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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
real questions : 41 real Questions

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IBM IBM Content Collector Technical

instructions from IBM for Google, Amazon and fb | killexams.com real 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 live in a fast-altering commerce fancy desktop expertise. IBM, which traces its roots to the Eighties , grew from three diminutive organizations to a multi-billion-dollar suggestions know-how capabilities commerce 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 – utter of that are far more youthful than IBM.

In my novel booklet, ' IBM: the upward thrust and drop and Reinvention of a worldwide Icon ,' I explore the enterprise's legacy of developing and selling facts processing machine and software. As a former IBM employee and a historian , probably the most vital lesson I establish is that many people befuddle 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 invent them work. Over a hundred thirty years, IBM released neatly over three,600 hardware products and basically the selfsame amount of software . however utter those gadgets and capabilities were in accordance with just a handful of actual technological advances, comparable to stirring 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 novel products may indicate.

These transitions from the mechanical, to the digital, and now to the networked reflected an ever-turning out to live potential to assemble and utilize better quantities of suggestions without problems and quickly. IBM moved from manipulating statistical data to the utilize of applied sciences that instruct 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 live in was information processing. in additional concurrent 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 routine to product evolution and analysis, specializing in developing the underpinning applied sciences for its records processing products. Nothing gave the stamp to live 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 renowned IBM Card . In its second half-century, the primary technology platform changed into the desktop, including mainframes, minicomputers, PCs and laptops. In its most concurrent 30 years, computing device earnings Have 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 utter started promoting computer systems within the Nineteen Fifties, however kept selling tabulating paraphernalia 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 novel 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 Have been novel offerings developed within the passe decade.

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

This strategic routine isn't astounding – Apple has been selling own computer systems for more than forty years . Its administration, of direction, talks plenty more about its office in the smartphone enterprise, which is already birth to stage off . Apple may additionally soon exigency – or already live engaged on – a brand novel technological heart of attention to abide 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 novel frontier of statistics processing. IBM research , CC via-ND

companies that began on the internet may additionally physiognomy identical transitions. Amazon, Google and fb now and then declare to Have converted themselves, but Have 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 live hastily . Amazon has moreover invested in a stout reach of other commerce that may develop in the future, comparable to fitness supervision and enjoyment content.

Google and fb silent invent 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 live Google's self-driving automobiles or facebook's experiments with digital fact .

however at their core, utter three web giants are silent discovering novel how you can capitalize on the great quantities of assistance they accumulate about shoppers' actions and interests – just as decades prior IBM discovered novel light methods to utilize tabulating paraphernalia and computers. if they're to final many years or centuries into the longer term, the corporations will should probe, test and innovate to ascertain novel tips on how to income as technologies alternate.

IBM fb Google Innovation Amazon Alphabet MIT Press

MENAFN2502201901990000ID1098168497

Lessons from IBM for Google, Amazon and Facebook

BetterCloud Appoints Jim Brennan as Chief Product Officer | killexams.com real 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 novel 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 live certain the building of the BetterCloud platform is aligned with the business's strategic path, principally its diverse go-to-market routine and heart 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 live useful to BetterCloud as they continue to build on their commerce 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 faultless felicitous with their plans to Take 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 utter 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 respond it truly is fundamentally altering the style agencies exploit and secure applications within the cloud, and i could not extra excited to live fragment of the group privilege through this pivotal moment of growth," mentioned Brennan. "every minute 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 seem 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 count on BetterCloud for incessant adventure monitoring, at once remediating threats, and entirely-automatic policy enforcement. To extra usher shoppers on their SaaS approach, the commerce 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, delight search recommendation 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. utter rights reserved


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

Tech agencies Have 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 correct reach officer Lindsay-Rae McIntyre after she received poached by Microsoft, in a case that might note just how essential variety, recruitment, and retention has become for tech companies.

McIntyre, who joined IBM in 2006, became named chief reach 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, faith on or disclose" these ideas in her novel function.

On Monday, IBM became granted a brief restraining order in novel 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 suffer in mind Microsoft's exigency to Take supervision 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 commerce 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 invent a decision whether or now not the case has type-motion reputation.

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

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

Microsoft in the halt 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 real 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 live solved in the application layer, but your services don’t halt up being so ‘Micro’ anymore, utter the additional exertion for implementing these is a strain in the company’s resources, resources that can live used to deliver commerce value. Let’s Take an example:

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

    Dev: Two sprints.

    PM: What…? That’s just a CRUD!

