Consider I have two collections in MongoDB. One for products with documents like:
{'_id': ObjectId('lalala'), 'title': 'Yellow banana'}
And another stores price changes with documents like:
{'product': DBRef('products', ObjectId('lalala')),
'since': datetime(2011, 4, 5),
'new_price': 150 }
One product may have many price changes. The price lasts until a new change with later time stamp. I guess you've caught idea.
Say, I have 100 products. I want to query my DB to get know what's the price of each product at the moment of June 9, 2011. What is the most efficient (quick) way to perform this query in MongoDB? Suppose I have no cache solution or cache is empty.
I thought about group statement on prices collection, where reduce function would select last since before a date provided, grouping by product.$id. But in this case I would not benefit from an index on since field and all documents would be scanned.
Any ideas?
I had a similar problem, but for GPS locations. I found the fastest way was to set up a query for each item, which is rather counter-intuitive if your used to SQL databases.
Query for the item where it's timestamp is less or equal than the date your looking for, and limit the result to 1. Repeat for each item. To really speed things up, run multiple querys in parallel to utilise all the cores on the MongoDB server.
Related
I’m working on a web service for an API that provides a feed of posts. Right now the posts are organized chronologically, and I paginate with opaque before and after tokens which are essentially timestamps. However, we want to move from a chronological feed to an algorithmic one. While I can calculate the post scores and send the first page of data, I’m not sure how to paginate relative to that. I suppose snapshot it and bundle up like 200 sorted post IDs and serialize them into an HMAC blob for the tokens, but this is a nontrivial overhead for each request. Is there a better way to handle this kind of pagination?
If you can store post score in database you can make an index on them and access them fast. Top pages will be fast anyway. If you need pagination by rating with big depth standard approach with order by rating desc limit 50 offset 10000 will be slow. Here you can find a second order field - eg timestamp. If there’re several posts with the same ration - which one should be on top? Add this field to the sort index and query DB like where rating < ..., timestamp <... order by rating, timestamp.
If you recalculate rating often I recommend to store it on a separate table like post_id, rating. Query this table for post_ids - it should be faster then walk through the whole table and join posts on it.
I'm researching how to implement leaderboards for my game with the parse.com SDK, my plan is to submit a score for the user every time they finish a level, attached to a "parent" leaderboard. I need to submit all scores because I need to retrieve leaderboards within time ranges (eg "all time", "last week", "last month", etc). The problem is, there'll be multiple scores for each user on the same leaderboard, and I only need to highest one. Is there a way to drop duplicate keys from a query? Is this the correct strategy? Everything else (sorting, paging, etc) seems to be in place.
Thanks.
You just need a table with 'User_id', 'Score', 'Level', and 'Date' (or whatever you need). Each time that a player finishes a level, you put the score into the table.
Then you have to calculate each (all time, last week, etc) in the query.
E.g.
10 Highs of the day:
SELECT TOP 10 User_id, Score, Date FROM Scores
WHERE Date = getdate()
ORDER BY Score DESC
I don't know if I understood the question. Let me know if I didn't.
Hope it helps.
As far as I understand from your question you want to retrieve data from Parse class. At the same time you want to eliminate the duplicate entry because user has multiple scores in different days. So to get the highest one, you have to query the class via query (based on SDK Android,iOS) and order by descending(based on your criteria), then obtain the first item in the result.
Or you can get the user all scores and create a structure where you can store the user scores as array list day by day. Based on day you can get the latest max or min scores. I hope I understand your question.
Hope this helps.Regards
We have a database with 2,00,000 vendor in 100 plus category, if someone visit the website we want to allow them to select a category and show them 25 Vendor per page, first we kept order by VendorId but it always use to get first 25, but we removed it, but now in paging it sometime repeat the vendor, is there a way to get random 25 vendor and also keep the paging.
Regards
you can randomize your result but everytime you dot he query, it will create new random list so unless you randomize and save the randomized state in your Code and page over it, it cant be done straightforward way.
refer, SQL Query results pagination with random Order by in SQL Server 2008
I believe this requirement is impossible to implement if a new random order is needed every time, there needs to be good performance and every item should have equal chance to get selected. I believe you should redesign the way your application works.
