I have enabled the Query Store for two of my databases (acceptance and production), which are both running on the same instance of SQL Server 2016 Standard Edition.
The Query Store records query history on the acceptance database, but on the production database it does not record any data.
The two databases are configured identically, with the exception of mirroring that is only enabled for the production database. The mirroring mode used is "High safety with automatic failover (synchronous)".
Query Store feature is introduced to monitor performance and is still evolving. There are certain known limitations around it.
As of now, it does not work on Read-Only databases (Including read-only AG replicas). Since readable secondary replicas are read-only,
the query store on those secondary replicas is also read-only. This
means runtime statistics for queries executed on those replicas are
not recorded into the query store.
Known Limitations of Query Store
No Information on Who ran / Which Program ran since Query Store provides no data related to Application Name.
Query store cannot be enabled for system databases like master or tempdb
Lack of Control - Multiple DBAs could change settings
Data is stored in Primary filegroup
Data captured at batch level is not available.
Query Wait Stats Store available starting from SQL Server 2017
Arbitrary values are not allowed for INTERVAL_LENGTH_MINUTES in Statistics Collection Interval. (1, 5, 10, 15, 30, 60, or 1440 minutes)
Related
Typically on an on-premise SQL server ETL workflow via SSIS, we load data from anywhere into staging tables and then apply validation and transformations to load/merge them into downstream data warehouse tables.
My question is if we should do something similar on Azure, where we have set of staging tables and downstream tables in azure SQL database or use azure storage area as staging and move data from there into final downstream tables via ADF.
As wild is it may seem, we also have a proposal to have separate staging database and downstream database, between which we move using ADF.
There are different models for doing data movement pipelines and no single one is perfect. I'll make a few comments on the common patterns I see in case that will help you make decisions on your application.
For many data warehouses where you are trying to stage in data and create dimensions, there is often a process where you load the raw source data into some other database/tables as raw data and then process it into the format you want to insert into your fact and dimension tables. That process is complicated by the fact that you may have data arrive late or data that is corrected on a later day, so often these systems are designed using partitioned tables on the target fact tables to allow re-processing of a partition worth of data (e.g. a day) without having to reprocess the whole fact table. Furthermore, the transformation process on that staging table may be intensive if the data itself is coming in a form far away from how you want to represent it in your DW. Often in on-premises systems, these are handled in a separate database (potentially on the same SQL Server) to isolate it from the production system. Furthermore, it is sometimes the case that these staging tables are re-creatable from original source data (CSV files or similar), so it is not the store of record for that source material. This allows you to consider using simple recovery mode on that database (which reduces the Log IO requirements and recovery time compared to full recovery). While not every DW uses full recovery mode for the processed DW data (some do dual load to a second machine instead since the pipeline is there), the ability to use full recovery plus physical log replication (AlwaysOn Availability Groups) in SQL Server gives you the flexibility to create a disaster recovery copy of the database in a different region of the world. (You can also do query read scale-out on that server if you would like). There are variations on this basic model, but a lot of on-premises systems have something like this.
When you look at SQL Azure, there are some similarities and some differences that matter when considering how to set up an equivalent model:
You have full recovery on all user databases (but tempdb is in simple recovery). You also have quorum-commit of your changes to N replicas (like in Availability Groups) when using v-core or premium dbs which matters a fair amount because you often have a more generic network topology in public cloud systems vs. a custom system you build yourself. In other words, log commit times may be slower than your current system. For batch systems it does not necessarily matter too much, but you need to be careful to use large enough batch sizes so that you are not waiting on the network all the time in your application. Given that your staging table may also be a SQL Azure database, you need to be aware that it also has quorum commit so you may want to consider which data is going to stay around day-over-day (stays in SQL Azure DB) vs. which can go into tempdb for lower latencies and be re-created if lost.
There is no intra-db resource governance model today in SQL Azure (other than elastic pools which is partial and is targeting a different use case than DW). So, having a separate staging database is a good idea since it isolates your production workload from the processing in the staging database. You avoid noisy neighbor issues with your primary production workload being impacted by the processing of the day's data you want to load.
When you provision machines for on-premises DW, you often buy a sufficiently large storage array/SAN that you can host your workload and potentially many others (consolidation scenarios). The premium/v-core DBs in SQL Azure are set up with local SSDs (with Hyperscale being the new addition where it gives you some cross-machine scale-out model that is a bit like a SAN in some regards). So, you would want to think through the IOPS required for your production system and your staging/loading process. You have the ability to choose to scale up/down each of these to better manage your workload and costs (unlike a CAPEX purchase of a large storage array which is made up front and then you tune workloads to fit into it).
Finally, there is also a SQL DW offering that works a bit differently than SQL Azure - it is optimized for larger DW workloads and has scale-out compute with the ability to scale that up/down as well. Depending on your workload needs, you may want to consider that as your eventual DW target if that is a better fit.
