I want to understand how surrogate keys are leveraged in real-time DWH environments. I get that they add the benefit of not being dependent on source-generated data to store each dimension key and also avoid having composite key built out of natural keys from dimensions in the fact, For eg, (prod id + cust id+ time id)
But does it not add the complexity of having to maintain the lookup of (natural key, surrogate key) while we load data into facts. I have been working in BI/DW teams for last 3 years and we do not maintain any surrogate keys in our systems. We leverage natural keys to build our datamarts. One sample usecase is revenue data which is stored in transactional system, which is loaded into warehouse at customer, product, time period granularity using the same natural keys from source. We use the same to join with corresponding dimensions to build STAR schema.
Main reason I think it makes sense in our case is that business uses EDW data to do micro-analysis of data at account level, not just trending analysis. We would need to maintain data integrity in that case which we achieve using natural keys. I want to understand how other DW environments work. How do you leverage surrogate keys or natural keys in your systems.
Thanks!
One reason is to maintain and being able to compare historical changes.
Example, if one of your product attributes changes and you wanted to look at and compare revenue before and after the attribute change, how would you do that without using surrogate product keys? Using a natural key would just overwrite the old value when you ETL.
The lookup doesn't have to be very complex to maintain. Most ETL tools have support for this and usually have some caching mechanism built in to cache lookup values.
Also, what do you mean when you say "real-time" data warehouse? Are you using ROLAP, DirectQuery or something similar? If so, you might be building your marts directly on your OLTP system and de-normalize in some semantic model. Then you could use your natural keys because there is no traditional ETL/data warehouse to do lookups and store your surrogate keys.
Lastly, granularity is not related to what type of key you are using.
If your business is stable and runs on top of a single application for everything, natural keys will work just fine, as your experience tells you.
Most businesses are not in such a state or not for very long. Mergers happen, new applications are introduced, legacy stuff refuses to die. New lines of business are started or split off and require wholesale renaming of existing natural key schemes.
Surrogate keys provide great benefits in keeping reporting dimensions stable and usable across the business when you have a bunch of separate new and legacy applications that all have their own versions of your customers and products and regularly get migrated or swapped out for similar systems with new natural key definitions. The major work is linking the various natural keys of a customer/product/whatever, assigning a surrogate key is just a simple and very helpful step in that.
Even in your scenario, I would use surrogate keys as they prepare you for future changes and are very helpful with historical data (as NITHIN B also answered) in Type 2 Dimensions.
It's quite possible to do versioning with natural keys by adding a version field to your dimension and fact tables, but it makes the joins harder to write for reporting and your whole system still gets messy if business or application changes cause the natural keys to change.
To illustrate:
Select bla
from Fact F
inner join Dim_Customer DC
on F.Surrogate_key = DC.Surrogate_Key
is almost foolproof. If you mess this up, it will be immediately obvious in your report.
Select bla
from Fact F
inner join Dim_Customer DC
on F.Natural_key = DC.Natural_Key
and F.Version = DC.Version
does the same job, but if you forget that last line, everything will look normal but your numbers will be inflated depending on how many versions there are on average. Kinda painful when that 25% sales increase turns out to be an error.
An additional reason, which has not been mentioned yet, is performance. Sometimes (very often in my experience) natural keys are strings, sometimes long strings.
It seems not a big deal using 10, 20 or 30 byte string instead of a 4 byte integer, but when you have 10 dimension and hundred of millions of rows, it adds up fast.
Could you please post a sample design.
I would be interested to see how you can load a fact table with Dimension Keys which are natural keys. Kimball design never recommends it.
My stand on Surrogate Keys in DWH.
Surrogate keys give you a lot of flexibility with Type 2 Dimensions,
ie if you have Type 2 Dimensions. For eg: You can track changes of a customer
if he or she changes her second name. You can have rows withe old values and
new values.
Fact tables usually hold keys which are surrogate keys. It makes your star
schema neat and tidy and robust.
However I am not jumping queues here, would wait for your design before going pro or against your stand.
