Intelligent Automation: The New Ally for High Availability in SQL and NoSQL Environments

Automation in database administration is not a new concept. For decades, IT teams have used scripts, in shell, Python, or PowerShell, to automate routine tasks, from backups to log cleaning. A natural extension of this practice was the creation of scripts to orchestrate failover in high-availability architectures. These scripts, often triggered by a simple ping check or service status, represent the first generation of automation. And they are dangerously fragile.

Traditional automation, based on binary triggers and simple logic, is inadequate for the complexity of modern data systems, whether they are SQL clusters or NoSQL replicas. A failover is not a trivial event; it is a surgical operation that, if executed based on incomplete information or at an inopportune moment, can cause more damage than the original failure, leading to scenarios of split-brain, data loss, or prolonged downtime.

The necessary evolution is the transition from traditional automation to intelligent automation. This is not a synonym for artificial intelligence, but for a reliability engineering (SRE) approach, where automation is a robust system that perceives the state of the environment through rich telemetry, analyzes multiple factors to make a decision, and acts in an orchestrated and safe manner.

At HTI Tecnologia, ensuring high availability in our 24/7 support service is built on this philosophy. We do not depend on fragile scripts; we design and manage automation systems that increase resilience.

This article dissects the failures of traditional automation and details the pillars of intelligent automation, demonstrating why it is a requirement, not a luxury, for mission-critical environments.

The Fragility of Traditional Automation

Traditional failover scripts usually fail for three fundamental reasons, all rooted in their inability to understand context.

1. Single-Signal Triggers (Brittle Triggers): Most scripts are triggered by a single binary metric: is the primary node responding to pings? Is the database process running? This is a dangerous model. A server can be “alive” (responding to pings) but have its I/O subsystem completely saturated, making it functionally useless. A failover based only on a ping would not be triggered, while the application would already be suffering an effective outage. Conversely, a transient network failure (a “network partition”) can make a node appear “dead” to the monitoring system, triggering an unnecessary failover that causes downtime.

2. Absence of Quorum and Consensus Validation: A simple failover script, executed from a single point of observation, has no way to distinguish between a real failure of the primary node and a connectivity failure with itself. If it acts alone, it might promote a secondary node while the primary is still active and receiving transactions from other parts of the application. This is the classic split-brain scenario, which leads to data divergence and, often, corruption.

3. Incomplete Action Logic: A successful failover is much more than just running a promote command on the secondary node. What happens to the old primary node? If it comes back online, it must be prevented from accepting new writes. This process, known as fencing or STONITH (Shoot The Other Node In The Head), is often neglected in simple scripts. Furthermore, how is the application redirected to the new primary? The automation needs to integrate with service discovery systems (like Consul), load balancers, or DNS, a complexity that goes beyond a linear script.

Traditional automation treats failover as a command. Intelligent automation treats it as a distributed engineering process.

The Pillars of Intelligent Automation for High Availability

Intelligent automation is a software system that operates in a continuous cycle of perception, analysis, and action. It is designed for resilience and safety, not just for task execution.

Pillar 1: Perception Through High-Granularity Telemetry

The basis of any intelligent decision is high-quality data. Automation cannot depend on a ping. It needs to consume a rich stream of telemetry from each node in the environment to build a complete and contextualized view of the system’s state.

DBMS Health Metrics: Instead of just checking if the process is running, the system collects internal metrics.
- PostgreSQL/MySQL: Continuous analysis of the replication lag and, more importantly, its temporal derivative. A growing lag is a precursor to problems.
- SQL Server Always On: Monitoring the state of the Availability Group, the depth of the send queue, and the redo queue.
- MongoDB: Tracking the oplog window to ensure that replicas can synchronize, and the state of the replica set members (health, stateStr).
- Galera Clusters (Percona/MariaDB): Monitoring Flow Control metrics (wsrep_flow_control_paused) and the size of the certification queues (wsrep_cert_queue_size), which are direct indicators of a node acting as a bottleneck.
Host Health Metrics: Collection of operating system metrics that impact the DBMS, such as disk I/O latency, CPU iowait, network interface saturation, and packet loss.
Validation from Multiple Points: A node’s health is not checked from a single point, but from multiple observers (other cluster nodes, monitoring probes in different network segments) to avoid false positives caused by network partitions.

