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HP0-781 - NonStop Performance Analysis and Tuning - Dump Information

Vendor : HP
Exam Code : HP0-781
Exam Name : NonStop Performance Analysis and Tuning
Questions and Answers : 154 Q & A
Updated On : February 15, 2019
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HP0-781 Questions and Answers

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HP0-781 NonStop Performance Analysis and Tuning

Study Guide Prepared by Killexams.com HP Dumps Experts

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

Test Code : HP0-781
Test Name : NonStop Performance Analysis and Tuning
Vendor Name : HP
Q&A : 154 Real Questions

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HP NonStop Performance Analysis and

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

(PRLEAP.COM) July 10, 2018 (tremendous Neck, big apple) – TIC application, the main provider of utility solutions and consulting services for HPE NonStop modernization, is joyful to announce the addition of Matt Riesz to their make use 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 field of efficiency analytics to help TIC clients. He'll work 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 center of attention has been on pre-and submit-sales technical help for software and methods. His potential in gadget structure, analytics and efficiency could be leveraged to support TIC valued clientele realize most fulfilling productivity from their NonStop techniques.

Mr. Riesz could be providing operations aid for TIC clients via performance analysis, besides integrating new applied sciences into the firm's ongoing NonStop modernization efforts. Some of those new 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 standard 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 our crew," explained Phil Ly, President, TIC application. "Matt's massive adventure with NonStop shoppers in the field, and his gadget analysis and structure advantage and competencies, will aid our consumers improve their IT operations via more desirable efficiency and productivity. We seem to be ahead to offering Matt's abilities to all our 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, we continue to satisfy these modernization challenges, in addition to present migration options to groups trying to discover 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 standard Returns In Its trade | killexams.com Real Questions and Pass4sure dumps

    are looking to take part in a brief research examine? assist form the future of investing tools and also you could win a $250 gift card!

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

    First, we’ll go over how we calculate ROCE. subsequent, we’ll compare it to others in its industry. ultimately, we’ll analyze how its existing liabilities affect 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. All else being equal, a stronger enterprise may have 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 excessive ROCE can point out that ‘one greenback invested in the company generates value of a couple of dollar’.

    How Do You Calculate Return On Capital Employed?

    The formula for calculating the return 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 our newest evaluation for HP

    Does HP Have a fine ROCE?

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

    As we 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 consider the company may be improving.

    NYSE:HPQ ultimate Perf February eighth 19


    When given that ROCE, keep in mind that it reflects the previous and doesn't always predict the longer term. ROCE will also be misleading for cyclical corporations, as returns can seem 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 see analyst predictions in our 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 be paid inside one year. as a result of the style ROCE is calculated, a high degree of current liabilities makes a company look as though it has much less capital employed, and consequently can (once in a while unfairly) enhance the ROCE. To counteract this, we investigate if a company has excessive current liabilities, relative to its complete belongings.

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

    Our take on HP’s ROCE

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

    Story continues

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

    To support readers see past the short term volatility of the monetary market, we goal to bring you an extended-term focused analysis evaluation in basic terms driven through basic information. note that our 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 please contact the editor at editorial-group@simplywallst.com.

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

    it's easy to see why HP desires to help guide 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 way 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 new HP technology; the HP Multi Jet Fusion (MJF) 3D printing equipment (launched in late 2014).

    I received a demonstration of the FitStation technology at the swift 3D Printing change reveal 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 moving 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 also become a power delicate mat that you also 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 have viewed a extra sensor-packed piece of device.

    a range of businesses are the use of the FitStation as neatly because the HP MJF printer:

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

    As part of the alliance, Go four-D will distribute FitStation powered through HP all 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 Go 4-D to manufacture relaxed, precise, and cost-efficient customized orthotics the usage of slicing-aspect HP Jet Fusion 3D printing methods.

    Brooks running business all started working with HP in 2017 as one of the vital first to create custom running shoes and they use 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 be obtainable for runners in the U.S. in early 2019.

