I have an MySQL database with 500,000 rows.
I have a list of 500,000 combination Strings such as First_Name and Last_Name.
I am trying to search the 500,000 rows with a similar query
select count(*) FROM data WHERE first='wadaw' AND last='wdvv';
The problem is that it takes too much time, I am using multiple threads and it doesn't seem to be very efficient considering the communication overhead between MySQL and the running time of the queries. I thought to start improve by changing the settings of my database to better fit my data and optimize MySQL database for it.
From my experience with search algorithms, an unsorted list would take n*log(n) with the most widely used methods and N with radix sort etc. which makes it n^2 or n^2log(n) for my case, which is not that good if you have 1,000,000 fields.
But with Binary search it would take Log(n) and thus n*log(n) for my case.
I am looking for a way to make the best out of my database.
Any suggestions?
Try using an index for both fields you are using. In your example:
create index idx_data_name_last on data (first, last);
That will use just one index and so the time will be log(n) and not n*log(n).
I'm building a fairly large database where I will have a lot of tables with various data.
But each table has similar fields, for example video title or track title.
Now the problem I'm facing is how to build a query which would look for a keyword match across five or more tables, keep in mind that each table can potentially have from 100k to 1million rows or in some cases even couple million rows.
I think using joins or separate queries for each table would be very slow, so what I thought of is to make one separate table where I would store search data.
For example I think it could have fields like these,
id ---- username ---- title ---- body ---- date ---- belongs_to ---- post_id
This way I think it would perform a lot faster searches, or am I totally wrong?
The only problem with this approach that I can think of it is that it would be hard to manage this table because if original record from some of the tables is deleted I would also need to delete record from 'search' table as well.
Don't use MySQL for joining lots of tables, I would suggest you to take a look at Apache Solr, with RDBMS
Take a look at some information retrieval systems. They also require their own indices, so you need to index the data after each update (or in regular intervals) to keep the search index up to date. But they offer the following advantages:
much faster, because they use special algorithms and data structures designed for specifically that purpose
ability to search for documents based on a set of terms (and maybe also a set of negative terms that must not appear in the result)
search for phrases (i.e. terms that appear after each other in a specific order)
automatic stemming (i.e. stripping the endings of words like "s", "ed", "ing" ...)
detection of spelling mistakes (i.e. "Did you mean ...?")
stopwords to avoid indexing really common meaningless words ("a", "the", etc.)
wildcard queries
advanced ranking strategies (i.e. rank by relevance, based on the number and the position of each occurrences of the search terms)
I have used xapian in the past for my projects and I was quite happy with it. Lucene, Solr and elastic search are some other really popular projects that might fit your needs.
I'm porting my application searches over to Sphinx from MySQL and am having a hard time figuring this one out, or if it even needs to be ported at all (I really want to know if it's worth using sphinx for this specific case for efficiency/speed):
users
uid uname
1 alex
2 barry
3 david
friends
uid | fid
1 2
2 1
1 3
3 1
Details are:
- InnoDB
- users: index on uid, index on uname
- friends: combined index on uid,fid
Normally, to search all of alex's friends with mysql:
$uid = 1
$searchstr = "%$friendSearch%";
$query = "SELECT f.fid, u.uname FROM friends f
JOIN users u ON f.fid=u.uid
WHERE f.uid=:uid AND u.uname LIKE :friendSearch";
$friends = $dbh->prepare($query);
$friends->bindParam(':uid', $uid, PDO::PARAM_INT);
$friends->bindParam(':friendSearch', $searchstr, PDO::PARAM_STR);
$friends->execute();
Is it any more efficient to find alex's friends with sphinx vs mysql or would that be an overkill? If sphinx would be faster for this as the list hits thousands of people,
what would the indexing query look like? How would I delete a friendship that no longer exists with sphinx as well, can I have a detailed example in this case? Should I change this query to use Sphinx?
Ok this is how I see this working.
I have the exact same problem with MongoDB. MongoDB "offers" searching capabilities but just like MySQL you should never use them unless you wanna be choked with IO, CPU and memory problems and be forced to use a lot more servers to cope with your index than you normally would.
The whole idea if using Sphinx (or another search tech) is to lower cost per server by having a performant index searcher.
