The 4 laws of Durability

When it comes to having durable data, there are four ways to do it: undo log, redo log, shadow copy and shadow data.

 

Let's start with the preliminaries.

So what do we mean by "durable"?

Well, durable means that whatever data you're trying to save, has reached your storage device in a consistent way. It means that when you write to storage you want it to be "permanent", whether that storage device is a USB key, a CD ROM, an hard drive, and SSD, or a non-volatile memory DIMM like Intel's Optane DC PM.

For any of these storage devices, the algorithms are always the same: you have to use one of the four mentioned above.

Keep in mind that these are needed if the data needs to be consistent, i.e. you want to see the whole data before the storage or none of the data. I mean, if we were ok with having garbled data, then why would we bother saving it in permanent storage? The whole point of making data durable is because it has important information and therefore, it implies consistency.

 

Now that the basics are out of the way, what are exactly these four algorithms?

I'm going to focus on these in the context of transactions, but they don't have to be necessarily about that.

 

Undo log is technique where we write to durable storage a log entry before each write is done to storage. It allows multiple independent (non-atomic) writes to become durable in an all-or-nothing way, like a transaction, or a checkpoint.

In the context of persistent memory, libpmemobj in PMDK is an example of a transactional system that uses undo log.

 

In Redo log we write the log with multiple entries to storage before writing the actual data. The difference between redo and undo is that undo log does one entry in the log at a time followed by one modification, while the redo log does all entries in the log in one shot and then all the modifications in one shot.

Mnemosyne and OneFile are examples of transactional systems that utilize redo log.

 

Shadow copy, sometimes called Copy-On-Write (COW) creates a new replica of the data and writes the new data along with the unchanged contents to durable storage, before swapping some kind of pointer to indicate the this is the new object/data and the old one can be discarded. COW can't really be used by itself for transactions over multiple objects, but it can be combined with redo log to make it more efficient.

One example is SAP HANA which uses redo log with COW.

 

Shadow data can sometimes be confused with COW but it is not the same thing. In shadow data two (or more) replicas of the entire data are kept in durable storage and they both are updated with the modifications, one at a time. First one replica, then a logical pointer and then the second replica. On the next set of atomic writes the recently updated replica is the first to be updated.

Examples of shadow data transactional systems are Romulus, RedoDB and Trinity to some extent.

 

 

We though long and hard at the similarities and differences between these four algorithms for durable transactions, and we found they possess four common characteristics, regardless of the underlying storage media for which they are intended.

Each one of these characteristics reveals an important insight into the concept of durability and we believe these to be empirical rules to which all durable techniques abide. These rules are:

1.  There must be a replica of the data;
2.  There must be a durable state indicating which of the replicas is consistent;
3. All algorithms require at least one ordering constraint of the writes to durable storage; 
4. A modification is durable only after a round-trip fence to the storage hardware;

 

 

The first key insight regarding durable transactions is that a consistent and durable replica of the data must exist at all times. This replica may be a full copy of the data, such as on shadow data, or it may be a logical replica, such as on undo log and redo log.

Intuitively, there has to be a consistent replica of the data, so that there is a way to recover data to its original consistent state in the event of a failure. Shadow data keeps a full replica of the data thus incurring a high permanent usage of the durable media (space amplification), while the undo log and redo log approaches have to write in durable storage, not just the new data but also, encoded information about the location and size of the modification (write amplification).

There's clearly an important trade-off here: log-based algorithms will increase (amortized) write amplification but shadow-data-based algorithms will increase space amplification.

 

The second empirical rule implies that the algorithm must ensure that, irrespective of when a failure occurs, there is a way for the recovery procedure to determine which of the replicas is consistent.

Shadow data like Romulus uses a two-bit variable to determine which of the two replicas is consistent, while redo log and undo log can use the size of the log (zero or non-zero) to indicate if the log is consistent.

By itself, there is no significant difference in any of the approaches however, the exact mechanics, will influence the number of ordering constraints in the algorithm.

 

This leads us to the third insight, that data consistency is possible only through ordering of some of the writes.

For shadow-copying, the modifications on the new block must be made durable before the pointer swap, otherwise a failure occurring after the pointer swap is made durable, would leave the pointer referencing an inconsistent block. This means that apart from block allocation and de-allocation details, shadow-copying has a single ordering constraint, or in other words, a single ordering fence.

