Paging with Spring Data

This blog shows by an example on how to use paging support in Spring Data. Let start with following repository without paging


public interface ProductRepository extends JpaRepository<Product, Long> {

 @Query("SELECT p FROM product p WHERE p.category= :category")
 List<Product> findByCategory(@Param("category") String category);



The above query will return all products with matching category. To change it to only return products required to fill in a single page,  the method is updated to:

public interface ProductRepository extends JpaRepository<Product, Long> {

 @Query("SELECT p FROM product p WHERE p.category= :category")
 Page<Product> findByCategory(@Param("category") String category, 
     Pageable pageable);



  1. The method now returns an object with class Page instead of List
  2. A new argument with type Pageable is added

The Pageable argument allows you to specify a few things including which page to return, the number of items in each page and properties used to sort the results. For example, to return the 2nd page where size of each page is 10, the method can be called with Pageable below

Pageable pageable = new PageRequest(1, 10);

Class PageRequest is a concrete implementation of Pageable provided by Spring Data. Note page number is 0-indexed.

To define how the results should be ordered, a Sort object can be supplied as the 3rd argument of the PageRequest constructor. For example, to return list of products sorted from highest price and then by name in alphabetical order:

Sort sort = new Sort(new Order(Direction.DESC, "price"), 
                     new Order(Direction.ASC, "name"));

Pageable pageable = new PageRequest(1, 10, sort);

Finally, note that the Page object returned from the method contains not just the list of products but also other values useful for implementing pagination, e.g. total number of results, number of pages. See the javadoc here for more detail.




Using Google Maps Directions APIs with more than 8 waypoints

Google Maps Directions APIs are great to create routes and directions for use in many practical business problems, e.g. delivery routing. One limitation is it only allows 8 (or 23 in premium plan) waypoints between the origin and destination locations. This blog demonstrates how to work around this limitation with the Maps JavaScript API. I am implementing the solution with TypeScript for use in an AngularJS 1.x application.

The idea here is straight forward: Given a route with more than 8 waypoints, i.e. 10 or more locations, we divide the route into multiple legs where each leg can have at most 8 waypoints. That is for a route with locations

[1,2,3,...,n] where n > 10

we would like to end up with multiple legs like:

leg 1: [1,2,3,...,10]
leg 2: [10,11,...,19]
leg 3: [...,n]

then we can use the directions API to get the route for each leg and concatenate the results back together as a single route on Google Maps.

First let define the objects to represent the route, waypoints and legs.

// Class to represent a route
export class Route {
 totalDistanceInKm : number;
 totalDurationInMins : number;
 drops : Drop[];
 legs : Leg[];

// Class to represent a drop (or waypoint)
export class Drop {
 location : google.maps.LatLng;
 // route details from directions service
 distanceInKm : number; // route distance from previous drop
 durationInMin : number; // route duration from previous drop

// Class to represent a leg of a route, used as input to directions service
export class Leg {
 origin : google.maps.LatLng;
 destination : google.maps.LatLng;
 waypoints : Array<google.maps.LatLng>;

A route can have multiple drops. The first and last drop corresponds to route origin and destination respectively. A drop consists of just a latlng location and a number of variables to store results, e.g. distance and duration, from the directions service response. The Leg class stores the information required as input requests to the directions service.

To create a route from a list of locations, use the following methods (assuming the class member _currentRoute contains the list of drops of the input route.

export class DeliveryRouteService implements IDeliveryRouteService {


 private createLegs() : void {
      var route = this._currentRoute;
      var drops = route.drops;
      route.legs = [];
      var i = 0; 
      var j = Math.min(drops.length, 10);

      while (j <= drops.length && j > i + 2) {
           route.legs.push(this.createLeg( drops.slice(i,j))); 
           i = j - 1;
           j = Math.min(drops.length, i + 10);

 private createLeg(drops : Drop[]) : Leg {
      var leg = new Leg();
      leg.origin = drops[0].location;
      leg.destination = drops[drops.length-1].location;
      leg.waypoints = [];
      if (drops.length > 2) {
           for (var k = 1; k < drops.length - 1; k++) {
      return leg;

