by gustaf | Aug 29, 2021
I was recently in charge of a large migration. It all went fine but not without hickups that typically are connected to moving large amounts of data to dataverse. We were using SSIS with Kingswaysoft and ended up using a local SQL database as staging database too. This article will discuss the different lessons learned and give some concrete tips when doing similar migrations.
One of my more popular articles is the article that describes how to optimize the writing of data to Dataverse/CDS. If you are working with migration of large amounts of data, as I will be describing here, I do suggest you have a look at it: https://powerplatform.se/fast-data-management-in-a-limited-cds-world/. I will not discuss those concepts in any detail here but we did use all aspects mentioned in that article.
I recently was in charge of a migration which used CSV-file exports from an old German system (with German field names!) which had many millions of records, in both large tables like “Contact” and “Sales Order”. However, the system we migrated from had a completely different data modell than the one used in Dynamics. For instance, each row describing a “Flight” had to be divided into two rows, one for the outgoing flight and one for the homecoming flight, in the order detail table. We also had to create a lot of related data which was referenced from the “Flight” table, for example location, agent and brand. In other words, there was quite a lot of heavy transformations going on and a lot of logic involved, such as change format on the old data to match the Dataverse model and apply rules to resolve old issues, such as bugs.
Initially we only got a quite small subset of the entire database load, and we started our migration journey by creating all the migration logic in SSIS (which facilitates the script and makes updates easy to handle). The script did include some functions that “joined” rather large tables, both from the CSV files but also related data fetched from the Dataverse based on primary and alternate keys. I was clear with the customer from the very beginning that I wanted a full export with the same amount of data that we could expect in the final migration, mainly for the opportunity to stress-test the SSIS script before the migration to the production environment took place and after a while we got the big files…
…And this was when the excrement hit the wind generator. The afore mentioned lookups just stalled forever. We noted that having a lookup (using Kingswaysoft Premium Lookup) works fine on a computer with 16 GB memory up to a few 100k of records. However, once the data starts reaching 500k and more, it just stalls forever (and don’t even get me start on the sort tools…). Not sure exactly if it would have been possible to fix this by adding more cores and memory, we didn’t try. We hence had to rewrite the script and implement a staging database instead. What we found, is that a dataflow with 1M+ records of lookups will be 100x faster if you import the data into SQL and do a join instead. Lookups still works for smaller tables and I am not against them per se, as they do make the migration simpler. Adding more tables to a migration database will increase complexity, and if you want to add a column in a table, that table do not only has to be added to one SSIS dataflow, but probably a few more. And you also must do an ALTER TABLE in SQL to add the field there too. It is therefore important to have a good mapping set before you start to create the script. And keep the complexity as simple as possible. You can also use SQL tasks in the migration script to update the tables straight after you read them to the staging database per automation, if you need to apply some kind of rules after the read to the staging database, and find it easiest with an SQL query.
The method we used for developing the migration was to first make a “skeleton” migration, based on the target data model. In other words, we started with trying to get a few of the easiest fields, not all, from all tables that was to be involved in the migration – maybe it could be called – model-first-approach, instead of starting with one table, completing this and then moving on to the next. The advantage of the model-first-approach is that you quite early can start some tests on the data, for instance setting up some quantitative test by checking in the source system for the quantities of contacts and then comparing these quantities to the target. The tests can typically be done by other people than the people building the migration scripts and hence this methods scales a lot better than table-by-table-approach. It is also possible for several devs to work in parallell with different tasks. Typically the more senior will build the skeleton and then more junior can add fields by field to each respective table. A negative aspect of this approach is that it requires a lot of re-loads (keep in mind that this was a first migration, so there are no prior data in the Dataverse that we needed to consider) and re-mapping. And it may be easier to “fall out of” the structure, if you just need “to add a little bit here and there”. It is however indeed hard to go table for table, especially with related data. If you already have a lot of live data, you should think about a way to easy identify the migrated data so you can bulk-deleted. And do not forget to engage the client early with raised questions and the mapping to make sure you have understood everything correctly and avoid unnecessary errors.
We also tried to create unique row identities that strictly was based on the source data. This is very useful as that allows for delta-migration, or to continue where we left off in case of a problem. Let’s say for instance that you want to migrate 3 million contacts. If, after 2.1 Million contacts the script breaks for some reason, it is good to be able to continue at 2.1M instead of restarting. In this case we didn’t use modifiedon-date to be able to do a full delta migration logic but it is certainly possible. For this we used the cache-transforms, easily fetch the already migrated data (if any) with the unique and sort out the already migrated data if it matched the key.
