denormalization

问题 I have a large database of normalized order data that is becoming very slow to query for reporting. Many of the queries that I use in reports join five or six tables and are having to examine tens or hundreds of thousands of lines. There are lots of queries and most have been optimized as much as possible to reduce server load and increase speed. I think it's time to start keeping a copy of the data in a denormalized format. Any ideas on an approach? Should I start with a couple of my worst

问题 Ok Im starting out fresh with Firebase. I've read this: https://www.firebase.com/docs/data-structure.html and I've read this: https://www.firebase.com/blog/2013-04-12-denormalizing-is-normal.html So I'm suitably confused as one seems to contradict the other. You can structure your data hierarchically, but if you want it to be scalable then don't. However that's not the actual problem. I have the following structure (please correct me if this is wrong) for a blog engine: "authors" : { "

问题 For performance reasons, I've got a denormalized database where some tables contain data which has been aggregated from many rows in other tables. I'd like to maintain this denormalized data cache by using SQLAlchemy events. As an example, suppose I was writing forum software and wanted each Thread to have a column tracking the combined word count of all comments in the thread in order to efficiently display that information: class Thread(Base): id = Column(UUID, primary_key=True, default

问题 Normalization leads to many essential and desirable characteristics, including aesthetic pleasure. Besides it is also theoretically "correct". In this context, denormalization is applied as a compromise, a correction to achieve performance. Is there any reason other than performance that a database could be denormalized? 回答1: The two most common reasons to denormalize are: Performance Ignorance The former should be verified with profiling, while the latter should be corrected with a rolled-up

问题 Normalization leads to many essential and desirable characteristics, including aesthetic pleasure. Besides it is also theoretically "correct". In this context, denormalization is applied as a compromise, a correction to achieve performance. Is there any reason other than performance that a database could be denormalized? 回答1: The two most common reasons to denormalize are: Performance Ignorance The former should be verified with profiling, while the latter should be corrected with a rolled-up

问题 I'm doing some research into databases and I'm looking at some limitations of relational DBs. I'm getting that joins of large tables is very expensive, but I'm not completely sure why. What does the DBMS need to do to execute a join operation, where is the bottleneck? How can denormalization help to overcome this expense? How do other optimization techniques (indexing, for example) help? Personal experiences are welcome! If you're going to post links to resources, please avoid Wikipedia. I

问题 We're trying to come up with the data model of Payment Method. There can be several kinds of payment methods like Card, Bank Transfer, Wallet, which further can be categorized for e.g. Card into credit/debit cards, Bank Transfer into ACH/SEPA and the like. So this is about modelling inheritance into database tables. One option is to use single table inheritance. Senior folks in my team call single table as denormalized table. But I don't understand why? I don't see any insert/delete/update

问题 I need a table to hold downtime, basically a downtime event contains the following info: EVENT_ID, START_TIME, END_TIME, SERVICES, CAUSES The main issue is that I don't know how to set this up because I don't want to end up with a mess like this: ID | EVENT_ID | START_TIME | END_TIME | SERVICES | CAUSES 01 455 12:00 12:30 FINANCE NETWORK 02 455 12:00 12:30 ADVANCE NETWORK ... 13 455 12:00 12:30 REFRESH DATABASE Basically...for a single outage, I would have many many entries in the table,

问题 Can I denormalize (by joining) multiple large tables in bigquery? Table1 is 400M rows Table2 is 2M rows Table3 is 800K rows If not, do I have to do it in my relational database before I upload? That would be a difficult solution. Should I chunk the tables into smaller pieces and run iterations of joins over the tables so that it is always large to small? This would also be a difficult solution. Thank you. 回答1: BigQuery now supports "Big JOINs" which allows you skip the LIMIT's in your JOIN

问题 I am fitting an exponential decay function with lsqvurcefit in Matlab . To do this I first normalize my data because they differ several orders of magnitude. However Im not sure how to denormalize my fitted parameters. My fitting model is s = O + A * exp(-t/T) where t and s are known and t is in the order of 10^-3 and s in the order of 10^5. So I subtract from them their mean and divide them by their standarddeviation. My goal is to find the best A, O and T that at the given times t will