reuvenlax commented on a change in pull request #10767: Document Beam Schemas
URL: https://github.com/apache/beam/pull/10767#discussion_r386038301
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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
Review comment:
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