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1Z1-450 exam Dumps Source : Oracle Application Express 3.2-(R) Developing Web Applications

Test Code : 1Z1-450
Test denomination : Oracle Application Express 3.2-(R) Developing Web Applications
Vendor denomination : Oracle
: 49 true Questions

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Oracle Oracle Application Express 3.2-(R)

Oracle organisation (ORCL) CEO Safra Catz And brand Hurd On Q1 2019 effects - income denomination Transcript | killexams.com true Questions and Pass4sure dumps

No outcomes found, are attempting original key phrase!when it comes to ecosystems, GAAP applications complete revenues gain been $ ... I’ll talk a bit bit about a corporation referred to as Federal express. FedEx is -- in the FedEx aspect of the house, a traditional Oracle person ...

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image of expose hall entrance for IOUG & OAUG Collaborate17

I’ve submitted some abstracts for three shows at Collaborate17 next yr in Las Vegas, Apri 2–6, 2017, fingers crossed. The IOUG and OAUG group should subsist making choices via early November. I’ll sequel up the link here to the authorised abstracts as soon as introduced.

records stories: Predicting Asset fees with Oracle statistics Visualization laptop, statistics assortment, and R

Your statistics has a fable to inform, and if advised appropriately, your data can predict the future. during this session they simplify the complicated assignment of asset valuation with an exciting fable the consume of a strategy that you can observe to any asset you want to purchase — be it your subsequent car, boat, home, or airplane. They consume a company aircraft for instance to operate asset valuation. you will find out how that you may perquisite now and easily: scrape the statistics from a market web site, consume Oracle information Visualization computing device to identify the expense drivers of this market, and categorical a simple R commentary in order to predict the cost of any asset out there.

targets
  • reveal an smooth formulation to duty records mining via scraping information from a web market.
  • exhibit the Oracle statistics Visualization desktop and consume it to determine expense drivers inside the mined facts.
  • Produce a predictive mannequin the consume of the R language that expresses the existing and future value of any asset out there.
  • replacing a Legacy gasoline Pipeline Accounting sub-ledger with the private Cloud

    Oil and fuel organizations are going to remarkable lengths to skinny out their operations within the latest market lows whereas combating growing older software This session examines how Williams greater person journey, addressed cell clients, reduced expenses and gained efficiencies through re-authoring their legacy pipeline accounting and client interface systems with a synchronous all-Oracle application stack interfacing with Oracle EBS r12 using Oracle Database 12c, APEX 5.0, Node.js, RESTful statistics features, Javascript and Open supply.

    goals
  • explain the enterprise challenges with intra-state pipeline accounting and delivery and Williams’ solution.
  • display the completed solution, an utility working in Williams inner most cloud and a SAAS for shoppers.
  • How up to date building within the Oracle toolset can enlarge consumer journey, accelerate building timelines
  • Co-Presenters

    Jeff Thomas

    EBS in the box: constructing Offline desktop and mobile applications for E-business Suite

    Do you've got EBS clients in the box, the air, or at sea? in that case these clients may additionally gain drugs or telephones, however more commonly than no longer they’re the consume of a desktop or other computing platform that requires offline use. during this session they explore an smooth consume case where they build a cross platform desktop utility for container provider technicians that connects to Oracle commercial enterprise Asset administration… after which they sever the connection! They divulge simple internet features for EAM written in Oracle relaxation records functions and the computer utility written within the wildly time-honored Electron Framework used with the aid of Slack, GitHub, facebook, and Docker.

    goals
  • determine the consume case for computing device purposes including offline mode, local instruments, kiosk class apps, and other makes consume of.
  • exhibit a course to build accepted functions for Oracle EBS and EAM the consume of ORDS, Oracle RESTful information features.
  • clarify the Electron framework, its industry success, and the course to construct a simple entrance-end for EBS EAM.
  • Co-presenters

    Erik Espinoza


    10 SQL tricks that you didn’t suppose were practicable | killexams.com true Questions and Pass4sure dumps

    This publish became at the birth published over at jooq.org, a weblog specializing in everything issues open source, Java and application construction from the viewpoint of jOOQ.

    Listicles fondness these accomplish toil – no longer most effective accomplish they appeal to attention, if the content is furthermore positive (and in this case it's, reliance me), the article structure will furthermore subsist extraordinarily entertaining.

    this text will carry you 10 SQL hints that many of you may now not gain understanding had been possible. The article is a abstract of my new, extraordinarily fast-paced, ridiculously infantile-humored speak, which I’m giving at conferences (lately at JAX, and Devoxx France). You may additionally quote me on this:

    the complete slides may furthermore subsist viewed on SlideShare:

    … and that i’m inescapable there’ll subsist a recording on video soon. listed here are 10 SQL hints that you simply Didn’t believe gain been feasible:

    Introduction

    in order to recollect the cost of these 10 SQL tricks, it's first crucial to Take into account the context of the SQL language. Why accomplish I talk about SQL at Java conferences? (and that i’m constantly the only one!) here is why:

    sql-tricks-slide-006

    From early days onwards, programming language designers had this desire to design languages during which you recount the computer WHAT you want because of this, now not how to acquire it. for instance, in SQL, you inform the machine that you simply wish to “join” (be fragment of) the user table and the wield table and determine the clients that live in Switzerland. You don’t keeping HOW the database will retrieve this suggestions (e.g. should the users table subsist loaded first, or the address desk? may noiseless the two tables subsist joined in a nested loop or with a hashmap? should noiseless everything records subsist loaded in recollection first after which filtered for Swiss clients, or should they only load Swiss addresses in the first location? and many others.)

    As with every abstraction, you are going to noiseless should comprehend the basics of what’s happening in the back of the scenes in a database to assist the database gain the revise choices should you query it. as an instance, it makes sense to:

  • set up a proper international key relationship between the tables (this tells the database that every address is inescapable to gain a corresponding user)
  • Add an index on the search field: The country (this tells the database that particular international locations may furthermore subsist present in O(log N) as an alternative of O(N))
  • however as soon as your database and your software matures, you will gain sequel everything of the crucial meta records in vicinity and you may focus of attention to your industry edifying judgment most effective. perquisite here 10 tricks expose astonishing performance written in barely a brace of traces of declarative SQL, producing primary and furthermore advanced output.

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    learn about Containers,continual birth, DevOps culture, Cloud systems & protection with articles via experts fondness Michiel Rook, Christoph Engelbert, Scott Sanders and many extra.

    1. everything is a desk

    this is the most paltry of tricks, and not even basically a trick, nonetheless it is simple to a radical figuring out of SQL: everything is a table! if you betide to descry a SQL observation fondness this:

    opt for * FROM adult

    … you're going to directly spot the desk person sitting revise there in the FROM clause. That’s cool, that's a desk. but did you understand that the total remark is furthermore a table? for instance, that you can write:

    select * FROM ( pick * FROM person ) t

    And now, you gain got created what's known as a “derived table” – i.e. a nestedSELECT remark in a FROM clause.

    That’s trivial, but if you feel of it, quite based. which you could furthermore create advert-hoc, in-reminiscence tables with the VALUES() constructor as such, in some databases (e.g. PostgreSQL, SQL Server):

    opt for * FROM ( VALUES(1),(2),(three) ) t(a)

    Which effectively yields:

    If that clause is not supported, that you can revert to derived tables, e.g. in Oracle:

    opt for * FROM ( select 1 AS a FROM dual UNION ALL opt for 2 AS a FROM dual UNION ALL pick 3 AS a FROM dual ) t

    Now that you just’re when you account that VALUES() and derived tables are definitely the identical issue, conceptually, let’s evaluate the INSERT remark, which comes in two flavors:

    -- SQL Server, PostgreSQL, some others: INSERT INTO my_table(a) VALUES(1),(2),(three); -- Oracle, many others: INSERT INTO my_table(a) choose 1 AS a FROM dual UNION ALL choose 2 AS a FROM dual UNION ALL opt for three AS a FROM twin

    In SQL every thing is a table. should you’re inserting rows perquisite into a table, you’re now not truly inserting particular person rows. You’re truly inserting entire tables. Most americans just gyrate up to insert a single-row-desk most of the time, and for that judgement don’t know what INSERT really does.

    every shrimp thing is a table. In PostgreSQL, even features are tables:

    opt for * FROM substring('abcde', 2, three)

    The above yields:

    if you’re programming in Java, you could consume the analogy of the Java 8 journey API to Take this one step further. account the following equal ideas:

    table : circulation<Tuple<..>> opt for : map() distinctive : different() be a fragment of : flatMap() the status / HAVING : filter() group by means of : bring together() ORDER by means of : sorted() UNION everything : concat()

    With Java 8, “every shrimp thing is a move” (as quickly as you birth working with Streams, at the least). No depend how you radically change a circulate, e.g. with map() or filter(), the ensuing sort is everything the time a movement once again.

    We’ve written a entire article to clarify this extra deeply, and to examine the stream API with SQL:regular SQL Clauses and Their Equivalents in Java 8 Streams

    And if you’re trying to find “more advantageous streams” (i.e. streams with even more SQL semantics), accomplish try jOOλ, an open supply library that brings SQL window features to Java.

    2. information era with recursive SQL

    typical desk Expressions (also: CTE, often known as subquery factoring, e.g. in Oracle) are the most effective mode to declare variables in SQL (apart from the imprecise WINDOW clause that simplest PostgreSQL and Sybase SQL anywhere subsist awake of).

    here's an impressive thought. extraordinarily potent. accept as genuine with here statement:

    -- desk variables WITH t1(v1, v2) AS (select 1, 2), t2(w1, w2) AS ( select v1 * 2, v2 * 2 FROM t1 ) select * FROM t1, t2

    It yields

    v1 v2 w1 w2 ----------------- 1 2 2 four

    the consume of the fundamental WITH clause, that you may specify a listing of table variables (remember: every shrimp thing is a desk), which may additionally even depend upon each and every different.

    it truly is convenient to gain in mind. This makes CTE (average desk Expressions) already very effective, but what’s in fact basically astounding is that they’re allowed to subsist recursive! reliance perquisite here PostgreSQL instance:

    WITH RECURSIVE t(v) AS ( pick 1 -- Seed Row UNION ALL pick v + 1 -- Recursion FROM t ) opt for v FROM t restrict 5

    It yields

    v—12345

    How does it work? It’s particularly convenient, once you descry in the course of the many key phrases. You define a typical table expression that has precisely two UNION everything subqueries.

    the primary UNION everything subquery is what I usually muster the “seed row”. It “seeds” (initialises) the recursion. it could bear one or several rows on which they will recurse afterwards. endure in mind: every shrimp thing is a table, so their recursion will gyrate up on an entire table, not on an individual row/value.

