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P2070-072 IBM Content Collector Technical Mastery Test v1

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P2070-072 exam Dumps Source : IBM Content Collector Technical Mastery Test v1

Test Code : P2070-072
Test denomination : IBM Content Collector Technical Mastery Test v1
Vendor denomination : IBM
: 41 actual Questions

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IBM IBM Content Collector Technical

instructions from IBM for Google, Amazon and fb | killexams.com actual Questions and Pass4sure dumps

(MENAFN - The dialog) it's staggering when corporations ultimate for decades – or even more than a century – and especially so when they may breathe in a fast-altering industry fancy desktop expertise. IBM, which traces its roots to the Eighties , grew from three small organizations to a multi-billion-dollar suggestions know-how capabilities industry today. Its united statesand downs along the manner tender some insights into the global expertise business, and might hold some instructive instructions for up-and-coming digital giants fancy Google, Amazon and fb – every bit of of that are far more youthful than IBM.

In my recent booklet, ' IBM: the upward thrust and tumble and Reinvention of a worldwide Icon ,' I explore the enterprise's inheritance of developing and selling facts processing machine and software. As a former IBM employee and a historian , probably the most vital lesson I found is that many people discombobulate incremental adjustments in technology with greater fundamental ones that in fact shape the course of a company's fate.

there's a inequity between particular person items – successive models of PCs or typewriters – and the underlying applied sciences that do them work. Over a hundred thirty years, IBM released neatly over three,600 hardware products and basically the same amount of software . however every bit of those gadgets and capabilities were in accordance with just a handful of actual technological advances, comparable to affecting from mechanical machines to people who relied on computing device chips and utility, and later to networks fancy the information superhighway. The transitions between these advances took location much more slowly than the constant stream of recent products may indicate.

These transitions from the mechanical, to the digital, and now to the networked reflected an ever-turning out to breathe potential to assemble and spend better quantities of suggestions without problems and quickly. IBM moved from manipulating statistical data to the spend of applied sciences that teach themselves what people want and are interested in seeing.

a spotlight that may adapt

Between 1914 and 1918, IBM administration decided that the company the enterprise would breathe in was information processing. in additional coincident phrases, that enterprise has turn into 'massive statistics' and analytics. nonetheless it's nonetheless accumulating and organizing records, and performing calculations and computations on it.

on the grounds that the early Twenties, IBM has taken a disciplined manner to product evolution and analysis, specializing in developing the underpinning applied sciences for its records processing products. Nothing gave the impression to breathe completed unintentionally.

A blue IBM punch card. Gwern/Wikimedia Commons

In its first half-century, IBM's primary technology platform from which many products emerged was the punch-card, yielding tabulators, card sorters, card readers and the distinguished IBM Card . In its second half-century, the primary technology platform changed into the desktop, including mainframes, minicomputers, PCs and laptops. In its most coincident 30 years, computing device earnings indulge in introduced in a declining share of the business's complete earnings, as IBM transitions to presenting more cyber web-primarily based functions, including software and technical and managerial consulting.

the upward thrust of each and every succeeding know-how happened throughout the maturity and decline of its predecessor. IBM first every bit of started promoting computer systems within the Nineteen Fifties, however kept selling tabulating apparatus that nonetheless used punch playing cards unless the early Nineteen Sixties. As these days as the early Nineties, over ninety p.c of IBM's revenues came from selling computers, although it turned into introducing recent features fancy management and technique consulting, assistance technology management and software income.

It wasn't except the conclusion of 2018 that IBM announced that fifty percent of its enterprise now got here from services and application, most of that indulge in been recent offerings developed within the primitive decade.

The advice media – and even IBM employees – may indulge in perceived that IBM became transforming itself quickly and frequently . really the industry had planted seeds for growth early and punctiliously tended recent technologies unless they bore fruit – fortunately, across the same time as prior methods indulge in been ending their positive lives.

This strategic manner isn't incredible – Apple has been selling own computer systems for more than forty years . Its administration, of direction, talks plenty more about its function in the smartphone enterprise, which is already birth to stage off . Apple may additionally soon requisite – or already breathe engaged on – a brand recent technological hub of attention to stay imperative.

The future of the giants

Microsoft, fancy Apple, evolved far from promoting just laptop software and operating techniques. It started web-based initiatives fancy its Bing search engine and OneDrive cloud storage – in addition to providing cloud-based mostly computing functions for groups.

IBM is already exploring quantum computing, as a brand recent frontier of statistics processing. IBM research , CC via-ND

companies that began on the internet may additionally visage identical transitions. Amazon, Google and fb now and then declare to indulge in converted themselves, but indulge in not yet utterly left their customary agencies.

Amazon nevertheless makes most of its funds selling physical items online, although its internet-based mostly cloud services division is starting to breathe hastily . Amazon has moreover invested in a mountainous compass of other industry that may develop in the future, comparable to fitness care and enjoyment content.

Google and fb still do most of their money promoting counsel about how users behave to advertisers and corporations that are looking to appeal to americans to a selected factor of view. both are exploring other avenues, no matter if it breathe Google's self-driving automobiles or facebook's experiments with digital fact .

however at their core, every bit of three web giants are still discovering recent how you can capitalize on the large quantities of assistance they accumulate about shoppers' actions and interests – just as decades prior IBM discovered recent effortless methods to spend tabulating apparatus and computers. if they're to final many years or centuries into the longer term, the corporations will should probe, test and innovate to discover recent tips on how to income as technologies alternate.

IBM fb Google Innovation Amazon Alphabet MIT Press

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Lessons from IBM for Google, Amazon and Facebook

BetterCloud Appoints Jim Brennan as Chief Product Officer | killexams.com actual Questions and Pass4sure dumps

big apple, March 5, 2019 /PRNewswire/ -- BetterCloud, the main SaaS Operations administration platform, introduced nowadays it has multiplied its executive team with the appointment of Jim Brennan as Chief Product Officer. In his recent position, Brennan will lead the company's product management, product operations and design groups. moreover, as a member of the govt management group, Brennan will breathe sure the pile of the BetterCloud platform is aligned with the business's strategic path, principally its diverse go-to-market manner and hub of attention on bringing SaaS Operations administration to agencies international.

"Jim has worked at one of the crucial biggest and most valuable businesses in the world, including IBM, and the skills he brings in line with those experiences will breathe useful to BetterCloud as they continue to build on their industry main SaaS Operations management platform," stated David Politis, CEO of BetterCloud. "Jim's profound technical potential in protection is exciting and his confirmed leadership abilities and entrepreneurial spirit are a flawless meet with their plans to grasp BetterCloud's product construction efforts to the subsequent level."

Brennan brings greater than two decades of event within the building, administration, and marketing of expertise options, together with 18 years within the information security industry. Most currently, he served as vice chairman of Product administration at IBM where he turned into liable for every bit of facets of product strategy, roadmap and execution across the business's safety Operations & Response portfolio. in this function, he led a global crew of product managers, and a brace of go-practical groups involving engineering, marketing, earnings and support. He additionally held a number of Director-degree positions at the business. just before joining IBM, Brennan become Director of Product administration at Dell SecureWorks where he led a group of product managers across the machine administration, monitoring and log management services offerings.