Sphinx however is not a storage engine. It is not as simple to query exact relationships across tables, they have remmedied this a little with SphinxQL but due to the nature of the full text index it still doesn't do an integral join like you would get in MySQL.
Instead I would store the relationships within MySQL but have an index of "users" within Sphinx.
In my website I personally have 2 indexes:
main (houses users,videos,channels and playlists)
help (help system search)
These are delta updated once every minute. Since realtime indexes are still bit experimental at times and I personally have seen problems with high insertion/deletion rates I keep to delta updates. So I would use a delta index to update the main searchable objects of my site since this is less resource intensive and more performant than realtime indexes (from my own tests).
Do note inorder to process deletions and what not your Sphinx collection through delta you will need a killlist and certain filters for your delta index. Here is an example from my index:
source main_delta : main
{
sql_query_pre = SET NAMES utf8
sql_query_pre =
sql_query = \
SELECT id, deleted, _id, uid, listing, title, description, category, tags, author_name, duration, rating, views, type, adult, videos, UNIX_TIMESTAMP(date_uploaded) AS date_uploaded \
FROM documents \
WHERE id>( SELECT max_doc_id FROM sph_counter WHERE counter_id=1 ) OR update_time >( SELECT last_index_time FROM sph_counter WHERE counter_id=1 )
sql_query_killlist = SELECT id FROM documents WHERE update_time>=( SELECT last_index_time FROM sph_counter WHERE counter_id=1 ) OR deleted = 1
}
This processes deletions and additions once every minute which is pretty much realtime for a real web app.
So now we know how to store our indexes. I need to talk about the relationships. Sphinx (even though it has SphinxQL) won't do integral joins across data so I would personally recommend doing the relationship outside of Sphinx, not only that but as I said this relationship table will get high load so this is something that could impact the Sphinx index.
I would do a query to pick out all ids and using that set of ids use the "filter" method on the sphinx API to filter the main index down to specific document ids. Once this is done you can search in Sphinx as normal. This is the most performant method I have found to date of dealing with this.
The key thing to remember at all times is that Sphinx is a search tech while MySQL is a storage tech. Keep that in mind and you should be ok.
Edit
As #N.B said (which I overlooked in my answer) Sphinx does have SphinxSE. Although primative and still in sort of testing stage of its development (same as realtime indexes) it does provide an actual MyISAM/InnoDB type storage to Sphinx. This is awesome. However there are caveats (as with anything):
The language is primative
The joins are primative
However it can/could do the job your looking for so be sure to look into it.
so I'm going to go ahead and kinda outline what -I- feel the best use cases for sphinx are and you can kinda decide if it's more or less in line for what you're looking to do.
If all you're looking to do is a string search one one field; then with MySQL you can do wild card searches without much trouble and honstly with an index on it unless you're expecting millions of rows you are going to be fine.
Now take facebook, that is not only indexing names, but pages ect or even any advanced search fields. Sphinx can take in x columns from MySQL, PostGRES, MongoDB, (insert your db you want here) and create a searchable full-text index across all of those.
Example:
You have 5 fields (house number, street, city, state, zipcode) and you want to do a full text search across all of those. Now with MySQL you could do searches on every single one, however with sphinx you can glob them all together then sphinx does some awesome statistical findings based on the string you've passed in and the matches which are resulting from it.
This Link: PHP Sphinx Searching does a great job at walking you through what it would look like and how things work together.
So you aren't really replacing a database; you're just adding a special daemon to it (sphinx) which allows you to create specialized indexes and run your full text searches against it.
No index can help you with this query, since you're looking for the string as an infix, not a prefix (you're looking for '%friendname%', not 'friendname%'.
Moreover, the LIKE solution will get you into corners: suppose you were looking for a friend called Ann. The LIKE expression will also match Marianne, Danny etc. There's no "complete word" notion in a LIKE expression.
A real solution is to use a text index. A FULLTEXT index is only available on MyISAM, and MySQL 5.6 (not GA at this time) will introduce FULLTEXT on InnoDB.
Otherwise you can indeed use Sphinx to search the text.
With just hundreds or thousands, you will probably not see a big difference, unless you're really going to do many searches per second. With larger numbers, you will eventually realize that a full table scan is inferior to Sphinx search.