With the route and legs created, you could call the directions API to obtain the route one leg at a time and concatenate the results for the whole route in your own codes. The following code snippets demonstrate how this could be done:

public directionsForLeg(idx : number, dropIdx : number) {
      var timeout : ng.ITimeoutService = this._timeoutService;
      var leg = this._currentRoute.legs[idx];
      var waypoints : google.maps.DirectionsWaypoint[] = [];
      for (var i = 0; i < leg.waypoints.length; i++) {
           waypoints.push(<google.maps.DirectionsWaypoint> {
           location : leg.waypoints[i],
           stopover : true
      // (1) Create directions API request
      var request = <google.maps.DirectionsRequest> {
           origin : leg.origin,
           destination : leg.destination,
           waypoints : waypoints,
           optimizeWaypoints : false,
           travelMode : google.maps.TravelMode.DRIVING
      // (2) invoke directions service using wrapper service
      this._googleMapService.directions(request).then(function(result) {
           var resultLegs = result.routes[0].legs;
           for (var j = 0; j < resultLegs.length; j++) {
             // do something with results here, e.g. get leg distance/duration

           if (idx < that._currentRoute.legs.length - 1) {
                // (3) do the next leg if there are more, 
                // put some delay to avoid hitting request limit
                timeout(function() {
                 that.directionsForLeg(idx+1, dropIdx);
                }, 1000, true);
           } else {
                // all done, notify route/directions is ready
      }, function(e) {
           that._logger.error('directions service failed: ' + e);

Note the recursion used to call directions service for the next leg in (3). To get route and directions for the entire route,  just call

 directionsForLeg(0, 1); // start with 1, first drop is origin

Below is the wrapper service for Google Maps JavaScript API

export class GoogleMapsService {
 static id : string = 'GoogleMapsService';
 private _map : google.maps.Map;
 private _renderers : google.maps.DirectionsRenderer[];
 private _directionsService : google.maps.DirectionsService;


      directions(request : google.maps.DirectionsRequest) : ng.IPromise<any> {
           var deferred = this.$q.defer();
           // create renderer
           var opts = <google.maps.DirectionsRendererOptions> {
               map : this._map,
               suppressMarkers : true
           var renderer = new google.maps.DirectionsRenderer(opts);

           this._directionsService.route(request, function(response, status) {
               if (status == google.maps.DirectionsStatus.OK) {
               } else {
           return deferred.promise;


Each call to the directions service will create and render the route for the input leg on the map.

The work around here has one obvious limitation – you can’t optimize the waypoints for the entire route. In practice, this may not be a severe issue if the waypoints are reasonably well ordered. It is still possible to apply optimization to each leg to improve the overall route distance and duration.


Monitoring Spring Boot Applications with Prometheus – Part 2

In my previous post, I describe how to use Prometheus and its JVM client library in a Spring Boot application to gather common JVM metrics. In this blog, I will demonstrate how to write your own application specific metrics using the client library. Prometheus client libraries support 4 core metric types: Counter, Gauge, Histogram and Summary. Its documentation has a brief but clear explanation of each metric types.

I will create 2 metrics to gather statistics about incoming requests to a Spring web application. In particular, a counter to count how many requests the web application handles and a summary for measuring the processing time of the incoming requests.

Application Set Up

To demonstrate the metrics we are going to implement, I have the following controller implementing two endpoints /endpointA and /endpointB. They do nothing except wasting a random amount of time between 0 and 100 ms.

public class HomeController {
     private Logger logger = LoggerFactory.getLogger(getClass());
     public void handlerA() throws InterruptedException {
          Thread.sleep(RandomUtils.nextLong(0, 100));
     public void handlerB() throws InterruptedException {
          Thread.sleep(RandomUtils.nextLong(0, 100));

Counter Metric

A counter is a metric of numeric value that never goes down. Here we add a request handler to keep track of the cumulative number of requests the web application receives:

import io.prometheus.client.Counter;
public class RequestCounterInterceptor extends HandlerInterceptorAdapter {