"BetterCloud has built a pioneering reply it truly is fundamentally altering the style agencies manipulate and secure applications within the cloud, and i could not extra excited to breathe section of the group prerogative through this pivotal instant of growth," mentioned Brennan. "every diminutive thing from their unbelievable crew of seasoned leaders and their world-category platform to the partnerships they've developed with businesses fancy Google, Okta and Dropbox, made it a straightforward option. The value BetterCloud is providing to businesses these days is unmatched and i look ahead to playing a key position during this subsequent chapter in BetterCloud's background."

About BetterCloudBetterCloud is the primary SaaS Operations administration platform, empowering IT and safety groups to define, remediate, and implement administration and safety guidelines for SaaS purposes. Over 2,500 customers in 60+ nations signify on BetterCloud for perpetual adventure monitoring, at once remediating threats, and entirely-automatic policy enforcement. To extra pilot shoppers on their SaaS approach, the industry established the first-ever "SaaS utility management and protection Framework" in its newly published ebook, Controlling Your SaaS environment. BetterCloud is headquartered in long island metropolis with an engineering office in Atlanta, GA. For extra assistance, tickle quest advice from www.bettercloud.com.

View long-established content material:http://www.prnewswire.com/news-releases/bettercloud-appoints-jim-brennan-as-chief-product-officer-300806598.html

source BetterCloud

Copyright (C) 2019 PR Newswire. every bit of rights reserved


Microsoft simply hired a primary variety officer — and IBM is suing over it | killexams.com actual Questions and Pass4sure dumps

Tech agencies indulge in a lower than stellar list hiring girls and minorities. but these companies will apparently conclude something it takes — including launching a prison battle — to rent one class of person: a first-rate diversity Officer.

IBM is suing its former reform compass officer Lindsay-Rae McIntyre after she received poached by Microsoft, in a case that might point to just how essential variety, recruitment, and retention has become for tech companies.

McIntyre, who joined IBM in 2006, became named chief compass officer of Microsoft on Sunday, after serving within the identical position and as VP of human supplies at IBM. IBM, in its criticism, argues that McIntyree had access to diversity records, techniques, methodologies and initiatives that are confidential, and that she "will use, trust on or disclose" these ideas in her recent function.

On Monday, IBM became granted a brief restraining order in recent york federal courtroom, which prevents McIntyre from working for Microsoft until the court docket decides otherwise.

"McIntyre become at the middle of tremendously exclusive and competitively sensitive assistance that has fueled IBM's success in these areas," a consultant for IBM mentioned in an announcement. "whereas they bear in mind Microsoft's requisite to grasp care of mounting criticism of its record on variety, IBM intends to totally implement Ms. McIntyre's non-compete constrict to tender protection to their competitive counsel."

at the heart of IBM's criticism is an on-going lawsuit from 2015, Moussouris v. Microsoft employer. The lawsuit alleges that Microsoft systematically discriminated in opposition t women in technical and engineering roles at the industry when it got here to opinions, pay, and promotions.

The briefings for that lawsuit wrapped up on February 9. Now it's up to the courts to do a determination whether or now not the case has type-motion reputation.

As a section of its non-compete case, IBM is calling to do spend of Microsoft's personal criminal arguments from the Moussouris lawsuit to do its own factor.

In April 2017, to steer pellucid of unavoidable assistance from being launched publicly within the discovery process, Microsoft argued that such variety records is "no longer merely personal, but so elegant and doubtlessly destructive to Microsoft if published to its opponents (e.g., IBM), that the courtroom may still grasp the surprising measure of inserting the tips under seal," in keeping with IBM's complaint.

Microsoft in the cessation obtained some of the files in the case sealed, and others launched with redactions.

In its legitimate response to IBM, Microsoft argued that implementing McIntyre's non-compete constrict is "draconian," as McIntyre has promised no longer to share private information with Microsoft.

it's worth noting that IBM's worker ranks aren't a paragon of variety: according to IBM's website, ladies comprised 31.8% of its international team of workers in 2016.


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IBM Content Collector Technical Mastery Test v1

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Back to Microservices with Istio (Part 1) | killexams.com actual questions and Pass4sure dumps

Istio is an Open Source project developed in partnership between teams from Google, IBM, and Lyft and it provides a solution to the complexities of microservice based application, to denomination a few:

  • Traffic management: Timeouts, retries, load balancing,
  • Security: End-user Authentication and Authorization,
  • Observability: Tracing, monitoring, and logging.
  • All of these can breathe solved in the application layer, but your services don’t cessation up being so ‘Micro’ anymore, every bit of the additional effort for implementing these is a strain in the company’s resources, resources that can breathe used to deliver industry value. Let’s grasp an example:

    PM: How long will it grasp to add a feedback feature?

    Dev: Two sprints.

    PM: What…? That’s just a CRUD!

    Dev: Creating the CRUD is the effortless section but then they requisite to authenticate and empower users and services. And because the network is not reliable they requisite to implement retries, and circuit breakers in the clients, and to do sure that they conclude not grasp the whole system down they requisite Timeouts and Bulkheads, additionally to detect issues they requisite monitoring, tracing […]

    PM: Let’s just stick it in the Product Service then. Jeez!

    You procure the idea, every bit of the ceremony, and effort that must Go in, for us to add one service is enormous. In this article, we’ll showcase how Istio removes every bit of the above-mentioned cross-cutting concerns from their services.

    Figure 1. The Ceremony of a Microservice

    Note: This article assumes that you indulge in a working information of Kubernetes. If it’s not the case I recommend you to read my introduction to Kubernetes, and then continue with this article.

    The persuasion of Istio

    In a world without Istio one service makes direct requests to another and in cases of failure, the service needs to ply it by retrying, timeouting, opening the circuit breaker etc.

    Figure 2. Network traffic in Kubernetes

    To resolve this, Istio provides an ingenious solution by being completely separated from the services and act only by intercepting every bit of network communication. And doing so it can implement:

  • Fault Tolerance — Using response status codes it understands when a request failed and retries.
  • Canary Rollouts — Forward only the specified percentage of requests to a recent version of the service.
  • Monitoring and Metrics — The time it took for a service to respond.
  • Tracing and Observability — It adds special headers in every request and traces them in the cluster.
  • Security — Extracts the JWT Token and Authenticates and Authorizes users.
  • To denomination a few (for actual just a few) and procure you intrigued! Let’s procure to the Technical details!

    Istio’s Architecture

    Istio intercepts every bit of network traffic and applies a set of rules by injecting an knowing proxy as a sidecar in every pod. The proxies that enable every bit of the features comprise The Data Plane and those are dynamically configurable by The Control Plane.

    The Data Plane

    The injected proxies enable Istio to easily achieve their requirements. For an case let’s check out the retrying and Circuit breaking functionalities.

    Figure 3. How envoys implement Retries & CircuitBreaking

    To summarize:

  • Envoy sends the request to the first instance of service B and it fails.
  • The Envoy Sidecar retries. (1)
  • Returns a failed request to the calling proxy.
  • Which opens the Circuit Breaker and calls the next Service on subsequent requests. (2)
  • This means that you don’t indulge in to spend another Retry library, you don’t indulge in to develop your own implementation of Circuit Breaking and Service Discovery in Programming Language X, Y or Z. every bit of of those and more are provided out of the box by Istio and NO code changes are required.

