I have a database with lots of customers in it. A user of the system wants to be able to look up a customer's account by name, amongst other things.
What I have done is create a new table called CustomerFullText, which just has a CustomerId and an nvarchar(max) field "CustomerFullText". In "CustomerFullText" I keep concatenated together all the text I have for the customer, e.g. First Name, Last Name, Address, etc, and I have a full-text index on that field, so that the user can just type into a single search box and gets matching results.
I found this gave better results that trying to search data stored in lots of different columns, although I suppose I'd be interested in hearing if this in itself is a terrible idea.
Many people have names which sound the same but which have different spellings: Katherine and Catherine and Catharine and perhaps someone who's record in the database is Katherine but who introduces themselves as Kate. Also, McDonald vs MacDonald, Liz vs Elisabeth, and so on.
Therefore, what I'm doing is, whilst storing the original name correctly, making a series of replacements before I build the full text. So ALL of Katherine and Catheine and so on are replaced with "KATE" in the full text field. I do the same transform on my search parameter before I query the database, so someone who types "Catherine" into the search box will actually run a query for "KATE" against the full text index in the database, which will match Catherine AND Katherine and so on.
My question is: does this duplicate any part of existing SQL Server Full Text functionality? I've had a look, but I don't think that this is the same as a custom stemmer or word breaker or similar.
Rather than trying to phonetically normalize your data yourself, I would use the Double Metaphone algorithm, essentially a much better implementation of the basic SOUNDEX idea.
You can find an example implementation here: http://www.sqlteam.com/forums/topic.asp?TOPIC_ID=13574, and more are listed in the Wikipedia link above.
It will generate two normalized code versions of your word. You can then persist those in two additional columns and compare them against your search text, which you would convert to Double Metaphone on the fly.
Related
I have a list of companies on a spreadsheet that is rarely updated. I'll call it List A.
I also have a constantly updating weekly list of companies (List B) that should have entries that match some entries on List A.
The reality is that the data extracted from List B's company names are often inconsistent due to various business abbreviations (e.g. The Company, Company Ltd., Company Accountants Limited). Sometimes, these companies are under different trading names or have various mispellings.
My initial very not intelligent reaction was to construct a table of employer alias names, with the first column being the true employer name and the following columns holding alises, something like this: [https://i.stack.imgur.com/2cmYv.png]
On the left is a sample table, and the far right is a column where I am using the following array formula template:
=ArrayFormula(INDEX(A30:A33,MATCH(1,MMULT(--(B30:E33=H30),TRANSPOSE(COLUMN(B30:E33)^0)),0)))
I realized soon after that I needed to create a new entry for every single exact match variation (Ltd., Ltd, and Limited), so I looked into fuzzy lookups. I was really impressed by Alan's Fuzzy Matching UDFs, but my needs heavily lean towards using Google Spreadsheets rather than VBA.
Sorry for the long post, but I would be grateful if anyone has any good suggestions for fuzzy lookups or can suggest an alternative solution.
The comments weren't exactly what I was looking for, but they did provide some inspiration for me to come up with a bandaid solution.
My original array formula needed exact matches, but the problem was that there were simply too many company suffixes and alternate names, so I looked into fuzzy lookups.
My current answer is to abandon the fuzzy lookup proposal and instead focus on editing the original data string (i.e. company names) into more simplified substring. Grabbing with a few codes floating around, I came up with a combined custom formula that implements two lines for GApps Script:
var companysuffixremoval = str.toString().replace(/limited|ltd|group|holdings|plc|llp|sons|the/ig, "");
var alphanumericalmin = str.replace(/[^A-Za-z0-9]/g,"")
The first line is simply my idea of removing popular company suffixes and the "the" from the string.
The second line is removing all non-alphanumerical characters, as well as any spaces and periods.
This ensure "The First Company Limited." and "First Company Ltd" become "FirstCompany", which should work with returning the same values from the original array formula in the OP. Of course, I also implemented a trimming and cleaning line for any trailing/leading/extra spaces for the initial string, but that's probably unncessary with the second line.
If someone can come up with a better idea, please do tell. As it stands, I'm just tinkering with a script with minimal experience.
Imagine a web form with a set of check boxes (any or all of them can be selected). I chose to save them in a comma separated list of values stored in one column of the database table.
