I am developing a site that will allow for users to track sales numbers for personal crafts. The way it will work is that a user will be able to submit/edit weekly sales data, and then once the data is stored, be able to view it in various forms of table or graph, track trends, etc.
My concern is that as the userbase grows, if it grows, I want a database design that will scale with it, and be manageable. I am self taught when it comes to proper web apps like this, and while I have all the PHP and JS knowledge I need to assemble the site, and I've worked with jQuery before, this one I am less sure about.
Am I better off storing users' weekly reports in one big table, or creating a separate database, in which each user has their own table, in turn containing that user's weekly reports? There's going to be far more pulling this data for charts done than there is altering or adding to it, so my goal is primarily efficiency and simplicity of storing/recalling the data.
The thing that has me most stumped is the best way to handle the fact that different users will have different amounts of products, and those amounts will change. In a user's first week, perhaps they log sales for 2 items, but come the third week, they add a new item to the list of things they are selling. The database needs to allow for this kind of thing with low overhead, as most users will have more than 1 product.
How would you structure this database?
I would suggest one large table with the innoDB engine for row-level locking instead of table locking. Then create an index on the username and entry time.
I would suggest that the table per user be a bit much, you would be wasting space on the harddisk and database allocated for the table which a user may not need. There is no problem with mySQL support 5+ million rows, if your table even gets that big.
Simplicity is best.
Related
I currently already have a website running using CodeIgniter and MySQL. The MySQL database is around 110 tables big and contains mainly website specific data, like user data, vacancy data, etc.
Now I want to extend this website to include a complete statistical module as well. We would capture a lot of user actions and other aggregations from the data gather on our own website, and would also pull in some data from google analytics API to use in our statistics (we will generate a report in Excel but also show statistical graphs and numbers on a page (using chart.js)).
We are not thinking (in a forseeable future) to use this data in other programs, but we need to be able to open some data to the public using an API.
We expect to start with about 300.000-350.000 data points gathered per day, but this amount will keep on growing every day of course, the more users we get.
Using multiple databases in CodeIgniter seems to not be an issue, so the main problem I am left with is how I should create the architecture for this statistical module.
I have a couple of idea's on how to start doing this, but I am not aware if there is performance impact from one to another solution or other things to take into consideration.
My main idea boils down to having a table containing all "events", which just insert in that table every time an action is performed, eg "user is registered", "user put account on private", "user clicked on X", ...
Then once a day (probably at around midnight), a CRON job would run over that table for the past day and aggregate all the values into a format usable for our statistical metrics. Those aggregated values would be stored in a new table. This way we can clean up the "event" table quite regularly since that will become very big very fast.
Idea 1: Extend the current MySQL database architecture with new tables to incorporate the statistics. I would keep on using the current database architecture and add 2 new tables for the events and the aggregated values.
Idea 2: Create a new database, separate from the current existing one, and use this to insert all the events in a table there and the aggregated values in a new table there.
Note: we already have quite a few CRONS running on our current database, updating statusses and dates, sending emails, ...
Note2: sync issues between databases is not an issue since we will never be storing statistics on a per-user level.
MySQL does not care whether tables are in the same database or separate databases. It is just a convenience for the user. Some things:
You might need db1.tbla JOIN db2.tblb to talk across dbs.
It is convenient to have different GRANTs for different databases, but clumsy to have different GRANTs for 110 tables.
I can't think of any performance differences.
Nightly aggregation is a middle-of-the road approach. Using IODKU gives you 'immediate' aggregation, but is probably more burden on the system.
My blog on Summary Tables .
350K rows inserted per day is about 5/second, which is comfortably low, so I don't think we need to discuss performance issues there.
"Summarize and toss" (for events) -- Yes. I like that approach. (Most people fail to think of this option.)
Do the math. Which table is the largest after a year? How many GB will it be? Then think about whether you can shrink any of the columns in it: SMALLINT instead of INT, normalization of long, oft-repeated, strings, etc.