Another pattern that we used was that, after creating a specific record, like contact, we reimported the recordid (in this case contactid) together with the legacyid. This allowed us to directly join with this table when later adding tables with dependencies like lookups towards the contact table, could be joined with this mapping table so that we directly got the contactid when querying the related table.
Tips
- When migrating from CSV, import them directly as source tables in the staging database. That way, in case you need to fix something, you have a good reference for quantities.
- Get an example of the full data load as early as possible. A script that works for a subset might not work at all for the full dataload as was the case for us.
- Automate as much as possible. Don’t use any hardcoded values that are environent specific, such as transactioncurrencyid, but rather read these to small tables or to SSIS variables. Use SQL Truncate to remove all data quickly in a table, and make this part of the SSIS script as an SQL task at the appropriate stage.
- Always check the quantities. How many rows in source data, how many rows after a match and check if it differs so you very early can identify bugs in your script that might be the reason for dropping rows. For example, you might use a JOIN when you should use an OUTER JOIN. Always check the total number and see if it is what you expect. Watch out for duplicates, and always check so your unique IDs (if you got some from the source data) really are unique and not NULL. Do note that if you have duplicates, that you join on, that will create multiplications. Hence it is possible, after a select-statement with joins to get more records that the initial table.
- Define reasonable goals and test cases for the migration. Some examples:
- 99.9% of all contacts to be migrated correctly. With 1 M records, this means that anything lower than 1000 incorrect migrated contacts/missed, is defined as still ok.
- Randomly pick 10-20 records on a base level, like 20 customer, and then compare these in the UAT/Test environment to the source system, as it is seen there. This needs to be done by the business people, so that they can have a say if the migrated data is fine.
- Select some filters, like “all customers in Munich” and some other segmentations and compare source system to destination. If there are large amounts of errors, backtrack to the staging database to see where you did loose some records or created too many (not uncommon).
- Complete entire transformation to destination tables in the staging db. Then you can move directly from there to dataverse. This is particularly important when moving large quantities of data when managing the data in SSIS can be problematic.
- Make sure to have unique identifiers on all tables that preferably can be regenerated from the data. Store these in some “Legacy ID” field. This allows for delta-migration logic, ie. where part of the data is migrated and then the rest later. If you have some issues during one of the dataflows, and it stops on 3 230 234-th record of 6 M, you can continue from there and you don’t have to redo it all. If there is no decent way of getting a legacy id, you can generate classic row numbers by creating an identity column. This will make the migration utilize this, but only within that particular instance and load of the staging db. Hence you must be careful everytime you reload the database.
- Utilize the backup-restore functionality of the dataverse environments. Do note that you can make manual backups just before you start migration. If you have a production environment, this will need to be converted to a sandbox environment before you can restore to it. Another option I got from a colleague was to use 3 different environments, with temporary names, and then just rename the final one when done.
- Once you have transfered an entire table to the source system, it is typically very useful to have a mapping table, with just the table record id and the legacy id. So for instance, after migrating Contact, read all contacts from dataverse with the contactid and the legacy id. That way, when later migrating “salesorders”, which identify the customer by legacy id, it is easy to just join with this table to get the contactid.
- Production environments are faster. Fastest is to ask Microsoft Support to relax throttles on all environments that are used during migration.
- Use a VM that is located geographically (or really with low latency and high throughput) to where the environments are hosted. This is a very common recommendation by Kingswaysoft too.
- The settings for number of threads and batch size needs to be set based on some factors, namely:
- Production/Sandbox
- Have throttles been relaxed
- Size of payload (ie how many columns) – larger payload -> smaller batches.
- Type of action – creates are faster than deletes. Updates are in-between.
I hope these tips can help you along. If you have any comments or you have other experiences in this subject, don’t hesitate to leave a comment.
During this migration and the writing of this article, I had excellent help from my highly intelligent colleague Ebba Linnea Nilsson and it is certainly true that two heads are better than one, and the end result is often a lot better than just the sum of two people. So for my final recommendation, make sure to have a good colleague with you to help you out, as you most probably will run into some issues and having someone to discuss with is really great!
Good luck on your migration challenge!
by gustaf | Apr 8, 2021
Should a five user organization be entitled to the same amount of compute as a 5 000 user organization?
Entitlements are the limitations that Microsoft have set on the platform that are based on which type of license each user has. This is not the same as the API Service limits which are much more liberal. The entitlements have not yet been fully enforced as the reporting capabilities of the platform have not been rolled out fully yet. But they will. With this blog post I attempt to give my perspective on entitlements on the Power Platform and Dynamics 365 (CRM part).
