Inside the InfluxDB 3 Plugin Ecosystem

By Allyson Boate / Developer
Oct 02, 2025

Companies today face growing pressure to manage and analyze massive flows of time series data, from IoT sensors to cloud-native infrastructure. Storing this information is relatively straightforward. The greater obstacle is keeping it useful and consistent while balancing a wide range of tools and modern technology platforms that continue to evolve.

InfluxDB was created with that obstacle in mind. Purpose-built for time series workloads, it stores and queries high-volume data efficiently while offering a broad plugin and integration ecosystem. Plugins extend the database with targeted functions such as downsampling, transformations, or alerting. Integrations connect InfluxDB to external platforms that organizations already depend on, from collaboration tools to enterprise analytics. Together, they create a living ecosystem that keeps data moving, surfaces insights quickly, and supports systems that can adapt and scale with confidence.

Plugins and the InfluxDB ecosystem

Plugins, small modular extensions to InfluxDB, are integral to adapting the database for modern workloads. These components run within the system and shape time series data for dashboards, trigger alerts, or export it to external platforms. Together they creating a flexible layer that adapts the database to different needs without custom code.

The true strength of plugins comes from the ecosystem around them. Rather than relying on fragile scripts or single-purpose connectors, organizations can choose from a library of plugins to adapt their data pipelines and monitoring workflows to specific needs. This shared library reduces overhead, simplifies daily operations, and makes data more actionable. InfluxData’s GitHub repository contains dozens of ready-to-use options that adapt seamlessly to existing workflows.

Getting started with plugins

Getting started with the InfluxDB 3 plugin ecosystem is straightforward, with just a few setup steps before you can begin extending the database for your own use cases:

• Set up InfluxDB 3: Download and install InfluxDB 3 Core or Enterprise.
• Enable the Processing Engine: Start the server with a plugin directory. Clone the influxdb3_plugins repository or reference plugins directly from GitHub using the gh: prefix.
• Configure plugins: Plugins run in a virtual environment. Some require extra dependencies, listed in their requirements.txt or README.
• Create triggers: Run plugins on a schedule, on new data, or through HTTP requests to make them actionable.

With these steps, InfluxDB 3 is ready to run plugins that fit smoothly into your workflows.

Essential plugins for InfluxDB 3

Below, we’ll cover some of the key plugins in the InfluxDB 3 ecosystem that are widely used for analytics, monitoring, and data management.

Iceberg Export: Bridge to Enterprise Analytics

Modern analytics often call for more than real-time dashboards. Teams want to combine fast metrics with deeper historical context, run queries across years of data, and plug into their broader data lake ecosystem. That’s where the InfluxDB to Iceberg plugin comes in.

Apache Iceberg is an open table format designed for huge analytic datasets. It makes data lakes behave more like databases, with reliable schema handling, ACID transactions, and SQL compatibility.

With this plugin, you can send time series data from InfluxDB 3 directly into Iceberg tables. That means long-term storage, seamless integration with tools like Spark, and the ability to bridge real-time monitoring with large-scale analytics. The plugin supports both scheduled transfers for pipelines that run continuously and on-demand transfers through an HTTP API.

Where this helps

• Data lake integration: Feed time series into Iceberg so you can join operational metrics with business data.
• Long-term retention: Offload historical records without slowing down your InfluxDB cluster.
• Hybrid pipelines: Keep real-time queries in InfluxDB while sending richer datasets to Iceberg for exploration and machine learning.

Quick start with the Iceberg plugin

• Start InfluxDB 3 with plugins enabled
• Install pandas, pyarrow, and pyiceberg
• Create a trigger to send data to Iceberg
• Write test data and confirm it appears in Iceberg

Next, try filtering fields, running transfers on demand, or connecting to S3 or Hive for larger workloads.

For more information, check out the InfluxDB to Iceberg plugin documentation.

Monitoring Data with the State Change Plugin

Catching unexpected shifts in your data is just as important as storing it. Whether it’s a sudden CPU spike, a system status change, or a sensor that starts misbehaving, you need to know when values cross thresholds or fluctuate too often. That’s where the State Change plugin comes in.

The State Change plugin continuously monitors InfluxDB 3 measurements for changes or threshold conditions and triggers alerts when criteria are met. It supports two modes: scheduled checks over time windows and real-time monitoring of new data writes. You can configure stability checks to cut down on noisy alerts and send notifications to multiple channels, including Slack, Discord, SMS, and more.

Where this helps

• System monitoring: Detect CPU, memory, or disk usage spikes and push alerts to your ops team.
• Application health: Track error rates, response times, and status values to catch downtime quickly.
• IoT and sensors: Monitor temperature, pressure, or other field values and act when they change unexpectedly.

Quick start with the State Change plugin

• Start InfluxDB 3 with plugins enabled
• Install the requests package
• Create a trigger to monitor a measurement and define thresholds
• Enable the trigger and confirm notifications fire when conditions are met

Next, try customizing notification templates, combining multiple conditions, or connecting to multiple alerting channels.

