Sunday, 9 September 2018

EPM Cloud – Managing users with EPM Automate and REST API

New functionality has been added in the EPM Cloud 18.09 release to provide the ability to manage users and roles at an identity domain level with either EPM Automate or the REST API. In this post I am going to cover this functionality starting off with EPM Automate.

Four new commands have been added to EPM Automate and these are:
  • addusers – Creates new users in the identity domain based on the contents of a comma separated file.
  • removeusers – Deletes identity domain accounts based on the contents of a comma separated file.
  • assignrole – Assigns an identity domain role to all users that are contained in a comma separated file.
  • unassignrole – Unassigns an identity domain role to all users that are contained in a comma separated file.
Please note, to be able to use these commands you will need to be logged in with an account that has the “Identity Domain Administrator” role.

The comma separated files have to be uploaded to the cloud instance first using the “uploadfile” command before you can use the new commands, I would have preferred it if it could have been done in a single command without having to upload files but unfortunately that is not the way it has been developed.

I am quickly going to go through each command and provide an example.

Let’s start off with adding new users to the identity domain.

Before you can use the “addusers” command, you will need a file containing the new user information in the correct format.

The file can contain as many users you would to like to add, for demo purposes I am just going to be adding one user.

The file is required to be in the following format:

The format is the same as if you were importing a batch of users through Oracle Cloud My Services.

Now that I have the file ready I can upload it using EPM Automate, I have assumed the user has been logged in and a file with the same name does not already exist in the cloud instance, you can easily use the “deletefile” command first to remove it if needed.

The file will then be available in the “Inbox/Outbox Explorer”

The format to add users with EPM Automate is:

epmautomate addusers <FILE_NAME> <userPassword=PASSWORD > <resetPassword=true|false>

FILE_NAME is the name of the comma separated file containing the new user information which I just uploaded.

userPassword is a default password that is assigned to the new users. It will need to meet the minimal password requirements for identity domain passwords.

resetPassword defines whether the new users must change the password the first time they log in. I recommend this is always set to true.

An example to create the users contained in the file that was just uploaded is:

The response from issuing the command will include how many new users were processed, including the number of successful and failed user creations.

If successful, the user should then be available in the identity domain. Oracle cloud should also send out an email to the new user with their account information.

As I set the reset password parameter to true, the first time the user logs in they will be redirected to the “Identity Self Service” and require changing their password.

Now that the user has been created we can assign a new identity domain role.

The format for the EPM Automate command is:

epmautomate assignrole <FILE_NAME> <ROLE>

FILE_NAME is the name of a comma separated file containing the user login for the users that you want to assign an identity domain role to.

ROLE is one of the predefined identity domain roles which are:
  • Service Administrator
  • Power User
  • User
  • Viewer
The file requires to be in the following format:

I hoped I could use the same file used for creating the users as it also contains the “User Login” information, but when I tried that I received the following EPM Automate error:

EPMAT-1:Failed to assign role for users. File does not have valid header. Please provide a valid header in file.

Before being able to use the “assignrole” command the above file was uploaded using EPM Automate.

After the file has been uploaded, the “assignrole” command can be executed, in my example I am assigning the “Power User” role to the user in the “assignUsers.csv” file.

The response is the same as when adding users, as the command was successful the user has been assigned the role.

Unassigning a role is no different to assigning a role, the format for the command is:

epmautomate unassignrole <FILE_NAME> <ROLE>

I don’t feel I need to explain what the parameters are this time.

In the following example I am using the same file I uploaded for assigning roles to users, this time I am going to unassign the “Power User” role.

The EPM Automate command completed successfully and a quick check in “My Services” confirms the role has been removed.

On to the final command to delete users from the identity domain.

The format for the EPM Automate command is:

epmautomate removeusers FILE_NAME

The filename should contain all the users you want to remove, and the file format is the same as when assigning/unassigning roles.

In the following example using EPM Automate, I upload the file containing the users to remove and then remove them with the “removeusers” command.

Back in “My Services” the user has been deleted.

As the EPM Automate utility is built on top of the REST API then all the above commands will be available using REST.

So let us repeat the available functionality using a REST client. There are lots of free clients out there so pick the one you prefer. As usual I will be using the Boomerang REST client for Chrome.

