---
id: version-2.4.2-schema-understand
title: Understand schema
sidebar_label: Understand schema
original_id: schema-understand
---

This chapter explains the basic concepts of Pulsar schema, focuses on the topics of particular importance, and provides additional background.

## SchemaInfo

Pulsar schema is defined in a data structure called `SchemaInfo`. 

The `SchemaInfo` is stored and enforced on a per-topic basis and cannot be stored at the namespace or tenant level.

A `SchemaInfo` consists of the following fields:

<table style="table">

<tr>

<th>

Field

</th>

<th>

Description

</th>

</tr>

<tr>

<td> 

`name` 

</td> 

<td> 

Schema name (a string).

</td>

</tr>

<tr>

<td> 

`type` 

</td> 

<td> 

Schema type, which determines how to interpret the schema data.

* Predefined schema: see [here](schema-understand.md#schema-type).

* Customized schema: it is left as an empty string.

</td>

</tr>

<tr>

<td> 

`schema`（`payload`)  

</td> 

<td> 

Schema data, which is a sequence of 8-bit unsigned bytes and schema-type specific. 

</td>

</tr>

<tr>

<td> 

`properties` 

</td> 

<td> 

It is a user defined properties as a string/string map. 

Applications can use this bag for carrying any application specific logics. 

Possible properties might be the Git hash associated with the schema, an environment string like `dev` or `prod`.

</td>

</tr>

</table>

**Example**

This is the `SchemaInfo` of a string.

```text
{
    “name”: “test-string-schema”,
    “type”: “STRING”,
    “schema”: “”,
    “properties”: {}
}
```

## Schema type

Pulsar supports various schema types, which are mainly divided into two categories: 

* Primitive type 

* Complex type

### Primitive type

Currently, Pulsar supports the following primitive types:

| Primitive Type | Description |
|---|---|
| `BOOLEAN` | A binary value |
| `INT8` | A 8-bit signed integer |
| `INT16` | A 16-bit signed integer |
| `INT32` | A 32-bit signed integer |
| `INT64` | A 64-bit signed integer |
| `FLOAT` | A single precision (32-bit) IEEE 754 floating-point number |
| `DOUBLE` | A double-precision (64-bit) IEEE 754 floating-point number |
| `BYTES` | A sequence of 8-bit unsigned bytes |
| `STRING` | A Unicode character sequence |
| `TIMESTAMP` (`DATE`, `TIME`) |  A logic type represents a specific instant in time with millisecond precision. <br>It stores the number of milliseconds since `January 1, 1970, 00:00:00 GMT` as an `INT64` value | 

For primitive types, Pulsar does not store any schema data in `SchemaInfo`. The `type` in `SchemaInfo` is used to determine how to serialize and deserialize the data. 

Some of the primitive schema implementations can use `properties` to store implementation-specific tunable settings. For example, a `string` schema can use `properties` to store the encoding charset to serialize and deserialize strings.

The conversions between **Pulsar schema types** and **language-specific primitive types** are as below.

| Schema Type | Java Type| Python Type | Go Type |
|---|---|---|---|
| BOOLEAN | boolean | bool | bool |
| INT8 | byte | | int8 |
| INT16 | short | | int16 |
| INT32 | int | | int32 |
| INT64 | long | | int64 |
| FLOAT | float | float | float32 |
| DOUBLE | double | float | float64|
| BYTES | byte[], ByteBuffer, ByteBuf | bytes | []byte |
| STRING | string | str | string| 
| TIMESTAMP | java.sql.Timestamp | | |
| TIME | java.sql.Time | | |
| DATE | java.util.Date | | |

**Example**

This example demonstrates how to use a string schema.

