In ms access I have been using soundex for possible matching options but now i want to use full text searching is there any code available for that eg: rose give letter to ram will possible match to ram got letter rose like fuzzy
Access does not have a full text indexing option.
You could I suppose write your own routines, but that would take considerable work. As a result, best effort would be to migrate the data file to say a free edition of SQL server – the express editions do support text indexing, and thus allow searching of text by keywords, even when not in the same order as the text or memo column in question.
I suppose if you only searching say one column, and you willing to write VBA code to take the key words, and then loop each given keyword, and do a VBA “instr” command on the one column, then this could work, but it would be slow, and you would not have say use of SQL queries to do all the heavy lifting for you.
It is likely less work to send the data to a free edition of SQL server then it is to write such code. A migration of the data to SQL server will still allow you to use your existing application and VBA anyway.
I did some research on this and couldn't find many introductory resources for a beginner so I'm looking for a basic understanding here of how the process works. The problem I'm trying to solve is as such: I want to move data from an old database to a new one with a slightly different structure, possibly mutating the data a little bit in the process. Without going into the nitty gritty detail.. what are the general steps involved in doing this?
From what I gathered I would either be...
writing a ton of SQL queries manually (eesh)
using some complex tool that may be overkill for what I'm doing
There is a lot of data in the database so writing INSERT queries from a SQL dump seems like a nightmare. What I was looking for is some way to write a simple program that inserts logic like for each row in the table "posts", take the value of the "body" attribute and put it in the "post-body attribute of the new database or something like that. I'm also looking for functionality like append a 0 to the data in the "user id" column then insert it in the new database (just an example, the point is to mutate the data slightly).
In my head I can easily construct the logic of how the migration would go very easily (definitely not rocket science here).. but I'm not sure how to make this happen on a computer to iterate over the ridiculous amount of data without doing it manually. What is the general process for doing this, and what tools might a beginner want to use? Is this even a good idea for someone who has never done it before?
Edit: by request, here is an example of a mutation I'd like to perform:
Old database: table "posts" with an attribute post_body that is a varchar 255.
New database: table "posts" with an attribute body" that is a text datatype.
Want to take post-body from the old one and put it in body in the new one. Realize they are different datatypes but they are both technically strings and should be fine to convert, right? Etc. a bunch of manipulations like this.
Usually, the most time-consuming step of a database conversion is understanding both the old and the new structure, and establishing the correspondance of fields in each structure.
Compared to that, the time it takes to write the corresponding SQL query is ridiculously short.
for each row in the table "posts", take the value of the "body" attribute and put it in the "post-body attribute of the new database
INSERT INTO newdb.postattribute (id, attribute, value)
SELECT postid, 'post-body', body FROM olddb.post;
In fact, the tool that allows such data manipulation is... SQL! Really, this is already a very high-level language.
For a college assignment I need to run an experiment related to MySQL. I have chosen to compare the performance of LIKE to MATCH AGAINST in the context of searching text fields.
The MySQL sample "Employees" DB was recommended to us but it doesn't really have any text fields with long text values. I'm of the opinion that i'd need something along the lines of a paragraph of text for each record to give decent results. I guess something like a paragraph about each employees background would be ideal. BUT, there are approximately 300k employees in that database.
Also, i'm guessing that the text values would want to be pretty distinct for each employee. Couldn't just use the same few for all employees.
Am I right in my assumptions and if so. Are there any other sample databases out there that would suit me?
Any ideas?
You might try any of the following links
Library of free data models
Wikimedia dumps in xml and sql form
MySQL documentation #1 #2
You might want to look at this previous post. One of those dataset HAS to be in mysql if not you can use www.talend.com to load it into your application.
Where can I find sample databases with common formatted data that I can use in multiple database engines?
I am pretty excited about the new Mysql XMl Functions.
Now I can finally embed something like "object oriented" documents in my oldschool relational database.
For an example use-case consider a user who sings up at your website using facebook connect.