     // @formatter:off
     // Note (1)
     private static final Counter requestTotal =
          .labelNames("method", "handler", "status")
          .help("Http Request Total").register();
     // @formatter:on

     public void afterCompletion(HttpServletRequest request, HttpServletResponse response, Object handler, Exception e)
 throws Exception {
          // Update counters
          String handlerLabel = handler.toString();
          // get short form of handler method name
          if (handler instanceof HandlerMethod) {
               Method method = ((HandlerMethod) handler).getMethod();
               handlerLabel = method.getDeclaringClass().getSimpleName() + "." + method.getName();
          // Note (2)
          requestTotal.labels(request.getMethod(), handlerLabel, Integer.toString(response.getStatus())).inc();


  1. We implement a counter using io.prometheus.client.Counter class to keep track of number of incoming requests handled by this handler. The counter is named http_requests_total and consists of a number of labels (method, handler and status). A label is an attribute of a metric which can be used in query to filter and aggregate metrics.
  2. The counter is incremented using the Counter’s inc() method. The values of the metric labels method, handler and status are populated with the request http method (get/post), the spring mvc controller and method, and response http status respectively.

Summary Metric

A summary is a complex metric which track a number of observations as well as their counts. See here for a comprehensive explanation of summary and histogram metrics. Similar to previous section, we implement the metric within a request handler class:

import io.prometheus.client.Summary;
public class RequestTimingInterceptor extends HandlerInterceptorAdapter {

      private static final String REQ_PARAM_TIMING = "timing";

      // @formatter:off
      // Note (1)
      private static final Summary responseTimeInMs = Summary
           .labelNames("method", "handler", "status")
           .help("Request completed time in milliseconds")
      // @formatter:on

      public boolean preHandle(HttpServletRequest request, HttpServletResponse response, Object handler) throws Exception {
           // Note (2)
           request.setAttribute(REQ_PARAM_TIMING, System.currentTimeMillis());
           return true;

      public void afterCompletion(HttpServletRequest request, HttpServletResponse response, Object handler, Exception ex)
 throws Exception {
           Long timingAttr = (Long) request.getAttribute(REQ_PARAM_TIMING);
           long completedTime = System.currentTimeMillis() - timingAttr;
           String handlerLabel = handler.toString();
           // get short form of handler method name
           if (handler instanceof HandlerMethod) {
                Method method = ((HandlerMethod) handler).getMethod();
                handlerLabel = method.getDeclaringClass().getSimpleName() + "." + method.getName();
         // Note (3)
         responseTimeInMs.labels(request.getMethod(), handlerLabel, Integer.toString(response.getStatus())).observe(


  1. We implement a metric with class io.prometheus.client.Summary. The same set of labels as used in the request counter is used here.
  2. The response time is measured here as time lapsed between the calls to preHandle and afterCompletion methods. This is just for illustration only so I can demonstrate the use of Prometheus client library.
  3. The summary metric is updated by calling the observe() method with the response time value.

Collecting Metrics

Now that we implement the request handler interceptors, we can register them to the test endpoints and start up the Spring Boot web application. Hit the two endpoints using the browser and then go to the Prometheus url, e.g. http://localhost:8080/prometheus, should return something similar to the following:

# HELP http_response_time_milliseconds Request completed time in milliseconds
# TYPE http_response_time_milliseconds summary
http_response_time_milliseconds_count{method="GET",handler="HomeController.handlerA",status="200",} 3.0
http_response_time_milliseconds_sum{method="GET",handler="HomeController.handlerA",status="200",} 169.0
http_response_time_milliseconds_count{method="GET",handler="HomeController.handlerB",status="200",} 1.0
http_response_time_milliseconds_sum{method="GET",handler="HomeController.handlerB",status="200",} 59.0
# HELP http_requests_total Http Request Total
# TYPE http_requests_total counter
http_requests_total{method="GET",handler="HomeController.handlerA",status="200",} 3.0
http_requests_total{method="GET",handler="HomeController.handlerB",status="200",} 1.0

Note the summary metric http_response_time_milliseconds actually collects two time series data: _count and _sum. In practice, we don’t require a separate counter metric. It is more to demonstrate how to implement a simple counter with the Prometheus client library.

