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HP0-781 NonStop Performance Analysis and Tuning

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HP0-781 exam Dumps Source : NonStop Performance Analysis and Tuning

Test Code : HP0-781
Test title : NonStop Performance Analysis and Tuning
Vendor title : HP
: 154 existent Questions

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HP NonStop Performance Analysis and

TIC On the current – provides HPE NonStop performance expert to crew | killexams.com existent Questions and Pass4sure dumps

(PRLEAP.COM) July 10, 2018 (tremendous Neck, mountainous apple) – TIC application, the main provider of utility solutions and consulting services for HPE NonStop modernization, is joyful to broadcast the addition of Matt Riesz to their construct exhaust of as a client advisor. Mr. Riesz is a 35-yr veteran of Tandem/Compaq/HP/HPE and brings to endure immense event and capabilities within the bailiwick of efficiency analytics to back TIC clients. He'll labor with TIC's NonStop customers, conserving their systems operating at top efficiency and productivity degrees.

Mr. Riesz's career spans the gamut of corporate transitions inside what's now known as HPE NonStop. hired by means of Tandem computer systems in 1982 as a techniques engineer, his career evolved because the company morphed from Tandem to Compaq, and finally to HP/HPE, where he retired as a master methods architect. Matt's headquarters of attention has been on pre-and submit-sales technical back for software and methods. His potential in gadget structure, analytics and efficiency could breathe leveraged to back TIC valued clientele realize most fulfilling productivity from their NonStop techniques.

Mr. Riesz could breathe providing operations aid for TIC clients via performance analysis, besides integrating unusual applied sciences into the firm's ongoing NonStop modernization efforts. Some of those unusual applied sciences consist of:

  • Amazon web services (AWS) – offering subscription-in keeping with-demand cloud computing
  • accurate NonStop GUI – giving NonStop clients useful access from a criterion windows interface
  • Blockchain expertise – distributed digital data – shared & reconciled throughout the internet
  • "we are excited to add an skilled knowledgeable of Matt's caliber to their crew," explained Phil Ly, President, TIC application. "Matt's massive adventure with NonStop shoppers in the field, and his gadget analysis and structure odds and competencies, will aid their consumers improve their IT operations via more desirable efficiency and productivity. They appear to breathe ahead to offering Matt's abilities to every solitary their NonStop modernization valued clientele'!"

    About TIC software:TIC software turned into created to address the challenges of keeping Tandem/HP NonStop legacy programs existing with ever-evolving "modernization" know-how – presenting consulting capabilities and utility options designed to hold mission-essential purposes up-and-working. nowadays, they continue to fullfil these modernization challenges, in addition to present migration options to groups trying to learn other technological alternatives. talk over with www.ticsoftware.com for more.

    TIC utility – NonStop Innovation

    MEDIA CONTACT: Ryan Ly, TIC software, C: 310-409-5082


  • HP Inc. (NYSE:HPQ) Earns among the gold criterion Returns In Its trade | killexams.com existent Questions and Pass4sure dumps

    are looking to recall piece in a brief research examine? assist shape the future of investing tools and too you could win a $250 gift card!

    nowadays they are going to examine HP Inc. (NYSE:HPQ) to stare even if it might possibly breathe a pretty funding prospect. above all, we’re going to figure its revert On Capital Employed (ROCE), in the hopes of getting some insight into the business.

    First, we’ll depart over how they figure ROCE. subsequent, we’ll compare it to others in its industry. ultimately, we’ll dissect how its existing liabilities move its ROCE.

    Return On Capital Employed (ROCE): what is it?

    ROCE measures the ‘return’ (pre-tax profit) a company generates from capital employed in its enterprise. every solitary else being equal, a stronger enterprise may believe a more robust ROCE. In short, it is a useful tool, however is not without drawbacks. favorite funding researcher Michael Mauboussin has cautioned that a unreasonable ROCE can point out that ‘one greenback invested in the company generates value of a couple of dollar’.

    How accomplish You figure revert On Capital Employed?

    The formula for calculating the revert on capital employed is:

    Return on Capital Employed = salary earlier than interest and Tax (EBIT) ÷ (total assets – current Liabilities)

    Or for HP:

    0.46 = US$four.3b ÷ (US$35b – US$25b) (in keeping with the trailing twelve months to October 2018.)

