Question:
Should I write my application to directly access a database Image Repository or write a middleware piece to handle document requests.
Background:
I have a custom Document Imaging and Workflow application that currently stores about 15 million documents/document images (90%+ single page, group 4 tiffs, the rest PDF, Word and Excel documents). The image repository is a commercial, 3rd party application that is very expensive and frankly has too much overhead. I just need a system to store and retrieve document images.
I'm considering moving the imaging directly into a SQL Server 2005 database. The indexing information is very limited - basically 2 index fields. It's a life insurance policy administration system so I index images with a policy number and a system wide unique id number. There are other index values, but they're stored and maintained separately from the image data. Those index values give me the ability to look-up the unique id value for individual image retrieval.
The database server is a dual-quad core windows 2003 box with SAN drives hosting the DB files. The current image repository size is about 650GB. I haven't done any testing to see how large the converted database will be. I'm not really asking about the database design - I'm working with our DBAs on that aspect. If that changes, I'll be back :-)
The current system to be replaced is obviously a middleware application, but it's a very heavyweight system spread across 3 windows servers. If I go this route, it would be a single server system.
My primary concerns are scalabity and performace - heavily weighted toward performance. I have about 100 users, and usage growth will probably be slow for the next few years.
Most users are primarily read users - they don't add images to the system very often. We have a department that handles scanning and otherwise adding images to the repository. We also have a few other applications that receive documents (via ftp) and they insert them into the repository automatically as they are received, either will full index information or as "batches" that a user reviews and indexes.
Most (90%+) of the documents/images are very small, < 100K, probably < 50K, so I believe that storage of the images in the database file will be the most efficient rather than getting SQL 2008 and using a filestream.
Oftentimes scalability and performance are ultimately married to each other in the sense that six months from now management comes back and says "Function Y in Application X is running unacceptably slow, how do we speed it up?" And all too the often the answer is to upgrade the back end solution. And when it comes to upgrading back ends, its almost always going to less expensive to scale out than to scale up in terms of hardware.
So, long story short, I would recommend building a middleware app that specifically handles incoming requests from the user app and then routes them to the appropriate destination. This will sufficiently abstract your front-end user app from the back end storage solution so that when scalability does become an issue only the middleware app will need to be updated.
This is straightforward. Write the application to an interface, use some kind of factory mechanism to supply that interface, and implement that interface however you want.
Once you're happy with your interface, then the application is (mostly) isolated from the implementation, whether it's talking straight to a DB or to some other component.
Thinking ahead a bit on your interface design but doing bone stupid, "it's simple, it works here, it works now" implementations offers a good balance of future proofing the system while not necessarily over engineering it.
It's easy to argue you don't even need an interface at this juncture, rather just a simple class that you instantiate. But if your contract is well defined (i.e. the interface or class signature), that is what protects you from change (such as redoing the back end implementation). You can always replace the class with an interface later if you find it necessary.
As far as scalability, test it. Then you know not only if you may need to scale, but perhaps when as well. "Works great for 100 users, problematic for 200, if we hit 150 we might want to consider taking another look at the back end, but it's good for now."
That's due diligence and a responsible design tactic, IMHO.
I agree with gabriel1836. However, an added benefit would be that you could for a time run a hybrid system for a time since you aren't going to convert 14 millions documents from your proprietary system to you home grown system overnight.
Also, I would strongly encourage you to store the documents outside of a database. Store them on a file system (local, SAN, NAS it doesn't matter) and store pointers to the documents in the database.
I'd love to know what document management system you are using now.
Also, don't underestimate the effort of replacing the capture (scanning and importing) provided by the proprietary system.
Related
I'm looking for a portable database solution I can use with a website that is designed to handle service outages. I need to nightly retrieve a list of users from SQL Server and upsert their details into a portable database. It's roughly about 250,000 users (and growing) and each one has probably 25 fields that are required. Of those fields, i'd say less than 5 need to be searched on. The rest just need retrieving.
The idea is, in times of a service outage, we can use a website that's designed to work from the portable database rather than SQL Server. Our long term goal, is to move to the cloud and handle things in an entirely different way, but for the short term this is our aim.
The website is going to be a .Net Core web api so will be being accessed by multiple users in multiple threads. The website will only ever need read access, it will not be updating these details what-so-ever.
To keep the portable database up-to-date i'm thinking of having another application that just runs nightly to update the data. Our business is 24 hours (albeit quieter overnight), so there is a potential this updater is in use while the website is in use. While service outage would assume the SQL Server is down, this may not be the case. There are other factors in play that could cause what we would describe as outages. This will be the only piece of software updating the database.
