Postgres design solution to user limiting in rooms (race condition)

Postgres design solution to user limiting in rooms (race condition) - sql

I have a chatting application with rooms in which users can freely join/leave any time they want. Rooms are limited to 8 users at a single time.
For simplification purposes, I have this relationship right now:
User -> (Many to one) -> Room
I'm checking if the room is full by querying
SELECT COUNT(*) FROM users WHERE room_id = x
before inserting the user, which works in normal cases.
However, if everybody joins at the same time, then it gets to a race condition and the limit is bypassed. How should I tackle this issue? Is Postgres suitable for this kind of operation?

While not wishing to be unkind, the previous answer is far from optimal.
While you can do this...
LOCK TABLE users IN ACCESS EXCLUSIVE MODE;
This is clearly quite heavy handed and is preventing all updates to the users table whether related to room changes or not.
A lighter approach would be instead to lock just the data you care about.
-- Move user 456 from room 122 to room 123
BEGIN;
SELECT true FROM rooms WHERE id = 123 FOR UPDATE;
SELECT true FROM users WHERE id = 456 AND room_id = 122 FOR UPDATE;
-- If either of the above failed to return a row, the starting condition of your database is not what you thought. Rollback
SELECT count(*) FROM users WHERE room_id = 123;
-- check count looks ok
UPDATE users SET room_id = 123 WHERE id = 456;
COMMIT;
This will lock two crucial items:
The new room being moved to.
The user being moved between rooms.
Since you don't care about people being moved out of rooms, this should be enough.
If two separate connections try to move different people into the same room simultaneously, one will be forced to wait until the other transaction commits or rolls back and releases its locks. If two separate connections try to update the same person, the same will happen.
As far as the other answer is concerned
Of course during the period the table is locked, if some thread in your application is trying to write users, it will get back an error.
No, it won't. Not unless you hold the lock for long enough to time out or try to take your own lock and tell it not to wait.
I think that you should probably synchronise the access and the writing from your calling application
Relational databases have been expressly designed to handle concurrent access from multiple clients. It is one of the things they are inarguably good at. If you are already using a RDBMS and implementing your own concurrency control then you are either in an exceptional situation or aren't using your RDBMS properly.

Related

Get distinct sets of rows for an INSERT executed in concurrent transactions

I am implementing a simple pessimistic locking mechanism using Postgres as a medium. The goal is that multiple instances of an application can simultaneously acquire locks on distinct sets of users.
The app instances are not trying to lock specific users. Instead they will take any user locks they can get.
Say, for example, we have three instances of the app running and there are currently 5 users that are not locked. All three instances attempt to acquire locks on up to three users at the same time. Their requests are served in arbitrary order.
Ideally, the first instance served would acquire 3 user locks, the second would acquire 2 and the third would acquire no locks.
So far I have not been able to write a Query that accomplishes this. I'll show you my best attempt so far.
Here are the tables for the example:
CREATE TABLE my_locks (
id bigserial PRIMARY KEY,
user_id bigint NOT NULL UNIQUE
);
CREATE TABLE my_users (
id bigserial PRIMARY KEY,
user_name varchar(50) NOT NULL
);
And this is the Query for acquiring locks:
INSERT INTO my_locks(user_id)
SELECT u.id
FROM my_users AS u
LEFT JOIN my_locks
AS l
ON u.id = l.user_id
WHERE l.user_id IS NULL
LIMIT 3
RETURNING *
I had hoped that folding the collecting of lockable users and the insertion of the locks into the database into a single query would ensure that multiple simultaneous requests would be processed in their entirety one after the other.
It doesn't work that way. If applied to the above example where three instances use this Query to simultaneously acquire locks on a pool of 5 users, one instance acquires three locks and the other instances receive an error for attempting to insert locks with non-unique user-IDs.
This is not ideal, because it prevents the locking mechanism from scaling. There are a number of workarounds to deal with this, but what I am looking for is a database-level solution. Is there a way to tweak the Query or DB configuration in such a way that multiple app instances can (near-)simultaneously acquire the maximum available number of locks in perfectly distinct sets?

