Production Grade

Always on. No exceptions.

Planned maintenance windows are a broken contract with your users. Modern AI systems — fraud decisions, real-time pricing, personalization — cannot tolerate downtime. Not even 30 seconds. Not even "just for the upgrade."

Tacnode Context Lake is built for continuous operation. Automated failover, zero-downtime rolling upgrades, and multi-node consensus ensure that node failures and deployments are invisible to your applications — and to your users.

Downtime0msFailover in progress — users see nothing
LEADERNode 1
Node 2Node 2
Node 3Node 3

When a node fails, a new leader is elected automatically — no pager alert, no manual step, no gap in service.

"Planned Downtime" Is Still Downtime

Traditional high availability was designed for a world where databases served internal business logic — where a 2 AM maintenance window was an acceptable tradeoff. That world is gone.

Today, the database is in the critical path of every AI decision. Fraud agents query it before approving a transaction. Pricing engines consult it before quoting. Personalization pipelines read it before rendering a page. When the database is down — even for 45 seconds — the entire decision layer stalls.

Traditional HA approaches paper over this with failover scripts, replica promotion runbooks, and maintenance windows negotiated at 2 AM. None of it changes the fundamental reality: there is a gap, and during that gap, your AI systems are flying blind.

Where Traditional HA Breaks Down

HA failures don't announce themselves as infrastructure failures. They show up as bad decisions, lost revenue, and eroded user trust.

Fraud Detection

failover gap

Symptom: Primary database goes down during failover. Fraud decisions queue up or fail open for 30–90 seconds.

Cost: Fraudulent transactions approved during the gap. Chargebacks follow weeks later.

Real-Time Pricing

planned downtime

Symptom: Maintenance window scheduled for 2 AM. Traffic spikes don't read the schedule.

Cost: Pricing API returns errors during flash sale. Revenue lost. Partners escalate.

Personalization Engine

restart gap

Symptom: Rolling restart causes brief unavailability. Load balancer retries hit a node mid-restart.

Cost: Degraded recommendations surface. User sees stale or empty suggestions. Session abandoned.

AI Agent Orchestration

manual failover

Symptom: Context store is unavailable for 45 seconds during leader election with manual intervention.

Cost: Agent pipeline stalls. Downstream tasks time out. Cascading retries overwhelm retry queues.

The Maintenance Window Is the Problem

When a database requires a restart to apply an upgrade, the engineering team faces an impossible choice: accept downtime on a schedule, or fall behind on updates. Neither is acceptable when real-time AI workloads depend on that database being available every millisecond.

Tacnode eliminates the maintenance window entirely. Upgrades are applied as rolling hot swaps across nodes — new code is loaded into running processes without bouncing them. From your application's perspective, there is no upgrade. There is only continuous availability.

Traditional HA

Timeline (60 min)

ONLINE
DOWN
ONLINE
00:00~5 min downtime01:00

Upgrade or failover requires a restart. The gap is "planned." Users don't care.

Tacnode HA

Timeline (60 min)

ONLINE — CONTINUOUSLY
00:000ms downtime01:00

Rolling upgrades run hot. No restart. No window. No negotiation with your users.

Traditional HA vs. Tacnode HA

Tacnode's failure model is built on a single principle: state and execution fail differently, so they must be separated. State is durably maintained and versioned. Execution is elastic and replaceable. When a compute node fails, it's a capacity event — not a semantic one. State is intact. A replacement node starts and resumes serving against the same state, with no rollback, reconciliation, or reprocessing.

Most databases treat high availability as a recovery story: something bad happens, and then the system recovers. Tacnode treats it as a continuity story: the system never stops, so there is nothing to recover from.

Traditional HATacnode HA
Failover methodManual promotion or scripted runbookAutomated leader election via multi-node consensus
Failover time30 seconds to several minutesSub-second — no human in the loop
Upgrade strategyRestart required — brief downtime acceptedRolling hot upgrade — code path swapped without restart
Manual interventionRequired for failover, scaling, and upgradesNone — all transitions are automated and self-healing
Data loss riskYes — unflushed writes during failoverNo — consensus-based writes are durable before acknowledgment

What Real High Availability Actually Requires

"High availability" is easy to claim. The properties below are what it actually takes to deliver it — not as a recovery capability, but as a continuous guarantee.

Automated Leader Election

When a leader node fails, consensus-based election promotes a hot standby in under a second — no ops team required
Primary/replica setups require a human (or an error-prone script) to detect failure and promote a replica

Zero-Downtime Upgrades

New code is loaded onto running nodes in a rolling fashion. Requests continue to be served throughout — there is no restart boundary
Upgrades require bouncing nodes. Even "rolling" restarts create a window where a node is unavailable and load spikes elsewhere

No Single Point of Failure

Every node can serve reads and writes. Losing any one node — or any two — does not interrupt operation
Single primary with replicas means primary failure is always an outage until failover completes

Durable Consensus Writes

A write is only acknowledged after a quorum of nodes confirms it — so no data is lost even if the acknowledging node fails immediately after
Writes acknowledged by the primary can be lost if the primary fails before replication completes

See Tacnode run without interruption

Automated failover. Zero-downtime upgrades. No maintenance windows. Production-grade availability built in from day one — not bolted on after the fact.