My previous post was about API Service limits which are commonly referred to as the throttling limits of the platform. The entitlements limits (and here) have another part in the Microsoft docs that go into these a bit deeper. I’d first like to go into why there are two different “protections” or limitations.
The API Service limits are there to protect the platform from noisy neighbours. Some of us, that have been around since the earlier days of Dynamics 365/CRM online remember that the performance used to be rather shaky. This could often be due to the fact that some other instance on the same hardware your instance was hosted on, was being slammed with massive amounts of requests, like during a migration. To make sure that this “noisy neighbour” problem doesn’t occur, the API-limits have been put in place and since they have, things have been a lot better so they do seem to work.
The Entitlements are there for another reason. Let’s say you buy two (2) Dynamics 365 Sales users and then use integrations with a custom built front end for B2C purposes with one of those users (or an app user), and, still within the limits of the API Service limits, hammer the API:s from day to night with an amazing amount of requests. The B2C aspect would be covered from a licensing perspective in what was previously called “external connector” license and is nowdays included in the normal license. However, the amount of compute that the instance is utilizing is way above what you are paying for. This is the reason why Microsoft have created the entitlements, as far as I know anyway. And I think it only makes sense that there is some kind of reasonable proportionality to that.
To quote the Microsoft docs page: “These limits represent the number of requests users are entitled to make each day. The allocated limit depends on the type of license assigned to each user.“
What is a request?
The first question is then, what is a request? Previously we were told, that a batch request (ExecuteMultiple) was one (1) request but that has since changed and is now considered to all the subparts. I would even think that a batch request has the extra overhead of the batch itself. Hence, a batch request with 10 creates, will actually be counted as 11 requests; 1 for the batch, and 10 for the creates. The exact definitions are not disclosed but we get a rather good description from the docs with this paragraph, where I have highlighted some interesting parts:
“For Dataverse, API requests include all data operations that interact with table rows where rows are created, retrieved, updated, or deleted (CRUD). Special operations such as share and assign are included because they are considered updates. These requests can be from any client or application and using any endpoint. These include, but are not limited to, operations performed by plug-ins, async workflows, custom controls, and $batch (ExecuteMultiple) operations. There are a small set of system internal operations that are excluded, like login, sign out, and system metadata operations.”
The important takeaway here is hence that you cannot create a workaround by using a plugin and using the internal context pseudo-api to do the calls, as these are counted as well. Difference might be that they are done in the context of a specific user and that user has a rather large entitlement, which might hence “flatten the curve” so to speak. An interesting aspect, though is the exception to this rule:
“Power Platform API request allocations include use of Power Automate, AI Builder, and Connector APIs. All requests through a connector that result in a Dataverse request will represent 1 Power Platform request.”
This strongly indicates that Microsoft wants us to use the Power Platform tools and that these should not at least have additional costs. There are, however, still some inconsistencies in this area that I really hope that they fix, such as:
- Microsoft supplied integrations in ADF
- Integrations to Dynamics 365 Finance & Operations
- Dynamics 365 Business Central
- Exports to ADLS
- Data Export Service
The latter two can be really heavy on the API:s if you have an enterprise system or a B2C system. I work with a customer which currently have a database of >400 GB which uses Data Export service and the amount of notifications on the Data Export Service just for Contacts for a year often exceed the hundreds of millions.
Other areas which are not mentioned but which I think are included are addon first-party apps like Customer Insight (Sales Insights) which actually uses a ADLS in the background (not that you can actually access it). I have heard stories of support tickets where Microsoft support have blamed the API Service protection for hitting the ceiling when it was Sales Insight that caused it, which would indicate that these are actually counted. I think the intention is to include all of these so that the license for these cover the API entitlements. I just wish they would fix the gaps as customers are being affected.
Entitlement telemetry might not be the same as API Service protection telemetry
That actually brings up another interesting aspect. The measurements that are used for the API Service protections are probably NOT the same as the measurements that are used for entitlements, but this is based on my personal hunch, and not any kind of facts. Mainly based on the assumption that I think that the areas that are excluded from entitlement measures above, probably are not excluded from the API Service protection.
Another definition of request!?
On this page there is another definition of what a request is that is different from the one above. I believe this is older than the one mentioned above, as it uses the term “CDS” which has been replaced by dataverse now. I am not sure though as this page last change is dated on the second of feb 2021 while the other the 5:th of March 2020. The main difference is that this does not make the exception mentioned in the article above, hence every call through a connector, every successful or failed call in Power Automate will be counted as one request. Hopefully Microsoft will clear this up soon.
