Background info before question
I use state session a lot to store my complex objects, I am also using at max like 8 tables. On those 8 tables I am using like about 25 SP to join users based on user id and some key values that the user selects. All this is done on SQL Server database.
zip codes spatial values
male or female
has pictures
approved profile
registered account(paying for services)
I store Images in a file system on my application server. I store the path on my db table.
Use Case
Dating website, unique payloads most of the time such as searching based on a certain criteria, updating and fetching personal profile with photos. I am using asp.net MVC, and this is a website only. (separate web pages for responsive designs for other devices)
Question
Can I just use Redis as my primary data store, instead of using SQL server Database based on my use case?
Key Points
I don't on plan on having more than like 10-12 total tables in the future. The data input are mostly strings. When I want to persist a complex object like profile information and image paths I use a Session State. I love what I read about the speed of Redis, and I see it being counter productive to duplicating updates to both Redis and a DB if I stack them.
I don't think you can easily replace your database with Redis because you are missing things like FK, indexes, constraints (Redis is a NoSQL so you don't have any relational attributes). So you will end up building those yourself, especially the indexes for your 25 stored procs which can become pretty complex stuff. Also the fact that you have 25 stored procs for your 8 tables kind of tells me you have quite some logic here which will be even harder to move to Redis or your application layer.
Sure, adding Redis to your stack is not easy and it will make your application more complex, so you must weight the benefits and the drawbacks. And Redis because it keeps all the stuff in memory it is best suited for a cache layer.
Related
I am using SignalR with my ASP.NET application. What my application needs is to pressist the groups data that is updated from various servers. According to SignalR documentation it's my responsibility to do this. It means that I need to use an external server/service that will collect the data from one or more servers and I can query that data from a single place.
I first thought that MemCached is the best candidate, because it's fast and the data that I need to put there is volatile. The problem is that I need to store collections, for example: collection A with user Ids, so I can have Collection A with 2000 user ids and Collection B with 40,000 ids. The problem is that I need to update this collection and remove and insert id very quickly. I afraid that because the commands will be initiated from several servers, and the fact that I might need to read the entire collection and update it on a either web servers, the data won't be consistent. Web Server A might update the data, but Server B will read the data before Server A finished updating it. There is a concurrency conflict.
I'm searching for the best way to implement this kind of strategy in my ASP.NET 4.5 application. I think that this might be a choice to use a in-memory database or that to insure no data integrity.
I want to ask you what is the best solution for my problem.
Here's an example for my problen:
MemCached Server - stores the collections (e.g. Collection A, B, C, D), each collection stores User Id's, which can be thousands of Ids and even much more.
Web Servers - My Amazon EC2 web servers with SignalR installed. Can be behind load balancer. Those servers need to gain access to the memcached server and get a complete collection items by the Collection name (e.g. "Collection_23"). They need to be able to remove items (User Id's) and add Items. All this should be fast as possible.
I hope that I explained myself right. Thanks.
Alternatively, you can use Redis, like Memcached everything is served from in-memory. Redis has many other capabilities beyond a simple key-value datastore; for your specific case you might use Redis transactions, which ensures data consistency.
In a comment in another post it shows a link to redis provider. The link is broken, it seems that it is now integrated in the main SignalR project: https://github.com/SignalR/SignalR/tree/master/src/Microsoft.AspNet.SignalR.Redis
You have the redis nuget here:
http://www.nuget.org/packages/Microsoft.AspNet.SignalR.Redis
and documentation here:
http://www.asp.net/signalr/overview/signalr-20/performance-and-scaling/scaleout-with-redis
I have an ASP.net application that I'm moving to Azure. In the application, there's a query that joins 9 tables to produce a user record. Each record is then serialized in json and sent back and forth with the client. To increase query performance, the first time the 9 queries run and the record is serialized in json, the resulting string is saved to a table called JsonUserCache. The table only has 2 columns: JsonUserRecordID (that's unique) and JsonRecord. Each time a user record is requested from the client, the JsonUserCache table is queried first to avoid having to do the query with the 9 joins. When the user logs off, the records he created in the JsonUserCache are deleted.
