reuvenlax commented on a change in pull request #10767: Document Beam Schemas
URL: https://github.com/apache/beam/pull/10767#discussion_r386038304
 
 

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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}
+While schemas themselves are language independent, they are designed to embed 
naturally into the programming languages
+of the Beam SDK being used. This allows Beam users to continue using native 
types while reaping the advantage of 
+having Beam understand their element schemas.
+ 
+ {:.language-java}
+ In Java you could use the following set of classes to represent the purchase 
schema.  Beam will automatically  
+ infer the correct schema based on the members of the class.
+
+```java
+@DefaultSchema(JavaBeanSchema.class)
+public class Purchase {
+  public String getUserId();  // Returns the id of the user who made the 
purchase.
+  public long getItemId();  // Returns the identifier of the item that was 
purchased.
+  public ShippingAddress getShippingAddress();  // Returns the shipping 
address, a nested type.
+  public long getCostCents();  // Returns the cost of the item.
+  public List<Transaction> getTransactions();  // Returns the transactions 
that paid for this purchase (returns a list, since the purchase might be spread 
out over multiple credit cards).
+  
+  @SchemaCreate
+  public Purchase(String userId, long itemId, ShippingAddress shippingAddress, 
long costCents, 
+                  List<Transaction> transactions) {
+      ...
+  }
+}
+
+@DefaultSchema(JavaBeanSchema.class)
+public class ShippingAddress {
+  public String getStreetAddress();
+  public String getCity();
+  @Nullable public String getState();
+  public String getCountry();
+  public String getPostCode();
+  
+  @SchemaCreate
+  public ShippingAddress(String streetAddress, String city, @Nullable String 
state, String country,
+                         String postCode) {
+     ...
+  }
+}
+
+@DefaultSchema(JavaBeanSchema.class)
+public class Transaction {
+  public String getBank();
+  public double getPurchaseAmount();
+ 
+  @SchemaCreate
+  public Transaction(String bank, double purchaseAmount) {
+     ...
+  }
+}
+```
+
+Using JavaBean classes as above is one way to map a schema to Java classes. 
However multiple Java classes might have
+the same schema, in which case the different Java types can often be used 
interchangeably. For example, the above
+`Transaction` class has the same schema as the following class:
+
+```java
+@DefaultSchema(JavaFieldSchema.class)
+public class TransactionPojo {
+  public String bank;
+  public double purchaseAmount;
+}
+```
+
+So if we had two `PCollection`s as follows
+
+```java
+PCollection<Transaction> transactionBeans = readTransactionsAsJavaBean();
+PCollection<TransactionPojos> transactionPojos = readTransactionsAsPojo();
+```
+
+Then these two `PCollection`s would have the same schema, even though their 
Java types would be different. This means
+for example the the following two code snippets are valid:
+
+```java
+transactionBeans.apply(ParDo.of(new DoFn<...>() {
+   @ProcessElement public void process(@Element TransactionPojo pojo) {
+      ...
+   }
+}));
+```
+
+and
+```java
+transactionPojos.apply(ParDo.of(new DoFn<...>() {
+   @ProcessElement public void process(@Element Transaction row) {
+    }
+}));
+```
+
+EEven though the in both cases the `@Element` parameter differs from the the 
`PCollection`'s Java type, since the
+schemas are the same Beam will automatically make the conversion. The built-in 
`Convert` transform can also be used
+to translate between Java types of equivalent schemas, as detailed below.