Currently I'm working on a dashboard in PHP/MySQL which contains several statistics/facts such as: amount of items sold, revenue, gender (male/female) ratio of users etc. (all filterable on last week/month/year). The amount of data is (currently) not that much: 20.000 user rows, 1.000 items, 500 items sold per day but is expected to grow in the future, perhaps even exponentially.
Now, there is a wish to have several graphs displaying the performance to see whether strategy changes have impacts on the amount of users, revenue, gender ratio etc. For this, it is necessary to have numbers per day. Currently, the dashboard can only display "NOW() - 1 week/1 month/1 year" but for showing a graph outlining the growth, these numbers should be saved on a daily basis.
My question is: what are the options in this case? A cronjob could be set in place to save these numbers and write them to a separate 'performance' or 'history' table that saves the visitors, sales, gender ratio etc. in rows linked to the date of that day. This is good for performance, but certain data gets lost. Another option is to compute these numbers with complex queries (group by day) etc, but that seems to intensive since the queries are performed on the production database. Especially since the database structure is a little complex. Thinking of avoiding doing this on the production database, is setting up a data-warehouse with ETL-processes a better option to avoid overloading the production database? In that case the data would not be displayed live.
I honestly have no idea what is the best option in this case. I'm very curious about the answers! Many thanks.
Running query on a production database (especially one which is growing in volume and complexity) become a losing proposition very quickly. There are a lot of possible alternative, basically the entire field of Business Intelligence is grown as as solution of this problem.
For a small system where you just want to avoid to query the production database probably the development of a full blown Data Warehouse is overkill. It is impossible to give a reasonable answer without knowing more, but I would go for one of the following (in growing order of complexity/degree of result):
Instead of directly show the result of the query, save it in a table and query the table
Clone your production database then query the clone
Extract relevant data from production database in a structure which save relevant data and preserve history (google Data Vault)
Direct over the production DB, or over solution 2 or 3 build a dimensional model (google Kimball Dimensional Model). Pay attention that to do a good job you have to consider what kind of queries you want to do. You could end up with different designs for different requirement.
It is also relevant which technology are you using and what are the options available on your available architecture. Depending on what you have on hand, you could have some solution, even complex ones, very much simplified. Do some research.
I need an expert advice for my database. Basically we have 100s of sensors around the world. We collect data from the sensors and store in the database for future use.
Currently I create a separate database table for each customer i.e. When a customer registers to the application, I create a separate table for them and the data from all the sensors from this customer goes to their separate database table.
Now the number of customers are increasing so does the number of tables and this approach is not looking good anymore (may be this approach wasn't right in the first place).
I now want to keep all the data in one table so I copied all the data from the customer's table into a new table. Now the size of the new table is over 5GB with more than 34 million rows (and growing).
If I want to insert new rows into this new table simultaneously, from multiple thread for each sensor, it takes too long. To access the data from the same table takes long time too.
How can I resolve this issue? Is there any other solution ? Should I use some external cloud service to store data ?
Thanks in advance!
EDIT:
I am using indexes. Here is the table schema
With UNIQUE INDEX idx_userInsDate ( userID,instrumentID,utcDateTime)
I have also looked into the database sharding but my main issue is, inserting rows to the same table from multiple threads and reading data from multiple threads is taking some time.
With this limited information here's my advice.
When collecting millions of rows from many different customers unless the data has to be collected together for "easy reporting" a customer specific table or even a customer specific database can definitely be used and that is absolutely fine.
This actually has several benefits including protecting you from exposing one customers information to another customer on accident since all their data is in 1 table.
As your number of customers goes up then you get either a new database for each customer or a new table and that is fine and that is probably something you would want to automate in your software. For instance, if a customer signs up, this table is automatically created.
Both scenarios and designs are common and perfectly fine depending on your situation. For instance, I once owned a product company and for that company every customer had their own entire database. So as my customer count went up my number of databases went up. This is no different really than you having a database or table per customer and if you choose that route that's okay.
Whatever you choose you must consider your sql backups, size of your database versus hard drive space available etc. If the number of tables continues to grow maybe each customer should get their own database but how hard would it then be for you to backup all of these databases and relate them to central db if you needed to do so. Just consider everything like this including security and your reporting needs, how much data you will need to keep etc.
