On this article we intention to indicate why taking an incremental method to
legacy cell utility modernization may be preferable to the classical
‘rewrite from scratch’ methodology. Thoughtworks has the good thing about working with
giant enterprise purchasers which might be depending on their in-house cell
functions for his or her core enterprise. We see a lot of them asking their
functions to do extra and evolve quicker, whereas on the identical time, we see an
rising rejection of reputationally damaging excessive threat releases.
As an answer, this text proposes different strategies of legacy
modernization which might be primarily based in Area Pushed Design and hinge on the
utility of the Strangler Fig sample. Whereas these ideas are removed from
new, we consider that their utilization in cell functions are novel. We really feel
that regardless of incurring a bigger momentary overhead from their utilization, that is
an appropriate tradeoff. We assert how the methodology is used to fight the
aforementioned attitudinal shifts in legacy cell utility growth
whereas gaining a platform to decrease threat and drive incremental worth
supply.
We talk about how this works in idea, diving into each the structure
and code. We additionally recount how this labored in apply when it was trialled on
a big, legacy cell utility at one in all Thoughtworks’ enterprise
purchasers. We spotlight how the sample enabled our shopper to quickly construct,
take a look at and productionize a modernized subset of area functionalities inside
an current legacy utility.
We transfer on to guage the effectiveness of the trial by highlighting the enterprise
dealing with advantages similar to a signficantly quicker time to worth and a 50% lowered median cycle
time. We additionally contact on different anticipated advantages that needs to be used to
measure the success of this system.
The Drawback with Cell Legacy Modernization
As functions age and develop, they have an inclination to deteriorate each in high quality
and efficiency. Options take longer to get to market whereas outages
and rolled again releases change into extra extreme and frequent. There’s a
nuanced complexity to be understood concerning the the explanation why this
happens each on the code and organizational degree.
To summarize although, sooner or later, an
group will develop uninterested in the poor outcomes from their
software program and begin the method of legacy substitute. The choice
to interchange could also be made primarily based on a number of elements, together with (however not restricted to)
price/profit evaluation, threat evaluation, or alternative price. Ultimately a legacy modernization technique can be chosen.
This can be depending on the group’s perspective to threat. For
instance, a fancy, excessive availability system might demand a extra
incremental or interstitial method to legacy
substitute/displacement than a less complicated, much less enterprise crucial one.
Within the case of cell utility modernization, these choices have
in current reminiscence been fairly clear lower. A cell utility was
usually designed to do a person thing- Apple’s “There’s an app for
that” nonetheless rings out loud and clear in individuals’s minds 15 years after
the preliminary batch of commercials. That message was one which was taken
to coronary heart by organizations and startups alike: If it’s good to do
one thing, write an app to do it. If it’s good to do one thing else, write
one other app to do this. This instance struck me after I was
pruning the apps on my telephone a few years in the past. On the time I observed I
had a number of apps from the producer of my automotive; an older one and a more recent
one. I additionally had two apps from my financial institution; one confirmed my checking account,
one other that analyzed and illustrated my spending habits. I had three apps
from Samsung for varied IoT gadgets, and at the least two from Philips that
managed my toothbrush and lightweight bulbs. The purpose I’m laboring right here is
{that a} cell utility was by no means allowed to get so sophisticated,
that it couldn’t be torn down, break up out or began from scratch once more.
However what occurs when this isn’t the case? Absolutely not all apps are
created equal? Many consider that the cell expertise of the long run
can be centered round so-called
“super-apps”; apps the place you may pay, socialize, store, name,
message, and sport, all beneath one utility. To some extent this has
already occurred in China with “do-everything” functions like
‘WeChat’ and ‘AliPay’- we see the cell machine and its working
system as extra of a car to permit the operating of those gigantic
items of software program. Feedback from business point out a realization
that the West
shouldn’t be fairly as far alongside as China on this regard. However whereas not
on the super-app, there isn’t a doubt that complexity of the cell
app expertise as an entire has elevated considerably in current
years. Take the instance of YouTube, when first put in, again in
the early 2010’s, the applying may play movies and never a lot
else. Opening the applying in the present day one is introduced with “Movies”
and “Shorts”, a information feed, controllable classes, subscriptions,
to not point out a content material enhancing and publishing studio. Equally
with the Uber app, the person is requested in the event that they need to order meals.
