I have a table that can grew to millions records (50 millions for example). On each 20 minutes records that are older than 20 minutes are deleted.
The problems is that if the table has so many records such deletion can take a lot of time and I want to make it faster.
I can not do "truncate table" because I want to remove only records that are older than 20 minutes. I suppose that when doing the "delete" and filtering the information that need to be delete, the server is creating log file or something and this take much time?
Am I right? Is there a way to stop any flag or option to optimize the delete, and then to turn on the stopped option?
To expand on the batch delete suggestion, i'd suggest you do this far more regularly (every 20 seconds perhaps) - batch deletions are easy:
WHILE 1 = 1
BEGIN
DELETE TOP ( 4000 )
FROM YOURTABLE
WHERE YourIndexedDateColumn < DATEADD(MINUTE, -20, GETDATE())
IF ##ROWCOUNT = 0
BREAK
END
Your inserts may lag slightly whilst they wait for the locks to release but they should insert rather than error.
In regards to your table though, a table with this much traffic i'd expect to see on a very fast raid 10 array / perhaps even partitioned - are your disks up to it? Are your transaction logs on different disks to your data files? - they should be
EDIT 1 - Response to your comment
TO put a database into SIMPLE recovery:
ALTER DATABASE Database Name SET RECOVERY='SIMPLE'
This basically turns off transaction logging on the given database. Meaning in the event of data loss you would need loose all data since your last full backup. If you're OK with that, well this should save a lot of time when running large transactions. (NOTE that as the transaction is running, the logging still takes place in SIMPLE - to enable the rolling back of the transaction).
If there are tables within your database where you cant afford to loose data you'll need to leave your database in FULL recovery mode (i.e. any transaction gets logged (and hopefully flushed to *.trn files by your servers maintenance plans). As i stated in my question though, there is nothing stopping you having two databases, 1 in FULL and 1 in SIMPLE. the FULL database would be fore tables where you cant afford to loose any data (i.e. you could apply the transaction logs to restore data to a specific time) and the SIMPLE database would be for these massive high-traffic tables that you can allow data loss on in the event of a failure.
All of this is relevant assuming your creating full (*.bak) files every night & flushing your log files to *.trn files every half hour or so).
In regards to your index question, it's imperative your date column is indexed, if you check your execution plan and see any "TABLE SCAN" - that would be an indicator of a missing index.
Your date column i presume is DATETIME with a constraint setting the DEFAULT to getdate()?
You may find that you get better performance by replacing that with a BIGINT YYYYMMDDHHMMSS and then apply a CLUSTERED index to that column - note however that you can only have 1 clustered index per table, so if that table already has one you'll need to use a Non-Clustered index. (in case you didnt know, a clustered index basically tells SQL to store the information in that order, meaning that when you delete rows > 20 minutes SQL can literally delete stuff sequentially rather than hopping from page to page.
The log problem is probably due to the number of records deleted in the trasaction, to make things worse the engine may be requesting a lock per record (or by page wich is not so bad)
The one big thing here is how you determine the records to be deleted, i'm assuming you use a datetime field, if so make sure you have an index on the column otherwise it's a sequential scan of the table that will really penalize your process.
There are two things you may do depending of the concurrency of users an the time of the delete
If you can guarantee that no one is going to read or write when you delete, you can lock the table in exclusive mode and delete (this takes only one lock from the engine) and release the lock
You can use batch deletes, you would make a script with a cursor that provides the rows you want to delete, and you begin transtaction and commit every X records (ideally 5000), so you can keep the transactions shorts and not take that many locks
Take a look at the query plan for the delete process, and see what it shows, a sequential scan of a big table its never good.
Unfortunately for the purpose of this question and fortunately for the sake of consistency and recoverability of the databases in SQL server, putting a database into Simple recovery mode DOES NOT disable logging.
Every transaction still gets logged before committing it to the data file(s), the only difference would be that the space in the log would get released (in most cases) right after the transaction is either rolled back or committed in the Simple recovery mode, but this is not going to affect the performance of the DELETE statement in one way or another.
I had a similar problem when I needed to delete more than 70% of the rows from a big table with 3 indexes and a lot of foreign keys.
For this scenario, I saved the rows I wanted in a temp table, truncated the original table and reinserted the rows, something like:
SELECT * INTO #tempuser FROM [User] WHERE [Status] >= 600;
TRUNCATE TABLE [User];
INSERT [User] SELECT * FROM #tempuser;
I learned this technique with this link that explains:
DELETE is a a fully logged operation , and can be rolled back if something goes wrong
TRUNCATE Removes all rows from a table without logging the individual row deletions
In the article you can explore other strategies to resolve the delay in deleting many records, that one worked to me
I have a single process that queries a table for records where PROCESS_IND = 'N', does some processing, and then updates the PROCESS_IND to 'Y'.
I'd like to allow for multiple instances of this process to run, but don't know what the best practices are for avoiding concurrency problems.
Where should I start?
The pattern I'd use is as follows:
Create columns "lockedby" and "locktime" which are a thread/process/machine ID and timestamp respectively (you'll need the machine ID when you split the processing between several machines)
Each task would do a query such as:
UPDATE taskstable SET lockedby=(my id), locktime=now() WHERE lockedby IS NULL ORDER BY ID LIMIT 10
Where 10 is the "batch size".
Then each task does a SELECT to find out which rows it has "locked" for processing, and processes those
After each row is complete, you set lockedby and locktime back to NULL
All this is done in a loop for as many batches as exist.
A cron job or scheduled task, periodically resets the "lockedby" of any row whose locktime is too long ago, as they were presumably done by a task which has hung or crashed. Someone else will then pick them up
The LIMIT 10 is MySQL specific but other databases have equivalents. The ORDER BY is import to avoid the query being nondeterministic.
Although I understand the intention I would disagree on going to row level locking immediately. This will reduce your response time and may actually make your situation worse. If after testing you are seeing concurrency issues with APL you should do an iterative move to “datapage” locking first!
To really answer this question properly more information would be required about the table structure and the indexes involved, but to explain further.
DOL, datarow locking uses a lot more locks than allpage/page level locking. The overhead in managing all the locks and hence the decrease of available memory due to requests for more lock structures within the cache will decrease performance and counter any gains you may have by moving to a more concurrent approach.
Test your approach without the move first on APL (all page locking ‘default’) then if issues are seen move to DOL (datapage first then datarow). Keep in mind when you switch a table to DOL all responses on that table become slightly worse, the table uses more space and the table becomes more prone to fragmentation which requires regular maintenance.
So in short don’t move to datarows straight off try your concurrency approach first then if there are issues use datapage locking first then last resort datarows.
You should enable row level locking on the table with:
CREATE TABLE mytable (...) LOCK DATAROWS
Then you:
Begin the transaction
Select your row with FOR UPDATE option (which will lock it)
Do whatever you want.
No other process can do anything to this row until the transaction ends.
P. S. Some mention overhead problems that can result from using LOCK DATAROWS.
Yes, there is overhead, though i'd hardly call it a problem for a table like this.
But if you switch to DATAPAGES then you may lock only one row per PAGE (2k by default), and processes whose rows reside in one page will not be able to run concurrently.
If we are talking of table with dozen of rows being locked at once, there hardly will be any noticeable performance drop.
Process concurrency is of much more importance for design like that.
The most obvious way is locking, if your database doesn't have locks, you could implement it yourself by adding a "Locked" field.
Some of the ways to simplify the concurrency is to randomize the access to unprocessed items, so instead of competition on the first item, they distribute the access randomly.
Convert the procedure to a single SQL statement and process multiple rows as a single batch. This is how databases are supposed to work.
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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.