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feat: support new metrics firehose api with get_usage() #404

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2 changes: 1 addition & 1 deletion .lintr
Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
linters: linters_with_defaults(
line_length_linter = line_length_linter(120L),
object_name_linter = object_name_linter(styles = c("snake_case", "symbols", "CamelCase")),
cyclocomp_linter = cyclocomp_linter(30L),
cyclocomp_linter = NULL, # Issues with R6 classes.
object_length_linter(32L),
indentation_linter = indentation_linter(hanging_indent_style = "tidy"),
return_linter = NULL
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1 change: 1 addition & 0 deletions NAMESPACE
Original file line number Diff line number Diff line change
Expand Up @@ -98,6 +98,7 @@ export(get_tag_data)
export(get_tags)
export(get_thumbnail)
export(get_timezones)
export(get_usage)
export(get_usage_shiny)
export(get_usage_static)
export(get_user_permission)
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5 changes: 5 additions & 0 deletions NEWS.md
Original file line number Diff line number Diff line change
@@ -1,5 +1,10 @@
# connectapi (development version)

## New features

- New `get_usage()` function returns content usage data from Connect's `GET
v1/instrumentation/content/hits` endpoint on Connect v2025.04.0 and higher.
(#390)

## Enhancements and fixes

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27 changes: 27 additions & 0 deletions R/connect.R
Original file line number Diff line number Diff line change
Expand Up @@ -818,6 +818,33 @@ Connect <- R6::R6Class(
self$GET(path, query = query)
},

#' @description Get content usage data.
#' @param from Optional `Date` or `POSIXt`; start of the time window. If a
#' `Date`, coerced to `YYYY-MM-DDT00:00:00` in the caller's time zone.
#' @param to Optional `Date` or `POSIXt`; end of the time window. If a
#' `Date`, coerced to `YYYY-MM-DDT23:59:59` in the caller's time zone.
Comment on lines +821 to +825
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I'm not sure this is doing what we expect with timezones. And actually, I'm not even totally sure what is intended. So maybe we should work that out here and then adapt the code to fit? When we send timestamps to Connect with this function do we want them to be transformed to UTC from the caller's local timezone before being sent? Or some other behavior?

One thing to note: If I'm reading this correctly, make_timestamp() has slightly different behavior if one sends a non-character than if one sends a character input. Where the character string version will not be parsed and also not be transformed into UTC. And so this function will do the same.

connectapi/R/parse.R

Lines 16 to 28 in e8c8075

make_timestamp <- function(input) {
if (is.character(input)) {
# TODO: make sure this is the right timestamp format
return(input)
}
# In the call to `safe_format`:
# - The format specifier adds a literal "Z" to the end of the timestamp, which
# tells Connect "This is UTC".
# - The `tz` argument tells R to produce times in the UTC time zone.
# - The `usetz` argument says "Don't concatenate ' UTC' to the end of the string".
safe_format(input, "%Y-%m-%dT%H:%M:%SZ", tz = "UTC", usetz = FALSE)
}

inst_content_hits = function(from = NULL, to = NULL) {
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I don't think this method name benefits from having the inst_ prefix on it.

More of a philosophical musing, but I also am not sure it's worth packing more things into methods on the Connect R6 object. get_usage() is a pretty thin wrapper around this. We could just put this inside of get_usage(), not sure there's much benefit to separating things like this. I know this is a pattern in parts of the package, but that doesn't mean we have to keep doing it.

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Agreed that I don't think the method name at all benefits from inst_ prefix; to the reader, it isn't at all clear what it means.

I would also be completely fine with beginning to move everything into the functions; I agree that I don't think we get much benefit at all from having the logic in the R6 object. In fact, I think it adds confusion: it was hard for me to decide where we want different bits of logic to live, i.e. within the method or the outer function. It's kind arbitrary.

I guess you could argue that the goal of the methods is to enumerate the available endpoints that operate on different objects, but it feels like a pretty verbose way to do that.

error_if_less_than(self$version, "2025.04.0")

# If this is called with date objects with no timestamp attached, it's
# reasonable to assume that the caller is indicating the days as an
# inclusive range.
if (inherits(from, "Date")) {
from <- as.POSIXct(paste(from, "00:00:00"))
}
if (inherits(to, "Date")) {
to <- as.POSIXct(paste(to, "23:59:59"))
}

self$GET(
v1_url("instrumentation", "content", "hits"),
query = list(
from = make_timestamp(from),
to = make_timestamp(to)
)
)
},

#' @description Get running processes.
procs = function() {
warn_experimental("procs")
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66 changes: 66 additions & 0 deletions R/get.R
Original file line number Diff line number Diff line change
Expand Up @@ -526,6 +526,72 @@ get_usage_static <- function(
return(out)
}

#' Get usage information for deployed content
#'
#' @description

#' Retrieve content hits for all available content on the server. Available
#' content depends on the user whose API key is in use. Administrator accounts
#' will receive data for all content on the server. Publishers will receive data
#' for all content they own or collaborate on.
#'
#' If no date-times are provided, all usage data will be returned.

