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C2090-317 - DB2 11 System Administrator for z/OS - Dump Information

Vendor : IBM
Exam Code : C2090-317
Exam Name : DB2 11 System Administrator for z/OS
Questions and Answers : 60 Q & A
Updated On : February 20, 2019
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C2090-317 Questions and Answers

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C2090-317 DB2 11 System Administrator for z/OS

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C2090-317 exam Dumps Source : DB2 11 System Administrator for z/OS

Test Code : C2090-317
Test Name : DB2 11 System Administrator for z/OS
Vendor Name : IBM
Q&A : 60 Real Questions

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IBM DB2 11 System Administrator

IBM Db2 question Optimization the use of AI | killexams.com Real Questions and Pass4sure dumps

In September 2018, IBM introduced a brand new product, IBM Db2 AI for z/OS. This artificial intelligence engine monitors statistics access patterns from executing SQL statements, uses machine studying algorithms to pick out most effective patterns and passes this suggestions to the Db2 question optimizer for use by subsequent statements.

laptop researching on the IBM z Platform

In can also of 2018, IBM announced edition 1.2 of its desktop studying for z/OS (MLz) product. this is a hybrid zServer and cloud utility suite that ingests efficiency statistics, analyzes and builds fashions that represent the fitness status of a variety of warning signs, monitors them over time and provides precise-time scoring capabilities.

several aspects of this product offering are geared toward assisting a neighborhood of model builders and executives. as an example:

  • It helps numerous programming languages corresponding to Python, Scala and R. This makes it possible for records modelers and scientists to make use of a language with which they are usual;
  • A graphical consumer interface referred to as the visual model Builder publications mannequin builders devoid of requiring highly-technical programming capabilities;
  • It contains assorted dashboards for monitoring model consequences and scoring services, as well as controlling the gadget configuration.
  • This desktop gaining knowledge of suite become at the beginning aimed at zServer-primarily based analytics purposes. some of the first evident decisions become zSystem performance monitoring and tuning. system administration Facility (SMF) facts that are immediately generated by using the working device deliver the raw information for system resource consumption akin to central processor utilization, I/O processing, reminiscence paging and so forth. IBM MLz can compile and shop these information over time, and build and educate fashions of system behavior, ranking those behaviors, determine patterns not effectively foreseen with the aid of people, increase key efficiency indications (KPIs) and then feed the mannequin outcomes lower back into the equipment to affect equipment configuration alterations that can enhance performance.

    The subsequent step was to put in force this suite to investigate Db2 performance information. One answer, referred to as the IBM Db2 IT Operational Analytics (Db2 ITOA) answer template, applies the machine researching technology to Db2 operational statistics to gain an figuring out of Db2 subsystem fitness. it might probably dynamically build baselines for key performance indications, deliver a dashboard of these KPIs and provides operational staff actual-time perception into Db2 operations.

    while popular Db2 subsystem efficiency is a vital component in average utility health and efficiency, IBM estimates that the DBA guide staff spends 25% or more of its time, " ... combating access path problems which trigger efficiency degradation and repair have an effect on.". (See Reference 1).

    AI comes to Db2

    believe the plight of contemporary DBAs in a Db2 ambiance. In trendy IT world they should aid one or greater large information purposes, cloud software and database capabilities, utility installation and configuration, Db2 subsystem and application performance tuning, database definition and administration, catastrophe healing planning, and greater. query tuning has been in existence considering the fact that the origins of the database, and DBAs are usually tasked with this as neatly.

    The heart of query route analysis in Db2 is the Optimizer. It accepts SQL statements from applications, verifies authority to entry the records, experiences the places of the objects to be accessed and develops an inventory of candidate statistics entry paths. These entry paths can encompass indexes, desk scans, a number of desk join methods and others. in the information warehouse and massive facts environments there are continually further choices accessible. One of those is the existence of abstract tables (occasionally called materialized question tables) that include pre-summarized or aggregated records, hence allowing Db2 to steer clear of re-aggregation processing. an extra choice is the starjoin entry direction, standard within the facts warehouse, the place the order of table joins is changed for performance reasons.

    The Optimizer then experiences the candidate access paths and chooses the access path, "with the lowest charge." charge during this context ability a weighted summation of useful resource utilization together with CPU, I/O, reminiscence and other supplies. finally, the Optimizer takes the bottom cost entry path, stores it in reminiscence (and, optionally, within the Db2 listing) and starts off access direction execution.

    big statistics and facts warehouse operations now consist of application suites that allow the enterprise analyst to use a graphical interface to construct and manipulate a miniature statistics mannequin of the information they wish to analyze. The applications then generate SQL statements in keeping with the clients’ requests.

    The problem for the DBA

    in order to do respectable analytics in your distinct facts outlets you need an outstanding knowing of the statistics requirements, an figuring out of the analytical services and algorithms accessible and a high-performance information infrastructure. regrettably, the quantity and location of facts sources is increasing (each in dimension and in geography), data sizes are growing to be, and functions proceed to proliferate in quantity and complexity. How should still IT managers support this atmosphere, mainly with probably the most skilled and mature body of workers nearing retirement?

    have in mind also that a large part of decreasing the full can charge of ownership of these methods is to get Db2 functions to run sooner and extra efficiently. This constantly translates into the usage of fewer CPU cycles, doing fewer I/Os and transporting less information across the community. on account that it's commonly intricate to even identify which functions could advantage from efficiency tuning, one strategy is to automate the detection and correction of tuning concerns. here's the place desktop discovering and artificial intelligence can be used to first-rate effect.

