---
type: docs
title: "How-To: Use output bindings to interface with external resources"
linkTitle: "How-To: Output bindings"
description: "Invoke external systems with output bindings"
weight: 300
---
Output bindings enable you to invoke external resources without taking dependencies on special SDK or libraries.
For a complete sample showing output bindings, visit this [link](https://github.com/dapr/quickstarts/tree/master/tutorials/bindings).
## Example:
The below code example loosely describes an application that processes orders. In the example, there is an order processing service which has a Dapr sidecar. The order processing service uses Dapr to invoke external resources, in this case a Kafka, via an output binding.
## 1. Create a binding
An output binding represents a resource that Dapr uses to invoke and send messages to.
For the purpose of this guide, you'll use a Kafka binding. You can find a list of the different binding specs [here]({{< ref setup-bindings >}}).
Create a new binding component with the name of `checkout`.
Inside the `metadata` section, configure Kafka related properties such as the topic to publish the message to and the broker.
{{< tabs "Self-Hosted (CLI)" Kubernetes >}}
{{% codetab %}}
Create the following YAML file, named `binding.yaml`, and save this to a `components` sub-folder in your application directory.
(Use the `--components-path` flag with `dapr run` to point to your custom components dir)
```yaml
apiVersion: dapr.io/v1alpha1
kind: Component
metadata:
name: checkout
spec:
type: bindings.kafka
version: v1
metadata:
# Kafka broker connection setting
- name: brokers
value: localhost:9092
# consumer configuration: topic and consumer group
- name: topics
value: sample
- name: consumerGroup
value: group1
# publisher configuration: topic
- name: publishTopic
value: sample
- name: authRequired
value: "false"
```
{{% /codetab %}}
{{% codetab %}}
To deploy this into a Kubernetes cluster, fill in the `metadata` connection details of your [desired binding component]({{< ref setup-bindings >}}) in the yaml below (in this case kafka), save as `binding.yaml`, and run `kubectl apply -f binding.yaml`.
```yaml
apiVersion: dapr.io/v1alpha1
kind: Component
metadata:
name: checkout
spec:
type: bindings.kafka
version: v1
metadata:
# Kafka broker connection setting
- name: brokers
value: localhost:9092
# consumer configuration: topic and consumer group
- name: topics
value: sample
- name: consumerGroup
value: group1
# publisher configuration: topic
- name: publishTopic
value: sample
- name: authRequired
value: "false"
```
{{% /codetab %}}
{{< /tabs >}}
## 2. Send an event (Output binding)
Below are code examples that leverage Dapr SDKs to interact with an output binding.
{{< tabs Dotnet Java Python Go Javascript>}}
{{% codetab %}}
```csharp
//dependencies
using System;
using System.Collections.Generic;
using System.Net.Http;
using System.Net.Http.Headers;
using System.Threading.Tasks;
using Dapr.Client;
using Microsoft.AspNetCore.Mvc;
using System.Threading;
//code
namespace EventService
{
class Program
{
static async Task Main(string[] args)
{
string BINDING_NAME = "checkout";
string BINDING_OPERATION = "create";
while(true) {
System.Threading.Thread.Sleep(5000);
Random random = new Random();
int orderId = random.Next(1,1000);
using var client = new DaprClientBuilder().Build();
//Using Dapr SDK to invoke output binding
await client.InvokeBindingAsync(BINDING_NAME, BINDING_OPERATION, orderId);
Console.WriteLine("Sending message: " + orderId);
}
}
}
}
```
Navigate to the directory containing the above code, then run the following command to launch a Dapr sidecar and run the application:
```bash
dapr run --app-id orderprocessing --app-port 6001 --dapr-http-port 3601 --dapr-grpc-port 60001 --app-ssl dotnet run
```
{{% /codetab %}}
{{% codetab %}}
```java
//dependencies
import io.dapr.client.DaprClient;
import io.dapr.client.DaprClientBuilder;
import io.dapr.client.domain.HttpExtension;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.util.Random;
import java.util.concurrent.TimeUnit;
//code
@SpringBootApplication
public class OrderProcessingServiceApplication {
private static final Logger log = LoggerFactory.getLogger(OrderProcessingServiceApplication.class);
public static void main(String[] args) throws InterruptedException{