Now, I know that the correct solution would be to create a second table and properly normalize the database. It was quicker to implement the easy solution, and I wanted to have a proof-of-concept of that application quickly and without having to spend too much time on it.
I thought the saved time and simpler code was worth it in my situation, is this a defensible design choice, or should I have normalized it from the start?
Some more context, this is a small internal application that essentially replaces an Excel file that was stored on a shared folder. I'm also asking because I'm thinking about cleaning up the program and make it more maintainable. There are some things in there I'm not entirely happy with, one of them is the topic of this question.
In addition to violating First Normal Form because of the repeating group of values stored in a single column, comma-separated lists have a lot of other more practical problems:
Can’t ensure that each value is the right data type: no way to prevent 1,2,3,banana,5
Can’t use foreign key constraints to link values to a lookup table; no way to enforce referential integrity.
Can’t enforce uniqueness: no way to prevent 1,2,3,3,3,5
Can’t delete a value from the list without fetching the whole list.
Can't store a list longer than what fits in the string column.
Hard to search for all entities with a given value in the list; you have to use an inefficient table-scan. May have to resort to regular expressions, for example in MySQL:
idlist REGEXP '[[:<:]]2[[:>:]]' or in MySQL 8.0: idlist REGEXP '\\b2\\b'
Hard to count elements in the list, or do other aggregate queries.
Hard to join the values to the lookup table they reference.
Hard to fetch the list in sorted order.
Hard to choose a separator that is guaranteed not to appear in the values
To solve these problems, you have to write tons of application code, reinventing functionality that the RDBMS already provides much more efficiently.
Comma-separated lists are wrong enough that I made this the first chapter in my book: SQL Antipatterns, Volume 1: Avoiding the Pitfalls of Database Programming.
There are times when you need to employ denormalization, but as #OMG Ponies mentions, these are exception cases. Any non-relational “optimization” benefits one type of query at the expense of other uses of the data, so be sure you know which of your queries need to be treated so specially that they deserve denormalization.
"One reason was laziness".
This rings alarm bells. The only reason you should do something like this is that you know how to do it "the right way" but you have come to the conclusion that there is a tangible reason not to do it that way.
Having said this: if the data you are choosing to store this way is data that you will never need to query by, then there may be a case for storing it in the way you have chosen.
(Some users would dispute the statement in my previous paragraph, saying that "you can never know what requirements will be added in the future". These users are either misguided or stating a religious conviction. Sometimes it is advantageous to work to the requirements you have before you.)
There are numerous questions on SO asking:
how to get a count of specific values from the comma separated list
how to get records that have only the same 2/3/etc specific value from that comma separated list
Another problem with the comma separated list is ensuring the values are consistent - storing text means the possibility of typos...
These are all symptoms of denormalized data, and highlight why you should always model for normalized data. Denormalization can be a query optimization, to be applied when the need actually presents itself.
In general anything can be defensible if it meets the requirements of your project. This doesn't mean that people will agree with or want to defend your decision...
In general, storing data in this way is suboptimal (e.g. harder to do efficient queries) and may cause maintenance issues if you modify the items in your form. Perhaps you could have found a middle ground and used an integer representing a set of bit flags instead?
Yes, I would say that it really is that bad. It's a defensible choice, but that doesn't make it correct or good.
It breaks first normal form.
A second criticism is that putting raw input results directly into a database, without any validation or binding at all, leaves you open to SQL injection attacks.
What you're calling laziness and lack of SQL knowledge is the stuff that neophytes are made of. I'd recommend taking the time to do it properly and view it as an opportunity to learn.
Or leave it as it is and learn the painful lesson of a SQL injection attack.
I needed a multi-value column, it could be implemented as an xml field
It could be converted to a comma delimited as necessary
querying an XML list in sql server using Xquery.
By being an xml field, some of the concerns can be addressed.
With CSV: Can't ensure that each value is the right data type: no way to prevent 1,2,3,banana,5
With XML: values in a tag can be forced to be the correct type
With CSV: Can't use foreign key constraints to link values to a lookup table; no way to enforce referential integrity.
With XML: still an issue
With CSV: Can't enforce uniqueness: no way to prevent 1,2,3,3,3,5
With XML: still an issue
With CSV: Can't delete a value from the list without fetching the whole list.
With XML: single items can be removed
With CSV: Hard to search for all entities with a given value in the list; you have to use an inefficient table-scan.