1. Create a producer with a string schema and send messages.

    ```text
    Producer<String> producer = client.newProducer(Schema.STRING).create();
    producer.newMessage().value("Hello Pulsar!").send();
    ```

2. Create a consumer with a string schema and receive messages.  

    ```text
    Consumer<String> consumer = client.newConsumer(Schema.STRING).create();
    consumer.receive();
    ```

### Complex type

Currently, Pulsar supports the following complex types:

| Complex Type | Description |
|---|---|
| `keyvalue` | Represents a complex type of a key/value pair. |
| `struct` | Supports **AVRO**, **JSON**, and **Protobuf**. |

#### keyvalue

`Keyvalue` schema helps applications define schemas for both key and value. 

For `SchemaInfo` of `keyvalue` schema, Pulsar stores the `SchemaInfo` of key schema and the `SchemaInfo` of value schema together.

Pulsar provides two methods to encode a key/value pair in messages：

* `INLINE`

* `SEPARATED`

Users can choose the encoding type when constructing the key/value schema.

##### INLINE

Key/value pairs will be encoded together in the message payload.

##### SEPARATED

Key will be encoded in the message key and the value will be encoded in the message payload. 
  
**Example**
    
This example shows how to construct a key/value schema and then use it to produce and consume messages.

1. Construct a key/value schema with `INLINE` encoding type.

    ```java
    Schema<KeyValue<Integer, String>> kvSchema = Schema.KeyValue(
    Schema.INT32,
    Schema.STRING,
    KeyValueEncodingType.INLINE
    );
    ```

2. Optionally, construct a key/value schema with `SEPARATED` encoding type.

    ```java
    Schema<KeyValue<Integer, String>> kvSchema = Schema.KeyValue(
    Schema.INT32,
    Schema.STRING,
    KeyValueEncodingType.SEPARATED
    );
    ```

3. Produce messages using a key/value schema.

    ```java
    Schema<KeyValue<Integer, String>> kvSchema = Schema.KeyValue(
    Schema.INT32,
    Schema.STRING,
    KeyValueEncodingType.SEPARATED
    );

    Producer<KeyValue<Integer, String>> producer = client.newProducer(kvSchema)
        .topic(TOPIC)
        .create();

    final int key = 100;
    final String value = "value-100”;

    // send the key/value message
    producer.newMessage()
    .value(new KeyValue<>(key, value))
    .send();
    ```

4. Consume messages using a key/value schema.

    ```java
    Schema<KeyValue<Integer, String>> kvSchema = Schema.KeyValue(
    Schema.INT32,
    Schema.STRING,
    KeyValueEncodingType.SEPARATED
    );

    Consumer<KeyValue<Integer, String>> consumer = client.newConsumer(kvSchema)
        ...
        .topic(TOPIC)
        .subscriptionName(SubscriptionName).subscribe();

    // receive key/value pair
    Message<KeyValue<Integer, String>> msg = consumer.receive();
    KeyValue<Integer, String> kv = msg.getValue();
    ```

#### struct

Pulsar uses [Avro Specification](http://avro.apache.org/docs/current/spec.html) to declare the schema definition for `struct` schema. 

This allows Pulsar:

* to use same tools to manage schema definitions

* to use different serialization/deserialization methods to handle data

There are two methods to use `struct` schema：

* `static`

* `generic`

##### static

You can predefine the `struct` schema, and it can be a POJO in Java, a `struct` in Go, or classes generated by Avro or Protobuf tools. 

**Example** 

Pulsar gets the schema definition from the predefined `struct` using an Avro library. The schema definition is the schema data stored as a part of the `SchemaInfo`.

1. Create the _User_ class to define the messages sent to Pulsar topics.

    ```text
    public class User {
        String name;
        int age;
    }
    ```

2. Create a producer with a `struct` schema and send messages.

    ```text
    Producer<User> producer = client.newProducer(Schema.AVRO(User.class)).create();
    producer.newMessage().value(User.builder().userName("pulsar-user").userId(1L).build()).send();
    ```

3. Create a consumer with a `struct` schema and receive messages

    ```text
    Consumer<User> consumer = client.newConsumer(Schema.AVRO(User.class)).create();
    User user = consumer.receive();
    ```

##### generic

Sometimes applications do not have pre-defined structs, and you can use this method to define schema and access data.

