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Introduction to DB2 backup

DB2 backup command is used to store a copy of the current database or specified tables for the usage of this data for restoring in case if there happens any sort of data loss due to any reason. This ensures that the data’s security and availability are available throughout the period, and the performance and availability do not get hamper on the user end. In this article, we will study the scope, authorization, connection that is required, syntax, and the implementation of the backup command, along with the help of certain examples.

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Scope of backup command

We can make use of the back command in DB2 to create the backup of the current partition if nothing is specified externally. In case if we have specified the option of partitioned backup, then it takes the backup of all the data on the catalog node only. In case if we want to perform the backup operation for all the partitions of our current database, we can specify the option saying that all the partitions are mentioned in the list of chúng tôi file of the database server should be backed up. In the remaining cases, the partitions that are specified in the backup command get copied for backup.

Authorization required

The user who performs the backup command execution should have one of the following privileges with him/ her –

SYSCTRL – Privilege for system control

SYSADM – Privilege for system administration

SYSMAINT – Privilege for system maintenance

The connection that is required

Internally, DB2 automatically creates a new connection for performing the backup operation exclusively to the database which is specified. If the connection specified in the command already exists for the same database, then that connection is terminated, and a new connection is initiated exclusively for performing a backup of the data.


In the above syntax, the different terms used are described as below –

Name_of_database – It helps to tell which alias of the database is to back up.

User – We can specify the name of the user after specifying the USER keyword. Along with that, if there is an optional authorization assigned to the user, we can specify the password of the user after USING the keyword.

ON – This keyword is used to specify the set of the partitions of the database.

(ALL) DBPARTITIONNUM(s) partition_number(s) – We can specify DBPARTITIONNUM along with all to specify that we have to backup all the partitions if our database is partitioned. The usage of just DBPARTITIONNUM followed by the partition numbers is used to tell that only the specified partition having these partition numbers should be backed up. In case if we use DBPARTITIONNUMS, the range of partition numbers can be specified to be backed up.

TABLESPACE followed by the name of table space – It helps specify the table space list that we have to backup.


We can perform the backup operation in DB2 either online or offline mode. By default, when not specified, the backup is considered to be in offline mode. In case if we want to conduct the backup online, then we have to specify this keyword. We can carry out online backup only on the database, which is configured with enabled logarchmeth1.


It helps in specifying that only cumulative that is data that is changed from the time of most recently conducted full backup of the database is considered for creating the backup image for this backup operation.


DELTA helps to specify that the incremental backup needs to be carried out for the data that is modified from the last recently carried out the backup operation of any type, full or partial or specified.


We can specify the external things which are to be used, followed by the USE keyword. Some of the most used things are as specified below –

TSM – Tivoli Storage Manager should be used for backup.

SNAPSHOT – If we want to carry out snapshot backup, then none of the following parameters must be used –








XBSA – Backup Services API that is the XBSA interface, should be used as the data storage management facility to carry out the backup.

SCRIPT – We can specify the name of the executable script which can carry out the snapshot backup. Note that the name of the script that will be specified should contain its fully qualified filename.


Examples of DB2 backup

Let us consider the example of the offline backup to be carried out step by step –

Step 1 –

List all the databases or applications available in the system. The below command can do that –

db2 list application

The execution of the above command gives the following output –

Step 2 –

Using the handled id retrieved from the list of applications, we can force the application using the app as shown below –

db2 “force application (40)”

The output shown is somewhat shown as below –

Step 3 –

db2 terminate;

Step 4 –

We have to deactivate the database in order to stop all the operations that will be carried out on the database after this, which can lead to data modification by using the following command –

db2 deactivate database sample_database

Step 5 –

Now, we are ready to take the backup of our database named sample_database. For this, we will use the DB2 backup command and take the backup in the location C:UsersPayal UdhaniDesktopArticles.

db2 backup database sample_database to C:UsersPayal UdhaniDesktopArticles

The execution of the above command gives the following output –

In this way, we have got a new file at the specified location, which can be used to restore the data when we face loss or unavailability.

Conclusion – DB2 backup

DB2 backup command is used to perform backup operations online, or offline which creates the backup file. This backup file can be used in scenarios where we face data loss due to unavoidable circumstances so that data available at the user end doesn’t get affected.