    The 2nd UNION everything subquery is where the recursion happens. if you seem intently, you will observe that it selects from t. I.e. the 2nd subquery is allowed to opt for from the very CTE that we’re about to declare. Recursively. It for this judgement has additionally access to the column v, which is being declared by using the CTE that already uses it.

    In their instance, they seed the recursion with the row (1), after which recurse by course of including v + 1. The recursion is then stopped on the use-web site through surroundings aLIMIT 5 (watch out for doubtlessly countless recursions – identical to with Java eight Streams).

    aspect observe: Turing completeness

    Recursive CTE gain SQL:1999 turing finished, which potential that any program will furthermore subsist written in SQL! (if you’re loopy enough)

    One wonderful instance that often indicates up on blogs: The Mandelbrot Set, e.g. as displayed on http://explainextended.com/2013/12/31/satisfied-new-yr-5/

    WITH RECURSIVE q(r, i, rx, ix, g) AS ( opt for r::DOUBLE PRECISION * 0.02, i::DOUBLE PRECISION * 0.02, .0::DOUBLE PRECISION , .0::DOUBLE PRECISION, 0 FROM generate_series(-60, 20) r, generate_series(-50, 50) i UNION ALL select r, i, CASE WHEN abs(rx * rx + ix * ix) &amp;amp;lt;= 2 THEN rx * rx - ix * ix halt + r, CASE WHEN abs(rx * rx + ix * ix) &amp;amp;lt;= 2 THEN 2 * rx * ix conclusion + i, g + 1 FROM q where rx isn't NULL AND g &amp;amp;lt; 99 ) select array_to_string(array_agg(s ORDER by using r), '') FROM ( opt for i, r, substring(' .:-=+*#%@', max(g) / 10 + 1, 1) s FROM q community by means of i, r ) q group through i ORDER with the aid of i

    Run the above on PostgreSQL, and furthermore you’ll congregate anything like

    .-.:-.......==..*.=.::-@@@@@:::.:.@..*-. =. ...=...=...::+%.@:@@@@@@@@@@@@@+*#=.=:+-. ..- .:.:=::*....@@@@@@@@@@@@@@@@@@@@@@@@=@@.....::...:. ...*@@@@=.@:@@@@@@@@@@@@@@@@@@@@@@@@@@=.=....:...::. .::@@@@@:-@@@@@@@@@@@@@@@@@@@@@@@@@@@@:@..-:@=*:::. .-@@@@@-@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@.=@@@@=..: ...@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@:@@@@@:.. ....:-*@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@:: .....@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@-.. .....@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@-:... .--:+.@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@... .==@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@-.. ..+@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@-#. ...=+@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@.. -.=-@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@..: .*%:@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@:@- . ..:... ..-@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ .............. ....-@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@%@= .--.-.....-=.:..........::@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@.. ..=:-....=@+..=.........@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@:. .:+@@::@==@-*:%:+.......:@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@. ::@@@-@@@@@@@@@-:=.....:@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@: .:@@@@@@@@@@@@@@@=:.....%@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ .:@@@@@@@@@@@@@@@@@-...:@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@:- :@@@@@@@@@@@@@@@@@@@-..%@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@. %@@@@@@@@@@@@@@@@@@@-..-@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@. @@@@@@@@@@@@@@@@@@@@@::+@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@+ @@@@@@@@@@@@@@@@@@@@@@:@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@.. @@@@@@@@@@@@@@@@@@@@@@-@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@- @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@.

    stunning, huh?

    three. operating total Calculations

    This blog is replete of working total examples. They’re some of the most academic examples to learn about advanced SQL, as a result of there are at least a dozen of how a course to enforce a working complete.

    A running complete is effortless to preserve in mind, conceptually.

    eder 1

    In Microsoft Excel, you may effortlessly reckon a sum (or change) of two previous (or subsequent) values, after which consume the positive crosshair cursor to drag that mode through your entire spreadsheet. You “run” that complete during the spreadsheet. A “working complete”.

    In SQL, the most desirable strategy to try this is through the consume of window services, a different subject that this weblog has covered many repeatedly.

    Window capabilities are an impressive understanding – now not so handy to preserve in intellect at first, however basically, they’re basically really handy:

    Window functions are aggregations / rankings on a subset of rows relative to the current row being converted through opt for

    That’s it.:)

    What it essentially capacity is that a window feature can discharge calculations on rows which are “above” or “below” the existing row. not fondness gauge aggregations and neighborhood by using, despite the fact, they don’t seriously change the rows, which makes them very useful.

    The syntax will furthermore subsist summarized as follows, with particular person parts being not obligatory

    function(...) OVER ( PARTITION via ... ORDER through ... ROWS BETWEEN ... AND ... )

    So, they now gain any sort of duty (we’ll descry examples for such services later), adopted through this OVER() clause, which specifies the window. I.e. this OVER()clause defines:

  • The PARTITION: handiest rows that are in the identical partition because the latest row may subsist considered for the window
  • The ORDER: The window can furthermore subsist ordered independently of what we’re making a selection on
  • The ROWS (or range) carcass definition: The window will furthermore subsist confined to a set quantity of rows “ahead” and “in the back of”
  • That’s everything there is to window features.

    Now how does that assist us reckon a running total? accept as genuine with the following data:

    | identification | VALUE_DATE | amount | steadiness | |------|------------|--------|------------| | 9997 | 2014-03-18 | 99.17 | 19985.eighty one | | 9981 | 2014-03-sixteen | 71.44 | 19886.sixty four | | 9979 | 2014-03-16 | -ninety four.60 | 19815.20 | | 9977 | 2014-03-sixteen | -6.96 | 19909.80 | | 9971 | 2014-03-15 | -65.ninety five | 19916.76 |

    Let’s import on that steadiness is what they want to reckon from quantity

    Intuitively, they can immediately descry that here holds true:

    sql-tricks-slide-081

    So, in undeniable English, any steadiness can subsist expressed with here pseudo SQL:

    TOP_BALANCE – SUM(quantity) OVER (“all the rows on top of the current row”)

    In true SQL, that might then subsist written as follows:

    SUM(t.quantity) OVER ( PARTITION via t.account_id ORDER with the aid of t.value_date DESC, t.identification DESC ROWS BETWEEN UNBOUNDED preceding AND 1 previous )

    rationalization:

  • The partition will reckon the sum for each and every checking account, no longer for the entire facts set
  • The ordering will gain inescapable that transactions are ordered (in the partition) in advance of summing
  • The rows clause will accord with most effective previous rows (in the partition, given the ordering) ahead of summing
  • All of this could betide in-reminiscence over the facts set that has already been selected by means of you on your FROM .. where and so on. clauses, and is accordingly extremely quick.

    Intermezzo

    earlier than they movement on to everything of the other extraordinary tricks, accord with this: We’ve seen

  • (Recursive) regular desk Expressions (CTE)
  • Window services
  • each of these points are:

  • dazzling
  • Exremely effective
  • Declarative
  • a fragment of the SQL typical
  • purchasable in most universal RDBMS (apart from MySQL)
  • Very crucial constructing blocks
  • If the relaxation may furthermore subsist concluded from this text, it's the fact that you'll want to completely know these two constructing blocks of up to date SQL. Why? as a result of:

    eder 2

    four. discovering the greatest sequence without a gaps

    Stack Overflow has this very attribute characteristic to inspire individuals to live on their website for provided that possible. Badges:

    sql-tricks-slide-090

    For scale, you can descry what number of badges I actually have. tons.

    How accomplish you reckon these badges? Let’s gain a notice at the “fanatic” and the “Fanatic”. These badges are awarded to any one who spends a given volume of consecutive days on their platform. inspite of any marriage ceremony date or spouse’s birthday, you ought to LOG IN, or the counter begins from zero once more.

    Now as we’re doing declarative programming, they don’t keeping about holding any condition and in-memory counters. They want to express this in the form of on-line analytic SQL. I.e. accord with this data:

    | LOGIN_TIME | |---------------------| | 2014-03-18 05:37:13 | | 2014-03-sixteen 08:31:47 | | 2014-03-16 06:11:17 | | 2014-03-16 05:59:33 | | 2014-03-15 11:17:28 | | 2014-03-15 10:00:eleven | | 2014-03-15 07:45:27 | | 2014-03-15 07:42:19 | | 2014-03-14 09:38:12 |

    That doesn’t uphold tons. Let’s remove the hours from the timestamp. That’s handy:

    choose distinctive cast(login_time AS DATE) AS login_date FROM logins the status user_id = :user_id

    Which yields:

    | LOGIN_DATE | |------------| | 2014-03-18 | | 2014-03-16 | | 2014-03-15 | | 2014-03-14 |

    Now, that we’ve realized about window services, let’s simply add an smooth row quantity to each of those dates:

    opt for login_date, row_number() OVER (ORDER by means of login_date) FROM login_dates

    Which produces:

    | LOGIN_DATE | RN | |------------|----| | 2014-03-18 | four | | 2014-03-sixteen | 3 | | 2014-03-15 | 2 | | 2014-03-14 | 1 |

    still convenient. Now, what occurs, if as a substitute of determining these values one after the other, they subtract them?

    opt for login_date - row_number() OVER (ORDER with the aid of login_date) FROM login_dates

    We’re getting whatever thing fondness this:

    | LOGIN_DATE | RN | GRP | |------------|----|------------| | 2014-03-18 | 4 | 2014-03-14 | | 2014-03-16 | three | 2014-03-13 | | 2014-03-15 | 2 | 2014-03-13 | | 2014-03-14 | 1 | 2014-03-13 |

    Wow. pleasing. So, 14 – 1 = 13, 15 – 2 = 13, sixteen – three = 13, but 18 – 4 = 14. nobody can philosophize it better than Doge:

    eder 3

    There’s a simple illustration for this conduct:

  • ROW_NUMBER() never has gaps. That’s the course it’s defined
  • Our records, youngsters, does
  • So once they subtract a “gapless” chain of consecutive integers from a “gapful” chain of non-consecutive dates, they can congregate the identical date for each “gapless” subseries of consecutive dates, and we’ll congregate a brand original date once more where the date collection had gaps.

    Huh.

