Narrow Vs Wide Transformation
Difference between Narrow and Wide Transformation
| Narrow transformations | Wide transformations | |
| Data scope | Narrow transformations are transformations that are performed on individual partitions of data in an RDD or Dataframe/Dataset. These transformations are performed on each partition in parallel and do not require shuffling of data between partitions. Examples of narrow transformations include filter, map, and flatMap. | Wide transformations, on the other hand, are transformations that require shuffling data between partitions. These transformations are typically more complex and require more resources to perform. Examples of wide transformations include groupByKey, reduceByKey, and join. |
| Lazy evaluation | Both narrow and wide transformations in Spark are considered as “lazy” operations, which means they are not executed immediately when called, but rather they are recorded in a lineage of transformations. The transformations are only executed when an action is called on the RDD or Dataframe/Dataset, this allows Spark to optimize the execution plan and minimize the data shuffling. | Both narrow and wide transformations in Spark are considered as “lazy” operations, which means they are not executed immediately when called, but rather they are recorded in a lineage of transformations. The transformations are only executed when an action is called on the RDD or Dataframe/Dataset, this allows Spark to optimize the execution plan and minimize the data shuffling. |
| Performance | Narrow transformations are generally more efficient than wide transformations in terms of performance because they do not require shuffling data between partitions. | wide transformations are necessary for certain types of data processing and analysis, such as aggregation and joining. The Dataframe and Dataset API use a more optimized and efficient query engine called the Catalyst Optimizer, that can perform both narrow and wide transformations more efficiently. |
| Data locality | Narrow transformations preserves data locality, meaning the data is processed on the same node where it is stored, which can improve performance by reducing network overhead. | Wide transformations, on the other hand, require shuffling data between partitions, which can increase network overhead and negatively impact performance. |
| Resource usage | Narrow transformations typically require less resources to perform as they only operate on a single partition of data. | Wide transformations, on the other hand, require more resources as they need to shuffle and operate on a large dataset. |
| Data format | Narrow transformations can be performed on various data formats, such as unstructured, structured, and semi-structured data. | Wide transformations, however, typically require structured data in order to perform the necessary operations. |
n conclusion, narrow and wide transformations are important concepts in Apache Spark when working with RDDs and Dataframe/Dataset API. Narrow transformations are simple, single partition transformations that are typically used for basic data processing tasks. Wide transformations are more complex, dataset-wide transformations that are used for more advanced data processing tasks and require more resources. The choice between narrow and wide transformations will depend on the specific use case and requirements of the application, and the Dataframe and Dataset API provides more optimized and efficient way of performing these operations.
Narrow Transformation
Narrow transformations in Apache Spark refer to the way data is transformed when using the Resilient Distributed Datasets (RDD) and Dataframe/Dataset API. These transformations are performed on individual partitions of data and do not require shuffling of data between partitions. Narrow transformations are simple, single partition operations that are typically used for basic data processing tasks.
- Filter:
- This is a narrow transformation that filters out unwanted records from a dataset. It can be used to remove null values, remove duplicates, or only select certain records based on a condition.
- Map:
- This is a narrow transformation that applies a function to each element of an RDD or Dataframe/Dataset. It can be used to convert data from one format to another, or to perform simple calculations on the data.
- FlatMap:
- This is similar to the map transformation but it can return multiple items for each input item. It can be used to split a single record into multiple records or to perform complex calculations on the data.
- Distinct:
- This is a narrow transformation that removes duplicates from a dataset. It can be used to de-duplicate data before further processing.
- Sample:
- This is a narrow transformation that returns a random sample of the data in an RDD or Dataframe/Dataset. It can be used to randomly select a subset of data for testing or validation.
- Union:
- This is a narrow transformation that combines two RDDs or Dataframes/Datasets into a single RDD or Dataframe/Dataset. It can be used to merge data from multiple sources.
- Intersection:
- This is a narrow transformation that returns the common elements between two RDDs or Dataframes/Datasets. It can be used to find common elements between datasets.
In conclusion, narrow transformations in Spark are simple, single partition operations that are typically used for basic data processing tasks. They are “lazy” operations and are only executed when an action is called on the RDD or Dataframe/Dataset. They are efficient, as they do not require shuffling data between partitions, and preserves data locality
Wide Transformation
Wide transformations in Apache Spark refer to the way data is transformed when using the Resilient Distributed Datasets (RDD) and Dataframe/Dataset API. These transformations are performed on the entire dataset and require shuffling of data between partitions. Wide transformations are more complex, dataset-wide operations that are typically used for more advanced data processing tasks and require more resources.
- groupByKey:
- This is a wide transformation that groups the elements of an RDD or Dataframe/Dataset by key. It can be used to perform aggregate operations such as counting, averaging, and summing on the data.
- reduceByKey:
- This is a wide transformation that combines the values of elements with the same key in an RDD or Dataframe/Dataset. It can be used to perform aggregate operations such as counting, averaging, and summing on the data.
- join:
- This is a wide transformation that joins two RDDs or Dataframes/Datasets based on a common key. It can be used to combine data from multiple sources and perform complex calculations.
- cogroup:
- This is a wide transformation that groups the elements of two RDDs or Dataframes/Datasets by key. It can be used to perform aggregate operations such as counting, averaging, and summing on the data.
- repartition:
- This is a wide transformation that redistributes the data in an RDD or Dataframe/Dataset across a specified number of partitions. It can be used to balance the load of data across the cluster and improve performance.
- sortByKey:
- This is a wide transformation that sorts the elements of an RDD or Dataframe/Dataset by key. It can be used to sort data based on a specific key or criteria.
- aggregateByKey:
- This is a wide transformation that performs a specific operation on the values of elements with the same key in an RDD or Dataframe/Dataset. It can be used to perform complex calculations on