#' @param client A `Connect` R6 client object.
#' @param from Optional `Date` or date-time (`POSIXct` or `POSIXlt`). Only
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If it's optional, what happens if I omit it?

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Why am I having so much trouble coming up with the language for this? 😂

If it's omitted, the time constraint is unbounded on the lower side.

#' records after this time are returned. If a `Date`, treated as the start of
#' that day in the local time zone; if a date-time, used verbatim.
#' @param to Optional `Date` or date-time (`POSIXct` or `POSIXlt`). Only records
#' before this time are returned. If a `Date`, treated as end of that day
#' (`23:59:59`) in the local time zone; if a date-time, used verbatim.
#'
#' @return A tibble with columns:
#' * `id`: An identifier for the record.
#' * `user_guid`: The GUID of logged-in visitors, NA for anonymous.
#' * `content_guid`: The GUID of the content.
#' * `timestamp`: The time of the hit as `POSIXct`.
#' * `path`: The path of the hit. Not recorded for all content types.
#' * `user_agent`: If available, the user agent string for the hit. Not
#' available for all records.
#'
#' @details
#'
#' The data returned by `get_usage()` includes all content types. For Shiny
#' content, the `timestamp` indicates the *start* of the Shiny session.
#' Additional fields for Shiny and non-Shiny are available respectively from
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Why are the additional fields not included in this endpoint?

#' `get_usage_shiny()` and `get_usage_static()`.
#'
#' When possible, however, we recommend using `get_usage()` over
#' `get_usage_static()` or `get_usage_shiny()`, as it will be much faster for
#' large datasets.
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What does "large datasets" mean here?

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I mean, not large in the "big data" sense, but like, to fetch 100,000 records on the old API requires 200 requests, which starts to lead to delays of a multiple seconds scaling linearly. Perhaps best to just not describe it?

#'
#' @examples
#' \dontrun{
#' client <- connect()
#'
#' # Fetch the last 2 days of hits
#' usage <- get_usage(client, from = Sys.Date() - 2, to = Sys.Date())
#'
#' # Fetch usage after a specified date
#' usage <- get_usage(
#' client,
#' from = as.POSIXct("2025-05-02 12:40:00", tz = "UTC")
#' )
#'
#' # Fetch all usage
#' usage <- get_usage(client)
#' }
#'
#' @export
get_usage <- function(client, from = NULL, to = NULL) {
usage_raw <- client$inst_content_hits(
from = from,
to = to
)

usage <- parse_connectapi_typed(usage_raw, connectapi_ptypes$usage)
fast_unnest_character(usage, "data")
}

#' Get Audit Logs from Posit Connect Server
#'
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63 changes: 63 additions & 0 deletions R/parse.R
Original file line number Diff line number Diff line change
Expand Up @@ -58,15 +58,19 @@ ensure_column <- function(data, default, name) {
# manual fix because vctrs::vec_cast cannot cast double -> datetime or char -> datetime
col <- coerce_datetime(col, default, name = name)
}

if (inherits(default, "fs_bytes") && !inherits(col, "fs_bytes")) {
col <- coerce_fsbytes(col, default)
}

if (inherits(default, "integer64") && !inherits(col, "integer64")) {
col <- bit64::as.integer64(col)
}

if (inherits(default, "list") && !inherits(col, "list")) {
col <- list(col)
}

col <- vctrs::vec_cast(col, default, x_arg = name)
}
data[[name]] <- col
Expand Down Expand Up @@ -101,6 +105,65 @@ parse_connectapi <- function(data) {
))
}

# nolint start
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What linting are we escaping here?

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Commented code maybe?

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Yeah, commented code — it's not roxygen2 docs, just a regular comment, because it's not an exported function.

# Unnests a list column similarly to `tidyr::unnest_wider()`, bringing the
# entries of each list-item up to the top level. Makes some simplifying
# assumptions for the sake of performance:
# 1. All inner variables are treated as character vectors;
# 2. The names of the first entry of the list-column are used as the
# names of variables to extract.
# Performance example:
# > nrow(x_raw)
# [1] 373632
# > nrow(x_raw)
# [1] 373632
# > t_tidyr <- system.time(
# + x_tidyr <- tidyr::unnest_wider(x_raw, data)
# + )
# > t_custom <- system.time(
# + x_custom <- fast_unnest_character(x_raw, "data")
# + )
# > identical(x_tidyr, x_custom)
# [1] TRUE
# > t_tidyr
# user system elapsed
# 7.018 0.137 7.172
# > t_custom
# user system elapsed
# 0.281 0.005 0.285
# nolint end
fast_unnest_character <- function(df, col_name) {
if (!is.character(col_name)) {
stop("col_name must be a character vector")
}
if (!col_name %in% names(df)) {
stop("col_name is not present in df")
}

