Flutter is becoming a serious developer platform and with it grows a need for Flutter databases. A quick note on Flutter and Dart: As it is pretty young and you might either be looking for a Flutter database, a Dart database or truly a Flutter Dart database. Flutter is an open-source UI software development kit created by Google. Dart is the programming language in which developers code Flutter apps. Dart is an object-oriented programming language.
Flutter databases / Flutter Dart data persistence
While the database market is huge and dynamic, there are only few options to choose from if you are a Flutter / Dart app developer. Before we dive into the Flutter database options, advantages and disadvantages, we’re taking a very quick look at databases to make sure, we share a common ground. Accordingly, we’ll not get theoretical and extensive, but focus on what we mean here.
What is a database?
A database is a piece of software that allows the storage and systematic use of digital information, in other words: data persistence. As opposed to mere caching, data is reliably stored and available to work with unless actively deleted. A database typically allows developers to store, access, search, update, query, and otherwise manipulate data in the database via a developer language or API. These types of operations are done within an application, in the background, typically hidden from end users. Many applications need a database as part of their technology stack. The most typical database operations are CRUD: Create, Read, Update, Delete.
What are the major types of databases?
There are many types of databases. For our purpose, the most important differentiations are non-relational (NoSQL) versus relational databases (SQL), cloud databases versus edge databases, and maybe embedded versus in-memory. However, databases can be further distinguished by additional criteria e.g. the data types they support, or the way they scale – and definitions can vary.
What is an ORM?
An Object relational Mapper (ORM) is not a database. We’re bringing this up mainly, because we see it confused often. It is a layer that sits on top of a database and makes it easier to use. This is typically especially relevant when the database is a relational database (SQL) and the programming language used is object-oriented. As noted above, Dart is an object-oriuented programming language.
The Flutter Dart data persistence landscape
At this point in time, the database landscape for Flutter Dart is still very limited. So, let us quickly introduce the current market players. Note: We are adding in Moor, because with that few player it is just one more option available and therefore, at this moment in time, should be part of the Flutter Dart data persistence landscapes, in our minds.
Firebase Realtime DBis a cloud-hosted database. It stores data as JSON and synchronizes it to connected clients.
Hive is a lightweight key-value database written in Dart for Flutter applications, inspired by Bitcask.
ObjectBox DB is a highly performant lightweight NoSQL database. It stores objects.
sqflite is a wrapper around SQLite, which is a relational database without direct support for Dart objects.
Moor is a reactive persistence library for Flutter and Dart, built ontop of sqlite.
What is the best Flutter Dart database?
This of course depends… Make up your own mind with the following comparison matrix as a starting point. Note: With very few options to choose from, the following overview is sometimes a bit comparing apples🍎 and pears🍐.
As with any benchmark, you need to take a look at the details. We take benchmarking very serious and strive to get accurate results. Therefore, we also always open source the benchmarking code and encourage you to check it out. If you note anything that does not even out in your oppinion, do let us know. We have a long history of updating and improving our benchmarks continually and are happy to take any recommendations.
Performance Benchmark Test Setup
We used an Android 10 device with a Kirin 980 CPU to run the benchmarks as a Flutter app. The app executed all operations (ops) in batches of 10.000 objects. Each batch formed a single transaction. We ran each test 50 times. The results you see in the diagram are averages across all runs. We set it up that way to ensure that neither the Virtual Machine warmup during the first run nor the garbage collections affect the overall result significantly.
Flutter Databases CRUD Performance Results
Summary of the Flutter Dart DB Benchmarks
Hive and ObjectBox clearly outperform sqflite across all CRUD operations. The results show ObjectBox performing with up to 70 times the speedup for create and update operations. With regards to comparing Hive and ObjectBox, the results vary more. Hive can be faster at reading objects than ObjectBox. However, strictly speaking it’s not a fair comparison, because in Hive, the high read numbers result from Dart objects already cached in memory. If the objects are fetched using the async API from disk, the numbers drop by factor 1000.
As a cloud-based online database, Firebase is not really comparable. Local data persistence, an edge database, will typically always beat a cloud-based solutions with regards to response times. But of course cloud-based solutions have their own advantages and there may be reasons why you would choose to use Firebase over an edge database.It still may be a great option for you, depending on the use case.
Moor was not part of the benchmarking as it is an ORM. However, it is very likely it will perform similarly as sqflite, reflecting primarily the performance of SQLite.