    So, HP has an ROCE of 46%.

    check out their newest evaluation for HP

    Does HP believe a fine ROCE?

    a technique to determine ROCE is to compare similar groups. HP’s ROCE looks to breathe noticeably greater than the 12% generic in the Tech trade. they might deem this a positive, as it suggests it's the exhaust of capital more effortlessly than different an identical organizations. placing apart its position relative to its commerce for now, in absolute terms, HP’s ROCE is at present very first rate.

    As they will see, HP currently has an ROCE of forty six% compared to its ROCE three years in the past, which turned into 6.1%. This makes us deem the company may breathe improving.

    NYSE:HPQ ultimate Perf February eighth 19

    more

    When given that ROCE, retain in judgement that it reflects the previous and doesn't always foretell the longer term. ROCE will too breathe misleading for cyclical corporations, as returns can appear staggering in growth instances, and terribly low in downturns. ROCE is barely a degree-in-time measure. Future efficiency is what concerns, and you'll notice analyst predictions in their free file on analyst forecasts for the company.

    Do HP’s existing Liabilities Skew Its ROCE?

    brief term (or existing) liabilities, are things like service provider invoices, overdrafts, or tax bills that deserve to breathe paid inside one year. as a result of the style ROCE is calculated, a lofty degree of current liabilities makes a company stare as though it has much less capital employed, and consequently can (once in a while unfairly) enhance the ROCE. To counteract this, they investigate if a company has unreasonable current liabilities, relative to its complete belongings.

    HP has total liabilities of US$25b and complete assets of US$35b. for this judgement its latest liabilities are corresponding to approximately 73% of its total assets. HP’s lofty flat of current liabilities enhance the ROCE – however its ROCE continues to breathe astounding.

    Our recall on HP’s ROCE

    So we'd breathe drawn to doing greater analysis perquisite here — there could breathe an opportunity! Of path you can breathe able to find a higher stock than HP. so that you may additionally want to notice this free assortment of alternative organizations which believe grown profits strongly.

    Story continues

    for people that want to locate profitable investments this free record of turning out to breathe corporations with recent insider paying for, may breathe simply the ticket.

    To back readers notice past the short term volatility of the monetary market, they goal to bring you an extended-term focused analysis evaluation in basic terms driven through basic information. note that their analysis does not ingredient in the latest expense-sensitive company bulletins.

    The creator is an unbiased contributor and on the time of e-book had no position within the stocks outlined. For errors that warrant correction gratify contact the editor at editorial-group@simplywallst.com.


    When Your Foot Hits The ground HP wants to customize The journey | killexams.com existent Questions and Pass4sure dumps

    it's simple to notice why HP desires to back lead your steps. The U.S. Athletic sneakers industry earnings Grew 2 p.c to $19.6 Billion in 2017, NPD group stories. Add to that the North American foot orthotic insoles market is expected to develop to more than $1.7 billion by artery of 2022, based on Statista.

    FitStation is a platform combining 3D foot scanning with gait analysis to create custom 3D printed orthotic insoles. The 3D printing is carried out on a different pretty unusual HP technology; the HP Multi Jet Fusion (MJF) 3D printing rig (launched in late 2014).

    I received a demonstration of the FitStation technology at the swift 3D Printing change disclose in 2016 and it is stunning. in view that then, HP and the platform were on a boom spurt in orthotics and in custom running shoes.

    The technology looks like a small treadmill but as an alternative of a affecting rubber mat, you step onto a tumbler plate similar to a copy machine with short walls (stuffed with other sensors) to measure your gait and step. In my demo there too become a power exquisite mat that you too walked on that measured a considerable number of power elements in accordance with how your foot sunk into the mat enabling a extremely precise 3D mannequin of your foot. The requisites I pulled from their web site state: “Gait sample seize. FDA-approved with 8,192 sensors taking pictures 500 information features per 2d.”  I don’t recognize that I actually believe viewed a extra sensor-packed piece of device.

    a ambit of businesses are the exhaust of the FitStation as neatly because the HP MJF printer:

    In late 2018, HP Inc. announced a strategic alliance with depart four-D, a 3D printed clinical orthotics commerce visionary, to modernize the system used to create custom orthotics.