Pillar 2: Analysis and Decision Based on Engineering Logic

With rich data in hand, the automation’s “brain” executes a decision logic that mimics the diagnostic process of an expert DBA, but at machine speed. This logic is, in practice, a coded incident response runbook.

Quorum and Consensus Logic: The decision to initiate a failover is never made by a single entity. The system uses a consensus mechanism (such as that of tools like Orchestrator, Patroni, or the DBMS’s own election mechanism) to ensure that a majority of the nodes agree on the state of the primary and on which node should be the successor. This prevents split-brain.
Correlational Analysis: Before acting, the system correlates multiple data points. For example, if the primary is unreachable, but the telemetry also shows a massive packet loss across the entire network, the automation may decide not to initiate the failover, as the problem is with the network, not the node. It might, instead, alert the network team.
Prerequisite Check: Before promoting a replica, the automation runs a validation checklist:
- Is the candidate replica’s replication lag below a safe threshold (e.g., < 1 second)? Promoting a replica with significant lag means data loss (RPO violation).
- Is the candidate replica healthy in terms of host resources (CPU, I/O)? Promoting a replica that is already overloaded just moves the problem.
- Is the candidate replica accessible by the applications?

Pillar 3: Orchestrated and Safe Action

Once the decision to act is made, the execution is a sequence of choreographed steps to ensure a safe and clean transition.

Fencing the Old Node: The most critical step. Before promoting a new primary, the system must ensure that the old primary is isolated and can no longer accept writes. This can be done in several ways:
- Hardware shutdown (STONITH): Commands sent to the server’s power controller (BMC/IPMI).
- Network isolation: Changing firewall rules or VLANs.
- Revoking access via the DBMS: Changing permissions or terminating connections.
Promotion of the New Primary Instance: The promote command is executed on the chosen replica. The automation then waits and validates that the replica has successfully assumed the primary role.
Reconfiguration of the Cluster and Application: The system reconfigures the other nodes so that they start replicating from the new primary. Crucially, it updates the service discovery layer—be it a registry like Consul, a load balancer, or a DNS endpoint—so that application traffic is redirected to the new primary.
Post-Failover Validation: The automation runs a series of health checks on the new primary and tests the application’s connectivity to ensure the operation was successful. Only after this validation is the incident considered resolved.

Human Expertise: The Architect Behind the Automation

Intelligent automation does not eliminate the need for an expert DBA; it elevates their role. Building, testing, and maintaining a robust automation system like the one described is a complex software development project, not a secondary task.

The expert DBA or SRE is the system’s architect.

Design and Implementation: They choose, configure, and customize the automation tools (like Patroni for PostgreSQL, Orchestrator for MySQL, or Kubernetes operators for containerized databases), codifying the decision logic specific to the workload and the business’s SLAs.
Testing and Chaos Engineering: The expert designs and executes rigorous failover tests and chaos engineering practices to validate the automation’s behavior and discover its weaknesses in a controlled environment.
Edge Case Analysis and Continuous Improvement: The automation handles 99% of common failures. The expert is responsible for analyzing the “near misses” and complex incidents that the automation could not resolve, using these learnings (post-mortems) to refine and improve the system’s logic.

This is a highly specialized discipline that requires deep knowledge of both the DBMS and software and distributed systems engineering.

The HTI Advantage of Specialization

For most companies, building and maintaining this capability in-house is unfeasible. Partnering with a specialized service like HTI Tecnologia offers a direct path to resilience.

Expertise as a Service: Our team already has experience in implementing and managing these intelligent automation tools in hundreds of client environments, covering a vast spectrum of SQL and NoSQL technologies. We bring this proven expertise to your operation.
Focus on Reliability Engineering: Our Database Consulting services go beyond simple administration, focusing on the design of resilient architectures and the implementation of robust automation.
24/7 Support with Intelligence: Our 24/7 Support and Sustaining service not only monitors alerts but also manages and refines the automation systems that ensure the high availability of your environment, with human experts always available to intervene in the most complex cases.

High availability in mission-critical environments cannot depend on fragile scripts and the hope that they will work in a crisis. It needs to be an engineered property of the system, guaranteed by intelligent, robust, and tested automation.

The transition from traditional automation to intelligent automation is the transition from a reactive and risky approach to a discipline of reliability engineering. It is the recognition that, in complex systems, the only way to ensure business continuity is through systems designed for resilience.

Does your high-availability strategy still depend on scripts and manual intervention? Schedule a conversation with one of our specialists and discover how intelligent automation can protect your operation.

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