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I work for a ticket re-sale eCommerce site, and one of the problems that we have is that during on-sale periods our database is bombarded with thousands of requests.
the table that holds the tickets is constantly updated and read from and this is a major bottleneck for the site.
We considered reading from replicated databases but these replicated servers are some times hours out of sync.
one Idea was to use triggers on the tickets table and according to Update,Insert,Delete actions populate a denormalized table, and use this denormalized table to do the reads on. this might make queries a bit faster.
We have considered CQRS but due to the nature of our site, and the following reasons we think that it would not be a good fit:
each ticket is unique, since it is uploaded by a seller, and multiple buyers will be competing for the same tickets concurrently.
we experience bursty traffic when popular events are listed, and tickets are sold in a request-response manner.
are there any other techniques that we can use distribute some of the load?
Can you tell us more about which version of SQL Server you are using (2008R2, 2012, edition, etc.) and what Isolation Level are you running? As far as triggers go, they are rarely synonymous with "performance improvement" =) Have you been able to identify the specific waits in your DB? Are reads waiting on a long-winded update transaction or delete of some sort? Or are you experiencing memory pressure on the db server? Do you have auto update statistics on? Are your writes to the table also bursty? If your stats are out of date, you might be picking up inefficient query plans along the way. If you are not already using it, I'd highly recommend sp_Blitz from Brent Ozar to give you some more insight.
Once you know more about those items, you'll probably have a better idea of whether or not you NEED to actually distribute load vs. just do some tuning.
As far as load distribution, SQL Server AlwaysOn Availability Groups are potentially an answer, though they take some finessing. A readable secondary can be created that is asynchronously replicated which, in my experience at least, generally maintains fairly low latency. A synchronous replica can also be spun up, but that could compound wait issues...you'd have to do a fair amount of testing on that one.
You are basically building another eBay, with the same scaling issues they have.
There are some descriptions of their architecture: http://www.quora.com/What-is-eBays-architecture, http://highscalability.com/ebay-architecture, and many others on google.
Basically though, it comes down to using asynchronous processing whenever possible (learn about queues), and offloading as much from your main database server as possible, having a good real-time search server (which is not your database server), and scale horizontally by moving as much logic as possible into the app layer.
This will require that you give up ACID principals, and embrace eventual consistency. Eventual doesn't mean hours though, as you learn about queues, you will realize that allowing for a .5 second delay allows MUCH greater scalability.
So, from a back-of-the-napkin architecture, I would suggest you move your search to some fairly real-time search engine (like elasticsearch), offload most of your metadata to some no-sql platform (like MongoDB, or Cassandra) and reserve your database for processing bids against tickets. These bids shouldn't go straight to the database, but should be put in a queue, which will enforce ordering, and allow another process to execute them against the database.
Any one of these architectural changes will help with your load, but the asynchronous updating will make the biggest difference.
I have a question, just looking for suggestions here.
So, my application is 'modernizing' a desktop application by converting it to the web, with an ICEFaces UI and server side written in Java. However, they are keeping around the same Oracle database, which at current count has about 700-900 tables and probably a billion total records in the tables. Some individual tables have 250 million rows, many have over 25 million.
Needless to say, the database is not scaling well. As a result, the performance of the application is looking to be abysmal. The architects / decision makers-that-be have all either refused or are unwilling to restructure the persistence. So, basically we are putting a fresh coat of paint on a functional desktop application that currently serves most user needs and does so with relative ease. The actual database performance is pretty slow in the desktop app now. The quick performance I referred to earlier was non-database related stuff (sorry I misspoke there). I am having trouble sleeping at night thinking of how poorly this application is going to perform and how difficult it is going to be for everyday users to do their job.
So, my question is, what options do I have to mitigate this impending disaster? Is there some type of intermediate layer I can put in between the database and the Java code to speed up performance while at the same time keeping the database structure intact? Caching is obviously an option, but I don't see that as being a cure-all. Is it possible to layer a NoSQL DB in between or something?