Flutter and Data persistence landscape Conclusion
Flutter is becoming a serious developer platform and developers need a data persistence solution. There are currently only few databases supporting the Flutter community and 2021 will be an interesting year to watch where this is going. If you are interested to learn more about the database space, DB-engines and the database of databases are great starting points. Otherwise, go, check out ObjectBox Dart for Flutter Dart and share your thoughts with us – it’s not too late yet; we are shortly before releasing 1.0 and your feedback counts 🙂
For those of you new to ObjectBox: ObjectBox is a superfast NoSQL object database for Flutter / Dart and here is how you can save data in your Dart / Flutter apps:
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@Entity()
classPerson{
intid;
StringfirstName;
StringlastName;
}
finalstore=Store(getObjectBoxModel());
finalbox=store.box<Person>();
varperson=Person()
..firstName="Joe"
..lastName="Green";
finalid=box.put(person);// Create
person=box.get(id);// Read
person.lastName="Black";
box.put(person);// Update
box.remove(person.id);// Delete
// find all people whose name start with letter 'J'
To learn about more ObjectBox features, like relations, queries and data sync, check our ObjectBox pub.dev page.
How fast is ObjectBox Dart? Performance Benchmarks
Speed is important for data persistence solutions. Accordingly, we wanted to test how ObjectBox compares performance-wise to other Flutter Dart database options. Therefore, we looked for libraries with comparable levels of storage abstraction and feature set – so not just plain SQL/Key-value storage but also ORM-like features. There doesn’t seem to be that much choice…
We looked at some two popular approaches: sqflite a SQLite wrapper for Flutter (no Dart Native support), and Hive, a key-value store with Class-adapters which seems still popular although its technology is phased out (see below). As a third alternative we pulled in Firestore, which does not really fit as it is no local database, but would be fun to compare anyway.
What we tested
To get an overview of the databases, we tested CRUD operations (create, read, update, delete). Each test was run multiple times and executed manually outside of the measured time. Data preparation and evaluation was also done outside of the measured time.
We tried to keep the test implementations as close as possible to each other while picking the approaches recommended by the docs for each database. We open sourced the test code at https://github.com/objectbox/objectbox-dart-performance if you want to have a closer look.
Performance Benchmark Results
Looking at the results, we can see ObjectBox performing significantly faster than sqflite across the board, with up to 70 times speedup in case of create & update operations. Compared to Hive, the results are a little more varied, with Hive being faster at reading objects than ObjectBox (we come to that later in our outlook), and ObjectBox being faster at creating objects, about four times faster at updates and three times for deletes. As a mostly-online database, it becomes clear that Firestore’s performance is not really comparable.
Implementation notes
ObjectBox: This release largely boosted the performance. The remaining bottlenecks are due to Dart itself and how it allows to modify byte buffers. There’s potential to double the speed if we look at other languages supported by ObjectBox. And if that’s not happening soon, we’d still have the option to do some low-level hacks…
Sqflite: a wrapper around SQLite, which is a relational database without direct support for Dart objects. Each dart object field is mapped to a column in the database, as per sqflite docs, i.e. converting between the Dart class and a Map.
Hive: We’ve tested with the latest Hive release, which is technically discontinued. The author hit two architectural roadblocks (RAM usage and queries) and is currently in the process to do a rewrite from scratch. Update: strictly speaking it’s not straightforward to directly compare e.g. ObjectBox vs. Hive. In Hive, the high read numbers result from Dart objects already cached in memory. If the objects are fetched using the async API from disk, the numbers drop by factor 1000.
Firestore: This is totally apples and oranges, but we still decided to include Firebase/Firestore as it seems at least somewhat popular to “persist data”. It’s quite Cloud centric and thus offers limited offline features. For example, in order to use batches (“transactions”), an internet connection is required to “commit”. Also, due to its low performance, the test configuration was different: batches of 500 objects and only 10 runs.
Test setup
We ran the benchmarks as a Flutter app on a Android 10 device with a Kirin 980 CPU. The app executed all operations in batches of 10.000 objects, with each batch forming a single transaction. Each test was run 50 times, averaging the results over all the runs. This ensured the VM warmup (optimization) during the first run and garbage collections don’t affect the overall result significantly. (We care about accurate benchmarks; read more about our benchmarking best practices here.)
