Category: Local influxdb docker

I don't know about everyone else, but it seems like I find a analytics problem at least once a week that could use a simple clean graphing tool. Using Docker. So without further distraction I'll show you what I did. Notice that two ports are exposed to the host machine. You can use all the default options for our purposes. Loading influx with data is extremely easy using the influx api libraries.

In particular I've used the python and nodejs libraries. Here is an example of loading points in python. Create a new connection to influxdb which is extremely simple.

local influxdb docker

You need the influxdb package installed. In the above code the "fetchedData" variable is just a dataset from a mysql db. If you'd like more information on building a dataset send me a message, and I'll help out.

If you made it this far you probably realized that I typed a lot more than I needed to. InfluxDB is running, it contains some time series data, and you are ready to see some graphs. Grafana makes graphing extremely easy when you have a backing influx data store. Influx provides the engine, and grafana provides the interior of the vehicle. To get the password which is generated each time a new container is built simply execute the following in a shell.

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They are really beautiful! The only thing you need to change in the examples is the data source from graphite to influx. In my opinion tutum. Their documentation is complete, and easy to read.

How To Install InfluxDB Telegraf and Grafana on Docker

Check out the two containers I used above if you'd like to get more information on using docker links rather than the port mappings. Great article! Do you know if it's possible to view more than one InfluxDB database on the same host with one Grafana instance? February 25, Last Updated: February 25, Why I don't know about everyone else, but it seems like I find a analytics problem at least once a week that could use a simple clean graphing tool. InfluxDBClient "localhost","root", "root", "mydata" Insert some points An empty array to store data to avoid making multiple calls to influx.

Written by Matt Conroy. December 04, Hi Matt. Sponsored by. Filed Under Tools. Awesome Job. See All Jobs.Maintained by : InfluxData. Supported architectures : more info amd64arm32v7arm64v8. InfluxDB is a time series database built from the ground up to handle high write and query loads. InfluxDB is meant to be used as a backing store for any use case involving large amounts of timestamped data, including DevOps monitoring, application metrics, IoT sensor data, and real-time analytics.

A typical invocation of the container might be:. The administrator interface is not automatically exposed when using docker run -P and is disabled by default.

The adminstrator interface requires that the web browser have access to InfluxDB on the same port in the container as from the web browser. Since -P exposes the HTTP port to the host on a random port, the administrator interface is not compatible with this setting.

InfluxDB can be either configured from a config file or using environment variables. To mount a configuration file and use it with the server, you can use this command:. Then start the InfluxDB container. If the variable isn't in a section, then omit that part. Find more about configuring InfluxDB here. InfluxDB supports the Graphite line protocol, but the service and ports are not exposed by default.

To run InfluxDB with Graphite support enabled, you can either use a configuration file or set the appropriate environment variables. Run InfluxDB with the default Graphite configuration:. In order to take advantage of graphite templates, you should use a configuration file by outputting a default configuration file using the steps above and modifying the [[graphite]] section.

The administrator interface is deprecated as of 1. It is disabled by default. If needed, it can still be enabled by setting an environment variable like below:. Read more about this in the official documentation.

The InfluxDB image contains some extra functionality for initializing a database. These options are not suggested for production, but are quite useful when running standalone instances for testing. The database initialization script will only be called when running influxd. It will not be executed when running any other program. The InfluxDB image uses several environment variables to automatically configure certain parts of the server.

They may significantly aid you in using this image. Enables authentication. If this is unset, a random password is generated and printed to standard out. The name of a user to be created with no privileges.

If the Docker image finds any files with the extensions. The order they are executed in is determined by the shell. This is usually alphabetical order. It takes the same parameters as the influxd run command. As an example:.

local influxdb docker

The above would create the database db0create an admin user with the password supersecretpasswordthen create the telegraf user with your telegraf's secret password. It would then exit and leave behind any files it created in the volume that you mounted. This is the defacto image.

If you are unsure about what your needs are, you probably want to use this one. It is designed to be used both as a throw away container mount your source code and start the container to start your appas well as the base to build other images off of.This tutorial will walk you through the process of creating a Dockerfile that will utilize supervisord to run a combined install of InfluxDB and nginx for Grafana.

At the end of this tutorial, you should have a container configured to accept server metrics information from collectd. InfluxDB is a time-series database that has been built to work best with metrics, events, and analytics. The solution is written completely in Go and relies on no external dependencies to run.

It is being maintained here. You would use Grafana to visualize the various metrics data you are pushing into InfluxDB. In our case the type of metrics we're preparing for are system metrics we hope to gather using collectd. You can either create a folder and place your Dockerfile and all configuration files in it or you can clone these files from our repository here. We recommend cloning so you capture all required configuration files, plus a pre-built Dockerfile.