    This capacity they can now quite simply group by course of this capricious date value:

    choose min(login_date), max(login_date), max(login_date) - min(login_date) + 1 AS periodFROM login_date_groups neighborhood with the aid of grp ORDER with the aid of size DESC

    And we’re executed. The greatest sequence of consecutive dates without a gaps has been discovered:

    | MIN | MAX | size | |------------|------------|--------| | 2014-03-14 | 2014-03-sixteen | three | | 2014-03-18 | 2014-03-18 | 1 |

    With the complete query being:

    WITH login_dates AS ( select diverse solid(login_time AS DATE) login_date FROM logins the status user_id = :user_id ), login_date_groups AS ( opt for login_date, login_date - row_number() OVER (ORDER by using login_date) AS grp FROM login_dates ) opt for min(login_date), max(login_date), max(login_date) - min(login_date) + 1 AS lengthFROM login_date_groups group by using grp ORDER by length DESC

    eder 4

    now not that challenging in the end, appropriate? Of route, having the understanding makes the entire difference, however the question itself is in fact very very simple and chic. No approach you may sequel in constrain some imperative-fashion algorithm in a leaner course than this.

    Whew.

    5. discovering the length of a series

    previously, they had considered collection of consecutive values. That’s handy to deal with as they will ill-treat of the consecutiveness of integers. What if the definition of a “series” is much less intuitive, and furthermore to that, a brace of chain hold the identical values? believe here statistics, the status size is the size of each sequence that they exigency to calculate:

    | id | VALUE_DATE | amount | length | |------|------------|--------|------------| | 9997 | 2014-03-18 | ninety nine.17 | 2 | | 9981 | 2014-03-16 | seventy one.forty four | 2 | | 9979 | 2014-03-sixteen | -ninety four.60 | 3 | | 9977 | 2014-03-16 | -6.96 | three | | 9971 | 2014-03-15 | -65.ninety five | 3 | | 9964 | 2014-03-15 | 15.13 | 2 | | 9962 | 2014-03-15 | 17.47 | 2 | | 9960 | 2014-03-15 | -3.55 | 1 | | 9959 | 2014-03-14 | 32.00 | 1 |

    yes, you’ve guessed correct. A “series” is described via the fact that consecutive (ordered by means of id) rows gain the selfsame signal(volume). investigate again the records formatted as under:

    | identification | VALUE_DATE | amount | length | |------|------------|--------|------------| | 9997 | 2014-03-18 | +99.17 | 2 | | 9981 | 2014-03-16 | +71.44 | 2 | | 9979 | 2014-03-16 | -94.60 | 3 | | 9977 | 2014-03-16 | - 6.ninety six | 3 | | 9971 | 2014-03-15 | -65.ninety five | three | | 9964 | 2014-03-15 | +15.13 | 2 | | 9962 | 2014-03-15 | +17.forty seven | 2 | | 9960 | 2014-03-15 | - 3.fifty five | 1 | | 9959 | 2014-03-14 | +32.00 | 1 |

    How accomplish they accomplish it? “convenient”😉 First, let’s accomplish away with the entire noise, and add yet another row number:

    opt for identification, amount, sign(quantity) AS sign, row_number() OVER (ORDER by identity DESC) AS rn FROM trx

    this could supply us:

    | identification | quantity | mark | RN | |------|--------|------|----| | 9997 | ninety nine.17 | 1 | 1 | | 9981 | seventy one.forty four | 1 | 2 | | 9979 | -94.60 | -1 | three | | 9977 | -6.96 | -1 | 4 | | 9971 | -65.95 | -1 | 5 | | 9964 | 15.13 | 1 | 6 | | 9962 | 17.forty seven | 1 | 7 | | 9960 | -three.55 | -1 | eight | | 9959 | 32.00 | 1 | 9 |

    Now, the subsequent purpose is to bear perquisite here desk:

    | id | quantity | mark | RN | LO | hi | |------|--------|------|----|----|----| | 9997 | 99.17 | 1 | 1 | 1 | | | 9981 | 71.forty four | 1 | 2 | | 2 | | 9979 | -ninety four.60 | -1 | three | three | | | 9977 | -6.96 | -1 | 4 | | | | 9971 | -sixty five.ninety five | -1 | 5 | | 5 | | 9964 | 15.13 | 1 | 6 | 6 | | | 9962 | 17.47 | 1 | 7 | | 7 | | 9960 | -3.fifty five | -1 | 8 | 8 | eight | | 9959 | 32.00 | 1 | 9 | 9 | 9 |

    in this table, they are looking to reproduction the row quantity cost into “LO” at the “lower” halt of a series, and into “hello” on the “higher” conclusion of a series. For this we’ll subsist the usage of the magical LEAD() and LAG(). LEAD() can entry the n-th next row from the present row, whereas LAG() can entry the n-th previous row from the current row. for example:

    choose lag(v) OVER (ORDER by course of v), v, lead(v) OVER (ORDER with the aid of v) FROM ( VALUES (1), (2), (three), (four) ) t(v)

    The above question produces:

    eder 4

    That’s surprising! remember, with window services, that you could discharge rankings or aggregations on a subset of rows relative to the present row. in the case of LEAD() and LAG(), they with no worry access a single row relative to the existing row, given its offset. here's helpful in so many situations.

    carrying on with with their “LO” and “hello” illustration, they are able to simply write:

    choose trx.*, CASE WHEN lag(sign) OVER (ORDER by course of identity DESC) != mark THEN rn halt AS lo, CASE WHEN lead(signal) OVER (ORDER with the aid of id DESC) != signal THEN rn halt AS hello, FROM trx

    … by which they examine the “old” signal (lag(sign)) with the “existing” mark (sign). if they’re distinctive, they sequel the row quantity in “LO”, as a result of that’s the lower inescapable of their series.

    Then they examine the “next” signal (lead(signal)) with the “present” mark (sign). in the event that they’re distinct, they sequel the row quantity in “hi”, as a result of that’s the upper inescapable of their series.

    ultimately, a shrimp handrum NULL coping with to congregate everything right, and we’re finished:

    select -- With NULL handling... trx.*, CASE WHEN coalesce(lag(sign) OVER (ORDER by id DESC), 0) != signal THEN rn conclusion AS lo, CASE WHEN coalesce(lead(sign) OVER (ORDER by course of identity DESC), 0) != signal THEN rn conclusion AS hello, FROM trx

    subsequent step. They exigency “LO” and “hello” to seem in everything rows, no longer just on the “reduce” and “upper” bounds of a series. E.g. fondness this:

    | id | amount | mark | RN | LO | hi | |------|--------|------|----|----|----| | 9997 | ninety nine.17 | 1 | 1 | 1 | 2 | | 9981 | 71.44 | 1 | 2 | 1 | 2 | | 9979 | -ninety four.60 | -1 | three | three | 5 | | 9977 | -6.ninety six | -1 | 4 | 3 | 5 | | 9971 | -65.ninety five | -1 | 5 | three | 5 | | 9964 | 15.13 | 1 | 6 | 6 | 7 | | 9962 | 17.47 | 1 | 7 | 6 | 7 | | 9960 | -3.55 | -1 | eight | eight | eight | | 9959 | 32.00 | 1 | 9 | 9 | 9 |

    We’re the consume of a characteristic it truly is accessible as a minimum in Redshift, Sybase SQL anywhere, DB2, Oracle. We’re the usage of the “IGNORE NULLS” clause that can subsist handed to a few window capabilities:

    choose trx.*, last_value (lo) IGNORE NULLS OVER ( ORDER by course of id DESC ROWS BETWEEN UNBOUNDED preceding AND present ROW) AS lo, first_value(hello) IGNORE NULLS OVER ( ORDER by course of id DESC ROWS BETWEEN latest ROW AND UNBOUNDED FOLLOWING) AS hi FROM trx

    lots of key words! but the essence is at everything times the same. From any given “current” row, they study everything the “outdated values” (ROWS BETWEEN UNBOUNDED previous AND latest ROW), but ignoring the entire nulls. From these outdated values, they Take the closing cost, and that’s their original “LO” price. In other phrases, they Take the “closest previous” “LO” cost.

    The identical with “hi”. From any given “existing” row, they examine everything the “subsequent values” (ROWS BETWEEN existing ROW AND UNBOUNDED FOLLOWING), however ignoring everything the nulls. From the next values, they Take the primary cost, and that’s their original “hello” price. In other words, they Take the “closest following” “hello” value.

    defined in Powerpoint:

    eder 4

    Getting it 100% proper, with a bit handrum NULL fiddling:

    select -- With NULL managing... trx.*, coalesce(last_value (lo) IGNORE NULLS OVER ( ORDER by means of identity DESC ROWS BETWEEN UNBOUNDED previous AND latest ROW), rn) AS lo, coalesce(first_value(hi) IGNORE NULLS OVER ( ORDER by means of identity DESC ROWS BETWEEN present ROW AND UNBOUNDED FOLLOWING), rn) AS hello FROM trx

    finally, we’re just doing a paltry terminal step, conserving in intellect off-by course of-1 blunders:

    choose trx.*, 1 + hi - lo AS lengthFROM trx

    And we’re completed. perquisite here’s their outcome:

    | id | volume | mark | RN | LO | hi | length| |------|--------|------|----|----|----|-------| | 9997 | ninety nine.17 | 1 | 1 | 1 | 2 | 2 | | 9981 | 71.44 | 1 | 2 | 1 | 2 | 2 | | 9979 | -94.60 | -1 | three | 3 | 5 | 3 | | 9977 | -6.ninety six | -1 | four | 3 | 5 | three | | 9971 | -sixty five.ninety five | -1 | 5 | 3 | 5 | 3 | | 9964 | 15.13 | 1 | 6 | 6 | 7 | 2 | | 9962 | 17.47 | 1 | 7 | 6 | 7 | 2 | | 9960 | -3.55 | -1 | 8 | 8 | 8 | 1 | | 9959 | 32.00 | 1 | 9 | 9 | 9 | 1 |

    And the replete question here:

    WITH trx1(identification, amount, signal, rn) AS ( pick identification, amount, sign(amount), row_number() OVER (ORDER through id DESC) FROM trx ), trx2(identity, quantity, signal, rn, lo, hello) AS ( select trx1.*, CASE WHEN coalesce(lag(sign) OVER (ORDER by id DESC), 0) != mark THEN rn conclusion, CASE WHEN coalesce(lead(signal) OVER (ORDER with the aid of id DESC), 0) != mark THEN rn conclusion FROM trx1 ) select trx2.*, 1 - last_value (lo) IGNORE NULLS OVER (ORDER by course of identity DESC ROWS BETWEEN UNBOUNDED previous AND latest ROW) + first_value(hello) IGNORE NULLS OVER (ORDER with the aid of id DESC ROWS BETWEEN latest ROW AND UNBOUNDED FOLLOWING) FROM trx2

    eder 4

    Huh. This SQL issue does birth getting exciting!

    ready for more?