    Great! Now you want to combine the voyage with Istio, but you still indulge in some doubts, some open questions. Is this a One-Size-Fits-All-Solution, which you’re suspicious about, as it always ends up being One-Size-Fits not any solution!

    You finally mutter the question: “Is this configurable?”

    Welcome to the cruise my friend and let’s procure introduced to the Control Plane.

    The Control Plane

    Is composed of three components: The Pilot, the Mixer, and the Citadel that in combination configure Envoys to route traffic, implement policies and collect telemetry data. Visually presented in the image below.

    Figure 4. Control Plane in relation to Data Plane

    The envoys (i.e. the data plane) are configured using Kubernetes Custom Resource Definitions defined by Istio and specialized for this purpose. Which means that for you it’s just another Kubernetes Resource with a familiar syntax. Which after being created will breathe picked up by the control plane that applies it to the envoys.

    Relation of Services to Istio

    We described the relation of Istio to their Services, but not the other way around, what’s the relation of their Services to Istio?

    Frankly, their services indulge in as much information of Istio’s presence, as fish conclude of water, they will put a question to themselves “What the hell is water?”.

    Drawing by Victoria Dimitrakopoulos

    This means that you can pick a working cluster and after deploying the components of Istio, the services within will continue to work and in the same manner, you can remove the components and everything will breathe just fine. Understandably, you would lose the capabilities provided by Istio.

    We had enough of theory, let’s rescue this information into practice!

    Istio in Practice

    Istio requires a Kubernetes Cluster with at least 4 vCPU and 8 GB of RAM. To quickly set up a cluster and follow up with this article, I recommend using the Google Cloud Platform, which provides recent users with a $300 free trial.

    After creating the cluster and configuring access with the Kubernetes command line tool we’re ready to install Istio using the Helm Package manager.

    Installing Helm

    Install the Helm client on your computer as explained in the official docs. Which they will spend to generate the Istio installation templates in the next section.

    Installing Istio

    Download Istio’s resources from the latest release, extract the contents into one directory that they will advert to as [istio-resources].

    To easily identify the Istio resources create a namespace istio-system in your Kubernetes Cluster:

    $ kubectl create namespace istio-system

    Complete the installation by navigating to [istio-resources] directory and executing the command below:

    $ helm template install/kubernetes/helm/istio \--set global.mtls.enabled=false \--set tracing.enabled=true \--set kiali.enabled=true \--set grafana.enabled=true \--namespace istio-system > istio.yaml

    The above command prints out the core components of Istio into the file istio.yaml. They customized the template using the following parameters:

  • global.mtls.enabled is set to unsuitable to preserve the introduction focused.
  • tracing.enabled enables tracing of requests using jaeger.
  • kiali.enabled installs Kiali in their cluster for Visualizing Services and Traffic
  • grafana.enabled installs Grafana to visualize the collected metrics.
  • Apply the generated resources by executing the command:

    $ kubectl apply -f istio.yaml

    This marks the completion of the Istio installation in their cluster! Wait until every bit of pods in the istio-system namespace are in Running or Completed state by executing the command below:

    $ kubectl procure pods -n istio-system

    Now we’re ready to continue with the next section, where they will procure the sample application up and running.

    Sentiment Analysis Application Architecture

    We will spend the same microservice application used in the Kubernetes Introduction article, it’s complicated enough to showcase Istio’s features in practice.

    The App is composed of four microservice:

  • SA-Frontend service: Serves the frontend Reactjs application.
  • SA-WebApp service: Handles requests for Sentiment Analysis.
  • SA-Logic service: Performs sentiment analysis.
  • SA-Feedback service: Receives feedbacks from the users about the accuracy of the analysis.
  • Figure 6 Sentiment Analysis microservices

    In figure 6, besides the services they view the Ingress Controller which in Kubernetes routes incoming requests to the confiscate services, Istio uses a similar concept called Ingress Gateway, which will breathe introduced in continuation of the article.

    Running the Application with Istio Proxies

    To follow up with this article clone the repository istio-mastery, containing the application and the manifests for Kubernetes and Istio.

    Sidecar Injection

    The injection is done Automatically or Manually. To enable automatic sidecar injection, they requisite to label the namespace with istio-injection=enabled, by executing the command below:

    $ kubectl label namespace default istio-injection=enablednamespace/default labeled

    From now every pod that gets deployed into the default namespace will procure the sidecar injected. To verify this let’s deploy the sample application by navigating to the root folder of the [istio-mastery] repository and executing the following command:

    $ kubectl apply -f resource-manifests/kubepersistentvolumeclaim/sqlite-pvc createddeployment.extensions/sa-feedback createdservice/sa-feedback createddeployment.extensions/sa-frontend createdservice/sa-frontend createddeployment.extensions/sa-logic createdservice/sa-logic createddeployment.extensions/sa-web-app createdservice/sa-web-app created

    With the services deployed verify that the pods indulge in two containers (the service and the sidecar) by executing the command kubectl procure pods and ensuring that under the column ready, they view the value “2/2” indicating that both containers are running. As seen below:

    $ kubectl procure podsNAME READY STATUS RESTARTS AGEsa-feedback-55f5dc4d9c-c9wfv 2/2 Running 0 12msa-frontend-558f8986-hhkj9 2/2 Running 0 12msa-logic-568498cb4d-2sjwj 2/2 Running 0 12msa-logic-568498cb4d-p4f8c 2/2 Running 0 12msa-web-app-599cf47c7c-s7cvd 2/2 Running 0 12m

    Visually presented in figure 7.

    Figure 7. Envoy proxy in one of the Pods

    With the application up and running now they requisite to allow incoming traffic to gain their application.

    Ingress Gateway

    A best drill for allowing traffic into the cluster is through Istio’s Ingress Gateway which positions itself at the edge of the cluster and on incoming traffic enables Istio’s features fancy routing, load balancing, security, and monitoring.

    During Istio’s installation, the Ingress Gateway component and a service that exposes it externally were installed into the cluster. To procure the services External IP execute the command below:

    $ kubectl procure svc -n istio-system -l istio=ingressgatewayNAME TYPE CLUSTER-IP EXTERNAL-IPistio-ingressgateway LoadBalancer 10.0.132.127 13.93.30.120

    In the continuation of this article they will access the application on this IP (referred to as the EXTERNAL-IP), for convenience, rescue it in a variable by executing the command below:

    $ EXTERNAL_IP=$(kubectl procure svc -n istio-system \-l app=istio-ingressgateway \-o jsonpath='{.items[0].status.loadBalancer.ingress[0].ip}')

    If you gain this IP in your browser and you will procure a Service Unavailable error, as by default Istio blocks any incoming traffic until they define a Gateway.

    The Gateway Resource

    A Gateway is a Kubernetes Custom Resource Definition defined upon Istio’s installation in their cluster that enables us to specify the Ports, Protocol and Hosts for which they want to allow incoming traffic.

