📚 Tip and Trick: MongoDB Attribute Pattern 💻

Situation:

• Single View applications 🔍
• Incorporate any type of data 🌐
• Dynamic schemas to iterate 🔀
• Expressive query language, indexing, aggregation to find, filter the data 🔍

Challenge:

• Diverse Data Types: Reconciling schemas from different systems is hard 🤯
• Rigid Schemas: Cannot change on schema 📛

Shortcomings:

• Only can fine-grained data schema 📋
• Cannot add ad hoc queries 🚫
• Cannot add secondary indexes 🚫

Solution: MongoDB Documents model 💥

• Use any type of data (numbers, strings, binary data, arrays) 🔢🔤🔣🔢
• Dynamic Schemas: Don't need to have all the same fields 🔀
• Query example: Indexing to find and filter data 🔍
• Indexing: pet

{
    "name": "Peter"
}
{
    "name": "Mary",
    "pet": "cat"
}

Result:

• Only can query Mary because she has pet on indexing 😕

Attribute Pattern: 🔑

• Subset: Big documents with many similar fields 📜
• Moving subset of data into a key-value sub-document 📂
• Sort fields in a small subset of documents 🗂️

Advantage:

• Avoid create a lot of indexes and reduce performance 💪

Attribute Pattern Use Case 1: Insurance Company 🏦

• Aggregates data from multiple sources into a central place 🌐
• Get a 360° picture of a customer 🔍
• Better customer service at a reduced cost 💰
• Innovate quickly 🚀

Before:

{
    release_Italy: ISODate("1977-10-20T01:00:00+01:00"),
    release_France: ISODate("1977-10-19T01:00:00+01:00"),
    release_UK: ISODate("1977-12-27T01:00:00+01:00"),
}

Index:

{release_Italy: 1}
{release_France: 1}
{release_UK: 1}

After:

{
    releases: [
        {
        location: "USA",
        date: ISODate("1977-05-20T01:00:00+01:00")
        },
        {
        location: "France",
        date: ISODate("1977-10-19T01:00:00+01:00")
        }
    ],
}

Index:

{ "releases.location": 1, "releases.date": 1}

Non-Deterministic Naming: 🔖

• Easy to add extra qualifiers 🔍
• Clearly state relationship of original field and value 📝

{
    "specs": [
        { k: "volume", v: "500", u: "ml" },
        { k: "volume", v: "12", u: "ounces" }
    ]
}

Index

{"specs.k": 1, "specs.v": 1, "specs.u": 1}

Attribute Pattern Use Case 2: Clothing 👕

• Sizes that are expressed in small, medium, large 📏
• Some clothing in collection may be expressed in numbers (11, 12, 13) 🔢
• Characteristics are seldom common across the assets 🎨

Catalog are similar, such as name, vendor, manufacturer, country of origin 🏭

• Provides good structure for data 🗂️

{
    "specs": [
        { k: "size", v: "11", u: "CHINA" },
        { k: "size", v: "large", u: "USA" }
    ]
}

Index:

{"specs.k": 1, "specs.v": 1, "specs.u": 1}

Reference:

https://www.mongodb.com/developer/products/mongodb/attribute-pattern/
Building with Patterns: The Attribute Pattern

https://www.mongodb.com/solutions/use-cases/single-view
Single View applications

https://www.mongodb.com/blog/post/building-with-patterns-a-summary
Building with Patterns: A Summary

https://www.mongodb.com/blog/post/building-with-patterns-the-approximation-pattern
Building with Patterns: The Approximation Pattern

Editor

Danny Chan, specialty of FSI and Serverless

Kenny Chan, specialty of FSI and Machine Learning