    6. The subset sum issue with SQL

    here is my customary!

    what is the subset sum problem? find a fun clarification perquisite here:https://xkcd.com/287

    And a avoid of the mill one here:https://en.wikipedia.org/wiki/Subset_sum_problem

    essentially, for each of those totals…

    | identity | complete | |----|-------| | 1 | 25150 | | 2 | 19800 | | three | 27511 |

    … they are looking to determine the “top-rated” (i.e. the closest) sum feasible, together with any combination of these objects:

    | identity | detail | |------|-------| | 1 | 7120 | | 2 | 8150 | | three | 8255 | | four | 9051 | | 5 | 1220 | | 6 | 12515 | | 7 | 13555 | | 8 | 5221 | | 9 | 812 | | 10 | 6562 |

    As you’re everything brief together with your intellectual mathemagic processing, you gain instantly calculated these to subsist the most advantageous sums:

    | complete | most fulfilling | CALCULATION |-------|-------|-------------------------------- | 25150 | 25133 | 7120 + 8150 + 9051 + 812 | 19800 | 19768 | 1220 + 12515 + 5221 + 812 | 27511 | 27488 | 8150 + 8255 + 9051 + 1220 + 812

    how to accomplish it with SQL? effortless. simply create a CTE that carries the entire 2n *viable* sums and then determine the closest one for each and every total:

    -- the entire practicable 2N sums WITH sums(sum, max_id, calc) AS (...) -- locate the most preempt sum per “total” select totals.complete, something_something(complete - sum) AS top-quality, something_something(complete - sum) AS calc FROM draw_the_rest_of_the_*bleep*_owl

    As you’re studying this, you could subsist fondness my friend perquisite here:

    eder 4

    but don’t agonize, the respond is – once again – now not everything that tough (although it doesn’t discharge as a result of the character of the algorithm):

    WITH sums(sum, id, calc) AS ( pick merchandise, id, to_char(merchandise) FROM gadgets UNION ALL pick detail + sum, gadgets.identity, calc || ' + ' || item FROM sums subsist a fragment of objects ON sums.id &lt; items.identity ) opt for totals.identity, totals.complete, min (sum) maintain ( DENSE_RANK FIRST ORDER with the aid of abs(complete - sum) ) AS optimal, min (calc) retain ( DENSE_RANK FIRST ORDER through abs(total - sum) ) AS calc, FROM totals cross subsist fragment of sums community by using totals.identification, totals.total

    in this article, I gained’t clarify the details of this solution, because the instance has been taken from a previous article so you might locate here:

    how to find the closest subset sum with SQL

    relish analyzing the details, however gain sure to attain back back perquisite here for the terminal 4 tricks:

    7. Capping a running total

    to date, we’ve considered how to reckon an “normal” running complete with SQL the consume of window functions. That became effortless. Now, how about if they cap the working complete such that it certainly not goes below zero? almost, they wish to reckon this:

    | DATE | quantity | complete | |------------|--------|-------| | 2012-01-01 | 800 | 800 | | 2012-02-01 | 1900 | 2700 | | 2012-03-01 | 1750 | 4450 | | 2012-04-01 | -20000 | 0 | | 2012-05-01 | 900 | 900 | | 2012-06-01 | 3900 | 4800 | | 2012-07-01 | -2600 | 2200 | | 2012-08-01 | -2600 | 0 | | 2012-09-01 | 2100 | 2100 | | 2012-10-01 | -2400 | 0 | | 2012-eleven-01 | 1100 | 1100 | | 2012-12-01 | 1300 | 2400 |

    So, when that great indigent volume -20000 changed into subtracted, as an alternative of displaying the genuine complete of -15550, they without hardship divulge 0. In different words (or information sets):

    | DATE | amount | complete | |------------|--------|-------| | 2012-01-01 | 800 | 800 | finest(0, 800) | 2012-02-01 | 1900 | 2700 | optimum(0, 2700) | 2012-03-01 | 1750 | 4450 | foremost(0, 4450) | 2012-04-01 | -20000 | 0 | top-quality(0, -15550) | 2012-05-01 | 900 | 900 | most advantageous(0, 900) | 2012-06-01 | 3900 | 4800 | ideal(0, 4800) | 2012-07-01 | -2600 | 2200 | most useful(0, 2200) | 2012-08-01 | -2600 | 0 | ultimate(0, -400) | 2012-09-01 | 2100 | 2100 | gold standard(0, 2100) | 2012-10-01 | -2400 | 0 | superior(0, -300) | 2012-11-01 | 1100 | 1100 | highest quality(0, 1100) | 2012-12-01 | 1300 | 2400 | most excellent(0, 2400)

    How will they accomplish it?

    eder 4

    exactly. With obscure, seller-certain SQL. in this case, we’re the consume of Oracle SQL

    eder 4

    How does it work? extraordinarily convenient!

    just add model after any desk, and you’re opening up a can of extraordinary SQL worms!

    opt for ... FROM some_table -- sequel this after any table mannequin ...

    as soon as they sequel mannequin there, they can implement spreadsheet logic without leisurely in their SQL statements, just as with Microsoft Excel.

    the following three clauses are probably the most valuable and general (i.e. 1-2 per year by anybody on this planet):

    model -- The spreadsheet dimensions DIMENSION with the aid of ... -- The spreadsheet telephone type MEASURES ... -- The spreadsheet formulas suggestions ...

    The that means of every of these three extra clauses is ultimate explained with slides once again.

    The DIMENSION via clause specifies the size of your spreadsheet. in contrast to in MS Excel, that you can gain any variety of dimensions in Oracle:

    eder 4

    The MEASURES clause specifies the values that are available in each telephone of your spreadsheet. not fondness in MS Excel, that you can gain an entire tuple in every cell in Oracle, not only a single price.

    eder 4

    The guidelines clause specifies the formulas that apply to each cell on your spreadsheet. unlike in MS Excel, these guidelines / formulas are centralized at a single location, as an alternative of being sequel inside of each cellphone:

    eder 4

    This design makes mannequin a bit more durable to consume than MS Excel, but lots greater powerful, if you dare. The entire question will then subsist “trivially”:

    opt for * FROM ( pick date, amount, 0 AS total FROM amounts ) mannequin DIMENSION through (row_number() OVER (ORDER by using date) AS rn) MEASURES (date, volume, complete) suggestions ( complete[any] = choicest(0, coalesce(total[cv(rn) - 1], 0) + amount[cv(rn)]) )

    This entire factor is so powerful, it ships with its personal white paper by means of Oracle, so in preference to explaining issues further perquisite here listed here, gladden accomplish read the remarkable white paper:

    http://www.oracle.com/technetwork/middleware/bi-basis/10gr1-twp-bi-dw-sqlmodel-131067.pdf

    eight. Time collection pattern cognizance

    if you’re into fraud detection or some other box that runs true time analytics on tremendous facts units, time sequence pattern recognition is not at everything a original time period to you.

    If they evaluate the “length of a series” information set, they could wish to generate triggers on complicated routine over their time sequence as such:

    | identification | VALUE_DATE | amount | LEN | trigger |------|------------|---------|-----|-------- | 9997 | 2014-03-18 | + 99.17 | 1 | | 9981 | 2014-03-16 | - seventy one.44 | four | | 9979 | 2014-03-sixteen | - ninety four.60 | 4 | x | 9977 | 2014-03-sixteen | - 6.ninety six | 4 | | 9971 | 2014-03-15 | - sixty five.ninety five | 4 | | 9964 | 2014-03-15 | + 15.13 | three | | 9962 | 2014-03-15 | + 17.47 | three | | 9960 | 2014-03-15 | + three.fifty five | three | | 9959 | 2014-03-14 | - 32.00 | 1 |

    the rule of thumb of the above trigger is:

    trigger on the 3rd repetition of an event if the suffer happens extra than 3 times.

    comparable to the outdated model clause, they are able to accomplish this with an Oracle-certain clause that become introduced to Oracle 12c:

    opt for ... FROM some_table -- sequel this after any desk to sample-match -- the desk’s contents MATCH_RECOGNIZE (...)

    The least difficult feasible software of MATCH_RECOGNIZE includes perquisite here subclauses:

    choose * FROM collectionMATCH_RECOGNIZE ( -- pattern matching is done during this order ORDER through ... -- These are the columns produced by means of suits MEASURES ... -- a brief specification of what rows are -- again from each and every fit everything ROWS PER match -- «ordinary expressions» of events to in shape pattern (...) -- The definitions of «what is an experience» define ... )

    That sounds loopy. Let’s notice at some sample clause implementations

    select * FROM seriesMATCH_RECOGNIZE ( ORDER by identity MEASURES classifier() AS trg everything ROWS PER healthy pattern (S (R X R+)?) outline R AS sign(R.volume) = prev(sign(R.quantity)), X AS signal(X.amount) = prev(signal(X.volume)) )

    What will they accomplish perquisite here?

  • We order the desk with the aid of id, which is the order by which they are looking to hale movements. convenient.
  • We then specify the values that they desire as a result. They want the “MEASURE” trg, which is defined because the classifier, i.e. the literal that we’ll consume within the pattern afterwards. Plus they want the entire rows from a healthy.
  • We then specify an everyday expression-like pattern. The pattern is an suffer “S” for birth, adopted optionally by “R” for Repeat, “X” for their particular event X, followed by one or extra “R” for iterate once again. If the total pattern suits, they congregate SRXR or SRXRR or SRXRRR, i.e. X will subsist on the third position of a collection of size >= 4
  • eventually, they define R and X as being the equal issue: The adventure whenSIGN(amount) of the existing row is the selfsame as signal(volume) of the previous row. They don’t gain to define “S”. “S” is only another row.
  • This question will magically bear perquisite here output:

    | identity | VALUE_DATE | quantity | TRG | |------|------------|---------|-----| | 9997 | 2014-03-18 | + 99.17 | S | | 9981 | 2014-03-sixteen | - 71.44 | R | | 9979 | 2014-03-16 | - ninety four.60 | X | | 9977 | 2014-03-sixteen | - 6.96 | R | | 9971 | 2014-03-15 | - 65.95 | S | | 9964 | 2014-03-15 | + 15.13 | S | | 9962 | 2014-03-15 | + 17.47 | S | | 9960 | 2014-03-15 | + 3.55 | S | | 9959 | 2014-03-14 | - 32.00 | S |

    we will descry a single “X” in their adventure flow. exactly the status they had anticipated it. on the third repetition of an event (identical sign) in a chain of size > three.

    boom!