If you spot an issue or have an improvement, feel free to issue a PR, too!

If you cloned the repo then you should have these files; however, note, that some of the files must be edited with your IP address, desired database name, etc. The important one that needs to be altered is Grafana's config. Grafana is configured to use InfluxDB for its configurtion. While you can use the same db for both it is not recommended. We copy this file over when we're dong our adds in our Dockerfile. The one provided in the repo was pulled from collectdb.

It is very important to understand the ephemeral nature of containers. This means that data created and stored within the container can disappear if the container restarts or is stopped. This allows us to pass the -v switch to docker to tell it to map the path in the container to a local path in our host system. When data is written to InfluxDB the it is now not stored within the container itself and is persistent beyond a restart.

Now that you have put your Dockerfile and configuration files together it is time to build your container. You will need to ensure you're working directory has your Dockerfile and configuration files in it. To build a container from your Dockerfile you would run the following command:. The -t parameter tags the image with the name influx.

The build will execute the Dockerfile. Inspect the output for any errors. If all looks good you should be ready to start the container up. Building a container will not automatically start the container.It is widely used for monitoring and dashboarding in the DevOps industry. Used by many successful companies in the world, InfluxDB is often used in distributed and cloud environments in order to be highly available to the applications it is connected to.

On the other hand, Docker is a virtualization environment that provides an easy way to create, manage and delete containers on the fly. If you are trying to build reliable monitoring architectures, one solution would be to install InfluxDB on Docker and to manage it with Kubernetes.

Note : InfluxDB is currently shifting to Influx 2. As a consequence, another tutorial will be available for InfluxDB 2. Before starting, it is important to make ensure that all the prerequisites are met to install InfluxDB on Docker.

Data pipeline with Docker, InfluxDB, and Grafana

To install Docker on Ubuntu and Debianyou can follow this tutorial. By default, you will install InfluxDB, that will expose useful ports like the one to your current network stack. Later on, you will bind Telegraf to it, but Telegraf does not have to expose any ports to your current host stack. Later on, we will add Grafana to our bridge network in order to visualize metrics gathered by Telegraf. You can either prepare your filesystem manually, and run the InfluxDB on a Docker container with no initialization scripts.

This method should be used if you plan on running InfluxDB on a single instance, if your initial InfluxDB configuration is very simple, or if you prefer to have full control over your containers.

This is the version that you should use if you are automating a lot of servers with InfluxDB with Chef or Puppet for exampleand you want to have the same initial setup on all your instances. The official InfluxDB image for Docker is named : influxdb. It is part of the Docker Official Imagesso you can check that you are running an official version of InfluxDB on your system.

The InfluxDB image is going to install the InfluxDB server responsible for storing time series metrics on your system. If you are familiar with Docker, you already know that you can map volumes from your local filesystem to your container in order to manipulate data easier in your container. Configuration files as well as directories storing actual data will be stored on our local filesystem.

If you carefully followed the tutorial on setting up InfluxDB on Ubuntuyou know that you are going to create a specific user for your InfluxDB database. As the —rm option is set, Docker will run a container in order to execute this command and the container will be deleted as soon as it exits.

Instead of having the configuration file printed on the standard output, it will be redirected to our InfluxDB configuration file. Again, make sure that the permissions are correctly set for your container to write into this folder.

With the InfluxDB image, there is a way to automate database initialization on your containers. As an example, we will instruct our Docker container to create an administrator account, a regular user account for Telegrafand a database with a custom retention via a custom InfluxQL script. On container boot, the entrypoint. You can execute the entry point script in order to launch a simple InfluxDB instance on your container.

This is for example what we have done in the previous section. We specified the configuration flag and it was used in order to set your InfluxDB server initialization. However, there is a second way to execute the entrypoint script : by executing the init-influxdb script. Execute the following command for meta folder in the influxdb folder to be updated with the correct information.

In order for the initialization scripts to be run on initialization, they have to be mapped to the docker-entrypoint-initdb. This simple initialization script will create a database for weather dataand it will assign a one week retention policy for it. Authentication is enabled in one of the next sections, this parameter is only used for the initialization script. If this is not the case, make sure that you specified the correct environments variables for your container.

If you chose to create initialization scripts for your container, you should also a log line for it. As a last verification step, you can inspect your meta. Head over to the [http] section of your configuration and make sure that it is enabled.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. What I love about containers is how fast you can get up and running using a Dockerfile to automate the build of your image. This tutorial will walk you through the process of creating a Dockerfile that will utilize supervisord to run a combined install of InfluxDB and nginx for Grafana.

At the end of this tutorial, you should have a container configured to accept server metrics information from collectd. InfluxDB is a time-series database that has been built to work best with metrics, events, and analytics. The solution is written completely in Go and relies on no external dependencies to run.