Cheers
Nithin
(my reason for asking this question is based on having read this answer, which made me rethink my current setup)
I currently am developing a ruby on rails application in which there are many languages, each of which has a dictionary of base words attached to it, as well as a list of the words that map to each base word. The way I currently have it set up, there is a base_words table that contains the base_word as a string, along with the language_id as a foreign key. There is also a words table, each row of which contains a word string, along with the base_word_id as a foreign key. There is also a language_id indexed on each column, although I'm almost positive that this is superfluous due to the language_id on base_word, so I'm planning to take it off (although this could be a bad assumption on my part).
In sum, on the contrary to the answer I mentioned in the beginning, the tables are not separated by language, because I've reasoned that I can simply pull out the language words programmatically when the time comes. However, my application will also have translation(s) associated with each base word (as did the answer I referenced), and so I'm doubting my structure due to the realization that each translation will actually be a base_word in the same table as itself, which would mean that the translation would actually be just an id of another base word in said table. This may be completely fine, or it may not be - I have no clue (this is my first ever programming project).
Is this ok? Do I need to separate my base_words into separate tables for each language, or can I leave it all in one table?
Another example: I also need to store many phrases for each language, along with their translations. Should I have one table where each row has the appropriate translation of the phrase, or one table where each row contains simply one phrase and a language_id, or multiple tables (one for each language)?
Un saludo,
Michael
As in the other scenario, you'll have a translations table. There is no technical reason it couldn't have multiple foreign keys to base_words (a source_word_id and target_word_id, perhaps). So yes, you can absolutely store all your words in one table. There are some minor side effects involved with translations being directional relationships: it becomes possible to have translations which only work one way, and there will be many pairs of entries with opposite source and target. Neither of these is much of a worry: the first is even potentially desirable in order to represent words with double meanings in one language but not the other, and as for the second, space is cheap and indexing is easy.
You are correct that you do not need words.language_id, so long as you always join base_words when you're querying words and the language matters. This obviously changes if you have a use case where it makes sense to leave base_words out, but that scenario sounds unlikely based on what you describe.
As for phrases: why should they be handled any differently than base_words?
In my star schema, I have a project dimension which has columns such as start_date, finish_date, service_date, onhold_date, resume_date etc.
Should I introduce foreign keys for all the dates in the fact table and connect them to a date dimension or should I snowflake the project_dimension with date_dimension? Not all the dates are available for a given project so keeping all these columns in a fact_table may result in having null keys in fact_table.
What is the best way to handle dates in this scenario?
In a data warehouse, I always prefer a general star schema, snowflaked as little as possible, although this is obviously a bit of personal preference, and can depend on what environment you are using. For Oracle (the environment I am most used to) it supports snowflaking physically, but best practice denotes not to snowflake the business model (logical) layer.
Personally, I would push for putting the FKs on the fact for a few reasons. One, that maintains a star, which generally performs better as snowflakes introduce more joins, and stars handle aggregation quicker. Two, if you have users combining this data with data from other facts, having a conformed date dimension just makes sense, can help query performance, and is more robust. Finally, stars are probably most common, so having others work on this area in the future should be easier/the data may work better with other applications in the future.
For null FKs, I would default to whatever default date your system has, for us, our unspecified record is 01/01/1901. I would not leave them null, unless it is desired to not see 1901 by business users, and even then, I would probably null them out with a case statement, but still leave the field filled on the table.
Here is a good article describing the advantages/disadvantages of each type. Like I said, neither is completely right or wrong.
http://www.dataonfocus.com/star-schema-and-snowflake-schema/
I've been seeing a lot of commentary (from an NHibernate perspective) about using Guid as opposed to an int (and presumably auto-id in the database), with the conclusion that using auto-identity breaks the UoW pattern.
This post has a short description of the issue, but it doesn't really tell me "why" it breaks the pattern (unless I'm misunderstanding which is likely the case.
Can someone enlighten me?
There are a few major reasons.
Using a Guid gives you the ability to identify a single entity across many databases, including six relational databases with the same schema but different data, a document database, etc. This becomes important any time you have more than one single place where data goes - and that means your case too: you have a dev database and a prod database, right?
