I want auto search option in textbox and data is fetching from database. I have thousands of data in my database table (almost 8-10000 rows). I know how to achieve this but as I am fetching thousands of data, it will take a lot of time to fetch. How to achieve this without getting slow down? Should I follow any other methodology to achieve this apart from simple fetching methods? I am using Oracle SQL Developer for database.
Besides the obvious solutions involving indexes and caching, if this is web technology and depending on your tool you can sometimes set a minimum length before the server call is made. Here is a jquery UI example: https://api.jqueryui.com/autocomplete/#option-minLength
"The minimum number of characters a user must type before a search is performed. Zero is useful for local data with just a few items, but a higher value should be used when a single character search could match a few thousand items."
It depends on your web interface, but you can use two tecniques:
Paginate your data: if your requirements are to accept empty values and to show all the results load them in block of a predefined size. goggle for example paginates search results. On Oracle pagination is made using the rownum special variable (see this response). Beware: you must first issue a query with a order by and then enclose it in a new one that use rownum. Other databases that use the limit keyword behave in a different way. If you apply the pagination techique to a drop down you end up with an infinite scroll (see this response for example)
Limit you data imposing some filter that limits the number of rows returned; your search display some results only after the user typed at least n chars in the field
You can combine 1 & 2, but unless you find an existing web component (a jquery one for example) it may be a difficult task if you don't have a Javascript knowledge.
I have a community website with 20.000 members using everyday a search form to find other members. The results are sorted by the last connected members. I'd like to use Solr for my search (right now it's mysql) but I'd like to know first if it's good practice to update the document of every member who would login in order to change their login date and time ? There will be around 20.000 update of documents a day, I don't really know if it's too much updating and could alter performances ? Tank you for your help.
20k updates/day is not unreasonable at all for Solr.
OTOH, for very frequently updating fields (imagine one user could log in multiple times a day so you might want to update it all those times), you can use External Fields to keep that field stored outside the index (in a text file) and still use it for sorting in solr.
Generally, Solr does not be used for this purpose, using your database is still better.
However, if you want to use Solr, you will deal with it in a way like the database. i.e every user document should has a unique field, id for example. When the user make log in, you may use an update query for that user's document last_login_date field by its id. You could able to know more about Partial Update from this link.
I have an entity Kind A with around 1.3 million entities. It has several fields, two of which I use as filters in a Datastore query - K (which is a Key) and S (which is a string).
I perform the following query, and it takes it almost 8 seconds to complete:
SELECT * FROM A WHERE K=KEY('...') AND S='...' LIMIT 1
(I do this by constructing a query with filters in java, but it works just as slow in GQL.)
Is this reasonable? Can this be improved in any way?
Replacing the key query with other fields (or removing completely), speeds things up. Does it make sense to save the key field as a string, and query on that instead of on the original Key type field?
Can you please send your index.yaml file, or at least paste all the indexes for Kind A? My initial feeling is that you don't have a proper index for K and S, so it's doing a full table scan or using another index to project the results. Check out https://developers.google.com/appengine/docs/python/datastore/indexes for an explanation.
If you're still banging your head, I would suggest turning appstats on and determining the slowness from the visuals on your local dev server (https://developers.google.com/appengine/docs/python/tools/appstats).
Finally, I wanted to point out that single property lookups are fast in App Engine, so either K= or S= lookups would be fast. Together, they require special attention (indexing, memcache, etc.).
I have a location auto-complete field which has auto complete for all countries, cities, neighborhoods, villages, zip codes. This is part of a location tracking feature I am building for my website. So you can imagine this list will be in the multi-millions of rows. Expecting over 20 million atleast with all the villages and potal codes. To make the auto-complete work well I will use memcached so we dont hit the database always to get this list. It will be used a lot as this is the primary feature on the site. But the question is:
Is only 1 instace of the list stored in memcached irrespective of the users pulling the info or does it need to maintain a separate instance for each? So if say 20 million people are using it at the same time, will that differ from just 1 person using the location auto-complete? I am open to other ideas also on how to implement this location auto complete so it performs well.
Or can i do something like this: When a user logs in in the background I send them the list anyways, so by the time they reach the auto complete textfield their computer will have it ready to load instant?
Take a look at Solr (or Lucene itself), using NGram (or EdgeNGram) tokenizers you can get good autocomplete performance on massive datasets.
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