Differences Between Spark SQL and Presto: A Comparative Overview

Presto is an open‑source distributed SQL query engine originally built for Apache Hadoop, designed for interactive analytics on data sets of any size.

Spark SQL is a distributed in‑memory compute engine that adds a SQL layer on top of structured and semi‑structured data, offering high‑speed processing because it runs in memory.

Key differences include:

Spark SQL introduces a programming module for structured data called Spark SQL, which provides a DataFrame abstraction that can act as a distributed SQL query engine.

Presto was created to achieve interactive analysis speeds comparable to Facebook’s massive data warehouses.

Spark SQL is a component of Spark Core and adds the SchemaRDD (Resilient Distributed Dataset) abstraction to support structured and semi‑structured data.

Presto was designed as an alternative to MapReduce‑based tools (e.g., Hive, Pig) for querying HDFS, but it is not limited to HDFS.

Spark SQL follows in‑memory processing, boosting speed, and Spark handles a wide range of workloads such as batch queries, iterative algorithms, interactive queries, and streaming.

Presto can perform federated queries across multiple data sources.

Assuming a relational table sample1 in MySQL and a Hive table sample2 in the Testdb database, Presto can query both in a single statement once connectors are configured:

presto> <Function (select/Group by ..etc)> hive.Testdb.sample2

Spark SQL’s architecture consists of Spark SQL, SchemaRDD, and DataFrames; DataFrames are collections of data organized into columns, analogous to relational tables. SchemaRDD is a special data structure in Spark Core, and Spark SQL works with schemas, tables, and records, allowing temporary tables to be created from SchemaRDD.

DataFrames can process data ranging from kilobytes to gigabytes on a single‑node cluster and support various formats (CSV, Elasticsearch, Cassandra, etc.) and storage systems (HDFS, Hive tables, MySQL, etc.), offering APIs in Python, Java, Scala, and R.

Presto is a distributed engine that runs on a cluster with a coordinator and multiple workers; the Presto CLI submits SQL statements to the coordinator, which parses, plans, and distributes execution to workers.

Companies using Presto include Facebook, Netflix, Airbnb, Dropbox, among others.

Apache Spark is used in finance, retail, healthcare, travel, and e‑commerce (e.g., eBay, Alibaba, Pinterest) for analyzing massive datasets.

Installation differences: Presto requires a coordinator and workers, with queries submitted via the CLI; Spark SQL is available out‑of‑the‑box when an Apache Spark cluster is installed, as Spark is a Hadoop sub‑project focused on fast, distributed computation.

Features and capabilities:

Presto can query many data sources (Hive, Cassandra, RDBMS, etc.) via pluggable connectors.

Spark SQL integrates with external data sources through DataFrames and JDBC connectors, supporting federated queries.

Both engines support federated queries: Presto via its configurable connectors and CLI, Spark SQL via built‑in JDBC support and the Spark Thrift server.

Typical users include data analysts, data engineers, data scientists, and Spark developers.

Conclusion: Spark SQL and Presto are both viable distributed SQL engines; Presto excels at heterogeneous federated queries, while Spark SQL offers superior performance for large analytical workloads, with Spark’s installation being more involved but offering broader processing capabilities.

Recommended further reading:

Apache Spark vs Apache Flink – 8 useful things to know

Apache Hive vs Apache Spark SQL – 13 surprising differences

Best 6 comparisons between Hadoop and SQL

Hadoop vs Teradata – valuable distinctions

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