First, I am going to delete the CSV file in the cloud instance containing the users and then upload a new one.

The REST URL format to delete files is:


A DELETE method is used, so to delete the “newusers.csv” file a request would be made to the following:

If the deletion was successful a status of 0 will be returned.

If the file does not exist, a status of 8 will be returned and an error message.

Now to upload a new file containing the users to add in the identity domain. I have covered uploading files using the REST API in the past which you can read about here, so there is no need for me to go into much detail again.

The REST URL to upload files is:


A POST method is required, for example:

In the body of the post I added the user information for the file.

You can include as many users as you want to add.

The request header content type equals “application/octet-stream”

A response status of 0 will be returned if the file was uploaded.

If the file already exists, you would receive something like:

The file is now available in the “Inbox/Output Explorer”.

On to adding the user contained in the file using the REST API.

The URL format for managing users is:


A POST method is required, and the body of the request should contain the filename and the default and reset password values. These are the same parameters which are used with EPM Automate commands.

It is a shame that the user information could not have been included the body of the request instead of having to upload a file.

The response will contain job information for adding new users. It includes a URL which can be accessed to check the job status.

A GET request can be made to keep checking the job status until it completes.

A status of 0 means the operation was successful, just like with EPM Automate details are included to inform how many new users creations were processed and how many succeeded or failed.

As the job was successful the new user has been added and is available in “My Services”

We can now move on to assigning a role for the new user.

I uploaded the following file:

In my example the file only contains a single user but it can contain as many as you want to assign a role to.

To assign a role to the users contained in a file the same URL format is required as when adding users.

A PUT method is required, and the body of the request should include the filename, the role name and a job type of “ASSIGN_ROLE”

This time I am going to assign the “User” identity domain role.

The current job status for assigning the role is returned, once again it includes a URL to check the job status.

The job status can then be checked until it completes.

As the operation was successful, the “User” role has been assigned to the user contained in the file.

To unassign roles is very similar to assigning. A PUT method is required, and the body of the request should contain the filename containing the users that the role should be unassigned from, the role name and a job type of “UNASSIGN_ROLE”

The current job status is returned in the response.

The job status can be checked until it completes.

As the job was successful the “User” role has been removed for the user contained in the specified file.

To remove users, a file should be uploaded containing the user login details of the users to remove.

I uploaded the following file.

A DELETE method is required, and the URL should include the filename containing the users to remove.

No surprise that job status information is returned in the response.

The job status can be checked until it completes.

The response confirms the user was removed. A look in “My Services” confirms the user has been removed.

I was going to include an example using scripting, but I think you should get the idea. I have covered scripting with the REST API many times in the past so just have a look at my previous posts if you are unclear.

Saturday, 8 September 2018

EPM Cloud Data Management – Incremental File Adapter

New functionality has been added to Data Management in the EPM Cloud 18.09 release, and in this post I am going to cover the incremental file adapter.

The monthly cloud release documentation contains the following summary:

“Data Management now includes an incremental file adapter to optimize the data load process. The new Incremental adapter compares the source data file with a prior version of the source data file and identifies new or changed records and loads only this data set. The user has the option to sort the input source data file before making the comparison or they can provide a pre-sorted file for better performance.”

So basically, if you are loading files which contains a full data set then before this release you would need to replace the existing the existing data in Data Management and reload, which, depending on the size on the source file, can cause performance issues. With the new incremental file adapter, the previous file will be compared with the latest file and only the differences will be loaded, so in theory performance should be much improved.

To get a good understanding of the adapter it is worth going through a simple example.

There are some important points that should be considered when working with the adapter:
  • The source data file must be a delimited data file.
  • Data files used must contain a one-line header, which describes the delimited columns.
  • Both numeric and non-numeric data can be loaded.
  • Any deleted records between the two files is ignored. In this case, you have to handle the deleted records manually.
  • If the file is missing (or you change the last ID to a non-existent run), the load completes with an error.
  • Sort options determine the level of performance using this feature. Sorting increases the processing time. Pre-sorting the file makes the process faster.
  • Only single period data loads are supported for an incremental load. Multi-period loads are not supported.
  • Drill down is not supported for incremental loads since incremental files are loaded in Replace mode and only the last version of the file comparison is present in the staging table.
  • Copies of the source data file are archived for future comparison. Only the last 5 versions are retained. Files are retained for a maximum of 60 days. If no incremental load is performed for more than 60 days, then set the Last Process ID to 0 and perform the load.
Some of the above points will become clearer with the following example, so let’s get started.