I'm using Sphinx a lot, on dozens and sometimes hundreds of millions large texts, and can testify it works like a charm.
The problem with Sphinx is, of course, that it's an external tool. With Sphinx you have to tell it to read data from your database. You can do so (using crontab for example) every 5 minutes, every hour, etc. So if rows are DELETEd, they will only be removed from sphinx the next time it reads the data from table. If you can live with that - that's the simplest solution.
If you can't, there are real time indexes in sphinx, so you may directly instruct it to remove certain rows. I am unable to explain everything in this port, so here are a couple links for you:
Index updates
Real time indexes
As final conclusion, you have three options:
Risk it and use a full table scan, assuming you won't have high load.
Wait for MySQL 5.6 and use FULLTEXT with InnoDB.
Use sphinx
At this point in time, I would certainly use option #3: use sphinx.
Take a look at the solution I propose here:
https://stackoverflow.com/a/22531268/543814
Your friend names are probably short, and your query looks simple enough. You can probably afford to store all suffixes, perhaps in a separate table, pointing back to the original table to get the full name.
This would give you fast infix search at the cost of a little bit more storage space.
Furthermore, to avoid finding 'Marianne' when searching for 'Ann', consider:
Using case-sensitive search. (Fragile; may break if your users enter their names or their search queries with incorrect capitalization.)
After the query, filtering your search results further, requiring word boundaries around the search term (e.g. regex \bAnn\b).
We need to be able to perform two types of queries efficiently against a table containing several million records:
1) Return the "x" most recent records which contain keyword "y".
2) Return the "x" most frequent keywords for a group of records.
We have been thinking about using some external search server such as Sphinx or Solr, but we are not sure if any of those will be able to support both types of queries.
So, which is the most efficient way to be able to perform those types of queries?
Solr can definitely do both of those things, assuming you've set up your schema.xml file properly. Your queries might look something like this:
1 - http://localhost:8983/solr/solr-index/select?q=y&rows=x&sort=date+desc
2 - http://localhost:8983/solr/solr-index/select?q=*:*&rows=0&facet=true&facet.field=description
In fact your main problem with Solr might be getting the data into the index. But even indexing and optimization are fast.
Sphinx can do 1) without even breaking a sweat. No problem them.
2) Is more tricky. Its not supported out of the box. But it can be done. Need to do a fair amount of extra work. Basically you need to tokenize the text yourself, and store ids as Multi-Value attribute. Can then run group by query on this mva column.
If the above sounds in anyway scary, you probably best using another solution - from the last reply sounds like Solr will do it.
In my mysql db I have a user table consisting of 37,000 (or thereabouts) users.
When a user search for another user on the site, I perform a simple like wildcard (i.e. LIKE '{name}%}) to return the users found.
Would it be more efficient and quicker to use a search engine such a solr to do my 'LIKE' searches? furthermore? I believe in solr I can use wildcard queries (http://www.lucidimagination.com/blog/2009/09/08/auto-suggest-from-popular-queries-using-edgengrams/)
To be honest, it's not that slow at the moment using a LIKE query however as the number of users grows it'll become slower. Any tips or advice is greatly appreciated.
We had a similar situation about a month ago, our database is roughly around 33k~ and due to the fact our engine was InnoDB we could not utilize the MySQL full-text search feature (that and it being quite blunt).
We decided to implement sphinxsearch (http://www.sphinxsearch.com) and we're really impressed with the results (me becoming quite a 'fanboy' of it).
If we do a large index search with many columns (loads of left joins) of all our rows we actually halved the query response time against the MySQL 'LIKE' counterpart.
Although we havn't used it for long - If you're going to build for future scailablity i'd recommend sphinx.
you can speed up if the searchword must have minimum 3 chars to start the search and index your search column with a index size of 3 chars.
It's actually already built-in to MySQL: http://dev.mysql.com/doc/refman/5.0/en/fulltext-search.html
we're using solr for this purpose, since you can search in 1-2 ms even with milions of documents indexed. we're mirroring our mysql instance with Data Import Handler and then we search on Solr.
as neville pointed out, full text searches are built-in in mysql, but solr performances are way better, since it's born as a full text search engine