ObjectBox Database Java 3.1 – Flex type

ObjectBox Database Java 3.1 – Flex type

We are happy to announce version 3.1 of ObjectBox for Java and Kotlin. The major feature of this version is the new Flex type. For a long time, ObjectBox worked on rigid data schemas, and we think that this is a good thing. Knowing what your data looks like is a feature – similar to programming languages that are statically typed. Fixed schemas make data handling more predictable and robust. Nevertheless, sometimes there are use cases which require flexible data structures. ObjectBox 3.1 allows exactly this.

Flex properties

Expanding on the string and flexible map support in 3.0.0, this release adds support for Flex properties where the type must not be known at compile time. To add a Flex property to an entity use Object in Java and Any? in Kotlin. Then at runtime store any of the supported types.

For example, assume a customer entity with a tag property:

Then set a String tag on one customer, and an Integer tag on another customer and just put them:

When getting the customer from its box the original type is restored. For simplicity the below example just casts the tag to the expected type:

A Flex property can be not justString or Integer. Supported types are all integers (Byte, Short, Integer, Long), floating point numbers (Float, Double), String and byte arrays.

It can also hold a List<Object> or a Map<String, Object> of those types. Lists and maps can be nested.

Behind the scenes Flex properties use a FlexBuffer converter to store the property value, so some limitations apply. See the FlexObjectConverter class documentation for details.

Query for map keys and values

If the Flex property contains integers or strings, or a list or map of those types, it’s also possible to do queries. For example, take this customer entity with a properties String to String map:

Why is properties not of type Object? ObjectBox supports using Map<String, String> (or Map<String, Object>) directly and will still create a Flex property behind the scenes.

Then put a customer with a premium property:

To query for any customers that have a premium key in their properties map, use the containsElement condition:

Or to only match customers where the map key has a specific value, here a specific premium tier, use the containsKeyValue condition:

What’s next?

ObjectBox database is free to use. Check out our docs and this video tutorial to get started today.

We strive to bring joy to mobile developers and appreciate all kinds feedback, both positive and negative. You can always raise an issue on GitHub or post a question on Stackoverflow. Otherwise, star the ObjectBox  Java database GitHub repo and up-vote the features you’d like to see in the next release.

 

ObjectBox Database Java / Kotlin 3.0 + CRUD Benchmarks

ObjectBox Database Java / Kotlin 3.0 + CRUD Benchmarks

The Android database for superfast Java / Kotlin data persistence goes 3.0. Since our first 1.0-release in 2017 (Android-first, Java), we have released C/C++, Go, Flutter/Dart, Swift bindings, as well as Data Sync and we’re thrilled that ObjectBox has been used by over 800,000 developers. 

We love our Java / Kotlin community ❤️ who have been with us since day one. So, with today’s post, we’re excited to share a feature-packed new major release for Java Database alongside CRUD performance benchmarks for MongoDB Realm, Room (SQLite) and ObjectBox.

What is ObjectBox?

ObjectBox is a high performance database and an alternative to SQLite and Room. ObjectBox empowers developers to persist objects locally on Mobile and IoT devices. It’s a NoSQL ACID-compliant object database with an out-of-the-box Data Sync providing fast and easy access to decentralized edge data (Early Access).

New Query API

A new Query API is available that works similar to our existing Dart/Flutter Query API and makes it easier to create nested conditions:

In Kotlin, the condition methods are also available as infix functions. This can help make queries easier to read:

Unique on conflict replace strategy

One unique property in an @Entity can now be configured to replace the object in case of a conflict (“onConflict”) when putting a new object.

This can be helpful when updating existing data with a unique ID different from the ObjectBox ID. E.g. assume an app that downloads a list of playlists where each has a modifiable title (e.g. “My Jam”) and a unique String ID (“playlist-1”). When downloading an updated version of the playlists, e.g. if the title of “playlist-1” has changed to “Old Jam”, it is now possible to just do a single put with the new data. The existing object for “playlist-1” is then deleted and replaced by the new version.

Built-in string array and map support

String array or string map properties are now supported as property types out-of-the-box. For string array properties it is now also possible to find objects where the array contains a specific item using the new containsElement condition. 

Kotlin Flow, Android 12 and more

Kotlin extension functions were added to obtain a Flow from a BoxStore or Query:

Data Browser has added support for apps targeting Android 12.

For details on all changes, please check the ObjectBox for Java changelog.

Room (SQLite), Realm & ObjectBox CRUD performance benchmarks

We compared against the Android databases, MongoDB Realm and Room (on top of SQLite) and are happy to share that ObjectBox is still faster across all four major database operations: Create, Read, Update, Delete.

Android database comparative benchmarks for ObjectBox, Realm, and Room

We benchmarked ObjectBox along with Room 2.3.0 using SQLite 3.22.0 and MongoDB Realm 10.6.1 on an Samsung Galaxy S9+ (Exynos) mobile phone with Android 10. All benchmarks were run 10+ times and no outliers were discovered, so we used the average for the results graph above. Find our open source benchmarking code on GitHub and as always: feel free to check them out yourself. More to come soon, follow us on Twitter or sign up to our newsletter to stay tuned (no spam ever!).