Google Maps can present a 3D view of a avenue and Amazon now recommends
scrollable product-recommendation temper boards. These further options
have definitely enriched a person’s expertise however additionally they make the
conventional construct, use, rebuild method far more troublesome.
This issue may be defined by contemplating a few of the current
frequent issues of cell utility growth:
- Huge View Controllers/Actions/Fragments
- Direct manipulation of UI components
- Platform particular code
- Poor Separation of Issues
- Restricted Testability
With self-discipline, these issues may be managed early on. Nonetheless, with
a big utility that has grown chaotically inline with the enterprise it
helps, incremental change can be troublesome regardless. The answer then, as
earlier than, is to construct new and launch suddenly. However what for those who solely need
so as to add a brand new characteristic, or modernize an current area? What if you wish to
take a look at your new characteristic with a small group of customers forward of time whereas
serving everybody else the previous expertise? What for those who’re glad along with your
app retailer opinions and don’t need to threat impacting them?
Taking an incremental method to app substitute then is the important thing to
avoiding the pitfalls related to ‘large bang releases’. The Strangler
Fig sample is usually used to rebuild a legacy utility in
place: a brand new system is steadily created across the edges of an previous
one by way of frequent releases. This sample is well-known, however
not broadly utilized in a cell context. We consider the explanation for that is that there are a number of conditions that have to be in
place earlier than diving headfirst into the sample.
Of their article on Patterns
of Legacy Displacement, the authors describe 4 broad
classes (conditions) used to assist break a legacy downside into
smaller, deliverable elements:
- Perceive the outcomes you need to obtain
- Resolve learn how to break the issue up into smaller elements
- Efficiently ship the elements
- Change the group to permit this to occur on an ongoing
foundation
Solely within the third level, can we envisage the invocation of the Strangler Fig
sample. Doing so with out an understanding of why, what or the way it would possibly
proceed sooner or later is a recipe for failure.
Going ahead, the article charts how Thoughtworks was in a position to assist one
of its enterprise purchasers broaden its current cell legacy modernization
efforts right into a profitable experiment that demonstrated the worth behind
using the Strangler Fig sample in a cell context.
Satisfying the Conditions
At this level, it appears acceptable to introduce the shopper that
impressed the writing of this text – a globally distributed enterprise
with a longtime retail group that had embraced cell
functions for a few years. Our shopper had realized the advantages an
app introduced to offer a self-service expertise for his or her
merchandise. That they had shortly expanded and developed their app domains to permit hundreds of thousands
of consumers to take full benefit of all of the merchandise they bought.
The group had already spent a major period of time and
effort modernizing its cell functions in its smaller
sub-brands. Responding to an absence of reuse/important duplication of
efforts, excessive
cognitive load in app groups and sluggish characteristic supply, the
group selected a cell expertise stack that leveraged a
Modular Micro-app structure. This technique had been largely
profitable for them, enabling proliferation of options frequent to
the group (e.g. ‘login/registration/auth’ or ‘grocery purchasing’)
throughout totally different manufacturers and territories, in a fraction of the time it
would have taken to put in writing all of them individually.
The diagram above is a simplified illustration of the modular
structure the group had efficiently carried out. React
Native was used attributable to its potential to completely encapsulate a
area’s bounded context inside an importable element. Every
element was underpinned by its personal backend
for frontend (BFF) that got here with the infrastructure as code to
instantiate and run it. The host apps, proven above as UK and US,
have been merely containers that supplied the app particular configuration
and theming to the person micro-apps. This ‘full slice’ of
performance has some great benefits of each permitting re-use and
decreasing complexity by abstracting utility domains to micro-apps
managed by particular person groups. We converse in depth concerning the outcomes of
this structure within the already referenced article on ‘Linking
Modular Structure’.
As touched upon earlier, the group’s cell property was made up of
numerous smaller sub-brands that served comparable merchandise in different
territories. With the modular structure sample tried and examined, the
group needed to focus efforts on its ‘home-territory’ cell
utility (serving its essential model). Their essential cell app was a lot
bigger by way of characteristic richness, income and person volumes to that of
the sub manufacturers. The app had been gaining options and customers over many
years of product growth. This regular however important development had
introduced success by way of how well-regarded their software program was on each
Google and Apple shops. Nonetheless, it additionally began to indicate the
attribute indicators of decay. Change frequency within the utility
had moved from days to months, leading to a big product backlog and
pissed off stakeholders who needed an utility that would evolve as
quick as their merchandise did. Their lengthy launch cycle was associated to threat
aversion: Any outage within the utility was a severe lack of income to
the group and likewise triggered their prospects misery because of the
important nature of the merchandise they bought. Adjustments have been at all times examined
exhaustively earlier than being put stay.
