Understanding Active-Active Clustering: A Comprehensive Guide 101

By: Published: May 13, 2022

While installing a server infrastructure, the common problem faced by administrators is to deploy a fault-tolerant and nonresistant design that has the potential to handle a single or multi-node failure. Most enterprises and companies require zero downtime for their customers to prevent node failure. This is where the concept of Active-Active Clustering comes into the picture. Active-Active Clustering architecture is a perfect approach to eliminate zero downtime. 

In this article, you will gain information about Active-Active Clustering. You will also gain a holistic understanding of the architecture of ACtive-Active Clustering, its advantages, and disadvantages, and comparison with Active-Passive Clustering.

Read along to find out in-depth information about Active-Active Clustering.

Table of Contents

What is Active-Active Clustering?

In Active-Active Clustering, a data adaptability architecture is deployed to distribute client tasks over two to three nodes in a cluster format to store data in a safe, protected, and accessible position. By keeping data in a cluster format, the workload of client data will remain protected from the possibility of an unplanned segment failure. The architecture of this clustering model is usually made up of more than two nodes that operate simultaneously to achieve redundancy and load balancing. 

With a load balancer, the workload is spread all over the nodes to avoid any idle node from being overloaded. The presence of more than two nodes will help improve the throughput and response times of service requests.

Some administrators are also called Active-Active clustering as dual-active clustering due to the availability of two nodes. All the idle or independent node points have their own replicated database server, and these nodes can access this copied database at any instance, resulting in high performance of applications running over the architecture.

Another benefit of using a replicated database is to experience the synchronization of data defined as continuous availability where a pair of companion nodes are present. If the primary node, for instance, fails to respond, then the secondary node comes into action and resumes the work of a primary node. Thus, the design will not suffer any node failure.

The job of a secondary node is to monitor all the traffic held by the primary node until it comes back from recovery mode and resumes its work. All the unissued transactions are kept and then resent to the presently operating node during the failover and failback process to prevent duplication and clean data thoroughly. 

Simplify Data Transformation using Hevo’s No-code Data Pipeline

Hevo Data, a Fully-managed Data Pipeline platform, can help you automate, simplify & enrich your data replication process in a few clicks. With Hevo’s wide variety of connectors and blazing-fast Data Pipelines, you can extract & load data from 100+ Data Sources straight into Data Warehouses, or any Databases of your choice. To further streamline and prepare your data for analysis, you can process and enrich raw granular data using Hevo’s robust & built-in Transformation Layer without writing a single line of code!


Hevo is the fastest, easiest, and most reliable data replication platform that will save your engineering bandwidth and time multifold. Try our 14-day full access free trial today to experience an entirely automated hassle-free Data Replication!

Architecture of Active-Active Clustering

To understand the architecture of Active-Active Clustering, you can check the following infrastructure wherein the load balancer is connected to two HTTP nodes and multiple web clients. The purpose of a load balancer is to indirectly connect web clients to servers. The assignment of web clients to nodes in a cluster depends on the algorithm of a load balancer.

For example, if the load balancer is running a Round Robin algorithm, then the first client will go to the first node, the second client will go to the second node, the third client will be connected to a first node, and so on. For high availability and redundancy configuration during disaster recovery mode, all nodes must operate in a virtually identical manner. 

Active-active Clustering: Architecture
Image Source

Advantages of Active-Active Clustering

The deployment of Active-Active clustering offers the following benefits:

  • It provides high availability data architecture to annihilate a single point of failure, ensuring the full-time response of mission-critical applications, solutions, systems, and databases.
  • It continuously maintains uptime longer than usual time.
  • It enables the architecture to balance the load over a cluster of servers.
  • It’s easy to increase the capacity of the methodology as traffic increases. Consequently, the network team can add additional nodes whenever it’s required. 
  • As there’s no single point of failure in this system, the admin can experience high reliability and complete backup. 
  • Provision of data monotony and resiliency as both failures and recovery become transparent across architecture and network.
  • Presence of zero Recovery Point Object (RPO) and zero Recovery Time Objective (RTO) functionalities.

