Document Validation for Polymorphic Collections

Andrew Morgan6 min read • Published Aug 10, 2023 • Updated Oct 01, 2024

MongoDB

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In data modeling design reviews with customers, I often propose a schema where different documents in the same collection contain different types of data. This makes it efficient to fetch related documents in a single, indexed query. MongoDB's flexible schema is great for optimizing workloads in this way, but people can be concerned about losing control of what applications write to these collections.

Customers are often concerned about ensuring that only correctly formatted documents make it into a collection, and so I explain MongoDB's schema validation feature. The question then comes: "How does that work with a polymorphic/single-collection schema?" This post is intended to answer that question — and it's simpler than you might think.

The banking application and its data

The application I'm working on manages customer and account details. There's a many-to-many relationship between customers and accounts. The app needs to be able to efficiently query customer data based on the customer id, and account data based on either the id of its customer or the account id.

Here's an example of customer and account documents where my wife and I share a checking account but each have our own savings account:

1 {
2     "_id": "kjfgjebgjfbkjb",
3     "customerId": "CUST-123456789",
4     "docType": "customer",
5     "name": {
6         "title": "Mr",
7         "first": "Andrew",
8         "middle": "James",
9         "last": "Morgan"
10     },
11     "address": {
12         "street1": "240 Blackfriars Rd",
13         "city": "London",
14         "postCode": "SE1 8NW",
15         "country": "UK"
16     },
17     "customerSince": ISODate("2005-05-20")
18 }
19 
20 {
21     "_id": "jnafjkkbEFejfleLJ",
22     "customerId": "CUST-987654321",
23     "docType": "customer",
24     "name": {
25         "title": "Mrs",
26         "first": "Anne",
27         "last": "Morgan"
28     },
29     "address": {
30         "street1": "240 Blackfriars Rd",
31         "city": "London",
32         "postCode": "SE1 8NW",
33         "country": "UK"
34     },
35     "customerSince": ISODate("2003-12-01")
36 }
37 
38 {
39     "_id": "dksfmkpGJPowefjdfhs",
40     "accountNumber": "ACC1000000654",
41     "docType": "account",
42     "accountType": "checking",
43     "customerId": [
44         "CUST-123456789",
45         "CUST-987654321"
46     ],
47     "dateOpened": ISODate("2003-12-01"),
48     "balance": NumberDecimal("5067.65")
49 }
50 
51 {
52     "_id": "kliwiiejeqydioepwj",
53     "accountNumber": "ACC1000000432",
54     "docType": "account",
55     "accountType": "savings",
56     "customerId": [
57         "CUST-123456789"
58     ],
59     "dateOpened": ISODate("2005-10-28"),
60     "balance": NumberDecimal("10341.21")
61 }
62 
63 {
64     "_id": "djahspihhfheiphfipewe",
65     "accountNumber": "ACC1000000890",
66     "docType": "account",
67     "accountType": "savings",
68     "customerId": [
69         "CUST-987654321"
70     ],
71     "dateOpened": ISODate("2003-12-15"),
72     "balance": NumberDecimal("10341.89")
73 }

As an aside, these are the indexes I added to make those frequent queries I referred to more efficient:

1 const indexKeys1 = { accountNumber: 1 };
2 const indexKeys2 = {  customerId: 1, accountType: 1 };
3 const indexOptions1 = { partialFilterExpression: { docType: 'account' }};
4 const indexOptions2 = { partialFilterExpression: { docType: 'customer' }};
5 
6 db.getCollection(collection).createIndex(indexKeys1, indexOptions1);
7 db.getCollection(collection).createIndex(indexKeys2, indexOptions2);

Adding schema validation

To quote the docs…

Schema validation lets you create validation rules for your fields, such as allowed data types and value ranges.

MongoDB uses a flexible schema model, which means that documents in a collection do not need to have the same fields or data types by default. Once you've established an application schema, you can use schema validation to ensure there are no unintended schema changes or improper data types.

