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Data Processing and Performance - A comprehensive guide of tables, and design
Overview To maintain well performing application, one must understand how the underlying database works and more importantly its limitations. Understanding how a system works, allows designers and administrators to create reliable, stable, and optimal performing applications. This white paper is intended to guide the design of those optimal data processing strategies for the OneStream platform. First, this document will provide a detailed look at the data structures used by the stage engine as well as those used by the in-memory financial analytic engine, providing a deep understanding of how the OneStream stage engine functions in relation to the in-memory financial analytic engine. The relationship between stage engine data structures and finance engine data structures will be discussed in detail. Understanding how data is stored and manipulated by these engines will help consultants build OneStream applications that are optimized for high-volume data processing. Second, the workflow engine configuration will be examined in detail throughout the document since it acts as the controller / orchestrated of most tasks in the system. The workflow engine is the primary tool used to configure data processing sequences and performance characteristics in an OneStream application. The are many different workflow structures and settings that specifically relate to data processing and these settings will be discussed in relation to the processing engine that they impact. Finally, this document will define best practices and logical data processing limits. This will include suggestions on how to create workflow structures and settings for specific data processing workloads. With respect to data defining processing limits, this document will help define practical / logical data processing limits in relation to hard/physical data processing limits and will provide a detailed explanation of the suggested logical limits. This is an important topic because in many situations the physical data processing limit will accept/tolerate that amount of data that is being processed, but the same data may be able to be processed in a much more efficient manner by adhering to logical limits and building the appropriate workflow structures to partition data. These concepts are particularly important because they enable efficient storage, potential parallel processing and high-performance reporting/consumption when properly implemented. Conclusion Large Data Units can create problems for loading, calculating, consolidating, and reporting data. This really is a limitation of what the hardware and networks can support. Your design needs to consider this. This paper provides some options to relieve some of the pressure points that could appear. NOTE: some tables mentioned in the paper have changed in version 9+. See this note for further details.
Matrix Consolidation: Eliminating Beyond Legal Entity
Purpose of the document To goal of this document is to share our experience in designing for a matrix consolidation requirement, as well as to drive discussion on the topic. What we mean by “matrix consolidation” Matrix consolidation is a term commonly used when finance teams want to prepare their management & statutory financials concurrently. This prevents the need to maintain separate scenarios and processes in the system. It will usually involve running eliminations on something more than just legal entity. In OneStream this can mean using a user defined dimension as part of the elimination process. The Requirement A common use case is to run eliminations between Profit Centres or Segments. Whilst inter-profit-centre eliminations will be used as the example in this document, it is not the only potential use case. The requirement we will attempt to solve for is that the customer wants an elimination to only occur at the first common parent in both the Legal Entity and Profit Centre hierarchies. First let’s look at the two broad ways in which this can be achieved. ENTITY DIMENSION OR USER DEFINED (UD) DIMENSION So, there is a requirement to do eliminations on a level of detail below the legal entity/company code level. The example we will use in the section is where the customer wants to generate eliminations between Profit Centres (PC). You have two main options on how to tackle this, outlined in the following two sub-sections. OPTION 1: ENTITY DIMENSION Include this Profit Centre detail in your entity dimension as base members. These will be children of the legal entity members. OPTION 1: ENTITY DIMENSION Include this Profit Centre detail in your entity dimension as base members. Pros Cons Business Logic · No additional Logic required to get eliminations running by Profit Centre. · Impacts consolidation performance more than option 2 in a typical setup, due to multiplication of members in the entity dimension (i.e. more data units) that are to be consolidated. Exact impact need to be analysed in each project. · If you move a PC in the entity hierarchy, you will need to reconsolidate all history. Dimensions · Uses fewer UD dimensions than option 2. · Generally, only appropriate when Profit Centres are unique to entities, since otherwise they will need to be duplicated for each entity. · Can end up with a very large entity dimension · To achieve some reporting, alternative entity hierarchies (and therefore additional consolidations) may be required. · Often leads to creation of additional “dummy” or “journal” Profit Centre entities to contain data that doesn’t need to be captured by Profit Centre (e.g. Balance Sheet Data). This creates even more entities to be consolidated! · When Profit Centres are not unique