Glen Mazza's Weblog Sunday June 16, 2019

TightBlog 3.5 Released!

And this blog is running it! Information here, including migration-from-3.4 notes. Alas, a couple of bugs found when running this version, I hope to get a 3.5.1 out next weekend to fix them. Update: 3.5.1 released!

Posted by Glen Mazza in Programming at 07:57PM Jun 16, 2019 | Comments[0] Saturday October 27, 2018

ElasticSearch Notes: Complex Date Filtering, Bulk Updates

Some things learned this past week with ElasticSearch:

Advanced Date Searches: A event search page my company provides for its Pro customers allows for filtering by start date and end date, however some events do not have an end date defined. We decided to have differing business rules on what the start and end dates will filter based on whether or not the event has an end date, specifically:

  • If an event has both start and end dates:
    1. The start date of the range filter, if provided, must be before the end date of the event
    2. The end date of the range filter, if provided, must be after the start date of the event
  • If an event does not have an end date:
    1. The start date of the range filter, if provided, must be before the start date of the event
    2. The end date of the range filter, if provided, must be after the start date of the event

The above business logic had to be implemented in Java but as an intermediate step I first worked out an ElasticSearch query out of it using Kibana. Creating the query first helps immensely in the subsequent conversion to code. For the ElasticSearch query, this is what I came up with (using arbitrary sample dates to test the queries):

GET events-index/_search
    "query": {
    "bool": {
        "should" : [
        {"bool" :
            {"must": [
                { "exists": { "field": "eventMeta.dateEnd" }},
                { "range" : { "eventMeta.dateStart": { "lte": "2018-09-01"}}},
                { "range" : { "eventMeta.dateEnd": { "gte": "2018-10-01"}}}
        {"bool" :
            {"must_not": { "exists": { "field": "eventMeta.dateEnd"}},
             "must": [
                { "range" : { "eventMeta.dateStart": { "gte": "2018-01-01", "lte": "2019-12-31"}}}

As can be seen above, I first used a nested Bool query to separate the two main cases, namely events with and without and end date. The should at the top-level bool acts as an OR, indicating documents fitting either situation are desired. I then added the additional date requirements that need to hold for each specific case.

With the query now available, mapping to Java code using ElasticSearch's QueryBuilders (API) was very pleasantly straightforward, one can see the roughly 1-to-1 mapping of the code to the above query (the capitalized constants in the code refer to the relevant field names in the documents):

private QueryBuilder createEventDatesFilter(DateFilter filter) {

    BoolQueryBuilder mainQuery = QueryBuilders.boolQuery();

    // query modeled as a "should" (OR), divided by events with and without an end date,
    // with different filtering rules for each.
    BoolQueryBuilder hasEndDateBuilder = QueryBuilders.boolQuery();
    hasEndDateBuilder.must().add(fillDates(EVENT_START_DATE, null, filter.getStop()));
    hasEndDateBuilder.must().add(fillDates(EVENT_END_DATE, filter.getStart(), null));

    BoolQueryBuilder noEndDateBuilder = QueryBuilders.boolQuery();
    noEndDateBuilder.must().add(fillDates(EVENT_START_DATE, filter.getStart(), filter.getStop()));

    return mainQuery;

Bulk Updates: We use a "sortDate" field to indicate the specific date front ends should use for sorting results (whether ascending or descending, and regardless of the actual source of the date used to populate that field). For our news stories we wanted to rely on the last update date for stories that have been updated since their original publish, the published date otherwise. For certain older records loaded it turned out that the sortDate was still at the publishedDate when it should have been set to the updateDate. For research I used the following query to determine the extent of the problem:

GET news-index/_search
   "query": {
   "bool": {
      "must": [
         { "exists": { "field": "meta.updateDate" }},
            "script": {
               "script": "doc['meta.dates.sortDate'].value.getMillis() < doc['meta.updateDate'].value.getMillis()"

For the above query I used a two part Bool query, first checking for a non-null updateDate in the first clause and then a script clause to find sortDates preceding updateDates. (I found I needed to use .getMillis() for the inequality check to work.)