Shadow data like Romulus uses a two-bit state (though one bit would suffice) to indicate which of the two replicas is the consistent one, or whether both are consistent. If the state variable indicating which replica is the consistent one becomes durable before or after the modifications on either replica and a crash occurs, upon recovery it may be referencing the inconsistent replica. For this algorithm, three ordering constraints exist: one to prevent the state from changing to COPYING before the modifications in main replica are done; another to prevent the modifications in back replica from being done before the state changes to COPYING; and another one to prevent the state change to IDLE before the changes on back replica are durable.

The undo log technique has two constraints per modified object/range: the log entry must contain the old value before the entry is added to the log; and the entry must be added to the log before the modification is done on the data. Undo log has one extra constraint per transaction, requiring the last modification to be durable before the log is reset.

The Redo log technique has three constraints per transaction: all the log entries must be durable before the log size is set; the log size must be set before the modifications are done on the data; the modifications on the data must be durable before the log is reset.

 

The fourth and final rule addresses the need for a round-trip synchronization mechanism to the storage domain, such that the hardware can guarantee that it contains, in stable durable storage, all the previously written data. The cost of such a fence is typically of the order of the storage device's latency.

Fast devices like PM implement this round-trip fence orders of magnitude faster than slower devices, like hard drives.

Without such a mechanism, it is not possible to have durable operations, even if ordering constraints are set: in the event of a failure, the ordering constraints impose a temporal sequence of which the writes will be made durable, but there is no guarantee on durability.

A corollary of this is that all algorithms require one and only one such fence, strategically placed.

Notice that the ordering constraints may be replaced by such synchronous fences, at the detriment of performance, and in fact, many storage systems make no distinction between the two. Ordering is typically achieved with an asynchronrous fence and it relates to the order to which certain writes will be made durable in the storage media.

On block based storage, this is typically implemented with fsync() or fdatasync().

In Persistent Memory (PM) ordering can be achieved through the combination of flushes (clwb) and fences (sfence) or by writing to the same cache line. The round-trip guarantee of durability is given by a synchronous fence, either fsync()/fdatasync() on block storage, or sfence on PM storage.

 

In case you haven't noticed, the fact that all algorithms require one round-trip fence to the device (psync), but may require multiple ordering fences (pfence) has implications in performance. This is specially true given that the psync has inescapable physical implications: it is not possible to have all-or-nothing consistent durability without a psync that physically does a round trip to the storage device (or at least the storage domain) and therefore the latency cost of this single round trip is inescapable.

However, different algorithms may have different ordering constraints (pfences) and these may have different costs.

 

Yes, fsync() is used for both sync and ordering on block devices, and the sfence instruction is also used for both in PM, however, there are tricks. In PM, writes to the same cache line are guaranteed to be ordered and therefore, no sfence is needed to order them, as long as store with memory_order_release is used.

Seen as these round trips are typically the bottleneck when doing random writes to PM, the fact that we can create an algorithm with a lower number of psyncs means we can have a performance gain that is nearly proportional to the reduction in the number of such fences.

 

This is exactly what we've done with Trinity.

Trinity is a novel durability technique that needs just two fences per transaction and reduces the number of flushes when doing random writes. It consumes more memory than the other previous techniques but it has significant higher performance.

Moreover, we combined it with our own variant of TL2 for highly scalable durable linearizable transactions, and we used that to make a K/V store, which is likely that fastest K/V store on the planet with full transactions (though you need Optane Persistent Memory to be able to run it).

 

If you want to see the video, it's here:

https://youtu.be/vdInrf_kk1w

 

If you want the source code, it's here:

https://github.com/pramalhe/durabletx/blob/master/ptms/trinity/TrinityVRTL2.hpp

Dealing with bugs in Concurrency and Durability

Everyone knows that Concurrency is hard, in fact, it's NP-hard: the total number of interleavings typically grow exponentially with the number of lines of code (LOC) in a program. Determining if any of these interleavings causes incorrect behavior, becomes problematic for anything but very small programs.
This never prevented anyone from writing large multi-threaded programs, and the usual trick is to use locks. A code block surround by a lock does not have interleavings with other code blocks for the same lock, and the problem becomes "tractable" again.

