Designing Stateful Alert Engines with SQLite

Building Deduplication, Cooldown, Acknowledgement, Escalation, and Recovery Logic

In our previous article, Detecting Anomalies in Sensor Data with SQLite, we moved beyond simply collecting telemetry. We used thresholds, rolling statistics, historical baselines, and window functions to identify readings that looked unusual.

That gives us a signal.

It does not yet give us a good alerting system.

As we explored when storing IoT telemetry streams with SQLite, a monitoring system may continuously collect temperature, vibration, pressure, and other measurements from connected devices.

Imagine an industrial motor whose normal operating temperature is around 60°C. Something goes wrong and the temperature climbs above its safe threshold.

The sensor reports every ten seconds.

Without additional logic, our system might produce:

14:32:10  High temperature
14:32:20  High temperature
14:32:30  High temperature
14:32:40  High temperature
14:32:50  High temperature
...

Five minutes later, one overheating motor has generated 30 alerts.

Nothing useful happened 30 times. One problem remained active for five minutes.

A real alert engine needs to understand that distinction.

It needs to know whether a condition is new, already active, acknowledged, getting worse, temporarily suppressed, or recovered.

That means alerting requires state.

SQLite is particularly useful here because the state can live transactionally beside the telemetry, anomaly records, device information, and local application state that produced it.

Let’s build an alert engine that remembers what is happening instead of reacting to every reading as though it were the first.

An Event Is Not an Alert

This distinction is the foundation of the design.

Suppose our anomaly detector produces:

Device: Motor-17
Metric: Temperature
Value: 78.4°C
Condition: HIGH_TEMPERATURE

That is an event.

An alert represents the continuing operational problem associated with that event.

If ten more high-temperature readings arrive, they may all belong to the same alert.

Event
Event
Event
Event
   ↓
ONE ACTIVE ALERT

The alert might tell us:

Motor-17 is overheating
Started: 14:32
Current value: 81.2°C
Peak value: 84.6°C
Occurrences: 27
Status: Active

We’ve transformed a stream of repeated detections into a useful piece of operational state.

Start with Alert Rules

We first need to describe what conditions can produce alerts.

For example:

CREATE TABLE AlertRule (
    AlertRuleID INTEGER PRIMARY KEY,
    MetricID INTEGER NOT NULL,
    RuleName TEXT NOT NULL,
    Severity TEXT NOT NULL,
    ThresholdValue REAL,
    CooldownSeconds INTEGER NOT NULL DEFAULT 300,
    EscalationSeconds INTEGER,
    Enabled INTEGER NOT NULL DEFAULT 1
);

A temperature rule might look conceptually like:

Rule: Motor High Temperature
Threshold: 75°C
Severity: Warning
Cooldown: 5 minutes
Escalate after: 15 minutes

The rule describes what should happen.

The alert table records what is happening right now.

Designing the Alert State

A useful alert record needs more than a timestamp.

CREATE TABLE Alert (
    AlertID INTEGER PRIMARY KEY,
    AlertRuleID INTEGER NOT NULL,
    DeviceID INTEGER NOT NULL,
    MetricID INTEGER NOT NULL,

    Status TEXT NOT NULL,

    FirstTriggeredAt INTEGER NOT NULL,
    LastTriggeredAt INTEGER NOT NULL,
    LastValue REAL,
    PeakValue REAL,
    OccurrenceCount INTEGER NOT NULL DEFAULT 1,

    AcknowledgedAt INTEGER,
    AcknowledgedBy TEXT,

    EscalationLevel INTEGER NOT NULL DEFAULT 0,
    LastEscalatedAt INTEGER,

    RecoveredAt INTEGER,

    FOREIGN KEY (AlertRuleID)
        REFERENCES AlertRule(AlertRuleID)
);

Now the database can answer questions such as:

• When did the problem begin?
  

• When was it last observed?
  

• How many times has the condition occurred?
  

• What was the worst value?
  

• Has anyone acknowledged it?
  

