Category: Tech Tips

Part Three: ServiceNow Hyperautomation – Process Mining

Automate, Blog, ServiceNow, Tech TipsKelly Cooke

ServiceNow, a leading platform for process mining and automation, recently launched its Utah release with several new features and enhancements, including multidimensional mining.

Part Three of this blog series will explore how you can analyse and improve your IT service management (ITSM) workflows using process mining and automation.

What is Process Mining?

Process mining is a technique that applies data science to discover, validate and improve workflows based on event logs from information systems. It uses artificial intelligence and machine learning to automatically extract process models from your ServiceNow data and visualise them in an interactive dashboard. You can then use the dashboard to identify bottlenecks, inefficiencies, deviations and best practices in your ITSM processes, and take action to optimise them with automation solutions.

How can ServiceNow Process Mining help you to enhance your organisation’s incident management process?

ServiceNow Process Mining can help your organisation enhance your incident management process by providing you with valuable insights into how your process is actually performed, how it deviates from the expected or desired behavior, and how it can be improved.

Process overview shows you the key metrics and statistics of your process, such as the number of cases, the average duration, the throughput time or the compliance rate. You can also filter and segment your data by various criteria, such as category, urgency, assignment group or resolution code.

Process Map provides your organisation with a graphical representation of your process model, which is automatically generated using your data. You can see the flow of cases from start to end, the frequency and duration of each activity, and the variants and deviations of your process. You can also drill down into specific cases or activities to see more details.

Process Analysis presents you the results of various analyses that are performed on your organisation’s process model, such as bottleneck analysis, root cause analysis, conformance analysis and best practice analysis. You can use these to identify and understand the causes and effects of problems and opportunities in your process.

Using these three capabilities, you can gain a comprehensive understanding of your organisation’s incident management process and discover ways to optimise it through elimination of unnecessary steps or automation of repetitive tasks.

Final Thought

As an Elite ServiceNow Partner, JDS can help you navigate the complex and fast-changing world of hyper-automation. We have the expertise, experience and passion to support your organisation’s digital transformation journey. Whether you need to automate your business processes, optimise your IT operations, or leverage artificial intelligence and machine learning, we can guide you every step of the way.

What is the difference between a Vulnerability Scan and a Penetration Test?

Blog, JDSSecurity, Secure, Tech Tips, TestKelly Cooke

Vulnerability Scanning and Penetration Testing are terms that are often interchanged and even confused for the same activity, and while they are similar, they are not the same. So what are the key differences, and how and when should they be carried out?

What is a Vulnerability Scan?

A Vulnerability Scan is performed within your network, systems, services, or applications in the security perimeter concerned. Generally speaking, a Vulnerability Scan is fully automated, providing detailed reports on vulnerabilities found such as out-of-date frameworks and dependencies, publicly known exploits, loopholes, and common configuration issues that could lead to further vulnerabilities.

Typically, these scans are run with tools like Tenable Nessus, Rapid7 Nexpose, and Qualys, among many others, and may be tailored to your requirements.

A Vulnerability Scan is limited to providing a report on raw data and is, for the most part, unable to paint a full picture of where greater issues can arise.

One major difference between using a scanning tool and a human tester is understanding where the pieces of the puzzle can come together, such as chaining low-level vulnerabilities into a high-level critical exploit which may need far more urgent and immediate action than a scan report may suggest.

While often very detailed and including information like CVE details, CVSS scores, and overviews of the vulnerability, etc., there are often false positives presented in even the most finely tuned scans due to the lack of a discerning human eye and experience. Depending on the level of the report, the provided data may still require significant human attention to filter through and further verify, as a scan does not attempt to actively exploit its findings.

What is a Penetration Test?

As suggested by its name, a Penetration Test is a test that attempts to penetrate a system or service from outside of the security perimeter.

A Penetration Test is handled by a human penetration tester and aided by the many tools and techniques available to them, to identify and further exploit any vulnerabilities found on the subject of the test.

Having a human rather than an automated tool may be slower and more expensive on a per-engagement basis, but can provide more accurate results limiting false positives, and providing proof of concept exploitation, experience-driven vulnerability overviews, exploit pivoting, and quality remediation advice with the context of your system or service in mind.

Another advantage of a Penetration Test over a Vulnerability Scan is the ability to research on the fly, find unknown exploits including Zero Days, and find vulnerabilities that may not have been added to the Vulnerability Scanners library yet.

What and When?

While it may seem that a Penetration Test may be the best overall service to take due to its accuracy, there is a time and a place for both services, or even have them work hand in hand.

Where security is concerned, both services are valid but are not providing the complete picture when they are used separately.

Due to the automated nature of a vulnerability scanning tool, it can be set to scan at specified intervals to report changes between two or more points in time, providing a real-time surface view of your systems, network, or other services, and generating a human-friendly report, all whilst running hands-off in the background.

A penetration tester can interpret reports provided by a vulnerability scan and this can supplement a penetration test itself, in many cases helping the human tester speed up the overall engagement by targeting identified points of vulnerability rather than having to manually find them.

