Go: Inside sync.Map — How does sync.Map work internally?

NOTE: this article is a long one. So it takes time to load (especially on mobile phones). By the way, I’m actively looking for a job (update Aug 1: I’m still looking for a job. Help me start my career with your company!) :-) More on this in the “letter to the reader” part at the end (which may never finish loading).

Contents

Introduction

This article briefly introduces how sync.Map can be used, and also explains how sync.Map works.

Most operations rely on a hash map in their code. Therefore, they end up being a crucial bottleneck for performance if they are slow. Before the introduction of sync.Map the standard library only had to rely on the built-in map which was not thread safe. When it had to be called from multiple goroutines sync.RWMutex was relied on to synchronize access. But this did not really scale well with additional processor cores, and this was addressed by implementing sync.Map for go1.9. When run with 64 processor cores, the performance of sync.RWMutex significantly degrades.

With sync.RWMutex, the value readerCount in sync.RWMutex was being incremented with each call to RLock , leading to cache contention. And this lead to a decrease in performance with additional processor cores. It is because with each additional core, the overhead of publishing updates to each core’s cache increased.

The documentation says: (here)

The Map type is optimized for two common use cases:

(1) when the entry for a given key is only ever written once but read many times, as in caches that only grow, or

(2) when multiple goroutines read, write, and overwrite entries for disjoint sets of keys. In these two cases, use of a Map may significantly reduce lock contention compared to a Go map paired with a separate Mutex or RWMutex.

map with sync.RWMutex significantly reduces performance with the first case above, when the calls to atomic.AddInt32(&rw.readerCount, 1) and atomic.AddInt32(&rw.readerWait, -1) are too frequent. The overhead of writes cannot be avoided, but we must expect reads to be extremely fast especially if it is a cache that is supposed to help with processing data in a fast pipeline.

A brief introduction to concurrency and how it applies in this context

Modern applications have to deal with historically large amounts of data within short periods of time. For this, they rely on multi-core processors with high processing speeds.

Real-time inputs handled by these systems usually have unpredictable arrival times. This is where OS level threads and goroutines truly shine. These rely on a combination of locks and semaphores to put them to “sleep” while waiting for an input. This frees the CPU resources utilized by the virtual thread until it receives an interrupt that awakens it. A thread or a goroutine awaiting an interrupt is similar to a callback waiting to be invoked on the arrival of an input — the interrupt signal originates when unlocking a mutex it was waiting on (or when a semaphore becomes zero).

Ideally, we would expect an application designed to run in a multi-threaded environment to scale with the increase in the number of processor cores. But not all applications scale well even if it is built to run multiple tasks on separate goroutines.

Certain blocks of code might need to synchronize with locks or atomic instructions, turning those parts into a forcefully synchronized execution blocks. An example for this could be a central cache which is accessible by each goroutine. These lead to lock contention preventing the application from improving in performance with additional cores. Or worse, these could lead to a degradation in performance. Atomic instructions also come with an overhead but it is much smaller than what is caused by locks.

Hardware level atomic instructions used to access the memory do not always guarantee reading the latest value. This is because, each processor core maintains a local cache which could be outdated. To avoid this problem, atomic write operations are usually followed by instructions forcing each cache to update. And to top that, it must also prevent memory reordering which is in place (both at the hardware and software level) to improve performance.

The map structure is so widely used that almost every practical application rely on a it in their code. And to use one in a concurrent application reads and writes to it must be synchronized with sync.RWMutex in the Go world. Doing so leads to the excessive use of atomic.AddInt32(...) which leads to frequent cache-contention, forcing cache update and memory ordering. This reduces performance.

sync.Map uses a combination of both the atomic instructions and locks, but ensures the path to the read operations are as short as possible, with just one atomic load operation for each call to Load(...) in most cases. Atomic store instructions are usually the ones that force the caches (of each core) to be updated, whereas, atomic load might just have to enforce memory ordering along with ensuring its atomicity. And atomic.AddInt32(...) is worse, as its contention with other atomic updates to the same variable will cause it to busy wait until the update which relies on a compare-and-swap instruction succeeds.

