I am at the planning stage of a web application that will be hosted in Azure with ASP.NET for the web site and Silverlight within the site for a rich user experience. Should I use Azure Tables or SQL Azure for storing my application data?
Azure Table Storage appears to be less expensive than SQL Azure. It is also more highly scalable than SQL Azure.
SQL Azure is easier to work with if you've been doing a lot of relational database work. If you were porting an application that was already using a SQL database, then moving it to SQL Azure would be the obvious choice, but that's the only situation where I would recommend it.
The main limitation on Azure Tables is the lack of secondary indexes. This was announced at PDC '09 and is currently listed as coming soon, but there hasn't been any time-frame announcement. (See http://windowsazure.uservoice.com/forums/34192-windows-azure-feature-voting/suggestions/396314-support-secondary-indexes?ref=title)
I've seen the proposed use of a hybrid system where you use table and blob storage for the bulk of your data, but use SQL Azure for indexes, searching and filtering. However, I haven't had a chance to try that solution yet myself.
Once the secondary indexes are added to table storage, it will essentially be a cloud based NoSQL system and will be much more useful than it is now.
Despite similar names SQL Azure Tables and Table Storage have very little in common.
Here are a two links that might help you:
Table Storage, a 100x cost factor
Fat Entities on Table Storage
Basically, the first question should wonder about is Does my app really need to scale? If not, then go for SQL Azure.
For those trying to decide between the two options, be sure to factor reporting requirements into the equation. SQL Azure Reporting and other reporting products support SQL Azure out of the box. If you need to generate complex or flexible reports, you'll probably want to avoid Table Storage.
Azure tables are cheaper, simpler and scale better than SQL Azure. SQL Azure is a managed SQL environment, multi-tenant in nature, so you should analyze if your performance requirements are fit for SQL Azure. A premium version of SQL Azure has been announced and is in preview as of this writing (see HERE).
I think the decisive factors to decide between SQL Azure and Azure tables are the following:
Do you need to do complex joins and use secondary indexes? If yes, SQL Azure is the best option.
Do you need stored procedures? If yes, SQL Azure.
Do you need auto-scaling capabilities? Azure tables is the best option.
Rows within an Azure table cannot exceed 4MB in size. If you need to store large data within a row, it is better to store it in blob storage and reference the blob's URI in the table row.
Do you need to store massive amounts of semi-structured data? If yes, Azure tables are advantageous.
Although Azure tables are tremendously beneficial in terms of simplicity and cost, there are some limitations that need to be taken into account. Please see HERE for some initial guidance.
One other consideration is latency. There used to be a site that Microsoft ran with microbenchmarks on throughput and latency of various object sizes with table store and SQL Azure. Since that site's no longer available, I'll just give you a rough approximation from what I recall. Table store tends to have much higher throughput than SQL Azure. SQL Azure tends to have lower latency (by as much as 1/5th).
It's already been mentioned that table store is easy to scale. However, SQL Azure can scale as well with Federations. Note that Federations (effectively sharding) adds a lot of complexity to your application. I'm also not sure how much Federations affects performance, but I imagine there's some overhead.
If business continuity is a priority, consider that with Azure Storage you get cheap geo-replication by default. With SQL Azure, you can accomplish something similar but with more effort with SQL Data Sync. Note that SQL Data Sync also incurs performance overhead since it requires triggers on all of your tables to watch for data changes.
I realize this is an old question, but still a very valid one, so I'm adding my reply to it.
CoderDennis and others have pointed out some of the facts - Azure Tables is cheaper, and Azure Tables can be much larger, more efficient etc. If you are 100% sure you will stick with Azure, go with Tables.
However this assumes you have already decided on Azure. By using Azure Tables, you are locking yourself into the Azure platform. It means writing code very specific to Azure Tables that is not just going to port over to Amazon, you will have to rewrite those areas of your code. On the other hand programming for a SQL database with LINQ will port over much more easily to another cloud service.
This may not be an issue if you've already decided on your cloud platform.
I suggest looking at Azure Cache in combination with Azure Table. Table alone has 200-300ms latencies, with occasional spikes higher, which can significantly slow down response times / UI interactivity. Cache + Table seems to be a winning combination, for me.
For your question, I want to talk about how to decide with logic choose SQL Table and which need to use Azure Table.
As we know SQL Table is a relational database engine. but if you have a big data in one table the SQL Table is not applicable, because SQL query get big data is slow.