Unfortunately, we can't use this trick for lock-free and wait-free code. Lock-free algorithms are typically done with atomic instructions that can interleave with each other.
Verifying correctness of these algorithms is a difficult problem.
There are static verification tools like SPIN, JSF, TLA+ and others, but such tools only work for a small number of threads (no more than 6 threads) and small-size programs (less than 100 LOC). As if this wasn't bad enough, these tools enforce the user to write the program in a particular language, apart from JSF, which is almost Java. The program will have to be re-written later in the target language that is meant for, and during this "translation" errors can be introduced.

Now, don't get me wrong, software has bugs. It's just part of life as a software developer to deal with them and try to minimize them.
Entire industry trends like Test Driven Development (TDD) and Test Automation revolve around the concept of testing to reduce the amount of bugs.
In other words, there's this idea that the more time you invest in tests, the more quality the final program will have. IMO this is true, but obviously it's not a linear effort: beyond a certain number of tests, the number of new bugs you'll detect will be small compared to the effort of maintaining those tests.

Concurrency bugs can, and should, also be approached in this manner, but this is far from sufficient.
Moreover, testing a concurrent program requires a different set of techniques and mindset than testing a sequential program: instead of exercising input-output responses, we should test program invariants; instead of mocking components, we should stress the code by increasing the number of threads or changing timings to exercise different interleavings and code paths.
This is where run-time verification can provide a good help. Tools like valgrind and Address Sanitizer become a need-to-have if you're writing C/C++ multi-threaded applications and there is a good deal of research being done on this field, so I'm sure that more capabilities and more efficient tools will show up in the future. However...

Suppose I have a new lock-free algorithm and I implement it. Then I bother to write some tests for it (which most researchers won't do) and then run it with address sanitizer and whatever other run-time tools I can get my hands on. None of my tests or run-time tools encounter any error. Now what? Is my algorithm ready for publication? How will the peer reviewers know that it is correct? How will I convince them of that?
And here lies the big problem in researching concurrent algorithms that people don't usually talk about: "How do I know if the lock-free algorithm in this paper is correct?"

As I said before, it's ok to have bugs in your code, all software has bugs, and software developers are used to this inevitability.
What is not ok is to have bugs in your algorithm. It's very hard to prove the correctness of a lock-free algorithm. Although it's usually simple to verify that a bug exists, once someone tells you the sequence of steps to reproduce that bug.

This can lead to surreal scenarios that typically look like this:
Me: Read the latest paper on a novel lock-free algorithm. Spend weeks thinking about it. Come up with a scenario where the algorithm doesn't work. Send an email to authors describing the scenario.
Authors: Spend a week thinking if the scenario is actually a bug and how to fix it, propose a slight variation of the algorithm.
Me: Spend another week thinking about it, come up with a difference scenario where there is a race condition.
Authors: Spend another week thinking about it and propose a modified algorithm.
Me: Spend another week thinking about this different algorithm. Get to the conclusion that the algorithm is no longer lock-free. Send a new email explaning why.
Authors: Spend another week thinking about it and propose yet another version of the algorithm.
Me: Spend another week thinking about it, only to reach the conclusion that this new algorithm is not even linearizable.
Authors: We weren't aiming for linearizability anyways...
Me: Lose all faith on the work done in the field of concurrent algorithms, and lose faith in mankind in general... at least for a couple of weeks, then I read another paper that looks interesting and the cycle restarts.

This can sound bad but it's a description of when things turn out good. Other times I send the email and the authors just ignore me, or they answer something that shows that they don't even understand there is a bug, or there is no bug and the whole things was a result of me missing some vital detail in the algorithm.
Ultimately, either I shame the authors by pointing out their mistakes or I shame myself for being too dumb to understand their paper. Never a good turnout and it's not a good way of "making friends". Unfortunately I can't help myself, I'm in it because I want to learn, and you learn the most when you're willing to make a fool of yourself  ;)

This is not about incompetence, this is about the inherent difficulty in writing lock-free algorithms. The authors are not sure that their algorithms are correct, the reviewers are not sure that the algorithms are correct and the readers of the papers are not sure that the algorithms are correct.  And don't get me started on the actual "implementations" of these algorithms.