to entities, shared Profit Centres will create lots of duplicate entities and should be avoided. · Less flexible since profit centres need to be created & moved within the entity hierarchy. Workflows · If the responsibility structure (and therefore workflow design) is by Profit centre, then can make workflow design/build better. · If the responsibility structure is not based around Profit Centre, more entities will need to be assigned and considered in workflow design. · Makes Profit Centres the basis for everything where data is stored, processed and locked. Reporting & Matching · May align with the way it was done in legacy systems, so users are familiar with the approach. · Standard IC matching reports will work for Profit Centre matching (although this requirement is less common from our experience). · Out-of-the-box matching will now only be at PC level. Legal Entity matching will require custom reporting. · Alternative entity hierarchies (and therefore consolidations) may be required to achieve some reporting. · Execution of consolidation required to see legal entity level data (as they will be parent entities) Security · Native using the entity dimension. · Requires maintenance on a Profit Centre level even if not required on that level. OPTION 2: USER DEFINED DIMENSION Create a Profit Centre dimension in a UD. OPTION 2: USER DEFINED (UD) DIMENSION Include the Profit Centre detail in a User Defined Dimension Pros Cons Business Logic · Logic can be customised to specific requirements. · Does not add additional members to the entity dimension (i.e. data units), which is beneficial for consolidation performance in a typical setup. · Running a consolidation, will run a statutory and management consolidation in parallel. · Requires additional development time for business logic if not part of a starter kit. Dimensions · A cleaner entity dimension to support legal entity and group reporting. · Matrix view of consolidation can be created (e.g. with entities in rows and Profit Centres in columns) · Can be combined with extensibility if Profit Centres aren’t applicable to all entities/divisions. · Requires the use of two UD dimensions (one for Profit Centre and another for PC counterparty). This is discussed later. Workflows · Often more closely aligns with the responsibility structure for Actuals (by legal entity). Reporting & Matching · Standard IC matching reports will support legal entity matching. · Consolidation not required to view total legal entity values (pre-elimination data) · Custom IC matching reports may be required for Profit Centre matching. This is less common as a requirement. Security · Native using the entity dimension if security is driven by Legal Entity. · Requires slice security (via Cube Data Access security) if required at Profit Centre level (can impact reporting performance if security is complex). Other Design Considerations Data quality of matrix counterpart – Remember, all intercompany data from the source system needs to be sourced for all matrix dimensions. It will negatively impact user experience if this data is not readily and accurately available in the source system (lots of manual input will be required). Stability of the matrix dimension – This is to say, in your situation, will the Profit Centre hierarchy change regularly with relationships changing? This requires significant consideration in the design phase. Some discussion points are included in a later section. New or existing application – The choice of solution may depend on whether this is a new implementation or an addition to an existing one. It will likely be easier to add a new UD to an existing application rather than re-develop the entity dimension! Performance – Common design considerations of performance, data unit sizes, number of data units etc. apply. Elimination vs. Matching – Remember that just because a customer wants their eliminations to happen on PC doesn’t mean that they need to do their month end intercompany matching at this level. It’s important to clarify this as two separate requirements during gathering. Workflow – Ensure you consider the responsibility structure of the organisation as this will have a big impact on the decision. If the true process (loading, locking, calculating & certifying the data) is by Profit Centre, then this could be a good justification for using the Entity dimension (option 1). However, it’s much more typical that these are based on legal entity for Actuals, making a UD solution (option 2) more appropriate. Option Overview The best approach will vary depending on the specific requirements, but the above gives some common indications of the benefits & drawbacks of each approach. Adding members to the entity dimension creates additional overhead during consolidation since the system must run the data unit calculation sequence (DUCS), consolidate and check the status on each entity member. Therefore, including profit centre in the entity dimension will often be slower than using a UD (in the presence of typical data volumes, exceptions always apply!). Regardless of the approach, remember that with the default eliminations always occur at the first common parent in the Entity dimension. If the client wants something different, then you would be looking at a “non-matrix” solution (i.e. separate cubes for statutory and management). But that is a different topic… Since option 1 mostly uses system-default logic for processing and eliminating the data, the setup is mostly straight forward. Therefore, the rest of the document focuses on how to design for Option 2, using a user defined dimension to contain this detail and run eliminations. Out of the Box - view of Eliminations It is worth stating that just because a client says “we need the eliminations to run by profit