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1T6-521 exam Dumps Source : Application Performance Analysis and Troubleshooting

Test Code : 1T6-521
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Network-General Network-General Application Performance Analysis

network favorite broadcasts Sniffer allotted four.6; most recent liberate of commercial enterprise community Instrumentation Enhances software evaluation Capabilities in high efficiency Networks | killexams.com real Questions and Pass4sure dumps

SAN JOSE, Calif.--(enterprise WIRE)--July 18, 2005--community usual(TM) agency, the global chief in business community and utility performance evaluation, nowadays announced the commonplace availability of Sniffer(R) distributed 4.6 for the s4000 collection and s6040 platforms. The latest release of Sniffer distributed gives not obligatory application-layer visibility inside a Gigabit community environment.

The vogue toward information headquarters consolidation and the expanding deployment of bandwidth-intensive applications are riding the claim for stronger efficiency in the core of the enterprise. due to this fact, IT organizations are impulsively deploying Gigabit know-how in the core to exist unavoidable ample throughput and response time for users throughout the business. This creates a necessity for network instrumentation that can deliver both network-layer and utility-layer visibility and evaluation of any essential site visitors within the Gigibit core community. network generic is assembly this starting to exist market claim with the brand modern Sniffer distributed solution.

"we're cozy to peer the endured enhancements that network frequent is making to the Sniffer disbursed platform," stated candid Lohse, community administrator at prosystems IT GmbH, in Bonn, Germany. "community time-honored recognizes that a transforming into variety of IT agencies -- including their personal -- deserve to maintain utility-layer visibility in a high-pace core network the Place Gigabit Ethernet is deployed. The modern version four.6 software will give that stage of visibility."

With the start of Sniffer dispensed 4.6, network typical continues to exhibit its leadership in network and application efficiency evaluation. in addition to presenting utility-layer visibility in a Gigabit ambiance, Sniffer distributed four.6 too offers a few different modern aspects, together with:

-- Filtering enhancements for managing MPLS (Multi-Protocol Label Switching) traffic flows at Gigabit speeds

-- The s4100 E2GB and improved s6040 systems, which deliver "hardware-assisted" Gigabit Ethernet usher designed for high-pace, high-density environments corresponding to those on the community core

-- potent management utility that enables users to manipulate any Sniffer systems from a separate console

-- improved assist for Modular Decode and professional Packs, obtainable to valued clientele with lively abet contracts

Deployed in hundreds of agencies worldwide, Sniffer disbursed combines extensive protocol decodes, award-winning knowledgeable analysis expertise, and short- and lengthy-time age reporting into an impressive, modular and in your price compass solution. These capabilities permit IT gurus to at once determine, video display, measure, manipulate, and win to the bottom of performance problems and bottlenecks -- before they maintain an result on the efficiency of the enterprise. due to this fact, Sniffer dispensed helps to empower IT organizations, simplify network management tasks, and offer protection to the extended value and life of IT property.

About community ordinary corporation

community widely wide-spread(TM) business enterprise, headquartered in San Jose, California, is the international chief in enterprise network and application efficiency evaluation. The network regular portfolio carries resourceful utility options and intelligent home equipment, deployed on crucial networks links throughout the disbursed business. together, these solutions provide IT gurus with a unified view of community and application efficiency and enable swift identification and resolution of efficiency issues. For more tips on community typical, dial 408-571-5000 or fade to www.networkgeneral.com.

be aware: network prevalent, Sniffer, and the network regularly occurring emblem are registered emblems or logos of community established organization and/or its affiliates in the united states and/or different international locations. only network widely wide-spread business enterprise makes Sniffer(R) company items. any other registered and unregistered logos herein are the sole property of their respective owners. (C)2005 community well-known agency. any RIGHTS RESERVED.


Sniffer updates nothing to sneeze at | killexams.com real Questions and Pass4sure dumps

Andrew R. Hickey, advice creator

still reeling from its messy breakup with application maker McAfee Inc., network prevalent Corp. hopes modern developments to its Sniffer items proves as soon as and for any that the San Jose, Calif.-based mostly dealer is lower back.

community touchstone announced a yoke of modern updates to its commercial enterprise community administration and efficiency evaluation platform this week on the Sniffer person zenith in Miami.

leading the can permeate is a modern incarnation of the enterprise's statistics trap tool, the InfiniStream. edition 2.5 gives real-time, circulate-to-disk trap of Gigabit and 10/100 Ethernet, WAN and Asynchronous switch Mode (ATM) statistics. It too uses Sniffer decodes and expert evaluation. mixed, the capabilities deliver what network everyday calls "Retrospective evaluation," returned-in-time and real-time network and application evaluation performed by recording and examining site visitors over a few hours, days or weeks. The InfiniStream 2.5 stores up to four terabytes of facts.