    As piece of the alliance, depart four-D will deal FitStation powered through HP every solitary over the North American medical market. HP’s FitStation platform combines 3D foot scanning with dynamic gait evaluation to enable each orthotic ideas and 3D printed custom orthotics, prescribed from each and every adult’s entertaining biomechanics. this could allow depart 4-D to manufacture relaxed, precise, and cost-efficient customized orthotics the usage of slicing-aspect HP Jet Fusion 3D printing methods.

    Brooks running commerce every solitary started working with HP in 2017 as one of the vital first to create custom running shoes and they exhaust FitStation for the insole and to assess other aspects of their shoes. In may 2018, they announced the Brooks Genesys—the company’s first personalised working shoe— would breathe obtainable for runners in the U.S. in early 2019.

    HP too has a partnership with Superfeet  using the HP Jet Fusion printer to create their custom $150 insoles. The small Washington State-based mostly enterprise is turning out to breathe quickly with its energetic and fun branding as well as quickly construction strategies.

    truly, Superfeet created its own manufacturing plant, Flowbuilt, to retain it up accurate of custom insole and custom shoe market opportunities. at this time, the FitStation setup—which is in 26 working shops across the country—can exhaust that rendering to recommend present footwear for you, as well as 3D-print customized $a hundred and fifty Superfeet insoles. in the future, once you’ve had your feet scanned, you’ll breathe able to load your records into the application and congregate your own version of the Brooks Genesys. FitStation takes your foot shape, gait, and stance under consideration to shape the insole and check the midsole’s stiffness.

    in the exertion to greater mildew and kind a shoe or an orthotic to your foot, HP is looking for to enrich your widely wide-spread walking undergo and your efficiency operating dreams. for people that believe long wanted both much less ache from their toes or a superior capability to escape sooner and more correctly, HP is perquisite here to help.

    replace: In Shanghai, China, final week, SIGG outside ("SIGG") formally announced a partnership with HP. in line with the FitStation platform, the two organizations will jointly analysis and develop essentially the most personalised and technologically superior expert digital and dynamically-personalized running shoes international. in accordance with the plan, SIGG, the unique partner of HP FitStation in China, will debut this innovative product at the ISPO, which can breathe held in China in January 2019, and roll out the product to chinese buyers in March 2019.


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    NonStop Performance Analysis and Tuning

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    I'm Pandora Music Analyst Hannah Glass, and This Is How I labor | killexams.com existent questions and Pass4sure dumps

    Pandora’s musical curation is a mountainous judgement the site silent stands out among competition from Spotify, Google, Apple, and YouTube. Professional music analysts listen to tracks and identify common elements, edifice the Music Genome Project that powers Pandora’s customized user stations. As a Senior Music Analyst, Hannah Glass is deciding what you listen to next. They talked to her about her labor at Pandora, her musical background, and her own projects as a performer.

    Location: Oakland, CACurrent gig: Senior Music AnalystCurrent Computer: MacBook AirCurrent Mobile Device: iPhone 5COne word that best describes how you work: Flexible

    Every song has its own fingerprint, and I map that out.First of all, show us a itsy-bitsy about your background and how you got to where you are today.

    I grew up in a musical family. My mom is a piano teacher and my dad is a bass player, so my brother and I were raised on many different genres and took classical training. Music followed me to UC Berkeley where I studied music and rhetoric. Most of my college undergo was spent performing, arranging and directing for a cappella, as well as volunteering at KALX, the student-run FM radio station, and playing Baroque violin. After graduating I worked at Rasputin Music off of Telegraph Avenue near campus.

    All of these experiences helped me congregate to my current role as a Music Analyst for Pandora. Growing up listening to a lot of varying music styles as a child, as well as having the practical undergo as I got older, has helped my ear tremendously.

    Take us through a recent workday.

    Morning

    What I do: Wake up, browse my phone, construct coffee, water plants, play a crossword puzzle. I usually labor from home, so I cleave out the prep and commute time.

    What I hear: tocsin (Cloud by Sia), meme videos, boiling water, chirping birds, dogs barking, maybe some early jazz.

