We Keep Coding

High performance Custom user fields

looking for examples/tutorial for custom user fields, not via EAV
EAV is going to be problematic for various reasons such as performance
there are many base entities/tables with over 100000 records each
there will likely be over a dozen attributes
the records are to be displayed in a flat ui grid incl. custom fields so flattening them would be an issue while maintaining performance
Looking at enabling this via DDL where all custom fields would go into a matching table such as
<tablename>_custom_<userid>
and all user attributes would map to a column each and all their metadata stored in a metadata table
the retrieval would be simpler where the query would simply be
select *
from <tablename> A, tableName_custom_userid B
where B.KeyField = A.KeyField --( perhaps using outer join, haven't gone that far yet )
Wondering if there are any gotchas down the road that i need to be aware of ?
of course any samples/pointers would be helpful to kickstart the effort
specifically would appreciate any advice on using DDL for Sql Server compact 4

One technique I have seen used is to use a sort of 'hard-coded' EAV pattern. Don't hang up! It worked well with the dataset sizes you were talking about and didn't actually use EAV - it was only EAV-esque.
The idea is to have a set of tables to store these custom attributes within it, with some triggers (described below) on them. The custom attributes tablesets store metadata about the attribute (what table it goes with, data type, constraints, etc). You can get very fancy with this but I did not haev the need.
The triggers on your meta-tables are there to re-generate views that rollup base+extension into first class objects within the DB. So instead of table person + employee extension table, you have an employee view that includes both. When you drop a new value into the custom attributes tables, the triggers will re-roll the views and include the new stuff. If you wanted to go nuts, you could also have the triggers re-write stored procedures as well. Depending on how your mid-tier code is structured, you would still be forced to re-code some, however this would be the case anyway should you be applying rules that read the data.
In testing, I found that for the relatively small # of records you're talking about, performance was somewhat slower but followed roughly the same pattern of degradation (2x the number of records, ~2x as slow).
-- edits --
How I saw it done, you had a table that represented your first class objects, so a row for 'person' and a row for 'employee,' etc. We'll call that FCO. Then you had a secondary table that stored what tables represented the FCO. We'll call that Srcs.. For person, there would be one row, which is the person table. For Employee, there would be two rows, the person table and the Employee extension. There is a third table, called Attribs, which stores the columns from the tables that constitute the FCO. For simplicity, we'll say Employee has ID, Name and Address, and Employee has Hire Date and Department, and obviously PersonID referring back to Person table. So, 2 rows in FCO table (person and employee), 3 rows in Src table, 8 rows in Attribs.
The view, we'll call it vw_Employee, selects PersonID, Name, Address, Hire Date, Department from the two tables. It is built by a SQL stored procedure we'll call OnMetadataChange.
This SP is fired (by trigger or batch process), and its purpose is to generate the CREATE VIEW statements. It will iterate through every First Class Object, collect which fields from which tables constitute the view, and will issue a CREATE statement based on that. So OnMetadataChange produces a DROP and CREATE for each view, it generates a dynamic SQL statement that is executed once per entry in FCO table. It is preferable to do this with Triggers but not necessary. Hopefully your FCO definitions won't change too often, and when they do, there will probably be a code release as well. You can run your OnMetadataChange SP at that time.
The end result is a 2-layer database. The views constitute the First Class Object layer, which is meaningful to the application. The application only uses views. The tables constitute the 'physical' layer, which the application shouldn't care about. The meta-tables are essentially your mapping between the FCO layer and the physical layer. It takes some time to set it up, but it's quite effective, and gives you many of the benefits of EAV, while at the same time giving you the concrete benefits of 3nf tables (indexability, etc).
If you'd like I can throw some sample SQL out there.