The InfiniStream finds the foundation reasons of network performance problems and makes it feasible for for troubleshooting on each topology, together with VoIP, spoke of Tom Bienkowski, network widely wide-spread's director of product management. It helps directors remedy efficiency complications extra straight away with out interruption, Bienkowski mentioned. InfiniStream 2.5 too integrates with different Sniffer tools, which enhances vogue analysis and capacity planning-primarily based reporting.

along with the InfiniStream replace, network habitual the day past additionally brought the Sniffer business Administrator 4.0, which makes it feasible for centralized management, administration and security of Sniffer resources. It automates basic processes and permits IT administrators to configure lots of of alternatives on one Sniffer agent and then clone the configuration throughout other agents any over the commercial enterprise. When mixed with the device's capacity to automatically down load the working gadget and Sniffer updates and patches, the device ensures security and coverage compliance, Bienkowski spoke of.

The Sniffer business Administrator offers a single-console view throughout multiple home equipment, allowing separate sign-on and administration of any Sniffer contraptions from one console, cutting down on time and doing away with the necessity for directors to bodily entry the Sniffer gadgets.

other bulletins made the day before today with the aid of network gardenvariety include:

  • The Sniffer MultiSegment Intelligence four.0, a device that detects inefficiencies on as much as eight network segments as site visitors flows between community facets. It lets administrators locate the Place a problem is and examine whether it's a network or application flaw.
  • The Sniffer business Visualizer four.0, which offers visibility into the health and availability of purposes, LAN and WAN site visitors, and allows quick tuning of functions. It presents vogue reviews with self assurance scores and gives in-depth, conclusion-to-end visibility into clients, applications and subnets.
  • The Sniffer InfiniStream i120, a line fee, circulation-to-disk implement that monitors, measures, manages and resolves community complications on the network's portion or in little deployments. It shops as much as 300 GB of records that network managers can consume to dissect network efficiency and trends.
  • Robert Whiteley, analyst with Cambridge, Mass.-based Forrester research Inc., referred to network conventional's movement to consist of utility performance analysis with its community and protocol evaluation line will abet for more desirable conclusion-to-end administration of the complete community.

    "It ultimately makes it feasible for the finger-pointing to cease," he stated. "Now they can definitively publish no matter if it exist an application or network issue. here is a suited characteristic when any is said and accomplished."

    Whiteley spoke of bringing community analysis capabilities into the software house is a "natural extension" for network ordinary.

    "or not it's a administration interface that plugs into a sort of supervisor of managers very seamlessly," he observed.

    Whiteley added: "Kudos to network prevalent for waking up … they're coming again around and saying, 'Let's exit and result positive not one of the little guys birth to snip away at us.'"


    equipped for VoIP: community management Architectures: ClearSight Networks | killexams.com real Questions and Pass4sure dumps

    WEBINAR:On-Demand

    EUC with HCI: Why It concerns

    ClearSight Networks, Inc., headquartered in Fremont, Calif., is a company of network monitoring and analysis tools for actual-time software troubleshooting.

    The business become headquartered in early 2001 by using former employees of community typical and founders of Cinco Networks. (Cinco was got via network typical in 1997.) in the past referred to as AppDancer Networks, ClearSight Networks acquired a majority investment in April of 2003 from Japan-based mostly Toyo, and became then positioned beneath modern management.

    ClearSight claims to exist the best enterprise that identifies community issues on the utility layer, enabling IT directors to effectively and automatically visualize and pinpoint the source of community complications, leading to quicker conclusion that ensures enterprise continuity.

    The company is privately held, and has about 60 employees. Its items are offered worldwide through an immediate and reseller channel.

    The ClearSight Analyzer product line consist of the ClearSight Analyzers, the ClearSight disbursed far flung monitoring device, and the network Time laptop, a information seize and evaluation system.

    The ClearSight Analyzer is an advanced network monitoring and troubleshooting implement that illustrates community undertaking by artery of an easy, intuitive person interface. This analyzer comprises aid for triple-play techniques, including IPTV.

    The ClearSight Analyzer Gigabit and Clearsight Analyzer 10G assist networks of up to 10 Gigabit Ethernet ability. The systems abet the evaluation of numerous signaling protocols including SIP, H.323, Cisco SCCP, MGCP, MEGACO, and RTSP.

    The VoIP protocol decodes supported encompass: H.323, SIP, MGCP, MEGACO or H.248, SCCP ("Skinny"), RTSP, MSN, SKYPE and RTP/RTCP. In total, the Clearsight Analyzer supports very nearly 1,000 protocol decodes, which the company claims is among the most wide lists within the trade.

    probably the most potent fits of the ClearSight Analyzer is its capacity to supply a ladder screen of the communique sequences; denomination reports, alarms, and packet decode functions (see motif 1).

    it will probably too generate a QoS document for a VoIP call easily through making a selection on the favored denomination and clicking the 'file' tab (see motif 2). The QoS record comprises evaluation of voice attribute parameters (together with packet loss, jitter, R-price, and MOS) mentioned as VQFactor™ and MDI for a video stream.

