Clickhouse vs Druid: What are the differences?

Introduction

ClickHouse and Druid are both powerful analytical databases that are designed to handle large volumes of data and provide fast query performance. While they share some similarities, there are several key differences between the two.

1. Architecture: ClickHouse is a columnar database that is optimized for online analytical processing (OLAP) workloads. It uses a shared-nothing architecture, where the data is spread across multiple nodes and each node processes data independently. On the other hand, Druid is a distributed, column-oriented OLAP database that uses a shared-disk architecture. It separates storage and compute, allowing for horizontal scalability and efficient data ingestion.

2. Data Model: ClickHouse supports a traditional relational data model with tables, columns, and rows. It uses a SQL-like query language for data retrieval and manipulation. Druid, on the other hand, uses a multidimensional data model with dimensions, measures, and hierarchies. It is optimized for time series data and provides a JSON-based query language called Druid Query Language (DSL).

3. Data Ingestion: ClickHouse supports batch and real-time data ingestion through various methods such as file uploads, replication, Kafka integration, and more. It also provides built-in support for merging and transforming data during ingestion. Druid, on the other hand, is designed for real-time data streaming and supports high-speed data ingestion from various sources like Kafka, AWS Kinesis, and more. It also supports batch ingestion for historical data.

4. Scalability: ClickHouse can scale horizontally across multiple nodes by adding more servers to the cluster. It provides automatic sharding and distribution of data across nodes for efficient data processing. Druid, on the other hand, is designed to handle large-scale data sets and can scale horizontally by adding more nodes to the cluster. It uses a distributed storage system for efficient data storage and retrieval.

5. Query Performance: ClickHouse is known for its fast query performance, especially for analytical queries that involve aggregations and complex calculations. It can handle millions of queries per second and provides various optimizations like data compression and indexing to improve query speed. Druid, on the other hand, is designed for real-time analytics and provides low latency queries on large data sets. It uses advanced caching techniques and indexing structures to optimize query performance.

6. Use Cases: ClickHouse is commonly used for OLAP workloads, ad-hoc analytics, and business intelligence applications. It is popular in industries like e-commerce, finance, and telecommunications. Druid, on the other hand, is often used for real-time analytics, monitoring, and visualization of time series data. It is used in applications like user behavior tracking, log analytics, and IoT analytics.

In summary, ClickHouse and Druid differ in their architecture, data model, data ingestion methods, scalability, query performance, and use cases. Each database has its own strengths and could be chosen based on specific requirements and use case scenarios.