• Has it escalated?
  

• Has the condition recovered?
  

This is already much richer than storing independent notification records.

Think of Alerts as State Machines

An alert should move through a defined lifecycle.

A simple model might be:

NORMAL
   ↓
ACTIVE
   ↓
ACKNOWLEDGED
   ↓
RECOVERED

But real situations aren’t always perfectly linear.

An acknowledged alert may continue getting worse.

A recovered condition may return shortly afterward.

An active alert may need escalation before anyone acknowledges it.

So a more useful mental model is:

                 ┌──────────────┐
                 │    ACTIVE    │
                 └──────┬───────┘
                        │
             ┌──────────┼───────────┐
             ↓          ↓           ↓
       ACKNOWLEDGED  ESCALATED   RECOVERED
             │          │           │
             └──────────┴───────────┘

The exact schema can vary, but the principle should remain:

State transitions should be explicit.

That makes alert behavior predictable and testable.

Deduplication: One Problem, One Active Alert

Suppose Motor 17 remains above the high-temperature threshold.

Each new reading should update the existing alert rather than create another one.

We therefore need a way to identify the operational condition.

A useful identity might be:

AlertRuleID + DeviceID

If rules are scoped differently, it might include additional fields.

For our example, we can prevent multiple open alerts for the same rule and device with a partial unique index:

CREATE UNIQUE INDEX ux_alert_open_rule_device
ON Alert(AlertRuleID, DeviceID)
WHERE Status IN ('active', 'acknowledged', 'escalated');

Now SQLite itself helps enforce an important business rule:

Only one unresolved alert for this rule and device may exist at a time.

This is much safer than relying entirely on application code.

Updating an Existing Alert

Suppose the first abnormal reading creates:

AlertID: 412
Device: Motor-17
FirstTriggeredAt: 14:32
LastTriggeredAt: 14:32
LastValue: 76.1
PeakValue: 76.1
OccurrenceCount: 1
Status: active

Ten seconds later:

Value: 77.4

We don’t insert another alert.

We update:

UPDATE Alert
SET
    LastTriggeredAt = ?,
    LastValue = ?,
    PeakValue = MAX(PeakValue, ?),
    OccurrenceCount = OccurrenceCount + 1
WHERE AlertRuleID = ?
  AND DeviceID = ?
  AND Status IN ('active', 'acknowledged', 'escalated');

After several minutes:

FirstTriggeredAt: 14:32
LastTriggeredAt: 14:38
LastValue: 80.3
PeakValue: 82.1
OccurrenceCount: 37

The alert has accumulated context instead of producing noise.

Make Alert Creation Atomic

There is a concurrency problem hiding here.

Imagine two worker threads detect the same condition at nearly the same time.

Both ask:

Is there an active alert?

Both see none.

Both attempt to create one.

If uniqueness exists only in application logic, duplicate alerts can appear.

The database constraint protects us.

We can also structure the operation as an upsert.

For example, a schema can use an explicit active-condition table with a unique key:

CREATE TABLE ActiveAlert (
    AlertRuleID INTEGER NOT NULL,
    DeviceID INTEGER NOT NULL,
    AlertID INTEGER NOT NULL,

    PRIMARY KEY (AlertRuleID, DeviceID)
);

Now claiming the active alert becomes a transactional operation.

This illustrates a broader principle:

Let SQLite enforce invariants that must never be violated.

Application checks are useful. Database constraints are stronger.

Deduplication Is Not Suppression

These concepts are easy to confuse.

Deduplication says:

These repeated detections belong to the same operational problem.

Suppression says:

We know about this problem, but we don’t want to send another notification right now.

You may still update an alert every ten seconds while sending a notification only once every thirty minutes.

The alert state and notification state should therefore be separate.

Add a Notification Table

Let’s record actual delivery attempts independently.