String BINDING_NAME = "checkout";
String BINDING_OPERATION = "create";
while(true) {
TimeUnit.MILLISECONDS.sleep(5000);
Random random = new Random();
int orderId = random.nextInt(1000-1) + 1;
DaprClient client = new DaprClientBuilder().build();
//Using Dapr SDK to invoke output binding
client.invokeBinding(BINDING_NAME, BINDING_OPERATION, orderId).block();
log.info("Sending message: " + orderId);
}
}
}
```
Navigate to the directory containing the above code, then run the following command to launch a Dapr sidecar and run the application:
```bash
dapr run --app-id orderprocessing --app-port 6001 --dapr-http-port 3601 --dapr-grpc-port 60001 mvn spring-boot:run
```
{{% /codetab %}}
{{% codetab %}}
```python
#dependencies
import random
from time import sleep
import requests
import logging
import json
from dapr.clients import DaprClient
#code
logging.basicConfig(level = logging.INFO)
BINDING_NAME = 'checkout'
BINDING_OPERATION = 'create'
while True:
sleep(random.randrange(50, 5000) / 1000)
orderId = random.randint(1, 1000)
with DaprClient() as client:
#Using Dapr SDK to invoke output binding
resp = client.invoke_binding(BINDING_NAME, BINDING_OPERATION, json.dumps(orderId))
logging.basicConfig(level = logging.INFO)
logging.info('Sending message: ' + str(orderId))
```
Navigate to the directory containing the above code, then run the following command to launch a Dapr sidecar and run the application:
```bash
dapr run --app-id orderprocessing --app-port 6001 --dapr-http-port 3601 --app-protocol grpc python3 OrderProcessingService.py
```
{{% /codetab %}}
{{% codetab %}}
```go
//dependencies
import (
"context"
"log"
"math/rand"
"time"
"strconv"
dapr "github.com/dapr/go-sdk/client"
)
//code
func main() {
BINDING_NAME := "checkout";
BINDING_OPERATION := "create";
for i := 0; i < 10; i++ {
time.Sleep(5000)
orderId := rand.Intn(1000-1) + 1
client, err := dapr.NewClient()
if err != nil {
panic(err)
}
defer client.Close()
ctx := context.Background()
//Using Dapr SDK to invoke output binding
in := &dapr.InvokeBindingRequest{ Name: BINDING_NAME, Operation: BINDING_OPERATION , Data: []byte(strconv.Itoa(orderId))}
err = client.InvokeOutputBinding(ctx, in)
log.Println("Sending message: " + strconv.Itoa(orderId))
}
}
```
Navigate to the directory containing the above code, then run the following command to launch a Dapr sidecar and run the application:
```bash
dapr run --app-id orderprocessing --app-port 6001 --dapr-http-port 3601 --dapr-grpc-port 60001 go run OrderProcessingService.go
```
{{% /codetab %}}
{{% codetab %}}
```javascript
//dependencies
import { DaprServer, DaprClient, CommunicationProtocolEnum } from 'dapr-client';
//code
const daprHost = "127.0.0.1";
var main = function() {
for(var i=0;i<10;i++) {
sleep(5000);
var orderId = Math.floor(Math.random() * (1000 - 1) + 1);
start(orderId).catch((e) => {
console.error(e);
process.exit(1);
});
}
}
async function start(orderId) {
const BINDING_NAME = "checkout";
const BINDING_OPERATION = "create";
const client = new DaprClient(daprHost, process.env.DAPR_HTTP_PORT, CommunicationProtocolEnum.HTTP);
//Using Dapr SDK to invoke output binding
const result = await client.binding.send(BINDING_NAME, BINDING_OPERATION, { orderId: orderId });
console.log("Sending message: " + orderId);
}
function sleep(ms) {
return new Promise(resolve => setTimeout(resolve, ms));
}
main();
```
Navigate to the directory containing the above code, then run the following command to launch a Dapr sidecar and run the application:
```bash
dapr run --app-id orderprocessing --app-port 6001 --dapr-http-port 3601 --dapr-grpc-port 60001 npm start
```
{{% /codetab %}}
{{< /tabs >}}
All that's left now is to invoke the output bindings endpoint on a running Dapr instance.
You can also invoke the output bindings endpoint using HTTP:
```bash
curl -X POST -H 'Content-Type: application/json' http://localhost:3601/v1.0/bindings/checkout -d '{ "data": { "orderId": "100" }, "operation": "create" }'
```
As seen above, you invoked the `/binding` endpoint with the name of the binding to invoke, in our case its `checkout`.
The payload goes inside the mandatory `data` field, and can be any JSON serializable value.
You'll also notice that there's an `operation` field that tells the binding what you need it to do.