Using a Guid gives NHibernate the ability to batch more statements together, perform more database work at the very end of the unit of work / transaction, and reduce the total number of roundtrips to the database, increasing performance as well as conferring other benefits.
Comment:
Random Guids do not create poor indexes - natively, they create poor clustered indexes. There are two solutions.
Use a partially sequential Guid. With NHibernate, this means using the guid.comb id generator rather than the guid id generator. guid.comb is partially sequential for good performance, but retains a very high degree of randomness.
Have your Guid primary key be a nonclustered index, and put a clustered index on another auto-incrementing column. You may decide to map this column, in which case you lose the benefit of better batching and fewer roundtrips, but you regain all the benefits of short numbers that fit easily in a URL. Or you may decide not to map this column and have it remain completely within the database, in which case you gain better performance for Guids as primary keys as well as better performance for NHibernate doing fewer roundtrips.
My take would be that the key breaking factor is that getting the auto-incremented value requires an actual write to the database, which nHibernate would have deferred or possibly never performed.
Using identity and in a parent-child scenario the database has round trip the database to get the ID of a parent so that it can associate the child correctly. This means that the parent has to be committed at this time. Should there be a problem with the child you would then need to delete the parent in order to exit the UoW correctly.
I inherited a database built with the idea that composite keys are much more ideal than using a unique object ID field and that when building a database, a single unique ID should never be used as a primary key. Because I was building a Rails front-end for this database, I ran into difficulties getting it to conform to the Rails conventions (though it was possible using custom views and a few additional gems to handle composite keys).
The reasoning behind this specific schema design from the person who wrote it had to do with how the database handles ID fields in a non-efficient manner and when it's building indexes, tree sorts are flawed. This explanation lacked any depth and I'm still trying to wrap my head around the concept (I'm familiar with using composite keys, but not 100% of the time).
Can anyone offer opinions or add any greater depth to this topic?
Most of the commonly used engines (MS SQL Server, Oracle, DB2, MySQL, etc.) would not experience noticeable issues using a surrogate key system. Some may even experience a performance boost from the use of a surrogate, but performance issues are highly platform-specific.
In general terms, the natural key (and by extension, composite key) verses surrogate key debate has a long history with no likely “right answer” in sight.
The arguments for natural keys (singular or composite) usually include some the following:
1) They are already available in the data model. Most entities being modeled already include one or more attributes or combinations of attributes that meet the needs of a key for the purposes of creating relations. Adding an additional attribute to each table incorporates an unnecessary redundancy.
2) They eliminate the need for certain joins. For example, if you have customers with customer codes, and invoices with invoice numbers (both of which are "natural" keys), and you want to retrieve all the invoice numbers for a specific customer code, you can simply use "SELECT InvoiceNumber FROM Invoice WHERE CustomerCode = 'XYZ123'". In the classic surrogate key approach, the SQL would look something like this: "SELECT Invoice.InvoiceNumber FROM Invoice INNER JOIN Customer ON Invoice.CustomerID = Customer.CustomerID WHERE Customer.CustomerCode = 'XYZ123'".
3) They contribute to a more universally-applicable approach to data modeling. With natural keys, the same design can be used largely unchanged between different SQL engines. Many surrogate key approaches use specific SQL engine techniques for key generation, thus requiring more specialization of the data model to implement on different platforms.
Arguments for surrogate keys tend to revolve around issues that are SQL engine specific:
1) They enable easier changes to attributes when business requirements/rules change. This is because they allow the data attributes to be isolated to a single table. This is primarily an issue for SQL engines that do not efficiently implement standard SQL constructs such as DOMAINs. When an attribute is defined by a DOMAIN statement, changes to the attribute can be performed schema-wide using an ALTER DOMAIN statement. Different SQL engines have different performance characteristics for altering a domain, and some SQL engines do not implement DOMAINS at all, so data modelers compensate for these situations by adding surrogate keys to improve the ability to make changes to attributes.