To get to your original question - can you run a data load pipeline on SQL Azure? Yes you can. There are a few caveats compared to your existing experiences on-premises, but it will work. To be fair, there are also people who just load from CSV files or similar directly without using a staging table. Often they don't do as many transformations, so YMMV based on your needs.
Hope that helps.
I have a SQL Server 2012 database which currently used as a transactional database and reporting database. The application reads/writes into the same database and the reports are also generated against the same database.
Due to some performance issue, I have decided to maintain the two copies of the database. One will be a transactional database which will be accessed by the application. The other database will be the exact copy of the transactional database and it will only be used by the reporting service.
Following are the requirements:
The reporting database should be synched with transactional database in every one hour. That is, the reporting database can have stale data for maximum of 1 hour.
It must be read-only database.
The main intension is NOT recovery or availability.
I am not sure which strategy, transactional log shipping, mirroring or replication, will be best suited in my case. Also if I do the synch operation more frequently (say in every 10 minutes), will there be any impact on the transactional database or the reporting service?
Thanks
I strongly recommend you to use a standby database in readonly state. And every 15 minutes your sqlserveragent has a scheduled job to: a) generate a new .trn logfile within main db, and b) restore it into standby one(your reports db). The only issue is: using this technique your session will be disconnected while agent restores the .trn logfile. But if you can stop the restore job, run your reports and then reactivate it, there is no problem. Seems to be exactly what you need. Or if your reports are fast to run, probably will not be disconnected...if im not wrong restore job can also be configured to wait opened session to finish or to close it. I can check it this last doubt for you tomorrow if you don't find..
Once it is running in the same sql server instance, you don't have to worry about extra licensing...
I want to understand the best approach for SQL Server architecture on production environment.
Here is my problem:
I have database which has on average around 20,000 records being inserted every second in various tables.
We have reports also implemented for the same, now what's happening is whenever reports is searched by user, performance of other application steeps down.
We have implemented
Table Partitioning
Indexing
And all other required things.
My question is: can anyone suggest an architecture that have different SQL Server databases for reports and application, and they can sync themselves online every time when new data is entered in master SQL Server?
Some what like Master and Slave Architecture. I understand Master and Slave architecture, however need to get more idea around it.
Our main tables are having around 40 millions rows (table partitioning done)
In SQL Server 2008R2 you have database mirroring and replication available, which will keep two databases in sync.
A schema which is efficient for OLTP is unlikely to be efficient for large volume reporting. The 'live' and 'reporting' databases should have different schema with an ETL process moving data from one to the other. I'd would like to negotiate with the business just how synchronised the reporting database needs to be. If the reports are processing large amounts of data they will take some time to run so a lag in data replication will not be noticed, I would suggest. In extremis you could construct a solution using Service Broker to move the data and processing on the reporting server to distribute it amonst the reporting tables.
The numbers you quote (20,000 inserts per second, 40 millions rows in largest table) suggests a record doesn't reside in the DB for long. You would have a significant load performing DELETEs. Optimising these out of peak hours could be sufficient to solve your problems.
If my source data is on Server 1, the cube is on Server 2, and I manually initiate cube/dimension processing in BIDS on my local machine, where exactly does processing occur?
With the traditinal MOLAP storage mode, the query (for raw data) will be executed on Server 1, the aggregation calculations will be done by the server which hosts the SSAS instance (Server 2).
The ROLAP mode will create indexed views on the source system and queries those views, so the 'calculations' are performed by the database engine (Server 1).
With HOLAP mode, it depends on the query since the aggregations are stored in the AS database (calculated by the AS engine while processing), while raw data is accessed from the source system (during a drill-down for example).
Basically you can say: All information stored in the AS database, the calculations are made by the AS engine.
For more information about storage modes see http://msdn.microsoft.com/en-us/library/ms174915.aspx
I have a online Database which will be updated Daily from various Sources.
I need to have a local Database with some tables from Server Database which have to check for any changes or new rows in tables in server and update the local Database for particular Intervals of Time. How can I Achieve this???
You may want to look into SQL Server Replication.
Replication will manage the data synchronization between the two copies of your database. You can configure replication for any tables in the database, including all tables. Replication will take care of checking for updates, adds and deletes from the Server Database and transfer the changes to the local database.
You can setup replication to update the local database at near-real-time or you can schedule periodic updates.
Replication is a high-maintenance solution. It's designed to maintain two copies of the same database with significant reliability. This makes replication a good solution when you must avoid data problems or recover from problems with little to no data loss.
If you don't require the high-maintenance solution, then SQL Server Integration Services (SSIS) may be a good alternative. With SSIS, you develop the data transfer and data management solution. Along with managing data problems, you design the solution to identify data adds, deletes and updates.