DragonflyDB Monitoring: Complete Guide to Metrics, Alerts, and Production Setup

DragonflyDB is a modern in-memory datastore built as a drop-in replacement for Redis and Memcached. It uses a shared-nothing architecture that saturates modern multi-core CPUs, delivering dramatically higher throughput on smaller hardware. But like any in-memory system, DragonflyDB requires careful monitoring because when memory limits are exceeded, data eviction or instability follows immediately.

This guide covers how DragonflyDB monitoring works, what metrics matter most, how to set up Prometheus and Grafana, and what to watch when running DragonflyDB in production especially in Kubernetes environments where auto-scaling and replication add complexity.

What Is DragonflyDB Monitoring

DragonflyDB monitoring is the practice of tracking memory usage, throughput, client connections, replication lag, and cluster health in real time to prevent evictions, detect bottlenecks, and ensure stable performance in production.

Unlike disk-based databases, in-memory stores like DragonflyDB face a hard constraint: if memory fills up, the system must either evict keys or fail. Monitoring catches this before it happens by surfacing memory consumption trends, tracking how close you are to configured limits, and alerting when client connections spike or replication falls behind.

DragonflyDB exposes Prometheus-compatible metrics natively at http://<dragonfly-host>:6379/metrics by default. This eliminates the need for a separate exporter, making it easier to integrate with existing observability stacks compared to Redis, which requires the Redis Exporter to expose metrics in Prometheus format.

How DragonflyDB Monitoring Works

DragonflyDB monitoring combines native metric exposure, time-series storage, visualization, and alerting. The flow looks like this:

1. Metric collection: DragonflyDB exposes Prometheus-compatible metrics at /metrics on its main TCP port (default 6379) or admin port (9999 in Kubernetes deployments).
2. Scraping and storage: Prometheus scrapes these metrics at regular intervals (typically every 15 seconds) and stores them as time-series data.
3. Visualization: Grafana queries Prometheus and displays metrics on dashboards showing memory usage, client counts, key operations, and replication health.
4. Alerting: Prometheus Alertmanager or Grafana triggers alerts when thresholds are breached, for example when memory usage exceeds 80% of the configured limit or replication lag grows beyond acceptable bounds.

This architecture mirrors standard infrastructure monitoring platforms but is tuned specifically for in-memory datastores where memory exhaustion is the primary failure mode.

Native Prometheus compatibility vs. Redis

Redis requires a separate Prometheus exporter to expose metrics in a format Prometheus can scrape. DragonflyDB eliminates this step by exposing metrics natively at the /metrics endpoint. This means one less component to deploy, no exporter version drift, and immediate metric availability the moment DragonflyDB starts.

Core DragonflyDB Metrics to Monitor

DragonflyDB exposes over 40 metrics covering memory, keys, clients, network, and replication. The most critical ones for production monitoring fall into five categories.

Memory metrics

Memory is the most important signal for any in-memory datastore. DragonflyDB tracks three key memory dimensions:

• dragonfly_memory_used_bytes: Total memory allocated by DragonflyDB’s allocator. This is the primary metric for understanding actual memory consumption.
• dragonfly_used_memory_rss_bytes: Resident Set Size as seen by the operating system. This includes memory overhead beyond what DragonflyDB reports and is what top or ps shows.
• dragonfly_memory_max_bytes: The configured memory limit set via the maxmemory directive. When used_memory approaches this limit, eviction policies activate.

When dragonfly_memory_used_bytes exceeds 80% of dragonfly_memory_max_bytes, you are entering a danger zone where evictions or out-of-memory conditions become likely.

Client connection metrics

Tracking how many clients are connected and how many are blocked helps detect connection leaks and application-level issues.

• dragonfly_connected_clients: Number of active client connections. A sudden drop can indicate network issues or application failures. A steady climb without corresponding disconnections suggests connection leaks.
• dragonfly_blocked_clients: Number of clients waiting on blocking operations like BLPOP, BRPOP, or BRPOPLPUSH. Sustained high values indicate that consumers are waiting for data, which may signal producer slowdowns or queue depth problems.

Connection monitoring prevents scenarios where an application opens thousands of idle connections, exhausting DragonflyDB’s connection limit and blocking new legitimate clients.

Key and operation metrics

These metrics track the data stored in DragonflyDB and how frequently it changes.