Outlook
With this latest release, we’re not far away from a stable API for a 1.0 release (🎉), so please share your thoughts and feedback. For the next release, we’ll add features like async operations, more relation types and some smaller improvements. We are also working on an ObjectBox variant for the Web platform that is planned close to the 1.0 release. And of course there is ObjectBox Data Sync for Flutter/Dart. If you want to be first in line to try, drop us a line, we can put you on the shortlist.
ObjectBox for Swift 1.4 makes object relations more natural and intuitive for Swift developers. For example, let’s take the teacher-student relation to Swift and how you store objects in the database. Let’s say “Teacher” is a Swift class that has a collection called “students”. Now let’s say we have a new teacher with new students and want to store them in the ObjectBox database. It’s done like this:
This is pretty much standard Swift. A single put command is enough to store all three new objects in the database (sorry for the “try”, Yoda, but you know, IO…). Now let’s see how this works. The students’ property in the Teacher’s class is of type ToMany<Student> and works like any Swift collection. This is because ToMany implements the protocols RandomAccessCollection and RangeReplaceableCollection. Under the hood however, it tracks all changes. Thus, when ObjectBox is instructed to put Yoda in a box, it also knows that two students were added. It also knows that our two Jedi students are new and thus puts them in database too. If you supply students that have been already persisted, it won’t put them. You can also mix new and existing objects.
Version 1.4 does not only bring TooMany (sic) improvements, but also brings a couple of new features, e.g. a bulk-get and read-only stores. You also may have heard of Sync (some kind of teleportation for objects, my young padawan). We’re still working on that, but we started to expose the Sync API with this release. It doesn’t come with any (space consuming) implementation so it’s really about getting early awareness and feedback. A full changelog is available at the docs.
So, time to start your (cocoa) pod again and let us know what you think. May the for… um, OK, that’s getting too many references for one article. One to many.
We speak with Ian Alexander, founder and lead developer at MoodSpace, a beautiful app making mental health exercises accessible to everyone. MoodSpace was released in 2019, and has over 150k+ downloads. The COVID-crises highlights the importance of digital support for wellbeing and saw MoodSpace surge. After trying several databases, Ian settled on ObjectBox because of its high performance and ease of use.
Alyssa: Hi Ian, thank you so much for joining me and for using ObjectBox. Let’s start with the basics about MoodSpace and your role there.
Ian: Hi, Alyssa, thanks for having me. I’m the software developer, founder, and runner of the company – a jack of all trades. MoodSpace is an app that teaches concepts from mental health. There is a massive problem with accessibility to mental health. In the UK, for example, you have something like 1 in 4 people that have some sort of mental health problem, but only 1 in 113 go through therapy and complete it. So our essential goal is to take concepts from therapy and bring them closer to people, teaching them techniques that they can do on an ongoing basis. There’s no end date like in therapy, no waiting list, and it’s a lot easier to use it in places where you wouldn’t necessarily have access to a therapist. In the western world it is much easier to access therapy, still difficult in some ways, but much of the world doesn’t have that benefit. So that’s the goal we’re trying to reach. We started last year, we released the MoodSpace MVP in September, and now we’re going through the next stage of trying to raise our next round of funding – it’s quite exciting.
A: That’s great, congratulations! Can you tell me a bit more about your team?
I: We’re based in the UK, and in terms of the technical side, it’s just me. We also have various other roles: designer, copywriter, and another co-founder who handles much of the business side. But in terms of technical, it is just me for now. Hopefully after we get funding, we’ll be able to expand the technical team..
A: What’s your background, what did you do before MoodSpace?
I: Actually, I was originally a chemical engineer – I worked in oil & gas for a couple of years, but then I taught myself to develop and for the last 5-6 years, I’ve worked across a lot of startups, for example the dating app Once, when they were just starting up, also ITV, and then started on MoodSpace last year. Moodspace has actually existed for quite some time, it started as a hobby project of mine about 5 years ago.
A: There are other mental health apps out there, what makes MoodSpace special?
