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I am starting to create my first web application in my career using mysql.
I am going to make table which contain users information (like id, firstname, lastname, email, password, phone number).
Which of the following is better?
Put all data into one single table (userinfo).
Divide all data by alphabet character and put data into many tables. for example, if user's email id is Joe#gmail.com that put into table (userinfo_j) and if user's email id is kevin#gmail.com that put into table (userinfo_k).
I don't want to sound condescending, but I think you should spend some time reading up on database design before tackling this project, especially the concept of normalization, which provides consistent and proven rules for how to store information in a relational database.
In general, my recommendation is to build your database to be easy to maintain and understand first and foremost. On modern hardware, a reasonably well-designed database with indexes running relational queries can support millions of records, often tens or hundreds of millions of records without performance problems.
If your database has a performance problem, tune the query first; add indexes second, buy better hardware third, and if that doesn't work, you may consider a design that makes the application harder to maintain (often called denormalization).
Your second solution will almost certainly be slower for most cases.
Relational databases are really, really fast when searching by indexed fields; searching for "email like 'Joe#gmail.com'" on a reasonable database will be too fast to measure on a database with tens of millions of records.
However, including the logic to find the right table in which to search will almost certainly be slower than searching in all the tables.
Especially if you want to search by things other than email address - imagine finding all the users who signed up in the last week. Or who have permission to do a certain thing in your application. Or who have a #gmail.com account.
So, the second solution is bad from a design/maintenance point of view, and will almost certainly be slower.
First one is better. In second you will have to write extra logic to find out which table you will start looking into. And for speeding up the search you can implement indexers. Here I suppose you will do equal operations more often rather than less than or more than operations so you can try implementing indexer with Hash. For comparison operations B-Tree are better.
Like others said, the first one is better. Specially if you need to add other tables in your database and link them to user´s table, as the second one will soon get impossible to work and create relationships when your number of tables increase.
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I'm building a website to monitor a bunch of IOT devices. E.g. Online/Offline status of each devices and some device specific information it may report back, IP address, Temperature etc this will vary. FYI These devices report back to my site via a processor/computer that poles these devices and then reports back (a maximum of 255 devices but in most cases between 10 - 100 devices).
To date, my approach had been that for each processor I would create a new table with just that processors devices would reside within. However in discussions with a colleague he suggested this might not be the best way to go, as it isn't particularly efficient and could be problematic later on e.g. if you wanted to add another column later on, having to add this to possible 50+ different processor tables etc.
Instead because all these tables would have the same structure e.g. identical amount of columns etc just the amount of devices e.g. rows would vary, would one big table with all these rows was a better way to go?
I know that in MySQL terms "scanning" is an expensive operation, and with one big table I would argue there would be more scanning as I would have to filter as I would have to take one big data set each time, and filter it down into a view, e.g. Processor or location against 5000+ rows vs lots of smaller tables of 100 rows. Also I would argue the data in this table would be written to allot e.g. each time a device goes offline the offline flag is updated, so I'm not sure if that makes it more suitable to a single table vs one large table.
Appropriate there's many different ways of approaching this, I just don't want to go down one rabbit hole and regret it later on. Front end will be PHP if that counts for anything.
Your friend is correct. Creating many tables to store very similar data would be a waste of configuration time and an inefficient way to store this information. Instead, creating a table that has columns which can differentiate your machines from each other (ID of machine, type, whatever), as well as columns for the information that all machines will be reporting (temperature, IP, etc), you will have a much more organized database and it will be much simpler when you want to update your table later on.
SQL is very well-optimized for search queries, and unless you're storing millions of rows, I think you'll be just fine in terms of performance.
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Currently I am working on a database that requires me to take raw data from a 3rd party and store it into a database. The problem is that the raw data is obviously not optimized, and the people who I'm building the database for, don't want any data entry involved when uploading the raw data into the database, they pretty much just want to upload the data and be done with it. Some of the raw data files have empty cells all over the place and many instances of duplicate names/numbers/entries. Is there a way to still optimize the data quickly and efficiently without too much data entry or reworking each time data is uploaded or is this an instant where optimization is impossible due to constrants? Does this happen a lot, or do I need to tell them their dreams of just uploading are not possible for long team success?
