There are some naive ways to do this but I was wondering whether there is a more proficient way to achieve this functionality. I have a django rest framework backend and a react native front end. I have a table with objects that includes an Address object with the object's street address. What I would like to do is to return results sorted by distance from the user. Right now the database is small but soon we may have to paginate results.
I have a couple of ideas but I'm wondering whether there is a cleaner way to accomplish this.
For example the address field contains a lat/lng portion that is currently left blank, one idea is to populate this field on create/update operations using the google maps API geocoding functionality, then when the user makes a search request we order by l2 distance from their coordinates. One issue is that this could become very inefficient as the table grows larger. If we need O(N log N) operations every time the user is at a different location - even using tricks like caching. I know that some larger companies shard their database over regions to help with overhead in situations like this but we are relatively small right now and so such an approach would be overkill.
I would appreciate any advice that could help me tailor a solution to this problem. I can provide more information as well.
Related
The design pattern is explained here:
http://www.tutorialspoint.com/design_pattern/filter_pattern.htm
I'm working on a software very similar to Adobe Lightroom or ACDSee but with different purposes. The user (photographer) is able to import thousands of images from his hard drive (it wouldn't be weird to have over 100k/200k images).
We have a side panel where users can create custom "filters" which are expressions like:
Does contain the keyword: "car"
AND
Does not contain the keyword "woods"
AND
(
Camera model is "Nikon D300s"
OR
Camera model is "Canon 7D Mark II"
)
AND
NOT
Directory is "C:\today_pictures"
You can get the idea from the above example.
We have a SQLite database where all image information is stored. The question is, should we load ALL Photo objects into memory from the database the first time the program is loaded and implement the Criteria/Filter design pattern as explained in the website cited above so our Criteria classes filter objects or is better to do the criteria classes actually generate an SQL query that is finally executed in order to retrieve only what's needed from the database?
We are developing the program with C++ (QT).
TL;DR: It's already properly implemented in SQLITE3, and look at how long that took. You'll face the same burden.
It'd be a horrible case of data duplication to read the data from the database and store it again in another data structure. Use database queries to implement the query that the user gave you. Let the database execute the query. That's what databases are for.
By reimplementing a search/query system for ~500k records, you'll be rewriting large chunks of a bog-standard database yourself. It'd be a mostly pointless exercise. SQLITE3 is very well tested and is essentially foolproof. It'll cost you thousands of hours of work to reimplement even a small fraction of its capabilities and reliability/resiliency. If that doesn't scream "reinventing the wheel", I don't know what does.
The database also allows you to very easily implement lookahead/dropdowns to aid the user in writing the query. For example, as you're typing out "camera model is", the user can have an option of autocompletion or a dropdown to select one or more models from.
You paid the "price" of a database, it'd be a shame for it all to go to waste. So, use it. It'll give you lots of leverage, and allow you to implement features two orders of magnitude faster than otherwise.
The pattern you've linked to is just a pattern. It doesn't mean that it's an exact blueprint of how to design your application to perform on real data. You'll be, eventually, fighting things such as concurrency (a file scanning thread running to update the metadata), indexing, resiliency in face of crashes, etc. In the end you'll end up with big chunks of SQLITE reimplemented for your particular application. 500k metadata records are nothing much, if you design your query translator well and support it with proper indexes, it'll work perfectly well.
So I implemented Haystack with ElasticSearch a week ago within our BETA application. One thing I can notice is that getting some data (large amount) back to our users (for example listing all the users within the application) is much faster by going through Haystack then Django's ORM. Now, I will be releasing a REST service (with TastyPie) to serve the possible tablets within the next weeks, as I want to be able to access the information from iPads, Nexus tablets and so on.
One thing I was wondering, is when should I be querying the ORM vs Haystack/ElasticSearch? For example, if the user on the tablet is requesting a specific set of users, should we let TastyPie query the ORM, or go to ElasticSearch?
If we look at this answer Django: Haystack or ORM, we can all agree that a DB is made to retrieve and write data. However, could we say that retrieving faster can be faster with Haystack/ElasticSearch once the search engine was updated?