CREATE TABLE AlertNotification (
    NotificationID INTEGER PRIMARY KEY,
    AlertID INTEGER NOT NULL,
    Channel TEXT NOT NULL,
    NotificationType TEXT NOT NULL,
    CreatedAt INTEGER NOT NULL,
    SentAt INTEGER,
    DeliveryStatus TEXT NOT NULL,

    FOREIGN KEY (AlertID)
        REFERENCES Alert(AlertID)
);

Now one alert can produce several notifications:

14:32  Initial warning
14:47  Escalation
15:02  Reminder
15:11  Recovery

while remaining a single alert throughout its lifecycle.

This separation will become increasingly important as the system grows.

Cooldowns Prevent Notification Storms

Suppose an alert remains active for three hours.

We probably don’t want an email every ten seconds.

A cooldown defines how soon another notification may be sent.

For example:

Cooldown = 30 minutes

If the last notification was at:

14:32

the next ordinary reminder cannot be sent before:

15:02

A query might inspect the most recent successful notification:

SELECT MAX(SentAt)
FROM AlertNotification
WHERE AlertID = ?
  AND DeliveryStatus = 'sent';

Then the application checks:

CurrentTime >= LastSentAt + Cooldown

If not, the alert continues updating silently.

The problem is still being tracked.

We’re simply controlling how often humans are interrupted.

Cooldowns Should Not Hide Escalation

Suppose a motor triggers a warning at 75°C.

Five minutes later it reaches 95°C.

If the ordinary cooldown is thirty minutes, should the system remain silent?

Probably not.

A higher-severity transition may need to bypass the normal reminder cooldown.

For example:

75°C → warning
85°C → critical

When severity increases:

warning → critical

the alert engine can immediately produce an escalation notification.

This is why cooldown should be treated as a notification policy, not as a blanket instruction to ignore the alert.

Acknowledgement Changes Human Workflow

Imagine an operator receives the alert and begins investigating.

They click:

Acknowledge

What should happen?

The condition still exists.

The motor is still hot.

So acknowledgement must not mean recovery.

It means:

A human has seen this alert and taken ownership of it.

We can record:

UPDATE Alert
SET
    Status = 'acknowledged',
    AcknowledgedAt = ?,
    AcknowledgedBy = ?
WHERE AlertID = ?
  AND Status = 'active';

Now dashboards can distinguish:

ACTIVE
Nobody has acknowledged the problem.

ACKNOWLEDGED
Someone is handling the problem.

That distinction is essential in multi-operator environments.

Acknowledgement Should Not Freeze the Alert

Suppose an engineer acknowledges a temperature warning at 78°C.

Then the motor climbs to 96°C.

The system shouldn’t think:

Someone acknowledged this, so we’re done.

New readings should continue updating:

LastValue
PeakValue
OccurrenceCount
LastTriggeredAt

and escalation rules should continue running.

Acknowledgement affects workflow.

It does not make the underlying condition disappear.

Escalation Adds Time to Severity

Some conditions become more serious simply because they persist.

Suppose:

0 minutes     Warning created
5 minutes     Still active
15 minutes    Escalate
30 minutes    Escalate again

The value may not have changed at all.

Time itself has become part of the alert logic.

A query can find alerts eligible for escalation:

SELECT
    AlertID,
    AlertRuleID,
    DeviceID,
    FirstTriggeredAt,
    EscalationLevel
FROM Alert
WHERE Status IN ('active', 'acknowledged', 'escalated')
  AND FirstTriggeredAt <= ?;

The application then evaluates the escalation policy.

For example:

Level 0:
Local dashboard

Level 1:
Notify technician

Level 2:
Notify supervisor

Level 3:
Trigger critical workflow

Escalation turns a detection engine into an operational system.

Escalation Should Be Idempotent

Imagine the escalation worker crashes immediately after sending a notification.

When it restarts, it may evaluate the same alert again.

Without protection, the supervisor could receive the same escalation twice.

We therefore need to record escalation state transactionally.