Both a Vulnerability Scan and a Penetration Test have their strengths and weaknesses and typically speaking, one’s strength covers the weakness of the other.

It isn’t uncommon for an organization to have a vulnerability scanner conducting day-to-day scans of systems and networks, and periodically have a human penetration tester validate and carry out further tests based on the outputs provided by the scanner. When used together in this way, you can achieve the highest level of security assurance for your organisation.

Manual Security Testing vs Automated Scanning?

Blog, JDSSecurity, Secure, Tech Tips, TestKelly Cooke

The art of penetration testing has evolved over the years. What began with testing arrows on armour, has now become testing tools and techniques on systems and applications. Without a doubt, we are still mostly using manually driven techniques, however this can be slow, cumbersome, and subject to the human element which can result in faults and missed opportunities.

Over the last decade or so, tools to aid and automate security testing have rapidly entered the fray and are increasingly taking the burden off some of the more time-intensive tasks in the cyber security sphere, such as scanning, brute-forcing, or even full-fledged attacks commanded with single line commands. Tools such as BurpSuite, Nmap, SQLMap, Metasploit, and Nessus, among many others, have certainly sped up the discovery and exploitation of vulnerabilities, allowing more in-depth testing within often limited test windows.

Looking at the bounty of tools available to us, you may start to wonder why manual testing is required anymore. Here is a quick rundown on some of the benefits and disadvantages of both, and how using both on engagements, big and small, can be greatly beneficial.

Manual Testing – The Old Reliable

Manual testing, simply put, is the act of using little to no automation for tasks. A great example of this would be the manual exploration of a website while data is being captured by BurpSuite, where the tester can manually analyse the headers and requests as its own task later, rather than immediately after every click.

Manual testing also extends to the exploitation stage of an engagement, where the tester may need to utilise very specific commands or customised scripts to achieve the desired result.

While manual testing can be very meticulous, and provide a detailed and deep understanding of the subject of the test, it can be very time-consuming, possibly taking days longer than an automation-driven test. There are some vulnerabilities that just simply can’t be automated entirely, or are very prone to false positives if automated, and therefore will require further investigation, possibly using more time than if done entirely manually from the beginning.

Some examples of vulnerabilities that require manual testing to correctly identify and safely exploit are:

  • Social Engineering
  • Access Control Violations
  • Password Spraying and Credential Stuffing Attacks
  • SQL Injection
  • Cross-Site Request Forgery (CSRF)

Another advantage of manual testing over automation is the ability to find, and use, newly or not yet discovered zero-day exploits, which can take a significant amount of time to be implemented into commonly used tools.

Automated Testing – The Shiny New Tools

Automated penetration testing is really what is written on the package – it is the process of utilizing automation tools, such as applications, platforms, and scripts, rather than the expertise and efforts of a human tester. It can be significantly cheaper and far more time efficient (which also adds to cost efficiency) than manual testing by one or more human ethical hackers.

Automation tools can perform actions such as content discovery, vulnerability analysis, and brute forcing, in a matter of minutes or seconds, where it could take a manual tester hours or days to get the same results. Automated tools, namely scanning, can be left to run in the background while manual testing is also performed, or set to periodically scan for issues, such as Tenable Nessus keeping an eye on things and providing reports at set intervals or upon request.

When it comes to regular penetration testing, companies factor in cost, and it can be rather expensive to hire human penetration testers for regular tests or as in-house, so it can be more cost-effective to have automated tools do the day-to-day, then infrequently have a human run further tests and analysis.

There is no doubt that automation is the way of the future, and will continuously improve; however, there are many tasks that are best suited to manual testing, either due to the simple inability to automate or due to the hassle of false positives (and false negatives).

Another advantage to automation is consistency, in both its actions and results, and with the reporting at the end. As the scans and processes run are mostly, if not entirely hands-off, there is less room for human error or deviation, and therefore don’t require a highly trained expert to perform the required tasks, which ultimately can save money for the organization. Automation, however, is often unable to fully assess a threat and how it can impact you in context to your application, platform, infrastructure, network, or organisation as a whole, which is something a sufficiently trained human penetration tester can do, and make new actions accordingly. A vulnerability that may be picked up and reported as a low finding by an automation tool, could have much more critical consequences when chained with other low, or even informational, vulnerabilities.

So, what’s better? A manual or automated approach?

Simply put, both manual and automated testing methods have their place, and should always be used in penetration tests of all kinds. The level of detail and effectiveness provided by manual testing is unsurpassed, as well as contextual reporting and risk analysis that simply cannot be provided by even the best automation tools on the market. However, where speed and consistency of tasks are concerned, automation wins without question.

Although both methods can provide you with a satisfactory outcome in terms of vulnerability identification, what is best for your organization will come down to what level of detail and quality your organisation requires, the frequency of the tests, and the cost factor.

