Real-Time Alerting on Bigtable Metrics with Cloud Monitoring

Google Cloud Bigtable integrates tightly with Cloud Monitoring, allowing you to create real-time alerts when performance, latency, throughput, or health metrics reach critical thresholds. This helps detect:

Hotspots and uneven traffic distribution

High read/write latency

Node saturation

High CPU usage

Storage pressure

Overloaded clusters

Application-side issues (timeouts, RPC errors)

You can push alerts to:

Slack

PagerDuty

Pub/Sub

Webhooks

Opsgenie

SMS

1. Bigtable Metrics Available in Cloud Monitoring

Bigtable exposes dozens of metrics. Common ones used for alerting:

Performance

Latency

bigtable.googleapis.com/server/latencies

P99, P95, P50 read/write latency

Node Health

CPU Utilization

bigtable.googleapis.com/cluster/cpu_load

Disk Utilization

bigtable.googleapis.com/cluster/storage_utilization

Node Count

Detect unexpected scaling or node failures.

Traffic / Throughput

Requests per second (RPS)

bigtable.googleapis.com/server/request_count

Throttle Count

bigtable.googleapis.com/server/throttled_requests

RPC Errors

bigtable.googleapis.com/server/error_count

Hotspot Detection

High latency + high CPU on a single node usually indicates a hotspot.

2. Creating Real-Time Alerts (Step-by-Step)

Step 1: Go to Cloud Monitoring

Google Cloud Console → Monitoring → Alerting → Create Policy

Step 2: Add a Condition

Choose Add Condition → Select Metric.

Search for Bigtable metrics, e.g.:

Bigtable Server » Latencies

Bigtable Cluster » CPU Load

Bigtable Cluster » Storage Utilization

Step 3: Configure Thresholds

Examples:

Latency Alert

Metric: Bigtable Server Latency (P99)

Condition: Above 50 ms

Duration: 5 minutes

CPU Spike Alert

Metric: Cluster CPU Load

Condition: Above 0.8 (80%)

Duration: 5 minutes

Storage Low-Capacity Alert

Metric: Storage Utilization

Condition: Above 0.7 (70%)

Duration: 10 minutes

High Throttled Requests

Metric: Throttled Requests Count

Condition: Above 0 (any throttling)

Duration: 2 minutes

You can filter by:

Project

Instance ID

Cluster ID

Zone

Table ID (some metrics)

Step 4: Add Notification Channels

Select:

Slack

PagerDuty

Pub/Sub

Webhook

SMS

You can add multiple notification channels per policy.

Step 5: Name and Save the Alert Policy

3. Recommended Alert Policies for Production

Below are typical SRE-grade alerts for Bigtable.

1. High Latency

P99 read latency > 50–100 ms for 5 minutes

P99 write latency > 50–100 ms for 5 minutes

2. CPU Overload

Cluster CPU > 70% for 10 minutes

Above this level, Bigtable auto-scaling may not react fast enough; consider adding nodes.

3. Storage Pressure

Cluster Storage Utilization > 60–70%

Bigtable performance degrades if disks fill too high.

4. Throttled Requests or Rate Limiting

Throttled requests > 0 for 2 minutes

Indicates application overloading or uneven workload distribution.

5. RPC Failures

Error count > 1% of requests

Useful for detecting network issues or client hot-spotting.

6. Node Count Change

Node count decreased unexpectedly

Detects failed nodes or misconfigurations.

4. Visualizing Bigtable Metrics in Dashboards

You can build a Bigtable SRE dashboard in Cloud Monitoring:

Include charts for:

P99 latency (reads/writes)

CPU usage across nodes

RPS (reads vs writes)

Error counts

Bigtable cluster storage usage

Autoscaler activity (if using autoscaling)

Use per-cluster and per-zone views for precision.

5. Advanced: Alerting on Metric-Based SLIs / SLOs

To track SLIs such as:

Request success rate

Latency SLO (e.g., 99% under 50 ms)

Use:

Monitoring → SLOs → Create SLO → Create Alert

This provides burn-rate alerts like:

Fast burn (5 minutes)

Slow burn (1 day)

Great for production SLA/SLO-driven environments.

6. Advanced: Use Pub/Sub Notifications

For programmatic workflows:

Trigger Cloud Functions / Cloud Run for auto-scaling

Forward alerts into SIEM tools

Trigger custom remediation scripts

Example: Add more Bigtable nodes automatically (if using manual scaling).

7. Best Practices for Bigtable Alerting

✔ Use P99 latency, not just averages

✔ Alert on CPU AND Storage, not just one

✔ Detect hotspotting early through latency + CPU

✔ Use per-zone metrics for multi-zone clusters

✔ Tune thresholds per workload

✔ Use rate-based suppression to avoid alert storms

Summary

Real-time alerting on Bigtable metrics using Cloud Monitoring gives you full visibility into:

Performance (latency, throughput)

Resource usage (CPU, storage, nodes)

Application issues (hotspots, throttling)

Cluster health (errors, autoscaling events)

Using Cloud Monitoring alerts, dashboards, SLOs, and Pub/Sub integration, you can build end-to-end SRE-grade observability for your Bigtable workloads.

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