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Take the first step towards transforming your apps into cloud-native

If you are a medium-sized or large organization which depends on your IT teams to provide you with on-demand infrastructure, and support for your business critical applications; if you are an organization with sprawling thousands of applications and are planning to take the journey, or are already on a path, to cloud-native, you must have faced questions like "where do I start in migrating these applications to a new cloud platform?" Or "Once I've migrated an application on to a cloud platform, how do I make sure my application code updates don't drift away from leveraging the most a cloud platform has to offer?".

Let's take a real world example. An organization that has 1500 Applications, about 80% of which are Commercial-Off-The-Shelf (COTS) apps, and about 20% are custom home-grown. These applications are mostly run on unix-based systems, with some instances run on Windows hosts, and they are looking for assistance on getting started with questions like – how do they decide which applications to move? what changes need to be made to these applications to be compatible with the new platform? what risks and vulnerabilities are associated in not taking any actions on these applications? how long will the effort be to migrate these applications? are these applications even ready to be migrated? and so on.

CAST Software provides organizations the ability to have automated application assessments for an entire portfolio of applications using various programming languages, and profile them based on multiple quality and quantity indicators. Using a combination of an assessment questionnaire and the automated code insights, CAST helps you decide which applications to target migrating first, how code changes affect an application resiliency, identify security vulnerabilities in the existing application code, and much more. CAST also provides the ability for you to export the results of your application assessment without the need to export any source code. This can be done by leveraging CAST API. For the example customer mentioned above with thousands of applications, the process of evaluating their entire portfolio of applications can be easily automated leveraging this function.

Here is an example of how the command line API call would look like, that would export the application metrics without the need to export any source code -

java -jar HighlightAutomation.jar --workingDir "/samples/pathToWorkingDir" --sourceDir "/samples/sourceDir/src/" --skipUpload

Since jar files can be run on Unix and Windows systems alike, the command remains the same for both platforms. You can also use the command wrapper created by Keyva (https://github.com/keyva/casthighlight_wrapper) to run the assessment.

For the aforementioned customer, coupling up the ability to run API commands with their configuration management system or a workflow automation system like Red Hat Ansible, they can scan for source code on their server inventory for all on-premises or cloud based servers, and automatically create an application portfolio assessment report on a scheduled basis.

To get started with our application assessment questionnaire, please visit us at https://keyvatech.com/survey/. We also provide a free assessment for one of your applications built using Java or Python and help you roadmap the required effort and the steps you would need to take to assess and migrate your entire portfolio of applications.

[post_title] => Transform into Cloud-Native [post_excerpt] => [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => transformintocloud-native [to_ping] => [pinged] => [post_modified] => 2019-09-30 18:53:00 [post_modified_gmt] => 2019-09-30 18:53:00 [post_content_filtered] => [post_parent] => 0 [guid] => https://keyvatech.com/?p=1531 [menu_order] => 27 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) [1] => WP_Post Object ( [ID] => 1270 [post_author] => 7 [post_date] => 2019-05-15 20:40:41 [post_date_gmt] => 2019-05-15 20:40:41 [post_content] => Organizations today are rattled with managing their infrastructure and application performance metrics. This is only exacerbated by the myriad of technology and tools in the market that now exists. How can Operations teams keep up with increasing demand of managing larger and ever-changing workloads under shrinking budgets? That’s a question which many organizational teams are left to answer and find a solution that fits their needs. Are you faced with this challenge? One step that can get your team closer to the solution is to genericize your processes independent of tools, so that teams can follow a unified process and gain end to end visibility in to their entire infrastructure – regardless of the underlying technology that is deployed. Closed-Loop-Incident-Process is one such operational process, that has tremendous benefits when coupled with automation and tools consolidation. A Closed-Loop-Incident-Process A Closed-Loop-Incident-Process, or CLIP for short, is when you automatically take actions on alerts on your Network Operations Center (NOC) unified console including auto remediation, while integrating the remediation process with your ticketing system (e.g. Incident tickets). It does not matter whether you use one of the APM tools, or infrastructure monitoring tools, and the process holds true for any and all IT Services Management (ITSM) and Continuous Management Data Base (CMDB) systems you have. Once your teams agree on an end to end process that works for their environment and organization, you can begin the work of integrating the various tool sets you have to achieve the end goal. [caption id="attachment_1278" align="alignleft" width="212"]

