What is auto failover?
Imagine this: a bank sends a client an OTP code to confirm a transaction. If the primary message broker crashes, the code never arrives. As a result, the client can’t log in to the app, the transaction fails, and support requests surge. This scenario frequently occurs without auto failover. And it costs businesses a lot of money.
According to this EMA research (2024), unplanned downtime costs organizations an average of $14,056 per minute. For large enterprises, this figure escalates to $23,750 per minute. Meanwhile, 83% of consumers say that sending and receiving text messages is their primary mobile activity, surpassing social media and email. As businesses increasingly rely on messaging channels, failures are becoming more and more costly.
You need to use auto failover to avoid situations like these. Here’s how.
Auto failover is a mechanism that automatically detects system failures and switches to a backup component without any human intervention.
Simply put, it ensures that failures don’t visibly disrupt the user experience.
If one server goes down, the system automatically locates a replacement, reroutes traffic, and continues processing messages, often without the user even noticing that anything went wrong.
How it works
Data replication
To ensure the system can survive a failure, data must not be stored in a single location. In Apache Kafka, for example, each piece of data is stored on multiple servers.
One server acts as the primary (leader), while the others act as backups (followers), constantly replicating its state. If the primary server fails, one of the backups automatically takes its place.
This allows the system to continue operating without data loss or downtime.
Failure detection
The system must detect that something has gone wrong.
To achieve this, nodes regularly exchange signals (heartbeats). If one of them stops responding, it is considered unavailable, and a failover process is initiated.
Leader election
After a failure, the system must quickly determine which node will act as the primary.
The Apache Kafka cluster has a special component (the controller) that monitors the state of nodes and appoints new leaders for partitions. This happens automatically and simultaneously for multiple partitions, speeding up the process. Even if the controller itself fails, another node automatically takes over.
Automatic client failover
After a leadership change, it’s important that clients continue to operate.
In Kafka, clients automatically update cluster information, find a new leader, and continue sending messages. This typically takes fractions of a second to a few seconds.
In RabbitMQ, the system handles this: connections and subscriptions are automatically restored without any action required from the client.
How failover operates at multiple levels
Failover combines several mechanisms that function across different levels of the system.
Level 1. Infrastructure failover
First, the infrastructure itself may fail. If one of the servers or the message broker goes down, the system switches to a backup server, and message processing continues. This is the basic level of autofailover.
Level 2. Client-side failover
However, simply replacing the server isn’t enough. Client applications and services must also recognize the new server. This is where the second level of failover comes into play. The client reconnects, updates its metadata, and resumes sending or receiving messages.
Level 3. Channel failover
And even this doesn’t always solve the problem. Sometimes the infrastructure is operational, but the delivery channel itself is down. For example, SMS messages aren’t delivered due to carrier issues.
Then the third level (channel failover) is activated. The system can send the same code via another channel, such as WhatsApp, push notifications, or email.
Ultimately, a reliable system consists of multiple layers of protection that reinforce one another.
Types of failover in messaging systems
At the infrastructure level, several approaches can be implemented to ensure failover capabilities. These include hot, warm and cold standbys.
Hot standby
In this setup, the backup server is already running and fully synchronized with the primary server. Switchover occurs almost instantly, allowing for seamless continuity, but it does require more resources.
Warm standby
Here, the backup server is running but is not actively processing traffic. In the event of a failure, it takes a few seconds to become operational and handle the load.
Cold standby
In this case, the backup server is turned off and activates only when a failure occurs. While this is more cost-effective, it results in significant downtime during the switch.
In addition to these infrastructure-level redundancies, it’s advisable to use alternative channels (e.g., WhatsApp, push notifications, voice, or email) as backups. If the SMS channel becomes unavailable or degraded, the system can automatically switch to another delivery method.
Autofailover key metrics and settings
Monitoring auto failover key metrics and settings is essential because they directly impact system reliability and user experience. Keeping an eye on these settings ensures that critical messages are delivered without unnecessary downtime or data loss. And don’t even get us started on revenue loss!
- RTO (Recovery Time Objective). This metric defines the maximum acceptable duration for recovery. Essentially, it measures how long the system remains unresponsive before users notice. In a well-configured Apache Kafka cluster, RTO typically ranges from 1 to 3 seconds. During overload or network instability, this latency may increase.
- RPO (Recovery Point Objective). This indicates the maximum acceptable data loss, ideally aiming for zero. For instance, using acks=all in Kafka and enabling durable queues in RabbitMQ ensures messages are written to multiple copies simultaneously, minimizing the risk of message loss. However, some data may still be lost in rare scenarios, such as write failures.
- Replication factor. This refers to the number of data copies stored. A single copy offers no failover capability, while two copies allow the system to endure one failure but remain vulnerable. Therefore, a minimum of three copies is typically used in production to provide a safety margin, albeit at a higher resource cost.
- Heartbeat interval. This determines how quickly the system can detect issues. Nodes regularly check each other’s health, with more frequent checks leading to faster failure detection and quicker failover initiation. However, overly frequent checks can increase system load.
Automated failover in messaging systems is essential for any scenario where messages represent business value, such as OTP codes, transaction notifications, monitoring alerts, and operational events. Modern cloud messaging systems incorporate buffering, automatic failover, and intelligent load balancing across geographically distributed data centers.
Massiva’s CPaaS platform stands out for its robust and reliable auto failover capabilities for SMS, ensuring that your critical communications never miss a beat.
Timely notifications and transaction confirmations often determine customer satisfaction, Massiva’s auto failover system guarantees that messages are consistently delivered, even in the event of an infrastructure failure.
With real-time monitoring and automatic channel switching, Massiva ensures that your SMS messages reach their destination seamlessly, enhancing your communication strategy without worry.
Additionally, Massiva’s user-friendly dashboard provides insights and analytics, enabling you to monitor performance and optimize messaging strategies in real time.
With features designed to enhance scalability and adaptability, Massiva empowers businesses to grow without the fear of communication breakdowns.
By choosing Massiva, you not only invest in a reliable SMS solution but also a partner committed to delivering excellence in customer communication, no matter the challenges that arise.