Reads the metadata in the RRD and adjusts the parameters accordingly in order to expose all RRAs in their entirety.
Value
Returns a named list of data.frames. Each data frame corresponds to an RRA (see read_rra()). The list has names constructed as "consolidation function" + "step" - e.g. "AVERAGE15".
See also
Other rrd functions:
describe_rrd(),
read_rra()
Examples
rrd_cpu_0 <- system.file("extdata/cpu-0.rrd", package = "rrd")
describe_rrd(rrd_cpu_0)
#> An RRD file with 10 RRA arrays and step size 60
#> [1] AVERAGE_60 (43200 rows)
#> [2] AVERAGE_300 (25920 rows)
#> [3] MIN_300 (25920 rows)
#> [4] MAX_300 (25920 rows)
#> [5] AVERAGE_3600 (8760 rows)
#> [6] MIN_3600 (8760 rows)
#> [7] MAX_3600 (8760 rows)
#> [8] AVERAGE_86400 (1825 rows)
#> [9] MIN_86400 (1825 rows)
#> [10] MAX_86400 (1825 rows)
cpu <- read_rrd(rrd_cpu_0)
names(cpu)
#> [1] "AVERAGE60" "AVERAGE300" "MIN300" "MAX300" "AVERAGE3600"
#> [6] "MIN3600" "MAX3600" "AVERAGE86400" "MIN86400" "MAX86400"
head(cpu[[1]])
#> # A tibble: 6 × 9
#> timestamp user sys nice idle wait irq softirq stolen
#> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2018-04-02 12:24:00 0.0104 0.00811 0 0.981 0 0 0 0.000137
#> 2 2018-04-02 12:25:00 0.0126 0.00630 0 0.979 0 0 0 0.00192
#> 3 2018-04-02 12:26:00 0.0159 0.00808 0 0.976 0 0 0 0
#> 4 2018-04-02 12:27:00 0.00853 0.00647 0 0.985 0 0 0 0
#> 5 2018-04-02 12:28:00 0.0122 0.00999 0 0.978 0 0 0 0
#> 6 2018-04-02 12:29:00 0.0106 0.00604 0 0.983 0 0 0 0
tail(cpu[[1]])
#> # A tibble: 6 × 9
#> timestamp user sys nice idle wait irq softirq stolen
#> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2018-05-02 12:17:00 0.00545 0.00144 0 0.992 1.44e-3 0 0 0
#> 2 2018-05-02 12:18:00 0.00602 0.00201 0 0.991 5.69e-4 0 0 0
#> 3 2018-05-02 12:19:00 0.00889 0.000569 0 0.991 0 0 0 0
#> 4 2018-05-02 12:20:00 0.00715 0 0 0.991 0 0 0 1.44e-3
#> 5 2018-05-02 12:21:00 0.00602 0.00144 0 0.992 0 0 0 5.68e-4
#> 6 2018-05-02 12:22:00 0.00171 0.000569 0 0.998 0 0 0 0
tail(
cpu$AVERAGE60$sys
)
#> [1] 0.0014390667 0.0020080000 0.0005689333 0.0000000000 0.0014390667
#> [6] 0.0005689333