Fig 1a.[/caption] It is important to keep your CMDB accurate and current, and many organizations end up spending a lot of cycles and redundant time trying to achieve that state. Eventually, organizations can use the CIs and CI relationships within the CMDB to implement event correlation and operational intelligence that can proactively reduce alerts that would’ve been classified as noise. Check out the CLIP framework here (fig 1a) here. How Kevya Can Help Keyva has helped several customers integrate and automate their operational processes to achieve time and cost savings. Keyva can help genericize the many different processes you may have and integrate tool sets to achieve end to end use case automation, with the end goal of achieving Operational Intelligence so the operations teams can put time towards automating complex remediation tasks, rather than on repetitive manual tasks. Are you ready to automate? If you have any questions, or feedback, please reach out to a Keyva associate at [email protected] -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Anuj Tuli serves as the Chief Technology Officer for Keyva. In his current role at Keyva, Anuj helps organizations adopt IT Process Automation, Containers, implement CI/CD methodology, modernize their applications, and develop an automation framework which supports end-to-end application lifecycle - planning, development, testing, deployment, and operations. He joined Keyva from Tech Data where he was the Director of Automation Solutions. In this role, he specialized in developing and delivering vendor-agnostic solutions that avoid the “rip-and-replace” of existing IT investments. Tuli has worked on Cloud Automation, DevOps, Cloud Readiness Assessments and Migrations projects for healthcare, banking, ISP, telecommunications, government and other sectors. During his previous years at Avnet, Seamless Technologies, and other organizations, he held multiple roles in the Cloud and Automation areas. Most recently, he led the development and management of Cloud Automation IP (intellectual property) and related professional services. He holds certifications for AWS, VMware, HPE, BMC and ITIL, and offers a hands-on perspective on these technologies. Like what you read? Follow Anuj on LinkedIn at https://www.linkedin.com/in/anujtuli/ [post_title] => The Closed Loop Incident Process (CLIP) [post_excerpt] => [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => closed-loop-incident-process [to_ping] => [pinged] => [post_modified] => 2019-05-16 14:21:01 [post_modified_gmt] => 2019-05-16 14:21:01 [post_content_filtered] => [post_parent] => 0 [guid] => https://keyvatech.com/?p=1270 [menu_order] => 31 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) [2] => WP_Post Object ( [ID] => 1258 [post_author] => 7 [post_date] => 2019-04-25 15:18:38 [post_date_gmt] => 2019-04-25 15:18:38 [post_content] => Keyva announces the release of open source version for ServiceNow App that integrates with Red Hat Ansible using Ansible Tower (or AWX) APIs. The integration allows users to trigger Ansible jobs from within ServiceNow Catalog Requests or Change tickets. Users have the ability to customize triggers to suit their own needs – to not only launch the Ansible job from a specific ServiceNow Application, but also being able to define the specific conditions (e.g. Status field set to ‘In Progress’). Many organizations use Ansible as the automation and orchestration layer, while using ServiceNow as their ITSM suite and CMDB. There are several common use cases that require an integration between the two offerings. [caption id="attachment_1260" align="alignleft" width="211"]

Fig 1a - Sample Provisioning Use Case[/caption] A similar use case can be implemented using this integration for Day 2 tasks like Patching, or Unprovisioning. Customers that are looking to launch a service request through a centralized portal like ServiceNow, and have Ansible as their orchestration fulfillment engine can leverage this open sourced integration. Check out the sample provisioning use case (fig 1a) here. You can check out the integration on our GitHub repository here - https://github.com/keyva/ansible If you have any questions, or feedback, please reach out to a Keyva associate at [email protected] [post_title] => ServiceNow App for Red Hat Ansible Automation [post_excerpt] => [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => servicenow-app-for-red-hat-ansible-automation [to_ping] => [pinged] => [post_modified] => 2024-05-15 18:57:15 [post_modified_gmt] => 2024-05-15 18:57:15 [post_content_filtered] => [post_parent] => 0 [guid] => https://keyvatech.com/?p=1258 [menu_order] => 32 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) [3] => WP_Post Object ( [ID] => 1187 [post_author] => 7 [post_date] => 2019-02-28 19:55:38 [post_date_gmt] => 2019-02-28 19:55:38 [post_content] => Many organizations have started utilizing DevOps practices and tools for data warehousing and data lake setups. Data Analysts and Database Managers can follow DevOps practices for managing updates and new database releases across various environments in a uniform fashion, to produce repeatable results. Just like application teams create and manage the CI/CD pipeline for applications, the data that these applications consume can have its own release pipeline that is managed by the database teams. In many cases, cloud based data warehousing platforms provide the ability to host the applications that consume this data, all within the same environment. Applications that consume data housed in a data warehouse may also leverage Kafka or other DevOps tools to achieve low latency query performance. As you release updates to your applications, you may also need to account for the updates to the service bus layer and the database layer. Continuous deployment and continuous integration becomes all the more important. Data teams that institute DevOps practices and tools for data warehousing can promote an agile culture within their silos. This includes the process of fetching or discovering the data for data warehousing, the process of making sure it is current and accurate for the consuming applications, and the process of organizing it for data mining and analysis. You can apply DevOps practices and policies to data automation (just like infrastructure automation). Starting from self-service models to request new data instances, to requesting updates, and other data lifecycle steps. There are many organizations that have built entire data platforms on containers. For infrastructure and database teams, it is imperative to provide data "as-a-service" with measured and tracked SLAs and costs – whether these services are provided on container platforms or otherwise. Public cloud platforms have made it easy for consumers to leverage SaaS data warehousing solutions. Using DevOps practices do not have to be limited to providing the underlying infrastructure or service, but can also be applied to the building of reports. Jenkins automation can be used to release database updates, integration tools can be used to fetch the relevant data from multiple sources to populate the target systems, and opensource tools like Grafana can be used for dashboards. Primary objective of such a setup would be to capture data from various components and locations within the environment to a centralized location via ETL, and process that data to produce business intelligence. When bringing data in from multiple sources for data warehousing, the exercise of data mapping and data reconciliation and sanitization usually take the most time and effort upfront. Architectural considerations also include the paradigm of monitoring the data warehouse components, as well as the data within it. Data processing engines like Hadoop MapReduce or Spark, along with the database serving platforms form the core components of any data warehouse setup. By implementing the best practices architecture, and tuning specifically for your environment, you can optimize your data warehouse setup to achieve a balance between performance and cost. Various industry use cases like fraud prevention in banking, storing health records and doctors notes in healthcare, customer profiling for retail, real time streaming in media, and others, have already leveraged the benefits provided by data lakes for capturing and storing unstructured data, and data warehousing for structured data. With the adoption of blockchain technologies, the relevance of Big Data is only anticipated to grow. Most enterprises depend heavily on applications for their business, and thereby have adopted agile processes for application releases. Combining the consumption of Big Data with emphasis on extracting relevant and accurate data at the right time, is paramount for business critical applications. The adoption of DevOps practices and tools for data warehousing within data teams is still in its nascent stage, but is being picked up by more and more data experts every day. If you need assistance with data warehousing to move your disparate data from various sources, or need help assessing the feasibility of a data warehouse platform without substantially affecting your business critical applications, Keyva can help. Associates at Keyva have worked with many different organizations in various verticals to help in data migration and application modernization projects. These include things like creating a data migration factory, creating ETL strategies with data mapping, refactoring existing applications, adding a wrapper over current applications so they can be consumed easily by DevOps processes, modifying existing applications to consume data from SaaS platforms, and more. If you'd like to have us review your environment and provide suggestions on what might work for you, please contact us at [email protected].