I: If you look at apps in the space, they’re generally fairly small and limited – they’ll have maybe 4 or 5 exercises. Versus the realm of therapy, which has literally thousands of exercises. Any app that exists at the moment takes just a fraction of a percent of therapy and tries to teach it. Our USP is that we are a very technically minded team, and with new technologies which have come out along with our internal processes, we can make a much bigger app, building something far bigger than what currently exists, much cheaper and far faster. Our USP is strangely, less about the app, and more about our process and the technology that we use to make the app. The tech we will be using is Kotlin Multiplatform, which is a cross platform framework which lets us maintain a single codebase which enables us to build fully native apps with full access to native APIs.
A: It sounds like the app is quite comprehensive – who is your target audience?
I: At the moment, we haven’t hit the product-market-fit stage. We’re still figuring out who the typical user is. We find that the unique art style of our app has helped our growth so far, we often find a lot of people sharing screenshots of the app on social media. So we seem to have hit a niche, but we’re still figuring out what that niche is!
A: Do you have any direct interaction with your users?
I: Mid-last year, I put a survey in the app, so after using it for a certain time users get the survey. There are some questions like who you are, why you are using it, and they gave us way more knowledge about who is using it and what they use it for, which was very helpful. Apart from that though, it is very difficult to know.
A: Yes, it can be very challenging, we’re familiar with that struggle at ObjectBox as well. Switching gears a bit – I’d love to hear a bit more about how and why you ObjectBox.
I: As I mentioned, MoodSpace is about 5 years old, so it’s gone through several databases. One of them was really time consuming to make – it wasn’t ORM based, so you had to write a lot of stuff yourself. Then the next was an ORM called Sugar, but it stopped being developed – it was a side project by someone, so maybe I shouldn’t have used it in the first place (laughs).
I: So then we switched to ObjectBox, and actually the reason we switched was essentially to skip asynchronous code – I’ve always been a frontend developer and what I’ve come across is that asynchronous code makes things very complex, and it means app development takes much longer. Because we had a lot of time constraints and we wanted to develop as much as quickly as possible, I actually wanted to completely skip asynchronous code – which I wouldn’t recommend – but essentially ObjectBox let us do that because it’s very fast. You’d have to have a ton of data in the app, before it would visibly slow it down – and I did a lot of testing around that and it would have needed several years of data before noticeably slowing down the app. So, that was our original reason, perhaps a bit of a strange reason. And we’ve since changed the app so it’s asynchronous, so it won’t slow down any longer, no matter how much data you add in the app. Overall, I like ObjectBox a lot – it’s just simple, very easy to use.
A: What features in your app use the database?
I: Actually everything is in the app, as we don’t have a backend. So we need it to store all the data in the app.
A: Okay, sure. Keeping everything in the app is also practical from a data privacy standpoint. How did you actually find ObjectBox?
I: It was someone I used to work with at Once – they used greenDAO and mentioned that ObjectBox (by the same people) was coming out. I looked into it a little bit and wanted to use it for a while, but it wasn’t I started developing MoodSpace again that I had a chance to.
ObjectBox is very fast, it would have needed several years of data before noticeably slowing down the app. Overall, I like ObjectBox a lot – it’s just simple, very easy to use.
A: Are there any other developer tools that you’re excited about and would want to share with the community?
I: Yes, Kotlin Multiplatform. Having been an Android developer, having used Kotlin for quite some time and having tried cross-platform tools before, I think Kotlin Multiplatform will change the way you make cross platform apps, because it lets you share so much of the code base without sacrificing the native experience. It has the potential of leading to massive cost savings in app development. Maybe in the next year or two I can see it having a huge impact on frontend development across mobile, web, and desktop.
A: What are your big picture goals for MoodSpace? Upcoming milestones? Does ObjectBox help with those at all?
I: Actually, it potentially will, with regards to ObjectBox Sync, which is part of my plan for that app. The app right now is only available on Android, and providing we get our next round of funding, we are going to be adding iOS – where we’ll need some sort of backend. We want to avoid, again, spending much money, and one of ObjectBox Sync, Realm Cloud or Firestore can help us do that – obviously as ObjectBox Sync is nearly ready, we’d want to use that. The main point around that is cost saving because it solves a lot of problems that otherwise we would have to solve ourselves – things like offline access and syncing with an API, that would otherwise be very time intensive.
A: Ian, thank you for your time and sharing more about MoodSpace and working with ObjectBox. We wish you the best of luck with your fundraising round!
Update: newer versions have been released. Check the changelog.
ObjectBox Swift 1.0 is here! Since the first public alpha released 10 months ago, we’ve worked hard and made major changes to put Swift first, tune the performance, and iterate on the API. We hope you love the result and appreciate your feedback.
