R/dataset.R
In MicroStrategy/mstrio: Interface for 'MicroStrategy' REST API
# datasets.R
# Create and interact with MicroStrategy datasets
#' @title Create, update, delete and certify MicroStrategy datasets
#'
#' @description When creating a new dataset, provide a dataset name and an optional description.
#' When updating a pre-existing dataset, provide the dataset identifier. Tables are added to the
#' dataset in an iterative manner using `add_table()`.
#' @field connection MicroStrategy connection object
#' @field name Name of the dataset
#' @field description Description of the dataset. Must be less than or equal to 250 characters
#' @field folder_id If specified the dataset will be saved in this folder
#' @field dataset_id Identifier of a pre-existing dataset. Used when updating a pre-existing dataset
#' @field owner_id Owner ID
#' @field path Cube path
#' @field modification_time Last modification time, "yyyy-MM-dd HH:mm:ss" in UTC
#' @field size Cube size
#' @field cube_state Cube status,for example, 0=unpublished, 1=publishing, 64=ready
#' @field verbose If True (default), displays additional messages.
#' @examples
#' \dontrun{
#' # Create data frames
#' df1 <- data.frame("id" = c(1, 2, 3, 4, 5),
#' "first_name" = c("Jason", "Molly", "Tina", "Jake", "Amy"),
#' "last_name" = c("Miller", "Jacobson", "Turner", "Milner", "Cooze"))
#'
#' df2 <- data.frame("id" = c(1, 2, 3, 4, 5),
#' "age" = c(42, 52, 36, 24, 73),
#' "state" = c("VA", "NC", "WY", "CA", "CA"),
#' "salary" = c(50000, 100000, 75000, 85000, 250000))
#'
#' # Create a list of tables containing one or more tables and their names
#' my_dataset <- Dataset$new(connection=conn, name="HR Analysis")
#' my_dataset$add_table("Employees", df1, "add")
#' my_dataset$add_table("Salaries", df2, "add")
#' my_dataset$create()
#'
#' # By default Dataset$create() will upload the data to the Intelligence Server and publish the
#' dataset.
#' # If you just want to create the dataset but not upload the row-level data, use
#' Dataset$create(auto_upload=FALSE)
#'
#' # followed by
#' Dataset$update()
#' Dataset$publish()
#'
#' # When the source data changes and users need the latest data for analysis and reporting in
#' # MicroStrategy, mstrio allows you to update the previously created dataset.
#'
#' ds <- Dataset$new(connection=conn, dataset_id="...")
#' ds$add_table(name = "Stores", data_frame = stores_df, update_policy = 'update')
#' ds$add_table(name = "Sales", data_frame = stores_df, update_policy = 'upsert')
#' ds$update(auto_publish=TRUE)
#'
#' # By default Dataset$update() will upload the data to the Intelligence Server and publish the
#' dataset.
#' # If you just want to update the dataset but not publish the row-level data, use
#' Dataset$update(auto_publish=FALSE)
#'
#' # By default, the raw data is transmitted to the server in increments of 100,000 rows. On very
#' # large datasets (>1 GB), it is beneficial to increase the number of rows transmitted to the
#' # Intelligence Server with each request. Do this with the chunksize parameter:
#'
#' ds$update(chunksize = 500000)
#'
#' # If you want to cerfify an existing dataset, use
#' ds$certify()
#' }
#' @docType class
#' @importFrom R6 R6Class
#' @importFrom jsonlite toJSON
#' @export
Dataset <- R6Class("Dataset",
public = list(
# instance variables
connection = NULL,
name = NULL,
description = NULL,
folder_id = NULL,
dataset_id = NULL,
owner_id = NULL,
path = NULL,
modification_time = NULL,
size = NULL,
cube_state = NULL,
verbose = NULL,
#' @description
#' Interface for creating, updating, and deleting MicroStrategy in-memory datasets.
