Apache Parquet vs Druid: What are the differences?

Introduction

Apache Parquet and Druid are both columnar data storage formats used for analytics and reporting. While they serve similar purposes, there are several key differences between the two. In this article, we will explore and compare these differences.

1. Data Organization: Apache Parquet stores data in a nested, columnar format which allows for efficient compression and encoding of data. On the other hand, Druid organizes data in a segmented, columnar format, dividing data into segments or shards for parallel processing and faster query performance.

2. Data Ingestion: Apache Parquet requires data to be processed and transformed before it can be loaded into the format. This processing often involves converting data types, optimizing for compression, and partitioning the data. In contrast, Druid supports real-time data ingestion, allowing data to be loaded and indexed immediately as it becomes available.

3. Query Capabilities: Apache Parquet provides a wide range of querying capabilities, including support for complex SQL-like queries, predicate pushdown, and column pruning. It also integrates well with other query engines like Apache Spark and Apache Hive. On the other hand, Druid is specifically designed for fast, real-time OLAP queries, with a focus on aggregations, filtering, and time-series analysis.

4. Scalability: Apache Parquet is highly scalable and can handle large volumes of data, often in the petabyte range. It can be easily distributed across multiple nodes and can be parallelized for efficient processing. Druid, on the other hand, is designed for horizontal scalability, allowing data to be distributed across a cluster of nodes for improved performance and fault tolerance.

5. Data Retention: Apache Parquet is primarily meant for long-term data storage and archiving. It provides efficient compression, reducing storage costs, and can be easily integrated with different storage systems like Hadoop Distributed File System (HDFS) or Amazon S3. In contrast, Druid is designed for real-time analytics and has built-in data expiration and eviction policies. It is optimized for storing and processing recent, time-based data.

6. Data Update and Append: Apache Parquet is a write-once, immutable data format, meaning that data cannot be updated or appended once it is written into the format. If updates or appends are required, the entire dataset needs to be rewritten. In contrast, Druid supports updates and appends to data in real-time. It has mechanisms to handle incremental updates and manages data versions efficiently.

In summary, Apache Parquet is a versatile columnar storage format that excels at long-term data storage and querying, while Druid is specifically designed for real-time analytics and aggregations with a focus on speed and performance.