Next, I used ES' Update by Query API to do an all-at-once update of the records. The API has two parts, an optional query element to indicate the documents I wish to have updated (strictly speaking, in ES, to be replaced with a document with the requested changes) and a script element to indicate the modifications I want to have done to those documents. For my case:

POST news-index/_update_by_query
   "script": {
   "source": "ctx._source.meta.dates.sortDate = ctx._source.meta.updateDate",
   "lang": "painless"
   "query": {
      "bool": {
         "must": [
            { "exists": { "field": "meta.updateDate" }},
               "script": {
                  "script": "doc['meta.dates.sortDate'].value.getMillis() < doc['meta.updateDate'].value.getMillis()"

For running your own updates, good to test first by making a do-nothing update in the script (e.g., set sortDate to sortDate) and specifying just one document to be so updated, which can be done by adding a document-specific match requirement to the filter query (e.g., { "match": { "id": "...." }},"). Kibana should report that just one document was "updated", if so switch to the desired update to confirm that single record was updated properly, and then finally remove the match filter to have all desired documents updated.

Posted by Glen Mazza in Programming at 07:00AM Oct 27, 2018 | Comments[0] Sunday October 07, 2018

Using functions with a single generic method to convert lists

For converting from a Java collection say List<Foo> to any of several other collections List<Bar1>, List<Bar2>, ... rather than create separate FooListToBar1List, FooListToBar2List, ... methods a single generic FooListToBarList method and a series of Foo->Bar1, Foo->Bar2... converter functions can be more succinctly used. The below example converts a highly simplified List of SaleData objects to separate Lists of Customer and Product information, using a common generic saleDataListToItemList(saleDataList, converterFunction) method along with passed-in converter functions saleDataToCustomer and saleDataToProduct. Of particular note is how the converter functions are specified in the saleDataListToItemList calls. In the case of saleDataToCustomer, which takes two arguments (the SailData object and a Region string), a lambda expression is used, while the Product converter can be specified as a simple method reference due to it having only one parameter (the SailData object).

import java.util.ArrayList;
import java.util.List;
import java.util.Optional;
import java.util.function.Function;

public class Main {

    public static void main(String[] args) {

        List saleDataList = new ArrayList<>();
        saleDataList.add(new SaleData("Bob", "radio"));
        saleDataList.add(new SaleData("Sam", "TV"));
        saleDataList.add(new SaleData("George", "laptop"));

        List customerList = saleDataListToItemList(saleDataList, sd -> Main.saleDataToCustomerWithRegion(sd, "Texas"));
        System.out.println("Customers: ");

        List productList = saleDataListToItemList(saleDataList, Main::saleDataToProduct);
        System.out.println("Products: ");

    private static  List saleDataListToItemList(List sdList, Function converter) {
        // handling potentially null sdList:
        return Optional.ofNullable(sdList).map(List::stream).orElse(Stream.empty()).map(converter).collect(Collectors.toList());

    private static Product saleDataToProduct(SaleData sd) {
        return new Product(sd.getProductName());

    private static Customer saleDataToCustomerWithRegion(SaleData sd, String region) {
        return new Customer(sd.getCustomerName(), region);

    private static class SaleData {
        private String customerName;
        private String productName;

        SaleData(String customerName, String productName) {
            this.customerName = customerName;
            this.productName = productName;

        String getProductName() {
            return productName;

        String getCustomerName() {
            return customerName;


    private static class Product {
        private String name;

        Product(String name) {
   = name;

        public String toString() {
            return "Product{" +
                    "name='" + name + '\'' +

    private static class Customer {
        private String name;
        private String region;

        Customer(String name, String region) {
   = name;
            this.region = region;

        public String toString() {
            return "Customer{" +
                    "name='" + name + '\'' +
                    ", region='" + region + '\'' +


Output from running:

Customer{name='Bob', region='Texas'}
Customer{name='Sam', region='Texas'}
Customer{name='George', region='Texas'}

Posted by Glen Mazza in Programming at 07:00AM Oct 07, 2018 | Comments[0] Sunday June 24, 2018

TightBlog 3.0 Released!

