Spark Difference between Cache and Persist?

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Spark Cache and persist are optimization techniques for iterative and interactive Spark applications to improve the performance of the jobs or applications. In this article, you will learn What is Spark Caching and Persistence, the difference between cache() vs persist() methods and how to use these two with RDD, DataFrame, and Dataset with Scala examples.

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Though Spark provides computation 100 x times faster than traditional Map Reduce jobs, If you have not designed the jobs to reuse the repeating computations you will see a degrade in performance when dealing with billions or trillions of data. Hence, we may need to look at the stages and use optimization techniques as one of the ways to improve performance.

Key Points to Note:

  • RDD.cache() caches the RDD with the default storage level MEMORY_ONLY
  • DataFrame.cache() caches the DataFrame with the default storage level MEMORY_AND_DISK
  • The persist() method is used to store it to the user-defined storage level
  • On Spark UI, the Storage tab shows where partitions exist in memory or disk across the cluster.
  • Dataset cache() is an alias for persist(StorageLevel.MEMORY_AND_DISK)
  • Caching of Spark DataFrame or Dataset is a lazy operation, meaning a DataFrame will not be cached until you trigger an action. 

1. Spark Cache vs Persist

Using cache() and persist() methods, Spark provides an optimization mechanism to store the intermediate computation of an RDD, DataFrame, and Dataset so they can be reused in subsequent actions(reusing the RDD, Dataframe, and Dataset computation results).

Both caching and persisting are used to save the Spark RDD, Dataframe, and Datasets. But, the difference is, RDD cache() method default saves it to memory (MEMORY_ONLY) and, DataFrame cache() method default saves it to memory (MEMORY_AND_DISK), whereas persist() method is used to store it to the user-defined storage level.

When you persist a dataset, each node stores its partitioned data in memory and reuses them in other actions on that dataset. And Spark’s persisted data on nodes are fault-tolerant meaning if any partition of a Dataset is lost, it will automatically be recomputed using the original transformations that created it.

2. Advantages of Caching and Persistence

Below are the advantages of using Spark Cache and Persist methods.

  • Cost efficient – Spark computations are very expensive; hence, reusing the computations are used to save cost.
  • Time efficient – Reusing repeated computations saves lots of time.
  • Execution time – Saves execution time of the job and we can perform more jobs on the same cluster.

Below, I will explain how to use Spark Cache and Persist with DataFrame or Dataset.

3. Spark Cache Syntax and Example

Spark DataFrame or Dataset caching by default saves it to storage level `MEMORY_AND_DISK` because recomputing the in-memory columnar representation of the underlying table is expensive. Note that this is different from the default cache level of `RDD.cache()` which is ‘MEMORY_ONLY‘.

Syntax


// Syntax
cache() : Dataset.this.type

Spark cache() method in Dataset class internally calls persist() method, which in turn uses sparkSession.sharedState.cacheManager.cacheQuery to cache the result set of DataFrame or Dataset. Let’s look at an example.

Example


// Create sparkSession and apply cache() on DataFrame
val spark:SparkSession = SparkSession.builder()
    .master("local[1]")
    .appName("SparkByExamples.com")
    .getOrCreate()

import spark.implicits._
val columns = Seq("Seqno","Quote")
val data = Seq(("1", "Be the change that you wish to see in the world"),
    ("2", "Everyone thinks of changing the world, but no one thinks of changing himself."),
    ("3", "The purpose of our lives is to be happy."))
val df = data.toDF(columns:_*)

val dfCache = df.cache()
dfCache.show(false)

4. Spark Persist Syntax and Example

Spark persist() has two signatures. The first signature doesn’t take any argument, which by default saves it to MEMORY_AND_DISK storage level, and the second signature takes StorageLevel as an argument to store it at different storage levels.

Syntax


// Syntax
1) persist() : Dataset.this.type
2) persist(newLevel : org.apache.spark.storage.StorageLevel) : Dataset.this.type

Example


// Persist Example
val dfPersist = df.persist()
dfPersist.show(false)

Using the second signature you can save DataFrame/Dataset to One of the storage levels MEMORY_ONLY,MEMORY_AND_DISK, MEMORY_ONLY_SER, MEMORY_AND_DISK_SER, DISK_ONLY, MEMORY_ONLY_2,MEMORY_AND_DISK_2


// Persist with argument
val dfPersist = df.persist(StorageLevel.MEMORY_ONLY)
dfPersist.show(false)

This stores DataFrame/Dataset into Memory.

5. Unpersist syntax and Example

We can also unpersist the persistence DataFrame or Dataset to remove it from the memory or storage.

