grape

Grafana Prototyping Environment

Table of Contents

Table of Contents

1. Prequisities
2. Setup
3. Help
4. Create
5. Delete
6. Populate Database
7. Save
8. Load
9. Import
10. Export
11. Status
12. Tree
13. Tools
    1. csv2sql.py
    2. runpga.sh
    3. upload-json-dashboard.sh
14. Samples
    1. demo01
    2. demo02
    3. demo03
    4. demo04
15. Miscellaneous
    1. Grafana
    2. Postres
    3. pgAdmin
16. Acknowledgments

Overview

This repository contains a tool named grape that allows you to quickly create a local grafana visualization development environment using docker containers for the grafana server and the postgresql database which relieves you from having to install and configured grafana or postgresql yourself.

NOTE: if you are interested in how to run a grafana server and postgres database in containers and connect them manually without a python wrapper please see this gist.

It is more than a simple wrapper around docker build and docker run commands because it provides additional services. For example it can also be used to import external environments, modify them and then export them back to enable isolation for development.

In addition, a set of samples is provided that demonstrate how to use the tool for common operations.

The primary audience for this tool is folks who want to play around with grafana without having to set it up. As long as you have docker you are good to go.

I use it to experiment on production environments by using the import capability to duplicate the system of interest locally (on my laptop) with the caveat that you must know the passwords for the data sources because grafana filters them out of the REST responses.

I also use it to test out new ideas. Typically i spin up an environment populate the database with modeling data and start playing with visualization ideas.

Here is a screenshot from demo02:

See demo01 and demo02 for additional grafana screenshots and database access details using psql.

This tool has been tested on Mac OSX 10.15.6, Ubuntu 18.04 and Windows 10.

Prerequisites

To use this you must have:

1. docker (https://docs.docker.com/get-docker/)
   1. The docker group must be available on linux.
2. bash
   1. If you are running on windows you will need WSL2 or a linux VM.
   2. It is only needed for the samples.
3. python-3.8 or later
   1. This is because the log module uses the logger stacklevel argument.
4. pipenv
5. A recent version of git
6. gnu make
   1. If you are running on windows you will need WSL2 or a linux VM.
7. unzip
   1. If you are running on windows you will need WSL2 or a linux VM.
8. sudo
   1. On linux, the program must be able to rm the database volume mount.

Setup

Do these steps to get started:

$ git clone https://github.com/eSentire/grape.git
$ cd grape
$ make

This can take awhile to run.

To see all of the available make targets: make help.

On windows you may have to run something like dos2unix to convert the bash shell scripts or use WSL2 or a linux VM to run > the demos in samples/*/run.sh. This will enable commands like make demo01 to work.

Help

Program help is available via the -h command:

$ pipenv run grape help

To get help about the available make commands type: make help.

Create

To create the infrastructure:

$ pipenv run grape create -v -g 4600 -n example

This will create two docker containers: examplegr which is the grafana server and examplepg which is the postgresql server.and then connects them so that the postgresql container database becomes a datasource in the grafana container.

If the docker containers were previously killed because of something like a system crash, grape create will restart them in the same state. The grafana dashboards and postgresql database contents will not be lost. Beware that the grape delete operation will destroy the state data.

It will also create and map the local example/pg/mnt/pgdata directory to the database container to save database results and example/gr/mnt/grdata to the grafana container to save the grafana dashboard data. This is done so that if the container is restarted for any reason the postgresql database and grafana dashboards are not lost. And, finally, it will connect the database as a source in the grafana server.

This is what the persistent storage looks like on the host:

$ tree -L 3 example
example
├── gr
│   ├── mnt
│   │   └── grdata
│   └── start.sh
└── pg
    ├── mnt
    │   └── pgdata
    └── start.sh

6 directories, 2 files

This the persistent storage looks like from the containers.

$ docker exec -it examplepg ls -l /mnt
total 0
drwxr-xr-x  5 506 dialout 160 Aug 27 17:04 grdata
drwx------ 26 506 dialout 832 Aug 27 17:04 pgdata
$ docker exec -it examplegr ls -l /mnt
total 0
drwxr-xr-x    5 506      dialout        160 Aug 27 17:04 grdata
drwx------   26 506      dialout        832 Aug 27 17:04 pgdata

It also creates the database container start script in example/gr/start.sh and the grafana container start script in example/pg/start.sh. These scripts contain the raw docker commands to start the database and grafana containers with all existing data if either container is killed. Once started the containers may take up to 30 seconds to initialize.

This is what the automatically generated scripts look like on the host:

$ tree -L 2 example
example
├── gr
│   ├── mnt
│   └── start.sh
└── pg
    ├── mnt
    └── start.sh

4 directories, 2 files

You can now access the empty dashboard at http://localhost:4600 in your browser. The username is admin and the password is admin.

At this point you can create grafana visualizations using datasources, folders and dashboards as well as database tables.