Part of the problem you are having is that you are trying to store schema-less data in a SQL database, which is not its strength. There are three approaches that would make your life far easier:
1) Have a column which stores the serialized custom fields, with whatever format is mst convenient. For example, this column could store xml. Upsides are that you can use SQL Server Compact and pulling back a record is trivial. Downsides are that you always have to pull/push the entire xml blob to do an update, and it is difficult to impossible to query on any custom fields.
2) Upgrade to SQL Server Express, and use XML columns. This is nearly the same as the first suggestion, except that any server ready version of SQL Server has native support for XML data. These columns can have indexes added and fields within the data can be used in queries.
3) Use a Schema-less Database, like MongoDB or CouchDB. These databases are all about storing schemaless data, so your custom fields will be no different than any other field. As such, you can index and query custom fields. Upsides are that custom data is incredibly easy to work with, downsides are that you would have to spend some time rethinking how you store data to fit within their model.
If you do not need to query based on custom fields, or if you can query custom fields within business logic, then the first option can work for you. In any other case, I would err towards something with more capabilities than compact. If cost is the deciding factor, both SQL Server Express and MongoDB are free.

Strategies for modeling large (50~) number of properties

Scenario
I'm parsing emails and inserting them a database using an ORM (NHibernate to be exact). While my current approach does technically work I'm not very fond of it but can't of a better solution. The email contains 50~ fields and is sent from a third party and looks like this (obviously a very short dummy sample).
Field #1: Value 1 Field #2: Value 2
Field #3: Value 3 Field #4: Value 4 Field #5: Value 5
Problem
My problem is that with parsing this many fields the database table is an absolute monster. I can't create proper models employing any kind of relationships either AFAIK because each email sent is all static data and doesn't rely on any other sources.
The only idea I have is to find commonalities between each field and split them into more manageable chunks. Say 10~ fields per entity, so 5 entities total. However, I'm not terribly in love with that idea either seeing as all I'd be doing is create one-to-one relationships.
What is a good way of managing large number of properties that are out of your control?
Any thoughts?

Create 2 tables: 1 for the main object, and the other for the fields. That way you can programatically access each field as necessary, and the object model doesn't look to nasty.
But this is just off the top of my head; you have a weird problem.
If the data is coming back in a file that you can parse easily, then you might be able to get away with creating a command line application that will produce scripts and c# that you can then execute and copy, paste into your program. I've done that when creating properties out of tables from html pages (Like this one I had to do recently)

If the 50 properties are actually unique and discrete pieces of data regarding this one entity, I don't see a problem with having those 50 properties (even though that sounds like a lot) on one object. For example, the Type class has a large number of boolean properties relating to it's data (IsPublic, etc).
Alternatives:
Well, one option that comes to mind immediately is using dynamic object and overriding TryGetMember to lookup the 'property' name as a key in a dictionary of key value pairs (where your real set up of 50 key value pairs exists). Of course, figuring out how to map that from your ORM into your entity is the other problem and you'd lose intellisense support.
However, just throwing the idea out there.

Use a dictionary instead of separate fields. In the database, you just have a table for the field name and its value (and what object it belongs to).

Storing Data from Forms without creating 100's of tables: ASP.NET and SQL Server

Let me first describe the situation. We host many Alumni events over the course of each year and provide online registration forms for each event. There is a large chunk of data that is common for each event:
An Event with dates, times, managers, internal billing info, etc.
A Registration record with info about the payment and total amount charged per form submission
Bio/Demographic and alumni data about the 1 or more attendees (name, address, degree, etc.)
We store all of the above data within columns in tables as you would expect.
The trouble comes with the 'extra' fields we are asked to put on the forms. Maybe it is a dinner and there is a Veggie or Carnivore option, perhaps there is lodging and there are bed or smoking options, or perhaps there is an optional transportation option. There are tons of weird little "can you add this to the form?" types of requests we receive.
Currently, we JSONify any non-standard data and store it all in one column (per attendee) called 'extras'. We can read this data out in code but it is not well suited to querying. Our internal staff would like to generate a quick report on Veggie dinners needed for instance.
Other than creating a separate table for each form that holds the specific 'extra' data items, are there any other approaches that could make my life (and reporting) easier? Anyone working in a simialr environment?

