Impala vs Presto

Impala and Presto are both popular SQL query engines for big data but are optimized for slightly different use cases. Here’s a comparison to help you understand their differences: 1. Purpose and Use Case Impala: Primarily designed to provide low-latency SQL queries directly on data stored in HDFS (Hadoop Distributed File System) or HBase. It’s highly optimized for the Hadoop ecosystem and is ideal for real-time or interactive data analysis within Cloudera environments. Presto: A distributed SQL query engine optimized for ad-hoc and interactive queries across diverse data sources, not limited to Hadoop. Presto can query data from HDFS, Amazon S3, MySQL, Cassandra, and other data sources, making it highly flexible for multi-source analytics. 2. Architecture Impala: Works as a native component of the Hadoop ecosystem, where it operates directly on HDFS, utilizing the data locality provided by Hadoop clusters. This allows for faster query execution times in these environments. Presto: Has a flexible and pluggable architecture, allowing it to connect to a variety of data sources. It runs on a cluster of nodes with a coordinator-worker model, providing scalability across large datasets and diverse sources. 3. Performance Impala: Often faster for simple analytical queries on Hadoop because of its tight integration and optimized I/O performance. However, its performance can decline with complex queries or diverse data sources outside the Hadoop ecosystem. Presto: While it may not match Impala’s speed for single-source Hadoop queries, it performs very well on federated queries across multiple sources. Presto’s distributed nature makes it highly scalable and effective for large, complex queries. 4. SQL Support Impala: Offers ANSI SQL support but with limitations, especially in complex joins and subqueries. However, it supports data structures specific to Hadoop, such as Parquet and ORC, making it efficient for typical Hadoop data formats. Presto: Known for a comprehensive implementation of ANSI SQL, Presto offers better support for complex SQL functionalities, including window functions, complex joins, and aggregations, making it versatile for advanced querying. 5. Data Sources Impala: Primarily designed for data stored in HDFS and HBase, with limited flexibility for connecting to other sources. Presto: Supports a variety of data sources, including HDFS, S3, MySQL, Cassandra, Kafka, MongoDB, and many others, making it a great option for federated querying across diverse environments. 6. Community and Ecosystem Impala: Mostly used within Cloudera ecosystems and has a strong community around Hadoop-focused big data environments. Presto: Originally developed by Facebook and now maintained by PrestoDB and Trino communities, Presto has become popular across various industries for use cases that involve querying multiple data sources. 7. Scalability Impala: Scales well in Hadoop environments but may not be as flexible when querying external data sources. It’s optimized for low-latency queries on large datasets within Hadoop. Presto: Designed with scalability in mind, Presto can handle large-scale queries across multi-petabyte datasets and diverse sources, making it better suited for a broader range of environments. 8. Typical Use Cases Impala: Interactive analytics on Hadoop, particularly in Cloudera-managed environments. Presto: Federated querying across multiple data sources, supporting diverse analytics needs in multi-source environments. Summary Table Feature Impala Presto Best Use Case Low-latency queries on Hadoop Federated querying across data sources Architecture Hadoop-native Coordinator-worker, supports various sources Performance Fast on Hadoop Scalable for complex, multi-source queries SQL Support ANSI SQL (limited) Full ANSI SQL support Data Sources HDFS, HBase HDFS, S3, MySQL, Cassandra, etc. Community Strong in Cloudera environments Broad, includes PrestoDB and Trino Scalability Good on Hadoop Excellent across diverse sources Conclusion If your environment is centered around Hadoop and Cloudera, Impala might be the better choice for high-speed, low-latency querying. For federated querying across multiple data sources and environments, Presto offers flexibility and scalability that Impala may lack.