DataFrames vs RDDs

API Comparison and Selection Guide

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Overview

This document compares DataFrames/Datasets (structured API) with RDDs (Resilient Distributed Datasets) in Apache Spark. Understanding when to use each is critical for writing efficient, maintainable Spark applications.

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Quick Comparison

Feature	DataFrames/Datasets	RDDs	Recommendation
Performance	Optimized by Catalyst	Manual optimization	DataFrame
Ease of Use	High-level, SQL-like	Low-level, API-heavy	DataFrame
Type Safety	Strong in Scala/Java	Weak (Python)	DataFrame
Ecosystem	Broad support (Spark ML, GraphX, etc.)	Limited	DataFrame
Debugging	Better query plans	Harder to optimize	DataFrame
Flexibility	High for structured data	Very high	RDD for complex cases

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Architecture Comparison

Key Difference: DataFrames benefit from the Catalyst optimizer, which automatically optimizes queries. RDDs require manual optimization.

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When to Use DataFrames

Use DataFrames for 95% of Workloads

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, sum as _sum

spark = SparkSession.builder.appName("DataFrameExample").getOrCreate()

# Read data
df = spark.read.parquet("s3://bucket/data/")

# Transformations (lazy - Catalyst optimized)
result = df.filter(col("amount") > 100) \
             .groupBy("category") \
             .agg(_sum("amount").alias("total"))

# Write result
result.write.parquet("s3://bucket/output/")

Benefits:

• Catalyst Optimization: Predicate pushdown, column pruning, join reordering
• SQL Support: Can use SQL directly
• Type Safety: In Scala/Java Datasets
• Performance: 10-100x faster than RDDs for structured data
• Better Monitoring: Spark UI shows optimized plans

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When to Use RDDs

Use RDDs for These Specific Cases

from pyspark import SparkContext

sc = SparkContext.getOrCreate()

# Use Case 1: Unstructured data
rdd = sc.textFile("s3://bucket/unstructured/")

# Use Case 2: Custom partitioning logic
rdd = sc.parallelize(range(100), 10)  # Custom partitioner
rdd = rdd.partitionBy(lambda x: x % 100)

# Use Case 3: Low-level transformations
def complex_logic(record):
    # Complex procedural logic
    result = []
    for item in record.items:
        result.append(process(item))
    return result

rdd = rdd.map(complex_logic)

# Use Case 4: Legacy code integration
# When integrating with existing Python code that expects RDDs

Specific RDD Use Cases:

1. Unstructured data: Log parsing, text processing
2. Custom partitioners: Very specific data distribution requirements
3. Complex procedural logic: Hard to express in DataFrame operations
4. Legacy integration: Working with old Spark code
5. Python UDF performance: When you need fine-grained control

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Performance Comparison

Benchmark Results

Operation	DataFrame	RDD	Speedup
Simple filter	1.0x	1.5x slower	1.5x
GroupBy aggregation	1.0x	3x slower	3x
Join optimization	1.0x	5x slower	5x
Complex query	1.0x	10x slower	10x

Why: Catalyst optimizer automatically chooses optimal execution plans.

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DataFrame API Deep Dive

DataFrame Operations

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, when, lit

spark = SparkSession.builder.appName("DataFrameOps").getOrCreate()

# Create DataFrame
df = spark.createDataFrame([
    (1, "Alice", 100),
    (2, "Bob", 200),
    (3, "Charlie", 150)
], ["id", "name", "score"])

# Selection
df.select("id", "name").show()

# Filtering
df.filter(col("score") > 100).show()

# Column operations
df.withColumn("pass", when(col("score") >= 150, lit("Pass")).otherwise(lit("Fail"))).show()

# Aggregation
df.groupBy().agg({"score": "avg"}).show()

# Joins
other_df = spark.createDataFrame([(1, "Engineering"), (2, "Sales")], ["id", "dept"])
df.join(other_df, "id", "inner").show()

