Let's say I have list of persons in my datastore. Each person there may have the following fields:
last name (*)
first name
middle name
id (*)
driving licence id (*)
another id (*)
date of birth
region
place of birth
At least one of the fields marked with (*) must exist.
Now user provides me with the same list of fields (and again at least one of the fields marked with (*) must be provided). I should search for the person user provided. But not all fields should be matched. I should display to the user somehow how I am sure in the results of search. Something like:
if person matched by id and last name (and user provided just these 2 fields for the search), then I am sure that result is correct (100%);
if person matched by id and last name (and user provided other fields, which were found in the database, but were not matched), then I am sure that result is almost correct by 60%;
etc.
(numbers are provided just as example)
How can I organize such search? Is there any standard algorithm? I also would like to minimize number of requests to the database.
P.S. I can not provide user with the actual field values from the database.
It sounds like your logic for determining the quality of a match will be too complex to handle at the database layer. I think you'll get the best performance by retrieving all of the records that match at least one of the mandatory keys, calculating the match score for each of them in memory, and returning the best score. For example, if the user provides you with an id, last name and place of birth, your query would look something like:
SELECT * FROM users WHERE id = `the_id` OR last_name = `the_last_name`;
This could be a performance problem if you have a VERY large dataset with lots of common last names but otherwise I would expect not to see too many collisions. You can check this on your own dataset outside of GAE. You could also get better performance if all mandatory fields MUST match by changing the OR to an AND.
Related
I'm new to Elasticsearch and I need to execute a complex query, but I need some help.
Here is my use case:
I would like to recommend a new place to each of my users everyday.
However:
The place must be opened this week day
The chosen place must be near of the user (closer places have higher score)
The place should not be one of the last 10 places a user have already been/suggested (if a place has already been visited by a user in his last 10 visits, this place should have a lower score)
My first guess is to have 2 documents types as follow:
user_history
user_id
place_id
date
place
place_id
opening_days (array with week days the place opens)
location geo position of the place
Given a user with position [lat, lon] and id user_1, what could be the search query to execute to retrieve X places sorted by score? (better score is near of user and not in the 10 last places a user have already been).
This query seems to be a basic but I can't figure out how to "mix" data from user_history and from place to get places I want.
But that's not all!
With this query, if I want to attribute to each user a place I need 3 steps:
retrieve all users (with their position)
for each user, search for the best place
once I have this place, add it to the user_history
This seems very time consuming task. Is it possible to simplify it with less Elasticsearch queries?
For instance, having something like this:
retrieving for each user his best place (with 1 query, search for all users and find them the best place)
add the place to the history
Or event better:
retrieving for each user his best place and add it to the history (with 1 query, perform all the 3 tasks above)
I don't know if it's possible to create queries that complex. That's why I need your help to tell me if it's possible and how it could be accomplished.
I have an application written in Nodejs that needs to find ONE row based on a city name (this could just be the table's name, different cities will be categorized as different tables), and a field named "currentJobLoads" which is a number. For example, a user might want to find ONE row with the city name "Chicago" and the lowest currentJobLoads. How can I achieve this in Dynamodb without scan operations(since scan would be slower and can only read so much data before it gets terminated)? Any suggestions would be highly appreciated.
You didn't specify what your current partition key and sort key for the table are, but I'm guessing the currentJobLoads field isn't one of them. So you would need to create a Global Secondary Index on the currentJobLoads field, at which point you will be able to run query operations against that field.
I have a dataset which has files of hotel reviews. Each file contains multiple reviews for a single hotel. Here are my two relations in BCNF:
Hotel(hotelID, OverallRating, AveragePrice, URL)
Review(hotelID, Author, Content, Date, No. Reader, No. Helpful,
Overall, Value, Rooms, Location, Cleanliness, Checkin / front desk,
Service, Business Service)
I am trying to write the following query in relational algebra:
Find all the reviews by the same user (i.e., given a user ID, return the list of all their
reviews).
By User ID, the question is referring to the Author attribute found in my second relation. The way I understand the question, it must take a user ID as an argument. Maybe you see it differently?
Here is what I have so far:
(Selection) Author = $1 (Review)
Replace selection with the sigma symbol used to represent selection in relational algebra, I was having trouble inserting it into my question. $1 represents where it would take the user ID argument, this is just to show my thinking, I do not think its correct.
Thanks for your time
Query will be:
σ(Author="Your_User Id") ( Hotel Join(X)(Hotel.hotelID=Review.hotelID) Review )
Where
σ = Selection Operator
X= Join Operator
(-----) = Condition
Hope it helps. For More detail Refer My notes for DBMS: Relational Algebra
Search "Relational Algebra" Term in site to find your exact information fast.
I've been doing a lot of reading lately on Cassandra, and specifically how to structure rows to take advantage of indexing/sorting, but there is one thing I am still unclear on; how many "index" items (or filters if you will) should you include in a column family (CF) row?
Specifically: I am building an app and will be using Cassandra to archive log data, which I will use for analytics.
Example types of analytic searches will include (by date range):
total visits to specific site section
total visits by Country
traffic source
I plan to store the whole log object in JSON format, but to avoid having to go through each item to get basic data, or to create multiple CF just to get basic data, I am curious to know if it's a good idea to include these above "filters" as columns (compound column segment)?
Example:
Row Key | timeUUID:data | timeUUID:country | timeUUID:source |
======================================================
timeUUID:section | JSON Object | USA | example.com |
So as you can see from the structure, the row key would be a compound key of timeUUID (say per day) plus the site section I want to get stats for. This lets me query a date range quite easily.
Next, my dilemma, the columns. Compound column name with timeUUID lets me sort & do a time based slice, but does the concept make sense?
Is this type of structure acceptable by the current "best practice", or would it be frowned upon? Would it be advisable to create a separate "index" CF for each metric I want to query on? (even when it's as simple as this?)
I would rather get this right the first time instead of having to restructure the data and refactor my application code later.
I think the idea behind this is OK. It's a pretty common way of doing timeslicing (assuming I've understood your schema anyway - a create table snippet would be great). Some minor tweaks ...
You don't need a timeUUID as your row key. Given that you suggest partitioning by individual days (which are inherently unique) you don't need a UUID aspect. A timestamp is probably fine, or even simpler a varchar in the format YYYYMMDD (or whatever arrangement you prefer).
You will probably also want to swap your row key composition around to section:time. The reason for this is that if you need to specify an IN clause (i.e. to grab multiple days) you can only do it on the last part of the key. This means you can do WHERE section = 'foo' and time IN (....). I imagine that's a more common use case - but the decision is obviously yours.
If your common case is querying the most recent data don't forget to cluster your timeUUID columns in descending order. This keeps the hot columns at the head.
Double storing content is fine (i.e. once for the JSON payload, and denormalised again for data you need to query). Storage is cheap.
I don't think you need indexes, but it depends on the queries you intend to run. If your queries are simple then you may want to store counters by (date:parameter) instead of values and just increment them as data comes in.
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?