    HP also 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 be quickly with its energetic and fun branding as well as quickly construction strategies.

    truly, Superfeet created its own manufacturing plant, Flowbuilt, to keep 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 use 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 be able to load your records into the application and get 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 effort to greater mildew and kind a shoe or an orthotic to your foot, HP is looking for to enrich your widely wide-spread walking experience and your efficiency operating dreams. for people that have long wanted both much less ache from their toes or a superior capability to run sooner and more correctly, HP is right 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 be held in China in January 2019, and roll out the product to chinese buyers in March 2019.

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

    Pandora’s musical curation is a big reason the site still stands out among competition from Spotify, Google, Apple, and YouTube. Professional music analysts listen to tracks and identify common elements, building 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. We talked to her about her work 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, tell us a little 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 experience 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 get 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 experience as I got older, has helped my ear tremendously.

    Take us through a recent workday.


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

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


    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 we analyze it, the music can spin on any station with similar musical characteristics. I don’t do 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 new rap and hip hop: Usually male 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. 


    What I do: Often I will have a rehearsal or performance, so when I come home late, that’s when I catch up on work, prep for the next day, and eat dinner. I like to keep 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 analyze 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 we 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 use a free app on my phone).

    What’s your workspace setup like?

    My workspace is pretty non-existent. I have my laptop, my isolating, comfortable 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 work at my kitchen table. One of my favorite places to work is outside in the sun at my house in Oakland.

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

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

    What’s your best life hack?

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

    Take us through an interesting process you have in place at work.

    One of the more interesting parts of my job, because it can be repetitive, is attending monthly group meetings where Pandora analysts discuss different genes of the Music Genome Project. In the rap and hip hop meetings, we talk about lyrics and musical discrepancies that we have to unpack, because it’s just part of our job. A lot of the material is NSFW, which makes for an inherently entertaining work 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 valid perspective on the music. It’s amazing to have all of these rockstars and true music geeks in a Skype session get fired up about the interpretation of a song’s harmonic, instrumental or lyrical elements.

    Who are the people who help you get things done, and how do you rely on them?

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

    How do you keep track of what you have to do?

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

    How do you recharge or take a break?

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

    What’s your favorite side project?

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

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

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

    Who else would you like to see answer these questions?

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

    What’s the best advice 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 all on our own track. Life is not a race.

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

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

    SRE: Performance Analysis: Tuning Methodology Using a Simple HTTP Webserver In Go | killexams.com real questions and Pass4sure dumps


    Nov 19, 2018

    Performance analysis and tuning is a powerful technique to verify if client focused performance requirements are being met. Performance analysis can be 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 general approach for performance analysis and tuning using a go HTTP service as an example. Go is particularly well suited for performance analysis and tuning because it provides pprof profiling tool chain in its standard library.


    Let’s try to establish a loose framework to begin a structured analysis of our program. The only thing we will try and do will be to use data to guide our decisions instead of making changes based on intuition or hunches. In order to do this we 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 be using a small HTTP golang server. All code for this post can be found at: https://github.com/dm03514/sre-tutorials/tree/master/performance/analysis-methodology-simple-http

    The application that will be analyzed is a go 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 level metrics:

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

    Determine Goals (Dimensions)

    This step outlines a specific goal. What are we trying to analyze? How do we know when our efforts are complete?

    For this post we’ll pretend that we have clients and that on average our service will be called 10000 requests / second.

    The Google SRE Book goes into a lot of depth on how to choose 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 use the minimal amount of money that we think is reasonably possible. In order to do this throughput should be maximized.
  • Capacity Planning — An understanding of how many instances will be required for launch and the general scaling function should be documented. What’s the number of instances we’ll need to meet our expected initial load requirements and achieve n+1 redundancy?
  • Latency may be 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 business as represented by the product owner. What’s really cool about this is that our service may be 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 our system. In order to analyze the performance of the HTTP service data will have to be generated.

