(Why) do we need to call cache or persist on a RDD

Question

When a resilient distributed dataset (RDD) is created from a text file or collection (or from another RDD), do we need to call "cache" or "persist" explicitly to store the RDD data into memory? Or is the RDD data stored in a distributed way in the memory by default?

val textFile = sc.textFile("/user/emp.txt")

As per my understanding, after the above step, textFile is a RDD and is available in all/some of the node's memory.

If so, why do we need to call "cache" or "persist" on textFile RDD then?

Answer 1

Most RDD operations are lazy. Think of an RDD as a description of a series of operations. An RDD is not data. So this line:

val textFile = sc.textFile("/user/emp.txt")

It does nothing. It creates an RDD that says "we will need to load this file". The file is not loaded at this point.

RDD operations that require observing the contents of the data cannot be lazy. (These are called actions.) An example is RDD.count — to tell you the number of lines in the file, the file needs to be read. So if you write textFile.count, at this point the file will be read, the lines will be counted, and the count will be returned.

What if you call textFile.count again? The same thing: the file will be read and counted again. Nothing is stored. An RDD is not data.

So what does RDD.cache do? If you add textFile.cache to the above code:

val textFile = sc.textFile("/user/emp.txt")
textFile.cache

It does nothing. RDD.cache is also a lazy operation. The file is still not read. But now the RDD says "read this file and then cache the contents". If you then run textFile.count the first time, the file will be loaded, cached, and counted. If you call textFile.count a second time, the operation will use the cache. It will just take the data from the cache and count the lines.

The cache behavior depends on the available memory. If the file does not fit in the memory, for example, then textFile.count will fall back to the usual behavior and re-read the file.

Answer 2

I think the question would be better formulated as:

When do we need to call cache or persist on a RDD?

Spark processes are lazy, that is, nothing will happen until it's required. To quick answer the question, after val textFile = sc.textFile("/user/emp.txt") is issued, nothing happens to the data, only a HadoopRDD is constructed, using the file as source.

Let's say we transform that data a bit:

val wordsRDD = textFile.flatMap(line => line.split("\\W"))

Again, nothing happens to the data. Now there's a new RDD wordsRDD that contains a reference to testFile and a function to be applied when needed.

Only when an action is called upon an RDD, like wordsRDD.count, the RDD chain, called lineage will be executed. That is, the data, broken down in partitions, will be loaded by the Spark cluster's executors, the flatMap function will be applied and the result will be calculated.

On a linear lineage, like the one in this example, cache() is not needed. The data will be loaded to the executors, all the transformations will be applied and finally the count will be computed, all in memory - if the data fits in memory.

cache is useful when the lineage of the RDD branches out. Let's say you want to filter the words of the previous example into a count for positive and negative words. You could do this like that:

val positiveWordsCount = wordsRDD.filter(word => isPositive(word)).count()
val negativeWordsCount = wordsRDD.filter(word => isNegative(word)).count()

Here, each branch issues a reload of the data. Adding an explicit cache statement will ensure that processing done previously is preserved and reused. The job will look like this:

val textFile = sc.textFile("/user/emp.txt")
val wordsRDD = textFile.flatMap(line => line.split("\\W"))
wordsRDD.cache()
val positiveWordsCount = wordsRDD.filter(word => isPositive(word)).count()
val negativeWordsCount = wordsRDD.filter(word => isNegative(word)).count()

For that reason, cache is said to 'break the lineage' as it creates a checkpoint that can be reused for further processing.

Rule of thumb: Use cache when the lineage of your RDD branches out or when an RDD is used multiple times like in a loop.

Answer 3

Do we need to call "cache" or "persist" explicitly to store the RDD data into memory?

Yes, only if needed.

The RDD data stored in a distributed way in the memory by default?

No!

And these are the reasons why :

• Spark supports two types of shared variables: broadcast variables, which can be used to cache a value in memory on all nodes, and accumulators, which are variables that are only “added” to, such as counters and sums.

• RDDs support two types of operations: transformations, which create a new dataset from an existing one, and actions, which return a value to the driver program after running a computation on the dataset. For example, map is a transformation that passes each dataset element through a function and returns a new RDD representing the results. On the other hand, reduce is an action that aggregates all the elements of the RDD using some function and returns the final result to the driver program (although there is also a parallel reduceByKey that returns a distributed dataset).

• All transformations in Spark are lazy, in that they do not compute their results right away. Instead, they just remember the transformations applied to some base dataset (e.g. a file). The transformations are only computed when an action requires a result to be returned to the driver program. This design enables Spark to run more efficiently – for example, we can realize that a dataset created through map will be used in a reduce and return only the result of the reduce to the driver, rather than the larger mapped dataset.

• By default, each transformed RDD may be recomputed each time you run an action on it. However, you may also persist an RDD in memory using the persist (or cache) method, in which case Spark will keep the elements around on the cluster for much faster access the next time you query it. There is also support for persisting RDDs on disk, or replicated across multiple nodes.

For more details please check the Spark programming guide.

Answer 4

Below are the three situations you should cache your RDDs:

using an RDD many times

performing multiple actions on the same RDD

for long chains of (or very expensive) transformations

Answer 5

Adding another reason to add (or temporarily add) cache method call.

for debug memory issues

with cache method, spark will give debugging informations regarding the size of the RDD. so in the spark integrated UI, you will get RDD memory consumption info. and this proved very helpful diagnosing memory issues.