My third annual release currently powering this blog. See here for a listing of enhancements over the previous TightBlog 2.0, here for all the enhancements over the original Apache Roller 5.1.0 I had forked in 2015. Screenshots are here.

Posted by Glen Mazza in Programming at 04:36PM Jun 24, 2018 | Comments[1] Sunday February 18, 2018

Sending Custom Metrics from Spring Boot to Datadog

This tutorial shows how Datadog's API can be used to send custom metrics for a Spring Boot web application and see how the results can be viewed graphically from Datadog dashboards. Samantha Drago's blog post provides a background of Datadog custom metrics which require a paid Datadog account. Note as an alternative not covered here, custom metrics can be defined via JMX with Datadog's JMX Integration used to collect them, this integration in particular provides a list of standard metrics that can be used even with the free DD account.

To facilitate metric accumulation and transferring of metrics to Datadog, Spring Boot's ExportMetricReader and ExportMetricWriter implementations will be used. Every 5 milliseconds by default (adjustable via the spring.metrics.export.delay-millis property), all MetricReader implementations marked @ExportMetricReader will have their values read and written to @ExportMetricWriter-registered MetricWriters. The class ("exporter") that handles this within Spring Boot is the MetricCopyExporter, which treats metrics starting with a "counter." as a counter (a metric that reports deltas on a continually growing statistic, like web hits) and anything else as a gauge (an standalone snapshot value at a certain timepoint, such as JVM heap usage.) Note, however, Datadog apparently does not support "counter" type metric collection using its API (everything is treated as a gauge), I'll be showing at the end how a summation function can be used within Datadog to work around that.

Spring Boot already provides several web metrics that can be sent to Datadog without any explicit need to capture those metrics, in particular, the metrics listed here that start with "counter." or "gauge.". These provide commonly requested statistics such as number of calls to a website and average response time in milliseconds. The example below will report those statistics to Datadog along with application-specific "" and "" metrics that are maintained by our application.

  1. Create the web application. For our sample, Steps #1 and #2 of the Spring Boot to Kubernetes tutorial can be followed for this. Ensure you can see "Hello World!" at localhost:8080 before proceeding.

  2. Modify the Spring Boot application to send metrics to Datadog. Note for tutorial brevity I'm condensing the number of classes that might otherwise be used to send metrics to DD. Additions/updates to make:

    • In the project build.gradle, the gson JSON library and Apache HTTP Client libraries need to be added to support the API calls to DD:

      dependencies {
      	...other libraries...
    • The needs to be included, it serves as both the reader of our application-specific metrics (not those maintained by Spring Boot--those are handled by BufferMetricReader included within the framework) and as the writer of all metrics (app-specific and Spring Boot) to Datadog. Please see the comments within the code for implementation details.
      package com.gmazza.demo;
      import org.apache.http.HttpEntity;
      import org.apache.http.StatusLine;
      import org.apache.http.client.methods.CloseableHttpResponse;
      import org.apache.http.client.methods.HttpPost;
      import org.apache.http.entity.ByteArrayEntity;
      import org.apache.http.impl.client.CloseableHttpClient;
      import org.apache.http.impl.client.HttpClients;
      import org.apache.http.util.EntityUtils;
      import org.slf4j.Logger;
      import org.slf4j.LoggerFactory;
      import org.springframework.beans.factory.annotation.Value;
      import org.springframework.boot.actuate.metrics.Metric;
      import org.springframework.boot.actuate.metrics.reader.MetricReader;
      import org.springframework.boot.actuate.metrics.writer.Delta;
      import org.springframework.boot.actuate.metrics.writer.MetricWriter;
      import org.springframework.stereotype.Component;
      import javax.annotation.PostConstruct;
      import java.math.BigDecimal;
      import java.util.ArrayList;
      import java.util.Arrays;
      import java.util.Date;
      import java.util.HashMap;
      import java.util.List;
      import java.util.Map;
      public class DemoMetricReaderWriter implements MetricReader, MetricWriter, Closeable {
          private static final Logger logger = LoggerFactory.getLogger(DemoMetricReaderWriter.class);
          private Metric<Integer> accessCounter = null;
          private Map<String, Metric<?>> metricMap = new HashMap<>();
          private static final String DATADOG_SERIES_API_URL = "";
          private String apiKey = null;
          private CloseableHttpClient httpClient;
          private Gson gson;
          public void init() {
              httpClient = HttpClients.createDefault();
              // removes use of scientific notation, see
              GsonBuilder gsonBuilder = new GsonBuilder();
              gsonBuilder.registerTypeAdapter(Double.class, (JsonSerializer<Double>) (src, typeOfSrc, context) -> {
                  BigDecimal value = BigDecimal.valueOf(src);
                  return new JsonPrimitive(value);
              this.gson = gsonBuilder.create();
          public void close() throws IOException {
          // besides the app-specific metrics defined in the below method, Spring Boot also exports metrics
          // via its BufferMetricReader, for those with the "counter." or "gauge.*" prefix here:
          public void updateMetrics(long barGauge) {
              // Using same timestamp for both metrics, makes it easier to match/compare if desired in Datadog
              Date timestamp = new Date();
    "Updating foo-count and bar-gauge of {} for web call", barGauge);
              // Updates to values involve creating new Metrics as they are immutable
              // Because this Metric starts with a "counter.", MetricCopyExporter used by Spring Boot will treat this
              // as a counter and not a gauge when reading/writing values.
              accessCounter = new Metric<>("",
                      accessCounter == null ? 0 : accessCounter.getValue() + 1, timestamp);
              metricMap.put("", accessCounter);
              // Does not start with "counter.", therefore a gauge to MetricCopyExporter.
              metricMap.put("", new Metric<>("", barGauge, timestamp));
          // required by MetricReader
          public Metric<?> findOne(String metricName) {
    "Calling findOne with name of {}", metricName);
              return metricMap.get(metricName);
          // required by MetricReader
          public Iterable<Metric<?>> findAll() {
    "Calling findAll(), size of {}", metricMap.size());
              return metricMap.values();
          // required by MetricReader
          public long count() {
    "Requesting metricMap size: {}", metricMap.size());
              return metricMap.size();
          // required by CounterWriter (in MetricWriter), used only for counters
          public void increment(Delta<?> delta) {
    "Counter being written: {}: {} at {}", delta.getName(), delta.getValue(), delta.getTimestamp());
              if (apiKey != null) {
                  sendMetricToDatadog(delta, "counter");
          // required by CounterWriter (in MetricWriter), but implementation optional (MetricCopyExporter doesn't call)
          public void reset(String metricName) {
              // not implemented
          // required by GaugeWriter (in MetricWriter), used only for gauges
          public void set(Metric<?> value) {
    "Gauge being written: {}: {} at {}", value.getName(), value.getValue(), value.getTimestamp());
              if (apiKey != null) {
                  sendMetricToDatadog(value, "gauge");
          // API to send metrics to DD is defined here:
          private void sendMetricToDatadog(Metric<?> metric, String metricType) {
              // let's add an app prefix to our values to distinguish from other apps in DD
              String dataDogMetricName = "app.glendemo." + metric.getName();
    "Datadog call for metric: {} value: {}", dataDogMetricName, metric.getValue());
              Map<String, Object> data = new HashMap<>();
              List<List<Object>> points = new ArrayList<>();
              List<Object> singleMetric = new ArrayList<>();
              singleMetric.add(metric.getTimestamp().getTime() / 1000);
              // additional metrics could be added to points list providing params below are same for them
              data.put("metric", dataDogMetricName);
              data.put("type", metricType);
              data.put("points", points);
              // InetAddress.getLocalHost().getHostName() may be accurate for your "host" value.
              data.put("host", "localhost:8080");
              // optional, just adding to test
              data.put("tags", Arrays.asList("demotag1", "demotag2"));
              List<Map<String, Object>> series = new ArrayList<>();
              Map<String, Object> data2 = new HashMap<>();
              data2.put("series", series);
              try {
                  String urlStr = DATADOG_SERIES_API_URL + "?api_key=" + apiKey;
                  String json = gson.toJson(data2);
                  byte[] jsonBytes = json.getBytes("UTF-8");
                  HttpPost httpPost = new HttpPost(urlStr);
                  httpPost.addHeader("Content-type", "application/json");
                  httpPost.setEntity(new ByteArrayEntity(jsonBytes));
                  try (CloseableHttpResponse response = httpClient.execute(httpPost)) {
                      StatusLine sl = response.getStatusLine();
                      if (sl != null) {
                          // DD sends 202 (accepted) if it's happy
                          if (sl.getStatusCode() == 202) {
                              HttpEntity responseEntity = response.getEntity();
                          } else {
                              logger.warn("Problem posting to Datadog: {} {}", sl.getStatusCode(), sl.getReasonPhrase());
                      } else {
                          logger.warn("Problem posting to Datadog: response status line null");
              } catch (Exception e) {
                  logger.error(e.getMessage(), e);
    • The file needs updating to wire in the DemoMetricReaderWriter. It's "Hello World" endpoint is also updated to send a duration gauge value (similar to but smaller than the more complete gauge.response.root Spring Boot metric) to the DemoMetricReaderWriter.
      package com.gmazza.demo;
      import org.springframework.boot.SpringApplication;
      import org.springframework.boot.actuate.autoconfigure.ExportMetricReader;
      import org.springframework.boot.actuate.autoconfigure.ExportMetricWriter;
      import org.springframework.boot.autoconfigure.SpringBootApplication;
      import org.springframework.context.annotation.Bean;
      import org.springframework.web.bind.annotation.RequestMapping;
      import org.springframework.web.bind.annotation.RestController;
      public class DemoApplication {
          public static void main(String[] args) {
    , args);
          private DemoMetricReaderWriter demoMetricReaderWriter = new DemoMetricReaderWriter();
          DemoMetricReaderWriter getReader() {
              return demoMetricReaderWriter;
          String home() throws Exception {
              long start = System.currentTimeMillis();
              // insert up to 2 second delay for a wider range of response times
              Thread.sleep((long) (Math.random() * 2000));
              // let that delay become the metric value
              long barValue = System.currentTimeMillis() - start;
              return "Hello World!";
    • The in your resources folder is where you provide your Datadog API key as well as some other settings. A few other spring.metrics.export.* settings are also available.