#' @details
#' When creating a new dataset, provide a dataset name and an optional description. When
#' updating a pre-existing dataset, provide the dataset identifier. Tables are added to the
#' dataset in an iterative manner using `add_table()`.
#' @param connection MicroStrategy connection object returned by `Connection$New()`.
#' @param name (character): Name of the dataset.
#' @param description (character, optional): Description of the dataset. Must be less than or equal to 250 characters.
#' @param dataset_id (character, optional): Identifier of a pre-existing dataset. Used when updating a pre-existing
#' dataset.
#' @param verbose Setting to control the amount of feedback from the I-Server.
#' @return A new `Datasets` object
initialize = function(connection, name = NULL, description = NULL, dataset_id = NULL, verbose = TRUE) {
self$connection <- connection
self$dataset_id <- dataset_id
self$verbose <- verbose
if (!is.null(name)) {
private$check_param_str(name, msg = "Dataset name should be a string.")
private$check_param_len(name, msg = "Dataset name should be <= 250 characters.",
max_length = private$max_desc_len)
}
self$name <- name
if (!is.null(description)) {
private$check_param_str(description, msg = "Dataset description should be a string.")
private$check_param_len(description, msg = "Dataset description should be <= 250 characters.",
max_length = private$max_desc_len)
}
self$description <- description
if (!is.null(dataset_id)) {
private$check_param_str(dataset_id, msg = "Dataset ID should be a string.")
private$load_definition()
}
},
#' @description Add a data.frame to a collection of tables which are later used to update the MicroStrategy dataset
#' @details Add tables to the dataset in an iterative manner using `add_table()`.
#' @param name (character): Logical name of the table that is visible to users of the dataset in MicroStrategy.
#' @param data_frame (`data.frame`): R data.frame to add or update.
#' @param update_policy (character): Update operation to perform. One of 'add' (inserts new, unique rows), 'update'
#' (updates data in existing rows and columns), 'upsert' (updates existing data and inserts new rows), or 'replace'
#' (replaces the existing data with new data).
#' @param to_metric (optional, vector): By default, R numeric data types are treated as metrics in
#' the MicroStrategy dataset while character and date types are treated as attributes. For example, a
#' column of integer-like strings ("1", "2", "3") would, by default, be an attribute in the newly created
#' dataset. If the intent is to format this data as a metric, provide the respective column name as
#' a character vector in `to_metric` parameter.
#' @param to_attribute (optional, vector): Logical opposite of `to_metric`. Helpful for formatting an
#' integer-based row identifier as a primary key in the dataset.
add_table = function(name, data_frame, update_policy, to_metric = NULL, to_attribute = NULL) {
if (class(data_frame) != "data.frame") {
stop("data_frame must be a valid R data.frame.")
}
if (!update_policy %in% private$valid_policy) {
stop("Invalid update policy. Only 'add', 'update', 'replace', and 'upsert' are supported.")
}
# info <- check_version(self$connection$base_url, )
ver <- utils::compareVersion(self$connection$iserver_version, "11.2.0300")
new_policy <- ver >= 0
if (is.null(self$dataset_id) && update_policy != 'replace' && new_policy) {
stop("Update policy has to be 'replace' if a dataset is created or overwritten.")
}
table <- list("table_name" = name,
"data_frame" = data_frame,
"update_policy" = tolower(update_policy))
if (any(to_attribute %in% to_metric)) {
stop(paste0("Column name(s) present in `to_attribute` also present in 'to_metric'."))
}
if (!is.null(to_attribute)) {
if (!all(to_attribute %in% names(data_frame))) {
stop(paste0("Column name(s) in `to_attribute` were not found in `names(data_frame)`."))
} else {
table["to_attribute"] <- list(to_attribute)
}
}
if (!is.null(to_metric)) {
if (!all(to_metric %in% names(data_frame))) {
stop(paste0("Column name(s) in `to_metric` were not found in `names(data_frame)`."))
