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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 title : IBM Content Collector Technical Mastery Test v1
Vendor title : 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 subsist in a fast-altering industry enjoy desktop expertise. IBM, which traces its roots to the Eighties , grew from three tiny organizations to a multi-billion-dollar suggestions know-how capabilities industry today. Its united statesand downs along the manner offer some insights into the global expertise business, and might contain some instructive instructions for up-and-coming digital giants enjoy Google, Amazon and fb – every lone of that are far more youthful than IBM.

In my recent booklet, ' IBM: the upward thrust and plunge 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 institute 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 disagreement 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 selfsame amount of software . however every lone those gadgets and capabilities were in accordance with just a handful of actual technological advances, comparable to touching from mechanical machines to people who relied on computing device chips and utility, and later to networks enjoy 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 subsist potential to assemble and expend better quantities of suggestions without problems and quickly. IBM moved from manipulating statistical data to the expend of applied sciences that educate 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 subsist in was information processing. in additional simultaneous phrases, that enterprise has spin 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 course to product development and analysis, specializing in developing the underpinning applied sciences for its records processing products. Nothing gave the stamp to subsist 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 eminent IBM Card . In its second half-century, the primary technology platform changed into the desktop, including mainframes, minicomputers, PCs and laptops. In its most simultaneous 30 years, computing device earnings contain introduced in a declining participate 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 lone started promoting computer systems within the Nineteen Fifties, however kept selling tabulating materiel 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 enjoy 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 contain been recent offerings developed within the primitive decade.

The tidings media – and even IBM employees – may contain 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 selfsame time as prior methods contain been ending their positive lives.

This strategic course isn't fabulous – Apple has been selling own computer systems for more than forty years . Its administration, of direction, talks plenty more about its role in the smartphone enterprise, which is already genesis to stage off . Apple may additionally soon requisite – or already subsist engaged on – a brand recent technological center of attention to abide imperative.

The future of the giants

Microsoft, enjoy Apple, evolved far from promoting just laptop software and operating techniques. It started web-based initiatives enjoy 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 contain converted themselves, but contain 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 subsist hastily . Amazon has also invested in a colossal orbit of other industry that may develop in the future, comparable to fitness custody 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 subsist Google's self-driving automobiles or facebook's experiments with digital fact .

however at their core, every lone 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 light methods to expend tabulating materiel 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

MENAFN2502201901990000ID1098168497

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 subsist sure the building of the BetterCloud platform is aligned with the business's strategic path, principally its diverse go-to-market course and center 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 subsist useful to BetterCloud as they continue to build on their industry main SaaS Operations management platform," stated David Politis, CEO of BetterCloud. "Jim's abysmal technical potential in protection is exciting and his confirmed leadership abilities and entrepreneurial spirit are a flawless fortunate with their plans to lift 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 lone 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 pair 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 maneuver and secure applications within the cloud, and i could not extra excited to subsist Part of the group perquisite through this pivotal flash of growth," mentioned Brennan. "every runt thing from their unbelievable crew of seasoned leaders and their world-category platform to the partnerships they've developed with businesses enjoy 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 count on BetterCloud for perennial adventure monitoring, at once remediating threats, and entirely-automatic policy enforcement. To extra steer 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, 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. every lone rights reserved


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

Tech agencies contain a lower than stellar list hiring girls and minorities. but these companies will apparently enact 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 orbit officer Lindsay-Rae McIntyre after she received poached by Microsoft, in a case that might attest just how essential variety, recruitment, and retention has become for tech companies.

McIntyre, who joined IBM in 2006, became named chief orbit 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, rely 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 intelligence Microsoft's requisite to lift custody of mounting criticism of its record on variety, IBM intends to totally implement Ms. McIntyre's non-compete shrink to offer 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 decision whether or now not the case has type-motion reputation.

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

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

Microsoft in the conclude 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 shrink is "draconian," as McIntyre has promised no longer to participate 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 title a few:

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

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

    Dev: Two sprints.

    PM: What…? That’s just a CRUD!

    Dev: Creating the CRUD is the light Part but then they requisite to authenticate and accredit users and services. And because the network is not amenable they requisite to implement retries, and circuit breakers in the clients, and to do sure that they enact not lift the entire 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 bag the idea, every lone the ceremony, and distress that must retrograde in, for us to add one service is enormous. In this article, we’ll showcase how Istio removes every lone the above-mentioned cross-cutting concerns from their services.