The validation rules are pretty simple to set up, and tools like Hackolade can make it simpler still — even reverse-engineering your existing documents.

It's simple to imagine setting up a JSON schema validation rule for a collection where all documents share the same attributes and types. But what about polymorphic collections? Even in polymorphic collections, there is structure to the documents. Fortunately, the syntax for setting up the validation rules allows for the required optionality.

I have two different types of documents that I want to store in my Accounts collection — customer and account. I included a docType attribute in each document to identify which type of entity it represents.

I start by creating a JSON schema definition for each type of document:

1 const customerSchema = {
2     required: ["docType", "customerId", "name", "customerSince"],
3     properties: {
4         docType: { enum: ["customer"] },
5         customerId: { bsonType: "string"},
6         name: {
7             bsonType: "object",
8             required: ["first", "last"],
9             properties: {
10                 title: { enum: ["Mr", "Mrs", "Ms", "Dr"]},
11                 first: { bsonType: "string" },
12                 middle: { bsonType: "string" },
13                 last: { bsonType: "string" }
14             }
15         },
16         address: {
17             bsonType: "object",
18             required: ["street1", "city", "postCode", "country"],
19             properties: {
20                 street1: { bsonType: "string" },
21                 street2: { bsonType: "string" },
22                 postCode: { bsonType: "string" },
23                 country: { bsonType: "string" }  
24             }
25         },
26         customerSince: {
27             bsonType: "date"
28         }
29     }
30 };
31 
32 const accountSchema = {
33     required: ["docType", "accountNumber", "accountType", "customerId", "dateOpened", "balance"],
34     properties: {
35         docType: { enum: ["account"] },
36         accountNumber: { bsonType: "string" },
37         accountType: { enum: ["checking", "savings", "mortgage", "loan"] },
38         customerId: { bsonType: "array" },
39         dateOpened: { bsonType: "date" },
40         balance: { bsonType: "decimal" }
41     }
42 };

Those definitions define what attributes should be in the document and what types they should take. Note that fields can be optional — such as name.middle in the customer schema.

It's then a simple matter of using the oneOf JSON schema operator to allow documents that match either of the two schema:

1 const schemaValidation = {
2     $jsonSchema: { oneOf: [ customerSchema, accountSchema ] }
3 };
4 
5 db.createCollection(collection, {validator: schemaValidation});

I wanted to go a stage further and add some extra, semantic validations:

For customer documents, the customerSince value can't be any earlier than the current time.
For account documents, the dateOpened value can't be any earlier than the current time.
For savings accounts, the balance can't fall below zero.

These documents represents these checks:

1 const badCustomer = {
2     "$expr": { "$gt": ["$customerSince", "$$NOW"] }
3 };
4 
5 const badAccount = {
6     $or: [ 
7         {
8             accountType: "savings",
9             balance: { $lt: 0}
10         },
11         {
12             "$expr": { "$gt": ["$dateOpened", "$$NOW"]}
13         }
14     ]
15 };
16 
17 const schemaValidation = {
18     "$and": [
19         { $jsonSchema: { oneOf: [ customerSchema, accountSchema ] }},
20         { $nor: [
21             badCustomer,
22             badAccount
23          ]
24         }
25     ]
26 };

I updated the collection validation rules to include these new checks:

1 const schemaValidation = {
2     "$and": [
3         { $jsonSchema: { oneOf: [ customerSchema, accountSchema ] }},
4         { $nor: [
5             badCustomer,
6             badAccount
7          ]
8         }
9     ]
10 };
11 
12 db.createCollection(collection, {validator: schemaValidation} );

If you want to recreate this in your own MongoDB database, then just paste this into your MongoDB playground in VS Code:

1 const cust1 = {
2     "_id": "kjfgjebgjfbkjb",
3     "customerId": "CUST-123456789",
4     "docType": "customer",
5     "name": {
6         "title": "Mr",
7         "first": "Andrew",
8         "middle": "James",
9         "last": "Morgan"
10     },
11     "address": {
12         "street1": "240 Blackfriars Rd",
13         "city": "London",
14         "postCode": "SE1 8NW",
15         "country": "UK"
16     },
17     "customerSince": ISODate("2005-05-20")
18 }
19 
20 const cust2 = {
21     "_id": "jnafjkkbEFejfleLJ",
22     "customerId": "CUST-987654321",
23     "docType": "customer",
24     "name": {
25         "title": "Mrs",
26         "first": "Anne",
27         "last": "Morgan"
28     },
29     "address": {
30         "street1": "240 Blackfriars Rd",
31         "city": "London",
32         "postCode": "SE1 8NW",
33         "country": "UK"
34     },
35     "customerSince": ISODate("2003-12-01")
36 }
37 
38 const futureCustomer = {
39     "_id": "nansfanjnDjknje",
40     "customerId": "CUST-666666666",
41     "docType": "customer",
42     "name": {
43         "title": "Mr",
44         "first": "Wrong",
45         "last": "Un"
46     },
47     "address": {
48         "street1": "240 Blackfriars Rd",
49         "city": "London",
50         "postCode": "SE1 8NW",
51         "country": "UK"
52     },
53     "customerSince": ISODate("2025-05-20")
54 }
55 
56 const acc1 = {
57     "_id": "dksfmkpGJPowefjdfhs",
58     "accountNumber": "ACC1000000654",
59     "docType": "account",
60     "accountType": "checking",
61     "customerId": [
62         "CUST-123456789",
63         "CUST-987654321"
64     ],
65     "dateOpened": ISODate("2003-12-01"),
66     "balance": NumberDecimal("5067.65")
67 }
68 
69 const acc2 = {
70     "_id": "kliwiiejeqydioepwj",
71     "accountNumber": "ACC1000000432",
72     "docType": "account",
73     "accountType": "savings",
74     "customerId": [
75         "CUST-123456789"
76     ],
77     "dateOpened": ISODate("2005-10-28"),
78     "balance": NumberDecimal("10341.21")
79 }
80 
81 const acc3 = {
82     "_id": "djahspihhfheiphfipewe",
83     "accountNumber": "ACC1000000890",
84     "docType": "account",
85     "accountType": "savings",
86     "customerId": [
87         "CUST-987654321"
88     ],
89     "dateOpened": ISODate("2003-12-15"),
90     "balance": NumberDecimal("10341.89")
91 }
92 
93 const futureAccount = {
94     "_id": "kljkdfgjkdsgjklgjdfgkl",
95     "accountNumber": "ACC1000000999",
96     "docType": "account",
97     "accountType": "savings",
98     "customerId": [
99         "CUST-987654333"
100     ],
101     "dateOpened": ISODate("2030-12-15"),
102     "balance": NumberDecimal("10341.89")
103 }
104 
105 const negativeSavings = {
106     "_id": "shkjahsjdkhHK",
107     "accountNumber": "ACC1000000666",
108     "docType": "account",
109     "accountType": "savings",
110     "customerId": [
111         "CUST-9837462376"
112     ],
113     "dateOpened": ISODate("2005-10-28"),
114     "balance": NumberDecimal("-10341.21")
115 }
116 
117 const indexKeys1 = { accountNumber: 1 }
118 const indexKeys2 = {  customerId: 1, accountType: 1 } 
119 const indexOptions1 = { partialFilterExpression: { docType: 'account' }}
120 const indexOptions2 = { partialFilterExpression: { docType: 'customer' }}
121 
122 const customerSchema = {