This is actually one of the toughest problem to solve efficiently. The SQL Server Customer Advisory Team has dedicated a white-paper to the topic which I highly recommend you read: Best Practices for Semantic Data Modeling for Performance and Scalability.
You basically have 3 options:
semantic database (entity-attribute-value)
XML column
sparse columns
Each solution comes with ups and downs. Out of the top of my hat I'd say XML is probably the one that gives you the best balance of power and flexibility, but the optimal solution really depends on lots of factors like data set sizes, frequency at which new attributes are created, the actual process (human operators) that create-populate-use these attributes etc, and not at least your team skill set (some might fare better with an EAV solution, some might fare better with an XML solution). If the attributes are created/managed under a central authority and adding new attributes is a reasonable rare event, then the sparse columns may be a better answer.

Well you could also have the following db structure:
Have a table to store custom attributes
AttributeID
AttributeName
Have a mapping table between events and attributes with:
AttributeID
EventID
AttributeValue
This means you will be able to store custom information per event. And you will be able to reuse your attributes. You can include some metadata as
AttributeType
AllowBlankValue
to the attribute to handle it easily afterwards

Have you considered using XML instead of JSON? Difference: XML is supported (special data type) and has query integration ;)

quick and dirty, but actually nice for querying: simply add new columns. it's not like the empty entries in the previous table should cost a lot.
more databasy solution: you'll have something like an event ID in your table. You can link this to an n:m table connecting events to additional fields. And then store the additional field data in a table with additional_field_id, record_id (from the original table) and the actual value. Probably creates ugly queries, but seems politically correct in terms of database design.
I understand "NoSQL" (not only sql ;) databases like couchdb let you store arbitrary fields per record, but since you're already with SQL Server, I guess that's not an option.

This is the solution that we first proposed in ASP.NET Forums (that later became Community Server), and that the ASP.NET team built a similar version of in the ASP.NET 2.0 Membership when they released it:
Property Bags on your domain objects
For example:
Event.Profile() or in your case, Event.Extras().
Basically, a property bag is a serialized collection of data stored in a name/value pair in a column (or columns). The ASP.NET 2.0 Membership went the route of storing names in a semi-colon delimited list, and values in the same:
Table: aspnet_Profile
Column: PropertyNames (separated by semi-colons, and has start index and end index)
Column: PropertyValues (separated by semi-colons, and only stores the string value)
The downside to that approach is it is all strings, and manually has to be parsed (even though the membership system does it for you automatically).
Recently, my current method is I've built FormCollection and NameValueCollection C# extension methods that automatically serialize the collections to an XML result. And I store that XML in the table in it's own column associated with that entity. I also have a deserializer C# extension on XElement that deserializes that data back to the collection at runtime.
This gives you the power of actually querying those properties in XML, via SQL (though, that can be slow though - always flatten out your read-only data).
The final note is runtime querying: The general rule we follow is, if you are going to query a property of an entity in normal application logic, then you move that property to an actual column on the table - and create the appropriate indexes. If that data will never be queried directly (for example, Linq-to-Sql or EF), then leave it in the XML Property Bag.
Property Bags gives you the power of extending your domain models however you like, without having to modify the db schema.

