So, my data model is similar to an assignment problem. So, let's assume we have a firm that provides suitable workers for requested jobs.
For now, I have such relations:
Customer (id)
Job (id)
Worker (id, available)
Jobs In Progress (customer_id, job_id)
Busy Workers (customer_id, worker_id)
There are many-to-many between Customer and Job and many-to-many between Customer and Worker. This data is like real-time, so it's highly dynamic.
We want to maintain such queries:
Request a worker for a job.
Return the worker when the job has finished.
This queries requires to read, update, delete and insert data in several tables.
For example, if customer requests for a worker, we have to check whether this customer already exists in the table; whether he already owns a suitable worker in Busy Workers; if no, find a suitable available worker in Worker; check whether such a job already registered in Job. And in the worst case, we have to atomically insert customer in Customer, insert job in Job, insert a corresponding row in Jobs In Progress, decrement Worker.avaiable and insert a row in Busy Workers.
In the second query, we have to do all of this stuff in a reversed order: increment Worker.available, delete the customer if he has no jobs, delete the job if no one customer needs it and so on.
So we have a lot of consistency rules: number of busy workers have to be consistent with Worker.available, a customer has to be present in the table only if he has requested not finished jobs, a job has to be present in the table only if there are no customers with such a job requested.
I read a lot about isolations levels and locking in databases, but I still don't understand how to ensure consistency across multiple tables. It seems like isolation levels don't work because multiple tables are involved and data may become inconsistent between select from two tables. And it seems like locks don't work too, because AFAIK SQL Server can't atomically acquire a lock on multiple tables and therefore data may become inconsistent between locks.
And, actually, I'm looking for a solution or idea of a solution in general, without referencing to a concrete RDBMS, it should be something that applicable one way or another to the most famous RDBMS's like MySQL, PostgreSQL, SQL Server, and Oracle. So it does not have to be a proper solution with examples with all of this RDMS's, maybe some practices, tips or
references.
I apologize for my English and thank you in advance.
First: Think about your model a bit more. I would not keep so much redundant information. "decrement Worker.avaiable and insert a row in Busy Workers" are completely superfluous because you can get the information easily by asking the other tables. You might say that is more costly to query. That I would call premature optimization. Redundancy is very costly per se.
Second: Think of locks as exclusive resources that only one may get. So the most simple way to ensure consistency would be to let all dbms-users lock just one record in the database using select ... for update. All changes would be serialized. If you use a MVCC-Dbms like postgres, oracle or even sql-server, the readers would always see a consistent situation.
Third: Doing your change perhaps you just need to detect, if another user/transaction already changed a certain record. This can be done by maintaining, so called, version-attributes and checking during updates, if those attributes where changed. If a change was detected, you have to repeat the complete transaction. That is called optimistic locking.
Fourth or better the most important point: I hope you understood the concept of dbms-transaction as a means to bring a dbms from one consistent state to the other.
I have some Rails ActiveRecord code that looks like this:
new_account_number = Model.maximum(:account_number)
# Some processing that usually involves incrementing
# the new account number by one.
Model.create(foo: 12, bar: 34, account_number: new_account_number)
This code works fine on its own, but I have some background jobs that are processed by DelayedJob workers. There are two workers and if they both start processing a batch of jobs that deal with this code, they end up creating new Model records that has the same account_number, because of the delay between finding the maximum and creating a new record with an even higher account number.
For now, I have solved it by adding a uniqueness constraint at database level to the models table and then retry by re-selecting the maximum in case this constraint triggers an exception.
However it feels like a hack.
Adding auto incrementing at database level to the account_number column is not an option, because the account_number assigning entails more than just incrementing.
Ideally I would like to lock the table in question for reading, so no other can execute the maximum select query against the table until I am done. However, I'm not sure how to go about that. I'm using Postgresql.
Based on the ActiveRecord::Locking docs it looks like Rails doesn't provide a built-in API for table-level locks.
