Comparing SQL Engines on Amazon EMR for Modern Data Platforms

Overview

Running scalable, efficient, cost-effective SQL queries across large datasets is essential in modern data platforms. Amazon EMR supports open-source engines like Apache Hive, Presto, and Trino, providing flexibility and performance for data processing and analytics on Amazon S3 and HDFS. This blog overviews these engines, highlights key Amazon EMR features, outlines migration strategies, and offers best practices for effectively managing and tuning workloads.

Apache Hive on Amazon EMR

Apache Hive is a widely used SQL-based data warehouse engine built for batch processing large datasets. On Amazon EMR, Hive supports various file formats (e.g., ORC, Parquet, JSON) and data sources (e.g., HDFS, Amazon S3, Amazon DynamoDB). It is primarily used for ETL, reporting, and interactive queries.

Amazon EMR enhances Hive with features such as:

• ACID transaction support (from EMR 6.1)
• LLAP (Low Latency Analytical Processing) for faster performance
• Optimized EMRFS S3 committers for better write performance
• Integration with AWS Glue Data Catalog or Amazon RDS for metadata management

Amazon EMR Hive benefits from up to 15x faster write performance using the “zero-rename” feature and persistent Tez UI for query monitoring.

Presto and Trino on Amazon EMR

Presto is a distributed SQL query engine designed for interactive analytics over petabyte-scale datasets. Trino, a fork of PrestoSQL, continues this lineage with enhanced capabilities. Both engines are supported in Amazon EMR.

Key features include:

• Memory-based parallel architecture for fast query execution
• Connectors to multiple data sources (Amazon Redshift, Kafka, HDFS, Amazon S3)
• Fine-grained Lake Formation policy enforcement (Presto)
• Iceberg and Delta Lake support (Trino)
• Up to 3.1x better performance on Amazon EMR compared to open-source Trino

Amazon EMR provides Presto and Trino with security features like SSL encryption and LDAP authentication, while Presto Web UI allows users to monitor and troubleshoot queries in real time.

Configuring and Running Workloads

Amazon EMR supports multiple interfaces and orchestration options for running Hive and Presto workloads:

• CLI (Hive CLI, Beeline, Presto CLI)
• JDBC/ODBC (Simba drivers)
• Notebooks (EMR Studio, Zeppelin, Hue)
• AWS orchestration tools (Step Functions, MWAA)

Hive configuration can be customized using hive-site, tez-site, and hive-env configurations. For Presto, tuning is done via coordinator and worker configurations, connectors, and security settings.

Migration Strategy

A successful migration to Amazon EMR involves the following steps:

1. Data Migration to Amazon S3

Use tools like distcp, AWS CLI, AWS DataSync, or Snowball to transfer datasets from on-prem HDFS to Amazon S3. Data should be structured for parallel processing and optimized for query performance.

2. Hive Metastore Migration

Move on-prem Hive metastores to either Amazon RDS or AWS Glue. This allows multiple Amazon EMR clusters to share metadata and decouples compute from storage.

3. Script and Query Updates

Update DDLs to reflect Amazon S3 locations. DMLs typically require minimal changes. Hive external tables are recommended for persistent data access from Amazon S3.

4. Security Configuration

Implement authentication using LDAP/Kerberos and authorization using SQL standards or Apache Ranger with Amazon EMR.

Performance Tuning and Best Practices

To achieve optimal performance on Amazon EMR, follow these best practices:

Partitioning and Bucketing

• Use partitions to reduce the data scanned during queries.
• Apply bucketing for frequently filtered high-cardinality columns.
• Combine partitioning and bucketing for significant improvements in performance and cost.

File Formats and Sizes

• Use columnar formats like Parquet for efficient reads.
• Avoid small files; aim for files larger than 128MB.
• Compress data using splittable formats for better parallelism.

Query Tuning

• Use APPROX_DISTINCT() for faster cardinality estimation.
• Limit selected columns to only what’s needed.
• Use LIMIT with ORDER BY to avoid memory-intensive operations.
• Arrange JOIN operations with the smaller table on the right to optimize memory usage.
• Order GROUP BY columns by decreasing cardinality.

Presto vs Amazon Athena vs Amazon Redshift Spectrum

Choose the engine based on query complexity, concurrency needs, and cost model.

Conclusion

By following best practices in partitioning, file formats, and query tuning, businesses can significantly improve query efficiency and reduce costs. With the right engine choice and optimization strategies, Amazon EMR offers scalable foundation for modern data analytics.

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