123     required: ["docType", "customerId", "name", "customerSince"],
124     properties: {
125         docType: { enum: ["customer"] },
126         customerId: { bsonType: "string"},
127         name: {
128             bsonType: "object",
129             required: ["first", "last"],
130             properties: {
131                 title: { enum: ["Mr", "Mrs", "Ms", "Dr"]},
132                 first: { bsonType: "string" },
133                 middle: { bsonType: "string" },
134                 last: { bsonType: "string" }
135             }
136         },
137         address: {
138             bsonType: "object",
139             required: ["street1", "city", "postCode", "country"],
140             properties: {
141                 street1: { bsonType: "string" },
142                 street2: { bsonType: "string" },
143                 postCode: { bsonType: "string" },
144                 country: { bsonType: "string" }  
145             }
146         },
147         customerSince: {
148             bsonType: "date"
149         }
150     }
151 }
152 
153 const accountSchema = {
154     required: ["docType", "accountNumber", "accountType", "customerId", "dateOpened", "balance"],
155     properties: {
156         docType: { enum: ["account"] },
157         accountNumber: { bsonType: "string" },
158         accountType: { enum: ["checking", "savings", "mortgage", "loan"] },
159         customerId: { bsonType: "array" },
160         dateOpened: { bsonType: "date" },
161         balance: { bsonType: "decimal" }
162     }
163 }
164 
165 const badCustomer = {
166     "$expr": { "$gt": ["$customerSince", "$$NOW"] }
167 }
168 
169 const badAccount = {
170     $or: [ 
171         {
172             accountType: "savings",
173             balance: { $lt: 0}
174         },
175         {
176             "$expr": { "$gt": ["$dateOpened", "$$NOW"]}
177         }
178     ]
179 }
180 
181 const schemaValidation = {
182     "$and": [
183         { $jsonSchema: { oneOf: [ customerSchema, accountSchema ] }},
184         { $nor: [
185             badCustomer,
186             badAccount
187          ]
188         }
189     ]
190 }
191 
192 const database = 'MongoBank';
193 const collection = 'Accounts';
194 
195 use(database);
196 db.getCollection(collection).drop();
197 db.createCollection(collection, {validator: schemaValidation} )
198 db.getCollection(collection).replaceOne({"_id": cust1._id}, cust1, {upsert: true});
199 db.getCollection(collection).replaceOne({"_id": cust2._id}, cust2, {upsert: true});
200 db.getCollection(collection).replaceOne({"_id": acc1._id}, acc1, {upsert: true});
201 db.getCollection(collection).replaceOne({"_id": acc2._id}, acc2, {upsert: true});
202 db.getCollection(collection).replaceOne({"_id": acc3._id}, acc3, {upsert: true});
203 
204 // The following 3 operations should fail
205 
206 db.getCollection(collection).replaceOne({"_id": negativeSavings._id}, negativeSavings, {upsert: true});
207 db.getCollection(collection).replaceOne({"_id": futureCustomer._id}, futureCustomer, {upsert: true});
208 db.getCollection(collection).replaceOne({"_id": futureAccount._id}, futureAccount, {upsert: true});
209 
210 db.getCollection(collection).dropIndexes();
211 db.getCollection(collection).createIndex(indexKeys1, indexOptions1);
212 db.getCollection(collection).createIndex(indexKeys2, indexOptions2);