All of this, to bring you the features you expect from a database, but more importantly – the features that we think delight developers and sets ObjectBox apart from other databases out there. Let’s swiftly (cheap pun intended) dive into ObjectBox Swift 1.0:
Built with Swift in Mind
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// objectbox: entity
classOlympian{
varid:Id=0
...
}
let store=tryStore(directoryPath:dbURL.path)
let box=store.box(for:Olympian.self)
let olympians:[Olympian]=trybox.all()
let goldMedalist:Olympian=trybox.get(sarahHoefflinId)
ObjectBox isn’t just a database bolted onto Swift. Your database entities are regular Swift classes or structs that you devise. No need to subclass a particular class (as with CoreData’s NSManagedObject), nor to write tedious serialization code. ?
All you need to do is add one property for the unique ID, build your project, and ObjectBox’s code generator will write a little bit of code for you, just like the Swift compiler does for Codable objects. All that’s left then, is to call a simple method like put() on the object to write it out:
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trybox.put(ruthJebet)
We’ve tried to keep this simplicity throughout the Swift binding, e.g. making it very easy to use any RawRepresentable enum without writing any conversion code.
Automatic Schema Migrations
A common chore with databases is schema migration. ObjectBox takes care of that. If you add a new property or class there are no additional migration steps required. Old objects will keep working, and new objects will be saved with the additional fields. Similarly, adding new classes will add them to the database without any error-prone migration steps.
Moreover, you do not need to maintain a dedicated schema, because your classes and structs are the schema in the first place.
Relations
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classAuthor:Entity{
varid:Id=0
varname:String
varbooks:ToMany<Book>=nil
...
}
amandaPalmer.books.append(theArtOfAsking)
tryamandaPalmer.books.applyToDb()
To ensure ObjectBox knows how to save object references, you use a wrapper class. Either ToOne or ToMany, instead of a straight reference or an array. This lets ObjectBox lazily load the related objects from the database, only when you’re actually accessing a related object.
The Swift 1.0 release brings you our complete set of relations: One-to-many, many-to-many, and their corresponding back-links. ToMany behaves just like any other Swift collection, you can add or remove objects as you please with your familiar methods like append().
Queries
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let query=trybox.query{Author.fname=="Jeaniene"}.build()
let results=tryquery.find()// All Authors matching query.
// All last names of the matching authors:
let names=tryquery.property(Author.lname)
.distinct().findStrings()
Of course ObjectBox lets you perform queries to collect data; either complete objects or individual properties (basic Swift data types).
But with ObjectBox you don’t mess around with query strings or unpack data from cursors. You simply write Swift expressions with function calls and operators you’re already used to.
Also, you get to keep the type-safety guarantees and compile-time checking. So you don’t have to spend hours figuring out why your query doesn’t return the proper results, just to discover you made a typo in a field name in a query string.
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subscription=query.subscribe(){authors,error in
iflet error=error{self.reportError(error);return}
self.authors=authors
self.tableView.reloadData()
}
ObjectBox lets you then operate on these objects, watch a query for changes, retrieve the results, delete the objects matching a query etc. The source code even contains a file that adds Combine support so you can integrate with its pipelines to take advantage of Apple’s newest technology.
Open Source Swift Binding
If you’re curious how things work behind the scenes, feel free to check out the Swift source code. The source code for the Swift binding, as well as our code generator based on Sourcery, are available among other projects through our Github account.
How-to Get Started
It’s a matter of minutes to get started with ObjectBox. Check our setup instructions (based on CocoaPods) and jump right into code with the getting started guide.
Your Feedback. And what’s Next?
As always, we would love to hear your feedback! Do you like ObjectBox as much as we do? We put our hearts in this product and are excited to learn your thoughts: What features are you most excited about, what are we missing?
We haven’t written much about a topic very dear to us: performance. We will cover this in a follow up post. Also, look forward to our ObjectBox Swift 1.0 benchmarks, which we will release soon including the sources.
——
Looking for a fast and simple data synchronization solution?
Today, over 90 percent of enterprise data is sent to the cloud. In the next years, this number will drop to just 25 percent according to Gartner. Where is the rest of the data going? It’s not going anywhere. It is being stored and used locally, on the device it was created on. This is Edge Computing.