It's a mess, and it creates a lot of distrust in the field, specially during peer revieweing.
Papers end up being accepted only if there is an expert reviewer that really knows about this stuff and decides to spend the several weeks of his personal time it takes to fully understand that algorithm and then comes back to the other reviewers and manages to convince them that this stuff is correct.
Most of the times, academic papers gets accepted just because they're not obviously wrong. But the problem is that may be simply because the algorithm is so complex that no one is willing to "dig into it". I've seen examples of papers accepted into major conferences, where the basic algorithm was wrong (and there is not obvious way of fixing it) and then the authors will continue convinced that approach is valid and correct and write follow up papers that build on that algorithm and those papers continue to be accepted in major conferences, when it's all crap.
Luckily, this is rare. Most of the bugs I've seen in lock-free algorithms are things that can be fixed without a major re-design of the algorithm.

There is also the effect that if the algorithm is too simple (there aren't many of those, but still) then the reviewers may think that the work doesn't have enough value. But if the algorithm is too complex, then they wouldn't be able to understand if it is correct or not.
This kind of creates the incentive for papers to have incremental algorithms based on prior published work, or to have several simple algorithms as opposed to a single complex one.

I've noticed that when it comes to durability (persistence), the problem is similar.
Durable algorithms are not NP-Hard, the failure can occur at a single point in the code, which means that the complexity is linearly proportional to the LOCs, as opposed to concurrency algorithms where the complexity grows exponentially with the LOCs in the algorithm.
However, the pattern of me reading papers on durable algorithms and then sending emails and then the authors fixing but not really fixing is pretty much the same.

It's still a hard problem, particularly now with Optane DC Persistent Memory that has a special set of rules for data to be correctly persisted.
On Persistent Memory it is very easy to forget a flush or a persistent fence. This means the implementation is incorrect, however, in practice it may have no issue, because the probability of having a crash exactly in the place where the fence is missing is very low.
On the other hand, if there are fences missing all over the place or if there is something being used that simply doesn't make sense, then no matter where the program crashes, the data will end up corrupted. So there are different shades of grey here.

Again here the problem repeats itself when it comes to getting a paper accepted at a conference: unless one of the reviewers really knows its stuff and is willing to invest the time it takes to deep dive on the algorithm presented on the paper, getting a "correct" durable algorithm accepted is just a game of luck. The best that can be done is to make a convincing proof or at least explain some invariants.

I believe that as time goes by, there will be better tools that will help writing and verifying correct durable algorithms. The thing is, by the time that happens (years from now), all or most of the interesting durable algorithms will be published by then, so it kind of defeats the purpose. Moreover, for the complicated stuff we can always use transactional durable techniques (undo-log, redo-log, copy-on-write, shadow-data) or a universal construction, like the one we presented at EuroSys2020 : link here.

For concurrency it's really black and white: you miss a fence, you're algorithm is wrong and you WILL see a bug happen eventually.
I don't see any real solution for concurrent algorithms coming anytime soon. And that's a shame because as long as things remain the way they are, it will be very hard for the good (correct) algorithms to be distinguished from the bad (incorrect) ones. It becomes a kind of "reputation game" where papers from people who have made correct algorithms in the past, will likely have correct algorithms later as well, but this is not really reliable seen as we're all humans we're bound to make mistakes at some point. For now, all we can do is test our implementations of these algorithms as extensively as possible so as to minimize the chances of bugs, but it will never be 100% certain.

This discussion may seem a bit boring: software will always have bugs, so what's the problem with having buggy algorithms?
My argument is that it's a big deal, actually, it's a very BIG deal!I've seen many papers that build on previous papers and then I go look at the previous work on which they're based, and those are incorrect. We need solid foundations on which to build, to be able to progress further and faster.
If we don't give the proper value to the correct algorithms and point out the incorrect ones, then we're creating the wrong incentive. Researchers will not bother to spend the time testing or validating their algorithms. This creates the climate of suspicion that we're in today.

Ultimately, designing lock-free algorithms will never be "easy", but designing lock-free and persistent data structures is easy when you use universal constructions or a lock-free software transactional memory. This means all these are problems are an issue only for the researchers of concurrent algorithms, but not really a problem for people that need a peristent or lock-free data structure.