With XML: xml field can be indexed
With CSV: Hard to count elements in the list, or do other aggregate queries.**
With XML: not particularly hard
With CSV: Hard to join the values to the lookup table they reference.**
With XML: not particularly hard
With CSV: Hard to fetch the list in sorted order.
With XML: not particularly hard
With CSV: Storing integers as strings takes about twice as much space as storing binary integers.
With XML: storage is even worse than a csv
With CSV: Plus a lot of comma characters.
With XML: tags are used instead of commas
In short, using XML gets around some of the issues with delimited list AND can be converted to a delimited list as needed
Yes, it is that bad. My view is that if you don't like using relational databases then look for an alternative that suits you better, there are lots of interesting "NOSQL" projects out there with some really advanced features.
Well I've been using a key/value pair tab separated list in a NTEXT column in SQL Server for more than 4 years now and it works. You do lose the flexibility of making queries but on the other hand, if you have a library that persists/derpersists the key value pair then it's not a that bad idea.
I would probably take the middle ground: make each field in the CSV into a separate column in the database, but not worry much about normalization (at least for now). At some point, normalization might become interesting, but with all the data shoved into a single column you're gaining virtually no benefit from using a database at all. You need to separate the data into logical fields/columns/whatever you want to call them before you can manipulate it meaningfully at all.
If you have a fixed number of boolean fields, you could use a INT(1) NOT NULL (or BIT NOT NULL if it exists) or CHAR (0) (nullable) for each. You could also use a SET (I forget the exact syntax).
Use Case
When a user goes to my website, they will be confronted with a search box much like SO. They can search for results using plan text. ".net questions", "closed questions", ".net and java", etc.. The search will function a bit different that SO, in that it will try to as much as possible of the schema of the database rather than a straight fulltext search. So ".net questions" will only search for .net questions as opposed to .net answers (probably not applicable to SO case, just an example here), "closed questions" will return questions that are closed, ".net and java" questions will return questions that relate to .net and java and nothing else.
Problem
I'm not too familiar with the words but I basically want to do a keyword to SQL driven search. I know the schema of the database and I also can datamine the database. I want to know any current approaches there that existing out already before I try to implement this. I guess this question is for what is a good design for the stated problem.
Proposed
My proposed solution so far looks something like this
Clean the input. Just remove any special characters
Parse the input into chunks of data. Break an input of "c# java" into c# and java Also handle the special cases like "'c# java' questions" into 'c# java' and "questions".
Build a tree out of the input
Bind the data into metadata. So convert stuff like closed questions and relate it to the isclosed column of a table.
Convert the tree into a sql query.
Thoughts/suggestions/links?
I run a digital music store with a "single search" that weights keywords based on their occurrences and the schema in which Products appear, eg. with different columns like "Artist", "Title" or "Publisher".
Products are also related to albums and playlists, but for simpler explanation, I will only elaborate on the indexing and querying of Products' Keywords.
Database Schema
Keywords table - a weighted table for every word that could possibly be searched for (hence, it is referenced somewhere) with the following data for each record:
Keyword ID (not the word),
The Word itself,
A Soundex Alpha value for the Word
Weight
ProductKeywords table - a weighted table for every keyword referenced by any of a product's fields (or columns) with the following data for each record:
Product ID,
Keyword ID,
Weight
Keyword Weighting
The weighting value is an indication of how often the words occurs. Matching keywords with a lower weight are "more unique" and are more likely to be what is being searched for. In this way, words occurring often are automatically "down-weighted", eg. "the", "a" or "I". However, it is best to strip out atomic occurrences of those common words before indexing.
I used integers for weighting, but using a decimal value will offer more versatility, possibly with slightly slower sorting.
Indexing
Whenever any product field is updated, eg. Artist or Title (which does not happen that often), a database trigger re-indexes the product's keywords like so inside a transaction:
All product keywords are disassociated and deleted if no longer referenced.
Each indexed field (eg. Artist) value is stored/retrieved as a keyword in its entirety and related to the product in the ProductKeywords table for a direct match.
The keyword weight is then incremented by a value that depends on the importance of the field. You can add, subtract weight based on the importance of the field. If Artist is more important than Title, Subtract 1 or 2 from its ProductKeyword weight adjustment.
Each indexed field value is stripped of any non-alphanumeric characters and split into separate word groups, eg. "Billy Joel" becomes "Billy" and "Joel".