You can fetch an object for the user using the graph api, and get nice information. This information however can vary vastly. Some fields may or may not be set, some may be added over time and so on.
Well if you are just intersted in very special fields (for example friends relations, gender, movies...), you can project them into your relational database scheme.
However using the XMl functions you could store the whole object inside a field and then your different models can access the data using the ExtractValue function. You can store everything right away without needing to worry what you will need later.
But what will the performance be?
For example I have a table with 50 000 entries which represent useres.
I have an enum field that states "male", "female" (or various other genders to be politically correct).
The performance of for example fetching all males will be very fast.
But what about something like WHERE ExtractValue(userdata, '/gender/') = 'male' ?
How will the performance vary if the object gets bigger?
Can I maby somehow put an Index on specified xpath selections?
How do field types work together with this functions/performance. Varchar/blob?
Do I need fulltext indexes?
To sum up my question:
Mysql XML functins look great. And I am sure they are really great if you just want to store structured data that you fetch and analyze further in your application.
But how will they stand battle in procedures where there are internal scans/sorting/comparision/calculations performed on them?
Can Mysql replace document oriented databases like CouchDB/Sesame?
What are the gains and trade offs of XML functions?
How and why are they better/worse than a dynamic application that stores various data as attributes?
For example a key/value table with an xpath as key and the value as value connected to the document entity.
Anyone made any other experiences with it or has noticed something mentionable?
I tend to make comments similar to Pekka's, but I think the reason we cannot laugh this off is your statement "This information however can vary vastly." That means it is not realistic to plan to parse it all and project it into the database.
I cannot answer all of your questions, but I can answer some of them.
Most notably I cannot tell you about performance on MySQL. I have seen it in SQL Server, tested it, and found that SQL Server performs in memory XML extractions very slowly, to me it seemed as if it were reading from disk, but that is a bit of an exaggeration. Others may dispute this, but that is what I found.
"Can Mysql replace document oriented databases like CouchDB/Sesame?" This question is a bit over-broad but in your case using MySQL lets you keep ACID compliance for these XML chunks, assuming you are using InnoDB, which cannot be said automatically for some of those document oriented databases.
"How and why are they better/worse than a dynamic application that stores various data as attributes?" I think this is really a matter of style. You are given XML chunks that are (presumably) documented and MySQL can navigate them. If you just keep them as-such you save a step. What would be gained by converting them to something else?
The MySQL docs suggest that the XML file will go into a clob field. Performance may suffer on larger docs. Perhaps then you will identify sub-documents that you want to regularly break out and put into a child table.
Along these same lines, if there are particular sub-docs you know you will want to know about, you can make a child table, "HasDocs", do a little pre-processing, and populate it with names of sub-docs with their counts. This would make for faster statistical analysis and also make it faster to find docs that have certain sub-docs.
Wish I could say more, hope this helps.
Are there any open source or commercial tools available that allow for text fragment indexing of database contents and can be queried from Java?
Background of the question is a large MySQL database table with several hundred thousand records, containing several VARCHAR columns. In these columns people would like to search for fragments of the contents, so a fulltext index (which is based on word boundaries) would not help.
EDIT: [Added to make clear why these first suggestions would not solve the problem:]
This is why MySQL's built in fulltext index will not do the job, and neither will Lucene or Sphinx, all of which were suggested in the answers. I already looked at both those, but as far as I can tell, these are based on indexing words, excluding stop words and doing all sorts of sensible things for a real fulltext search. However this is not suitable, because I might be looking for a search term like "oison" which must match "Roisonic Street" as well as "Poison-Ivy". The key difference here is that the search term is just a fragment of the column content, that need not be delimited by any special characters or white space.
EDIT2: [Added some more background info:]
The requested feature that is to be implemented based on this is a very loose search for item descriptions in a merchandise management system. Users often do not know the correct item number, but only part of the name of the item. Unfortunately the quality of these descriptions is rather low, they come from a legacy system and cannot be changed easily. If for example people were searching for a sledge hammer they would enter "sledge". With a word/token based index this would not find matches that are stored as "sledgehammer", but only those listen "sledge hammer". There are all kinds of weird variances that need to be covered, making a token based approach impractical.
