Azure Analysis Services Out of Memory - azure

We have our data stored in azure analysis services B1 Tier. Power bi report deployed in a premium workspace is
consuming the Model using live query. We are facing issue with AAS that the model is not releasing the memory
once the report is consumed by the users. Memory is released only after the service is restarted. Is this because of the B1 tier that we use? Is there any better way to
handle the caching ? Any help is appreciated
Thanks

Analysis Services releases memory as required, based on the demands placed on the model. It does not do this as soon as a report is rendered as this is not how it is designed to work and would negatively impact performance.
You see the memory utilisation drop after restarting because the cached data generated by the Vertipaq engine is flushed, though this will be rebuilt as soon as you start querying the model again.
It sounds like you have some rather intensive dashboard measures or too much data for the Basic tier, which is a general-purpose tier and recommended for production solutions with small Tabular models, limited user concurrency and simple data refresh requirements.
Your solution here is to do some combination of:
Upgrade your AAS instance
Reduce the amount of data in your model
Reduce the number of users
Reduce the complexity of your reports
Optimise your DAX measures

Related

PowerBi Embedded and SQL Datewarehouse in different locations

I want to create Webapps with PowerBI Embedded from the german central datacenter. Unfortnuatly this service is not available and i don't know when it will become available.
Therefore my idea is to migrate PowerBi Embedded later and start with all other services located in german central. Is this possible or strongly recommended to have the PowerBi Embedded service and the azure SQL Datawarehouse in the same place?
When you place your data source (SQL Data warehouse) and your BI tool (Power BI) in different datacenters there are two things that should be mindful of:
Latency and network speed between the data centers may affect the performance of your BI solution significantly (in a negative way), especially if you are manipulating and analysing large amounts of data. It depends a little on how you set up your Power BI embedded. If you use DirectQuery then you will be hit with the latency penalty every time the query runs (whenever you look at your report), if not then you will only be hit with the latency when you refresh your imported data. However, without DirectQuery you may have to import more data in order to aggregate etc from the imported dataset.
Egress, you pay for traffic going out of data centers. If you continuously send large amounts of data between two data centers then the egress cost can be a factor for you. In a normal setup the traffic charges are almost negliable, but if your BI solution streams a lot of data on every refresh then it may build up to a lot of money.

Azure SQL Performance

I have an Azure SQL Database S1 Standard (20DTU) and I'm seeing vast variations in performance. I have a number of queries that power a set of reports on a small web site. When running these queries through the Management Studio the performance varies from 0 to 60 seconds. The site isn't public so there's no traffic yet - only me. Looking at the DTU usage, it spikes at around 50%. Can anyone help me understand where the performance difference is coming from?
You can follow the link http://social.technet.microsoft.com/wiki/contents/articles/1104.troubleshoot-and-optimize-queries-with-azure-sql-database.aspx to troubleshoot your query performance. Enabling Query data store is another option if you are on V12.
There could be various factors that imapcts query performance, buffer pool, sql instance restarts because of azure maintenance (which clears buffer pool) etc.

New Azure SQL Database Services, how scalable and what are DTUs

The new new Azure SQL Database Services look good. However I am trying to work out how scalable they really are.
So, for example, assume a 200 concurrent user system.
For Standard
Workgroup and cloud applications with "multiple" concurrent transactions
For Premium
Mission-critical, high transactional volume with "many" concurrent users
What does "Multiple" and "Many" mean?
Also Standard/S1 offers 15 DTUs while Standard/S2 offers 50 DTUs. What does this mean?
Going back to my 200 user example, what option should I be going for?
Azure SQL Database Link
Thanks
EDIT
Useful page on definitions
However what is "max sessions"? Is this the number of concurrent connections?
There are some great MSDN articles on Azure SQL Database, this one in particular has a great starting point for DTUs. http://msdn.microsoft.com/en-us/library/azure/dn741336.aspx and http://channel9.msdn.com/Series/Windows-Azure-Storage-SQL-Database-Tutorials/Scott-Klein-Video-02
In short, it's a way to understand the resources powering each performance level. One of the things we know when talking with Azure SQL Database customers, is that they are a varied group. Some are most comfortable with the most absolute details, cores, memory, IOPS - and others are after a much more summarized level of information. There is no one-size fits all. DTU is meant for this later group.
Regardless, one of the benefits of the cloud is that it's easy to start with one service tier and performance level and iterate. In Azure SQL Database specifically you can change the performance level while you're application is up. During the change there is typically less than a second of elapsed time when DB connections are dropped. The internal workflow in our service for moving a DB from service tier/performance level follows the same pattern as the workflow for failing over nodes in our data centers. And nodes failing over happens all the time independent of service tier changes. In other words, you shouldn’t notice any difference in this regard relative to your past experience.
If DTU's aren't your thing, we also have a more detailed benchmark workload that may appeal. http://msdn.microsoft.com/en-us/library/azure/dn741327.aspx
Thanks Guy
It is really hard to tell without doing a test. By 200 users I assume you mean 200 people sitting at their computer at the same time doing stuff, not 200 users who log on twice a day. S2 allows 49 transactions per second which sounds about right, but you need to test. Also doing a lot of caching can't hurt.
Check out the new Elastic DB offering (Preview) announced at Build today. The pricing page has been updated with Elastic DB price information.
DTUs are based on a blended measure of CPU, memory, reads, and writes. As DTUs increase, the power offered by the performance level increases. Azure has different limits on the concurrent connections, memory, IO and CPU usage. Which tier one has to pick really depends upon
#concurrent users
Log rate
IO rate
CPU usage
Database size
For example, if you are designing a system where multiple users are reading and there are only a few writers, and if your application middle tier can cache the data as much as possible and only selective queries / application restart hit the database then you may not worry too much about the IO and CPU usage.
If many users are hitting the database at the same time, you may hit the concurrent connection limit and requests will be throttled. If you can control user requests coming to the database in your application then this shouldn't be a problem.
Log rate: Depends upon the volume of the data changes (including additional data pumping in the system). I have seen application steadily pumping the data vs data being pumped all at once. Selecting the right DTU again depends upon how one can do throttling at the application end and get steady rate.
Database size: Basic, standard, and premium has different allowed max sizes, and this is another deciding factor. Using table compression kind of features helps reducing the total size, and hence total IO.
Memory: Tuning the expesnive queries (joins, sorts etc), enabling lock escalation / nolock scans help controlling the memory usage.
The very common mistake people usually do in database systems is scaling up their database instead of tuning the queries and application logic. So testing, monitoring the resources / queries with different DTU limits is the best way of dealing this.
If choose the wrong DTU, don't worry you can always scale up/ down in SQL DB and it is completely online operation
Also unless a strong reason migrate to V12 to get even better performance and features.