But you can still do this with raw SQL. For Postgres, this looks like
ActiveRecord::Base.transaction do
ActiveRecord::Base.connection.execute('LOCK table_name IN ACCESS EXCLUSIVE MODE')
...
end
The lock must be acquired within a transaction, and is automatically freed once the transaction ends.
Note that the SQL you use here will be different depending on your database.
Obviously locking the entire table is not elegant or efficient, but for small apps, for some time, it may indeed be the best solution. It's simple and easy to reason about. In general, an advisory lock is a better fit for this kind of data race.
There are already answers on how to lock the entire table, but I believe you should try to avoid that. Instead I believe you should give advisory locks a look. It makes sure the same block of code isn't executed on two machines simultaneously, while still keeping the table open for other business.
It still uses the database, but it doesn't lock your tables.
You can use the gem called "with_advisory_lock" like this:
Model.with_advisory_lock("ADVISORY_LOCK_NAME") do
# Your code
end
https://github.com/ClosureTree/with_advisory_lock
It doesn't work with SQLite.
Setting unique constraint IS NOT a hack. It is thing that makes your data consistent.
By the way you have a few more options here:
Lock some DB resource (e.g. it could be a unique record) using
SELECT FOR UPDATE or PostreSQL's Advisory Locks (see docs).
Use a sequence (docs).
The main difference between two approaches is #1 does not allow to have gaps in your numbers because other session will wait for transaction commit and #2 allows.
you don't have to lock the hall table to lock a piece of code for a single process at a time. locking a full table causes performence problems.you can lock a single same row all the time with "with_lock" method.this way code is fully protected. no extra gem is needed. it also creates a transaction. like this:
m = Model.order(:id).first
m.with_lock do #aquire lock
#some code here for a single process at a time
end #release lock
Well, technically it's the same to lock a table or to always lock a record of another table before accessing the table.
So you may have another table with max one record, alway lock that record with http://api.rubyonrails.org/classes/ActiveRecord/Locking/Pessimistic.html before read/write from the table you want to lock:
LockTable.last.with_lock do
// the things that needed for your table
end
This question already has answers here:
Only inserting a row if it's not already there
(7 answers)
Closed 9 years ago.
I have a DB table with a field that must be unique. Let's say the table is called "Table1" and the unique field is called "Field1".
I plan on implementing this by performing a SELECT to see if any Table1 records exist where Field1 = #valueForField1, and only updating or inserting if no such records exist.
The problem is, how do I know there isn't a race condition here? If two users both click Save on the form that writes to Table1 (at almost the exact same time), and they have identical values for Field1, isn't it possible that the following would happen?
User1 makes a SQL call, which performs the select operation and determines there are no existing records where Field1 = #valueForField1. User1's process is preempted by User2's process, which also finds no records where Field1 = #valueForField1, and performs an insert. User1's process is allowed to run again, and inserts a second record where Field1 = #valueForField1, violating the requirement that Field1 be unique.
How can I prevent this? I'm told that transactions are atomic, but then why do we need table locks too? I've never used a lock before and I don't know whether or not I need one in this case. What happens if a process tries to write to a locked table? Will it block and try again?
I'm using MS SQL 2008R2.
Add a unique constraint on the field. That way you won't have to SELECT. You will only have to insert. The first user will succeed the second will fail.
On top of that you may make the field autoincremented, so you won't have to care on filling it, or you may add a default value, again not caring on filling it.
Some options would be an autoincremented INT field, or a unique identifier.
You can add a add a unique constraint. Example from http://www.w3schools.com/sql/sql_unique.asp:
CREATE TABLE Persons
(
P_Id int NOT NULL UNIQUE
)
EDIT: Please also read Martin Smith's comment below.
jyparask has a good answer on how you can tackle this specific problem. However, I would like to elaborate on your confusion over locks, transactions, blocking, and retries. For the sake of simplicity, I'm going to assume transaction isolation level serializable.
