Читать книгу Official Google Cloud Certified Professional Data Engineer Study Guide - Dan Sullivan - Страница 41

Let’s consider the simple product catalog example again. There are many ways that shoppers might want to search for information about products. If they are looking for a dishwasher, for example, they might want to search based on size or power consumption. When searching for furniture, style and color are important considerations.

{ {’id’: ’123456’, ’product_type’: ’dishwasher’, ’length’: ’24 in’, ’width’: ’34 in’, ’weight’: ’175 lbs’, ’power’: ’1800 watts’ } {’id’:’987654’, ’product_type’: ’chair’, ’weight’: ’15 kg’, ’style’: ’modern’, ’color’: ’brown’ } }

To search efficiently by attributes, document databases allow for indexes. If you use Cloud Datastore, for example, you could create indexes on each of the attributes as well as a combination of attributes. Indexes should be designed to support the way that data is queried. If you expect users to search for chairs by specifying style and color together, then you should create a style and color index. If you expect customers to search for appliances by their power consumption, then you should create an index on power.

Creating a large number of indexes can significantly increase the amount of storage used. In fact, it is not surprising to have total index storage greater than the amount of storage used to store documents. Also, additional indexes can negatively impact performance for insert, update, and delete operations, because the indexes need to be revised to reflect those operations.