IBM Cloudant Aggregated Query

IBM Cloudant is a Distributed database that can store and process large-scale data in a cloud environment. It supports aggregate queries and uses the MapReduce function to generate results from different data views. The following are several common aggregation queries in Cloudant: 1. MapReduce: -Table structure and data examples: Assuming there is a collection of documents with name and age fields, as follows: { "_id": "1", "name": "John", "age": 25 }, { "_id": "2", "name": "Alice", "age": 30 }, { "_id": "3", "name": "Bob", "age": 27 } -Query example: Write Map and Reduce functions to calculate the number of people in different age groups. //Map function function (doc) { emit(doc.age, 1); } //Reduce function function (keys, values, rereduce) { return sum(values); } After executing the query, a result grouped by different age groups will be returned, such as: { "25": 1, "27": 1, "30": 1 } 2. Mango query: Cloudant also supports Mango Query language, which provides a structured query syntax similar to SQL for querying and filtering document collections. Mango queries support various aggregation functions, such as $sum, $avg, $max, $min, etc. -Table structure and data example: Same as the above example. -Query example: Calculate the average age of all people. //Mango Query { "selector": { "age": { "$gt": 0 } }, "fields": ["name", "age"], "aggregator": [ {"$group": { "_id": null, "averageAge": {"$avg": "$age"} }} ] } After executing the query, a result containing the average age will be returned, such as: { "docs": [ { "averageAge": 27.333333333333332, "_id": null } ], "bookmark": "g1AAA...", "execution_stats": { ... } } 3. Cloudant Query: Cloudant Query is a simplified method of writing queries using JavaScript. It provides flexible query syntax and can be optimized using MapReduce for queries. -Table structure and data example: Same as the above example. -Query example: Calculate the number of people in different age groups. // Cloudant Query { "selector": { "age": { "$gt": 0 } }, "fields": ["age"], "reduce": "_count", "group": "age" } After executing the query, a result grouped by different age groups will be returned, such as: { "rows": [ { "key": 25, "value": 1 }, { "key": 27, "value": 1 }, { "key": 30, "value": 1 } ], "total_rows": 3, "bookmark": "g1AAA...", "execution_stats": { ... } } The above are some examples of aggregation queries supported in IBM Cloudant. Different Query language can be used to perform aggregation operations according to specific business requirements.