} else {
table["to_metric"] <- list(to_metric)
}
}
# add the new dataframe to the list of dataframes
private$tables <- c(private$tables, list(table))
},
#' @description Create a new dataset.
#' @param folder_id ID of the shared folder that the dataset should be created within. If `None`, defaults to the
#' user's My Reports folder.
#' @param auto_upload (default TRUE) If True, automatically uploads the data to the I-Server. If False, simply
#' creates the dataset definition but does not upload data to it.
#' @param auto_publish (default TRUE) If True, automatically publishes the data used to create the dataset
#' definition. If False, simply creates the dataset but does not publish it. To publish the dataset, data has to be
#' uploaded first.
#' @param chunksize (int, optional) Number of rows to transmit to the I-Server with each request when uploading.
create = function(folder_id = NULL, auto_upload = TRUE, auto_publish = TRUE, chunksize = 100000) {
if (auto_publish && !auto_upload) {
stop("Data needs to be uploaded to the I-Server before the dataset can be published.")
}
# Check that tables object contains data
private$check_tables(private$tables)
if (!is.null(folder_id)) {
self$folder_id <- folder_id
} else {
self$folder_id <- ""
}
# generate model of the dataset
private$build_model()
# makes request to create the dataset definition on the server
response <- create_multitable_dataset(self$connection,
body = private$model_list$json,
verbose = private$debug)
response <- content(response, as = "parsed", type = "application/json")
self$dataset_id <- response$id
if (self$verbose) {
print(sprintf("Created dataset %s with ID: %s", self$name, self$dataset_id))
}
# if desired, automatically upload and publish the data to the new dataset
if (auto_upload) {
self$update(chunksize = chunksize, auto_publish = auto_publish)
}
},
#' @description Updates an existing dataset with new data.
#' @param chunksize (int, optional): Number of rows to transmit to the I-Server with each request when uploading.
#' @param auto_publish (default TRUE) If True, automatically publishes the data. If False, data will be uploaded but
#' the cube will not be published
update = function(chunksize = 100000, auto_publish = TRUE) {
# Check that tables object contains data
private$check_tables(private$tables)
# form request body and create a session for data uploads
private$form_upload_body()
response <- upload_session(connection = self$connection, dataset_id = self$dataset_id,
body = private$upload_body$json, verbose = private$debug)
response <- content(response, as = "parsed", type = "application/json")
private$session_id <- response$uploadSessionId
# upload each table
for (table in private$tables) {
# break the data up into chunks
rows <- 0
total <- nrow(table$data_frame)
chunks <- split(table$data_frame, rep(1:ceiling(nrow(table$data_frame) / chunksize),
each = chunksize,
length.out = nrow(table$data_frame)))
for (i in seq_along(chunks)) {
# base64 encode the data
enc <- Encoder$new(chunks[[i]], "multi")
b64_enc <- enc$encode()
# form body of the request
body <- toJSON(list("tableName" = table$table_name,
"index" = i,
"data" = b64_enc),
auto_unbox = TRUE)
# make request to upload the data
response <- upload(self$connection, dataset_id = self$dataset_id, session_id = private$session_id,
body = body, verbose = private$debug)
if (http_error(response)) {
# http != 200
response_handler(response, msg = "Error uploading data.", throw_error = FALSE)
publish_cancel(self$connection, self$dataset_id, private$session_id, verbose = private$debug)
}
rows <- rows + nrow(chunks[[i]])
if (self$verbose) {
private$upload_progress(table$table_name, rows, total)
}
}
}
# if desired, automatically publish the data to the new dataset
if (auto_publish) {
self$publish()
}
},
#' @description Publish the uploaded data to the selected dataset. A dataset can be published just once.
