Browsing the web, I found that many people have different ways of pushing or pulling data to or from big query. So the question is the following. If I have a very large pandas dataset (more than 10 GB), and I need to push this entire dataset into a big query table, what is the most efficient way of doing that? At the moment, the way I decided to do that, is to save this large DataFrame into multiple parquet files, following also the guidelines
- Keep row sizes to 50 MB or less.
- If your input data contains more than 100 columns, consider reducing the page size to be smaller than the default page size (1 * 1024 * 1024 bytes). This is especially helpful if you are using significant compression.
Then, I push all these parquet files into the cloud storage and I tell the cloud storage to push them into big query at batches. However:
1. I'm not sure to correctly follow the guidelines for the parquet best chuck sizes
2. I don't know if this is the best approach.
I attach my way of storing the DataFrame into multiple parquet, which at the moment saves a huge number of tiny parquet files:
def save_dataframe_to_parquet_chunks(df, output_dir, prefix, max_file_size_mb=50, max_rows_per_file=None,
page_size=256 * 1024, compression='SNAPPY'):
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Estimate the number of rows per file based on the size limit if not provided
if max_rows_per_file is None:
row_size = df.memory_usage(deep=True).sum() / len(df) # Average size of a row in bytes
max_rows_per_file = int((max_file_size_mb * 1024 * 1024) / row_size)
# Split the DataFrame into chunks and save each as a separate Parquet file
for i, chunk in enumerate(range(0, len(df), max_rows_per_file)):
df_chunk = df.iloc[chunk:chunk + max_rows_per_file]
output_file = os.path.join(output_dir, f"{prefix}_part_{i + 1}.parquet")
# Save the chunk to a Parquet file
df_chunk.to_parquet(
output_file,
engine='pyarrow',
compression=compression,
row_group_size=page_size
)
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