Transactions are atomic. The database guarantees that if you have two transactions, then all operations in one transaction occur completely before the next one starts, no matter what kind of race conditions there are. Even if two users access the same row at the same time (multiple cores), there is no chance of a race condition, because the database will ensure that one of them will fail.
How does the database do this? With locks. When you select a row, SQL Server will lock the row, so that all other clients will block when requesting that row. Block means that their query is paused until that row is unlocked.
The database actually has a couple of things it can lock. It can lock the row, or the table, or somewhere in between. The database decides what it thinks is best, and it's usually pretty good at it.
There is never any retrying. The database will never retry a query for you. You need to explicitly tell it to retry a query. The reason is because the correct behavior is hard to define. Should a query retry with the exact same parameters? Or should something be modified? Is it still safe to retry the query? It's much safer for the database to simply throw an exception and let you handle it.
Let's address your example. Assuming you use transactions correctly and do the right query (Martin Smith linked to a few good solutions), then the database will create the right locks so that the race condition disappears. One user will succeed, and the other will fail. In this case, there is no blocking, and no retrying.
In the general case with transactions, however, there will be blocking, and you get to implement the retrying.
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What is the advantage of doing a logical/soft delete of a record (i.e. setting a flag stating that the record is deleted) as opposed to actually or physically deleting the record?
Is this common practice?
Is this secure?
Advantages are that you keep the history (good for auditing) and you don't have to worry about cascading a delete through various other tables in the database that reference the row you are deleting. Disadvantage is that you have to code any reporting/display methods to take the flag into account.
As far as if it is a common practice - I would say yes, but as with anything whether you use it depends on your business needs.
EDIT: Thought of another disadvantange - If you have unique indexes on the table, deleted records will still take up the "one" record, so you have to code around that possibility too (for example, a User table that has a unique index on username; A deleted record would still block the deleted users username for new records. Working around this you could tack on a GUID to the deleted username column, but it's a very hacky workaround that I wouldn't recommend. Probably in that circumstance it would be better to just have a rule that once a username is used, it can never be replaced.)
Are logical deletes common practice? Yes I have seen this in many places. Are they secure? That really depends are they any less secure then the data was before you deleted it?
When I was a Tech Lead, I demanded that our team keep every piece of data, I knew at the time that we would be using all that data to build various BI applications, although at the time we didn't know what the requirements would be. While this was good from the standpoint of auditing, troubleshooting, and reporting (This was an e-commerce / tools site for B2B transactions, and if someone used a tool, we wanted to record it even if their account was later turned off), it did have several downsides.
The downsides include (not including others already mentioned):
Performance Implications of keeping all that data, We to develop various archiving strategies. For example one area of the application was getting close to generating around 1Gb of data a week.
Cost of keeping the data does grow over time, while disk space is cheap, the amount of infrastructure to keep and manage terabytes of data both online and off line is a lot. It takes a lot of disk for redundancy, and people's time to ensure backups are moving swiftly etc.
When deciding to use logical, physical deletes, or archiving I would ask myself these questions:
Is this data that might need to be re-inserted into the table. For example User Accounts fit this category as you might activate or deactivate a user account. If this is the case a logical delete makes the most sense.
Is there any intrinsic value in storing the data? If so how much data will be generated. Depending on this I would either go with a logical delete, or implement an archiving strategy. Keep in mind you can always archive logically deleted records.
It might be a little late but I suggest everyone to check Pinal Dave's blog post about logical/soft delete:
I just do not like this kind of design [soft delete] at all. I am firm believer of the architecture where only necessary data should be in single table and the useless data should be moved to an archived table. Instead of following the isDeleted column, I suggest the usage of two different tables: one with orders and another with deleted orders. In that case, you will have to maintain both the table, but in reality, it is very easy to maintain. When you write UPDATE statement to the isDeleted column, write INSERT INTO another table and DELETE it from original table. If the situation is of rollback, write another INSERT INTO and DELETE in reverse order. If you are worried about a failed transaction, wrap this code in TRANSACTION.
What are the advantages of the smaller table verses larger table in above described situations?