    Afternoon

    What I do: I pound out analysis using Pandora’s Music Genome Project interface. The music is ingested like that of any radio station, and then after they dissect it, the music can spin on any station with similar musical characteristics. I don’t accomplish the curation; I just enter data using harmonic analysis and other critical listening skills. Every song has its own fingerprint, so to speak, and I map that out.

    What I hear: Email notifications, then 15-25 tracks of unusual rap and hip hop: Usually virile voices sing-rapping over electronic trap beats. Once in a while I listen to traditional Celtic tracks for a change of pace. For breaks, I sit at my piano and sight read Romantic or Classical pieces. 

    Evening

    What I do: Often I will believe a rehearsal or performance, so when I Come home late, that’s when I trap up on work, prep for the next day, and devour dinner. I like to retain my musical interests diverse: on Monday it’s a 12-person a cappella practice. Tuesday is a stadium pop-rock band. Wednesday is a face-melting rock trio. Thursday is a bluegrass string band. Saturday is a string quartet. Sunday is an Argentine milonga ensemble. Then I dissect a bit more music before going to bed.

    What I hear: My Pandora app as I drive to rehearsal. Tuning, laughter, drum kits, FX pedals, amp feedback, constructive feedback, alto sectionals, metronomes, pages turning, cursing, and a lot of “1, 2, 3, 4,” “Turn up the violin,” “This one’s in D,” and “Where are they starting?” Back home, a few hours of analysis. I unwind to LoFi hip hop. Then burning of a candle. Finally, silence. 

    What apps, gadgets, or tools can’t you live without?

    For work, simple wifi connection does the trick! For gigs, I can’t live without Lyft. A metronome is always helpful (I exhaust a free app on my phone).

    What’s your workspace setup like?

    My workspace is pretty non-existent. I believe my laptop, my isolating, cozy collapsible headphones, and a travel-size numerical keyboard. I’ve worked at cafes in Paris and on a tour bus on the highway, but I mainly labor at my kitchen table. One of my favorite places to labor is outside in the sun at my house in Oakland.

    What are your music listening habits? How has your labor affected them?

    My listening habits change often, but I treasure silence once in a while to reset my ears. I’m certainly not sick of music after analyzing it for a living. I accomplish believe a very professional relationship with rap, as I spend most of my time listening to it for work, so I watch to listen to other genres when relaxing. As a music academic, I’ve listened to songs analytically for years, so I know how to swirl it on and off.

    What’s your best life hack?

    Keep snacks in your purse at every solitary times! As a musician, I’m often pressed for time driving from one position to another, especially around dinner time. My purse is always full of snacks.

    Take us through an spellbinding process you believe in position at work.

    One of the more spellbinding parts of my job, because it can breathe repetitive, is attending monthly group meetings where Pandora analysts dispute different genes of the Music Genome Project. In the rap and hip hop meetings, they talk about lyrics and musical discrepancies that they believe to unpack, because it’s just piece of their job. A lot of the material is NSFW, which makes for an inherently entertaining labor space.

    Another rewarding aspect is that every piece of the Music Genome Project—even after 15 years of algorithms—is open for discussion and revision. Every analyst coworker of mine has a cogent perspective on the music. It’s Amazing to believe every solitary of these rockstars and legal music geeks in a Skype session congregate fired up about the interpretation of a song’s harmonic, instrumental or lyrical elements.

    Who are the people who back you congregate things done, and how accomplish you reckon on them?

    My job is mostly straightforward, listening to a unusual song every ten minutes. But my manager helps me improve the attribute of my analysis and makes confident I don’t burn out. Any length of analysis past six hours can definitely swirl a brain to mush.

    How accomplish you retain track of what you believe to do?

    Old school: a CVS planner. Sometimes I even exhaust gel pens.

    How accomplish you recharge or recall a break?

    Some analysts labor in blocks, others labor in between other gigs. After a few hours of analysis, I play the piano, lay out in the sun, or devour a snack.

    What’s your favorite side project?

    I treasure performing in general, and my musical instruments are my favorite side projects.

    What are you currently reading, or what accomplish you recommend?

    At the moment I’m reading Ray Bradbury’s The Day it Rained Forever, a compilation of short stories. I’d too recommend the fiction writing of Jonathan Lethem. It’s fantastic.

    Who else would you like to notice confess these questions?

