Efficiently determining if a business is open or not based on store hours

2024/11/13 12:07:29

Given a time (eg. currently 4:24pm on Tuesday), I'd like to be able to select all businesses that are currently open out of a set of businesses.

  • I have the open and close times for every business for every day of the week
  • Let's assume a business can open/close only on 00, 15, 30, 45 minute marks of each hour
  • I'm assuming the same schedule each week.
  • I am most interested in being able to quickly look up a set of businesses that is open at a certain time, not the space requirements of the data.
  • Mind you, some my open at 11pm one day and close 1am the next day.
  • Holidays don't matter - I will handle these separately

What's the most efficient way to store these open/close times such that with a single time/day-of-week tuple I can speedily figure out which businesses are open?

I am using Python, SOLR and mysql. I'd like to be able to do the querying in SOLR. But frankly, I'm open to any suggestions and alternatives.

Answer

If you are willing to just look at single week at a time, you can canonicalize all opening/closing times to be set numbers of minutes since the start of the week, say Sunday 0 hrs. For each store, you create a number of tuples of the form [startTime, endTime, storeId]. (For hours that spanned Sunday midnight, you'd have to create two tuples, one going to the end of the week, one starting at the beginning of the week). This set of tuples would be indexed (say, with a tree you would pre-process) on both startTime and endTime. The tuples shouldn't be that large: there are only ~10k minutes in a week, which can fit in 2 bytes. This structure would be graceful inside a MySQL table with appropriate indexes, and would be very resilient to constant insertions & deletions of records as information changed. Your query would simply be "select storeId where startTime <= time and endtime >= time", where time was the canonicalized minutes since midnight on sunday.

If information doesn't change very often, and you want to have lookups be very fast, you could solve every possible query up front and cache the results. For instance, there are only 672 quarter-hour periods in a week. With a list of businesses, each of which had a list of opening & closing times like Brandon Rhodes's solution, you could simply, iterate through every 15-minute period in a week, figure out who's open, then store the answer in a lookup table or in-memory list.

https://en.xdnf.cn/q/72304.html

Related Q&A

parsing .xsd in python

I need to parse a file .xsd in Python as i would parse an XML. I am using libxml2. I have to parse an xsd that look as follow: <xs:complexType name="ClassType"> <xs:sequence><x…

How to get the params from a saved XGBoost model

Im trying to train a XGBoost model using the params below: xgb_params = {objective: binary:logistic,eval_metric: auc,lambda: 0.8,alpha: 0.4,max_depth: 10,max_delta_step: 1,verbose: True }Since my input…

Reverse Label Encoding giving error

I label encoded my categorical data into numerical data using label encoderdata[Resi] = LabelEncoder().fit_transform(data[Resi])But I when I try to find how they are mapped internally usinglist(LabelEn…

how to check if a value exists in a dataframe

hi I am trying to get the column name of a dataframe which contains a specific word,eg: i have a dataframe,NA good employee Not available best employer not required well mana…

Do something every time a module is imported

Is there a way to do something (like print "funkymodule imported" for example) every time a module is imported from any other module? Not only the first time its imported to the runtime or r…

Unit Testing Interfaces in Python

I am currently learning python in preperation for a class over the summer and have gotten started by implementing different types of heaps and priority based data structures.I began to write a unit tes…

Python Pandas average based on condition into new column

I have a pandas dataframe containing the following data:matchID server court speed 1 1 A 100 1 2 D 200 1 3 D 300 1 …

Merging same-indexed rows by taking non-NaNs from all of them in pandas dataframe

I have a sparse dataframe with duplicate indices. How can I merge the same-indexed rows in a way that I keep all the non-NaN data from the conflicting rows?I know that you can achieve something very c…

Approximating cos using the Taylor series

Im using the Taylors series to calculate the cos of a number, with small numbers the function returns accurate results for example cos(5) gives 0.28366218546322663. But with larger numbers it returns i…

How to apply max min boundaries to a value without using conditional statements

Problem:Write a Python function, clip(lo, x, hi) that returns lo if x is less than lo; hi if x is greater than hi; and x otherwise. For this problem, you can assume that lo < hi.Dont use any conditi…