Apache Parquet vs RocksDB: What are the differences?

Introduction

Apache Parquet and RocksDB are two different technologies used in the field of data storage and processing. While both serve a similar purpose, they have key differences in terms of their functionality and use cases.

1. Storage Format: Apache Parquet is a columnar storage format that is optimized for use in big data processing systems. It is designed to efficiently store and retrieve large amounts of structured and semi-structured data. On the other hand, RocksDB is a key-value store that is optimized for high performance and low latency. It is typically used for transactional workloads that require fast and random access to data.

2. Data Organization: Parquet organizes data in a columnar format, where each column is stored separately, allowing for efficient compression and pruning of data during query execution. RocksDB, on the other hand, organizes data in key-value pairs, where each key is associated with a value. This allows for fast lookups and updates of individual records.

3. Data Types: Parquet supports a wide range of data types, including primitive types (such as integers, floats, and booleans) as well as complex types (such as arrays and structs). RocksDB, on the other hand, is a schema-less storage engine, which means it can store any type of value, including complex data structures like JSON or binary blobs.

4. Data Access: Parquet is typically used in conjunction with big data processing frameworks like Apache Spark or Apache Hive. It provides efficient data access for analytical workloads, where queries are executed on large datasets in parallel. RocksDB, on the other hand, is often used in embedded systems or as a persistent cache, where fast and low-latency data access is critical.

5. Durability and Fault Tolerance: Parquet is designed to be durable and fault-tolerant, with support for data replication and data recovery mechanisms. It can be used in distributed storage systems to ensure that data is not lost in the event of hardware failures or network partitions. RocksDB, on the other hand, focuses on providing high performance and low latency, with support for ACID transactions and write-ahead logging.

6. Scalability: Parquet is a scalable storage format that can handle large volumes of data across multiple nodes in a distributed system. It can efficiently store and process data in parallel, making it suitable for large-scale analytical workloads. RocksDB, on the other hand, is optimized for single-node deployments and is not designed to scale horizontally across multiple machines.

In Summary, Apache Parquet and RocksDB differ in their storage format, data organization, data types, data access patterns, durability, fault tolerance, and scalability.