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

Vendor Name : HP
Exam Code : HP0-781
Exam Name : NonStop Performance Analysis and Tuning
Questions and Answers : 154 Q & A
Updated On : March 22, 2019
PDF Download Mirror : HP0-781 Braindumps
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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 circulate – provides HPE NonStop efficiency professional to team | killexams.com Real Questions and Pass4sure dumps

(PRLEAP.COM) July 10, 2018 (extraordinary Neck, big apple) – TIC software, the main company of application solutions and consulting functions for HPE NonStop modernization, is glad to announce the addition of Matt Riesz to their employ as a client consultant. Mr. Riesz is a 35-12 months veteran of Tandem/Compaq/HP/HPE and brings to bear big journey and abilities in the field of efficiency analytics to aid TIC consumers. He'll work with TIC's NonStop customers, keeping their systems operating at peak efficiency and productiveness levels.

Mr. Riesz's profession spans the gamut of company transitions within what's now referred to as HPE NonStop. employed by means of Tandem computers in 1982 as a methods engineer, his career advanced as the company morphed from Tandem to Compaq, and finally to HP/HPE, the place he retired as a master programs architect. Matt's center of attention has been on pre-and submit-earnings technical support for utility and techniques. His knowledge in equipment structure, analytics and efficiency can be leveraged to help TIC consumers realize most reliable productivity from their NonStop methods.

Mr. Riesz might be providing operations guide for TIC clients via efficiency analysis, in addition to integrating new applied sciences into the enterprise's ongoing NonStop modernization efforts. Some of those new technologies consist of:

  • Amazon web capabilities (AWS) – presenting subscription-in accordance with-demand cloud computing
  • excellent NonStop GUI – giving NonStop users helpful access from a common windows interface
  • Blockchain know-how – dispensed digital facts – shared & reconciled across the cyber web
  • "we are excited so as to add an experienced expert of Matt's caliber to our group," defined Phil Ly, President, TIC software. "Matt's colossal event with NonStop shoppers within the container, and his gadget analysis and architecture potential and abilities, will support our consumers increase their IT operations through more suitable performance and productivity. We seem to be ahead to offering Matt's knowledge to all our NonStop modernization valued clientele'!"

    About TIC software:TIC software become created to tackle the challenges of conserving Tandem/HP NonStop legacy techniques present with ever-evolving "modernization" technology – providing consulting expertise and application options designed to preserve mission-vital purposes up-and-operating. today, we continue to fulfill these modernization challenges, in addition to present migration options to corporations trying to explore different technological options. consult with www.ticsoftware.com for more.

    TIC software – NonStop Innovation

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

  • HP Inc. (NYSE:HPQ) Earns among the many most beneficial Returns In Its trade | killexams.com Real Questions and Pass4sure dumps

    want to take part in a brief analysis analyze? assist form the future of investing equipment and also you may win a $250 gift card!

    today we're going to look at HP Inc. (NYSE:HPQ) to see whether it might possibly be a gorgeous funding prospect. certainly, we’re going to calculate its Return On Capital Employed (ROCE), within the hopes of getting some perception into the business.

    First, we’ll go over how we calculate ROCE. subsequent, we’ll examine it to others in its business. eventually, we’ll study how its existing liabilities affect its ROCE.

    Return On Capital Employed (ROCE): what's it?

    ROCE measures the ‘return’ (pre-tax income) an organization generates from capital employed in its business. All else being equal, an improved company can have a better ROCE. In short, it is a great tool, however it isn't devoid of drawbacks. well-known investment researcher Michael Mauboussin has cautioned that a high ROCE can point out that ‘one greenback invested in the enterprise generates price of multiple dollar’.

    How Do You Calculate Return On Capital Employed?

    The formulation for calculating the return on capital employed is:

    Return on Capital Employed = income before pastime and Tax (EBIT) ÷ (total property – latest Liabilities)

    Or for HP:

    0.46 = US$4.3b ÷ (US$35b – US$25b) (in response to the trailing twelve months to October 2018.)