Currently the only thing we can do is a LIKE '%searchterm%' query, effectively disabling any index use and requiring lots of resources and time.
Ideally any such tool would create an index that allowed me to get results for suchlike queries very quickly, so that I could implement a spotlight-like search, only retrieving the "real" data from the MySQL table via the primary key when a user picks a result record.
If possible the index should be updatable (without needing a full rebuild), because data might change and should be available for search immediately by other clients.
I would be glad to get recommendations and/or experience reports.
EDIT3: Commercial solution found that "just works"
Even though I got a lot of good answers for this question, I wanted to note here, that in the end we went with a commercial product called "QuickFind", made and sold by a German company named "HMB Datentechnik". Please note that I am not affiliated with them in any way, because it might appear like that when I go on and describe what their product can do. Unfortunately their website looks rather bad and is German only, but the product itself is really great. I currently have a trial version from them - you will have to contact them, no downloads - and I am extremely impressed.
As there is no comprehensive documentation available online, I will try and describe my experiences so far.
What they do is build a custom index file based on database content. They can integrate via ODBC, but from what I am told customers rarely do that. Instead - and this is what we will probably do - you generate a text export (like CSV) from your primary database and feed that to their indexer. This allows you to be completely independent of the actual table structure (or any SQL database at all); in fact we export data joined together from several tables. Indexes can be incrementally updated later on the fly.
Based on that their server (a mere 250kb or so, running as a console app or Windows service) serves listens for queries on a TCP port. The protocol is text based and looks a little "old", but it is simple and works. Basically you just pass on which of the available indexes you want to query and the search terms (fragments), space delimited.
There are three output formats available, HTML/JavaScript array, XML or CSV. Currently I am working on a Java wrapper for the somewhat "dated" wire protocol. But the results are fantastic: I currently have a sample data set of approximately 500.000 records with 8 columns indexed and my test application triggers a search across all 8 columns for the contents of a JTextField on every keystroke while being edited and can update the results display (JTable) in real-time! This happens without going to the MySQL instance the data originally came from. Based on the columns you get back, you can then ask for the "original" record by querying MySQL with the primary key of that row (needs to be included in the QuickFind index, of course).
The index is about 30-40% the size of the text export version of the data. Indexing was mainly bound by disk I/O speed; my 500.000 records took about a minute or two to be processed.
It is hard to describe this as I found it even hard to believe when I saw an in-house product demo. They presented a 10 million row address database and searched for fragments of names, addresses and phone numbers and when hitting the "Search" button, results came back in under a second - all done on a notebook! From what I am told they often integrate with SAP or CRM systems to improve search times when call center agents just understand fragments of the names or addresses of a caller.
So anyway, I probably won't get much better in describing this. If you need something like this, you should definitely go check this out. Google Translate does a reasonably good job translating their website from German to English, so this might be a good start.
This may not be what you want to hear, because I presume you are trying to solve this with SQL code, but Lucene would be my first choice. You can also build up fairly clever ranking and boosting techniques with additional tools. Lucene is written in Java so it should give you exactly the interface you need.
If you were a Microsoft shop, the majority of what you're looking for is built into SQL Server, and wildcards can be enabled which will give you the ability to do partial word matches.
In Lucene and Lucene.Net, you can use wildcard matches if you like. However, it's not supported to use wildcards as the first symbol in a search. If you want the ability to use first character wildcards, you'll probably need to implement some sort of trie-based index on your own, since it's an expensive operation in many cases to filter the set of terms down to something reasonable for the kind of index most commonly needed for full text search applications, where suffix stemming is generally more valuable.
You can apparently alter the Query Parser instance in Lucene to override this rule by setting setAllowLeadingWildcard to true.