publish = function() {
response <- publish(connection = self$connection,
dataset_id = self$dataset_id,
session_id = private$session_id,
verbose = private$debug)
if (http_error(response)) {
# on error, cancel the previously uploaded data
response <- publish_cancel(self$connection, self$dataset_id, private$session_id, verbose = private$debug)
} else {
status <- 6 # default initial status
while (status != 1) {
pub <- publish_status(connection = self$connection,
dataset_id = self$dataset_id,
session_id = private$session_id,
verbose = private$debug)
pub <- content(pub, as = "parsed", type = "application/json")
status <- pub$status
if (status == 1 & self$verbose) {
print(sprintf("Dataset '%s' published successfully.", self$name))
}
}
}
},
#' @description Check the status of data that was uploaded to a dataset.
#' @return Response status code
publish_status = function() {
response <- publish_status(connection = self$connection,
dataset_id = self$dataset_id,
session_id = private$session_id,
verbose = private$debug)
status <- content(response, as = "parsed", type = "application/json")
if (self$verbose) {
print(sprintf("Publish message: %s", status$message))
print(sprintf("Publish status: %s", status$status))
} else {
return(status)
}
},
#' @description Delete a dataset that was previously created using the REST API.
#' @return Response object from the Intelligence Server acknowledging the deletion process.
delete = function() {
response <- delete_dataset(connection = self$connection,
dataset_id = self$dataset_id,
verbose = private$debug)
if (self$verbose) {
print(paste("Successfully deleted dataset with ID:", self$dataset_id))
} else {
return(response)
}
},
#' @description Certify a dataset that was previously creted using the REST API
#' @return Response object from the Intelligence Server acknowledging the certification process.
certify = function() {
response <- toggle_dataset_certification(connection = self$connection,
dataset_id = self$dataset_id,
verbose = private$debug)
if (self$verbose) {
print(sprintf("The dataset with ID: %s has been certified.", self$dataset_id))
} else {
return(response)
}
}
),
private = list(
tables = list(),
model_list = NULL,
upload_body = NULL,
session_id = NULL,
valid_policy = c("add", "update", "replace", "upsert"),
max_desc_len = 250,
debug = FALSE,
build_model = function() {
# generate model of the dataset using Models class
model <- Model$new(tables = private$tables, name = self$name, description = self$description, folder_id = self$folder_id)
private$model_list <- model$get_model()
},
form_upload_body = function() {
# Form request body for creating an upload session for data uploads
body <- list("tables" = lapply(private$tables, function(x) {
list("name" = x$table_name,
"updatePolicy" = x$update_policy,
"columnHeaders" = as.list(names(x$data_frame)))
}))
body_json <- toJSON(body, auto_unbox = TRUE)
private$upload_body <- list("raw" = body,
"json" = body_json)
},
load_definition = function() {
# Load definition of an existing dataset
response <- cube_info(connection = self$connection,
cube_id = self$dataset_id,
verbose = private$debug)
response <- content(response, as = "parsed", type = "application/json")
self$name <- response$cubesInfos[[1]]$cubeName
self$owner_id <- response$cubesInfos[[1]]$ownerId
self$path <- response$cubesInfos[[1]]$path
self$modification_time <- response$cubesInfos[[1]]$modificationTime
self$size <- response$cubesInfos[[1]]$size
self$cube_state <- response$cubesInfos[[1]]$status
},
upload_progress = function(table_name, rows, total) {
# Prints status of dataset upload
print(sprintf("%s status: %s of %s rows. %1.0f%%", table_name, rows, rows, 100 * rows / total))
},
check_param_len = function(param, msg, max_length) {
if (nchar(param) > max_length) {
stop(msg)
} else {
return(TRUE)
}
},
check_param_str = function(param, msg) {
if (class(param) != "character") {
stop(msg)
} else {
return(TRUE)
}
},
check_tables = function(tables) {
if (length(tables) == 0) {
stop("No tables have been added to the dataset. Use `Dataset$add_table()` to add a table.")