The problem with using a map with sync.RWMutex

An example of using sync.RWMutex to synchronize access to the map: https://github.com/golang/go/blob/912f0750472dd4f674b69ca1616bfaf377af1805/src/sync/map_reference_test.go#L25

For convenience, the above code is mirrored here:

Source 1: RWMutexMap

When performance is critical we can address this either by reducing the frequency of acquiring the same locks in parallel (thus, reducing the frequency of lock contention) or by entirely replacing locks with atomic instructions like atomic-load, atomic-store and atomic-compare-and-swap. Atomic operations are also not a silver bullet, as state updates which rely on atomic-compare-and-swap run in an infinite loop until the update succeeds. The updates usually never happen when there is a contention, therefore leading them to busy wait when there are a lot of concurrent updates.

Most applications usually rely on a combination of both. Even those applications try to choose a more faster alternative, like reducing the number of calls to atomic-compare-and-swap instructions spinning in a loop.

sync.RWMutex uses a combination of semaphores along with two additional variables readerCount and readerWait to keep track on the number of read accesses.

To understand why we need to go for sync.Map when our environment has too many processor cores, instead of using the built-in map guarded by sync.RWMutex, we must dive into how sync.RWMutex works internally.

Introducing sync.Map

sync.Map contains these following methods, which are self explanatory (taken from here)

Source 2: sync.Map methods documentation

Before going ahead trying to understand how sync.Map works, it is important to understand why do we absolutely need the methods LoadAndDelete(...) and LoadOrStore(...).

sync.Map maintains two map objects at any given time, one for the reads and one for the writes. The read map is partially immutable, whereas the write map is fully mutable.

At any given time, the readable map is not expected to be fully updated, whereas, the writable map is assumed to be fully updated representing the store’s accurate state (while it was not set to nil). Whenever there is a key miss in the read-map, it is read from the write-map with a sync.Mutex lock held.

While deleting a key, the key could either be present only in the writable map or it could be present in the writable map and the readable map. If it was present only in writable map, it is completely removed with the built-in delete operation. But if it was present in both the writable map and the readable map, it is only set to nil (note, the update operation of setting the field to nil is reflected within the read-map and the write-map simultaneously, as they point to the same entry object; more on this later).

Every access to the writable map is guarded by sync.Mutex and they also have the overhead of atomic instructions. Therefore, it must have an overhead slightly greater than a built-in map guarded by sync.RWMutex.

So one of the objectives of this implementation should be to reduce the frequency of read-misses. This is done by promoting the writable map as the next readable map (while setting the pointer to the writable map as nil) when the number of read misses are larger than the length of the writable map (this length is computed using len). The outdated readable map is then discarded.

Upon the first attempt to add a key to the writable map, a new map object is created by copying the contents of the new readable map to the writable map. Immediately after the promotion, the readable map will represent the store’s most accurate state. But after being promoted it becomes “immutable” and there will not be any new keys added to it. Therefore, adding additional keys to sync.Map will make the readable map outdated.

There are two different ways in which an update happens. If the key being added (with the sync.Map’s Store(...) or LoadOrStore(…) operation) already exists in the readable map, the value associated with the key in the readable map is updated atomically (note: the changes done to the value field this way will be immediately reflected on the writable map as well. How this happens will be explained later in this article). If the key only exists in the writable map or if the key does not exist at all, the update is made only in the writable region with a sync.Mutex lock held.

Where is sync.Map used?

sync.Map was originally created to reduce the overhead incurred by the Go’s standard library packages that have been using a map guarded by sync.RWMutex. So the Go authors discovered that having a store like sync.Map does not increase the memory overhead too much (an alternate memory intensive solution is to have a separate copy of the map for each goroutine used, but have them updated synchronously) while also improving the performance in a multi-core environment. Therefore, the original purpose of sync.Map was to address the problems within the standard packages, but it was still made public in hope that people find it useful.

The documentation does not really go into the details of where exactly will sync.Map be of most use. Benchmarking sync.Map has revealed there is a gain in efficiency over the use of map guarded by sync.RWMutex only when it is run on a system with more than 4 cores. And the most ideal use case for sync.Map is having it used as cache that witnesses a frequent access to disjoint keys; or having it extensively read from the same set of keys.

