I currently have the following code to find all the unique IDs of comments placed by a user. This works "fine". However, it's really slow for users with a lot of comments and I'm trying to figure out if there is a more elegant way of handling this as it doesn't seem to be the best solution.
def find_unique_user_grades
#comments = []
#environment.users.includes(:comments).map(&:comments).select do |comments|
comments.select { |comment| #comments.push(comment.id) }
end
#comments.uniq!
end
I'm hoping someone can help me with this.
You should always prefer to do this in the database, not in Ruby. The code you've posted will load all users from the database, and then convert the raw row data to ActiveRecord objects, which is (comparatively) extremely expensive. You don't need any of that data to join through to comments. Then, you'll do the same for every user's comments (query and create ActiveRecord objects) and again, you don't need any of that to get at the comment's id column.
What you're after (assuming I've guessed correctly at the shape your schema) is a simply join followed by a pluck. This will run a single query and return a single array of numbers, without any of the cost of loading users, creating objects, loading comments, creating objects, or iterating in Ruby.
Finally, it will also perform the distinct query in the database, where it can take advantage of any relevant indexes, rather than uniqing in Ruby.
The correct query is something near to:
#environment.users.joins(:comments).distinct.pluck('comments.id')
I want to store employees record. I don't want to use any external libraries or framework. I am trying to build the data structure from scratch.
There will be three fields,
EmployeeName
Age
Salary
We also want to query like,
Get all the salary where EmployeeName = "Bill"
Get all the EmployeeName where salary > 2000
Get all the Salary where age='50'
I am open to use any language but not any built-in package. What is the recommended data-structure to achieve it ?
I assume that the purpose of this exercise is self-education.
If so, Where to begin reading SQLite source code? is a great place to start reading to understand how this kind of software can be built.
If you really want to roll your own, I would suggest storing your data in an array of structs/objects/dictionaries (what they are called will depend on your language), hidden behind an object so that your insert/update/delete methods on the table go through well-defined access functions. Your operations can be implemented inefficiently with grep, filter, etc depending on your language. In addition to the obvious fields, include deleted as a field. That way you can just update that to delete a record, rather than try to modify the table.
To make them more efficient, read through https://cstack.github.io/db_tutorial/parts/part7.html for how to write a b-tree. Then create a b-tree mapping EmployeeName to the list of indexes of records with that name, ditto for age and salary. Now modify the access methods to update the indexes for those fields when you modify the table. Your searches can now go through the b-tree to find the indexes of the records that you want, and then you can look in the table for them.
This is massively simplified compared to what a database gives you, but you're on your way to understanding how databases work. Both in terms of why they scale, and also why they aren't magically fast.
I am in the midst of designing an application following the mvc paradigm. I'm using the sqlalchemy expression language (not the orm), and pyramid if anyone was curious.
So, for a user class, that represents a user on the system, I have several accessor methods for various pieces of data like the avatar_url, name, about, etc. I have a method called getuser which looks up a user in the db(by name or id), retrieves the users row, and encapsulates it with the user class.
However, should I have to make this look-up every-time I create a user class? What if a user is viewing her control panel and wants to change avatars, and sends an xhr; isn't it a waste to have to create a user object, and look up the users row when they wont even be using the data retrieved; but simply want to make a change to subset of the columns? I doubt this lookup is negligible despite indexing because of waiting for i/o correct?
More generally, isn't it inefficient to have to query a database and load all a model class's data to make any change (even small ones)?
I'm thinking I should just create a seperate form class (since every change made is via some form), and have specific form classes inherit them, where these setter methods will be implemented. What do you think?
EX: Class: Form <- Class: Change_password_form <- function: change_usr_pass
I'd really appreciate some advice on creating a proper design;thanks.
SQLAlchemy ORM has some facilities which would simplify your task. It looks like you're having to re-invent quite some wheels already present in the ORM layer: "I have a method called getuser which looks up a user in the db(by name or id), retrieves the users row, and encapsulates it with the user class" - this is what ORM does.