    The system too contains a VoIP denomination Log Browser, that could extract call history (delivery time, caller, callee, MOS, and period) from the VoIP denomination log suggestions kept in ClearSight Analyzer's history database in keeping with divide circumstances, and checklist the effects in this file. Time range, call period, and MOS cost can too exist combined as extraction conditions. The extracted denomination log can then exist exported in CSV (comma-separated values) structure, and can too exist compiled in a divide spreadsheet software (see determine 3).

    The analyzer additionally measures audio or video pleasant for each RTP stream and shows designated statistical information because of this. MOS and R-value are supported as QoS metrics for voice; VQFactor (a video first-rate metric developed with ClearSight's patent pending algorithm) and the Media start Index (MDI-a trademark for video transport performance) are supported for video (see determine four).

    ClearSight Analyzer too helps the performance evaluation and troubleshooting of video streams in IPTV environments. proposing an IPTV channel zapping evaluation graph, the analyzer categorizes multicast streams under IPTV environments into every channel and graphically expose alterations within the packet expense (see determine 5).

    ClearSight dispensed is a distributed device with a next-generation utility circulation analyzer. the usage of the germane Agent supervisor, it allows for remote network monitoring, software performance evaluation, and troubleshooting for multiple LAN segments that are physically separated from one a different. Monitoring and evaluation of network traffic is finished inside a far flung Agent, which minimizes the management site visitors overhead between the supervisor and probe. The Agent detects malfunctions on the community and generates alarms, that may comprehend lots of moves, reminiscent of sending an e-mail, web page, or SNMP tempt message, or the execution of a Java script.

    The ClearSight Packet Generator is a software application for transmitting site visitors via 10/100Mb Ethernet from a home windows pc. The application helps separate packet mode for again and again sending the identical packet, and buffer mode for regenerating traffic captured from the specific network. The Packet Generator can exist used at the side of ClearSight Analyzer to check transmission of packets across a community any over community setting up. The number of packets and transmission rate can exist adjusted from Packet Generator and the received packets monitored with the aid of ClearSight Analyzer after passing throughout the community and community contraptions.

    further particulars on the Clearsight Networks architecture and items can too exist discovered at www.clearsightnet.com. Their subsequent tutorial will continue their examination of providers' network management architectures.

    writer's BiographyMark A. Miller, P.E., is President of DigiNet organization, a Denver-based mostly consulting engineering company. he's the creator of many books on networking technologies, including Voice over IP technologies, and web applied sciences instruction manual, each posted with the aid of John Wiley & Sons.

    Article courtesy of business VoIP Planet, © 2008 DigiNet enterprise, any Rights Reserved

    Mark A. Miller, P.E. is President of DigiNetB. service provider, a Denver-based consulting engineering company. he's the creator of many books on networking applied sciences, including Voice over IP applied sciences, and cyber web technologies instruction manual, both published by using John Wiley & Sons.




    Killexams.com 1T6-521 Dumps and real Questions

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    1T6-521 exam Dumps Source : Application Performance Analysis and Troubleshooting

    Test Code : 1T6-521
    Test denomination : Application Performance Analysis and Troubleshooting
    Vendor denomination : Network-General
    : 120 real Questions

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    Application Performance Analysis and Troubleshooting

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    Spark Application Performance Monitoring using Uber JVM Profiler, InfluxDB and Grafana | killexams.com real questions and Pass4sure dumps

    Key Takeaways
  • Running spark applications continuously & reliably is a challenging task and a suited performance monitoring system is needed.
  • The performance monitoring system designed with three objectives - collect server and application metrics, store metrics in a time-series database and provide a dashboard for data visualization.
  • Uber JVM Profiler is used to monitor Spark application. Other technologies used are InfluxDB for storing time-series data and Grafana data visualization tool.
  • The performance monitoring system helps DevOps teams to efficiently monitor the system to meet compliance and service plane agreements of an application.
  • Many industries are using Apache Spark for edifice colossal data processing applications. Spark provides implicit data parallelism and fault-tolerance for this sort of application. The application could exist a stream processing, batch processing, SQL dataset processing or machine learning application. Spark’s fast, in-memory data processing engine runs these applications in a cluster and transforms & processes large amount of data in data pipelines. Running these applications continuously & reliably is a challenging task, and a suited performance monitoring system is needed. As Spark is being widely adopted by industries, performance monitoring, metric analysis and tuning the issues of Spark applications are getting more attention. Uber has recently open sourced their JVM Profiler for Spark. In this article they will discuss how they can extend Uber JVM Profiler and consume it with InfluxDB and Grafana for monitoring and reporting the performance metrics of a Spark application.

    Spark Application Performance Monitoring System

    A performance monitoring system is needed for optimal utilisation of available resources and early detection of feasible issues. It should provide comprehensive status reports of running systems and should dispatch alerts on component failure. When they talk of large-scale distributed systems running in a Spark cluster along with different components of Hadoop reflect system, the necessity for a fine-grained performance monitoring system becomes predominant. The Spark application performs distributed processing of data and works on shared resources which makes the job of the DevOps team very complex. The DevOps team has to manage the available resources effectively and monitor different components of the system closely to avoid any downtime. The complete stack visibility provided by an efficient performance monitoring system helps theDevOps team to understand the system behaviour and to react quickly on the production issues. This ensures reliability, scalability and performance of the Spark application.