It is being maintained here. You would use Grafana to visualize the various metrics data you are pushing into InfluxDB. In our case the type of metrics we're preparing for are system metrics we hope to gather using collectd.

You can either create a folder and place your Dockerfile and all configuration files in it or you can clone these files from our repository here.

local influxdb docker

We recommend cloning so you capture all required configuration files, plus a pre-built Dockerfile. If you spot an issue or have an improvement, feel free to issue a PR, too! If you cloned the repo then you should have these files; however, note, that some of the files must be edited with your IP address, desired database name, etc. The important one that needs to be altered is Grafana's config.

Grafana is configured to use InfluxDB for its configurtion. While you can use the same db for both it is not recommended.

It is very important to understand the ephemeral nature of containers. This means that data created and stored within the container can disappear if the container restarts or is stopped. This allows us to pass the -v switch to docker to tell it to map the path in the container to a local path in our host system.

When data is written to InfluxDB the it is now not stored within the container itself and is persistent beyond a restart.In this article, I will explain how to use docker to setup a simple monitoring stack by using collectdinfluxdb and grafana. If you are not familiar with these tools, I will introduce them to give you a brief introduction.

And it can send these data to some data store. Here comes the data store, influxdb is a time series database designed to store and analyse time-series data.

And of course InfluxDB is on the officially supported data source, we can see how to see it in action later. After we finish modify the default configuration file, now we can start the collectd container by this docker command:. Now the collectd daemon process is running and trying to send data to In this section, we will use the official influxdb docker image I will use the 1. Also, we need to modify the default configuration file a bit to let influxdb receive the collectd data we setup above.

The official image provides a way to easily get the default configuration file, here is how:. Now we have the file, and we only need to modify one place:. Leave the other values as default, and you can see why we use as collectd port in previous section, because here influxdb use it as default collectd listening port. One more thing to take care, you see there is a typesdb configuration in [[collectd]] section. So we need to get this file and put it into this path inside the influxdb container.

So even we remove or restart the container later, we still have the data. The port mapping contains 3 port:. To check the data, select collectd on the up-right dropdown button, and input this query in the input box:. Start the grafana container is easy, here is the command:. We will show how to setup it soon. After save our dashboard, we can see it is empty, and there is a single default rowwe can add a graph panel to this row by move the mouse to the left green bar.

After we add the graph panel, we will be brought to the edit dialog, you can set the panel name to Memory in the General tab, then go to the Metrics tab, here is the place we get the memory data and show in the graph.

In the Panel data source dropdown button, select influxdb. Click the first query item named as Awhich is provided as default to us. Set the values like below picture. When we finish set the query parameters, the memory graph immediately show in the above grid. Now as we finish, click Back to dashboard link on the upper-right area, it will show our dashboard with the memory graph we just configured, looks nice!

And Grafana has great document to guide you make beautiful dashboard.

Grafana Docker for Home Assistant on Synology - #003

For all the above setup, I also made a all-in-one docker compose file, if you want to just start all of them and try it in your local, head to my github repository and run docker-compose up. My original purpose to setup this stack was to try out InfluxDB, but as I got the whole picture, this stack is very good example for a typical collecting and monitoring infrastructure.The following discussion is based on the tutorial project package named tik-docker-tutorial.

It will create a running deployment of these applications that can be used for an initial evaluation and testing of Kapacitor. Chronograf is currently not included in the package. This tutorial depends on Docker Compose 3. To use this package Docker and Docker Compose should be installed on the host machine where it will run.

Docker installation is covered at the Docker website. Docker Compose installation is also covered at the Docker website. In order to keep an eye on the log files, this document will describe running the reference package in two separate consoles. In the first console Docker Compose will be run.

The second will be used to issue commands to demonstrate basic Kapacitor functionality. As of this writing, the package has only been tested on Linux Ubuntu It contains a docker-compose. Please clone or copy the package to the host machine and open two consoles to its install location before continuing.

The core of the package is the docker-compose. Standard Unix style directories have also been prepared. These are mapped into the docker containers to make it easy to access scripts and logs in the demonstrations that follow. Here the kapacitor. In the first console, in the root directory of the package, to start the stack and leave the logs visible run the following:. The console logs should be similar to the above sample. In the second console the status can be confirmed by using docker directly.

Take note of the container names, especially for Kapacitor. If the Kapacitor container name in the current deployment is not the same i. A bridge network has been defined in the docker-compose. This bridge network features a simple name resolution service, that allows the container names to be used as the server names in the configuration files just mentioned. The running configuration can be further inspected by using the influx command line client directly from the InfluxDB Container.

The top level nodes of a TICKscript define the mode by which the underlying node chain is to be executed. They can be setup so that Kapacitor receives processed data in a steady stream, or so that it triggers the processing of a batch of data points, from which it will receive the results.


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