The keys that are newly added are likely to stay in the writable map, while the keys that were accessed most frequently are likely to stay in the readable map. sync.Map will perform the least when a finite set of keys are being added, read from and deleted where each operation happens with the same frequency.This happens when you do not let the keys in the write-map to be promoted by frequently adding and deleting them.In this situation, we might be better off using map with sync.RWMutex or sync.Mutex (The exact choice for a concurrent hash-map is usually decided through benchmarking)

Whenever a key is deleted in sync.Map it only has its associated value field marked as nil but the key isn’t really removed until the first write after the writable-map was promoted as the readable-map. This leads to a memory overhead. But this overhead is only temporary, as with the next promotion cycle, the overhead comes down.

sync.Map: the implementation details

sync.map’s structure is given here for convenience:

source 3: Map structure

As we can see, sync.Map has one dirty map store and one atomic.Value field that is used for storing the “clean” read map. All accesses to the dirty map are always guarded by mu. Before we look at how each individual methods work we must understand the working of sync.Map and its design ideas from a higher level.

The entry structure is vital for the function of sync.Map .

Source 4: entry structure

The entry structure acts as a “container” or a “box” that holds the value being stored associated with the key. Instead of directly storing a value with m[key] = value, you have the value encapsulated within a convenient container of type entry. The address of entry is then stored into the map object associated to the key.

At any given time, entry is assumed to satisfy one of these three properties:

It is extremely convenient to wrap up the pointer under a structure like entry. If you are changing the state of an entry, you can expect it to be instantly updated at every other location that has the pointer to the entry object.

So, if you share the address to entry between the readable map and the writable map, updating the readable map’s entry will reflect the changes on the writable map as well and visa-verse (note that, in both source 3 and source 5 the map is defined as map[inerface{}]*entry , where the key is an interface{} and the entry is stored as a pointer).

Source 5: readOnly structure

The read field of sync.Map structure is an atomic.Value field, which holds a reference to readOnly(source 5). This structure contains another field amended which is true when the sync.Map object was extended by adding a new key and a promotion did not happen yet since the new key was added. In this case, the key goes into the writable map without there being a record of it in the readable map. The amended field being true signifies this specific state where the dirty map contains a record read doesn’t.

How does load, store and delete work at a high level?

The read part of these operations are usually inexpensive (relatively). This is because, the pointer to a readOnly object is retrieved from the atomic.Value field and the value associated with the key is quickly searched for. If the value exists, it gets returned.

If it doesn’t exist, then it is likely to have been added more recently. In which case the dirty field needs to be checked (with mu held). If the key exists in the dirty field, it is retrieved as the result.

The read operations have a slow path in this implementation. With each miss in the readOnly object, the value of the field misses is atomically incremented. When the misses count is larger than the size of dirty, it gets promoted (directly moving its pointer) to the read field, by creating a new readOnly object to contain it. The previous value of the read field is discarded when this happens.

All operations involving the dirty map are carried out within a region guarded by the mutex mu .

When record is being stored into sync.Map, it is handled in one of these three ways:

In case of delete, if the key was present in the readOnly object in read, its entry instance is atomically updated to be nil. This operation does not need mu to be acquired. But mu will need to be acquired when the key is searched for in the dirty field. If the key is not present in read, it is looked for in dirty while being guarded by mu. If it is found only in dirty, the entry object is directly removed with the built-in delete function.

Therefore the read, write and delete operations will be fully atomic when the key is found in the readOnly instance held by the read field. Whereas, the other cases that needs to go though dirty will be guarded by mu.

And any modification done to the entry field will be reflected at both the readOnly object in read and the dirty map. Because, whenever a new map[interface{}]*entry object is created from copying the readOnly instance, only the address of the entry objects are copied.

The difference between storing expunged and just simply nil

The following properties are always said to hold:

When misses > len(dirty) (misses is a field in sync.Map structure), the dirty map is copied into the read field, which is of type atomic.Value. The code for doing that is: m.read.Store(readOnly{m: m.dirty}) where, the Store here is an atomic store operation.