Each separate word group for each field value is soundexed and stored/retrieved as a keyword and associated with the product in the same way as in step 2. If a keyword has already been associated with a product, its weight is simply adjusted.
Querying
Take the input query search string in its entirety and look for a direct matching keyword. Retrieve all ProductKeywords for the keyword in an in-memory table along with Keyword weight (different from ProductKeyword weight).
Strip out all non-alphanumeric characters and split query into keywords. Retrieve all existing keywords (only a few will match). Join ProductKeywords to matching keywords to in-memory table along with Keyword weight, which is different from the ProductKeyword weight.
Repeat Step 2 but use soundex values instead, adjusting weights to be less relevant.
Join retrieved ProductKeywords to their related Products and retrieve each product's sales, which is a measure of popularity.
Sort results by Keyword weight, ProductKeyword weight and Sales. The final summing/sorting and/or weighting depends on your implementation.
Limit results and return product search results to client.
What you are looking for is Natural Language Processing. Strangely enough this used to be included free as English Query in SQL Server 2000 and prior. But it's gone now
Some other sources are :
http://devtools.korzh.com/eq/dotnet/
http://www.easyask.com/products/business-intelligence/index.htm
The concept is a meta data dictionary mapping words to table, columns, relationships etc and an English sentence parser combined together to convert a English sentence ( or just some keywords) into a real query
Some people even user English Query with speech recognition for some really cool demos, never saw it used in anger though!
If you're using SQL Server, you can simply use its Full-Text Search feature, which is specifically designed to solve your problem.
You could use a hybrid approach, take the full text search results and further filter them based on the meta data from your #4. For something more intelligent you could create a simple supervised learning solution by tracking what links the user clicks on after the search and store that choice with the key search words in a decision tree. Searches would then be mined from this decision tree
I am trying to visualize how to create a search for an application that we are building. I would like a suggestion on how to approach 'searching' through large sets of data.
For instance, this particular search would be on a 750k record minimum table, of product sku's, sizing, material type, create date, etc;
Is anyone aware of a 'plugin' solution for Coldfusion to do this? I envision a google like single entry search where a customer can type in the part number, or the sizing, etc, and get hits on any or all relevant results.
Currently if I run a 'LIKE' comparison query, it seems to take ages (ok a few seconds, but still), and it is too long. At times making a user sit there and wait up to 10 seconds for queries & page loads.
Or are there any SQL formulas to help accomplish this? I want to use a proven method to search the data, not just a simple SQL like or = comparison operation.
So this is a multi-approach question, should I attack this at the SQL level (as it ultimately looks to be) or is there a plug in/module for ColdFusion that I can grab that will give me speedy, advanced search capability.
You could try indexing your db records with a Verity (or Solr, if CF9) search.
I'm not sure it would be faster, and whether even trying it would be worthwhile would depend a lot on how often you update the records you need to search. If you update them rarely, you could do an Verity Index update whenever you update them. If you update the records constantly, that's going to be a drag on the webserver, and certainly mitigate any possible gains in search speed.
I've never indexed a database via Verity, but I've indexed large collections of PDFs, Word Docs, etc, and I recall the search being pretty fast. I don't know if it will help your current situation, but it might be worth further research.
If your slowdown is specifically the search of textual fields (as I surmise from your mentioning of LIKE), the best solution is building an index table (not to be confiused with DB table indexes that are also part of the answer).
Build an index table mapping the unique ID of your records from main table to a set of words (1 word per row) of the textual field. If it matters, add the field of origin as a 3rd column in the index table, and if you want "relevance" features you may want to consider word count.
Populate the index table with either a trigger (using splitting) or from your app - the latter might be better, simply call a stored proc with both the actual data to insert/update and the list of words already split up.
This will immediately drastically speed up textual search as it will no longer do "LIKE", AND will be able to use indexes on index table (no pun intended) without interfering with indexing on SKU and the like on the main table.
Also, ensure that all the relevant fields are indexed fully - not necessarily in the same compund index (SKU, sizing etc...), and any field that is searched as a range field (sizing or date) is a good candidate for a clustered index (as long as the records are inserted in approximate order of that field's increase or you don't care about insert/update speed as much).
For anything mode detailed, you will need to post your table structure, existing indexes, the queries that are slow and the query plans you have now for those slow queries.
