Accumulators and broadcast variables in spark
In Apache Spark, accumulators and broadcast variables are two types of shared variables that are used to share data across tasks in a parallel and fault-tolerant way. While they serve similar purposes, they have some key differences that make them more suitable for different use cases.
An accumulator is a variable that can be used to accumulate values across multiple tasks in a parallel and fault-tolerant way. An accumulator is created by calling the SparkContext.accumulator() method, which takes an initial value as an argument. Once created, the accumulator can be used by any task in the Spark job to add to its value. The final value of the accumulator can be accessed by calling the value() method on the accumulator object. An accumulator is useful in situations where you need to keep track of a value that is being updated across multiple tasks in a parallel and fault-tolerant way, for example counting the number of occurrences of a specific error message in log files.
A broadcast variable, on the other hand, is a read-only shared variable that is cached on each executor, so that tasks can access them without the need to send data over the network. They are used to cache a value on each executor so that it can be reused across multiple tasks, without the need to send the value over the network multiple times. For example, If you have a large lookup table that is used in multiple tasks, you can use a broadcast variable to cache the lookup table on each executor, so that tasks can access it without having to send the entire table over the network multiple times.
It’s worth noting that, accumulator can only be updated by the tasks, and its value is only accessible by the driver program. While, the broadcast variable can be updated by the driver program and its value can be accessed by both the driver program and tasks.
In summary, Accumulators and broadcast variables are two types of shared variables in Spark that are used to share data across tasks in a parallel and fault-tolerant way. Accumulators are used to accumulate values across multiple tasks, and are useful in situations where you need to keep track of a value that is being updated across multiple tasks, while broadcast variables are read-only shared variables that are cached on each executor and are useful in situations where you need to share large read-only data across multiple tasks, to avoid the overhead of sending data over the network multiple times.
Difference between Accumulators and Broadcast Variable in Apache Spark
An accumulator is a variable that can be used to accumulate values across multiple tasks in a parallel and fault-tolerant way. An accumulator is created by calling the SparkContext.accumulator() method, which takes an initial value as an argument. Once created, the accumulator can be used by any task in the Spark job to add to its value. The final value of the accumulator can be accessed by calling the value() method on the accumulator object. An accumulator is useful in situations where you need to keep track of a value that is being updated across multiple tasks in a parallel and fault-tolerant way, for example counting the number of occurrences of a specific error message in log files.
A broadcast variable, on the other hand, is a read-only shared variable that is cached on each executor, so that tasks can access them without the need to send data over the network. They are used to cache a value on each executor so that it can be reused across multiple tasks, without the need to send the value