    Db2 12 for z/OS and artificial Intelligence

    Db2 edition 12 on z/OS uses the desktop gaining knowledge of amenities outlined above to collect and save SQL question text and entry course particulars, as well as genuine performance-related old assistance such as CPU time used, elapsed times and result set sizes. This providing, described as Db2 AI for z/OS, analyzes and stores the records in computer learning fashions, with the model evaluation effects then being scored and made obtainable to the Db2 Optimizer. The subsequent time a scored SQL observation is encountered, the Optimizer can then use the mannequin scoring statistics as enter to its access route alternative algorithm.

    The influence should be a reduction in CPU consumption as the Optimizer makes use of mannequin scoring enter to opt for more desirable access paths. This then lowers CPU fees and speeds software response times. a major talents is that the use of AI software doesn't require the DBA to have facts science competencies or deep insights into question tuning methodologies. The Optimizer now chooses the ideal access paths based no longer simplest on SQL question syntax and data distribution facts however on modelled and scored historic performance.

    This can be chiefly crucial if you store facts in numerous areas. for example, many analytical queries in opposition t massive data require concurrent entry to certain records warehouse tables. These tables are frequently referred to as dimension tables, and they contain the information aspects continually used to manage subsetting and aggregation. for instance, in a retail environment accept as true with a table known as StoreLocation that enumerates each keep and its vicinity code. Queries towards shop earnings statistics may want to combination or summarize earnings via vicinity; therefore, the StoreLocation table will be used by some big statistics queries. during this atmosphere it's regular to take the dimension tables and copy them consistently to the big statistics utility. in the IBM world this area is the IBM Db2 Analytics Accelerator (IDAA).

    Now think about SQL queries from each operational purposes, statistics warehouse users and large facts company analysts. From Db2's perspective, all these queries are equal, and are forwarded to the Optimizer. besides the fact that children, in the case of operational queries and warehouse queries they should definitely be directed to access the StoreLocation table in the warehouse. even so, the question from the enterprise analyst against massive facts tables should probably access the reproduction of the table there. This consequences in a proliferations of expertise access paths, and greater work for the Optimizer. thankfully, Db2 AI for z/OS can give the Optimizer the suggestions it should make sensible access path selections.

    how it Works

    The sequence of hobbies in Db2 AI for z/OS (See Reference 2) is often right here:

  • throughout a bind, rebind, prepare or clarify operation, an SQL statement is passed to the Optimizer;
  • The Optimizer chooses the statistics entry direction; because the choice is made, Db2 AI captures the SQL syntax, access direction alternative and question efficiency data (CPU used, and so forth.) and passes it to a "gaining knowledge of task";
  • The getting to know project, which can be completed on a zIIP processor (a non-conventional-goal CPU core that doesn't component into software licensing costs), interfaces with the computer learning utility (MLz mannequin functions) to shop this counsel in a model;
  • because the quantity of facts in each model grows, the MLz Scoring carrier (which also can be achieved on a zIIP processor) analyzes the model information and rankings the habits;
  • all the way through the subsequent bind, rebind, put together or clarify, the Optimizer now has access to the scoring for SQL models, and makes acceptable changes to entry course decisions.
  • There are additionally quite a few person interfaces that supply the administrator visibility to the reputation of the collected SQL observation efficiency statistics and mannequin scoring.

    abstract

    IBM's computing device gaining knowledge of for zOS (MLz) providing is being used to splendid impact in Db2 edition 12 to enhance the performance of analytical queries in addition to operational queries and their associated functions. This requires management consideration, as you need to assess that your enterprise is prepared to eat these ML and AI conclusions. How will you measure the expenses and advantages of the usage of computing device researching? Which IT guide team of workers must be tasked to reviewing the effect of model scoring, and perhaps approving (or overriding) the effects? How will you overview and justify the assumptions that the software makes about access route decisions?

    In different phrases, how smartly do you know your statistics, its distribution, its integrity and your existing and proposed entry paths? this may verify where the DBAs spend their time in supporting analytics and operational application performance.

    # # #

    Reference 1

    John Campbell, IBM Db2 unique EngineerFrom "IBM Db2 AI for z/OS: increase IBM Db2 application efficiency with desktop discovering"https://www.worldofdb2.com/pursuits/ibm-db2-ai-for-z-os-increase-ibm-db2-software-performance-with-ma

    Reference 2

    Db2 AI for z/OShttps://www.ibm.com/support/knowledgecenter/en/SSGKMA_1.1.0/src/ai/ai_home.html

    See all articles by way of Lockwood Lyon


    speakme to IBM DB2 with Golang | killexams.com Real Questions and Pass4sure dumps

    In my fresh assignment, I need to discuss with DB2 database to assemble some leisure API capabilities.

    first of all, we want a DB2 driver for golang software to talk to DB2. There are some previous art accomplished via people. although it became either now not actively maintained or doesn’t guide on all of the main systems. I determined to use the typical ODBC driver to communicate with the DB2.

    Setup ODBC/CLI for DB2

    down load the DB2 ODBC/CLI driver from IBM web page.

    Then simply untar/unzip it to a directory.

    Configure DB2 ODBC/CLI on home windows

    My development and checking out atmosphere is on home windows. I setup the db2 driver on windows first.