I don't understand how to reconcile two things you said.
Needless to say, the database is not scaling well
and
currently serves most user needs and does so with relative ease and quick performance.
You don't say you are adding new users or new function, just making the same function accessible via a web interface.
So why is there a problem. Your Web App will be doing more or less the same database work as before.
In fact introducing a web tier could well give new caching opportunities so reducing the work the DB is doing.
If your early pieces of web app development are showing poor performance then I would start by trying to understand how the queries you are doing in the web app differ from those done by the existing app. Is it possible that you are using some tooling which is taking a somewhat naive approach to generating queries?
If the current app performs well and your new java app doesn't, the problem is not in the database layer, but in your application layer. If performance is as bad as you say, they should notice fairly early and have the option of going back to the Desktop application.
The DBA should be able to readily identify the additional workload on the database from your application. Assuming the logic hasn't changed it is unlikely to be doing more writes. It could be reads or it could be 'chattier' (moving the same amount of information but in smaller parcels). Chatty applications can use a lot of CPU. A lot of architects try to move processing from the database layer into the application layer because "work on the database is expensive" but actually make things worse due to the overhead of the "to-and-fro".
PS.
There's nothing 'bad' about having 250 million rows in a table. Generally you access a table through an index. There are typically 2 or 3 hops from the top of an index to the bottom (and then one more to the table). I've got a 20 million row table with a BLEVEL of 2 and a 120+ million row table with a BLEVEL of 3.
Indexing means that you rarely hit more than a small proportion of your data blocks. The frequently used index blocks (and data blocks) get cached in the database server's memory. The DBA would be able to see if this memory area is too small for the workload (ie a lot of physical disk IO).
If your app is getting a lot of information that it doesn't really need, this can put pressure on the memory space. Don't be greedy. if you only need three columns from a row, don't grab the whole row.
What you describe is something that Oracle should be capable of handling very easily if you have the right equipment and database design. It should scale well if you get someone on your team who is a specialist in performance tuning large applications.
Redoing the database from scratch would cost a fortune and would introduce new bugs and the potential for loss of critical information is huge. It almost never is a better idea to rewrite the database at this point. Usually those kinds of projects fail miserably after costing the company thousands or even millions of dollars. Your architects made the right choice. Learn to accept that what you want isn't always the best way. The data is far more important to the company than the app. There are many reasons why people have learned not to try to redesign the database from scratch.
Now there are ways to improve database performance. First thing I would consider with a database this size is partioning the data. I would also consider archiving old data to a data warehouse and doing most reporting from that. Other things to consider would be improving your servers to higher performing models, profiling to find slowest running queries and individually fixing them, looking at indexing, updating statistics and indexes (not sure if this is what you do on Oracle, I'm a SLQ Server gal but your dbas would know). There are some good books on refactoring old legacy databases. The one below is not datbase specific.
http://www.amazon.com/Refactoring-Databases-Evolutionary-Database-Design/dp/0321293533/ref=sr_1_1?ie=UTF8&s=books&qid=1275577997&sr=8-1
There are also some good books on performance tuning (look for ones specific to Oracle, what works for SQL Server or mySQL is not what is best for Oracle)
Personally I would get those and read them from cover to cover before designing a plan for how you are going to fix the poor performance. I would also include the DBAs in all your planning, they know things that you do not about the database and why some things are designed the way they are.
If you have a lot of lookups that are for items not in the database you can reduce the number by using a bloom filter. Add everything in the database to the bloom filter then before you do a lookup check the bloom first. Only if the bloom reports it present do you need to bother the database. The bloom will result in false positives but you can design it to the 'size vs false positive' trade off that best suits you.
The strategy is used by Google in their big-table database and they have reported that it significantly improves performance.
http://en.wikipedia.org/wiki/Bloom_filter
Good luck, working on tasks you don't believe in is tough.
So you put a fresh coat of paint on a functional and quick desktop application and then the system becomes slow?
And then you say that "it is needless to say that the database isn't scaling well"?