You can define the `struct` schema using the `GenericSchemaBuilder`, generate a generic struct using `GenericRecordBuilder` and consume messages into `GenericRecord`.

**Example** 

1. Use `RecordSchemaBuilder` to build a schema.

    ```text
    RecordSchemaBuilder recordSchemaBuilder = SchemaBuilder.record("schemaName");
    recordSchemaBuilder.field("intField").type(SchemaType.INT32);
    SchemaInfo schemaInfo = recordSchemaBuilder.build(SchemaType.AVRO);

    Producer<GenericRecord> producer = client.newProducer(Schema.generic(schemaInfo)).create();
    ```

2. Use `RecordBuilder` to build the struct records.

    ```text
    producer.newMessage().value(schema.newRecordBuilder()
                .set("intField", 32)
                .build()).send();
    ```

### Auto Schema

If you don't know the schema type of a Pulsar topic in advance, you can use AUTO schema to produce or consume generic records to or from brokers.

| Auto Schema Type | Description |
|---|---|
| `AUTO_PRODUCE` | This is useful for transferring data **from a producer to a Pulsar topic that has a schema**. |
| `AUTO_CONSUME` | This is useful for transferring data **from a Pulsar topic that has a schema to a consumer**. |

#### AUTO_PRODUCE

`AUTO_PRODUCE` schema helps a producer validate whether the bytes sent by the producer is compatible with the schema of a topic. 

**Example**

Suppose that:

* You have a producer processing messages from a Kafka topic _K_. 

* You have a Pulsar topic _P_, and you do not know its schema type.

* Your application reads the messages from _K_ and writes the messages to _P_.  
   
In this case, you can use `AUTO_PRODUCE` to verify whether the bytes produced by _K_ can be sent to _P_ or not.

```text
Produce<byte[]> pulsarProducer = client.newProducer(Schema.AUTO_PRODUCE())
    …
    .create();

byte[] kafkaMessageBytes = … ; 

pulsarProducer.produce(kafkaMessageBytes);
```

#### AUTO_CONSUME

`AUTO_CONSUME` schema helps a Pulsar topic validate whether the bytes sent by a Pulsar topic is compatible with a consumer, that is, the Pulsar topic deserializes messages into language-specific objects using the `SchemaInfo` retrieved from broker-side. 

Currently, `AUTO_CONSUME` only supports **AVRO** and **JSON** schemas. It deserializes messages into `GenericRecord`.

**Example**

Suppose that:

* You have a Pulsar topic _P_.

* You have a consumer (for example, MySQL) receiving messages from the topic _P_.

* You application reads the messages from _P_ and writes the messages to MySQL.
   
In this case, you can use `AUTO_CONSUME` to verify whether the bytes produced by _P_ can be sent to MySQL or not.

```text
Consumer<GenericRecord> pulsarConsumer = client.newConsumer(Schema.AUTO_CONSUME())
    …
    .subscribe();

Message<GenericRecord> msg = consumer.receive() ; 
GenericRecord record = msg.getValue();
…
```

## Schema version

Each `SchemaInfo` stored with a topic has a version. Schema version manages schema changes happening within a topic. 

Messages produced with a given `SchemaInfo` is tagged with a schema version, so when a message is consumed by a Pulsar client, the Pulsar client can use the schema version to retrieve the corresponding `SchemaInfo` and then use the `SchemaInfo` to deserialize data.

Schemas are versioned in succession. Schema storage happens in a broker that handles the associated topics so that version assignments can be made. 

Once a version is assigned/fetched to/for a schema, all subsequent messages produced by that producer are tagged with the appropriate version.

**Example**

The following example illustrates how the schema version works.

