When do we need to use a 1-to-1 relationship in database design? In my opinion, if two tables are in a 1-to-1 relationship, they can be combined into one table. Is this true?
Vertical partitioning for large tables to reduce I/O and cache requirements -- separate columns that are queried often vs rarely.
Adding a column to a production system when the alter table is "too expensive".
Super-type/subtype pattern.
Vertical partitioning to benefit from table (join) elimination -- providing optimizer
supports it (again to reduce I/O and cache) .
Anchor modeling -- similar to 4, but down to 6NF.
On occasion it's useful for table locks. When you add a column to a database, the whole table gets locked until it's fully rewritten. This has little to no impact when your database has 100k rows. But if you've 100M rows, or 1B rows, it's a whole different story...
It's also useful to avoid dead rows that take too much space. If you're using MVCC and some of your columns are regularly overwritten, it occasionally makes sense to place them in a separate table. Arguably, auto-vacuuming eventually kicks in, but for the sake of sparing hard drive work, better vacuum a few int fields in a separate table than a whole bunch of entire rows full of text, varchar(n) and who know what else.
A last reason would be the abuse of select * in ORMs. If you're storing images or blog posts/articles, for instance, it may make sense to store the blob/text field in a separate table. Because every time it gets loaded for a reason or the other, your ORM is going to load the whole row. When you only need the URL of your image or post, the last thing you want is to pull the whole binary/text from the database; and yet your ORM will do just that...
yes in general.
One exception may be if you want to assign privileges differently to a subset of columns.
also consider that this is true only when both sides are required.
One reason would be to put frequently accessed data in one table and extremely rarely accessed data in another table. It would run faster and save some memory.
But I would have to have my arm twisted hard before I would do it.
Related
I have to create a database to store information being sent and received to / from a 3rd party web service portal. There are about 150 fields of information to be sent though I can remove about 50 of those fields by normalising (there are three sets addresses that can be saved in an address table, for example). However, this still leaves a table that could potentially have 100 columns.
I've come up with two ways of handling this though I'm not sure which to use:
1. Have a table with 100 columns and three references to an address table.
2. Break it down into maybe 15-20 separate dedicated tables.
Option 1 seems the quickest as it involves the fewest joins but the idea of a table with 100 columns doesn't feel right.
Option 2 feels better and would break things down in to more managable chunks but it won't save any database space and will increase the number of joins. Pretty much all the columns in the database will have a value and I cannot normalise these columns any further.
My question is, in this situation is it acceptable to have a table with c.100 columns in it or should I try and break it down over several tables for presentation?
Please note: The table structure will not change over the course of it's useage, a new database would be created for a new version of the web service portal. I have no control over the web service data structure.
Edit: #Oded's answer below has made me think a bit more about how the data will be accessed; it will really only be accessed in whole and not in part. I wouldn't for example, need to return columns 5-20 on a regular basis.
Answer: I accepted Oded's answer based on the comments after he posted it helped me make my mind up and I decided to go with option 1. As the data is accessed in full then having one table seems the better solution. If, for example, I regularly wanted to access columns 5-20 rather than the full table row then I'd see about breaking it up into separate tables for performance reasons.
Speaking from a relational purist point of view - first, there is nothing against having 100 columns in a table, if they are related. The point here is that if after normalizing you still have 100 columns, that's OK.
But you should normalize, and in the process you may very well end up with 15-20 separate dedicated tables, which most relational database professionals would agree is a better design (avoid data duplication with the update/delete issues associated, smaller data footprint etc...).
Pragmatically, however, if there is a measurable performance problem, it may be sensible to denormalize your design for performance benefit. The key here - measureable. Don't optimize before you have an actual problem.
In that respect, I'd say you should go with the set of 15-20 tables as an initial design.
From MSDN:Maximum Capacity Specifications for SQL Server :
Columns per nonwide table: 1,024
Columns per wide table: 30,000
So I think 100 columns is ok in your case. And also maybe you need to note(from same link):
Columns per primary key: 16
Of course this is only in the case if need data only as Log for a service.
If after reading from service you need to maintain data -> then normalising seems better...
If you find it easier to "manage" tables with fewer columns, however you happen to define manageability (e.g. less horizontal scrolling when looking at the table data in SSMS), you can break the table up into several tables with 1-to-1 relationships without violating the rules of normalization.
I have a database table (called Fields) which has about 35 columns. 11 of them always contains the same constant values for about every 300.000 rows - and act as metadata.