I've tried using LiteDB but I couldn't get it working in a way that worked with my concurrency requirements. It did seem to do some of the job, and was easy to get running. However, i'd often run into locked files due to the nature of web api. I did work out a solution for that, but then the updater app couldn't access the database file.
Does anyone have any recommendations I can look into?
Given the description of the problem (1 table, 250k rows with - I assume - relative fast growth rate) and requirements, I don't think a relational database is what you are looking for.
I think nosql databases, or, more specifically, document oriented databases are more fitted to meet your requirements. There are many choices: Mongo, Cassandra, CouchDB, ... the choice is yours.
Personally I have some experience with ElasticSearch (https://www.elastic.co/elasticsearch), that is quite easy to learn, is portable (runs on Linux, Windows, Containers, etc...), is scalable, and it is fast. I mean, really, really fast, you can get results in 10-20 milliseconds (even less, sometimes).
The NEST nuget package acts as a high level client for working with ElasticSearch (https://www.elastic.co/guide/en/elasticsearch/client/net-api/7.x/nest-getting-started.html)
So I have this requirement that says the app must let users upload and download about 6000 files per month (mostly pdf, doc, xls).
I was thinking about the optimal solution for this. Question is whether I'll use BLOb's in my database or a simple file hierarchy for writing/reading these bunch of files.
The app architecture is based on Java 1.6, Spring 3.1 and DOJO, Informix 10.X.
So I'm here just to be advised based on your experience.
When asking what's the "best" solution, it's a good idea to include your evaluation criteria - speed, cost, simplicity, maintenance etc.
The answer Mikko Maunu gave is pretty much on the money. I haven't used Informix in 20 years, but most databases are a little slow when dealing with BLOBs - especially the step of getting the BLOB into and out of the database can be slow.
That problem tends to get worse as more users access the system simultaneously, especially if they use a web application - the application server has to work quite hard to get the files in and out of the database, probably consumes far more memory for those requests than normal, and probably takes longer to complete the file-related requests than for "normal" pages.
This can lead to the webserver slowing down under only moderate load. If you choose to store the documents in your database, I'd strongly recommend running some performance tests to see if you have a problem - this kind of solution tends to expose flaws in your setup that wouldn't otherwise come to light (slow network connection to your database server, insufficient RAM in your web servers, etc.)
To avoid this, I've stored the "master" copies of the documents in the database, so they all get backed up together, and I can ask the database questions like "do I have all the documents for user x?". However, I've used a cache on the webserver to avoid reading documents from the database more than I needed to. This works well if you have a "write once, read many" time solution like a content management system, where the cache can earn its keep.
If you have other data in database in relation to these files, storing files to file system makes it more complex:
Back-up should be done separately.
Transactions have to be separately implemented (as far as even possible for file system operations).
Integrity checks between database and file system structure do not come out of the box.
No cascades: removing users pictures as consequence of removing user.
First you have to query for path of file from database and then pick one from file system.
What is good with file system based solution is that sometimes it is handy to be able to directly access files, for example copying part of the images somewhere else. Also storing binary data of course can dramatically change size of database. But in any case, more disk storage is needed somewhere with both solutions.
Of course all of this can ask more DB resources than currently available. There can be in general significant performance hit, especially if decision is between local file system and remote DB. In your case (6000 files monthly) raw performance will not be problem, but latency can be.
I alway wondered how could a very big site like facebook to be faster than any other sites ,though the very big large amount of data which stored everyday ..
what they are using to store information and if I use sql server to store e.g news feed is that ok or what (the news feed will be stored in a separate table which called News) .
in the other hand what could happen if I joined many huge tables with each other - it should be slow (maybe) or it doesn't matter how big the table is !?
thanx :)
When you talk about scaling at the size of Facebook, is a whole different ball park. Latest estimates put Facebook datacenter at about 60000 servers (sixty thousand). Only the cache is estimated to be at about 30 TB (terabytes) ina a masive Memcached cluster. Although their back end is stil MySQL, is used as a pure key-value store, according to publicly available information:
Facebook uses MySQL, but
primarily as a key-value persistent
storage, moving joins and logic onto
the web servers since optimizations
are easier to perform there (on the
“other side” of the Memcached layer).