The group first thought-about a rewrite of the whole utility
and have been shocked by the fee and period of such a undertaking. The potential
destructive reception of a ‘large bang’ new launch to their app retailer
prospects additionally triggered considerations within the ranges of threat they may settle for.
Strategies of alpha and beta person teams have been thought-about unacceptable
given the massive volumes of customers the group was serving. On this
occasion, a modernization effort much like that seen of their sub-brands
was believed to be of significantly greater price and threat.
Thoughtworks steered an preliminary proof of idea that constructed on the
successes of the reusability already seen with a modular
structure. We addressed the group’s large bang threat aversion
by suggesting the Strangler
Fig sample to incrementally change particular person domains. By
leveraging each methods collectively we have been in a position to give the
group the flexibility to reuse production-ready domains from
their modernized cell apps inside their legacy app expertise. The
concept was to ship worth into the palms of consumers a lot sooner
with much less duplication than in a full rewrite. Our focus was not on
delivering probably the most stunning or cohesive full app expertise (-not
fairly but anyway). It was about acquiring confidence each within the
stability of the iterative substitute sample and likewise in how properly
the brand new product was being acquired. These items of data
allowed the group to make extra knowledgeable product choices
early on within the modernization course of. This ensured the completed product
had been extensively used and molded by the precise finish customers.
Strangler Fig and Micro-apps
So how far did we get with the proof of idea and extra importantly
how did we truly do that? Taking the learnings from Modular Micro-app
structure (described above), we theorized the design to be as follows:
The preliminary state of the applying concerned the identification of
domains and their navigation routes (Resolve learn how to break the issue into
smaller elements). We targeted our efforts on discovering navigation entry factors
to domains, we referred to as them our ‘factors of interception’. These acquainted
with cell utility growth will know that navigation is mostly
a properly encapsulated concern, that means that we could possibly be assured that we
may at all times direct our customers to the expertise of our selecting.
As soon as we recognized our ‘factors of interception’, we chosen a website
for incremental substitute/retirement. Within the instance above we deal with
the Grocery area inside the current utility. The ‘new‘ Grocery area,
was a micro-app that was already getting used inside the sub-brand apps. The
key to implementation of the Strangler Fig sample concerned embedding an
total React Native utility inside the present legacy utility.
The crew took the chance to comply with the nice modularity practices that
the framework encourages and constructed Grocery as an encapsulated element. This
meant that as we added extra domains to our Strangler Fig Embedded
Utility, we may management their enablement on a person degree.
As per the diagram, within the legacy app, Grocery performance was
underpinned by a monolithic backend. Once we imported the New Grocery
Micro-app, it was configured to make use of that very same monolithic backend. As
talked about beforehand, every micro-app got here with its personal Backend for
Frontend (BFF). On this occasion, the BFF was used as an anti-corruption
layer; creating an isolating layer to take care of the identical area mannequin as
the frontend. The BFF talked to the present monolith by way of the identical
interfaces the legacy cell utility did. Translation between each
monolith and micro-app occurred in each instructions as essential. This
allowed the brand new module’s frontend to not be constrained by the legacy API
because it developed.
We continued the within out substitute of the previous utility by
repeating the method once more on the following prioritized area. Though out
of scope for this proof of idea, the intention was that the method
proven be repeated till the native utility is finally only a shell
containing the brand new React Native utility. This then would enable the removing of the
previous native utility solely, leaving the brand new one as an alternative. The brand new
utility is already examined with the present buyer base, the
enterprise has confidence in its resilience beneath load, builders discover it
simpler to develop options and most significantly, unacceptable dangers
related to a typical large bang launch have been negated.
Diving Deeper…
To date we’ve introduced a really broad set of diagrams to
illustrate our Cell Strangler Fig idea. Nonetheless, there are
nonetheless many
excellent implementation-focused questions as a way to take idea
into
apply.