For example:

AlertID
EscalationLevel
LastEscalatedAt

A notification record can also carry a unique identity such as:

AlertID + NotificationType + EscalationLevel

and enforce it:

CREATE UNIQUE INDEX ux_alert_escalation
ON AlertNotification(
    AlertID,
    NotificationType,
    EscalationLevel
);

If EscalationLevel is part of the notification schema, retrying the same operation cannot create another identical escalation record.

This is the same production principle we used for rollups:

Anything that can be retried should be designed to tolerate retries.

Recovery Needs Its Own Rule

Suppose our high-temperature alert triggers when:

Temperature >= 75°C

Should it recover the moment the temperature reaches:

74.9°C

Not necessarily.

Consider:

75.2
74.8
75.1
74.9
75.3
74.7

If 75°C is both the trigger and recovery threshold, the alert may repeatedly open and close.

This is called flapping.

A better design uses hysteresis.

For example:

Trigger:
Temperature >= 75°C

Recover:
Temperature <= 70°C

Now the system requires a meaningful return toward normal before declaring recovery.

Recovery Can Require Persistence Too

Even crossing the recovery threshold once may not be enough.

Suppose:

69.8
76.0
69.9
75.4

The machine isn’t truly stable.

A stronger rule might be:

Recover only after temperature remains below 70°C for five minutes.

Now recovery is stateful too.

We may track:

RecoveryCandidateAt

The first qualifying reading starts the recovery timer.

If the value becomes abnormal again, reset it.

If the condition remains healthy for the required duration, transition the alert to recovered.

Recording Recovery

Once the condition has genuinely cleared:

UPDATE Alert
SET
    Status = 'recovered',
    RecoveredAt = ?
WHERE AlertID = ?
  AND Status IN ('active', 'acknowledged', 'escalated');

A recovery notification can then be queued:

Motor-17 temperature returned to normal.

Alert duration: 43 minutes
Peak temperature: 88.6°C
Acknowledged by: Operator 12

That final context is much more useful than simply saying:

Temperature normal.

Don’t Immediately Reopen a Recovered Alert

Imagine:

14:00  Alert starts
14:20  Recovers
14:21  Condition returns
14:24  Recovers
14:25  Condition returns

Technically, these could be separate incidents.

Operationally, they may represent one unstable problem.

This is where a reopen window can help.

For example:

Reopen window = 10 minutes

If the same condition returns within ten minutes of recovery, reopen the previous alert rather than creating a completely new incident.

If it returns two days later, create a new alert.

This preserves a more realistic incident history.

Cooldown and Reopen Windows Solve Different Problems

It’s worth keeping these separate.

A cooldown controls notification frequency while a condition is active.

A reopen window controls whether a recently recovered condition belongs to the previous incident.

For example:

Notification cooldown: 30 minutes
Recovery stability: 5 minutes
Reopen window: 10 minutes

Each timer solves a different operational problem.

Combining them into one generic “delay” setting makes alert behavior difficult to reason about.

Alert Rules Need More Than Thresholds

Our first AlertRule table was intentionally simple.

A more realistic rule might contain:

CREATE TABLE AlertRule (
    AlertRuleID INTEGER PRIMARY KEY,
    MetricID INTEGER NOT NULL,
    RuleName TEXT NOT NULL,

    TriggerOperator TEXT NOT NULL,
    TriggerValue REAL NOT NULL,

    RecoveryOperator TEXT NOT NULL,
    RecoveryValue REAL NOT NULL,

    TriggerDurationSeconds INTEGER NOT NULL DEFAULT 0,
    RecoveryDurationSeconds INTEGER NOT NULL DEFAULT 0,

    CooldownSeconds INTEGER NOT NULL DEFAULT 300,
    ReopenWindowSeconds INTEGER NOT NULL DEFAULT 600,

    Severity TEXT NOT NULL,
    Enabled INTEGER NOT NULL DEFAULT 1
);

Now alert behavior is configuration rather than scattered application logic.

Different metrics can use different policies without rewriting the engine.

Persistence Before Triggering

In the previous article, we discussed requiring an anomaly to persist before turning it into an operational alert.

