How to Calculate DataFrame Size in PySpark

Utilising Scala’s SizeEstimator in PySpark

Being able to estimate DataFrame size is a very useful tool in optimising your Spark jobs. In particular, knowing how big your DataFrames are helps gauge what size your shuffle partitions should be, something that can greatly improve speed and efficiency.

Estimating DataFrame size also plays a useful role in other areas, like memory management and cache optimisation. Knowing the approximate size of your data helps you decide how to cache data and tune the memory settings of Spark executors.

If you are only interested in the code that lets you estimate DataFrame size, skip to the ‘Accessing sizeEstimator in PySpark’ section.

Challenges in calculating precise DataFrame size

Because Spark distributes the DataFrame across a cluster, it makes it hard for Spark to know the exact size of the DataFrame. Since the data is not in stored in a contiguous block of memory, Spark would have to aggregate information from multiple nodes, each with its own memory and storage configuration.

Additionally, when data is distributed across a Spark cluster, it is often serialised, i.e. converted into a format suitable for transfer over the network or for storage. This serialised form of data can differ in size from its in-memory representation and the act of serialisation itself can vary depending on the serialisation technique.

Furthermore, the way data is stored in memory can vary. For example, data can be stored in compressed formats, and different types of data (like integers, strings, etc.) use different amounts of memory. This variability makes it hard to precisely calculate the size without actually iterating over and inspecting each element.

If it was easy to accurately calculate DataFrame size, that wouldn’t necessarily be desirable anyway — it would be expensive and take a long time to calculate the size of large data frames (which is why we’re using Spark in the first place). Estimation is usually enough for practical purposes like optimising memory usage, cache management, and performance tuning.

Due to the difficulties in accurately calculating DataFrame size, Spark provides a SizeEstimator to estimate size rather than an exactly measure it.

Spark’s SizeEstimator

SizeEstimator estimates the size of a DataFrame using sampling and extrapolation methods that provide a reasonably accurate approximation of the DataFrame’s size. While the estimation isn’t exact, it’s generally good enough for practical purposes. The primary aim in a distributed computing system like Spark is to optimise performance and resource utilisation rather than achieve precise memory measurements. The estimation offered by SizeEstimator strikes a balance between accuracy and computational efficiency, avoiding the resource-intensive process of calculating exact sizes.

The difficulty with SizeEstimator is that it isn’t readily available in PySpark because it is a Scala/Java utility. As a Scala function, it is employed within the JVM (Java Virtual Machine) environment, separate to the Python environment in which you use PySpark. Functions and utilities that are deeply integrated with the JVM, like SizeEstimator, are not exposed directly to the Python API because they are built to interact with the Scala/Java ecosystem of Spark.

There is, however, a way of achieving this via Python: Py4J.

Py4J

Py4J is a bridge between Python and Java that enables Python programs to dynamically access Java objects and invoke Java methods. PySpark uses Py4J to allow Python code to interact with the JVM.

This is what happens when you usually use PySpark:

1. Python API Calls: When you execute PySpark functions in Python, these calls are actually interfaces to Spark’s underlying Scala functions. PySpark is a wrapper to these Scala functions.
2. Py4J Bridge: PySpark uses Py4J to communicate between Python and the JVM. When you invoke a PySpark function, Py4J translates this Python command into a JVM command.
3. Execution in JVM: The actual processing and heavy lifting is done in the JVM. Spark’s core is written in Scala, which runs on the JVM. This is where RDD (Resilient Distributed Dataset) transformations, actions, and other computations take place.
4. Results to Python: Once the computation is done, the results are sent back from the JVM to the Python environment via Py4J.

You can explicitly use Scala/Java functions in your PySpark code by employing sc._jvm. This is how we use SizeEstimator.

Accessing sizeEstimator in PySpark

sc._jvm is as a gateway from Python to the JVM, provided by Py4J. It allows you to access functionalities and utilities that are natively part of the JVM.

scis the SparkContext. The SparkContext is the entry point from your code to Spark itself, and it is required to directly access the JVM. In Databricks this is imported automatically, but if you are working outside of Databricks, simply do the following:

from pyspark.sql import SparkSession

# Initialize SparkSession
spark = (
    SparkSession
        .builder 
        .appName("YourAppName") 
        .getOrCreate()
)

# Access SparkContext from SparkSession
sc = spark.sparkContext

Now that you have your SparkContext, here’s how you use it to employ SizeEstimator:

# create DataFrame
df = spark.range(1000000)

# Force cache DataFrame
df.cache().count()

# Get size of DataFrame in bytes 
size_in_bytes = sc._jvm.org.apache.spark.util.SizeEstimator.estimate(df._jdf)

We first generate a DataFrame with a column of 1 million rows. We then need to cache this DataFrame. Because cache is a transformation (as opposed to an action), we need to force spark to cache the DataFrame by triggering an action through count. Once the DataFrame is cached, we can use SizeEstimator to estimate its size.

The output is in bytes, so if we want to see the size in megabytes or gigabytes, we can do the following:

# size in megabytes
size_estimate_mb = size_estimate_bytes / (1024**2)

# size in gigabytes
size_estimate_gb = size_estimate_bytes / (1024**3)

Don’t forget to uncache your DataFrame once you are finished with unpersist:

df.unpersist()

You now have the size of your DataFrame in gigabytes and megabytes!

Best Practices and Considerations for Using SizeEstimator

Understanding the Estimation Process

Firstly, it’s important to reiterate that SizeEstimator provides an estimated size, not an exact measurement. This approximation is usually sufficient for optimising Spark jobs, but you should be aware that it is not entirely accurate.

Use Cases

SizeEstimator is particularly useful in scenarios such as memory management and determining shuffle partition sizes. To learn more about these challenges and how DataFrame size plays a role, read these articles:

Handling Large DataFrames

When dealing with large DataFrames, SizeEstimator might introduce additional processing overhead. In such cases, SizeEstimator might slow down your Spark application.

For very large DataFrames, consider using SizeEstimator on a subset of the DataFrame and extrapolating the total size from there. This may be less accurate, but sampling a subset of the data can provide a reasonable estimate of the total size without the need to process the entire DataFrame, which could significantly reduce the computational load and time required for size estimation.

Regular Monitoring and Review

Data characteristics and sizes can change over time, especially in dynamic data environments. Regularly monitoring and reviewing the estimated sizes can help in maintaining an efficient Spark application, adapting to data changes as needed.

On the other hand, if you know that the size of your data will remain stable, a one off calculation using SizeEstimator might be all you need. If not, you may want to integrate it in your pipeline. That said, it may not be necessary to use at all!

Conclusion

SizeEstimator is probably the simplest, most efficient, and most accurate method for estimating the size of a DataFrame. There are several techniques for estimating DataFrame size, each with their own pros and cons, but using the built-in SizeEstimator is generally the best approach. While this can be extremely useful when sizing partition shuffles and managing memory usage, it may not always be necessary to know the size of your data. Experiment, test, and adapt to your specific use case to determine when and how to utilise SizeEstimator effectively.

In the end, the goal is to strike a balance between gaining insightful data metrics and maintaining optimal performance of your Spark jobs. Remember, SizeEstimator is a tool to aid in decision-making, not a one-size-fits-all solution. As with any tool in data engineering, its value is maximized when used with a clear understanding of its capabilities and limitations in the context of your overall data processing strategy.