Why ClickHouse is better than PostgreSQL for real-time analytics?

ClickHouse is a column-oriented database management system that is designed for real-time analytics, and it has several features that make it well-suited for this type of workload, compared to PostgreSQL:

1. Performance: ClickHouse is optimized for high-performance analytical queries, and it can handle large amounts of data and high query loads. Its column-based storage allows for efficient compression and fast query execution, which can be especially beneficial for real-time analytics.
2. Scalability: ClickHouse is horizontally scalable, meaning that it can handle a large number of concurrent queries and users without a significant drop in performance. It also supports distributed queries and data replication, which allows for high availability and fault tolerance.
3. Data modeling: ClickHouse supports a wide range of data types, including arrays, nested data structures, and JSON. This makes it easier to model complex data sets and perform nested queries.
4. Real-time data ingestion: ClickHouse supports real-time data ingestion and can handle large write loads, which is useful for real-time analytics. It also supports different data loading methods like bulk load, Kafka and real-time streaming.
5. Memory Management: ClickHouse is optimized for in-memory operations. Data is compressed and stored in memory, which allows for faster read and write operations.
6. Cost-effective: ClickHouse is designed to work on commodity hardware and can handle large amounts of data with minimal hardware requirements. This makes it a cost-effective solution for real-time analytics.

While PostgreSQL is a powerful and versatile database management system, it may not be as well-suited for real-time analytics workloads as ClickHouse. Depending on the specific use case, ClickHouse may offer better performance, scalability, and data modeling capabilities for real-time analytics.