With ORM, you have a Session, which, apart from other things, serves as a cache for ORM objects, so you can avoid loading the same model more than once per transaction. You'll find that you need to load User object to authenticate the request anyway, so not querying the table at all is probably not an option.
You can also configure some attributes to be lazily loaded, so some rarely-needed or bulky properties are only loaded when you access them
You can also configure relationships to be eagerly loaded in a single query, which may save you from doing hundreds of small separate queries. I mean, in your current design, how many queries would the below code initiate:
for user in get_all_users():
print user.get_avatar_uri()
print user.get_name()
print user.get_about()
from your description it sounds like it may require 1 + (num_users*3) queries. With SQLAlchemy ORM you could load everything in a single query.
The conclusion is: fetching a single object from a database by its primary key is a reasonably cheap operation, you should not worry about that unless you're building something the size of facebook. What you should worry about is making hundreds of small separate queries where one larger query would suffice. This is the area where SQLAlchemy ORM is very-very good.
Now, regarding "isn't it a waste to have to create a user object, and look up the users row when they wont even be using the data retrieved; but simply want to make a change to subset of the columns" - I understand you're thinking about something like
class ChangePasswordForm(...):
def _change_password(self, user_id, new_password):
session.execute("UPDATE users ...", user_id, new_password)
def save(self, request):
self._change_password(request['user_id'], request['password'])
versus
class ChangePasswordForm(...):
def save(self, request):
user = getuser(request['user_id'])
user.change_password(request['password'])
The former example will issue just one query, the latter will have to issue a SELECT and build User object, and then to issue an UPDATE. The latter may seem to be "twice more efficient", but in a real application the difference may be negligible. Moreover, often you will need to fetch the object from the database anyway, either to do validation (new password can not be the same as old password), permissions checks (is user Molly allowed to edit the description of Photo #12343?) or logging.
If you think that the difference of doing the extra query is going to be important (millions of users constantly editing their profile pictures) then you probably need to do some profiling and see where the bottlenecks are.
Read up on the SOLID principle, paying particular attention to the S as it answers your question.
Create a single class to perform user existence check, and inject it into any class that requires that functionality.
Also, you need to create a data persistence class to store the user's data, so that the database doesn't have to be queried every time.
I'm about to embark on a project where a user will be able to create their own custom fields. MY QUESTION - what's the best approach for something like this?
Use case: we have medical records with attributes like first_name, last_name etc... However we also want a user to be able to log into their account and create custom fields. For instance they may want to create a field called 'second_phone' etc... They will then map their CRM to their fields within this app so they can import their data.
I'm thinking on creating tables like 'field_sets (has_many fields)', 'fields', 'field_values' etc...
This seems like it would be somewhat common hence why I thought I would first ask for opinions and/or existing examples.
This is where some modern schemaless databases can help you. My favourite is MongoDB. In short: you take whatever data you have and stuff a document with it. No hard thinking required.
If, however, you are in relational land, EAV is one of classic approaches.
I have also seen people do these things:
predefine some "optional" fields in the schema and use them if necessary.
serialize this optional data to string (using JSON, for example) and write it to text blob.
I am creating a laboratory database which analyzes a variety of samples from a variety of locations. Some locations want their own reference number (or other attributes) kept with the sample.
How should I represent the columns which only apply to a subset of my samples?
Option 1:
Create a separate table for each unique set of attributes?
SAMPLE_BOILER: sample_id (FK), tank_number, boiler_temp, lot_number
SAMPLE_ACID: sample_id (FK), vial_number
This option seems too tedious, especially as the system grows.
Option 1a: Class table inheritance (link): Tree with common fields in internal node/table
Option 1b: Concrete table inheritance (link): Tree with common fields in leaf node/table
Option 2: Put every attribute which applies to any sample into the SAMPLE table.
Most columns of each entry would most likely be NULL, however all of the fields are stored together.
