Apache Impala vs Apache Kudu: What are the differences?

Introduction

Apache Impala and Apache Kudu are both open-source technologies aimed at improving the performance and efficiency of data processing and analytics. While they can be used together to achieve better results, they serve different purposes and have distinct features.

1. Storage Model: Apache Impala is a massively parallel processing SQL query engine designed for high-performance analytics on structured data. It can process data stored in various formats, including Apache Parquet, Apache Avro, and Apache Kudu. On the other hand, Apache Kudu is specifically built for high-performance storage of structured data, allowing fast analytics and inserts/updates on the same dataset.

2. Data Updates: One key difference between Apache Impala and Apache Kudu is their approach to data updates. Impala allows read and write operations, but it performs best with read-heavy workloads. On the contrary, Apache Kudu is optimized for fast updates and inserts, making it a suitable choice for write-intensive workloads.

3. Data Encoding: In terms of data encoding, Impala uses a columnar format called Apache Parquet, which provides efficient compression and encoding techniques for high-performance analytics. Kudu, on the other hand, utilizes a unique update-friendly data format that enables both efficient storage and update operations.

4. Data Storage: Apache Impala is designed to work with various distributed file systems, including Hadoop Distributed File System (HDFS), Amazon S3, and Hadoop compatible storage systems. On the other hand, Apache Kudu provides its own native storage layer, which offers faster access and performance optimizations specifically tailored for its storage model.

5. Data Consistency: When it comes to data consistency, Impala relies on the underlying storage system for consistency guarantees. In contrast, Apache Kudu guarantees strong consistency for both reads and writes, as it ensures that all replicas of a row are always updated atomically.

6. Data Access Patterns: Impala is primarily optimized for analytical queries that involve aggregations, joins, and complex calculations on large volumes of data. Apache Kudu, on the other hand, excels at serving random and point queries due to its unique storage model, making it well-suited for real-time analytics and interactive workloads.

In summary, Apache Impala is a distributed SQL query engine optimized for high-performance analytics on structured data, while Apache Kudu is a columnar storage engine designed for fast writes and serves random access queries efficiently. Their differences lie in storage models, data updates, data encoding, data storage, data consistency, and data access patterns.