There are many ways to optimize data and one way to optimize data in one use case may be horrible in another use case. There are tools that will tell you there are multiple values in columns that need to be optimized but there is no single advice which works in all cases.
without specific details, this is always good:
With regards to empty entries, that should not be an issue
With regards to duplicate data, it may be worth considering adding a one to many relationship
One thing need to make sure is to put a key in any field you are going to search for, this will speed up a lot your queries no matter the dataset
as far as changing the database schema... rare are the schemas that do not change over time.
My advice is think through your schema but do not try to over optimize things because you can not plan in advance what the exact usage will be. As long as it is working and there is no bottleneck, focus on other areas. If there is a bottleneck, then by all means, rewrite the affected part, making sure indices are present (consider composite indices in some cases). Consider avoiding unions when possible. and remember the KISS principle (Keep It Simple and Sweet).
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I'm trying to build a activity stream which has the following structure :
------------------------------------------------------------------------------------
id | activity_by_user_id | activity_by_username | ... other activity related columns
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Is this a good approach to store the activity_by_username too in the activity table ? I understand that this will clutter up the table with the same username again and again. But If not, I will have to do a join with the users table to fetch the username.
The username in my web application never changes.
With this, I will no longer have to join this table with the users table. Is this an optimum way of achieving what I need ?
What you are proposing is to denormalize the data structure. There are advantages and disadvantages to this approach.
Clearly, you think that performance will be an advantage, because you will not need to look up the username on each row. This may not be true. The lookup should be on the primary key of the table and should be quite fast. There are even situations where storing the redundant information could slow down the query. This occurs when the field size is large and there are many apps with the same user. Then you are wasting lots of storage on redundant data, increasing the size of the table. Normally, though, you would expect to see a modest -- very modest -- improvement in performance.
Balanced against that is the fact that you are storing redundant data. So, if the user name were updated, then you would have to change lots of rows with the new information.
On balance, I would only advise you to go with such an approach if you tested it on real data in your environment and the performance improvement is worth it. I am skeptical that you would see much improvement, but the proof is in the pudding.
By the way, there are cases where denormalized data structures are needed to support applications. I don't think that looking up a field using a primary key is likely to be one of them.
There isn't a single answer to your question*
In general, relational database design seeks to avoid redundancy to limit the opportunities for data anomalies. For example, you now have the chance that two given rows might contain the same user id but different user names. Which one is correct? How do you prevent such discrepancies?
On the other hand, denormalization by storing certain columns redundantly is sometimes justified. You're right that you avoid doing a join because of that. But now it's your responsibility to make sure data anomalies don't creep in.
And was it really worth it? In MySQL, doing a join to look up a related row by its primary key is pretty efficient (you see this as a join type "eq_ref" in EXPLAIN). I wouldn't try to solve that problem until you can prove it's a bottleneck.
Basically, denormalization optimizes one type of query, at the expense of other types of queries. The extra work you do to prevent, detect, and correct data anomalies may be greater than any efficiency you gain by avoiding the join in this case. Or if usernames were to change sometimes, you'd have to change them in two places now (I know you said usernames don't change in your app).
The point is it depends entirely on your how frequently different queries are run by your application, so it's not something anyone can answer for you.
* That might explain why some people are downvoting your question -- some people in StackOverflow seem to have a rather strict idea about what is a "valid" question. I have seen questions closed or even deleted because they are too subjective and opinion-based. But I have also seen questions deleted because the answer is too "obvious". One of my answers with 100 upvotes was lost because a moderator thought the question of "Do I really need version control if I work solo?" was invalid. Go figure. I copied that one to my blog here.
I think it is bad idea. Databases are optimized for joins (assuming you did your job and indexed correctly) and denormalized data is notoriously hard to maintain. There may be no username changes now but can you guarantee that for the future, no. Risking your data integrity on such a thing is short-sighted at best.