    Figure 1. The Ceremony of a Microservice

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

    The understanding of Istio

    In a world without Istio one service makes direct requests to another and in cases of failure, the service needs to wield 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 lone 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 title a few (for actual just a few) and bag you intrigued! Let’s bag to the Technical details!

    Istio’s Architecture

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

    Great! Now you want to connect the voyage with Istio, but you still contain 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 no one at all solution!

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

    Welcome to the cruise my friend and let’s bag 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 intimate syntax. Which after being created will subsist 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 course around, what’s the relation of their Services to Istio?

    Frankly, their services contain as much lore of Istio’s presence, as fish enact of water, they will demand 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 selfsame manner, you can remove the components and everything will subsist just fine. Understandably, you would lose the capabilities provided by Istio.

    We had enough of theory, let’s achieve this lore 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 recent users with a $300 free trial.

    After creating the cluster and configuring access with the Kubernetes command line appliance 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 expend 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 erroneous 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 every lone pods in the istio-system namespace are in Running or Completed situation by executing the command below:

    $ kubectl bag pods -n istio-system

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

    Sentiment Analysis Application Architecture

    We will expend the selfsame microservice application used in the Kubernetes Introduction article, it’s tangled 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 design 6, besides the services they note the Ingress Controller which in Kubernetes routes incoming requests to the preempt services, Istio uses a similar concept called Ingress Gateway, which will subsist 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 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 bag 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 contain two containers (the service and the sidecar) by executing the command kubectl bag pods and ensuring that under the column ready, they note the value “2/2” indicating that both containers are running. As seen below:

    $ kubectl bag 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 design 7.

    Figure 7. Envoy proxy in one of the Pods

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

    Ingress Gateway

    A best exercise 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 enjoy 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 bag the services External IP execute the command below:

    $ kubectl bag 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, save it in a variable by executing the command below:

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

    If you achieve this IP in your browser and you will bag 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 lone 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 subsist routed to the sa-frontend, sa-web-app and sa-feedback services (show in design 8).

    Figure 8. Routes to subsist configured with VirtualServices

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

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

    The critical 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 also 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 design 9.

    Figure 9. Configuring Istio-IngressGateway to route requests

    The Sentiment Analysis app is now accessible on http://{EXTERNAL-IP}/. If you bag a Not institute status, enact not worry sometimes it takes a bit for the configuration to retrograde in consequence and update the envoy caches.

    Before touching to the next section expend the app to generate some traffic.

    Kiali — Observability

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

    $ kubectl port-forward \$(kubectl bag 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. lift some time to checkout Kiali before touching 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 bag 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 subsist 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 sanction of this. Let’s antecedent some load by executing the command below:

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

    Now they contain prettier graphs 😊, and additionally, they contain the fabulous 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 contain the harder it gets to pinpoint the antecedent of failure. Let’s lift 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 bag down to the logs of each. How many times enact you find yourself doing this? Their job feels more enjoy 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 achieve together. An instance is shown in design 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 bag access the Jaegers UI execute the command below:

    $ kubectl port-forward -n istio-system \$(kubectl bag 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 lone the traces will subsist shown, as seen in design 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 note it in the traces) and forwarded to the sa-web-app container instance.
  • Here the course sentimentAnalysis handles the request. These traces are generated by the application, acceptation that code changes were required).
  • From where a POST request is started to sa-logic. trail ID needs to subsist 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 ponderous 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 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 bag out of the box (or partially out of the box 😜) let’s bag 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 bag 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 contain two versions of an application (usually the versions differ visually) and they aren’t 100% sure which will augment 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 contain the label app: sa-frontend requests routed by the virtual service sa-external-services to the service sa-frontend will subsist forwarded to every lone of its instances and will subsist load balanced using the round robin algorithm, which causes the issue presented in design 16.

    Figure 16. Requested files are not found

    The files are not institute 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 subsist load balanced to the pods delivering the other version, where understandably the other files enact not exist.

    This means that for their app to work they requisite 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, enjoy for instance an HTTP header. Made possible 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 design 17.

    Figure 17. Traffic Management with Istio Resources

    Note: design 17, visualizes how Istio resources are affecting the network traffic, in an easily understandable way. But, to subsist 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 contain 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 bag the selfsame files when specifying the version header:

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

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

    DestinationRules contain more LoadBalancing capabilities, for every lone the details check out the official docs.