• dragonfly_db_keys: Total number of keys across all databases. A sudden drop may indicate unintended key expiration or deletion.
• dragonfly_expired_keys_total: Cumulative count of keys that reached their TTL and were deleted. Spikes here are normal after batch TTL expirations but sustained high rates may indicate overly aggressive TTLs.
• dragonfly_evicted_keys_total: Number of keys evicted due to memory pressure. Any non-zero value here means you hit the memory limit and DragonflyDB had to remove keys according to the eviction policy. This is a production incident signal.

Tracking evictions is critical because unlike disk-based systems where writes slow down, in-memory stores lose data when memory fills.

Replication metrics

DragonflyDB supports replication for high availability. The /metrics endpoint includes replication-specific signals that track master-replica sync status.

• dragonfly_role: Indicates whether this instance is a master or replica.
• dragonfly_connected_replicas: Number of replicas connected to this master instance.
• dragonfly_replication_lag_bytes: Bytes of replication lag between master and replica. High lag means the replica is falling behind and may serve stale data.

Replication lag matters most when using replicas for read scaling. If lag grows too large, clients reading from replicas see outdated data, which can break application logic that assumes near-real-time consistency.

System and network metrics

DragonflyDB also exposes metrics about its own performance and resource usage.

• dragonfly_connections_received_total: Total connections accepted since startup. Useful for tracking connection churn over time.
• dragonfly_commands_processed_total: Total commands executed. Sudden drops indicate client application failures or network issues.
• dragonfly_keyspace_hits_total and dragonfly_keyspace_misses_total: Cache hit and miss counts. A low hit rate suggests inefficient caching or incorrect TTL tuning.

Network-level metrics help diagnose whether performance issues originate in DragonflyDB itself or in the clients and networks connecting to it.

Setting Up DragonflyDB Monitoring with Prometheus and Grafana

This section walks through setting up Prometheus to scrape DragonflyDB metrics and Grafana to visualize them. The example uses Docker Compose for local testing but the same principles apply to Kubernetes deployments.

Step 1: Configure Prometheus to scrape DragonflyDB

Create a prometheus.yml configuration file that defines DragonflyDB as a scrape target.

global:
  scrape_interval: 15s

scrape_configs:
  - job_name: dragonfly_metrics
    static_configs:
      - targets: ['dragonfly:6379']

This tells Prometheus to scrape metrics from DragonflyDB every 15 seconds at http://dragonfly:6379/metrics. If you are using Kubernetes, replace dragonfly:6379 with the appropriate service name and port.

Step 2: Deploy DragonflyDB, Prometheus, and Grafana with Docker Compose

Create a docker-compose.yml file to spin up all three services.

version: '3'
services:
  dragonfly:
    image: 'docker.dragonflydb.io/dragonflydb/dragonfly'
    pull_policy: 'always'
    ulimits:
      memlock: -1
    ports:
      - "6379:6379"
    volumes:
      - dragonflydata:/data

  prometheus:
    image: prom/prometheus:latest
    restart: always
    ports:
      - "9090:9090"
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml
    command:
      - '--config.file=/etc/prometheus/prometheus.yml'
    depends_on:
      - dragonfly

  grafana:
    image: grafana/grafana:latest
    restart: always
    ports:
      - "3000:3000"

volumes:
  dragonflydata:

Start the stack with:

docker compose -p dragonfly-monitoring up

Verify that all three containers are running:

docker compose -p dragonfly-monitoring ps

You should see dragonfly, prometheus, and grafana listed with STATUS as running.

Step 3: Verify metrics in Prometheus

Navigate to http://localhost:9090 and go to Status → Targets. You should see the dragonfly_metrics job listed with state UP. If it shows DOWN, check that DragonflyDB is reachable at port 6379 and that the /metrics endpoint returns data.

To test a metric query, go to the Prometheus query page and enter:

dragonfly_memory_used_bytes

You should see the current memory usage value returned.

Step 4: Import a Grafana dashboard for DragonflyDB

Grafana provides visualization on top of Prometheus data. DragonflyDB maintains an official Grafana dashboard that covers the most important metrics.

1. Navigate to http://localhost:3000 and log in (default credentials: admin/admin).
2. Go to Connections → Data sources → Add data source.
3. Select Prometheus and set the URL to http://prometheus:9090.
4. Click Save & Test to verify the connection.
5. Download the official DragonflyDB Grafana dashboard from the DragonflyDB monitoring blog post.
6. Go to Dashboards → Import and upload the downloaded JSON file.