    In their scenario, they want to allow HTTP traffic on port 80, for every bit of hosts. Achieved with the following definition:

    All the configuration is self-explanatory besides the selector istio: ingressgateway. Using this selector, they can specify to which Ingress Gateway to apply the configuration, and in their case, it’s the default ingress gateway controller installed on Istio setup.

    Apply the configuration by executing the command below:

    $ kubectl apply -f resource-manifests/istio/http-gateway.yaml gateway.networking.istio.io/http-gateway created

    The gateway now allows access in port 80 but it has no concept where to route the requests. That is achieved using Virtual Services.

    The VirtualService resource

    The VirtualService instructs the Ingress Gateway how to route the requests that were allowed into the cluster.

    For their application requests coming through the http-gateway must breathe routed to the sa-frontend, sa-web-app and sa-feedback services (show in figure 8).

    Figure 8. Routes to breathe configured with VirtualServices

    Let’s fracture down the requests that should breathe routed to SA-Frontend:

  • Exact path /should breathe routed to SA-Frontend to procure the Index.html
  • Prefix path /static/* should breathe routed to SA-Frontend to procure any static files needed by the frontend, fancy Cascading Style Sheets and JavaScript files.
  • Paths matching the regex '^.*\.(ico|png|jpg)$' should breathe routed to SA-Frontend as it is an image, that the page needs to show.
  • This is achieved with the following configuration:

    The valuable points here are:

  • This VirtualService applies to requests coming through the http-gateway.
  • Destination defines the service where the requests are routed to.
  • Note: The configuration above is in the file sa-virtualservice-external.yaml, it moreover contains the configuration to route to SA-WebApp and SA-Feedback but was shortened for brevity.

    Apply the VirtualService by executing:

    $ kubectl apply -f resource-manifests/istio/sa-virtualservice-external.yamlvirtualservice.networking.istio.io/sa-external-services created

    Note: When they apply Istio resources the Kubernetes API Server creates an event received by Istio’s Control Plane which then applies the recent configuration to the envoy proxies of every pod. And the Ingress Gateway controller is another Envoy which is configured by the Control Plane, visually presented in figure 9.

    Figure 9. Configuring Istio-IngressGateway to route requests

    The Sentiment Analysis app is now accessible on http://{EXTERNAL-IP}/. If you procure a Not found status, conclude not worry sometimes it takes a bit for the configuration to Go in sequel and update the envoy caches.

    Before affecting to the next section spend the app to generate some traffic.

    Kiali — Observability

    To access Kiali’s Admin UI execute the command below:

    $ kubectl port-forward \$(kubectl procure pod -n istio-system -l app=kiali \-o jsonpath='{.items[0].metadata.name}') \-n istio-system 20001

    And open http://localhost:20001/ login using “admin” (without quotes) for user and password. There is a ton of useful features, for case checking the configurations of Istio Components, visualizing services according to the information collected by intercepting network requests and answering, “who is calling who?”, “which version of a service has failures?” etc. grasp some time to checkout Kiali before affecting on to the next goodie, visualizing metrics with Grafana!

    Figure 10. Kiali — Service Observability Grafana — Metrics Visualization

    The metrics collected by Istio are scraped into Prometheus and Visualized using Grafana. To access the Admin UI of Grafana execute the command below and open http://localhost:3000.

    $ kubectl -n istio-system port-forward \$(kubectl -n istio-system procure pod -l app=grafana \-o jsonpath={.items[0].metadata.name}) 3000

    On the top left click the menu Home and select Istio Service Dashboard and on the top left corner select the service starting with sa-web-app, you will breathe presented with the collected metrics, as seen on the image below:

    Holly molly that’s an blank and totally non-exciting view, management would never ratify of this. Let’s intuition some load by executing the command below:

    $ while true; conclude \curl -i http://$EXTERNAL_IP/sentiment \-H “Content-type: application/json” \-d ‘{“sentence”: “I fancy yogobella”}’; \sleep .8; done

    Now they indulge in prettier graphs 😊, and additionally, they indulge in the incredible tools of Prometheus for monitoring and Grafana for visualizing the metrics that enable us to know the performance, health, improvement or degradation of their services throughout time!

    Lastly, they will investigate Tracing requests throughout services.

    Jaeger — Tracing

    We requisite tracing because the more services they indulge in the harder it gets to pinpoint the intuition of failure. Let’s grasp the simple case in the image below:

    Figure 12. A commonly random failed request

    The request goes in, failure goes out, what was the cause? The first service? Or the second? Exceptions are in both, Let’s procure down to the logs of each. How many times conclude you find yourself doing this? Their job feels more fancy Software Detectives than Developers.

    This is a prevalent problem in Microservices and it’s solved using Distributed Tracing Systems where the services pass a unique header to each other and then this information is forwarded to the Distributed Tracing System where the request trace is rescue together. An case is shown in figure 13.

    Figure 13. TraceId used to identify the span of a request

    Istio uses Jaeger Tracer that implements the OpenTracing API, a vendor-neutral framework. To procure access the Jaegers UI execute the command below:

    $ kubectl port-forward -n istio-system \$(kubectl procure pod -n istio-system -l app=jaeger \-o jsonpath='{.items[0].metadata.name}') 16686

    Then open the UI in http://localhost:16686, select the sa-web-app service, if the service is not shown on the dropdown generate some activity on the page and hit refresh. Afterward click the button Find Traces, which displays the most recent traces, select any and a circumstantial breakdown of every bit of the traces will breathe shown, as seen in figure 14.

    Figure 14. Jaeger — a request trace

    The trace shows:

  • The request comes to the istio-ingressgateway (it’s the first contact with one of the services so for the request the Trace ID is generated) then the gateway forwards the request to the sa-web-app service.
  • In the sa-web-app service, the request is picked up by the Envoy sidecar and a span child is created (that’s why they view it in the traces) and forwarded to the sa-web-app container instance.
  • Here the manner sentimentAnalysis handles the request. These traces are generated by the application, import that code changes were required).
  • From where a POST request is started to sa-logic. Trace ID needs to breathe propagated by sa-web-app.
  • 5. …

    Note: At the 4th point their application needs to pick up the headers generated by Istio and pass them down on the next requests, as shown in the image below.

    Figure 15. (A) Istio propagating headers, (B) Services propagating headers

    Istio does the main weighty lifting as it generates the headers on incoming requests, creates recent spans on every sidecar, propagates them, but without their services propagating the headers as well, they will lose the full trace of the request.

    The headers to propagate are:

    x-request-idx-b3-traceidx-b3-spanidx-b3-parentspanidx-b3-sampledx-b3-flagsx-ot-span-context

    Despite it being a simple task, there are already many libraries that simplify the process, for case in the sa-web-app service, the RestTemplate client is instrumented to propagate the headers by simply adding the Jaeger and OpenTracing libraries in the dependencies.

    Note: The Sentiment Analysis app showcases implementations for Flask, Spring and ASP.NET Core.

    Now after investigating what they procure out of the box (or partially out of the box 😜) let’s procure to the main topic here, fine-grained routing, managing network traffic, security and more!

