I was recently helping my colleague Ebba Linnea Nilsson with a support ticket with data not being propagated correctly from dataverse to a datalake via Azure Synapse Link. It turned out that this was all by design. A design that might not be what normal users would expect.
Calculated columns and now recently the formula columns are both very useful way of being able to calculate data in a field that is based on other fields. Common scenarios are calculations like “Weighted revenue” which is the probability multiplied by the estimated revenue for an opportunity. However, there are scenarios where you need to be aware of how these fields actually work or you might get an unwanted or unexpected behaviour.
The first thing that needs to be understood is that these column types are calculated “on-the-fly” everytime dataverse attemts to access these columns. It might seem like the data is “in the columns” but it really isn’t, it is calculated. This is a big difference from for instance rollup-columns is that those columns are calculated on a regular interval by the system, and the result is stored in the record.
What does this mean for Azure Synapse Link? Well, let’s say we have a simple calculation, that sets the value “A” into all records for this calculated column. We then enable the Azure Synapse Link which will make an initial sync and set the column in the datalake to “A”. Now we change the calculation of the rule to output “B” instead. As no records are actually changed, this will not cause any records in the datalake to be updated, hence they will all still have the value “A”. From a user perspective comparing Dynamics 365 to the datalake without any underlying understanding of how this functions, it will look like an error. Same column has different values comparing what is in dataverse with what is in the datalake.
As soon as a record is actually changed, all columns for that record will then be sent to the datalake, and hence the calculated column will be set to “B” at that time. It is hence possible, to manually or semimanually force a resync, but it would require some bulk like for instance SSIS with Kingswaysoft especially for implementations with large amounts of records.
An important question to ask, is why would you want to calculate the data in dataverse and then use it in in the datalake. If you have a propper datalake architecture it should be easier to make calculated columns/fields in the datalake/datalakehouse. If the data is calculated only for use in the datalake, I would suggest moving the calculation to the datalake.
There are, of course, scenarios when it is preferrable to have calculations in one place and reuse the output in many places. However, this understanding of what can reasonably be expected is then essential.
A final note is that this type of unexpected behaviour is not limited to just Azure Synapse Link but really to any integrations based on either “modified on” or change tracking without doing periodic synchronizations. Hence I would also like to give a general warning about this.
The long awaited Long term retention feature for dataverse is now in public preview. You can read more about it on Microsoft Docs.
This is really a good functionality that many of us working with larger customers that have a lot of data, and hence paying a lot for it, have been waiting for.
It is a bit hard to test, and as it is preview, it is not recommended to be used in production. The reason is that for larger organizations, the cost of having a lot of data in non-production is usually too high to justify it. Due to this reason, I have a ask, that if you think this is an interesting functionality, please try it out and send any feedback to Microsoft on the idea-site. I have sent quite a few, and please vote for any ideas you think are good.
The key question, is of course, what the cost for storing data in the long term storage will be. As the data is stored in an internal datalake (from what I understad), I would presume it to be closer to file-storage costs than the db-storage costs. But i guess we’ll all see soon.
Storing data in dataverse is very expensive. Especially the data that is stored in the actual database (db data). Hence, for many customer with larger datasets, typically with some B2C type of business, it is a good practice to overviewing the data you have in the system and figure out different ways of keeping it from costing too much money.
Below are 10 tips you should consider to keep the data in check.
Make a deletion list – and set up deletion jobs
Flatten verbose fields
Use virtual tables
Use datalake with analytics
Use datalake for archiving
Compress complex structures
Clean up non-production environments
Set lifecycle for instances
1 – Make a deletion list and set up deletion jobs
The first thing you need to start doing is to go through the biggest storage contributors. You can find which these are by looking in the Power Platform Admin Center under “Resources -> Capacity”. Open the view of each of the dataverse instances by clicking on the small chart symbol next to the instance.
You can export the full list by clicking on the hamburger symbol in the right hand corner of the graph. See picture below:
You can then start by trying to break down each table from the top. Based on the picture above, ask yourself questions like;
Do we need to store and save all orders? By removing some orders, maybe cancelled orders, we can cut down on two of the three largest tables as the order rows are removed at the same time
What activities are really causing activitypointer (the common table for all activities) to become so large? It is quite common that the email table is a culprit here, as the body of the emails, is usually a quite a few bytes and with thousands or tens of thousands of emails, they do add up. Marketing integrations can also bloat the activitiy pointer. I suggest investigating this further by exporting the data to an Azure Datalake and analyzing it with PowerBI. It is also quite common that not all emails need to be saved, for instance if you have email enabled queues like email@example.com it is quite common that you get some spam into this. Maybe searching for “unsubscribe” in email body to see if there might be some newsletter looking spam which enable you to remove them.
