I have box with six most popular comments from last 4 days. I need to change it once at day (at midnight). How best to do this? I have function with getting most popular comments from last 4 days but how to update it only once at day?
24 hour cache with result is a solution?
You have a couple of choices. One is a simple cron task that executes a Python function that updates your data at midnight on whatever day. The other is a Celery task that you can manage through Django admin to update the cache.
Related
I have a simple django project and I am trying to keep track of ranks for certain objects to see how they change over time. For example, what was the rank of US GDP (compared to other countries) over last 3 years. Below is the postgres db structure I am working with:
Below is what I am trying to achieve:
What I am finding challenging is that the previous period value may or may not exist and it's possible that even the entity may or may not be in the pervious period. Period can be year, quarter or months but for a specific record it can be either of one and stays consistently same for all the years for that record.
Can someone guide me in the right direction to write a query to achieve those tables? I am trying to avoid writing heavy forloop queries because there may be 100s of entities and many years of data.
So far I have only been able to achieve the below output:
I am just trying to figure out how to use annotate to fetch previous period values and ranks but I am pretty much stuck.
I'm using the AWS Machine Learning regression to predict the waiting time in a line of a restaurant, in a specific weekday/time.
Today I have around 800k data.
Example Data:
restaurantID (rowID)weekDay (categorical)time (categorical)tablePeople (numeric)waitingTime (numeric - target)1 sun 21:29 2 23
2 fri 20:13 4 43
...
I have two questions:
1)
Should I use time as Categorical or Numeric?
It's better to split into two fields: minutes and seconds?
2)
I would like in the same model to get the predictions for all my restaurants.
Example:
I expected to send the rowID identifier and it returns different predictions, based on each restaurant data (ignoring others data).
I tried, but it's returning the same prediction for any rowID. Why?
Should I have a model for each restaurant?
There are several problems with the way you set-up your model
1) Time in the form you have it should never be categorical. Your model treats times 12:29 and 12:30 as two completely independent attributes. So it will never use facts it learn about 12:29 to predict what's going to happen at 12:30. In your case you either should set time to be numeric. Not sure if amazon ML can convert it for you automatically. If not just multiply hour by 60 and add minutes to it. Another interesting thing to do is to bucketize your time, by selecting which half hour or wider interval. You do it by dividing (h*60+m) by some number depending how many buckets you want. So to try 120 to get 2 hr intervals. Generally the more data you have the smaller intervals you can have. The key is to have a lot of samples in each bucket.
2) You should really think about removing restaurantID from your input data. Having it there will cause the model to over-fit on it. So it will not be able to make predictions about restaurant with id:5 based on the facts it learn from restaurants with id:3 or id:9. Having restaurant id there might be okay if you have a lot of data about each restaurant and you don't care about extrapolating your predictions to the restaurants that are not in the training set.
3) You never send restaurantID to predict data about it. The way it usually works you need to pick what are you trying to predict. In your case probably 'waitingTime' is most useful attribute. So you need to send weekDay, time and number of people and the model will output waiting time.
You should think what is relevant for the prediction to be accurate, and you should use your domain expertise to define the features/attributes you need to have in your data.
For example, time of the day, is not just a number. From my limited understanding in restaurant, I would drop the minutes, and only focus on the hours.
I would certainly create a model for each restaurant, as the popularity of the restaurant or the type of food it is serving is having an impact on the wait time. With Amazon ML it is easy to create many models as you can build the model using the SDK, and even schedule retraining of the models using AWS Lambda (that mean automatically).
I'm not sure what the feature called tablePeople means, but a general recommendation is to have as many as possible relevant features, to get better prediction. For example, month or season is probably important as well.
In contrast with some answers to this post, I think resturantID helps and it actually gives valuable information. If you have a significant amount of data per each restaurant then you can train a model per each restaurant and get a good accuracy, but if you don't have enough data then resturantID is very informative.
1) Just imagine what if you had only two columns in your dataset: restaurantID and waitingTime. Then wouldn't you think the restaurantID from the testing data helps you to find a rough waiting time? In the simplest implementation, your waiting time per each restaurantID would be the average of waitingTime. So definitely restaurantID is a valuable information. Now that you have more features in your dataset, you need to check if restaurantID is as effective as the other features or not.
2) If you decide to keep restaurantID then you must use it as a categorical string. It should be a non-parametric feature in your dataset and maybe that's why you did not get a proper result.
On the issue with day and time I agree with other answers and considering that you are building your model for the restaurant, hourly time may give a more accurate result.
i'm writing a django app that features a timer like in a game.
lets say that the game is a basketball game and i have 4 quarters of 10 min.
i need that in the end of each of the 10 min the db will be changed.
to set a timer that will change the db won't work for me because the quarter
won't always be of 10 min, and it will be changed while the app is on
production, i.e i save the quarter time in the db so i can change it whenever
i want.
i thought to use signals but i just could't find a way to make it work.
any help will be good
thx
one way to think about it would be to say it doesn't matter what state the db is in when nobody is looking at it... in other words you don't have to update the db after exactly 10 minutes
instead: as each request comes in first check if you are past the limit of the timer, if so then update the db before continuing with the usual view code
I'm trialing FluentMigrator as a way of keeping my database schema up to date with minimum effort.
For the release I'm currently building, I need to run a database script to make a simple change to a large number of rows of existing data (around 2% of 21,000,000 rows need to be updated).
There's too much data for to be updated in a single transaction (the transaction log gets full and the script aborts), so I use a WHILE loop to iterate through the table, updating 10,000 rows at a time, each batch in a separate transacticon. This works, and takes around 15 minutes to run to completion.
Now I have the script complete, I'm trying to integrate it into FluentMigrator.
FluentMigrator seems to run all the migrations for a single batch in one transaction.
How do I get FM to run each migration in a separate transaction?
Can I tell FM to not use a transaction for a specific migration?
This is not possible as of now.
There are ongoing discussions and some work already in progress.
Check it out here : https://github.com/schambers/fluentmigrator/pull/178
But your use case will surely help in pushing the things in the right direction.
You are welcome to take part to the discussion!
Maybe someone will find a temporary workaround?
After hours of searching the web (including SO), I am requesting advice from the community. RRD seems to be the right tool for this, but I could not get a straight answer until now.
My question is : Is it possible to get RRD output a graph for the day, that averages data from the past year ?
In other words, I want the "view span" to be one day long, but the "data span" to extend over the last 12 months, so that for 6pm, the value will be computed as the average value of ALL previous traffic measured at 6pm last 12 months.
Any hints, or instructions welcomed!
There is no direct way to create such a graph, at least in theory it would be possible using multiple DEF lines together with the SHIFT operation to create such a chart ... you would have to use a program to create the necessary command line though