Introduction

Apache Spark and Trino are two of the most powerful distributed query engines in modern data processing. While both are designed for large-scale analytics, they cater to different use cases, architectures, and performance optimizations.

1. Architecture 

Apache Spark

• General-Purpose Distributed Computing Framework
• Supports batch processing, streaming, machine learning, and graph processing.
• Uses RDDs (Resilient Distributed Datasets) as its core data structure.
• Built on a DAG (Directed Acyclic Graph) execution model.
• Has multiple execution engines: Spark Core, Spark SQL, Spark Streaming, MLlib, and GraphX.
• Uses JVM-based execution (Scala, Java, Python, R support).
• Optimized for iterative computations, making it suitable for machine learning and complex ETL.

Trino

• SQL Query Engine Focused on Interactive Querying
• Designed for fast, federated querying across multiple data sources.
• Uses a massively parallel processing (MPP) architecture.
• Stateless execution engine optimized for ad-hoc SQL queries.
• Written in Java, optimized for high concurrency and low-latency analytics.
• Ideal for querying distributed data lakes and data warehouses without moving data.

Key Differences:

• Spark is a general-purpose computing engine, while Trino is a federated SQL query engine.
• Trino is stateless and optimized for fast interactive queries, whereas Spark retains state for iterative processing.

2. Data Processing Models

Apache Spark

• Batch Processing: Uses Spark SQL and DataFrame API for batch analytics.
• Stream Processing: Supports micro-batch processing via Spark Streaming and continuous processing with Structured Streaming.
• Machine Learning: MLlib provides scalable machine learning models.
• Graph Processing: GraphX enables large-scale graph computations.

Trino

• Primarily SQL-based batch query processing
• No built-in support for streaming or iterative computations.
• Query federation: Can query multiple data sources (e.g., Hive, Iceberg, Delta Lake, MySQL, PostgreSQL, Snowflake) without data movement.

Key Differences:

• Spark supports streaming, ML, and graph processing, while Trino is optimized for SQL-based batch querying.
• Trino excels in federated querying, whereas Spark is better suited for ETL and data transformations.

3. Performance & Query Execution

Apache Spark

• Uses lazy evaluation for transformations to optimize execution plans.
• Catalyst Optimizer enhances SQL query performance.
• Tungsten Execution Engine optimizes memory and CPU efficiency.
• Supports columnar storage formats (Parquet, ORC, Delta Lake) for faster queries.
• Higher latency than Trino for ad-hoc queries due to DAG execution overhead.

Trino

• Uses a query optimizer similar to traditional MPP databases.
• Distributed execution model processes queries efficiently without intermediate storage.
• Dynamic query execution and predicate pushdown for optimized performance.
• Outperforms Spark for low-latency, interactive analytics on large datasets.

Key Differences:

• Trino is significantly faster for ad-hoc SQL queries, while Spark is better for long-running, complex transformations.
• Spark optimizes computation-heavy workloads, while Trino focuses on SQL query efficiency.

4. Data Storage & Compatibility

Apache Spark

• Works with HDFS, Amazon S3, Azure Blob, Google Cloud Storage.
• Native support for Delta Lake, an optimized storage layer for ACID transactions.
• Reads/writes Parquet, ORC, Avro, JSON, CSV efficiently.
• Supports external catalogs like Hive Metastore.

Trino

• Primarily queries data lakes, relational databases, and cloud warehouses.
• Supports Hive, Iceberg, Delta Lake, AWS Glue, PostgreSQL, MySQL, Snowflake, BigQuery, Redshift, and more.
• Works as a federated query engine, allowing users to query multiple sources at once.

Key Differences:

• Spark provides better write performance with ACID transactions (Delta Lake), whereas Trino is read-optimized and federated.
• Trino has broader connectivity to relational databases, whereas Spark is more focused on distributed file-based storage.

5. Scalability & Resource Management

Apache Spark

• Scales horizontally using YARN, Kubernetes, or Mesos.
• Can run on on-premises clusters or cloud platforms like Databricks.
• Uses dynamic resource allocation to optimize cluster usage.

Trino

• Also scales horizontally but requires coordinator and worker nodes.
• Stateless execution allows more efficient resource utilization for queries.
• Integrates with Kubernetes, AWS EMR, and on-premises clusters.

Key Differences:

• Spark requires more resource management tuning (memory, shuffle optimizations), while Trino’s stateless model is easier to scale dynamically.
• Trino scales better for concurrent users, while Spark scales better for large ETL workloads.

6. Security & Authentication

Apache Spark

• Supports Kerberos, LDAP, and Role-Based Access Control (RBAC).
• Works with Apache Ranger for fine-grained security.
• Data encryption options vary based on storage.

Trino

• Supports LDAP, OAuth, JWT, and Kerberos authentication.
• Fine-grained access control via Apache Ranger & SQL-based access policies.
• Integrates with enterprise authentication solutions.

Key Differences:

• Trino has stronger built-in security for SQL-based access control, while Spark security depends on external tools like Apache Ranger.

7. Use Cases & Real-World Applications

Conclusion:

• Apache Spark: ETL, batch processing, streaming analytics, or ML.

• Trino: high-performance, federated SQL querying across multiple data sources.

P Sakhib Rahil