    As they don’t definitely keeping about “S” and “R” activities, let’s simply eliminate them as such:

    choose identity, value_date, quantity, CASE trg WHEN 'X' THEN 'X' conclusion trg FROM seriesMATCH_RECOGNIZE ( ORDER by means of identification MEASURES classifier() AS trg everything ROWS PER fit pattern (S (R X R+)?) outline R AS sign(R.quantity) = prev(signal(R.quantity)), X AS signal(X.quantity) = prev(signal(X.amount)) )

    to produce:

    | id | VALUE_DATE | volume | TRG | |------|------------|---------|-----| | 9997 | 2014-03-18 | + 99.17 | | | 9981 | 2014-03-sixteen | - seventy one.forty four | | | 9979 | 2014-03-sixteen | - ninety four.60 | X | | 9977 | 2014-03-16 | - 6.96 | | | 9971 | 2014-03-15 | - sixty five.ninety five | | | 9964 | 2014-03-15 | + 15.13 | | | 9962 | 2014-03-15 | + 17.47 | | | 9960 | 2014-03-15 | + three.fifty five | | | 9959 | 2014-03-14 | - 32.00 | |

    thanks Oracle!

    eder 4

    again, don’t are expecting me to interpret this any more desirable than the striking Oracle white paper already did, which I strongly advocate analyzing in case you’re using Oracle 12c anyway:

    http://www.oracle.com/ocom/corporations/public/@otn/files/webcontent/1965433.pdf

    9. Pivoting and Unpivoting

    in case you’ve examine this far, the following should subsist nearly too embarassingly essential:

    here is their statistics, i.e. actors, film titles, and movie scores:

    | denomination | TITLE | ranking | |-----------|-----------------|--------| | A. provide | ANNIE id | G | | A. award | DISCIPLE mom | PG | | A. supply | GLORY TRACY | PG-13 | | A. HUDSON | LEGEND JEDI | PG | | A. CRONYN | IRON MOON | PG | | A. CRONYN | girl STAGE | PG | | B. WALKEN | SIEGE MADRE | R |

    here is what they denomination pivoting:

    | denomination | NC-17 | PG | G | PG-13 | R | |-----------|-------|-----|-----|-------|-----| | A. award | three | 6 | 5 | 3 | 1 | | A. HUDSON | 12 | 4 | 7 | 9 | 2 | | A. CRONYN | 6 | 9 | 2 | 6 | four | | B. WALKEN | eight | 8 | four | 7 | 3 | | B. WILLIS | 5 | 5 | 14 | three | 6 | | C. DENCH | 6 | 4 | 5 | four | 5 | | C. NEESON | three | eight | four | 7 | 3 |

    have a notice at how they kinda grouped via the actors after which “pivoted” the quantity films per rating every actor performed in. as an alternative of displaying this in a “relational” means, (i.e. each and every group is a row) they pivoted the entire issue to provide a column per neighborhood. they are able to accomplish this, because they recognize everything of the viable corporations in strengthen.

    Unpivoting is the opposite, when from the above, they are looking to congregate lower back to the “row per neighborhood” representation:

    | identify | rating | import number | |-----------|--------|-------| | A. award | NC-17 | 3 | | A. accouter | PG | 6 | | A. accouter | G | 5 | | A. provide | PG-13 | 3 | | A. provide | R | 6 | | A. HUDSON | NC-17 | 12 | | A. HUDSON | PG | 4 |

    It’s definitely basically convenient. here is how we’d accomplish it in PostgreSQL:

    choose first_name, last_name, import number(*) FILTER (where score = 'NC-17') AS "NC-17", import number(*) FILTER (the status score = 'PG' ) AS "PG", count(*) FILTER (the status rating = 'G' ) AS "G", count(*) FILTER (where score = 'PG-13') AS "PG-13", import number(*) FILTER (where ranking = 'R' ) AS "R" FROM actor AS a be a fragment of film_actor AS fa using (actor_id) be fragment of film AS f using (film_id) group via actor_id

    we will append a simple FILTER clause to an aggregate characteristic with a view to import number most effective one of the vital statistics.

    In everything different databases, we’d accomplish it fondness this:

    choose first_name, last_name, count(CASE rating WHEN 'NC-17' THEN 1 end) AS "NC-17", count(CASE ranking WHEN 'PG' THEN 1 end) AS "PG", count(CASE score WHEN 'G' THEN 1 end) AS "G", count(CASE score WHEN 'PG-13' THEN 1 conclusion) AS "PG-13", count(CASE rating WHEN 'R' THEN 1 end) AS "R" FROM actor AS a join film_actor AS fa the usage of (actor_id) be a fragment of movie AS f using (film_id) neighborhood through actor_id

    The distinguished aspect perquisite here is that combination functions usually only reliance non-NULL values, so if they gain everything the values NULL that don't seem to subsist arresting per aggregation, we’ll congregate the equal result.

    Now, if you’re the consume of both SQL Server, or Oracle, you could consume the developed-in PIVOT or UNPIVOT clauses instead. once more, as with mannequin or MATCH_RECOGNIZE, simply append this original key phrase after a desk and congregate the selfsame effect:

    -- PIVOTING opt for something, whatever FROM some_table PIVOT ( import number(*) FOR rating IN ( 'NC-17' AS "NC-17", 'PG' AS "PG", 'G' AS "G", 'PG-13' AS "PG-13", 'R' AS "R" ) ) -- UNPIVOTING opt for whatever thing, anything FROM some_table UNPIVOT ( import number FOR score IN ( "NC-17" AS 'NC-17', "PG" AS 'PG', "G" AS 'G', "PG-13" AS 'PG-13', "R" AS 'R' ) )

    effortless. subsequent.

    10. Abusing XML and JSON

    First off

    eder 4

    JSON is barely XML with less elements and less syntax

    Now, each person is awake of that XML is fabulous. The corollary is as a consequence:

    JSON is much less astounding

    Don’t consume JSON.

    Now that we’ve settled this, they will safely ignore the continuing JSON-in-the-database-hype (which most of you are going to feel sorry about in 5 years anyway), and circulate on to the closing illustration. the course to accomplish XML within the database.

    here is what they wish to do:

    eder 4

    Given the customary XML doc, they wish to parse that doc, unnest the comma-separated checklist of movies per actor, and bear a denormalized representation of actors/movies in a single relation.

    ready. Set. Go. here's the concept. we've three CTE:

    WITH RECURSIVE x(v) AS (select '...'::xml), actors( actor_id, first_name, last_name, films ) AS (...), movies( actor_id, first_name, last_name, film_id, film ) AS (...) choose * FROM films

    within the first one, they simply parse the XML. perquisite here with PostgreSQL:

    WITH RECURSIVE x(v) AS (choose ' Bud Spencer God Forgives... I Don’t, Double crisis, They muster Him Bulldozer Terence Hill God Forgives... I Don’t, Double drawback, fortunate Luke '::xml), actors(actor_id, first_name, last_name, movies) AS (...), movies(actor_id, first_name, last_name, film_id, movie) AS (...) opt for * FROM films

    handy.

    Then, they accomplish some XPath magic to extract the particular person values from the XML constitution and sequel those into columns:

    WITH RECURSIVE x(v) AS (select '...'::xml), actors(actor_id, first_name, last_name, movies) AS ( choose row_number() OVER (), (xpath('//first-identify/textual content()', t.v))[1]::text, (xpath('//final-name/text()' , t.v))[1]::textual content, (xpath('//movies/textual content()' , t.v))[1]::textual content FROM unnest(xpath('//actor', (opt for v FROM x))) t(v) ), movies(actor_id, first_name, last_name, film_id, movie) AS (...) opt for * FROM movies

    nevertheless convenient.

    finally, simply slightly of recursive commonplace expression pattern matching magic, and we’re done!

    WITH RECURSIVE x(v) AS (opt for '...'::xml), actors(actor_id, first_name, last_name, films) AS (...), films(actor_id, first_name, last_name, film_id, film) AS ( pick actor_id, first_name, last_name, 1, regexp_replace(films, ',.+', '') FROM actors UNION ALL pick actor_id, a.first_name, a.last_name, f.film_id + 1, regexp_replace(a.films, '.*' || f.movie || ', ?(.*?)(,.+)?', '\1') FROM films AS f subsist a fragment of actors AS a the consume of (actor_id) the status a.films not fondness '%' || f.film ) opt for * FROM movies

    Let’s conclude:

    eder 4

    Conclusion

    All of what this text has shown was declarative. and comparatively easy. Of path, for the fun sequel that I’m attempting to achieve in this speak, some exaggerated SQL became taken and that i expressly called every thing “effortless”. It’s not at everything handy, you should observe SQL. fondness many different languages, but a shrimp more durable as a result of:

  • The syntax is a bit of clumsy on occasion
  • Declarative considering isn't handy. as a minimum, it’s very diverse
  • however once you congregate a hold of it, declarative programming with SQL is completely value it as you can express intricate relationships between your statistics in very shrimp or no code with the aid of simply describing the result you are looking to congregate from the database.

    Isn’t that marvelous?

    And if that was a shrimp bit over the top, accomplish notice that I’m happy to dispute with your JUG / convention to supply this speak (simply contact us), or if you wish to congregate definitely down into the particulars of those things, they additionally present this talk as a public or in-apartment workshop. accomplish congregate involved! We’re searching ahead.

    See once again the complete set of slides here:


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    How to Create an Oracle Database Docker Image | killexams.com true questions and Pass4sure dumps

    Oracle has released Docker build files for the Oracle Database on GitHub. With those build files, you can travel ahead and build your own Docker image for the Oracle Database. If you don’t know what Docker is, you should travel and check it out. It’s a icy technology based on the Linux containers technology that allows you to containerize your application — whatever that application may be. Naturally, it didn’t Take long for people to start looking at containerizing databases, as well, which makes a lot of sense — especially for, but not only, progress and test environments. Here is a particular blog post on how to containerize your Oracle Database by using those build files that Oracle has provided.

    You will need:

    Environment

    My environment is as follows:

  • Oracle Linux 7.3 (4.1.12-94.3.8.el7uek.x86_64).
  • Docker 17.03.1-ce (docker-engine.x86_64 17.03.1.ce-3.0.1.el7).
  • Oracle Database 12.2.0.1 Enterprise Edition.
  • Docker Setup

    The first thing, if you gain not already done so, is to set up Docker on the environment. Luckily, this is fairly straightforward. Docker is shipped as an add-on with Oracle Linux 7 UEK4. As I’m running on such an environment, everything I gain to accomplish is to is enable the addons Yum Repository and install the docker-engine package. Note, this is done as the root Linux user.