centre” doesn’t mean that they need to implement a full matrix consolidation solution. If they don’t need the profit centre elimination to happen at the first common parent in the PC hierarchy, then the out of the box eliminations will suffice as you can report the eliminated data simply by selecting the correct combination of members (Origin, PC, etc.). For those less familiar with the topic, let’s just take a moment to set up a simple example that shows the default behaviour of eliminations in OneStream. We have a Profit Centre dimension in UD1, and an entity dimension for the legal entity members as follows (all entities are using USD only & are 100% owned, for the sake of a simple example): There is an intercompany transaction between the legal entities Manchester & Houston: Within Manchester it is captured within the Finance PC, and within the Houston Sales PC. When out-of-the-box eliminations are run, we will see the following results (eliminations in red, consolidated results in the blue box): The eliminations occur at the first common parent in the entity dimension (in this case the first common parent is the Main Group). In the UD1 the eliminations happen on the same member as the original data, so at the group level we still see the plug amounts by Profit Centre. If we zoom into the Profit Centre dimension (UD1) at the top Main Group reporting entity member, we see the following, where 100 is the aggregated difference on the plug account of the two base level Profit Centres Finance1 and Sales1: Matrix Consolidation - View of Eliminations Now let’s imagine we have the same setup but want to apply matrix consolidation. We have the same data, but now we are capturing the Counterpart Profit Centre for each transaction: Notice how in the below screenshot our eliminations will be happening on a new “elimination member” within UD1, rather than the member the data sits on (highlighted in the green boxes below; the required elimination members are discussed further in the next section on the setup). The member where the elimination occurs represents the first common parent of the PC & Counterparty PC in the hierarchy (in our example this is the “Top_PC” member in UD1). Again, if we look at this result in more detail at the Main Group entity level, you can now see that within the UD1 hierarchy, the elimination doesn’t occur until the first common parent in the UD dimension. So, at “Top_PC” the data is eliminated, but at descendant UD1 members, it is not (e.g. Admin_PC, Finance_PC, Sales_PC). Note that we have the same result at the Top Profit centre and Group entity, but the way we get there is different. Now that we understand the situation we are trying to tackle, let’s look at the setup used in this example. Setup The following items are configured in our matrix consolidation example. Entity No changes are required to the entity dimension for matrix consolidation. Account No changes are required to the account dimension for matrix consolidation. We will use the same plug accounts. UD1 – Profit centre Some additional elimination members are required in our UD1 as follows. Whilst UD1 is used in our example, the usual design decisions apply to which UD you use. These new elimination members will be required at every point an elimination may happen, so you can see that this can potentially add a large number of members to your existing hierarchy. A common naming convention is often used to allow the system to derive where to post the elimination. In this case you can see it is the parent member name with a prefix of “Elim_”. Alternatively, you could use text fields to store this information. Either way, the logic will rely on this being updated accurately and consistently. Tip: Ensure your consolidation logic provides helpful error messages if it finds that an elimination member does not exist or is misconfigured. UD7 - NEW Counterparty PROFIT CENTRE A new dimension is needed to capture the counterparty Profit Centre information. Like the Intercompany dimension in OneStream, this can be (and almost always is) a simple flat list of the base counterparties. All relevant intercompany data now needs to be analysed by this dimension so input forms & transformation rules will need updating. In data models where (almost) all UDs are already in use, this element can be challenging and requires consideration. Whilst UD7 is used in our example, the usual design decisions apply to which UD you use. Remember that this dimension is simply used to capture the counterparty so if your design is already using lots of dimensions then you may be able to combine this with other supplementary data, or maybe even use UD8 (although this will need additional consideration in your dynamic reporting design). Tip: Consider how this dimension will be maintained going forwards as it will be important for the logic that all members exist with the same naming in this counterparty dimension. Consider if the counterparty dimension could/should be automated to align with the main PC dimension. Business Logic Since, unlike the entity dimension, all parents in a UD are calculated on-the-fly this approach will require additional eliminations to be calculated. You will need to store your new matrix consolidation logic somewhere; in our case it is a business rule attached to the cube, but it could also be attached to a member formula: Tip: You DO NOT need to switch on custom consolidation algorithm on the cube to achieve a matrix consolidation result. Always consider the wider requirements & design. Reporting Custom reports will need to be developed to allow users to do IC matching and report on the resulting eliminations meaningfully. If the customer already does their elimination like this, they should have existing specifications that can