Now we can implement that idea properly.

Suppose vibration must remain abnormal for 60 seconds.

The first abnormal reading doesn’t create an alert immediately.

Instead, we create or update a candidate state:

CREATE TABLE AlertCandidate (
    AlertRuleID INTEGER NOT NULL,
    DeviceID INTEGER NOT NULL,
    FirstDetectedAt INTEGER NOT NULL,
    LastDetectedAt INTEGER NOT NULL,
    DetectionCount INTEGER NOT NULL,

    PRIMARY KEY (AlertRuleID, DeviceID)
);

If abnormal readings continue long enough:

FirstDetectedAt + TriggerDuration <= CurrentTime

the candidate becomes an alert.

If the readings return to normal before then, delete the candidate.

This prevents a single noisy measurement from becoming an incident.

Missing Data Can Have Stateful Alerts Too

Not every alert originates from an abnormal value.

Suppose a sensor should report every minute.

We might define:

Expected interval: 60 seconds
Warning after: 3 minutes
Critical after: 15 minutes

The alert engine can evaluate the last reading:

SELECT MAX(RecordedAt)
FROM Telemetry
WHERE DeviceID = ?
  AND MetricID = ?;

If silence persists:

3 minutes → active warning
15 minutes → escalation

When telemetry resumes:

new reading arrives → recovery candidate

This fits the same state model.

The source of the condition differs, but the alert lifecycle doesn’t have to.

Store State, Not Just History

It may be tempting to derive the current alert state every time by replaying all historical events.

That can work in some architectures, but for a local monitoring engine it often adds unnecessary complexity.

SQLite can maintain compact current state:

Current active alerts
Current acknowledgement
Current escalation level
Current recovery candidate
Latest notification state

while retaining historical records separately.

This gives dashboards fast answers.

For example:

SELECT
    AlertID,
    DeviceID,
    MetricID,
    Status,
    FirstTriggeredAt,
    LastValue,
    PeakValue,
    EscalationLevel
FROM Alert
WHERE Status IN ('active', 'acknowledged', 'escalated')
ORDER BY FirstTriggeredAt;

No reconstruction is necessary.

Keep an Alert Event History

Current state alone isn’t enough for investigation.

Suppose an alert currently says:

Status: recovered

An engineer may want to know exactly what happened before recovery.

Add an event log:

CREATE TABLE AlertEvent (
    AlertEventID INTEGER PRIMARY KEY,
    AlertID INTEGER NOT NULL,
    EventType TEXT NOT NULL,
    EventAt INTEGER NOT NULL,
    Value REAL,
    Details TEXT,

    FOREIGN KEY (AlertID)
        REFERENCES Alert(AlertID)
);

Possible events include:

triggered
updated
acknowledged
escalated
notification_sent
recovery_started
recovery_cancelled
recovered
reopened

Now we have both:

Alert table
→ current incident state

AlertEvent table
→ incident history

This combination is powerful.

Use Transactions for State Transitions

Imagine an alert is acknowledged.

We need to:

1. Update the alert.
   

2. Record the acknowledgement event.
   

Those two operations belong together.

BEGIN IMMEDIATE;

UPDATE Alert
SET
    Status = 'acknowledged',
    AcknowledgedAt = ?,
    AcknowledgedBy = ?
WHERE AlertID = ?
  AND Status = 'active';

INSERT INTO AlertEvent (
    AlertID,
    EventType,
    EventAt,
    Details
)
VALUES (?, 'acknowledged', ?, ?);

COMMIT;

If something fails, both changes should roll back.

We don’t want:

Alert says acknowledged
but history doesn't show it

or the reverse.

This is exactly the kind of consistency SQLite transactions handle well.

Beware of Invalid State Transitions

Application bugs can produce nonsense such as:

recovered → acknowledged

or:

recovered → escalated

Define allowed transitions explicitly.

For example:

Current stateAllowed next stateActiveAcknowledged, Escalated, RecoveredAcknowledgedEscalated, RecoveredEscalatedAcknowledged, RecoveredRecoveredReopened

Not every system needs exactly these states.