Anuj joined Keyva from Tech Data where he was the Director of Automation Solutions. In this role, he specializes in developing and delivering vendor-agnostic solutions that avoid the “rip-and-replace” of existing IT investments. Tuli has worked on Cloud Automation, DevOps, Cloud Readiness Assessments and Migrations projects for healthcare, banking, ISP, telecommunications, government and other sectors. During his previous years at Avnet, Seamless Technologies, and other organizations, he held multiple roles in the Cloud and Automation areas. Most recently, he led the development and management of Cloud Automation IP (intellectual property) and related professional services. He holds certifications for AWS, VMware, HPE, BMC and ITIL, and offers a hands-on perspective on these technologies. Like what you read? Follow Anuj on LinkedIn at https://www.linkedin.com/in/anujtuli/ [post_title] => DevOps and Data Warehousing [post_excerpt] => [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => devops-and-data-warehousing [to_ping] => [pinged] => [post_modified] => 2020-01-22 18:20:10 [post_modified_gmt] => 2020-01-22 18:20:10 [post_content_filtered] => [post_parent] => 0 [guid] => https://keyvatech.com/?p=1187 [menu_order] => 33 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) [4] => WP_Post Object ( [ID] => 1164 [post_author] => 7 [post_date] => 2019-02-25 20:16:42 [post_date_gmt] => 2019-02-25 20:16:42 [post_content] => This write-up walks through setting up a two node Hadoop v3.1.1 cluster, and running a couple of sample MapReduce jobs. Prerequisites:

Two machines set up with RHEL 7. You could use another distribution, but the commands may vary.
Perl, wget, and other required packages downloaded using yum
Disable the firewall, or open up connectivity between the two machines. Since we are setting it up as a lab instance, we will go ahead and disable the firewall

systemctl stop firewalld 
systemctl disable firewalld

hadoop1 will be the master node, and hadoop2 will be the datanode.
Add entry for hadoop1 and hadoop2 under /etc/hosts on both machines. We will need a JDK installation (on both machines):
```
yum install java-1.8.0-openjdk -y
```
You can validate that java is installed by querying for the installed version
```
java -version
```
Create a separate directory under '/' path where we will download the bits for hadoop (on both machines)
```
mkdir hadoop 
cd /hadoop/ 
wget http://mirror.cc.columbia.edu/pub/software/apache/hadoop/common/hadoop-3.1.1/hadoop-3.1.1.tar.gz 
tar -xzf hadoop-3.1.1.tar.gz
```
In order to point hadoop to the correct java installation, we will need to capture the full path of java install
```
readlink -f $(which java)
```
Export the path as environment variable (on both machines)
```
export JAVA_HOME=/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.191.b12-1.el7_6.x86_64/jre/
```
We will modify the bashrc profile file to make sure that all the required environment variables are available when we log in to the machine console. This change is made (on both machines):
```
vi ~/.bashrc
```
Add the following lines to the file

export HDFS_NAMENODE_USER="root"

export HDFS_DATANODE_USER="root" 
export HDFS_SECONDARYNAMENODE_USER="root" 
export YARN_RESOURCEMANAGER_USER="root" 
export YARN_NODEMANAGER_USER="root" 
export JAVA_HOME=/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.191.b12-1.el7_6.x86_64/jre/ 
export PATH=$PATH:$JAVA_HOME/bin