Option 3: Create _VALUE_ tables for each Oracle data type used.
This option is far more complex. Getting all of the attributes for a sample requires accessing all of the tables below. However, the system can expand dynamically without separate tables for each new sample type.
SAMPLE:
sample_id*
sample_template_id (FK)
SAMPLE_TEMPLATE:
sample_template_id*
version *
status
date_created
name
SAMPLE_ATTR_OF
sample_template_id* (FK)
sample_attribute_id* (FK)
SAMPLE_ATTRIBUTE:
sample_attribute_id*
name
description
SAMPLE_NUMBER:
sample_id* (FK)
sample_attribute_id (FK)
value
SAMPLE_DATE:
sample_id* (FK)
sample_attribute_id (FK)
value
Option 4: (Add your own option)
To help with Googling, your third option looks a little like the Entity-Attribute-Value pattern, which has been discussed on StackOverflow before although often critically.
As others have suggested, if at all possible (eg: once the system is up and running, few new attributes will appear), you should use your relational database in a conventional manner with tables as types and columns as attributes - your option 1. The initial setup pain will be worth it later as your database gets to work the way it was designed to.
Another thing to consider: are you tied to Oracle? If not, there are non-relational databases out there like CouchDB that aren't constrained by up-front schemas in the same way as relational databases are.
Edit: you've asked about handling new attributes under option 1 (now 1a and 1b in the question)...
If option 1 is a suitable solution, there are sufficiently few new attributes that the overhead of altering the database schema to accommodate them is acceptable, so...
you'll be writing database scripts to alter tables and add columns, so the provision of a default value can be handled easily in these scripts.
Of the two 1 options (1a, 1b), my personal preference would be concrete table inheritance (1b):
It's the simplest thing that works;
It requires fewer joins for any given query;
Updates are simpler as you only write to one table (no FK relationship to maintain).
Although either of these first options is a better solution than the others, and there's nothing wrong with the class table inheritance method if that's what you'd prefer.
It all comes down to how often genuinely new attributes will appear.
If the answer is "rarely" then the occasional schema update can cope.
If the answer is "a lot" then the relational DB model (which has fixed schemas baked-in) isn't the best tool for the job, so solutions that incorporate it (entity-attribute-value, XML columns and so on) will always seem a little laboured.
Good luck, and let us know how you solve this problem - it's a common issue that people run into.
Option 1, except that it's not a separate table for each set of attributes: create a separate table for each sample source.
i.e. from your examples: samples from a boiler will have tank number, boiler temp, lot number; acid samples have vial number.
You say this is tedious; but I suggest that the more work you put into gathering and encoding the meaning of the data now will pay off huge dividends later - you'll save in the long term because your reports will be easier to write, understand and maintain. Those guys from the boiler room will ask "we need to know the total of X for tank grouped by this set of boiler temperature ranges" and you'll say "no prob, give me half an hour" because you've done the hard yards already.
Option 2 would be my fall-back option if Option 1 turns out to be overkill. You'll still want to analyse what fields are needed, what their datatypes and constraints are.
Option 4 is to use a combination of options 1 and 2. You may find some attributes are shared among a lot of sample types, and it might make sense for these attributes to live in the main sample table; whereas other attributes will be very specific to certain sample types.
You should really go with Option 1. Although it is more tedious to create, Option 2 and 3 will bite you back when trying to query you data. The queries will become more complex.
In fact, the most important part of storing the data, is querying it. You haven't mentioned how you are planning to use the data, and this is a big factor in the database design.
As far as I can see, the first option will be most easy to query. If you plan on using reporting tools or an ORM, they will prefer it as well, so you are keeping your options open.
In fact, if you find building the tables tedious, try using an ORM from the start. Good ORMs will help you with creating the tables from the get-go.
I would base your decision on the how you usually see the data. For instance, if you get 5-6 new attributes per day, you're never going to be able to keep up adding new columns. In this case you should create columns for 'standard' attributes and add a key/value layout similar to your 'Option 3'.
