Spark vs Presto in PySpark: Key Differences and Usage

Apache Spark and Presto are both distributed query engines but differ in design and use. Spark is a general-purpose big data engine with strong support in PySpark, while Presto is a fast SQL query engine optimized for interactive analytics across multiple data sources.

Quick Comparison

This table summarizes key factors comparing Apache Spark and Presto when used with PySpark.

Factor	Apache Spark (PySpark)	Presto
Primary Use	Batch and stream processing, machine learning, ETL	Interactive SQL queries, ad-hoc analytics
Language Support	Python (PySpark), Scala, Java, R	Primarily SQL
Execution Engine	DAG-based with in-memory computation	Distributed SQL query engine with pipelined execution
Data Sources	Wide support including HDFS, S3, Cassandra, JDBC	Connects to many data sources via connectors
Performance	Good for large-scale complex jobs, slower startup	Faster for low-latency SQL queries
Integration with PySpark	Native and seamless	Requires external setup, not native in PySpark

Key Differences

Spark is a full big data processing framework that supports batch and streaming data, machine learning, and graph processing. It uses a directed acyclic graph (DAG) engine to optimize complex workflows and can cache data in memory for faster iterative processing. PySpark is Spark's Python API, allowing Python developers to write Spark jobs easily.

Presto, on the other hand, is designed specifically for fast, interactive SQL queries across large datasets. It does not support streaming or machine learning natively and focuses on querying data where it lives without moving it. Presto excels at low-latency queries but lacks the broader data processing capabilities of Spark.

While PySpark integrates directly with Spark's engine, Presto is a separate system that can be queried via JDBC or REST APIs. This means Presto is not embedded in PySpark but can be used alongside it for SQL analytics.

Code Comparison

Here is how you run a simple SQL query to count rows in a table using PySpark with Spark.

python

from pyspark.sql import SparkSession

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

# Create a sample DataFrame
data = [(1, "Alice"), (2, "Bob"), (3, "Cathy")]
columns = ["id", "name"]
df = spark.createDataFrame(data, columns)
df.createOrReplaceTempView("people")

# Run SQL query
result = spark.sql("SELECT COUNT(*) AS total FROM people")
result.show()

Output

+-----+
|total|
+-----+
|    3|
+-----+

Presto Equivalent

Presto queries are usually run via a CLI or JDBC connection. Here is an example using Python's prestodb client to run the same count query.

python

import prestodb

conn = prestodb.dbapi.connect(
    host='presto-coordinator-host',
    port=8080,
    user='user',
    catalog='hive',
    schema='default',
)
cursor = conn.cursor()
cursor.execute('SELECT COUNT(*) AS total FROM people')
rows = cursor.fetchall()
print(rows)

Output

[(3,)]

When to Use Which

Choose Apache Spark with PySpark when you need a versatile big data engine for batch processing, streaming, machine learning, or complex ETL workflows with Python support.

Choose Presto when you want fast, interactive SQL queries across multiple data sources without moving data, especially for ad-hoc analytics and BI reporting.

Use Spark if your workload involves heavy data transformations or iterative algorithms. Use Presto if your focus is on quick SQL analytics on large datasets with minimal setup.

Key Takeaways

Apache Spark with PySpark is a full big data engine supporting batch, streaming, and ML workloads.

Presto is optimized for fast, interactive SQL queries across diverse data sources.

PySpark integrates natively with Spark, while Presto requires separate setup and connections.

Use Spark for complex data processing and Presto for low-latency SQL analytics.

Choosing depends on workload type: ETL and ML favor Spark; ad-hoc SQL favors Presto.