Pandas read_sql with chunksize: Unlock Parallel Processing

In this tutorial, you’ll learn how to use the chunksize parameter in Pandasread_sql to optimize your data fetching.

You’ll learn how you can parallelize your SQL queries to enhance performance.



The Purpose of the chunksize Parameter

The chunksize parameter in Pandas read_sql function tells Pandas to fetch a specific number of rows at a time.

This method allows you to start processing the data without waiting for the entire query to complete.

Here’s how you can use chunksize with a sample SQL query:

import pandas as pd
from sqlalchemy import create_engine
engine = create_engine('sqlite:///sample_data.db')
query = """
SELECT user_id, session_start, session_end, bytes_sent, bytes_received
FROM data_usage_logs

# Use chunksize to read the SQL query in chunks
chunk_size = 5000
chunks = pd.read_read_sql(query, engine, chunksize=chunk_size)
for chunk in chunks:


   user_id       session_start         session_end  bytes_sent  bytes_received
0    10234 2023-01-01 00:01:00 2023-01-01 00:30:00       20480           40960
1    10235 2023-01-01 00:03:00 2023-01-01 00:33:00       10240           20480
2    10236 2023-01-01 00:05:00 2023-01-01 00:35:00       30720           61440
3    10237 2023-01-01 00:07:00 2023-01-01 00:37:00       40960           81920
4    10238 2023-01-01 00:09:00 2023-01-01 00:39:00       51200          102400

In this output, you see the first five rows of the first chunk.


Optimal chunksize Determination

Determining the optimal chunksize is a balancing act between your available memory and the need for efficient data processing.

Here are a few steps to guide you through determining an appropriate chunksize:

  1. Assess Your Available Memory: Check your system’s available memory, which can be done using Python’s psutil library or system monitoring tools.
  2. Estimate the Size of a Row: Calculate the approximate size of a single row in your dataset.
    This can be done by loading a small sample and using the getsizeof function from Python’s sys module.
  3. Calculate a Safe chunksize: Divide your available memory by the size of a row to find a safe chunksize that your system can handle.

Let’s go through an example:

import psutil
import pandas as pd
from sqlalchemy import create_engine
from sys import getsizeof

engine = create_engine('sqlite:///sample_data.db')
sample_df = pd.read_sql('SELECT * FROM data_usage_logs LIMIT 100', engine)

# Estimate the memory footprint of one row
average_row_size = getsizeof(sample_df) / len(sample_df)
print(f"Average memory size per row: {average_row_size} bytes")

# Assess your available memory (this is a simplistic approach)
available_memory = psutil.virtual_memory().available * 0.5  # using 50% of available memory

# Calculate a safe chunksize
safe_chunksize = available_memory // average_row_size
print(f"Safe chunksize: {safe_chunksize} rows per chunk")


Average memory size per row: 450 bytes
Safe chunksize: 11111 rows per chunk

In this example, we’ve determined that each row is approximately 450 bytes, and with the available memory, we can safely process 11,111 rows per chunk.

This number is just a guideline; you might adjust the chunk size smaller or larger depending on the actual performance and memory usage when you process the data.


Limitations of chunksize

The chunksize does not parallelize the execution of queries or the processing of data.

It controls the amount of data read into memory at any one time.

Each chunk is processed sequentially, and while this approach conserves memory, it doesn’t necessarily decrease the total time taken to process the entire dataset.

Here’s what you need to keep in mind regarding chunksize limitations:

  1. Sequential Processing: Pandas will process each chunk one after the other in a single thread. If you’re processing a very large dataset, even with an optimized chunksize, it may take a significant amount of time because of this sequential nature.
  2. No Parallel Processing: By default, there’s no built-in parallel processing in Pandas for handling different chunks simultaneously. Each chunk must be processed completely before moving on to the next one.

For example, you might use chunksize like this when fetching data:

import pandas as pd
from sqlalchemy import create_engine
engine = create_engine('sqlite:///sample_data.db')
query = "SELECT * FROM data_usage_logs"
for chunk in pd.read_sql(query, engine, chunksize=10000):
    # This will happen sequentially, not in parallel
    process(chunk)  # Assume 'process' is a function defined to handle the chunks

Even though you’re reading the data in chunks, it’s crucial to understand that the overall operation is still constrained by the single-threaded nature of this approach.

You can speed up the processing by implementing parallel processing logic, using multiple threads or processes.


Parallelizing Queries with chunksize

While chunksize doesn’t inherently parallelize queries, you can leverage Python’s concurrency features, such as concurrent.futures or multiprocessing, to process chunks in parallel.

Here’s an example of how you might implement parallel processing of chunks in Pandas:

from sqlalchemy import create_engine
import pandas as pd
from concurrent.futures import ThreadPoolExecutor
def process_chunk(chunk):
    # Perform some data processing here
    print(f"Processing {len(chunk)} records")
    return "Chunk processed"
engine = create_engine('sqlite:///sample_data.db')
query = "SELECT * FROM data_usage_logs"
chunk_iter = pd.read_sql(query, engine, chunksize=10000)

# Use ThreadPoolExecutor to process chunks in parallel
with ThreadPoolExecutor(max_workers=4) as executor:
    # Submit each chunk to the executor to be processed in parallel
    futures = [executor.submit(process_chunk, chunk) for chunk in chunk_iter]
    for future in concurrent.futures.as_completed(futures):


Processing 10000 records
Processing 10000 records
Chunk processed
Chunk processed

In this code snippet, ThreadPoolExecutor is used to process each chunk in parallel across different threads.

The chunk_iter variable is an iterator that lazily loads chunks of data from the SQL query.

Instead of processing the chunks sequentially, we submit each chunk to a pool of workers to be processed in parallel.

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