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Glide vs. Picasso

Glide and Picasso are the most used image loading library in the world of android applications. Most of the android application developers have used any of these or both libraries in their career. Both libraries provide number of features, very fast and optimized. They are also well tested on many applications. On the surface, it looks like both are working on the same mechanism. But they both are very different in the way how they download images, caches images and loads them into memory. Today, we are going to look into the core differences between both the libraries and try to figure out which one is the best for application development.

We are comparing Glide v3.7.0 and Picasso v2.5.2 versions for this article. The latest available version might be different depending on when you are reading this article.

Import to the project:

Picasso and Glide, both are on jcenter. You can simply import those into your project with below dependency.



support-v4 dependency is not needed for most of the projects as most of them uses support library by default.

Size and Method Count:

While comparing the sizes of the .jar file of both the libraries, Glide is almost 3.5 times larger than Picasso in size.


Picasso has method count of 849, while Glide has total 2678 method count. 2678 is quite a lot for 65535 methods limit of Android DEX file. You should enable ProGuard if you choose to use Glide.



Both libraries have almost same syntax if you want to simply load the image from the URL and display them into the image view. Both support the fade animations and center crop. You can also add placeholder image to display while loading the image or while the image loading fails.



One thing in which Glide dominates is, it is designed to work with Activity and Fragment’s life cycle too. You can pass the activity or fragment context with Glide.with() and it will brilliantly integrate with activity lifecycle callbacks such as onPause() or onResume().


Disk caching:

Both the library supports caching the image in the disk. They download the images from the URL and store those images on the disk by caching. But there are some differences how they store the images in the cache.

Picasso downloads the image and stores the full-size image (in my case the image resolution was 1160*750) in the cache and whenever we ask for the same image, it will return the full-size image and resize them to fit into the ImageView in real time.

On the other hand, Glide works differently. Glide downloads the image from the given URL, resize it to the size of the image view and stores it to the disk cache. So if you are loading the same image in two different sized image views, Glide will store two different copies of the same image in the cache with different resolutions. That will increase the disk cache size, but it has some of its own benefits. We will see that in the next section.

When I tried to adjust ImageView to the different sizes, Picasso cached only single size of the image and that is full-size. While Glide caches separate files for each size of ImageView. On down point of this approach is, although an image has already been loaded once, if you need to load the image in another size ImageView, it needs to be downloaded once again before be resized to the right resolution and then be cached.


By default, Glide uses RGB_555 configuration while Picasso loads images in ARGB_8888 configuration to load the bitmap into memory. So for fair comparison, I made some changes in GlideModule to load images in ARGB_8888 format by creating a new class which extended from GlideModule like this:

Below is the graph of memory consumption of loading the image in Glide and Picasso:


By looking into the graph, we can see that Glide is more memory efficient (about 8 MB) than Picasso (about 13 MB). This is pretty much understandable as we discussed in the earlier section, that Picasso loads the full-size image into the memory and relies on GPU to resize that image to fit into the size of the ImageView. While Glide loads an image that is already resized as per the ImageView, that requires significantly less memory than loading the full image like Picasso. This helps to prevent your app from throwing popular OutOfMemoryError.

Time taken to load the Image:

When we try to load an image from the URL, both libraries checks their local caches and if the image is not present in the cache, they will download the image from the URL.

When I tried to download images from the URL, Picasso was quite fast in loading the image from the internet than Glide. Maybe because after downloading the image, Picasso directly pushes the full-size image to the memory; while Glide resizes the image as per the dimension of the ImageView. That needs some time to resize the image. (You can reduce this time by using thumbnail() which we will discuss in later section.)


But while loading the image from the cache, Glide wins the battle. This is because of an advantage of the way Glide was designed. While the Picasso causes some delay on loading since it needs to be resized the image first in real-time before images are set to an ImageView. Even if you use .noFade()to appear it instantly.