I am a bit confused as to when, should we not be querying Haystack if it is much faster?!
To make things clear I guess you're talking about querying Elasticsearch via Haystack without later instantiating any objects for your search results with data from you database.
Some points to consider besides the points mentioned in the other post:
A search engine like Elasticsearch is highly optimized when dealing with full-text searches (When doing something with SQL it highly depends on the database/engine you are using)
Queries that are involving a lot of relations/joins will most like be easier to handle with the ORM, but on the other hand you can eg save data from foreign-key relations in a denormalized fashion when using ES which could give you a performance boost. Of course you can denormalize your database tables as well but this is quite often considered as a bad practice as long as you know what you are doing, eg when solving a performance bottleneck.
ES is somehow quite easy to scale while scaling your SQL DB might be more complicated.
Most likely this is a decision that depends very much on your use case, the amount of data to process and the queries you are intending to run. So the best thing of course is - as always - to do some benchmarking yourself and compare this two solutions. But don't do any premature optimisations as one big advantage of the ORM is to keep things simple - you don't have to care much about the integrity of your data and maintain an additional system.
We are developing an online school diary application using django. The prototype is ready and the project will go live next year with about 500 students.
Initially we used sqlite and hoped that for the initial implementation this would perform well enough.
The data tables are such that to obtain details of a school day (periods, classes, teachers, classrooms, many tables are used and the database access takes 67ms on a reasonably fast PC.
Most of the data is static once the year starts with perhaps minor changes to classrooms. I thought of extracting the timetable for each student for each term day so no table joins would be needed. I put this data into a text file for one student, the file is 100K in size. The time taken to read this data and process it for a days timetable is about 8ms. If I pre-load the data on login and store it in sessions it takes 7ms at login and 2ms for each query.
With 500 students what would be the impact on the web server using this approach and what other options are there (putting the student text files into a sort of memory cache rather than session for example?)
There will not be a great deal of data entry, students adding notes, teachers likewise, so it will mostly be checking the timetable status and looking to see what events exist for that day or week.
What is your expected response time, and what is your expected number of requests per minute? One twentieth of a second for the database access (which is likely to be slow part) for a request doesn't sound like a problem to me. SQLite should perform fine in a read-mostly situation like this. So I'm not convinced you even have a performance problem.
If you want faster response you could consider:
First, ensuring that you have the best response time by checking your indexes and profiling individual retrievals to look for performance bottlenecks.
Pre-computing the static parts of the system and storing the HTML. You can put the HTML right back into the database or store it as disk files.
Using the database as a backing store only (to preserve state of the system when the server is down) and reading the entire thing into in-memory structures at system start-up. This eliminates disk access for the data, although it limits you to one physical server.
This sounds like premature optimization. 67ms is scarcely longer than the ~50ms where we humans can observe that there was a delay.
SQLite's representation of your data is going to be more efficient than a text format, and unlike a text file that you have to parse, the operating system can efficiently cache just the portions of your database that you're actually using in RAM.
You can lock down ~50MB of RAM to cache a parsed representation of the data for all the students, but you'll probably get better performance using that RAM for something else, like the OS disk cache.
I agree with some of other answers which suggest to use MySQL or PostgreSQL instead of SQLite. It is not designed to be used as production db. It is great for storing data for one-user applications such as mobile apps or even a desktop application, but it falls short very quickly in server applications. With Django it is trivial to switch to any other full-pledges database backend.
If you switch to one of those, you should not really have any performance issues, especially if you will do all the necessary joins using select_related and prefetch_related.
If you will still need more performance, considering that "most of the data is static", you actually might want to convert Django site a static site (a collection of html files) and then serve those using nginx or something similar to that. The simplest way I can think of doing that is to just write a cron-job which will loop over all needed url-configs, request the page from Django and then save that as an html file. If you want to go into that direction, you also might want to take a look at Python's static site generators: Hyde and Pelican.
This approach will certainly work much faster then any caching system however you will loose any dynamic components of the site. If you need them, then caching seems like the best and fastest solution.
You should use MySQL or PostgreSQL for your production database. sqlite3 isn't a good idea.
