vsphere-influxdb-go/vendor/github.com/influxdata/influxdb/client/README.md

InfluxDB Client

Description

NOTE: The Go client library now has a "v2" version, with the old version being deprecated. The new version can be imported at import "github.com/influxdata/influxdb/client/v2". It is not backwards-compatible.

A Go client library written and maintained by the InfluxDB team. This package provides convenience functions to read and write time series data. It uses the HTTP protocol to communicate with your InfluxDB cluster.

Getting Started

Connecting To Your Database

Connecting to an InfluxDB database is straightforward. You will need a host name, a port and the cluster user credentials if applicable. The default port is 8086. You can customize these settings to your specific installation via the InfluxDB configuration file.

Though not necessary for experimentation, you may want to create a new user and authenticate the connection to your database.

For more information please check out the Admin Docs.

For the impatient, you can create a new admin user bubba by firing off the InfluxDB CLI.

influx
> create user bubba with password 'bumblebeetuna'
> grant all privileges to bubba

And now for good measure set the credentials in you shell environment. In the example below we will use $INFLUX_USER and $INFLUX_PWD

Now with the administrivia out of the way, let's connect to our database.

NOTE: If you've opted out of creating a user, you can omit Username and Password in the configuration below.

package main

import (
	"log"
	"time"

	"github.com/influxdata/influxdb/client/v2"
)

const (
	MyDB = "square_holes"
	username = "bubba"
	password = "bumblebeetuna"
)


func main() {
	// Create a new HTTPClient
	c, err := client.NewHTTPClient(client.HTTPConfig{
		Addr:     "http://localhost:8086",
		Username: username,
		Password: password,
	})
	if err != nil {
		log.Fatal(err)
	}

	// Create a new point batch
	bp, err := client.NewBatchPoints(client.BatchPointsConfig{
		Database:  MyDB,
		Precision: "s",
	})
	if err != nil {
		log.Fatal(err)
	}

	// Create a point and add to batch
	tags := map[string]string{"cpu": "cpu-total"}
	fields := map[string]interface{}{
		"idle":   10.1,
		"system": 53.3,
		"user":   46.6,
	}

	pt, err := client.NewPoint("cpu_usage", tags, fields, time.Now())
	if err != nil {
		log.Fatal(err)
	}
	bp.AddPoint(pt)

	// Write the batch
	if err := c.Write(bp); err != nil {
		log.Fatal(err)
	}
}

Inserting Data

Time series data aka points are written to the database using batch inserts. The mechanism is to create one or more points and then create a batch aka batch points and write these to a given database and series. A series is a combination of a measurement (time/values) and a set of tags.

In this sample we will create a batch of a 1,000 points. Each point has a time and a single value as well as 2 tags indicating a shape and color. We write these points to a database called square_holes using a measurement named shapes.

NOTE: You can specify a RetentionPolicy as part of the batch points. If not provided InfluxDB will use the database default retention policy.

func writePoints(clnt client.Client) {
	sampleSize := 1000

	bp, err := client.NewBatchPoints(client.BatchPointsConfig{
		Database:  "systemstats",
		Precision: "us",
	})
	if err != nil {
		log.Fatal(err)
	}

    rand.Seed(time.Now().UnixNano())
	for i := 0; i < sampleSize; i++ {
		regions := []string{"us-west1", "us-west2", "us-west3", "us-east1"}
		tags := map[string]string{
			"cpu":    "cpu-total",
			"host":   fmt.Sprintf("host%d", rand.Intn(1000)),
			"region": regions[rand.Intn(len(regions))],
		}

		idle := rand.Float64() * 100.0
		fields := map[string]interface{}{
			"idle": idle,
			"busy": 100.0 - idle,
		}

		pt, err := client.NewPoint(
			"cpu_usage",
			tags,
			fields,
			time.Now(),
		)
		if err != nil {
			log.Fatal(err)
		}
		bp.AddPoint(pt)
	}

	if err := clnt.Write(bp); err != nil {
		log.Fatal(err)
	}
}

Querying Data

One nice advantage of using InfluxDB the ability to query your data using familiar SQL constructs. In this example we can create a convenience function to query the database as follows:

// queryDB convenience function to query the database
func queryDB(clnt client.Client, cmd string) (res []client.Result, err error) {
	q := client.Query{
		Command:  cmd,
		Database: MyDB,
	}
	if response, err := clnt.Query(q); err == nil {
		if response.Error() != nil {
			return res, response.Error()
		}
		res = response.Results
	} else {
		return res, err
	}
	return res, nil
}

Creating a Database

_, err := queryDB(clnt, fmt.Sprintf("CREATE DATABASE %s", MyDB))
if err != nil {
	log.Fatal(err)
}

Count Records

q := fmt.Sprintf("SELECT count(%s) FROM %s", "value", MyMeasurement)
res, err := queryDB(clnt, q)
if err != nil {
	log.Fatal(err)
}
count := res[0].Series[0].Values[0][1]
log.Printf("Found a total of %v records\n", count)

Find the last 10 shapes records

q := fmt.Sprintf("SELECT * FROM %s LIMIT %d", MyMeasurement, 20)
res, err = queryDB(clnt, q)
if err != nil {
	log.Fatal(err)
}

for i, row := range res[0].Series[0].Values {
	t, err := time.Parse(time.RFC3339, row[0].(string))
	if err != nil {
		log.Fatal(err)
	}
	val := row[1].(string)
	log.Printf("[%2d] %s: %s\n", i, t.Format(time.Stamp), val)
}

Using the UDP Client

The InfluxDB client also supports writing over UDP.

func WriteUDP() {
	// Make client
	c, err := client.NewUDPClient("localhost:8089")
	if err != nil {
		panic(err.Error())
	}
	
	// Create a new point batch
	bp, _ := client.NewBatchPoints(client.BatchPointsConfig{
		Precision: "s",
	})

	// Create a point and add to batch
	tags := map[string]string{"cpu": "cpu-total"}
	fields := map[string]interface{}{
		"idle":   10.1,
		"system": 53.3,
		"user":   46.6,
	}
	pt, err := client.NewPoint("cpu_usage", tags, fields, time.Now())
	if err != nil {
		panic(err.Error())
	}
	bp.AddPoint(pt)

	// Write the batch
	c.Write(bp)
}

Point Splitting

The UDP client now supports splitting single points that exceed the configured payload size. The logic for processing each point is listed here, starting with an empty payload.

1. If adding the point to the current (non-empty) payload would exceed the configured size, send the current payload. Otherwise, add it to the current payload.
2. If the point is smaller than the configured size, add it to the payload.
3. If the point has no timestamp, just try to send the entire point as a single UDP payload, and process the next point.
4. Since the point has a timestamp, re-use the existing measurement name, tagset, and timestamp and create multiple new points by splitting up the fields. The per-point length will be kept close to the configured size, staying under it if possible. This does mean that one large field, maybe a long string, could be sent as a larger-than-configured payload.

The above logic attempts to respect configured payload sizes, but not sacrifice any data integrity. Points without a timestamp can't be split, as that may cause fields to have differing timestamps when processed by the server.

Go Docs

Please refer to http://godoc.org/github.com/influxdata/influxdb/client/v2 for documentation.

See Also

You can also examine how the client library is used by the InfluxDB CLI.