Other features that Glide support but not Picasso:

  • Animated GIF support: Glide supports animated GIF images out of the box. Loading GIF is simple. Just use the same Glide.with(...).load(...)call and if the image you load is an animated GIF. Also, because of Glide integrates with your activity life-cycle, animated GIFs are also paused in onStop() to avoid draining the battery in the background. While as of now, Picasso does not support animated GIF images. So, if you want to display some animated GIF, then Glide is the only option here.
  • Thumbnail support: With Glide, you can load multiple images into the same view at the same time. To first load a thumbnail at 1/10th the size of your view and then load the full image on top, you can use below code:

 This will reduce the time your user has to see image loading spinners without sacrificing quality.

  • Configurations & Customization: One good thing about Glide is, it provides various conflagrations and customization options. So, you can tweak Glide library as per your requirement. You can read more about this at configuration page.


Neither Glide nor Picasso is perfect. You can use any of this library based on your application requirement. If you want the smaller app and you require very fewer features in the image loading, than Picasso is best. But If you want more customization, animated GIF support, and very better memory management, you should use Glide as your image loader library.

In my opinion, Glide is the winner over here as it handles memory very well and prevents my app from OutOfMemoryError. Also, loads image much faster than Picasso. And yes, the support for animated GIFs is almost killer.


How to decrease your Gradle build time by 65%?

Gradle is very powerful build system. It handles the very complex process of building .dx files from java source code, merging all the resources and assets into the application package (.apk) and signing the application.

But gradle isn’t perfect. It is comparatively slower than other build systems out there.

Why should you care about build time of your project?

For a long time, I was OKAY with my build timings. I thought I can live with this. I used to do other stuff like surfing the internet, making coffee and even taking my lunch while gradle builds my project.

Now, let’s say you work on the above I/O’16 application project for 6 hours a day and run gradle build to install debug apk file on your test device for almost 15 times, you will spend 35 minutes a day and almost 3 hours/week(almost 10% of total time) just sitting idle and watching gradle build running.

That’s why it is very important that you optimize your build timing. The less time you spend watching gradle build your project, the more you can be productive. After all, time is precious.

I applied some modifications in my build process to decrease the build time and here are a few tips for you that will help you to decrease your build timings.

Here for example purpose, we will use Google I/O application as the sample project. We are going to run all the benchmarks on that project. This project contains more than 28 dependencies and 41538 method references. Also, the source code of the application is openly available on GitHub.


Build without any modifications:

Let’s measure the time to build and debug app from the project with the current configuration using below command.

./gradlew android:assembleDebug --profile

— profile will tell gradle to measure the time taken to execute each task and dump that data into HTML file. You can find that report under /projectDir/build/reports/profile directory.


  • As you can see it takes 2 minutes and 43 seconds to build the debug apk from the source on my machine. Keep this figure in your mind.

These benchmark timings may vary on your machine depending on your system configuration. Here I am going to use my 13″ Macbook Pro 2016 with touch barto run all these tasks.

Now, let’s try to decrease this time.

Gradle Build Cycle:

Gradle has three distinct phases in build life cycle:

  1. InitialisationIn this stage of the build life cycle, gradle picks the project and decides what things to build.
  2. ConfigurationHere, gradle will evaluate your build.gradle script, configure all the plugins and evaluate the task graph.
  3. ExecutionIn this phase gradle actually runs all the task those were evaluated in previous phase to get the work done and build the application.

As you can see that out of all three phases, only execution phase performs all the operations. That means that first two phases are just overhead to the gradle build process. We don’t care about them. But, sadly whenever you do anything with gradle this two phases will always get executed.

So, how can we measure the time taken to execute first two phases? Luckily, gradle provides — dry-run command, that tells gradle to evaluate the project but don’t run any task. Thus execution phase won’t get executed. So, run this command in your terminal:

./gradlew android:assembleDebug --dry-run --profile

Here is the time taken by first two phase on over Google I/O project.