At this time you can choose Azure Table, the Azure Table query is so fast then SQL Table for big data, for example, in our website, someone subscribed many articles, we make the article as feed to user, every user have a copy of article title and description, so in the article table there are lots of data, if we use SQL Table, each query execution maybe take more than 30 seconds. But in Azure Table get users article feed by PartitionKey and RowKey is so fast.
From this example you may know how to choose between in SQL Table and Azure Table.
I wonder whether we are going to end up with some "vendor independent" cloud api libraries in due course?
I think that you have first to define what your application usage funnels are. Will your data model be subjected to frequent changes or it is a stable one? You have to be able to perform ultra fast inserts and reads are not so complicated? Do you need advance google like search? Storing BLOBS?
Those are the questions (and not only) that you have to ask and answer yourself in order to decide if you are more likely going to use NoSql or SQL approach in storing your data.
Please consider that both approaches can easily coexist and can be extended with BLOB storage as well.
Both Azure Tables and SQL Azure are two different beasts.Both are meant for different scenarios, one con to azure table is that you cannot move from azure to any other platform, unless you write providers in your code that can handle such shifts.
Related
The project I am working on as an architect has got an IOT setup where lots of sensors are sending data like water pressure, temperature etc. to an FTP(cant change it as no control over it due to security). From here few windows service on Azure pull the data and store it into an Azure SQL Database.
Here is my observation with respect to this architecture:
Problems: 1 TB limit in Azure SQL. With higher tier it can go to 4 TB but that's the max. So it does not appear to be infinitely scalable plus with size, the query issues could be a problem. Columnstore index and partitioning seem to be options but size limitation and DTUs is a deal breaker.
Problem-2- IOT data and SQL Database(downstream storage) seem to be tightly coupled. If some customer wants to extract few months of data or even more with millions of rows, DB will get busy and possibly throttle other customers due to DTU exhaustion.
I would like to have some ideas on possibly scaling this further. SQL DB is great and with JSON support it is awesome but a it is not horizontally scalable solution.
Here is what I am thinking:
All the messages should be consumed from FTP by Azure IOT hub by some means.
From the central hub, I want to push all messages to Azure Blob Storage in 128 MB files for later analysis at cheap cost.
At the same time, I would like all messages to go to IOT hub and from there to Azure CosmosDB(for long term storage)\Azure SQL DB(Long term but not sure due to size restriction).
I am keeping data in blob storage because if client wants or hires a Machine learning team to create some models, I would prefer them to pull data from Blob storage rather than hitting my DB.
Kindly suggest few ideas on this. Thanks in advance!!
Chandan Jha
First, Azure SQL DB does have Hyperscale which is much larger than 4TB. That said, there is a tipping point where it makes sense to consider alternative architectures when you get to be bigger than what one machine can handle for your solution. While CosmosDB does give you a horizontal sharding solution, you can do the same with N SQL Databases (there are libraries to help there). Stepping back, it is actually pretty important to understand what you want to do with the data if it were in a database. Both CosmosDB and SQL DB are set up for OLTP-style operations (with some limited forms of broader queries - SQL DB supports columnstore and batch mode, for example, which means you could do a reasonably-sized data mart just fine there too). If you are just storing things in the database in the hope of needing to support future data scientists, then you may or may not really need either of these two OLTP stores.
Synapse SQL is set up for analytics and generally has support to read from data in formats in Azure Storage. So, this may be a better strategy if you want to support arbitrarily-large IoT data and do analytics/ML processing over it.
If you know your solution will never be above , you may not need to consider something like Synapse, but it is set up for those scenarios if you are of sufficient size.
Option - 1:
Why don't you extract and serialize the data based on the partition id (device id), send it over the to IoT hub, where you can have the Azure Functions or Logic Apps that de-serializes the data into files that are stored in the blob containers.
Option - 2:
You can also attempt to create a module that extracts the data into excel file, which is then sent to the IoT hub to be stored in the storage containers.
First time using Azure. I have a basic node js bot built with Microsofts Bot Framework, and deployed on Azure. What are my options for storage?
I will most likely just be needing simple key:value storage. Mongodb was my first though but I dont think Azure supports it nativeley.
That said, what are my options for storage on Azure? I usual shy away from MySQL just from preference, but theres no actual reason that wouldnt work either.
Take a look at Azure Table Storage for a NoSql solution
Table storage is a key/attribute store with a schemaless design. Because Table storage is schemaless, it's easy to adapt your data as the needs of your application evolve. Access to data is fast and cost-effective for all kinds of applications. Table storage is typically significantly lower in cost than traditional SQL for similar volumes of data.