Conclusion

I hope that this short article has shown how easy it is to use schema validations with MongoDB's polymorphic collections and single-collection design pattern.

I didn't go into much detail about why I chose the data model used in this example. If you want to know more (and you should!), then here are some great resources on data modeling with MongoDB:

Daniel Coupal and Ken Alger’s excellent series of blog posts on MongoDB schema patterns
Daniel Coupal and Lauren Schaefer’s equally excellent series of blog posts on MongoDB anti-patterns
MongoDB University Course, MongoDB Data Modeling Path

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The banking application and its data
Adding schema validation
Conclusion

MongoDB

Document Validation for Polymorphic Collections

The banking application and its data

Adding schema validation

Conclusion

Related

Optimizing $lookup Performance Using the Power of Indexing

Introduction to the MongoDB Aggregation Framework

Storing Large Objects and Files in MongoDB

Subscribe to MongoDB Change Streams Via WebSockets

Table of Contents

1	{
2	"_id": "kjfgjebgjfbkjb",
3	"customerId": "CUST-123456789",
4	"docType": "customer",
5	"name": {
6	"title": "Mr",
7	"first": "Andrew",
8	"middle": "James",
9	"last": "Morgan"
10	},
11	"address": {
12	"street1": "240 Blackfriars Rd",
13	"city": "London",
14	"postCode": "SE1 8NW",
15	"country": "UK"
16	},
17	"customerSince": ISODate("2005-05-20")
18	}
19
20	{
21	"_id": "jnafjkkbEFejfleLJ",
22	"customerId": "CUST-987654321",
23	"docType": "customer",
24	"name": {
25	"title": "Mrs",
26	"first": "Anne",
27	"last": "Morgan"
28	},
29	"address": {
30	"street1": "240 Blackfriars Rd",
31	"city": "London",
32	"postCode": "SE1 8NW",
33	"country": "UK"
34	},
35	"customerSince": ISODate("2003-12-01")
36	}
37
38	{
39	"_id": "dksfmkpGJPowefjdfhs",
40	"accountNumber": "ACC1000000654",
41	"docType": "account",
42	"accountType": "checking",
43	"customerId": [
44	"CUST-123456789",
45	"CUST-987654321"
46	],
47	"dateOpened": ISODate("2003-12-01"),
48	"balance": NumberDecimal("5067.65")
49	}
50
51	{
52	"_id": "kliwiiejeqydioepwj",
53	"accountNumber": "ACC1000000432",
54	"docType": "account",
55	"accountType": "savings",
56	"customerId": [
57	"CUST-123456789"
58	],
59	"dateOpened": ISODate("2005-10-28"),
60	"balance": NumberDecimal("10341.21")
61	}
62
63	{
64	"_id": "djahspihhfheiphfipewe",
65	"accountNumber": "ACC1000000890",
66	"docType": "account",
67	"accountType": "savings",
68	"customerId": [
69	"CUST-987654321"
70	],
71	"dateOpened": ISODate("2003-12-15"),
72	"balance": NumberDecimal("10341.89")
73	}

1	const indexKeys1 = { accountNumber: 1 };
2	const indexKeys2 = { customerId: 1, accountType: 1 };
3	const indexOptions1 = { partialFilterExpression: { docType: 'account' }};
4	const indexOptions2 = { partialFilterExpression: { docType: 'customer' }};
5
6	db.getCollection(collection).createIndex(indexKeys1, indexOptions1);
7	db.getCollection(collection).createIndex(indexKeys2, indexOptions2);

1	const customerSchema = {
2	required: ["docType", "customerId", "name", "customerSince"],
3	properties: {
4	docType: { enum: ["customer"] },
5	customerId: { bsonType: "string"},
6	name: {
7	bsonType: "object",
8	required: ["first", "last"],
9	properties: {
10	title: { enum: ["Mr", "Mrs", "Ms", "Dr"]},
11	first: { bsonType: "string" },
12	middle: { bsonType: "string" },
13	last: { bsonType: "string" }
14	}
15	},
16	address: {
17	bsonType: "object",
18	required: ["street1", "city", "postCode", "country"],
19	properties: {
20	street1: { bsonType: "string" },
21	street2: { bsonType: "string" },
22	postCode: { bsonType: "string" },
23	country: { bsonType: "string" }
24	}
25	},
26	customerSince: {
27	bsonType: "date"
28	}
29	}
30	};
31
32	const accountSchema = {
33	required: ["docType", "accountNumber", "accountType", "customerId", "dateOpened", "balance"],
34	properties: {
35	docType: { enum: ["account"] },
36	accountNumber: { bsonType: "string" },
37	accountType: { enum: ["checking", "savings", "mortgage", "loan"] },
38	customerId: { bsonType: "array" },
39	dateOpened: { bsonType: "date" },
40	balance: { bsonType: "decimal" }
41	}
42	};