Update the core-site file (on the master node) vi /hadoop/hadoop-3.1.1/etc/hadoop/core-site.xml Modify the <configuration> section as per below:

<configuration> 
 <property> 
  <name>fs.defaultFS</name> 
  <value>hdfs://hadoop1:9000</value> 
 </property> 
</configuration>

Update the hdfs-site file (on the master node)

vi /hadoop/hadoop-3.1.1/etc/hadoop/hdfs-site.xml

Modify the <configuration> section as per below:

<configuration> 
 <property> 
  <name>dfs.replication</name> 
  <value>1</value> 
 </property> 
</configuration>

Set up the machines for passwordless SSH access (on both machines):

 ssh-keygen 
 ssh-copy-id -i ~/.ssh/id_rsa.pub root@hadoop1 
 ssh-copy-id -i ~/.ssh/id_rsa.pub root@hadoop2

On the master node, update the workers file to reflect the slave nodes

vi /hadoop/hadoop-3.1.1/etc/hadoop/workers

Add the entry

hadoop2

And then on the master node, format the hdfs file system:

/hadoop/hadoop-3.1.1/bin/hdfs namenode -format

On the datanode, format the hdfs file system:

/hadoop/hadoop-3.1.1/bin/hdfs datanode –format

On the master node, start the dfs service:

/hadoop/hadoop-3.1.1/sbin/start-dfs.sh

On the master node, run the dfsadmin report, to validate the availability of datanodes

/hadoop/hadoop-3.1.1/bin/hdfs dfsadmin -report

The output of this command should show two entries for datanodes - one for hadoop1 and one for hadoop2. The nodes are now set up to handle MapReduce jobs. We will look at two examples. We will use the sample jobs from hadoop-mapreduce-examples-3.1.1.jar file under the share folder. There is a large number of opensource java projects available, which run various kinds of mapreduce jobs. We will run these exercises on the master node. Exercise 1: We will solve a sudoku puzzle using MapReduce. First we will need to create a sudoku directory under root folder in hdfs file system.

/hadoop/hadoop-3.1.1/bin/hdfs dfs -mkdir /sudoku

Then create an input file with the sudoku puzzle, under your current directory:

vi solve_this.txt

Update the file with the below text. Each entry on the same line is separated by a space.

? 9 7 ? ? ? ? ? 5
? 6 3 ? 4 ? 2 ? ?
? ? ? 9 ? ? ? 8 ?
? ? 9 ? ? ? ? 7 ?
? ? ? 1 ? 6 ? ? ?
2 5 4 8 3 ? ? ? 1
? 7 ? ? ? 1 8 ? ?
? 8 ? ? 7 ? 6 ? 4
5 ? ? ? ? 2 ? 9 ?

Now move (put) the file from your current directory in to the hdfs folder (sudoku) that we created earlier.

/hadoop/hadoop-3.1.1/bin/hdfs dfs -put solve_this.txt /sudoku/solve_this.txt

To make sure that the file was copied:

/hadoop/hadoop-3.1.1/bin/hdfs dfs -ls /sudoku

Run the mapreduce job, to solve the puzzle:

/hadoop/hadoop-3.1.1/bin/hadoop jar /hadoop/hadoop-3.1.1/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.1.jar sudoku solve_this.txt
Solving solve_this.txt
1 9 7 6 2 8 4 3 5
8 6 3 7 4 5 2 1 9
4 2 5 9 1 3 7 8 6
6 1 9 2 5 4 3 7 8
7 3 8 1 9 6 5 4 2
2 5 4 8 3 7 9 6 1
9 7 2 4 6 1 8 5 3
3 8 1 5 7 9 6 2 4
5 4 6 3 8 2 1 9 7

Found 1 solutions Exercise 2: We will run a wordcount method on the sudoku puzzle file. Run the wordcount method on the sudoku puzzle file, and have the output stored in wcount_result folder.

/hadoop/hadoop-3.1.1/bin/hadoop jar /hadoop/hadoop-3.1.1/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.1.jar wordcount /sudoku/solve_this.txt /sudoku/wcount_result

The lengthy output lists out the results of detailed analysis conducted on the file. We will cat the results of various results,

/hadoop/hadoop-3.1.1/bin/hdfs dfs -cat /sudoku/wcount_result/*
1 3
2 3
3 2
4 3
5 3
6 3
7 4
8 4
9 4
? 52

The above output captures the total number of times a particular digit is listed in the solved puzzle. To see all the different sample methods available under hadoop-mapreduce-examples-3.1.1.jar, run the following command:

/hadoop/hadoop-3.1.1/bin/hadoop jar /hadoop/hadoop-3.1.1/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.1.jar

If you have any questions about the steps documented here, would like more information on the installation procedure, or have any feedback or requests, please let us know at [email protected].