The down side of this structure is that, when i need to update those 11 columns values, i need to go and update all 300.000 rows.
I could move all the common data in a different table, and update it only one time, in one place, instead of 300.000 places.
However, if i do it like this, when i display the fields, i need to create INNER JOIN's between the two tables, which i know makes the SELECT statement slower.
I must say that updating the columns occurs more rarely than reading (displaying) the data.
How you suggest that i should store the data in database to obtain the best performances?
I could move all the common data in a different table, and update it only one time, in one
place, instead of 300.000 places.
I.e. sane database design and standad normalization.
This is not about "many empty fields", it is brutally about tons of redundant data. Constants you should have isolated. Separate table. This may also make things faster - it allows the database to use memory more efficient because your database is a lot smaller.
I would suggest to go with a separate table unless you've concealed something significant (of course it would be better to try and measure, but I suspect you already know it).
You can actually get faster selects as well: joining a small table would be cheaper then fetching the same data 300000 times.
This is a classic example of denormalized design. Sometimes, denormalization is done for (SELECT) performance, and always in a deliberate, measurable way. Have you actually measured whether you gain any performance by it?
If your data fits into cache, and/or the JOIN is unusually expensive1, then there may well be some performance benefit from avoiding the JOIN. However, the denormalized data is larger and will push at the limits of your cache sooner, increasing the I/O and likely reversing any gains you may have reaped from avoiding the JOIN - you might actually lose performance.
And of course, getting the incorrect data is useless, no matter how quickly you can do it. The denormalization makes your database less resilient to data inconsistencies2, and the performance difference would have to be pretty dramatic to justify this risk.
1 Which doesn't look to be the case here.
2 E.g. have you considered what happens in a concurrent environment where one application might modify existing rows and the other application inserts a new row but with old values (since the first application hasn't committed yet so there is no way for the second application to know that there was a change)?
The best way is to seperate the data and form second table with those 11 columns and call it as some MASTER DATA TABLE, which will be having a primary key.
This primary key can be referred as a foreign key in those 30,000 rows in the first table
I have encountered the following dilemma several times and would be interested to hear how others have addressed this issue or if there is a canonical way that the situation can be addressed.
In some domains, one is naturally led to consider very wide tables. Take, for instance, time series surveys that evolve over many years. Such surveys can have hundreds, if not thousands, of variables. Typically though there are probably only a few thousand or tens-of-thousands of rows. It is absolutely natural to consider such a result set as a table where each variable corresponds to a column in the table however, in SQL Server at least, one is limited to 1024 (non sparse) columns.
The obvious workarounds are to
Distribute each record over multiple tables
Stuff the data into a single table with columns of say, ResponseId, VariableName, ResponseValue
Number 2. I think is very bad for a number of reasons (difficult to query, suboptimal storage, etc) so really the first choice is the only viable option I see. This choice can be improved perhaps by grouping columns that are likely to be queried together into the same table - but one can't really know this until the database is actually being used.
So, my basic question is: Are there better ways to handle this situation?
You might want to put a view in front of the tables to make them appear as if they are a single table. The upside is that you can rearrange the storage later without queries needing to change. The downside is that only modifications to the base table can be done through the view. If necessary, you could mitigate this with stored procedures for frequently used modifications. Based on your use case of time series surveys, it sounds like inserts and selects are far more frequent than updates or deletes, so this might be a viable way to stay flexible without forcing your clients to update if you need to rearrange things later.
Hmmm it really depends on what you do with it. If you want to keep the table as wide as it is (possibly this is for OLAP or data warehouse), I would just use proper indexes. Also based on the columns that are selected more often , I could also use covering indexes. Based on the rows that are searched more often, I could also use filtered indexes. If there are, let’s say, billions of records in the table, you could partition the table as well. If you just want to store the table over multiple tables, definitely use proper normalization techniques, probably up to 3NF or 3.5NF, to divide the big table into smaller tables. I would use the first method of yours, normalization, to store data for the big table just because it seems like it makes sense better to me that way.
This is an old topic but something we are currently working on resolving. Neither of the above answers really give as many benefits as the solution we feel we have found.
We previously believed that having wide tables wasn't really a problem. Having spent time analysing this we have seen the light and realise that costs on inserts/updates are indeed getting out of control.
As John states above, the solution is really to create a VIEW to provide your application with a consistent schema. One of the challenges in any redesign may be, as in our case, that you have thousands or millions of lines of code referencing an old wide table and you may want to provide backwards compatibility.
