Project Metamorphosis: Wir präsentieren die Event-Streaming-Plattform der nächsten Generation. Mehr erfahren

Integrating Azure and Confluent: Real-Time Search Powered by Azure Cache for Redis and Spring Cloud

Self-managing a distributed system like Apache Kafka®, along with building and operating Kafka connectors, is complex and resource intensive. It requires significant Kafka skills and expertise in the development and operations teams of your organization. Additionally, the higher the volumes of real-time data that you work with, the more challenging it becomes to ensure that all of the infrastructure scales efficiently and runs reliably.

Confluent and Microsoft are working together to make the process of adopting event streaming easier than ever by alleviating the typical infrastructure management needs that often pull developers away from building critical applications. With Azure and Confluent seamlessly integrated, you can collect, store, process event streams in real-time and feed them to multiple Azure data services. The integration helps reduce the burden of managing resources across Azure and Confluent.

The unified integration with Confluent enables you to:

  • Provision a new Confluent Cloud resource from Azure client interfaces like Azure Portal/CLI/SDKs with fully managed infrastructure
  • Streamline single sign-on (SSO) from Azure to Confluent Cloud with your existing Azure Active Directory (AAD) identities
  • Get unified billing of your Confluent Cloud service usage through Azure subscription invoicing with the option to draw down on Azure commits; Confluent Cloud consumption charges simply appear as a line item on monthly Azure bills
  • Manage Confluent Cloud resources from the Azure portal and track them in the ”All Resources” page, alongside your Azure resources

Confluent has developed an extensive library of pre-built connectors that seamlessly integrate data from many different environments. With Confluent, Azure customers access fully managed connectors that stream data for low-latency, real-time analytics into Azure and Microsoft services like Azure Functions, Azure Blob Storage, Azure Event Hubs, Azure Data Lake Storage (ADLS) Gen2, and Microsoft SQL Server. More real-time data can now easily flow to applications for smarter analytics and more context-rich experiences.

Real-time search use case

In today’s rapidly evolving business ecosystem, organizations must create new business models, provide great customer experiences, and improve operational efficiencies to stay relevant and competitive. Technology plays a critical role in this journey with the new imperative being to build scalable, reliable, persistent real-time systems. Real-time infrastructure for processing large volumes of data with lower costs and reduced risk plays a key role in this evolution.

Apache Kafka often plays a key role in the modern data architecture with other systems producing/consuming data to/from it. These could be customer orders, financial transactions, clickstream events, logs, sensor data, and database change events. As you might imagine, there is a lot of data in Kafka (topics), but it’s useful only when processed (e.g., with Azure Spring Cloud or ksqlDB) or when ingested into other systems.

Let’s investigate an architecture pattern that transforms an existing traditional transaction system into a real-time data processing system. We‘ll describe a data pipeline that synchronizes data between MySQL and RediSearch, powered by Confluent Cloud on Azure. This scenario is applicable to many use cases, but we’ll specifically cover the scenario where batch data must be available to downstream systems in near real time to fulfill search requirements. The data can be further streamed to an ADLS store for correlation of real-time and historic data, analytics, and visualizations. This provides a foundation for other services through APIs to drive important parts of the business, such as a customer-facing website that can provide fresh, up-to-date information on products, availability, and more.

Real-time search using Azure Cache for Redis, Spring Cloud, and Confluent Cloud
Real-time search using Azure Cache for Redis, Spring Cloud, and Confluent

Below are the key elements and capabilities of the above-mentioned architecture:

The above-mentioned services use the JRediSearch library to interface with RediSearch in order to create indexes, add documents, and query.

Thanks to the JDBC source connector, data in MySQL (the products table) is sent to a Kafka topic. Here is what the JSON payload looks like:


Objectives

The data can be uploaded into a relational database on Azure Database for MySQL, in this case, through an application or a batch process. This data will be synchronized from Confluent Cloud on Azure to the RediSearch module available in the Azure Cache for Redis Enterprise service. This will enable you to perform real-time search with your data in a flexible way. The real-time data is also streamed to an ADLS store. All the service components can be deployed to one Azure region for low latency and performance. Additionally, these service components are deployed in a single Azure subscription to enable unified billing of your Confluent Cloud usage through Azure subscription invoicing.