Why use a PTM in your application?

When adapting or creating new applications that use Persistent Memory (PM), developers are faced with two main choices: using a PTM or manually placing flushes and fences.

If they chose hand-placed fences, they have to worry about the correct placement of the clwbs, sfences and, in which situations are non-temporal stores a better choice performance-wise.
Placing these fences correctly can be a difficult task that even expert researchers will occasionally get wrong.
One incorrect fence or flush is enough the prevent the recovery of the application in the event of a crash, thus losing or even corrupting data in PM permanently.
Moreover, dealing with concurrency must also be done by hand, yet another non-trivial task.
Finding expert developers that are capable of dealing with durability and concurrency is likely the reason why database engines are a costly piece of software that requires a team of experts to assemble.

If the application developer chooses instead to use a PTM, concerns of how to make the code durable and concurrent, disappear.
The developer's task now becomes the identification of which blocks of code and data must be accessed in an atomic way, and encapsulate those inside a transaction. Notice that identifying atomicity of data is a task required even when choosing the hand-placement of flushes and fences.
Another advantage of using a PTM is that any modifications to allocator metadata during allocation or de-allocation, becomes part of the transaction, implying that there will be no persistent memory leaks due to loss of metadata when a crash occurs.
For the end-developer, the code within a transaction block can be reasoned about as if it were sequential.
This shift of complexity from the application developer to the PTM library implementer, tremendously increases development speed for the application developer, reduces bug count and improves code maintainability.

Recovery on a hand-made durable data structure versus recovery on a Persistent Transactional Memory

A durable data structure is a data structure in Persistent Memory (PM) or Non-Volatile Memory, if you prefer.
These data structures have to be resilient to failures. In other words, if there is a power cut during an operation on the data structure, the data structure must be correctly recovered after the system restarts.
For the rest of this post, and likely for all other posts you read in this site, when I say "durable data structure" I mean a data structure that has "atomic durability": In the event of a failure, the side effects of all completed operations will be visible upon restart and recovery.

The are two main approaches to having a durable data structure in PM:
1) Write an algorithm for a new data structure. This algorithm has to be failure-resilient;
2) Write a regular data structure and wrap it in a PTM;

Making a new data structure is typically the subject of an entire research paper, while using a PTM is not. Researchers tend to lean on doing new data structures because it's more fun (we can't get a paper accepted where we describe "using" a data structure). Which approach is better depends on what you mean by "better", but my favorite is 2).

However, set's say you decide to use a hand-made durable data structure that someone else made. Now how do you use it in your program?

Most applications have many data structure instances. For an application that needs durability (or persistence, if you prefer) not all data needs to be persistent, which means that not all data structures need to be persistent either. However, let's assume it is more than 1 data structure instance.
When a crash occurs, it could have occurred during an operation on *any* of these data structures. This means we need to call the recovery() method for each of these data structure instances, just in case.

This by itself creates a nuisance, because now you need to have an explicit list of all the durable data structures in your application and iterate through all in your recovery code:
void recovery() {   // called after a restart
  ds1.recovery();
  ds2.recovery();
  ds3.recovery();
  ...
}
If you forget to add one of the data structures to the main recovery method, you risk doing operations on an inconsistent data structure and further corrupting your data. In PM, when you corrupt the data it's gone forever... unless you had backups, and even then you're going to lose all data from the last non-corrupt backup.

Another difficulty is that sometimes the number of durable data structures may be dynamic because the application creates and destroys data structures during execution, to store some data. This means that it's not possible to add each data structure instance in the recovery() method because they may not exist when the program restarts. This typically means you will need a registration mechanism where newly created data structure instances are added and later de-registered when the data structure is destroyed. Also, you need to save the root pointer to each of those data structure instances.
PTMs handle this transparently because the transaction has the all-or-nothing semantics. If new data structure instances are created or destroyed, you can use the root pointer API (in OneFile and Romulus we called it get_object() and put_object()). When there is a crash, any modifications to ongoing transactions will be reverted, including allocating or de-allocating a new data structure instance and including adding or removing a root pointer. By having a PTM whose transactions can encompass the modifications on the data structure and the allocation/de-allocation of the data structure and the adding/removing of the instance as a root pointer, we give transactions that are easy to reason about and make your code consistent at all times.