  • trade directory to where you unzip the motive force
  • As a administrator, run bin\db2cli set up -setup
  • find out the place is the db2cli.ini found with the aid of running bin\db2cli validate | findstr "typical App"
  • commonplace App records path : C:\ProgramData\IBM\DB2\Some_Directory_Namedb2cli.ini route : <normal App records course>\cfg\db2cli.ini

    four. Edit the db2cli.ini file the place you recognized from last step

    [MyDB]Protocol=TCPIPHostname=192.168.xxx.xxxPort=50000Database=mydbCurrentSchema=myschema

    5. Register the DB into ODBC as a gadget level DB

    bin\db2cli registerdsn -add -dsn MyDB -equipment

    At this stage we are able to consult with the DB2 with ODBC on home windows.

    Configure DB2 ODBC/CLI on Linux

    My Linux is Redhat.

  • If ODBC equipment is not installation. set up it. as an example,rpm -iv unixODBC-2.three.1–11.el7.x86_64.rpm
  • Untar the DB2 ODBC/CLI driver file downloaded.
  • update equipment ODBC drivers with the aid of updating the /etc/odbcinst.ini
  • [DB2]Description=DB2 DriverDriver=#target_dir/clidriver/lib/libdb2o.sofileusage=1dontdlclose=1

    exchange #targe_dir with the whole course identify of the place you untar the drivers.

    four. replace system /and so on/odbc.ini file

    [MyDB]Protocol=TCPIPHostname=192.168.xxx.xxxPort=50000Database=mydbCurrentSchema=myschemaDriver=#target_dir/clidriver/lib/libdb2o.so

    5. also replace the #target_dir/clidriver/clidriver/cfg/db2cli.ini with the identical content material as shown above. i am not certain why the ODBC CLI driver appears on the two ini data.

    So we're ready for the ODBC setup for each windows and Linux.

    Use ODBC to connect with DB2

    The connection to DB2 becomes straight forward. Use the ODBC driver,

    package restServer import ("fmt""os""regexp" _ "github.com/alexbrainman/odbc""github.com/jmoiron/sqlx""github.com/sirupsen/logrus") func DB2Connect() (*sqlx.DB, error) dbName := os.Getenv("MY_DBNAME")uid := os.Getenv("MY_USERNAME")password := os.Getenv("MY_CREDENTIAL") //some decryption code is overlooked here... db, err := sqlx.Open("odbc", fmt.Sprintf("DSN=%s;uid=%s;pwd=%s", dbName, uid, password))if err != nil return nil, err return db, nil

    we will then use all the normal golang DB programming stuff. i am the use of sqlx to operate question and execution, and squirrel to contruct SQL observation.

    Notes: We couldn't run cross compiling. different platform must be compiled one after the other.


    IBM DB2 relational DBMS overview | killexams.com Real Questions and Pass4sure dumps

    IBM DB2 is a relational DBMS with built-in help for a number of NoSQL capabilities, including XML, graph save and Java Script Object Notation (JSON). Used by companies of all sizes, DB2 offers an information platform for both transactional and analytical operations in addition to continual availability of statistics to preserve transactional workflows and analytics working correctly.

    OSes DB2 helps

    DB2 is accessible for 3 fundamental platforms:

  • computing device, with DB2 for Linux, Unix and windows (LUW)
  • Midrange, with DB2 for iSeries
  • Mainframe, with DB2 for z/OS, the predominant mainframe relational DBMS
  • DB2 helps these Unix versions: AIX, Solaris and HP-UX; Linux editions include red Hat, SUSE, Ubuntu and Linux on pSeries and gadget z.

    DB2 for i comes pre-put in on the iSeries server and, as such, won't be addressed right here.

    DB2 points

    The present edition of DB2 for LUW is eleven.1, launched in 2d quarter 2016. This release specializes in advancements to BLU Acceleration and pureScale functionality.

    BLU Acceleration, brought to DB2 in version 10.5, offers a column store ability, as well as technological performance boosts for actionable compression, exploitation of the single instruction varied statistics (SIMD) capabilities of modern CPUs, and records skipping expertise.

    For DB2 11, BLU Acceleration may also be deployed throughout a hugely parallel processing (MPP) network cluster structure. BLU Acceleration can be utilized in database partitioning facility environments for MPP scale out, that can enhance query performance for giant partitioned functions. moreover, this version improves in-memory capabilities for column-geared up tables and integration with IBM statistics Server manager to investigate workloads and investigate no matter if they can advantage from column-oriented tables.

    DB2 for LUW eleven also provides assist for top Availability catastrophe healing, or HADR, in pureScale clustered DB2 databases. records can also be mirrored from a major pureScale database cluster to a 2nd local or faraway standby cluster, thereby enhancing recoverability. An further development for pureScale is the start of online repair pack update guide, permitting users to observe DB2 11.1 fix packs to particular person members operating in a pureScale cluster with no need to take the total cluster offline.

    On the security entrance, DB2 eleven allows for the use of business key management programs for storing native encryption master keys. The DB2 superior healing function is a bundle of database backup, recovery and statistics extraction equipment that may help clients enhance availability, mitigate chance and optimize administrative initiatives.

    The latest edition of DB2 for z/OS is 11, which changed into launched in October 2013. Highlights of DB2 eleven for z/OS include performance and availability enhancements, stronger analytics capabilities, application compatibility features, more suitable entry direction stability and archive transparency. IBM additionally presents the IBM DB2 Analytics Accelerator, which is an add-on to DB2 for z/OS for rushing up analytical queries.

    DB2 for LUW requires two configuration data: the Database supervisor configuration file containing configuration parameters for a complete instance, and Database configuration information for each and every database.      