Ultimately, a combination of both manual and automated testing is the best way to get the highest quality outcome of a penetration test, with the most efficient use of time and money, to bring you a greater assurance of security and peace of mind that your assets are secure from malicious attack.

Five Reasons Why Your Organisation Should Be Penetration Testing

Blog, JDSSecurity, Secure, Tech Tips, TestKelly Cooke

Modern businesses require an advanced approach to security and due diligence.  Having anti-virus software and a firewall is no longer an efficient strategy to prevent highly sophisticated security attacks which can result in irreversible damage to your organisation.

A professional penetration testing service is the best way to identify the strengths, weaknesses and holes in your defences.  Read on to uncover the five best reasons why your organisation needs penetration testing.

1. Protect Your Organisation From Cyber Attacks

Reports of cyber crime within Australia have increased nearly 15% each year since 2019, with the average reported financial loss per successful cybercrime incident being $50,673. Regardless of your organisational size or sector, cyber criminals view every business as a potentially exploitable prospect. The internet is continuously being scanned in search of vulnerable systems.  Carrying out penetration tests will enable you to identify vulnerabilities that are most likely to be exploited, determine what the potential impact could be, and enable you to implement measures to mitigate or eliminate the threat.     

2. Identify and Prioritise Vulnerabilities

Put simply, a pen test will uncover all of the potential threats and vulnerabilities that could damage your organisation’s IT assets.  The resulting report prioritises these vulnerabilities from low to critical, and further categorises them by likelihood and impact.  This gives your team a clear picture of your security posture, and the opportunity to mitigate the greatest threats first before moving on to less risky ones.

3. Stay Compliant With Security Standards and Regulations

Regular penetration testing can help you to comply with security standards and regulations such as ISO 27001 and PCI.  These standards require company managers and system owners to conduct regular penetration tests and security audits to demonstrate ongoing due diligence and maintenance of required security controls. Not only does pen testing identify potential vulnerabilities, ensuring that you are protecting your customers and assets, but it also helps to avoid costly fines and fees connected with non-compliance. 

4. Reduce Financial Losses and Downtime

Recent studies have reported that the average financial impact of a major data breach in Australia is around $3.7million per incident.  This takes into account expenditures on customer data protection programs, regulatory fines, and loss of revenue due to business operability.  System downtime is incredibly costly – the longer your system is down, the more exorbitant the cost.  A penetration test is a proactive solution to highlight and fix your system’s most critical vulnerabilities, and ensure your team are ready to act if your systems were to go down unexpectedly. 

5. Protect Your Reputation and Company Loyalty

Consumers are extremely quick to lose trust in companies and brands, and all it takes is one security breach or data leak to tarnish your reputation.  Customers and partners of your organisation want to know that their private data is safe in your hands, so it is in your best interest to be aware of any vulnerabilities which may put the company’s reputation and reliability in jeopardy.  

This is just a handful of reasons why organisations should carry out regular penetration tests, but there are many more.  Connect with JDS to discuss your pen testing needs and get a full scope of work customised to your requirements.

Splunk

Blog, Case Study, News, Splunk, Tech TipsKelly Cooke

As a Splunk Premier partner, JDS has a dedicated Splunk practice with expertise spanning ITOps, AIOps, and Security. JDS has proven to be trusted advisors and provide a safe pair of hands to architect, implement and manage Splunk for many organisations across a wide range of use cases.

IT Service Intelligence / Business Service Insights

Customisable business dashboards, mapped to key performance indicators, can provide invaluable real-time visibility into the health of your digital services. Our skilled JDS team have extensive experience in implementing Splunk’s unique platform to assist organisations ensure uninterrupted access to critical services.


IT Operations, Analytics and AIOps

JDS can transform your entire IT Ops approach with a suite of tools that put AI and machine learning at their core, allowing you to predict and prevent, instead of triage and react.  We enable a genuine understanding of the complete environment to get ahead of issues before they occur.


End to End Application Visibility

Gaining End-to-End Visibility means unifying business, application and infrastructure health for full-stack observability of critical apps and services.  With JDS and Splunk, gain the ability to visualise the health of your services at a glance, and make smarter, data-driven decisions.

Enterprise Security and Analytics

Splunk is renowned for its Security Information and Event Management (SIEM) and Security Orchestration, Automation and Response (SOAR) capabilities and JDS has the experience to establish and build out these capabilities along with the integrations to related systems.


Call Centre Visibility

Having insights into how your call centre is responding to customers can improve efficiency, effectiveness, quality of service and the overall customer experience. Using Splunk’s Contact Center Analytics, JDS can unlock this vital visibility, whether you’re working with centralized call centres or remote agents.

Splunk Cloud Migrations

JDS will help you to minimise downtime whilst maximising your architecture when migrating to Splunk Cloud. Our experts lead a collaborative engagement to make the transition as seamless as possible, while maintaining full visibility into your infrastructure before, during and after migration.

Success Stories

Helping one of Australia’s largest banking institutions migrate seamlessly to Splunk Cloud >

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Mastering Modal Dialog Boxes

Blog, ServiceNow, Tech TipsKelly Cooke

Modal Dialog boxes are a great way to enhance the user experience and provide more detailed feedback to users than a default browser popup.