be designed for. But if not, end users will need to understand Matrix consolidation when they build their own reports/Quick Views or just run LE-based reports with “Top” for Profit Centres. Tip: You can use Data Set business rules to help you efficiently gather your data for interactive dashboard & reports. Business Logic Consolidation Algorithm When a matrix consolidation requirement exists, it has been commonly observed that consultants will switch on the Custom Consolidation algorithm on the relevant cube(s). However simply because this stores the Share data, this has a negative impact on consolidation performance, and database size. Before you reach for the Custom algorithm though, I would recommend considering calculating the matrix elimination adjustments during the calculation pass of C#Elimination, within a UD member (potentially within your data audit dimension). This will allow you to remain on the Standard or Org-by-Period algorithm and within this member you can update the standard eliminations with the PC detail. Of course, you may have other requirements that lead to you using the Custom algorithm, in which case the approach for matrix eliminations can be determined in the context of the overall design. Tip: Consider whether matrix eliminations are required for all processes & scenarios and ensure it is only running on those where it is truly required. Useful Snippets The general approach for writing a matrix consolidation rule is to check that the elimination only occurs at the first common parent – other than that it will follow standard OneStream rule writing techniques such as using data buffers. The following functions can be useful (comments correct as of version 8.5): Function Comment api.Members.GetFirstCommonParent() You will want to use this function to check both the entity and profit centre parents to see if they’re common to the IC or counterparty member. api.Members.IsDescendant() Note that this doesn’t check whether a descendant has a consolidation percentage greater than zero. So, if doing org-by-period this may need additional consideration. api.Entity.PercentConsolidation() Useful for checking whether entity is being consolidated. Ensure you only pass valid parent/entity combinations into the function. Example rule Attached to this paper you will find a sample rule that can be used as a starting point to implement matrix consolidation. Disclaimer: The provided rule is an example only, and its purpose it to demonstrate an approach that can be taken. If used as a starting point, then all care should be taken to adapt and thoroughly test it before implementation. Updates may be made, without notice, to the example in future. Whilst there are arguably different ways to approach this, the example takes the following approach: Retrieves a data buffer of the system generated eliminations. Reallocates the elimination (both IC & Plug account entry) to the correct PC. Reverses lower-level eliminations from current eliminations (without this step the process will repeat at each parent after the first common parent). Clears the system Elimination as “No Data”. Save Results. This should be assigned to the cube when using the standard or org-by-period consolidation algorithm. It reallocates the out-of-the-box eliminations to the relevant UD member. With some quick reconfiguration of the dimensions & names referenced in the rule, it should work with the setup described in the previous section. org-by period in the UD With regards to matrix consolidation, I have previously been asked the following question: “What happens to the eliminations if we change our Profit Centre structure?” Well in our Entity dimension, we have built in tools to handle org-by-period, so that entities can have relationship properties vary by time period. Data is also stored on parent entities which aids in this org-by-period. Within our UD, no such functionality exists, so if I move a member, that member is moved for all history. If I duplicate a member, the values are duplicated (depending on the aggregation weight of course, but keep in mind that this cannot be varied by time!) So how can we approach this is when a Profit Centre needs to change parents one month: Change the main hierarchy – The old view will no longer be visible. The added complexity is that the eliminations for prior periods will occur in the “wrong” place in the UD hierarchy from a historical point of view unless a consolidation is rerun on all those periods. Of course, if you rerun the consolidation on prior periods, then all your results will change (although not at the top level provided nothing else has changed). This implies that the elimination will correctly display the elimination after the change; Historical data will not be kept for the re-consolidated periods. Create alternate hierarchies (e.g. Top_2023, Top_2024 etc.) – New hierarchies can be created, with unique parents that will allow the old hierarchy to be preserved. As with the first option, re-consolidation of prior periods will be required to view historic data in the same format. However, if the data is only required in the new format going forwards then re-consolidation of prior periods can be avoided. Tip: For every alternate hierarchy in which you run your matrix eliminations, the eliminations will be “duplicated”. Therefore, your business logic should allow you to configure, by time period & scenario, which hierarchies are eliminated to ensure only necessary calculations are run. This could be done, for instance, through tags on text fields of the members. Whilst not such a common scenario, it is a consideration worth making during the requirements gathering & design.Question: Client wants to be able to input parent entity adjustments using forms rather than journals.
