In Scala, you can nest methods and functions inside a method. This is a useful feature when you want to encapsulate a part of the logic specific to the enclosing method. The following is an example of a nested method.
nestedMethod(x," a nested method")
When you run this code, it will print ‘I am a nested function’.
Similarly, you can do nested functions as below.
Passing a function as a parameter helps in dividing complexity logically. For example, a function that iterates through a list of words and converts them to the upper case can be passed to any method that needs this functionality without exposing the list. It lets you delegate the complexity to the function. Here is the code both in Scala and Java.
First define a method that takes a function as a parameter.
The above function fn, takes no parameters and outputs a String. Let’s define a function that takes no parameters and outputs a String, so we can pass it to methodA.
val functionB=()=>"Hi, I am functionB from Scala, I am passed to functionA as a parameter."
Pass functionB to functionA as below.
You should see “Hi, I am functionB from Scala, I am passed to functionA as a parameter.” in your browser. You can download the complete working example for both Scala and Java from my Git repo below. Here is the video version of this post.
Let’s implement the same thing in Java.
IFunc functionB=()->"Hi, I am functionB from Java, I am passed to functionA as a parameter.";
The main advantages of containerizing the microservices are
It makes applications portable, as dependencies can be packaged together with applications. For example, if one application needs JRE7 and the other needs JRE8, they can be packaged separately with their respective dependencies and deployed on a server, regardless of what JRE version exists on the server.
It removes the bottleneck of being limited by a number of servers during the release process, as you can deploy multiple versions of the same image or multiple features at the same time. For example, you can deploy multiple features for QA at the same time, regardless of how many servers you have.
You can start multiple instances of the application to handle increasing load.
As the containers can be isolated from each other, it will help in security.
Docker is a command line program, a background daemon. Docker containers run natively on Linux and share the kernel, making it very light weight as compared to VMs.
In this post, we will containerize a Spring Microservice with Docker. First, install Docker and make sure it is working as listed at Install Docker.
Then go to Spring Initializer site and add Web as a dependency and click on Generate Project to download a template project. Import the project into your favorite IDE and add the @RestController annotation to the main class. Add additional method with @RequestMapping as shown below.
Run the application and go to http://localhost:8080 and you should see “My First Dockerized Microservice” in your browser. This ensures, our service builds and runs fine without Docker. Stop the application and close your IDE.
Add a file called Dockerfile to the project directory and copy and paste the following contents into it.
In the 2nd command -d tells Docker to run it as detached so your Terminal is not tied up. Port number on the left is host machine port, it can be any valid port. Docker container is an instance of an image. Think of an image as a Java class and a container as an instance of that class.
Once, you are done stop and remove containers with the following commands. Note that, each time you use Docker run, it creates a new container from the image.
docker ps(shows all the running containers along with container ids)
docker rm<container id>(Thiswill delete the container)
As you create more microservices, it is hard to keep track. Eureka helps in discovering and locating the services. It acts as a load balancer and service registry. All the services are identified by their names without port information. If service A running on port 8080 at URL http://localhost:8080 registers itself as SERVCIEA on Eureka, other services on Eureka can call it as SERVCIEA instead of calling http://localhost:8080.
If you rather watch the video on what’s in this post, see below, otherwise continue reading.
Eureka acts as an internal DNS and middle tier load balancer. With Eureka, load balancing happens at the instance level and the client instances know the information about which servers they need to talk to, making it ideal for the client-based load balancer.
To implement it follow the steps listed below.
Go to https://start.spring.io/ and download the project with Eureka Server as a dependency.
Add the following text to the application.properties.
Below, we are configuring the name for this service, port to run at and telling it not to register itself as a service.
Add the following annotation to SpringBoot main class and run the application.
Back propagation is used by Optimization algorithms to adjust w and b.