A smaller table is easy to maintain
Index Rebuild operations are much faster
Moving the archive data to another filegroup will reduce the load of primary filegroup (considering that all filegroups are on different system) – this will also speed up the backup as well.
Statistics will be frequently updated due to smaller size and this will be less resource intensive.
Size of the index will be smaller
Performance of the table will improve with a smaller table size.
I'm a NoSQL developer, and on my last job, I worked with data that was always critical for someone, and if it was deleted by accident in the same day that was created, I were not able to find it in the last backup from yesterday! In that situation, soft deletion always saved the day.
I did soft-deletion using timestamps, registering the date the document was deleted:
IsDeleted = 20150310 //yyyyMMdd
Every Sunday, a process walked on the database and checked the IsDeleted field. If the difference between the current date and the timestamp was greater than N days, the document was hard deleted. Considering the document still be available on some backup, it was safe to do it.
EDIT: This NoSQL use case is about big documents created in the database, tens or hundreds of them every day, but not thousands or millions. By general, they were documents with the status, data and attachments of workflow processes. That was the reason why there was the possibility of a user deletes an important document. This user could be someone with Admin privileges, or maybe the document's owner, just to name a few.
TL;DR My use case was not Big Data. In that case, you will need a different approach.
One pattern I have used is to create a mirror table and attach a trigger on the primary table, so all deletes (and updates if desired) are recorded in the mirror table.
This allows you to "reconstruct" deleted/changed records, and you can still hard delete in the primary table and keep it "clean" - it also allows the creation of an "undo" function, and you can also record the date, time, and user who did the action in the mirror table (invaluable in witch hunt situations).
The other advantage is there is no chance of accidentally including deleted records when querying off the primary unless you deliberately go to the trouble of including records from the mirror table (you may want to show live and deleted records).
Another advantage is that the mirror table can be independently purged, as it should not have any actual foreign key references, making this a relatively simple operation in comparison to purging from a primary table that uses soft deletes but still has referential connections to other tables.
What other advantages? - great if you have a bunch of coders working on the project, doing reads on the database with mixed skill and attention to detail levels, you don't have to stay up nights hoping that one of them didn’t forget to not include deleted records (lol, Not Include Deleted Records = True), which results in things like overstating say the clients available cash position which they then go buy some shares with (i.e., as in a trading system), when you work with trading systems, you will find out very quickly the value of robust solutions, even though they may have a little bit more initial "overhead".
Exceptions:
- as a guide, use soft deletes for "reference" data such as user, category, etc, and hard deletes to a mirror table for "fact" type data, i.e., transaction history.
I used to do soft-delete, just to keep old records. I realized that users don't bother to view old records as often as I thought. If users want to view old records, they can just view from archive or audit table, right? So, what's the advantage of soft-delete? It only leads to more complex query statement, etc.
Following are the things i've implemented, before I decided to not-soft-delete anymore:
implement audit, to record all activities (add,edit,delete). Ensure that there's no foreign key linked to audit, and ensure this table is secured and nobody can delete except administrators.
identify which tables are considered "transactional table", which very likely that it will be kept for long time, and very likely user may want to view the past records or reports. For example; purchase transaction. This table should not just keep the id of master table (such as dept-id), but also keep the additional info such as the name as reference (such as dept-name), or any other necessary fields for reporting.
Implement "active/inactive" or "enable/disable" or "hide/show" record of master table. So, instead of deleting record, the user can disable/inactive the master record. It is much safer this way.
Just my two cents opinion.
I'm a big fan of the logical delete, especially for a Line of Business application, or in the context of user accounts. My reasons are simple: often times I don't want a user to be able to use the system anymore (so the account get's marked as deleted), but if we deleted the user, we'd lose all their work and such.
Another common scenario is that the users might get re-created a while after having been delete. It's a much nicer experience for the user to have all their data present as it was before they were deleted, rather than have to re-create it.
I usually think of deleting users more as "suspending" them indefinitely. You never know when they'll legitimately need to be back.