I don't get it. I think that there is something wrong with your fresh coat of paint, not with the database.
Don't be put down by this sort of thing. See it as a challenge, rather than something to be losing sleep over! I know it's tempting as a programmer to want to rip everything out and start over again, but from a business perspective, it's just not always viable. For example, by using the same database, the business can continue to use the old application while the new one is being developed and switch over customers in groups, rather than having to switch everyone over at the same time.
As for what you can do about performance, it depends a lot on the usage pattern. Caching can help greatly with mostly read-only databases. Even with read/write database, it can still be a boon if correctly designed. A NoSQL database might help with write-heavy stuff, but it might also be more trouble than it's worth if the data has to end up in a regular database anyway.
In the end, it all depends greatly on your application's architecture and usage patterns.
Good luck!
Well without knowing too much about what kinds of queries that are mostly done (I would expact lookups to be more common) perhaps you should try caching first. And cache at different layers, at the layer before the app server if possible and of course what you suggested caching at the layer between the app server and the database.
Caching works well for read data and it might not be as bad as you think.
Have you looked at Terracotta ? They do have some caching and scaling stuff that might be relavant to you.
Take it as a challenge!
The way to 'mitigate this impending disaster' is to do what you should be doing anyway. If you follow best practices the pain of switching out your persistence layer at a later stage will be minimal.
Up until the time that you have valid performance benchmarks and identified bottlenecks in the system talk of performance is premature. In any case I would be surprised if many of the 'intermediate layer' strategies aren't already implemented at the database level.
If the database is legacy and enormous, then
1) it cannot be changed in a way that will change the interface, as this will break too many existing applications. Or, if you change the interface, this has to be coordinated with modifying multiple applications with associated testing.
2) If the issue is performance, then there are probably many changes that can be made to optimize the database without changing the interface.
3) Views can be used to maintain the existing interfaces while restructuring tables for more efficiency, or possibly to allow more efficient access in the future.
4) Standard database optimizations, such as performance analysis, indexing, caching can probably greatly increase efficiency and performance without changing the interface.
There's a lot more that can be done, but you get the idea. It can't really be updated in one single big change. Changes have to be incremental, or transparent to the applications that use it.
The database is PART of the application. Don't consider them to be separate, it isn't.
As developer, you need to be free to make schema changes as necessary, and suggest data changes to improve performance / functionality in production (for example archiving old data).
Your development system presumably does not have that much data, but has the exact same schema.
In order to do performance testing, you will need a system with the same hardware and same size data (same data if possible) as production. You should explain to management that performance testing is absolutely necessary as you feel the app isn't going to perform.
Of course making schema changes (adding / removing indexes, splitting tables out etc) may affect other parts of the system - which you should consider as parts of a SYSTEM - and hence do the necessary regression testing and fixing.
If you need to modify the database schema, and make changes to the desktop client accordingly, to make the web app perform, that is what you have to do - justify your design decision to the management.
I have created a prototype of a custom ORM tool using aspect oriented programming (PostSHarp) and achieving persistence ignorance (before compile-time). Now I tried to find out how much overhead does it introduce compared to using pure DataReader and ADO.NET. I made a test case - insert, read, delete data (about 1000 records) in MS SQL Server 2008 and MySQL Community Edition. I run this test multiple times using pure ADO.NET and my custom tool.
I expected that results will depend on many factors - memory, swapping, CPU, other processes so I ran tests for many times (20-40). But the results were really unexpected. They just differed too much between those cases. If there were just some extreme values, I could ignore them (maybe swapping ocurred or smth. like that) but they were so different that I am sure I cannot trust this kind of testing. Almost half of times my ORM showed 10% better performance than pure ADO.NET, other times it was -10%.
Is there any way I can make those tests reliable? I do not have a powerful computer with lots of memory, but maybe I somehow can make MS SQL and MySQL or ADO.NET to be as consistent as possible during those tests? And how about count of records - which is more reliable, using small amount of records and running more times or other way?