    Besides reading what my mom and dad would believe to say, I’d treasure to Lifehack Thomas Newman. He’s the film composer behind American Beauty, Finding Nemo, Wall-E, and Skyfall to title a few.

    What’s the best recommendation you’ve ever received?

    Don’t compare yourself to other people. It’s not a race—trust your own process. This is super helpful to remember as a songwriter. There are so many artists in the world, only some of whom are big, but we’re every solitary on their own track. Life is not a race.

    What’s a problem you’re silent trying to solve?

    Time management and prioritization. It’s been a fun challenge juggling every solitary of my extracurricular activities since elementary school, and now I’m mindful of making time to breathe who I want to be. I’ve create that taking a step back, re-calibrating, and establishing what I exigency to accomplish helps a lot.


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    dm03514BlockedUnblockFollowFollowing

    Nov 19, 2018

    Performance analysis and tuning is a powerful technique to verify if client focused performance requirements are being met. Performance analysis can breathe used to characterize where a program is spending its time and supports a scientific approach to testing the efficacy of tuning experiments. This post defines a generic approach for performance analysis and tuning using a depart HTTP service as an example. depart is particularly well suited for performance analysis and tuning because it provides pprof profiling implement chain in its criterion library.

    Strategy

    Let’s try to establish a lax framework to launch a structured analysis of their program. The only thing they will try and accomplish will breathe to exhaust data to lead their decisions instead of making changes based on intuition or hunches. In order to accomplish this they will:

  • Establish which dimensions we’re optimizing for (Requirements)
  • Create a harness to apply transactional load to the system
  • Execute a Test— (Generate data)
  • Observe
  • Analyze — Are the requirements met?
  • Tune — Scientific Method — Form A Hypothesis
  • Execute experiment to test this hypothesis
  • Simple HTTP Server Architecture

    For this blog post we’ll breathe using a small HTTP golang server. every solitary code for this post can breathe create at: https://github.com/dm03514/sre-tutorials/tree/master/performance/analysis-methodology-simple-http

    The application that will breathe analyzed is a depart HTTP server that queries PostgreSQL on each request. Additionally, there is Prometheus, node_exporter, and Grafana in order to collect and visualize application and system flat metrics:

    For simplicity this post assumes that in order to scale horizontally (and simplify their calculations) each HTTP service and Postgres Database will breathe deployed together:

    Determine Goals (Dimensions)

    This step outlines a specific goal. What are they trying to analyze? How accomplish they know when their efforts are complete?

    For this post we’ll assume that they believe clients and that on middling their service will breathe called 10000 requests / second.

    The Google SRE bespeak goes into a lot of depth on how to elect and model these. In the spirit of SRE we’ll model ours as:

  • Latency — 99% of requests should complete in < 60ms
  • Cost— The service should exhaust the minimal amount of money that they contemplate is reasonably possible. In order to accomplish this throughput should breathe maximized.
  • Capacity Planning — An understanding of how many instances will breathe required for launch and the generic scaling office should breathe documented. What’s the number of instances we’ll exigency to meet their expected initial load requirements and achieve n+1 redundancy?
  • Latency may breathe require an optimization in addition to analysis but throughput is an analysis. Using SRE SLO process the latency requirement would Come from the client and or commerce as represented by the product owner. What’s really chilly about this is that their service may breathe able to meet this commitment from the outset without any required tuning!

    Setup Test Harness

    The test harness will apply a fixed amount load to their system. In order to dissect the performance of the HTTP service data will believe to breathe generated.

    Transactional Load

    This harness just uses Vegeta to construct HTTP requests at a configurable rate until stopped:

    $ construct load-test LOAD_TEST_RATE=50echo "POST http://localhost:8080" | vegeta bombard -body tests/fixtures/age_no_match.json -rate=50 -duration=0 | tee results.bin | vegeta report Observe

    During execution an unending transaction load will breathe applied (load test). In addition to application (requests rate, request latencies) and system flat metrics (memory, CPU, IOPS) this time the application will breathe profiled in order to understand where it is spending its time.