Implanting the Strangler Fig
A very good begin could be, how did we summary the complexity of
constructing each native and non-native codebases?
Beginning with the repository construction, we turned our authentic native
utility construction inside out. By inverting the management
of the native utility to a React Native (RN) utility
we averted important duplication related to nesting
our RN listing twice inside every cell working system’s
folder. In truth, the react-native init default
template gave a construction to embed our iOS and Android
subfolders.
From a developer perspective, the code was largely unchanged. The
legacy utility’s two operating-system-separated groups have been in a position to
goal their authentic directories, solely this time it was inside a single
repository. The diagram under is a generalized illustration (that’s,
relevant to each iOS and Android) of the present pipeline from the
Shopper as we understood:
Bi-Directional Communication utilizing the Native Bridge
We’ve already touched on navigation with our beforehand talked about
‘factors of interception’. It’s value trying deeper into how we
facilitated communication and the switch of management between native and
React Native as it will be simple to oversimplify this space.
The React
Native ‘Bridge’ allows communication between each
worlds. Its objective is to function the message queue for
directions like rendering views, calling native capabilities,
occasion handlers, passing values and many others. Examples of
properties handed throughout the bridge can be isCartOpen
or sessionDuration. Whereas an instance of a bridge
operate name could be js invocations of the machine’s native geolocation
module.
The diagram above additionally references the idea of a ‘React Native
Micro App’. We launched this idea earlier within the article once we
described our app by way of journeys. To recap although, a micro-app is a self-contained
encapsulation of UI and performance associated to a single
area. A React Native app could also be made up of many micro-apps
much like the micro
frontend sample. Along with these benefits we’ve got already mentioned, it additionally permits us to have a better
diploma of management over how our Strangler Fig utility
grows and is interacted with. For instance, in a state of affairs
the place we’ve got extra confidence in one in all our new journeys
than one other we’re afforded the choice to divert a bigger
proportion of visitors to 1 micro-app with out impacting
one other.
Bringing each ideas collectively, we utilized the bridge to
seamlessly transfer our customers forwards and backwards throughout experiences.
The flexibility to move info allowed us to protect any
instant state or motion from the UI that wanted to
persevere throughout experiences. This was notably helpful
in our case because it helped us to decouple domains at
acceptable fracture factors with out worrying whether or not we
would lose any native state once we crossed the bridge.
Dealing with Delicate Knowledge
To date we’ve mentioned shifting between legacy and new codebases as
atomic entities. We’ve touched on how native state may be
shared throughout the bridge, however what about extra delicate
information? Having just lately changed their login and registration (auth)
course of of their different customer-facing React Native apps
with a modular, configurable, model agnostic one, the shopper
was eager for us to reuse that have. We set ourselves
the duty of integrating this expertise as an
preliminary demonstration of the Strangler Fig sample in
motion.
We leveraged the methods already mentioned to implant the
Strangler Fig: i.e. the brand new authentication journey on the
React Native aspect. When a buyer efficiently logged in or
registered, we wanted to make sure that in the event that they moved away from
the brand new expertise (again into the legacy journey), their
authentication standing was preserved irrespective of the place they
have been.
For this, we utilized the native module code calling aspect of the
bridge. The diagram above explains how we achieved this by
utilizing a React Native library that served as a wrapper to
save authentication information to the Android
EncryptedSharedPreferences or iOS Keychain after a
profitable login. Because of the versatile construction of the info
contained in the keystore, it allowed us to seamlessly share the
(re)authentication course of regardless of whether or not
the person was within the native or non-native expertise. It additionally
gave us a sample for the safe sharing of any delicate
information between experiences.
Regression Testing at Area Boundaries
An necessary a part of a cutover technique is the flexibility to know
from any vantage level (in our case, totally different groups working inside the identical app) whether or not a change made affected the
total performance of the system. The embedded app
sample described above presents a singular problem on this
regard round scalable testability of a multi-journey
expertise. Furthermore one that’s managed by a number of groups
with quite a few branching paths.
The interplay diagram above exhibits an instance journey circulation
inside the embedded app. One factor to note is the quantity
of branching complexity throughout a journey that’s carrying
out simply two concurrent experiments. We converse extra on unintentional complexity later on this part.