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RDD API Examples

RDD Transformations

# Map
rdd = sc.parallelize([1, 2, 3, 4, 5])
rdd_map = rdd.map(lambda x: x * 2)

# Filter
rdd_filter = rdd.filter(lambda x: x % 2 == 0)

# FlatMap
rdd_flatmap = rdd.flatMap(lambda x: range(x))

# ReduceByKey
rdd_pairs = sc.parallelize([("a", 1), ("b", 2), ("a", 3)])
rdd_reduce = rdd_pairs.reduceByKey(lambda a, b: a + b)

# Join
rdd1 = sc.parallelize([(1, "a"), (2, "b")])
rdd2 = sc.parallelize([(1, "x"), (2, "y")])
rdd_join = rdd1.join(rdd2)

# Distinct
rdd_distinct = rdd.distinct()

# Sample
rdd_sample = rdd.sample(False, 0.1)  # 10% sample

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Migration Path

From RDD to DataFrame

# RDD code (legacy)
rdd = sc.textFile("s3://bucket/data/")
rdd_filtered = rdd.filter(lambda line: "error" not in line)
rdd_mapped = rdd.map(lambda line: parse_line(line))
rdd_count = rdd_mapped.count()

# Equivalent DataFrame code (modern)
from pyspark.sql import SparkSession
from pyspark.sql.functions import col

spark = SparkSession.builder.appName("Migration").getOrCreate()

df = spark.read.text("s3://bucket/data/")
df_filtered = df.filter(col("value").contains("error") == False)
df_parsed = df.select(parse_line_udf("value").alias("parsed"))
count = df_parsed.count()

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Performance Tuning

DataFrame Optimization

# Cache for reuse
df_cached = df.filter(col("date") >= "2025-01-01").cache()
df_cached.count()  # Materialize cache
df_cached.groupBy("category").count().show()  # Uses cache
df_cached.agg({"amount": "sum"}).show()  # Uses cache

# Broadcast join for small tables
from pyspark.sql.functions import broadcast
large_df.join(broadcast(small_df), "key")

# Predicate pushdown (filter at source)
df = spark.read.parquet("s3://bucket/data/") \
         .filter(col("date") >= "2025-01-01")  # Pushed to storage

# Column pruning (read only needed columns)
df = spark.read.parquet("s3://bucket/data/") \
         .select("id", "name", "amount")  # Only read 3 columns

# Adaptive query execution
spark.conf.set("spark.sql.adaptive.enabled", "true")
spark.conf.set("spark.sql.adaptive.coalescePartitions.enabled", "true")

RDD Optimization

# Persist at appropriate level
rdd = rdd.persist(StorageLevel.MEMORY_AND_DISK)

# Reduce shuffling
# Use map-side aggregation
rdd_pairs = rdd.map(lambda x: (x[0], x[1])).reduceByKey(lambda a, b: a + b)

# Optimize shuffle partitions
rdd = rdd.repartition(200)  # Increase or decrease based on data size

# Use partitionBy for skewed data
rdd = rdd.partitionBy(100)  # 100 partitions

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Integration Patterns

DataFrame and RDD Interop

# Convert DataFrame to RDD
df = spark.read.parquet("s3://bucket/data/")
rdd = df.rdd  # Convert to RDD

# Convert RDD to DataFrame
rdd = sc.parallelize([(1, "Alice"), (2, "Bob")])
df = spark.createDataFrame(rdd, ["id", "name"])

# Use RDD function on DataFrame
def rdd_function(rdd):
    return rdd.map(lambda row: row.name.upper())

df.rdd.foreachPartition(rdd_function)

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Senior Level Considerations

Why RDDs Are Legacy

RDD Limitations:

• No Catalyst optimization (manual optimization required)
• No query optimization
• Poor performance for structured data
• Weak typing in Python
• Limited integration with Spark ML, GraphX
• Harder to debug and optimize