Are there any patterns of contacts and/or leads that can be removed? Working with B2C you might find that there are quite a lot of contacts and leads with incorrect data or similar. Ask yourself what value a lead with no phone number and no email has… removing contact data will also remove customeraddress as there will be at least two customer address records for each contact automatically created. The same goes for leads but in that case it is the leadaddress where extra data is being created.
The point with the list is trying to identify possible patterns of data that can be deleted. I typically have one row per rule like so:
status code = inactive AND modifiedon older than 6m
body contains “unsubscribe” AND modifiedon older than 1m
Flatten w Power Automate
Outgoing AND subject = “Covid information”
2 – Flatten verbose fields
Sometime it can be a good idea to just clean out or some “body” data. For example if you have sent emails to a lot of customers with the same Covid related information, the actual content of that email is the same for all and is known by everyone. Hence the body can be removed which can typically be done with a Power Automate Flow or SSIS/Kingswaysoft depending on the size of the data.
3 – Use virtual tables
Not all data needs to be stored in the expensive dataverse database. The recommendation from Microsoft is typically that data which you interact with should be in dataverse and the rest can be somewhere else. As it is now possible to use SQL as a source for virtual tables, as well as Cosmos DB and many other sources, moving data out of dataverse and accessing it through virtual tables can be a viable option.
Virtual tables is also something that should be considered when designing integrations. It is not always critical that all data actually reside in dataverse. Sometime just being able to access the data through dataverse is good enough. Especially if the data source is fast, it isn’t that much data, the data only needs to be accessed seldomly, It is also easier to use virtual tables if the data is read-only and used for reference.
4 – Use datalake for analytics
Dataverse isn’t really a good source for analytics. All endpoints (even the T-SQL) go through the application layer to the database. This makes large data crunching less than optimal in dataverse. This is also why Microsoft have developed methods for replicating the dataverse data to external stores. The just discontinued Data Export Service (DES) which synchronized data to an Azure SQL and the new Azure Synapse Link which synchronizes data to a datalake, are clear examples of this.
Hence, a good architecture for analytics of dataverse data is doing the analytics outside dataverse and the current best place for this is in a datalake with Azure Synapse Analytics.
This also has a direct implication on the data stored in Dataverse, the data you have in dataverse that is only needed for analytics, might not actually be needed in dataverse anymore. This can be anything from old orders, and of course all old communication like emails and phone calls. Datalakes are also way more efficient for doing large scale AI/ML analytics, which many organizations are looking for.
5 – Use datalake for archiving
If you havn’t already thought about it, a datalake can hence be used as an archive for data that you might need but isn’t actually something that you will use every day. Often a lot of data is stored for compliance reasons. When setting up archiving, it is important to make sure that what ever method you use, you probably want to keep the data in the archive after it has been deleted in dataverse, hence you might need to move the data from the initial storage container to the actual archive making sure to not actually move any deletes.
Microsoft have announced that they will be putting their own hot/cold-storage solution for dataverse into public preview this spring. This sounds a lot like some archiving functionality and I am certainly looking forward to seeing what it will be, how it will work and the licensing for it.
6 – Compress complex structures
For some businesses it is often needed to have quite a complex datastructure to handle the operations of what is done. It can be complex order structures with allocations of order rows to specific users or product configurations with billing, logistics and provisioning settings. Once an order has bee fullfilled in all aspects, it might not be required to store and keep all that complexity. It might just be required to keep the order header and the most critical information about what the order rows were about. Perhaps even flattened into a JSON or text field for future reference. If it is possible to move from a order header + 10 records in related tables to just a order header with a summarized text, for thousands of orders, than can save quite a lot of space.
7 – Clean up non-production environments
Data isn’t only stored in the production instances of dataverse. Many times production instances are copied and turned into test or staging instances. Integrations may be running towards development and test instances generating substantial amounts of data. Have a look at your non-production instances and check which data you actually need in them.