Recently, we had a challenging set of requirements from a customer. They needed a bunch of mandatory fields (easy enough with UI Policies) but there was a twist—they needed:

  • Fields that were mandatory ONLY if the person wanted to send the record for approval
  • Four fields but ONLY one of the four is mandatory (ie, put data in any one field and all four are good to go)
  • There had to be at least one record in a related list

Modal dialogs are awesome!

We developed a simple approval dialog box for a UI action to cater to these requirements. It’s a good example of how complex requirements can be distilled into a simple solution.

Have fun and happy coding!

Understanding your Customer Journeys in Salesforce with AppDynamics

AppDynamics, Blog, Tech TipsJDS

The Problem

JDS Australia works with numerous customers who utilise the force.com platform as the primary interface for their end users (internal and external) to execute business critical services. The flexibility and extensibility of the component based Lightning framework has allowed businesses to customise the platform to meet their specific requirements.

However, many of these companies struggle to monitor, quantify or pinpoint the impact of performance in the Salesforce platform to their end users and ultimately their business. Furthermore, there is limited capability to provide detailed Salesforce information for root cause analysis (e.g. is the problem with a particular lightning component(s) vs core Salesforce platform vs multiple pages?).

The Solution

Using AppDynamics Real User Browser Monitoring (RUM) coupled with advanced JavaScript configuration, we have created a solution.

Unlike traditional methods involving logfile or API based monitoring; real user monitoring collects rich metrics from the end users’ perspective. JDS has also further integrated  additional custom code to identify AJAX requests and inject page names into the stream and provide business context and make sense of the data.

Dashboards provide Salesforce Performance at a glance

Additionally, AppDynamics RUM is able to identify and dynamically visualise each step of Customer Journeys as they traverse Salesforce, in near real time. 

Using the collated metrics these businesses have been able to proactively alert support teams of issues, and also utilise the historic data to analyse customer behaviour to understand how customers are using the platform. For example, expected user journeys vs actual user journeys.

AppDynamics RUM captures detailed diagnostic information to help triage issues, including:

  • Single Page Application performance
  • Page Component load details, 
  • AJAX requests, 
  • Detailed Error Snapshots
  • Dynamic Business Transaction Baselining (Normal vs Slow Performance)
  • User browser version and device type,
  • Geographic location of users,
  • Connection method (e.g. browser vs mobile), and device type. 

AppDynamics RUM can also provide direct correlation to AppDynamics APM agents to combine the ‘front-end’ and ‘back-end’ of these user sessions where Salesforce may traverse additional down-stream applications and infrastructure.

Why JDS?

As experts in Application Performance Management (APM) and Observability, JDS have extensive experience in helping our customers determine the root cause of performance issues.

Contact us at [email protected] to discuss how monitoring Salesforce can be used to understand your end users and make informed decisions with quantifiable metrics. 

Implementing Salesforce monitoring in Splunk

Blog, Splunk, Tech TipsKelly Cooke

The problem

A JDS customer embarked on a project to implement Salesforce to provide their users a single user interface to fulfil their customer needs.  Their aim, to make the interface easy to use and reduce the time to process customer requests.  At the same time, the business had to ensure that their customer data stored in Salesforce was secure and to be able to detect any malicious use.

The Solution

Implementing Splunk with the Splunk Add-on for Salesforce enabled the collection of logs and objects from Salesforce using REST APIs.  This in turn, enabled proactive alerting and the creation of informative dashboards and reports to satisfy the business’ security requirements.

Scenarios detected:

  • Failed or unusual login attempts (same user tries to login from multiple IP addresses)
  • Large amounts of data extracted from Salesforce
  • Unauthorised changes in Salesforce configuration such as Connected Apps settings or Authentication Provider settings
  • Integration user account activity occurring outside of scheduled job runs
  • Privileged user activity
  • Apex code execution

All of this was achieved by setting up the required data inputs via the Splunk Add-on.  Creating lookups to enhance the alert content with meaningful information and macros for re-usability and ease of administration, then adding alerts to ensure the required conditions were notified to the operational support teams.

Splunk dashboards and reports built on Salesforce data allowed the business to easily view login patterns and analyse EventLog events and Setup Audit Trail changes.  Additionally, Salesforce data ingestion and alert summary dashboards were added to assist the support team to identify issues or delays in data ingestion as well as review the number of alerts being generated over time.

When developing any application that provides access to secure information, it’s important to not only monitor in terms of user experience, but also look at security aspects. Our customer was able to satisfy the security monitoring requirements of the business with the Splunk Add-on for Salesforce and achieved their go-live target date. The configured alerts will keep them informed of any potential security issues, giving them confidence that the platform is secure. The accompanying dashboards provide an intuitive summary of user actions, all backed by an extended data retention policy in Splunk to satisfy regulatory compliance. With SalesForce data now available in Splunk, they are planning additional use cases to not only monitor security, but get insights into how the platform is used by employees.

