In the field of databases and big data, the architectural debate between “storage-computing integration” and “storage-computing separation” has never ceased. Some people question, “Is storage-computing separation really necessary? Isn’t the performance of local disks sufficient?” The answer is not black and white — the key to technology selection lies in the precise matching of business scenarios and resource requirements. This article takes Apache Doris as an example to analyze the essential differences, advantages and disadvantages, and implementation scenarios of the two architectures.
Storage-Computing Integration vs. Storage-Computing Separation
Storage-Computing Integration: The Tightly-Coupled “All-Rounder”
Definition: Data storage and computing resources are bound to the same node (such as a local disk + server), and local reading and writing are used to reduce network overhead. Typical examples include the early architecture of Hadoop and traditional OLTP databases.