    The example performance monitoring system for such a complex system must maintain the following features:

  • The monitoring system should provide granular visibility about each component inside cluster. They should able to win detailed metrics about CPU, Memory, Storage, Disk I/O for local file and HDFS, stack traces etc. These metrics abet to quickly troubleshoot the faulty instances.
  • The monitoring system should provide code plane metrics for applications (e.g. execution time, arguments used by different methods) running on spark. This will abet to identify behind running methods, disk hot-spotting etc.
  • The monitoring system should store metrics for every second and allow us to analyse metrics by navigating across different time periods. They should able to achieve second and sub second plane dissection of these data. They should able to control the data retention age and easily access and analyse the past data when needed. This helps in analysing the current trend and predicting the future trend.
  • The monitoring system should provide an efficient artery of extracting meaningful information from a large volume of metrics being collected continuously. This includes querying of data using SQL or API, filtering of data, aggregation of values and applying custom analysis. This helps in smooth transformation and faster analysis of data.
  • The monitoring system should provide smooth access of information deduced from the analysis of metric data. There should exist the option to display data in dashboard using different forms affection charts, graphs tables, etc. There should exist the option to categorise the data based on host, time or job, and users should exist able to further drill them down to corelate the different data points. There should exist a provision for configuring alerts and notifications for user defined thresholds on metrics data. This helps DevOps teams and other stockholders of an organisation to win the desired set of information quickly when it is needed.
  • In this article, we’ll develop a performance monitoring system using opensource tools & technologies. The spark application performance monitoring system is designed with three objectives:

  • Collect the performance metrics of system (driver & executors) and application code
  • Store these metrics in durable storage for timeseries analysis (batch & live)
  • Generate reports from metrics data in the profile of graphs and charts
  • Apache Spark provides a web-ui and leisure API for metrics. Spark too provides a variety of sinks including Consoles, JMX, Servlet, Graphite etc. There are few other open source performance monitoring tools available affection dr-elephant, sparklint, prometheus, etc. Metrics provided by these tools are mostly server plane metrics, and few of them too provide information of running applications.

    Uber JVM Profiler collects both server plane and application code metrics. This profiler can collect any metrics (cpu, memory, buffer-pool etc) from the driver, executor or any JVM. It can instrument existing code without modifying it, so it can collect metrics about methods, arguments and execution time. For storing metrics for timeseries analysis, they will consume InfluxDB, which is a powerful timeseries database. They will extend Uber JVM Profiler and add a modern reporter for InfluxDB so metrics data can exist stored using HTTP API. For the dashboard of graphs and charts they will consume Grafana, which will query the InfluxDB for metrics data.

    Below are the details about tools/technologies used for the spark application performance monitoring system.

    Uber JVM Profiler   

    Uber JVM Profiler is a distributed profiler which collects performance metrics and resource utilisation metrics from different nodes across the cluster. This JVM Profiler runs as a Java Agent along with the application and collects the different metrics. It publishes these metrics to a designated reporter for further analysis and reporting. Uber JVM Profiler was developed for profiling spark applications, but this can exist used for profiling any JVM based application. There are three main components of Uber JVM Profiler:

  • Profiler: Uber JVM Profiler comes with the following built in profilers:
  • CpuAndMemory Profiler – This profiler collects Buffer Pool (direct & mapped), Garbage Collection (count & time), pile memory (Committed & TotalUsed), Non- pile memory (committed & total used), CPU (load & time), memory Pool details (EdenSpace, SurvivorSpace, TenuredGen,  CodeCache, CompressedClassSpace, Metaspace), vmHWM and vmRSS metrics.
  • IO Profiler – IO profiler collects CPU stat (idle, nice, system, user, iowait) and Disk io read/write bytes.
  • Stacktrace Profiler – This profiler collects Thread Name, Thread status and Stack vestige metrics.
  • ProcessInfo Profiler – This profiler collects Agent Version, JVM Class Path, JVM Input Arguments and xmxBytes metrics.
  • MethodDuration Profiler – This profiler collects metrics for the execution time of a fashion which includes Class Name, fashion denomination and Process Name.
  • MethodArgument Profiler – This profiler collects metrics for fashion controversy which includes Class Name, fashion denomination and Process Name.
  • Transformer: This is a Class File Transformer to instrument java fashion bytecodes. This transformer is used to instrument methods from MethodDuration and MethodArgument profilers.
  • Reporter: The following reporters are available:
  • KafkaOutputReporter – To dispatch performance metrics to Kafka topic.
  • FileOutputReporter – To write metrics in a file.
  • ConsoleOutputReporter – Sends metrics to console.
  • RedisOutputReporter – Store metrics to Redis database.
  • For more details about JVM profiler gratify advert to Uber’s blog post.