The file I am going to begin with only contains a few records as this is just meant to be a simple example.

The file satisfies the first two points, it contains a header record which describes the columns, and it is delimited.

I first thought that the incremental file adapter would be configured from the source system area in Data Management, but this is not the case; it is created as a target application.

You need to go to target application and add a new data source.

Once “Data Source” has been selected, a new window will open where you can select “Incremental File” as the source system.

This will open the file select window where you can select an existing file or upload one.

The filename will be the basis for the name of the target application

If you intend to use multiple files with the same naming convention, then you should use the prefix option to make the target applications unique.

Selecting the file at this point is only to define the dimension details in the application details.

The dimension names are automatically created from the header record in the source file.

The steps to create an integration are the same as you would normally go through, so I am only going to highlight some of them.

In the import format creation, the newly created application should be available as a source.

The source columns of the file are then mapped to the target application.

The location set up is no different.

On to the data load rule which is slightly different than a standard file-based load, the file is selected in the source filters section.

There are two other properties in the source filters, the incremental processing options has the following possible values:

Do not sort source file - Source file is compared as provided. This option assumes that the source file is generated in the same order each time. In this case, the system performs a file comparison, and then extracts the new and changed records. This option makes the incremental file load perform faster.

Sort source file—Source file is sorted before performing the file comparison for changes. In this option the source file is first sorted. The sorted file is then compared to the prior sorted version of this file. Sorting a large file consumes a lot system resources and performs slower.

For this example, I am going to go with not sorting the source file, not that it would make much difference in my case because the file is so small. Once I get the opportunity I would like to test the performance of having to sort a large file.

Next is the last process ID, the first time you load a file this would be set to 0, when the load is run again the ID will be updated to match the process ID.

If there are no differences between the latest file and the previous file or the source file doesn’t exist, the ID will be kept as the last successful load ID.

To reload all the data the process ID should be set back to 0 and the file defined in the source filter will be considered the baseline file.

If you are going to change the sorting option then to stop data issues you will need to reset the process ID back to 0 and reload.

After selecting the source file, some simple pass through mappings are added, which means the file can be loaded.

The data load acts like any other, it is only when you take a look at the log you get an idea of what is going on.

INFO  [AIF]: Executing the following script: /u02/Oracle/Middleware/EPMSystem11R1/products/FinancialDataQuality/bin/plugin/
INFO  [AIF]: ************************************************************************************
INFO  [AIF]: * Started for LoadID: 95
INFO  [AIF]: ************************************************************************************
DEBUG [AIF]: loadID: 95
DEBUG [AIF]: ruleID: 14
DEBUG [AIF]: Source file: /u03/inbox/inbox/incremental.csv
DEBUG [AIF]: Data file: /u03/inbox/data/appdata/sourcefiles/14/Vision_SOURCE_95.dat
DEBUG [AIF]: Deleting old source files from /u03/inbox/data/appdata/sourcefiles/14
DEBUG [AIF]: impGroupKey: MT_Incremental_IF
DEBUG [AIF]: Import format delimiter = ,
DEBUG [AIF]: Instantiated FileDiffUtils.
DEBUG [AIF]: Full load.  No incremental file.
DEBUG [AIF]: dataFileName: Vision_SOURCE_95.dat

It looks like it is a python/jython script that is handling the incremental files and differences, the file is moved into a source file directory which is numbered by the load rule ID, in this case 14 and the file format is <TARGET_APP>_SOURCE_<LOADID>.dat

As the last process ID is 0 then it is considered a full load and no incremental differences are required and the file is loaded.

If you go back into the load rule you can see the last process ID has been updated to reflect the load ID of the last import.

Now, time to add additional records to the same file. I selected not to sort the source file in the load rule, so I made sure the records were sorted.