You can see in above statistics that gradle passed almost 7.8 seconds just to initialise and configure the project. That is completely waste of time for us.

Let’s see if we can decrease the time taken to initialise and configure the project.

Configure on demand:

Google I/O app has two components/modules:

  • Android : That contains the source code related to the andorid application and
  • Server : This contains code related to backend server.

Now, if you are building an android application, you don’t want to configure server component. Right? But previous statistic shows that gradle passed almost a second to just configure server component.

Gradle provides — configure-on-demand flag, that will tell gradle to only build the projects that it really needs to build. Let’s run this command.

./gradlew android:assembleDebug --dry-run --profile --configure-on-demand

As you can see, if we use — configure-on-demandgradle will skip configuring the server module and it will decrease the build time by almost a second. I know this is not a huge gain, but small drops can fill the whole lake!!!

Time decreased : 1 second (6% win).

How you can enable configure-on-demand in android studio?

You can enable — configure-on-demand for every build you can add org.gradle.configureondemand=true in your file or in your android studio, navigate to Preferences > Build, Execution, Deployment > Compiler and check configure on demand option.


Enable daemon:

Gradle has a very good feature called Gradle DaemonDaemon keeps the instance of the gradle up and running in the background even after your build finishes. This will remove the time required to initialize the gradle and decrease your build timing significantly.

You won’t be able to see the time difference in your first build as gradle has to initialize and start the daemon, but build times will decrease in subsequent builds as gradle daemon is already initialised.

./gradlew android:assembleDebug --dry-run --profile --configure-on-demand --daemon


In our case, the build time is decreased by about 4.5 seconds. Hurray!!!

Time decreased by : 4.5 seconds (71% win)

How you can enable daemon in android studio?

If you are using the gradle version 3.0 or above, the gradle daemon is by default enabled. But, if you are running on older versions of gradle then you can enable it for every build by adding org.gradle.daemon=true in your file.

Always keep your gradle up-to-date:

Gradle itself got faster with each new releases. There are many tweaks and performance improvements in the latest versions of gradle. Also, if you are running on gradle 2.4 or above, gradle will also cache build artifacts and that will improve the build time significantly.

So, always make sure that you are using the latest version of the gradle. You can go to /gradle/wrapper/ and upgrade gradle version by changing distributionUrl.

While writing this article, the gradle the latest gradle version is 3.3. Here is the updated file for I/O application.1*77iuK0YxGUcRmcTPNHpeXg.png

./gradlew android:assembleDebug --dry-run --profile --configure-on-demand --daemon


Time decreased by : 0.7 seconds (82% win 🍾)

So far we achived almost 82% gain in initialise and configuration timings. Now, let’s try to decrease the time required in execution phase.

Increase the heap size:

Since android studio 2.0, gradle uses dex in the process to decrease the build timings for the project.

“Dex in process” is a process that allows to run multiple dex processes to run within the single VM that is also shared with the gradle.

Generally, while building the applications, multiple dx processes runs on different VM instances.1*L-XJGmxyWGvmqYErQ7klZw.png

But starting from the Android Studio 2.0, all these dx processes runs in the single VM and that VM is also shared with the gradle.(You need to have build tool version 23.0.2 or above to enable dex in the process.)


This decreases the build time significantly as all the dex process runs on the same VM instances. But this requires larger memory to accommodate all the dex processes and gradle. That means you need to increase the heap size required by the gradle daemon. By default, the heap size for the daemon is about 1GB. You have to increase the heap size by adding below line in your

org.gradle.jvmargs=-Xmx3072m -XX:MaxPermSize=512m -XX:+HeapDumpOnOutOfMemoryError -Dfile.encoding=UTF-8

The required heap size varies from machine to machine. For my machine, I found that 3GB heap size is perfect. Any more increase in the heap size doesn’t affect the build timing significantly.