    Enable OL7 addons repo:

    [root@localhost ~]# yum-config-manager enable *addons* Loaded plugins: langpacks ================================================================== repo: ol7_addons ================================================================== [ol7_addons] async = True bandwidth = 0 base_persistdir = /var/lib/yum/repos/x86_64/7Server baseurl = http://public-yum.oracle.com/repo/OracleLinux/OL7/addons/x86_64/ cache = 0 cachedir = /var/cache/yum/x86_64/7Server/ol7_addons check_config_file_age = True compare_providers_priority = 80 cost = 1000 deltarpm_metadata_percentage = 100 deltarpm_percentage = enabled = True enablegroups = True exclude = failovermethod = priority ftp_disable_epsv = False gpgcadir = /var/lib/yum/repos/x86_64/7Server/ol7_addons/gpgcadir gpgcakey = gpgcheck = True gpgdir = /var/lib/yum/repos/x86_64/7Server/ol7_addons/gpgdir gpgkey = file:///etc/pki/rpm-gpg/RPM-GPG-KEY-oracle hdrdir = /var/cache/yum/x86_64/7Server/ol7_addons/headers http_caching = all includepkgs = ip_resolve = keepalive = True keepcache = False mddownloadpolicy = sqlite mdpolicy = group:small mediaid = metadata_expire = 21600 metadata_expire_filter = read-only:present metalink = minrate = 0 mirrorlist = mirrorlist_expire = 86400 name = Oracle Linux 7Server Add ons (x86_64) old_base_cache_dir = password = persistdir = /var/lib/yum/repos/x86_64/7Server/ol7_addons pkgdir = /var/cache/yum/x86_64/7Server/ol7_addons/packages proxy = False proxy_dict = proxy_password = proxy_username = repo_gpgcheck = False retries = 10 skip_if_unavailable = False ssl_check_cert_permissions = True sslcacert = sslclientcert = sslclientkey = sslverify = True throttle = 0 timeout = 30.0 ui_id = ol7_addons/x86_64 ui_repoid_vars = releasever, basearch username =

    Install docker-engine:

    [root@localhost ~]# yum install docker-engine Loaded plugins: langpacks, ulninfo Resolving Dependencies --> Running transaction check ---> Package docker-engine.x86_64 0:17.03.1.ce-3.0.1.el7 will subsist installed --> Processing Dependency: docker-engine-selinux >= 17.03.1.ce-3.0.1.el7 for package: docker-engine-17.03.1.ce-3.0.1.el7.x86_64 --> Running transaction check ---> Package selinux-policy-targeted.noarch 0:3.13.1-102.0.3.el7_3.16 will subsist updated ---> Package selinux-policy-targeted.noarch 0:3.13.1-166.0.2.el7 will subsist an update --> Processing Dependency: selinux-policy = 3.13.1-166.0.2.el7 for package: selinux-policy-targeted-3.13.1-166.0.2.el7.noarch --> Running transaction check ---> Package selinux-policy.noarch 0:3.13.1-102.0.3.el7_3.16 will subsist updated ---> Package selinux-policy.noarch 0:3.13.1-166.0.2.el7 will subsist an update --> Finished Dependency Resolution Dependencies Resolved ====================================================================================================================================================== Package Arch Version Repository Size ====================================================================================================================================================== Installing: docker-engine x86_64 17.03.1.ce-3.0.1.el7 ol7_addons 19 M Updating: selinux-policy-targeted noarch 3.13.1-166.0.2.el7 ol7_latest 6.5 M Updating for dependencies: selinux-policy noarch 3.13.1-166.0.2.el7 ol7_latest 435 k Transaction Summary ====================================================================================================================================================== Install 1 Package Upgrade 1 Package (+1 relative package) Total download size: 26 M Is this ok [y/d/N]: y Downloading packages: No Presto metadata available for ol7_latest (1/3): selinux-policy-3.13.1-166.0.2.el7.noarch.rpm | 435 kB 00:00:00 (2/3): selinux-policy-targeted-3.13.1-166.0.2.el7.noarch.rpm | 6.5 MB 00:00:01 (3/3): docker-engine-17.03.1.ce-3.0.1.el7.x86_64.rpm | 19 MB 00:00:04 ------------------------------------------------------------------------------------------------------------------------------------------------------ Total 6.2 MB/s | 26 MB 00:00:04 Running transaction check Running transaction test Transaction test succeeded Running transaction Updating : selinux-policy-3.13.1-166.0.2.el7.noarch 1/5 Updating : selinux-policy-targeted-3.13.1-166.0.2.el7.noarch 2/5 Installing : docker-engine-17.03.1.ce-3.0.1.el7.x86_64 3/5 Cleanup : selinux-policy-targeted-3.13.1-102.0.3.el7_3.16.noarch 4/5 Cleanup : selinux-policy-3.13.1-102.0.3.el7_3.16.noarch 5/5 Verifying : selinux-policy-targeted-3.13.1-166.0.2.el7.noarch 1/5 Verifying : selinux-policy-3.13.1-166.0.2.el7.noarch 2/5 Verifying : docker-engine-17.03.1.ce-3.0.1.el7.x86_64 3/5 Verifying : selinux-policy-targeted-3.13.1-102.0.3.el7_3.16.noarch 4/5 Verifying : selinux-policy-3.13.1-102.0.3.el7_3.16.noarch 5/5 Installed: docker-engine.x86_64 0:17.03.1.ce-3.0.1.el7 Updated: selinux-policy-targeted.noarch 0:3.13.1-166.0.2.el7 Dependency Updated: selinux-policy.noarch 0:3.13.1-166.0.2.el7 Complete!

    And that’s it! Docker is now installed on the machine. Before I proceed with building an image I first gain to configure my environment appropriately.

    Enable Non-Root User

    The first thing I want to accomplish is to enable a non-root user to communicate with the Docker engine. Enabling a non-root user is fairly straightforward, as well. When Docker was installed, a original Unix group docker was created along with it. If you want to allow a user to communicate with the Docker daemon directly (hence avoiding to avoid as the root user), everything you gain to accomplish is to add that user to the docker group. In my case, I want to add the oracle user to that group:

    [root@localhost ~]# id oracle uid=1000(oracle) gid=1001(oracle) groups=1001(oracle),1000(dba) [root@localhost ~]# usermod -a -G docker oracle [root@localhost ~]# id oracle uid=1000(oracle) gid=1001(oracle) groups=1001(oracle),1000(dba),981(docker) Increase ground Image Size

    Before I travel ahead and avoid the image build, I want to double-check one essential parameter: the default ground image size for the Docker container. In the past, Docker came with a maximum container size of 10 GB by default. While this is more than enough for running some applications inside Docker containers, this needed to subsist increased for Oracle Database. The Oracle Database 12.2.0.1 image requires about 13GB of space for the image build.

    Recently, the default size has been increased to 25GB, which will subsist more than enough for the Oracle Database image. The setting can subsist found and double-checked in /etc/sysconfig/docker-storage as the storage-opt dm.basesize parameter:

    [root@localhost ~]# cat /etc/sysconfig/docker-storage # This file may subsist automatically generated by an installation program. # By default, Docker uses a loopback-mounted sparse file in # /var/lib/docker. The loopback makes it slower, and there are some # restrictive defaults, such as 100GB max storage. # If your installation did not set a custom storage for Docker, you # may accomplish it below. # Example: consume a custom pair of raw logical volumes (one for metadata, # one for data). # DOCKER_STORAGE_OPTIONS = --storage-opt dm.metadatadev=/dev/mylogvol/my-docker-metadata --storage-opt dm.datadev=/dev/mylogvol/my-docker-data DOCKER_STORAGE_OPTIONS= --storage-driver devicemapper --storage-opt dm.basesize=25G Start and Enable the Docker Service

    The final step is to start the docker service and configure it to start at boot time. This is done via the systemctl command:

    [root@localhost ~]# systemctl start docker [root@localhost ~]# systemctl enable docker Created symlink from /etc/systemd/system/multi-user.target.wants/docker.service to /usr/lib/systemd/system/docker.service. [root@localhost ~]# systemctl status docker ● docker.service - Docker Application Container Engine Loaded: loaded (/usr/lib/systemd/system/docker.service; enabled; vendor preset: disabled) Drop-In: /etc/systemd/system/docker.service.d └─docker-sysconfig.conf Active: active (running) since Sun 2017-08-20 14:18:16 EDT; 5s ago Docs: https://docs.docker.com Main PID: 19203 (dockerd) Memory: 12.8M CGroup: /system.slice/docker.service ├─19203 /usr/bin/dockerd --selinux-enabled --storage-driver devicemapper --storage-opt dm.basesize=25G └─19207 docker-containerd -l unix:///var/run/docker/libcontainerd/docker-containerd.sock --metrics-interval=0 --start-timeout 2m --state...

    As the last step, you can verify the setup and the ground image size (check for Base Device Size:) via docker info:

    [root@localhost ~]# docker info Containers: 0 Running: 0 Paused: 0 Stopped: 0 Images: 0 Server Version: 17.03.1-ce Storage Driver: devicemapper Pool Name: docker-249:0-202132724-pool Pool Blocksize: 65.54 kB Base Device Size: 26.84 GB Backing Filesystem: xfs Data file: /dev/loop0 Metadata file: /dev/loop1 Data Space Used: 14.42 MB Data Space Total: 107.4 GB Data Space Available: 47.98 GB Metadata Space Used: 581.6 kB Metadata Space Total: 2.147 GB Metadata Space Available: 2.147 GB Thin Pool Minimum Free Space: 10.74 GB Udev Sync Supported: true Deferred Removal Enabled: false Deferred Deletion Enabled: false Deferred Deleted Device Count: 0 Data loop file: /var/lib/docker/devicemapper/devicemapper/data WARNING: Usage of loopback devices is strongly discouraged for production use. consume `--storage-opt dm.thinpooldev` to specify a custom obscure storage device. Metadata loop file: /var/lib/docker/devicemapper/devicemapper/metadata Library Version: 1.02.135-RHEL7 (2016-11-16) Logging Driver: json-file Cgroup Driver: cgroupfs Plugins: Volume: local Network: bridge host macvlan null overlay Swarm: inactive Runtimes: runc Default Runtime: runc Init Binary: docker-init containerd version: 4ab9917febca54791c5f071a9d1f404867857fcc runc version: 54296cf40ad8143b62dbcaa1d90e520a2136ddfe init version: 949e6fa Security Options: seccomp Profile: default selinux Kernel Version: 4.1.12-94.3.8.el7uek.x86_64 Operating System: Oracle Linux Server 7.3 OSType: linux Architecture: x86_64 CPUs: 1 Total Memory: 7.795 GiB Name: localhost.localdomain ID: D7CR:3DGV:QUGO:X7EB:AVX3:DWWW:RJIA:QVVT:I2YR:KJXV:ALR4:WLBV Docker Root Dir: /var/lib/docker Debug Mode (client): false Debug Mode (server): false Registry: https://index.docker.io/v1/ Experimental: false Insecure Registries: 127.0.0.0/8 Live Restore Enabled: false

    That concludes the installation of Docker itself.