There are various other technologies in use there:
HipHop to compile PHP into native code
Haystack for media (photo) storage
BigPipe for HTTP delivery
Cassandra for Inbox search
You can also watch this year SIGMOD 2010 key address Building Facebook: Performance at big scale. They even present their basic internal API:
cache_get ($ids,
'cache_function',
$cache_params,
'db_function',
$db_params);
So if you connect the dots you'll see that at such scale you no longer talk about a 'big database'. You talk about huge clusters of services, key-value storage partitioned across thousands of servers, many technologies used together and so on and so forth.
As a side note, you can also see a pretty good presentation of MySpace internals. Although the technology stack is completely different (Microsoft .Net and SQL Server based, with a huge emphasis on message passing via Service Broker) there are similar points in how they approach storage. To sum up: application layer partitioning.
It depends, Facebook is very fast because they have a server farm, so queries are optimised and each single query hits many servers.
In regards to huge tables, they can be fast as long as you have enough physical memory to index whatever you need to search on. Having correct index's can improve database performance hugely (When it comes to retrieving data).
As long as it makes sense to join many huge tables together into one then yes, but if they're separate, and not related then no. If you provide more details on what kind of tables you would be looking to merge, we might be able to help you more.
According to link text and other pages Facebook uses a technique called Sharding.
It simply uses a bunch of databases with a small portion of the site on each database. A simple algorithm for deciding which database to use could be using the first letter in the username as an index for the database. One database for 'a', one for 'b', etc. I'm sure Facebook has a more advanced scheme than that, but the principle is the same.
The result is many small independent databases that are small enough to handle the load. Facebook and all other major sites has all sorts of similar tricks to make the sites fast and responsive.
They continuously monitor the sites for performance and other metrics and come up with solutions to the issues the find.
I think the monitoring part is more important to the performance success than the actual techniques used to gain the performance. You can not make a fast site by blindly throw some "good performance spells" at it. You have to know where and why you have bottlenecks before you can remove them.
Depends what the performance bottleneck is. One problem is often using the wrong technology for the problem, eg using a relational DB when an object DB or document store would be better, or vice versa of course.
Some people try and use the same DB for everything which is not always the answer. Sometimes it is useful to have multiple denormalizations of the same data for different purposes.
Thinking about the nature of the data and how it is written, read, queried etc is important. You can put all write-once data in one DB and optimize that db for that. Other data that is written frequently could be stored on a db optimized for that.
Distribution techniques can also assist with upscaling.
I'm using SqlServer to drive a WPF application, I'm currently using NHibernate and pre-read all the data so it's cached for performance reasons. That works for a single client app, but I was wondering if there's an in memory database that I could use so I can share the information across multiple apps on the same machine. Ideally this would sit below my NHibernate stack, so my code wouldn't have to change. Effectively I'm looking to move my DB from it's traditional format on the server to be an in memory DB on the client.
Note I only need select functionality.
I would be incredibly surprised if you even need to load all your information in memory. I say this because, just as one example, I'm working on a Web app at the moment that (for various reasons) loads thousands of records on many pages. This is PHP + MySQL. And even so it can do it and render a page in well under 100ms.
Before you go down this route make sure that you have to. First make your database as performant as possible. Now obviously this includes things like having appropriate indexes and tuning your database but even though are putting the horse before the cart.
First and foremost you need to make sure you have a good relational data model: one that lends itself to performant queries. This is as much art as it is science.
Also, you may like NHibernate but ORMs are not always the best choice. There are some corner cases, for example, that hand-coded SQL will be vastly superior in.
Now assuming you have a good data model and assuming you've then optimized your indexes and database parameters and then you've properly configured NHibernate, then and only then should you consider storing data in memory if and only if performance is still an issue.
To put this in perspective, the only times I've needed to do this are on systems that need to perform millions of transactions per day.
One reason to avoid in-memory caching is because it adds a lot of complexity. You have to deal with issues like cache expiry, independent updates to the underlying data store, whether you use synchronous or asynchronous updates, how you give the client a consistent (if not up-to-date) view of your data, how you deal with failover and replication and so on. There is a huge complexity cost to be paid.
Assuming you've done all the above and you still need it, it sounds to me like what you need is a cache or grid solution. Here is an overview of Java grid/cluster solutions but many of them (eg Coherence, memcached) apply to .Net as well. Another choice for .Net is Velocity.
It needs to be pointed out and stressed that something like NHibernate is only consistent so long as nothing externally updates the database and that there is exactly one NHibernate-enabled process (barring clustered solutions). If two desktop apps on two different PCs are both updating the same database with NHibernate the caching simply won't work because the persistence units simply won't be aware of the changes the other is making.
http://www.db4o.com/ can be your friend!
