What is the Difference Between Cache and Persist in Apache Spark?

To understand the difference between cache and persist in Apache Spark, it’s essential to delve into how Spark’s in-memory storage works and how both these methods are used for performance optimizations.

Cache

The cache method in Spark is a shorthand way to persist an RDD (Resilient Distributed Dataset) using the default storage level, which is MEMORY_ONLY. This means that the data will be stored in memory only, and if there is not enough memory, some data will be recomputed the next time it is needed.

Example:

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("CacheExample").getOrCreate()
data = [("Java", 20000), ("Python", 100000), ("Scala", 3000)]
df = spark.createDataFrame(data, ["Language", "Users"])

df.cache()

Persist

The persist method offers more flexibility than cache. With persist, you can specify the storage level explicitly. This allows you to control how and where the data will be stored, such as in memory, on disk, or both. Common storage levels include MEMORY_AND_DISK, MEMORY_ONLY_SER, DISK_ONLY, etc.

Common Storage Levels:

• MEMORY_ONLY: Store RDD as deserialized Java objects in the JVM. If the RDD does not fit in memory, some partitions will not be cached and will be recomputed on the fly as needed.
• MEMORY_AND_DISK: Store RDD as deserialized Java objects in the JVM. If the RDD does not fit in memory, store the partitions not fitting on disk and read them as needed.
• DISK_ONLY: Store the RDD partitions only on disk.
• MEMORY_ONLY_SER: Store RDD as serialized objects (one byte array per partition). This is more space-efficient but incurs the overhead of deserialization.

Example:

from pyspark import StorageLevel

df.persist(StorageLevel.MEMORY_AND_DISK)

Key Differences

In summary, cache is a more convenient but less flexible method for persisting data compared to persist, which extends greater control over how the data should be stored.