The dashboard includes panels for memory usage, client connections, key counts, evictions, and replication lag.

Monitoring DragonflyDB in Kubernetes with the Dragonfly Operator

When running DragonflyDB in Kubernetes using the Dragonfly Operator, metrics are exposed on the admin port 9999 instead of the main port 6379. This separation prevents metric scraping from interfering with client traffic.

Configuring Prometheus to scrape Kubernetes DragonflyDB instances

If you are using the Prometheus Operator, create a ServiceMonitor resource to automatically discover and scrape DragonflyDB pods.

apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: dragonfly-metrics
  namespace: default
spec:
  selector:
    matchLabels:
      app: dragonfly
  endpoints:
    - port: admin
      path: /metrics
      interval: 15s

This assumes your DragonflyDB service exposes the admin port with a label app: dragonfly. Adjust the selector to match your actual service labels.

Handling IPv6-only Kubernetes clusters

If your Kubernetes cluster uses IPv6 only, you must explicitly bind DragonflyDB to :: instead of the default 0.0.0.0. Add these arguments to your Dragonfly custom resource:

apiVersion: dragonflydb.io/v1alpha1
kind: Dragonfly
metadata:
  name: dragonfly-sample
spec:
  replicas: 3
  args:
    - "--bind=::"
    - "--admin_bind=::"

Without these flags, DragonflyDB will fail to accept connections in IPv6-only environments.

Scaling and replication monitoring in Kubernetes

The Dragonfly Operator supports horizontal and vertical scaling. Monitoring becomes more complex when instances scale up or down dynamically because Prometheus must track metrics across multiple pods.

Key metrics to watch during scaling:

• dragonfly_connected_replicas: Should equal the number of replica pods configured in your Dragonfly resource.
• dragonfly_replication_lag_bytes: Should remain low across all replicas. High lag on any replica indicates that it cannot keep up with the master’s write load.
• dragonfly_db_keys: Should be consistent across master and replicas. Divergence suggests replication failures.

When you scale up the number of replicas using:

kubectl patch dragonfly dragonfly-sample --type merge -p '{"spec":{"replicas":5}}'

Monitor the dragonfly_connected_replicas metric to confirm that all new replicas connect successfully.

Monitoring DragonflyDB with CubeAPM

CubeAPM provides unified observability for DragonflyDB alongside your application traces, logs, and infrastructure metrics. Instead of managing separate Prometheus, Grafana, and alert routing systems, CubeAPM offers a single platform that collects DragonflyDB metrics via OpenTelemetry or Prometheus scraping and correlates them with application performance data.

How CubeAPM monitors DragonflyDB

CubeAPM connects to DragonflyDB using the native Prometheus metrics endpoint at /metrics. It scrapes the same metrics covered earlier in this guide, memory usage, client connections, key counts, evictions, replication lag, and stores them in a time-series database with unlimited retention at a flat $0.15/GB ingestion cost.

Because CubeAPM runs on your infrastructure rather than as a SaaS platform, all DragonflyDB metrics remain within your VPC. This eliminates data egress costs and satisfies data residency requirements.

Setting up DragonflyDB monitoring in CubeAPM

1. Deploy the CubeAPM agent in your environment. The agent supports both Prometheus scraping and OpenTelemetry Collector configurations.
2. Configure the agent to scrape the DragonflyDB /metrics endpoint. If using the OpenTelemetry Collector, add a prometheus receiver:

receivers:
  prometheus:
    config:
      scrape_configs:
        - job_name: dragonfly
          static_configs:
            - targets: ['dragonfly:6379']

1. Metrics flow into CubeAPM where they are indexed and searchable immediately. You can filter by instance, database, or replication role.
2. Create dashboards in CubeAPM’s UI that mirror the Grafana DragonflyDB dashboard layout, or build custom views that combine DragonflyDB metrics with application traces and logs.

Alerting on DragonflyDB metrics in CubeAPM

CubeAPM includes built-in alerting that triggers notifications based on metric thresholds or anomaly detection. To set up an alert for high memory usage:

1. Navigate to Alerts → Create Alert in the CubeAPM dashboard.
2. Select dragonfly_memory_used_bytes as the metric.
3. Define a threshold, for example alert when memory usage exceeds 80% of dragonfly_memory_max_bytes.
4. Route the alert to Slack, PagerDuty, email, or any webhook endpoint.