Have you seen ORMBattle.NET? See FAQ there, there are some ideas related to measuring performance overhead introduced by a particular ORM tool. Test suite is open source.
Concerning your results:
Some ORM tools automatically batch statement sequences (i.e. send several SQL statements together). If this feature is implemented well in ORM, it's easy to beat plain ADO.NET by 2-4 times on CRUD operations, if ADO.NET test does not involve batching. Tests on ORMBattle.NET test both cases.
A lot depends on how you establish transaction boundaries there. Please refer to ORMBattle.NET FAQ for details.
CRUD tests aren't best performance indicator at all. In general, it's pretty easy to get
peak possible performance here, since in general, RDBMS must do much more than ORM in this case.
P.S. I'm one of ORMBattle.NET authors, so if you're interested in details / possible contributions, you can contact me directly (or join ORMBattle.NET Google Groups).
I would run the test for a longer duration and with many more iterations as small differences would average out over time and you should get a clearer picture. Also, make sure you eliminate any external things that may be affecting your test, such as other processes running, non enough free memory, cold start vs warm start, network usage, etc.
Also, make sure that your database file and log file have enough free space allocated so you aren't waiting for the DB to grow the file during certain tests.
First of all you need to find out where does the variance come from. The ORM layer itself or the database?
Many times the source of such variance is the database itself. Databases are very complex systems, with many active processes inside that can interact with the result of performance measurements. To achieve some reproductible results you'll have to place your database under 'laboratory' conditions and make sure nothing unexpected happens. what that means depends from vendor to vendor and you need know some pretty advanced topics in order to tacle something like this. For instance, on a SQL Server database the typical sources of variation are:
cold cache vs. warm cache (both data and procedures)
log and database growth events
maintenance jobs
ghost cleanup
lazy writer
checkpoints
external memory pressure
There is frequently the need to synchronize data from master tables in one database to clone tables in other databases, often on other servers. For example, consider the case where a backend system manages inventory data and that inventory data ultimately must be pushed to one or more databases that are part of a web site application.
The source data in the backend system is heavily normalized, with dozens of tables and foreign key constraints. It is a well-designed OLTP RDBMS system. Many of the tables in question contain millions of rows. The need is to push this data out to the other databases regularly. As frequently as feasible; latency can be tolerated. Above all, maximum uptime of both the backend and remote databases is imperative.
I am using SQL Server and am familiar with change tracking, rowversion, triggers, and so on. I know that Microsoft pushes replication, SyncFx, and SSIS heavily for these scenarios. However, there is quite a difference between vendor whitepapers and overviews recommending technologies and the actual implementation, deployment, and maintenance of the solution. In the SQL Server world, replication is often viewed as the turnkey solution, but I am trying to explore alternate solutions. (There is some fear that replication is difficult to administer, makes it hard to change schema, and in the event that a re-initialize is ever required there would be large downtime for critical systems.)
There are lots of gotchas. Due to the complex foreign key relationships among large numbers of tables, determining what order to perform captures or to apply updates is not trivial. Due to unique indexes, two rows might be interlocked in such a way that row-at-a-time update will not even work (need to perform intermediate updates to each row before the final update). These are not necessarily show-stoppers, as unique indexes can often be changed to regular indexes and foreign keys can be disabled (though disabling the foreign keys is extremely undesirable). Often, you will hear, "just" use SQL 2008 change tracking and SSIS or SyncFx. These kinds of answers really do not do justice to the practical difficulties. (And of course, clients really have a hard time wrapping their heads over how copying data could be so difficult, making a difficult situation all the worse!)
This issue is ultimately very generic: perform one-way synchronization of many heavily related database tables with lots of rows. Almost everyone involved in databases has to deal with this kind of issue. Whitepapers are common, practical expertise hard to find. We know this can be a difficult issue, but the job must get done. Let's hear about what has worked for you (and what to avoid). Tell your experience with Microsoft products or products from other vendors. But if you personally have not battle-tested the solution with large numbers of heavily-related tables and rows, please refrain from answering. Let's keep this practical -- not theoretical.