I'm fairly sure that wildcard-on-both-ends-of-a-word searches are inherently inefficient. Skip lists are sometimes used to improve performance on such searches with plaintext, but I think you're more likely to find an implementation like that in something like grep than a generalized text indexing tool.
There are other solutions for the problem that you describe where one word may occur spelled as two, or vice versa. Fuzzy queries are supported in Lucene, for example. Orthographic and morphological variants can be handled using either by providing a filter that offers suggestions based on some sort of Bayesian mechanism, or by indexing tricks, namely, taking a corpus of frequent variants and stuffing the index with those terms. I've even seen knowledge from structured data stuffed into the full text engine (e.g. adding city name and the word "hotel" to records from the hotel table, to make it more likely that "Paris Hotels" will include a record for the pension-house Caisse des Dépôts.) While not exactly a trivial problem, it's manageable without destroying the advantages of word-based searches.
I haven't had this specific requirement myself, but my experience tells me Lucene can do the trick, though perhaps not standalone. I'd definitely use it through Solr as described by Michael Della Bitta in the first answer. The link he gave was spot on - read it for more background.
Briefly, Solr lets you define custom FieldTypes. These consist of an index-time Analyzer and a query-time Analyzer. Analyzers figure out what to do with the text, and each consists of a Tokenizer and zero to many TokenFilters. The Tokenizer splits your text into chunks and then each TokenFilter can add, subtract, or modify tokens.
The field can thus end up indexing something quite different from the original text, including multiple tokens if necessary. So what you want is a multiple-token copy of your original text, which you query by sending Lucene something like "my_ngram_field:sledge". No wildcards involved :-)
Then you follow a model similar to the prefix searching offered up in the solrconfig.xml file:
<fieldType name="prefix_token" class="solr.TextField" positionIncrementGap="1">
<analyzer type="index">
<tokenizer class="solr.WhitespaceTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory" />
<filter class="solr.EdgeNGramFilterFactory" minGramSize="1" maxGramSize="20"/>
</analyzer>
<analyzer type="query">
<tokenizer class="solr.WhitespaceTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory" />
</analyzer>
</fieldType>
The EdgeNGramFilterFactory is how they implement prefix matching for search box autocomplete. It takes the tokens coming from the previous stages (single whitespace-delimited words transformed into lower case) and fans them out into every substring on the leading edge. sledgehammer = s,sl,sle,sled,sledg,sledge,sledgeh, etc.
You need to follow this pattern, but replace the EdgeNGramFilterFactory with your own which does all NGrams in the field. The default org.apache.solr.analysis.NGramFilterFactory is a good start, but it does letter transpositions for spell checking. You could copy it and strip that out - it's a pretty simple class to implement.
Once you have your own FieldType (call it ngram_text) using your own MyNGramFilterFactory, just create your original field and the ngram field like so:
<field name="title" type="text" indexed="true" stored="true"/>
<field name="title_ngrams" type="ngram_text" indexed="true" stored="false"/>
Then tell it to copy the original field into the fancy one:
<copyField source="title" dest="title_ngrams"/>
Alright, now when you search "title_ngrams:sledge" you should get a list of documents that contain this. Then in your field list for the query you just tell it to retrieve the field called title rather than the field title_ngrams.
That should be enough of a nudge to allow you to fit things together and tune it to astonishing performance levels rather easily. At an old job we had a database with over ten million products with large HTML descriptions and managed to get Lucene to do both the standard query and the spellcheck in under 200ms on a mid-sized server handling several dozen simultaneous queries. When you have a lot of users, caching kicks in and makes it scream!
Oh, and incremental (though not real-time) indexing is a cinch. It can even do it under high loads since it creates and optimizes the new index in the background and autowarms it before swapping it in. Very slick.
Good luck!