8 – Revise datamodel
Sometimes datamodels can become unnecessarily complex. This is most common when someone with little experience of dataverse and model driven applications design solutions. Other typical problems I have seen are repurposing of tables like leads, accounts, sales order to other simpler purposes. The problem from a dataverse storage perspective becomes that the additional storage overhead in these cases, especially in cases with businesses that have a lot of customers, can be rather drastic. Redesigning the datamodel to be more sleek can make it both more user friendly and use less storage.
9 – Revise integrations
Integrating data to dataverse can be a large culprit for data. For instance, many marketing applications generate quite a lot of data regarding the behaviors of customers. This data can then be integrated to dataverse to be used there. As behavioural data can be very granular, to the level of “Customer x has be send email y”, “customer x has read email y”, “customer x has clicked link z in email y” this data can take up substantial amounts of storage if integrated to dataverse. What options exist for this are different for different marketing applications. For ex Adobe Marketing can shut off this integration and can instead synchronize behavioral data to an Azure datalake where it can be unified with other dataverse data.
ERP integrations are also a common area of problem. ERP systems often have a very deep granualar level of data which might not be needed in dataverse/Dynamics 365 CE. Sometime just enabling deeplinking directly to the ERP system can be a better method, combined with virtual tables. However, this is not always the case and sometimes the best solution is just synchornization of the data.
However, do recognize that there should be a reason for having the data in dataverse. It should genrally be actively used.
10 – Set lifecycle for instances
Having dataverse instances with a bit unclear usage and not knowing what they are for and if they can be removed is, of course, a source of quite a lot of data consumption. By clearly assigning owners, reasons etc for all instances it makes answering the question “Do we really need ‘Internal test applications sprint 7′” or is it just an old remnant from a two year old development. This is part of the Center of Excellence starter kit and the processes that are important to set up in relation to this.
I hope any of these 10 tips have been useful. If you think so, the best way to thank me is to tweet or share your thoughts on this in social media. Also feel free to leave a comment below.
Perhaps you have some other thing that you think should be done or that I missed something. If so, please share in the comments below.
Have you ever tried creating bulk deletes in dataverse/Dynamics 365? It is still a very old interface and it is hard to control the exact definition of what is to be deleted as you cannot see the actual FetchXML that is being used. It is also hard to see existing recurring bulk delete jobs and what FetchXML they are using. Based on these facts, my colleague Ebba Linnea Nilsson and I decided to make our first plugin for XrmToolBox (XTB). We are now proud to announce that it is released and available for free (as usual in XTB). We have also identified a bug in the platform related to this, which I will describe below.
The bulk delete functionality in dataverse and hence in Dynamics 365 CE is an essential function for many organizations. This is especially true since GDPR was introduced and there is a strong legislative requirement to remove personal data that cannot be justified to be stored. There are also other reasons why the bulk delete functionality is more and more important and that is based on the capacity costs that can be inferred on a Power Platform tennant. Firstly just storing data, especially in the database storage in dataverse has a non trivial cost at $40/GB. There is a lot of value per GB, so it might not be justified to say that it is too expensive but removing unnecessary data is definitely something that can be worthwhile especially for larger implementations. I personally work with a customer in the travel retail industry which has millions of customers and some tables have 40M+ records. There are also a lot of integrations and automations causing a lot of data to be created. Data that at some point needs to be removed. As all data should, or there should at least be a conscious decision not to remove it and why if so. As you might be aware, if you have read my other articles in this blog, I have previously used SSIS and Kingswaysoft to remove massive amounts of data. However, now that the API Entitlements will be introduced (6 months after the report for API Entitlements is made Generally Available), we need to start to become more and more restrictive in using the APIs for massive data management, like deletes. Hence, we try to move as much as possible to the built in Bulk Delete.
Working with the built in bulk delete functionality is a bit sad. It is very old and you have to click through a wizard kind of experience to be able to set them up. But the most limiting factor is that you cannot see the actual FetchXML of an active recurring bulk delete and you cannot input FetchXML directly into the bulk delete.