You can check [here]({{< ref supported-bindings >}}) which operations are supported for every output binding.
Watch this [video](https://www.youtube.com/watch?v=ysklxm81MTs&feature=youtu.be&t=1960) on how to use bi-directional output bindings.
## 1. Create a binding
An output binding represents a resource that Dapr uses to invoke and send messages to.
For the purpose of this guide, you'll use a Kafka binding. You can find a list of the different binding specs [here]({{< ref setup-bindings >}}).
Create a new binding component with the name of `checkout`.
Inside the `metadata` section, configure Kafka related properties such as the topic to publish the message to and the broker.
{{< tabs "Self-Hosted (CLI)" Kubernetes >}}
{{% codetab %}}
Create the following YAML file, named `binding.yaml`, and save this to a `components` sub-folder in your application directory.
(Use the `--components-path` flag with `dapr run` to point to your custom components dir)
```yaml
apiVersion: dapr.io/v1alpha1
kind: Component
metadata:
name: checkout
spec:
type: bindings.kafka
version: v1
metadata:
# Kafka broker connection setting
- name: brokers
value: localhost:9092
# consumer configuration: topic and consumer group
- name: topics
value: sample
- name: consumerGroup
value: group1
# publisher configuration: topic
- name: publishTopic
value: sample
- name: authRequired
value: "false"
```
{{% /codetab %}}
{{% codetab %}}
To deploy this into a Kubernetes cluster, fill in the `metadata` connection details of your [desired binding component]({{< ref setup-bindings >}}) in the yaml below (in this case kafka), save as `binding.yaml`, and run `kubectl apply -f binding.yaml`.
```yaml
apiVersion: dapr.io/v1alpha1
kind: Component
metadata:
name: checkout
spec:
type: bindings.kafka
version: v1
metadata:
# Kafka broker connection setting
- name: brokers
value: localhost:9092
# consumer configuration: topic and consumer group
- name: topics
value: sample
- name: consumerGroup
value: group1
# publisher configuration: topic
- name: publishTopic
value: sample
- name: authRequired
value: "false"
```
{{% /codetab %}}
{{< /tabs >}}
## 2. Send an event (Output binding)
Below are code examples that leverage Dapr SDKs to interact with an output binding.
{{< tabs Dotnet Java Python Go Javascript>}}
{{% codetab %}}
```csharp
//dependencies
using System;
using System.Collections.Generic;
using System.Net.Http;
using System.Net.Http.Headers;
using System.Threading.Tasks;
using Dapr.Client;
using Microsoft.AspNetCore.Mvc;
using System.Threading;
//code
namespace EventService
{
class Program
{
static async Task Main(string[] args)
{
string BINDING_NAME = "checkout";
string BINDING_OPERATION = "create";
while(true) {
System.Threading.Thread.Sleep(5000);
Random random = new Random();
int orderId = random.Next(1,1000);
using var client = new DaprClientBuilder().Build();
//Using Dapr SDK to invoke output binding
await client.InvokeBindingAsync(BINDING_NAME, BINDING_OPERATION, orderId);
Console.WriteLine("Sending message: " + orderId);
}
}
}
}
```
Navigate to the directory containing the above code, then run the following command to launch a Dapr sidecar and run the application:
```bash
dapr run --app-id orderprocessing --app-port 6001 --dapr-http-port 3601 --dapr-grpc-port 60001 --app-ssl dotnet run
```
{{% /codetab %}}
{{% codetab %}}
```java
//dependencies
import io.dapr.client.DaprClient;
import io.dapr.client.DaprClientBuilder;
import io.dapr.client.domain.HttpExtension;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.util.Random;
import java.util.concurrent.TimeUnit;
//code
@SpringBootApplication
public class OrderProcessingServiceApplication {
private static final Logger log = LoggerFactory.getLogger(OrderProcessingServiceApplication.class);
public static void main(String[] args) throws InterruptedException{
String BINDING_NAME = "checkout";
String BINDING_OPERATION = "create";
while(true) {
TimeUnit.MILLISECONDS.sleep(5000);
Random random = new Random();
int orderId = random.nextInt(1000-1) + 1;
DaprClient client = new DaprClientBuilder().build();
//Using Dapr SDK to invoke output binding
client.invokeBinding(BINDING_NAME, BINDING_OPERATION, orderId).block();
log.info("Sending message: " + orderId);
}
}
}
```
