What are the clusters? What are their advantages?

本文深入探讨了数据库集群的概念,包括其组成、创建方式、集群键的作用以及带来的存储和性能优势。通过实例展示了如何定义一个名为workers的集群,包含特定的集群键department,并解释了这种设计如何提高查询效率并节省存储空间。

A cluster contains data from one or more tables. All these tables have one or more columns in common. All the rows from all the tables that share the same cluster key.

Example: cluster named workers with the cluster key column department, a cluster size of 512 bytes, and storage parameter values

CREATE CLUSTER workers
(department NUMBER(4))
SIZE 512
STORAGE (initial 100K next 50K);

Advantages:

  • Clusters can speed up join queries.
  • Storage is saved since storing the field or fields comprising the Cluster Key are stored once instead of multiple times.
  • Clustering does not does not affect the relational model (table schemas).
MapReduce is a programming model and software framework for processing large sets of data in a distributed and parallel manner. It allows for processing of large data sets on clusters of computers using a simple model for parallel processing. In the MapReduce framework, the input data is divided into chunks and each chunk is processed by a separate mapper function. The mapper function processes the input data and generates intermediate key-value pairs. These key-value pairs are then sorted and grouped by key, and passed on to the reducer function. The reducer function then aggregates the intermediate key-value pairs and generates a final output. For example, let's say we have a large data set of customer orders that we want to process using MapReduce. The mapper function will read each order and generate intermediate key-value pairs where the key is the customer ID and the value is the order amount. The intermediate key-value pairs might look like this: ``` customer1: 10.99 customer2: 25.55 customer1: 5.99 customer3: 15.00 customer2: 12.50 ``` These intermediate key-value pairs are then sorted and grouped by key: ``` customer1: [10.99, 5.99] customer2: [25.55, 12.50] customer3: [15.00] ``` The reducer function then aggregates the values for each key and generates a final output: ``` customer1: 16.98 customer2: 38.05 customer3: 15.00 ``` In this example, the MapReduce framework allowed us to process a large data set in a distributed and parallel manner, making it faster and more efficient.
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