Your current workflow is bottlenecked by single-threaded processing on the driver, unnecessary disk writes/copies, and manual cleanup. Let's revamp it for speed, scalability, and reliability using Spark's distributed capabilities:

Core Optimizations Overview #

• Distribute the workload: Let Spark executors handle ZIP processing directly (no more driver bottleneck)
• In-memory processing: Skip writing to driver/executor local disks entirely
• Eliminate DBFS copy: Move data directly from S3 to Delta without intermediate storage
• Parallelize everything: Use your Spark cluster's resources to process multiple ZIPs at once

Step-by-Step Optimized Code #

First, define a function that each executor runs to process batches of ZIP files. This function reads directly from S3, unzips in memory, parses CSVs, and returns rows for Spark to assemble into a DataFrame:

from pyspark.sql import Row
import io
import boto3
import zipfile
from pyspark.sql.types import StructType, StructField, StringType, IntegerType  # Adjust schema to match your CSV

def process_zip_batch(s3_path_batch):
    # Initialize S3 client on each executor (safe and efficient)
    s3 = boto3.client('s3')
    parsed_rows = []
    
    # Define your CSV schema explicitly (critical for performance and accuracy)
    csv_schema = StructType([
        StructField("customer_id", StringType(), nullable=True),
        StructField("order_date", StringType(), nullable=True),
        StructField("amount", IntegerType(), nullable=True),
        # Add all your CSV columns here
    ])
    
    for s3_path in s3_path_batch:
        # Parse bucket and key from the S3 path
        path_parts = s3_path.split('/')
        bucket = path_parts[2]
        zip_key = '/'.join(path_parts[3:])
        
        try:
            # Fetch ZIP file directly from S3 into memory
            zip_response = s3.get_object(Bucket=bucket, Key=zip_key)
            zip_bytes = zip_response['Body'].read()
            
            # Unzip and process CSV files entirely in memory
            with io.BytesIO(zip_bytes) as zip_buffer:
                with zipfile.ZipFile(zip_buffer, 'r') as zip_ref:
                    # Only process CSV files inside the ZIP
                    for file_name in zip_ref.namelist():
                        if file_name.lower().endswith('.csv'):
                            with zip_ref.open(file_name) as csv_file:
                                # Read and decode CSV content
                                csv_content = csv_file.read().decode('utf-8').split('\n')
                                # Skip header row (remove this if your CSVs don't have headers)
                                header = csv_content[0].split(',')
                                for line in csv_content[1:]:
                                    if line.strip():  # Skip empty lines
                                        values = line.split(',')
                                        # Map values to schema fields and create a Spark Row
                                        row = Row(**dict(zip(header, values)))
                                        parsed_rows.append(row)
        except Exception as e:
            # Handle individual ZIP failures without crashing the entire job
            print(f"Failed to process {s3_path}: {str(e)}")
            continue
    
    return parsed_rows

# Your list of S3 ZIP paths
zip_paths = ["s3://mybucket/file1.zip", "s3://mybucket/file2.zip", ..., "s3://mybucket/fileN.zip"]

# Parallelize the ZIP paths across your Spark cluster
# Adjust numSlices based on your cluster size (e.g., 2-4 slices per executor core)
zip_rdd = spark.sparkContext.parallelize(zip_paths, numSlices=len(zip_paths))

# Process batches of ZIPs on executors
processed_rdd = zip_rdd.mapPartitions(process_zip_batch)

# Convert RDD to DataFrame using your predefined schema
csv_df = spark.createDataFrame(processed_rdd, schema=csv_schema)

# Write directly to Delta table (no DBFS copy needed!)
csv_df.write.format("delta")\
    .mode("overwrite")\
    .option("mergeSchema", "true")\
    .save("/path/to/your/delta/table")

Key Improvements Over Your Original Workflow #

1. Parallel Processing: Each executor handles multiple ZIP files simultaneously, utilizing your entire cluster's compute power instead of just the driver.
2. No Disk I/O: Everything happens in memory—no writing to driver/executor local disks, so you avoid disk space errors and slow disk operations.
3. Skip DBFS Copy: Data moves directly from S3 → Executor memory → Delta table, cutting out the unnecessary intermediate step.
4. Automatic Cleanup: Since we never write to local disks, there's no need for shutil.rmtree or dbutils.fs.rm calls—no residual files left behind.
5. Fault Tolerance: The try-except block ensures that a single corrupted ZIP won't crash the entire job.

Additional Tips for Even Better Performance #

• Tune Parallelism: Adjust numSlices to match your cluster's resources. A good starting point is 2-4 slices per available core.
• Use Snappy Compression: When writing Delta, add .option("compression", "snappy") to reduce storage size and speed up reads/writes.
• Partition the Delta Table: If your data has a natural partition key (like order_date), partition the Delta table to improve future query performance.
• Pre-Install Dependencies: If using a custom environment, ensure boto3 and zipfile are installed on all executors (Databricks clusters usually have these pre-installed).

内容的提问来源于stack exchange，提问作者Eric Bellet