Why choose JDS?

JDS has experience and expertise in bringing SalesForce application data into Splunk . If your focus is on security, performance, or custom monitoring, speak to JDS today about how we can convert your SalesForce data into useful insights.

Finding Exoplanets with Splunk

Blog, Splunk, Tech TipsJDS

Splunk is a software platform designed to search, analyze and visualize machine-generated data, making sense of what, to most of us, looks like chaos.

Ordinarily, the machine data used by Splunk is gathered from websites, applications, servers, network equipment, sensors, IoT (internet-of-things) devices, etc, but there’s no limit to the complexity of data Splunk can consume.

Splunk specializes in Big Data, so why not use it to search the biggest data of all and find exoplanets?

What is an exoplanet?

An exoplanet is a planet in orbit around another star.

The first confirmed exoplanet was discovered in 1995 orbiting the star 51 Pegasi, which makes this an exciting new, emerging field of astronomy. Since then, Earth-based and space-based telescopes such as Kepler have been used to detect thousands of planets around other stars.

At first, the only planets we found were super-hot Jupiters, enormous gas giants orbiting close to their host stars. As techniques have been refined, thousands of exoplanets have been discovered at all sizes and out to distances comparable with planets in our own solar system. We have even discovered exomoons!

 

How do you find an exoplanet?

Imagine standing on stage at a rock concert, peering toward the back of the auditorium, staring straight at one of the spotlights. Now, try to figure out when a mosquito flies past that blinding light. In essence, that’s what telescopes like NASA’s TESS (Transiting Exoplanet Survey Satellite) are doing.

The dip in starlight intensity can be just a fraction of a percent, but it’s enough to signal that a planet is transiting the star.

Transits have been observed for hundreds of years in one form or another, but only recently has this idea been applied outside our solar system.

Australia has a long history of human exploration, starting some 60,000 years ago. In 1769 after (the then) Lieutenant James Cook sailed to Tahiti to observe the transit of Venus across the face of the our closest star, the Sun, he was ordered to begin a new search for the Great Southern Land which we know as Australia. Cook’s observation of the transit of Venus used largely the same technique as NASA’s Hubble, Kepler and TESS space telescopes but on a much simpler scale.

Our ability to monitor planetary transits has improved considerably since the 1700s.

NASA’s TESS orbiting telescope can cover an area 400 times as broad as NASA’s Kepler space telescope and is capable of monitoring a wider range of star types than Kepler, so we are on the verge of finding tens of thousands of exoplanets, some of which may contain life!

How can we use Splunk to find an exoplanet?

 Science thrives on open data.

All the raw information captured by both Earth-based and space-based telescopes like TESS are publicly available, but there’s a mountain of data to sift through and it’s difficult to spot needles in this celestial haystack, making this an ideal problem for Splunk to solve.

While playing with this over Christmas, I used the NASA Exoplanet Archive, and specifically the PhotoMetric data containing 642 light curves to look for exoplanets. I used wget in Linux to retrieve the raw data as text files, but it is possible to retrieve this data via web services.

MAST, the Mikulski Archive for Space Telescopes, has made available a web API that allows up to 500,000 records to be retrieved at a time using JSON format, making the data even more accessible to Splunk.

Some examples of API queries that can be run against the MAST are:

The raw data for a given observation appears as:

Information from the various telescopes does differ in format and structure, but it’s all stored in text files that can be interrogated by Splunk.

Values like the name of the star (in this case, Gliese 436) are identified in the header, while dates are stored either using HJD (Heliocentric Julian Dates) or BJD (Barycentric Julian Dates) centering on the Sun (with a difference of only 4 seconds between them).

Some observatories will use MJD which is the Modified Julian Date (being the Julian Date minus 2,400,000.5 which equates to November 17, 1858). Sounds complicated, but MJD is an attempt to simplify date calculations.

Think of HJD, BJD and MJD like UTC but for the entire solar system.

One of the challenges faced in gathering this data is that the column metadata is split over three lines, with the title, the data type and the measurement unit all appearing on separate lines.

The actual data captured by the telescope doesn’t start being displayed until line 138 (and this changes from file to file as various telescopes and observation sets have different amounts of associated metadata).

In this example, our columns are…

  • HJD - which is expressed as days, with the values beyond the decimal point being the fraction of that day when the observation occurred
  • Normalized Flux - which is the apparent brightness of the star
  • Normalized Flux Uncertainty - capturing any potential anomalies detected during the collection process that might cast doubt on the result (so long as this is insignificant it can be ignored).

Heliocentric Julian Dates (HJD) are measured from noon (instead of midnight) on 1 January 4713 BC and are represented by numbers into the millions, like 2,455,059.6261813 where the integer is the days elapsed since then, while the decimal fraction is the portion of the day. With a ratio of 0.00001 to 0.864 seconds, multiplying the fraction by 86400 will give us the seconds elapsed since noon on any given Julian Day. Confused? Well, your computer won’t be as it loves working in decimals and fractions, so although this system may seem counterintuitive, it makes date calculations simple math.