The locking clause SKIP LOCKED should be perfect for you. Added with Postgres 9.5.
The manual:
With SKIP LOCKED, any selected rows that cannot be immediately locked are skipped.
FOR NO KEY UPDATE should be strong enough for your purpose. (Still allows other, non-exclusive locks.) And ideally, you take the weakest lock that's strong enough.
Work with just locks
If you can do your work while a transaction locking involved users stays open, then that's all you need:
BEGIN;
SELECT id FROM my_users
LIMIT 3
FOR NO KEY UPDATE SKIP LOCKED;
-- do some work on selected users here !!!
COMMIT;
Locks are gathered along the way and kept till the end of the current transaction. While the order can be arbitrary, we don't even need ORDER BY. No waiting, no deadlock possible with SKIP LOCKED. Each transaction scans over the table and locks the first 3 rows still up for grabs. Very cheap and fast.
Since transaction might stay open for a while, don't put anything else into the same transaction so not to block more than necessary.
Work with lock table additionally
If you can't do your work while a transaction locking involved users stays open, register users in that additional table my_locks.
Before work:
INSERT INTO my_locks(user_id)
SELECT id FROM my_users u
WHERE NOT EXISTS (
SELECT FROM my_locks l
WHERE l.user_id = u.id
)
LIMIT 3
FOR NO KEY UPDATE SKIP LOCKED
RETRUNGING *;
No explicit transaction wrapper needed.
Users in my_locks are excluded in addition to those currently locked exclusively. That works under concurrent load. While each transaction is open, locks are active. Once those are released at the end of the transaction they have already been written to the locks table - and are visible to other transaction at the same time.
There's a theoretical race condition for concurrent statements not seeing newly committed rows in the locks table just yet, and grabbing the same users after locks have just been released. But that would fail trying to write to the locks table. A UNIQUE constraint is absolute and will not allow duplicate entries, disregarding visibility.
Users won't be eligible again until deleted from your locks table.
Further reading:
Postgres UPDATE ... LIMIT 1
Select rows which are not present in other table
Aside:
... multiple simultaneous requests would be processed in their entirety one after the other.
It doesn't work that way.
To understand how it actually works, read about the Multiversion Concurrency Control (MVCC) of Postgres in the manual, starting here.

SQL Server 2008 Express locking

OK so I have read a fair amount about SQL Server's locking stuff, but I'm struggling to understand it all.
What I want to achieve is thus:
I need to be able to lock a row when user A SELECTs it
If user B then tries to SELECT it, my winforms .net app needs to set all the controls on the relevant form to be disabled, so the user can't try and update. Also it would be nice if I could throw up a messagebox for user B, stating that user A is the person that is using that row.
So basically User B needs to be able to SELECT the data, but when they do so, they should also get a) whether the record is locked and b) who has it locked.
I know people are gonna say I should just let SQL Server deal with the locking, but I need User B to know that the record is in use as soon as they SELECT it, rather than finding out when they UPDATE - by which time they may have entered data into the form, giving me inconsistency.
Also any locks need to allow SELECTs to still happen - so when user B does his SELECT, rather than just being thrown an exception and receiving no/incomplete data, he should still get the data, and be able to view it, but just not be able to update it.
I'm guessing this is pretty basic stuff, but there's so much terminology involved with SQL Server's locking that I'm not familiar with that it makes reading about it pretty difficult at the moment.
Thanks

To create this type of 'application lock', you may want to use a table called Locks and insert key, userid, and table names into it.
When your select comes along, join into the Locks table and use the presence of this value to indicate the record is locked.
I would also recommend adding a 'RowVersion' column to your table you wish to protect. This field will assist in identifying if you are updating or querying a row that has changed since you last selected it.

This isn't really what SQL Server locking is for - ideally you should only be keeping a transaction (and therefore a lock) open for the absolute minimum needed to complete an atomic operation against that database - you certainly shouldn't be holding locks while waiting for user input.
You would be better served keeping track of these sorts of locks yourself by (for example) adding a locked bit column to the table in question along with a locked_by varchar column to keep track of who has the row locked.
The first user should UPDATE the row to indicate that the row is locked and who has it locked:
UPDATE MyTable
SET `locked` = 1
AND `locked_by` = #me
WHERE `locked` = 0
The locked = 0 check is there to protect against potential race conditions and make sure that you don't update a record that someone else has already locked.
This first user then does a SELECT to return the data and ensure that they did really manage to lock the row.

Deleting rows from a contended table

I have a DB table in which each row has a randomly generated primary key, a message and a user. Each user has about 10-100 messages but there are 10k-50k users.
I write the messages daily for each user in one go. I want to throw away the old messages for each user before writing the new ones to keep the table as small as possible.
Right now I effectively do this:
delete from table where user='mk'
Then write all the messages for that user. I'm seeing a lot of contention because I have lots of threads doing this at the same time.
I do have an additional requirement to retain the most recent set of messages for each user.
I don't have access to the DB directly. I'm trying to guess at the problem based on some second hand feedback. The reason I'm focusing on this scenario is that the delete query is showing a lot of wait time (again - to the best of my knowledge) plus it's a newly added bit of functionality.
Can anyone offer any advice?
Would it be better to:
select key from table where user='mk'
Then delete individual rows from there? I'm thinking that might lead to less brutal locking.

If you do this everyday for every user, why not just delete every record from the table in a single statement? Or even
truncate table whatever reuse storage
/
edit
The reason why I suggest this approach is that the process looks like a daily batch upload of user messages preceded by a clearing out of the old messages. That is, the business rules seems to me to be "the table will hold only one day's worth of messages for any given user". If this process is done for every user then a single operation would be the most efficient.
However, if users do not get a fresh set of messages each day and there is a subsidiary rule which requires us to retain the most recent set of messages for each user then zapping the entire table would be wrong.