    DB2 for LUW is also embedded into IBM's database PureData appliance, enabling turnkey implementation of DB2 devoid of the time-ingesting setting up and configuration required of a normal DBMS installation.

    DB2 for z/OS is installed as a subsystem with accessories for database functions, lock management, distributed request processing and connecting to different mainframe brokers. The subsystem is configured with a collection of parameters referred to as the DSNZPARMs.

    DB2 offerings

    As of edition eleven, IBM offers eight variants of DB2 for LUW:

  • DB2 advanced commercial enterprise Server edition presents the maximum level of functionality attainable for DB2 and is relevant for transactional, warehouse and combined workloads. This edition has no processor, memory or database measurement limits and springs with a full complement of warehouse equipment, InfoSphere Optim equipment and IBM facts Studio.
  • DB2 superior Workgroup Server edition is similar to the DB2 advanced enterprise Server edition, apart from it places limits on processor, reminiscence and database measurement. This version is choicest-applicable for deployment in a departmental, workgroup or midsize company atmosphere.
  • DB2 business Server edition is appropriate for transactional and combined workloads, and like the advanced version, it has no processor, memory or database measurement limits. besides the fact that children, in contrast to the superior version, it lacks column-equipped tables, in-reminiscence database, records compression, workload administration, replication and dispensed partitioning capabilities.
  • DB2 Workgroup Server edition is primarily for transactional workloads. This edition areas limits on processor, memory and database dimension, which makes it most advantageous for medium-size workloads.
  • DB2 Direct superior edition is a edition of DB2 advanced Workgroup Server version for digital birth.
  • DB2 Direct regular edition is a digital version of DB2 Workgroup Server version for digital delivery.
  • DB2 for large records combines the relational engine of DB2 with IBM's BigInsights platform for massive statistics and analytics. This version is designed to raise portability of information across relational techniques and Hadoop.
  • DB2 Developer version is designed for a single software developer to design, construct and prototype functions for deployment on any of the IBM counsel administration customer or server platforms.
  • moreover, DB2 specific-C version, an entry-level edition of the DB2 records server for the developer and associate group, is attainable at no cost. It can be put in on actual or digital techniques with any amount of CPU and RAM, and is optimized to burn up to a optimum of two cores and 16 GB of reminiscence.

    DB2 information varieties

    DB2 supports the regular numeric, character, and date/time information forms well as LOBs, XML and user-described kinds. JSON is supported by means of DB2 JSON, a driver-based mostly solution that provides JSON statistics representation inside the context of an RDBMS.

    DB2 benchmarks

    IBM has posted numerous TPC benchmarks for DB2 on all systems supported. IBM sells DB2 for LUW direct, via business companions, on-line and thru utility selected licensing, which makes it possible for providers corresponding to SAP to license and promote DB2 relational DBMS as a part of their applications.

    Pricing is according to processor price unit (PVU), which is a unit of measure that IBM makes use of to license its software. IBM applies a PVU count to every core of a processor. The pricing is then in response to the number of PVUs for the processor and via the number of processors made purchasable to DB2.

    The developer edition is licensed with the aid of approved user, no longer PVU. There are additional nuances to pricing and licensing in keeping with DB2 versions that aren't coated here.

    DB2 licensing

    The license cost for DB2 contains first-year guide and subscription, entitling the proprietor to contact IBM for help in addition to to download fixes and enhancements to future models/releases of the product.

    DB2 for z/OS relational DBMS is offered directly via IBM and is licensed as a monthly license can charge (MLC) product. There are a lot of metrics obtainable for pricing MLC items. A 90-day free trial download is accessible from the IBM site.

    concerning the authorCraig S. Mullins is a knowledge management strategist, researcher, consultant and author with more than 30 years of adventure in all aspects of database systems construction. he's president and fundamental consultant of Mullins Consulting Inc. and publisher/editor of TheDatabaseSite.com. e mail him at craig@craigmullins.com.

    e mail us at editor@searchdatamanagement.com and follow us on Twitter: @sDataManagement.

    this article became updated in September 2016.


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    DB2 11 System Administrator for z/OS

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    Connecting DSX — RStudio to on-prem DB2 for z/OS database | killexams.com real questions and Pass4sure dumps

    IBM Data Science Experience provides an environment that brings together all of a data scientist’s tools into one location. It includes the most popular Open Source tools and IBM value-add functionality with integration of community and social features to make data scientists more successful.

    A wide variety of cloud data sources are easily connected to DSX, or a user could upload data from DB2 on z/OS (by creating a copy) into the environment. However, the ability to access to data in-place on the system of record, has great appeal.

    In this guide, I will outline the steps needed to configure DSX to access an on-prem DB2 for z/OS database using JDBC and provide the ability to utilize RStudio to perform advanced analytics operations on this data.

    Here is an outline of the steps we will go through in the guide to set up the DSX environment for DB2 for z/OS connectivity:

  • Examine the RStudio interface.
  • Identify the JDBC driver files and configure the initial environment with RStudio.
  • If required, setup a Bluemix Secure Gateway.
  • Use the RJDBC package to connect with a DB2 for z/OS database, and run a query.
  • Data Science Experience — RStudio Interface

    (Note: The screenshots in this tutorial are from a Mac OSX environment running Firefox. If you are running from Windows or Linux, there may some differences in the appearance of the native dialogs and browser interfaces.)