CubeAPM’s anomaly detection can also flag unusual patterns, such as a sudden spike in dragonfly_evicted_keys_total, even if it has not crossed a hard threshold.

Correlating DragonflyDB metrics with application traces

One of CubeAPM’s strengths is its ability to correlate infrastructure metrics with distributed traces. If your application experiences slow response times, you can view DragonflyDB memory usage, client connection counts, and replication lag in the same timeline as your APM traces.

For example, if an application trace shows a slow Redis command, you can immediately check whether DragonflyDB was under memory pressure or experiencing high client connection churn at that exact moment. This correlation speeds up root cause analysis compared to switching between separate Prometheus, Grafana, and APM tools.

Best Practices for DragonflyDB Monitoring in Production

Effective DragonflyDB monitoring requires more than just collecting metrics. The following practices help teams catch issues early and maintain stable production performance.

Set memory alert thresholds at 80% of maxmemory

DragonflyDB’s eviction behavior activates when memory usage approaches the maxmemory limit. Waiting until you hit 100% means evictions are already happening. Set alerts at 80% to give yourself time to investigate and either increase memory limits or optimize key TTLs.

Monitor eviction rates even when memory is not full

A non-zero dragonfly_evicted_keys_total value is a production incident. Even if memory usage looks normal, evictions indicate that at some point memory filled up and keys were lost. Always investigate spikes in this metric.

Track client connection churn over time

Sustained growth in dragonfly_connected_clients without corresponding disconnections suggests a connection leak in the application layer. Monitor the rate of change, not just the absolute count. A steady climb of 10 connections per hour over days will eventually exhaust connection limits.

Use replication lag alerts for read-replica setups

If you are using DragonflyDB replicas to scale read traffic, set alerts on dragonfly_replication_lag_bytes. Replicas with high lag serve stale data, which can break application logic that assumes near-real-time consistency. Alert when lag exceeds 1 MB or when it persists for more than 5 minutes.

Collect metrics at 15-second intervals or faster

In-memory stores change state quickly. Scraping metrics every minute can miss short-lived spikes in client connections or memory usage. A 15-second scrape interval is the standard for DragonflyDB monitoring and strikes a balance between granularity and storage overhead.

Enable persistent snapshots and monitor snapshot intervals

DragonflyDB supports snapshotting to persistent volumes or S3 for data durability. Monitor the time between successful snapshots and alert if the interval grows beyond your recovery point objective (RPO). If snapshots fail due to disk space or permission issues, you lose the ability to recover data after a crash.

Tools for DragonflyDB Monitoring

Several observability platforms support DragonflyDB monitoring through Prometheus scraping or OpenTelemetry ingestion. The table below compares options based on deployment model, pricing, and ease of setup.

Tool	Deployment	Pricing	DragonflyDB support	Best for
CubeAPM	Self-hosted, vendor-managed	$0.15/GB ingestion, unlimited retention	Native Prometheus scraping, OpenTelemetry support	Teams that want unified DragonflyDB + APM + logs in one platform
Prometheus + Grafana	Self-hosted, DIY	Free open source	Native /metrics endpoint	Teams already running Prometheus for other services
Datadog	SaaS only	Starts at $15/host/month for infrastructure monitoring	Prometheus integration, Datadog agent required	Teams already using Datadog for multi-cloud monitoring
New Relic	SaaS only	$0.40/GB beyond 100 GB free tier	Prometheus remote write, requires New Relic agent	Teams using New Relic for full-stack observability
Elastic APM	Self-hosted or Elastic Cloud	Free open source, Elastic Cloud starts at $99/month	Metricbeat with Prometheus module	Teams already on the ELK stack

For teams that want to avoid managing Prometheus and Grafana themselves while keeping data on-prem, CubeAPM offers the most direct path. For teams already invested in Prometheus, adding DragonflyDB as a scrape target takes minutes.

Frequently Asked Questions

Disclaimer: The information in this article reflects the latest details available at the time of publication and may change as technologies and products evolve. Features, pricing, and plan limits can change over time. Always verify the latest information directly with the vendor before making purchasing or deployment decisions.