Delete

The delete operation deletes all artifacts created by the create operation.

$ pipenv run grape delete -v -n example

It will remove the containers and the local database storage.

Populate Database

You can access the database using psql interactively like this:

$ docker exec -it examplepg psql -d postgres -U postgres

You can also load batch commands like this by taking care to make them visible to the tool

$ edit x.sql
$ cp x.sql example/mnt/  # makes it visible as /mnt/x.sql
$ docker exec -it examplepg psql -d postgres -U postgres -1 < /mnt/x.sql

Save

The save operation captures the specified model in a zip file. It is what you use to capture changes.

$ pipenv run grape save -v -n example -g 4760 -f /mnt/save.zip

Load

The load operation updates the model from a saved state (zip file).

$ pipenv run grape load -v -n example -g 4600 -f /mnt/save.zip

Import

The import operation captures an external grafana environment for the purposes of experimenting or working locally.

It imports rhe datasources without passwords because grafana never exports passwords which means that they have to be updated manually after the import operation completes or by specifying the passwords in the associated conf file. It does not import databases.

The import operation creates a zip file that can be used by a load operation. It requires a conf file that is specified by the -x option.

Operations

Here is the sequence of operations that define an import flow:

1. create the external conf file
2. import the external grafana
3. load it into the local model

Commands

Below are the actual commands for downloading an external grafana service into the local foo model.

$ rm -rf foo.zip
$ edit import.yaml  # to set the external access parameters.
$ time pipenv run grape del -v -g 4800 -n foo
$ time pipenv run grape import -v -x import.yaml -f foo.zip
$ time pipenv run grape load -v -f foo.zip -n foo -g 4800

Once the above steps are complete, you will be able to access the local version at http://localhost:4800.

External Conf YAML

This is what a sample external conf file looks like:

# Data to access an external grafana server.
# If any fields are not present, then the user
# will be prompted for them.
url: 'https://official.grafana.server'
username: 'bigbob'
password: 'supersecret'

# The passwords for each database can optionally
# be specified. If they are not specified, then
# they must be entered manually because grafana
# does not export them.
databases:
  - name: 'PostgreSQL'
    password: 'donttellanyone'
  - name: 'InfluxDB'
    password: 'topsecret!'

Export

The export operation is the inverse of the import operation. You use it to update an external grafana server.

Operations

The sequence of operations to perform an export operation are:

1. create the external conf file
2. save the local grafana service to a zip file
3. export to the external service.

Commands

Here are the commands:

$ edit export.yaml
$ time pipenv run grape save -v -n foo -g 4800 -f foo.zip
$ time pipenv run grape export -v -x export.yaml -f foo.zip

External Conf YAML

This is what a sample external conf file looks like:

# Data to access an external grafana server.
# If any fields are not present, then the user
# will be prompted for them.
url: 'https://official.grafana.server'
username: 'bigbob'
password: 'supersecret'

# The passwords for each database can optionally
# be specified. If they are not specified, then
# they must be entered manually because grafana
# does not export them.
databases:
  - name: 'PostgreSQL'
    password: 'donttellanyone'
  - name: 'InfluxDB'
    password: 'topsecret!'

Status

Generate a status report of all docker containers associated with grape.

$ grape status -v
INFO 2021-01-11 16:36:14,134 status.py:187 - status
  Name           Type  Version  Status   Started                         Elapsed   Id          Image              Created                         Port
  grape_test1gr  gr    0.4.3    running  2021-01-12T00:34:48.618074371Z  00:01:25  f85986a629  sha256:9ad3ce931a  2021-01-12T00:34:48.259793436Z  4700
  grape_test1pg  pg    0.4.3    running  2021-01-12T00:34:49.13129136Z   00:01:25  28e4372c1b  sha256:0b0b68fee3  2021-01-12T00:34:48.673554391Z  4701
  grape_test2gr  gr    0.4.3    running  2021-01-12T00:35:11.212822109Z  00:01:02  632389a8d8  sha256:9ad3ce931a  2021-01-12T00:35:10.865475598Z  4710
  grape_test2pg  pg    0.4.3    running  2021-01-12T00:35:11.692884845Z  00:01:02  65b9b2186b  sha256:0b0b68fee3  2021-01-12T00:35:11.256244428Z  4711
  jbhgr          gr    0.4.3    running  2021-01-11T16:47:24.427558155Z  07:48:49  5368be647f  sha256:9ad3ce931a  2021-01-11T16:47:24.099948853Z  4640
  jbhpg          pg    0.4.3    running  2021-01-11T16:47:24.907664979Z  07:48:49  7cf96782d7  sha256:0b0b68fee3  2021-01-11T16:47:24.459861004Z  4641
INFO 2021-01-11 16:36:14,221 status.py:222 - done

Tree

Generate a tree view of a grape grafana server datasources, folders and dashboards.