One possible workaround is to have a couple of columns in a table and fill them with random numbers. When a user requests the list assign the random column to him (stick it in the URL for example). Then do an order by that column and display the results. Randomly switch 4-5 columns to create the appearance of randomness. Update the random numbers in the columns once a day.
Suppose I have a large (300-500k) collection of text documents stored in the relational database. Each document can belong to one or more (up to six) categories. I need users to be able to randomly select documents in a specific category so that a single entity is never repeated, much like how StumbleUpon works.
I don't really see a way I could implement this using slow NOT IN queries with large amount of users and documents, so I figured I might need to implement some custom data structure for this purpose. Perhaps there is already a paper describing some algorithm that might be adapted to my needs?
Currently I'm considering the following approach:
Read all the entries from the database
Create a linked list based index for each category from the IDs of documents belonging to the this category. Shuffle it
Create a Bloom Filter containing all of the entries viewed by a particular user
Traverse the index using the iterator, randomly select items using Bloom Filter to pick not viewed items.
If you track via a table what entries that the user has seen... try this. And I'm going to use mysql because that's the quickest example I can think of but the gist should be clear.
On a link being 'used'...
insert into viewed (userid, url_id) values ("jj", 123)
On looking for a link...
select p.url_id
from pages p left join viewed v on v.url_id = p.url_id
where v.url_id is null
order by rand()
limit 1
This causes the database to go ahead and do a 1 for 1 join, and your limiting your query to return only one entry that the user has not seen yet.
Just a suggestion.
Edit: It is possible to make this one operation but there's no guarantee that the url will be passed successfully to the user.
It depend on how users get it's random entries.
Option 1:
A user is paging some entities and stop after couple of them. for example the user see the current random entity and then moving to the next one, read it and continue it couple of times and that's it.
in the next time this user (or another) get an entity from this category the entities that already viewed is clear and you can return an already viewed entity.
in that option I would recommend save a (hash) set of already viewed entities id and every time user ask for a random entity- randomally choose it from the DB and check if not already in the set.
because the set is so small and your data is so big, the chance that you get an already viewed id is so small, that it will take O(1) most of the time.
Option 2:
A user is paging in the entities and the viewed entities are saving between all users and every time user visit your page.
in that case you probably use all the entities in each category and saving all the viewed entites + check whether a entity is viewed will take some time.
In that option I would get all the ids for this topic- shuffle them and store it in a linked list. when you want to get a random not viewed entity- just get the head of the list and delete it (O(1)).
I assume that for any given <user, category> pair, the number of documents viewed is pretty small relative to the total number of documents available in that category.
So can you just store indexed triples <user, category, document> indicating which documents have been viewed, and then just take an optimistic approach with respect to randomly selected documents? In the vast majority of cases, the randomly selected document will be unread by the user. And you can check quickly because the triples are indexed.
I would opt for a pseudorandom approach:
1.) Determine number of elements in category to be viewed (SELECT COUNT(*) WHERE ...)
2.) Pick a random number in range 1 ... count.
3.) Select a single document (SELECT * FROM ... WHERE [same as when counting] ORDER BY [generate stable order]. Depending on the SQL dialect in use, there are different clauses that can be used to retrieve only the part of the result set you want (MySQL LIMIT clause, SQLServer TOP clause etc.)
If the number of documents is large the chance serving the same user the same document twice is neglibly small. Using the scheme described above you don't have to store any state information at all.
You may want to consider a nosql solution like Apache Cassandra. These seem to be ideally suited to your needs. There are many ways to design the algorithm you need in an environment where you can easily add new columns to a table (column family) on the fly, with excellent support for a very sparsely populated table.
edit: one of many possible solutions below:
create a CF(column family ie table) for each category (creating these on-the-fly is quite easy).
Add a row to each category CF for each document belonging to the category.
Whenever a user hits a document, you add a column with named and set it to true to the row. Obviously this table will be huge with millions of columns and probably quite sparsely populated, but no problem, reading this is still constant time.
Now finding a new document for a user in a category is simply a matter of selecting any result from select * where == null.
You should get constant time writes and reads, amazing scalability, etc if you can accept Cassandra's "eventually consistent" model (ie, it is not mission critical that a user never get a duplicate document)
I've solved similar in the past by indexing the relational database into a document oriented form using Apache Lucene. This was before the recent rise of NoSQL servers and is basically the same thing, but it's still a valid alternative approach.