Entitlements per user
At this link you can find the specific entitlements per license. They are all measured on a 24 h period and range from 20 000 for the full enterprise versions of Dynamics 365 to Power Apps per app plan which get 1000 requests.
Entitlements for non-licensed users, which mainly will be application registrations/application users are fixed per tennant based on the highest licensed purchased on the tennant. This means the following pooled included non-licensed entitlements.
- 1 Sales Enterprise -> 100 000
- 1 Sales Professional -> 50 000
- 1 Power Apps license -> 25 000
- 1 Sales Enterprise, 1000 Power Apps -> 100 000
- 10 000 Sales Enterprise, 10 000 Power Apps -> 100 000
The important note here is that this does not scale at all, but is fixed. And if you plan to do some integrations with a Power Apps only tennant, you’d be wise to buy at least one Dynamics 365 Enterprise, just to get the non-licensed user entitlements, as the Sales Enterprise is around $95 and each additional 10 000 is $50, which means that the saving to get to a 100 000 calls / 24h is:
50*(100 000 – 20 000)/10 000-95= 400 – 95 = $305/month
Buying extra capacity
It is also possible to buy extra API capacity. You can read more about this in the Licensing Guide for the Power Platform. I am not able to find a current price for this at this time, but the list price was previously set at $50 (per 10 000 for 24h). These are then to be allocated to the users as you wish.
Overshooting
“Users will not be blocked from using apps for occasional and reasonable overages at this point of time.“
What will happen when or if you overshoot? A very important question. Most organizations will at some time do this, most probably during migration of data from the old systems. The statement from Microsoft above, especially the highlighted “at this point of time.” is rather omnious. It does indicate that at some time the hammer will come down. But at this time it won’t, admins will be harassed with emails about overshooting and just as with overshooting data capacity, they might start with blocking some features when you are overshooting. It is mentioned in one of the articles in the FAQ that after the transition period they will start blocking. So that will be a real fact unless they change their mind on that.
My very strong advice, is hence that all organizations that are not compliant need to start looking at this as soon as possible. I have some tips on what you can do further down in this article. Please refer to these and feel free to leave a comment if you have questions on the subject not answered here.
ISV Bundling
There are many ISV:s which export rather large amounts of data. The first ones that come to mind are the Marketing Automation products like Adobe Marketing, Click Dimensions, Dot Digital and more. These all synchronize contacts, marketinglists and marketinglistmembers, at least, which for larger installations can be quite large datasets. I do think it would be advantageous if these ISV:s could include the API Entitlements that are required, or if they are billed by Microsoft to the ISV which in turn bills the customer with a surcharge. At the very least Microsoft have to take ISV:s into the equation here as they are an essential part of the ecosystem, especially from the customer perspective.
Tips on how to handle future entitlement enforcement
- Start by using the PPAC to get an overview of how your situation looks even though you might not get an exact picture.
- Consider the overhead of batching. There can be performance advantages to batching as mentioned in my previous article. But there needs to be
- Consider “outsourcing large datasets” to ADLS – although the ADLS export also uses API-calls.
- Maybe not a problem if short term – for now
- Consider using official connectors or Power Automate instead (although that might cause costs in itself)
- If building Power App licens based solutions and you have heavy integrations, buy one Dynamics 365 Enterprise license.
- If possible impersonate the data load over all the users. This can be done with plugins and synchronous workflows for instance. Patterns that can be used in this case can be staging tables in dataverse where the owner is set and then a plugin is triggered that slices the row into many pieces as the owner of the import record. I am not sure if impersonation using the API will have any effect on this. That needs to be investigated. If it can be used to spread the load, that would be a good pattern to use.
- Refactor inefficient code. Depending on implementation maybe increase use of caching or other techniques to reduce the amount of requests. Make sure you have skilled Power Platform/Dynamics 365 developers working with development as knowing how to do this very particular to this platform.
Microsoft representatives, locally in Sweden anyway, are saying to our customers and potential customers that they need not worry about this. I find that message a bit mixed with what I read here. On the other hand I think this will be a very rough change for many organizations. If your organization will be very negativly affected by this and you feel that you are still paying “fairly” for your part, then I suggest you contact Microsoft and describe your business scenario in detail. If you need help with who to contact you can always start with the people who have written the articles who you can ask to forward the articles to the right people, use your local user group or ask some local MVP for help as they often have contacts directly with the product group (and many other experts do too).
Good luck and do leave a comment or share this if you like it!
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