}
}
)
)
MicroStrategy/mstrio documentation built on March 29, 2024, 9:44 p.m.
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# datasets.R
# Create and interact with MicroStrategy datasets
#' @title Create, update, delete and certify MicroStrategy datasets
#'
#' @description When creating a new dataset, provide a dataset name and an optional description.
#' When updating a pre-existing dataset, provide the dataset identifier. Tables are added to the
#' dataset in an iterative manner using `add_table()`.
#' @field connection MicroStrategy connection object
#' @field name Name of the dataset
#' @field description Description of the dataset. Must be less than or equal to 250 characters
#' @field folder_id If specified the dataset will be saved in this folder
#' @field dataset_id Identifier of a pre-existing dataset. Used when updating a pre-existing dataset
#' @field owner_id Owner ID
#' @field path Cube path
#' @field modification_time Last modification time, "yyyy-MM-dd HH:mm:ss" in UTC
#' @field size Cube size
#' @field cube_state Cube status,for example, 0=unpublished, 1=publishing, 64=ready
#' @field verbose If True (default), displays additional messages.
#' @examples
#' \dontrun{
#' # Create data frames
#' df1 <- data.frame("id" = c(1, 2, 3, 4, 5),
#' "first_name" = c("Jason", "Molly", "Tina", "Jake", "Amy"),
#' "last_name" = c("Miller", "Jacobson", "Turner", "Milner", "Cooze"))
#'
#' df2 <- data.frame("id" = c(1, 2, 3, 4, 5),
#' "age" = c(42, 52, 36, 24, 73),
#' "state" = c("VA", "NC", "WY", "CA", "CA"),
#' "salary" = c(50000, 100000, 75000, 85000, 250000))
#'
#' # Create a list of tables containing one or more tables and their names
#' my_dataset <- Dataset$new(connection=conn, name="HR Analysis")
#' my_dataset$add_table("Employees", df1, "add")
#' my_dataset$add_table("Salaries", df2, "add")
#' my_dataset$create()
#'
#' # By default Dataset$create() will upload the data to the Intelligence Server and publish the
#' dataset.
#' # If you just want to create the dataset but not upload the row-level data, use
#' Dataset$create(auto_upload=FALSE)
#'
#' # followed by
#' Dataset$update()
#' Dataset$publish()
#'
#' # When the source data changes and users need the latest data for analysis and reporting in
#' # MicroStrategy, mstrio allows you to update the previously created dataset.
#'
#' ds <- Dataset$new(connection=conn, dataset_id="...")
#' ds$add_table(name = "Stores", data_frame = stores_df, update_policy = 'update')
#' ds$add_table(name = "Sales", data_frame = stores_df, update_policy = 'upsert')
#' ds$update(auto_publish=TRUE)
#'
#' # By default Dataset$update() will upload the data to the Intelligence Server and publish the
#' dataset.
#' # If you just want to update the dataset but not publish the row-level data, use
#' Dataset$update(auto_publish=FALSE)
#'
#' # By default, the raw data is transmitted to the server in increments of 100,000 rows. On very
#' # large datasets (>1 GB), it is beneficial to increase the number of rows transmitted to the
#' # Intelligence Server with each request. Do this with the chunksize parameter:
#'
#' ds$update(chunksize = 500000)
#'
#' # If you want to cerfify an existing dataset, use
#' ds$certify()
#' }
#' @docType class
#' @importFrom R6 R6Class
#' @importFrom jsonlite toJSON
#' @export
Dataset <- R6Class("Dataset",
public = list(
# instance variables
connection = NULL,
name = NULL,
description = NULL,
folder_id = NULL,
dataset_id = NULL,
owner_id = NULL,
path = NULL,
modification_time = NULL,
size = NULL,
cube_state = NULL,
verbose = NULL,
#' @description
#' Interface for creating, updating, and deleting MicroStrategy in-memory datasets.
