I'm working on a analysis assignment, we got a partial data-set from the university Library containing almost 300.000.000 rows.
Each row contains:
ID
Date
Owner
Deadline
Checkout_date
Checkin_date
I put all this inside a MySQL table, then I started querying that for my analysis assignment, however simple query (SELECT * FROM table WHERE ID = something) where taking 9-10 minutes to complete. So I created an index for all the columns, which made it noticeable faster ~ 30 sec.
So I started reading similar issues, and people recommended switching to a "Wide column store" or "Search engine" instead of "Relational".
So my question is, what would be the best database engine to use for this data?
Using a search engine to search is IMO the best option.
Elasticsearch of course!
Disclaimer: I work at elastic. :)
The answer is, of course, "it depends". In your example, you're counting the number of records in the database with a given ID. I find it hard to believe that it would take 30 seconds in MySQL, unless you're on some sluggish laptop.
MySQL has powered an incredible number of systems because it is full-featured, stable, and has pretty good performance. It's bad (or has been bad) at some things, like text search, clustering, etc.
Systems like Elasticsearch are good with globs of text, but still may not be a good fit for your system, depending on usage. From your schema, you have one text field ("owner"), and you wouldn't need Elasticsearch's text searching capabilities on a field like that (who ever needed to stem a user name?). Elasticsearch is also used widely for log files, which also don't need a text engine. It is, however, good with blocks of text and with with clustering.
If this is a class assignment, I'd stick with MySQL.
I have a mysql database where large amounts of text are constantly added. (10 pages of text per hour). Text is stored as plaintext in text fields. Every row contacts a page or two of texts.
I need to do full text search (search for a keyword in the text and do complex queries) on this database on a regular basis. I just need to search for newly added text. But it is very important for added text to be immediately searchable (within a minute or two).
From what i've read, fulltext with mysql is very inefficient. I know lucene is an option but i'm not sure yet how quickly it can index new text.
So what are my options? is there a way to make mysql more efficient? is lucene my best solution? anything more appropriate?
thanks
I have done benchmarking for Indexing Times for Sphinx & Solr. Sphinx is far ahead as compared to Solr with respect to Indexing Algorithms (super fast indexing times and small index size).
When you say 10 pages of text, it seems you even dont need Real Time Sphinx Indexing. You can follow the main + delta indexing scheme in Sphinx (you can find that on Sphinx Documentation). It would be superfast and near real time. If you want more help on this please feel free to ask, would be glad to explain you.
Solr is great but when it comes to optimized Algorithms Sphinx rocks!! Try Sphinx.
Coming to your questions in the comment, Solr/Lucene supports incremental indexing (known as delta imports in their terminology) and its quiet easy to configure however they are pretty slow as compared to the method used by Sphinx.
Main+Delta is quick enough because what you can do is create a temporary table store you new text in that and index that. According to the documentation:Sphinx supports "live" (almost real time) index updates and it could be implemented using
so called "main+delta" scheme. The idea is to set up two sources and two indexes, with one "main" index for the data, and one "delta" for the new documents.
Say for example you have 10 Million records so you can keep that as the main index and all the new documents get added to a new table which will act as the delta. This new table can be indexed from time to time (say every 1hr) and the data gets searchable within very few seconds as you have 10 pages of text. Now after your new records are being searched you can merge the documents of the main table + delta table which can be carried out without interfering your search. When the documents are merged, empty the new table and again after an hour you can carry out the whole process again. I hope you got that else please feel free to ask any question.
You have a couple of options:
Sphinx Search: Can integrate directly with your MySQL DB. Has support for real-time indexes, with limitations
Solr/Lucene: Feed it data via JSON or XML from your DB. Has rich querying capabilities. Current versions are not real-timey w/o some edge builds. You have to re-index your data and commit it for changes to appear. Which depending on your amount of data, you could do a commit every 10 min. This wont be an issue until you have 100K / 1M+ documents as Lucene is very fast at indexing. 10 pages / hour is pretty trivial.
ElasticSearch: Is Java based like Solr/Lucene but appears to be the truly "near real time" enough. Its engineered out of the box to be distributed and support linear scale-out. You feed it data via JSON and query via JSON.
It really depends on your needs and capabilities. Sphinx might be the easiest to get started. But its Real Time index limitations might not work for you.
My website stores several million entities. Visitors search for entities by typing words contained only in the titles. The titles are at most 100 characters long.
This is not a case of classic document search, where users search inside large blobs.