Using a fast on-device database matters

A fast local database is more than just a “nice-to-have.” It saves device resources, so you have more resources (CPU, Memory, battery) left for other resource-heavy operations. Also, a faster database allows you to keep more data locally with the device and user, thus improving privacy and data ownership by design. Keeping data locally and reducing data transferal volumes also has a significant impact on sustainability.

Sustainable Data Sync

Some data, however, you might want or need to synchronize to a backend. Reducing overhead and synchronizing data selectively, differentially, and efficiently reduces bandwidth strain, resource consumption, and cloud / Mobile Network usage – lowering the CO2 emissions too. Check out ObjectBox Data Sync, if you are interested in an out-of-the-box solution.

Get Started with ObjectBox for Java / Kotlin Today

ObjectBox is free to use and you can get started right now via this getting-started article, or follow this video.

Already an ObjectBox Android database user and ready to take your application to the next level? Check out ObjectBox Data Sync, which solves data synchronization for edge devices, out-of-the-box. It’s incredibly efficient and (you guessed it) superfast 😎

We ❤️ your Feedback

We believe, ObjectBox is super easy to use. We are on a mission to make developers’ lives better, by building developer tools that are intuitive and fun to code with. Now it’s your turn: let us know what you love, what you don’t, what do you want to see next? Share your feedback with us, or check out GitHub and up-vote the features you’d like to see next in ObjectBox.

Dart Flutter Database ObjectBox 1.0 Release

Dart Flutter Database ObjectBox 1.0 Release

In 2019 we first introduced the ObjectBox database v0.1 for Flutter/Dart. Our team has loved the engagement and feedback we’ve received from the developer community since, and we’re thrilled to announce the first stable version 1.0 for ObjectBox Dart/Flutter today.

With this release we bring you the fast and easy to use ObjectBox database for Dart objects: optimized for high performance on mobile and desktop devices. ObjectBox persists your Dart objects (null safe, of course) and comes with relations, queries, transactions, and Data Sync. For a feature list and more, please also check the pub.dev page.

ObjectBox by Example

For those of you new to ObjectBox, here is how you can use it (or check the docs if you want to dive deep right away). By annotating a class with @Entity you tell ObjectBox that you want to persist its objects, which is done putting the object in a Box:

What’s new with the 1.0?

Version 1.0 delivers a stabilized API and adds new essential features like async writes and query streams. We’ve also extended support for Flutter desktop. Let’s look at queries and how they can be used depending on the use case:

There are two new approaches to do async puts for asynchronous database writes: putAsync() returns a Future to check if the call was successful.

Or you can use a background queue if you don’t need individual Futures, the following code inserts 100 objects and only waits once:

If you are interested in further improvements we made to 1.0, please check out the full changelog.

Dart Flutter Database Benchmarks

ObjectBox Dart v1.0 also comes with considerable optimizations bringing a new level of database performance to Flutter apps. ObjectBox enables data-heavy apps that were not possible on Flutter before. Consider this a first sneak-peek; stay tuned for detailed performance benchmarks to be released including queries (hint: they are really fast) along with updated benchmarking code.

What we tested

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 creator abandoned it for architectural shortcomings (it has memory problems and does not support queries). In the previous benchmark we’ve also had a look at Firestore, but being an online-only database it was thousands of times slower than the rest so we’ve left it to rest this time around. Check our previous benchmark if you’re interested.

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 were also done outside of the measured time.

ObjectBox, sqflite, Hive performance comparison across CRUD

Looking at the results, we can see ObjectBox performing significantly faster than sqflite across the board, with up to 100 time speed-up in case of create & update operations. Compared to Hive, the results are a little closer in some cases (read) though ObjectBox still comes out on top in all the metrics. Considering that Hive keeps all Dart objects in memory (!) while ObjectBox does not, should give you a good impression of how fast object persistence with ObjectBox is.

ObjectBox Database for Flutter/Dart Highlights

For those of you new to ObjectBox, here’s a quick summary of what our super-fast embedded database offers, out of the box:

  • automatic schema migration: adding new classes or fields just works
  • type-safe APIs, e.g. no interface{} arguments
  • embedded edge database – no server needed, store all data directly on the device
  • no ORM, no SQL
  • relations: to-one, to-many (eager and lazy fetching)       
  • robust query support, including indexes for scalable lookups
  • Support for implicit (automatic) and explicit (user defined)
  • transactions: ACID compliant with superfast bulk/batch operations
  • low memory usage
  • runs across operating systems: 64-bit Linux, macOS, Windows, small 32-bit ARM-based Linux devices (e.g. Raspberry Pi)
  • Data Sync: an efficient and easy way to synchronize data between your app and the cloud

Getting Started with ObjectBox for Flutter/Dart Today

ObjectBox is free to use and you can get started right now with the Flutter database on GitHub, via the docs, pub.dev, or this getting-started video tutorial, or getting-started article.

We ❤️ your Feedback

Now it’s your turn: let us know what you love, what you don’t, what do you want to see next? Share your feedback with us, or check out GitHub and up-vote the features you’d like to see next in ObjectBox.