    Before touching 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 strike 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 critical issue and makes a colossal 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 every lone instances are labeled with the respective versions and additionally with app=sa-logic:

    $ kubectl bag 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 subsist load balanced between every lone instances, as shown in design 18.

    Figure 18. Round Robin load balancing

    But they want requests to subsist routed to the instances with version v1 and mirrored to the instances with version v2, as shown in design 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 title used when routing to instances of a subset.
  • Label defines the key-value pairs that requisite to match for an instance to subsist Part 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 lag 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 note 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; enact curl -v http://$EXTERNAL_IP/sentiment \-H “Content-type: application/json” \-d ‘{“sentence”: “I adore yogobella”}’; \sleep .8; done

    Check the results in Grafana, where they can note that the buggy version is failing about 60% of the requests, but no one at all of the failures affected the end-users as they were responded by the currently lively 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 note 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 tiny set of users, as a step to verify the absence of issues, and then with a higher assurance of property 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 robust service by applying the VirtualService below:

  • Weight specifies the percentage of requests to subsist 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 note that some of their requests are failing:

    $ while true; enact \curl -i http://$EXTERNAL_IP/sentiment \-H “Content-type: application/json” \-d ‘{“sentence”: “I adore 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 expend 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 expend the buggy version of sa-logic, where the random failures simulate the unreliability of the network.

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

    To alleviate these issues and help the user undergo 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 subsist waiting for longer than 8 seconds and they retry three times in case of failures, increasing the desultory 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 design 21).

    Figure 21. Improvement after using Timeouts & Retries

    Before touching 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 critical 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 robust instances of that service (increasing success rate).

    The Bulkhead pattern isolates failures from taking the entire system down, to lift an example, Service B is in a vitiate situation and another service (a client of Service B) makes requests to Service B this will result that the client will expend up its own thread pool and won’t subsist 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 course too excited to showcase Authentication and Authorization, which will subsist the topic of the next article.

    Part I — Summary

    In this article, they deployed Istio in a Kubernetes cluster and using its Custom Resource Definitions enjoy 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 lone were possible without code changes or any additional dependencies, keeping your services small, light to operate and maintain.

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

    I would adore to hear your thoughts in the comments below and feel free to achieve 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 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 advocate for JavaScript and Node.JS, with beta advocate 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 subsist 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 account for the history of GraalVM? Where did it achieve from?

    Thomas Wuerthinger: The colossal understanding was that a compiler didn't contain to contain any built-in lore of the language semantics. The judgement for this was that it was very pellucid from the genesis that groups within Oracle had interest in a very diverse orbit of languages including JavaScript, R and Ruby.

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

  • The common faith of VM architects throughout the software industry at that time was that a language VM needed to subsist designed specifically with the semantics of the language in intelligence if you wanted to bag optimal performance.
  • The Java architects worked to advocate 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 distress could subsist much more efficient and a 100% compatible implementation at that.
  • We also 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 uncanny and spellbinding properties that they thought it was a righteous proof point that their multilingual compiler was fully general. Later they brought those language projects into Oracle Labs to bag an industrial-quality implementation for those languages.

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

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

    Wuerthinger: The key technology understanding 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 building an [Abstract Syntax Tree] AST interpreter, and enact not contain to worry about writing a code generator or other low-level runtime functionality enjoy a garbage collector.

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

    This means that you contain to designate recollection and copy data to subsist an "object" in most VMs.

    In Graal, you can contain zero-cost interop between languages, because a JavaScript demur can subsist made to behave enjoy a JavaScript demur or an R demur or whatever, without the requisite for additional demur creation & data copies.

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

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

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

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

    A final and separate technology piece of GraalVM that was added later is called SubstrateVM. The understanding here was to do VMs embeddable on a merge and match basis. A conventional language VM enjoy Java Hotspot wants to control everything: which threads are scheduled, how recollection 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 advocate a dynamic JIT compiler (like a garbage collector). When you are running in a database, for example, the database wants to control recollection management, thread allocations, and so forth.