dynamic data model

I have a project that requires user-defined attributes for a particular object at runtime (Lets say a person object in this example). The project will have many different users (1000 +), each defining their own unique attributes for their own sets of 'Person' objects.
(Eg - user #1 will have a set of defined attributes, which will apply to all person objects 'owned' by this user. Mutliply this by 1000 users, and that's the bottom line minimum number of users the app will work with.) These attributes will be used to query the people object and return results.
I think these are the possible approaches I can use. I will be using C# (and any version of .NET 3.5 or 4), and have a free reign re: what to use for a datastore. (I have mysql and mssql available, although have the freedom to use any software, as long as it will fit the bill)
Have I missed anything, or made any incorrect assumptions in my assessment?
Out of these choices - what solution would you go for?
Hybrid EAV object model. (Define the database using normal relational model, and have a 'property bag' table for the Person table).
Downsides: many joins per / query. Poor performance. Can hit a limit of the number of joins / tables used in a query.
I've knocked up a quick sample, that has a Subsonic 2.x 'esqe interface:
Select().From().Where ... etc
Which generates the correct joins, then filters + pivots the returned data in c#, to return a datatable configured with the correctly typed data-set.
I have yet to load test this solution. It's based on the EA advice in this Microsoft whitepaper:
SQL Server 2008 RTM Documents Best Practices for Semantic Data Modeling for Performance and Scalability
Allow the user to dynamically create / alter the object's table at run-time. This solution is what I believe NHibernate does in the background when using dynamic properties, as discussed where
http://bartreyserhove.blogspot.com/2008/02/dynamic-domain-mode-using-nhibernate.html
Downsides:
As the system grows, the number of columns defined will get very large, and may hit the max number of columns. If there are 1000 users, each with 10 distinct attributes for their 'Person' objects, then we'd need a table holding 10k columns. Not scalable in this scenario.
I guess I could allow a person attribute table per user, but if there are 1000 users to start, that's 1000 tables plus the other 10 odd in the app.
I'm unsure if this would be scalable - but it doesn't seem so. Someone please correct me if I an incorrect!
Use a NoSQL datastore, such as CouchDb / MongoDb
From what I have read, these aren't yet proven in large scale apps, based on strings, and are very early in development phase. IF I am incorrect in this assessment, can someone let me know?
http://www.eflorenzano.com/blog/post/why-couchdb-sucks/
Using XML column in the people table to store attributes
Drawbacks - no indexing on querying, so every column would need to be retrieved and queried to return a resultset, resulting in poor query performance.
Serializing an object graph to the database.
Drawbacks - no indexing on querying, so every column would need to be retrieved and queried to return a resultset, resulting in poor query performance.
C# bindings for berkelyDB
From what I read here: http://www.dinosaurtech.com/2009/berkeley-db-c-bindings/
Berkeley Db has definitely proven to be useful, but as Robert pointed out – there is no easy interface. Your entire wOO wrapper has to be hand coded, and all of your indices are hand maintained. It is much more difficult than SQL / linq-to-sql, but that’s the price you pay for ridiculous speed.
Seems a large overhead - however if anyone can provide a link to a tutorial on how to maintain the indices in C# - it could be a goer.
SQL / RDF hybrid.
Odd I didn't think of this before. Similar to option 1, but instead of an "property bag" table, just XREF to a RDF store?
Querying would them involve 2 steps - query the RDF store for people hitting the correct attributes, to return the person object(s), and use the ID's for these person object in the SQL query to return the relational data. Extra overhead, but could be a goer.

The ESENT database engine on Windows is used heavily for this kind of semi-structured data. One example is Microsoft Exchange which, like your application, has thousands of users where each user can define their own set of properties (MAPI named properties). Exchange uses a slightly modified version of ESENT.
ESENT has a lot of features that enable applications with large meta-data requirements: each ESENT table can have about ~32K columns defined; tables, indexes and columns can be added at runtime; sparse columns don't take up any record space when not set; and template tables can reduce the space used by the meta-data itself. It is common for large applications to have thousands of tables/indexes.
In this case you can have one table per user and create the per-user columns in the table, creating indexes on any columns that you want to query. That would be similar to the way that some versions of Exchange store their data. The downside of this approach is that ESENT doesn't have a query engine so you will have to hand-craft your queries as MakeKey/Seek/MoveNext calls.
A managed wrapper for ESENT is here:
http://managedesent.codeplex.com/

In a EAV model you don't have to have many joins, as you can just have the joins you need for the query filtering. For the resultset, return property entries as a separate rowset.
That is what we are doing in our EAV implementation.
For example, a query might return persons with extended property 'Age' > 18:
Properties table:
1 Age
2 NickName
First resultset:
PersonID Name
1 John
2 Mary
second resultset:
PersonID PropertyID Value
1 1 24
1 2 'Neo'
2 1 32
2 2 'Pocahontas'
For the first resultset, you need an inner join for the 'age' extended property
to query the basic Person object entity part:
select p.ID, p.Name from Persons p
join PersonExtendedProperties pp
on p.ID = pp.PersonID
where pp.PropertyName = 'Age'
and pp.PropertyValue > 18 -- probably need to convert to integer here
For the second resultset, we are making an outer join of the first resultset with PersonExtendedProperties table to get the rest of the extended properties. It's a 'narrow' resultset, we do not pivot the properties in sql, so we don't need multiple joins here.
Actually we use separate tables for different types to avoid data type conversion, to have extended properties indexed and easily queriable.