We can reverse engineer Epoch dates and regular dates from HJD/BJD, giving Splunk something to work with other than obscure heliocentric dates.

  • As Julian Dates start at noon rather than midnight, all our calculations are shifted by half a day to align with Epoch (Unix time)
  • The Julian date for the start of Epoch on CE 1970 January 1st 00:00:00.0 UT is 2440587.500000
  • Any-Julian-Date-minus-Epoch = 2455059.6261813 - 2440587.5 = 14472.12618
  • Epoch-Day = floor(Any-Julian-Date-minus-Epoch) * milliseconds-in-a-day = 14472 * 86400000 = 1250380800000
  • Epoch-Time = floor((Any-Julian-Date-minus-Epoch – floor(Any-Julian-Date-minus-Epoch)) * milliseconds-in-a-day = floor(0. 6261813 * 86400000) = 10902064
  • Observation-Epoch-Day-Time = Epoch-Day + Epoch-Time = 1250380800000 + 10902064 = 1250391702064

That might seem a little convoluted, but we now have a way of translating astronomical date/times into something Splunk can understand.

I added a bunch of date calculations like this to my props.conf file so dates would appear more naturally within Splunk.

[exoplanets]

SHOULD_LINEMERGE = false

LINE_BREAKER = ([\r\n]+)

EVAL-exo_observation_epoch = ((FLOOR(exo_HJD - 2440587.5) * 86400000) + FLOOR(((exo_HJD - 2440587.5) - FLOOR(exo_HJD - 2440587.5))  *  86400000))

EVAL-exo_observation_date = (strftime(((FLOOR(exo_HJD - 2440587.5) * 86400000) + FLOOR(((exo_HJD - 2440587.5) - FLOOR(exo_HJD - 2440587.5))  *  86400000)) / 1000,"%d/%m/%Y %H:%M:%S.%3N"))

EVAL-_time = strptime((strftime(((FLOOR(exo_HJD - 2440587.5) * 86400000) + FLOOR(((exo_HJD - 2440587.5) - FLOOR(exo_HJD - 2440587.5))  *  86400000)) / 1000,"%d/%m/%Y %H:%M:%S.%3N")),"%d/%m/%Y %H:%M:%S.%3N")

Once date conversions are in place, we can start crafting queries that map the relative flux of a star and allow us to observe exoplanets in another solar system.

Let’s look at a star with the unassuming ID 0300059.

sourcetype=exoplanets host="0300059"

| rex field=_raw "\s+(?P<exo_HJD>24\d+.\d+)\s+(?P<exo_flux>[-]?\d+.\d+)\s+(?P<exo_flux_uncertainty>[-]?\d+.\d+)" | timechart span=1s avg(exo_flux)

And there it is… an exoplanet blotting out a small fraction of starlight as it passes between us and its host star!

What about us?

While curating the Twitter account @RealScientists, Dr. Jessie Christiansen made the point that we only see planets transit stars like this if they’re orbiting on the same plane we’re observing. She also pointed out that “if you were an alien civilization looking at our solar system, and you were lined up just right, every 365 days you would see a (very tiny! 0.01%!!) dip in the brightness that would last for 10 hours or so. That would be Earth!”

There have even been direct observations of planets in orbit around stars, looking down from above (or up from beneath depending on your vantage point). With the next generation of space telescopes, like the James Webb, we’ll be able to see these in greater detail.

 

Image credit: NASA exoplanet exploration

Next steps

From here, the sky’s the limit—quite literally.

Now we’ve brought data into Splunk we can begin to examine trends over time.

Astronomy is BIG DATA in all caps. The Square Kilometer Array (SKA), which comes on line in 2020, will create more data each day than is produced on the Internet in a year!

Astronomical data is the biggest of the Big Data sets and that poses a problem for scientists. There’s so much data it is impossible to mine it all thoroughly. This has led to the emergence of citizen science, where regular people can contribute to scientific discoveries using tools like Splunk.

Most stars have multiple planets, so some complex math is required to distinguish between them, looking at the frequency, magnitude and duration of their transits to identify them individually. Over the course of billions of years, the motion of planets around a star fall into a pattern known as orbital resonance, which is something that can be predicted and tested by Splunk to distinguish between planets and even be used to predict undetected planets!

Then there’s the tantalizing possibility of exomoons orbiting exoplanets. These moons would appear as a slight dip in the transit line (similar to what’s seen above at the end of the exoplanet’s transit). But confirming the existence of an exomoon relies on repeated observations, clearly distinguished from the motion of other planets around that star. Once isolated, the transit lines should show a dip in different locations for different transits (revealing how the exomoon is swinging out to the side of the planet and increasing the amount of light being blocked at that point).

Given its strength with modelling data, predictive analytics and machine learning, Splunk is an ideal platform to support the search for exoplanets.