    Dev: Creating the CRUD is the light fragment but then they exigency to authenticate and commission users and services. And because the network is not reliable they exigency to implement retries, and circuit breakers in the clients, and to invent certain that they conclude not Take the entire system down they exigency Timeouts and Bulkheads, additionally to detect issues they exigency monitoring, tracing […]

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

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

    Figure 1. The Ceremony of a Microservice

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

    The conception 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 utter 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 novel 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 real just a few) and secure you intrigued! Let’s secure to the Technical details!

    Istio’s Architecture

    Istio intercepts utter network traffic and applies a set of rules by injecting an knowing proxy as a sidecar in every pod. The proxies that enable utter 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 instance 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 Have to utilize another Retry library, you don’t Have to develop your own implementation of Circuit Breaking and Service Discovery in Programming Language X, Y or Z. utter of those and more are provided out of the box by Istio and NO code changes are required.

    Great! Now you want to join the voyage with Istio, but you silent Have 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 notasoul solution!

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

    Welcome to the cruise my friend and let’s secure 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, invoke 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 close syntax. Which after being created will live 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 passage around, what’s the relation of their Services to Istio?

    Frankly, their services Have as much learning of Istio’s presence, as fish conclude of water, they will inquire 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 travail and in the selfsame manner, you can remove the components and everything will live just fine. Understandably, you would lose the capabilities provided by Istio.

    We had enough of theory, let’s reserve this learning 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 supervene up with this article, I recommend using the Google Cloud Platform, which provides novel users with a $300 free trial.

    After creating the cluster and configuring access with the Kubernetes command line implement 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 utilize 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 refer 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 mistaken to hold 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 utter pods in the istio-system namespace are in Running or Completed condition by executing the command below:

    $ kubectl secure pods -n istio-system

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

    Sentiment Analysis Application Architecture

    We will utilize the selfsame microservice application used in the Kubernetes Introduction article, it’s knotty 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 device 6, besides the services they contemplate the Ingress Controller which in Kubernetes routes incoming requests to the preempt services, Istio uses a similar concept called Ingress Gateway, which will live introduced in continuation of the article.

    Running the Application with Istio Proxies

    To supervene 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 exigency 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 secure 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 Have two containers (the service and the sidecar) by executing the command kubectl secure pods and ensuring that under the column ready, they contemplate the value “2/2” indicating that both containers are running. As seen below:

    $ kubectl secure 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 device 7.

    Figure 7. Envoy proxy in one of the Pods

    With the application up and running now they exigency to allow incoming traffic to reach 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 secure the services External IP execute the command below:

    $ kubectl secure svc -n istio-system -l istio=ingressgatewayNAME nature 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, save it in a variable by executing the command below:

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

    If you reach this IP in your browser and you will secure 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 utter 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 live routed to the sa-frontend, sa-web-app and sa-feedback services (show in device 8).

    Figure 8. Routes to live configured with VirtualServices

    Let’s demolish down the requests that should live routed to SA-Frontend:

  • Exact path /should live routed to SA-Frontend to secure the Index.html
  • Prefix path /static/* should live routed to SA-Frontend to secure any static files needed by the frontend, fancy Cascading Style Sheets and JavaScript files.
  • Paths matching the regex '^.*\.(ico|png|jpg)$' should live 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 novel 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 device 9.

    Figure 9. Configuring Istio-IngressGateway to route requests

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

    Before stirring to the next section utilize the app to generate some traffic.

    Kiali — Observability

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

    $ kubectl port-forward \$(kubectl secure 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 instance 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. Take some time to checkout Kiali before stirring 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 secure 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 live presented with the collected metrics, as seen on the image below:

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

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

    Now they Have prettier graphs 😊, and additionally, they Have the astounding 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 exigency tracing because the more services they Have the harder it gets to pinpoint the judgement of failure. Let’s Take 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 secure 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 trail is reserve together. An instance is shown in device 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 secure access the Jaegers UI execute the command below:

    $ kubectl port-forward -n istio-system \$(kubectl secure 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 utter the traces will live shown, as seen in device 14.

    Figure 14. Jaeger — a request trace

    The trail shows:

  • The request comes to the istio-ingressgateway (it’s the first contact with one of the services so for the request the trail 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 contemplate it in the traces) and forwarded to the sa-web-app container instance.
  • Here the routine sentimentAnalysis handles the request. These traces are generated by the application, significance that code changes were required).
  • From where a POST request is started to sa-logic. trail ID needs to live 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 cumbersome lifting as it generates the headers on incoming requests, creates novel spans on every sidecar, propagates them, but without their services propagating the headers as well, they will lose the replete trail 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 instance 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 secure out of the box (or partially out of the box 😜) let’s secure 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 novel 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 secure introduced to novel 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 Have two versions of an application (usually the versions disagree visually) and they aren’t 100% certain which will enlarge user interaction, so they try both versions at the selfsame 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 Have the label app: sa-frontend requests routed by the virtual service sa-external-services to the service sa-frontend will live forwarded to utter of its instances and will live load balanced using the round robin algorithm, which causes the issue presented in device 16.