1	const schemaValidation = {
2	$jsonSchema: { oneOf: [ customerSchema, accountSchema ] }
3	};
4
5	db.createCollection(collection, {validator: schemaValidation});

1	const badCustomer = {
2	"$expr": { "$gt": ["$customerSince", "$$NOW"] }
3	};
4
5	const badAccount = {
6	$or: [
7	{
8	accountType: "savings",
9	balance: { $lt: 0}
10	},
11	{
12	"$expr": { "$gt": ["$dateOpened", "$$NOW"]}
13	}
14	]
15	};
16
17	const schemaValidation = {
18	"$and": [
19	{ $jsonSchema: { oneOf: [ customerSchema, accountSchema ] }},
20	{ $nor: [
21	badCustomer,
22	badAccount
23	]
24	}
25	]
26	};

1	const cust1 = {
2	"_id": "kjfgjebgjfbkjb",
3	"customerId": "CUST-123456789",
4	"docType": "customer",
5	"name": {
6	"title": "Mr",
7	"first": "Andrew",
8	"middle": "James",
9	"last": "Morgan"
10	},
11	"address": {
12	"street1": "240 Blackfriars Rd",
13	"city": "London",
14	"postCode": "SE1 8NW",
15	"country": "UK"
16	},
17	"customerSince": ISODate("2005-05-20")
18	}
19
20	const cust2 = {
21	"_id": "jnafjkkbEFejfleLJ",
22	"customerId": "CUST-987654321",
23	"docType": "customer",
24	"name": {
25	"title": "Mrs",
26	"first": "Anne",
27	"last": "Morgan"
28	},
29	"address": {
30	"street1": "240 Blackfriars Rd",
31	"city": "London",
32	"postCode": "SE1 8NW",
33	"country": "UK"
34	},
35	"customerSince": ISODate("2003-12-01")
36	}
37
38	const futureCustomer = {
39	"_id": "nansfanjnDjknje",
40	"customerId": "CUST-666666666",
41	"docType": "customer",
42	"name": {
43	"title": "Mr",
44	"first": "Wrong",
45	"last": "Un"
46	},
47	"address": {
48	"street1": "240 Blackfriars Rd",
49	"city": "London",
50	"postCode": "SE1 8NW",
51	"country": "UK"
52	},
53	"customerSince": ISODate("2025-05-20")
54	}
55
56	const acc1 = {
57	"_id": "dksfmkpGJPowefjdfhs",
58	"accountNumber": "ACC1000000654",
59	"docType": "account",
60	"accountType": "checking",
61	"customerId": [
62	"CUST-123456789",
63	"CUST-987654321"
64	],
65	"dateOpened": ISODate("2003-12-01"),
66	"balance": NumberDecimal("5067.65")
67	}
68
69	const acc2 = {
70	"_id": "kliwiiejeqydioepwj",
71	"accountNumber": "ACC1000000432",
72	"docType": "account",
73	"accountType": "savings",
74	"customerId": [
75	"CUST-123456789"
76	],
77	"dateOpened": ISODate("2005-10-28"),
78	"balance": NumberDecimal("10341.21")
79	}
80
81	const acc3 = {
82	"_id": "djahspihhfheiphfipewe",
83	"accountNumber": "ACC1000000890",
84	"docType": "account",
85	"accountType": "savings",
86	"customerId": [
87	"CUST-987654321"
88	],
89	"dateOpened": ISODate("2003-12-15"),
90	"balance": NumberDecimal("10341.89")
91	}
92
93	const futureAccount = {
94	"_id": "kljkdfgjkdsgjklgjdfgkl",
95	"accountNumber": "ACC1000000999",
96	"docType": "account",
97	"accountType": "savings",
98	"customerId": [
99	"CUST-987654333"
100	],
101	"dateOpened": ISODate("2030-12-15"),
102	"balance": NumberDecimal("10341.89")
103	}
104
105	const negativeSavings = {
106	"_id": "shkjahsjdkhHK",
107	"accountNumber": "ACC1000000666",
108	"docType": "account",
109	"accountType": "savings",
110	"customerId": [
111	"CUST-9837462376"
112	],
113	"dateOpened": ISODate("2005-10-28"),
114	"balance": NumberDecimal("-10341.21")
115	}
116