Syntax


// unpersist() Syntax
unpersist() : Dataset.this.type
unpersist(blocking : scala.Boolean) : Dataset.this.type

Example


// unpersist() Example
val dfPersist = dfPersist.unpersist()
dfPersist.show(false)

unpersist(Boolean) with boolean as argument blocks until all blocks are deleted.

6. Spark Persistance storage levels

All different storage level Spark supports are available at org.apache.spark.storage.StorageLevel class. The storage level specifies how and where to persist or cache a Spark DataFrame and Dataset.

MEMORY_ONLY – This is the default behavior of the RDD cache() method and stores the RDD or DataFrame as deserialized objects to JVM memory. When there is no enough memory available it will not save DataFrame of some partitions and these will be re-computed as and when required. This takes more memory. but unlike RDD, this would be slower than MEMORY_AND_DISK level as it recomputes the unsaved partitions and recomputing the in-memory columnar representation of the underlying table is expensive

MEMORY_ONLY_SER – This is the same as MEMORY_ONLY but the difference being it stores RDD as serialized objects to JVM memory. It takes lesser memory (space-efficient) then MEMORY_ONLY as it saves objects as serialized and takes an additional few more CPU cycles in order to deserialize.

MEMORY_ONLY_2 – Same as MEMORY_ONLY storage level but replicate each partition to two cluster nodes.

MEMORY_ONLY_SER_2 – Same as MEMORY_ONLY_SER storage level but replicate each partition to two cluster nodes.

MEMORY_AND_DISK – This is the default behavior of the DataFrame or Dataset. In this Storage Level, The DataFrame will be stored in JVM memory as a deserialized object. When required storage is greater than available memory, it stores some of the excess partitions into the disk and reads the data from the disk when required. It is slower as there is I/O involved.

MEMORY_AND_DISK_SER – This is the same as MEMORY_AND_DISK storage level difference being it serializes the DataFrame objects in memory and on disk when space is not available.

MEMORY_AND_DISK_2 – Same as MEMORY_AND_DISK storage level but replicate each partition to two cluster nodes.

MEMORY_AND_DISK_SER_2 – Same as MEMORY_AND_DISK_SER storage level but replicate each partition to two cluster nodes.

DISK_ONLY – In this storage level, DataFrame is stored only on disk and the CPU computation time is high as I/O is involved.

DISK_ONLY_2 – Same as DISK_ONLY storage level but replicate each partition to two cluster nodes.

Below are the table representation of the Storage level, Go through the impact of space, cpu and performance choose the one that best fits for you. 


Storage Level    Space used  CPU time  In memory  On-disk  Serialized   Recompute some partitions
----------------------------------------------------------------------------------------------------
MEMORY_ONLY          High        Low       Y          N        N         Y    
MEMORY_ONLY_SER      Low         High      Y          N        Y         Y
MEMORY_AND_DISK      High        Medium    Some       Some     Some      N
MEMORY_AND_DISK_SER  Low         High      Some       Some     Y         N
DISK_ONLY            Low         High      N          Y        Y         N

7. Some Points to note on Persistence

  • Spark automatically monitors every persist() and cache() calls you make and it checks usage on each node and drops persisted data if not used or using least-recently-used (LRU) algorithm. As discussed in one of the above section you can also manually remove using unpersist() method.
  • Spark caching and persistence is just one of the optimization techniques to improve the performance of Spark jobs.
  • For RDD cache(), the default storage level is ‘MEMORY_ONLY’ but, for DataFrame and Dataset, the default is ‘MEMORY_AND_DISK‘
  • On Spark UI, the Storage tab shows where partitions exist in memory or disk across the cluster.
  • Dataset cache() is an alias for persist(StorageLevel.MEMORY_AND_DISK)
  • Caching of Spark DataFrame or Dataset is a lazy operation, meaning a DataFrame will not be cached until you trigger an action. 

Conclusion

In this article, you have learned Spark cache and Persist methods are optimization techniques to save interim computation results and use them subsequently and learned what is the difference between Spark Cache and Persist and finally saw their syntaxes and usages with Scala examples.

Happy Learning !!

Reference

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This Post Has 2 Comments

  1. NNK December 5, 2022

    Hi, They are not contradictory. They are the default behavior of RDD and Dataset.

    RDD.cache() uses MEMORY_ONLY
    Dataset.cache() uses MEMORY_AND_DISK

  2. Micha Nelis December 1, 2022

    “Dataset cache() is an alias for persist(StorageLevel.MEMORY_AND_DISK)”

    “MEMORY_ONLY – This is the default behavior of the RDD cache()”

    Arent these two lines contradictory?

Comments are closed.