I commonly use logical deletions - I find they work well when you also intermittently archive off the 'deleted' data to an archived table (which can be searched if needed) thus having no chance of affecting the performance of the application.
It works well because you still have the data if you're ever audited. If you delete it physically, it's gone!
I almost always soft delete and here's why:
you can restore deleted data if a customer asks you to do so. More happy customers with soft deletes. Restoring specific data from backups is complex
checking for isdeleted everywhere is not an issue, you have to check for userid anyway (if the database contains data from multiple users). You can enforce the check by code, by placing those two checks on a separate function (or use views)
graceful delete. Users or processes dealing with deleted content will continue to "see" it until they hit the next refresh. This is a very desirable feature if a process is processing some data which is suddenly deleted
synchronization: if you need to design a synchronization mechanism between a database and mobile apps, you'll find soft deletes much easier to implement
Re: "Is this secure?" - that depends on what you mean.
If you mean that by doing physical delete, you'll prevent anyone from ever finding the deleted data, then yes, that's more or less true; you're safer in physically deleting the sensitive data that needs to be erased, because that means it's permanently gone from the database. (However, realize that there may be other copies of the data in question, such as in a backup, or the transaction log, or a recorded version from in transit, e.g. a packet sniffer - just because you delete from your database doesn't guarantee it wasn't saved somewhere else.)
If you mean that by doing logical delete, your data is more secure because you'll never lose any data, that's also true. This is good for audit scenarios; I tend to design this way because it admits the basic fact that once data is generated, it'll never really go away (especially if it ever had the capability of being, say, cached by an internet search engine). Of course, a real audit scenario requires that not only are deletes logical, but that updates are also logged, along with the time of the change and the actor who made the change.
If you mean that the data won't fall into the hands of anyone who isn't supposed to see it, then that's totally up to your application and its security structure. In that respect, logical delete is no more or less secure than anything else in your database.
Logical deletions if are hard on referential integrity.
It is the right think to do when there is a temporal aspect of the table data (are valid FROM_DATE - TO_DATE).
Otherwise move the data to an Auditing Table and delete the record.
On the plus side:
It is the easier way to rollback (if at all possible).
It is easy to see what was the state at a specific point in time.
I strongly disagree with logical delete because you are exposed to many errors.
First of all queries, each query must take care the IsDeleted field and the possibility of error becomes higher with complex queries.
Second the performance: imagine a table with 100000 recs with only 3 active, now multiply this number for the tables of your database; another performance problem is a possible conflict with new records with old (deleted records).
The only advantage I see is the history of records, but there are other methods to achieve this result, for example you can create a logging table where you can save info: TableName,OldValues,NewValues,Date,User,[..] where *Values can be varchar and write the details in this form fieldname : value; [..] or store the info as xml.
All this can be achieved via code or Triggers but you are only ONE table with all your history.
Another options is to see if the specified database engine are native support for tracking change, for example on SQL Server database there are SQL Track Data Change.
It's fairly standard in cases where you'd like to keep a history of something (e.g. user accounts as #Jon Dewees mentions). And it's certainly a great idea if there's a strong chance of users asking for un-deletions.
If you're concerned about the logic of filtering out the deleted records from your queries getting messy and just complicating your queries, you can just build views that do the filtering for you and use queries against that. It'll prevent leakage of these records in reporting solutions and such.
There are requirements beyond system design which need to be answered. What is the legal or statutory requirement in the record retention? Depending on what the rows are related to, there may be a legal requirement that the data be kept for a certain period of time after it is 'suspended'.
On the other hand, the requirement may be that once the record is 'deleted', it is truly and irrevocably deleted. Before you make a decision, talk to your stakeholders.
Mobile apps that depend on synchronisation might impose the use of logical rather than physical delete: a server must be able to indicate to the client that a record has been (marked as) deleted, and this might not be possible if records were physically deleted.
I just wanted to expand on the mentioned unique constraint problem.