Here's another article I wrote some time ago on multi-tenant data architecture.
https://stackoverflow.com/a/38555345/671343
Check it out and hopefully this helps you. Your not the only one to struggle with a design decision about this. Just weigh all your options considering reporting, security, backups and more.
Hope thats helpful
Use Mongo or similar DB for your scenerio , that is the exact scenerio which requires Mongo .
Multiple Record Insertion at once is very fast and isolated from other records hence faster\
Reading is Faster if you have proper Data structure Tree formed for your data.
Proper structuring will furhter help to reduce the requirement of creating new table for each customer.
I've created many databases before, but I have never linked two tables together. I've tried looking around, but cannot find WHY one would need to link two or more tables together.
There is a good tutorial here that goes over database relationships, but does not explain why they would be needed. He just simply says that they are.
Are they truly necessary? I understand that (in his example) all orders have a customer, and so one would link the orders table to the customers table, but I just don't see why this would be absolutely necessary. I can (and have) created shopping carts and other complex databases that work just fine without creating any table relationships.
I've just started playing around with MySQL Workbench v6.0 for a new project that has a fairly large and complex database, and so I'm wondering if I am losing anything by creating the entire project without relationships?
NOTE: Please let me know if this question is too general or off topic, and I will change it. I understand that a lot can be said about this topic, and so I'm really just looking to know if I am opening myself up to any security issues or significant performance issues by not using relationships. Please be specific in your response; "Yes you are opening yourself up to performance issues" is useless and not helpful for myself, nor for anyone else looking at this thread at a later date. Please include details and specifics in your response.
Thank you in advance!
As Sam D points out in the comments, entire books can be written about database design and why having tables with relationships can make a lot of sense.
That said, theoretically, you lose absolutely no expressive/computational power by just putting everything in the same table. The primary arguments against doing so likely deal with performance and maintenance issues that might arise.
The answer revolves around granularity, space consumption, speed, and detail.
Inherently different types of data will be more granular than others, as items can always be rolled up to a larger umbrella. For a chain of stores, items sold can be rolled up into transactions, transactions can be rolled up into register batches, register batches can be rolled up to store sales, store sales can be rolled up to company sales. The two options then are:
Store the data at the lowest grain in a single table
Store the data in separate tables that are dedicated to purpose
In the first case, there would be a lot of redundant data, as each item sold at location 3 of 430 would have store, date, batch, transaction, and item information. That redundant data takes up a large volume of space, when you could very easily create separated tables for their unique purpose.
In this example, lets say there were a thousand transactions a day totaling a million items sold from that one store. By creating separate tables you would have:
Stores = 430 records
Registers = 10 records
Transactions = 1000 records
Items sold = 1000000 records
I'm sure your asking where the space savings comes in ... it is in the detail for each record. The store table has names, address, phone, etc. The register has number, purchase date, manager who reconciles, etc. Transactions have customer, date, time, amount, tax, etc. If these values were duplicated for every record over a single table it would be a massive redundancy of data adding up to far more space consumption than would occur just by linking a field in one table (transaction id) to a field in another table (item id) to show that relationship.
Additionally, the amount of space consumed, as well as the size of the overall table, inversely impacts the speed of you querying that data. By keeping tables small and capitalizing on the relationship identifiers to link between them, you can greatly increase the response time. Every time the query engine needs to find a value, it traverses the table until it finds it (that is a grave oversimplification, but not untrue), so the larger and broader the table the longer the seek time. These problems do not exist with insignificant volumes of data, but for organizations that deal with millions, billions, trillions of records (I work for one of them) storing everything in a single table would make the application unusable.
There is so very, very much more on this topic, but hopefully this gives a bit more insight.