2) They enable easier implementations of concurrency than natural keys. In the natural key case, if two users are concurrently working with the same information set, such as a customer row, and one of the users modifies the natural key value, then an update by the second user will fail because the customer code they are updating no longer exists in the database. In the surrogate key case, the update will process successfully because immutable ID values are used to identify the rows in the database, not mutable customer codes. However, it is not always desirable to allow the second update – if the customer code changed it is possible that the second user should not be allowed to proceed with their change because the actual “identity” of the row has changed – the second user may be updating the wrong row. Neither surrogate keys nor natural keys, by themselves, address this issue. Comprehensive concurrency solutions have to be addressed outside of the implementation of the key.
3) They perform better than natural keys. Performance is most directly affected by the SQL engine. The same database schema implemented on the same hardware using different SQL engines will often have dramatically different performance characteristics, due to the SQL engines data storage and retrieval mechanisms. Some SQL engines closely approximate flat-file systems, where data is actually stored redundantly when the same attribute, such as a Customer Code, appears in multiple places in the database schema. This redundant storage by the SQL engine can cause performance issues when changes need to be made to the data or schema. Other SQL engines provide a better separation between the data model and the storage/retrieval system, allowing for quicker changes of data and schema.
4) Surrogate keys function better with certain data access libraries and GUI frameworks. Due to the homogeneous nature of most surrogate key designs (example: all relational keys are integers), data access libraries, ORMs, and GUI frameworks can work with the information without needing special knowledge of the data. Natural keys, due to their heterogeneous nature (different data types, size etc.), do not work as well with automated or semi-automated toolkits and libraries. For specialized scenarios, such as embedded SQL databases, designing the database with a specific toolkit in mind may be acceptable. In other scenarios, databases are enterprise information resources, accessed concurrently by multiple platforms, applications, report systems, and devices, and therefore do not function as well when designed with a focus on any particular library or framework. In addition, databases designed to work with specific toolkits become a liability when the next great toolkit is introduced.
I tend to fall on the side of natural keys (obviously), but I am not fanatical about it. Due to the environment I work in, where any given database I help design may be used by a variety of applications, I use natural keys for the majority of the data modeling, and rarely introduce surrogates. However, I don’t go out of my way to try to re-implement existing databases that use surrogates. Surrogate-key systems work just fine – no need to change something that is already functioning well.
There are some excellent resources discussing the merits of each approach:
http://www.google.com/search?q=natural+key+surrogate+key
http://www.agiledata.org/essays/keys.html
http://www.informationweek.com/news/software/bi/201806814
I've been developing database applications for 15 years and I have yet to come across a case where a non-surrogate key was a better choice than a surrogate key.
I'm not saying that such a case does not exist, I'm just saying when you factor in the practical issues of actually developing an application that accesses the database, usually the benefits of a surrogate key start to overwhelm the theoretical purity of non-surrogate keys.
the primary key should be constant and meaningless; non-surrogate keys usually fail one or both requirements, eventually
if the key is not constant, you have a future update issue that can get quite complicated
if the key is not meaningless, then it is more likely to change, i.e. not be constant; see above
take a simple, common example: a table of Inventory items. It may be tempting to make the item number (sku number, barcode, part code, or whatever) the primary key, but then a year later all the item numbers change and you're left with a very messy update-the-whole-database problem...
EDIT: there's an additional issue that is more practical than philosophical. In many cases you're going to find a particular row somehow, then later update it or find it again (or both). With composite keys there is more data to keep track of and more contraints in the WHERE clause for the re-find or update (or delete). It is also possible that one of the key segments may have changed in the meantime!. With a surrogate key, there is always only one value to retain (the surrogate ID) and by definition it cannot change, which simplifies the situation significantly.
It sounds like the person who created the database is on the natural keys side of the great natural keys vs. surrogate keys debate.
I've never heard of any problems with btrees on ID fields, but I also haven't studied it in any great depth...
I fall on the surrogate key side: You have less repetition when using a surrogate key, because you're only repeating a single value in the other tables. Since humans rarely join tables by hand, we don't care if it's a number or not. Also, since there's only one fixed-size column to look up in the index, it's safe to assume surrogates have a faster lookup time by primary key as well.