    Once setup with your account, click on the DSX Logo at the top of your screen, and this will take you to the main Community page. Once there, if you click the far left drop-down menu, you will see your project options, including RStudio. Click on the the RStudio option, and this will open up your RStudio instance.

    If you have opened DSX RStudio before, the instance opened will have retained any history or enviroment settings that you had saved/autosaved from before. If you are familiar with RStudio and have used a native version of RStudio on Windows/Mac OSX, it will look exactly the same. The only difference is that the underlying infrastucture is running in a Bluemix virtual machine. There is limited access to the underlying operating system, but we can access the filesystem to upload the JDBC driver and license file and configure the connection for DB2 for z/OS.

    The RStudio interface should look similar to what is shown below……

    (For a complete guide to using RStudio, please see the documentation linked to in the ‘Help’ Section)

    Next, we will confirm that the correct packages are installed/loaded that enable JDBC access to our on-prem database.

    In the bottom right panel of the RStudio desktop you will see the ‘Packages’ tab, and within that is an ‘Install’ tab. If you click “Install”, you will get a dialog box that allows you to load the required packages from the CRAN repo. You will need to install ‘RJDBC’. If the ‘Install dependencies’ box is checked, you will also be installing ‘rJava’ and ‘DBI’ (which load automatically). Once you install these packages, you should see them in the User Library. If they are unchecked, please check them, and they will be enabled in the environment.

    Next, we will need to upload the appropriate DB2 for z/OS data server driver .jar files to the DSX environment. The files we will need are: db2jcc.jar and db2jcc_license_cisuz.jar

    If you already have a licensed version of DB2 Connect installed, these driver files are already available on one of your existing client environment setups. Ask your DB2 system administrator for the location of these files in your client environment.

    This guide is based on DB2Connect Version 11.1 for Linux, Unix and Windows (client) and DB2 for z/OS Unlimited Edition (server). If you are not currently licensed for DB2Connect, see the Appendix at the end of this guide for instructions on how to obtain the correct data server drivers and license files that are required.

    From the RStudio desktop (shown above), go to the lower right panel again, to the “Files” section. Then, click “Upload” and you will get a dialog box that allows you to browse your local file system to upload the driver files. For both db2jcc.jar and db2jcc_license_cisuz.jar, upload these to the current directory of your DSX/RStudio environment.

    Connecting to the z Server over TCPIP — Secure Gateway

    With the JDBC driver files in place, we now need to configure access to the server which is running the DB2 for z/OS instance. The assumption for most IBM customers is that their target z server is running on a secure internal network, which is accessed externally through a VPN or from an internal private network access point. If this is the case, you will also need to set up a Bluemix Secure Gateway, that will allow the Bluemix/DSX cloud app to access your z server through a firewall, using the same VPN/internal network acces point that you use for other client connections from a desktop.

    For more general information on Bluemix Secure Gateways, please go to: https://console.ng.bluemix.net/docs/services/SecureGateway/secure_gateway.html

    If you do not need to configure a Secure Gateway, you can skip to the “Connecting to DB2/zOS with JDBC” section.

    To configure the secure gateway, please go to https://console.ng.bluemix.net/dashboard/services/, log in to Bluemix with the same credentials you are using for DSX, and then use the drop-down menu on the left to go to the ‘Services’ and then ‘Dashboard’ menu.

    Once within the Services dashboard, click the “Create Service +” button.

    Next, we will go to “Integrate” and then choose the “Secure Gateway” service from the menu.

    Leave the service “Unbound” and click “Create”.

    And, then “+ Add Gateway’

    Choose a name for your gateway, uncheck both of the boxes listed, and then click “Add Gateway”.

    Now, we can setup the destination details, and the client. Click on the gateway.

    Then click on “+ Add Destination”.

    Choose “On-Premises” and click Next.

    On the next screen add your host name/IP , and the DB Port of your DB2z Subsystem. Click ‘Next’

    Then, select TCP protocol on the next screen, and click “Next” again.

    The next screen setting is related to server side authentication. If you typically connect with just a user and password, then choose “None”. If you are not sure whether or not your server requires additional authentication, ask your System Administrator, and you can change this later. Click ‘Next’.

    You can skip over the next setting, unless you want to privatize the IP address and port or your connection. Click ‘Next’.

    To complete the destination settings for the back-end server, we just need to give a name for our connection. Choose a name for your destination that describes the DB2 Subsystem you are connecting to, and then click “Finish”.

    Now let’s install the client for our secure gateway. Choose “+ Add Clients”

    Choose your preferred method of hosting the gateway client. For this demonstration, we will be using IBM Installer — Mac OSX version on a laptop that has a VPN connection to access an on-prem z server. The details for different OS installs and Using Docker or IBM DataPower are covered in detail in the online instructions. For specific install details, See the “Bluemix Documentation” link.

    At this time, copy the Gateway ID, for input later into the gateway client command-line interface.

    After Downloading the client, double-clicking the image, you might have to go to your ‘Finder’ to see the mount point for the image.

    Click on the mount-point and then drag the ‘ibm’ folder into the Applications Folder.

    Now, we can go into the ‘IBM’ folder within Applicatons, and double-click to run the segw.command to open the CLI for the gateway.

    Enter the gateway ID you copied earlier, and then enter ‘none’ for the security token.

    Next, note the web address of the gateway interface — localhost:9003/dashboard  Enter that web address in your web browser.

    Below, we see the gateway web UI. You can see all the details of your secure gateway from this interface. There is one more setting, for the access control list. Please click “Access Control List”, and then enter your destination hostname/IP and port once again.