+
+### 6.3. Schema definition {#schema-definition}
+The schema for a `PCollection` defines elements of that `PCollection` as an 
ordered list of named fields. Each field
+has a name, a type, and possibly a set of user options. The type of a field 
can be primitive or composite. The following
+are the primitive types currently supported by Beam:
+
+<table>
+  <thead>
+    <tr class="header">
+      <th>Type</th>
+      <th>Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>BYTE</td>
+      <td>An 8-bit signed value</td>
+    </tr>
+     <tr>
+       <td>INT16</td>
+       <td>A 16-bit signed value</td>
+     </tr>
+     <tr>
+       <td>INT32</td>
+       <td>A 32-bit signed value</td>
+     </tr>
+    <tr>
+      <td>INT64</td>
+       <td>A 64-bit signed value</td>
+     </tr>
+     <tr>
+       <td>DECIMAL</td>
+       <td>An arbitrary-precision decimal type</td>
+     </tr>
+     <tr>
+       <td>FLOAT</td>
+       <td>A 32-bit IEEE 754 floating point number</td>
+     </tr>
+     <tr>
+       <td>DOUBLE</td>
+       <td>A 64-bit IEEE 754 floating point number</td>
+     </tr>
+     <tr>
+       <td>STRING</td>
+       <td>A string</td>
+     </tr>
+     <tr>
+       <td>DATETIME</td>
+       <td>A timestamp represented as milliseconds since the epoch</td>
+     </tr>  
+     <tr>
+       <td>BOOLEAN</td>
+       <td>A boolean value</td>
+     </tr>
+     <tr>
+       <td>BYTES</td>
+       <td>A raw byte array</td>
+     </tr>             
+  </tbody>
+</table>
+<br/>
+
+A field can also reference a nested schema. In this case, the field will have 
type ROW, and the nested schema will 
+be an attribute of this field type.
+
+Three collection types are supported as field types: ARRAY, ITERABLE and MAP:
+* **ARRAY** This represents a repeated value type, where the repeated elements 
can have any supported type. Arrays of 
+nested rows are supported, as are arrays of arrays.
+* **ITERABLE** This is very similar to the array type, it represents a 
repeated value, but one in which the full list of 
+items is not known until iterated over. This is intended for the case where an 
iterable might be larger than the 
+available memory, and backed by external storage (for example, this can happen 
with the iterable returned by a 
+`GroupByKey`). The repeated elements can have any supported type.
+* **MAP** This represents an associative map from keys to values. All schema 
types are supported for both keys and values.
+ Values that contain map types cannot be used as keys in any grouping 
operation.
+
+### 6.4. Logical types {#logical-types}
+Users can extend the schema type system to add custom logical types that can 
be used as a field. A logical type is 
+identified by a unique identifier and an argument. A logical type also 
specifies an underlying schema type to be used 
+for storage, along with conversions to and from that type. As an example, a 
logical union can always be represented as 
+a row with nullable fields, where the user ensures that only one of those 
fields is ever set at a time. However this can
+be tedious and complex to manage. The OneOf logical type provides a value 
class that makes it easier to manage the type
+as a union, while still using a row with nullable fields as its underlying 
storage. Each logical type also has a 
+unique identifier, so they can be interpreted by other languages as well. More 
examples of logical types are listed 
+below.
+
+#### 6.4.1. Defining a logical type {#defining-a-logical-type}
+To define a logical type you must specify a Schema type to be used to 
represent the underlying type as well as a unique
+identifier for that type. A logical type imposes additional semantics on top a 
schema type. For example, a logical 
+type to represent nanosecond timestamps is represented as a schema containing 
an INT64 and an INT32 field. This schema
+alone does not say anything about how to interpret this type, however the 
logical type tells you that this represents
+a nanosecond timestamp, with the INT64 field representing seconds and the 
INT32 field representing nanoseconds.
+
+Logical types are also specified by an argument, which allows creating a class 
of related typed. For example, a 
+limited-precision decimal type would have an integer argument indicating how 
many digits of precision are represented.
+The argument is represented by a schema type, so can itself be a complex type.
+
+ {:.language-java}
+In Java, a logical type is specified as a subclass of the `LogicalType` class. 
A custom Java class can be specified to 
+represent the logical type and conversion functions must be supplied to 
convert back and forth between this Java class
+and the underlying Schema type representation. For example, the logical type 
representing nanosecond timestamp might
+be implemented as follows
+
+```java
+// A Logical type using java.time.Instant to represent the logical type.
+public class TimestampNanos implements LogicalType<Instant, Row> {
+  // The underlying schema used to represent rows.
+  private final Schema SCHEMA = 
Schema.builder().addInt64Field("seconds").addInt32Field("nanos").build();
+  @Override public String getIdentifier() { return "timestampNanos"; }
+  @Override public FieldType getBaseType() { return schema; }
+  
+  // Convert the representation type to the underlying Row type. Called by 
Beam when necessary.