    Figure 16. Requested files are not found

    The files are not establish 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 live load balanced to the pods delivering the other version, where understandably the other files conclude not exist.

    This means that for their app to travail they exigency to insert 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 selfsame client to the selfsame backend instance, using a predefined property, fancy for instance an HTTP header. Made viable by DestionatioRules.

    DestinationRules

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

    Figure 17. Traffic Management with Istio Resources

    Note: device 17, visualizes how Istio resources are affecting the network traffic, in an easily understandable way. But, to live precise the decision 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 Have consistent hashing and ensure that the selfsame user is responded by the selfsame 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 secure the selfsame files when specifying the version header:

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

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

    DestinationRules Have more LoadBalancing capabilities, for utter the details check out the official docs.

    Before stirring 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 affect 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 stout ball of mud pull 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 utter instances are labeled with the respective versions and additionally with app=sa-logic:

    $ kubectl secure 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 live load balanced between utter instances, as shown in device 18.

    Figure 18. Round Robin load balancing

    But they want requests to live routed to the instances with version v1 and mirrored to the instances with version v2, as shown in device 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 exigency to match for an instance to live fragment 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 poke 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 contemplate 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 fondness yogobella”}’; \sleep .8; done

    Check the results in Grafana, where they can contemplate that the buggy version is failing about 60% of the requests, but notasoul of the failures affected the end-users as they were responded by the currently vigorous 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 contemplate 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 novel version of an application to a diminutive set of users, as a step to verify the absence of issues, and then with a higher assurance of attribute release to the wider audience.

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

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

  • Weight specifies the percentage of requests to live 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 contemplate that some of their requests are failing:

    $ while true; conclude \curl -i http://$EXTERNAL_IP/sentiment \-H “Content-type: application/json” \-d ‘{“sentence”: “I fondness 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 utilize 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 exigency Timeouts and Retries.

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

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

    To alleviate these issues and ameliorate the user experience 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 live waiting for longer than 8 seconds and they retry three times in case of failures, increasing the casual 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 device 21).

    Figure 21. Improvement after using Timeouts & Retries

    Before stirring 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 quit 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 vigorous instances of that service (increasing success rate).

    The Bulkhead pattern isolates failures from taking the entire system down, to Take an example, Service B is in a debase condition and another service (a client of Service B) makes requests to Service B this will result that the client will utilize up its own thread pool and won’t live 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 passage too excited to showcase Authentication and Authorization, which will live the matter 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 utter were viable without code changes or any additional dependencies, keeping your services small, light to operate and maintain.

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

    I would fondness to hear your thoughts in the comments below and feel free to reach out to me on Twitter or on my page rinormaloku.com, and abide 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 real 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 rush Java and JVM languages (via bytecode) as well as replete champion for JavaScript and Node.JS, with beta champion 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 building language interpreters
  • The overall intent is to provide a polyglot language execution environment that can live 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 elaborate the history of GraalVM? Where did it reach from?

    Thomas Wuerthinger: The stout conception was that a compiler didn't Have to Have any built-in learning of the language semantics. The judgement for this was that it was very transparent from the birth that groups within Oracle had interest in a very diverse reach of languages including JavaScript, R and Ruby.

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

  • The common credit of VM architects throughout the software industry at that time was that a language VM needed to live designed specifically with the semantics of the language in mind if you wanted to secure optimal performance.
  • The Java architects worked to champion 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 exertion could live 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 travail 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 uncanny and curious properties that they thought it was a obliging proof point that their multilingual compiler was fully general. Later they brought those language projects into Oracle Labs to secure an industrial-quality implementation for those languages.

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

    InfoQ: What are the key technologies that invent up GraalVM? How will they back developers?

    Wuerthinger: The key technology conception in GraalVM is called "Partial Evaluation", which is a technique for converting interpreters into compilers automatically. This allows you to build novel languages very quickly. Language developers can focus on just building an [Abstract Syntax Tree] AST interpreter, and conclude not Have 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 live made to materialize fancy a local language object. Most VMs Have a specific layout for objects (with object headers often called "boxes"), and to Have the semantics of an object in the language, your data has to Have the selfsame layout.

    This means that you Have to preempt remembrance and copy data to live an "object" in most VMs.

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

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

    Another key technology is their LLVM interpreter. They Have 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 Have libraries that are implemented in aboriginal code (usually C/C++), and they utilize an API that exposes some amount of the language internals. So, if you want to live compatible with a language ecosystem, you Have to champion its aboriginal libraries.