Find out more

If you’d like to learn more about how Splunk can help your organization reach for the stars, contact one of our account managers.

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Glide Variables

ServiceNow, Tech TipsJDS

ServiceNow uses a special type of super flexible variable to store information in what appears like a single field, but is actually a complex storage/management system with a database column type called glide_var.As each record can have a different number of variables stored as key/value pairs, there’s no easy way of dot-walking to the name of the variable within the glide_var as the names can change from record to record within the same table! You can, however, detect and retrieve variables from a glide_var by treating the gliderecord field as an object.

In this example, from an automated test framework step, you can see each of the variables and their values from the database glide_var column inputs.

var gr = new GlideRecord('the table you are looking at')
gr.get('sys_id of the record you are looking at')

for(var eachVariable in gr.inputs){
gs.info(eachVariable + ' : ' + gr.inputs[eachVariable])
}

If you run this in a background script you’ll see precisely which variables exist and what their values are.

It doesn’t need to be complicated! Reach out to us and we can help you.

Fast-track ServiceNow upgrades with Automated Testing Framework (ATF)

ServiceNow, Tech TipsJDS

Why automate?

ServiceNow provides two releases a year, delivering new features to consistently support best practice processes. ServiceNow has flagged they will move towards “N-1” upgrades in 2019, meaning every customer will need to accept each release in the future. To keep up, ServiceNow customers should consider automated testing. This can provide regression testing, completed consistently for each release, and reduce the time and effort needed per upgrade. Effective test automation on ServiceNow is provided by the Automated Testing Framework (ATF), which is included in the ServiceNow platform for all applications. It enables no-code and low-code users to create automated tests with ease. Aided by complementary processes and methodology, ATF reduces upgrade pain by cutting back on manual tests, reducing business impact and accelerating development efficiency.

What is the Automated Test Framework?

Testing frameworks can be viewed as a set of guidelines used for creating and designing test cases. Test automation frameworks take this a step further by using a range of automation tools designed to make the practice of quality assurance more manageable for testing teams.

ServiceNow’s ATF application combines the above into an easy to use and implement solution specifically for ServiceNow applications and functionality. Think of ATF as a tool to streamline your upgrade and QA processes by building tests to check if software or configuration changes have potentially ‘broken’ any existing functionality. It also means developers would no longer be required to start invasive activities like code refactoring to generate new test cases.

Overview of test creation in ServiceNow ATF - click to enlarge

ATF’s unique features include:

  • Codeless setup of test steps and templates (reusable across multiple tests)
  • Testing Service Catalog from end-to-end including submitting Catalog forms and request fulfilment
  • Module testing e.g. ITSM (Incident, Problem & Change Management)
  • Testing forms in the Service Portal (Included in London release)
  • Batching with test suites e.g. Execute multiple tests in a defined order and trigger based on a schedule
  • Custom step configurations & unit testing using the Jasmine test framework
        

Using the codeless test step configuration in ATF to set a Variable value on a Service Catalog form

Simplify your upgrades

For most software upgrades, testing is a burdensome and complicated process, with poorly defined test practices often leading to compromised software quality and potentially, failed projects. Without automation, teams are forced to manually test upgrades - a costly, time-consuming and often ineffective exercise. In most cases, this can be attributed to a lack of testing resources and missing test processes, leading to inconsistent execution.

To address these, ATF introduces structure and enforces consistency across different tests applied to common ServiceNow use cases. Test consistency is important, as this forms a baseline of instance stability based on existing issues, meaning defects caused by upgrades are more reliably determined. ATF allows for full automation of test suites, where the run order can be configured based on tests that depend on each other.  

The following illustrates a simple test dependency created in ATF:

An example ‘hierarchical’ test structure implemented within ATF

A common issue resolved by automated testing is the impact on Agile development cycles. Traditionally, developers would run unit tests by hand, based on steps also written by the developer, potentially leading to missed steps and incomplete scenarios. ATF can be used to run automated unit tests between each build cycle, saving time through early detection and remediation of software issues along with shortened development cycles. The fewer issues present before the upgrade, the fewer issues introduced during the upgrade.

A common requirement of post-upgrade activities is to execute a full regression test against the platform. This means accounting for countless business logic, client scripts, UI policies and web forms that may have been affected. Rather than factoring all of these scenarios into lengthy UAT/Regression cycles, ATF can reduce the load on the business by running those common and repetitive test cases involving multiple user interactions and data entry.

The below example shows how a common use case for testing, field ‘state’ validation on a Service Portal form, is applied using the test step module:

Validating visible & mandatory states of variables with ATF against a Record Producer in Service Portal

Unfortunately, not everything can be automated with ATF out of the box, there are some gaps that are a result of complex UI components, portal widgets and custom development work. It is also important to note custom functionality will add overhead to the framework implementation, and will require specialised scripting knowledge and making use of the ‘Step Configurations’ module to create custom test steps.

When configured properly, it’s possible to automate between 40-60% of test cases with ATF depending on environment complexity and timeframes. The benefits can largely be seen during regression testing post-upgrade, and unit testing during development projects.