+  @Override public Row toBaseType(Instant instant) {
+    return Row.withSchema(schema).addValues(instant.getEpochSecond(), 
instant.getNano()).build();
+  }
+  
+  // Convert the underlying Row type to and Instant. Called by Beam when 
necessary.
+  @Override public Instant toInputType(Row base) {
+    return Instant.of(row.getInt64("seconds"), row.getInt32("nanos"));
+  }
+
+     ...
+}
+```
+
+#### 6.4.2. Useful logical types {#built-in-logical-types}
+##### **EnumerationType**
+EnumerationType allows creating an enumeration type consisting of a set of 
named constants.
+
+```java
+Schema schema = Schema.builder()
+               …
+     .addLogicalTypeField(“color”, EnumerationType.create(“RED”, “GREEN”, 
“BLUE”))
+     .build();
+```
+
+The value of this field is stored in the row as an INT32 type, however the 
logical type defines a value type that lets 
+you access the enumeration either as a string or a value. For example:
+
+```java
+EnumerationType.Value enumValue = enumType.valueOf(“RED”);
+enumValue.getValue();  // Returns 0, the integer value of the constant.
+enumValue.toString();  // Returns “RED”, the string value of the constant
+```
+
+Given a row object with an enumeration field, you can also extract the field 
as the enumeration value.
+
+```java
+EnumerationType.Value enumValue = row.getLogicalTypeValue(“color”, 
EnumerationType.Value.class);
+```
+
+Automatic schema inference from Java POJOs and JavaBeans automatically 
converts Java enums to EnumerationType logical 
+types.
+
+##### **OneOfType**
+OneOfType allows creating a disjoint union type over a set of schema fields. 
For example:
+
+```java
+Schema schema = Schema.builder()
+               …
+     .addLogicalTypeField(“oneOfField”, 
+        OneOfType.create(Field.of(“intField”, FieldType.INT32),
+                         Field.of(“stringField”, FieldType.STRING),
+                         Field.of(“bytesField”, FieldType.BYTES)))
+      .build();
+```
+
+The value of this field is stored in the row as another Row type, where all 
the fields are marked as nullable. The 
+logical type however defines a Value object that contains an enumeration value 
indicating which field was set and allows
+ getting just that field:
+
+```java
+// Returns an enumeration indicating all possible case values for the enum.
+// For the above example, this will be 
+// EnumerationType.create(“intField”, “stringField”, “bytesField”);
+EnumerationType oneOfEnum = onOfType.getCaseEnumType();
+
+// Creates an instance of the union with the string field set.
+OneOfType.Value oneOfValue = oneOfType.createValue(“stringField”, “foobar”);
+
+// Handle the oneof
+switch (oneOfValue.getCaseEnumType().toString()) {
+  case “intField”:  
+    return processInt(oneOfValue.getValue(Integer.class));
+  case “stringField”:
+    return processString(oneOfValue.getValue(String.class));
+  case “bytesField”:
+    return processBytes(oneOfValue.getValue(bytes[].class));
+}
+```
+
+In the above example we used the field names in the switch statement for 
clarity, however the enum integer values could
+ also be used.
+
+### 6.5. Creating Schemas {#creating-schemas}
+
+In order to take advantage of schemas, your `PCollection`s must have a schema 
attached to it. Often, the source 
+itself will attach a schema to the PCollection. For example, when using 
`AvroIO` to read Avro files, the source can
+automatically infer a Beam schema from the Avro schema and attach that to the 
Beam `PCollection`. However not all sources 
+produce schemas. In addition, often Beam pipelines have intermediate stages 
and types, and those also can benefit from
+the expressiveness of schemas.
+ 
+#### 6.5.1. Inferring schemas {#inferring-schemas}
+{:.language-java}
+Beam is able to infer schemas from a variety of common Java types. The 
`@DefaultSchema` annotation can be used to tell
+Beam to infer schemas from a specific type. The annotation takes a 
`SchemaProvider` as an argument, and `SchemaProvider` 
+classes are already built in for common Java types. The `SchemaRegistry` can 
also be invoked programmatically for cases 
+where it is not practical to annotate the Java type itself.