Ok, ok, so it's not that bad for the hand-made durable data structures. We can do automatic checks for these scenarios by adding registration mechanisms in the constructor of the data structure, or even some kind of static check or compile-time assertion to make sure everything is as it should be.
But the thing is, if you use a PTM, you only have to call the recovery method of the PTM, one time, regardless of the number of data structure instances you have in your application!
In the case of OneFile, it has null recovery which means there is no recovery method: the PTM will simply continue execution where it left of.


This is yet another thing where PTMs are better than an hand-made durable data structures.

Atomic Durability - How do databases recover from a crash ?

In this post we're going to talk about the four different ways of having durable transactions. If you want to know how databases and file systems guarantee correct data recovery after a power failure, then keep reading!

Writing to a durable/persistent media can be a complex task. If there is a failure half-way through, you may end up with corrupted data.
By durable media I mean a disk drive, an SD card, a USB stick, a Persistent Memory module (NV-DIMM), a network attached NAS, and many other like these.

Suppose you're changing a customer address in a database. The current address is "Bag End, Shire" and we want to change it to "Rivendell".
What happens of there is a crash half-way through the write?
Suppose the first four bytes were written before the crash occurred. Upon restart, the address in the database is now "Rivend, Shire"... clearly an incorrect address. What should we do?

This is a typical problem encountered by the designers of DBMS (Database Management Systems) and file systems.
Generally speaking, there are four solutions to this problem and they are:
1) Undo-Log;
2) Redo-Log;
3) Copy-On-Write (sometimes called Shadow Copy or Shadow Paging);
4) Romulus;


Before we explain how each of these works, we need to introduce two things which we're going to call "ordering fence" and "synchronization fence".
An "ordering fence" is something that guarantees that previous writes will reach the durable media before subsequent writes. For example:

write(a);
order_fence();
write(b);
implies that "a" will always be written to the durable media before "b" is written.

A "synchronization fence" is something that guarantees that previously written data is now durable. And by durable I mean it's persistent in the media and in the event of a crash, this data will not be lost. For example:

write(a);
sync_fence();
computer_beeps();
if you heard a beep and immediately after there is a power cut, you know for sure that the media contains the data "a" because the beep occurred after the sync_fence() which was after the write(a) was persisted.


The actual semantics depend on what hardware we're talking about, but the idea is pretty much the same: you need order_fence() and you need sync_fence().
When writing to disk, both the order_fence() and sync_fence() have to be implemented as fsync() or fdatasync(). When writing to PM they're made up of a combination of CLWB and SFENCE instructions.

Now back to the actual algorithms

1) Undo-Log

This technique is at least 30 years old and dates back to AIRES.
As the name indicates, it's an undo-log technique. Before writing to the durable media, we copy the original contents to a log, then we indicate that the log is consistent, then write the new values on the actual data, and finally reset the log:

1:  write(dst, src, size) {
2:    memcpy(src, log, size);   // copy original data to log
3:    order_fence();
4:    recover_from_log = true;
5:    order_fence();
6:    memcpy(dst, src, size);   // write new data
7:    order_fence();
8:    recover_from_log = false;
9:    sync_fence();
10: }
Let's examine what happens if there is a crash in:
- Lines 1 to 4: no data has been over written, nothing happens;
- Lines 4 to 6: 'recover_from_log' is seen as 'true' and the recovery will overwrite the contents of 'src' with the contents of 'log'. They are the same, therefore, no change;
- Lines 6 to 8: the recovery method will overwrite the new data with the contents of the log, restoring the old values, as it should be;
- After line 8: assuming 'recover_from_log' is durable, nothing will be reverted, the write() has committed successfully;



Above, we saw the algorithm for a single write. If we want to have multiple writes with all-or-nothing (transaction) semantics, the algorithm changes slightly:

1:  write(dst, src, size) {
2:    memcpy(src, &log[entries].data, size);   // copy original data to log entry
3:    log[entries].size = size;                // save the size of this write()
4:    log[entries].addr = dst;                 // save the destination address
3:    order_fence();
4:    entries++;
5:    order_fence();
6:    memcpy(dst, src, size);                  // write new data
7:  }
8:  commit() {
9:    sync_fence();
10:   entries = 0;
11:   order_fence();
12: }

When used in this way, the number of fences is 2 per call to write() plus 2 at commit time. The higher the number of write()s in the transaction, the more fences will be executed.

