So i have a website with a product catalog, this page has 4 product sliders one for recent products, another for bestsellers a third one for special offers.
Is it better to create a query for each type of slider, or should I get all products and then have php sort them out and separate them into three distict arrays one for each slider?
Currently I am just doing
SELECT * FROM products WHERE deleted = 0
For testing.
It's almost always best to refine the query so that it just returns not only the records you actually need, but also only the columns you really need. So, in your case this would look like
SELECT id, description, col3
FROM products
WHERE deleted = 0
AND -- conditions that make it fit in the proper slider
The reason is that it also costs resources (time and bandwidth) to transport a result set over to the processing program and processing time to evaluate the individual return records, and thus the "cost" of the query will evolve with the table size, not with the size of the dataset you actually need.
Just an example, but let's suppose you want to create a top 10 seller list, and you have 100 items in story. You'll retrieve 100 records and retain 10. No big deal. Now, your shop grows and you have 100000 items in store. For your top 10 you'll have to plow through all of theses and throw away 99990 records. 99990 records you'll have the db server read and transfer to you, and 99990 records you have to individually inspect to see whether it's a top 10 item.
As this type of query is going to be executed often, it's also a good idea to optimize them by indexing the search columns, as indexed searches in the db server are much faster.
I said "almost always" because there are rare cases where you have a hard time expressing in SQL what you actually need, or where you need to use fairly exotic techniques like query hints to force the database engine to execute your query in a reasonably efficient way. But these cases are fairly rare, and when a query doesn't perform as expected, with some analysis you'll manage to improve its performance in most cases - have a look at the literally thousands of questions regarding query optimization here on SO.
I work on a site which sells let's say stuff and offers a "vendors search". On this search you enter your city, or postal code, or region and a distance (in km or miles) then the site gives you a list of vendors.
To do that, I have a database with the vendors. In the form to save these vendors, you enter their full address and when you click on the save button, a request to google maps is made in order to get their latitude and longitude.
When someone does a search, I look on a table where I store all the search terms and their lat/lng.
This table looks like
+--------+-------+------+
| term | lat | lng |
+--------+-------+------+
So the first query is something very simple
select lat, lng from my_search_table where term = "the term"
If I find a result, I then search with a nice method for all the vendors in the range the visitor wants and print the result on a map.
If I don't find a result, I search with a levenshtein function because people writing bruxelle or bruxeles instead of bruxelles is something really common and I don't want to make a request to google maps all the time (I also have a "how many time searched" column in my table to get some stats)
So I request my_search_time with no where clause and loop through all results to get the smallest levensthein distance. If the smallest result is greater than 2, I request coordinates from google maps.
Here is my problem. For some countries (we have several sites all around the world), my_search_table has 15-20k+ entries... and php doesn't (really) like looping on such data (which I perfectly understand) and my request falls under the php timeout. I could increase this timeout but the problem will be the same in a few months.
So I tried a levensthein MySQL function (found on stackoverflow btw) but it's also very slow.
So my question is "is there any way to make this search fast even on very large datasets ?"
My suggestion is based on three things:
First, your data set is big. That means - it's: big enough to reject the idea of "select all" + "run levenshtein() in PHP application"
Second, you have control over your database. So you can adjust some architecture-related things
Finally, performance of SELECT queries is the most important thing, while performance for adding new data doesn't matter.
The thing is you can not perform fast levenshtein search because levenshtein itself is very slow. I mean, calculating levenshtein distance is a slow thing. Thus, you'll not be able to resolve the issue with only "smart search". You'll have to prepare some data.
Possible solution will be: create some group index and assign it during adding/updating data. That means - you'll store additional column which will store some hash (numeric, for example). When adding new data, you'll:
Perform search with levenshtein distance (for that you may either use your application or that function which you've (already mentioned) over all records in your table against inserted data
Set group index for new row to value of index which found rows in previous step have.
If nothing found, set some new group index value (it' the first row and there are no similar rows yet) - which will be different from any group index values that already present in table
To search desired rows, you'll need just select rows with same group index value. That means: your select queries will be very fast. But - yes, this will cause extremely huge overhead when adding/changing your data. Thus, it isn't applicable for case, when performance of updating/inserting matters.
You could try MySQL function SOUNDS LIKE
SELECT lat, lng FROM my_search_table WHERE term SOUNDS LIKE "the term"
You can use a kd-tree or a ternary tree to speed up the search. The idea is to use a binary search.