Using 'unique (object) ID' fields simplifies joins, but you should aim to have the other (possibly composite) key still unique -- do NOT relax the not-null constraints and DO maintain the unique constraint.
If the DBMS can't handle unique integers effectively, it has big problems. However, using both a 'unique (object) ID' and the other key does use more space (for the indexes) than just the other key, and has two indexes to update on each insert operation. So it isn't a freebie -- but as long as you maintain the original key, too, then you'll be OK. If you eliminate the other key, you are breaking the design of your system; all hell will break loose eventually (and you might or might not spot that hell broke loose).
I basically am a member of the surrogate key team, and even if I appreciate and understand arguments such as the ones presented here by JeremyDWill, I am still looking for the case where "natural" key is better than surrogate ...
Other posts dealing with this issue usually refer to relational database theory and database performance. Another interesting argument, always forgotten in this case, is related to table normalisation and code productivity:
each time I create a table, shall I
lose time
identifying its primary key and its
physical characteristics (type,
size)
remembering these characteristics
each time I want to refer to it in
my code?
explaining my PK choice to other
developers in the team?
My answer is no to all of these questions:
I have no time to lose trying to
identify "the best Primary Key" when
dealing with a list of persons.
I do not want to remember that the
Primary Key of my "computer" table
is a 64 characters long string (does
Windows accepts that many characters
for a computer name?).
I don't want to explain my choice to
other developers, where one of them
will finally say "Yeah man, but
consider that you have to manage
computers over different domains?
Does this 64 characters string allow
you to store the domain name + the
computer name?".
So I've been working for the last five years with a very basic rule: each table (let's call it 'myTable') has its first field called 'id_MyTable' which is of uniqueIdentifier type. Even if this table supports a "many-to-many" relation, such as a 'ComputerUser' table, where the combination of 'id_Computer' and 'id_User' forms a very acceptable Primary Key, I prefer to create this 'id_ComputerUser' field being a uniqueIdentifier, just to stick to the rule.
The major advantage is that you don't have to care animore about the use of Primary Key and/or Foreign Key within your code. Once you have the table name, you know the PK name and type. Once you know which links are implemented in your data model, you'll know the name of available foreign keys in the table.
I am not sure that my rule is the best one. But it is a very efficient one!
A practical approach to developing a new architecture is one that utilizes surrogate keys for tables that will contain thousands of multi-column highly unique records and composite keys for short descriptionary tables. I usually find that the colleges dictate the use of surrogate keys while the real world programmers prefer composite keys. You really need to apply the right type of primary key to the table - not just one way or the other.
using natural keys makes a nightmare using any automatic ORM as persistence layer. Also, foreign keys on multiple column tend to overlap one another and this will give further problem when navigating and updating the relationship in a OO way.
Still you could transform the natural key in an unique constrain and add an auto generated id; this doesn't remove the problem with the foreign keys, though, those will have to be changed by hand; hopefully multiple columns and overlapping constraints will be a minority of all the relationship, so you could concentrate on refactoring where it matter most.
natural pk have their motivation and usages scenario and are not a bad thing(tm), they just tend to not get along well with ORM.
my feeling is that as any other concepts, natural keys and table normalization should be used when sensible and not as blind design constraints
I'm going to be short and sweet here: Composite primary keys are not good these days. Add in surrogate arbitrary keys if you can and maintain the current key schemes via unique constraints. ORM is happy, you're happy, original programmer not-so-happy but unless he's your boss then he can just deal with it.
Composite keys can be good - they may affect performance - but they are not the only answer, in much the same way that a unique (surrogate) key isn't the only answer.
What concerns me is the vagueness in the reasoning for choosing composite keys. More often than not vagueness about anything technical indicates a lack of understanding - maybe following someone else's guidelines, in a book or article....
There is nothing wrong with a single unique ID - infact if you've got an application connected to a database server and you can choose which database you're using it will all be good, and you can pretty much do anything with your keys and not really suffer too badly.