Question Answer The relevant account(s) needs the follow setting updating to Data Entry rather than Journals See knowledge base article KB0010218 for complete instructions https://onestreamsoftware.service-now.com/sp?id=kb_article&sys_id=1bc22565db472b008feee3a84b961963 Source: Office Hours 2020-07-02 - Partner EnablementOneStream 9.0 Stage Schema Changes
Introduction As part of our ongoing commitment to improving functionality and performance, the 9.0 OneStream release includes significant changes to the database schema related to Stage operations. These changes are designed to enhance data management, streamline processes, and provide increased flexibility for future developments. Overview of Changes The following tables have been replaced by views: StageSourceData StageSummaryTargetData StageTargetData The following tables have been added, to increase normalization of Stage-related data structures: StageDataName StageSourceDataSlim StageSummaryTargetDataSlim StageTargetDataSlim The following views have been added: vStageSourceAndAttributeData vStageSourceAndAttributeDataSlim vStageSourceAndTargetDataR vStageSourceAndTargetDataSlim vStageSourceAndTargetDataWithAttributesR vStageSourceAndTargetDataWithAttributesSlim vStageSourceData vStageTargetData vStageTargetDataR The StageRules table now has an additional RuleIdentityId column, which can be leveraged in joins with the new “Slim” tables. StageRulesHistory and AuditStageRules also contain the new column. Indexing and partitioning strategies on Stage* and Taskflow* tables have been streamlined and optimized. See the Appendix for details of modified indexes. Impact on Existing Applications The following advice applies only to custom queries that developers, partners, or administrators might implement in their applications. Internal operations (e.g. standard Import/Validate/Load workflows) are unaffected. The changes to the database schema in OneStream 9.0 might require small modifications to existing applications. Although we tried to maintain compatibility as much as possible, it is suggested to review and update SQL queries and application logic that interact with tables StageSourceData, StageSummaryTargetData, and StageTargetData. In most cases no change should be necessary, as SQL statements will typically retrieve data from a named object regardless of whether it is a table or a view, and the old table names are now aliases for views. However, if the query or api is explicitly looking for one of the removed tables (rather than a table or view), it will be necessary to change it so that the query can accept a view as well as a table. Note that performance of such queries might differ from previous releases. Impact on New Development The following advice applies only to custom queries that developers, partners, or administrators might implement in their applications. Internal operations (e.g. standard Import/Validate/Load workflows) are unaffected. Member names are now stored only once, in the StageDataName table, and referred to from “Slim” tables and views via IDs. In future development, in a few cases it might be more performant to move from operating on the old Stage tables to working with the new “Slim” counterparts, joining on StageDataName only where necessary. Note that these members are not necessarily related to Metadata members; they represent any type of item name involved in stage loads (source members, etc.) These JOINs should be done sparingly; in most cases, leveraging views will likely be preferable, since they already do that work for you (returning actual member names as strings) and allow for better caching. Unless there is a clear and significant performance difference, queries should be performed on views. In these new “Slim” tables, the string and uniqueidentifier columns have been replaced with integer types that reference the “NameId” column in the StageDataName table. When querying “Slim” tables, the integer values in columns are used to join with the “NameId” table of the StageDataName table to retrieve the actual names. As an example, if we wanted to obtain the text value of the “CnId” column in the StageSourceDataSlim table, we would now need to perform a SQL join to the StageDataName on the NameId column. SELECT TOP (100) Slim.Wfk, Slim.Wsk, Slim.Wtk, Slim.CnId, Sdn.[Name] FROM [dbo].[StageSourceDataSlim] AS Slim LEFT OUTER JOIN [dbo].[StageDataName] AS Sdn ON Sdn.NameId = Slim.CnId IMPORTANT! The collation of the “Name” column in the StageDataName table has been set to “Latin1_General_100_BIN2”. The “Latin1_General_100_BIN2” collation allows us to compare string values properly between Unicode and non-unicode values. If you need to perform a join to the StageDataName table on the “Name” column, you will need to force the source column in the join to use this collation. An example of this is below where a string column using the default database collation is joined with the [Name] column of the StageDataName table. The “COLLATE” clause is added to the join to force a binary comparison of the two column values. SELECT Src.Et, Sdn.NameId FROM [dbo].[StageSourceDataSlim] AS Src LEFT OUTER JOIN [dbo].