    InfluxDB and Grafana

    InfluxDB: InfluxDB is an open source time progression database which is designed for efficiently storing and querying a large amount of time-stamped data. This data could exist IoT sensor data, real-time analytics data, application metrics data or dev-ops monitoring data. It provides automatic data lifecycle management by expiring & deleting obsolete data. It provides write and query capabilities using SQL-like query language, HTTP API and client libraries. Check out the details here.

    Grafana: Grafana is an open source metric dashboard and graph editor. Grafana too supports alerts and notifications for metrics. It supports many data sources affection Graphite, InfluxDB, OpenTSDB, Prometheus, Elasticsearch and CloudWatch. Many Dashboard and Plugins (both opensource and commercial) are available at Grafana’s website. For more details about Grafana visit the website.

    System Architecture

    Spark application runs on cluster network which may consist of a few nodes to thousands of nodes. For collecting metrics from this distributed system and sending the metrics to other systems for further analysis, they necessity a loosely coupled and foible tolerant architecture. Publishing the metrics to Kafka topic is one of the best solutions. Uber JVM profiler comes with a “KafkaOutputReporter” which can exist used for this purpose. Another solution approach for storing and reading the metrics is by using the InfluxDB. InfluxDB provides HTTP API which can exist used to query and write to database. This API supports both basic and JWT token authentication and URLS can exist configured for HTTPS access. “InfluxDBOutputReporter” that they are discussing in this article invokes Write HTTP Endpoint to store the metrics collected by different profilers. Grafana provides a wealthy data source plugin for InfluxDB. Grafana uses Query HTTP Endpoint to fetch metrics data from InfluxDB  and it displays the data on the dashboard in the profile of wealthy graphs and tables. These graphs and tables are auto refreshed at fixed intervals, and time intervals can exist configured in Grafana settings.

    The architecture diagram of the performance monitoring system for spark application using Uber JVM Profiler, InfluxDB and Grafana is illustrated in motif 1 below.

    Figure 1. Performance Monitoring System Architecture Diagram

    Technologies and Tools

    The following Table shows the technologies and tools used for a performance monitoring system.

    Please advert to the documentation for installing and configuring these tools.

    Design and Implementation

    The following sections provide design and implementation details for a performance monitoring system for spark application. Uber JVM profiler collects metrics from driver and executors and these metrics maintain detailed information affection role (driver/executor), processUuid and host. These details are useful for identifying the different systems and analysing their metrics. In InfluxDB they can consume these details to query performance metrics of different executors. They can add processUuid in InfluxDb tags, so query performance will exist high. First, they will create “metrics” database in InfluxDB, then they will add an “InfluxDBOutputReporter” in JVM Profiler code base, and finally they will configure the Grafana dashboard and graphs.

    Create Metrics Database in InfluxDB
  • Start the InfluxDB server, which by default runs on 8086 port. For example, in Ubuntu system open a terminal and execute “influxd” command.
  • /user/bin$ sudo influxd

  • Once the server is up and running, start “influx” utility in another terminal. And then execute command to create “metrics” database.
  • /user/bin$ sudo influx            

    CREATE DATABASE metrics

    Implement InfluxDBOutputReporter

    We will implement “InfluxDBOutputReporter” in JVM-Profile code base. advert to the InfluxDBOutputReporter.java file at “influxdb_reporter” arm at GitHub for the implementation details discussed in this section.

  • We will consume influxdb-java library to interact with InfluxDB database server. It uses HTTP API to read and write in the InfluxDB database. Update the pom.xml file and add maven dependency for influxdb-java library.
  • Define host, port and database properties for InfluxDB database server in InfluxDBOutputReporter class. Default values for these properties are “127.0.0.1”, “8086” and “metrics”, respectively. Create connection to InfluxDB using API provided by influxdb-java library.
  • InfluxDBOutputReporter class needs to implement com.uber.profiling.Reporter interface. They necessity to override public void report(String profilerName, Map<String, Object> metrics) fashion and public void close() method. The “profilerName” will exist the measurement denomination in “metrics” database.
  • As InfluxDB uses Line Protocol to store key=value pairs, they necessity to process metrics data available in Map<String, Object> sent by Profiler and transfigure them to a profile so that it can exist stored in a Point of separate series. They can consume the value of “name” property from metrics, if it is available, to profile the denomination of field. If “name” property is not available, then they can consume a counter value. InfluxDB supports regular expression for realm denomination in query, so querying these fields won’t exist an issue.
  • Create Point and Batchpoint using influxdb-java library APIs and write the Batchpoints to InfuxDB database.
  • For reading database connection properties from the yaml file they maintain to consume com.uber.profiling.YamlConfigProvider and com.uber.profiling.Argument classes. Add a reference of InfluxDBOutputReporter class in controversy class and invoke setProperties fashion to pass properties coming from yaml file. A sample yaml file for influxdb is available at resources/influxdb.yaml at GitHub.
  • Add Data Source and Dashboard in Grafana