      # Just logging will occur if api.key not defined
      # Datadog can keep per-second metrics, but using every 15 seconds per Datadog's preference
      # disabling security for this tutorial (don't do in prod), allows seeing all metrics at http://localhost:8080/metrics
  3. Make several web calls to http://localhost:8080 from a browser to send metrics to Datadog. May also wish to access metrics at .../metrics a few times, you'll note the app-specific metrics and become listed in the web page that is returned, also that accessing /metrics sends additional *.metrics (counter.status.200.metrics and gauge.response.metrics) stats to Datadog. We configured the application in to send Datadog metrics every 15 seconds, if running in your IDE, you can check the application logging in the Console window to see the metrics being sent.

  4. Log into Datadog and view the metrics sent. Two main options from the left-side Datadog menu: Metrics -> Explorer and Dashboards -> New Dashboard. For the former, one can search on the metric names in the Graph: field (see upper illustration below), with charts of the data appearing immediately to the right. For the latter (lower illustration), I selected "New Timeboard" and added three Timeseries and one Query Value for the two main Spring Boot and two application-specific metrics sent.

    Metrics Explorer

    Datadog TimeBoard

    Again, as the "counter" type is presently not supported via the Datadog API, for dashboards the cumulative sum function can be used to have the counter metrics grow over time in charts:

    Cumulative Sum function

Posted by Glen Mazza in Programming at 07:00AM Feb 18, 2018 | Comments[0] Monday February 12, 2018

TightBlog 2.0.4 Patch Release

I made a 2.0.4 Patch Release of TightBlog to fix two pressing issues, the blog hit counter was not resetting at the end of each day properly and the "Insert Media File" popup on the blog entry edit page was also not working. Upgrading is as simple as swapping out the 2.0.3 WAR with this one. For first-time installs, see the general installation instructions, Linode-specific instructions are here.