Anuj joined Keyva from Tech Data where he was the Director of Automation Solutions. In this role, he specializes in developing and delivering vendor-agnostic solutions that avoid the “rip-and-replace” of existing IT investments. Tuli has worked on Cloud Automation, DevOps, Cloud Readiness Assessments and Migrations projects for healthcare, banking, ISP, telecommunications, government and other sectors. During his previous years at Avnet, Seamless Technologies, and other organizations, he held multiple roles in the Cloud and Automation areas. Most recently, he led the development and management of Cloud Automation IP (intellectual property) and related professional services. He holds certifications for AWS, VMware, HPE, BMC and ITIL, and offers a hands-on perspective on these technologies. Like what you read? Follow Anuj on LinkedIn at https://www.linkedin.com/in/anujtuli/ [post_title] => How to set up Hadoop two node cluster and run MapReduce jobs [post_excerpt] => [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => how-to-set-up-hadoop-two-node-cluster-and-run-mapreduce-jobs [to_ping] => [pinged] => [post_modified] => 2023-06-28 18:07:13 [post_modified_gmt] => 2023-06-28 18:07:13 [post_content_filtered] => [post_parent] => 0 [guid] => https://keyvatech.com/?p=1164 [menu_order] => 34 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) [5] => WP_Post Object ( [ID] => 1149 [post_author] => 7 [post_date] => 2019-02-19 19:48:42 [post_date_gmt] => 2019-02-19 19:48:42 [post_content] => This technical guide will walk you through the installations of Ansible v2.4.2.0, an open source configuration management and deployment tool, and Ansible Tower (web layer for Ansible) v3.4.1 on a RHEL 7 virtual machine. Ansible Tower is a RedHat supported and paid version of AWX, which is open source. We will first enable the required repos:

sudo subscription-manager repos --enable rhel-7-server-ansible-2.6-rpms 
subscription-manager repos --enable rhel-7-desktop-optional-rpms

You can install the latest version of ansible using Yum:

yum install ansible

(Since we will be installing Ansible Tower on this same machine, it is recommended to use the Yum method to install Ansible). -OR- You can build the RPM package by downloading the latest version of Ansible code from Git. If choosing this method, first we will need to get all the pre-requisite libraries ready (some of these are optional):

yum update 
yum install python-dev python-pip wget 
yum install git 
yum update -y nss curl libcurl 
yum install rpm-build 
yum -y install python

Download the latest code, and build:

mkdir ansible 
cd ansible/ 
git clone https://github.com/ansible/ansible.git 
systemctl stop firewalld 
systemctl disable firewalld 
cd ./ansible/ 
make rpm 
rpm -Uvh ./rpm-build/ansible-*.noarch.rpm

Once installed, you can view and modify the default Ansible hosts file at /etc/ansible/hosts You can also verify successful installation using the command:

ansible –-version

Now, we can go ahead and set up Ansible Tower on this machine. We will be using the integrated installation, which installs the GUI, the REST API, and the database – all on the same machine:

mkdir ansible-tower 
cd ansible-tower/ 
wget https://releases.ansible.com/ansible-tower/setup-bundle/ansible-tower-setup-bundle-3.4.1-1.el7.tar.gz 
tar xvzf ansible-tower-setup-bundle-3.4.1-1.el7.tar.gz 
cd ansible-tower-setup-bundle-3.4.1-1.el7/

Tower connects to the PostgreSQL database using password authentication. We will need to create a md5 hash to configure Tower to talk with the database. Replace <CUSTOM-DB-PASSWORD> with a password of your choosing:

python -c 'from hashlib import md5; print("md5" + md5("<CUSTOM-DB-PASSWORD>" + "awx").hexdigest())'

Make a note of the hash key generated from this command. We will use it for our next step. We have to now update the inventory file (located within ansible-tower-setup-bundle-3.4.1-1.el7 directory) with the passwords for the database, the hash key generated above, and a custom password of our choosing for rabbit_mq. Find the following lines and update them accordingly. First, for setting the admin password for the console

admin_password='AdminPassword'

Next, set the password for database connectivity. Please note, this password should be the same as what you used to replace <CUSTOM-DB-PASSWORD> during the hash key generation step above. Also, we will paste the copied hash key, and set it for the hashed password line

pg_password='password' 
pg_hashed_password='md5f58b4d5d85dbde46651335d78bb56b8c'

And finally, choose a custom password for rabbit_mq

rabbitmq_password='password'

We are now ready to run the setup script

./setup.sh

Once all the steps are completed successfully, you can verify the Tower installation by going to the URL

https://<MACHINE-IP-OR-FQDN>:443

You can use the admin credentials (username: admin, password: admin password as defined in the inventory file) to log in and access the console. You can request a free Ansible Tower license for an evaluation environment of up to 10 nodes, or can purchase a RedHat subscription for larger environments, and some additional logging, management and support features. If you have any questions about the steps documented here, would like more information on the installation procedure, or have any feedback or requests, please let us know at [email protected].