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A Practical Implementation Of The Faster R


Which algorithm do you use for object detection tasks? I have tried out quite a few of them in my quest to build the most precise model in the least amount of time. And this journey, spanning multiple hackathons and real-world datasets, has usually always led me to the R-CNN family of algorithms.

It has been an incredible useful framework for me, and that’s why I decided to pen down my learnings in the form of a series of articles. The aim behind this series is to showcase how useful the different types of R-CNN algorithms are. The first part received an overwhelmingly positive response from our community, and I’m thrilled to present part two!

In this article, we will first briefly summarize what we learned in part 1, and then deep dive into the implementation of the fastest member of the R-CNN family – Faster R-CNN. I highly recommend going through this article if you need to refresh your object detection concepts first: A Step-by-Step Introduction to the Basic Object Detection Algorithms (Part 1).

Part 3 of this series is published now and you can check it out here: A Practical Guide to Object Detection using the Popular YOLO Framework – Part III (with Python codes)

We will work on a very interesting dataset here, so let’s dive right in!

Table of Contents

A Brief Overview of the Different R-CNN Algorithms for Object Detection

Understanding the Problem Statement

Setting up the System

Data Exploration

Implementing Faster R-CNN

A Brief Overview of the Different R-CNN Algorithms for Object Detection

Let’s quickly summarize the different algorithms in the R-CNN family (R-CNN, Fast R-CNN, and Faster R-CNN) that we saw in the first article. This will help lay the ground for our implementation part later when we will predict the bounding boxes present in previously unseen images (new data).

R-CNN extracts a bunch of regions from the given image using selective search, and then checks if any of these boxes contains an object. We first extract these regions, and for each region, CNN is used to extract specific features. Finally, these features are then used to detect objects. Unfortunately, R-CNN becomes rather slow due to these multiple steps involved in the process.

Fast R-CNN, on the other hand, passes the entire image to ConvNet which generates regions of interest (instead of passing the extracted regions from the image). Also, instead of using three different models (as we saw in R-CNN), it uses a single model which extracts features from the regions, classifies them into different classes, and returns the bounding boxes.

All these steps are done simultaneously, thus making it execute faster as compared to R-CNN. Fast R-CNN is, however, not fast enough when applied on a large dataset as it also uses selective search for extracting the regions.

Faster R-CNN fixes the problem of selective search by replacing it with Region Proposal Network (RPN). We first extract feature maps from the input image using ConvNet and then pass those maps through a RPN which returns object proposals. Finally, these maps are classified and the bounding boxes are predicted.

I have summarized below the steps followed by a Faster R-CNN algorithm to detect objects in an image:

Take an input image and pass it to the ConvNet which returns feature maps for the image

Apply Region Proposal Network (RPN) on these feature maps and get object proposals

Apply ROI pooling layer to bring down all the proposals to the same size

Finally, pass these proposals to a fully connected layer in order to classify any predict the bounding boxes for the image

What better way to compare these different algorithms than in a tabular format? So here you go!

Algorithm Features Prediction time / image Limitations

CNN Divides the image into multiple regions and then classifies each region into various classes. – Needs a lot of regions to predict accurately and hence high computation time.

R-CNN Uses selective search to generate regions. Extracts around 2000 regions from each image. 40-50 seconds High computation time as each region is passed to the CNN separately. Also, it uses three different models for making predictions.

Fast R-CNN Each image is passed only once to the CNN and feature maps are extracted. Selective search is used on these maps to generate predictions. Combines all the three models used in R-CNN together. 2 seconds Selective search is slow and hence computation time is still high.

Faster R-CNN Replaces the selective search method with region proposal network (RPN) which makes the algorithm much faster. 0.2 seconds Object proposal takes time and as there are different systems working one after the other, the performance of systems depends on how the previous system has performed.

Now that we have a grasp on this topic, it’s time to jump from the theory into the practical part of our article. Let’s implement Faster R-CNN using a really cool (and rather useful) dataset with potential real-life applications!