    Below, once you have added the host/IP and port, you can close the browser window, or minimize it.

    We are almost done. The Secure Gateway config is complete.

    Now we will return to the main Secure Gateway dashboard to copy the cloud hostname/port for the gateway. It will be used for the JDBC connection string in DSX/RStudio. From the Services Dashboard, click on the gateway you created.

    On this screen, click on the ‘settings gear’ of the destination server connection details. A dialog box will open.

    Copy the ‘Cloud Host : Port’ address to use in your JDBC connection in DSX/RStudio.

    Return to the RStudio desktop, and we can setup the JDBC driver and run a query.

    Connecting to DB2/zOS with JDBC

    In the RStudio desktop, we are now ready to enter the JDBC commands at the console prompt to setup the connection to our DB2 for z/OS database. Make sure the RJDBC, DBI, and rJava packages have loaded.

    Also, at this time, make sure you are running any VPN connection that is normally required to access your backend system from a desktop client.

    Enter the following commands in the RStudio console, noting the results you get for the classpath display:

    .jinit()  .jclassPath()  .jaddClassPath("/home/rstudio/db2jcc_license_cisuz.jar") .jclassPath() drv <- JDBC("com.ibm.db2.jcc.DB2Driver","/home <br> /rstudio/db2jcc.jar") .jclassPath()

    (* note the addition of the license file….its position in the classpath is important *)


    IBM Db2 Query Optimization Using AI | killexams.com real questions and Pass4sure dumps

    In September 2018, IBM announced a new product, IBM Db2 AI for z/OS. This artificial intelligence engine monitors data access patterns from executing SQL statements, uses machine learning algorithms to decide on optimal patterns and passes this information to the Db2 query optimizer for use by subsequent statements.

    Machine Learning on the IBM z Platform

    In May of 2018, IBM announced version 1.2 of its Machine Learning for z/OS (MLz) product. This is a hybrid zServer and cloud software suite that ingests performance data, analyzes and builds models that represent the health status of various indicators, monitors them over time and provides real-time scoring capabilities.

    Several features of this product offering are aimed at supporting a community of model developers and managers. For example:

  • It supports multiple programming languages such as Python, Scala and R. This allows data modelers and scientists to use a language with which they are familiar;
  • A graphical user interface called the Visual Model Builder guides model developers without requiring highly-technical programming skills;
  • It includes multiple dashboards for monitoring model results and scoring services, as well as controlling the system configuration.
  • This machine learning suite was initially aimed at zServer-based analytics applications. One of the first obvious choices was zSystem performance monitoring and tuning. System Management Facility (SMF) records that are automatically generated by the operating system provide the raw data for system resource consumption such as central processor usage, I/O processing, memory paging and the like. IBM MLz can collect and store these data over time, and build and train models of system behavior, score those behaviors, identify patterns not readily foreseen by humans, develop key performance indicators (KPIs) and then feed the model results back into the system to affect system configuration changes that can improve performance.

    The next step was to implement this suite to analyze Db2 performance data. One solution, called the IBM Db2 IT Operational Analytics (Db2 ITOA) solution template, applies the machine learning technology to Db2 operational data to gain an understanding of Db2 subsystem health. It can dynamically build baselines for key performance indicators, provide a dashboard of these KPIs and give operational staff real-time insight into Db2 operations.

    While general Db2 subsystem performance is an important factor in overall application health and performance, IBM estimates that the DBA support staff spends 25% or more of its time, " ... fighting access path problems which cause performance degradation and service impact.". (See Reference 1).

    AI Comes to Db2

    Consider the plight of modern DBAs in a Db2 environment. In today's IT world they must support one or more big data applications, cloud application and database services, software installation and configuration, Db2 subsystem and application performance tuning, database definition and management, disaster recovery planning, and more. Query tuning has been in existence since the origins of the database, and DBAs are usually tasked with this as well.

    The heart of query path analysis in Db2 is the Optimizer. It accepts SQL statements from applications, verifies authority to access the data, reviews the locations of the objects to be accessed and develops a list of candidate data access paths. These access paths can include indexes, table scans, various table join methods and others. In the data warehouse and big data environments there are usually additional choices available. One of these is the existence of summary tables (sometimes called materialized query tables) that contain pre-summarized or aggregated data, thus allowing Db2 to avoid re-aggregation processing. Another option is the starjoin access path, common in the data warehouse, where the order of table joins is changed for performance reasons.

    The Optimizer then reviews the candidate access paths and chooses the access path, "with the lowest cost." Cost in this context means a weighted summation of resource usage including CPU, I/O, memory and other resources. Finally, the Optimizer takes the lowest cost access path, stores it in memory (and, optionally, in the Db2 Directory) and begins access path execution.

    Big data and data warehouse operations now include software suites that allow the business analyst to use a graphical interface to build and manipulate a miniature data model of the data they wish to analyze. The packages then generate SQL statements based on the users’ requests.

    The Problem for the DBA

    In order to do good analytics on your multiple data stores you need a good understanding of the data requirements, an understanding of the analytical functions and algorithms available and a high-performance data infrastructure. Regrettably, the number and location of data sources is expanding (both in size and in geography), data sizes are growing, and applications continue to proliferate in number and complexity. How should IT managers support this environment, especially with the most experienced and mature staff nearing retirement?

