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From David E Jones <d...@me.com>
Subject Re: Optimistic locking based on timestamps
Date Sat, 14 Aug 2010 15:46:35 GMT

Timestamp-based optimistic locking is a feature of the Entity Engine, but it is not used very
much in OFBiz. In fact, I'm not sure if it's used at all. The way it came into this discussion
was, I suppose, as a possible solution to the synchronization problems people were having
with race conditions.

As you mentioned here, which is correct, optimistic locking is really only helpful if two
people are possibly editing the same data at the same time and you want to notify a user if
another user has changed the data they are working on between the time they got the data from
the database, and the time they saved their changes to the database. With most manual editing,
as you mentioned, the reading and writing are done in two separate transactions, so that is
a case where a SELECT FOR UPDATE would not help. As you said, in order for that to be helpful
in the common case where optimistic locks are used the transaction would have to live for
many minutes and lock resources for that entire time (ie a pessimistic lock).

There are certainly cases where optimistic locks might be useful, and they would be things
mostly done manually like editing product information or any content that lives in the database.
Two people could accidentally be working on the same product or content at the same time,
and without optimistic locking the person who saved second would wipe out the changes of the
person who saved first, but neither would know it unless they manually review the data at
a later time. If pessimistic locking were used in these scenarios it would be like those REALLY
annoying old source repositories where if you check out a file it is "locked" and no one else
can change it until you check it back in and release the lock (ie they didn't bother to implement
any sort of merging). With the Entity Engine optimistic lock it won't try to do any merging,
the purpose is to notify the user that someone else changed the data they were working on
between the time they read the data to edit and the time they tried to save it (the separate
read and write transactions).

For many race conditions that cause bigger problems the scenario is very different. In your
example of order data that is likely to be very low conflict, but there are many data structures
that tend to be higher conflict, like inventory data. In order for there to be conflict in
inventory data all it takes is for two customers to order the same product at roughly the
same time (ie within the span of the time it takes the order transaction to execute, which
can be tens of seconds sometimes). For a popular item on a busy site this isn't just possible,
it's really likely. In this case optimistic locking wouldn't be that helpful, ie you don't
want the behavior where the system essentially says "someone else is ordering that product
right now, please try again later". What you would want is for the database to lock certain
records so that the second user waits until the first user makes any changes. And, what you
want them to wait on is being able to READ the data, not waiting to WRITE it. The common scenario
is that two different threads read the current inventory value, then both are working on things
including decrementing the inventory value, then both write it. In the end the result will
be wrong because they both started with the same value and subtracted from it, and basically
whoever writes first will have their value ignored and the total at the end will just be the
original value minus whatever the second thread to write subtracted.

That is a case where pessimistic locking is necessary, and a case where things aren't as simple
as they may seem.

To understand some of the complexity check out the concept of "transaction isolation". The
big trick is that for performance and concurrency reasons databases do NOT totally isolate
transactions and update conflicts can easily occur:


Many databases don't even support the more strict transaction isolation levels, and even if
they do they are not commonly used except for special purposes. With things like SERIALIZABLE
the problem is that you end up locking, in many cases, entire tables and not just rows within
those tables and you have HUGE concurrency and deadlock problems that result.

The most common level you'll see used is READ COMMITTED, and sometimes READ UNCOMMITTED if
the database doesn't support READ COMMITTED. You can see these settings in the entityengine.xml

That is where SELECT FOR UPDATE is useful. You don't want to use the SERIALIZABLE transaction
isolation, but you want this certain record locked even though it hasn't been changed so that
other transactions don't read the incorrect value.


On Aug 14, 2010, at 12:45 AM, Matt Warnock wrote:

> I'm still a bit confused.  I think I understand the issues, but not why
> so many people are apparently having trouble with them.  Or maybe I
> misunderstand them completely.
> Optimistic locking (as I understand it) is used primarily when editing
> an existing record by hand, since record creation and programmed updates
> can just use transactions, which are better for most operations anyway.
> Most common business cases I can imagine would not usually involve 2
> people editing (not just viewing) the same record at the same time.
> What business scenario causes these apparently common collisions?
> Most high-volume business uses don't edit other people's records.  If I
> enter an e-commerce order for example, I create the header record,
> several line item records, perhaps some other stuff.  Eventually I
> commit the whole order at once, when it is assigned an order number and
> becomes part of the main database, which can all be done in a single
> transaction.  
> Others may be entering similar orders, but they are creating different
> header records with different associated line items.  These records
> should all be accumulated into memory-only or temporary tables (I would
> assume) until they are committed to the database, and optimistic locking
> should never really enter into it, as these records are private to the
> user and current session (like an e-commerce shopping cart) until they
> are committed.  If they are abandoned before they commit, they should
> never leave a trace in the main database, as I see it.  Any code that
> updates the record (to total it, apply taxes, figure shipping, or
> whatever) can work in-memory, or in a single transaction on the
> temporary records, until the whole thing is committed.
> If I then go back and edit an order, it is usually one I just recently
> entered, and in most cases, no one else should be using it.  When I do
> that, the optimistic lock code should read the record data and note the
> time that the record was last modified (or the data itself). I then edit
> that data on-screen, and when I commit, it first checks to see that the
> data was not modified in the meantime.  In most cases, it wasn't
> modified, and the new data is written, again within the scope of a
> single transaction.
> If the last-modified date (or the original data) has changed, then a
> collision has occurred, and the system should cancel my commit, because
> I was editing data which has changed while I was editing it, and is now
> stale.  In most cases, any manual edit takes much more than a second, so
> the chance of a time granularity collision on an actual record edit
> seems miniscule. If there is a collision, the system re-reads the
> recently updated data, tells me about the collision, probably discards
> the previous edits, and I can then edit again if necessary. 
> It's a poor substitute for an update transaction, but you don't want to
> lock a database up for several minutes while a user edits a record by
> hand, and most transactions will timeout long before the user finishes
> the edit.
> Programmatic data updates like Mike Z describes are much more common,
> but they can usually be managed in a single transaction too.  I don't
> need a lock to calculate a total, enter a timestamp, or similar updates,
> as these can all be done inside an ACID transaction, thereby protected
> from other threads, users, application servers, or whatever.  We can
> even suspend one transaction to run an unrelated one, then resume the
> first, as David suggested earlier in this thread.
> Can you give me an example of the kind of update that leads to the kind
> of concurrency issues you describe?  Is OFBiz using optimistic locks
> where transactions are really required?  Or what about James' inventory
> count scenario prevents using a transaction instead of an optimistic
> lock?  What am I missing?  Just want to know where the big bear traps
> might be.  Thanks in advance.
> -- 
> Matt Warnock <mwarnock@ridgecrestherbals.com>
> RidgeCrest Herbals, Inc.
> On Fri, 2010-08-13 at 19:52 -0700, Mike Z wrote:
>> This has been a very useful thread.  I now know that I need to dump
>> MySQL asap.   I planned on running multiple ofbiz instances for
>> ecommerce and had no idea that this may cause issues.  Thanks for the
>> input.
>> On Fri, Aug 13, 2010 at 5:31 PM, Brett Palmer <brettgpalmer@gmail.com> wrote:
>>> James,
>>> We have run into this same problem on MySQL and ofbiz.  We worked around the
>>> problem by creating a custom method that got a direction connection from the
>>> transaction manager.  Then we wrote a custom SELECT for UPDATE on that
>>> connection.  We needed this functionality because we had multiple
>>> application servers hitting the same database and ran into concurrency
>>> problems without it.
>>> I would like to see the optimistic locking feature enhanced in ofbiz.  Maybe
>>> we could move away from timestamps and use an increasing unique ID as a
>>> replacement.  This is definitely a problem with MySQL.  We may move away
>>> from MySQL if we can find a good replication solution from Postgres.
>>> Brett
>>> On Thu, Aug 12, 2010 at 2:15 PM, James McGill <
>>> james.mcgill@ableengineering.com> wrote:
>>>> We are having problems with the optimistic locking.   With "enable-lock"
>>>> set
>>>> on an Entity, updates in GenericDAO use a timestamp to do locking.
>>>> There are a number of issues with this.  The biggest one is that it's not
>>>> synchronized operation, so there's potential for a race condition within
>>>> customUpdate, which we are actually seeing in production.
>>>> I added code to introduce the "FOR UPDATE" expression when reading the
>>>> timestamp.  This brings up another issue, that the timestamp field in MySQL
>>>> has resolution only to the second.  So even if you don't have contention
>>>> the optimistic lock SELECT, you still have to be lucky that your
>>>> transactions are more than one second apart.
>>>> I realize this is a fairly difficult problem to address, in general, and
>>>> that "fixing" many concurrency issues leads to risks of deadlock.  But we
>>>> are seeing errors in data where the "last update wins."
>>>> Has anyone else had concurrency problems when multiple threads are updating
>>>> entities?  Are there any locking provisions in the Delegator that would
>>>> allow us to prevent this kind of problem?
>>>> --
>>>> James McGill
>>>> Phoenix AZ

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