Understanding the Problem Statement

We will be working on a healthcare related dataset and the aim here is to solve a Blood Cell Detection problem. Our task is to detect all the Red Blood Cells (RBCs), White Blood Cells (WBCs), and Platelets in each image taken via microscopic image readings. Below is a sample of what our final predictions should look like:

The reason for choosing this dataset is that the density of RBCs, WBCs and Platelets in our blood stream provides a lot of information about the immune system and hemoglobin. This can help us potentially identify whether a person is healthy or not, and if any discrepancy is found in their blood, actions can be taken quickly to diagnose that.

Manually looking at the sample via a microscope is a tedious process. And this is where Deep Learning models play such a vital role. They can classify and detect the blood cells from microscopic images with impressive precision.

The full blood cell detection dataset for our challenge can be downloaded from here. I have modified the data a tiny bit for the scope of this article:

The bounding boxes have been converted from the given .xml format to a .csv format

I have also created the training and test set split on the entire dataset by randomly picking images for the split

Note that we will be using the popular Keras framework with a TensorFlow backend in Python to train and build our model.

Setting up the System

Before we actually get into the model building phase, we need to ensure that the right libraries and frameworks have been installed. The below libraries are required to run this project:




keras – 2.0.3





Most of the above mentioned libraries will already be present on your machine if you have Anaconda and Jupyter Notebooks installed. Additionally, I recommend downloading the chúng tôi file from this link and use that to install the remaining libraries. Type the following command in the terminal to do this:

pip install -r requirement.txt

Alright, our system is now set and we can move on to working with the data!

Data Exploration

It’s always a good idea (and frankly, a mandatory step) to first explore the data we have. This helps us not only unearth hidden patterns, but gain a valuable overall insight into what we are working with. The three files I have created out of the entire dataset are:

train_images: Images that we will be using to train the model. We have the classes and the actual bounding boxes for each class in this folder.

test_images: Images in this folder will be used to make predictions using the trained model. This set is missing the classes and the bounding boxes for these classes.

train.csv: Contains the name, class and bounding box coordinates for each image. There can be multiple rows for one image as a single image can have more than one object.

Let’s read the .csv file (you can create your own .csv file from the original dataset if you feel like experimenting) and print out the first few rows. We’ll need to first import the below libraries for this:

# importing required libraries import pandas as pd import matplotlib.pyplot as plt %matplotlib inline from matplotlib import patches # read the csv file using read_csv function of pandas train = pd.read_csv(‘train.csv’) train.head()

There are 6 columns in the train file. Let’s understand what each column represents:

image_names: contains the name of the image

cell_type: denotes the type of the cell

xmin: x-coordinate of the bottom left part of the image

xmax: x-coordinate of the top right part of the image

ymin: y-coordinate of the bottom left part of the image

ymax: y-coordinate of the top right part of the image

Let’s now print an image to visualize what we’re working with:

# reading single image using imread function of matplotlib image = plt.imread('images/1.jpg') plt.imshow(image)

This is what a blood cell image looks like. Here, the blue part represents the WBCs, and the slightly red parts represent the RBCs. Let’s look at how many images, and the different type of classes, there are in our training set.

# Number of unique training images train['image_names'].nunique()

So, we have 254 training images.

# Number of classes train['cell_type'].value_counts()

We have three different classes of cells, i.e., RBC, WBC and Platelets. Finally, let’s look at how an image with detected objects will look like:

fig = plt.figure() #add axes to the image ax = fig.add_axes([0,0,1,1]) # read and plot the image image = plt.imread('images/1.jpg') plt.imshow(image) # iterating over the image for different objects for _,row in train[train.image_names == "1.jpg"].iterrows(): xmin = row.xmin xmax = row.xmax ymin = row.ymin ymax = row.ymax width = xmax - xmin height = ymax - ymin # assign different color to different classes of objects if row.cell_type == 'RBC': edgecolor = 'r' ax.annotate('RBC', xy=(xmax-40,ymin+20)) elif row.cell_type == 'WBC': edgecolor = 'b' ax.annotate('WBC', xy=(xmax-40,ymin+20)) elif row.cell_type == 'Platelets': edgecolor = 'g' ax.annotate('Platelets', xy=(xmax-40,ymin+20)) # add bounding boxes to the image rect = patches.Rectangle((xmin,ymin), width, height, edgecolor = edgecolor, facecolor = 'none') ax.add_patch(rect)

This is what a training example looks like. We have the different classes and their corresponding bounding boxes. Let’s now train our model on these images. We will be using the keras_frcnn library to train our model as well as to get predictions on the test images.