The readOnly object has two fields in it. One for the map, and the other for the amended variable which says if a new key was added to the sync.Map object after a promotion. The first attempt to insert a key following a promotion causes the map inside the readOnly object to be copied to the dirty map key by key. Every key that associates to an entry object storing nil is not copied into the dirty map and has its entry object updated to contain expunged.

Therefore, expunged keys are only present in the “clean” map without having them copied into the new dirty map. This is done with an assumption that the key that was deleted once is not likely to be added again.

There are two separate unexported methods,missLocked()and dirtyLocked() defined for sync.Map. These are respectively responsible for,

missLocked() is called each time there is a key miss while reading the readOnly object. The call could be triggered by every exported method defined in sync.Map other than Range(…), as they all try retrieving the stored record first and accept a key as an argument. missLocked() only promotes the dirty map when the size of it is smaller than the number of misses.

Store(key, value interface{}):

Source 6: the Store method

Let’s break the above code into four parts (the region containing each part is marked above). The first part attempts to retrieve the value from read, which is a sync.Value field containing the readOnly object. If the read was successful, it attempts to store the value into the entry object atomically. Source 7 shows how it is done.

The atomic update in the tryStore(…) operation contains an infinite loop that terminates when the value was previously expunged. This is because, an expunged value signifies that there isn’t a copy of the same key field in the dirty map, while there is one in the readOnly object. Therefore, updating the pointer in this case will not reflect on the dirty map, which is always supposed to contain the fully updated and accurate state of sync.Map. Except for the case where it was just recently promoted. In which case, the dirty field will temporary hold nil until the first attempted write to it since it was set to nil.

The infinite loop in source 7 is there to cause the function to stay in a “busy wait” state until the update succeeds. Atomic compare and swap operation accepts three arguments. The pointer to be modified, the likely old value contained in the pointer and the new value. The new value is stored in the pointer if the old value was the same as the original value held in the pointer.

In source 7, the pointer is first atomically loaded from the entry object and it is checked if it was expunged. If it was, then tryStore fails because, an expunged entry signifies that the entry object is not present in the dirty map associated with its key. Therefore, storing the value into the entry object retrieved from the read map will no longer be useful (as the modifications will not reflect on the dirty map).

The atomic-compare-and-swap instruction in line 11 of source 7 is responsible for adding the new value, if e.p (which is the pointer stored inside the entry object) was same as the value of p previously read at line 8 of source 7. If a method running in a different goroutine had atomically modified the underlying value of p before the execution of line 11, the compare-and-swap-pointer operation fails causing the loop to continue. If p was modified to hold expunged, then the loop breaks because of the conditional branch at line 8.

This infinite loop will go on until e.p was not modified by a different goroutine while statements from line 7 to 11 were being executed. This is why contention will be more heavy on the CPU when we use atomic instructions instead of locks that put goroutines to sleep until they are needed to run again. Atomic instructions cause a “busy wait” to occur until there are no contentions.

Source 7: the tryStore method

The remaining parts 2, 3 and 4 (in Source 6) all run within a region guarded by the mu lock. Part 1 returns when the key was present in the readOnly object and the tryStore operation (explained above) was successful.

But Part 1 failing signifies that either the key was not present in the readOnly object or it was expunged. Proceeding to Part 2, the read value is reloaded within the locked region again.

This is done because the pointer to the readOnly object stored within atomic.Value could have been replaced following a call to missLocked() executed from a different goroutine, which will also be executed after acquiring mu(Note, every function with the postfix “Locked” is supposed to be executed within the locked region guarded by mu). But because part 1 does not acquire mu the value retrieved at line 6 in source 6 can become outdated.

In Part 2 the entry pointer is retrieved again. Now, a call to e.unexpungeLocked() checks if the value stored in the entry was expunged or not:

A call to unexpungeLocked() executes the statement return atomic.CompareAndSwapPointer(&e.p, expunged, nil) (which is the only statement in its definition). This ensures that e.p is only updated when it is expunged. You don’t have to busy wait here to allow the update to happen. This is because the “old pointer” argument of CompareAndSwapPointer(…) is a constant (expunged) and it can never change.