You should also avoid pre-loading data on login. Since your records can be inserted in advance, write django management commands and run the import to your chosen database before hand and design your models such that when a user logs in, the user would already be able to access and view/edit his or her related data (which are pre-inserted before the application even goes live). Hardcoding data operations when log in does not smell right at all from an application design point-of-view.
https://docs.djangoproject.com/en/dev/howto/custom-management-commands/
The benefit of designing your django models and using custom management commands to insert the records right way before your application goes live implies that you can use django orm to make the appropriate relationships between users and their records.
I suspect - based on your description of what you need above - that you need to re-look at the approach you are creating this application.
With 500 students, we shouldn't even be talking about caching. If you want response speed, you should deal with the following issues in priority:-
Use a production quality database
Design your application use case correctly and design your application model right
Pre-load any data you need to the production database
front end optimization comes first (css/js compression etc)
use django debug toolbar to figure out if any of your sql is slow and optimize specifically those
implement caching (memcached etc) as needed
As a general guideline.
I'm writing a project in C++/Qt and it is able to connect to any type of SQL database supported by the QtSQL (http://doc.qt.nokia.com/latest/qtsql.html). This includes local servers and external ones.
However, when the database in question is external, the speed of the queries starts to become a problem (slow UI, ...). The reason: Every object that is stored in the database is lazy-loaded and as such will issue a query every time an attribute is needed. On average about 20 of these objects are to be displayed on screen, each of them showing about 5 attributes. This means that for every screen that I show about 100 queries get executed. The queries execute quite fast on the database server itself, but the overhead of the actual query running over the network is considerable (measured in seconds for an entire screen).
I've been thinking about a few ways to solve the issue, the most important approaches seem to be (according to me):
Make fewer queries
Make queries faster
Tackling (1)
I could find some sort of way to delay the actual fetching of the attribute (start a transaction), and then when the programmer writes endTransaction() the database tries to fetch everything in one go (with SQL UNION or a loop...). This would probably require quite a bit of modification to the way the lazy objects work but if people comment that it is a decent solution I think it could be worked out elegantly. If this solution speeds up everything enough then an elaborate caching scheme might not even be necessary, saving a lot of headaches
I could try pre-loading attribute data by fetching it all in one query for all the objects that are requested, effectively making them non-lazy. Of course in that case I will have to worry about stale data. How would I detect stale data without at least sending one query to the external db? (Note: sending a query to check for stale data for every attribute check would provide a best-case 0x performance increase and a worst-caste 2x performance decrease when the data is actually found to be stale)
Tackling (2)
Queries could for example be made faster by keeping a local synchronized copy of the database running. However I don't really have a lot of possibilities on the client machines to run for example exactly the same database type as the one on the server. So the local copy would for example be an SQLite database. This would also mean that I couldn't use an db-vendor specific solution. What are my options here? What has worked well for people in these kinds of situations?
Worries
My primary worries are:
Stale data: there are plenty of queries imaginable that change the db in such a way that it prohibits an action that would seem possible to a user with stale data.
Maintainability: How loosely can I couple in this new layer? It would obviously be preferable if it didn't have to know everything about my internal lazy object system and about every object and possible query
Final question
What would be a good way to minimize the cost of making a query? Good meaning some sort of combination of: maintainable, easy to implement, not too aplication specific. If it comes down to pick any 2, then so be it. I'd like to hear people talk about their experiences and what they did to solve it.
As you can see, I've thought of some problems and ways of handling it, but I'm at a loss for what would constitute a sensible approach. Since it will probable involve quite a lot of work and intensive changes to many layers in the program (hopefully as few as possible), I thought about asking all the experts here before making a final decision on the matter. It is also possible I'm just overlooking a very simple solution, in which case a pointer to it would be much appreciated!
Assuming all relevant server-side tuning has been done (for example: MySQL cache, best possible indexes, ...)
*Note: I've checked questions of users with similar problems that didn't entirely satisfy my question: Suggestion on a replication scheme for my use-case? and Best practice for a local database cache? for example)
If any additional information is necessary to provide an answer, please let me know and I will duly update my question. Apologies for any spelling/grammar errors, english is not my native language.