#' @details
#' When creating a new dataset, provide a dataset name and an optional description. When
#' updating a pre-existing dataset, provide the dataset identifier. Tables are added to the
#' dataset in an iterative manner using `add_table()`.
#' @param connection MicroStrategy connection object returned by `Connection$New()`.
#' @param name (character): Name of the dataset.
#' @param description (character, optional): Description of the dataset. Must be less than or equal to 250 characters.
#' @param dataset_id (character, optional): Identifier of a pre-existing dataset. Used when updating a pre-existing
#' dataset.
#' @param verbose Setting to control the amount of feedback from the I-Server.
#' @return A new `Datasets` object
initialize = function(connection, name = NULL, description = NULL, dataset_id = NULL, verbose = TRUE) {
self$connection <- connection
self$dataset_id <- dataset_id
self$verbose <- verbose
if (!is.null(name)) {
private$check_param_str(name, msg = "Dataset name should be a string.")
private$check_param_len(name, msg = "Dataset name should be <= 250 characters.",
max_length = private$max_desc_len)
}
self$name <- name
if (!is.null(description)) {
private$check_param_str(description, msg = "Dataset description should be a string.")
private$check_param_len(description, msg = "Dataset description should be <= 250 characters.",
max_length = private$max_desc_len)
}
self$description <- description
if (!is.null(dataset_id)) {
private$check_param_str(dataset_id, msg = "Dataset ID should be a string.")
private$load_definition()
}
},
#' @description Add a data.frame to a collection of tables which are later used to update the MicroStrategy dataset
#' @details Add tables to the dataset in an iterative manner using `add_table()`.
#' @param name (character): Logical name of the table that is visible to users of the dataset in MicroStrategy.
#' @param data_frame (`data.frame`): R data.frame to add or update.
#' @param update_policy (character): Update operation to perform. One of 'add' (inserts new, unique rows), 'update'
#' (updates data in existing rows and columns), 'upsert' (updates existing data and inserts new rows), or 'replace'
#' (replaces the existing data with new data).
#' @param to_metric (optional, vector): By default, R numeric data types are treated as metrics in
#' the MicroStrategy dataset while character and date types are treated as attributes. For example, a
#' column of integer-like strings ("1", "2", "3") would, by default, be an attribute in the newly created
#' dataset. If the intent is to format this data as a metric, provide the respective column name as
#' a character vector in `to_metric` parameter.
#' @param to_attribute (optional, vector): Logical opposite of `to_metric`. Helpful for formatting an
#' integer-based row identifier as a primary key in the dataset.
add_table = function(name, data_frame, update_policy, to_metric = NULL, to_attribute = NULL) {
if (class(data_frame) != "data.frame") {
stop("data_frame must be a valid R data.frame.")
}
if (!update_policy %in% private$valid_policy) {
stop("Invalid update policy. Only 'add', 'update', 'replace', and 'upsert' are supported.")
}
# info <- check_version(self$connection$base_url, )
ver <- utils::compareVersion(self$connection$iserver_version, "11.2.0300")
new_policy <- ver >= 0
if (is.null(self$dataset_id) && update_policy != 'replace' && new_policy) {
stop("Update policy has to be 'replace' if a dataset is created or overwritten.")
}
table <- list("table_name" = name,
"data_frame" = data_frame,
"update_policy" = tolower(update_policy))
if (any(to_attribute %in% to_metric)) {
stop(paste0("Column name(s) present in `to_attribute` also present in 'to_metric'."))
}
if (!is.null(to_attribute)) {
if (!all(to_attribute %in% names(data_frame))) {
stop(paste0("Column name(s) in `to_attribute` were not found in `names(data_frame)`."))
} else {
table["to_attribute"] <- list(to_attribute)
}
}
if (!is.null(to_metric)) {
if (!all(to_metric %in% names(data_frame))) {
stop(paste0("Column name(s) in `to_metric` were not found in `names(data_frame)`."))