The fields are very short. Also, the main issue here is performance (and not relevance) seeing as entities are provided "as you type" (auto-suggested).
What would be the smarter route?
Create a MySql table [word, entity_id], have 'word' indexed, and then query using
select entity_id from search_index where word like '[query_word]%
This obviously requires me to break down each title to its words and add a row for each word.
Use Solr or some similar search engine, which from my reading are more oriented towards full text search.
Also, how will this affect me if I'd like to introduce spelling suggestions in the future.
Thank you!
Pro's of a Database Only Solution:
Less set up and maintenance (you already have a database)
If you want to JOIN your search results with other data or otherwise manipulate them you will be able to do so natively in the database
There will be no time lag (if you periodically sync Solr with your database) or maintenance procedure (if you opt to add/update entries in Solr in real time everywhere you insert them into the database)
Pro's of a Solr Solution:
Performance: Solr handles caching and is fast out of the box
Spell check - If you are planning on doing spell check type stuff Solr handles this natively
Set up and tuning of Solr isn't very painful, although it helps if you are familiar with Java application servers
Although you seem to have simple requirements, I think you are getting at having some kind of logic around search for words; Solr does this very well
You may also want to consider future requirements (what if your documents end up having more than just a title field and you want to assign some kind of relevancy? What if you decide to allow people to search the body text of these entities and/or you want to index other document types like MS Word? What if you want to facet search results? Solr is good at all of these).
I am not sure if you would need to create an entry for every word in your database, vs. just '%[query_word]%' search if you are going to create records with each word anyway. It may be simpler to just go with a database for starters, since the requirements seem pretty simple. It should be fairly easy to scale the database performance.
I can tell you we use Solr on site and we love the performance and we use it for even very simple lookups. However, one thing we are missing is a way to combine Solr data with database data. And there is extra maintenance. At the end of the day there is not an easy answer.
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'.
We have a hosted application that manages pages of content. Each page can have a number of customized fields, and some standard fields (timestamp, user name, user email, etc).
With potentially hundreds of different sites using the system -- what is an efficient way to handle filtering/searching? Picture a grid view that you want to narrow down. You can filter on specific fields (userid, date) or you can enter a full-text search.
For example, "all pages started by userid 10" would be a pretty quick query against a MySQL database. But things like "all pages started by a user whose userid is 10 and matches [some search query]" would suck against the database, so it's suited for a search engine like Lucene.
Basically I'm wondering how other large sites do this sort of thing. Do they utilize a search engine 100% for all types of filtering? Do they mix database queries with a search engine?
If we use only a search engine, there's a problem with the delay time it takes for a new/updated object to appear in the search index. That is, I've read that it's not smart to update the index immediately, and to do it in batches instead. Even if this means every 5 minutes, users will get confused when their recently added page isn't immediately listed when they view a simple page listing (say a search query of "category:5").
We are using MySQL and have been looking closely at Lucene for searching. Is there some other technology I don't know about?
My thought is to offer a simple filtering page which uses MySQL to filter on basic fields. Then offer a separate fulltext search page that would present results similar to Google. Is this the only way?
Solr or grassyknoll both provide slightly more abstract interfaces to Lucene.
That said: Yes. If you are a primarily content driven site, providing fulltext searching over your data, there is something in play beyond LIKE. While MySql's FULLTEXT indexies aren't perfect, it might be an acceptable placeholder in the interim.
Assuming you do create a Lucene index, linking Lucene Documents to your relational objects is pretty straightforward, simply add a stored property to the document at index time (this property can be a url, ID, GUID etc.) Then, searching becomes a 2 phase system:
1) Issue query to Lucene indexies (Display simple results like title)
2) Get more detailed information about the object from your relational stores by its key
Since instantiation of Documents is relatively expensive in Lucene, you only want to store fields searched in the Lucene index, as opposed to complete clones of your relational objects.
Don't write-off MySQL so readily!
Implement it using the database e.g. a select with a 'like' in the where-clause or whatever.
Profile it, add indexes if necessary. Roll out a beta, so you get real numbers from user's actual data patterns - not all columns might be equally asked after, etc.
If the performance does suck, then thats when you consider other options. You can consider tuning your SQL, your database, the machine the database is running on, and finally using another technology stack...
In case you want to use MySQL or PostgreSQL, a open source solution that works great with it is Sphinx:
http://www.sphinxsearch.com/
We are having the same problem and considering Sphinx and Lucene as possible solutions.