In summary, implementing an ATF is a great way of delivering value to ServiceNow upgrade projects and enabling development teams to be more agile. Assessing the scope and depth of testing using an agreed methodology is a great way to determine what is required, and to achieve ‘buy-in’ from others, rather than taking the ‘automate everything’ approach.

JDS is here to help

Recognised as experts in the local Test Automation space for over 12 years, JDS' specialist team has adapted this experience to provide a proven framework for ServiceNow ATF implementation.

We have developed a Rapid Automated Testing tool which can use your existing data to take up to 80% of the work out of building your automated tests. Contact us today to find out how we can build test cases based on real data and automate the development of your testing suite in a fraction of the time that manual test creation would require.

We can help you get started with ServiceNow ATF. In just a few days, a ServiceNow ATF “Kick Start” engagement will provide you with the detail you need to scope and plan ATF on your platform.

Conclusion

Want to know more? Email [email protected] or call 1300 780 432 to reach our team.

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Is DevPerfOps a thing?

Tech TipsJDS

New technology terms are constantly being coined. One of our lead consultants answers the question: Is DevPerfOps a thing?

Hopefully it’s no surprise that traditional performance testing has struggled to adapt to the new paradigm of DevOps, or even Agile. The problems can be boiled down to a few key factors:

  1. Performance testing takes time—time to script, and time to execute. A typical performance test engagement is 4-6 weeks, during which time the rest of the project team has made significant progress. 
  2. Introducing performance testing to a CI/CD pipeline is really challenging. Tests typically run for an hour or more and can hold up the pipeline, and they are extremely sensitive to application changes as the scripts operate at the protocol level (typically HTTP requests). 
  3. Performance testing often requires a full-sized, production-like infrastructure environment. These aren’t cheap and are normally unavailable during early development, making “early performance testing” more of an aspirational idea, rather than something that is always practical.

All the software vendors will tell you that DevOps with performance testing is easy, but none of them have solved the above problems. For the most part, they have simply added a CI/CD hook without even attempting to challenge the concept of performance testing in DevOps at all.

A new world

So what would redefining the concept of performance testing look like? Allow me to introduce DevPerfOps. We would define the four main activities of a DevPerfOps engineer as:

  1. Tactical Load Tests
  2. Microbenchmarking
  3. APM and Code Reviews
  4. Container Workloads

Let’s start at the top.

Tactical Load Testing

This is can be broadly defined as running performance tests within the limitations of a CD/CI pipeline. Is it perfect? No. Is it better than not doing anything or struggling with a full end-to-end test? Absolutely, yes!

Tactical Load Testing has the following objectives:

  • Leverage CI toolkits like Jenkins for execution and results collection.
  • Use lightweight toolsets for execution (JMeter, Taurus, Gatling, LoadRunner, etc. are all perfectly good choices).
  • Configure performance tests to run for short durations. 15-20 minutes is the ideal balance. This can be achieved by minimising the run logic, reducing think times, and focusing on only testing individual business processes.
  • Expect to run your performance tests on a scaled-down environment, or a single server node. Think about what this will do to your workload profile.

Microbenchmarking

These are mini unit or integration tests designed to ensure that a component operates within a performance benchmark and that deviations are detected.

Microbenchmarking can target things like core application code, SQL queries, and Web Service integrations. There is an enormous amount of potential scope here; for example, you can write a microbenchmark test for a login, or a report execution, or a data transformation--anything goes if it makes sense.

Most importantly, microbenchmarking is a great opportunity to test early in the project lifecycle and provide early feedback.

APM and Code Reviews

In past years, having access to a good APM tool or profiler, or even the source code, has been a luxury. Not anymore--these tools are everywhere, and while a fully featured tool like AppDynamics or New Relic is beneficial for developers and operations, a lot can be achieved with low-cost tools like YourKit Profiler or VisualVM.

APM or profilers allow slow execution paths to be identified, and memory usage and CPU utilisation to be measured. Resource intensive workloads can be easily identified and performance baked into the application.

Container Workloads

Containers will one day rule the world. If you don’t know much about containers, you need to learn about them fast.

In terms of performance engineering, each container handles a small unit of workload and container orchestration engines like Kubernetes will auto-scale that container horizontally across the entire data centre. It’s not uncommon for applications to run on hundreds or thousands of containers.

What’s important here is that the smallest unit of workload is tiny, and scaling is something that a well-designed application will get for free. This allows your performance tests to scale accordingly, and it allows the whole notion of “what is application infrastructure” to be challenged. In the container world, an application is a service definition… and the rest is handled for you.

So, is DevPerfOps a thing? We think so, and we think it will only continue to grow and expand from here. It’s time that performance testing meets the needs of 2018 IT teams, and JDS can help. If you have any questions or want to discuss more, please get in touch with our performance engineering team by emailing [email protected]. If you’re looking for a quick, simple way of getting actionable insights into the performance health of your website or application, check out our current One Second Faster promotion.

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