Now, let’s run full clean build using the below command:

./gradlew android:assembleDebug --profile --configure-on-demand --daemon


Time decrease by : 1 min 43 sec (63% win) — From 2 min 43 sec to 1 min

Modularise and Parallelise build:

Allow gradle to build your project in parallel. If you have multiple modules in you project, then by enabling this, gradle can run build operations for independent modules parallelly. This may decrease the build time significantly for very complex and multi-module projects.

You can enable parallelism by adding org.gradle.parallel=true in your file.

As our project doesn’t have multiple modules, building parallel won’t make much difference.


Time decrease by : 1 min 45 sec (64.5% win)

Other small things to remember:

Avoid heavy computations, like increasing the version code from git commits count or downloading some files from url while building the project. Gradle will spend time to perform those computations or network operations and that will afftect build timings.

Don’t use dynamic dependency like,

compile ''

If you are using the dynamic dependencies, gradle will go online and check if there is any newer version for the library is available or not? (Most of the time, it is just waste of the precious build time). Instead, use the fixed dependencies and update them manually time to time.

compile ''


The long story short, add below lines to your file and you will get almost 65% decrease in your build time.



Continuous Integration and Code Coverage in Android using Travis CI

Testing has always been a trending topic in the world of Android. It becomes a time consuming task in large android projects where multiple developers are working together, maintaining the code quality and resolving bugs; which is a very difficult task.

This is a very serious issue in the open source projects where multiple developers from different environment and different level of expertise contribute to a single code base. Everyone is not the perfect and someone can commit changes that break the application code and we should have something that can alert us if someone is doing something wrong as soon as possible.

It would be great if we can find out that whether the code we are going to push onto our master branch of repository contains any possible bugs that can break the system. Here, continuous integration comes into play.

What is Continuous Integration?

Continuous Integration (CI) is a development practice that requires developers to integrate code into a shared repository several times a day. Each check-in is then verified by an automated build, allowing teams to detect problems early.

In simple words, each time you commit and push your code to the remote repository, continuous integration will check if everything is working correctly in the code before you merge it into your master (assuming you are using git for version control).

With continuous integration, you can:

  •  Automated build process. Every commit/push to a git repo will automatically trigger a new build.
  • Find out Android Lint errors.
  • Coding styles to ensure that the new code is matching the code format that your team has decided.
  • Get the code coverage percentage. This will be really helpful to figure out if any part of the code is remaining to test.
  • Run Unit tests located on your /test directory of project.
  • Run Instrumentation tests located in your /androidTest project.

How to configure Continuous Integration?

As the title of this post suggests, here I am going to use Travis CI. You can use other services available to the market like CircleCI, Codeship, etc. But here, I will stick to Travis CI as it is most used CI service for GitHub projects and also it is free for Open Source projects.


  • An account on GitHub. I believe nowadays every developer has it.
  • An account in Travis CI. If you don’t have it yet you can create it from here.

Let’s start with the integration. For the demo purpose, I am going to use API 25 as the targeted Android version in our Gradle and we are going to test our build on API 21 and API 19.

We have skipped the description of some lines as we don’t want to make this article too long and boring. We will concentrate more on the lines which are more important and difficult to understand.

Step-1 : Creating .yml file

First, create a .travis.yml file into your project’s root directory. This file will contain the configuration script for the automated build testing.


Step-2 : Basic setup:

Add above lines at the starting of the file. This basically tells Travis that this repository is an android application source code and it has to use Java 8 for compilation. The notification section will provide email notifications on the email you provided whenever a build gets completed successfully or fails due to some error.

Step-3 : Defining android targets:

Next, add above lines to your .travis.yml file. The line under the matrix section defines the Android API version and the processor architecture, on which the code will be tested. These lines re-executes the script for each variable. So if I write another line with: “– ANDROID_TARGET=android-16 ANDROID_ABI=armeabi-v7a”, it will execute the whole script with the 21, 19 and 16 APIs. We can say it is really useful to test different API levels. So, each commit you push to the repository will start two separate builds and test prepossess. You can see this in Travis dashboard like below.