    So, HP has an ROCE of 46%.

    take a look at our newest analysis for HP

    Does HP Have a superb ROCE?

    a technique to assess ROCE is to examine similar agencies. HP’s ROCE seems to be extensively more advantageous than the 12% common in the Tech business. we might consider this a favorable, as it suggests it's the usage of capital extra without difficulty than other equivalent companies. putting aside its place relative to its business for now, in absolute terms, HP’s ROCE is currently very first rate.

    As we can see, HP at present has an ROCE of forty six% compared to its ROCE three years ago, which changed into 6.1%. This makes us think the company might possibly be enhancing.

    NYSE:HPQ closing Perf February eighth 19


    When given that ROCE, keep in mind that it reflects the previous and doesn't necessarily predict the longer term. ROCE can be deceptive for cyclical groups, as returns can seem brilliant in boom times, and terribly low in downturns. ROCE is only some extent-in-time measure. Future performance is what concerns, and you can see analyst predictions in our free record on analyst forecasts for the business.

    Do HP’s latest Liabilities Skew Its ROCE?

    brief time period (or existing) liabilities, are issues like enterprise invoices, overdrafts, or tax bills that need to be paid within twelve months. due to the style ROCE is calculated, a high level of current liabilities makes a corporation seem to be as although it has much less capital employed, and for this reason can (once in a while unfairly) raise the ROCE. To counteract this, we investigate if an organization has excessive present liabilities, relative to its complete assets.

    HP has total liabilities of US$25b and total belongings of US$35b. hence its present liabilities are comparable to approximately 73% of its total assets. HP’s excessive degree of present liabilities raise the ROCE – but its ROCE continues to be astonishing.

    Our take on HP’s ROCE

    So we might be attracted to doing greater analysis here — there can be a chance! Of direction you can be able to find a higher stock than HP. so that you may also wish to see this free assortment of different groups which have grown income strongly.

    Story continues

    for those that want to locate successful investments this free listing of turning out to be companies with recent insider paying for, may be simply the ticket.

    To assist readers see past the short term volatility of the fiscal market, we intention to carry you a long-time period concentrated research evaluation in simple terms driven by means of simple statistics. be aware that our analysis does not aspect in the newest fee-sensitive enterprise bulletins.

    The creator is an impartial contributor and at the time of booklet had no place within the shares mentioned. For error that warrant correction please contact the editor at editorial-team@simplywallst.com.

    world statistics center Networking Market 2019 – trade evaluation, dimension, Share, ideas and Forecast To 2025 | killexams.com Real Questions and Pass4sure dumps

    Feb 13, 2019 (MarketersMedia via COMTEX) -- New analyze On "2019-2025 statistics middle Networking Market global Key participant, Demand, boom, alternatives and evaluation Forecast" brought to shrewd man reports Database

    Pune , India - February 13, 2019 /MarketersMedia/ --

    global data core Networking business

    New examine On "2019-2025 statistics middle Networking Market global Key participant, Demand, growth, alternatives and analysis Forecast" delivered to intelligent guy stories Database

    The information center networking companies are specializing in providing premiere-in-class dynamic network infrastructure that can support to consolidate purposes and servers and virtualizes its system resources together with improving normal efficiency, availability, and power effectivity, thereby proposing a greater bendy and dynamic assistance expertise (IT) infrastructure. modern facts core networking infrastructure answer is in response to open requisites. It optimizes efficiency and enables consolidation which in turn raises community scalability and resiliency, simplifies operations and streamlines administration along with decreasing the overall cost of possession (TCO). This solution also automates the network infrastructure administration, making latest infrastructure with ease adaptable and flexible, particularly for cloud service suppliers, for third celebration software deployments.In 2018, the global records center Networking market dimension became xx million US$ and it is anticipated to attain xx million US$ by using the end of 2025, with a CAGR of 10.eight% right through 2019-2025.