Also, data for each metric are collected separately for each distinct set of label values. In this example, we have metrics for each individual endpoints given the label values for the label handler is different. Since we also include the response status as a label, we will potentially get other metrics with non 200 status value, for example

http_response_time_milliseconds_count{method="GET",handler="HomeController.handlerA",status="500",} 3.0

Queries in Prometheus

One of the useful features of Prometheus is its powerful query language for manipulating the time series data collected.

For example, the cumulative value of total number of http requests of the counter we implement is not of much use. Typically we would want to get the number of requests a server received per second to get an idea of the system load at various time of the day. This can be achieved by query like the following:

rate(http_requests_total{job="blog", handler="HomeController.handlerA"}[5m])

The above query uses the built-in rate() function to return per-second rate of the http requests measured over the last 5 minutes ([5m]) for the endpoint /endpointA, as specified by the handler filters handler=”HomeController.handlerA”.  The query can be modified to only gather requests that are not successful (not 2xx) by including the label status in the query:

rate(http_requests_total{job="blog", handler="HomeController.handlerA", status!~"^2..$"}[5m])

Prometheus include 2 labels names job and instance for the target as defined in the prometheus.yml configuration file. It is recommended to always include the job name in the query.

Similarly, we can create query to get the average response time over last 5 minutes with the following query


For more details on operations and functions Prometheus support, see their documentation here

This blog post introduces with examples how to implement application specific metrics using Prometheus JVM client library as well as how to use query functions provided by Prometheus to filter and query the data collected. The ability for developers to implement their own metrics together with support with powerful query language by Prometheus is particularly useful when one needs to implement, monitor and analysis specific application functions beyond typical JVM and application metrics.

Monitoring Spring Boot Applications with Prometheus – Part 1

This blog post will demonstrate how to use Prometheus to monitor a spring boot web application. Prometheus is an open source tool for monitoring systems by collecting metrics from target systems as time series data. It supports multiple approaches for instrumenting the application codes. I am going to show how to do this using the Prometheus JVM client library.

Instrumenting with Prometheus JVM client

POM setup

I set up a Spring Boot project in Maven and include the following dependency for the Prometheus JVM client (version 0.0.16):

 <!-- Hotspot JVM metrics -->
 <!-- Exposition servlet -->
 <!-- The client -->

Configure and implement Metric endpoint

The main method for Prometheus to collect metrics is via scraping an endpoint implemented by the target application on regular intervals. To do that, include a Java configuration class as follows:

public class PrometheusConfiguration {

     CollectorRegistry metricRegistry() {
         return CollectorRegistry.defaultRegistry;

     ServletRegistrationBean registerPrometheusExporterServlet(CollectorRegistry metricRegistry) {
           return new ServletRegistrationBean(new MetricsServlet(metricRegistry), "/prometheus");


The above code adds the endpoint (/prometheus) to the Spring Boot application. Now we are ready to add some metrics to it. The Prometheus JVM client includes a number of standard exporters to collect common JVM metrics such as memory and cpu usages. Let’s add them to our new prometheus endpoint

First, we create a exporter register class

 * Metric exporter register bean to register a list of exporters to the default
 * registry
public class ExporterRegister {

     private List<Collector> collectors; 

     public ExporterRegister(List<Collector> collectors) {
          for (Collector collector : collectors) {
          this.collectors = collectors;

     public List<Collector> getCollectors() {
          return collectors;


The above class is just a utility class to register a collection of metric collectors with the registry. Now add the standard exporters from Prometheus JVM client:

import io.prometheus.client.hotspot.MemoryPoolsExports;
import io.prometheus.client.hotspot.StandardExports;
     ExporterRegister exporterRegister() {
           List<Collector> collectors = new ArrayList<>();
           collectors.add(new StandardExports());
           collectors.add(new MemoryPoolsExports());
           ExporterRegister register = new ExporterRegister(collectors);
           return register;

We just added 2 exporters: (1) StandardExports provides CPU usage metrics and (2) MemoryPoolExports add memory usage by the JVM and host. To see what metrics are now available, go to the URL in the browser:


The browser should display something like below (truncated as it is too long to list)

# HELP jvm_up_time_seconds System uptime in seconds.
# TYPE jvm_up_time_seconds gauge
jvm_up_time_seconds 15.0
# HELP jvm_cpu_load_percentage JVM CPU Usage %.
# TYPE jvm_cpu_load_percentage gauge
jvm_cpu_load_percentage 37.18078068931383
# HELP os_cpu_load_percentage System CPU Usage %.
# TYPE os_cpu_load_percentage gauge

Install and Setup Prometheus

Now we have implemented the metric endpoint for the Spring Boot application, we are ready to install and configure Prometheus. Following the instruction here to install Prometheus and start up the server. You should now start up and access the server in your browser, e.g. http://localhost:9090/targets


By default, Prometheus is configured to monitor itself, handy. Now let’s update the configuration to scrape our Spring Boot app. Open the file prometheus.yml in the Prometheus folder and add the following lines under the scrape_configs section:

 - job_name: 'blog'

scrape_interval: 5s

 metrics_path: '/prometheus'
 - targets: ['localhost:8080']

Restart Prometheus and refresh your browser to show the following:


Prometheus provides a rather basic graphing function. I will show how to integrate Prometheus with other graphing software in a later post. For now, let’s try to display memory usages of the Spring Boot application. Go to Graph tab and select from the dropdown the metric jvm_memory_bytes_used and click Execute buttonThis should end up like the following in the browser. Note the metric has two different set of time series data, head and nonheap usage.


Summary and What Next

In this blog, I describe how to add monitoring by Prometheus via its JVM client in a Spring Boot application, generate some JVM metrics using the provided exporter classes  and how to configure Prometheus to scrape the data. I end by demonstrating how to use the metrics with the graphing features in Prometheus.

In future blog, I will show how to implement custom metrics in the Spring Boot application using Prometheus JVM client as well as using its expression language to query time series data to return metrics relevant for monitoring purpose. I will also demonstrate how to create dashboard in Grafana using data from Prometheus.


Using catch-exception in JUnit Tests

I use ExpectedException a lot in my JUnit test to verify exceptions thrown. In some circumstances, you would also want to run some assertions in the tests after the exception is thrown. This is impossible with ExpectedException. In this post, I will demonstrate how to use catch-exception instead to achieve this.

Set up

Let say we have the following class we want to test:

public class MyService {

 private MyDao dao;
 public MyService(MyDao dao) {
      this.dao = dao;
 public void someMethod() {
      // Some codes here. Throw an exception for demo purpose
      if (true) {
           throw new NullPointerException("NPE!");

Note the method someMethod() will throw an NullPointerException. This simulates real codes that throws an exception when the object in certain state or when one of the methods it calls within returns with an exception.


ExpectedException works by wrapping the entire test method by its own try-catch block. As a result, any assertion statements after the method that throws the exception are not called.

public class MyTest {

 public ExpectedException none = ExpectedException.none();
 public void testUsingExpectedException() {
      MyDao mockDao = mock(MyDao.class);
      MyService service = new MyService(mockDao);
      // Note the following line is not called. Test still passes

Note the test above verifies that the NPE is thrown as expected. The dao’s afterException method is never called and the test will still pass.


Catch-exception is a library that would address the issue above with ExpectedException. To use it, include the following Maven dependency

      <scope>test</scope> <!-- test scope to use it only in tests -->

and update the test as follows

 public void testUsingCatchException() {
      MyDao mockDao = mock(MyDao.class);
      MyService service = new MyService(mockDao);
      // Hamcrest
      assertThat(caughtException(), instanceOf(NullPointerException.class));
      // Mockito verify got called
      verify(mockDao, never()).afterException();

Now the assertion (mockito verify) statements are called. This could be useful if you want to verify that the object is in the correct state or as in the example certain methods of the object’s member is (not) called when the method throws an exception.

Securing Multiple Resources in OAuth2 Resource Server using Spring Security

In my previous blog post I demonstrate how to setup an OAuth2 authorization server using Spring Security. In this post, I will demonstrate how to setup the security configurations in a resource server to secure multiple resources.