Both tryStore() and unexpungeLocked() can update e.p though they aren’t mutually guarded by the same mutex. Thus they could potentially attempt to update e.p simultaneously from different goroutines. But this does not become a race condition as unexpungeLocked() is supposed to modify the entry object only when its pointer (the p field of the entry object) was set to expunged.

unexpungeLocked() running on a different goroutine could execute its CompareAndSwapPointer(…) statement anywhere between lines 7 and 11 in Source 7. If it was executed between these lines, the value of the underlying pointer will have been changed before the compare-and-swap operation at line 11 which will cause the loop to fail and repeat again. Therefore, a successful execution of the region between the lines 7 and 11 in Source 7 cannot occur if,

In Part 2, the value is finally stored into the entry object by a call to storeLocked(...). Now moving on to Part 3. The condition in Part 2 fails on two possibilities (note, we have already ruled out the possibility that the key could be present in the readOnly object):

Part 3 handles the first case. It simply stores the record into the entry object. Now part 4 handles these following two possibilities:

If the dirty map was nil, it must be created by copying the entries from readOnly object before the new key is added. Otherwise, the new key is added directly without any changes.

The dirty map being nil also signifies that no new entries were added into the sync.Map object ever since its dirty map was promoted as the “clean” map. Therefore, the field read.amended in source 5 is supposed to be false if dirty was nil.

While promoting a dirty map to a clean map (this happens with a call to missLocked() defined within the structure sync.Map; it is only executed when Load(…), LoadOrStore(…), LoadAndDelete(…) or Delete(…) are called) it just gets directly copied into the “clean” map. When this happens, the users are free to delete the keys in the clean map concurrently (which will just be an atomic operation) and re-add them. But with the first attempt to add a new key into the map after the promotion, the contents of the clean map are copied into the dirty map. But while this happens, the keys with nil in their entry object’s pointer fields are ignored and within the “clean” map, they are atomically changed to expunged.

In part 4, a call to dirtyLocked() is made to populate the dirty map. The source for dirtyLocked() is given below:

Source 8: dirtyLocked() — creates a new map by copying contents of the readable map and stores it in dirty

tryExpungeLocked() is defined below:

Source 9: tryExpungeLocked() — updates the entry’s pointer with expunged if it was nil

Like tryStore(…) defined in source 7, source 8 also relies on a completely atomic operation in setting expunged to the pointer. As we can see in Source 8, the creation of the new dirty map and the eventual update only happens when dirty is nil . And the line 9 of source 8 makes it clear that only if tryExpungeLocked() fails, the key is added into the dirty map.

tryExpungeLocked() ensures that the pointer field in the entry object is set to expunged if it was originally nil (see line 4 of source 9). If the pointer was modified before the compare-and-swap operation, the swap fails and the loop exits. This loop continues until p is not nil. This could change from being nil before the compare-and-swap is executed; for example, it could be replaced by a goroutine executing a call to the Store(...) method in the sync.Map structure.

Following the call to dirtyLocked(), the read map is marked as “amended”. This expresses that the map in the read field is outdated.

Load(key interface{}) (value interface{}, ok bool) :

Note: if you haven’t read the Store(…) part, I recommend you to do so. The line of reasoning I used there also applies here and every other methods that are explained below. Therefore I won’t be reexplaining them here.

Source 10: Load

LoadOrStore(key, value interface{}) (actual interface{}, loaded bool)

The source for LoadOrStore(…) can be found here.

For the most part LoadOrStore(…) is similar to Load(…) except that it uses e.tryLoadOrStore(value) in place of e.tryLoad() and also makes a call to missLocked(…) if the key was absent in the readable map.

The implementation of tryLoadOrStore(…) is similar to any atomic update strategy using the compare-and-swap instruction. This method only succeeds when the entry object holds any pointer other than expunged (which includes nil).

LoadAndDelete(key interface{}) (value interface{}, loaded bool):

The strategy followed isn’t very unique from the ones mentioned above. First read is attempted with the readOnly object. If it succeeds, e.delete() is called which atomically sets the entry object’s pointer to nil. If it fails, the entry object is retrieved from the dirty map with within a locked region. The call to e.delete() is made if the dirty map had the key.

The key is not removed at this point. It is just set to nil.