You would create a Lucene Document for each of your texts with a textId (relational database id) field and multi valued categoryId and userId fields. Populate the categoryId field appropriately. When a user reads a text, add their id to the userId field. A simple query will return the set of documents with a given categoryId and without a given userId - pick one randomly and display it.
Store a users past X selections in a cookie or something.
Return the last selections to the server with the users new criteria
Randomly choose one of the texts satisfying the criteria until it is not a member of the last X selections of the user.
Return this choice of text and update the list of last X selections.
I would experiment to find the best value of X but I have in mind something like an X of say 16?
I read a lot of documents about AppFabric caching but most of them cover simple scenarios.
For example adding city list data or shopping card data to the cache.
But I need adding product catalog data to the cache.
I have 4 tables:
Product (1 million rows), ProductProperty (25 million rows), Property (100 rows), PropertyOption (300 rows)
I display paged search results querying with some filters for Product and ProductProperty tables.
I am creating criteria set over searched result set. For example (4 Items New Product, 34 Items Phone, 26 Items Book etc.)
I query for grouping over Product table with columns of IsNew, CategoryId, PriceType etc.
and also another query for grouping over ProductProperty table with PropertyId and PropertyOptionId columns to get which property have how many items
Therefore to display search results I make one query for search result and 2 for creating criteria list (with counts)
Search result query took 0,7 second and 2 grouping queryies took 1,5 second in total.
When I run load test I reach 7 request per second and %10 dropped by IIS becasue db could not give response.
This is why I want to cache Product and property records.
If I follow items below (in AppFabric);
Create named cache
Create region for product catalog data (a table which have 1 million rows and property table which have 25 million rows)
Tagging item for querying data and grouping.
Can I query with some tags and get 1st or 2nd page of results ?
Can I query with some tags and get counts of some grouping results. (displaying filter options with count)
And do I have to need 3 servers ? Can I provide a solution with only one appfabric server (And of course I know risk.)
Do you know any article or any document explains those scenarios ?
Thanks.
Note:
Some additional test:
I added about 30.000 items to the cache and its size is 900 MB.
When I run getObjectsInRegion method, it tooks about 2 minutes. "IList> dataList = this.DataCache.GetObjectsInRegion(region).ToList();"
The problem is converting to IList. If I use IEnumerable it works very quicly. But How can I get paging or grouping result without converting it to my type ?
Another test:
I tried getting grouping count with 30.000 product item and getting result for grouping took 4 seconds. For example GetObjectByTag("IsNew").Count() and other nearly 50 query like that.
There is, unfortunately, no paging API for AppFabric in V1. Any of the bulk APIs, like GetObjectsByTag, are going to perform the query on the server and stream back all the matching cache entries to the client. From there you can obviously use any LINQ operators you want on the IEnumerable (e.g. Skip/Take/Count), but be aware that you're always pulling the full result set back from the server.
I'm personally hoping that AppFabric V2 will provide support via IQueryable instead of IEnumerable which will give the ability to remote the full request to the server so it could page results there before returning to the client much like LINQ2SQL or ADO.NET EF.
For now, one potential solution, depending on the capabilities of your application, is you can actually calculate some kind of paging as you inject the items into the cache. You can build ordered lists of entity keys representing each page and store those as single entries in the cache which you can pull out in one request and then individually (in parallel) or bulk fetch the items in the list from the cache and join them together with an in-memory LINQ query. If you wanted to trade off CPU for Memory, just cache the actual list of full entities rather than IDs and having to do the join for the entities.
You would obviously have to come up with some kind of keying mechanism to quickly pull these lists of objects from the cache based on the incoming search criteria. Some kind of keying like this might work:
private static string BuildPageListCacheKey(string entityTypeName, int pageSize, int pageNumber, string sortByPropertyName, string sortDirection)
{
return string.Format("PageList<{0}>[pageSize={1};pageNumber={2};sortedBy={3};sortDirection={4}]", entityTypeName, pageSize, pageNumber, sortByPropertyName, sortDirection);
}
You may want to consider doing this kind of thing with a separate process or worker thread that's keeping the cache up to date rather than doing it on demand and forcing the users wait if the cache entry isn't populated yet.
Whether or not this approach ultimately works for you depends on several factors of your application and data. If it doesn't exactly fit your scenarios maybe it will at least help shift your mind into a different way of thinking about solving the problem.