Run the process again and this time in the logs you can see that a difference file has been created and loaded, the format of the difference file is <TARGET_APP>_DIFF_<LOADID>.dat

DEBUG [AIF]: Source file: /u03/inbox/inbox/incremental.csv
DEBUG [AIF]: Data file: /u03/inbox/data/appdata/sourcefiles/14/Vision_SOURCE_96.dat
DEBUG [AIF]: Deleting old source files from /u03/inbox/data/appdata/sourcefiles/14
DEBUG [AIF]: impGroupKey: MT_Incremental_IF
DEBUG [AIF]: Import format delimiter = ,
DEBUG [AIF]: Instantiated FileDiffUtils.
DEBUG [AIF]: Diff file: /u03/inbox/data/Vision_DIFF_96.dat
DEBUG [AIF]: Header line: Account,Entity,Product,Amount
DEBUG [AIF]: dataFileName: Vision_DIFF_96.dat

Back in the load rule the last process ID has been updated.

In the workbench I spotted an issue.

There was one record that was from the previous load, in theory this should not have been loaded again.

I was thinking maybe it was the way I sorted the file, so I decided to run the process again but this time set the processing option to sort the file.

I reset the last process ID back to zero and uploaded my original file and ran the process again.

In the logs you can see entries to show that the source file is being sorted.

DEBUG [AIF]: Sorting data file started.
DEBUG [AIF]: Sorting data file completed.

Uploaded the incremental file and ran the process.

In the logs you can see the source file is being sorted and a difference file created.

DEBUG [AIF]: Sorting data file started.
DEBUG [AIF]: Sorting data file completed.
DEBUG [AIF]: Header line: Account,Entity,Product,Amount
DEBUG [AIF]: dataFileName: Vision_DIFF_98.dat

I was hoping that the issue would be fixed and the record from the full data load would not be included in the incremental load.

Unfortunately, it is still loading the record, so unless I am missing something there is a problem with the script that is working out the differences.

I thought I would have a look at the files that have been generated by Data Management, the files can be downloaded using EPM Automate or REST.

The files were sorted in exactly the same way as in my original testing so at least I know I got that correct, though the difference file is not correct as it contains a record from the first full load.

I found out if I created the incremental file by appending the records to the end of the original file.

Then set the processing option not to sort the source file.

Reset the last process ID back to zero, reloaded both files, this time the incremental data was correct and did not contain the record from the original load.

Anyway, moving on, if I try to load the file again with no changes then the difference file will contain no records.

The process will be displayed as a warning.

No records will exist in the workbench.

Going back to one of the earlier points:

“Only the last 5 versions are retained. Files are retained for a maximum of 60 days.”

You will see an entry in the log when go over 5 versions.

DEBUG [AIF]: Deleting old source files from /u03/inbox/data/appdata/sourcefiles/14
DEBUG [AIF]: Deleted file: /u03/inbox/data/appdata/sourcefiles/14/Vision_SOURCE_95.dat

For each load rule there will be a maximum of 5 archived source files on the file system.

It is still possible to run the data load rule with EPM Automate or REST and pass in the filename.

First the file can be uploaded.

Next the data load rule can be executed by passing in the filename.

In the process logs you will see an entry for EPM Automate and the filename.

DEBUG [AIF]: epmAutomateFileName: sep-18-actuals.csv
DEBUG [AIF]: Source file: /u03/inbox/inbox/sep-18-actuals.csv

I did begin to have a look at loading larger files to check out the performance when sorting is enabled, but I seem to hit more problems.

I started with a file like in my previous example but this time containing 50,000 records. I then loaded an incremental file containing 100,000 records which included the 50,000 from the original file.

For some reason 199,000 records were loaded to Data Management.

In the log I could see:

DEBUG [AIF]: dataFileName: Vision_DIFF_116.dat

HEADER_ROW] Account,Entity,Product,Amount
INFO  [AIF]: EPMFDM-140274:Message - [TC] - [Amount=NN] 99990,4000,1200,9299Account,Entity,Product,Amount
INFO  [AIF]: EPMFDM-140274:Message - Rows Loaded: 199999
Rows Rejected: 1

I downloaded the file containing the differences and the file contained 200,001 records.

It seems to have created the difference file by joining the incremental file to itself.

I am concerned about some of these issues I have seen, it may just be me but if you are going to use the adapter functionality then please do carry out testing first.

I am going to leave it there for this introduction to the incremental file adapter, if I find any more details about the problems I have experienced then I will update this post.