    This section provides brief details on the steps needed for adding a graph for metric data from InfluxDB in Grafana. gratify check on the Grafana documents for in-depth details:

  • Start the Grafana server. On Ubuntu they can execute the below command. By default, it runs on port number 3000.
  • sudo service grafana-server start

  • Open http://localhost:3000/ in a browser and create data source for InfluxDB. Add denomination as “InfluxDBDataSource”, sort as “InfluxDB”, default URL for InfluxDB is “http://localhost:8086” and Database denomination will exist “metrics”.
  • Create a modern dashboard by clicking on Graph, click Edit and add query for graph. Below is a sample query for InfluxDB database.
  • select "heapMemoryCommitted" as Committed, "heapMemoryTotalUsed" as Used from "metrics"."autogen"."CpuAndMemory” where “role" = 'driver' AND time > now() – 5m
  • Grafana provides the option of defining template variables which can exist passed in query. This will exist useful for displaying data from multiple executors on dashboard. For example, they can create variables for “executorProcessUuid” & “timeInterval” and consume them in query as below.
  • select "heapMemoryCommitted" as Committed, "heapMemoryTotalUsed" as Used from "metrics"."autogen"."CpuAndMemory” where "processUuid" =~ /^$executorProcessUuid$/ AND time > now() - $timeInterval

    A sample Spark-Metrics-Dashboard JSON file has been provided at GitHub. This file can exist imported at Grafana server. Open the URL http://localhost:3000/dashboard/import in a browser and click on “Upload.json File”.

    Build and Deploy

    This section describes the steps to build and deploy a performance monitoring system. Application code can exist cloned from the “influxdb_reporter” arm at GitHub.

  • Use the below command to build the JVM profiler with “InfluxDBOutputReporter”.
  • mvn clean package

  • Copy the JVM Profiler-0.0.9.jar file created by maven build to some directory. (e.g /opt/profiler). They can preserve influxdb.yaml file as well in this directory.
  • We will consume the existing JavaNetworkWordCount application for profiling which is shipped with Apache Spark. Source code is available at /spark-2.3.1-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/streaming.
  • To rush JavaNetworkWordCount application, they necessity to rush Netcat server using the below command.
  • nc -lk 9999

  • Go to /spark-2.3.1-bin-hadoop2.7/sbin and start the Master using the below command.
  • ./start-master.sh

  • We can win the master URL from log file. Pass this URL to command to start the Worker.
  • ./start-slave.sh -c 2 spark://192.168.1.6:7077

  • Go to /spark-2.3.1-bin-hadoop2.7/bin and execute the below command. This command will execute JavaNetworkWordCount application and start the JVM profilers for driver and executor. Check the Uber JVM profiler GitHub readme page for details about the parameters.
  • spark-submit --master spark://192.168.1.6:7077 --conf "spark.driver.extraJavaOptions=-javaagent:/opt/profiler/jvm-profiler-0.0.9.jar=reporter=com.uber.profiling.reporters.InfluxDBOutputReporter,metricInterval=5000,sampleInterval=5000,ioProfiling=true" --conf "spark.executor.extraJavaOptions=-javaagent:/opt/profiler/jvm-profiler-0.0.9.jar=reporter=com.uber.profiling.reporters.InfluxDBOutputReporter,tag=influxdb,configProvider=com.uber.profiling.YamlConfigProvider,configFile=/opt/profiler/influxdb.yaml,metricInterval=5000,sampleInterval=5000,ioProfiling=true" --class org.apache.spark.examples.streaming.JavaSqlNetworkWordCount ../examples/jars/spark-examples_2.11-2.3.1.jar localhost 9999
  • Optionally, they can rush the application with yaml file. Pass “configProvider” and “configFile” parameters in command as below.
  • spark-submit --master spark://192.168.1.6:7077 --conf "spark.driver.extraJavaOptions=-javaagent:/opt/profiler/jvm-profiler-0.0.9.jar=reporter=com.uber.profiling.reporters.InfluxDBOutputReporter,configProvider=com.uber.profiling.YamlConfigProvider,configFile=/opt/profiler/influxdb.yaml,metricInterval=5000,sampleInterval=5000,ioProfiling=true" --conf "spark.executor.extraJavaOptions=-javaagent:/opt/profiler/jvm-profiler-0.0.9.jar=reporter=com.uber.profiling.reporters.InfluxDBOutputReporter,tag=influxdb,configProvider=com.uber.profiling.YamlConfigProvider,configFile=/opt/profiler/influxdb.yaml,metricInterval=5000,sampleInterval=5000,ioProfiling=true" --class org.apache.spark.examples.streaming.JavaSqlNetworkWordCount ../examples/jars/spark-examples_2.11-2.3.1.jar localhost 9999
  • Go to “influx” terminal and execute the below commands.
  • use metrics

    show measurements

  • We will win the denomination of “Measurements” created in “metrics” database as below.
  • CpuAndMemory