At the end of a forward propagation (see my previous post), output layer results in a predicted value, we compare this predicted value with the corresponding actual value from our training set to figure out the difference, also referred to as cost function. Cost function measures how weight w and bias b are doing on a training item to come up with a good prediction.
If the cost function is high, it means network predicted a value that is far from the actual value. For example actual value is 6, network predicted 2.
If the cost function is low, it means network predicted a value that is close to the actual value. For example actual value is 6, network predicted 5.
So the goal is to minimize the cost function. Weight w and bias b impact how close or far prediction is from the actual value. Optimization algorithms like Gradient Descent, Adam etc., update w and b to minimize the cost function.
Back propagation figures out, impact on cost function (sensitivity) , in relation to w and b, but it does not update w and b. Optimization algorithms like Gradient descent determine how much to change and update w and b based on the sensitivity.
For example in a simple 2 layered neural network, back propagation determines that increasing w in layer1, from 1 to 2, increases the cost function from 3 to 6. This means if you increase w by one unit, cost function goes up by 3 times the change. In other words, 3 is the derivative of the cost function with respect to w. Similarly back propagation calculates derivate of b. Gradient descent uses these derivatives to update w and b in order to minimize the cost function.
In the previous post, we talked about standing up a Spring Cloud Config server. In this post, we will discuss how client applications can get different properties for different environments like development, test, production etc from the Config server.
If you rather watch the video on what’s in this post, see below, otherwise continue reading.
First, let’s make sure, your Config server is running as indicated in the previous post.
Next, create a client application to test the Config server. Go to the Spring Initializr site https://start.spring.io/ and in the dependencies search box add web, config client dependencies (notice that for the Config server in the previous post, we added Config server as a dependency) and click on Generate Project to download an empty Spring boot project.
Create a file named bootstrap.properties or bootstrap.yaml in the resources folder and add the following properties.
The first property sets the name of the application. Note, this should exactly match the name of the properties file you created in the previous post (remember you created a property file called clientapp.properties). This is how the Spring Config server knows, what properties to serve to this application.
Instead of using different branches to store properties for different environments, you can also use different repos.
In most cases, you can also use application.properties instead of bootstrap.properties. but note that bootstrap gets higher precedence. I think it is better to keep external properties in bootstrap and the rest in applcation.properties for a clean separation.
When multiple microservices are moving through a release cycle from development, test, UAT to production, it is a big setback to on-demand release process to update environment specific properties like DB connection strings, message broker parameters, email server properties etc. for each of the environments.
Spring Cloud Config server resolves this issue, by moving the properties out of the applications and centralizing them. There is no need to keep updating properties from one environment to the other, as applications move along in the release process. This offers a lot of flexibility and speeds up the entire deployment cycle.
With Config server, you have a central place to manage the properties across all environments.
For a configuration server to serve properties to client applications, it needs to be pointed to the source where properties exist. While you can use a file system or database, in this post we focus on Git repo backed Config server which is the most common set up.
Create an empty folder called config-properties-env anywhere on your system and inside the folder create a file called clientapp.properties. Add the following to the file and save.
message=I am a property from Config server for Development Environment
Initialize a Git repo, add the file and commit.
git add client.properties
git commit-m"dev properties"
Standing up Spring Cloud Config server is very easy, it is like any other microservice with an additional annotation and a dependency.
Go to the Spring Initializr site https://start.spring.io/ and in the dependencies search box, add Config server, web dependencies and click on Generate Project to download an empty Spring boot project.
Add @EnableConfigServer annotation to the main class, as shown below.
Add the following to the application.properties file.
You have now successfully set up the Config server and ready to serve the properties to the client applications.
Here is the video version of it.
In the next post, I will discuss how client applications can use the Config server to get properties, how to organize properties for different environments, and how to encrypt passwords in property files.
We recently moved a few applications to AWS cloud and immediately started noticing a significant drop in performance. This was happening for the applications that were being developed locally on-premise, and access Oracle database on the cloud. Performance decreased by many folds. One of the applications that used to take 5 minutes to process 35 million records, was taking 3 hours.