Work on the future TightBlog 3.0 is continuing, it has much simpler blog template extraction, better caching design, and uses Thymeleaf instead of Velocity as the blog page template language. Non-test Java source files have fallen to 126 vs. the 146 in TightBlog 2.0, and one fewer database table is needed (now down to 12).

Posted by Glen Mazza in Programming at 07:23PM Feb 12, 2018 | Comments[0] Sunday February 11, 2018

Hosting Spring Boot Applications on Kubernetes

Provided here are simple instructions for deploying a "Hello World" Spring Boot application to Kubernetes, assuming usage of Amazon Elastic Container Service (ECS) including its Elastic Container Repository (ECR). Not covered are Kubernetes installation as well as proxy server configuration (i.e., accessibility of your application either externally or within an intranet) which would be specific to your environment.

  1. Create the Spring Boot application via the Spring Initializr. I chose a Gradle app with the Web and Actuator dependencies (the latter to obtain a health check /health URL), as shown in the following illustration.

    References: Getting Started with Spring Boot / Spring Initializr

  2. Import the Spring Boot application generated by Initializr into your favorite Java IDE and modify the to expose a "Hello World" endpoint:

    package com.gmazza.demo;
    import org.springframework.boot.SpringApplication;
    import org.springframework.boot.autoconfigure.SpringBootApplication;
    import org.springframework.boot.*;
    import org.springframework.boot.autoconfigure.*;
    import org.springframework.stereotype.*;
    import org.springframework.web.bind.annotation.*;
    public class DemoApplication {
    	public static void main(String[] args) {, args);
    	String home() {
    		return "Hello World!";

    Let's make sure the application works standalone. From a command-line window in the Demo root folder, run gradle bootRun to activate the application. Ensure you can see "Hello World!" from a browser window at localhost:8080 and the health check at localhost:8080/health ({"status":"UP"}") before proceeding.

  3. Create a Docker Image of the Spring Boot application. Steps:

    1. Create a JAR of the demo application: gradle clean build from the Demo folder will generate a demo-0.0.1-SNAPSHOT.jar in the demo/build/libs folder.

    2. Create a new folder separate from the demo application, any name, say "projdeploy". Copy the demo JAR into this directory and also place there a new file called "Dockerfile" within it having the following code:

      FROM openjdk:8u131-jdk-alpine
      RUN echo "networkaddress.cache.ttl=60" >> /usr/lib/jvm/java-1.8-openjdk/jre/lib/security/
      ADD demo-0.0.1-SNAPSHOT.jar demo.jar
      ENTRYPOINT ["java","-Xmx2000m", "-Dfile.encoding=UTF-8","-jar","demo.jar" ]

      The above command creates a docker image building off of the OpenJDK image along with a recommended adjustment to the caching TTL. The ADD command performs a rename of the JAR file, stripping off the version from the name for subsequent use in the ENTRYPOINT command.

    3. Next, we'll generate the docker image. From the projdeploy folder, docker build -t demo:0.0.1-SNAPSHOT. Run the docker images command to view the created image in your local respository:

      $ docker images
      REPOSITORY                                                 TAG                                 IMAGE ID            CREATED             SIZE
      demo                                                       0.0.1-SNAPSHOT                      7139669729bf        10 minutes ago      116MB

      Repeated docker build commands with the same repository and tag will just overwrite the previous image. Images can also be deleted using docker rmi -f demo:0.0.1-SNAPSHOT.

  4. Push the target image to ECR. The ECR documentation provides more thorough instructions. Steps:

    1. Install the AWS Command-Line Interface (AWS CLI). Step #1 of AWS guide gives the OS-specific commands to use. In the aws ecr get-login... command you may find it necessary to specify the region where your ECR is hosted (e.g., --region us-west-1). Ensure you can log in from the command line (it will output "Login Succeeded") before continuing.