} else {
table["to_metric"] <- list(to_metric)
}
}
# add the new dataframe to the list of dataframes
private$tables <- c(private$tables, list(table))
},
#' @description Create a new dataset.
#' @param folder_id ID of the shared folder that the dataset should be created within. If `None`, defaults to the
#' user's My Reports folder.
#' @param auto_upload (default TRUE) If True, automatically uploads the data to the I-Server. If False, simply
#' creates the dataset definition but does not upload data to it.
#' @param auto_publish (default TRUE) If True, automatically publishes the data used to create the dataset
#' definition. If False, simply creates the dataset but does not publish it. To publish the dataset, data has to be
#' uploaded first.
#' @param chunksize (int, optional) Number of rows to transmit to the I-Server with each request when uploading.
create = function(folder_id = NULL, auto_upload = TRUE, auto_publish = TRUE, chunksize = 100000) {
if (auto_publish && !auto_upload) {
stop("Data needs to be uploaded to the I-Server before the dataset can be published.")
}
# Check that tables object contains data
private$check_tables(private$tables)
if (!is.null(folder_id)) {
self$folder_id <- folder_id
} else {
self$folder_id <- ""
}
# generate model of the dataset
private$build_model()
# makes request to create the dataset definition on the server
response <- create_multitable_dataset(self$connection,
body = private$model_list$json,
verbose = private$debug)
response <- content(response, as = "parsed", type = "application/json")
self$dataset_id <- response$id
if (self$verbose) {
print(sprintf("Created dataset %s with ID: %s", self$name, self$dataset_id))
}
# if desired, automatically upload and publish the data to the new dataset
if (auto_upload) {
self$update(chunksize = chunksize, auto_publish = auto_publish)
}
},
#' @description Updates an existing dataset with new data.
#' @param chunksize (int, optional): Number of rows to transmit to the I-Server with each request when uploading.
#' @param auto_publish (default TRUE) If True, automatically publishes the data. If False, data will be uploaded but
#' the cube will not be published
update = function(chunksize = 100000, auto_publish = TRUE) {
# Check that tables object contains data
private$check_tables(private$tables)
# form request body and create a session for data uploads
private$form_upload_body()
response <- upload_session(connection = self$connection, dataset_id = self$dataset_id,
body = private$upload_body$json, verbose = private$debug)
response <- content(response, as = "parsed", type = "application/json")
private$session_id <- response$uploadSessionId
# upload each table
for (table in private$tables) {
# break the data up into chunks
rows <- 0
total <- nrow(table$data_frame)
chunks <- split(table$data_frame, rep(1:ceiling(nrow(table$data_frame) / chunksize),
each = chunksize,
length.out = nrow(table$data_frame)))
for (i in seq_along(chunks)) {
# base64 encode the data
enc <- Encoder$new(chunks[[i]], "multi")
b64_enc <- enc$encode()
# form body of the request
body <- toJSON(list("tableName" = table$table_name,
"index" = i,
"data" = b64_enc),
auto_unbox = TRUE)
# make request to upload the data
response <- upload(self$connection, dataset_id = self$dataset_id, session_id = private$session_id,
body = body, verbose = private$debug)
if (http_error(response)) {
# http != 200
response_handler(response, msg = "Error uploading data.", throw_error = FALSE)
publish_cancel(self$connection, self$dataset_id, private$session_id, verbose = private$debug)
}
rows <- rows + nrow(chunks[[i]])
if (self$verbose) {
private$upload_progress(table$table_name, rows, total)
}
}
}
# if desired, automatically publish the data to the new dataset
if (auto_publish) {
self$publish()
}
},
#' @description Publish the uploaded data to the selected dataset. A dataset can be published just once.