Active-Active vs Active-Passive Clustering

In Active-Active Clustering architecture, the units of a client are fastened to a load balancer to allocate workloads onto multiple active servers. Here, a user can access all the resources of computing servers during the regular function of architecture. 

In Active-Passive Clustering architecture, the systems of a client are joined to the main server instead of a load balancer to hold the full workload, while a backup server stays idle. It only gets activated in the event of an outage. Here, the backup server examines action during outage failure. 

Although the Active-Passive cluster also contains two nodes, it connects web clients directly to nodes, as shown in the following image. Unlike Active-Active clustering architecture, if the first node is active, the second node will remain in a standby position, and vice versa. Therefore, the second node or server takes full responsibility for the web clients when the primary server faces any failure or event outage. For this reason, the configuration setting of nodes or servers must be identical. 

Active-active Clustering: Architecture of Active-Passive Cluster
Image Source

What makes Hevo’s ETL Process Best-In-Class

Providing a high-quality ETL solution can be a difficult task if you have a large volume of data. Hevo’s automated, No-code platform empowers you with everything you need to have for a smooth data replication experience.

Check out what makes Hevo amazing:

  • Fully Managed: Hevo requires no management and maintenance as it is a fully automated platform.
  • Data Transformation: Hevo provides a simple interface to perfect, modify, and enrich the data you want to transfer.
  • Faster Insight Generation: Hevo offers near real-time data replication so you have access to real-time insight generation and faster decision making. 
  • Schema Management: Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
  • Scalable Infrastructure: Hevo has in-built integrations for 100+ sources (with 40+ free sources) that can help you scale your data infrastructure as required.
  • Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
Sign up here for a 14-day free trial!

Disadvantages of Active-active Clustering

The deployment of Active-Active clustering has the following limitations:

1) Requirement of Load Balancers

One of the most significant disadvantages suffered by administrators while deploying Active-Active clustering is that the load balancer bears all the workload of a web client. In the presence of a single load balancer, the network servers along with the user sessions would become slow or perform a limited operation during the failure of an independent load balancer. To avoid such a type of incident, it is recommended to embed more than one load balancer in the architecture of Active-Active clustering. 

2) Cost

The Active-Active clustering architecture approach is comparatively more expensive than the Active-Passive method due to the presence of additional load balancers. Moreover, the hardware and design of such type of infrastructure must be of premium quality to run all the operations smoothly. 

3) Maintenance

Active-Active clustering architecture requires 24/7 maintenance and monitoring of hardware, network nodes, and design to improve infrastructure reliability. 


Active-Active Clustering architecture is one of the most robust, trusted, reliable and upgradeable server setups currently in use. With this configuration, the admin team receives a complete view of the network status, infrastructure and load balancer output at all times. It also enables the team members to keep an eye on potential problems and unveil what solutions can be used to solve those issues. 

Hevo Data, a No-code Data Pipeline provides you with a consistent and reliable solution to manage data transfer between a variety of sources and a wide variety of Desired Destinations with a few clicks.

Visit our Website to Explore Hevo

Hevo Data, with its strong integration with 100+ Data Sources (including 40+ Free Sources) allows you to not only export data from your desired data sources & load it to the destination of your choice but also transform & enrich your data to make it analysis-ready. Hevo also allows the integration data from non-native sources using Hevo’s in-built REST API & Webhooks Connector. You can then focus on your key business needs and perform insightful analysis using BI tools. 

Want to give Hevo a try? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You may also have a look at the amazing price, which will assist you in selecting the best plan for your requirements.

Share your experience of understanding Active-Active Clustering in the comment section below! We would love to hear your thoughts.

Syeda Famita Amber
Freelance Technical Content Writer, Hevo Data

Syeda is a freelance writer having passion towards wiriting about data industry who creates informative content on data analytics, machine learning, AI, big data, and business intelligence topics.

No-code Data Pipeline for your Data Warehouse