    This document focuses on the world information middle Networking reputation, future forecast, boom probability, key market and key players. The analyze pursuits are to existing the records core Networking development in united states, Europe and China.

    the key avid gamers lined in this studyAlcatel-LucentBrocadeCiscoDellEMCExtreme NetworksEquinixFujitsuHewlett Packard (HP)Hitachi facts SystemsIBMIntelJuniper NetworksMicrosoftNECVmware

    are trying sample record @ https://www.wiseguyreports.com/sample-request/3665388-global-statistics-middle-networking-market-size-popularity-and-forecast-2019-2025

    Market segment by using classification, the product can also be split intoType IType II

    Market segment through software, break up intoEnterprisesCloud provider ProvidersTelecommunication carrier suppliers

    Market phase by regions/countries, this report coversUnited StatesEuropeChinaJapanSoutheast AsiaIndiaCentral & South america

    The examine goals of this report are:to research world facts middle Networking popularity, future forecast, increase opportunity, key market and key avid gamers.To current the facts center Networking construction in united states, Europe and China.To strategically profile the important thing avid gamers and comprehensively analyze their development plan and strategies.To outline, describe and forecast the market via product type, market and key regions.

    For detailed analyzing Please talk over with WiseGuy reviews @ https://www.wiseguyreports.com/studies/3665388-global-facts-core-networking-market-size-popularity-and-forecast-2019-2025Some essential aspects from table of content:

    1 record Overview1.1 analyze Scope1.2 Key Market Segments1.three players Covered1.4 Market analysis by way of Type1.four.1 global data center Networking Market size boom price by using classification (2014-2025)1.four.2 class I1.four.3 type II1.5 Market via Application1.5.1 global statistics middle Networking Market Share through application (2014-2025)1.5.2 Enterprises1.5.three Cloud carrier Providers1.5.four Telecommunication carrier Providers1.6 analyze Objectives1.7 Years considered

    12 overseas gamers Profiles12.1 Alcatel-Lucent12.1.1 Alcatel-Lucent enterprise Details12.1.2 business Description and business Overview12.1.three information middle Networking Introduction12.1.4 Alcatel-Lucent salary in statistics center Networking company (2014-2019)12.1.5 Alcatel-Lucent fresh Development12.2 Brocade12.2.1 Brocade enterprise Details12.2.2 business Description and enterprise Overview12.2.three facts middle Networking Introduction12.2.4 Brocade income in statistics middle Networking business (2014-2019)12.2.5 Brocade fresh Development12.3 Cisco12.3.1 Cisco business Details12.3.2 business Description and enterprise Overview12.3.3 facts center Networking Introduction12.three.4 Cisco salary in records core Networking enterprise (2014-2019)12.3.5 Cisco fresh Development12.four Dell12.four.1 Dell enterprise Details12.four.2 enterprise Description and business Overview12.four.three information center Networking Introduction12.4.four Dell salary in information center Networking enterprise (2014-2019)12.4.5 Dell contemporary Development12.5 EMC12.5.1 EMC business Details12.5.2 company Description and business Overview12.5.3 records core Networking Introduction12.5.four EMC revenue in facts core Networking business (2014-2019)12.5.5 EMC recent Development12.6 extreme Networks12.6.1 intense Networks business Details12.6.2 company Description and business Overview12.6.3 records center Networking Introduction12.6.four extreme Networks revenue in records core Networking enterprise (2014-2019)12.6.5 extreme Networks recent Development12.7 Equinix12.7.1 Equinix business Details12.7.2 enterprise Description and company Overview12.7.3 data center Networking Introduction12.7.4 Equinix salary in data middle Networking enterprise (2014-2019)12.7.5 Equinix fresh Development12.eight Fujitsu12.8.1 Fujitsu business Details12.eight.2 company Description and enterprise Overview12.8.three facts core Networking Introduction12.eight.4 Fujitsu revenue in records core Networking business (2014-2019)12.8.5 Fujitsu recent Development12.9 Hewlett Packard (HP)12.9.1 Hewlett Packard (HP) company Details12.9.2 company Description and business Overview12.9.three records center Networking Introduction12.9.4 Hewlett Packard (HP) revenue in statistics core Networking enterprise (2014-2019)12.9.5 Hewlett Packard (HP) fresh Development12.10 Hitachi records Systems12.10.1 Hitachi statistics methods company Details12.10.2 company Description and enterprise Overview12.10.three information middle Networking Introduction12.10.4 Hitachi data systems earnings in data middle Networking enterprise (2014-2019)12.10.5 Hitachi information programs contemporary Development12.11 IBM12.12 Intel12.13 Juniper Networks12.14 Microsoft12.15 NEC12.16 Vmware