Suppose that a Pulsar [Java client](client-libraries-java.md) created using the code below attempts to connect to Pulsar and begins to send messages:

```text
PulsarClient client = PulsarClient.builder()
        .serviceUrl("pulsar://localhost:6650")
        .build();

Producer<SensorReading> producer = client.newProducer(JSONSchema.of(SensorReading.class))
        .topic("sensor-data")
        .sendTimeout(3, TimeUnit.SECONDS)
        .create();
```

The table below lists the possible scenarios when this connection attempt occurs and what happens in each scenario:

<table class="table">
    
<tr>

<th>Scenario</th>

<th>What happens</th>

</tr>

<tr>
    
<td> 

* No schema exists for the topic. 
 
</td>
    
<td> 
    
(1) The producer is created using the given schema.

(2) Since no existing schema is compatible with the `SensorReading` schema, the schema is transmitted to the broker and stored.

(3) Any consumer created using the same schema or topic can consume messages from the `sensor-data` topic.

</td>

</tr>

<tr>
    
<td> 

* A schema already exists. 
  
* The producer connects using the same schema that is already stored.

</td>
    
<td> 

(1) The schema is transmitted to the broker. 

(2) The broker determines that the schema is compatible. 

(3) The broker attempts to store the schema in [BookKeeper](concepts-architecture-overview.md#persistent-storage) but then determines that it's already stored, so it is used to tag produced messages. 

</td>

<tr>

<td> 

* A schema already exists. 

* The producer connects using a new schema that is compatible.
 
</td>
    
<td> 
    
(1) The schema is transmitted to the broker. 

(2) The broker determines that the schema is compatible and stores the new schema as the current version (with a new version number).

</td>

</tr>

</table>

## How does schema work

Pulsar schemas are applied and enforced at the **topic** level (schemas cannot be applied at the namespace or tenant level). 

Producers and consumers upload schemas to brokers, so Pulsar schemas work on the producer side and the consumer side.

### Producer side

This diagram illustrates how does schema work on the Producer side.

![Schema works at the producer side](assets/schema-producer.png)

1. The application uses a schema instance to construct a producer instance. 

    The schema instance defines the schema for the data being produced using the producer instance. 

    Take AVRO as an example, Pulsar extract schema definition from the POJO class and construct the `SchemaInfo` that the producer needs to pass to a broker when it connects.

2. The producer connects to the broker with the `SchemaInfo` extracted from the passed-in schema instance.
   
3. The broker looks up the schema in the schema storage to check if it is already a registered schema. 
   
4. If yes, the broker skips the schema validation since it is a known schema, and returns the schema version to the producer.
  
5. If no, the broker validates the schema based on the schema compatibility check strategy defined for the topic. 
  
6. If the schema is compatible, the broker stores it and returns the schema version to the producer. 

    All the messages produced by this producer are tagged with the schema version. 

7. If the schema is incompatible, the broker rejects it.

### Consumer side

This diagram illustrates how does Schema work on the consumer side. 

![Schema works at the consumer side](assets/schema-consumer.png)

1. The application uses a schema instance to construct a consumer instance.
   
    The schema instance defines the schema that the consumer uses for decoding messages received from a broker.

2. The consumer connects to the broker with the `SchemaInfo` extracted from the passed-in schema instance.
   
3. The broker looks up the schema in the schema storage to check if it is already a registered schema. 
   
4. If yes, the broker skips the schema validation since it is a known schema, and returns the schema version to the consumer.

5. If no, the broker validates the schema based on the schema compatibility check strategy defined for the topic. 
   
6. If the schema is compatible, the broker stores it and returns the schema version to the consumer. 
   
7. If the schema is incompatible, the consumer will be disconnected.

8. The consumer receives the messages from the broker. 

    If the schema used by the consumer supports schema versioning (for example, AVRO schema), the consumer fetches the  `SchemaInfo` of the version tagged in messages, and use the passed-in schema and the schema tagged in messages to decode the messages.