Pros/Cons:
Disadvantage 1) Two fences per write(). Can be very slow;
Advantage 1) Reading and writing is done in place;

In the context of Persistent Memory, undo-log is what libpmemobj does. This is the library in PMDK that provides durable transactions.
In the context of databases, AIRES was the first to use undo-log.

2) Redo-Log

In the redo-log technique the approach is almost opposite from the undo-log: we write the new data on the log, make the log consistent and after, copy the new contents from the log to the data:

1:  write(dst, src, size) {
2:    memcpy(log, src, size);   // copy new data to log
3:    order_fence();
4:    recover_from_log = true;
5:    order_fence();
6:    memcpy(dst, log, size);   // copy new data from log
7:    sync_fence();
8:    recover_from_log = false;
9:  }

If there is a crash after the log has been made consistent, the modifications in the log will be re-applied.
Let's examine what happens if there is a crash in:
- Lines 1 to 4: recovery does nothing;
- Lines 4 to 8: recovery method will re-apply the contents of the log to the data (redo) overwriting any old values that may have been left incomplete (in case the crash occurred during line 6);
- After line 8: recovery does nothing;


Above, we saw the algorithm for a single write. If we want to have multiple writes with all-or-nothing semantics (transaction), the algorithm changes slightly:

1:  write(dst, src, size) {
2:    memcpy(&log[entries].data, src, size);   // copy new data to log entry
3:    log[entries].size = size;                // save the size of this write()
4:    log[entries].addr = dst;                 // save the destination address
5:    non_durable_entries++;
6:  }
7:  commit() {
8:    order_fence();
9:    entries = non_durable_entries;           // make the log consistent
10:   sync_fence();
11:   for (i : entries) memcpy(log[i].addr, &log[i].data, log[i].size);
12:   order_fence();
13:   entries = 0;
14:   order_fence();
15: }

When used for transactions, a redo-log always does 4 fences (3 orders plus 1 sync), regardless of the size of the transaction. No matter how many write()s are done in the transaction, it's always 4 fences. This number can be brought down to 2 fences, but the algorithm becomes more complex.

Pros/Cons:
Advantage 1) Only four fences per transaction;
Disadvantage 1) Reading data requires a lookup on the log, so as to cover the read-after-write scenarios. Without it, we would be reading stale data and break invariants.


In the context of Persistent Memory, redo-log is what Mnemosyne and Onefile do. Mnemosyne was the first blocking PTM and OneFile was the first wait-free PTM.
In the context of databases, Microsoft Hekaton, MySQL InnoDB engine, PostgreSQL and OracleDB all use a redo-log technique to provide transactions, while IBM DB2 uses a combination of undo and redo logs.

3) Copy-On-Write

Copy-On-Write (COW) is sometimes named "shadow copying" or "shadow paging".
This technique doesn't allow for transactional semantics. For that, it needs to be coupled with either undo-log or redo-log.
The COW technique is always applied to a block or an object. First, a new block/object is reserved, which is a replica of the block/object that is to be modified, then the modifications are executed on the new block/object and finally, a single pointer swap makes the new block/object accessible (durable):

1:  modify(obj_ptr, mutation()) {
2:    new_obj_ptr = malloc(sizeof(*obj_ptr));          // allocate a new object (2 fences or more)
3:    memcpy(new_obj_ptr, obj_ptr, sizeof(*obj_ptr));  // copy the contents of the old to the new object
4:    new_obj_ptr->mutation();                         // apply the modification on the new object
5:    order_fence();
6:    old_obj_ptr = obj_ptr;
7:    obj_ptr = new_obj_ptr;                           // pointer swap (atomic)
8:    sync_fence();
9:    free(obj_ptr);                                   // de-allocate old object (2 fences or more)
10: }