The important part is to decide what is valid rather than letting arbitrary updates happen.

Restarting Must Not Lose Alert State

This is where SQLite becomes especially valuable at the edge.

Suppose a monitoring application crashes while three alerts are active.

If alert state exists only in memory:

Application restart
      ↓
Alert knowledge disappears

The system may resend notifications, forget acknowledgements, or incorrectly treat continuing problems as new incidents.

If the state lives in SQLite:

Application restart
      ↓
Load unresolved alerts
      ↓
Resume timers and evaluation

The engine remembers:

When the alert started
Whether it was acknowledged
Who acknowledged it
When it last notified
Its escalation level
Whether recovery had begun

This is what makes the engine stateful in a durable sense. Because this operational state can be critical during an incident, it should also be considered as part of your broader SQLite backup and data protection strategy.

Timers Should Be Stored as Timestamps

Avoid relying entirely on in-memory timers.

Suppose the application schedules:

Escalate in 15 minutes

and then crashes after ten minutes.

An in-memory timer disappears.

Instead, persist the information needed to reconstruct the decision.

For example:

FirstTriggeredAt = 14:00
Escalation interval = 15 minutes

After restart at 14:12, the application can calculate:

Next escalation = 14:15

Nothing important was lost.

Store facts and deadlines, not only running timers.

What Happens When the Device Was Offline for Hours?

Suppose an edge monitoring application stops at midnight and restarts at 06:00.

An alert had been active since 23:50.

Should the system immediately send six hours of missed reminders?

Usually not.

On startup, the engine should reconstruct current state and evaluate what is relevant now.

For example:

Alert still active?
Yes.

Escalation overdue?
Yes.

Send appropriate current escalation.

Not:

Replay every notification that would
have happened while offline.

Alert recovery after downtime needs policy, not blind replay.

Separate Alert State from Delivery

This separation becomes even more important when notifications leave the device.

Imagine:

Alert created successfully
      ↓
Internet unavailable
      ↓
Email cannot be delivered

The alert itself still exists.

Its state should not depend on whether an external service is reachable.

Think of the architecture as:

Telemetry
    ↓
Detection
    ↓
Alert State
    ↓
Notification Intent
    ↓
Delivery Worker
    ↓
Email / SMS / Push / Cloud

SQLite can safely preserve the notification intent until delivery becomes possible.

That prevents network failures from corrupting alert state.

Indexing the Alert Engine

Alert tables are usually much smaller than telemetry tables, but indexes still matter.

A common query is:

Find unresolved alerts for a device or rule.

Our partial unique index already helps.

We may also frequently ask:

Which alerts need escalation?

A targeted index could help depending on the schema and workload.

For example:

CREATE INDEX idx_alert_status_triggered
ON Alert(Status, FirstTriggeredAt);

For notification delivery:

CREATE INDEX idx_notification_pending
ON AlertNotification(DeliveryStatus, CreatedAt);

As always, measure the actual queries before adding many indexes.

The alert engine writes frequently, so unnecessary indexes still have a cost.

A Production Alert Flow

Let’s put the complete design together.

A new sensor reading arrives:

Sensor Reading
      ↓
Validate
      ↓
Store Telemetry
      ↓
Anomaly / Rule Evaluation

If normal:

Existing alert?
      ↓
Evaluate recovery

If abnormal:

Trigger duration satisfied?
      ↓
Find existing active alert
      ↓
YES → Update alert
NO  → Create alert

Then:

Alert State
      ↓
Severity changed?
      ↓
Escalation due?
      ↓
Cooldown expired?
      ↓
Create Notification Intent

Meanwhile:

Human Operator
      ↓
Acknowledgement
      ↓
Update Alert + Record Event

And eventually:

Condition Normal
      ↓
Recovery Duration
      ↓
Recovered
      ↓
Recovery Notification

This is much closer to what production monitoring actually requires.