The take a look at
pyramid is a well-known heuristic that recommends a
relationship between the price of a take a look at (upkeep and
writing) and its amount within the system. Our shopper had saved
to the take a look at pyramid and we discovered unit, subcutaneous and
journey-centric UI-driving exams once we examined their
code. The answer subsequently was to proceed to comply with the
sample: Increasing the variety of exams throughout all layers and
additionally extending the suite of journey exams to include the
leaping out and in of our embedded Strangler Fig app. However
there was a possible downside, possession. We realized
that it will be unreasonable to tie the success of one other
crew’s construct to code they didn’t write or have been in charge of.
We subsequently proposed the next take a look at technique throughout
groups:
| Take a look at Sort | Native | React Native |
|---|---|---|
| Unit | X | X |
| Subcutaneous | X | X |
| Legacy Journey | X | |
| e2e Micro-app Journey | X | |
| Contract exams for interactions with ‘The Bridge’ (journeys with each legacy and micro-app elements) | X | X |
On the final desk row, by contract we merely imply:
If I work together with the bridge interface a specific manner, I
count on a particular occasion to fireplace
For Native to RN interactions, these contracts act as blueprints
for micro-apps and allow unit testing with mocks. Mocks
simulate the conduct of the micro-app, making certain it makes use of
the required context accurately.
The opposite manner round (RN to Native) was comparable. We recognized
the Native performance we wished to name by way of the
Bridge. RN then supplied us with an object referred to as
NativeModules which, when mocked, allowed us to say
towards the ensuing context.
Defining these boundaries of duty meant that we may
restrict the ‘regression-related’ cognitive load on groups by way of
‘hand-off’ factors with out compromising on total app take a look at
protection.
This technique was largely properly acquired by each the native and
non-native groups. The place we did run into friction was the
complexity behind the implementation of the contract exams
throughout the bridge. The crew operating the legacy utility
merely didn’t have the bandwidth to know and write a
new class of exams. As a compromise, throughout
the PoC, all contract exams have been written by the React Native
crew. From this we realized that any interstitial state
required considered paid to the developer expertise. In
our case, merely layering complexity to attain our targets
was solely a part of the issue to be solved.
Creating the Experiment
Bringing every part collectively to kind an experiment was the final
hurdle we needed to overcome. We wanted a method to have the ability to
reveal measurable success from two totally different
experiences and still have a capability to shortly backout and
revert a change if issues have been going fallacious.
The group had an current integration with an
experimentation instrument, so out of ease, we selected it as our
instrument for metric seize and experiment measurement. For experiment
person choice, we determined machine degree person choice (IMEI
quantity) can be extra consultant. This was because of the
potential for a number of machine utilization throughout a single account
skewing the outcomes.
We additionally utilized the characteristic
flagging element of the experimentation instrument to permit us to ‘flip off’ the experiment (revert to
native app solely) with out the necessity for a launch; significantly
decreasing the time taken to get better ought to any outage happen.
Outcomes
We’ve advised the story of how we carried out the Strangler Fig sample
towards a big, complicated legacy utility, however how
profitable was it with our shopper?
Our shopper selected a website/journey that mapped to an current smaller
micro-app to be the primary that might be incrementally changed
contained in the legacy utility. This was as a result of the micro-app was
tried and examined in different functions across the enterprise and was
generic sufficient that it could possibly be simply ‘white labeled’ by our crew.
Following the success of the primary micro-app integration, a second,
bigger micro-app was then implanted to reveal the sample
was extensible. These have been the outcomes:
Time to First Worth
Getting a product in entrance of customers early allows worth to be
realized cumulatively over time and precise person suggestions to be collected
and iterated upon. An extended time to worth will increase the affect of
altering necessities and delays the belief of advantages. The primary
metric involved time to first worth for our new expertise. This determine
is derived from the time it took to create the Strangler Fig framework
inside the present legacy app and all regression/integration actions
across the first micro-app.
By comparability, our shopper had been quoted
round two years for a whole utility rewrite. Within the case of the Strangler Fig, It took round 1 month to implant the micro-app construction into the present
utility, 3 months to construct the primary micro-app, and 5 months for the
second. Therefore, from a clean web page, it will take 4 months to yield first
worth (implantation plus first app). Whereas that is the fairest solution to
make the comparability, actually the shopper noticed first worth a lot faster.