    Traffic Management

    Using the Envoy’s Istio provides a host of recent capabilities to your cluster enabling:

  • Dynamic request routing: Canary deployments, A/B testing,
  • Load balancing: Simple and Consistent Hash balancing,
  • Failure Recovery: Timeouts, retries, circuit breakers,
  • Fault Injection: Delays, abort requests etc.
  • In the sequence of this article, we’ll showcase these capabilities in their application and procure introduced to recent concepts along the way. The first concept they will delve into is DestinationRules and using those we’ll enable A/B Testing.

    A/B Testing — Destination Rules in Practice

    A/B Testing is used when they indulge in two versions of an application (usually the versions disagree visually) and they aren’t 100% sure which will enlarge user interaction, so they try both versions at the same time and collect metrics.

    Execute the command below to deploy a second version of the frontend, needed for demonstrating A/B Testing:

    $ kubectl apply -f resource-manifests/kube/ab-testing/sa-frontend-green-deployment.yamldeployment.extensions/sa-frontend-green created

    The deployment manifest for the green version differs in two points:

  • The image is based on a different tag: istio-green and
  • Pods are labeled with version: green.
  • And as both deployments indulge in the label app: sa-frontend requests routed by the virtual service sa-external-services to the service sa-frontend will breathe forwarded to every bit of of its instances and will breathe load balanced using the round robin algorithm, which causes the issue presented in figure 16.

    Figure 16. Requested files are not found

    The files are not found because they are named differently in the different versions of the app. Let’s verify that:

    $ curl --silent http://$EXTERNAL_IP/ | tr '"' '\n' | grep main/static/css/main.c7071b22.css/static/js/main.059f8e9c.js$ curl --silent http://$EXTERNAL_IP/ | tr '"' '\n' | grep main/static/css/main.f87cd8c9.css/static/js/main.f7659dbb.js

    This means that the index.html that requests one version of the static files might breathe load balanced to the pods delivering the other version, where understandably the other files conclude not exist.

    This means that for their app to work they requisite to interpolate the restriction that “the version of the app that served the index.html, must serve subsequent requests”.

    We’ll achieve this using Consistent Hash Loadbalancing, which is the process that forwards requests from the same client to the same backend instance, using a predefined property, fancy for case an HTTP header. Made feasible by DestionatioRules.

    DestinationRules

    After a request gets routed by the VirtualService to the reform service, then using DestinationRules they can specify policies that apply to the traffic intended for the instances of this Service, as seen in figure 17.

    Figure 17. Traffic Management with Istio Resources

    Note: figure 17, visualizes how Istio resources are affecting the network traffic, in an easily understandable way. But, to breathe precise the determination to which instance to forward the request to is made in the Ingress Gateway’s Envoy, configured by the CRDs.

    Using Destination Rules they can configure load balancing to indulge in consistent hashing and ensure that the same user is responded by the same instance of the service. Achieved with the following configuration:

  • Generate a consistent hash according to the contents of the “version” header.
  • Apply the configuration by executing the command below and give it a try!

    $ kubectl apply -f resource-manifests/istio/ab-testing/destinationrule-sa-frontend.yamldestinationrule.networking.istio.io/sa-frontend created

    Execute the command below and verify that you procure the same files when specifying the version header:

    $ curl --silent -H "version: yogo" http://$EXTERNAL_IP/ | tr '"' '\n' | grep main

    Note: You can spend this chrome extension to add different values to the version header, to test in your browser.

    DestinationRules indulge in more LoadBalancing capabilities, for every bit of the details check out the official docs.

    Before affecting on to explore VirtualService in more detail, remove the green version of the app and the destination rule by executing the commands below:

    $ kubectl delete -f resource-manifests/kube/ab-testing/sa-frontend-green-deployment.yamldeployment.extensions “sa-frontend-green” deleted$ kubectl delete -f resource-manifests/istio/ab-testing/destinationrule-sa-frontend.yamldestinationrule.networking.istio.io “sa-frontend” deleted Shadowing — Virtual Services in Practice

    Shadowing or Mirroring is used when they want to test a change in production but not influence end-users, so they mirror the requests into a second instance that has the change and evaluate it. To phrase it simpler it’s when one of your colleagues picks the most censorious issue and makes a mountainous ball of mud drag Request, that nobody can really review.

    To test out this feature lets create a second instance of SA-Logic (which is buggy) by executing the command below:

    $ kubectl apply -f resource-manifests/kube/shadowing/sa-logic-service-buggy.yamldeployment.extensions/sa-logic-buggy created

    Execute the following command and verify that every bit of instances are labeled with the respective versions and additionally with app=sa-logic:

    $ kubectl procure pods -l app=sa-logic --show-labelsNAME READY LABELSsa-logic-568498cb4d-2sjwj 2/2 app=sa-logic,version=v1sa-logic-568498cb4d-p4f8c 2/2 app=sa-logic,version=v1sa-logic-buggy-76dff55847-2fl66 2/2 app=sa-logic,version=v2sa-logic-buggy-76dff55847-kx8zz 2/2 app=sa-logic,version=v2

    As the service sa-logic targets pods labeled with app=sa-logic, any incoming requests will breathe load balanced between every bit of instances, as shown in figure 18.

    Figure 18. Round Robin load balancing

    But they want requests to breathe routed to the instances with version v1 and mirrored to the instances with version v2, as shown in figure 19.

    Figure 19. Routing to v1 and Mirroring to v2

    This is achieved using a VirtualService in combination with a DestinationRule, where the destination rule specifies the subsets and VirtualService routes to the specific subset.

    Specifying Subsets with Destination Rules

    We define the subsets with the following configuration:

  • Host defines that this rule applies only when routing has occurred towards sa-logic service
  • Subset denomination used when routing to instances of a subset.
  • Label defines the key-value pairs that requisite to match for an instance to breathe section of the subset.
  • Apply the configuration executing the command below:

    $ kubectl apply -f resource-manifests/istio/shadowing/sa-logic-subsets-destinationrule.yamldestinationrule.networking.istio.io/sa-logic created

    With the subsets defined they can scurry on and configure a VirtualService to apply to requests towards sa-logic where the requests are:

  • Routed to the subset named v1 and,
  • Mirrored to the subset named v2.
  • And this is achieved with the manifest below:

    As everything is self-explanatory let’s just view it in action:

    $ kubectl apply -f resource-manifests/istio/shadowing/sa-logic-subsets-shadowing-vs.yamlvirtualservice.networking.istio.io/sa-logic created

    Add some load by executing the following command:

    $ while true; conclude curl -v http://$EXTERNAL_IP/sentiment \-H “Content-type: application/json” \-d ‘{“sentence”: “I fancy yogobella”}’; \sleep .8; done

    Check the results in Grafana, where they can view that the buggy version is failing about 60% of the requests, but not any of the failures affected the end-users as they were responded by the currently active service.

    Figure 20. The success rate of sa-logic service versions

    In this section, they saw for the first time a VirtualService that was applied to the envoys of their services, when the sa-web-app makes a request towards sa-logic this goes through the sidecar envoy, which via the VirtualService is configured to route to the subset v1 and mirror to the subset v2 of the sa-logic service.