    2. Create an additional tag for your image to facilitate pushing to ECR, as explained in Step #4 in the ECR w/CLI guide. For this example:

      docker tag demo:0.0.1-SNAPSHOT

      Note in the above command, the "demo" at the end refers to the name of the ECR repository where the image will ultimately be placed, if not already existing it will need to be created beforehand for the next command to be successful or another existing repository name used. Also, see here for determining your account ID. You may wish to run docker images again to confirm the image was tagged.

    3. Push the newly tagged image to AWS ECR (replacing the "demo" below if you're using another ECR repository):

      docker push
    4. At this stage, good to confirm that the image was successfully loaded by viewing it in ECR repositories (URL to do so should be

  5. Deploy your new application to Kubernetes. Make sure you have kubectl installed locally for this process. Steps:

    1. Create a deployment.yaml for the image. It is in this configuration file that your image's deployment, declare the image to use, and its service and ingress objects. A sample deployment.yaml would be as follows:


      kind: Deployment
      apiVersion: extensions/v1beta1
        name: demo
        replicas: 1
              app: demo
            - name: demo
              - containerPort: 80
                  memory: "500Mi"
                  memory: "1000Mi"
                  scheme: HTTP
                  path: /health
                  port: 8080
                initialDelaySeconds: 15
                periodSeconds: 5
                timeoutSeconds: 5
                successThreshold: 1
                failureThreshold: 20
                  scheme: HTTP
                  path: /health
                  port: 8080
                initialDelaySeconds: 15
                periodSeconds: 15
                timeoutSeconds: 10
                successThreshold: 1
                failureThreshold: 3
      kind: Service
      apiVersion: v1
        name: demo
          app: demo
          - protocol: TCP
            port: 80
            targetPort: 8080
      kind: Ingress
      apiVersion: extensions/v1beta1
        name: demo
        - host:
            - path:
                serviceName: demo
                servicePort: 80

      Take particular note of the bolded deployment image (must match what was deployed to ECR) and the Ingress loadbalancer host, i.e., the URL to be used to access the application.

    2. Deploy the application onto Kubernetes. The basic kubectl create (deploy) command is as follows:

      kubectl --context ??? --namespace ??? create -f deployment.yaml

      To determine the correct context and namespace values to use, first enter kubectl config get-contexts to get a table of current contexts, the values will be under in the second column, "Name". If your desired context is not the current one (first column), enter kubectl config use-context context-name to switch to that one. Either way, then enter kubectl get namespaces for a listing of available namespaces under that context, picking one of those or creating a new namespace.

      Once your application is created, good to go to the Kubernetes dashboard to confirm it has successfully deployed. In the "pod" section, click the next-to-last column (the one with the horizontal lines) for the deployed pod to see startup logging including error messages, if any.

    3. Determine the IP address of the deployed application to configure routing. The kubectl --context ??? --namespace ??? get ingresses command (with context and namespace determined as before) will give you a list of configured ingresses and their IP address, configuration of the latter with Route 53 (at a minimum) will probably be needed for accessing your application.

      Once the application URL is accessible, you should be able to retrieve the same "Hello World!" and health check responses you had obtained in the first step from running locally.

    4. To undeploy the application, necessary for redeploying it via kubectl create, the application, service, and ingress can be individually deleted from the Kubernetes Dashboard. As an alternative, the following kubectl commands can be issued to delete the application's deployment, service, and ingress:

      kubectl --context ??? --namespace ??? delete deployment demo
      kubectl --context ??? --namespace ??? delete service demo
      kubectl --context ??? --namespace ??? delete ingress demo

      If it is desired to just reload the current application, deletion of the application's pod by default will accomplish that.