Prerequisites

  1. An Azure account
  2. Install the Azure CLI to deploy and manage the infrastructure components
  3. JDK 11 or above for e.g. Open JDK
  4. A recent Maven release
  5. Install Git

Set up the Azure cloud environment

  1. Create an Azure Database for MySQL server
  2. Create an instance of Apache Kafka on Confluent Cloud
  3. Create a Redis Enterprise cache instance with the RediSearch module enabled
  4. Provision an instance of Azure Spring Cloud

Build and deploy applications to Azure Spring Cloud

  1. Set up the consumer application to process events from Kafka topics to Redis
  2. Set up the search app to query records from RediSearch
  3. Build and deploy the application JAR file

Use the search application to query data

  1. Use curl or an HTTP client to invoke the Search API

Export data to Azure Data Lake

  1. Setup ADLS Gen 2 connector to export data from Kafka topics

Clean up

  1. Delete the resources (MySQL, Confluent Cloud organization, Redis, and your Azure Spring Cloud instance) individually or delete the resource group

Configure MySQL and Confluent Cloud on Azure

MySQL instance on Azure

Create an Azure Database for MySQL server using the Azure CLI (or the Azure portal if that’s what you prefer):

Kafka cluster in Confluent Cloud

  • Set up and subscribe for Apache Kafka on Confluent Cloud, which you can easily discover via Azure Marketplace
  • Provide configuration details for creating a Confluent Cloud organization on Azure
  • Provisioning in Azure:  Seamlessly provision Confluent organizations through the Azure portal
  • Single sign-on to Confluent Cloud: Log in directly to Confluent Cloud
    Single sign-on to Confluent Cloud
  • Create Confluent Cloud fully managed resources like clusters, topics, and connectors
    Creating a cluster
  • Create a topic (optional): The connector automatically creates a topic (based on the default convention); create the topic manually if you want to override its default settings, though make sure to use the same topic name while configuring the connector
  • Configure and launch the MySQL source connector using the portal
Note

Before you move ahead, make sure that the basic pipeline is operational. Insert a record in MySQL and ensure that the Kafka topic is receiving messages.

For your Redis instance on Azure, ensure that you have the hostname and access keys handy.

Build and deploy applications to Azure Spring Cloud

Start by cloning the GitHub repository and go into the mysql-kafka-redis-integration directory:

git clone https://github.com/Azure-Samples/mysql-kafka-redis-integration
cd mysql-kafka-redis-integration

For both services, update the application.yaml file in the src/main/resources folder with the connection details for Azure Cache for Redis and the Confluent Cloud cluster.

Here is a trimmed down version for the change events processor service:

redis:
  host: <enter redis host>
  port: <enter redis port>
  password: <enter redis access key> 
topic:
  name: <topic name e.g. myserver.products>
  partitions-num: 6
  replication-factor: 3
spring:
  kafka:
    bootstrap-servers:
      - <enter Confluent Cloud bootstrap server>
    properties:
      ssl.endpoint.identification.algorithm: https
      sasl.mechanism: PLAIN
      request.timeout.ms: 20000
      retry.backoff.ms: 500
      sasl.jaas.config: org.apache.kafka.common.security.plain.PlainLoginModule required username="<enter Confluent Cloud API key>" password="<enter Confluent Cloud API secret>";
      security.protocol: SASL_SSL
...

The config for the Search API service is quite compact:

redis:
  host: <enter redis host>
  port: <enter redis port>
  password: <enter redis access key>

Build JAR files for the Spring applications:

export JAVA_HOME=<enter path to JDK e.g. /Library/Java/JavaVirtualMachines/zulu-11.jdk/Contents/Home>

# Change Events Processor service
mvn clean package -f change-events-processor/pom.xml

# Search API service
mvn clean package -f search-api/pom.xml

Install the Azure Spring Cloud extension for the Azure CLI:

az extension add --name spring-cloud

Create the Azure Spring Cloud applications corresponding to both of the services:

# Change Events Processor service
az spring-cloud app create -n change-events-processor -s <enter the name of Azure Spring Cloud service instance> -g <enter azure resource group name> --runtime-version Java_11

# Search API service
az spring-cloud app create -n search-api -s <enter the name of Azure Spring Cloud service instance> -g <enter azure resource group name> --runtime-version Java_11 --is-public true

Deploy the JAR files for the respective applications that you just created:

# for the Change Events Processor service
az spring-cloud app deploy -n change-events-processor -s <enter the name of Azure Spring Cloud service instance> -g <enter azure resource group name> --jar-path change-events-processor/target/change-events-processor-0.0.1-SNAPSHOT.jar

# for the Search API service
az spring-cloud app deploy -n search-api -s <enter the name of Azure Spring Cloud service instance> -g <enter azure resource group name> --jar-path search-api/target/search-api-0.0.1-SNAPSHOT.jar

Time to see real-time search in action!

Now that we have all the components in place, we can test the end-to-end functionality. We will start by adding new product data to the MySQL database and use the Search app to make sure it has propagated all the way to Redis.