If you don't expect to see this, I'd go with Option 1 for now, and modify your design to 'Option 3' only if you get to the point that it is turning into too much work. It could end up that you have 25 attributes added in the first few weeks and then nothing for several months. In which case you'll be glad you didn't do the extra work.
As for Option 2, I generally advise against this as Null in a relational database means the value is 'Unknown', not that it 'doesn't apply' to a specific record. Though I have disagreed on this in the past with people I generally respect, so I wouldn't start any wars over it.
Whatever you do option 3 is horrible, every query will have join the data to create a SAMPLE.
It sounds like you have some generic SAMPLE fields which need to be join with more specific data for the type of sample. Have you considered some user_defined fields.
Example:
SAMPLE_BASE: sample_id(PK), version, status, date_create, name, userdata1, userdata2, userdata3
SAMPLE_BOILER: sample_id (FK), tank_number, boiler_temp, lot_number
This might be a dumb question but what do you need to do with the attribute values? If you only need to display the data then just store them in one field, perhaps in XML or some serialised format.
You could always use a template table to define a sample 'type' and the available fields you display for the purposes of a data entry form.
If you need to filter on them, the only efficient model is option 2. As everyone else is saying the entity-attribute-value style of option 3 is somewhat mental and no real fun to work with. I've tried it myself in the past and once implemented I wished I hadn't bothered.
Try to design your database around how your users need to interact with it (and thus how you need to query it), rather than just modelling the data.
If the set of sample attributes was relatively static then the pragmatic solution that would make your life easier in the long run would be option #2 - these are all attributes of a SAMPLE so they should all be in the same table.
Ok - you could put together a nice object hierarchy of base attributes with various extensions but it would be more trouble than it's worth. Keep it simple. You could always put together a few views of subsets of sample attributes.
I would only go for a variant of your option #3 if the list of sample attributes was very dynamic and you needed your users to be able to create their own fields.
In terms of implementing dynamic user-defined fields then you might first like to read through Tom Kyte's comments to this question. Now, Tom can be pretty insistent in his views but I take from his comments that you have to be very sure that you really need the flexibility for your users to add fields on the fly before you go about doing it. If you really need to do it, then don't create a table for each data type - that's going too far - just store everything in a varchar2 in a standard way and flag each attribute with an appropriate data type.
create table sample (
sample_id integer,
name varchar2(120 char),
constraint pk_sample primary key (sample_id)
);
create table attribute (
attribute_id integer,
name varchar2(120 char) not null,
data_type varchar2(30 char) not null,
constraint pk_attribute primary key (attribute_id)
);
create table sample_attribute (
sample_id integer,
attribute_id integer,
value varchar2(4000 char),
constraint pk_sample_attribute primary key (sample_id, attribute_id)
);
Now... that just looks evil doesn't it? Do you really want to go there?
I work on both a commercial and a home-made system where users have the ability to create their own fields/controls dynamically. This is a simplified version of how it works.
Tables:
Pages
Controls
Values
A page is just a container for one or more controls. It can be given a name.
Controls are linked to pages and represents user input controls.
A control contains what datatype it is (int, string etc) and how it should be represented to the user (textbox, dropdown, checkboxes etc).
Values are the actual data that the users have typed into the controls, a value contains one column for every datatype that it can represent (int, string, etc) and depending on the control type, the relevant column is set with the user input.
There is an additional column in Values which specifies which group the value belong to.
Each time a user fills in a form of controls and clicks save, the values typed into the controls are saved into the same group so that we know that they belong together (incremental counter).
CodeSpeaker,
I like you answer, it's pointing me in the right direction for a similar problem.
But how would you handle drop-downlist values?
I am thinking of a Lookup table of values so that many lookups link to one UserDefinedField.
But I also have another problem to add to the mix. Each field must have multiple linked languages so each value must link to the equivilant value for multiple languages.
Maybe I'm thinking too hard about this as I've got about 6 tables so far.