1. Architecture: QuestDB is a high-performance database designed for time-series data, utilizing a specialized column-oriented storage engine for efficient data storage and retrieval. In contrast, Vitess is a sharding middleware for MySQL databases, allowing horizontal scaling by distributing data across multiple MySQL instances.
2. Use Case: QuestDB is ideal for applications that require high-speed data ingestion and complex analytical queries on large volumes of time-series data, such as financial services and IoT applications. Vitess is suited for applications that require horizontal scaling and high availability in MySQL databases, often used in web applications and e-commerce platforms.
3. Query Language Support: QuestDB supports SQL queries leveraging a subset of PostgreSQL syntax with extensions for time-series data manipulation, making it easier for users familiar with SQL to work with the database. Vitess, on the other hand, is compatible with MySQL and supports SQL queries that are familiar to users of MySQL databases.
4. Data Sharding: QuestDB does not natively support sharding, as it is designed for single-node deployments with the focus on high performance and low latency for time-series data. Vitess, on the contrary, provides automatic sharding capabilities for MySQL databases, enabling horizontal scaling to handle growing data volumes.
5. Community and Ecosystem: QuestDB has a growing open-source community but is relatively newer compared to Vitess, which has been utilized by several large companies like YouTube and Slack, leading to a more extensive ecosystem of tools, plugins, and support resources available for users.
6. Consistency vs. Performance: QuestDB prioritizes performance by leveraging its column-oriented storage engine for optimized query execution speed, while Vitess emphasizes consistency by providing features like distributed transactions and strict consistency to ensure data integrity across sharded instances.

In Summary, the key differences between QuestDB and Vitess lie in their architecture, use case, query language support, data sharding capabilities, community ecosystem, and the balance between consistency and performance.