Anuj joined Keyva from Tech Data where he was the Director of Automation Solutions. In this role, he specializes in developing and delivering vendor-agnostic solutions that avoid the “rip-and-replace” of existing IT investments. Tuli has worked on Cloud Automation, DevOps, Cloud Readiness Assessments and Migrations projects for healthcare, banking, ISP, telecommunications, government and other sectors. During his previous years at Avnet, Seamless Technologies, and other organizations, he held multiple roles in the Cloud and Automation areas. Most recently, he led the development and management of Cloud Automation IP (intellectual property) and related professional services. He holds certifications for AWS, VMware, HPE, BMC and ITIL, and offers a hands-on perspective on these technologies. Like what you read? Follow Anuj on LinkedIn at https://www.linkedin.com/in/anujtuli/ [post_title] => Installation Guide: Ansible & Ansible Tower - Day 1 [post_excerpt] => [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => installing-guide-ansible-and-ansible-tower-day-1 [to_ping] => [pinged] => [post_modified] => 2019-02-21 19:08:28 [post_modified_gmt] => 2019-02-21 19:08:28 [post_content_filtered] => [post_parent] => 0 [guid] => https://keyvatech.com/?p=1149 [menu_order] => 35 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) [6] => WP_Post Object ( [ID] => 1144 [post_author] => 7 [post_date] => 2019-01-30 20:13:42 [post_date_gmt] => 2019-01-30 20:13:42 [post_content] => You may have heard the term Microservices when talking about applications, and some cases even when talking about underlying infrastructure. If you've worked with any OOPS language, you must be familiar with the concept of abstraction and modularization. Extend that idea to an application - the breakdown of independent functions abstracted in to separate services; with a bunch of services working together forming an application. Typically, the more fine grained and decoupled your services are, the more scalability can be achieved. Let's take a look at some of the reasons why it is important for your critical applications to be be implemented with a microservices architecture framework -

Scalability: By splitting down the function of an application in to a microservices, it can be scaled independently of other functions or components. For example, a microservice responsible for managing database connections can scale independently of the web tier, if the db connection pool size is to be increased.

Decoupling: Separating the functions into individual services provides flexibility for design, implementation, and maintenance of those individual services.

Continuous Delivery & Updates: Functional upgrades can be released without affecting other components. Each function or service can have its own release pipeline, list of enhancements, and priority for feature releases.

Error Micro-segmentation: Error in one service will be isolated within that service. Faults will not propagate to other functions as they are modularized and separated. Also, releasing an update to address the issue is quicker and more efficient in this architecture.

Parallel Development & Domain Expertise: Each service can have domain specific experts working on it. In the case of monolithic applications, the entire application stack needs to be updated for feature releases, as well as development is stymied because of complexity and interdependencies.

Reduced Deployment Time: Individual services can be deployed with a focus on function specific features and environments. Development, testing, and pipeline release is for smaller modules rather than the entire application stack. Frequent updates can be made to individual services, and those updates can be deployed in production much faster.

If you need assistance in determining the feasibility of transforming your applications from monolithic to microservices architecture, Keyva can help. Associates at Keyva have worked with many different organizations in various verticals to help in application modernization projects. These include things like refactoring existing applications, adding a wrapper over current applications so they can be consumed easily by DevOps processes, and more. If you'd like to have us review your environment and provide suggestions on what might work for you, please contact us at [email protected].

Anuj joined Keyva from Tech Data where he was the Director of Automation Solutions. In this role, he specializes in developing and delivering vendor-agnostic solutions that avoid the “rip-and-replace” of existing IT investments. Tuli has worked on Cloud Automation, DevOps, Cloud Readiness Assessments and Migrations projects for healthcare, banking, ISP, telecommunications, government and other sectors. During his previous years at Avnet, Seamless Technologies, and other organizations, he held multiple roles in the Cloud and Automation areas. Most recently, he led the development and management of Cloud Automation IP (intellectual property) and related professional services. He holds certifications for AWS, VMware, HPE, BMC and ITIL, and offers a hands-on perspective on these technologies. Like what you read? Follow Anuj on LinkedIn at https://www.linkedin.com/in/anujtuli/ [post_title] => What is Microservices Architecture? [post_excerpt] => [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => what-is-microservices-architecture [to_ping] => [pinged] => [post_modified] => 2019-01-30 20:13:53 [post_modified_gmt] => 2019-01-30 20:13:53 [post_content_filtered] => [post_parent] => 0 [guid] => https://keyvatech.com/?p=1144 [menu_order] => 36 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) [7] => WP_Post Object ( [ID] => 1139 [post_author] => 7 [post_date] => 2019-01-21 19:22:52 [post_date_gmt] => 2019-01-21 19:22:52 [post_content] => If you've tried to install Kong from scratch, you know that it is a little more involved than what is posted on the official website guide. There are some pre-requisites that the system needs to be set up with, in order to make sure the service installs and runs successfully. Let us take a look at the steps involved in setting up a Kong instance from the ground up. This guide is valid for RHEL 7, PostgreSQL 11, and Kong 0.14.1. We will set up Kong on a VMware virtual machine. If you are using another hypervisor, the steps for hardware configurations may differ slightly. First, let us take a look at the pre-requisite steps that need to be addressed before we begin our work.