Having worked with this wizard you might also have noticed something a bit off. If you create a view which shows all contacts that have no activities. Then this will work when using it in the system like a view. However, if you try to use this view in a bulk delete it will “simplify” this and remove that part of the query. My thought that this was a limitation in the UI based on the fact that it is very old and hasn’t gotten any love for many years (decades). My assumption was hence that creating a bulk delete via the API would allow me to create bulk deletes that were based on FetchXMLs that you couldn’t even input from the UI. These were the reasons for us starting to create this plugin and it was so useful that I used it in debug mode for several weeks before finalizing it and publishing it.
Now we have released the first version and you can download it directly from XTB. I would like to give a huge thanks to Jonas Rapp who helped us out a lot with both connection to his tool FetchXML Builder but also the general setup of the plugins and the details of getting it approved as a Plugin for XTB.
If you have any suggestions, comments or otherwise, leave them on the GitHub repo https://github.com/crmgustaf/BulkDeleteManager or down below. We already have a bunch of stuff we want to do. Ah, yes, and the bug we found, it seems that the outer joins that was a rather recent addon to FetchXML is not supported by the actual platform. Hence the UI and the platform match in that perspective. Just to make it clear, what happens is that you input a FetchXML saying “All contacts with no activities” or something like that, which it will simplify to “All contacts” which is not really what you want.
As it is supported to create bulk delete jobs via the API, I do think that this still can be seen as a bug as there is no clear documentation on this or even a control when creating the bulk delete job with a FetchXML that will be incorrectly parsed. My suggestion is hence to implement the new FetchXML parser in Bulk Delete functionality, at least on the platform side. With the current setup, it is very possible that bulk deletes are created that remove a lot more than what was initially intended which can be very damaging to any organization. And from a GDPR perspective, this type of query is rather common, as it might be definied that contacts with no cases can only be stored for 2 years, but with cases for 10. To remove the ones without the cases, you would make the question “Remove all contacts with no cases with created on > 2 years”. This would then cause all contacts with created on > 2 years to be removed regardless of if it has a case or not.
To inform users of this, we have added a warning, every time a new bulk delete job is to be saved. Hopefully Microsoft will fix this soon.
On the page in Microsoft docs where they discuss API Service Protections there is towards the end of the page a part which gives some recommendations. Some are great, like the recommendation to use many threads and remove the affinity cookie, however when I read it I really bounced at the recommendation that batching shouldn’t be used. That just didn’t rime with my experiences of doing heavy dataloads to dataverse. So I thought I might just test to see if it was true or not by creating a simple script in SSIS with Kingswaysoft. My results, using batching compared to not using it gives more than a 10x performance increase. Continue reading to understand more about how I tested this and some deeper analysis.
Parameters and excel
The first thing I did was to create an excel sheet for storing all the results. I really did have to think about the different parameters that could affect the result, so I chose the following columns:
Dataload – how many records. This needed to be a bit larger to make sure that the throttling time of 5 min was passed.
Operation – Different dataverse operation take different amounts of time. For instance, creates are typically rather fast, but deletes, depending on table, can be a lot slower as the platform might execute cascading deletes based on one single delete. For instance, if you remove a contact with 100 tasks connected to it with the regarding relation set to “parental” or “cascade delete” it will actually remove all the 100 tasks. If set to “remove link”, the platform has to make an update to each of the tasks, removing the link. There are also special operations like merge which are rather complex.
Table – There is a large difference between the different tables. Some of the OOB tables have a lot of built in logic and really small non-activity custom tables can be a lot quicker to create, update or delete.
Threads – How many threads were used.
Batch – The size of the batches being used.
Duration / Duration (ms) – Duration is where I input the duration as a normal time. I created a calculation to calculate the corresponding amount of milliseconds.
Time per record (ms) – This is the division of the duration in ms with the total number of records. During this first test, I always used 100 000 records as the dataload, but it could be interesting in the future to see the differences between different dataloads, with all else being the same. This is also the main output from this test.
Strategy – It is possible to have different strategies. In this first version I just ran everything at once, hence I called the strategy “All at once”. Different strategies might be “5 on, 5 off”, meaning that you design the script to run superfast for 5 minutes, the throttling limit, and then stop and do nothing for 5 minutes and then loop this. Not always possible to use that kind of strategy, but for massive deletes of for instance market list members (cannot be removed with bulk delete) that might be an option.