    IO

    ProcessInfo

    Stacktrace

    We can check the data populated in these Measurements. For example, they consume the below command to fetch a separate record from CpuAndMemory measurement.

    select * from CpuAndMemory restrict 1

  • Go to Grafana dashboard for spark. Graphs for metrics should exist populated with data. Below is the sample dashboard.
  • Figure 2. Sample Grafana dashboard for spark metrics

    Pros and Cons

    The performance monitoring system they discussed in this article collects different system and application metrics using Uber JVM Profiler and stores them in the InfluxDB time-series database. The time-series database provides data retention policies, continuous queries, flexible time aggregations and efficient handling of millions of records both for live and batch processing. These timeseries data helps us to analyse how system changed in the past, how the system is behaving in the present and foretell how it may change in the future. They can identify the patterns of failures by correlating these metrics collected over time. They created a dashboard using Grafana which helps us in smooth access and navigation across different types of metrics. The DevOps team can consume these graphs and charts to corelate the different metrics to understand the system behaviour and can identify hotspots in the data. This helps in maintaining the compliance and achieving the service plane agreements of an application. Overall this performance monitoring system helps in achieving the Continuous Monitoring of the system.

    The limitations of this performance monitoring system are in line with the limitations they maintain for the agent-based profiler. The agent-based profiler may consume a unavoidable amount of computing resources. Sometimes it may require troubleshooting and patching, which might exist difficult for large geographically distributed system. You might necessity to complete the paper toil before installing the agents on the production system. They too maintain to consider the security, scalability and availability of the performance monitoring system, i.e. monitor the monitoring system. Most of these challenges can exist overcome by proper designing and tuning of the application and system. If you are not allowed to install profiler agents on a production system, then you can consider anagent-less system, but it has its own limitations, such as less granular metrics and increased network overload.

    Summary

    For complex spark applications identifying, debugging and resolving, the production issues are not smooth tasks and they necessity an efficient performance monitoring system which can assist us with this. Uber JVM Profiler is a distinguished addition to the open source community. This JVM Profiler can exist extended and a modern reporter for publishing metrics can exist added. Performance metrics for spark applications collected by different profilers can exist stored in InfluxDB. The “InfluxDBOutputReporter” that they discussed in this article uses HTTP API to write metrics for spark drivers and executors to InfluxDB. Grafana provides a plugin for InfluxDB and uses HTTP API for querying the metrics. They can create a dashboard with charts, tables and graphs for these metrics, which can exist auto refreshed in fixed intervals. Code for “InfluxDBOutputReporter” is available here, and Spark-InfluxDB-Grafana.json file is available here.

    References About The Author

    Amit Baghel is a software architect with over 17 years of undergo in design and evolution of enterprise applications and products around Java ecosystem. His current focus is on IoT, Cloud Computing, colossal Data Solutions, Micro Services, DevOps and Continuous Integration and Delivery. Baghel  can exist reached via e-mail.


    High Performance Computing Market Segmented by Product , by Applications and Geography, Analysis of Growth, Trends and Forecasts (2018-2025) | killexams.com real questions and Pass4sure dumps

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    Enterprise high Performance Computing 2018 Market Segmentation,Application,Technology & Market Analysis Research Report to 2023 | killexams.com real questions and Pass4sure dumps

    "Enterprise high Performance Computing Market"

    WiseGuyReports.Com Publish a modern Market Research Report On –“ Enterprise high Performance Computing 2018 Market Segmentation,Application,Technology & Market Analysis Research Report to 2023”.

    Description:-

    Over the past two decades, enterprises maintain realized the value of using clusters of computers to resolve complex mathematical, computational, and simulation/modeling problems. By addressing these massive problems using parallel computing techniques (allowing the problem to exist split into parts that can exist tackled by individual or groups of processors), the time to complete a solution can exist drastically reduced.

    Scope of the Report:

    The market is currently dominated by HPC rig utilized for traditional consume cases, or situations in which an HPC system is used for heavy-duty number crunching, simulation, and analysis, techniques that require the brute force of cluster computing to reduce the time to complete complex calculations. In these cases, HPC is not used to back any ersatz intelligence (AI)-related techniques, such as machine learning, profound learning, or natural language processing.

    The global Enterprise high Performance Computing market is valued at xx million USD in 2017 and is expected to reach xx million USD by the quit of 2023, growing at a CAGR of xx% between 2017 and 2023.

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    The Asia-Pacific will occupy for more market share in following years, especially in China, too snappily growing India and Southeast Asia regions.

    North America, especially The United States, will soundless play an principal role which cannot exist ignored. Any changes from United States might impress the evolution trend of Enterprise high Performance Computing.

    Europe too play principal roles in global market, with market size of xx million USD in 2017 and will exist xx million USD in 2023, with a CAGR of xx%.