It is because each micro-apps had already been constructed to be used in
separate cell functions. So the time to first worth on this case
was solely the implantation time of 1 month.
Cycle Time
Our second measurement is Cycle Time. It represents the time to
make a change contained in the micro-app code and contains time taken for
regression with the Strangler Fig app. It excludes pushing an app
to the shop – a variable size course of that app kind has no bearing on.
Within the case of our legacy app, we calculated cycle time because the period
it took to make and regression take a look at a change within the current native code
base.
The metric is helpful as a result of its uplift represents a shift in
organizational threat aversion towards the product; modifications up to now
being exhaustively examined because of the potential for unrelated aspect
results and outages. As our current micro app was a completely
encapsulated area, we knew that the overwhelming majority of modifications can be
owned by the micro-app crew and subsequently absolutely testable contained in the micro-app
itself. Any exceptions the place the bridge was invoked (e.g. native
performance requested) could possibly be mapped to contract exams on the
boundaries.
| App Sort | Median Cycle Time (over 30 days) |
|---|---|
| Micro-App 1 | 9 days |
| Micro-App 2 | 10 days |
| Legacy App | 20 days |
The
outcomes above present a major uplift in
velocity to make code modifications inside
encapsulated area boundaries (micro-apps)
when in comparison with a coupled monolithic
app construction.
Limitations and Recognized Drawbacks
To date we’ve largely highlighted the advantages of a Strangler Fig
method to legacy cell App displacement. Nonetheless, there are some
important limitations to this sample that needs to be taken under consideration
earlier than selecting to copy our experiment. We acknowledge that our use
of the
sample originated from a proof of idea: A request from a shopper
unwilling to just accept that there was just one choice to interchange their legacy
utility. Whereas the info we see to date is encouraging by way of
cumulative worth supply and enhancements in cycle time, it’s arduous to
ignore an absence of information from the proper aspect of the event course of. Earlier than
recommending this as an choice for legacy substitute, we would wish to
see information on app resilience similar to time to revive service and quantity/severity of outages. Considering additional forward, we additionally acknowledge the
limitations of solely making use of the sample to 2 of the numerous domains the
shopper’s app was composed of. It stays to be seen if there are any
complexity issues created when extra domains are launched to the
interstitial app state.
Abstract
Recapping, we began this text by explaining why, as cell
apps have grown in complexity, incremental legacy
modernization has change into extra engaging. From there, we
launched the Strangler Fig sample for Cell
Purposes. We confirmed the assorted levels within the course of
from preliminary characteristic deployment by way of to eventual full
substitute. We examined a few of the extra complicated
implementation challenges intimately. We demonstrated how our
Strangler Fig was implanted into the legacy app. We dove deeper into the idea by inspecting the React
Native Bridge as a method to facilitate communication between
previous and new. We mentioned how the dealing with of delicate information happened. We additionally confirmed how efficient regression
take a look at protection may occur when confronted with a number of unbiased groups. Lastly, we touched on how leveraging experimentation towards the sample, was helpful in an incremental supply setting.
We found encouraging leads to that our PoC was in a position to
considerably shorten the trail to first worth when in comparison with the estimated time for a full app rewrite.
Our use of modular micro-apps additionally confirmed a 50% enchancment within the median cycle time when
in contrast towards that of the present
legacy cell app. With that being stated, we acknowledge the
limitations of our standing as a PoC and the unintentional complexity incurred that wanted managing. We
counsel additional exploration of the resiliency and scalability of the
sample earlier than it’s a dependable different
to the normal strategies of cell app modernization.
To sum up, we consider that it’s innevitable cell apps will proceed to
improve in scope and complexity.
We additionally assume that attitudes round threat mitigation and quicker worth
supply will change into extra commonplace
when contemplating modernization of a sufficiently complicated app. To
some extent, this calls for a brand new method, maybe that which was
proposed on this article. Nonetheless, regardless of the successes we’ve got
seen, this shouldn’t be overplayed
as greater than a instrument as a part of a wider ‘legacy modernization
toolbelt’. These trying to replicate
ought to perceive in the beginning that Legacy Modernization,
no matter expertise, is a multifaceted
downside that calls for important evaluation and alignment. Placing in
the funding upfront, is not going to solely assist you choose
the right instrument to your state of affairs, however be sure that your app is
higher aligned to the purchasers it serves
and the issues it solves.