Example: Motor Temperature Incident

Let’s walk through one complete incident.

Normal temperature:

62°C

Rule:

Trigger at: 75°C
Trigger duration: 60 seconds
Recover below: 70°C
Recovery duration: 5 minutes
Cooldown: 30 minutes
Escalate after: 15 minutes

At 14:00:

76°C

Candidate starts.

At 14:01:

78°C

The condition has persisted for one minute.

Alert created.

Status: active
FirstTriggeredAt: 14:00

Initial notification sent.

At 14:05:

81°C

Same alert updated.

No duplicate notification because the cooldown hasn’t expired.

At 14:08:

An engineer acknowledges it.

Status: acknowledged
AcknowledgedAt: 14:08

At 14:15:

The condition still exists.

Escalation policy activates.

EscalationLevel: 1

A supervisor notification is generated even though the alert was acknowledged.

At 14:27:

69°C

Recovery candidate starts.

At 14:29:

72°C

Recovery is cancelled.

At 14:36:

68°C

Recovery starts again.

The temperature remains below 70°C.

At 14:41:

Status: recovered
RecoveredAt: 14:41

The incident lasted approximately 41 minutes.

One physical problem produced:

Hundreds of sensor readings
Dozens of abnormal detections
One alert
One acknowledgement
One escalation
One recovery

That is the difference between detecting conditions and managing incidents.

What Belongs in SQLite?

SQLite is a strong fit for the durable core of a local alert engine:

• Alert rules
  

• Trigger candidates
  

• Current alert state
  

• Acknowledgements
  

• Escalation state
  

• Recovery state
  

• Notification intents
  

• Delivery attempts
  

• Alert history
  

• Retry-safe identifiers
  

External systems can still handle:

Email delivery
SMS
Push notifications
Pager services
Central fleet dashboards

SQLite doesn’t need to replace those services.

It gives them a reliable local source of truth.

Best Practices

When designing a stateful alert engine with SQLite:

• Treat repeated detections as events belonging to an alert, not as separate alerts.
  

• Define an explicit alert lifecycle.
  

• Enforce important uniqueness rules in SQLite.
  

• Separate alert state from notification delivery.
  

• Use cooldowns to control repeated notifications.
  

• Allow important severity changes to bypass ordinary cooldowns.
  

• Treat acknowledgement as ownership, not recovery.
  

• Continue evaluating acknowledged alerts.
  

• Make escalation retry-safe.
  

• Use separate trigger and recovery thresholds where appropriate.
  

• Require stable recovery when noisy signals can flap.
  

• Consider a reopen window for rapidly recurring incidents.
  

• Persist trigger candidates when conditions must last before alerting.
  

• Detect missing telemetry as an alertable condition.
  

• Keep current state and historical events separately.
  

• Use transactions for state transitions.
  

• Define valid transitions explicitly.
  

• Persist timestamps and deadlines instead of relying on memory-only timers.
  

• Reconstruct alert state after application restarts.
  

• Don’t blindly replay every missed notification after downtime.
  

• Index operational queries, but avoid unnecessary write overhead.
  

Closing Thoughts

An anomaly detector answers:

Does this measurement look unusual?

An alert engine answers a much harder set of questions:

Is this a new problem? Is it still happening? Has someone seen it? Is it getting worse? Should we notify again? Has it actually recovered?

Those questions require memory.

By storing that memory in SQLite, we can turn a stream of noisy detections into a durable incident lifecycle with deduplication, cooldowns, acknowledgement, escalation, recovery, and restart-safe state.

The result is not just fewer alerts.

It is better operational information.

One overheating motor should look like one evolving incident, not hundreds of unrelated warnings.

And once our system can detect problems and manage their alert lifecycle locally, another challenge appears.

What happens when the device needs to send those alerts, summaries, or telemetry elsewhere, but the network is unreliable?

That takes us to the next article:

SQLite as a Durable Store-and-Forward Buffer

Keeping data flowing reliably through intermittent and unreliable networks.

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