services
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3.6.9
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This article assumes a knowledge of services, their rates and related concepts as documented in and in the article on the service statement
The services statement is used to create or modify multiple services based on the data in a .
services{ param1 = value [ ... paramN = value] }
Example:
For many of the parameters to the services statement there are two ways of using a column name:
The values in the column are extracted from the usage data and those values are embedded as literals into the service definition.
The column name itself is used in the service definition such that the reporting engine dynamically determines the values to use for any given day when generating report data
Using the second method requires only a single rate revision which identifies by name the column(s) containing rate and/or COGS information. When a report is run, the charge engine then obtains the correct rate information for any given day from the data in those named columns.
The parameters supported by the services statement are summarised in the following table. The Type column in the table indicates the way the column name is used as described in How column names are used above. Additional information about each parameter can be found below the summary table itself.
Parameter
Type
Meaning
usages_col
2
The name of the column from which units of consumption are derived
service_type
n/a
Determines how the usages_col and consumption_col values are used when interrogating the usage data to get the units of consumption
consumption_col
2
The name of the column containing units of consumption (AUTOMATIC services only - see below for more details)
instance_col
2
Values in this column are used to distinguish between service instances
description_col
1
Values in this column determine the service description for each service definition
category or group
n/a
A specific category string to use for all service definitions
category_col or group_col
1
Values in this column determine the service category for each service definition
interval
n/a
A specific charging interval to use for all service defintions
interval_col
1
Values in this column determine the charging interval for each service definition
model
n/a
A specific proration setting to use for all service definitions
model_col
1
Values in this column determine the proration setting for each service definition
charge_model
n/a
A specific charge model to use for all service definitions (may be peak , average, last_day or day_xxx, where xxx is number in range 1-28)
charge_model_col
1
Values in this column determine the charge_model for each service definition
unit_label
n/a
A specific unit label to use for all service definitions
unit_label_col
1
Values in this column determine the unit label for each service definition
rate_col
2
Set the column name from which Edify will determine rate per unit at report time
set_rate_using
1
Values in this column determine the rate per unit for each service definition
cogs_col
2
Set the column name from which Edify will determine the COGS rate per unit for each service definition
set_cogs_using
1
Values in this column determine the COGS rate per unit for each service definition
set_min_commit_using
1
Values in this column determine the minimum commit for each service definition
effective_date_col
1
Values in this column determine the effective date of the rate revision created for each service definition
effective_date
n/a
A specific effective date to use in the rate revision created for each service definition
The usages_col parameter is the name of a column containing service keys. A service will be created for each distinct value in this column, and these values will be used as the service keys.
In order to calculate the charges associated with a service it is necessary to know the number of units of that service that were consumed. Exivity supports two methods of retrieving the units of consumption from usage data and the service_type determines which of these is applied.
Any given service may use one or other of these methods, which are as follows:
Manual services: service_type = MANUAL
Automatic services: service_type = AUTOMATIC
Transformer
(service_type)
API
(service.attributes.type)
GUI (Service name source)
MANUAL
service_name_in_header
In header, consumption in data
AUTOMATIC
service_name_in_data
In data, consumption in separate column
Manual services
Manual services require that the units of consumption for each service named in the usages_col column are stored in separate columns whose names correlate to the service keys themselves.
To illustrate this, consider the following fragment of usage data:
In this case, the service_name column contains a list of service keys and for each of those service keys there is a corresponding column containing the units of consumption for that service. Thus in the above example we can see that there are two services, "Small VM" and "Large VM" and that the units of consumption for each of these services are in the columns of the same name.
The more manual services that are represented in the data, the more columns are required.
Automatic services
Automatic services require that the units of consumption for each service named in the usages_col column are stored in the column named by the consumption_col parameter. To represent the same information as that shown in the example above, the following would be used:
It can be seen that any number of automatic services, along with their consumption figures, can be represented using only two columns of data.
The consumption_col parameter is only required when creating automatic services and determines the column containing the units of consumption for each service as described above.
It is not enough to know only the units of consumption for each service, as this merely provides the total consumption for each service across the entire usage. In the examples above, for example, the "Large VM" service has 10 units of consumption but using that information alone there is no way to know if this represents one instance of a VM used 10 times, 10 instances of VMs used once each, or something in between.
The instance_col parameter is therefore required to tell the difference. Typically this will be a unique identifier which groups the units of consumption into 'buckets'. In the case of a VM this may be a VM ID which remains constant throughout the life of a VM in the cloud.
To illustrate this, we can supplement the example usage fragment used previously with additional information to use as the instance_col as follows:
By specifying instance_col = vmid we can now see that the usage represents:
5 instances of a single Small VM with an ID of 444
6 instances of a Large VM with an ID of 555
4 instances of a Large VM with an ID of 666
If specified, the description_col denotes a column containing a friendly description for reports to identify the service with.
Typically in cloud usage data, services are identified using unique IDs (referred to as keys in Exivity) which are often non-meaningful to human eyes, so Exivity supports a 'friendly' description for each service for display purposes when generating a report.
For example description_col = description may be used in conjunction with the following data to map the service_id to a friendly name:
It is not mandatory to provide a description_col parameter. If one is not supplied then the description will be set to a duplicate of the service key (as derived via the usages_col parameter).