There has been, and will be, a lot written about this, because there is no single answer. There are methods and approaches that need to be applied carefully in a skilled manner.
I've had lots of problems with ID's being provided automatically by the database - and I avoid them wherever possible, but still use them occasionally.
... how the database handles ID fields in a non-efficient manner and when it's building indexes, tree sorts are flawed ...
This was almost certainly nonsense, but may have related to the issue of index block contention when assigning incrementing numbers to a PK at a high rate from different sessions. If so then the REVERSE KEY index is there to help, albeit at the expense of a larger index size due to a change in block-split algorithm. http://download.oracle.com/docs/cd/B19306_01/server.102/b14220/schema.htm#sthref998
Go synthetic, particularly if it aids more rapid development with your toolset.
I am not a experienced one but still i m in favor of Using primary key as id here is the explanation using an example..
The format of external data may change over time. For example, you might think that the ISBN of a book would make a good primary key in a table of books. After all, ISBNs are unique. But as this particular book is being written, the publishing industry in the United States is gearing up for a major change as additional digits are added to all ISBNs.
If we’d used the ISBN as the primary key in a table of books, we’d have to update each row to reflect this change. But then we’d have another problem. There’ll be other tables in the database that reference rows in the books table via the primary key. We can’t change the key in the books table unless we first go through and update all of these references. And that will involve dropping foreign key constraints, updating tables, updating the books table, and finally reestablishing the constraints. All in all, this is something of a pain.
The problems go away if we use our own internal value as a primary key. No third party can come along and arbitrarily tell us to change our schema—we control our own keyspace. And if something such as the ISBN does need to change, it can change without affecting any of the existing relationships in the database. In effect, we’ve decoupled the knitting together of rows from the external representation of data in those rows.
Although the explanation is quite bookish but i think it explains the things in a simpler way.
#JeremyDWill
Thank you for providing some much-needed balance to the debate. In particular, thanks for the info on DOMAINs.
I actually use surrogate keys system-wide for the sake of consistency, but there are tradeoffs involved. The most common cause for me to curse using surrogate keys is when I have a lookup table with a short list of canonical values—I'd use less space and all my queries would be shorter/easier/faster if I had just made the values the PK instead of having to join to the table.
You can do both - since any big company database is likely to be used by several applications, including human DBAs running one-off queries and data imports, designing it purely for the benefit of ORM systems is not always practical or desirable.
What I tend to do these days is to add a "RowID" property to each table - this field is a GUID, and so unique to each row. This is NOT the primary key - that is a natural key (if possible). However, any ORM layers working on top of this database can use the RowID to identify their derived objects.
Thus you might have:
CREATE TABLE dbo.Invoice (
CustomerId varchar(10),
CustomerOrderNo varchar(10),
InvoiceAmount money not null,
Comments nvarchar(4000),
RowId uniqueidentifier not null default(newid()),
primary key(CustomerId, CustomerOrderNo)
)
So your DBA is happy, your ORM architect is happy, and your database integrity is preserved!
I just wanted to add something here that I don't ever see covered when discussing auto-generated integer identity fields with relational databases (because I see them a lot), and that is, it's base type can an will overflow at some point.
Now I'm not trying to say this automatically makes composite ids the way to go, but it's just a matter of fact that even though more data could be logically added to a table (which is still unique), the single auto-generated integer identity could prevent this from happening.
Yes I realize that for most situations it's unlikely, and using a 64bit integer gives you lots of headroom, and realistically the database probably should have been designed differently if an overflow like this ever occurred.
But that doesn't prevent someone from doing it... a table using a single auto-generated 32bit integer as it's identity, which is expected to store all transactions at a global level for a particular fast-food company, is going fail as soon as it tries to insert it's 2,147,483,648th transaction (and that is a completely feasible scenario).
It's just something to note, that people tend to gloss over or just ignore entirely. If any table is going to be inserted into with regularity, considerations should be made to just how often and how much data will accumulate over time, and whether or not an integer based identifier should even be used.