117	const indexKeys1 = { accountNumber: 1 }
118	const indexKeys2 = { customerId: 1, accountType: 1 }
119	const indexOptions1 = { partialFilterExpression: { docType: 'account' }}
120	const indexOptions2 = { partialFilterExpression: { docType: 'customer' }}
121
122	const customerSchema = {
123	required: ["docType", "customerId", "name", "customerSince"],
124	properties: {
125	docType: { enum: ["customer"] },
126	customerId: { bsonType: "string"},
127	name: {
128	bsonType: "object",
129	required: ["first", "last"],
130	properties: {
131	title: { enum: ["Mr", "Mrs", "Ms", "Dr"]},
132	first: { bsonType: "string" },
133	middle: { bsonType: "string" },
134	last: { bsonType: "string" }
135	}
136	},
137	address: {
138	bsonType: "object",
139	required: ["street1", "city", "postCode", "country"],
140	properties: {
141	street1: { bsonType: "string" },
142	street2: { bsonType: "string" },
143	postCode: { bsonType: "string" },
144	country: { bsonType: "string" }
145	}
146	},
147	customerSince: {
148	bsonType: "date"
149	}
150	}
151	}
152
153	const accountSchema = {
154	required: ["docType", "accountNumber", "accountType", "customerId", "dateOpened", "balance"],
155	properties: {
156	docType: { enum: ["account"] },
157	accountNumber: { bsonType: "string" },
158	accountType: { enum: ["checking", "savings", "mortgage", "loan"] },
159	customerId: { bsonType: "array" },
160	dateOpened: { bsonType: "date" },
161	balance: { bsonType: "decimal" }
162	}
163	}
164
165	const badCustomer = {
166	"$expr": { "$gt": ["$customerSince", "$$NOW"] }
167	}
168
169	const badAccount = {
170	$or: [
171	{
172	accountType: "savings",
173	balance: { $lt: 0}
174	},
175	{
176	"$expr": { "$gt": ["$dateOpened", "$$NOW"]}
177	}
178	]
179	}
180
181	const schemaValidation = {
182	"$and": [
183	{ $jsonSchema: { oneOf: [ customerSchema, accountSchema ] }},
184	{ $nor: [
185	badCustomer,
186	badAccount
187	]
188	}
189	]
190	}
191
192	const database = 'MongoBank';
193	const collection = 'Accounts';
194
195	use(database);
196	db.getCollection(collection).drop();
197	db.createCollection(collection, {validator: schemaValidation} )
198	db.getCollection(collection).replaceOne({"_id": cust1._id}, cust1, {upsert: true});
199	db.getCollection(collection).replaceOne({"_id": cust2._id}, cust2, {upsert: true});
200	db.getCollection(collection).replaceOne({"_id": acc1._id}, acc1, {upsert: true});
201	db.getCollection(collection).replaceOne({"_id": acc2._id}, acc2, {upsert: true});
202	db.getCollection(collection).replaceOne({"_id": acc3._id}, acc3, {upsert: true});
203
204	// The following 3 operations should fail
205
206	db.getCollection(collection).replaceOne({"_id": negativeSavings._id}, negativeSavings, {upsert: true});
207	db.getCollection(collection).replaceOne({"_id": futureCustomer._id}, futureCustomer, {upsert: true});
208	db.getCollection(collection).replaceOne({"_id": futureAccount._id}, futureAccount, {upsert: true});
209
210	db.getCollection(collection).dropIndexes();
211	db.getCollection(collection).createIndex(indexKeys1, indexOptions1);
212	db.getCollection(collection).createIndex(indexKeys2, indexOptions2);