    Profiling

    Profiling is a nature of measurement and allow us to to notice where the program is spending its time. It reports where a program is spending its time. Profiling can breathe used to identify which functions are being called and how much time the program is spending in each one:

    This data can breathe used during analysis to gain visibility into unnecessary labor that the program is doing. depart (pprof) is able to generate profiles, and visualize them as a flame graph, using the criterion toolchain. We’ll cover using these to lead tuning decisions later in this post.

    Execute/Observe/Analyze

    Let’s launch executing experiments. We’ll execute, celebrate and dissect until they their performance requirements believe been invalidated. We’ll elect an whimsical low amount of load to apply in order to generate the first observations and analysis. Each step that their performance requirements hold legal we’ll increase the load by some random-ish scaling factor. Each of the load tests are executed by adjustng the rate of:make load-test LOAD_TEST_RATE=X.

    50 requests / second

    Notice the top two graphs. The upper left hand shows us that their application is registering that it is processing 50 requests per second (from its point of view) and the upper perquisite reporting the duration of each HTTP request. These together back us celebrate and dissect if their performance requirements are being met. The red line on the HTTP Request Latency is at the SLO of 60 ms. This shows that they are well below it for their max response times.

    On the cost front:

    10k requests / second / 50 requests / machine = 200 machines + 1

    we can silent accomplish much better.

    500 Requests / second

    Things start to congregate more spellbinding when 500 requests per second are applied:

    Once again the upper left hand graph shows that the application is registering the intended load. If it weren’t it might identify an issue in the host machine between the load test command and the application server. The latency graph in the upper perquisite graph shows that applying 500 requests / second transmit the tail up to 25–40ms per HTTP requests. The p99 is silent well within the 60ms SLO that was specified above.

    On the cost front:

    10k requests / second / 500 requests / machine = 20 machines + 1

    we can silent accomplish much better!

    1000 requests / second

    This is a noteworthy one! The program is logging 1000 requests / second, but on the latency side the SLO has been violated. This can breathe seen by the P99 line in the upper left hand graph. While the tail p100 max is much higher p99 tracks above the 60ms maximum. It’s time to dive in and profile what the application is actually doing.

    Profile

    In order to profile we’ll apply 1000 request per second load and then exhaust pprof to sample the stacks in order to congregate an thought of where their program is spending its time. This is done by enabling the pprof HTTP endpoint and curling a track while load is being applied:

    $ curl http://localhost:8080/debug/pprof/profile?seconds=29 > cpu.1000_reqs_sec_no_optimizations.prof

    The track can then breathe visualized using:

    $ depart implement pprof -http=:12345 cpu.1000_reqs_sec_no_optimizations.prof

    The flame graph shows where the application is spending its time and how much time time is being spent! From Brendan Gregg’s description:

    The x-axis shows the stack profile population, sorted alphabetically (it is not the passage of time), and the y-axis shows stack depth, counting from zero at the [top]. Each rectangle represents a stack frame. The wider a frame is is, the more often it was present in the stacks. The top edge shows what is on-CPU, and beneath it is its [children]. The colors are usually not significant, picked randomly to differentiate frames.

    Analysis — Hypothesis

    In order to lead tuning we’ll focus on trying to find unnecessary work. We’ll try and find the largest source of unnecessary labor and remove it. Since profiling exposes where exactly the service is spending its time it requires identifying potentially duplicate work, modifying the code to change it, re running the test and observing if the performance is closer to its goal.

    Following Bredan Gregg’s description the depart pprof flame graph is read from top to bottom. Each row represents a stack frame (function call). The first row is the entry point to the program and is the parent of every solitary other calls (ie every solitary other calls believe the first line in their stack). The next row diverges from this:

    Hovering over the office title in the flame graph shows the total amount of time it was on the stack during tracing. HTTPServe was on the stack for 65% of the profiles while various depart runtime methods runtime.mcall , mstart, gc compose the comfort of this. One spellbinding thing is that 5% of the program is being spent in DNS Lookups:

    The only addresses that the program should breathe resolving are for Postgres. Clicking on FindByAge shows:

    This is spellbinding it shows roughly 3 main sources which add to the latency: Closing/Releasing Connections, Querying Data, and Connecting.Based on this flame graph DNS lookups and connection closing/opening amounts for ~13% of the total service time.

    Hypothesis: Reusing connections through pooling will reduce HTTP transaction time allow for higher throughput and lower latency.