Posted by Glen Mazza in Programming at 06:10AM Feb 11, 2018 | Comments[0] Sunday November 26, 2017

Streaming Salesforce notifications to Kafka topics

Salesforce CRM's Streaming API allows for receiving real-time notifications of changes to records stored in Salesforce. To enable this functionality, the Salesforce developer creates a PushTopic channel backed by a SOQL query that defines the changes the developer wishes to be notified of. Record modifications (Create, Update, Delete, etc.) fitting the SOQL query are sent on the channel and can be picked up by external systems. Salesforce provides instructions on how its Workbench tool can be used to create, view and test PushTopic notifications, which is a useful first step. For Java clients, Salesforce also provides an tutorial using an EMPConnector sample project. At least the username-password version of that sample worked following the instructions given in the tutorial, but the tutorial was vague on how to get the bearer token version to work.

For Kafka, Confluent's Jeremy Custenborder has written a Salesforce source connector for placing notifications from a Salesforce PushTopic to a Kafka topic. His simplified instructions in the GitHub README assume usage of Confluent's wrap of Kafka including the Confluent-only Schema Registry with Avro-formatted messages. I'm expanding on his instructions a bit to make them more end-to-end and also to show how the connector can be used with pure Kafka, no schema registry, and JSON-formatted messages:

  1. Follow the API Streaming Quick Start Using Workbench to configure your SF PushTopic. Before proceeding, make sure records created from the Workbench generate notifications on the SF PushTopic. It's a quick, efficient tutorial.

  2. Create a Connected Application from your account. Connected Apps allow for external access to your Salesforce data. I mostly relied on Calvin Froedge's article for configuring the Connected App.

  3. (Optional) To confirm the Connected App is working properly before moving on to Kafka, you may wish to run the EMPConnector sample mentioned above.

  4. If you haven't already, download a Kafka distribution and expand it, its folder will be referred to as KAFKA_HOME below.

  5. Clone and build the Salesforce source connector in a separate directory.

  6. Open a terminal window with five tabs. Unless stated otherwise, all commands should be run from the KAFKA_HOME directory.

    1. First and second tabs, activate ZooKeeper and the Kafka broker using the commands listed in Step #2 of the Kafka Quick Start.

    2. Third tab, create a Kafka topic to receive the notifications placed on the Salesforce PushTopic:

      bin/ --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic sf_invoice_statement__c

      The name of the topic can be different from the one given above, just be sure to update the connector configuration file given in the next step accordingly.

    3. Fourth tab, start the Salesforce Connector. First, navigate to the config folder under the base folder of the connector and make a file:

      # Set these required values


      • The password token can be obtained via's Reset My Security Token screen.
      • The consumer key and secret are available from the Connected App configuration within
      • Having already created the Salesforce PushTopic, I set salesforce.push.topic.create to false in the configuration above. Alternatively, I could have set it to true and provided the salesforce.object property to have the Salesforce Connector dynamically create the PushTopic. However, the auto-created PushTopic did not (at least for me) do a good job of bringing all the fields of the object ("description" was missing from InvoiceStatement notifications); manually creating the PushTopic will (in most cases) provide the fields given in the SELECT list of the SOQL query you create for the PushTopic.

      Next, from the Connector base folder, create the CLASSPATH and activate the connector as follows:

      export CLASSPATH="$(find target/ -type f -name '*.jar'| tr '\n' ':')"
      $KAFKA_HOME/bin/ $KAFKA_HOME/config/ config/ 

      Important: Note the export statement above is different than the one in the GitHub instructions, I created a not-yet-applied PR to fix the latter.

    4. For the fifth tab, we need to create a consumer to read the SF messages that the SF connector places on our Kafka topic, this worked for me:

      bin/ --bootstrap-server localhost:9092 --topic sf_invoice_statement__c --from-beginning
  7. Use the Workbench Insert page to create a new Invoice Statement record, and view the Kafka consumer output to confirm the Kafka topic received the notification:

    Once created, you should see a message for it output by the Kafka consumer:

          {"type":"string","optional":false,"doc":"Unique identifier for the object.","field":"Id"},
          "CreatedById":null,"LastModifiedDate":null,"LastModifiedById":null,"SystemModstamp":null, "Status__c":"Negotiating",
          "Description__c":"Hello 11/27","_ObjectType":"Invoice_Statement__c","_EventType":"created"}}

Posted by Glen Mazza in Programming at 07:00AM Nov 26, 2017 | Comments[0]

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