Tutorial do MongoDB Atlas Data Federation: consultas federadas e $out para AWS S3

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git clone https://github.com/mongodb-developer/data-lake-tutorial.git

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from pymongo import MongoClient
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from datetime import datetime
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client = MongoClient('mongodb+srv://<username>:<password>@m0.lbtrerw.mongodb.net/')
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…

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pip3 install -r requirements.txt
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python3 insert_data.py

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{
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  "databases": [
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    {
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      "name": "test",
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      "collections": [
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        {
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          "name": "orders",
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          "dataSources": [
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            {
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              "storeName": "M0",
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              "database": "test",
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              "collection": "orders"
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            },
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            {
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              "storeName": "cold-data-mongodb",
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              "path": "/*"
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            }
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          ]
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        }
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      ],
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      "views": []
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    }
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  ],
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  "stores": [
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    {
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      "name": "M0",
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      "provider": "atlas",
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      "clusterName": "M0",
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      "projectId": "<your-project-id>"
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    },
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    {
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      "name": "cold-data-mongodb",
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      "provider": "s3",
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      "bucket": "cold-data-mongodb",
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      "prefix": "",
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      "delimiter": "/"
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    }
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  ]
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}

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from datetime import datetime
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from pymongo import MongoClient
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client = MongoClient('<YOUR_FEDERATED_INSTANCE_URI>')
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db = client.get_database('test')
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coll = db.get_collection('orders')
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start_date = datetime(2020, 5, 1)  # May 1st
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end_date = datetime(2020, 6, 1)  # June 1st
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pipeline = [
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    {
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        '$match': {
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            'created': {
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                '$gte': start_date,
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                '$lt': end_date
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            }
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        }
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    },
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    {
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        '$out': {
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            's3': {
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                'bucket': 'cold-data-mongodb',
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                'region': 'eu-west-1',
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                'filename': start_date.isoformat('T', 'milliseconds') + 'Z-' + end_date.isoformat('T', 'milliseconds') + 'Z',
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                'format': {'name': 'json', 'maxFileSize': '200MiB'}
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            }
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        }
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    }
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]
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coll.aggregate(pipeline)
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print('Archive created!')

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python3 archive.py

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from datetime import datetime
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from pymongo import MongoClient
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client = MongoClient('<YOUR_ATLAS_URI>')
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db = client.get_database('test')
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coll = db.get_collection('orders')
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start_date = datetime(2020, 5, 1)  # May 1st
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end_date = datetime(2020, 6, 1)  # June 1st
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query = {
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    'created': {
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        '$gte': start_date,
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        '$lt': end_date
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    }
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}
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result = coll.delete_many(query)
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print('Deleted', result.deleted_count, 'orders.')

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python3 remove.py

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{"_id":{"$numberDouble":"1.0"},"created":{"$date":{"$numberLong":"1590796800000"}},"items":[{"$numberDouble":"1.0"},{"$numberDouble":"3.0"}],"price":{"$numberDouble":"20.0"}}
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{"_id":{"$numberDouble":"2.0"},"created":{"$date":{"$numberLong":"1590883200000"}},"items":[{"$numberDouble":"2.0"},{"$numberDouble":"3.0"}],"price":{"$numberDouble":"25.0"}}

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from pymongo import MongoClient
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client = MongoClient('<YOUR_FEDERATED_INSTANCE_URI>')
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db = client.get_database('test')
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coll = db.get_collection('orders')
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print('All the docs from S3 + Atlas:')
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docs = coll.find()
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for d in docs:
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    print(d)
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pipeline = [
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    {
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        '$group': {
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            '_id': None,
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            'total_price': {
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                '$sum': '$price'
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            }
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        }
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    }, {
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        '$project': {
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            '_id': 0
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        }
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    }
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]
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print('\nI can also run an aggregation.')
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print(coll.aggregate(pipeline).next())

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python3 federated_queries.py

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All the docs from S3 + Atlas:
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{'_id': 1.0, 'created': datetime.datetime(2020, 5, 30, 0, 0), 'items': [1.0, 3.0], 'price': 20.0}
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{'_id': 2.0, 'created': datetime.datetime(2020, 5, 31, 0, 0), 'items': [2.0, 3.0], 'price': 25.0}
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{'_id': 3.0, 'created': datetime.datetime(2020, 6, 1, 0, 0), 'items': [1.0, 3.0], 'price': 20.0}
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{'_id': 4.0, 'created': datetime.datetime(2020, 6, 2, 0, 0), 'items': [1.0, 2.0], 'price': 15.0}
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I can also run an aggregation:
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{'total_price': 80.0}