Adding Audit Columns to Existing Tables: Comparing Approaches for Large Datasets

Adding Audit Columns to Existing Tables: Comparing Approaches for Large Datasets Introduction In data engineering, adding audit columns like bd_insert_dtm and bd_updated_dtm to track when records are created or modified is a common requirement. When dealing with large datasets (2-5GB files), choosing the right approach becomes critical for performance and resource utilization. This post compares four different methods to implement this seemingly simple task, helping you choose the right tool for your specific needs. The Challenge We need to add audit timestamp columns to existing tables with file sizes ranging from 2GB to 5GB. Let's explore our options: Approach 1: PySpark PySpark leverages distributed computing, making it ideal for large datasets. While it might seem like overkill for 2-5GB files, it scales beautifully as your data grows. from pyspark.sql import SparkSession from pyspark.sql.functions import current_timestamp # Initialize Spark session spark = SparkSession.builder \ .appName("Add Audit Columns") \ .getOrCreate() # Read your data df = spark.read.format("csv").option("header", "true").load("your_file.csv") # Add audit columns df_with_audit = df.withColumn("bd_insert_dtm", current_timestamp()) \ .withColumn("bd_updated_dtm", current_timestamp()) # Write the result df_with_audit.write.format("csv").mode("overwrite").option("header", "true").save("output_path") Pros: Highly scalable to much larger datasets Parallelized processing Built-in functions for timestamps Cons: Startup overhead Requires Spark environment More complex for simple tasks Approach 2: Pandas Pandas offers simplicity and ease of use, loading the entire dataset into memory: import pandas as pd from datetime import datetime # Read your data df = pd.read_csv("your_file.csv") # Add audit columns current_time = datetime.now() df["bd_insert_dtm"] = current_time df["bd_updated_dtm"] = current_time # Write the result df.to_csv("output_path.csv", index=False) Pros: Simple and straightforward Familiar API for data scientists Great for quick iterations Cons: Loads entire dataset into memory May struggle with 2GB+ files on machines with limited RAM Single-threaded operations Approach 3: Dask Dask combines the familiar Pandas API with out-of-core processing for larger-than-memory datasets: import dask.dataframe as dd from datetime import datetime # Read your data df = dd.read_csv("your_file.csv") # Add audit columns current_time = datetime.now() df["bd_insert_dtm"] = current_time df["bd_updated_dtm"] = current_time # Write the result df.to_csv("output_path/*.csv", index=False) Pros: Pandas-like API Handles larger-than-memory datasets Parallel execution Cons: More complex than pandas Output is split across multiple files by default Some operations require careful consideration Approach 4: Using Generators Generators provide the most memory-efficient solution by processing the file line by line: import csv from datetime import datetime def add_audit_columns(input_file, output_file): with open(input_file, 'r') as infile, open(output_file, 'w', newline='') as outfile: reader = csv.reader(infile) writer = csv.writer(outfile) # Handle header header = next(reader) header.extend(["bd_insert_dtm", "bd_updated_dtm"]) writer.writerow(header) # Process rows current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S") for row in reader: row.extend([current_time, current_time]) writer.writerow(row) # Usage add_audit_columns("your_file.csv", "output_file.csv") Pros: Minimal memory footprint Works on any machine Simple to understand Cons: Sequential processing Limited functionality compared to dataframe libraries Manual handling of data types Comparison and Recommendations Approach Memory Usage Speed Scalability Ease of Use PySpark Medium Fast Excellent Complex Pandas High Medium Poor Simple Dask Medium Fast Good Medium Generator Low Slow Poor Medium For 2-5GB files: With sufficient RAM: Pandas offers the simplest solution With limited RAM: Dask or generators are better choices With an existing Spark environment: PySpark makes sense For absolute memory efficiency: Go with generators Conclusion When adding audit columns to existing tables, the best approach depends on your specific constraints. For most cases with 2-5GB files, Dask provides an excellent balance between ease of use and performance. However, generators shine when working in extremely memory-constrained environments, while PySpark is the go-to solution if you anticipate scaling to much larger datasets in the future. What approach are you using for adding audit columns to your tables? Let me know in the comments!