Suppose I have a table with two columns: id and my_column. To support soft-deletes I need to update my table definition to this:
create table mytable (
id serial primary key,
my_column varchar unique not null,
deleted_at datetime
)
But if a row is soft-deleted, I want my_column constraint to be ignored, because deleted data should not interfere with non-deleted data. My original model will not work.
I would need to update my data definition to this:
create table mytable (
id serial primary key,
my_column varchar not null,
my_column_repetitions integer not null default 0,
deleted_at datetime,
unique (my_column, my_column_repetitions),
check (deleted_at is not null and my_column_repetitions > 0 or deleted_at is null and my_column_repetitions = 0)
)
And apply this logic: when a row is current, i.e. not deleted, my_column_repetitions should hold the default value 0 and when the row is soft-deleted its my_column_repetitions needs to be updated to (max. number of repetitions on soft-deleted rows) + 1.
The latter logic must be implemented programmatically with a trigger or handled in my application code and there is no check that I could set.
Repeat this is for every unique column!
I think this solution is really hacky and would favor a separate archive table to store deleted rows.
They don't let the database perform as it should rendering such things as the cascade functionality useless.
For simple things such as inserts, in the case of re-inserting, then the code behind it doubles.
You can't just simply insert, instead you have to check for an existence and insert if it doesn't exist before or update the deletion flag if it does whilst also updating all other columns to the new values. This is seen as an update to the database transaction log and not a fresh insert causing inaccurate audit logs.
They cause performance issues because tables are getting glogged with redundant data. It plays havock with indexing especially with uniqueness.
I'm not a big fan of logical deletes.
To reply to Tohid's comment, we faced same problem where we wanted to persist history of records and also we were not sure whether we wanted is_deleted column or not.
I am talking about our python implementation and a similar use-case we hit.
We encountered https://github.com/kvesteri/sqlalchemy-continuum which is an easy way to get versioning table for your corresponding table. Minimum lines of code and captures history for add, delete and update.
This serves more than just is_deleted column. You can always backref version table to check what happened with this entry. Whether entry got deleted, updated or added.
This way we didn't need to have is_deleted column at all and our delete function was pretty trivial. This way we also don't need to remember to mark is_deleted=False in any of our api's.
Soft Delete is a programming practice that being followed in most of the application when data is more relevant. Consider a case of financial application where a delete by the mistake of the end user can be fatal.
That is the case when soft delete becomes relevant. In soft delete the user is not actually deleting the data from the record instead its being flagged as IsDeleted to true (By normal convention).
In EF 6.x or EF 7 onward Softdelete is Added as an attribute but we have to create a custom attribute for the time being now.
I strongly recommend SoftDelete In a database design and its a good convention for the programming practice.
Most of time softdeleting is used because you don't want to expose some data but you have to keep it for historical reasons (A product could become discontinued, so you don't want any new transaction with it but you still need to work with the history of sale transaction). By the way, some are copying the product information value in the sale transaction data instead of making a reference to the product to handle this.
In fact it looks more like a rewording for a visible/hidden or active/inactive feature. Because that's the meaning of "delete" in business world. I'd like to say that Terminators may delete people but boss just fire them.
This practice is pretty common pattern and used by a lot of application for a lot of reasons. As It's not the only way to achieve this, so you will have thousand of people saying that's great or bullshit and both have pretty good arguments.
From a point of view of security, SoftDelete won't replace the job of Audit and it won't replace the job of backup too. If you are afraid of "the insert/delete between two backup case", you should read about Full or Bulk recovery Models. I admit that SoftDelete could make the recovery process more trivial.
Up to you to know your requirement.
To give an alternative, we have users using remote devices updating via MobiLink. If we delete records in the server database, those records never get marked deleted in the client databases.
So we do both. We work with our clients to determine how long they wish to be able to recover data. For example, generally customers and products are active until our client say they should be deleted, but history of sales is only retained for 13 months and then deletes automatically. The client may want to keep deleted customers and products for two months but retain history for six months.
So we run a script overnight that marks things logically deleted according to these parameters and then two/six months later, anything marked logically deleted today will be hard deleted.
