Recently, there are two new data abstractions released dataframe and datasets in apache spark. Now, \u00a0it might be difficult to understand the relevance of each one. Also, not easy to decide which one to use and which one not to.<\/p>\n
By keeping this point in mind this blog is introduced here, we will discuss both the APIs: spark dataframe and datasets on the basis of their features. We will learn complete comparison between DataFrame vs DataSets here.<\/p>\n
In addition, we will also learn the usage of spark datasets and dataframes. But to understand all first, we need to know the brief introduction of dataframe vs datasets.<\/p>\n
DataFrames gives a schema view of data basically, it is an abstraction. In dataframes, view of data is organized as columns with column name and types info. In addition, we can say data in dataframe is as same as the table in relational database.<\/p>\n
As similar as RDD, execution in dataframe too is lazy triggered. Moreover, to allow efficient processing datasets is structure as a distributed collection of data. Spark also uses catalyst optimizer along with dataframes.<\/p>\n
In Spark, datasets are an extension of dataframes. Basically, it earns two different APIs characteristics, such as strongly<\/span> typed and untyped. Datasets are by default a collection of strongly<\/span> typed JVM objects, unlike dataframes.<\/p>\n Moreover, it uses Spark\u2019s Catalyst optimizer<\/strong>. For exposing expressions & data field to a query planner.<\/p>\n Let\u2019s discuss the difference between apache spark Datasets &\u00a0 spark DataFrame, on the basis of their features:<\/p>\n DataFrame-<\/strong> \u00a0In Spark 1.3<\/em> Release, dataframes are introduced.<\/p>\n DataSets-<\/strong> \u00a0In Spark 1.6<\/em> Release, datasets are introduced.<\/p>\n DataFrame- <\/strong>\u00a0Dataframes organizes the data in the named column. Basically, dataframes can efficiently process unstructured and\u00a0structured data. Also, allows the Spark to manage schema.<\/p>\n DataSets-<\/strong> \u00a0<\/b>As similar as dataframes, it also efficiently processes unstructured and structured data. Also, represents data in the form of a collection of row object or JVM objects of row. Through encoders, is represented in tabular forms.<\/p>\n DataFrame- <\/strong>\u00a0In dataframe data is organized into named columns. Basically,\u00a0 it is as same as a table in a relational database.<\/p>\n DataSets-<\/strong> <\/b>\u00a0As we know, it is an extension of dataframe API, which provides the functionality of type-safe, object-oriented programming interface of the RDD API. Also, performance benefits of the Catalyst query optimizer.<\/p>\n DataFrame-<\/strong>\u00a0There is a case if we try to access the column which is not on the table. Then, dataframe APIs does not support compile-time error<\/em>.<\/p>\n DataSets-<\/strong> Datasets offers compile-time type safety<\/em>.<\/p>\n DataFrame-<\/strong> It allows data processing in different formats, for example, AVRO, CSV, JSON, and storage system HDFS, HIVE tables, MySQL.<\/p>\n DataSets-<\/strong> It also supports data from different sources.<\/p>\n DataFrame-<\/strong> Once transforming into dataframe, we cannot regenerate a domain object.<\/p>\n DataSets-<\/strong> Datasets overcomes this drawback of dataframe to regenerate the RDD from dataframe. It also allows us to convert our existing RDD and dataframes into datasets.<\/p>\n DataFrame-<\/strong>\u00a0By using off-heap memory for serialization, reduce the overhead.<\/p>\n DataSets-<\/strong> \u00a0<\/b>It allows to perform an operation on serialized data. Also, improves memory usage.<\/p>\n DataFrame-<\/strong> In dataframe, can serialize data into off-heap storage in binary format. Afterwards, it performs many transformations directly on this off-heap memory.<\/p>\n DataSets-<\/strong> \u00a0<\/b>In Spark, dataset API has the concept of an encoder. Basically, it handles conversion between JVM objects to tabular representation. Moreover, by using spark internal tungsten binary format it stores, tabular representation. Also, allows to perform an operation on serialized data and also improves memory usage.<\/p>\n DataFrame-<\/strong> As same as RDD, Spark evaluates dataframe lazily too.<\/p>\n DataSets-<\/strong> As similar to RDD, and Dataset it also evaluates lazily.<\/p>\n DataFrame-<\/strong> Through spark\u00a0catalyst optimizer<\/strong>, optimization takes place in dataframe.<\/p>\n DataSets-<\/strong> \u00a0For optimizing query plan, it offers the concept of dataframe catalyst optimizer.<\/p>\n DataFrame-<\/strong> Through the Hive meta store, it auto-discovers the schema. We do not need to specify the schema manually.<\/p>\n DataSets-<\/strong> \u00a0Because of using spark SQL engine, it auto discovers the schema of the files.<\/p>\n DataFrame-<\/strong> \u00a0In 4 languages like Java, Python, Scala, and R dataframes are available.<\/p>\n DataSets-<\/strong> Only available in Scala and Java.<\/p>\n DataFrame-<\/strong><\/p>\n DataSets- <\/strong><\/p>\n As a result, we have seen that both dataframes and datasets in apache spark allow custom view and structure. Moreover, both offers high-level domain-specific operations. Also saves space, and executes at high speed.<\/p>\n Hence, by analyzing the difference between dataframe vs datasets, we can select one out of dataframes or dataset that meets our requirements.<\/p>\n","protected":false},"excerpt":{"rendered":" Recently, there are two new data abstractions released dataframe and datasets in apache spark. Now, \u00a0it might be difficult to understand the relevance of each one. Also, not easy to decide which one to...<\/p>\n","protected":false},"author":1,"featured_media":73102,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[614],"tags":[834,889,890,891,892,893],"class_list":["post-2025","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-apache-spark","tag-apache-spark-rdd-vs-dataframe-vs-dataset","tag-apache-spark-dataframe-vs-datasets","tag-dataframe-or-dataset","tag-dataframes-vs-datasets-in-spark","tag-datasets-vs-dataframes","tag-spark-dataframes-vs-datasets"],"yoast_head":"\nComparison: Spark DataFrame vs DataSets, on the basis of Features<\/h3>\n
a. Spark Release<\/h4>\n
b. Data Formats<\/h4>\n
c. Data Representation<\/h4>\n
d. Compile-time type safety<\/h4>\n
e. Data Sources API<\/h4>\n
f. Immutability and Interoperability<\/h4>\n
g. Efficiency\/Memory use<\/h4>\n
h. Serialization<\/h4>\n
i. Lazy Evaluation<\/h4>\n
j. Optimization<\/h4>\n
k. Schema Projection<\/h4>\n
l. Programming Language Support<\/h4>\n
m. Usage of Datasets and Dataframes<\/h4>\n
\n
\n
Conclusion<\/h3>\n