I am trying to create a script that finds a matching percentage between my table rows. For example my mySQL database in the table products contains the field name (indexed, FULLTEXT) with values like
LG 50PK350 PLASMA TV 50" Plasma TV Full HD 600Hz
LG TV 50PK350 PLASMA 50"
LG S24AW 24000 BTU
Aircondition LG S24AW 24000 BTU Inverter
As you may see all of them have some same keyword. But the 1st name and 2nd name are more similar. Additionally, 3rd and 4th have more similar keywords between them than 1st and 2nd.
My mySQL DB has thousands of product names. What I want is to find those names that have more than a percentage (let's say 60%) of similarity.
For example, as I said, 1st, 2nd (and any other name) that match between them with more than 60%, will be echoed in a group-style-format to let me know that those products are similar. 3rd and 4th and any other with more than 60% matching will be echoed after in another group, telling me that those products match.
If it is possible, it would be great to echo the keywords that satisfy all the grouped matching names. For example LG S24AW 24000 BTU is the keyword that is contained in 3rd and 4th name.
At the end I will create a list of all those keywords.
What I have now is the following query (as Jitamaro suggested)
Select t1.name, t2.name From products t1, products t2
that creates a new name field next to all other names. Excuse me that I don't know how to explain it right but this is what it does: (The real values are product names like above)
Before the query
-name-
A
B
C
D
E
After the query
-name- -name-
A A
B A
C A
D A
E A
A B
B B
C B
D B
E B
.
.
.
Is there a way either with mySQL or PHP that will find me the matching names and extract the keywords as I described above? Please share code examples.
Thank you community.
Query the DB with LIKE OR REGEXP:
SELECT * FROM product WHERE product_name LIKE '%LG%';
SELECT * FROM product WHERE product_name REGEXP "LG";
Loop the results and use similar_text():
$a = "LG 50PK350 PLASMA TV 50\" Plasma TV Full HD 600Hz"; // DB value
$b = "LG TV 50PK350 PLASMA 50\"" ; // USER QUERY
$i = similar_text($a, $b, $p);
echo("Matched: $i Percentage: $p%");
//outputs: Matched: 21 Percentage: 58.3333333333%
Your second example matches 62.0689655172%:
$a = "LG S24AW 24000 BTU"; // DB value
$b = "Aircondition LG S24AW 24000 BTU Inverter" ; // USER QUERY
$i = similar_text($a, $b, $p);
echo("Matched: $i Percentage: $p%");
You can define a percentage higher than, lets say, 40%, to match products.
Please note that similar_text() is case SensItivE so you should lower case the string.
As for your second question, the levenshtein() function (in MySQL) would be a good candidate.
When I look at your examples, I consider how I would try to find similar products based on the title. From your two examples, I can see one thing in each line that stands out above anything else: the model numbers. 50PK350 probably doesn't show up anywhere other than as related to this one model.
Now, MySQL itself isn't designed to deal with questions like this, but some bolt-on tools above it are. Part of the problem is that querying across all those fields in all positions is expensive. You really want to split it up a certain way and index that. The similarity class of Lucene will grant a high score to words that rarely appear across all data, but do appear as a high percentage of your data. See High level explanation of Similarity Class for Lucene?
You should also look at Comparison of full text search engine - Lucene, Sphinx, Postgresql, MySQL?
Scoring each word against the Lucene similarity class ought to be faster and more reliable. The sum of your scores should give you the most related products. For the TV, I'd expect to see exact matches first, then some others of the same size, then brand, then TVs in general, etc.
Whatever you do, realize that unless you alter the data structures by using another tool on top of the SQL system to create better data structures, your queries will be too slow and expensive. I think Lucene is probably the way to go. Sphinx or other options not mentioned may also be up for consideration.
This is trickier than it seems and there is information missing in your post:
How are people going to use this auto-complete function?
Is it relevant that you can find all names for a product? Because apparently not all stores name their products similarly so a clerk might not be able to find the product (s)he found.
Do you have information about which product names are for the same product?
Is it relevant from which store you're searching? where is this auto-complete used?
Should the auto-complete really only suggest products that match all the words you typed? (it's not so hard, technically, to correct typos)
I think you need a more clear picture of what you (or better yet: the users) want this auto-complete function to do.
An auto-complete function is very much a user-friendly type feature. It aids the user, possibly in a fuzzy way so there is no single right answer. You have to figure out what works best, not what is easiest to do technically.