    Understand also that a large part of reducing the total cost of ownership of these systems is to get Db2 applications to run faster and more efficiently. This usually translates into using fewer CPU cycles, doing fewer I/Os and transporting less data across the network. Since it is often difficult to even identify which applications could benefit from performance tuning, one approach is to automate the detection and correction of tuning issues. This is where machine learning and artificial intelligence can be used to great effect.

    Db2 12 for z/OS and Artificial Intelligence

    Db2 version 12 on z/OS uses the machine learning facilities mentioned above to gather and store SQL query text and access path details, as well as actual performance-related historical information such as CPU time used, elapsed times and result set sizes. This offering, described as Db2 AI for z/OS, analyzes and stores the data in machine learning models, with the model analysis results then being scored and made available to the Db2 Optimizer. The next time a scored SQL statement is encountered, the Optimizer can then use the model scoring data as input to its access path choice algorithm.

    The result should be a reduction in CPU consumption as the Optimizer uses model scoring input to choose better access paths. This then lowers CPU costs and speeds application response times. A significant advantage is that using AI software does not require the DBA to have data science skills or deep insights into query tuning methodologies. The Optimizer now chooses the best access paths based not only on SQL query syntax and data distribution statistics but on modelled and scored historical performance.

    This can be particularly important if you store data in multiple places. For example, many analytical queries against big data require concurrent access to certain data warehouse tables. These tables are commonly called dimension tables, and they contain the data elements usually used to control subsetting and aggregation. For example, in a retail environment consider a table called StoreLocation that enumerates every store and its location code. Queries against store sales data may wish to aggregate or summarize sales by Location; hence, the StoreLocation table will be used by some big data queries. In this environment it is common to take the dimension tables and copy them regularly to the big data application. In the IBM world this location is the IBM Db2 Analytics Accelerator (IDAA).

    Now think about SQL queries from both operational applications, data warehouse users and big data business analysts. From Db2's perspective, all these queries are equal, and are forwarded to the Optimizer. However, in the case of operational queries and warehouse queries they should most likely be directed to access the StoreLocation table in the warehouse. On the other hand, the query from the business analyst against big data tables should probably access the copy of the table there. This results in a proliferations of potential access paths, and more work for the Optimizer. Luckily, Db2 AI for z/OS can provide the Optimizer the information it needs to make smart access path selections.

    How It Works

    The sequence of events in Db2 AI for z/OS (See Reference 2) is generally the following:

  • During a bind, rebind, prepare or explain operation, an SQL statement is passed to the Optimizer;
  • The Optimizer chooses the data access path; as the choice is made, Db2 AI captures the SQL syntax, access path choice and query performance statistics (CPU used, etc.) and passes it to a "learning task";
  • The learning task, which can be executed on a zIIP processor (a non-general-purpose CPU core that does not factor into software licensing charges), interfaces with the machine learning software (MLz Model Services) to store this information in a model;
  • As the amount of data in each model grows, the MLz Scoring Service (which also can be executed on a zIIP processor) analyzes the model data and scores the behavior;
  • During the next bind, rebind, prepare or explain, the Optimizer now has access to the scoring for SQL models, and makes appropriate changes to access path choices.
  • There are also various user interfaces that give the administrator visibility to the status of the accumulated SQL statement performance data and model scoring.

    Summary

    IBM's machine learning for zOS (MLz) offering is being used to great effect in Db2 version 12 to improve the performance of analytical queries as well as operational queries and their associated applications. This requires management attention, as you must verify that your business is prepared to consume these ML and AI conclusions. How will you measure the costs and benefits of using machine learning? Which IT support staff must be tasked to reviewing the result of model scoring, and perhaps approving (or overriding) the results? How will you review and justify the assumptions that the software makes about access path choices?

    In other words, how well do you know your data, its distribution, its integrity and your current and proposed access paths? This will determine where the DBAs spend their time in supporting analytics and operational application performance.

    # # #

    Reference 1

    John Campbell, IBM Db2 Distinguished EngineerFrom "IBM Db2 AI for z/OS: Boost IBM Db2 application performance with machine learning"https://www.worldofdb2.com/events/ibm-db2-ai-for-z-os-boost-ibm-db2-application-performance-with-ma

    Reference 2

    Db2 AI for z/OShttps://www.ibm.com/support/knowledgecenter/en/SSGKMA_1.1.0/src/ai/ai_home.html

    See all articles by Lockwood Lyon


    IBM DB2 relational DBMS overview | killexams.com real questions and Pass4sure dumps

    IBM DB2 is a relational DBMS with integrated support for a number of NoSQL capabilities, including XML, graph store and Java Script Object Notation (JSON). Used by organizations of all sizes, DB2 provides a data platform for both transactional and analytical operations as well as continuous availability of data to keep transactional workflows and analytics operating efficiently.

    OSes DB2 supports

    DB2 is available for three primary platforms:

  • Workstation, with DB2 for Linux, Unix and Windows (LUW)
  • Midrange, with DB2 for iSeries
  • Mainframe, with DB2 for z/OS, the predominant mainframe relational DBMS
  • DB2 supports these Unix variants: AIX, Solaris and HP-UX; Linux variants include Red Hat, SUSE, Ubuntu and Linux on pSeries and System z.

    DB2 for i comes pre-installed on the iSeries server and, as such, won't be addressed here.

    DB2 features

    The current version of DB2 for LUW is 11.1, released in second quarter 2016. This release focuses on improvements to BLU Acceleration and pureScale functionality.

