What is Spark DataFrame
In this tutorial, we shall be learning about Spark DataFrame. DataFrames are distributed collections of data arranged into rows and columns in Spark.
What is Spark DataFrame?
DataFrames are distributed collections of data arranged into rows and columns in Spark. Each column in a DataFrame has a name and a type assigned to it. DataFrames are structured and compact, similar to standard database tables. DataFrames are relational databases with improved optimization techniques.
Spark DataFrames can be derived from a variety of sources, including Hive tables, log tables, external databases, and existing RDDs. Massive volumes of data may be processed with DataFrames. A Schema is a blueprint that is used by every DataFrame. It can contain both general data types like string types and integer types, as well as spark-specific data types such as struct types.
DataFrames addressed the performance and scalability issues that arise when utilizing RDDs.
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RDDs fail to function properly when there is insufficient storage space in memory or on a disc. Furthermore, Spark RDDs lack the idea of schema, which is the structure of a database that defines its objects. RDDs hold both organized and unstructured data, which is inefficient.
RDDs cannot alter the system to make it run more efficiently. RDDs do not allow us to debug issues while they are running. They keep the data in the form of a collection of Java objects.
RDDs employ serialization (the act of turning an object into a stream of bytes to allow for faster processing) and garbage collection (an automatic memory management approach that discovers unneeded items and frees them from memory). Because they are so long, they put a strain on the system's memory.
Let's take a look at what makes Spark DataFrames so distinctive and popular.
Flexibility
DataFrames, like RDDs, can support a wide range of data formats which includes .CSV, Casandra, and many more.
Scalability
DataFrames may be coupled with a variety of different Big Data tools and can process data ranging from megabytes to petabytes at once.
Input Optimization Engine
To process data efficiently, DataFrames make use of input optimization engines, such as Catalyst Optimizer. The same engine can be used for any Python, Java, Scala, and R DataFrame APIs.
Handling Structured Data
DataFrames provide a graphical representation of data. When data is stored in this manner, it has some meaning.
Custom Memory Management
RDDs keep data in memory, whereas DataFrames store data off-heap (outside the main Java Heap region, but still inside RAM), reducing garbage collection overload.
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