The table JsonUserCache is SQL Server. I could simply leave everything as is but I'm wondering if there's a better way. I'm thinking about creating a simple dictionary that'll store the key/values and put that dictionary in AppFabric. I'm also considering using a NoSQL provider and if there's an option for Azure or if I should just stick to a dictionary in AppFabric. Or, is there another alternative?
Thanks for your suggestions.
"There are only two hard problems in Computer Science: cache invalidation and naming things."
Phil Karlton
You are clearly talking about a cache and as a general principle, you should not persist any cached data (in SQL or anywhere else) as you have the problem of expiring the cache and having to do the deletes (as you currently are). If you insist on storing your result somewhere and don't mind the clearing up afterwards, then look at putting it in an Azure blob - this is easily accessible from the browser and doesn't require that the request be handled by your own application.
To implement it as a traditional cache, look at these options.
Use out of the box ASP.NET caching, where you cache in memory on the web role. This means that your join will be re-run on every instance that the user goes to, but depending on the number of instances and the duration of the average session may be the simplest to implement.
Use AppFabric Cache. This is an extra API to learn and has additional costs which may get quite high if you have lots of unique visitors.
Use a specialised distributed cache such as Memcached. This has the added cost/hassle of having to run it all yourself, but gives you lots of flexibility in the long run.
Edit: All are RAM based. Using ASP.NET caching is simpler to implement and is faster to retrieve the data from cache because it is on the same machine - BUT requires the cache to be populated for each instance of the web role (i.e. it is not distributed). AppFabric caching is distributed but is also a bit slower (network latency) and, depending what you mean by scalable, AppFabric caching currently behaves a bit erratically at scale - so make sure you run tests. If you want scalable, feature rich distributed caching, and it is a big part of your application, go and put in Memcached.
I have developed a CRM for my company. Next I would like to take that system and make it available for others to use in a hosted format. Very much like a salesforce.com. The question is what type of database structure would I use. I see two options:
Option 1. Each time a company signs up, I clone the master database for them.
The disadvantage of this is that I could end up with thousands of databases. Thats a lot of databases to backup every night. My CRM uses cron jobs for maintanance, those jobs would have to run on all databases.
Each time I upgrade the system with a new feature, and need to add a new column to the database, I will have to add that column to thousands of databases.
Option 2. Use only one database.
At the beginning of EVERY table add "CompanyID". In EVERY SQL statement add "and
companyid={companyid}".
The advantage of this method is the simplicity of only one database. Just one database to backup each night. Just one database to update when needed.
The disadvantage is what if I get 1000 companies signing up, and each wants to store data on 100,000 leads, that 100,000,000 rows in the lead table, which worries me.
Does anyone know how the online hosted CRMs like salesforce.com do this?
Thanks
Wouldn't you clone a table structure style to each new database id all sheets archived in master base indexed client clone is hash verified to access specific sheet run through a host machine at the front end of the master system. Then directing requests as primary role. Internal access is batched to read/write slave systems in groups. Obviously set raid configurations to copy real time and scheduled. Balance requests for load to speed of system resources. That way you separated the security flawed from ui and connection to the retention schema. it seems like simplified structures and reducing policy requests cut down request rss in the query processing. or Simply a man in the middle approach from inside out.
1) Splitting your data into smaller groups in a safe, unthinking way (such as one database per grouping) is almost always best if you want to scale. In this case, unless for some reason you want to query between companies, keeping them in separate databases is best.
2) If you are updating all of the databases by hand, you are doing something wrong if you want to scale. You'd want to automate the process.
3) Ultimately, salesforce.com uses this as the basis of their DB infrastructure:
http://blog.database.com/blog/2011/08/30/database-com-is-open-for-business/
I'm developing a web app for which the client wants us to query their data as little as possible. The data will be coming from a Microsoft CRM instance.
So we've agreed that data will only be queried as and when it is needed, therefore if a web user wants to see a list of contacts (for example) that list is fetched into a local DataTable. Then if a new contact is created on the website the new contact is sent to CRM and added to the local DataTable at the same time. Likewise for edits.
If the user then looks at their contacts again the data will just come from the local DataTable.