First figure out what you want, then worry about technology.
One possible solution is to use Damerau-Levenstein distance. It could be used like this
select *
from products p
where DamerauLevenstein(p.name, '*user input here*')<=*X*
You'll have to figure out X that suites your needs best. It should be integer greater than zero. You could have it hard-coded, parameterized or calculated as needed.
The trickiest thing here is DamerauLevenstein. It has to be stored procedure, that implements Damerau-Levenstein algorithm. I don't have MySQL here, so I might write it for you later this day.
Update: MySQL does not support arrays in stored procedures, so there is no way to implement Damerau-Levenstein in MySQL, except using temporary table for each function call. And that will result in terrible performance. So you have two options: loop through the results in PHP with levenstein like Alix Axel suggests, or migrate your database to PostgreSQL, where arrays are supported.
There is also an option to create User-Defined function, but this requires writing this function in C, linking it to MySQL and possibly rebuilding MySQL, so this way you'll just add more headache.
Your approach seems sound. For matching similar products, I would suggest a trigram search. There's a pretty decent explanation of how this works along with the String::Trigram Perl module.
I would suggest using trigram search to get a list of matches, perhaps coupled with some manual review depending on how much data you have to deal with and how frequent you need to add new products. I've found this approach to work quite well in practice.
Maybe you want to find the longest common substring from the 2 strings? Then you need to compute a suffix tree for each of your strings see here http://en.wikipedia.org/wiki/Longest_common_substring_problem.
If you want to check all names against each other you need a cross join in mysql. There are many ways to achieve this:
1. Select a, b From t1, t2
2. Select a, b From t1 Join t2
3. Select a, b From t1 Cross Join t2
Then you can loop through the result. This is the same when I say create a 2d array with n^2-(n-1) elements and each element is connected with each other.
P.S.: Select t1.name, t2.name From products t1, products t2
It sounds like you've gone through all this trouble to explain a complex scenario, then said that you want to ignore the optimal answers and just get us to give you the "handshake" protocol (everything is compared to everything that hasn't been compared to it yet). So... pseudocode:
select * from table order by id
while (result) {
select * from table where id > result_id
}
That will do it.
If your database simply had a UPC code as one of it's fields, and this field was well-maintained, i.e., you could trust that it was entered correctly by the database maintainer and correctly reflected what the item was -- then you wouldn't need to do all of the work you suggest.
An even better idea might be to have a UPC field in your next database -- and constrain it as unique.
Database users attempt to put an-already-existing UPC into the database -- they get an error.
Database maintains its integrity.
And if such a database maintained its integrity -- the necessity of doing what you suggest never arises.
This probably doesn't help much with your current task (apologies) -- but for a future similar database -- you might wish to think about it...
I`d advise you to use some fulltext search engine, like sphinx. It has possibilities to implement any algorithm you want. For example, you may use "quorom" or "any" searches.
It seems that you might always want to return the shortest string?? That's more or a question than anything. But then you might have something like...
SELECT * FROM products LIMIT 1
WHERE product_name like '%LG%'
ORDER BY LENGTH(product_name) ASC
This is a clustering problem, which can be resolved by a data mining method. ( http://en.wikipedia.org/wiki/Cluster_analysis) It requires a lot of memory and computation intensive operations which is not suitable for database engine. Otherwise, separate data mining, text mining, or business analytics software wouldn't have existed.
This question is similar :) to this one:
What is the best way to implement a substring search in SQL?
Trigram can easily find similar rows, and in that question i posted a php+mysql+trigram solution.
You can use LIKE to find similar product names within the table. For example:
SELECT * FROM product WHERE product_name LIKE 'LG%';
Here is another idea (but I'm voting for levenshtein()):
Create a temporary table of all words used in names and their frequencies.
Choose range of results (most popular words are probably words like LCD or LED, most unique words could be good, they might be product actual names).
Suggest for each of result words either:
results with those words
results containing longest substring (like this: http://forums.mysql.com/read.php?10,277997,278020#msg-278020 ) of those words.
Ok, I think I was trying to implement very much similar thing. It can work the same as the google chrome address box. When you type the address it gives you the suggestions. This is what you are trying to achieve as far I am concerned.
I cannot give you exact solution to that but some advice.