    I can view you thinking “Darn man Virtual Services are simple!”, in the next section, we’ll extend the sentence to “Simply Amazing!”.

    Canary Deployments

    Canary Deployment is the process of rolling out a recent version of an application to a small set of users, as a step to verify the absence of issues, and then with a higher assurance of trait release to the wider audience.

    We will continue with the same buggy subset of sa-logic to demonstrate canary deployments.

    Let’s start boldly and send 20% of the users to the buggy version (this represents the canary deployment) and 80% to the robust service by applying the VirtualService below:

  • Weight specifies the percentage of requests to breathe forwarded to the destination or subset of the destination.
  • Update the previous sa-logic virtual service configuration using the following commands:

    $ kubectl apply -f resource-manifests/istio/canary/sa-logic-subsets-canary-vs.yamlvirtualservice.networking.istio.io/sa-logic configured

    We immediately view that some of their requests are failing:

    $ while true; conclude \curl -i http://$EXTERNAL_IP/sentiment \-H “Content-type: application/json” \-d ‘{“sentence”: “I fancy yogobella”}’ \--silent -w “Time: %{time_total}s \t Status: %{http_code}\n” \-o /dev/null; sleep .1; doneTime: 0.153075s Status: 200Time: 0.137581s Status: 200Time: 0.139345s Status: 200Time: 30.291806s Status: 500

    VirtualServices enable Canary Deployments and with this method, they reduced potential damages to 20% of their user base. Beautiful! Now they can spend Shadowing and Canary Deployments every time they are insecure about their code, in other words always. 😜

    Timeouts & Retries

    It’s not always that the code is buggy. In the list of “The 8 fallacies of distributed computing” the first fallacy is that “The network is reliable”. The network is NOT reliable, and that’s why they requisite Timeouts and Retries.

    For demonstration purposes, they will continue to spend the buggy version of sa-logic, where the random failures simulate the unreliability of the network.

    The buggy service has a one-third random of taking too long to respond, one-third random of ending in an Internal Server mistake and the comfort complete successfully.

    To alleviate these issues and better the user taste they can:

  • Timeout if the service takes longer than 8 seconds and
  • Retry on failed requests.
  • This is achieved with the following resource definition:

  • The request has a timeout of 8 seconds,
  • It attempts 3 times,
  • An attempt is marked as failed if it takes longer than 3 seconds.
  • This is an optimization, as the user won’t breathe waiting for longer than 8 seconds and they retry three times in case of failures, increasing the random of resulting in a successful response.

    Apply the updated configuration with the command below:

    $ kubectl apply -f resource-manifests/istio/retries/sa-logic-retries-timeouts-vs.yamlvirtualservice.networking.istio.io/sa-logic configured

    And check out the Grafana graphs for the improvement in success rate (shown in figure 21).

    Figure 21. Improvement after using Timeouts & Retries

    Before affecting into the next section delete sa-logic-buggy and the VirtualService by executing the command below:

    $ kubectl delete deployment sa-logic-buggydeployment.extensions “sa-logic-buggy” deleted$ kubectl delete virtualservice sa-logicvirtualservice.networking.istio.io “sa-logic” deleted Circuit Breaker and Bulkhead patterns

    Two valuable patterns in Microservice Architectures that enable self-healing of the services.

    The Circuit Breaker is used to stop requests going to an instance of a service deemed as unhealthy and enable it to recover, and in the meantime client’s requests are forwarded to the robust instances of that service (increasing success rate).

    The Bulkhead pattern isolates failures from taking the whole system down, to grasp an example, Service B is in a corrupt state and another service (a client of Service B) makes requests to Service B this will result that the client will spend up its own thread pool and won’t breathe able to serve other requests (even if those are not related to Service B).

    I will skip implementations of these patterns because you can check out implementations in the official docs and I’m way too excited to showcase Authentication and Authorization, which will breathe the subject of the next article.

    Part I — Summary

    In this article, they deployed Istio in a Kubernetes cluster and using its Custom Resource Definitions fancy Gateways, VirtualServices, DestinationRules and it’s components it enabled the following features:

  • Observability over their services by answering what services are running, how are they performing and how are they related, using Kiali.
  • Metric collection and visualization, with Prometheus and Grafana.
  • Request tracing with Jaeger (german for Hunter).
  • Full and fine-grained control over the network traffic, enabling Canary Deployments, A/B Testing, and Shadowing.
  • Easy implementation of Retries, Timeouts, and CircuitBreakers.
  • And every bit of were feasible without code changes or any additional dependencies, keeping your services small, effortless to operate and maintain.

    For your evolution team removing these cross-cutting concerns and centralizing them into Istio’s Control plane, means that recent services are effortless to breathe introduced, they aren’t resource-heavy as developers can focus in solving industry problems. And up to now, no developer complained about “having to solve knowing industry problems!”.

    I would fancy to hear your thoughts in the comments below and feel free to gain out to me on Twitter or on my page rinormaloku.com, and stay tuned for the next article where they tackle the final layers of Authentication and Authorization!


    Oracle Releases GraalVM 1.0, a Polyglot Virtual Machine and Platform | killexams.com actual questions and Pass4sure dumps

    Oracle has announced the 1.0 release of GraalVM, a polyglot virtual machine and platform. The initial release includes the capability to Run Java and JVM languages (via bytecode) as well as full support for JavaScript and Node.JS, with beta support for Ruby, Python, R and LLVM bitcode.

    The overall platform comprises a number of components:

  • Graal - a JIT compiler written in Java
  • SubstrateVM - a lightweight wrapper to abstract away the execution container
  • Truffle - a toolkit and API for pile language interpreters
  • The overall intent is to provide a polyglot language execution environment that can breathe embedded within another execution container - either an OpenJDK container or another possibility, such as within an Oracle or MySQL database.

    InfoQ spoke to Thomas Wuerthinger, research director at Oracle Labs, for an in-depth interview about GraalVM.

    InfoQ: Can you justify the history of GraalVM? Where did it approach from?

    Thomas Wuerthinger: The mountainous persuasion was that a compiler didn't indulge in to indulge in any built-in information of the language semantics. The intuition for this was that it was very pellucid from the birth that groups within Oracle had interest in a very diverse compass of languages including JavaScript, R and Ruby.

    There were two key areas they felt they could better upon with some exploration:

  • The common belief of VM architects throughout the software industry at that time was that a language VM needed to breathe designed specifically with the semantics of the language in mind if you wanted to procure optimal performance.
  • The Java architects worked to support multilingualism starting in Java 7 by conversions to Java bytecode by adding `invokedynamic` to the JVM specification. The GraalVM team felt that since Java bytecode had too much Java semantics built into it, a longer term effort could breathe much more efficient and a 100% compatible implementation at that.
  • We moreover had the goal of demonstrating a fully-multilingual engine with competitive performance, and they started off some collaborations with universities to work on other languages.

    A group at Purdue did an implementation of R, and a group at UC Irvine did an implementation of Python in 2012-2014. A student intern worked on Ruby as a thesis project, and it had enough weird and knowing properties that they thought it was a estimable proof point that their multilingual compiler was fully general. Later they brought those language projects into Oracle Labs to procure an industrial-quality implementation for those languages.