    Tune the application — Experiment

    The program will breathe updated to try and remove the postgres connection on every request. One artery to accomplish this is to exhaust an application flat connection pool. This experiment will configure a connection pool using the depart sql driver pool configuration options:

    db, err := sql.Open("postgres", dbConnectionString)db.SetMaxOpenConns(8)

    if err != nil {return nil, err}

    Execute/Observe/Analyze

    Re-Runing the 1000 test load test shows that the P99 HTTP Request Latency is well within the 60ms SLO!

    But on the cost front:

    10k requests / second / 1000 requests / machine = 10 machines + 1

    Let’s try and accomplish better!

    2000 Requests / Second

    Doubling the requests define the same. Once again the upper left hand graph shows that the application is receiving 2000 requests per second, and that while the p100 max client latency is above 60ms, the p99 is silent within the SLO.

    On the cost side:

    10k requests / second / 2000 requests / machine = 5 machines + 1

    3000 Requests / second

    At this point the service is able to achieve a rate of 3000 requests / second with < 60ms p99 latency, with the p100 rising significantly from 100–250ms to 250ms-1000ms between this escape and the 2000 requests / second run. While the SLO isn’t being violated and costs are acceptable with:

    10k requests / second / 3000 requests / machine = 4 machines + 1

    Let’s try one more round of analysis.

    Analysis — Hypothesis

    Generating and visualizing a profile of the application with 3000 requests / second load being applied shows:

    There is silent 6% of the FindByAge transaction length due to Dialing Connections!! Establishing a connection pool has increased performance but its silent observed that the program is silent doing repeated labor by continuing to construct unusual DB connections!

    Hypothesis: Even though connections are being pooled they are silent being recycled and cleaned up resulting in the application having to reconnect. Adjusting the number of connections idle to equal the pool size should back with latency by further minimizing the amount of time the application spends in creating database connections.

    Tune the application — Experiment

    Let’s try and set the MaxIdleConns equal to the pool size (also described here):

    db, err := sql.Open("postgres", dbConnectionString)db.SetMaxOpenConns(8)db.SetMaxIdleConns(8) if err != nil {return nil, err} Execute/Observe/Analyze

    3000 requests / second

    p99 is well below the <60ms! @ 3000 requests per second with a lower p100!

    Looking closely at the flame graph below now shows that the connection dial has no longer shows up! Looking closely at pg(*conn).query a whole leg of dialing is no longer there.

    Conclusion

    Performance analysis is critical for understanding if client expectations and non-functional requirements are being met. Analyzing performance by qualifying observations against client expectations can back in determining what is acceptable performance vs what is unacceptable. depart provides Amazing utilities baked into the criterion library to construct this sort of analysis simple and accessible.

    I treasure you reading this and would treasure feedback!


    Monitoring and Tuning Analysis Services with SQL Profiler, piece 1 | killexams.com existent questions and Pass4sure dumps

    Database administration expert Baya Dewald explores the possibilities of using the SQL Profiler framework to find common performance problems in your SQL Server Analysis Services (SSAS) cubes. In piece 1 of this two-part series, he discusses the various events and columns that SQL Profiler supports for SSAS.

    Like this article? They recommend 

    SQL Server Analysis Services (SSAS) is an enterprise-class data analytics and aggregation engine that offers much flexibility and ease for developing commerce intelligence applications. edifice a small, paltry cube isn't complicated, but when SSAS cubes grow to a reasonable size and complexity, querying and processing performances often don't live up to customers' expectations. Fortunately, Microsoft offers a few tools for looking under the hood and detecting the root occasions of issues. SQL Profiler is arguably the most informative implement for monitoring and troubleshooting Analysis Services (the implement works with Analysis Services 2005 and later versions).

    In this article, I dispute the SQL Profiler framework for identifying most commonly encountered problems. After you're confidential with Profiler events and columns, piece 2 of this succession examines how to exhaust Profiler events to detect and resolve querying, processing, and synchronization performance issues. I'll too dispute using Profiler for load testing to ensure that your application is ready for production use.

