is used to indicate if values of these fields can have null values.

All data types of Spark SQL are located in the package org.apache.spark.sql.types. You can access them by doing

{% include_example data_types scala/org/apache/spark/examples/sql/SparkSQLExample.scala %}

All data types of Spark SQL are located in the package of org.apache.spark.sql.types. To access or create a data type, please use factory methods provided in org.apache.spark.sql.types.DataTypes.

All data types of Spark SQL are located in the package of pyspark.sql.types. You can access them by doing {% highlight python %} from pyspark.sql.types import * {% endhighlight %}