RHEL 7 VM with minimum/base packages
A valid Subscription with RedHat with appropriate entitlements
Static IP configuration set up
/etc/hosts configured for name resolution
/etc/hostname set up with the fully qualified domain name
SSH Keys copied on your local workstation (optional)
Nameserver set up
firewalld service disabled
Yum enabled
Non-root user set up for sudo access (optional)
VMware tools package installed

Prepare and Install OS Packages Since we set up RHEL with minimal packages, we would need to enable all the needed rpms. First register with subscription manager using your Red Hat profile credentials: subscription-manager register subscription-manager refresh subscription-manager attach –-auto subscription-manager repos –-list subscription-manager repos --enable rhel-7-server-rh-common-beta-rpms subscription-manager repos --enable rhel-7-server-rpms subscription-manager repos --enable rhel-7-server-source-rpms subscription-manager repos --enable rhel-7-server-rh-common-source-rpms subscription-manager repos --enable rhel-7-server-rh-common-debug-rpms subscription-manager repos --enable rhel-7-server-optional-source-rpms subscription-manager repos --enable rhel-7-server-extras-rpms Finish setting up other utils: yum repolist yum -y update yum –y install wget PostgreSQL 11 Installation We will first have to set up PostgreSQL 9.5+ or Cassandra database to use with Kong. The version compatibility is listed on Kong's website. We decided to go with PostgreSQL 11. Download the latest build for PostgreSQL 11 curl -O https://download.postgresql.org/pub/repos/yum/11/redhat/rhel-7-x86_64/pgdg-redhat11-11-2.noarch.rpm Extract the package rpm -ivh pgdg-redhat11-11-2.noarch.rpm Find the relevant RPMs and install yum list postgres* yum install postgresql11-server.x86_64 Initialize the DB /usr/pgsql-11/bin/postgresql-11-setup initdb Start the PostgreSQL service systemctl enable postgresql-11.service systemctl start postgresql-11.service Verify the install was successful and the service is up, as well as set the database password su - postgres -c "psql" postgres=# \password postgres postgres-# \q Set up the database for Kong postgres=# create user kong; create database kong owner kong; Make a note of the configuration file location. We will need this later postgres=# show hba_file; hba_file ------------------------------------ /var/lib/pgsql/11/data/pg_hba.conf (1 row) We will also set (or update) the password for the kong user. It is recommended you use a password with no numbers (string only). It has been observed with previous versions that using numbers in the password can cause issues. Since this will be lab environment, it may be easier to choose a string only password and avoid troubleshooting any potential issues caused due to this. postgres=# alter user kong with password 'kong'; Kong 0.14.1 Installation Enable the relevant EPEL repository EL_VERSION=`cat /etc/redhat-release | grep -oE '[0-9]+\.[0-9]+'` && \ > sudo yum install https://dl.fedoraproject.org/pub/epel/epel-release-latest-${EL_VERSION%.*}.noarch.rpm Fetch the latest Kong package wget https://bintray.com/kong/kong-community-edition-rpm/download_file?file_path=rhel%2F7%2Fkong-community-edition-0.14.1.rhel7.noarch.rpm The saved file gets a strange name, so let's change the name to make it cleaner mv download_file\?file_path\=rhel%2F7%2Fkong-community-edition-0.14.1.rhel7.noarch.rpm kong-community-edition-0.14.1.rhel7.noarch.rpm Extract the package rpm -ivh kong-community-edition-0.14.1.rhel7.noarch.rpm You can now verify the install by running the command kong on the prompt. It will display various command options for kong. We will now need to modify the PostgreSQL config file to make sure the database allows connections from Kong. We had noted the location of the pg_hba.conf file earlier in the database install section. vi /var/lib/pgsql/11/data/pg_hba.conf Add the IP of Kong host (or let the default entry of 127.0.0.1 stay as is, if installing on the same host as the database) under the IPv4 section. Change the authentication method from ident to md5. This is important – otherwise you will get ident authentication errors when you try to start the kong service later. The Kong configuration file that ships with the package has an extension .default to it. We therefore copy the default file to the .conf extension before making changes cp /etc/kong/kong.conf.default /etc/kong/kong.conf By default, all entries in the configuration file are commented out. You can choose to let them be as is. Or you can decide to uncomment specific sections or specific lines. In our case, we will do the following: 1) Under the GENERAL section, uncomment the prefix line, and update it to the following path prefix = /etc/nginx/ssl 2) Under the NGINX section, uncomment the admin_listen line, and update it as per the following admin_listen = <Kong_Server_IP>:8001, 127.0.0.1:8001, 127.0.0.1:8444 ssl 3) Under the DATASTORE section, uncomment the lines for postgresql, and update them with the connection information for your database. database = postgres pg_host = 127.0.0.1 pg_port = 5432 pg_user = kong pg_password = <Password you set during DB initiation> pg_database = kong pg_ssl = off pg_ssl_verify = off Save and exit the configuration file. Also note that we changed the default prefix path; so we will need to manually create a directory for that path, and chmod the appropriate permissions for that folder. cd /etc mkdir nginx cd nginx mkdir ssl cd / chown –R root:root /etc/nginx/ssl chmod –R 600 /etc/nginx/ssl Run Kong migrations kong migrations up [-c /etc/kong/kong.conf] --v Start the Kong service kong start [-c /etc/kong/kong.conf] --v Verify the service is up and running curl -i http://<Kong_Server_IP>:8001/ You can also verify the service status by going to URL http://<Kong_Server_IP>:8001/ via a browser. Troubleshooting 1) Error: Absolute path to the certificate /usr/local/share/lua/5.1/kong/cmd/start.lua:71: /usr/local/share/lua/5.1/kong/cmd/start.lua:28: nginx configuration is invalid (exit code 1): nginx: [emerg] SSL_CTX_load_verify_locations("/etc/nginx/ssl/# Absolute path to the certificate") failed (SSL: error:02001002:system library:fopen:No such file or directory:fopen('/etc/nginx/ssl/# Absolute path to the certificate','r') error:2006D080:BIO routines:BIO_new_file:no such file error:0B084002:x509 certificate routines:X509_load_cert_crl_file:system lib) nginx: configuration file /etc/nginx/ssl/nginx.conf test failed If you get an error like the above, make sure that the DEVELOPMENT & MISCELLANEOUS section in the /etc/kong/kong.conf file are all commented out. If you are not using SSL certificates, you may want to comment out all lines that point to the certificate path (e.g. #lua_ssl_trusted_certificate = ) 2) Error: Permission Denied This could be caused by Kong trying to access the default prefix path of /usr/local/kong without proper permissions. If the install is being run by a non-root user, you will have to provide folder write permissions for that user as well. Alternatively, you can manually create a different folder (see instructions above) and assign appropriate write permissions to it. 3) Error: During service verification, the curl command works, but the browser URL access fails with Page Not Found Make sure that the admin_listen = <Kong_Server_IP>:8001, 127.0.0.1:8001, 127.0.0.1:8444 ssl line is not commented out in the /etc/kong/kong.conf file. Also, Kong_Server_IP should reflect the IPv4 address of the server where Kong is installed. 4) Error: Unauthorized ident access Make sure that in the postgresql configuration file (/var/lib/pgsql/11/data/pg_hba.conf), the access method for the kong host is set to md5. 5) Error: In postgres.lua Expected value but found T_END at character 1 Check the database configuration parameters in /etc/kong/kong.conf. Verify the database server name, IP, and credentials are correct. Also, try to drop the database and recreating it within PostgreSQL (in case the database got corrupt). You can do so by running the commands postgres=# drop database kong; postgres=# create database kong owner kong; and then restarting the PostgreSQL service from the console systemctl stop postgresql-11 systemctl start postgresql-11 If you have any questions about the steps documented here, would like more information on the installation procedure, or have any feedback or requests, please let us know at [email protected].