    SQL Profiler supports a army of Analysis Services events, and you can collect a number of columns for each event. Collecting every event and every column on a busy server is impractical because the resulting track would grow rapidly to unmanageable size and breathe difficult to decipher. For occasional troubleshooting, they exigency to focus on a few events that are pertinent for the job at hand. For ongoing monitoring, it's useful to collect a larger set of events and columns for historical analysis, which I'll dispute in piece 2 of this series.

    Table 1 quickly summarizes the Profiler events they can capture. Study this table to Come up with a list of events that would breathe helpful for your monitoring and tuning effort. gratify note that Microsoft tends to add Profiler events with each version, so the list available in your environment might differ slightly from the one in Table 1, depending on the Analysis Services version you're using.

    Table 1

    Analysis Services Profiler Events

    Event Category

    Event

    Description/Example

    Command Events

    Command Begin

    Command End

    Captures the following commands:

  • [lb] Data-definition language commands, including create, alter, delete, merge
  • [lb] Backup and restore commands: backup, restore, synchronize, detach, attach
  • [lb] every solitary processing commands
  • Discover Events

    Discover Begin

    Discover End

    Captures retrieval of metadata. For example, a learn command is executed when a user connects to SSAS by using SQL Server Management Studio and expands folders of databases, cubes, and measure groups; checks partition properties.

    Discover Server state Events

    Server state learn Begin

    Server state learn Data

    Server state learn End

    SSAS occasionally checks its own health through these events by taking a snapshot of server state. This is done by submitting learn connections, learn sessions, learn jobs, and other commands. Frequency of execution of these commands is controlled by the <FlightRecorder><SnapshotFrequencySec> setting in the msmdsrv.ini configuration file. The commands to breathe executed are included in the FlightRecorderSnapshotDef.xml file create in the bin folder of the SSAS installation directory.

    Errors and Warnings

    Error

    The mistake number reported by this event can breathe helpful when troubleshooting an issue with assistance from Microsoft support.

    Locks

    Deadlock

    Lock Timeout

    SSAS is predominantly a read-only system, but they write data when creating files through processing, synchronization, and other means. A deadlock normally occurs when two commands are attempting to modify the identical object. Lock timeout can breathe encountered if a long-running query blocks a processing or synchronization command, or vice versa.

    Notification Events

    Notification

    Records creation, modification, or deletion of an object. For example, as soon as you start a Profiler trace, it will capture a notification event recording its own creation.

    Progress Reports

    Progress Report Begin

    Progress Report Current

    Progress Report End

    Progress Report Error

    Records the current step of processing or querying a job's execution. For example, Progress Report Begin/End events are raised when they read a specific partition. Progress Report mistake is raised when a user cancels a long-running query, or processing fails due to missing-key errors.

    Queries Events

    Query Begin

    Query End

    These events are useful for checking MDX or DMX query start time and total duration.

    Query Processing

    Calculate Non-Empty Begin

    Calculate Non-Empty Current

    Calculate Non-Empty End

    Execute MDX Script Begin

    Execute MDX Script Current

    Execute MDX Script End

    Get Data from Aggregation

    Get Data from Cache

    Query Cube Begin

    Query Cube End

    Query Dimension

    Query Subcube

    Query Subcube Verbose

    Serialize Results Begin

    Serialize Results Current

    Serialize Results End

    The most informative category for comprehending how the query is resolved.

    Query Subcube event shows a vector of dimension attributes that SSAS reads to resolve the query. This is the identical succession of ones and zeros that you find in the query log. Each query could read multiple subcubes to derive the requested result.

    Query Subcube Verbose event returns minute information about the assign values being retrieved.

    Serialize Results * events witness that the requested cell-set is being displayed to the user who submitted the query.

    Security Audit

    Audit Backup/Restore Event

    Audit Login

    Audit Logout

    Audit object license Event

    Audit Server Starts and Stops

    Auditing various types of activity on the server. Primarily useful for long-term monitoring.

    Session Events

    Existing Connection

    Existing Session

    Session Initialize

    Displays existing connections and sessions, as well as requests for unusual sessions. This category is useful when you first start a track with the Profiler user interface, to gauge how busy your server is.

    From Table 1, they can surmise that progress reports, command events, query events, and query processing are the most useful categories for detecting query and processing performance issues. Other categories Come in handy for troubleshooting, though, so don't discount them yet!



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