Failure in converting the SparkDF to Pandas DF

Question

I have a Spark code running on a Dataproc cluster that reads a table from BigQuery into a Spark dataframe. In this code, there is a step where I need to perform some data processing using pandas dataframe logic. However, when I try to convert the Spark dataframe to a pandas dataframe, I encounter an error that I am unable to resolve. It's worth noting that this code works fine on Hadoop without any issues.

I would appreciate any assistance or guidance in resolving this issue with the conversion from Spark dataframe to pandas dataframe.

df=df.toPandas()
  File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/sql/pandas/conversion.py", line 141, in toPandas
  File "/opt/conda/default/lib/python3.8/site-packages/pandas/core/frame.py", line 2317, in from_records
    mgr = arrays_to_mgr(arrays, columns, result_index, typ=manager)
  File "/opt/conda/default/lib/python3.8/site-packages/pandas/core/internals/construction.py", line 153, in arrays_to_mgr
    return create_block_manager_from_column_arrays(
  File "/opt/conda/default/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 2142, in create_block_manager_from_column_arrays
    mgr._consolidate_inplace()
  File "/opt/conda/default/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 1829, in _consolidate_inplace
    self.blocks = _consolidate(self.blocks)
  File "/opt/conda/default/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 2272, in _consolidate
    merged_blocks, _ = _merge_blocks(
  File "/opt/conda/default/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 2297, in _merge_blocks
    new_values = np.vstack([b.values for b in blocks])  # type: ignore[misc]
  File "<__array_function__ internals>", line 180, in vstack
  File "/opt/conda/default/lib/python3.8/site-packages/numpy/core/shape_base.py", line 282, in vstack
    return _nx.concatenate(arrs, 0)
  File "<__array_function__ internals>", line 180, in concatenate
numpy.core._exceptions.MemoryError: Unable to allocate 69.9 MiB for an array with shape (4289, 2137) and data type int64

The code I am working on processes a relatively small amount of data, with the maximum number of rows being around 10,000. However, the main challenge lies in the fact that the data source has over 4,000 columns. In this particular example, the code failed while processing 2,137 rows of data.

Answer 1

When converting spark dataframe to pandas the data will be no longer shared across dataproc nodes and be collected to the driver machine that's why you get memory error instead of converting your data to pandas try to use spark dataframe API to process data

Answer 2

The error you're encountering suggests that the process is running out of memory during the conversion of a Spark DataFrame to a Pandas DataFrame. This is likely due to the large number of columns in your data. Although the dataset might not seem large in terms of rows, the high dimensionality (i.e., the large number of columns) can significantly increase memory usage, especially during operations that require holding the entire dataset in memory, like this conversion process.

Possible solutions:

1. Increase Memory: If possible, increase the memory allocation for your Spark driver node. This is the node where the .toPandas() operation is executed and thus needs substantial memory to handle the conversion of large DataFrames. How you do this depends on your specific environment and configuration (e.g., adjusting Spark properties like spark.driver.memory in your Spark submit command or Dataproc cluster configuration).

2. Reduce Data Size Before Conversion: Select Fewer Columns: If you don't need all 4,000+ columns for your Pandas DataFrame operations, select only the columns you need before converting the DataFrame.

3. Use Arrow for Conversion: PySpark supports converting Spark DataFrames to Pandas DataFrames using Apache Arrow, which can be more efficient than the default conversion process. To use Arrow, you need to enable it in your Spark session:

spark.conf.set("spark.sql.execution.arrow.enabled", "true")
df = df.toPandas()

Note that while Arrow can improve performance and reduce memory usage, it might not completely resolve the issue if the dataset's size is the root cause of the memory error.

4. Leverage Pandas UDFs: If the purpose of converting to a Pandas DataFrame is to perform certain operations that are easier or possible only in Pandas, consider using Pandas UDFs (User Defined Functions) in Spark. This allows you to apply a function that works on Pandas DataFrames to each partition of your Spark DataFrame, thereby avoiding the need to convert the entire DataFrame at once.