Preventing Alert Storms with InfluxDB 3's Processing Engine Cache

By Paul Dix / Developer
Mar 26, 2025

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A common problem in monitoring and alerting systems is not just alerting on what you’re seeing but preventing alert storms from overwhelming operators. When a system generates multiple notifications for the same incident, it leads to alert fatigue and can mask other important issues. For time series data, alert fatigue can result in missed anomalies, delayed responses to critical trends, and difficulty distinguishing real performance degradations from noise.

InfluxDB 3’s Processing Engine provides a solution to alert storms with its in-memory cache feature. This post demonstrates how to build a simple alert de-duplication system that prevents unnecessary additional notifications while delivering all alerts on important events.

The Processing Engine and in-memory cache

InfluxDB 3’s Processing Engine is an embedded Python environment that allows you to run code directly in your database, enabling real-time transformation, analytics, and responses to data as it arrives. One of its most powerful features is the in-memory cache, which enables plugins to:

  • Maintain state between executions
  • Share data across different plugins
  • Set expiration times for cached data
  • Operate in isolated or global namespaces

This stateful processing capability opens up new possibilities for designing intelligent monitoring systems directly within your database. Creating a more intelligent monitoring system reduces noise (aka alert storms). Without those distractions, you can focus on critical issues, streamlining incident response, and maintaining system performance.

Building an alert de-duplication plugin

Let’s build a plugin that demonstrates how to use the cache to prevent alert storms. The basic idea is simple:

  • When a metric exceeds a threshold, generate an alert
  • Store the alert time in the cache
  • Implement a cooldown period during which duplicate alerts are suppressed

Here’s the complete code for our alert de-duplication plugin:

def process_writes(influxdb3_local, table_batches, args=None):
    """

    Process incoming metrics data and generate alerts with 
de-duplication 
    to prevent alert storms.

    This plugin:
    1. Monitors incoming metrics for threshold violations
    2. Uses the in-memory cache to track alert states
    3. Implements cooldown periods to prevent alert storms
    4. Writes alert events to an 'alerts' table
    """
    # Get configuration from trigger arguments or use defaults
    threshold = float(args.get("threshold", "90"))
    cooldown_seconds = int(args.get("cooldown_seconds", "300"))  
# 5 minutes default
    metric_table = args.get("metric_table", "cpu_metrics")
    metric_field = args.get("metric_field", "usage_percent")
    alert_type = args.get("alert_type", "high_value")

    for table_batch in table_batches:
        table_name = table_batch["table_name"]

        # Check if this table matches our configured metric table
        if table_name != metric_table:
            continue

        for row in table_batch["rows"]:
            # Check if we have the necessary fields
            if "host" not in row["tags"] or metric_field not in 
   row["fields"]:   
                continue

            host = row["tags"]["host"]
            value = row["fields"][metric_field]
            timestamp = row["timestamp"]

            # Check if the metric exceeds our threshold
            if value > threshold:
                # Construct a unique alert ID
                alert_id = f"{host}:{alert_type}"                `

                # Check if we're in a cooldown period for this 
alert
                last_alert_time = 
influxdb3_local.cache.get(alert_id)
                current_time = timestamp / 1_000_000_000  # 
Convert ns to seconds

                if last_alert_time is None or (current_time - 
last_alert_time > cooldown_seconds):
                    # We're not in a cooldown period, so generate 
a new alert
                    influxdb3_local.info(f"{alert_type} alert for 
{host}: {value} (threshold: {threshold})")                  

                    # Store the alert time in cache
                    influxdb3_local.cache.put(alert_id, 
current_time)

                    # Create an alert record
                    line = LineBuilder("alerts")
                    line.tag("host", host)
                    line.tag("alert_type", alert_type)
                    line.tag("metric_table", metric_table)
                    line.tag("metric_field", metric_field)
                    line.float64_field("threshold", threshold)
                    line.float64_field("value", value)
                    line.string_field("message", f"{metric_field} 
exceeded threshold: {value}")
                    line.time_ns(timestamp)

                    # Write the alert to the database
                    influxdb3_local.write(line)
                else:
                    # We're in a cooldown period, log this but 
don't generate a new alert
                    cooldown_remaining = cooldown_seconds - 
(current_time - last_alert_time)
                    influxdb3_local.info(
                        f"Suppressing duplicate {alert_type} 
alert for {host}: {value} "
                        f"(cooldown: {int(cooldown_remaining)}s remaining)"
                    )

Key concepts explained

Let’s break down how this plugin uses the cache to prevent alert storms.

