influxdb-client-r

This repository contains R package for InfluxDB 2.0 client.

• Features
• Installing
• Installing dependencies
• Installing influxdbclient package
• Usage
• Client instantiation
• Querying data
  • Type mapping
  • Time series
  • Metadata
• Writing data
  • Type mapping
  • Output preview
  • Write retrying
• Getting status
  • Health
  • Readiness
• Advanced
• Known Issues
• Contributing
• License

Features

The InfluxDB 2.0 client supports:

• Querying data
• Writing data
• Getting status

Documentation

This section contains links to the client library documentation.

• Product documentation
• Getting Started
• Examples
• API Reference
• Changelog

Installing

The package requires R >= 3.4.

Installing dependencies

1. install.packages(c("httr", "bit64", "nanotime", "plyr"))

Installing influxdbclient package

The package is published on CRAN and can be installed with

1. install.packages("influxdbclient")

The latest development version can be installed with

1. # install.packages("remotes")
2. remotes::install_github("influxdata/influxdb-client-r")

Usage

Client instantiation

1. library(influxdbclient)
2. client <- InfluxDBClient$new(url = "http://localhost:8086",
3. token = "my-token",
4. org = "my-org")

Parameters

Hint: to avoid SSL certificate validation errors when accessing InfluxDB instance over https such as SSL certificate problem: unable to get local issuer certificate, you can try to disable the validation using the following call before using any InfluxDBClient method. Warning: it will disable peer certificate validation for the current R session.

1. library(httr)
2. httr::set_config(config(ssl_verifypeer = FALSE))

Querying data

Use query method.

1. client <- InfluxDBClient$new(url = "http://localhost:8086",
2. token = "my-token",
3. org = "my-org")

5. data <- client$query('from(bucket: "my-bucket") |> range(start: -1h) |> drop(columns: ["_start", "_stop"])')
6. data

Flux query can yield multiple results in one response, where each result may contain multiple tables. Return value is therefore a named list, where each element is a list of data frames that represent a result. Data frame represents Flux table. You can list the results using names method.

Quite often, though, there is just a single result and therefore the query by default flattens the return value to simple unnamed list of data frames. This behaviour controlled by flatSingleResult parameter. With flatSingleResult = FALSE, you can check that the return value contains one element with name "_result" (default result name when there is no explicit yield in the query) and use the name to retrieve it, like

1. > names(data)
2. [1] "_result"

4. > data[["_result"]]
5. [[1]]
6. time       name region sensor_id altitude grounded temperature
7. 1 2021-06-09T09:52:41+00:00 airSensors  south   TLM0101      549    FALSE  71.7844100
8. 2 2021-06-09T09:52:51+00:00 airSensors  south   TLM0101      547    FALSE  71.7684399
9. 3 2021-06-09T09:53:01+00:00 airSensors  south   TLM0101      563     TRUE  71.7819928
10. 4 2021-06-09T09:53:11+00:00 airSensors  south   TLM0101      560     TRUE  71.7487767
11. 5 2021-06-09T09:53:21+00:00 airSensors  south   TLM0101      544    FALSE  71.7335579

Parameters

Incoming type mapping

Using retrieved data as time series

Flux timestamps are parsed into nanotime (integer64 underneath) type, because R datetime types do not support nanosecond precision. nanotime is not a time-based object appropriate for creating a time series, though. By default, query coerces the _time column to time column of POSIXct type (see POSIXctCol parameter), with possible loss precision (which is unimportant in the context of R time series).

Select data of interest from the result like

1. # from the first data frame, pick subset containing `time` and `_value` columns only
2. df1 <- data[[1]][c("time", "_value")]

Then, a time series object can be created from the data frame, eg. using tsbox package:

1. ts1 <- ts_ts(ts_df(df1))

A data frame, or a time series object created from it, can be used for decomposition, anomaly detection etc, like

1. df1$`_value` %>% ts(freq=168) %>% stl(s.window=13) %>% autoplot()

or

1. ts1 %>% ts(freq=168) %>% stl(s.window=13) %>% autoplot()

Querying metadata

For queries returning records without time info (listing buckets, tag values etc.), set POSIXctCol to NULL.

1. buckets <- client$query('buckets()', POSIXctCol = NULL)

Writing data

Use write method.