Views can also be used for UPDATES and INSERTS as John alludes to, however a problem we found initially was that if you take the example of myWideTable which may have hundreds of columns and you want to split this to myWideTable_a with columns a, b and c and myWideTable_b with columns x, y and z then an insert to a view which only sets column a will only insert a record for myWideTable_a
This causes a problem when you want to later update your record and set myWideTable.z as this will fail.
The solution we're adopting, and performance testing, is to have an 'insteadof' trigger on the View insert to always insert to our split-tables so that we can continue to update or read from the view with impunity.
The question as to whether using this trigger on inserts provides more overhead than a wide table is still open, but it is clear that it will improve subsequent writes to columns in each split table.
I heard a lot about denormalization which was made to improve performance of certain application. But I've never tried to do anything related.
So, I'm just curious, which places in normalized DB makes performance worse or in other words, what are denormalization principles?
How can I use this technique if I need to improve performance?
Denormalization is generally used to either:
Avoid a certain number of queries
Remove some joins
The basic idea of denormalization is that you'll add redundant data, or group some, to be able to get those data more easily -- at a smaller cost; which is better for performances.
A quick examples?
Consider a "Posts" and a "Comments" table, for a blog
For each Post, you'll have several lines in the "Comment" table
This means that to display a list of posts with the associated number of comments, you'll have to:
Do one query to list the posts
Do one query per post to count how many comments it has (Yes, those can be merged into only one, to get the number for all posts at once)
Which means several queries.
Now, if you add a "number of comments" field into the Posts table:
You only need one query to list the posts
And no need to query the Comments table: the number of comments are already de-normalized to the Posts table.
And only one query that returns one more field is better than more queries.
Now, there are some costs, yes:
First, this costs some place on both disk and in memory, as you have some redundant informations:
The number of comments are stored in the Posts table
And you can also find those number counting on the Comments table
Second, each time someone adds/removes a comment, you have to:
Save/delete the comment, of course
But also, update the corresponding number in the Posts table.
But, if your blog has a lot more people reading than writing comments, this is probably not so bad.
Denormalization is a time-space trade-off. Normalized data takes less space, but may require join to construct the desired result set, hence more time. If it's denormalized, data are replicated in several places. It then takes more space, but the desired view of the data is readily available.
There are other time-space optimizations, such as
denormalized view
precomputed columns
As with any of such approach, this improves reading data (because they are readily available), but updating data becomes more costly (because you need to update the replicated or precomputed data).
The word "denormalizing" leads to confusion of the design issues. Trying to get a high performance database by denormalizing is like trying to get to your destination by driving away from New York. It doesn't tell you which way to go.
What you need is a good design discipline, one that produces a simple and sound design, even if that design sometimes conflicts with the rules of normalization.
One such design discipline is star schema. In a star schema, a single fact table serves as the hub of a star of tables. The other tables are called dimension tables, and they are at the rim of the schema. The dimensions are connected to the fact table by relationships that look like the spokes of a wheel. Star schema is basically a way of projecting multidimensional design onto an SQL implementation.
Closely related to star schema is snowflake schema, which is a little more complicated.
If you have a good star schema, you will be able to get a huge variety of combinations of your data with no more than a three way join, involving two dimensions and one fact table. Not only that, but many OLAP tools will be able to decipher your star design automatically, and give you point-and-click, drill down, and graphical analysis access to your data with no further programming.
Star schema design occasionally violates second and third normal forms, but it results in more speed and flexibility for reports and extracts. It's most often used in data warehouses, data marts, and reporting databases. You'll generally have much better results from star schema or some other retrieval oriented design, than from just haphazard "denormalization".
The critical issues in denormalizing are:
Deciding what data to duplicate and why
Planning how to keep the data in synch
Refactoring the queries to use the denormalized fields.
One of the easiest types of denormalizing is to populate an identity field to tables to avoid a join. As identities should not ever change, this means the issue of keeping the data in sync rarely comes up. For instance, we populate our client id to several tables because we often need to query them by client and do not necessarily need, in the queries, any of the data in the tables that would be between the client table and the table we are querying if the data was totally normalized. You still have to do one join to get the client name, but that is better than joining to 6 parent tables to get the client name when that is the only piece of data you need from outside the table you are querying.
However, there would be no benefit to this unless we were often doing queries where data from the intervening tables was needed.
Another common denormalization might be to add a name field to other tables. As names are inherently changeable, you need to ensure that the names stay in synch with triggers. But if this saves you from joining to 5 tables instead of 2, it can be worth the cost of the slightly longer insert or update.