If your table is MyISAM, you can use MySQL's full text search capabilites: http://dev.mysql.com/doc/refman/5.0/en/fulltext-search.html
If not, the "industry standard" is http://www.sphinxsearch.com/
Some ideas on what to do if you are using InnoDB: http://www.mysqlperformanceblog.com/2009/09/10/what-to-do-with-mysql-full-text-search-while-migrating-to-innodb/
Also, a good presentation that introduces Sphinx and explains architecture+usage
http://www.scribd.com/doc/2670976/Sphinx-High-Performance-Full-Text-Search-for-MySQL-Presentation
Update
Having read your clarification to the question -- Sphinx can do substring matches. You need to set "enable-star" and create an infix index with the appropriate min_infix_length (1 will give you all possible substrings, but obviously the higher the set it, the smaller your index will be, and the faster your searches). See http://sphinxsearch.com/docs/current.html for details.
I'd use Apache Solr. The indexing strategy is entirely tunable (see http://wiki.apache.org/solr/AnalyzersTokenizersTokenFilters), can incrementally read directly from your database to populate the index (see DataImportHandler in the same wiki), and can be queried from basically any language that speaks HTTP and XML or something like JSON.
what about using tools such as proposed above (lucene etc.) for full text indexing and having LIKE search for cases, where nothing was found? (i.e. run LIKE only after fulltext indexed search returned zero results)
What you're trying to do is unlikely to ever be all that much faster than LIKE '%searchterm%' without a great deal of custom code. The equivalent of LIKE 'searchterm%' ought to be trivial though. You could do what you're asking by building an index of all possible partial words that aren't covered by the trailing wild-card, but this would result in an unbelievably large index size, and it would be unusually slow for updates. Long tokens would result in Bad Things™. May I ask why you need this? Re: Spotlight... You do realize that Spotlight doesn't do this, right? It's token-based just like every other full-text indexer. Usually query expansion is the appropriate method of getting inexact matches if that's your goal.
Edit:
I had a project exactly like this at one point; part-numbers for all kinds of stuff. We finally settled on searchterm* in Xapian, but I believe Lucene also has the equivalent. You won't find a good solution that handles wild-card searches on either side of the token, but a trailing wild-card is usually more than good enough for what you want, and I suspect you'll find that users adapt to your system fairly quickly if they have any control over cleaning up the data. Combine it with query expansion (or even limited token expansion) and you should be pretty well set. Query expansion would convert a query for "sledgehammer" into "sledgehammer* OR (sledge* hammer*)" or something similar. Not every query will work, but people are already pretty well trained to try related queries when something doesn't work, and as long as at least one or two obvious queries come up with the results they expect, you should be OK. Your best bet is still to clean up the data and organize it better. You'd be surprised how easy this ends up being if you version everything and implement an egalitarian edit policy. Maybe let people add keywords to an entry and be sure to index those, but put limits on how many can be set. Too many and you may actually degrade the search results.
Shingle search could do the trick.
http://en.wikipedia.org/wiki/W-shingling
For example, if you use 3-character shingles, you can split "Roisonic" to: "roi", "son", "ic ", and store all three values, associating them with original entry. When searching for "oison", you first will search for "ois", "iso", "son". First you fuzzy-match all entries by shingles (finding the one with "son"), and then you can refine the search by using exact string matching.
Note that 3-character shingle require the fragment in query to be at least 5 characters long, 4-char shingle requires 7-char query and so on.
The exact answer to your question is right here Whether it will perform sufficiently well for the size of your data is another question.
I'm pretty sure Mysql offers a fulltext option, and it's probably also possible to use Lucene.
See here for related comments
Best efficient way to make a fulltext search in MySQL
A "real" full text index using parts of a word would be many times bigger than the source text and while the search may be faster any update or insert processing would be horibly slow.
You only hope is if there is some sort of pattern to the "mistakes' made. You could apply a set of "AI" type rules to the incoming text and produce cannonical form of the text which you could then apply a full text index to. An example for a rule could be to split a word ending in hammer into two words s/(\w?)(hammer)/\1 \2/g or to change "sledg" "sled" and "schledge" to "sledge". You would need to apply the same set of rules to the query text. In the way a product described as "sledgehammer" could be matched by a search for ' sledg hammer'.