    This report studies the Enterprise high Performance Computing market status and outlook of Global and major regions, from angles of players, countries, product types and quit industries; this report analyzes the top players in global market, and splits the Enterprise high Performance Computing market by product sort and applications/end industries.

    Market Segment by Companies, this report covers

    Amazon

    Bright Computing

    Cray

    Dell EMC

    Ephesoft

    Hewlett Packard Enterprise

    IBM

    Lenovo

    Mellanox Technologies

    Microsoft

    Panasas

    Pure Storage

    Verne Global

    Market Segment by Regions, regional analysis covers

    North America (United States, Canada and Mexico)

    Europe (Germany, France, UK, Russia and Italy)

    Asia-Pacific (China, Japan, Korea, India and Southeast Asia)

    South America (Brazil, Argentina, Colombia)

    Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)

    Market Segment by Type, covers

    On-Premise

    Cloud Services

    Market Segment by Applications, can exist divided into

    Automotive

    Manufacturing

    Financial Services

    Life Sciences

    Oil and Gas

    Others

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    Table Of Contents – Major Key Points

    1 Enterprise high Performance Computing Market Overview

    1.1 Product Overview and Scope of Enterprise high Performance Computing

    1.2 Classification of Enterprise high Performance Computing by Types

    1.2.1 Global Enterprise high Performance Computing Revenue Comparison by Types (2017-2023)

    1.2.2 Global Enterprise high Performance Computing Revenue Market share by Types in 2017

    1.2.3 On-Premise

    1.2.4 Cloud Services

    1.3 Global Enterprise high Performance Computing Market by Application

    1.3.1 Global Enterprise high Performance Computing Market Size and Market share Comparison by Applications (2013-2023)

    1.3.2 Automotive

    1.3.3 Manufacturing

    1.3.4 pecuniary Services

    1.3.5 Life Sciences

    1.3.6 Oil and Gas

    1.3.7 Others

    1.4 Global Enterprise high Performance Computing Market by Regions

    1.4.1 Global Enterprise high Performance Computing Market Size (Million USD) Comparison by Regions (2013-2023)

    1.4.1 North America (USA, Canada and Mexico) Enterprise high Performance Computing Status and Prospect (2013-2023)

    1.4.2 Europe (Germany, France, UK, Russia and Italy) Enterprise high Performance Computing Status and Prospect (2013-2023)

    1.4.3 Asia-Pacific (China, Japan, Korea, India and Southeast Asia) Enterprise high Performance Computing Status and Prospect (2013-2023)

    1.4.4 South America (Brazil, Argentina, Colombia) Enterprise high Performance Computing Status and Prospect (2013-2023)

    1.4.5 Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa) Enterprise high Performance Computing Status and Prospect (2013-2023)

    1.5 Global Market Size of Enterprise high Performance Computing (2013-2023)

    2 Manufacturers Profiles

    2.1 Amazon

    2.1.1 business Overview

    2.1.2 Enterprise high Performance Computing sort and Applications

    2.1.2.1 Product A

    2.1.2.2 Product B

    2.1.3 Amazon Enterprise high Performance Computing Revenue, shameful Margin and Market share (2016-2017)

    2.2 radiant Computing

    2.2.1 business Overview

    2.2.2 Enterprise high Performance Computing sort and Applications

    2.2.2.1 Product A

    2.2.2.2 Product B

    2.2.3 radiant Computing Enterprise high Performance Computing Revenue, shameful Margin and Market share (2016-2017)

    2.3 Cray

    2.3.1 business Overview

    2.3.2 Enterprise high Performance Computing sort and Applications

    2.3.2.1 Product A

    2.3.2.2 Product B

    2.3.3 Cray Enterprise high Performance Computing Revenue, shameful Margin and Market share (2016-2017)

    2.4 Dell EMC

    2.4.1 business Overview

    2.4.2 Enterprise high Performance Computing sort and Applications

    2.4.2.1 Product A

    2.4.2.2 Product B

    2.4.3 Dell EMC Enterprise high Performance Computing Revenue, shameful Margin and Market share (2016-2017)

    2.5 Ephesoft

    2.5.1 business Overview

    2.5.2 Enterprise high Performance Computing sort and Applications

    2.5.2.1 Product A

    2.5.2.2 Product B

    2.5.3 Ephesoft Enterprise high Performance Computing Revenue, shameful Margin and Market share (2016-2017)

    2.6 Hewlett Packard Enterprise

    2.6.1 business Overview

    2.6.2 Enterprise high Performance Computing sort and Applications

    2.6.2.1 Product A

    2.6.2.2 Product B

    2.6.3 Hewlett Packard Enterprise Enterprise high Performance Computing Revenue, shameful Margin and Market share (2016-2017)

    2.7 IBM

    2.7.1 business Overview

    2.7.2 Enterprise high Performance Computing sort and Applications

    2.7.2.1 Product A

    2.7.2.2 Product B

    2.7.3 IBM Enterprise high Performance Computing Revenue, shameful Margin and Market share (2016-2017)

    Continued……

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