Better ask on serverfault.com (I can't post comments, scripts are broken in SO, so I have to post a full answer)
Update: (switched to Safari, scripts work again, I can post properly)
There is no silver bullet. For ease of use and 'one key turn' deployment nothing can beat replication. Is the only solution that covers deeply conflict detection and resolution, has support for pushing schema changes and comes with a comprehensive set of tools for setting it up and monitoring it. It has been the MS poster child of data synchronization for many years before this 'agenda' was taken over by the .Net crowd. Replication has two underlying problems in my opinion:
The technology used to pushing changes is primitive, slow and unreliable. It requires file shares to initiate the replicas and it depends on T-SQL to actually replicate data, resulting in all sort of scalability problems: the replication threads use server worker threads and the fact that they interact with arbitrary tables and application queries lead to blocking and deadlocks. The biggest deployments I've heard of are around 400-500 sites and are done by superhuman MVPs and top dollar consultants. This stops on its track many projects that start at 1500 sites (way beyond largest deployed replication projects). I'm curious to hear if I'm wrong and you know of a SQL Server replication solution deployed with more than 500 sites.
The replication metaphor is too data centric. It does not take into account the requirements of distributed applications: need of versioned and formalized contracts, autonomy of data 'fiefdoms', loose coupling from availability and security pov. As a result replication based solution solve the immediate need to 'make data available there', but fail to solve the true problem of 'my app needs to talk with your app'.
At the other end of the spectrum you'll find solutions that truly address the problem of application communication, like services based on queued messaging. But are either painfully slow and riddled with problems rooted in the separation of the communication mechanism (web services and or msmq) and the data storage (DTC transactions between comm and db, no common high availability story, no common recoverability story etc etc). Solutions that are blazingly fast and fully integrated with DB exists in the MS stack, but nobody knows how to use them. Somewhere in between these and replication you'll find various intermediate solutions, like OCS/Synch framework and SSIS based custom solutions. None will offer the ease of setup and monitoring of replication, but they might scale and perform better.
I was involved with several projects that required 'data synchronization' on a very large scale (+1200 sites, +1600 sites) and my solution was to turn the problem on a 'application communication' problem. Once the mindset is changed to this and the data flow is no longer seen as 'record with key X of table Y' but instead 'message communicating the purchase of item X by customer Y' the solution becomes easier to understand and apply. You no longer think in terms of 'insert records in order X-Y-Z so FK relations don't break' but instead in terms of 'process purchase as described by message XYZ'.
In my view replication, and it derivatives (ie. data tracking and data-gram shipping), are solutions anchored in the '80 technologies and view of the data/applications. Obsolete dinosaurs (and by no way turning into birds).
I know this does not even begin to address all your (very legit) concerns, but writing out all I have to say/rant/rable on this topic would fill volumes of paperback...
Here at work (a multi-billion dollar manufaturing company with a 12 person Windows development team) we are about to go to a single master database for all new applications and will have it broken up with schemas for what we normally would have had databases for before. There will also be a few common schemas with stuff like employee directory and branch directory and so on...
I'm still not sure how I feel about this move, but we're about to have a meeting on this in a few hours to discuss pros, cons, best practices, pitfalls and so on... so I'm looking for your thoughts on this... Is it good? Is it bad? What problems are we going to run into a year from now?
Any thoughts, tips, or advice is welcome. Thanks
EDIT
In response to a comment on this question, we are using SQL Server 2005 and we are actually talking about moving what would have been seperate databases on the same instance into a single database. The driving issue is the complete lack of referential integrity accross databases as the majority of our applications need access to common data such as an employee record, or branch information.
UPDATE
Several people requested that I update this question with the results from our meeting so here it is. We debated back and forth the pros and cons of doing this (I even showed them this question using the projector) and by the time we were done we had pretty much covered the pros and cons covered here. About half of us thought we could get it done with the right resources and commitment, and about half thought we couldn't do it (or that it wouldn't work out well). We decided to use some time with Microsoft to get their thoughts and platform specific advice. I will be sure to update this question and my blog after we've talked to them. Thanks for all the help and helpful answers.