You need to implement the dropdown box where someone starts to enter the product they are looking for
Then you need to get the current value of the dropdown and then run query like guy posted above. Can be "SELECT * FROM product WHERE product_name LIKE 'LG%';"
Save results of the query
Refresh the page
Add the results of the query to the dropdown
Note:
You need to save the query results somewhere like the text file with the HTML code i.e. "option" LG TS 600"/option" (add <> brackets to option of course). This values will be used for populating your option box after the page refresh. You need to set up the users session for the user to get the same results for the same user, otherwise if more users would use the search at the same time it could clash. So, with the search id and session id you can match them then. You can save it in the file or the table. Table would be more convenient. It is actually in my sense the whole subsystem for that what are you looking for.
I hope it helps.
I'm not sure how to handle the following issues. So i hope, to get here some ideas or something like that.
I'm using lucene with solr. Every document (which is indexed in lucene) has an date-field an an topic - field (with some keywords)
By using faceted search, i'm able to calculate the frequency of every keyword at an specific date.
Example 1 (pseudo code):
1st search where date=today:
web=>70
apple=>35
blue=>32
2nd search where date=yesterday:
web=>65
blue=>55
apple=>5
But now i would like to combine the results into one solr/lucene query in order to calculate which word-frequency grows very strong and witch doesn't.
An result could be:
Example 2:
one search merging both querys from example 1
web=>(70,65) <- growth +7,69%
blue=>(32,55) <- growth -41,81%
apple=>(34,5) <- growth +680%
Is it possible (and useful) to do this consolidation (and calclulation) inside solr or is it better to start 2 solr querys (see example 1) an postprocess the results with PHP?
Than you!
If you have the facet values a priori, you could do this with facet queries, i.e. something like facet.query=category:web AND date:[2011-06-14T00:00:00Z TO 2011-06-14T23:59:59Z]&facet.query=category:web AND date:[2011-06-13T00:00:00Z TO 2011-06-13T23:59:59Z]&... so you would do the cartesian product of facet values * dates.
Otherwise, to do this inside Solr I think you'd have to write some custom Java faceting code. Or do it client-side, with multiple queries as you mentioned.
I have a table that lists people and all their contact info. I want for users to be able to perform an intelligent search on the table by simply typing in some stuff and getting back results where each term they entered matches at least one of the columns in the table. To start I have made a query like
SELECT * FROM contacts WHERE
firstname LIKE '%Bob%'
OR lastname LIKE '%Bob%'
OR phone LIKE '%Bob%' OR
...
But now I realize that that will completely fail on something as simple as 'Bob Jenkins' because it is not smart enough to search for the first an last name separately. What I need to do is split up the the search terms and search for them individually and then intersect the results from each term somehow. At least that seems like the solution to me. But what is the best way to go about it?
I have heard about fulltext and MATCH()...AGAINST() but that sounds like a rather fuzzy search and I don't know how much work it is to set up. I would like precise yes or no results with reasonable performance. The search needs to be done on about 20 columns by 120,000 rows. Hopefully users wouldn't type in more than two or three terms.
Oh sorry, I forgot to mention I am using MySQL (and PHP).
I just figured out fulltext search and it is a cool option to consider (is there a way to adjust how strict it is? LIMIT would just chop of the results regardless of how well it matched). But this requires a fulltext index and my website is using a view and you can't index a view right? So...
I would suggest using MATCH / AGAINST. Full-text searches are more advanced searches, more like Google's, less elementary.
It can match across multiple tables and rank them to how many matches they have.
Otherwise, if the word is there at all, esp. across multiple tables, you have no ranking. You can do ranking server-side, but that is going to take more programming/time.
Depending on what database you're using, the ability to do cross columns can become more or less difficult. You probably don't want to do 20 JOINs as that will be a very slow query.
There are also engines such as Sphinx and Lucene dedicated to do these types of searches.
BOOLEAN MODE
SELECT * FROM contacts WHERE
MATCH(firstname,lastname,email,webpage,country,city,street...)
AGAINST('+bob +jenkins' IN BOOLEAN MODE)
Boolean mode is very powerful. It might even fulfil all my needs. I will have to do some testing. By placing + in front of the search terms those terms become required. (The row must match 'bob' AND 'jenkins' instead of 'bob' OR 'jenkins'). This mode even works on non-indexed columns, and thus I can use it on a view although it will be slower (that is what I need to test). One final problem I had was that it wasn't matching partial search terms, so 'bob' wouldn't find 'bobby' for example. The usual % wildcard doesn't work, instead you use an asterisk *.