LUKI NOTES

If we want to know a correlation between a boxer winning a match and using red pants, we can mathematically determine it based on past records of those conditions. There are four possible combinations of the conditions. The boxer loses and he doesn’t use red pants (00) The boxer loses and he uses red pants (01) The boxer wins and he doesn’t use red pants (10) The boxer wins and he uses red pants (11) For example, we have the following past records. Condition “00” is 23 times Condition “01” is 10 times Condition “10” is 45 times Condition “11” is 9 times Then, we can use the phi coefficient formula. The result of this formula is between -1 and 1. If the result is close to 0, it means the conditions have no correlation. If it is close to 1, it means the conditions are strongly correlated. Meanwhile, if it is negative, the correlation is strong but in the opposite way. The formula is as follows. phi = (n11 x n00) – (n01 x n10) / sqrt(n1X x n0X x nX1 x nX0) n1X represents the condition wh...

If we build a backend service, we probably need to keep any statistics related to our service such as number of requests, average time for processing, number of errors, etc. We may also need to have statistic records in several time precisions for example hourly and daily. The simplest solution, we can just store any information into a table in our database, then calculate the summary when needed or by a request. But, it will cost our system storage and computing resources. Unlike relational databases or NoSQL that only have CRUD operations in general, Redis operations are related to the type of data that is stored. For example, a list will have push or pop operations, a sorted set will have a ranking, incrementing, or union operation, and so on. For instance for storing request statistics in Redis, we will store statistics of the response time of a backend application. The metrics are minimum time , maximum time , number of responses , and total response time . The last two metrics ...