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    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!

    Achieving the Best QoE: Performance Libraries Accelerate Code Execution | killexams.com real questions and Pass4sure dumps

    The increasing consumerization of IT means that even staid business applications like accounting need to have the performance and ease of use of popular consumer apps. For many organizations, performance has taken a back burner to functionality. Fortunately, developers now have access to a powerful group of libraries that can instantly increase application performance – with little or no rewriting of older code. You can download them and start benefitting from this acceleration today.

    Here’s a quick rundown of these Intel-provided libraries and how to get them. They’re offered at no cost as part of Intel’s mission to support innovation and impressive performance on Intel® architecture.

    Intel® Math Kernel Library (Intel® MKL) This popular library offers optimized, threaded, and vectorized functions to maximize performance on each processor family. It works with your choice of compilers, languages, operating systems, and linking and threading models to speed applications and cut development time. You’ll find algebra, FFTs, vector statistics & data fitting, vector math, and other miscellaneous solvers, and since it uses industry-standard C and Fortran APIs for compatibility with popular BLAS, LAPACK, and FFTW functions, no code changes are required.

    Intel® Integrated Performance Primitives (Intel® IPP) If you’re working with images, signals, or cryptography, this library is a must, speeding performance for imaging, vision, signal, security, and storage applications on multiple OSs and platforms. Intel® IPP is a one-stop shop for ready-to-use, royalty-free APIs that work in multicore, multiple OS, and multiplatform environments where computation speed and optimization can make the difference between project success and failure. Optimized using Intel® Streaming SIMD Extensions, Intel® Advanced Vector Extensions 2, and Intel® Advanced Vector Extensions 512, use IPP to have your applications perform faster than optimized compilers alone.

    Intel® Threading Building Blocks (Intel® TBB) Want to get the most out of every core and processor? This widely used C++ library is designed for shared-memory parallel programming and heterogeneous computing, offering advanced threading for fast, scalable parallel applications. TBB simplifies code by letting you specify logical parallelism instead of threads, by targeting threading for performance, and it’s compatible with other threading packages so you can leave your legacy code alone while using TBB for new implementations.

    Intel® Data Analytics Acceleration Library (Intel® DAAL) Big data, machine learning, and data science all have one thing in common: a need for reliable, fast analytics whether for business or scientific coding. This easy-to-use performance library features highly tuned functions for analytics performance across the spectrum of Intel® architecture devices. It helps apps deliver better predictions, faster. DAAL lets you analyze larger data sets with the same compute resources, and it optimizes data ingestion and algorithmic compute together for the highest performance achievable during all stages of the data analytics pipeline: preprocessing, transformation, analysis, modeling, validation, and decision-making.

    Intel® MPI Library If you have applications running on Intel® architecture-based clusters, this library will improve performance by delivering flexible, efficient, and scalable cluster messaging. It is a multi-fabric message-passing library that implements the open-source MPICH spec. Use it to create, maintain, and test advanced, complex applications that perform better on HPC clusters based on Intel® processors. The MPI library delivers maximum end-user performance without having to change the software or operating environment by achieving the best latency, bandwidth, and scalability through automatic tuning for the latest Intel® platforms.

    All of these libraries are available as part of the Intel® Parallel Studio XE and Intel® System Studio tool suites, and they’re all free to use.

    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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