Only denormalize in rare cases where there is an existing performance problem and other optimitization techniques have failed to improve the situation. Denormalizing isn't even always going to get you any performance improvement. As the tables get wider, it may even slow down performance. So don't do it unless you havea measuable performance problem and you measure and ensure the denormlaization actually helps. It is the last optimation technique to try out of all of them, so if you haven't gone through all the optimation techniques in the very large list of possibilities, first, then denormalization should not be an option.
No. This goes against all principles of data normalization.
And it won't even be that difficult (if I'm interpreting what you mean by id, user_id, and user_name); id will be the primary key tying everything together - and the linchpin of your JOINs. So you'll have your main table with id, other activity col, next activity col, etc. (not sure what you mean by activity). Then a 2nd table with just id and user_id and a third with id and username). And when you want to output whatever you're going to output, and do it by user_id or username, you'll just JOIN (implied join syntax - WHERE table1.id = table2.id).
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Okay, so I have my user table ready with columns for all the technical information, such as username, profile picture, password and so on. Now I'm at a situation where I need to add superficial profile information, such as location, age, self-description, website, Facebook account, Twitter account, interests etc. In total, I calculated this would amount to 12 new columns, and since my user table already has 18 columns, I come at a crossroads. Other questions I read about this didn't really give a bottom-line answer of the method that is most efficient.
I need to find out if there is a more efficient way, and what is the most efficient way to store this kind of information? The base assumption being that my website would in the future have millions of users, so an option is needed that is able to scale.
I have so far concluded two different options:
Option 1: Store superficial data in user table, taking the total column count in users table up to 30.
Or
Option 2: Store superficial data in separate table, connecting that with Users table.
Which of these has better ability to scale? Which is more efficient? Is there a third option that is better than these two?
A special extra question also, if anyone has information about this; how do the biggest sites on the internet handle this? Thanks to anyone who participates with an answer, it is hugely appreciated.
My current databse is MySQL with rails mysql2 gem in Rails 4.
In your case, I would go with the second option. I suppose this would be more efficient because you would retrieve data from table 1 whenever the user logins and you would use data from table 2 (superficial data) whenever you change his preferences. You would not have to retrieve all data each time you want to do something. In the bottom line, I would suggest modelling your data according to your usage scenarios (use cases), creating data entities (eg tables) matching your use case entities. Then you should take into account the database normalization principles.
If you are interested on how these issues are handled by the biggest sites in the world, you should know that they do not use relational (SQL) databases. They actually use NoSQL databases, which run on a distributed function. This is a much more complicated scenario than yours. If you want to see related tools, you could start reading about Cassandra and hadoop.
Hope I helped!
If you will need to access to these 30 columns of information frequently, you could put all of them into the same table. That's what some widely-used CMS-es do because even though a row is big, it's faster to retrieve one big row than plenty of small rows on various tables (more SQL requests, more searches, more indexes, ...).
Also a good read for your problem is Database normalization.
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So this is more of a design question.
I have one primary key (say the user's ID), and I have tons of information associated with that user.
Should I have multiple tables broken down into categories according to the information, or should I have just one table with many columns?
The way I used to do it was to have multiple tables, so say, one table for application usage data, one table for profile info, one table for back end tokens etc. to keep things looking organized.
Recently some one told me that it's better not to do it that way and having a table with lots of columns is fine. The thing is, all those columns have the same primary key.
I'm pretty new to database design so which approach is better and what are the pros and cons?
What's the conventional way of doing it?
Any time information is one-to-one (each user has one name and password), then it's probably better to have it one table, since it reduces the number of joins the database will need to do to retrieve results. I think some databases have a limit on the number of columns per table, but I wouldn't worry about it in normal cases, and you can always split it later if you need to.
If the data is one-to-many (each user has thousands of rows of usage info), then it should be split into separate tables to reduce duplicate data (duplicate data wastes storage space, cache space, and makes the database harder to maintain).
You might find the Wikipedia article on database normalization interesting, since it discusses the reasons for this in depth:
Database normalization is the process of organizing the fields and tables of a relational database to minimize redundancy and dependency. Normalization usually involves dividing large tables into smaller (and less redundant) tables and defining relationships between them. The objective is to isolate data so that additions, deletions, and modifications of a field can be made in just one table and then propagated through the rest of the database via the defined relationships.