We're less about data security than about having enormous databases on a client device with limited memory, such as a smartphone. A client who orders 200 products twice a week for four years will have over 81,000 lines of history, of which 75% the client doesn't care if he sees.
It all depends on the use case of the system and its data.
For example, if you are talking about a government regulated system (e.g. a system at a pharmaceutical company that is considered a part of the quality system and must follow FDA guidelines for electronic records), then you darned well better not do hard deletes! An auditor from the FDA can come in and ask for all records in the system relating to product number ABC-123, and all data better be available. If your business process owner says the system shouldn't allow anyone to use product number ABC-123 on new records going forward, use the soft-delete method instead to make it "inactive" within the system, while still preserving historical data.
However, maybe your system and its data has a use case such as "tracking the weather at the North Pole". Maybe you take temperature readings once every hour, and at the end of the day aggregate a daily average. Maybe the hourly data will no longer ever be used after aggregation, and you'd hard-delete the hourly readings after creating the aggregate. (This is a made-up, trivial example.)
The point is, it all depends on the use case of the system and its data, and not a decision to be made purely from a technological standpoint.
Well! As everyone said, it depends on the situation.
If you have an index on a column like UserName or EmailID - and you never expect the same UserName or EmailID to be used again; you can go with a soft delete.
That said, always check if your SELECT operation uses the primary key. If your SELECT statement uses a primary key, adding a flag with the WHERE clause wouldn't make much difference. Let's take an example (Pseudo):
Table Users (UserID [primary key], EmailID, IsDeleted)
SELECT * FROM Users where UserID = 123456 and IsDeleted = 0
This query won't make any difference in terms of performance since the UserID column has a primary key. Initially, it will scan the table based on PK and then execute the next condition.
Cases where soft deletes cannot work at all:
Sign-up in majorly all websites take EmailID as your unique identification. We know very well, once an EmailID is used on a website like facebook, G+, it cannot be used by anyone else.
There comes a day when the user wants to delete his/her profile from the website. Now, if you make a logical delete, that user won't be able to register ever again. Also, registering again using the same EmailID wouldn't mean to restore the entire history. Everyone knows, deletion means deletion. In such scenarios, we have to make a physical delete. But in order to maintain the entire history of the account, we should always archive such records in either archive tables or deleted tables.
Yes, in situations where we have lots of foreign tables, handling is quite cumbersome.
Also keep in mind that soft/logical deletes will increase your table size, so the index size.
I have already answered in another post.
However, I think my answer more fit to the question here.
My practical solution for soft-delete is archiving by creating a new
table with following columns: original_id, table_name, payload,
(and an optional primary key `id).
Where original_id is the original id of deleted record, table_name
is the table name of the deleted record ("user" in your case),
payload is JSON-stringified string from all columns of the deleted
record.
I also suggest making an index on the column original_id for latter
data retrievement.
By this way of archiving data. You will have these advantages
Keep track of all data in history
Have only one place to archive records from any table, regardless of the deleted record's table structure
No worry of unique index in the original table
No worry of checking foreign index in the original table
No more WHERE clause in every query to check for deletion
The is already a discussion
here explaining why
soft-deletion is not a good idea in practice. Soft-delete introduces
some potential troubles in future such as counting records, ...
It depends on the case, consider the below:
Usually, you don't need to "soft-delete" a record.
Keep it simple and fast.
e.g. Deleting a product no longer available, so you don't have to check the product isn't soft-deleted all over your app (count, product list, recommended products, etc.).
Yet, you might consider the "soft-delete" in a data warehouse model. e.g. You are viewing an old receipt on a deleted product.*
Advantages are data preservation/perpetuation. A disadvantage would be a decrease in performance when querying or retrieving data from tables with significant number of soft deletes.
In our case we use a combination of both: as others have mentioned in previous answers, we soft-delete users/clients/customers for example, and hard-delete on items/products/merchandise tables where there are duplicated records that don't need to be kept.