Delete()(value interface{}, ok bool):

This just calls LoadAndDelete(…)

Range(f func(key, value interface{}) bool)

Discussion: sync.Map performance vs RWMutex guarded map’s performance. A quick guide on optimization

This article (The new kid in town — Go’s sync.Map by Ralph Caraveo III) goes into a great detail of how a simple sync.RWMutex guarded map is way better than sync.Map when we are using only a few processor cores. The benchmarks in the article shows that beyond 4 cores, the performance of sync.Map seemed to be significantly higher than a map guarded by sync.RWMutex.

It it therefore necessary to build a prototype that can later be benchmarked to check its relative performance with each variant of its implementation. This can help us choose which variant will be most appropriate for each situation. This shares similarity with how deep neural networks work. In DNNs we have a loss function the value of which we try to minimize. The loss function in this case expresses the objective of the neural network model.

The same way, applications must view their test cases and benchmarks together as a “loss function” that expresses the objective of the entire project. If you measure the performance, in time you can improve it. Because, you cannot adjust what you don’t measure.

Premature optimization really becomes a problem when what you optimize does not improve the overall performance of your application, or the change in performance is just too negligibly small.

Let’s see an example:

Assume you have an application which is supposed to receive profile info along with a specific meta-data through API calls, transform it to a different format and send them over to a different microservice. Let’s say you write a procedure to filter a specific pattern in the data being read — assume you receive customer profile data and your application is supposed to filter profiles that are older than 8 years and send it’s ID over to a different message queue while also storing the profile in a cache.

Should you use sync.Map or a map guarded by sync.RWMutex?

Assume that out of every 100 profile data received by the application, one of them is 8 years old. So should you really bother thinking about which map to use? Either of those choices make no difference here because, the overall performance of the system is usually not impaired by your choice. Maybe because this cache isn’t the slowest step.

When we work as a team, it is not uncommon to have different people handle different tasks. So you won’t benefit from having people make their part of the code run faster through testing them with localized benchmarks. The benchmarks must reflect the overall objective of the application.

Any amount of work that goes into improving performance of a part of the code base will be wasted if it was not really the bottle neck. But things are a bit different when you are writing a library where most functions are exposed to the user. Writing benchmarks at the application level (including the exposed APIs) help set the objective the team wishes to achieve.

If reading this article had given you the impression that the Go authors were optimizing prematurely, I have to say, that’s not true. Go is a general purpose language and I am sure even its authors cannot fully anticipate all of its use cases. But it is always possible for them to figure out the most common use cases and optimize for that. Their priorities should be tweaking the parts that have the most impact in their ecosystem. For this, they definitely need a very rigid feedback loop from all their users. Their main focus should be on fixing the pain points.

What does an extreme level of optimization look like?

For the reasons I mentioned before it is not really needed to improve performance within an application beyond a point. That said, if you do want to make your application super fast for whatever reason, then read on!

SQL database systems do everything in their power to make things run faster. They do this by implementing multiple “strategies” for the same task. When you query for an unindexed record you will observe the worse-case performance. This happens when the query engine falls back to using brute force search.

But if you had an index built, the query engine has a chance to choose a better algorithm (of just referring to the index) for improving the query execution time. The “query planning” phase takes care of determining the right strategy to use. If you have multiple indexes built, you are essentially giving the query engine too many options and strategies to choose from. In this case, it may even rely on execution statistics to choose the best strategy to use internally.

Likewise, if you want a super fast concurrent map the following might help with that:

Source 11: super efficient sync map

The strategy pattern can be used almost everywhere giving different parts of your application a huge control over its performance. You could also write modules for noting down the statistics and devising the right strategy for your application more like how the query planner does it.

This way, your application will use a different set of strategy for each environment! This will be really useful if your customers are concerned for performance and you are supporting a wide range of platforms. Or, it will be most useful if you are building a language.

That said, premature optimization can always be avoided if you are clear about your team’s objectives. Without rigid objectives, you cannot really know what counts as “optimization” and what doesn’t.

Letter to the reader

Dear reader,

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My LinkedIn: https://www.linkedin.com/in/k-sreram-a04a90b7/

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