1. Configurable Parameters

The plugin accepts several arguments that make it adaptable to different monitoring scenarios:

threshold = float(args.get("threshold", "90"))
cooldown_seconds = int(args.get("cooldown_seconds", "300"))  # 5 
minutes default
metric_table = args.get("metric_table", "cpu_metrics")
metric_field = args.get("metric_field", "usage_percent")
alert_type = args.get("alert_type", "high_value")

This makes the plugin reusable across different metrics and alert types.

2. Unique Alert Identifiers

For each potential alert, we create a unique identifier based on the host and alert type:

alert_id = f"{host}:{alert_type}"

This allows us to track different alert types separately for each host.

3. Cache-Based Cooldown Period

The core of our alert de-duplication logic uses the in-memory cache:

last_alert_time = influxdb3_local.cache.get(alert_id)
current_time = timestamp / 1_000_000_000  # Convert ns to seconds

if last_alert_time is None or (current_time - last_alert_time > 
cooldown_seconds):
    # Generate alert and update cache
    influxdb3_local.cache.put(alert_id, current_time)
    # ...
else:
    # Suppress duplicate alert
    # ...

When an alert condition is detected, we check if we’re within the cooldown period for this specific alert. If not, we generate a new alert and update the cache with the current time.

4. Automatic Alert Generation

When a new alert is needed, we write to a dedicated “alerts” table:

line = LineBuilder("alerts")
line.tag("host", host)
line.tag("alert_type", alert_type)
# ...
influxdb3_local.write(line)

This creates a permanent record of alerts that can be queried for analysis or connected to notification systems. We could also enable this plugin to connect to third-party systems like PagerDuty, Slack, or Discord to send alerts.

Deploying the plugin

To deploy this plugin, save it as alert_deduplication.py in your InfluxDB plugin directory and create a trigger:

influxdb3 create trigger \
  --trigger-spec "table:system_metrics" \
  --plugin-filename "alert_deduplication.py" \
  --trigger-arguments 
  threshold=95,cooldown_seconds=600,metric_table=system_metrics,met
  ric_field=cpu_usage,alert_type=high_cpu \
  --database monitoring \
  cpu_alert_handler

You can create multiple triggers with different configurations to monitor various metrics:

influxdb3 create trigger \
  --trigger-spec "table:memory_metrics" \
  --plugin-filename "alert_deduplication.py" \
  --trigger-arguments 
  threshold=85,cooldown_seconds=300,metric_table=memory_metrics,met
  ric_field=memory_usage,alert_type=high_memory \
  --database monitoring \
  memory_alert_handler

Advanced configuration options

While our example focused on simple threshold-based alerts, you can extend this pattern to handle more sophisticated scenarios.

Dynamic Cooldown Periods

You could adjust the cooldown period based on the severity of the alert:

# Adjust cooldown period based on severity
severity = calculate_severity(value, threshold)
adjusted_cooldown = cooldown_seconds * (1 - severity/100)  # 
Shorter cooldown for more severe issues
influxdb3_local.cache.put(alert_id, current_time, 
ttl=adjusted_cooldown)

Alert Escalation

For persistent issues, you might implement escalation after repeated alerts:

# Get alert count from cache
alert_count = influxdb3_local.cache.get(f"{alert_id}:count", default=0)
alert_count += 1
influxdb3_local.cache.put(f"{alert_id}:count", alert_count)

# Escalate if this problem has triggered multiple alerts
if alert_count > 3:
    line.tag("priority", "high")
    line.string_field("message", f"ESCALATED: {message} (occurred {alert_count} times)")

Summary

The in-memory cache feature of InfluxDB 3’s Processing Engine enables powerful stateful processing directly within your database. By implementing alert de-duplication with configurable cooldown periods, you can create smarter monitoring systems that reduce noise while ensuring you’re notified of important events.

This simple example demonstrates one way to leverage the cache in your data processing pipelines. The same pattern can be applied to rate limiting, threshold adjustments, trend analysis, and many other scenarios where maintaining state between executions is valuable.

To learn more about InfluxDB 3’s Processing Engine and explore other capabilities, check out the documentation or try out some of the example plugins contributed by the community. Download InfluxDB 3 and get started with the Processing Engine today.