Implementing Faster R-CNN

For implementing the Faster R-CNN algorithm, we will be following the steps mentioned in this Github repository. So as the first step, make sure you clone this repository. Open a new terminal window and type the following to do this:

Move the train_images and test_images folder, as well as the chúng tôi file, to the cloned repository. In order to train the model on a new dataset, the format of the input should be:



filepath is the path of the training image

x1 is the xmin coordinate for bounding box

y1 is the ymin coordinate for bounding box

x2 is the xmax coordinate for bounding box

y2 is the ymax coordinate for bounding box

class_name is the name of the class in that bounding box

We need to convert the .csv format into a .txt file which will have the same format as described above. Make a new dataframe, fill all the values as per the format into that dataframe, and then save it as a .txt file.

data = pd.DataFrame() data['format'] = train['image_names'] # as the images are in train_images folder, add train_images before the image name for i in range(data.shape[0]):     data['format'][i] = 'train_images/' + data['format'][i] # add xmin, ymin, xmax, ymax and class as per the format required for i in range(data.shape[0]):     data['format'][i] = data['format'][i] + ',' + str(train['xmin'][i]) + ',' + str(train['ymin'][i]) + ',' + str(train['xmax'][i]) + ',' + str(train['ymax'][i]) + ',' + train['cell_type'][i] data.to_csv('annotate.txt', header=None, index=None, sep=' ')

What’s next?

Train our model! We will be using the file to train the model.

cd keras-frcnn python -o simple -p annotate.txt

It will take a while to train the model due to the size of the data. If possible, you can use a GPU to make the training phase faster. You can also try to reduce the number of epochs as an alternate option. To change the number of epochs, go to the file in the cloned repository and change the num_epochs parameter accordingly.

Every time the model sees an improvement, the weights of that particular epoch will be saved in the same directory as “model_frcnn.hdf5”. These weights will be used when we make predictions on the test set.

It might take a lot of time to train the model and get the weights, depending on the configuration of your machine. I suggest using the weights I’ve got after training the model for around 500 epochs. You can download these weights from here. Ensure you save these weights in the cloned repository.

So our model has been trained and the weights are set. It’s prediction time! Keras_frcnn makes the predictions for the new images and saves them in a new folder. We just have to make two changes in the file to save the images:


# cv2.waitKey(0)

Let’s make the predictions for the new images:

python -p test_images

Finally, the images with the detected objects will be saved in the “results_imgs” folder. Below are a few examples of the predictions I got after implementing Faster R-CNN:

End Notes

R-CNN algorithms have truly been a game-changer for object detection tasks. There has suddenly been a spike in recent years in the amount of computer vision applications being created, and R-CNN is at the heart of most of them.


A Complete Guide To The Db2 Version

Introduction to DB2

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Later, after 1990, universal database called as UDB which is one of the DB2 server was created after deciding that the product should be able to run on any given authorized operating system such as windows, unix and linux platforms. Here we will see various versions of the DB2 and how it has evolved by adding new functionalities and supporting new programming languages over the period of time.

Various DB2 Versions

Given below are the various DB2 versions:

Version Code Name

Version 3.4 Code language used to create this platform is Cobweb.

Version 8.1, Version 8.2 Code language used to create this platform is Stinger.

Version 9.1 Code language used to create this platform is Viper.

Version 9.5 Code language used to create this platform is Viper 2.

Version 9.7 Code language used to create this platform is Cobra.

Version 9.8 Code language used to create this platform is PureScale.

Version 10.1 Code language used to create this platform is Galileo.

Version 10.5 Code language used to create this platform is Kepler.

To check the version of DB2 on you LUW machine, you can type the following command on the terminal or console:


SELECT fixpack_num, service_level FROM TABLE (sysproc.env_get_inst_info()) as informationOfInstance


1. Viper 2. Viper 2 3. Cobra

IBM came up with a new version called cobra in the month of June in 2009 which is the codename for DB2 9.7 for LUW which introduced new features of temporary tables, compression of data for database indexes and storage of objects which are very large. Cobra also came up with support of various XML data partitioning techniques like range partitioning, hash partitioning and multidimensional clustering. Because of these features one can easily work with XML in environment of data warehousing.