The example here uses Spring Boot 1.2.7 and is a standalone OAuth2 resource server which secures multiple resources with their own ids and access rules. To do that, instead of using @EnableResourceServer, we have to define a ResourceServerConfiguration bean for each resource to be secured as shown below

public class OAuth2ServerConfig {
     protected ResourceServerConfiguration stockesources() {
          ResourceServerConfiguration resource = new ResourceServerConfiguration(){
                // Switch off the Spring Boot @Autowired configurers
                public void setConfigurers(List<ResourceServerConfigurer> configurers) {
                public int getOrder() {
                     return 30;
           resource.setConfigurers(Arrays.<ResourceServerConfigurer> asList(new ResourceServerConfigurerAdapter() {
                 public void configure(ResourceServerSecurityConfigurer resources) throws Exception {
                  public void configure(HttpSecurity http) throws Exception {
            return resource;

To secure the resource “stock”, we first implements a bean of ResourceServerConfiguration by manually setting the configurers by calling the super.setConfigurers() method and then set the order by overriding the getOrder() method. Then, security configuration for the resource can then be set up by using the return ResourceServerConfiguration object (variable resource) by implementing an anonymous ResourceSourceServerConfigurerAdapter.

To secure another resource, just define another bean of ResourceServerConfiguration similar to the above with a different resource id and its own OAuth2 access rules. Also, the order value has to be unique.

Note that I have to override the getOrder() method in the ResourceServerConfiguration here instead of line like

     ResourceServerConfiguration resource = ...

or spring security will throw exception like below

Caused by: java.lang.IllegalStateException: @Order on WebSecurityConfigurers must be unique. Order of 2147483626 was already used, so it cannot be used on . OAuth2ServerConfig$1@1a40489f too.


Writing parameterized Tests with Spring and JUnit 4

This blog will demonstrate how to write parameterized tests with JUnit4 with Spring.

Let say we have the following interface

public interface Logic {
     boolean xor(boolean a, boolean b);

and the corresponding implementation annotated as a Spring service component

public class LogicImpl implements Logic {
      public boolean xor(boolean a, boolean b) {
            return a ^ b;

To test the above class with different input combinations of arguments a and b, we can write the following parameterized test in JUnit

@RunWith(Parameterized.class) // Note 1
@SpringApplicationConfiguration(classes = BlogApplication.class)
public class LogicImplTest {

     private LogicImpl logic;

     // Manually config for spring to use Parameterised
     private TestContextManager testContextManager;

     @Parameter(value = 0) // Note 3i
     public boolean a;

     @Parameter(value = 1) // Note 3ii
     public boolean b;

     @Parameter(value = 2) // Note 3iii
     public boolean expected;

     @Parameters // Note 4
     public static Collection<Object[]> data() {
          Collection<Object[]> params = new ArrayList<>();
          params.add(new Object[] { true, true, false});
          params.add(new Object[] { true, false, true});
          params.add(new Object[] { false, true, true});
          params.add(new Object[] { false, false, false});

          return params;

     @Before // Note 2
     public void setUp() throws Exception {
          this.testContextManager = new TestContextManager(getClass());

     @Test // Note 5
     public void testXor() {
          assertThat(logic.xor(a, b), equalTo(expected));


A few things to note here:

  1. The test class is to be run with the Parameterized runner, instead of the normal SpringJUnit4ClassRunner class.
  2. We need to manually configure the test context manager as in the @Before method. This is typically done automatically by Spring
  3. Parameters are defined as public members of the class (as in 3i to 3iii) with the @Parameter annotation. Since we have more than 1 parameter, it is also neccessary to set the value attribute. This defines the index of the parameters to use.
  4. Parameter values are set by implementing a static  method and annotate it with @Parameters. In our example, the data() method returns a list of object arrays. Each value of the list params corresponds to a set of parameter values.
  5.  Tests now can use the parameter values.

Running the test class in Eclipse will give you something like this


Note testXor() is run 4 times, each using the parameter set of the values defined in the list returned by the data() method.