    BLU Acceleration, added to DB2 in Version 10.5, provides a column store capability, as well as technological performance boosts for actionable compression, exploitation of the single instruction multiple data (SIMD) capabilities of modern CPUs, and data skipping technology.

    For DB2 11, BLU Acceleration can be deployed across a massively parallel processing (MPP) network cluster architecture. BLU Acceleration can be used in database partitioning facility environments for MPP scale out, which can improve query performance for large partitioned applications. Additionally, this version improves in-memory capabilities for column-organized tables and integration with IBM Data Server Manager to analyze workloads and determine whether they can benefit from column-oriented tables.

    DB2 for LUW 11 also adds support for High Availability Disaster Recovery, or HADR, in pureScale clustered DB2 databases. Data can be mirrored from a primary pureScale database cluster to a second local or remote standby cluster, thereby improving recoverability. An additional improvement for pureScale is the delivery of online fix pack update support, allowing users to apply DB2 11.1 fix packs to individual members running in a pureScale cluster without having to take the entire cluster offline.

    On the security front, DB2 11 enables the use of enterprise key management systems for storing native encryption master keys. The DB2 Advanced Recovery Feature is a bundle of database backup, recovery and data extraction tools that can help users improve availability, mitigate risk and optimize administrative tasks.

    The current version of DB2 for z/OS is 11, which was released in October 2013. Highlights of DB2 11 for z/OS include performance and availability enhancements, improved analytics capabilities, application compatibility features, improved access path stability and archive transparency. IBM also offers the IBM DB2 Analytics Accelerator, which is an add-on to DB2 for z/OS for speeding up analytical queries.

    DB2 for LUW requires two configuration files: the Database Manager configuration file containing configuration parameters for an entire instance, and Database configuration files for each database.      

    DB2 for LUW is also embedded into IBM's database PureData appliance, enabling turnkey implementation of DB2 without the time-consuming installation and configuration required of a normal DBMS installation.

    DB2 for z/OS is installed as a subsystem with components for database services, lock management, distributed request processing and connecting to other mainframe agents. The subsystem is configured with a set of parameters called the DSNZPARMs.

    DB2 offerings

    As of Version 11, IBM offers eight editions of DB2 for LUW:

  • DB2 Advanced Enterprise Server Edition offers the highest level of functionality available for DB2 and is suitable for transactional, warehouse and mixed workloads. This edition has no processor, memory or database size limits and comes with a full complement of warehouse tools, InfoSphere Optim tools and IBM Data Studio.
  • DB2 Advanced Workgroup Server Edition is similar to the DB2 Advanced Enterprise Server Edition, except it places limits on processor, memory and database size. This edition is best-suited for deployment in a departmental, workgroup or midsize business environment.
  • DB2 Enterprise Server Edition is suitable for transactional and mixed workloads, and like the advanced version, it has no processor, memory or database size limits. However, unlike the advanced version, it lacks column-organized tables, in-memory database, data compression, workload management, replication and distributed partitioning capabilities.
  • DB2 Workgroup Server Edition is primarily for transactional workloads. This edition places limits on processor, memory and database size, which makes it ideal for medium-size workloads.
  • DB2 Direct Advanced Edition is a version of DB2 Advanced Workgroup Server Edition for digital delivery.
  • DB2 Direct Standard Edition is a digital version of DB2 Workgroup Server Edition for digital delivery.
  • DB2 for Big Data combines the relational engine of DB2 with IBM's BigInsights platform for big data and analytics. This edition is designed to enhance portability of data across relational systems and Hadoop.
  • DB2 Developer Edition is designed for a single application developer to design, build and prototype applications for deployment on any of the IBM Information Management client or server platforms.
  • Additionally, DB2 Express-C edition, an entry-level edition of the DB2 data server for the developer and partner community, is available for free. It can be installed on physical or virtual systems with any amount of CPU and RAM, and is optimized to use up to a maximum of two cores and 16 GB of memory.

    DB2 data types

    DB2 supports the standard numeric, character, and date/time data types well as LOBs, XML and user-defined types. JSON is supported via DB2 JSON, a driver-based solution that delivers JSON data representation within the context of an RDBMS.

    DB2 benchmarks

    IBM has published numerous TPC benchmarks for DB2 on all platforms supported. IBM sells DB2 for LUW direct, through business partners, online and through application specific licensing, which allows vendors such as SAP to license and sell DB2 relational DBMS as part of their applications.

    Pricing is based on processor value unit (PVU), which is a unit of measure that IBM uses to license its software. IBM applies a PVU count to each core of a processor. The pricing is then based on the number of PVUs for the processor and by the number of processors made available to DB2.

    The developer edition is licensed by authorized user, not PVU. There are additional nuances to pricing and licensing based on DB2 editions that aren't covered here.

    DB2 licensing

    The license cost for DB2 includes first-year support and subscription, entitling the owner to contact IBM for support as well as to download fixes and upgrades to future versions/releases of the product.

    DB2 for z/OS relational DBMS is sold directly by IBM and is licensed as a monthly license charge (MLC) product. There are numerous metrics available for pricing MLC products. A 90-day free trial download is available from the IBM website.

    About the authorCraig S. Mullins is a data management strategist, researcher, consultant and author with more than 30 years of experience in all facets of database systems development. He is president and principal consultant of Mullins Consulting Inc. and publisher/editor of TheDatabaseSite.com. Email him at craig@craigmullins.com.

    Email us at editor@searchdatamanagement.com and follow us on Twitter: @sDataManagement.

    This article was updated in September 2016.



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