list_col <- df[[col_name]]

new_cols <- names(list_col[[1]])

df2 <- df
for (col in new_cols) {
df2[[col]] <- vapply(
list_col,
function(row) {
if (is.null(row[[col]])) {
NA_character_
} else {
row[[col]]
}
},
"1",
USE.NAMES = FALSE
)
}

df2[[col_name]] <- NULL
df2
}
Comment on lines +135 to +165
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The data returned from the endpoint includes path and user_agent fields nested under a data field. Without special treatment these are returned a list-column, which is awkward. I initially experimented with tidyr::unnest(), but that was slow on the larger datasets returned by this endpoint, so I wrote a custom fast_unnest_character() function which runs in about 5% (!) of the time that tidyr::unnest() takes.

Thinking about this a bit more: this isn't a huge chunk of code of course, but it is another chunk that we will take on the maintenance of if we go this route. This is another example where having our data interchange within connectapi be all data frames means we have to worry about the performance of json-parsed list responses into data frames and make sure those data frames are in a natural structure for folks to use. If we relied instead on only the parsed list data as our interchange and then gave folks as.data.frame() methods, we could defer the (sometimes expense) reshaping until late in process and eaking out performance gains like this are much less important so we can rely on more off the shelf tools.

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I can see what you mean, and this does align with what you've been saying about other server objects.

I hear what you're saying about parsing to data frames for data interchange and I think that approach would be great to use for, say, the integrations endpoints that I just added stories for.

For the data from the hits endpoint, presenting it as anything other than a data frame goes back to feeling kinda not-R-idiomatic, as it isn't data that can… hmm…

So definitely one of the tasks, and maybe the main task that I can imagine for this data is to, like, treat it as a data frame and filter, plot, etc., it. But another thing you might want to do is, like, get the content item associated with this hit. And yeah, in that case, you might just want to be able to pass the hit, or hit$content_guid to content_item().

I still think we might want to keep code like this around in an as.data.frame() method — for making an unnested data frame out of nested data from the Connect API, tidyr::unnest() was 20x slower (which I was surprised by! it seems like a wild differential).

Open to a variety of options — let's discuss what the best approach would be to finalize and merge this PR.


coerce_fsbytes <- function(x, to, ...) {
if (is.numeric(x)) {
fs::as_fs_bytes(x)
Expand Down
9 changes: 8 additions & 1 deletion R/ptype.R
Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
NA_datetime_ <- # nolint: object_name_linter
vctrs::new_datetime(NA_real_, tzone = "UTC")
vctrs::new_datetime(NA_real_, tzone = Sys.timezone())
NA_list_ <- # nolint: object_name_linter
list(list())

Expand Down Expand Up @@ -38,6 +38,13 @@ connectapi_ptypes <- list(
"bundle_id" = NA_character_,
"data_version" = NA_integer_
),
usage = tibble::tibble(
"id" = NA_integer_,
"user_guid" = NA_character_,
"content_guid" = NA_character_,
"timestamp" = NA_datetime_,
"data" = NA_list_
),
content = tibble::tibble(
"guid" = NA_character_,
"name" = NA_character_,
Expand Down
22 changes: 22 additions & 0 deletions man/PositConnect.Rd

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67 changes: 67 additions & 0 deletions man/get_usage.Rd

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Original file line number Diff line number Diff line change
@@ -0,0 +1,52 @@
[
{
"id": 8966707,
"user_guid": null,
"content_guid": "475618c9",
"timestamp": "2025-04-30T12:49:16.269904Z",
"data": {
"path": "/hello",
"user_agent": "Datadog/Synthetics"
}
},
{
"id": 8966708,
"user_guid": null,
"content_guid": "475618c9",
"timestamp": "2025-04-30T12:49:17.002848Z",
"data": {
"path": "/world",
"user_agent": null
}
},
{
"id": 8967206,
"user_guid": null,
"content_guid": "475618c9",
"timestamp": "2025-04-30T13:01:47.40738Z",
"data": {
"path": "/chinchilla",
"user_agent": "Datadog/Synthetics"
}
},
{
"id": 8967210,
"user_guid": null,
"content_guid": "475618c9",
"timestamp": "2025-04-30T13:04:13.176791Z",
"data": {
"path": "/lava-lamp",
"user_agent": "Datadog/Synthetics"
}
},
{
"id": 8966214,
"user_guid": "fecbd383",
"content_guid": "b0eaf295",
"timestamp": "2025-04-30T12:36:13.818466Z",
"data": {
"path": null,
"user_agent": null
}
}
]
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