Azure table storage and caching

Is it worth caching data from Azure Table storage with the Azure Caching Preview?
Or is the table storage fast enough in large scale applications?
Thanks
The short answer is it depends. In the application I am currently working on there is some information that we use caching for to handle both the latency of retrieving data from Table Storage and to accommodate the desired number of transactions per second.
We started out serving the information from Table Storage and moved to caching only when our performance requirements dictated it. I'd recommend a similar approach: make it work, then make it fast.
In addition to what Robert said, you should also consider following points:
Windows Azure Table Storage allows to store up to 100TB in size (in chunks). At first glance, that size of data may seem overwhelming. However, Table Storage can be partitioned. Each partition of Table Storage can be moved to a separate server by the Azure controller thereby reducing the load on any single server and improving performance.
If you have very high load on your application, you cache with frequent inserts will approach the maximum cache size very quickly and then cache items eviction process starts. In most cases frequent inserts into cache and frequent cache items eviction processes end up with performance degradation instead of improvement. Then you would need to increase cache maximum size, which in turn will affect your application cost (sometimes this might be a blocker).
Last but not least, you can access Windows Azure Table Storage data using the OData protocol and LINQ queries with WCF Data Service .NET Libraries; you do not have that ability with Azure Cache.
Please bear in mind that those points may or may not be valid in your case. All depends on your system architecture; expected load etc.
I hope my answer will help you in making good system architecture decisions.

Is storing data in Windows Azure cheaper when using RavenDB rather than SQL Azure?

SQL Azure storage is a lot more expensive than Windows Azure Storage. Would implementing a no-sql solution like RavenDB allow me to store data on the cheaper Azure Storage?
Are there other things to consider, like backup, speed or security?
Thank you.
You have to consider that with SQL Azure you not only get the storage, but the database server too. If you implement RavenDB, you will will need a worker role to host it in and, in order to allow for failure of that worker role, another worker role (replica), which also doubles up the storage.
Bear in mind that with SQL Azure you get a highly available (3x replicated with failover) SQL solution that surfaces a familiar (ADO.NET) API. Make your choices based on aspects other than storage cost, such as operational effort and development effort. If you choose RavenDB it should be because of the potential cost savings in development effort (because of the closeness on the document API to the object graph) and operational cost, because RavenDB is 'administered' as part of the application. Cost of storage of actual bytes, particularly at scale, is a marginal consideration.
Adding a bit to #Simon's answer: When considering Table Storage and its low cost, also consider whether you can use it directly, instead of going with an installed-and-managed-by-you NoSQL database engine. As it stands, Table Storage offers a schemaless solution that lets you store essentially a property bag within a row, indexed by partitionkey+rowkey. Does that work for you? Could you work with a few extra tables to give you additional indexing? If so, your storage cost is going to be really low (and still durable, triple-replicated).
If you find yourself writing significant code to manage Table Storage, then it may be more efficient to invest in the Compute instances needed to run RavenDB. When considering this, also consider that you'll likely want larger VM sizes if you're moving significant data (as you get approx. 100Mbps per core). A database like MongoDB, working with memory-mapped files, really ramps up speed-wise with more RAM. Not sure if this is the same with RavenDB.

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