Many additional functionalities for users working with oracle database were also introduced in DB2 which included PL/ SQL syntax, commonly used syntaxes of SQL, scripting syntax and oracle database’s datatypes. Microsoft SQL server and oracle database users find it very familiar for using DB2 because of the exhibition of behavior of concurrency model.

4. Purescale

IBM launched a new DB2 version release in 2009 in the month of October named DB2 purescale which is the cluster database mainly designed for the non-main frame platforms which is mostly suitable for OLTP transactions that is online transaction processing. The design of the DB2 purescale is based on the DB2 data sharing with parallel sysplex on the main frame platforms. The architecture of DB2 purescale is completely fault-tolerant and has the storage of shared disk. This version of DB2 provides automatic load balancing and complete availability which can be extended upto 128 servers of database.

5. Galileo

IBM introduced to a new version of DB2 in early 2012 which was DB2 10.1 and the code name for this version was Galileo which can be used for windows, Linux and Unix platforms. One of the most promising features that became available was fine-level control over the database in which we can restrict the access on row level and column level. Besides this, Galileo came up with lot many new data management capabilities like multi-temperature management of the data which helps in deciding whether the data is hot or cold which means how often the data is accessed and accordingly we can decide the storage of the data. Another feature of Galileo includes the adaptive compression capability.

6. Kepler

IBM announced a new version of DB2 named Kepler in June 2013 which is the code name for DB2 10.5.

7. Other Versions

IBM launched a new version of DB2 distribution system for hadoop in the month of April 2024 to June 2024 which was named as DB2 LUW 11.1. IBM also relaunched a version of DB2 and dashDB in the middle of 2023 year and named them to DB2. An artificial intelligence enabled DB2 version was released by IBM on June 27 in 2023 which introduced many new performance improvements in query building and execution as well as features which can help in creating the applications which are enabled with AI.

Conclusion – DB2 Version Recommended Articles

This is a guide to DB2 Version. Here we discuss the introduction and the various DB2 versions for the better understanding. You may also have a look at the following articles to learn more –

Learn The Most Famous And Common Plugins Of Revit

Introduction to Revit plugins

Revit software can be associated with more than seventy-five plugins and Add-ons. These plugins are downloaded externally and then are incorporated with the Revit Program.

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Revit Plugins

Substantially, Revit is an Autodesk-owned 3D BIM (Building Information Modeling) software that is used to create and control digital depictions of material and practical aspects of buildings, infrastructures, railways, roadways, and many other places.

Although Revit has become a popular choice amongst the users, it still lacks some of the tools and functions of its own. This requires additional plugins and Add-on software that can be used and embraced within the program.

Some of the most common and popular plugins that are chiefly used with the program are-

ARCHSMARTER- Archsmarter provides an additional toolbox for Revit users. These tools dispense a platform for the users that makes the work more creative, productive, accurate, and easier.

RUSHFORTH TOOLS- Revit users commonly use these plugins for Model Management, Parameter Schedulers & Transformers, and Managing Imported Excel Files. The plugin is also very useful in controlling project and sheet previews and layouts. The users can create and update layout sheets automatically in Revit using this plugin.

PYREVIT- This particular plugin is used by programmers and coding professionals using the Revit program to create custom workflows, interfaces, add-ons, and toolsets. The plugin is compatible with Python, C#, and chúng tôi programming languages.

COLORS PLASHER- As the name itself says, this plugin is useful in properly managing color nodes based on the attributes given to the specific object. Although the user can achieve this technique using Revit tools, it makes the process complex and extensive. However, to overcome, the user can download and use this plugin effortlessly.

ENGIPEDIA LAYERS MANAGER-PRO- This plugin manages the layer structures, material layers, and material widths with efficient nodes and parameters. The plugin helps the users in differentiating the different groups of models and structures. Layers with core models are underlined, and materials that are used for structural designs are previewed in bold blue.

ALGO- This Plugin is helpful to the users in the early stages of planning and conceptualizing the details of the architecture. This plugin helps in distributing the deep and logical details to the customers and engineers. These details help them to conceive and plan the ideas in the inceptive stages of designing the process.

