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README.md

quasardb

⚠️WARNING: Early stage development version. Use at your own risk.

ℹ️NOTE: Supported systems: FreeBSD, Linux, 🍎macOS, Windows.

The quasardb package is the official R API for quasardb timeseries database.

Installation

On Unix-like systems, if you have quasardb C API installed system-wide, you can install quasardb R API directly from GitHub as follows:

if (!requireNamespace("devtools", quietly = TRUE)) {
  install.packages("devtools")
}
devtools::install_github("bureau14/qdb-api-r")

quasardb C API

To build the R API, you will need the C API. You can get the C API as well as other tools from our download page.

It can either be installed on the machine system-wide (e.g. on Unix in /usr/lib or /usr/local/lib) or you can unpack the C API archive in inst subdirectory of this R API source. On Windows, you need to verify additionally that the qdb_api.dll is in the PATH environment variable.

Building the extension from source

To perform a clean build:

devtools::build(binary = TRUE)

To update documentation and auto-generated files:

devtools::document()

Only update the documentation in man, but don't regenerate R files:

roxygen2::roxygenize()

To check the extension:

devtools::check()

You may wish to skip tests using:

devtools::check(args = "--no-tests")

To test the extension:

devtools::test()

Usage

⚠️WARNING: Most of these snippets require a running qdbd cluster running. You can get it from our download page.

Load library:

library(quasardb)

Get underlying C API version:

version()
#> [1] "2.7.0"

Get underlying C API build id and date:

build()
#> [1] "e09741b 2018-06-27 16:40:20 +0000"

Connect to a quasardb cluster at a default URI qdb://127.0.0.1:2836:

handle <- connect()

Connect to a quasardb cluster at a user-provided URI:

handle <- connect("qdb://127.0.0.1:2836")

Create a timeseries:

ts_create(handle, name = "timeseries1",
     columns = c("column1" = column_type$blob, "column2" = column_type$double))

Show information about the columns of a timeseries:

columns <- ts_show(handle, name = "timeseries1")
columns
#> column1 column2
#>       1       0
sapply(columns, function(ct) {
  names(which(ct == column_type))
})
#>  column1  column2
#>   "blob" "double"

Add a tag to an entry:

attach_tags(handle, entry = "timeseries1", tags = "my_tag")

Add many tags at once:

attach_tags(handle,
                entry = "timeseries1",
                tags = c("my_tag1", "my_tag2", "my_tag3"))

Get tags of an entry:

tags <- get_tags(handle, name = "timeseries1")
tags
#> [1] "my_tag3" "my_tag1" "my_tag"  "my_tag2"

Get all entries marked with a tag:

entries <- get_tagged(handle, tag = "my_tag")
entries
#> [1] "timeseries2" "timeseries1"

Get all entry keys matching given find query:

# Get all entries (precisely: their keys) tagged with 'my-tag' being timeseries.
keys <- query_find(handle, "find(tag='my_tag' and type=ts)")
keys
#> [1] "timeseries2" "timeseries1"

Untag an entry:

detach_tags(handle, entry = "timeseries2", tag = "my_tag")
# Now, timeseries2 is no more on the list.
keys <- query_find(handle, "find(tag='my_tag' and type=ts)")
keys
#> [1] "timeseries1"

Execute a select query:

# Get number of elements in each column of the timeseries in year 2017.
result <-
  query(handle, "select count(*) from timeseries1 in range(2017, +1y)")
result$scanned_point_count
#> [1] 0
result$tables[["timeseries1"]]$data
#>                            $timestamp count(column1) count(column2)
#> 1 2017-01-01T00:00:00.000000000+00:00              0              0

Remove an entry:

entry_remove(handle, name = "timeseries1")

TODO

  • Add ts_insert (only stub is currently implemented), has_tag.
  • Make a quasardb driver compliant with DBI package.
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