Pros/Cons:
Disadvantage 1) Every modification requires the allocation of a new block/object and the de-allocation of the old one. This can lead to high "fragmentation" on the durable media and it has a performance penalty because the act of allocating a new block/object requires two extra fences to change allocator metadata. This means that COW executes 4 fences per modified object (1 order + 1 sync for the allocator plus 1 order + 1 sync for the modification and pointer change);
Disadvantage 2) The entire block/object has to be copied, even if a single byte has changed. Depending on the usage pattern, this can create high write amplification which in turn affects performance and possibly media endurance (think flash and PM);
Disadvantage 3) Needs and undo-log or redo-log to provide transactional semantics, which can mean additional fences;
Disadvantage 4) Write amplification may be close to zero (just the pointer swap and allocator metadata changes), or it may be close to infinity (a single byte change on a very large object requires a full copy of that object, and allocator metadata changes and the pointer swap);

To be properly durable, COW techniques will always execute *at least* two fences per modified block/object (1 order + 1 sync). Extra fences may be needed for the allocation and de-allocation of the block/object (2 or more fences).

In the context of Persistent Memory, there is no PTM that uses COW but there are hand-made data structures that use this technique extensively, examples being the ones described in the MOD paper (although their algorithm requires two fences per operation to be correct, plus whatever the allocator does).
When it comes to databases, SAP HANA uses a combination of COW and redo-log to provide durable transactions.

4) Romulus

Since last year (2018), there is now a fourth way to achieve durability and it's called Romulus.
In Romulus, two twin replicas of the data are maintained at all times, with the modifications first executed in the first replica 'main' and after, applied to the other replica 'back':

1:  write(dst, src, size) {
2:    state = MUTATING;
3:    order_fence();
4:    memcpy(dst_main, src, size);   // copy new data to 'main'
5:    order_fence();
6:    state = COPYING;
7:    sync_fence();
8:    memcpy(dst_back, src, size);   // copy new data to 'back'
9:    order_fence();
10:   state = IDLE;
11: }

Let's examine what happens if there is a crash in:
- Lines 2 to 4: the recovery method see state at 'MUTATING' and copies the entire contents of 'back' to 'main' (no change);
- Lines 4 to 6: the recovery method copies 'back' to 'main', undoing the possibly incomplete modifications in 'main';
- Lines 6 to 10: assuming state is durable at 'COPYING', the recovery method will copy the contents of 'main' to back, effectively acting as a redo and duplicating the committed modifications;
- After line 10: recovery does nothing;




Above, we saw the algorithm for a single write. If we want to have multiple writes with all-or-nothing semantics (transaction), then it is important to have an optimization where a non-durable log is kept with the addresses and ranges of the modified data:

1:  write(dst, src, size) {
2:    state = MUTATING;
3:    order_fence();
4:    memcpy(dst_main, src, size);   // copy new data to 'main'
5:    log[non_durable_entries].addr = dst_main;
6:    log[non_durable_entries].size = size;
7:    non_durable_entries++;
8:  }
9:  commit() {
10:   order_fence(); 
11:   state = COPYING;
12:   sync_fence();
13:   // 'offset' is the delta between 'back' and 'main'
14:   for (i : non_durable_entries) {
15:     memcpy(log[i].addr+offset, log[i].addr, log[i].size);
16:   }
17:   order_fence();
18:   state = IDLE;
19: }

When used for transactions, romulus always does 4 fences (3 orders plus 1 sync), regardless of the size of the transaction. No matter how many write()s are done in the transaction. Similarly to redo-log, this number can be brought down to 2 fences, but the algorithm becomes more complex.

Pros/Cons:
Advantage 1) Reading and writing is done in place, directly from the data;
Advantage 2) Transactions are unbounded in size. The volatile log can grow dynamically and if it grows too much, we can just stop using it and copy the entire 'main' to 'back' at the end of the transaction. No other technique supports transactions of unbounded size;
Advantage 3) Write amplification is 1;
Advantage 4) Four fences per transaction;
Disadvantage 1) Only half the capacity of the media is available to the user. The redo-log and undo-log need to pre-reserve space for their logs but rarely do applications reserve half the capacity of the media to the logs, giving the advantage to undo/redo/cow.



Whether we're talking about filesystems, DBMS or some other kind of application that requires durability with resilience to failures, it must use one or a combination/variant of these four techniques. Each of them has different trade-offs in terms of performance, memory usage, ease-of-implementing and ease-of-use. In the end, it's an engineering choice which of these four algorithms is best suited for a particular application.