1. client <- InfluxDBClient$new(url = "http://localhost:8086",
2. token = "my-token",
3. org = "my-org")
4. data <- ...
5. response <- client$write(data, bucket = "my-bucket", precision = "us",
6. measurementCol = "name",
7. tagCols = c("region", "sensor_id"),
8. fieldCols = c("altitude", "temperature"),
9. timeCol = "time")

The example is valid for data.frame data like the following:

1. > print(data)
2. time       name region sensor_id altitude grounded temperature
3. 1 2021-06-09T09:52:41+00:00 airSensors  south   TLM0101      549    FALSE  71.7844100
4. 2 2021-06-09T09:52:51+00:00 airSensors  south   TLM0101      547    FALSE  71.7684399
5. 3 2021-06-09T09:53:01+00:00 airSensors  south   TLM0101      563     TRUE  71.7819928
6. 4 2021-06-09T09:53:11+00:00 airSensors  south   TLM0101      560     TRUE  71.7487767
7. 5 2021-06-09T09:53:21+00:00 airSensors  south   TLM0101      544    FALSE  71.7335579

9. > str(data)
10. 'data.frame':    5 obs. of  7 variables:
11. $ time       :integer64 1623232361000000000 1623232371000000000 1623232381000000000 1623232391000000000 1623232401000000000
12. $ name       : chr  "airSensors" "airSensors" "airSensors" "airSensors" ...
13. $ region     : chr  "south" "south" "south" "south" ...
14. $ sensor_id  : chr  "TLM0101" "TLM0101" "TLM0101" "TLM0101" ...
15. $ altitude   :integer64 549 547 563 560 544
16. $ grounded   : logi  FALSE FALSE TRUE TRUE FALSE
17. $ temperature: num  71.8 71.8 71.8 71.7 71.7

Parameters

Supported time column value types: nanotime, POSIXct. To write data points without timestamp, set timeCol to NULL. See Timestamp precision for details.

Response is either NULL on success, or errorr otherwise.

Note: default fieldCols value is suitable for writing back unpivoted data retrieved from InfluxDB before. For usual tables (“pivoted” in Flux world), fieldCols should be unnamed list, eg. c("humidity", "temperature", ...).

Outgoing type mapping

Output preview

To preview how input data are serialized to InfluxDB line protocol, pass the name of object to receive the output as object parameter value. It changes write to dry-run operation (nothing is sent to the database). The object will be assigned to the calling environment. This option is intended for debugging purposes.

1. data <- ...
2. response <- client$write(data, bucket = "my-bucket", precision = "us",
3. measurementCol = "name",
4. tagCols = c("region", "sensor_id"),
5. fieldCols = c("altitude", "temperature"),
6. timeCol = "time",
7. object = "lp")
8. lp

Sample output:

1. > print(lp)
2. [[1]]
3. [1] "airSensors,region=south,sensor_id=TLM0101 altitude=549i,temperature=71.7844100 1623232361000000"
4. [2] "airSensors,region=south,sensor_id=TLM0101 altitude=547i,temperature=71.7684399 1623232371000000"
5. [3] "airSensors,region=south,sensor_id=TLM0101 altitude=563i,temperature=71.7819928 1623232381000000"
6. [4] "airSensors,region=south,sensor_id=TLM0101 altitude=560i,temperature=71.7487767 1623232391000000"
7. [5] "airSensors,region=south,sensor_id=TLM0101 altitude=544i,temperature=71.7335579 1623232401000000"

Write retrying

By default, client will not retry failed writes. To instantiate a client with retry support, pass an instance of RetryOptions, eg:

1. client <- InfluxDBClient$new(url = "http://localhost:8086",
2. token = "my-token",
3. org = "my-org",
4. retryOptions = RetryOptions$new(maxAttempts = 3))

For retry strategy with default options just pass TRUE as retryOptions parameter value:

1. client <- InfluxDBClient$new(url = "http://localhost:8086",
2. token = "my-token",
3. org = "my-org",
4. retryOptions = TRUE)

Retryable InfluxDB write errors are 429 and 503 status codes. The retry strategy implements exponential backoff algorithm, customizable with RetryOptions.

Getting status

Health status

Use health method to get the health status.

1. client <- InfluxDBClient$new(url = "http://localhost:8086",
2. token = "my-token",
3. org = "my-org")

5. check <- client$health()

Response is list with health information elements (name, status, version, commit) or error.

Readiness status

Use ready method to get the readiness status.

1. client <- InfluxDBClient$new(url = "http://localhost:8086",
2. token = "my-token",
3. org = "my-org")

5. check <- client$ready()

Response is a list with status elements (status, started, up) or error.

Advanced

The client automatically follows HTTP redirects.

To use the client with proxy, use set_config to configure the proxy:

1. library(httr)
2. httr::set_config(
3. use_proxy(url = "my-proxy", port = 8080, username = "user",password = "password")
4. )

Known Issues

Contributing

Contributions are most welcome. The fastest way to get something fixed is to open a PR.

License

The client is available as open source under the terms of the MIT License.