Larger database are harder to maintain due to sheer size: backups take longer, disaster recovery is slower which in turn requires more often backups. You can address these by creating filegroups and using filegroup level backup in your maintenance plans and on crash recovery you can use the 'piecemeal restore' strategy to speed things up.
Proper use of filegroups will make most of the 'cons' cited by previous replies go away: they can distribute the I/O, they can sanitize your maintenance plans and backup/restore strategy, they offer availability by taking offline only the damaged portion of the the db in case of crash. So I'd say that while those 'cons' are legit concerns, they have can be mitigated by a proper deployment strategy. Its true though that these mitigation actions require a true, experienced, dba at the helm as they will go beyond the comfort zone of a developer turned dba by need.
Some of the pros I can think of quickly:
Consistency. You can have a backup-restore so that all data is consistent. Separate dbs don't allow this because you cannot coordinate a consistent set of backups unless you take them all offline, or make them r/o, during the backup.
Dirt cheap high availability: you can deploy database mirroring for disaster recoverability and high availability. Multiple databases have problems because one cannot coordinate a simultaneous failover and apps are faced with the dilemma of seeking each database current location.
Security. While most other posts see one database harder to secure, I'd say is easier to secure. Multiple databases seem harder to secure properly simply because what everyone does is they make one login and add it to that database db_owner group. Having one database will make things harder (unless you end up making everyone dbo, very bad) but once you start doing the right thing (granular access) then one db is not harder than multiple dbs, is actually easier because you won't have to copy/maintain some common groups/rights across multiple dbs.
Control. Will be easier to impose certain policies and good practices on a single db rather than multiple ones (no data access to developers, app data access only through execute rights on the schema to enforce procedures access etc).
There are also some cons I did not see in other posts:
This will be much harder to pull off that you think right now
Increase coupling between formerly separated applications will impose development restrictions: you can't simply alter your schema, you will have to coordinate it with the rest of the apps (you can argue that this was also the case before, but was brushed under the carpet by having separate dbs, and you're right)
Log writes that are now distributed across multiple db logs will be consolidated into one single log file. If your writes are significant, this may turn out to be a serious bottleneck and force you to buy some expensive fast drives for the new, consolidated, log file. In general this can be addresses by making the log drive a stripped array across as many stripes as needed to make it fast enough (usually raid 10).
GAM/SGAM/PFS allocations will also be consolidated, but again this will be alleviated by proper use of file groups.
Pros:
You only need to remember one connection string
When users report that access is slow, you know which DB is causing the trouble
Cons:
Backups of The One DB will take a long time and will get progressively longer over time.
Restoring data from a backup will get increasingly difficult.
Performance Tuning (SQL Profiler, Execution Plan estimation) for a feature for one app will slow down every app.
Restricting access to a single application's data is cumbersome if at all possible which will likely mean in practice that all devs and DBAs will be given keys to the ENTIRE kingdom.
New developers/DBAs have a much larger learning curve as they need to navigate a large and mostly useless (to them) database structure which means higher costs for training/ramp up.
When The One database goes down, everyone in your organization plays solitaire until it is restored.
Creating test instances for app development means copying your entire db
The only "Pro" I can think of is that all of your systems will be in the one database and therefore a single place to backup, store, etc. However, I would consider this to also be one of the biggest "Cons".
Some other general Cons:
Much harder to move an application to a different location/server in the future.
Possible locking issues if any applications make use of tempdb.
Possible unrelated performance degredation on one application when another application is being used.
Much harder to implement an application level security model if all tables are in the same database.
It sounds to me as though your company is transitioning between two completely distinct motives for using database technology. The first is application support. The second is data integration. If I'm right about this, the process will open up a huge can of worms, and many of the issues won't even be addressed by putting all the data in one big database.