No, it is always better to perform a single SQL statement on a set of rows than a series of "row-by-row" (or what Tom Kyte calls "slow-by-slow") operations. When you say you are "seeing a lot of contention", what are you seeing exactly? An obvious question: is column USER indexed?
(Of course, the column name can't really be USER in an Oracle database, since it is a reserved word!)
EDIT: You have said that column USER is not indexed. This means that each delete will involve a full table scan of up to 50K*100 = 5 million rows (or at best 10K * 10 = 100,000 rows) to delete a mere 10-100 rows. Adding an index on USER may solve your problems.

Are you sure you're seeing lock contention? It seems more likely that you're seeing disk contention due to too many concurrent (but unrelated updates). The solution to that is simply to reduce the number of threads you're using: Less disk contention will mean higher total throughput.

I think you need to define your requirements a bit clearer...
For instance. If you know all of the users who you want to write messages for, insert the IDs into a temp table, index it on ID and batch delete. Then the threads you are firing off are doing two things. Write the ID of the user to a temp table, Write the message to another temp table. Then when the threads have finished executing, the main thread should
DELETE * FROM Messages INNER JOIN TEMP_MEMBERS ON ID = TEMP_ID
INSERT INTO MESSAGES SELECT * FROM TEMP_messges
im not familiar with Oracle syntax, but that is the way i would approach it IF the users messages are all done in rapid succession.
Hope this helps

TALK TO YOUR DBA
He is there to help you. When we DBAs take access away from the developers for something such as this, it is assumed we will provide the support for you for that task. If your code is taking too long to complete and that time appears to be tied up in the database, your DBA will be able to look at exactly what is going on and offer suggestions or possibly even solve the problem without you changing anything.
Just glancing over your problem statement, it doesn't appear you'd be looking at contention issues, but I don't know anything about your underlying structure.
Really, talk to your DBA. He will probably enjoy looking at something fun instead of planning the latest CPU deployment.

This might speed things up:
Create a lookup table:
create table rowid_table (row_id ROWID ,user VARCHAR2(100));
create index rowid_table_ix1 on rowid_table (user);
Run a nightly job:
truncate table rowid_table;
insert /*+ append */ into rowid_table
select ROWID row_id , user
from table;
dbms_stats.gather_table_stats('SCHEMAOWNER','ROWID_TABLE');
Then when deleting the records:
delete from table
where ROWID IN (select row_id
from rowid_table
where user = 'mk');

Your own suggestion seems very sensible. Locking in small batches has two advantages:
the transactions will be smaller
locking will be limited to only a few rows at a time
Locking in batches should be a big improvement.

Best way to do 10,000 inserts in a SQL db?

What happens is whenever i upload media onto my site; everyone will get a notification. Each person can click a box to remove and it will be gone from their msg queue forever.
If i had 10,000 people on my site how would i add it to every person msg queue? I can imagine it takes a lot of time so would i opt for something like a filesystem journal? mark that i need to notify people, the data then my current position. Then update my position every 100 inserts or so? I would need a PK on my watcher list so if anyone registers in the middle of it my order will not be broken since i'll be sorting via PK?
Is this the best solution for a mass notification system?
-edit-
This site is a user created content site. Admins can send out global messages and popular people may have thousands of subscribers.

If 10000 inserts into a narrow many to many table linking the recipients to the messages (recipientid, messageid, status) is slow, I expect you've get bigger problems with your design.
This is the kind of operation I wouldn't typically even worry about batching or people subscribing in the middle of the post operation - basically:
Assuming #publisherid known, #msg known on SQL Server:
BEGIN TRANSACTION
INSERT INTO msgs (publisherid, msg)
VALUES(#publisherid, #msg)
SET #messageid = SCOPE_IDENTITY()
INSERT INTO msqqueue (recipientid, messageid, status)
SELECT subscriberid, #messageid, 0 -- unread
FROM subscribers
WHERE subscribers.publisherid = #publisherid
COMMIT TRANSACTION

Maybe just record for each user which notifications they have seen- so the set of notifications to show to a user are ones created before their "earliest_notification" horizon (when they registered, or a week ago...) minus the ones they have acknowledged. That way you delay inserting anything until it's a single user at once- plus if you only show users messages less than a week old, you can purge the read-this-notification flags that are a week or more old.
(Peformance optimisation hint from my DBA at my old job: "business processes are the easiest things to change, look at them first")

I'd say just let the database take care of things it is well capable of and designed to do. Insert and manage data. Don't try and do it in code, just write the SQL to insert the data all in one go. 10000 rows is a diddle for all real databases.

IMHO inserting records may not be the most efficient solution to this problem. Can you use a client side cookie to store whether the user removed the notification or do you have to keep track of this even if they clear cookies? If you upload a new video the app can just compare the cookie with the new video record id and decide to show or hide the notification based on whats stored in the cookie. This will save you a ton of database inserts, and keeps most heavy lifting on the client.

If you're using Postgres, you can use the COPY command which is the fastest method of them all:
COPY tablename (col1, col2, col3) FROM '/path/to/tabfile';
where tabfile is a TAB-separated file with lots of entries. This will fail if there are some UNIQUE constraints, though, and there are duplicates in the file.

Physical vs. logical (hard vs. soft) delete of database record? [closed]

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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.

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