$ pipenv run grape tree -g 4640
jbhgr:4640
  ├─ datasources
  │   └─ jbhpg:1:postgres
  └─ folders
      ├─ JBH:1
      │   └─ dashboards
      │       ├─ Northstar Dashboard Mock:id=5:uid=lC0QCuaMz:panels=33
      │       └─ OKR Initiatives Health:id=6:uid=peAwjuaMk:panels=6
      └─ Northstar:2
          └─ dashboards
              ├─ Jenkins Build Health Details:id=4:uid=ir0QjX-Mz:panels=9
              └─ Jenkins Build Health:id=3:uid=6Q0QCuaGk:panels=70

Tools

This section describes the tools in the local tools directory. They are not integrated into grape at this time because they don't fit the grape idiom but that is a completely subjective decision which can be revisitied at any time.

csv2sql.py

This is a standalone tool to read a CSV data file with a header row and convert it to SQL instructions to create and populate a table generically.

It is generic because it figures out the field types by analyzing the data.

There are a number of options for specifying the output, how to convert certain values and what SQL types to use for integers, floats, dates and strings.

It is useful for adding CSV data to your dashboards.

See the help (-h) for more detailed information.

runpga.sh

There is script called tools/runpga.sh that will create a pgAdmin container for you.

For demo01 you would run it like this:

$ tools/runpga.sh demo01pg

When it completes it prints out the information necessary to login into the pgAdmin and connect to the database.

upload-json-dashboard.sh

There is a script called tools/upload-json-dashboard.sh that will upload a JSON dashboard to a Grafana server from the command line.

The upload is limited to servers with simple authentication based on a username and password unless you override it using -x and -n.

The local dashboard JSON file is creatined by exporting the dashboard from the Grafana UI with the "Export for sharing externally" checkbox checked.

This script is useful for transferring a single dashboard from one server to another.

Although the same function can be accomplished in the UI, this script allows updates to be automated from the command line.

This script requires that "curl" is installed.

See the script help (-h) for more information and examples.

Samples

There are different samples of how to use this system in the samples directory tree. Each sample is in its own directory with a README.md that describes what it does and how to use it.

demo01

This demo is very basic. It shows, in detail, how to create a simple model with grafana and a database. There is more information in the demo README.

Here is how to run it.

$ make demo01

Note that you must have bash installed for it to work.

The demo01 dashboard looks like this.

Although it looks really simple, this demo shows an automatically generated dashboard that connects to the automatically generated database and displays its contents.

Click here for more information.

demo02

This demo is more realistic. It shows how to create a model from a publicly available dataset. There is more information in the demo README.

Here is how to run it.

$ make demo02

The demo02 dashboard looks like this.

Click here for more information.

demo03

This demo shows how to create a dashboard from local data. There is more information in the README.

Here is how to run it.

$ make demo03

The demo03 dashboard looks like this.

Click here for more information.

demo04

This demo shows how to create mock data in a panel which allows you to prototype dashboards without touching the database. It is a very powerful idiom.

Here is how to run it.

$ make demo04

The demo04 dashboard looks like this.

Click here for more information.

Miscellaneous

This section contains miscellaneous information.

Grafana

Grafana was chosen because it is a commonly used open source resource for querying, visualizing, alerting on, and exploring metrics you are interested in. Many organizations use it for understanding metrics related to business and engineering operations.

Postgres

Postgres was chosen for this project because it a popular database that supports relational data, time series data and document (NoSQL) data cleanly which supports experimenting with different data storage models. Other possibilities, like InfluxDB, tend to be more focused in a specific data storage model like time series data.

pgAdmin

For folks that like to use GUI interfaces to databases like pgAdmin or DBeaver, it is trivial to create such connections.

If you have pgAdmin installed on the host you can connect to the database as host localhost:4401 for demo01 or localhost:4411 for demo02.

If you would prefer to run pgAdmin from a local container, you need to get the gateway IP address of pgAdmin container and use that to connect to port 4401 or 4402. The gateway is a proxy for the host localhost. Here is how to obtain that for pgAdmin:

$ docker run -d -e PGADMIN_DEFAULT_EMAIL=me@example.com -e PGADMIN_DEFAULT_PASSWORD=password -h pgadmin4 --name pgadmin4 -p 4450:80 dpage/pgadmin4
$ docker inspect pgadmin4 | jq '.[]|.NetworkSettings.Networks.bridge.Gateway'
"172.17.0.1"

For the above example, the database host would be 172.17.0.1:4401 or demo01 or 172.17.0.1L4411 for demo02 when referenced from the pgAdmin docker container created above: http://localhost:4450.

Acknowledgments

• Many thanks to Deron Ferguson for helping me track down and debug problems on windows 10.
• Many thanks to Rob Rodrigues for helping me track down and debug problems on linux as well as his work to add the integration work flow.