ObjectBox Dart/Flutter v0.11 Database: Performance & Relations

ObjectBox Dart/Flutter v0.11 Database: Performance & Relations

Flutter Databases are few. Therefore, we’re happy to take a big step towards 1.0 with this ObjectBox Dart v0.11 release, improving performance and bringing the much-desired relations support known from other ObjectBox DB language bindings to Dart/Flutter.

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:

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 Swift 1.4 – In Relation to…

ObjectBox Swift 1.4 – In Relation to…

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:

let yoda = Teacher(name: "Yoda")
yoda.students.append(Student(name: "Luke"))
yoda.students.append(Student(name: "Anakin"))
try box.put(yoda)

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.

Time Series & Objects: Using Data on the Edge

Time Series & Objects: Using Data on the Edge

Many IoT projects collect, both time series data and other types of data. Typically, this means they will run two databases: A time-series database and a traditional database or key/value store. This creates fracture and overhead, which is why ObjectBox TS brings together the best of both worlds in one database (DB). ObjectBox TS is a hybrid database: an extremely fast object-oriented DB plus a time-series extension, specially optimized for time series data. In combination with its tiny footprint, ObjectBox is a perfect match for IoT applications running on the edge. The out-of-the-box synchronization takes care of synchronizing selected data sets super efficiently and it works offline and online, on-premise, in the cloud.

time-series-data-example-temperature

What is time series data?

There are a lot of different types of data that are used in IoT applications. Time-series is one of the most common data types in analytics, high-frequency inspections, and maintenance applications for IIoT / Industry 4.0 and smart mobility. Time series tracks data points over time, most often taken at equally spaced intervals. Typical data sources are sensor data, events, clicks, temperature – anything that changes over time.

Why use time series data on the edge?

Time-series data sets are usually collected from a lot of sensors, which sample at a high rate – which means that a lot of data is being collected.

For example, if a Raspberry Pi gateway collects 20 data points/second, typically that would mean 1200 entries a minute measuring e.g. 32 degrees. As temperatures rarely change significantly in short time frames, does all of this data need to go to the cloud? Unless you need to know the exact temperature in a central location every millisecond, the answer is no. Sending all data to the cloud is a waste of resources, causing high cloud costs without providing immediate, real-time insights.

time-series-objects-edge

The Best of Both Worlds: time series + object oriented data persistence

With ObjectBox you aren’t limited to only using time series data. ObjectBox TS is optimized for time series data, but ObjectBox is a robust object oriented database solution that can store any data type. With ObjectBox, model your world in objects and combine this with the power of time-series data to identify patterns in your data, on the device, in real time. By combining time series data with more complex data types, ObjectBox empowers new use cases on the edge based on a fast and easy all-in-one data persistence solution. 

Bring together different data streams for a fusion of data; mix and match sensor data with the ObjectBox time series dashboard and find patterns in your data. On top, ObjectBox takes care of synchronizing selected data between devices (cloud / on-premise) efficiently for you.

time-series-data-visualization-dashboard

Get a complete picture of your data in one place

Use Case: Automotive (Process Optimization)

Most manufacturers, whether they’re producing cars, the food industry, or utilities, have already been optimizing production for a long period of time. However, there are still many cases and reasons why costly manual processes prevail.  One such example is automotive varnish. In some cases, while the inspection is automatic and intelligent, a lot of cars need to be touched up by hand, because the factors leading to the errors in the paint are not yet discovered. While there is a lot of internal expert know-how available from the factory workers, their gut feel is typically not enough to adapt production processes.

How can this be improved using time series and object data? 

The cars (objects) are typically already persisted including all the mass customization and model information. If now, all data, including sensor data, of the manufacturing site like temperature, humidity, spray speed (all time-series data) is persisted and added to each car object, any kind of correlations between production site variables, individual car properties and varnish quality can be detected. Over time, patterns will emerge. The gut feel of the factory workers would provide a great starting point for analyzing the data to discover Quick Wins before longterm patterns can be detected. Over time, AI and automatic learning kicks in to optimize the factory setup best possible to reduce the need for paint touch ups as much as possible. 

Use Case: Smart Grids

Utility grid loads shift continually throughout the day, effecting grid efficiency, pricing, and energy delivery. Using Smart Grids, utilities companies can increase efficiency and reliability in real time. In order to get insights from Smart Grids, companies need to collect a large volume of data from existing systems. A huge portion of this data is time series, e.g. usage and load statistics. On top, they incorporate other forms of data, e.g. asset relationship data, weather conditions, and customer profiles. Using visualization and analytical tools, these data types can be brought together to generate business insights and actionable operative goals.

ObjectBox TS: time series with objects

Storing and processing both time series data and objects on the edge, developers can gather complex data sets and get real time insight, even when offline. Combining these data types gives a fuller understanding and context for data – not only what happens over time, but what other factors could be influencing results. Using a fast hybrid edge database allows developers to save resources, while maintaining speed and efficiency. By synchronizing useful data to the cloud, real time data can be used for both immediate action, and post-event analysis.

Get in touch with our team to get a virtual demo of ObjectBox TS, or check out the sample GitHub repo to see more about the code.