Note about "lazy"
A small example of what my code looks like (simplified of course):
QList<MyObject> myObjects = database->getObjects(20, 40); // fetch and construct object 20 to 40 from the db
// ...some time later
// screen filling time!
foreach (const MyObject& o, myObjects) {
o->getInt("status", 0); // == db request
o->getString("comment", "no comment!"); // == db request
// about 3 more of these
}
At first glance it looks like you have two conflicting goals: Query speed, but always using up-to-date data. Thus you should probably fall back to your needs to help decide here.
1) Your database is nearly static compared to use of the application. In this case use your option 1b and preload all the data. If there's a slim chance that the data may change underneath, just give the user an option to refresh the cache (fully or for a particular subset of data). This way the slow access is in the hands of the user.
2) The database is changing fairly frequently. In this case "perhaps" an SQL database isn't right for your needs. You may need a higher performance dynamic database that pushes updates rather than requiring a pull. That way your application would get notified when underlying data changed and you would be able to respond quickly. If that doesn't work however, you want to concoct your query to minimize the number of DB library and I/O calls. For example if you execute a sequence of select statements your results should have all the appropriate data in the order you requested it. You just have to keep track of what the corresponding select statements were. Alternately if you can use a looser query criteria so that it returns more than one row for your simple query that ought to help performance as well.
I have a request to return a list of the most popular search terms used when searching a Sitecore site.
I have no idea how to implement this sort of function using Sitecore or whether Sitecore has this kind of functionality all ready. I can't find any documentation detailing this.
I am currently using search based of the LuceneSearch module (http://trac.sitecore.net/LuceneSearch) but altered to bind to a ListView for easy pagination.
At the moment I am probably just going to build a standalone function/class to update an XML file or something unless someone is able to point me in the correct direction...?
I would frankly use OMS for that - this is what it is designed to do. No need of separate database. Just register the search events via API with OMS. There is an out of the box Search report. May require some tweaking, but this seems to be the most out of the box solution.
Take a look here for more details.
I don't know of any standard functionality in Sitecore that would help you achieve this, so you will probably have to approach this from ground up - unless someone else in here is able to point to a package deal somewhere :-)
Solving this, really breaks down into two tasks
1) Collecting search term information. Whenever a user enters a search term in the searchbox that I assume you have; normalise it and store it in a SQL table (essentially a [term] [count] type table. Update the counter on terms you already store.
By normalising, I mean lowercasing it and so on - possibly breaking each search term (word) down and storing them one for one if that is what your solution calls for (probably not the route I would go)
2) Realtime retreiving information from the table, based on what the user is typing in the searchbox. Assuming you want some sort of "amazon-like" - also found on almost all major search engines nowadays - autocompletion. I normally implement these in a web service that then gets called by Ajax, JQuery or whatever rich client implementation you prefer.
As for updating an XML file, I think locking issues and performance would kill that solution; though it could perhaps be made to work on a very small scale.
Sorry that I can't be more specific in my response, but your question is very open-ended.
Very interesting question. One thing you could do it have another database to store these search queries. An insert into this DB would not be very difficult and would get around the issue of locking on a XML file. Maybe insert the search query into a DB table then to get the top results just pull the top x rows ordered by that query field. As Mark Cassidy said before, maybe normalize the data before inserting it.
You could isolate this work on your search layout (or sublayout) so it runs on a specific part of the site, not on every page.
Sitecore has an out of the box "site search" report in the executive insight dashboard, this will give you an indication of what search terms are driving the most visits and of course engagement value.
You just need to configure it by registering a page event on the search page and passing the query otherwise sitecore wouldnt know what form field constitutes a search. See this post it explains it in more detail. For more information you can download the analytics configuration reference document from sdn.http://sdn.sitecore.net/upload/sitecore6/65/engagement_analytics_configuration_reference_sc65-usletter.pdf
And dont forget for performance sitecore caches the reports at various levels so during development it may be handy to know how to force a cache update, I talk about this in the following blog post:
http://andytsitecore.blogspot.co.uk/2013/10/sitecore-dms-and-analytics.html