publish = function() {
response <- publish(connection = self$connection,
dataset_id = self$dataset_id,
session_id = private$session_id,
verbose = private$debug)
if (http_error(response)) {
# on error, cancel the previously uploaded data
response <- publish_cancel(self$connection, self$dataset_id, private$session_id, verbose = private$debug)
} else {
status <- 6 # default initial status
while (status != 1) {
pub <- publish_status(connection = self$connection,
dataset_id = self$dataset_id,
session_id = private$session_id,
verbose = private$debug)
pub <- content(pub, as = "parsed", type = "application/json")
status <- pub$status
if (status == 1 & self$verbose) {
print(sprintf("Dataset '%s' published successfully.", self$name))
}
}
}
},
#' @description Check the status of data that was uploaded to a dataset.
#' @return Response status code
publish_status = function() {
response <- publish_status(connection = self$connection,
dataset_id = self$dataset_id,
session_id = private$session_id,
verbose = private$debug)
status <- content(response, as = "parsed", type = "application/json")
if (self$verbose) {
print(sprintf("Publish message: %s", status$message))
print(sprintf("Publish status: %s", status$status))
} else {
return(status)
}
},
#' @description Delete a dataset that was previously created using the REST API.
#' @return Response object from the Intelligence Server acknowledging the deletion process.
delete = function() {
response <- delete_dataset(connection = self$connection,
dataset_id = self$dataset_id,
verbose = private$debug)
if (self$verbose) {
print(paste("Successfully deleted dataset with ID:", self$dataset_id))
} else {
return(response)
}
},
#' @description Certify a dataset that was previously creted using the REST API
#' @return Response object from the Intelligence Server acknowledging the certification process.
certify = function() {
response <- toggle_dataset_certification(connection = self$connection,
dataset_id = self$dataset_id,
verbose = private$debug)
if (self$verbose) {
print(sprintf("The dataset with ID: %s has been certified.", self$dataset_id))
} else {
return(response)
}
}
),
private = list(
tables = list(),
model_list = NULL,
upload_body = NULL,
session_id = NULL,
valid_policy = c("add", "update", "replace", "upsert"),
max_desc_len = 250,
debug = FALSE,
build_model = function() {
# generate model of the dataset using Models class
model <- Model$new(tables = private$tables, name = self$name, description = self$description, folder_id = self$folder_id)
private$model_list <- model$get_model()
},
form_upload_body = function() {
# Form request body for creating an upload session for data uploads
body <- list("tables" = lapply(private$tables, function(x) {
list("name" = x$table_name,
"updatePolicy" = x$update_policy,
"columnHeaders" = as.list(names(x$data_frame)))
}))
body_json <- toJSON(body, auto_unbox = TRUE)
private$upload_body <- list("raw" = body,
"json" = body_json)
},
load_definition = function() {
# Load definition of an existing dataset
response <- cube_info(connection = self$connection,
cube_id = self$dataset_id,
verbose = private$debug)
response <- content(response, as = "parsed", type = "application/json")
self$name <- response$cubesInfos[[1]]$cubeName
self$owner_id <- response$cubesInfos[[1]]$ownerId
self$path <- response$cubesInfos[[1]]$path
self$modification_time <- response$cubesInfos[[1]]$modificationTime
self$size <- response$cubesInfos[[1]]$size
self$cube_state <- response$cubesInfos[[1]]$status
},
upload_progress = function(table_name, rows, total) {
# Prints status of dataset upload
print(sprintf("%s status: %s of %s rows. %1.0f%%", table_name, rows, rows, 100 * rows / total))
},
check_param_len = function(param, msg, max_length) {
if (nchar(param) > max_length) {
stop(msg)
} else {
return(TRUE)
}
},
check_param_str = function(param, msg) {
if (class(param) != "character") {
stop(msg)
} else {
return(TRUE)
}
},
check_tables = function(tables) {
if (length(tables) == 0) {
stop("No tables have been added to the dataset. Use `Dataset$add_table()` to add a table.")
}
}
)
)
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