Anuj joined Keyva from Tech Data where he was the Director of Automation Solutions. In this role, he specializes in developing and delivering vendor-agnostic solutions that avoid the “rip-and-replace” of existing IT investments. Tuli has worked on Cloud Automation, DevOps, Cloud Readiness Assessments and Migrations projects for healthcare, banking, ISP, telecommunications, government and other sectors. During his previous years at Avnet, Seamless Technologies, and other organizations, he held multiple roles in the Cloud and Automation areas. Most recently, he led the development and management of Cloud Automation IP (intellectual property) and related professional services. He holds certifications for AWS, VMware, HPE, BMC and ITIL, and offers a hands-on perspective on these technologies. Like what you read? Follow Anuj on LinkedIn at https://www.linkedin.com/in/anujtuli/ [post_title] => Installing Kong: A First Day Guide [post_excerpt] => [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => installing-kong-a-first-day-guide [to_ping] => [pinged] => [post_modified] => 2020-03-05 19:20:18 [post_modified_gmt] => 2020-03-05 19:20:18 [post_content_filtered] => [post_parent] => 0 [guid] => https://keyvatech.com/?p=1139 [menu_order] => 37 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) ) [post_count] => 8 [current_post] => -1 [before_loop] => 1 [in_the_loop] => [post] => WP_Post Object ( [ID] => 1531 [post_author] => 7 [post_date] => 2019-07-09 15:33:31 [post_date_gmt] => 2019-07-09 15:33:31 [post_content] =>

Take the first step towards transforming your apps into cloud-native

Here is an example of how the command line API call would look like, that would export the application metrics without the need to export any source code -

java -jar HighlightAutomation.jar --workingDir "/samples/pathToWorkingDir" --sourceDir "/samples/sourceDir/src/" --skipUpload

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