    With the release of GraalVM 1.0, they feel that they indulge in demonstrated both that a multilingual VM with competitive performance is possible, and that the best way to conclude it isn't through a language-specific bytecode fancy Java or Microsoft CLR.

    InfoQ: What are the key technologies that do up GraalVM? How will they assist developers?

    Wuerthinger: The key technology persuasion in GraalVM is called "Partial Evaluation", which is a technique for converting interpreters into compilers automatically. This allows you to build recent languages very quickly. Language developers can focus on just pile an [Abstract Syntax Tree] AST interpreter, and conclude not indulge in to worry about writing a code generator or other low-level runtime functionality fancy a garbage collector.

    A key technology they built into GraalVM is logical/physical data separation. This allows any remembrance data layout to breathe made to emerge fancy a local language object. Most VMs indulge in a specific layout for objects (with object headers often called "boxes"), and to indulge in the semantics of an object in the language, your data has to indulge in the same layout.

    This means that you indulge in to designate remembrance and copy data to breathe an "object" in most VMs.

    In Graal, you can indulge in zero-cost interop between languages, because a JavaScript object can breathe made to behave fancy a JavaScript object or an R object or whatever, without the requisite for additional object creation & data copies.

    This moreover allows, for example, exposing data from a custom binary format in the configuration of high-level objects to a program without ever actually allocating those high-level objects.

    Another key technology is their LLVM interpreter. They indulge in a GraalVM interpreter for LLVM bitcode, which is the output of the accepted LLVM compiler, which works well with languages targeting aboriginal binaries (like C++, FORTRAN, Rust, and so forth).

    One of the key problems in implementing languages is that they usually indulge in libraries that are implemented in aboriginal code (usually C/C++), and they spend an API that exposes some amount of the language internals. So, if you want to breathe compatible with a language ecosystem, you indulge in to support its aboriginal libraries.

    One of the problems with implementations fancy JRuby or Nashorn is that they don't work for modules containing aboriginal parts, which is a problem for compatibility.

    A final and separate technology piece of GraalVM that was added later is called SubstrateVM. The persuasion here was to do VMs embeddable on a mingle and match basis. A conventional language VM fancy Java Hotspot wants to control everything: which threads are scheduled, how remembrance allocations are parcelled out to those threads, access to the OS, etc.

    We wanted a technology layer that would allow an underlying system to provide some of those services, with SubstrateVM making up the missing pieces in terms of what is needed to support a dynamic JIT compiler (like a garbage collector). When you are running in a database, for example, the database wants to control remembrance management, thread allocations, and so forth.

    SubstrateVM is the "wafer-thin" VM that can sit on something fancy a database, so when you Run the Graal compiler there, the database can specify the maximum bank size rather than the Java mechanism, and the database can manage code artefacts rather than the JVM class loader, and so forth.

    One of the valuable ways the SubstrateVM operates is to conclude ahead-of-time compilation on Java code (like Graal) directly into a aboriginal binary, using a closed world assumption (all the classes to breathe used should breathe known at compile time). So, when you deploy the Graal compiler in a database, they grasp Graal and some set of GraalVM languages and compile them into a aboriginal shared library, and they don't indulge in to breathe compiled at runtime.

    InfoQ: What is Truffle? Why is it needed?

    Wuerthinger: Truffle is the API you indulge in to write a GraalVM interpreter to, when you are pile a recent GraalVM language. The way interpreters work is that they first parse source code into what is called an Abstract Syntax Tree or AST. For example, if you wrote code like: c = a + b then the AST would hold two nodes, one for the add operator and having child nodes of a and b, and a second one for the assignment having child nodes of c and the add operator node.

    The interpreter internals fancy the AST requisite to breathe inherited from Truffle interfaces, and if you conclude so, the Graal compiler will watch the activity of the language interpreter to learn the semantics of the language, so that a compiler for that language can breathe generated.

    InfoQ: One of GraalVM's most visible components is the Graal compiler - a Java-based JIT compiler, just known as "Graal". Can you speak to some of the difficulties that are present in C2 (the existing C++ JIT in OpenJDK) that Graal is looking to scurry past? Is it expected that Graal will supplant C2 / Tiered Compilation as default in the mainline OpenJDK?

    Wuerthinger: The Graal compiler is an experimental compiler in Java 10 and Oracle is optimistic that it can breathe the default in a future release.

    The mountainous edge that a compiler fancy Graal has is that it first compiles itself (since it is written in Java), and that makes it feasible to write Graal in a much higher-level way than working directly on the AST in C++ as C2 does. For example, this makes it easier for Graal to conclude more aggressive inlining and object escape analysis, which allows more object allocations to breathe removed from the program automatically. In general, the more abstractions your program has, the more the Graal compiler helps you, so you view the biggest performance benefits in things fancy the Stream & Lambda support in Java8 or in Scala.

    InfoQ: Oracle is heavily marketing GraalVM as a polyglot VM. How does the content of the 1.0 release disagree to what was shipped as section of Java 10? Should, for example, JRuby developers choose one release over the other? Is GraalVM seen as a separate project, or a section of OpenJDK? Will future releases of Graal breathe tied to the Java release cadence, or is it seen as independent?

    Wuerthinger: GraalVM and Java are 100% independent projects with large open source presence and with separate release cadences, and they both grab pieces of technology from each other. Java 10 is grabbing the compiler from GraalVM (as an experimental option), and GraalVM inbounds the bulk of Java 8 as one of the supported languages. GraalVM inbounds lots of languages, not just Java, so they spend technology from Node.js, JRuby, and from the GNU R implementation, among others.

    Like most technology producer/consumer relationships across organizations, the consumer waits for recent technology from others to stabilize before including it. Similarly, The Java Platform Group has to breathe conservative about including recent technology until they are sure it won't fracture existing deployments, and they indulge in lots of backwards and forwards compatibility concerns, so there is going to breathe some lag.

    InfoQ: IBM's OpenJ9 runtime takes a rather different approach to GraalVM. Can you justify the main differences between OpenJ9 / OMR as you view them, and speak to what makes GraalVM unique?

    Wuerthinger: OpenJ9 is trying to speed up existing single language runtimes (like Ruby MRI) by adding some limited just-in-time compilation functionality to them. The edge of their approach is that it is easier to meet into an existing runtime while staying compatible. The major drawback however is that the benefits are much more limited.

    First, the performance capitalize of their Ruby prototype is very small compared to the order of magnitude Ruby running on GraalVM achieves. The intuition is that GraalVM works with much more profiling and speculative optimizations while OMR keeps the data structures of the existing interpreter.

    Second, they require you to write a code generator and cannot automatically derive the compiled code from the interpreter via partial evaluation.

    Third, there is no built-in physical/logical data separation.

    This leads to most valuable fourth point: there is no faculty with their approach for efficient language interoperability.

    OMR can give marginal improvement to existing single-language runtimes fancy Ruby MRI that are not well-optimized already. GraalVM uses a completely recent way to write languages with an order of magnitude improvement and polyglot capabilities to Run any set of languages with zero overhead interoperability.