Short answer: In a relational database like MySQL Yes. Check this out about referential integrity http://databases.about.com/cs/administration/g/refintegrity.htm
That does not mean that you have to use relational databases for your project. In fact the trend is to use Non-Relational databases (NoSQL), like MongoDB to achieve same results with better performance. More about RDBMS vs NoSQL http://www.zdnet.com/rdbms-vs-nosql-how-do-you-pick-7000020803/
I think that with this example you will understand better:
Let's we want to create on-line store. We have at minimum Users, Payments and Events (events about the pages where the user navigates or other actions). In this scenario we want to link in a secure and relational way the Users with the Payments. We do not want a Payment to be lost or assigned to another User. So we can use a RDBMS like MySQL to create the tables Users and Payments and linked the with proper Foreign Keys. However for the events, we are going to be a lot of them per users (maybe millions) and we need to track them in a fast way without killing the relation database. In that case a No-SQL database like MongoDB makes totally sense.
To sum up to can use an hybrid of SQL and NO-SQL, but either if you use one, the other or both kind of solutions, do it properly.
I am working on an app right now which has the potential to grow quite large. The whole application runs through a single domain, with customers being given sub-domains, which means that it all, of course, runs through a common code-base.
What I am struggling with is the database design. I am not sure if it would be better to have a column in each table specifying the customer id, or to create a new set of tables (in the same database), or to create a complete new database per customer.
The nice thing about a "flag" in the database specifying the customer id is that everything is in a single location. The downfalls are obvious- Tables can (will) get huge, and maintenance can become a complete nightmare. If growth occurs, splitting this up over several servers is going to be a huge pain.
The nice thing about creating new tables it is easy to do, and also keeps the tables pretty small. And since customers data doesn't need to interact, there aren't any problems there. But again, maintenance might become an issue (Although I do have a migrations library that will do updates on the fly per customer, so that is no big deal). The other issue is I have no idea how many tables can be in a single database. Does anyone know what the limit is, and what the performance issues would be?
The nice thing about creating a new database per customer, is that when I need to scale, I will be able to, quite nicely. There are several sites that make use of this design (wordpress.com, etc). It has been shown to be effective, but also have some downfalls.
So, basically I am just looking for some advice on which direction I should (could) go.
Single Database Pros
One database to maintain. One database to rule them all, and in the darkness - bind them...
One connection string
Can use Clustering
Separate Database per Customer Pros
Support for customization on per customer basis
Security: No chance of customers seeing each others data
Conclusion
The separate database approach would be valid if you plan to support customer customization. Otherwise, I don't see the security as a big issue - if someone gets the db credentials, do you really think they won't see what other databases are on that server?
Multiple Databases.
Different customers will have different needs, and it will allow you to serve them better.
Furthermore, if a particular customer is hammering the database, you don't want that to negatively affect the site performance for all your other customers. If everything is on one database, you have no damage control mechanism.
The risk of accidentally sharing data between customers is much smaller with separate database. If you'd like to have all data in one place, for example for reporting, set up a reporting database the customers cannot access.
Separate databases allow you to roll out, and test, a bugfix for just one customer.
There is no limit on the amount of tables in MySQL, you can make an insane amount of them. I'd call anything above a hundred tables per database a maintenance nightmare though.
Are you planning to develop a Cloud App?
I think that you don´t need to make tables or data bases by customer. I recommend you to use a more scalable relational database management system. Personally I don´t know the capabilities of MySQL, but i´m pretty sure that it should support distributed data base model in order to handle the load.
creating tables or databases per customer can lead you to a maintenance nightmare.
I have worked with multi-company databases and every table contains customer ids and to access its data we develop views per customer (for reporting purposes)
Good luck,
You can do whatever you want.
If you've got the customer_id in each column, then you've got to write the whole application that way. That's not exactly true as there should be enough to add that column only to some tables, the rest could be done using some simple joins.
If you've got one database per user, there won't be any additional code in the application so that could be easier.
If you take to first approach there won't be a problem to move to many databases as you can have the customer_id column in all those tables. Of course then there will be the same value in this column in each table, but that's not a problem.
Personally I'd take the simple one customer one database approach. Easier to user more database servers for all customers, more difficult to show a customer data that belongs some other customer.