    Transactional Load

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

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

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


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

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


    Let’s begin executing experiments. We’ll execute, observe and analyze until we our performance requirements have been invalidated. We’ll choose an arbitrary low amount of load to apply in order to generate the first observations and analysis. Each step that our performance requirements hold true 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 our application is registering that it is processing 50 requests per second (from its point of view) and the upper right reporting the duration of each HTTP request. These together help us observe and analyze if our performance requirements are being met. The red line on the HTTP Request Latency is at the SLO of 60 ms. This shows that we are well below it for our max response times.

    On the cost front:

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

    we can still do much better.

    500 Requests / second

    Things start to get more interesting 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 right graph shows that applying 500 requests / second send the tail up to 25–40ms per HTTP requests. The p99 is still well within the 60ms SLO that was specified above.

    On the cost front:

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

    we can still do much better!

    1000 requests / second

    This is a great one! The program is logging 1000 requests / second, but on the latency side the SLO has been violated. This can be 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.


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

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

    The trace can then be visualized using:

    $ go tool 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 guide tuning we’ll focus on trying to find unnecessary work. We’ll try and find the largest source of unnecessary work 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 go 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 all other calls (ie all other calls have the first line in their stack). The next row diverges from this:

    Hovering over the function name 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 go runtime methods runtime.mcall , mstart, gc compose the rest of this. One interesting thing is that 5% of the program is being spent in DNS Lookups:

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

    This is interesting 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 be updated to try and remove the postgres connection on every request. One way to do this is to use an application level connection pool. This experiment will configure a connection pool using the go sql driver pool configuration options:

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

    if err != nil {return nil, err}


    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 do better!

    2000 Requests / Second

    Doubling the requests show 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 still 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 run 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 still 6% of the FindByAge transaction length due to Dialing Connections!! Establishing a connection pool has increased performance but its still observed that the program is still doing repeated work by continuing to make new DB connections!

    Hypothesis: Even though connections are being pooled they are still being recycled and cleaned up resulting in the application having to reconnect. Adjusting the number of connections idle to equal the pool size should help 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.


    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 help in determining what is acceptable performance vs what is unacceptable. Go provides amazing utilities baked into the standard library to make this sort of analysis easy and accessible.

    I appreciate you reading this and would love feedback!

    Monitoring and Tuning Analysis Services with SQL Profiler, Part 1 | killexams.com real 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 part 1 of this two-part series, he discusses the various events and columns that SQL Profiler supports for SSAS.

    Like this article? We recommend 

    SQL Server Analysis Services (SSAS) is an enterprise-class data analytics and aggregation engine that offers much flexibility and ease for developing business intelligence applications. Building a small, trivial 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 cause of issues. SQL Profiler is arguably the most informative tool for monitoring and troubleshooting Analysis Services (the tool works with Analysis Services 2005 and later versions).

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

    SQL Profiler supports a multitude 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 trace would grow rapidly to unmanageable size and be difficult to decipher. For occasional troubleshooting, we need to focus on a few events that are relevant for the task at hand. For ongoing monitoring, it's useful to collect a larger set of events and columns for historical analysis, which I'll discuss in part 2 of this series.

    Table 1 quickly summarizes the Profiler events we can capture. Study this table to come up with a list of events that would be helpful for your monitoring and tuning effort. Please 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



    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] All processing commands
  • Discover Events

    Discover Begin

    Discover End

    Captures retrieval of metadata. For example, a discover 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 Discover Begin

    Server State Discover Data

    Server State Discover End

    SSAS occasionally checks its own health through these events by taking a snapshot of server state. This is done by submitting discover connections, discover sessions, discover 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 be executed are included in the FlightRecorderSnapshotDef.xml file found in the bin folder of the SSAS installation directory.

    Errors and Warnings


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



    Lock Timeout

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

    Notification Events


    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 we read a specific partition. Progress Report Error 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 same series 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 detailed information about the attribute values being retrieved.

    Serialize Results * events indicate 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 Permission 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 new sessions. This category is useful when you first start a trace with the Profiler user interface, to gauge how busy your server is.

    From Table 1, we 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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