By default Exivity will group services on reports according to their category. Using categories ensures that charges for services that fall within the same category will appear in a contiguous block.
The category parameter specifies the category to which all services created will be assigned, thus specifying category = "Virtual Machines" might be appropriate for the example data used so far in this article.
Usage data normally contains information about a range of services of different types such as Virtual Machines, Storage, Networking and so on. By referencing a column in the usage data which identifies the correct category for each service, multiple categories will be created and each service assigned to the correct category by the services statement.
To illustrate this, let us extend the sample data as follows:
By specifying category_col = category each service will now be associated with the correct category.
The interval parameter is used to specify a literal interval for all the services created by the services statement.
The interval parameter may be any of:
individually
daily
monthly
If the interval parameter is not specified, then a default interval of monthly will be used.
In the event that different services in the usages_col require different charge intervals, a column name containing the interval to use may be specified using the interval_col column as follows:
By specifying interval_col = interval each service in the above usage data will be assigned the correct charge interval.
The model parameter is used to enable proration for monthly services. Either of unprorated or prorated may be specified.
If no model is specified, then a value of unprorated will be used by default.
In the event that different services in the consumptions_col require different proration settings, the model_col parameter can be used to specify which column contains the proration setting for each service.
By specifying model_col = model, each service in the above usage data will be assigned the correct proration model.
If specified as peak then the charge for any monthly services created will be calculated based on the day with the highest charge in the month.
If specified as average then the charge for any monthly services created will be calculated as the average unit price (for days that have usage only) multiplied by the average quantity (days with no usage will be treated as if they had a quantity of 0).
If specified as last_day then the charge for any monthly services created will be calculated based on the last day of the month.
If specified as day_xxx (where xxx is a number in 1-28 range) then the charge for any monthly services created will be calculated based on the specified day of the month.
In the event that different services in the consumptions_col require different charge models, the charge_model_col parameter can be used to specify which column contains the charge_model setting for each service. For the example data below charge_model_col would be set to chargetype:
The unit_label parameter is used by reports to provide a meaningful description of the units of consumption associated with a service. A virtual machine may have a unit label of Virtual Machines, but storage-related services may have a unit label of Gb for example.
If the unit_label parameter is not specified then a default lavel of Units will be used.
The unit label may be up to 63 characters in length. Longer values will be truncated.
In cases where the services contained in the usages_col column collectively require more than one unit label, the unit_label_col parameter can be used to identify a column in the usage data which contains an appropriate label for each service.
For example unit_label_col = label can be used to associate an appropriate label using the data below:
The rate_col parameter is used to determine the column in the usage data which contains the unit rates for the service definitions created by the services statement.
As each service definition is created, an initial rate revision is also created which contains the column named by the rate_col parameter. When a report is run, for each day in the reporting range the unit rate for that day will be determined by whatever value is in the column named by the rate_col parameter in the usage data.
This means that only a single rate revision is required, even if the actual value in the rate_col column is different from day to day.
The set_rate_using parameter is also used to determine the unit rate for each service. This differs from the rate_col parameter in that the values in the column named by set_rate_using are consulted when the service is created, and the literal values in that column are used to populate the initial rate revision.
This means that the unit cost is hard-coded into the rate revision and will apply indefinitely, or until such time as a new rate revision takes effect (see effective_date for more details)
The cogs_col parameter is used to determine the column in the usage data which contains the COGS rate associated with the service definitions created by the services statement.
As each service definition is created, an initial rate revision is also created which contains the column named by the cogs_col parameter. When a report is run, for each day in the reporting range the COGS rate for that day will be determined by whatever value is in the column named by the cogs_col parameter in the usage data.
If a monthly service has different COGS rates for different days in the month, then whichever results in the highest charge will be used.
This means that only a single rate revision is required, even if the actual value in the cogs_col column is different from day to day.
The set_cogs_using parameter is also used to determine the COGS rate for each service. This differs from the cogs_col parameter in that the values in the column named by set_cogs_using are consulted when the service is created, and the literal values in that column are used to populate the initial rate revision.
This means that the COGS rate is hard-coded into the rate revision and will apply indefinitely, or until such time as a new rate revision takes effect (see effective_date for more details)
The set_min_commit_using parameter is used to set the minimum commit value in the initial rate revision for each service.
The values in the column identified by set_min_commit_using are extracted from the usage data and used as numeric literals in the revision.
When creating the initial rate revision for a service, the value specified by the effective_date parameter is interpreted as a yyyyMMdd value to determine the date from which the revision should be applied.
If there is a column in the usage data containing yyyyMMdd values representing the desired effective date for the initial revision of each service, The effective_date_col parameter may be used to extract the values from this column and set the effective date for each service accordingly.
The services statement is used to create or modify multiple services from the data in a . The parameters supplied to the statement map columns in the usage data to attributes of the created services.
When creating or updating services using the first method, Transcript will create a new rate revision every time the rate information changes. For data sources such as Microsoft Azure where the rates can change daily this will result in a lot of rate revisions in the .
Don't confuse the service_type in Transform with the charge_type (Billing type) and cogs_type (COGS type) in the API, both use the terms 'manual' and 'automatic' but they refer to different concepts. The service_type in Transform defines how to retrieve the service name and usage data, the charge_type and cogs_type in the API define where rate information is stored. Also refer to the section .
If the effective_date parameter is omitted then the current will be used by default.