ENSCAPE- This plugin is popularly used with Revit software for rendering models and creating realistic architectural structures. The Plugin provides rendering in real-time and can render 3d as well as 2d models. The plugin uses the NVIDIA scheme to help the users in rendering realistic walkthroughs. Users who are into demonstrations and illustrations of models and structures can use Enscape to illustrate their ideas in virtual reality. The plugin supports the export of structures and videos in batches and contains an added library for modeling structures.

VRAY FOR REVIT – VRAY is a popular rendering engine amongst 3D programs. This plugin is created for Revit software itself that is used to enhance the experience of rendering with Revit software. This plugin combines with the interface of Revit without any effort. The plugin is used for top-notch lighting, super-efficient placement of the camera, and a realistic environment. With effortless blending with the Revit software, the whole process is created and concluded in the software itself.

FAMILY REVISER- This Plugin is very useful in editing and modifying the names and groups of modular families. Editing titles of models and structures can be extensive work for the users. This Plugin, however, helps in quick and easier modifications with the Find and Replace tools.

COINS AUTO SECTION BOX – This Plugin manages and controls the views while creating models and structures on Revit. It has tools for controlling markers, rulers, grids, and tags as well. This Plugin is helpful for the users who are juggling between 3D previews and the main model view. It also helps the user to create constant as well as provisional views.


Summarizing the above article, we have listed some of the most famous and common plugins used with Revit software to improve the program’s efficiency and workflow. These plugins offer great support and value to Revit Software.

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This is a guide to Revit plugins. Here we discuss the most famous and common plugins used with Revit software to improve the program’s efficiency and workflow. You may also have a look at the following articles to learn more –

Learn The Working Of Async In Java With Features

Introduction to Java async

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Working of async in java

In this article, we will discuss a callback method which is known as the async function in java. This function is also known as await in java. In java, to make or write asynchronous programming by starting a new thread by making it asynchronous themselves. The asynchronous callbacks are used only when the tasks are not dependent on each other, which might take some time for executing. So, in general, the async call can be explained by taking an example of online shopping where when we select some item and add it into the cart, then that item will not be blocked as it will also be available to others too where others don’t need to wait for the order of the item to finish. So, whenever we want to run any program that can be executed without blocking its execution, it is done using async programming.

1. completeableFutures

The completeableFutures is the java version of javascript promises known as completeableFutures which can implement two interfaces such as Future and CompletionStage, where a combination of these two interfaces completes this feature for writing or working with async programming. This feature provides many methods such as supplyAsync, runAsync, etc. These methods are used to start the asynchronous part of the code because supplyAsync method is used when we are doing something with the result, and if we do not want anything, we can use the runAsync method. There other different methods in completeableFutures such as thenCompose if we want to use multiple completeableFutures one after one or in simple when we want to use nested completeableFutures and if we want to combine the results of two completeableFutures, then there is a method named thenCombine method. So these all methods are handled in a completable future, which in turn has completion stage methods that hold all these methods.

Sample example: To create completeableFuture using no-arg constructor by the following syntax:

So to get the result, we have to use the get() method. So we can write it as

String result = completeableFuture.get()  where this gets () method will block until the Future completes, but this call will block it forever as Future is never completed. So we have to complete it manually by calling the below method.

Therefore the clients get specified results ignoring the subsequent calls. The program might look like below.

while (!completableFuture.isDone()) { System.out.println(“CompletableFuture is not finished yet…”); } long result = completableFuture.get();

2. EA Async

This is another feature in java for writing asynchronous code sequentially, which naturally provides easy programming and scales. This is Electronic Arts which got the async-await feature which is given to the java ecosystem through this ea-async library. This feature converts the runtime code and rewrites the call to await method, which works similarly as completableFuture. So we can implement the above completeableFuture code by using the EA-sync method known as the await method by making a call to the chúng tôi method for initializing Async runtime.

So let us consider an example of factorial of a number using both completeableFuture and EA sync.

while (!completableFuture.isDone()) { System.out.println(“The completeableFuture is not completed…”); } double res = completableFuture.get();

static { Async.init(); } public func_name(){….. same as above code of completeableFuture… double res Async.await(completableFuture);

From the above sample code, which is transformed code of completeableFuture code by using static block also for initializing the Async runtime so that the Async can transform the code of completeableFuture code during runtime and then to the await method, it can rewrite the calls which will now EA async will behave similarly to that of using the chain of completeableFuture or chúng tôi method. So now, when once the asynchronous execution of any method is completed, then the result from the Future method is passed to another method where the method is having the last execution using the CompleteableFuture.runAsync method.