I excepted a little bit performance hit, but not 36 times slower. First, I checked all our recent commits, to see if any of the recent changes were causing the issue, but I didn’t notice anything significant. Then, I checked SQL execution plan and fine-tuned SQL created indexes on the key columns. This didn’t make any difference at all. I also tried tracing network and traffic but didn’t see anything important.
Adding to the confusion, when I tried running the same queries as in the application with SQL clients like SqlPlus, Toad etc. selects ran in seconds. This ruled out the possibility of something wrong with the SQL. To narrow it down, I created a very simple microservice that ran same queries without any data processing, but it took hours to run. This pointed to the fact that, even though SQL itself was running fast, once it was used in an application, something was going wrong.
After fiddling with different configurations, I started noticing immediate improvements in performance, as soon as I added setFetchSize to Spring JdbcTemplate and tuned it, the performance came back to before migration levels, from 3 hours to 5 minutes. Here is the code in Scala, depending on your configuration your fetch size, may be different.
If you are using Spring’s NamedParameterJdbcTemplate, configure JdbcTemplate as above and pass it to NamedParameterJdbcTemplat as a constructor arg.
A neural network learns relationships between inputs and the corresponding outputs from sample data, it then applies the learnings to new inputs to predict the best possible outputs.
Weights and bias
A Neural network consists of neurons and one or more layers. As in the pic above, weights (w) connect neurons from one layer to the next and represent the importance of a given input to its respective output.
For example, how important are the following inputs to decide where to go for a vacation?
1.Price of tickets (w=1)
2. Price of hotels (w=1)
3. Weather (w=0)
Higher weight means more importance.
While weights represent the importance, bias is used to fine-tune the relationship between inputs and outputs.
A neuron computes the weighted sum of its inputs, adds bias to compute (z) and passes the z to the activation function (a) to get a single output. A neural network learns by collecting these outputs from each neuron; this is done by doing forward propagation, getting the loss, and updating w and b to decrease the loss (backward propagation).
Neural Network (NN) takes an input x from the input layer, it multiplies it with its respective weight, adds bias to it and passes the resulting value to an activation function. The resulting value is then passed to the next layer as an input.
z = w * x + b and z passed to, a = sigmoid(z)
At the end of the forward propagation, the output layer results in a predicted value, then we compare the predicted value with the actual value to figure out the difference, and update w and b to decrease the difference. This process is repeated multiple times to get to the prediction we like.
In the next posts, we will discuss activation functions, cost functions, and backward propagation.
If a model is doing great on a training set, but not on a test set, you can use regularization. For example, if training set error is 1%, but test set error is 11%, we may be dealing with an overfitting or high variance issue.
There are 2 ways of dealing with overfitting; one is to get more data to train or try a regularization technique. When data is hard to come by or expensive, you can try regularization. L2 is the most commonly used regularization. Similar to a loss function, it minimizes loss and also the complexity of a model by adding an extra term to the loss function.
L2 regularization defines regularization term as the sum of the squares of the feature weights, which amplifies the impact of outlier weights that are too big. For example, consider the following weights:
w1 = .3, w2= .1, w3 = 6, which results in 0.09 + 0.01 + 36 = 36.1, after squaring each weight. In this regularization term, just one weight, w3, contributes to most of the complexity.
L2 regularization prevents this by penalizing large weights for being too large, to make it simple.
You can fine-tune the model by multiplying the regularization term with the regularization rate. By increasing the regularization rate, you encourage weights to go towards 0, thus making your model simpler. By decreasing the regularization rate, you make your weights bigger, thus making your model more complex.
How Regularization reduces overfitting
Increasing the regularization rate makes the whole network simpler by making weights smaller, which reduces the impact of a lot of hidden units. This makes the activation function relatively linear as if each layer is linear. This is how L2 regularization solves overfitting.