Another item is to enure that as little of the fields are textual as possible, especially ones that are "decodable" - your comment mentioned "is it boxed" in the text fields set. If so, I assume the values are "yes"/"no" or some other very limited data set. If so, simply store a numeric code for valid values and do en/de-coding in your app, and search by the numeric code. Not a tremendous speed improvement but still an improvement.
I've done this using SQL's full text indexes. This will require very application changes and no changes to the database schema except for the addition of the full text index.
First, add the Full Text index to the table. Include in the full text index all of the columns the search should perform against. I'd also recommend having the index auto update; this shouldn't be a problem unless your SQL Server is already being highly taxed.
Second, to do the actual search, you need to convert your query to use a full text search. The first step is to convert the search string into a full text search string. I do this by splitting the search string into words (using the Split method) and then building a search string formatted as:
"Word1*" AND "Word2*" AND "Word3*"
The double-quotes are critical; they tell the full text index where the words begin and end.
Next, to actually execute the full text search, use the ContainsTable command in your query:
SELECT *
from containstable(Bugs, *, '"Word1*" AND "Word2*" AND "Word3*"')
This will return two columns:
Key - The column identified as the primary key of the full text search
Rank - A relative rank of the match (1 - 1000 with a higher ranking meaning a better match).
I've used approaches similar to this many times and I've had good luck with it.
If you want a truly plug-in solution then you should just go with Google itself. It sounds like your doing some kind of e-commerce or commercial site (given the use of the term 'SKU'), So you probably have a catalog of some kind with product pages. If you have consistent markup then you can configure a google appliance or service to do exactly what you want. It will send a bot in to index your pages and find your fields. No SQl, little coding, it will not be dependent on your database, or even coldfusion. It will also be quite fast and familiar to customers.
I was able to do this with a coldfusion site in about 6 hours, done! The only thing to watch out for is that google's index is limited to what the bot can see, so if you have a situation where you want to limit access based on a users role or permissions or group, then it may not be the solution for you (although you can configure a permission service for Google to check with)
Because SQL Server is where your data is that is where your search performance is going to be a possible issue. Make sure you have indexes on the columns you are searching on and if using a like you can't use and index if you do this SELECT * FROM TABLEX WHERE last_name LIKE '%FR%'
But it can use an index if you do it like this SELECT * FROM TABLEX WHERE last_name LIKE 'FR%'. The key here is to allow as many of the first characters to not be wild cards.
Here is a link to a site with some general tips. https://web.archive.org/web/1/http://blogs.techrepublic%2ecom%2ecom/datacenter/?p=173
I run (and am presently completely overhauling) a website that deals with theater (njtheater.com if you're interested).
When I query a list of plays from the database, I'd like "The Merchant of Venice" to sort under the "M"s. Of course, when I display the name of the play, I need the "The" in front.
What the best way of designing the database to handle this?
(I'm using MS-SQL 2000)
You are on the right track with two columns, but I would suggest storing the entire displayable title in one column, rather than concatenating columns. The other column is used purely for sorting. This gives you complete flexibility over sorting and display, rather than being stuck with a simple prefix.
This is a fairly common approach when searching (which is related to sorting). One column (with an index) is case-folded, de-punctuated, etc. In your case, you'd also apply the grammatical convention of removing leading articles to the values in this field. This column is then used as a comparison key for searching or sorting. The other column is not indexed, and preserves the original key for display.
Store the title in two fields: TITLE-PREFIX and TITLE-TEXT (or some such). Then sort on the second, but display the concatenation of the two, with a space between.
My own solution to the problem was to create three columns in the database.
article varchar(4)
sorttitle varchar(255)
title computed (article + sortitle)
"article" will only be either "The ", "A " "An " (note trailing space on each) or empty string (not null)
"sorttitle" will be the title with the leading article removed.
This way, I can sort on SORTTITLE and display TITLE. There's little actual processing going on the computed field (so it's fast), and there's only a little work to be done when inserting.
I agree with doofledorfer, but I would recommend storing spaces entered as part of the prefix instead of assuming it's a single space. It gives your users more flexibility. You may also be able to do some concatenation in your query itself, so you don't have to merge the fields as part of your business logic.
I don't know if this can be done in SQL Server. If you can create function based indexes you could create one that does a regex on the field or that uses your own function. This would take less space than an additional field, would be kept up to date by the database itself, and allows the complete title to be stored together.