In Java, as discussed above, there are many different ways for writing the asynchronous programming using various other methods.


In this article, we discussed java async where it is defined as a callback method that will continue the execution of the program without blocking by returning the calls to the callback function. This article saw how asynchronous programming is written in java using different features such as CompleteableFutures, EA async, Future Task, Guava, etc. In this article, we have seen two among these features for making the callback functions asynchronous by using various methods provided by the given respective features.

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Your Backup Drive Needs A Backup Plan: Three Ways To Safeguard The Data

Congratulations on backing up your PC—but you aren’t as safe as you may think you are. Files on your backup drive can be just as vulnerable to disaster as files on your main system are. Most recently, CryptoLocker demonstrated that an external drive connected to a PC—a secondary hard drive, for example, or an external USB hard drive used for backup—could fall victim to ransomware just as easily as the PC on the other end of the cable.

“A lot of people got burned by CryptoLocker because their attached backup drives were also encrypted by the Trojan,” says Dwayne Melancon, CTO of enterprise security company Tripwire. “CryptoLocker encrypts local data files, but it also looks for attached storage devices, network shares, and other storage locations connected to your computer.”

Don’t let a CryptoLocker-style catastrophe happen to you. Here are a few options for protecting your backup drive against such attacks.

Disconnect your backup data

Marc Maiffret, CTO of security software firm BeyondTrust, sums up the most common-sense solution: “Make sure to back up to a media that can be removed physically from your system and stored offline.”

This approach is less convenient, of course, but it’s a good habit to form for a couple of reasons. First, it moves your backup data out of harm’s way if ransomware ever infects your PC. Second, if you store the backup media in a fire safe—or better still, offsite in a safety deposit box, the backup may survive even if a natural or unnatural physical disaster destroys the original data.

Backing up your data to recordable CDs or DVDs can keep your backup safe from malware, though it may force you to use many discs.

One option is to back up your data to less-volatile media such as recordable CDs or DVDs. Once a recording session is finalized, the data should be safe from malware threats even if the disc remains in the drive. The downside of using optical discs is the media’s much smaller storage capacity compared to a modern hard drives, meaning that performing a full backup may require multiple discs.

Back up to the cloud

Rather than backing up locally, consider using the cloud. Cloud backup applications generally run as a background service that the system doesn’t view as an attached or networked drive. As a result, malware threats are unlikely to spread directly to cloud backup.

Most modern backup systems use a proprietary storage format for further protection. “This makes the backed-up files unable to be read or written to by common malware,” says Paul Lipman, CEO of Total Defense, which sells online backup services as well as antivirus and security software. “It doesn’t mean it’s impossible—it’s just highly unlikely. Malware generally works by attaching to existing files on the system; and in cases of proprietary storage formats, the malware would not be able to infect the backup directly.”

Using a cloud backup service like Backblaze improves the security of your data.

Note, however, that most cloud backup services automatically sync and update data. If your local PC is compromised, you’ll want to disable the service to prevent the compromised data from overwriting your good backup data.

Back up multiple versions

The most effective way to safeguard your backup is maintain more than one copy of your data.

There are two ways to do this. First, most security experts recommend backing up your important data to more than one location. For example, back up to an external USB drive that you disconnect when it’s not in use, and also use a cloud backup service. That way if infection or physical disaster compromises either backup, you’ll still have a good copy of the data.

Redundancy is the strongest protection for your backed up data. Crashplan makes it easy to back up to multiple locations.

The second way is to maintain version histories of your files: Save multiple backups from different points in time, and choose a cloud backup service that stores more than just the most recent backup, so you can restore data from a time before the compromise occurred.

“I go a step further and also create several generations of local and off-site image backups of my computer, so I can quickly restore one of them if my system is lost, compromised, or otherwise unusable,” Tripwire’s Melancon says.

Your backup drive needs a backup plan. Without one, you’re not much better off than if you’d never backed up in the first place. Follow one of the methods laid out here to ensure that your backup will be there—in readable form—when you need it most.

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