+
+##### **Java POJOs**
+A POJO (Plain Old Java Object) is a Java object that is not bound by any 
restriction other than the Java Language 
+Specification. A POJO can contain member variables that are primitives, that 
are other POJOs, or are collections maps or
+arrays thereof. POJOs do not have to extend prespecified classes or extend any 
specific interfaces.
+
+If a POJO class is annotated with `@DefaultSchema(JavaFieldSchema.class)`, 
Beam will automatically infer a schema for 
+this class. Nested classes are supported as are classes with `List`, array, 
and `Map` fields.
+
+For example, annotating the following class tells Beam to infer a schema from 
this POJO class and apply it to any 
+`PCollection<TransactionPojo>`.
+
+```java
+@DefaultSchema(JavaFieldSchema.class)
+public class TransactionPojo {
+  public final String bank;
+  public final double purchaseAmount;
+  @SchemaCreate
+  public TransactionPojo(String bank, double purchaseAmount) {
+    this.bank = bank.
+    this.purchaseAmount = purchaseAmount;
+  }
+}
+// Beam will automatically infer the correct schema for this PCollection. No 
coder is needed as a result.
+PCollection<TransactionPojo> pojos = readPojos();
+````
+
+The `@SchemaCreate` annotation tells Beam that this constructor can be used to 
create instances of TransactionPojo, 
+assuming that constructor parameters have the same names as the field names. 
`@SchemaCreate` can also be used to annotate
+static factory methods on the class, allowing the constructor to remain 
private. If there is no `@SchemaCreate`
+ annotation then all the fields must be non-final and the class must have a 
zero-argument constructor.
+
+There are a couple of other useful annotations that affect how Beam infers 
schemas. By default the schema field names 
+inferred will match that of the class field names. However `@SchemaFieldName` 
can be used to specify a different name to
+be used for the schema field. `@SchemaIgnore` can be used to mark specific 
class fields as excluded from the inferred
+schema. For example, it’s common to have ephemeral fields in a class that 
should not be included in a schema 
+(e.g. caching the hash value to prevent expensive recomputation of the hash), 
and `@SchemaIgnore` can be used to
+exclude these fields.
+
+In some cases it is not convenient to annotate the POJO class, for example if 
the POJO is in a different package that is
+not owned by the Beam pipeline author. In these cases the schema inference can 
be triggered programmatically in 
+pipeline’s main function as follows:
+
+```java
+ pipeline.getSchemaRegistry().registerPOJO(TransactionPOJO.class); 
+```
+
+##### **Java Beans**
+Java Beans are a de-facto standard for creating reusable property classes in 
Java. While the full 
+standard has many characteristics, the key ones are that all properties are 
accessed via getter and setter classes, and 
+the name format for these getters and setters is standardized. A Java Bean 
class can be annotated with 
+`@DefaultSchema(JavaBeanSchema.class)` and Beam will automatically infer a 
schema for this class. For example:
+
+```java
+@DefaultSchema(JavaBeanSchema.class)
+public class TransactionBean {
+  public TransactionBean() { … } 
+  public String getBank() { … }
+  public void setBank(String bank) { … }
+  public double getPurchaseAmount() { … }
+  public void setPurchaseAmount(double purchaseAmount) { … }
+}
+// Beam will automatically infer the correct schema for this PCollection. No 
coder is needed as a result.
+PCollection<TransactionBean> beans = readBeans();
+```
+
+The `@SchemaCreate` annotation can be used to specify a constructor or a 
static factory method, in which case the 
+setters and zero-argument constructor can be omitted.
+
+```java
+@DefaultSchema(JavaBeanSchema.class)
+public class TransactionBean {
+  @SchemaCreate
+  Public TransactionBean(String bank, double purchaseAmount) { … }
+  public String getBank() { … }
+  public double getPurchaseAmount() { … }
+}
+```
+
+`@SchemaFieldName` and `@SchemaIgnore` can be used to alter the schema 
inferred, just like with POJO classes.
+
+##### AutoValue
 
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