My recommendation:
Allow properties to be marked as indexable. Have a smallish hard limit on number of indexable properties, and on columns per object. Have a large hard limit on total column types in all objects.
Implement indexes as separate tables (one per index) joined with main table of data (main table has large unique key for object). (Index tables can then be created/dropped as required).
Serialize the data, including the index columns, plus put the index propertoes in first class relational columns in their dedicated index tables. Use JSON instead of XML to save space in the table. Enforce short column name policy (or long display name and short stored name policy) to save space and increase performance.
Use quarks for field identifiers (but only in the main engine to save RAM and speed some read operations -- don't rely on quark pointer comparison in all cases).
My thought on your options:
1 is a possible. Performance clearly will be lower than if field ID columns not stored.
2 is a no in general DB engines not all happy about dynamic schema changes. But a possible yes if your DB engine is good at this.
3 Possible.
4 Yes though I'd use JSON.
5 Seems like 4 only less optimized??
6 Sounds good; would go with if happy to try something new and also if happy about reliability and performance but usually would want to go with more mainstream technology. I'd also like to reduce the number of engines involved in coordinating a transaction to less then would be true here.
Edit: But of course though I've recommened something there can be no general right answer here -- profile various data models and approaches with your data to see what runs best for your application.
Edit: Changed last edit wording.

Assuming you an place a limit, N, on how many custom attributes each user can define; just add N extra columns to the Person table. Then have a separate table where you store per-user metadata to describe how to interpret the contents of those columns for each user. Similar to #1 once you've read in the data, but no joins needed to pull in the custom attributes.

For a problem similar to your problem, we have used the "XML Column" approach (the fourth one in your survey of methods). But you should note that many databases (DBMS) support index for xml values.
I recommend you to use one table for Person which contains one xml column along with other common columns. In other words, design the Person table with columns that are common for all person records and add a single xml column for dynamic and differing attributes.
We are using Oracle. it supports index for its xml-type. Two types of indices are supported: 1- XMLIndex for indexing elements and attributes within an xml, 2- Oracle Text Index for enabling full-text search in text fields of the xml.
For example, in Oracle you can create an index such as:
CREATE INDEX index1 ON table_name (XMLCast(XMLQuery ('$p/PurchaseOrder/Reference'
PASSING XML_Column AS "p" RETURNING CONTENT) AS VARCHAR2(128)));
and xml-query is supported in select queries:
SELECT count(*) FROM purchaseorder
WHERE XMLCast(XMLQuery('$p/PurchaseOrder/Reference'
PASSING OBJECT_VALUE AS "p" RETURNING CONTENT)
AS INTEGER) = 25;
As I know, other databases such as PostgreSQL and MS SQL Server (but not mysql) support such index models for xml value.
see also:
http://docs.oracle.com/cd/E11882_01/appdev.112/e23094/xdb_indexing.htm#CHDEADIH

How do I compare two datasets for equality

I have two datasets each with one data table pulled from different sources and I need to know if there are any differences in the data contained in the data tables. I'm trying to avoid looping and comparing each individual record or column, although there may be no other way. All I need to know is if there is a difference in the data, I do not need to know the details of any difference.
I have tried the below code, but it appears that dataset.Merge does not update rowstatus so dataset.HasChanges() always returns false. Any help is appreciated:
var currentDataSet = GetSomeData();
var historicalDataSet = GetSomeHistoricalData();
historicalDataSet.Merge(currentDataSet);
if (historicalDataSet.HasChanges()) DoSomeStuff();

I don't know of any built-in support for this and I wouldn't expect it either. So you'll have to do this by yourself in some way.
The most obvious way would be a brute force, table by table and row by row approach.
If you can rely on certain factors to be the same, ie exactly the same naming, ordering of records etc then you could test if saving both as XML and comparing the results might be an efficient trick.