[GitHub] [beam] TheNeuralBit commented on a change in pull request #10767: Document Beam Schemas

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TheNeuralBit commented on a change in pull request #10767: Document Beam Schemas
URL: https://github.com/apache/beam/pull/10767#discussion_r408487454
 
 

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 File path: website/src/documentation/programming-guide.md
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 @@ -1970,7 +1976,1076 @@ records.apply("WriteToText",
 See the [Beam-provided I/O Transforms]({{site.baseurl }}/documentation/io/built-in/)
 page for a list of the currently available I/O transforms.
 
-## 6. Data encoding and type safety {#data-encoding-and-type-safety}
+## 6. Schemas {#schemas}
+Often, the type of records being processed have an obvious structure. Common Beam sources produce
+JSON, Avro, Protocol Buffer, or database row objects; all of these types have well defined structures, 
+structures that can often be determined by examining the type. Even within a pipeline, Simple Java POJOs 
+(or  equivalent structures in other languages) are often used as intermediate types, and these also have a
+ clear structure that can be inferred by inspecting the class. By understanding the structure of a pipeline’s 
+ records, we can provide much more concise APIs for data processing.
+ 
+### 6.1. What is a schema {#what-is-a-schema}
+Most structured records share some common characteristics: 
+* They can be subdivided into separate named fields. Fields usually have string names, but sometimes - as in the case of indexed
+ tuples - have numerical indices instead.
+* There is a confined list of primitive types that a field can have. These often match primitive types in most programming 
+ languages: int, long, string, etc.
+* Often a field type can be marked as optional (sometimes referred to as nullable) or required.
+
+In addition, often records have a nested structure. A nested structure occurs when a field itself has subfields so the 
+type of the field itself has a schema. Fields that are  array or map types is also a common feature of these structured 
+records.
+
+For example, consider the following schema, representing actions in a fictitious e-commerce company:
+
+**Purchase**
+<table>
+  <thead>
+    <tr class="header">
+      <th><b>Field Name</b></th>
+      <th><b>Field Type</b></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>userId</td>
+      <td>STRING</td>      
+    </tr>
+    <tr>
+      <td>itemId</td>
+      <td>INT64</td>      
+    </tr>
+    <tr>
+      <td>shippingAddress</td>
+      <td>ROW(ShippingAddress)</td>      
+    </tr>
+    <tr>
+      <td>cost</td>
+      <td>INT64</td>      
+    </tr>
+    <tr>
+      <td>transactions</td>
+      <td>ARRAY[ROW(Transaction)]</td>      
+    </tr>                  
+  </tbody>
+</table>
+<br/>
+
+**ShippingAddress**
+<table>
+  <thead>
+    <tr class="header">
+      <th><b>Field Name</b></th>
+      <th><b>Field Type</b></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>streetAddress</td>
+      <td>STRING</td>      
+    </tr>
+    <tr>
+      <td>city</td>
+      <td>STRING</td>      
+    </tr>
+    <tr>
+      <td>state</td>
+      <td>nullable STRING</td>      
+    </tr>
+    <tr>
+      <td>country</td>
+      <td>STRING</td>      
+    </tr>
+    <tr>
+      <td>postCode</td>
+      <td>STRING</td>      
+    </tr>                  
+  </tbody>
+</table> 
+<br/>
+
+**Transaction**
+<table>
+  <thead>
+    <tr class="header">
+      <th><b>Field Name</b></th>
+      <th><b>Field Type</b></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>bank</td>
+      <td>STRING</td>      
+    </tr>
+    <tr>
+      <td>purchaseAmount</td>
+      <td>DOUBLE</td>      
+    </tr>                  
+  </tbody>
+</table>
+<br/>
+
+Purchase event records are represented by the aove purchase schema. Each purchase event contains a shipping address, which
+is a nested row containing its own schema. Each purchase also contains a list of credit-card transactions 
+(a list, because a purchase might be split across multiple credit cards); each item in the transaction list is a row 
+with its own schema.
+
+This provides an abstract description of the types involved, one that is abstracted away from any specific programming 
+language.
+
+Schemas provide us a type-system for Beam records that is independent of any specific programming-language type. There
+might be multiple Java classes that all have the same schema (for example a Protocol-Buffer class or a POJO class),
+and Beam will allow us to seamlessly convert between these types. Schemas also provide a simple way to reason about 
+types across different programming-language APIs.
+
+A `PCollection` with a schema does not need to have a `Coder` specified, as Beam knows how to encode and decode 
+Schema rows.
+
+### 6.2. Schemas for programming language types {#schemas-for-pl-types}
 
 Review comment:
   Ahh I see. Makes sense, that's a very good thing to clear up.

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