Apache Parquet vs Clickhouse: What are the differences?

Introduction

In this Markdown document, we will explore the key differences between Apache Parquet and ClickHouse, two popular technologies used for data storage and processing.

1. Performance: Apache Parquet is a columnar storage format that offers excellent performance for analytical processing. It uses a compression algorithm that reduces both disk space and I/O operations, resulting in faster query execution. ClickHouse, on the other hand, is a columnar database management system that is specifically designed for high-performance analytics. It leverages various optimizations like vectorized query execution and efficient data compression to achieve exceptional processing speeds.

2. Data Compression: Apache Parquet employs a compact, efficient compression scheme that minimizes the storage space required for data representation. It supports a variety of compression codecs such as Snappy, Gzip, and LZO, allowing users to choose the most suitable option for their specific use case. ClickHouse also provides powerful compression capabilities, using a combination of various techniques like delta coding, LZ4, and LZ77. This results in significant reduction in data size and consequently faster query processing.

3. Supported Data Types: Apache Parquet supports a wide range of data types, including primitive types like integers, floating-point numbers, booleans, and strings, as well as complex types like arrays, maps, and structs. ClickHouse, on the other hand, offers support for a similar set of data types, with the addition of several specialized types like UUID, IPv4, and IPv6. This comprehensive support for diverse data types makes both Parquet and ClickHouse suitable for handling complex datasets.

4. Query Language: Apache Parquet does not have its own query language. It is typically used in conjunction with other tools and frameworks like Apache Hive or Apache Spark, which provide SQL-like query capabilities. On the other hand, ClickHouse has its own query language called ClickHouse SQL. This language is specifically designed for analytical queries and provides a rich set of SQL features, including window functions, aggregate functions, and advanced join capabilities.

5. Data Partitioning: Apache Parquet supports partitioning of data based on one or more columns. Partitioning allows for efficient data pruning and improves query performance by minimizing the amount of data read from disk. ClickHouse also supports partitioning, but it takes a different approach. It uses a concept called 'sharding' where the data is divided across multiple physical servers. This allows for parallel processing of queries and enables horizontal scalability.

6. Data Replication and High Availability: ClickHouse supports data replication and high availability out of the box. It uses a replication mechanism called 'replicas' where multiple copies of data are stored across different servers. This ensures data durability and fault tolerance in case of hardware failures. Apache Parquet, on the other hand, is simply a file format and does not provide built-in replication or high availability features. It is typically used in conjunction with distributed storage systems like Apache Hadoop or distributed file systems like Apache HDFS.

In Summary, Apache Parquet and ClickHouse differ in terms of performance, data compression, supported data types, query language, data partitioning, and data replication/high availability. These differences make them suitable for different use cases, with Apache Parquet excelling in data storage and inter-operability, while ClickHouse is specifically designed for high-performance analytics.