I have read here and on other post and forums that the best place to save session state in Azure is AppFabric Cache, but I find that very expensive and would like to give a go to either table storage or a SQL database.
I read that a SQL database will be faster but I can't understand why it would be. Surely the SQL database will cache hot data in memory, but I would expect Table Storage to also do that (does it?). Otherwise I don't see why a SQL database would be faster at retrieving a row than Table Storage, in the end both are just retrieving data from a local disk based on a key. I would even expect that because Table Storage scales up well and automatically (vs a SQL databases that needs to be partitioned manually), it would be preferable as session state isn't a good candidate for local caching.
Does anyone have any experience or opinion on this?
thanks
Charles
You mentioned AppFabric Cache, which is a retired service. Regarding SQL vs Table: There isn't really a right answer to this. If you want to spin up a SQL Database instance (running about $2.50 monthly for a Basic-tier database), you'll have 2GB to work with. With Table storage, you'll pay about $0.15 for the same storage. Then there is Redis cache, your own cache (such as memcached), Azure Managed Cache service, etc. Performance-wise, you'd need to do some benchmarking to see how each performs. Any of these would work with Virtual Machines, Cloud Services (web/worker roles), and Web Sites, as they all have very well-defined APIs and, if using ASP.NET MVC, good provider support. Each has different capacity limits and different pricing.
One thing with Table storage: each entity (row) is limited to 1MB, so if you're attempting to cache > 1MB per cache entry, you'll need to consider another option.
#Gaurav mentioned in-role cache. This is a great way to use extra memory in your web/worker role instances. However: It's limited to web/worker Cloud Services; it doesn't help with Web Sites or Virtual Machines. For those, you really need some type of independent cache provider.
I need to consider a database to store large volumes of data. Though my initial requirement is to simply retrieve chunks of data and save them in excel file, I am expecting more complex use cases for this data in future where the data will be consumed by different applications especially for analytics - hence need to use aggregated queries.
I am open to use either cloud based storage or on-premises storage. I am considering azure storage table (when there is a need to use aggregated data, I can have a wrapper service + cache around azure table storage but eventually will end up with nosql type storage) and on-premises MongoDB. Can someone suggest pros and cons of saving large data in azure table storage Vs on-premises MongoDB/couchbase/ravendb? Cost factor can be ignored.
I suspect this question may end up getting closed due to its broad nature and potential for gathering more opinions than fact. That said:
This is really going to be an app-specific architecture issue, dealing with latency and bandwidth, as well as the need to maintain on-premises servers and other resources. On-prem, you'll have full control of your hardware resources, but if you're doing high-volume querying against your database, from the cloud, your performance will be hampered by latency and bandwidth. Cloud-based storage (whether in MongoDB or any other database) will have the advantage of being neighbors with your app if set up in the same data center.
Keep in mind: Any persistent database store will need to back its data in Azure Storage, meaning a mounted disk backed by Blob storage. You'll need to deal with the 1TB-per-disk size limit (expanding to 16TB on an 8-core box via stripe), and you'll need to compare this to your storage needs. If you need to go beyond 16TB, you'll need to either shard, go with 200TB Table storage, or go with on-prem MongoDB. But... MongoDB and Table Storage are two different beasts, one being document-based with a focus on query strength, the other a key/value store with very high speed discrete lookups. Comparing the two on the notion of on-prem vs cloud is secondary (in my opinion) to comparing functionality as it relates to your app.
I have an application that looks up data for a page. The data is looked up by primary key and row key in table storage.
I am considering SQL Azure storage. Is there some advantage in my going to this kind of storage being that the look up will always be very direct. Note that I do NOT need any reporting. ALL I want is single row look up
I am considering SQL Azure storage. Is there some advantage in my going to this kind of storage being that the look up will always be very direct. Note that I do NOT need any reporting. ALL I want is single row look up
Assuming that your requirements are fully stated as will only ever need single row access, and assuming that you only want to know about advantages and not disadvantages, then the only advantages I can think of are that SQL azure offers:
time-based subscription pricing instead of pricing per transaction
options for backup (in CTP)
options for replication/synchronisation
more client library options (e.g. Entity Framework, Linq2SQL, etc)
more data types supported
more options for moving your app outside of Azure if you ever want to
Use Table Storage if you don't need relational database functionality.