Pymongo tutorial (2) aggregate operations

Common aggregation operations in MongoDB include aggregation, mapreduce, and group. First, add some test data: db. things. insert ({x: 1, tags: [dog, cat]}) db. things. insert ({x: 2, tags: [cat]}) db. things. insert ({x: 2, tags: [mouse, cat, dog]})

Common aggregation operations in MongoDB include aggregation, map/reduce, and group. First, add some test data: db. things. insert ({"x": 1, "tags": ["dog", "cat"]}) db. things. insert ({"x": 2, "tags": ["cat"]}) db. things. insert ({"x": 2, "tags": ["mouse", "cat", "dog"]})

Common aggregation operations in MongoDB include aggregation, map/reduce, and group.

First, add some test data:

db.things.insert({"x": 1, "tags": ["dog", "cat"]})db.things.insert({"x": 2, "tags": ["cat"]})db.things.insert({"x": 2, "tags": ["mouse", "cat", "dog"]})db.things.insert({"x": 3, "tags": []})

Aggregation

The following example shows how many times each value in the tags field appears.

from bson.son import SONdb.things.aggregate([    {"$unwind": "$tags"},    {"$group": {"_id": "$tags", "count": {"$sum": 1}}},    {"$sort": SON([("count", -1), ("_id", -1)])}]){'ok': 1.0, 'result': [{'count': 3, '_id': 'cat'}, {'count': 2, '_id': 'dog'}, {'count': 1, '_id': 'mouse'}]}

Note: The aggregate operation requires that the server program version 2.1.0 or later be used. The PyMongo driver is later than 2.3.

Map/Reduce

The preceding operations can also be completed using Map/Reduce.

from bson.code import Codemapper = Code("""    function () {      this.tags.forEach(function(z) {        emit(z, 1);      });    }""")reducer = Code("""    function (key, values) {      var total = 0;      for (var i = 0; i < values.length; i++) {        total += values[i];      }      return total;    }""")result = db.things.map_reduce(mapper, reducer, "myresults")for doc in result.find():    print(doc){u'_id': u'cat', u'value': 3.0}{u'_id': u'dog', u'value': 2.0}{u'_id': u'mouse', u'value': 1.0}

Map and reduce are both javascript Functions. The map_reduce method saves the statistical results to a temporary dataset.

Group

The group operation is similar to the SQL GROUP BY operation and easier than Map/Reduce operation.

reducer = Code("""    function(obj, prev){      prev.count++;    }""")results = db.things.group(key={"x":1}, condition={}, initial={"count": 0}, reduce=reducer)for doc in results:    print(doc){'x': 1.0, 'count': 1.0}{'x': 2.0, 'count': 2.0}{'x': 3.0, 'count': 1.0}

Note: The group operation is not supported in the MongoDB cluster environment. You can use aggregation or map/reduce instead.

Complete MongoDB aggregation documentation: http://docs.mongodb.org/manual/aggregation/

Original article address: pymongo tutorial (2) -- aggregate operation. Thank you for sharing it with the original author.