Insert the following sample data:

INSERT INTO `products` VALUES (42, 'Outdoor chairs', NOW(), '{"brand": "Mainstays", "description": "Mainstays Solid Turquoise 72 x 21 in. Outdoor Chaise Lounge Cushion", "tags": ["Self ties cushion", "outdoor chairs"], "categories": ["Garden"]}');

INSERT INTO `products` VALUES (43, 'aPhone', NOW(), '{"brand": "Orange", "description": "An inexpensive phone", "tags": ["electronics", "mobile phone"], "categories": ["Electronics"]}');

Get the URL for the Search API service using the portal or the CLI:

az spring-cloud app show -n search-api -s <enter the name of Azure Spring Cloud service instance> -g <enter azure resource group name>

Use curl or another HTTP client to invoke the Search API. Each of these queries will return results in form of a JSON payload, like so:

[
    {
        "created": "1614235666000",
        "name": "Outdoor chairs",
        "description": "Mainstays Solid Turquoise 72 x 21 in. Outdoor Chaise Lounge Cushion",
        "id": "42",
        "categories": "Garden",
        "brand": "Mainstays",
        "tags": "Self ties cushion, outdoor chairs"
    },
    {
        "created": "1614234718000",
        "name": "aPhone",
        "description": "An inexpensive phone",
        "id": "43",
        "categories": "Electronics",
        "brand": "Orange",
        "tags": "electronics, mobile phone"
    }
]

Here are a few examples to get you started. Note that the query parameter q is used to specify the RediSearch query.

# search for all records
curl <search api URL>/search?q=*

# search for products by name
curl <search api URL>/search?q=@name:Outdoor chairs

# search for products by category
curl <search api URL>/search?q=@categories:{Garden | Electronics}

# search for products by brand
curl <search api URL>/search?q=@brand:Mainstays

# apply multiple search criteria
curl <search api URL>/search?q=@categories:{Electronics} @brand:Orange
Note

You can continue to add more product information and check the pipeline. You may also want to try the following:


Connect to the Azure Cache for Redis instance using the redis-cli:

redis-cli -h <enter host name> -p <enter port i.e. 10000> -a <enter redis password/access key> --tls

Export data to Azure Data Lake

If you want to store this data in Azure Data Lake Storage longer term (cold storage), Confluent’s ADLS Gen2 connector has you covered. In our scenario, we already have product data flowing into the Kafka topic in Confluent Cloud on Azure—all we need to do is configure the connector to get the job done.

And guess what—that’s available as a fully managed offering as well!

Here is what you need to do:

  • Create a storage account
  • Configure the connector and start it; make sure to use the same topic name as you did before (e.g., myserver.products)
  • Confirm that the data was exported to the Azure storage container in the ADLS account

For a step by step guide, please follow the documentation.

Delete Azure resources

Once you’re done, delete the services so that you do not incur unwanted costs. If they are in the same resource group, simply deleting the resource group will suffice. You can also delete the resources (MySQL, Confluent Cloud organization, Redis, and Azure Spring Cloud instance) individually.

Summary

The urgency for real-time applications will grow exponentially as more businesses undergo digital transformation. With the new integration between Confluent and Azure along with the fully managed Kafka connectors available to export and source data into Azure data and storage services, you will be able process huge volumes of data much faster, simplify integration, and avoid the challenges of setting up and maintaining complex distributed systems.

This complete guide showed you the high-level architecture on how to run this solution on Azure based on managed PaaS services. The benefit of this is that you don’t have to set up and maintain complex distributed systems, such as a database, event streaming platform, and runtime infrastructure for your Spring Boot Java apps.

Bear in mind that this is just one part of a potentially larger use case. Thanks to Kafka, you can extend this solution to integrate with other systems as well, such as Azure Data Lake, using yet another fully managed ADLS Gen2 connector.

Want to learn more?

If you’d like to learn more, Get started with Apache Kafka on Confluent Cloud via Azure Marketplace and follow the quick start. When you sign up, you’ll receive $200 of free usage each month for your first three months. Use the promo code CL60BLOG to receive an additional $60 of free usage.*

Loslegen

Ramya Oruganti is a senior product manager at Microsoft. She works on the Azure Developer Experience team focused on building integration services between Azure and Confluent. She has been working in the cloud and data space for more than a decade. From engineering to solution architecture roles at organizations like IBM, Oracle, and now Microsoft, Ramya has a wealth of hands-on understanding when it comes to cloud products.

Abhishek Gupta is a senior developer advocate at Microsoft where he helps developers be successful with the Azure platform. His key focus areas include Kafka, databases, and Kubernetes. Abhishek is also an open source contributor and a Confluent Community Catalyst. Previously, in his role as a product manager, he helped build and advocate for developer-focused PaaS products.

Did you like this blog post? Share it now

Subscribe to the Confluent blog

More Articles Like This

Debuting a Modern C++ API for Apache Kafka

Morgan Stanley uses Apache Kafka® to publish market data to internal clients and to persist it for replay purposes. We started out using librdkafka’s C++ API, which maintains C++98 compatibility.

Confluent and Elastic Partner to Deliver Optimized Search and Real-Time Analytics

Today, I am delighted to announce an expanded partnership with Elastic. Together, we’re enabling our joint customers to set data in motion, and through that, deliver optimized search, real-time analytics,

Setting Up Secure Networking in Confluent with Azure Private Link

We’re happy to announce that Confluent Cloud, our cloud-native service for Apache Kafka®, now supports Azure Private Link for secure network connectivity, in addition to the existing Azure Virtual Network