Clickhouse vs Vertica: What are the differences?

Introduction

ClickHouse and Vertica are both columnar database management systems that are designed for high-performance analytics. While they share similarities in terms of providing fast query processing and scalability, they also have distinct differences that set them apart.

1. Architecture: ClickHouse and Vertica have different architectural approaches. ClickHouse is built on a shared-nothing architecture where data is partitioned across a cluster of commodity hardware. On the other hand, Vertica utilizes a shared disk architecture where data is stored on a shared storage system accessible by multiple nodes. This architectural difference affects the way data is distributed, replicated, and processed, leading to variations in performance and fault tolerance.

2. Data Compression: ClickHouse and Vertica employ different techniques for data compression. ClickHouse uses a combination of dictionary and delta compression, along with efficient bit packing and SIMD instructions to achieve high compression ratios. Vertica, on the other hand, utilizes various compression algorithms such as Huffman, Run-Length Encoding (RLE), and delta encoding. These compression techniques impact the storage requirements and query performance of the system.

3. Indexing: ClickHouse and Vertica have different approaches to indexing. ClickHouse relies heavily on using an efficient MergeTree data structure, which is optimized for time-series data and supports granular partitioning and sorting. Vertica, on the other hand, leverages a combination of projection and segmentation to optimize query performance. These indexing strategies impact the speed and efficiency of querying data.

4. Data Distribution: ClickHouse and Vertica handle data distribution differently. ClickHouse uses a replication model where data is divided into parts and replicated across multiple nodes for increased fault tolerance. Vertica, on the other hand, uses a sharding model where data is partitioned based on a predefined key and distributed across different nodes. These data distribution mechanisms have implications on query execution, data access patterns, and fault tolerance.

5. Query Execution Model: ClickHouse and Vertica have different query execution models. ClickHouse employs a vectorized query execution model, where rows of data are processed in a batch-oriented manner to achieve high throughput. Vertica, on the other hand, uses a hybrid query execution model that combines a row-based execution approach with the ability to process multiple rows simultaneously. These execution models affect the performance characteristics of the systems in terms of query latency and throughput.

6. SQL Compatibility: ClickHouse and Vertica differ in terms of SQL compatibility. ClickHouse supports a subset of SQL standards, primarily focusing on analytical workloads and lacking certain advanced features found in traditional SQL implementations. Vertica, on the other hand, provides a more comprehensive SQL implementation that supports advanced features like window functions, user-defined functions (UDFs), and complex SQL constructs. This difference in SQL compatibility may impact the ease of migration and compatibility with existing SQL-based applications.

In summary, ClickHouse and Vertica differ in their architectural approach, compression techniques, indexing strategies, data distribution mechanisms, query execution models, and SQL compatibility. These differences impact various aspects of the systems' performance, scalability, and functionality.