[StageDataName] AS Sdn ON Sdn.Name = Src.Et COLLATE Latin1_General_100_BIN2 Failure to add this collate clause when joining on the [Name] column of the StageDataName table will result in an error like the one below. Msg 468, Level 16, State 9, Line 4 Cannot resolve the collation conflict between "SQL_Latin1_General_CP1_CI_AS" and "Latin1_General_100_BIN2" in the equal to operation. There is no requirement to use COLLATE when operating on the prebuilt views. Conclusion The database schema changes in OneStream 9.0 represent a significant step forward in our data management capabilities. By optimizing table structures and improving indexing, we are laying the groundwork for more robust and scalable applications. We appreciate your cooperation during this transition and are confident that these improvements will provide substantial benefits to our operations. Appendix Modified Indexes The following changes to indexes have been implemented: Table Name Index Name Comment Member IX_Member1 New nonclustered index on Name, DimTypeId, DimId StageArchivesInformation IX_StageArchivesInformation0 Index is now a unique clustered index. The “UniqueID” column has been added to this index. StageAttributeData IX_StageAttributeData0 This index has been removed. StateBiBlendInformation IX_StageBiBlendInformation0 New unique nonclustered index added using the column “BlendTableName” in ascending order. StageRuleGroupsHistory IX_StageRuleGroupsHistory0 Index has been removed. StageRuleProfileMembersHistory IX_StargeRuleProfileMembersHistory0 Index has been removed. StageRuleProfilesHistory IX_ StageRuleProfilesHistory0 Index has been removed. StageRules IX_StageRules1 New index on the “RuleIdentityId” column in ascending order was created. StageRulesHistory IX_StageRulesHistory0 Column “RuleIdentityId” added to index. Index changed to a unique nonclustered index. StageToFinanceDataUnit IX_StageToFinanceDataUnit0 Index is now nonclustered. “WorkflowProfileKey” column has been removed. “FillFactor” has been removed. StageToFinanceDataUnit IX_StageToFinanceDataUnit1 The “WorkflowProfileKey”, “WorkflowScenarioKey” and “WorkflowTimeKey” have been replaced with the “ScenarioId”, “TimeId”, “CubeId” and “EntityId” columns. StageToFinanceDataUnit IX_StageToFinanceDataUnit2 Index has been removed. StageToFinanceLoadResult IX_StageToFinanceLoadResult0 Index is now a unique clustered index. The “SummaryRowID” column has been added and the “FillFactor” has been removed. StageToFinanceLoadResult IX_StageToFinanceLoadResult1 The “WorkflowProfileKey” column has been removed. StageToFinanceValidationError IX_StageToFinanceValidationError0 Now a unique clustered index with “FillFactor” removed. StageToFinanceValidationError IX_StageToFinanceValidationError1 Index has been removed. TaskflowStepInstances IX_TaskflowStepInstances0 “FillFactor” increased from 80 to 95. TaskflowStepLog IX_TaskflowStepLog0 Now a unique clustered index. “UniqueID” column has been added. “FillFactor” increased from 80 to 95.
Question: Is it possible to filter the list of members returned in a parameter/drop-down box based on a user’s cube data cell (“slice”) security access?
Answer One approach would be to use a bound list parameter together with a dashboard dataset business rule. Source: Office Hours 2021-04-29 - Partner EnablementQuestion: Is there any security options to limit a user's ability to submit data using excel?
Answer Submitting data via excel will respect the security model set up in OneStream. So consider typical security setup. Also consider using constraints to manage "valid" data intersections. Inputting data via set cell will get tracked in the audit logs, however, clearing this data can be problematic if you wish to clear all out all submitted cells. Rather than using set cell, another option is to set up an excel import which would go through stage and allow for clearing of the data set. There is a pending enhancement request to have an option available to restrict some users from submitting via excel. Source: Office Hours 2020-7-2 - Partner EnablementDoes OneStream have the capability to refresh Cube View connections within Excel workbooks via XFDoc or some other type of functionality? Goal is to schedule the refresh & distribution of an Excel workbook via the Book or other functionality, so that both CVs and XFGetCells can be used. Per our prior testing, when using an XFDoc.Xlsx file type, the XFDoc will refresh XFGetCells when retrieved. However, it does not refresh cube view connections embedded in the Excel XFDoc.
Answer There are numerous Enhancement Requests related to this: PF-385, PF-1309 Source: Office Hours 2020-10-08 Partner EnablementQuestion: In a Cube View heading, how can I include the last day of the month as the time description in a page caption (e.g., display "as at 30 September 2020" instead of "September 2020“)?
Answer Assuming that the date being shown is always the last day of the month and the client is using calendar months, it is possible to use XFBR string rule to call a function to get the number of days in the month. Source: Office Hours 2020-01-17 Partner EnablementIs it possible to load multiple thing planning registers to meet different requirements and, if so, how?
Answer It is possible to create different "versions" of thing planning which could be then used for different requirements (e.g., travel planning and sales planning in the same application). Using the marketplace solution "Marketplace Solution Tools", you can create multiple instances of a marketplace solution which you would then load into the application as normal. Source: Office Hours 2020-12-03 Partner Enablement