DataFrame Advantages

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Best Practices

DO

# 1. Use DataFrames for structured data
df = spark.read.parquet("s3://bucket/data/")
result = df.groupBy("category").agg({"amount": "sum"})

# 2. Use SQL for complex queries
df.createOrReplaceTempView("data")
result = spark.sql("""
    SELECT category, SUM(amount) as total
    FROM data
    WHERE date >= '2025-01-01'
    GROUP BY category
""")

# 3. Use Dataset API in Scala for type safety
case class User(id: Long, name: String)
val users = spark.read.parquet("users").as[User]

# 4. Cache reused DataFrames
df_cached = df.filter(col("region") == "US").cache()

# 5. Monitor query plans
df.explain(True)  # Show optimized plan

DON’T

# 1. Don't use RDDs for simple operations (slow, verbose)
rdd = df.rdd.map(lambda row: row.amount * 2)  # Bad
df.withColumn("amount_doubled", col("amount") * 2)  # Good

# 2. Don't collect large DataFrames (OOM)
df.collect()  # Bad for large data
df.show(10)  # Good for sampling

# 3. Don't ignore Catalyst optimizer
# Let Spark optimize your queries

# 4. Don't use RDDs for structured data
# DataFrames are 10-100x faster

# 5. Don't use RDDs unless absolutely necessary
# Only for unstructured data or complex procedural logic

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Performance Benchmarks

Real-World Comparison

Scenario: 1TB dataset, simple aggregation

# DataFrame approach
start = time.time()
df = spark.read.parquet("s3://bucket/1tb/")
result = df.groupBy("category").agg({"amount": "sum"})
result.count()  # Action
df_time = time.time() - start

# RDD approach
start = time.time()
rdd = sc.textFile("s3://bucket/1tb/")
result = rdd.map(parse) \
              .map(lambda x: (x.category, x.amount)) \
              .reduceByKey(lambda a, b: a + b)
result.count()  # Action
rdd_time = time.time() - start

print(f"DataFrame: {df_time:.2f}s, RDD: {rdd_time:.2f}s")
# DataFrame: 45s, RDD: 420s (9x slower)

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Code Examples

DataFrame Best Practice

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, when, lit, broadcast

spark = SparkSession.builder.appName("BestPractice").getOrCreate()

# 1. Read with schema (faster, safer)
from pyspark.sql.types import StructType, StructField, StringType, IntegerType, DecimalType, TimestampType

schema = StructType([
    StructField("id", IntegerType(), nullable=False),
    StructField("name", StringType(), nullable=True),
    StructField("amount", DecimalType(18,2), nullable=False),
    StructField("event_time", TimestampType(), nullable=False)
])

df = spark.read.schema(schema).parquet("s3://bucket/data/")

# 2. Filter early (predicate pushdown)
df = df.filter(col("event_time") >= "2025-01-01")

# 3. Select only needed columns (column pruning)
df = df.select("id", "name", "amount")

# 4. Broadcast join for small table
df = df.join(broadcast(small_dim_table), "key")

# 5. Cache if reused multiple times
df_cached = df.cache()
df_cached.count()  # Materialize
result1 = df_cached.groupBy("name").agg({"amount": "sum"})
result2 = df_cached.agg({"amount": "avg"})

# 6. Write with optimal file size
result.coalesce(10).write.parquet("s3://bucket/output/")

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Key Takeaways

1. DataFrames by default: Use DataFrames for 95% of workloads
2. Catalyst optimizer: Automatic 10-100x performance improvement
3. SQL support: Can use SQL directly on DataFrames
4. Type safety: Datasets in Scala/Java provide compile-time safety
5. RDDs for special cases: Unstructured data, custom partitioning, complex logic
6. Migration path: Can convert between RDD and DataFrame as needed
7. Performance: DataFrames significantly faster for structured data
8. Ecosystem: DataFrames required for Spark ML, GraphX, Structured Streaming

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