Consider two of the points you made. The first is the complete lack of referential integrity across different databases. The second is the idea that each application will have its own schema. What this permits to happen is complete lack of referential integrity across schemas, putting you back in the quicksand you are in now.
Fixing the data so that referential integrity is present, and fixing the schemas so that referential integrity is enforced, and fixing the applications so that the applications agree with the new schemas will turn out to be a monumental task.
Here's what your company really needs to do: Have one single CONCEPTUAL database that contains all "enterprise data", and defined in such a way that both referential integrity and entity integrity are enforced. Revise existing schemas so that they conform to the CONCEPTUAL database except for data that is both purely local to that schema and undocumented in the unified conceptual database. Use constraints wherever needed to guarantee that the data covered by these schemas doesn't lose integrity.
Make the decision about whether these schemas belong in one database or many databases based on database administration, fail soft, security, and performance requirements and NOT on the need to integrate data. Whether you use one platform or multiple platforms is a separable decision.
Where necessary, maintain synchronized copies of the same data in separate databases. Include the overhead of doing this in your performance considerations above.
Document the conceptual database out the gazoo. Don't just settle for definitions of the FORM of data. Insist on definitions of the semantics of the data as well.
Notice that if you use ID fields instead of natural keys to enforce referential integrity, you will have to generate each ID field in one schema, and let the association between ID and dependent data propagate by means of synonyms, views, and synchronized replication.
This is not going to be easy.
If DB is getting bigger, making back-up is getting more difficult because of it's size.
This could mean a serious scalability problem if you want to add high-traffic applications in the future, since it is much easier to add new database servers which run seperate dbs than it is to parrallelize a single DB. At least in SQL Server.
Pros:
The convenience of having everything in one place
Thinking less about good database design
Cons:
Even unrelated things are in one place
Less thinking about good database design leading to poorly normalized data
To me this just sounds like laziness and a belief that all this "fancy ivory tower database stuff" is worthless.
I can see that being scary, but considering the number of businesses that use Oracle EBS, or SAP, or other systems that are, in essence, this same configuration, I don't see it being a Bad Thing™. It's a big move, and will be tough to get correct, but it can really improve integration across the enterprise in the long run.
I've never heard of this approach and would like to know how the meeting goes. I see no real benefit in combining multiple applications into a single database when the data doesn't relate to each other.
I'm thinking you might have issues if you decide that an application requires it's own database server at one point.
Ah, the old EggsInOneBasket design pattern. It's not a favourite.
You're just compounding any problems caused by damage to that database. Spread the risk!
For the referential integrity issue, you can make copies of those shared tables in the subsidiary databases. You can't use real replication, but what you do is deny everything but select on these to most users.
On the same server, you can either push or pull data from the official repository of the master data and insert any new rows/update any changed rows. You can even do this with a trigger in the master database (I don't recommend it, though).
If it's different instances or servers, you can use linked servers or SSIS.
You can put the common data into a "core" schema in each database. Then you can have tools to check that all your core tables in every subsidiary database are consistent. The worse that can happen is that an application is not seeing a new employee because the core isn't updated. And keeping your database separate gives you an ability to decouple and gives you maintenance windows. (You can even decouple and run "standalone" if your master is down for maintenance).
I expect you'll only be seeing a few dozen of these core entity tables in even a largish enterprise.
There are many other ways to solve the referential integrity (RI) issue. I am not as familiar with SQL Server as other DB's. In Informix you can use synonyms to point to objects in other DB's and use these for your RI. In Oracle you can make a DB links to one or more DB's to accomplish the same thing.
These approaches have the issue that if any of the DB's are down the RI will fail causing issues in the dependent DB's. selects would work, but inserts would fail.
Consolidation can be a good idea, depending upon the size of the schema's, and other issues with scalability. SQL Server has serious scalability issues. Other DB platforms allow horizontal scaling with either a share everything approach (Oracle's RAC, latest Informix release) or a partitioned share nothing approach (DB2's DPF, Informix XPS, Netezza, Teradata)
I am with some of the others here interested to hear the results of your meeting.