Check out the full State Change plugin documentation for configuration details and advanced examples.

Sending Alerts with the Notifier Plugin

Data monitoring only matters if the right people know when something goes wrong. The Notifier plugin gives InfluxDB 3 a flexible way to send alerts across multiple channels in real-time.

The Notifier plugin is a centralized dispatcher that routes notifications from InfluxDB 3 to Slack, Discord, HTTP webhooks, SMS, or WhatsApp. It receives messages from other plugins or external systems and delivers them where your team already works. That means you can connect your monitoring pipelines directly to the tools your ops team uses every day.

Where this helps

• Incident response: Alert your team in Slack or Discord when metrics cross a threshold.
• Custom workflows: Use HTTP webhooks to trigger downstream automations or ticketing systems.
• Mobile alerts: Deliver critical notifications directly by SMS or WhatsApp when real-time visibility matters most.

Quick start with the Notifier plugin

• Start InfluxDB 3 with plugins enabled
• Install the httpx and twilio packages
• Create a trigger that registers an HTTP endpoint (/api/v3/engine/notify)
• Send a test notification to Slack, Discord, or SMS and confirm delivery

Next, try setting up multi-channel notifications, customizing message templates, or adding retry logic for more reliable delivery.

Explore the Notifier plugin documentation for configuration details and advanced use cases.

Smarter Monitoring with the Threshold Deadman Checks Plugin

Sometimes it’s not just the values in your metrics that matter, but also whether the data is flowing at all. The Threshold Deadman Checks plugin helps you cover both cases by combining threshold alerts with deadman monitoring.

A “deadman check” is when the system alerts you if data stops arriving, such as no server logs for five minutes. The plugin also supports real-time threshold detection, allowing you to monitor values that exceed defined limits and trigger alerts with varying severity levels (INFO, WARN, ERROR, CRITICAL).

With support for both scheduled checks and real-time data write monitoring, Threshold Deadman Checks gives you flexible coverage across high-frequency data streams and longer aggregation windows.

Where this helps

• System uptime: Detect missing heartbeat signals or stalled data pipelines.
• Performance monitoring: Alert when CPU, memory, or latency values exceed set thresholds.
• IoT and sensors: Combine value thresholds with deadman checks to ensure both accuracy and continuity.

Quick start with the Threshold Deadman Checks plugin

• Start InfluxDB 3 with plugins enabled
• Install the requests package
• Create a trigger for threshold conditions or deadman monitoring
• Enable the trigger and confirm alerts are delivered through Slack, SMS, or another channel

You can try multi-level alerts (INFO, WARN, ERROR), combine threshold checks with deadman monitoring, or send notifications across multiple channels.

By uniting threshold detection and deadman monitoring, this plugin ensures you catch both spikes and silence.

Visit the Threshold Deadman Checks plugin documentation for detailed setup instructions and examples.

Reducing Data Volume with the Downsampler Plugin

High-resolution time series data is powerful, but it can also become overwhelming to store and query over long periods. The Downsampler plugin helps you keep data manageable by aggregating raw measurements into coarser time intervals without losing the big picture.

Downsampling means compressing many points into fewer, aggregated values. A company that collects one-minute CPU metrics can use the Downsampler plugin to convert them into hourly averages, reducing storage needs and improving query performance.

The plugin supports both scheduled downsampling for ongoing aggregation of new data and on-demand downsampling through HTTP requests for historical backfills. Each aggregated record also includes metadata, such as the number of raw points compressed and the time range they cover.

Where this helps

• System metrics: Store hourly averages instead of second-by-second CPU readings to reduce storage costs.
• IoT devices: Aggregate high-frequency sensor data into daily summaries for long-term retention.
• Analytics pipelines: Pre-aggregate data before joining it with other systems to improve performance.

Quick start with the Downsampler plugin

• Start InfluxDB 3 with plugins enabled
• Create a trigger that defines the source measurement, target measurement, and interval
• Run the trigger and confirm aggregated data appears in the target measurement

Next, try applying different aggregation functions (avg, sum, min, max, median), filtering specific fields, or scheduling backfills for historical datasets.

Exploring the ecosystem

The InfluxDB plugin ecosystem is about more than adding extra features. It gives teams the flexibility to shape InfluxDB 3 around their own use cases, whether that means exporting data into Iceberg, triggering state change alerts, sending notifications, running deadman checks, or downsampling high-frequency metrics.

By mixing and matching plugins, you can move beyond simple data collection to build monitoring and analytics pipelines that fit your business. That adaptability is what makes plugins such a powerful part of the InfluxDB ecosystem, letting you grow from fast queries today to full-scale insights tomorrow.

Ready to get started? Explore InfluxDB 3 Core OSS or InfluxDB 3 Enterprise to see how plugins can extend your workflows.

Want to make your own plugins? You can contribute to the influxdb3_plugins GitHub repo or connect with us on Discord, Slack, or the Community Forums.