Mist, fog, edge, cloud – the terms
To bring some light into the terminology mess: The terms “mist” and “cloud” constitute the ends of a continuum.
Mist covers the computing area that takes place on really tiny, distributed, and outspread devices, e.g. humidity or temperature sensors. To make it a bit more tangible: These devices generally are too small to run an operating system locally. They just generate data and send it to the network.
As opposed to this, the cloud refers to huge centralized data centers.
The terms “fog” and “edge” fall within this continuum and – depending on whose definition you follow – can be used interchangeably.
The underlying megatrend enabling this shift is cheaper and more efficient hardware, as well as the emergence of edge databases. Edge databases are specifically designed to run on the edge and therefore lean and efficient. Both the number of mobile and IoT devices, and alongside data volumes are exploding at exponential rates. At the same time computing capabilities on the edge level are advancing faster than those on the cloud level. So, the edge has increasingly more power – power that is currently often underused.
New applications and requirements drive the shift to the edge:
Offline Capability is necessary in some applications, but many offer it for a more seamless user experience (think about it: When you are mobile, at some point you will be offline)
Advantages of setting your project up in the cloud
So, what about the cloud; do we still need it in this era of Edge Computing? Setting up your project in the cloud has some advantages: First of all, the setup itself is comparably easy, as the cloud servers are managed by another organization. This also means that you do not need to worry about scalability of your servers or data loss, i.e. the need for redundancy, reducing overall downtimes. Additionally, cloud systems also are well tested, automatically updated and often encryption mechanisms are provided natively. This eases up on administration. Generally, you can use the cloud rather quickly without worrying about lengthy and error-prone setup tasks. Using servers also centralizes the logic: clients will just call a unified interface (e.g. Web/REST).
A typical IoT-setup would often be centralized and look like this today:
Advantages of running your application on the edge
Running an application on the edge, e.g. your Android phone, a Smart Home Server, or in the car, has a couple of advantages:
The application works everywhere, all of the time (offline / online)
Great supersmooth User Experience (UX) as the app can respond in (near) realtime
Data stays where it was produced and belongs, the user maintains data ownership
Cloud / Connectivity costs go way down
Some tangible use cases for edge computing:
Many Mobile Games run on the edge. As a game developer you really want your users to be able to engage with the game whenever they feel like it and have the time. And as a gamer, you may well want to play when offline, for example when commuting on the underground. Also, gamers really care about the user experience with very smooth animations and high-fidelity visuals.
Autonomous driving as well as any human safety application needs to work independent from an Internet connection and in realtime. Imagine crashing because the car was trying to connect to the cloud or still waiting for the database to respond.
Smart home or smart health applications should work even when there is no connection, but moreover: Why should you personal health data leave your private space? You probably would want to own that data and keep it safe in the local environment. That way it is much less likely that s.o. will try to hack your individual data versus millions of centrally stored data.
Predictive maintenance apps usually need to process tons of data or high-fidelity data like video streams. Transferring all this data to the cloud usually means such high cloud costs that the project becomes unprofitable. Therefore, they usually are run on the edge and only aggregated data transferred to a central server.
Unlikely. Often you want some data accessible from anywhere. Synchronizing parts of the data to the cloud (or an on-premise central server) allows you to combine many of the advantages from edge and cloud computing. Thus, the edge is a natural extension of the cloud that makes applications all the more powerful. We believe that future scenarios will often look like this:
This line of reasoning is supported by the fact that all major cloud companies, e.g. Amazon, Google, Microsoft, are pursuing an edge strategy.
Why should Android developers care about Edge Computing?
Here is why designing your app to run on the edge will help you be successful on the Play Store: There are roughly 2.1 mill. Android Apps to choose from today in the Play Store. To stand a chance in that market you need to delight your users and get good app store ratings. Edge Computing delivers on the app traits users care about most: performance, security, and availability.
Users care about performance – a lot
Whenever an app responds to a query directly instead of taking a round-trip to the cloud and back, it should be faster. More importantly, you can measure and optimize more reliably, as the latency is independent from the network connection. This enables the fast high-quality digital experience consumers want.
Reliable performance was found to be the second most important trait for app users in a study by PacketZoom.
A study by appdynamics found that more than eight out of ten respondents had deleted or uninstalled at least one mobile app because of performance issues.
