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#2 Understand Apache Spark Data Types

Data types serve as the cornerstone of all data-related endeavours. They are the very essence that defines the nature and behaviour of data within any system.

Understanding data types ensures data integrity, optimises performance, fosters interoperability, and facilitates effective manipulation and analysis. From efficient memory usage to error-free operations, data storage to seamless data exchange, every aspect of data-driven endeavours relies on a deep understanding of data types.

Here are the most common data types in Apache Spark:

Data TypeDescriptionExample
BinaryRepresents binary data.bytes([0x48, 0x65, 0x6C, 0x6C, 0x6F])
BooleanRepresents boolean values (True or False).TRUE or FALSE
ByteRepresents byte values (-128 to 127).42
DateRepresents date, 3, 18)
DecimalRepresents fixed precision decimal numbers.Decimal(‘3.141592653589793238’)
DoubleRepresents double-precision floating-point numbers.3.14
FloatRepresents single-precision floating-point numbers.3.14
IntegerRepresents integer numbers. i.e. a signed 32-bit integer35
LongRepresents long integer numbers. i.e. a signed 64-bit integer12345
NullRepresents null values.None
ArrayRepresents a collection of elements of the same type[1, 2, 3, 4, 5]
MapRepresents key-value pairs.{“key1”: “value1”, “key2”: “value2”}
ShortRepresents short integer numbers. i.e. A signed 16-bit integer.30
StringRepresents text strings.“Hello!”
CharRepresents character data.“Jason”
VarcharRepresents variable-length character data.“Smith”
StructRepresents a structure with multiple fields.struct_value = StructType([ StructField(“name”, StringType(), nullable=False), StructField(“age”, IntegerType(), nullable=True) ])
TimestampRepresents timestamp values.‘2024-03-01 12:00:00’
DayTimeIntervalRepresents intervals in days and seconds.1 day 3 hours 30 minutes
YearMonthIntervalRepresents intervals in years and months.2 years 6 months
Data Types in Apache Spark