In the global section, you can declare any global environment variable that is required to successfully complete the build process. This field is optional. Here, we defined “ADB_INSTALL_TIMEOUT =10”. This will increase the ADB connection timeout to 10 mins. Normally, it is 2 minutes by default.

Step-4 : Setting up the build configs

The android section contains the build related configurations. Specify the build tools version, targeted android API and extra repository you required to complete the build process like play services, m2repository, etc. At the end, specify emulator system images on which application testing will be performed. If you are going to use more than one API levels like this example, remember to define all the emulators needed. In our case, we are going to perform a test on API 21 and 19. So “sys-img-armeabi-v7a-android-25”, “sys-img-armeabi-v7a-android-18” are required emulator images.

Step-5: Enabling caching of resources:

Travis can cache directories that you need to speed up the subsequent builds by caching the Gradle dependency folders. To enable the caching, you have to specify which directories you want to cache under the cache section. Here, I am caching some of the .gradle folders.

Step-6: Writing build script:

In every project, we need to run the instrumentation tests. So we will run the emulator using “emulator -avd test -no-skin -no-audio -no-window &”. “ android-wait-for-emulator” script will wait until emulator is up and running. This will print “Waiting for emulator” in Travis log until emulator starts. The next two lines are printing the available devices for our information and sending the key “menu” to the emulator to unlock it.

Make sure you are using supported android targets and android ABI. You can list out supported android targets by running “android list targets”. You will get list of the supported android targets for given build environments in Travis log window like below:


Step-7: Enabling code coverage:

Code coverage is the best way to motivate you and your team to write more tests. Once we launched the tests and connected tests on Travis, we can generate the code coverage report with all the results. Once the jacoco (Java Code Coverage library) report is generated, it uploads all the reports tothe codecov server. You can see these reports online. (If you don’t know how to integrate jacoco in your android project, we found this article very useful. Or you can see the sample project, created by the codecov team.)

To run the code coverage after every successful build, you can integrate codecov by adding below lines in .travis.yml.

Congratulations! We completed all the required changes in our repository.

Your final .travis.yml file should look like this:


We are almost there. Stay with us. Now, sign in to Travis CI website and go to “Add new repository”. Select the repository in which you integrated .travis.yml file and enabled the Travis integration.

Enable travis integration by clicking the switch

That’s it. You are ready to push your commit with .travis.yml file.

Run your first build:

As soon as you push the commit to your GitHub repository, Travis will start building the project automatically. You can see the past build history by going to the “Build history” tab.


You can see your build status (whether it is running, passed or failed) right behind your GitHub repository commits list.


At this stage, if your build fails, try to find out what went wrong. You can find answers on how to fix those bugs on StackOverflow or paste your error in the comments below. We will try our best to resolve those the errors.

Here is one sample project to help you:

You can also find the test project that we used for the demonstration purpose in this tutorial in the below link. Go ahead and take a look into it.

This is an sample application which validates email address you entered in edit text. Here, we integrated Travis CI and CodeCov using Jacoco. You can see all the build statuses in Travis CI from here. If you want to see the latest code coverage report for the project, you can find it over here. This will give you an idea of how code coverage report looks like.


Continuous Integration (CI) is one of the good practice while developing the code. This lets you and your team concentrate on development and reduces the efforts required for maintaining the code. By outsourcing the maintenance of your test infrastructure, you can remove a whole bunch of work from your engineering team that can then be used to build your product. On another hand, code coverage gives you better idea of the amount of code remaining to test and inspires your team to write better test cases. As an overall effect, they both together help you and your team to develop robust product and allows you to deploy your code with confidence.

