In PySpark, cache() and persist() are powerful optimization techniques used with DataFrames, Datasets, and RDDs. These methods allow you to store intermediate results in memory or other storage levels, reducing the need for recomputation during subsequent transformations. This can significantly improve performance, especially in iterative or complex workflows.
Although both cache() and persist() serve a similar purpose—storing intermediate data—there are some key differences between them. Before diving into their comparison, let’s first explore what each method does individually. Understanding them separately will make their differences and use cases much clearer.
cache():
This is a PySpark DataFrame method used to store intermediate results. The cache() method doesn't support any arguments, so by default, the data is stored using a combination of memory and disk storage. For DataFrames, data is stored in memory and disk combination in a deserialized format, whereas for RDDs, data is stored in memory in a serialized format.
Here, deserialization means storing the object or data in its original format. Serialization means converting the object or data into byte format before storing it.
Syntax:
#for a Dataframe
Fabricofdata_DF.cache()
#for a RDD
fabricOfData_RDD.cache()
Even if you write code to cache a DataFrame (DF) or RDD, Spark will not actually store the result set until an action is performed on that object. Therefore, after caching an RDD or DF, you should always trigger an action—such as count()—to ensure the data gets cached.
Now, let's see this in practice. For demonstration purposes, we'll create a random DataFrame and RDD, and then cache them.
for RDD:
for DataFrame:
From the Spark UI, it's clear that the default storage type for RDDs is Memory Serialized, while for DataFrames it’s Memory and Disk Deserialized.
Note: In this example, the DataFrame size is small enough to fit entirely into available memory. However, if the DataFrame is too large to fit completely into memory, Spark will automatically spill the excess data onto the disk.
persist():
The persist() method is another DataFrame operation used to store intermediate data.
Unlike cache(), the persist() method allows you to pass arguments to override
the default storage behavior of an RDD or DataFrame.
Spark supports a variety of storage levels, all of which can be used only with the persist() method:
DISK_ONLYDISK_ONLY_2MEMORY_ONLYMEMORY_ONLY_2MEMORY_AND_DISKMEMORY_AND_DISK_2
To gain a deeper understanding of these storage levels, check out this guide:
👉
PySpark Storage Levels – Fabric of Data
syntax:
#for a Dataframe
Fabricofdata_DF.persist(*arg)
#for a RDD
fabricOfData_RDD.persist(*arg)
Similar to the cache() method, after using the persist() method to store the results of a DataFrame or RDD, you need to trigger an action to actually store (persist) the data.
Now, let's understand this practically. To demonstrate, we'll use the same DataFrame and RDD we created earlier for the cache() example.
For RDD:
For DataFrame:
If you carefully observe, we have not provided any arguments or parameters to the persist() method. In this scenario, both persist() and cache() methods share the same default storage levels for RDDs and DataFrames.
As mentioned earlier, if you wish to modify this default behavior, you can explicitly pass parameters using the StorageLevel() constructor with the following syntax:
StorageLevel(useDisk: bool, useMemory: bool, useOffHeap: bool, deserialized: bool, replication: int = 1)
Now, let's explore a few practical examples using the same DataFrame we've previously created.
⚠️ Important Note on Result Set Storage in Spark
While Spark provides powerful options like caching and persisting for result set storage, it's important to use these features judiciously. Overusing them can negatively affect program performance and execution time. Spark is inherently optimized for managing data storage and computation, so lean on its capabilities unless there's a strong use case.
Use caching or persisting only when it truly adds value.
Here are a few scenarios where applying these optimization techniques makes sense:
- Frequent Data Reuse: If a dataset is accessed multiple times across various transformations, caching or persisting can prevent redundant computations.
- Real-Time Data Processing: In Spark Streaming, caching dataframes can help maintain consistency in state across multiple batches, improving efficiency and reliability.
Choose your optimization strategies wisely to get the best performance out of your Spark applications.
🧠 Here are some commonly asked interview questions that can help reinforce your understanding of Spark caching and persisting techniques:
- What is caching in Spark?
- Why do we use caching and persisting in Spark?
- When should we avoid using caching?
- How can you uncache data in Spark?
- What is the difference between cache and persist?
- How do you use Cache and Persist in Spark SQL?
please check below the complete code used in this article.
mport random
from pyspark import SparkContext
from pyspark import StorageLevel
# Generate a large dataset
data = [i for i in range(1, 1000001)]
#Create a RDD and Cache it
rdd_data = sc.parallelize(data)
rdd_data.cache()
rdd_data.count()
# Generate a large dataset
data = [(f"Name_{i}", random.randint(18, 65), random.choice(["Engineering", "HR", "Finance", "Marketing","Data Engineering","IT","Logistics","Security","miscellaneous"])) for i in range(1, 1000001)]
columns = ["Name", "Age", "Department"]
# Create the DataFrame
fabricofdata_df = spark.createDataFrame(data, schema=columns)
fabricofdata_df.cache()
fabricofdata_df.count()
#Persist
rdd_data_persist = sc.parallelize(data)
rdd_data_persist.persist()
rdd_data_persist.count()
fabricofdata_df_persist = spark.createDataFrame(data, schema=columns)
fabricofdata_df_persist.persist()
fabricofdata_df_persist.count()
#Persist with Different Storage levels
persist_storagelevel1 = spark.createDataFrame(data, schema=columns)
persist_storagelevel2 = spark.createDataFrame(data, schema=columns)
persist_storagelevel3 = spark.createDataFrame(data, schema=columns)
persist_storagelevel4 = spark.createDataFrame(data, schema=columns)
#Different Storage Levels StorageLevel(useDisk: bool, useMemory: bool, useOffHeap: bool, deserialized: bool, replication: int = 1)
persist_storagelevel1.persist(StorageLevel(True,False,False,True,3))
persist_storagelevel2.persist(StorageLevel(True,True,False,False,1))
persist_storagelevel3.persist(StorageLevel(False,True,False,False,2))
persist_storagelevel4.persist(StorageLevel(False,True,False,True,1))
persist_storagelevel1.count()
persist_storagelevel2.count()
persist_storagelevel3.count()
persist_storagelevel4.count()