Velocity is an out of process object caching server designed by Microsoft to do pretty much what you want although it's only in CTP form at the moment.
I believe there are also wrappers for memcached, which can also be used to cache objects.
You can use HANA, express edition. You can download it for free, it's in-memory, columnar and allows for further analytics capabilities such as text analytics, geospatial or predictive. You can also access with ODBC, JDBC, node.js hdb library, REST APIs among others.
I'm working on a web app that is somewhere between an email service and a social network. I feel it has the potential to grow really big in the future, so I'm concerned about scalability.
Instead of using one centralized MySQL/InnoDB database and then partitioning it when that time comes, I've decided to create a separate SQLite database for each active user: one active user per 'shard'.
That way backing up the database would be as easy as copying each user's small database file to a remote location once a day.
Scaling up will be as easy as adding extra hard disks to store the new files.
When the app grows beyond a single server I can link the servers together at the filesystem level using GlusterFS and run the app unchanged, or rig up a simple SQLite proxy system that will allow each server to manipulate sqlite files in adjacent servers.
Concurrency issues will be minimal because each HTTP request will only touch one or two database files at a time, out of thousands, and SQLite only blocks on reads anyway.
I'm betting that this approach will allow my app to scale gracefully and support lots of cool and unique features. Am I betting wrong? Am I missing anything?
UPDATE I decided to go with a less extreme solution, which is working fine so far. I'm using a fixed number of shards - 256 sqlite databases, to be precise. Each user is assigned and bound to a random shard by a simple hash function.
Most features of my app require access to just one or two shards per request, but there is one in particular that requires the execution of a simple query on 10 to 100 different shards out of 256, depending on the user. Tests indicate it would take about 0.02 seconds, or less, if all the data is cached in RAM. I think I can live with that!
UPDATE 2.0 I ported the app to MySQL/InnoDB and was able to get about the same performance for regular requests, but for that one request that requires shard walking, innodb is 4-5 times faster. For this reason, and other reason, I'm dropping this architecture, but I hope someone somewhere finds a use for it...thanks.
The place where this will fail is if you have to do what's called "shard walking" - which is finding out all the data across a bunch of different users. That particular kind of "query" will have to be done programmatically, asking each of the SQLite databases in turn - and will very likely be the slowest aspect of your site. It's a common issue in any system where data has been "sharded" into separate databases.
If all the of the data is self-contained to the user, then this should scale pretty well - the key to making this an effective design is to know how the data is likely going to be used and if data from one person will be interacting with data from another (in your context).
You may also need to watch out for file system resources - SQLite is great, awesome, fast, etc - but you do get some caching and writing benefits when using a "standard database" (i.e. MySQL, PostgreSQL, etc) because of how they're designed. In your proposed design, you'll be missing out on some of that.
Sounds to me like a maintenance nightmare. What happens when the schema changes on all those DBs?
http://freshmeat.net/projects/sphivedb
SPHiveDB is a server for sqlite database. It use JSON-RPC over HTTP to expose a network interface to use SQLite database. It supports combining multiple SQLite databases into one file. It also supports the use of multiple files. It is designed for the extreme sharding schema -- one SQLite database per user.
One possible problem is that having one database for each user will use disk space and RAM very inefficiently, and as the user base grows the benefit of using a light and fast database engine will be lost completely.
A possible solution to this problem is to create "minishards" consisting of maybe 1024 SQLite databases housing up to 100 users each. This will be more efficient than the DB per user approach, because data is packed more efficiently. And lighter than the Innodb database server approach, because we're using Sqlite.
Concurrency will also be pretty good, but queries will be less elegant (shard_id yuckiness). What do you think?
If you're creating a separate database for each user, it sounds like you're not setting up relationships... so why use a relational database at all?
If your data is this easy to shard, why not just use a standard database engine, and if you scale large enough that the DB becomes the bottleneck, shard the database, with different users in different instances? The effect is the same, but you're not using scores of tiny little databases.
In reality, you probably have at least some shared data that doesn't belong to any single user, and you probably frequently need to access data for more than one user. This will cause problems with either system, though.
I am considering this same architecture as I basically wanted to use the server side SQLLIte databases as backup and synching copy for clients. My idea for querying across all the data is to use Sphinx for full-text search and run Hadoop jobs from flat dumps of all the data to Scribe and then expose the results as webservies. This post gives me some pause for thought however, so I hope people will continue to respond with their opinion.
Having one database per user would make it really easy to restore individual users data of course, but as #John said, schema changes would require some work.
Not enough to make it hard, but enough to make it non-trivial.