If you have certain requirement, like reporting etc., it can help to denormalize your database in various ways:
introduce certain data duplication to save yourself some JOINs (e.g. fill certain information into a table and be ok with duplicated data, so that all the data in that table and doesn't need to be found by joining another table)
you can pre-compute certain values and store them in a table column, insteda of computing them on the fly, everytime to query the database. Of course, those computed values might get "stale" over time and you might need to re-compute them at some point, but just reading out a fixed value is typically cheaper than computing something (e.g. counting child rows)
There are certainly more ways to denormalize a database schema to improve performance, but you just need to be aware that you do get yourself into a certain degree of trouble doing so. You need to carefully weigh the pros and cons - the performance benefits vs. the problems you get yourself into - when making those decisions.
Consider a database with a properly normalized parent-child relationship.
Let's say the cardinality is an average of 2x1.
You have two tables, Parent, with p rows. Child with 2x p rows.
The join operation means for p parent rows, 2x p child rows must be read. The total number of rows read is p + 2x p.
Consider denormalizing this into a single table with only the child rows, 2x p. The number of rows read is 2x p.
Fewer rows == less physical I/O == faster.
As per the last section of this article,
https://technet.microsoft.com/en-us/library/aa224786%28v=sql.80%29.aspx
one could use Virtual Denormalization, where you create Views with some denormalized data for running more simplistic SQL queries faster, while the underlying Tables remain normalized for faster add/update operations (so long as you can get away with updating the Views at regular intervals rather than in real-time). I'm just taking a class on Relational Databases myself but, from what I've been reading, this approach seems logical to me.
Benefits of de-normalization over normalization
Basically de-normalization is used for DBMS not for RDBMS. As we know that RDBMS works with normalization, which means no repeat data again and again. But still repeat some data when you use foreign key.
When you use DBMS then there is a need to remove normalization. For this, there is a need for repetition. But still, it improves performance because there is no relation among the tables and each table has indivisible existence.
DBA (with only 2 years of google for training) has created a massive data management table (108 columns and growing) containing all neccessary attribute for any data flow in the system. Well call this table BFT for short.
Of these columns:
10 are for meta-data references.
15 are for data source and temporal tracking
1 instance of new/curr columns for textual data
10 instances of new/current/delta/ratio/range columns for multi-value numeric updates
:totaling 50 columns.
Multi valued numeric updates usually only need 2-5 of the update groups.
Batches of 15K-1500K records are loaded into the BFT and processed by stored procs with logic to validate those records shuffle them off to permanent storage in about 30 other tables.
In most of the record loads, 50-70 of the columns are empty through out the entire process.
I am no database expert, but this model and process seems to smell a little, but I don't know enough to say why, and don't want to complain without being able to offer an alternative.
Given this very small insight to the data processing model, does anyone have thoughts or suggestions? Can the database (SQL Server) be trusted to handle records with mostly empty columns efficiently, or does processing in this manner wasted lots of cycles/memory,etc.
Sounds like he reinvented BizTalk.
I typically have multiple staging tables corresponding to the input loads. These may or may not correspond to the destination tables, but we don't do what you're talking about. If he doesn't like to have a lot of what are basically temporary work tables, they could be put into their own schema or even a separate database.
As far as the columns which are empty, if they aren't referenced in the particular query which is processing BFT it doesn't matter - HOWEVER, what will happen is that the indexing becomes much more crucial that the index chosen is a non-clustered covering index. When your BFT is used and a table scan or clustered index scan is chosen, the unused column have to be read and ignored or skipped, and this definitely seems to affect processing in my experience. Whereas with a non-clustered index scan or seek, less columns are read, and hopefully this doesn't include (m)any of the unused columns.
Normalization is the keyword here. If you have so many NULL values, chances are high that you're wasting a lot of space. Normalizing the table should also make data integrity in this table easier to enforce.
One thing that might make things a little more flexible (other than normalizing) could be to create one or more views or table functions to present the data. Particularly if the table is outside your control, these would enable you to filter the spurious crap out and grab only what you need from the table.
However, if you're going to be one of the people who will be working with (and frowning every time you have to crack open) that massive table, you might want to trump the DBA's "design" and normalize that beast, and maybe give the DBA the task of creating some views and/or table functions to help you out.
I currently work with a similar but not so huge table which has been around on our system for years and has had new fields and indices and constraints rather hastily tacked on Frankenstein-style. Unfortunately some other workgroups rely on the structure as gospel, so we've created such views and functions to enable us to "shape" the data the way we need it.