Compute size of Spark dataframe – SizeEstimator gives unexpected results

Error Overview

The error message “Compute size of Spark dataframe – SizeEstimator gives unexpected results” indicates that the Spark framework’s SizeEstimator utility is providing inaccurate or unexpected size estimates for a DataFrame. This can lead to challenges in performance optimization and resource allocation during Spark jobs, particularly when working with large datasets.

Common Causes

Several factors can contribute to this discrepancy in size estimation:

1. Overhead of Object References: SizeEstimator measures the memory footprint of objects in the JVM, including the references to those objects. This often results in inflated size estimates.
2. Caching Behavior: When a DataFrame is cached, the actual size may differ from the estimate provided by SizeEstimator, leading to unexpected results.
3. DataFrame Operations: Various transformations applied to DataFrames might affect the size measurement. For instance, some operations may change the internal representation of the data.
4. Spark Version Differences: Different versions of Spark might have variations in the SizeEstimator implementation, which can lead to discrepancies.
5. Logical vs. Physical Plans: Size calculations may vary based on whether the logical or physical execution plan is being analyzed.

Solution Methods

To address the error “Compute size of Spark dataframe – SizeEstimator gives unexpected results,” various methods can be employed to obtain more reliable size estimates.

Method 1: Cache and Use Query Execution

1. Cache the DataFrame:
   scala
df.cache.foreach(_ => ())
2. Get the Catalyst Plan:
   scala
val catalyst_plan = df.queryExecution.logical
3. Execute the Plan and Get Size:
   scala
val df_size_in_bytes = spark.sessionState.executePlan(catalyst_plan).optimizedPlan.stats.sizeInBytes

This method provides an accurate size of the DataFrame in bytes. Note that caching the DataFrame is a prerequisite for this method.

Method 2: Use RDD Storage Info

1. Extract RDD Storage Information:
   scala
val rddInfo = spark.sparkContext.getRDDStorageInfo
2. Analyze Cached RDDs:
   This will return information about cached RDDs, including memory size and disk usage, which can help cross-verify the DataFrame size.

Method 3: Utilize DataFrame Debugging

1. Debugging with toDebugString:
   To obtain detailed information about the DataFrame’s partitions and sizes:
   scala
println(df.rdd.toDebugString)
2. Count Records per Partition:
   “`scala
   df.rdd.mapPartitionsWithIndex