The same study found that 44% of respondents closed the app when experiencing poor network performance, but even worse: 32% even uninstall the app altogether. They also found, the reverse is true for fast performing and reliable apps with usage increasing.
As an Android developer you also know that many consumers cannot or do not care, why an app is not working. If the application is not working or just responding very slowly, users are dissatisified and annoyed. Therefore, as the developer you need to make sure your app always performs well.
“When your app depends on a network, latency is out of your control.”
Now, you might need to query data from the network. That’s fine; if most of your app is running independent from a connection, there are tons of ways to optimize user experience for connectivity loss and network latency.
Security is a hot topic and can be a USP
Users care about security, and this is a trend that will – in the face of the loss of huge amounts of personal data by tech giants – only continue to grow. When you leave data at the edge, on the device of the user, data security is much easier to provide. On top, data ownership is clear, easing up on data privacy.
Data is much more secure stored in one place than when transferred over the network – possibly again and again and again. Android provides a good basis for keeping internally stored data safe. If data security and privacy are important for you, your app, or your users, think about keeping data locally and then only synchronizing data you really need accessible from anywhere. Last not least, while an individual phone may be hacked, it is less likely to occur and only an individual dataset is compromised (as opposed to millions of datasets).
Offline first – deliver an always-on-feeling
Users do not care about connectivity, they simply want to use the application when they want to – at home, in a department store, in a train, on a flight, on vacation. And when they can’t access an app, their user experience is bad. Even today in a highly connected world, there are lots of times where people have no connectivity or need to switch data off to save battery. The app that supports them in these times ;), is the one they will love and use
The most important advantage of doing an offline-first app is the availability of the app. Google translate is a great example of an app that you want to be offline-capable. Chances are that when you need it most you are in a place where you do not have a (affordable) connection. But you might also appreciate being able to read and search through your mails when you are on a plane too. Or type WhatsApp messages that go out when connected again, or just enjoy a round of Subway Surfers.
Offline-first apps make it possible to move content off the server and onto the phone. If an app only has to go to the server when it needs to, rather than all the time, it will be faster and more reliable. This is particularly significant where content doesn’t change often, but users require fast access.
First of all the 5G rollouts and uptake still will take some time, but if you are reading this you are building a business now. Secondly, while it will bring a faster network connection to many areas, there are caveats to a central cloud-based application that won’t change:
When you are mobile, at some point you will be offline or your connection flaky.
Storing and transferring data to the cloud is costly.
Storing and transferring data unnecessarily to the cloud is wasteful.
Storing data centrally yields higher security risks for your user’s data; transferring data is an additional security risk. Any data you can just keep locally is safest.
If you leave the data with the user, data ownership is clear and you do not need to worry about privacy.
How do I bring my Android app to the edge?
As an Android developer, chances are, you are already doing Edge Computing in many of the apps you are developing.
First of all, offline-first does not work with typical web pages. Usually, you would go for a native app, or alternatively, a progressive web app (PWA) or similar technology. Apart from multiple UX benefits and speed, most users prefer native apps and users still spend 80% of their mobile usage time in apps.
Secondly, for an offline-first architecture you need a local storage as a primary source of data, e.g. a database. Changes to data are made in this layer. Application also can and usually do have networking components to synchronize data to a server. However, this connection to the backend is mainly used in the background to synchronize the local database.
So, why does not everybody do it all the time? Well, there are use cases where it does not make sense to go the extra mile for the limited functionality that edge computing would bring, e.g. in a parking app. Obviously, most relevant data points like your car and the availability of parking spots are changing all the time. So, you really are dependent upon a constant connection, or rather several constant connections: From the spots to the cloud and from the cloud to the app. However, there is another reason: Offline-capable apps are hard. That is why we developed ObjectBox Sync.
Does any app need to run at the edge? ⚖️
As always, there are some cases where the cloud makes perfect sense, indeed is the only option – and the same is true for the edge. You need to assess what you want to achieve and where the value lies for your application and users.
If you do not need to push all the data to the cloud, where large chunks of it might not even be used, you might want to take a step back and consider the broader picture: What do all these billions of mobile and IoT devices (that are quite capable) do while they wait for the cloud to respond? Nothing.
Sending data to the cloud unnecessarily is wasteful in two respects: Use of bandwidth and server capacity (which comes down to infrastructure, electricity and physical space) and the big waste of underused resources.