    SubstrateVM is the "wafer-thin" VM that can sit on something enjoy a database, so when you Run 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 critical ways the SubstrateVM operates is to enact ahead-of-time compilation on Java code (like Graal) directly into a native binary, using a closed world assumption (all the classes to subsist used should subsist known at compile time). So, when you deploy the Graal compiler in a database, they lift Graal and some set of GraalVM languages and compile them into a native shared library, and they don't contain to subsist compiled at runtime.

    InfoQ: What is Truffle? Why is it needed?

    Wuerthinger: Truffle is the API you contain to write a GraalVM interpreter to, when you are building a recent GraalVM language. The course 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 contain 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 enjoy the AST requisite to subsist inherited from Truffle interfaces, and if you enact 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 subsist 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 lag past? Is it expected that Graal will replace 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 subsist the default in a future release.

    The colossal edge that a compiler enjoy Graal has is that it first compiles itself (since it is written in Java), and that makes it possible to write Graal in a much higher-level course than working directly on the AST in C++ as C2 does. For example, this makes it easier for Graal to enact more aggressive inlining and demur shun analysis, which allows more demur allocations to subsist removed from the program automatically. In general, the more abstractions your program has, the more the Graal compiler helps you, so you note the biggest performance benefits in things enjoy the Stream & Lambda advocate in Java8 or in Scala.

    InfoQ: Oracle is heavily marketing GraalVM as a polyglot VM. How does the content of the 1.0 release differ to what was shipped as Part of Java 10? Should, for example, JRuby developers pick one release over the other? Is GraalVM seen as a separate project, or a Part of OpenJDK? Will future releases of Graal subsist 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 expend 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 subsist conservative about including recent technology until they are sure it won't fracture existing deployments, and they contain lots of backwards and forwards compatibility concerns, so there is going to subsist some lag.

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

    Wuerthinger: OpenJ9 is trying to hasten up existing lone 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 fortunate into an existing runtime while staying compatible. The major detriment however is that the benefits are much more limited.

    First, the performance benefit of their Ruby prototype is very tiny 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 critical fourth point: there is no ability with their approach for efficient language interoperability.

    OMR can give marginal improvement to existing single-language runtimes enjoy Ruby MRI that are not well-optimized already. GraalVM uses a completely recent course 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 undergo of working with non-Oracle developers? What does the future development of Graal discover 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 district for developers, since it is creating recent language features and recent libraries every lone the time. You contain to change your code to deal with many of the changes to OpenJDK, and the mission is to bag community consensus on which changes are worthwhile. The OpenJDK community is very large and there are a lot of opinions, and their process helps bag 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 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 much contributions from vendors enjoy 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 subsist very useful for us. You may subsist 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 bag 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 course Twitter has, and they contain internal users at Oracle.

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

    Wuerthinger: Well, the GraalVM team is in Oracle Labs, so they contain benevolent of the contradictory problem where they contain lots of research projects that are public that may or may not retrograde 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 contain one student who was thinking about linking GraalVM into Chromium so that you could expend any language in the browser and not just JavaScript, but I contemplate it is very pellucid that it is unlikely ever to subsist released as an Oracle product. You can certainly discover for academic papers based on GraalVM to note some other crazy ideas. We've done investigations into some other languages as research projects that may or may not retrograde anywhere, based on community interest.

    What I can divulge 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 advocate and also on advocate for the ARM platform (together with Red Hat). There is also 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 note in the next 18 months. But how GraalVM will discover enjoy in 18 months will also 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, also known as Linux Kongress, took Place 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 property talks from intimate 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 development 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 advocate in the Linux kernel and how capabilities such as ptrace, AIO and O_DIRECT do a disagreement to virtual machines. He also 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 ability 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 center automation policies using the Cluster Resource Manager (CRM).

    Mattias Rechenburg's presentation on "Using Enterprise Data Centers with OpenQRM" showcased the situation 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 advocate from Qlusters. The generic sentiment from the audience was they were not interested if they couldn't bag advocate 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 elevated throughput is not enough because customers increasingly also require latency guarantees, especially in particular military applications and trading systems. It was spellbinding 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 subsist used to evaluate the different approaches to enterprise real-time. This includes property of service, the amount of code inspection required when a recent feature is added, the API provided to applications, the relative complexity, vice isolation, and supported hardware and software configurations.

    Although IBM presently has only one customer that plans to deploy enterprise real-time computing, the ability to advocate large SMP systems, TCP/IP, commercially available middleware, and databases makes it an district to watch in the future. Ted also 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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