Cache vs Persist in Spark

What is Cache and Persist in Spark?

Cache

• Definition: The cache() method stores the RDD or DataFrame in memory. By default, it uses the MEMORY_AND_DISK storage level, which means data is stored in memory but will spill to disk if memory is insufficient.

• Usage: Ideal for reusing data across multiple actions or transformations.

Persist

• Definition: The persist() method allows more control over storage levels compared to cache(). It supports a variety of storage levels like MEMORY_ONLY, MEMORY_AND_DISK, DISK_ONLY, MEMORY_AND_DISK_SER, etc.

• Usage: Useful when specific storage strategies are required based on resource availability and application requirements.

Examples

Caching:

# Create an RDD or DataFrame
data = spark.range(1, 1000000)
cached_data = data.cache()

# Perform multiple actions on the cached DataFrame
cached_data.count()
cached_data.filter("id > 100").count()

Persisting

# Persist with a specific storage level
persisted_data = data.persist(StorageLevel.MEMORY_AND_DISK)

# Perform multiple actions on the persisted DataFrame
persisted_data.count()
persisted_data.filter("id > 100").count()

Layman Explanation Along with Technical One

Layman Explanation

• Cache: Imagine you’re baking cookies, and you’ve measured all the ingredients. Instead of measuring them again for each batch, you save the measured ingredients on the countertop (in memory). If the countertop runs out of space, you use the fridge (disk).

• Persist: You not only save the ingredients but also decide where to save them—countertop, fridge, or both, based on your convenience.

Technical Explanation

• Cache is a shortcut for persist(StorageLevel.MEMORY_AND_DISK).

• Persist gives more flexibility, allowing you to choose storage strategies like only memory, only disk, or a combination

Advantages and Disadvantages

Cache

Advantages:

• Simple to use; defaults to MEMORY_AND_DISK storage level.
• Optimized for common use cases.

Disadvantages:

• Limited flexibility in storage strategies.
• Not suitable if specific storage customization is required.

Persist

Advantages:

• Flexible storage level options.
• Allows tuning based on resource availability and workload.

Disadvantages:

• Slightly more complex to use compared to cache().
• Improper storage level choices can lead to inefficient resource utilization.

When to Use and Avoid Cache and Persist

When to Use

Cache:

• Data is reused multiple times, and default storage is sufficient.

• The dataset can fit in memory for most nodes.

Persist:

• Custom storage levels are needed, e.g., DISK_ONLY for large datasets.

• Data cannot fit in memory, or you want serialized storage.

When to Avoid

Cache and Persist:

• The dataset is used only once.

• Memory and storage resources are limited, and spilling to disk will impact performance.

Key Takeaways

1. Cache is a specialized case of Persist: cache() is equivalent to persist(StorageLevel.MEMORY_AND_DISK).

2. Choose cache for simplicity and persist for flexibility.

3. Avoid caching or persisting if the dataset is used only once or if resources are constrained.

Examples of Real-World Use Cases

Caching

1. Iterative Machine Learning Algorithms:
   • Repeatedly train a model on the same dataset.

      # Cache dataset for iterative processing
      df = spark.read.csv("large_dataset.csv")
      df.cache()
      model = train_model(df)
     

2. Exploratory Data Analysis:
   • Repeatedly query and transform a dataset during analysis.

      # Cache DataFrame for interactive analysis
      df = spark.read.parquet("data.parquet").cache()
      df.groupBy("category").count().show()
      df.filter("value > 100").show()
     

Persisting

1. Large ETL Pipelines:
   • Persist intermediate results to disk to free up memory for the next stages.

      # Persist with DISK_ONLY storage level
      df = spark.read.json("huge_data.json").persist(StorageLevel.DISK_ONLY)
      processed_df = df.filter("value > 100")
     

2. Fault Tolerance in Streaming Applications:
   • Persist streaming results to ensure recovery in case of node failure.

      streaming_df.persist(StorageLevel.MEMORY_AND_DISK_SER)