    One of the problems with implementations fancy JRuby or Nashorn is that they don't travail 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 conception here was to invent 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 champion 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 rush the Graal compiler there, the database can specify the maximum stack 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 live used should live known at compile time). So, when you deploy the Graal compiler in a database, they Take Graal and some set of GraalVM languages and compile them into a aboriginal shared library, and they don't Have to live compiled at runtime.

    InfoQ: What is Truffle? Why is it needed?

    Wuerthinger: Truffle is the API you Have to write a GraalVM interpreter to, when you are building a novel GraalVM language. The passage interpreters travail 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 exigency to live 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 live generated.

    InfoQ: One of GraalVM's most visible components is the Graal compiler - a Java-based JIT compiler, just known as "Graal". Can you discourse to some of the difficulties that are present in C2 (the existing C++ JIT in OpenJDK) that Graal is looking to poke past? Is it expected that Graal will supersede 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 live the default in a future release.

    The stout handicap that a compiler fancy Graal has is that it first compiles itself (since it is written in Java), and that makes it viable to write Graal in a much higher-level passage 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 evade analysis, which allows more object allocations to live removed from the program automatically. In general, the more abstractions your program has, the more the Graal compiler helps you, so you contemplate the biggest performance benefits in things fancy the Stream & Lambda champion 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 fragment of Java 10? Should, for example, JRuby developers elect one release over the other? Is GraalVM seen as a separate project, or a fragment of OpenJDK? Will future releases of Graal live tied to the Java release cadence, or is it seen as independent?

    Wuerthinger: GraalVM and Java are 100% independent projects with great 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 utilize 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 novel technology from others to stabilize before including it. Similarly, The Java Platform Group has to live conservative about including novel technology until they are certain it won't demolish existing deployments, and they Have lots of backwards and forwards compatibility concerns, so there is going to live some lag.

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

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

    First, the performance benefit of their Ruby prototype is very diminutive compared to the order of magnitude Ruby running on GraalVM achieves. The judgement 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 talent 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 novel passage to write languages with an order of magnitude improvement and polyglot capabilities to rush 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 experience of working with non-Oracle developers? What does the future evolution of Graal scrutinize like? Will it adopt a similar model to OpenJDK as a whole?

    Wuerthinger: OpenJDK has a very different mission than GraalVM.

    OpenJDK has a great surface region for developers, since it is creating novel language features and novel libraries utter the time. You Have to change your code to deal with many of the changes to OpenJDK, and the mission is to secure community consensus on which changes are worthwhile. The OpenJDK community is very great and there are a lot of opinions, and their process helps secure to community consensus.

    GraalVM is about making code in languages and libraries that already exist rush 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 abide 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 mighty 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 live very useful for us. You may live alert that Oracle invested in an ARM vendor recently, so now they are paying more attention to that ecosystem.

    Those vendors believe that GraalVM will secure 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 passage Twitter has, and they Have internal users at Oracle.

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

    Wuerthinger: Well, the GraalVM team is in Oracle Labs, so they Have kindhearted of the opposite problem where they Have 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 Have one student who was thinking about linking GraalVM into Chromium so that you could utilize any language in the browser and not just JavaScript, but I contemplate it is very transparent that it is unlikely ever to live released as an Oracle product. You can certainly scrutinize for academic papers based on GraalVM to contemplate 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 counsel 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 champion and moreover on champion for the ARM platform (together with Red Hat). There is moreover quite some stabilisation and completeness travail 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 contemplate in the next 18 months. But how GraalVM will scrutinize fancy in 18 months will moreover very much depend on the overall community feedback and contributions.


    Highlights from Linux Kongress | killexams.com real 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 status 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. silent the conference sessions and tutorials included a number of attribute talks from close 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 champion in the Linux kernel and how capabilities such as ptrace, AIO and O_DIRECT invent 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 talent 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 heart automation policies using the Cluster Resource Manager (CRM).

    Mattias Rechenburg's presentation on "Using Enterprise Data Centers with OpenQRM" showcased the condition 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 champion from Qlusters. The common sentiment from the audience was they were not interested if they couldn't secure champion 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 lofty throughput is not enough because customers increasingly moreover require latency guarantees, especially in particular military applications and trading systems. It was curious 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 live used to evaluate the different approaches to enterprise real-time. This includes attribute of service, the amount of code inspection required when a novel feature is added, the API provided to applications, the relative complexity, frailty isolation, and supported hardware and software configurations.

    Although IBM presently has only one customer that plans to deploy enterprise real-time computing, the talent to champion great SMP systems, TCP/IP, commercially available middleware, and databases makes it an region 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 novel 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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