Mosparo is an open-source project that provides a distributed and reliable time-series database. It's implemented in Go and can be used as a drop-in replacement for other time-series databases like Graphite and OpenTSDB. This tutorial provides step-by-step instructions on how to install Mosparo on NetBSD.
Before installing Mosparo, you must install Go on your NetBSD system. You can do this by following the instructions provided in the NetBSD documentation or by using package managers like pkgsrc.
Assuming you have installed Go successfully, you should be able to run the go
command in your terminal. Check this by running:
go version
The output should be similar to:
go version go1.16.2 netbsd/amd64
git clone https://github.com/mosparo/mosparo.git
cd mosparo
go build -o mosparo_server cmd/mosparo_server/main.go
./mosparo_server -config=config.yml
The server should start without any errors, and you should be able to connect to it from a client.
cp config.example.yml config.yml
Open config.yml
in your text editor and update the settings as needed. For example, you can change the HTTP and TCP ports used by Mosparo, the retention policies for time-series data, and the default number of replicas for data.
Start the Mosparo server with the newly created configuration file:
./mosparo_server -config=config.yml
You can now connect to Mosparo using clients like Moscap, Grafana, or Telegraf. You can send data to Mosparo using its HTTP or TCP endpoints.
Here's an example of how to send data to Mosparo using curl
:
curl -X POST -H "Content-Type: application/json" -d '[{"name":"temperature","tags":{"sensor":"1"},"datapoints":[[1587051000000,25.3],[1587051360000,24.8]]}]' http://localhost:8086/write?db=mydb
This command will add two data points for the temperature
time-series with sensor=1
as a tag. The HTTP endpoint used is http://localhost:8086/write?db=mydb
, where mydb
is the database where the data will be stored.
By following this tutorial, you should now have Mosparo installed and running on your NetBSD system. You can start using it as a time-series database for your applications or monitoring systems.
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