Apr 16, 2025 - 20:34

Adding Audit Columns to Existing Tables: Comparing Approaches for Large Datasets

Introduction

In data engineering, adding audit columns like bd_insert_dtm and bd_updated_dtm to track when records are created or modified is a common requirement. When dealing with large datasets (2-5GB files), choosing the right approach becomes critical for performance and resource utilization.

This post compares four different methods to implement this seemingly simple task, helping you choose the right tool for your specific needs.

The Challenge

We need to add audit timestamp columns to existing tables with file sizes ranging from 2GB to 5GB. Let's explore our options:

Approach 1: PySpark

PySpark leverages distributed computing, making it ideal for large datasets. While it might seem like overkill for 2-5GB files, it scales beautifully as your data grows.

from pyspark.sql import SparkSession
from pyspark.sql.functions import current_timestamp

# Initialize Spark session
spark = SparkSession.builder \
    .appName("Add Audit Columns") \
    .getOrCreate()

# Read your data
df = spark.read.format("csv").option("header", "true").load("your_file.csv")

# Add audit columns
df_with_audit = df.withColumn("bd_insert_dtm", current_timestamp()) \
                  .withColumn("bd_updated_dtm", current_timestamp())

# Write the result
df_with_audit.write.format("csv").mode("overwrite").option("header", "true").save("output_path")

Pros:

Highly scalable to much larger datasets
Parallelized processing
Built-in functions for timestamps

Cons:

Startup overhead
Requires Spark environment
More complex for simple tasks

Approach 2: Pandas

Pandas offers simplicity and ease of use, loading the entire dataset into memory:

import pandas as pd
from datetime import datetime

# Read your data
df = pd.read_csv("your_file.csv")

# Add audit columns
current_time = datetime.now()
df["bd_insert_dtm"] = current_time
df["bd_updated_dtm"] = current_time

# Write the result
df.to_csv("output_path.csv", index=False)

Pros:

Simple and straightforward
Familiar API for data scientists
Great for quick iterations

Cons:

Loads entire dataset into memory
May struggle with 2GB+ files on machines with limited RAM
Single-threaded operations

Approach 3: Dask

Dask combines the familiar Pandas API with out-of-core processing for larger-than-memory datasets:

import dask.dataframe as dd
from datetime import datetime

# Read your data
df = dd.read_csv("your_file.csv")

# Add audit columns
current_time = datetime.now()
df["bd_insert_dtm"] = current_time
df["bd_updated_dtm"] = current_time

# Write the result
df.to_csv("output_path/*.csv", index=False)

Pros:

Pandas-like API
Handles larger-than-memory datasets
Parallel execution

Cons:

More complex than pandas
Output is split across multiple files by default
Some operations require careful consideration

Approach 4: Using Generators

Generators provide the most memory-efficient solution by processing the file line by line:

import csv
from datetime import datetime

def add_audit_columns(input_file, output_file):
    with open(input_file, 'r') as infile, open(output_file, 'w', newline='') as outfile:
        reader = csv.reader(infile)
        writer = csv.writer(outfile)

        # Handle header
        header = next(reader)
        header.extend(["bd_insert_dtm", "bd_updated_dtm"])
        writer.writerow(header)

        # Process rows
        current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        for row in reader:
            row.extend([current_time, current_time])
            writer.writerow(row)

# Usage
add_audit_columns("your_file.csv", "output_file.csv")

Pros:

Minimal memory footprint
Works on any machine
Simple to understand

Cons:

Sequential processing
Limited functionality compared to dataframe libraries
Manual handling of data types

Comparison and Recommendations

Approach	Memory Usage	Speed	Scalability	Ease of Use
PySpark	Medium	Fast	Excellent	Complex
Pandas	High	Medium	Poor	Simple
Dask	Medium	Fast	Good	Medium
Generator	Low	Slow	Poor	Medium

For 2-5GB files:

With sufficient RAM: Pandas offers the simplest solution
With limited RAM: Dask or generators are better choices
With an existing Spark environment: PySpark makes sense
For absolute memory efficiency: Go with generators

Conclusion

When adding audit columns to existing tables, the best approach depends on your specific constraints. For most cases with 2-5GB files, Dask provides an excellent balance between ease of use and performance. However, generators shine when working in extremely memory-constrained environments, while PySpark is the go-to solution if you anticipate scaling to much larger datasets in the future.

What approach are you using for adding audit columns to your tables? Let me know in the comments!