Navigate to the directory containing the above code, then run the following command to launch a Dapr sidecar and run the application:
```bash
dapr run --app-id orderprocessing --app-port 6001 --dapr-http-port 3601 --dapr-grpc-port 60001 mvn spring-boot:run
```
{{% /codetab %}}
{{% codetab %}}
```python
#dependencies
import random
from time import sleep
import requests
import logging
import json
from dapr.clients import DaprClient
#code
logging.basicConfig(level = logging.INFO)
BINDING_NAME = 'checkout'
BINDING_OPERATION = 'create'
while True:
sleep(random.randrange(50, 5000) / 1000)
orderId = random.randint(1, 1000)
with DaprClient() as client:
#Using Dapr SDK to invoke output binding
resp = client.invoke_binding(BINDING_NAME, BINDING_OPERATION, json.dumps(orderId))
logging.basicConfig(level = logging.INFO)
logging.info('Sending message: ' + str(orderId))
```
Navigate to the directory containing the above code, then run the following command to launch a Dapr sidecar and run the application:
```bash
dapr run --app-id orderprocessing --app-port 6001 --dapr-http-port 3601 --app-protocol grpc python3 OrderProcessingService.py
```
{{% /codetab %}}
{{% codetab %}}
```go
//dependencies
import (
"context"
"log"
"math/rand"
"time"
"strconv"
dapr "github.com/dapr/go-sdk/client"
)
//code
func main() {
BINDING_NAME := "checkout";
BINDING_OPERATION := "create";
for i := 0; i < 10; i++ {
time.Sleep(5000)
orderId := rand.Intn(1000-1) + 1
client, err := dapr.NewClient()
if err != nil {
panic(err)
}
defer client.Close()
ctx := context.Background()
//Using Dapr SDK to invoke output binding
in := &dapr.InvokeBindingRequest{ Name: BINDING_NAME, Operation: BINDING_OPERATION , Data: []byte(strconv.Itoa(orderId))}
err = client.InvokeOutputBinding(ctx, in)
log.Println("Sending message: " + strconv.Itoa(orderId))
}
}
```
Navigate to the directory containing the above code, then run the following command to launch a Dapr sidecar and run the application:
```bash
dapr run --app-id orderprocessing --app-port 6001 --dapr-http-port 3601 --dapr-grpc-port 60001 go run OrderProcessingService.go
```
{{% /codetab %}}
{{% codetab %}}
```javascript
//dependencies
import { DaprServer, DaprClient, CommunicationProtocolEnum } from 'dapr-client';
//code
const daprHost = "127.0.0.1";
var main = function() {
for(var i=0;i<10;i++) {
sleep(5000);
var orderId = Math.floor(Math.random() * (1000 - 1) + 1);
start(orderId).catch((e) => {
console.error(e);
process.exit(1);
});
}
}
async function start(orderId) {
const BINDING_NAME = "checkout";
const BINDING_OPERATION = "create";
const client = new DaprClient(daprHost, process.env.DAPR_HTTP_PORT, CommunicationProtocolEnum.HTTP);
//Using Dapr SDK to invoke output binding
const result = await client.binding.send(BINDING_NAME, BINDING_OPERATION, { orderId: orderId });
console.log("Sending message: " + orderId);
}
function sleep(ms) {
return new Promise(resolve => setTimeout(resolve, ms));
}
main();
```
Navigate to the directory containing the above code, then run the following command to launch a Dapr sidecar and run the application:
```bash
dapr run --app-id orderprocessing --app-port 6001 --dapr-http-port 3601 --dapr-grpc-port 60001 npm start
```
{{% /codetab %}}
{{< /tabs >}}
All that's left now is to invoke the output bindings endpoint on a running Dapr instance.
You can also invoke the output bindings endpoint using HTTP:
```bash
curl -X POST -H 'Content-Type: application/json' http://localhost:3601/v1.0/bindings/checkout -d '{ "data": { "orderId": "100" }, "operation": "create" }'
```
As seen above, you invoked the `/binding` endpoint with the name of the binding to invoke, in our case its `checkout`.
The payload goes inside the mandatory `data` field, and can be any JSON serializable value.
You'll also notice that there's an `operation` field that tells the binding what you need it to do.
You can check [here]({{< ref supported-bindings >}}) which operations are supported for every output binding.
Watch this [video](https://www.youtube.com/watch?v=ysklxm81MTs&feature=youtu.be&t=1960) on how to use bi-directional output bindings.
## References
- [Binding API]({{< ref bindings_api.md >}})
- [Binding components]({{< ref bindings >}})
- [Binding detailed specifications]({{< ref supported-bindings >}})