Code your next android app using RxJava

RxJava is new hot topic in the world of Android Development. The only problem is that it is very difficult to understand. Especially, Functional reactive programming is very hard to understand when you come from Object Oriented Programming. So, I am developing a series of articles to help you understand the basic concept of Reactive Programming.

In the previous Part 1we get the basic concept of the reactive programming. If you did not read yet, I will encourage you to first go through Part 1 and get the basic idea about reactive programming.

As we discussed in Part 1, Rx is made up of three key component.


Let’s create each component one by one. But, first let’s start with integration.

Integrate RxAndroid in project

RxAndroid is basically an android specific wrapper that provides some functions explicit to android and those are not available in RxJava. So, if you want to integrate Rx into your java project only, you don’t need to implement RxAndroid library into your project.

Here is the gradle dependency of RxJava and RxAndroid.

The version of both the library may change, depends on when you are reading this article. You can find latest version from here.

Marble Diagram

Rx uses marble diagram to explain how any operator works. Marble diagrams are very convenient and easy to understand.

  • The line above the box indicates the raw data. This data needs to be emitted by the observable. Different types of objects are display with different shapes.
  • Box in the middle indicates operator. There are many other operators available, that basically controls when and how the observable should emit the data. We are going to look into those operators in future.
  • The line below the box indicates the data that is emitted by the observable. Observer in Rx receives this emitted data.


Let’s create Observable

As you know observable is nothing but a function that emits the data stream. Below is the observable that emits 1 to 5 one after another.

Here just() is called as an operator. It emits the values provided in arguments. (And that’s why they named as just.)


Sometimes we want to refine specific event only to be emitted by observable. Let’s say in our above example we only want to emit only odd numbers out of the observable. We can achieve this thing using another operator called filter(). As the name suggest filter operator filters items emitted by an Observable.


Creating observer

Observers consume the data stream emitted by the observable. Wheneverthe observable emits the data all the registered observer receives the data.

In RxJava there are three callbacks you are going to receive in an observer.

  • onNext() : This method will be called when there is any new data emitted by the observer. The object that is emitted by the observable can be found in argument parameters of this callback.
  • onError() : You will receive this callback whenever there is any error occurred on observable. (After all the world is not perfect.)
  • onComplete() : Whenever observable is done with emitting the data streams, you will receive this callback. This indicates that there is no more data to emit.

In many cases you don’t care about onCompleted() or onError(). So instead of using Observer<T> we can use a simpler class to define what to do during onNext() using Action1 class.

Here, onCall() is equivalent to onNext() in our first approach.

Manage concurrency using Scheduler

As you know scheduler in the reactive programming manages the concurrency.

In Android the most common operation when dealing with asynchronous tasks is to observe the task’s result or outcome on the main thread because you want to update the UI. Using vanilla Android, this would typically be accomplished with an AsyncTask. But with Rx you can achieve this by using the schedulers.

There are two methods, which controls thread management.

  • subscribeOn() : By using this method you can define on which thread the observable should run.
  • observeOn() : By using this method you can define on which thread the observer should run.

RxJava and RxAndroid library provides some predefined schedulers. Like, indicates the IO thread. While Schedulers.newThread() will create a new thread to run observer/observable. You can find other various types of schedulers here.

Finally, we will use subscribe() to subscribe the observer to receive the data issued by the observable. This will return Subscription object that holds the reference to that specific connection between observer and observable.

Let’s see the output of above program.


We can see from the output that only odd numbers were emitted by the observer. At the end, when all the data are emitted, onComplete() got execute.


If you want to unsubscribe the observer from observable, you can call unsubscribe.

In android this is essential that you call unsubscribe in onDesrtoy() of your activity/fragment to release the connection between observer and observable. Otherwise, it may call memory leak.

If you have multiple subscriptions in your class, then you can use CompositeSubscription to unregister all the subscriptions at once. Below is an example on how you can do that:

In the next part I am going to explain different operators used in RxJava. Meanwhile, If you liked the article, click the 💚 below so more people can see it!