API – There are currently two APIs that can be used. The new WebAPI which uses JSON payloads and the older SOAP API which used XML payloads. It stands to reason that the smaller JSON payload should cause the WebAPI to be faster than the corresponding SOAP API. However SSL encryption also causes the data to be compressed, which might make these differences smaller than expected. There is also a server side aspect to this, as the APIs will run through different parts of the code on the server side which could affect the performance.
No of columns – How many columns are being sent to the API. Of course there would be a difference if you send a create message with 3 columns compared to 30. Hence this is a relevant point. It is still a bit rough, as there is a huge difference in creating a boolean record, a 2000 character nvarchar or a lookup. This could also be something that was adapted.
Existing records – How many records existed in the system prior to running this? Not sure if this makes any difference, in other words, everything else equal, would it take more time to write 100k records to a system with 0 records or one with 10M records? As I don’t know, and cannot rule it out, I added it.
Latency (ms) – Daniel Cai, Founder of Kingswaysoft, always recommend that the SSIS script with Kingswaysoft be run “as close as possible to the dataverse”. That does in other words imply that the latency to the server affect the performance. Do calculate this, I used diag.aspx from the computer running the script.
Location – Which geo is the instance located in. This is more for general information, the latency is really the important factor here. The throughput might also have some affect if you are using a really bad line to the dataverse. I was using a wired 1 GBit line. In this test, I was using an instance I got hold of as MVP, which is located in the US and my own stationary computer at home (a AMD Ryzen 9 3900X 12-Core Processor 3.79 GHz with 32 GB of memory). Hence the latency was rather high and not in line with Daniel Cai’s recommendations. It is hence also something to investigate further.
No of users – As I, and some others in the community have described, throttling is based on a per-user and per-front end server basis. Hence utilizing several service principals/application users can effectivly multiply the throughput. In this test I used just one.
Instance type – It is well known that sandbox instances do not have the same performance as a production instance. If you find Microsoft support on a happy day and you are working with a larger (no of licenses) instance, you might also get them to relax the throttles a bit, especially if you mention that you are doing a migration. As these factors strongly affect the performance of large dataloads, I did have to add this. During this test I was using a non-enhanced production instance, in other words, a production instance on which no throttles had been relaxed.
DB Version – The final parameter that I thought might affect this is the actual version of the dataverse instance. As improvements and god forbidd sub optimal “improvements”, can cause enhancements or degradations of the performance, this is necessary to document.
For setup of create tests in SSIS with the Kingswaysoft addons I used a dataspawner (productivity pack) to generate the data. I then just sent this directly to the CDS Destination.
And the CDS Destination config
After each run, I checked the log from SSIS to see how long the entire process took. Due to the fact that I have a computer with many threads and for this case, enough memory, it is my perception that most of the threads allocated were also used.
What are the results? This is a picture of the excel:
As you can see I did try both Create and Delete operations, but the results are rather obvious.
20 threads/20 per batch of both create and delete, took around 45 minutes
Reducing to 16/10 made only a minor difference – 48 minutes
Microsofts recommendation of not using batching, ie 20 threads/1 batch – took over 10 h, both for delete and create.
Using only 1 thread with 1 batch was more or less the same as using 20/1
1 thread with 20 in every batch (1/20) took almost 5 h, which is around half the 1/1 or 20/1
I think the results clearly show, that Microsoft docs are currently incorrect in their recommendation to not use batching. Perhaps they will update this soon. From an entitlement perspective, one needs to understand the additional cost of the “batch unpacking” request that is made. With 20 in every batch, this is an overhead of 1/21 but if you would lower the batches to 4, it would be 1/5. Hence using as large batch as you can, without loosing performance, is generally what I would recommend.
As I have implied in this article, there are a lot of other parameters to investigate in the API. I have a hunch that a create with 10 lookups compared to 10 textfields, will also make a significant difference, but I will need to test it.
Also do consider the request timeout. When working with complex and large batches, one request may taker quite some time. You will know, however, as it will return a timeout exception if you exceed it. Note that some records in that batch may have been written anyway. Just that your client wasn’t waiting around for the answer.
I do also encourage others to try out other parameters in the API. What is really optimal from many different aspects. From a mathematical perspective this can really be seen as a multidimentional surface where we are attempting to find the highest points. I have now started this journey, and I hope it was an interesting read. Please leave a comment, if you have any experience to share or just want to comment.