At the moment local data is being kept in Session but my concern is that too much memory will start being used up. However traffic is expected to be pretty small, perhaps no more than 20 concurrent users so am I worrying about nothing or is there a better way you can suggest to handle this?
You worry about nothing. Basically it is a scalability dump - stupid desig. BUT: if you can throw 1gb of memory at the problem, for 20 users, storing 16mb of memory is not a problem.
The main problem starts when pepople count grows and the application needs to be rewritten.
20 concurrent users is not too many.
Clients "looks at their contacts": Depending on "contacts" table size, could you consider storing it in in-memory dataset( all contacts). You could then filter acc to primary key.
Alternative to session:Cache, Application
Cache with SqlCacheDependency and CacheItemRemovedCallback should be a good option to session.
XML files for each customer contacts.
So I have a challenge to build a site that people online can use to interact with organizations.: Asp.NET MVC Customer Application
One of the requirements is financial processing and accounting.
I'm very comfortable using SQL Transactions and stored procedures to do this; i.e. CreateCustomer also creates an entity, and an account record. We have a stored procedure to do this, that does a begin transaction, creates some setup records we need, then does a commit. I'm not seeing a good way to do this with an ORM, and after reading some great blog articles I'm starting to wonder if I'm going down the wrong path.
Part of the complexity here is the data itself:
I'm querying x databases (one per existing customer) to get some of my data, though my app has its own data store as well. I need to query the x databases, run stored procedures on the x databases, and also to my own datastore.
I'm not seeing strong support for things like stored procedures and thereby transactions, though it does seem to be present.
Maybe I'm just trying to make my app a nail here, cause the MVC hammer is sooo shiny. I'm plenty comfortable with raw ADO.NET of course, but I'm in love with the expressive feel to writing Linq code in C# and I'd rather not give up on it.
Down to the question:
Is this a bad idea? Should I try to use Linq / Entity Framework, or something like nHibernate... and stick with the ORM pattern or should I trash it and use raw ADO.NET data access?
Edit: a note on scale; from a queries per second standpoint this app is not "huge". But, from a data complexity perspective, it does need to query against 50+ databases (all identical, or close to it) to read data from an external application and publish data back to that application. ORM feels right when dealing with "my" data store, but feels very wrong for accessing the data from the external application.
From a certain size (number of databases) up, you have to change the paradigm. Are you at that size?
When you deploy what ultimately is a distributed application and yet try to controll it as an ordinary local application you are going to run into a set of fundamental issues around availability, scalability and correctness. If you use concepts like 'distributed transactions', 'linked servers' and 'ORM', your are down the wrong path. True distributed applications will use terms like 'message', 'queue' and and 'service'. Terms like Linq, EF, nHibernate are all fine and good, but none will bring you anything extra from what a simple Transact-SQL SELECT statement brings. In other words, if a SELECT solves your issues, then the client side various ORM will work. If not, they won't add any miraculos value.
I recommend you go over the slides on the SQLCAT: High Performance Distributed Applications in Real World Deployments which explain how a site like MySpace manages to read and write into a store of nearly 500 servers and thousands of databases.
Ultimately what you need to internalize is this: one database can have 95% availability (uptime and acceptable service response time). A system consiting of 10 databases with 95% availability has 59% availability. And a system of 100 databases each with 99.5% availability has 60% availability. 1000 databases with 99.95% availability (5 min downtime per week) have 60% availability. And this is for an ideal situation. In reality there is always a snowball effect caused by resource consumption (eg. threads blocked on trying to access an unavailable or slow resource) that makes things far worse.
This means that one cannot write a large distributed system relying on synchronous, tightly coupled operatiosn and transactions. Is simply impossible. You always rely on asynchronous operations (usually messaging and queues), which is something completely different from your run-of-the-mill database application.
use TransactionScope object available in System.Transaction.
What I have chosen is to use Entity Framework to allow access to the application's main data store, and create a custom DAL for access to external application data and for access to stored procedures within the application.
Here's hoping Entity Framework 4.0 fixes the issue. For now, I'm using the concept listed here.
http://social.msdn.microsoft.com/forums/en-US/adodotnetentityframework/thread/44a0a7c2-7c1b-43bc-98e0-4d072b94b2ab/