Denormalization is also something to be aware of, because there are cases where repeating data is better (since it reduces the amount of work the database needs to do when reading data). I'd highly recommend making your data as normalized as possible to start out, and only denormalize if you're aware of performance problems in specific queries.
One big table is often a poor choice. Related tables are what relational database were designed to work with. If you index properly and know how to write performant queries, they are going to perform fine.
When tables get too many columns, then you can run into issues with the actual size of the page that the database is storing the information on. Either the record can end up being too large for the page, in which can you may end up not being able to create or update a specific record which makes users unhappy or you may (in SQL Server at least) be allowed some overflow for particular datatypes (with a set of rules you need to look up if you are doing this) but if many records will overflow the page size you can create tremedous performance problems. Now how MYSQL handles the pages and whether you have a problem when the potential page size gets too large is something you would have to look up in the documentation for that database.
Came across this, and as someone who used to use MySQL a lot, and then switched over to Postgres recently, one of the big advantages is that you can add JSON objects to a field in Postgres.
So if you are in this situation, you don't have to necessarily decide between one large table with many columns and splitting it up, but you can merge columns into JSON objects to reduce it e.g. instead of address being 5 columns, it can just be one. You can also query on that object too.
I have a good example. Overly Normalized database with the following set of relationships:
people -> rel_p2staff -> staff
and
people -> rel_p2prosp -> prospects
Where people has names and persons details, staff has just the staff record details, prospects has just prospects details, and the rel tables are relationship tables with foreign keys from people linking to staff and prospects.
This sort of design carries on for entire database.
Now to query this set of relations it's a multi-table join every time, sometimes 8 and more table join. It has been working fine up to mid this year, when it started getting very slow now that we past 40000 records of people.
Indexing and all low hanging fruits had been used up last year, all queries are optimized to perfection. This is the end of the road for the particular normalized design and management now approved a rebuilt of entire application that depends on it as well as restructure of the database, over a term of 6 months. $$$$ Ouch.
The solution will be to have a direct relation for people -> staff and people -> prospect
ask yourself these questions if you put everything in one table, will you have multiple rows for that user? If you have to update a user do you want to keep an audit trail? Can the user have more than one instance of a data element? (like phone number for instance) will you have a case where you might want to add an element or set of elements later?
if you answer yes then most likely you want to have child tables with foreign key relationships.
Pros of parent/child tables is data integrity, performance via indexes (yes you can do it on a flat table also) and IMO easier to maintain if you need to add a field later, especially if it will be a required field.
Cons design is harder, queries become slightly more complex
But, there are many cases where one big flat table will be appropriate so you have to look at your situation to decide.
I'm already done doing some sort of database design. for me, it depends on the difficulty of the system with database management; yeah it is true to have unique data in one place only but it is really hard to make queries with overly normalized database with lots of record. Just combine the two schema; use one huge table if you feel that you'll be having a massive records that are hard to maintain just like facebook,gmail,etc. and use different table for one set of record for simple system... well this is just my opinion .. i hope it could help.. just do it..you can do it... :)
The conventional way of doing this would be to use different tables as in a star schema or snowflake schema. Howeevr, I would base this strategy to be two fold. I believe in the theory that data should only exist in one place, there for the schema I mentioned would work well. However, I also believe that for reporting engines and BI suites, a columnar approach would be hugely beneficial becuase it is more supportive of the the reporting needs. Columnar approaches like those with infobright.org have huge performance gains and compression that makes using both approaches incredibly useful. Alot of companies are starting to realize that have just one database architecture in the organization is not supportive of the full range of their needs. Alot of companies are implementing both the concept of having more than one database achitecture.
i think having a single table is more effective but you should make sure that the table is organised in a manner that it shows the relationship,trend as well as the difference in variables of the same row.
for example if the table shows age and grades of the students you should arange the table in a manner that thank highest scorer is well differentiated with the lowest scorer and the difference in the age of students is even.