    InfoQ: What about wider community involvement in Graal? Twitter are on record as being early adopters and running Graal in production - who else is already using it? What has been your taste of working with non-Oracle developers? What does the future evolution of Graal view like? Will it adopt a similar model to OpenJDK as a whole?

    Wuerthinger: OpenJDK has a very different mission than GraalVM.

    OpenJDK has a large surface belt for developers, since it is creating recent language features and recent libraries every bit of the time. You indulge in to change your code to deal with many of the changes to OpenJDK, and the mission is to procure community consensus on which changes are worthwhile. The OpenJDK community is very large and there are a lot of opinions, and their process helps procure to community consensus.

    GraalVM is about making code in languages and libraries that already exist Run more efficiently, in more places, and interoperate more, and not to change language semantics (for the most part). The only major piece of developer interface it has is the interoperabity API that allows you to invoke alien language functions in other runtimes, and they try to stay below the waterline as much as possible.

    The community for each language that GraalVM supports comes to its own conclusions on how it should evolve.

    As with OpenJDK, GraalVM has a lot of worthy contributions from vendors fancy Red Hat, Intel and AMD that are quite substantial. Red Hat took the initiative a while ago to build an ARM backend for GraalVM without much input from us, and that turned out to breathe very useful for us. You may breathe awake that Oracle invested in an ARM vendor recently, so now they are paying more attention to that ecosystem.

    Those vendors believe that GraalVM will procure enough uptake from others to warrant the investments, and their interest isn't primarily for their own usage. As you point out, Twitter has been contributing to GraalVM because they are using it themselves. There are some other major technology vendors using it that aren't going public the way Twitter has, and they indulge in internal users at Oracle.

    InfoQ: Oracle is usually reticent to dispute future roadmaps. Can you give us any indication of where the evolution track of GraalVM might grasp us in the next 18 months?

    Wuerthinger: Well, the GraalVM team is in Oracle Labs, so they indulge in benevolent of the antithetical problem where they indulge in lots of research projects that are public that may or may not Go anywhere beyond a paper in an academic conference. Oracle Labs has a cloud of graduate student collaborators looking to try out ideas using GraalVM.

    We indulge in one student who was thinking about linking GraalVM into Chromium so that you could spend any language in the browser and not just JavaScript, but I assume it is very pellucid that it is unlikely ever to breathe released as an Oracle product. You can certainly view for academic papers based on GraalVM to view some other crazy ideas. We've done investigations into some other languages as research projects that may or may not Go anywhere, based on community interest.

    What I can show you about the roadmap is that they are working on supporting more platforms. The initial release was just for a Linux/X86 and Mac/X86 to minimise the "release stress". They are actively working on Windows support and moreover on support for the ARM platform (together with Red Hat). There is moreover quite some stabilisation and completeness work necessary for the languages that are currently marked as "experimental"  i.e., R, Ruby, and Python.

    In terms of integrations, there are a number of ways that they are going to integrate GraalVM more deeply into MySQL and the Oracle RDBMS that you will view in the next 18 months. But how GraalVM will view fancy in 18 months will moreover very much depend on the overall community feedback and contributions.


    Highlights from Linux Kongress | killexams.com actual questions and Pass4sure dumps

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    September 27, 2006

    This article was contributed by Stacey Quandt

    The 13th annual International Linux System Technology Conference, moreover known as Linux Kongress, took position September 5 - 8 in Nürnberg, Germany. As a technical Linux event Linux Kongress is smaller in scale than the Ottawa Linux Symposium and linux.conf.au. Still the conference sessions and tutorials included a number of trait talks from familiar members of the Linux and open source communities such as Heinz Mauelshagen, Lars Mueller, Theodore Ts'o, Volker Lendecke, Alan Robertson, and Daniel Phillips.

    A few of the talks stood out. One such talk was Felix von Leitner's presentation titled "Benchmarking, round 2: I/O Performance", in which he tested file system performance on Linux, Windows, OpenSolaris, NetBSD, FreeBSD, and OpenBSD in order to better understand the scalability of different operating systems and IP stack throughput. Based on von Leitner's benchmarking methodology Linux has the fastest file system - reiser4.

    The testing theme continued with Poornima Bangalore, whose presentation was on the topic of "Best Practices in Linux Kernel Testing." Her talk circumstantial many of the key differences between traditional and open source testing. She pointed out that mainline kernel testing is more challenging than testing many other open source projects because of the rapid evolution and the different sub trees in the kernel: the stable kernels are released every 6 weeks or so, release candidate (-rc) kernels are available every week, and experimental (-mm) kernels are available every few days. Poornima shared best practices regarding kernel configuration, hardware configuration, test automation, test coverage, and first failure data capture.

    Heinz Mauelshagen gave a talk on device-mapper architecture features and the related target feature set. In the talk "Linux as a Hypervisor," Jeff Dike discussed the evolution of the hypervisor support in the Linux kernel and how capabilities such as ptrace, AIO and O_DIRECT do a inequity to virtual machines. He moreover talked about the implications of FUSE (filesystems in userspace) and the manageability benefits of exporting a UML filesystem to the host. Lars Marowsky-Bree's presentation on Heartbeat 2 and Xen explored Heartbeat's faculty to manage Xen guests. He expanded on Heartbeat's architecture and its integration with Xen to enable resource reallocation, globally ordered recovery actions, and data hub automation policies using the Cluster Resource Manager (CRM).

    Mattias Rechenburg's presentation on "Using Enterprise Data Centers with OpenQRM" showcased the state of OpenQRM an open source project to achieve high-availability, scalability, and deployment, service and server virtualization on a variety of operation system. In spite of OpenQRM's pluggable architecture, the audience focused on the fact that it depends on a binary module which requires support from Qlusters. The common sentiment from the audience was they were not interested if they couldn't procure support from Red Hat, IBM, Hewlett-Packard etc.

    In "Real-Time Approaches to Linux," Ted Ts'o shared his perspective on enterprise real-time computing and how it differs from so-called traditional real-time computing. He emphasized the changing requirements in enterprise software and how high throughput is not enough because customers increasingly moreover require latency guarantees, especially in particular military applications and trading systems. It was knowing to hear about the benefits and tradeoffs of different approaches to enterprise real-time including RTAI and Ingo Molnar's CONFIG_PREEMPT_RT.

    Ted suggested that guidelines outlined by his colleague Paul McKenney can breathe used to evaluate the different approaches to enterprise real-time. This includes trait of service, the amount of code inspection required when a recent feature is added, the API provided to applications, the relative complexity, failing isolation, and supported hardware and software configurations.

    Although IBM presently has only one customer that plans to deploy enterprise real-time computing, the faculty to support large SMP systems, TCP/IP, commercially available middleware, and databases makes it an belt to watch in the future. Ted moreover elaborated on the features of IBM's real-time JVM/SDK (aka IBM Websphere Real-Time v1.0) such as RTSJ (Real-time specification for Java), the Metronome real-time garbage collector, and AOT (Ahead of Time Compilation). The talk emphasized that there are many recent applications for real-time operating systems, and in particular enterprise real-time Linux.

    Maddog provided the final keynote on having fun with open source in his own inimitable way.

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