    Building the Oracle Database Docker Image

    Now that Docker is up and running, I can start building the image. First, I exigency to congregate the Docker build files and the Oracle install binaries. Both are smooth to obtain, as shown below. Note that I consume the oracle Linux user for everything the following steps, which I gain enabled previously to communicate with the Docker daemon.

    Obtaining the Required Files

    We exigency the GitHub build files and Oracle installation binaries.

    GitHub Build Files

    First, I gain to download the Docker build files. There are various ways to accomplish this. I can, for example, clone the Git repository directly. But for simplicity and for the people who aren’t close with Git, I will just consume the download option on GitHub itself. If you travel to the main repository URL, you will descry a green button aphorism Clone or download. By clicking on it, you will gain the option Download ZIP. Alternatively, you can just download the repository directly via the static URL.

    [oracle@localhost ~]$ wget https://github.com/oracle/docker-images/archive/master.zip --2017-08-20 14:31:32-- https://github.com/oracle/docker-images/archive/master.zip Resolving github.com (github.com)... 192.30.255.113, 192.30.255.112 Connecting to github.com (github.com)|192.30.255.113|:443... connected. HTTP request sent, awaiting response... 302 Found Location: https://codeload.github.com/oracle/docker-images/zip/master [following] --2017-08-20 14:31:33-- https://codeload.github.com/oracle/docker-images/zip/master Resolving codeload.github.com (codeload.github.com)... 192.30.255.120, 192.30.255.121 Connecting to codeload.github.com (codeload.github.com)|192.30.255.120|:443... connected. HTTP request sent, awaiting response... 200 OK Length: unspecified [application/zip] Saving to: ‘master.zip’ [ ] 4,411,616 3.37MB/s in 1.2s 2017-08-20 14:31:34 (3.37 MB/s) - ‘master.zip’ saved [4411616] [oracle@localhost ~]$ unzip master.zip Archive: master.zip 21041a743e4b0a910b0e51e17793bb7b0b18efef creating: docker-images-master/ extracting: docker-images-master/.gitattributes inflating: docker-images-master/.gitignore inflating: docker-images-master/.gitmodules inflating: docker-images-master/CODEOWNERS inflating: docker-images-master/CONTRIBUTING.md ... ... ... creating: docker-images-master/OracleDatabase/ extracting: docker-images-master/OracleDatabase/.gitignore inflating: docker-images-master/OracleDatabase/COPYRIGHT inflating: docker-images-master/OracleDatabase/LICENSE inflating: docker-images-master/OracleDatabase/README.md creating: docker-images-master/OracleDatabase/dockerfiles/ ... ... ... inflating: docker-images-master/README.md [oracle@localhost ~]$ Oracle Installation Binaries

    Just download the Oracle binaries from where you usually would. Oracle Technology Network is probably the status that most people travel to. Once you gain downloaded them, you can proceed with building the image:

    [oracle@localhost ~]$ ls -al *database*zip -rw-r--r--. 1 oracle oracle 1354301440 Aug 20 14:40 linuxx64_12201_database.zip Building the Image

    Now that I gain everything the files, it’s time to build the Docker image. You will find a separate README.md in the docker-images-master/OracleDatabase directory that explains the build process in more details. Make sure that you always read that file, as it will always reflect the latest changes in the build files! 

    You will furthermore find a buildDockerImage.sh shell script in the docker-images-master/OracleDatabase/dockerfiles directory that does the legwork of the build for you. For the build, it is essential that I copy the install files into the revise version directory. As I’m going to create an Oracle Database 12.2.0.1 image, I exigency to copy the installed ZIP file into docker-images-master/OracleDatabase/dockerfiles/12.2.0.1:

    [oracle@localhost ~]$ cd docker-images-master/OracleDatabase/dockerfiles/12.2.0.1/ [oracle@localhost 12.2.0.1]$ cp ~/linuxx64_12201_database.zip . [oracle@localhost 12.2.0.1]$ ls -al total 3372832 drwxrwxr-x. 2 oracle oracle 4096 Aug 20 14:44 . drwxrwxr-x. 5 oracle oracle 77 Aug 19 00:35 .. -rwxr-xr-x. 1 oracle oracle 1259 Aug 19 00:35 checkDBStatus.sh -rwxr-xr-x. 1 oracle oracle 909 Aug 19 00:35 checkSpace.sh -rw-rw-r--. 1 oracle oracle 62 Aug 19 00:35 Checksum.ee -rw-rw-r--. 1 oracle oracle 62 Aug 19 00:35 Checksum.se2 -rwxr-xr-x. 1 oracle oracle 2964 Aug 19 00:35 createDB.sh -rw-rw-r--. 1 oracle oracle 9203 Aug 19 00:35 dbca.rsp.tmpl -rw-rw-r--. 1 oracle oracle 6878 Aug 19 00:35 db_inst.rsp -rw-rw-r--. 1 oracle oracle 2550 Aug 19 00:35 Dockerfile.ee -rw-rw-r--. 1 oracle oracle 2552 Aug 19 00:35 Dockerfile.se2 -rwxr-xr-x. 1 oracle oracle 2261 Aug 19 00:35 installDBBinaries.sh -rw-r--r--. 1 oracle oracle 3453696911 Aug 20 14:45 linuxx64_12201_database.zip -rwxr-xr-x. 1 oracle oracle 6151 Aug 19 00:35 runOracle.sh -rwxr-xr-x. 1 oracle oracle 1026 Aug 19 00:35 runUserScripts.sh -rwxr-xr-x. 1 oracle oracle 769 Aug 19 00:35 setPassword.sh -rwxr-xr-x. 1 oracle oracle 879 Aug 19 00:35 setupLinuxEnv.sh -rwxr-xr-x. 1 oracle oracle 689 Aug 19 00:35 startDB.sh [oracle@localhost 12.2.0.1]$

    Now that the ZIP file is in place, I am ready to invoke the buildDockerImage.sh shell script in the dockerfiles folder. The script takes a brace of parameters: -v for the version and -e for telling it that I want Enterprise Edition. 

    Note: The build of the image will pull the Oracle Linux slim ground image and execute a yum install as well as a yum upgrade inside the container. For it to succeed, it needs to gain internet connectivity:

    [oracle@localhost 12.2.0.1]$ cd .. [oracle@localhost dockerfiles]$ ./buildDockerImage.sh -v 12.2.0.1 -e Checking if required packages are present and valid... linuxx64_12201_database.zip: OK ========================== DOCKER info: Containers: 0 Running: 0 Paused: 0 Stopped: 0 Images: 0 Server Version: 17.03.1-ce Storage Driver: devicemapper Pool Name: docker-249:0-202132724-pool Pool Blocksize: 65.54 kB Base Device Size: 26.84 GB Backing Filesystem: xfs Data file: /dev/loop0 Metadata file: /dev/loop1 Data Space Used: 14.42 MB Data Space Total: 107.4 GB Data Space Available: 47.98 GB Metadata Space Used: 581.6 kB Metadata Space Total: 2.147 GB Metadata Space Available: 2.147 GB Thin Pool Minimum Free Space: 10.74 GB Udev Sync Supported: true Deferred Removal Enabled: false Deferred Deletion Enabled: false Deferred Deleted Device Count: 0 Data loop file: /var/lib/docker/devicemapper/devicemapper/data WARNING: Usage of loopback devices is strongly discouraged for production use. consume `--storage-opt dm.thinpooldev` to specify a custom obscure storage device. Metadata loop file: /var/lib/docker/devicemapper/devicemapper/metadata Library Version: 1.02.135-RHEL7 (2016-11-16) Logging Driver: json-file Cgroup Driver: cgroupfs Plugins: Volume: local Network: bridge host macvlan null overlay Swarm: inactive Runtimes: runc Default Runtime: runc Init Binary: docker-init containerd version: 4ab9917febca54791c5f071a9d1f404867857fcc runc version: 54296cf40ad8143b62dbcaa1d90e520a2136ddfe init version: 949e6fa Security Options: seccomp Profile: default selinux Kernel Version: 4.1.12-94.3.8.el7uek.x86_64 Operating System: Oracle Linux Server 7.3 OSType: linux Architecture: x86_64 CPUs: 1 Total Memory: 7.795 GiB Name: localhost.localdomain ID: D7CR:3DGV:QUGO:X7EB:AVX3:DWWW:RJIA:QVVT:I2YR:KJXV:ALR4:WLBV Docker Root Dir: /var/lib/docker Debug Mode (client): false Debug Mode (server): false Registry: https://index.docker.io/v1/ Experimental: false Insecure Registries: 127.0.0.0/8 Live Restore Enabled: false ========================== Building image 'oracle/database:12.2.0.1-ee' ... Sending build context to Docker daemon 3.454 GB Step 1/16 : FROM oraclelinux:7-slim 7-slim: Pulling from library/oraclelinux 3152c71f8d80: pull complete Digest: sha256:e464042b724d41350fb3ac2c2f84bd9d28d98302c9ebe66048a5367682e5fad2 Status: Downloaded newer image for oraclelinux:7-slim ---> c0feb50f7527 Step 2/16 : MAINTAINER Gerald Venzl ---> Running in e442cae35367 ---> 08f875cea39d ... ... ... Step 15/16 : EXPOSE 1521 5500 ---> Running in 4476c1c236e1 ---> d01d39e39920 Removing intermediate container 4476c1c236e1 Step 16/16 : CMD exec $ORACLE_BASE/$RUN_FILE ---> Running in 8757674cc3d5 ---> 98129834d5ad Removing intermediate container 8757674cc3d5 Successfully built 98129834d5ad Oracle Database Docker Image for 'ee' version 12.2.0.1 is ready to subsist extended: --> oracle/database:12.2.0.1-ee Build completed in 802 seconds. Starting and Connecting to the Oracle Database Inside a Docker Container

    Once the build was successful, I can start and avoid the Oracle Database inside a Docker container. everything I gain to accomplish is to issue the docker run command and pass in the preempt parameters. One essential parameter is the -p for the mapping of ports inside the container to the outside world. This is required so that I can furthermore connect to the database from outside the Docker container. Another essential parameter is the -v parameter, which allows me to preserve the data files of the database in a location outside the Docker container. This is important, as it will allow me to preserve my data even when the container is thrown away. You should always consume the -v parameter or create a named Docker volume! The terminal useful parameter that I’m going to consume is the --name parameter which specifies the denomination of the Docker container itself. If omitted, a random denomination will subsist generated. However, passing on a denomination will allow me to refer to the container via that denomination later on:

    [oracle@localhost dockerfiles]$ cd ~ [oracle@localhost ~]$ mkdir oradata [oracle@localhost ~]$ chmod a+w oradata [oracle@localhost ~]$ docker avoid --name oracle-ee -p 1521:1521 -v /home/oracle/oradata:/opt/oracle/oradata oracle/database:12.2.0.1-ee ORACLE PASSWORD FOR SYS, SYSTEM AND PDBADMIN: 3y4RL1K7org=1 LSNRCTL for Linux: Version 12.2.0.1.0 - Production on 20-AUG-2017 19:07:55 Copyright (c) 1991, 2016, Oracle. everything rights reserved. Starting /opt/oracle/product/12.2.0.1/dbhome_1/bin/tnslsnr: gladden wait... TNSLSNR for Linux: Version 12.2.0.1.0 - Production System parameter file is /opt/oracle/product/12.2.0.1/dbhome_1/network/admin/listener.ora Log messages written to /opt/oracle/diag/tnslsnr/e3d1a2314421/listener/alert/log.xml Listening on: (DESCRIPTION=(ADDRESS=(PROTOCOL=ipc)(KEY=EXTPROC1))) Listening on: (DESCRIPTION=(ADDRESS=(PROTOCOL=tcp)(HOST=0.0.0.0)(PORT=1521))) Connecting to (DESCRIPTION=(ADDRESS=(PROTOCOL=IPC)(KEY=EXTPROC1))) STATUS of the LISTENER ------------------------ Alias LISTENER Version TNSLSNR for Linux: Version 12.2.0.1.0 - Production Start Date 20-AUG-2017 19:07:56 Uptime 0 days 0 hr. 0 min. 0 sec Trace smooth off Security ON: Local OS Authentication SNMP OFF Listener Parameter File /opt/oracle/product/12.2.0.1/dbhome_1/network/admin/listener.ora Listener Log File /opt/oracle/diag/tnslsnr/e3d1a2314421/listener/alert/log.xml Listening Endpoints Summary... (DESCRIPTION=(ADDRESS=(PROTOCOL=ipc)(KEY=EXTPROC1))) (DESCRIPTION=(ADDRESS=(PROTOCOL=tcp)(HOST=0.0.0.0)(PORT=1521))) The listener supports no services The command completed successfully [WARNING] [DBT-10102] The listener configuration is not selected for the database. EM DB Express URL will not subsist accessible. CAUSE: The database should subsist registered with a listener in order to access the EM DB Express URL. ACTION: Select a listener to subsist registered or created with the database. Copying database files 1% complete 13% complete 25% complete Creating and starting Oracle instance 26% complete 30% complete 31% complete 35% complete 38% complete 39% complete 41% complete Completing Database Creation 42% complete 43% complete 44% complete 46% complete 47% complete 50% complete Creating Pluggable Databases 55% complete 75% complete Executing Post Configuration Actions 100% complete Look at the log file "/opt/oracle/cfgtoollogs/dbca/ORCLCDB/ORCLCDB.log" for further details. SQL*Plus: Release 12.2.0.1.0 Production on Sun Aug 20 19:16:01 2017 Copyright (c) 1982, 2016, Oracle. everything rights reserved. Connected to: Oracle Database 12c Enterprise Edition Release 12.2.0.1.0 - 64bit Production SQL> System altered. SQL> Pluggable database altered. SQL> Disconnected from Oracle Database 12c Enterprise Edition Release 12.2.0.1.0 - 64bit Production ######################### DATABASE IS READY TO USE! ######################### The following output is now a tail of the alert.log: Completed: alter pluggable database ORCLPDB1 open 2017-08-20T19:16:01.025829+00:00 ORCLPDB1(3):CREATE SMALLFILE TABLESPACE "USERS" LOGGING DATAFILE '/opt/oracle/oradata/ORCLCDB/ORCLPDB1/users01.dbf' SIZE 5M REUSE AUTOEXTEND ON NEXT 1280K MAXSIZE UNLIMITED EXTENT MANAGEMENT LOCAL SEGMENT SPACE MANAGEMENT AUTO ORCLPDB1(3):Completed: CREATE SMALLFILE TABLESPACE "USERS" LOGGING DATAFILE '/opt/oracle/oradata/ORCLCDB/ORCLPDB1/users01.dbf' SIZE 5M REUSE AUTOEXTEND ON NEXT 1280K MAXSIZE UNLIMITED EXTENT MANAGEMENT LOCAL SEGMENT SPACE MANAGEMENT AUTO ORCLPDB1(3):ALTER DATABASE DEFAULT TABLESPACE "USERS" ORCLPDB1(3):Completed: ALTER DATABASE DEFAULT TABLESPACE "USERS" 2017-08-20T19:16:01.889003+00:00 ALTER SYSTEM SET control_files='/opt/oracle/oradata/ORCLCDB/control01.ctl' SCOPE=SPFILE; ALTER PLUGGABLE DATABASE ORCLPDB1 rescue STATE Completed: ALTER PLUGGABLE DATABASE ORCLPDB1 rescue STATE

    On the very first startup of the container, a original database is being created. Subsequent startups of the selfsame container or newly created containers pointing to the selfsame volume will just start up the database again. Once the database is created and or started the container will avoid a tail -f on the Oracle Database alert.log file. This is done for convenience so that issuing a docker logs command will actually print the logs of the database running inside that container. Once the database is created or started up, you will descry the line DATABASE IS READY TO USE! in the output. After that, you can connect to the database.

    Resetting the Database Admin Account Passwords

    The startup script furthermore generated a password for the database admin accounts. You can find the password next to the line ORACLE PASSWORD FOR SYS, SYSTEM AND PDBADMIN: in the output. You can either consume that password going forward or you can reset it to a password of your choice. The container provides a script called setPassword.sh for resetting the password. In a original shell, just execute the following command against the running container:

    [oracle@localhost ~]$ docker exec oracle-ee ./setPassword.sh LetsDocker The Oracle ground remains unchanged with value /opt/oracle SQL*Plus: Release 12.2.0.1.0 Production on Sun Aug 20 19:17:08 2017 Copyright (c) 1982, 2016, Oracle. everything rights reserved. Connected to: Oracle Database 12c Enterprise Edition Release 12.2.0.1.0 - 64bit Production SQL> User altered. SQL> User altered. SQL> Session altered. SQL> User altered. SQL> Disconnected from Oracle Database 12c Enterprise Edition Release 12.2.0.1.0 - 64bit Production Connecting to the Oracle Database

    Now that the container is running and port 1521 is mapped to the outside world, I can connect to the database inside the container:

    [oracle@localhost ~]$ sql system/LetsDocker@//localhost:1521/ORCLPDB1 SQLcl: Release 4.2.0 Production on Sun Aug 20 19:56:43 2017 Copyright (c) 1982, 2017, Oracle. everything rights reserved. Last Successful login time: Sun Aug 20 2017 12:21:42 -07:00 Connected to: Oracle Database 12c Enterprise Edition Release 12.2.0.1.0 - 64bit Production SQL> award connect, resource to gvenzl identified by supersecretpwd; Grant succeeded. SQL> conn gvenzl/supersecretpwd@//localhost:1521/ORCLPDB1 Connected. SQL> Stopping the Oracle Database Docker Container

    If you wish to quit the Docker container, you can just accomplish so via the docker stop command.  everything you will gain to accomplish is to issue the command and pass on the container denomination or ID. This will trigger the container to issue a shutdown immediate for the database inside the container. By default, Docker will only allow ten seconds for the container to shut down before killing it. For applications that may subsist fine but for persistent containers such as the Oracle Database container, you may want to give the container a bit more time to shut down the database appropriately. You can accomplish that via the -t option, which allows you to pass on a original timeout in seconds for the container to shut down successfully.

    I will give the database 30 seconds to shut down, but it’s essential to point out that it doesn’t really matter how long you give the container to shut down. Once the database is shut down, the container will exit normally. It will not wait everything the seconds that you gain specified until returning control. So even if you give it ten minutes (600 seconds), it will noiseless recrudesce as soon as the database is shut down.

    Just preserve that in intellect when specifying a timeout for industrious database containers:

    [oracle@localhost ~]$ docker quit -t 30 oracle-ee oracle-ee Restarting the Oracle Database Docker Container

    A stopped container can always subsist restarted via the docker start command:

    [oracle@localhost ~]$ docker start oracle-ee oracle-ee

    The docker start command will sequel the container into the background and recrudesce control immediately. You can check the status of the container via the docker logs command, which should print the same DATABASE IS READY TO USE! line. You will furthermore descry that this time, the database was just restarted rather than created.

    Note: A docker logs -f will follow the log output, i.e. preserve on printing original lines:

    [oracle@localhost ~]$ docker logs oracle-ee ... ... ... SQL*Plus: Release 12.2.0.1.0 Production on Sun Aug 20 19:30:31 2017 Copyright (c) 1982, 2016, Oracle. everything rights reserved. Connected to an idle instance. SQL> ORACLE instance started. Total System Global zone 1610612736 bytes Fixed Size 8793304 bytes Variable Size 520094504 bytes Database Buffers 1073741824 bytes Redo Buffers 7983104 bytes Database mounted. Database opened. SQL> Disconnected from Oracle Database 12c Enterprise Edition Release 12.2.0.1.0 - 64bit Production ######################### DATABASE IS READY TO USE! ######################### The following output is now a tail of the alert.log: ORCLPDB1(3):Undo initialization finished serial:0 start:6800170 end:6800239 diff:69 ms (0.1 seconds) ORCLPDB1(3):Database Characterset for ORCLPDB1 is AL32UTF8 ORCLPDB1(3):Opatch validation is skipped for PDB ORCLPDB1 (con_id=0) ORCLPDB1(3):Opening pdb with no Resource Manager end active 2017-08-20T19:30:43.703897+00:00 Pluggable database ORCLPDB1 opened read write

    Now that the database is up and running again, I can connect once more to the database inside:

    [oracle@localhost ~]$ sql gvenzl/supersecretpwd@//localhost:1521/ORCLPDB1 SQLcl: Release 4.2.0 Production on Sun Aug 20 20:10:28 2017 Copyright (c) 1982, 2017, Oracle. everything rights reserved. Connected to: Oracle Database 12c Enterprise Edition Release 12.2.0.1.0 - 64bit Production SQL> select sysdate from dual; SYSDATE --------- 20-AUG-17 SQL> exit Disconnected from Oracle Database 12c Enterprise Edition Release 12.2.0.1.0 - 64bit Production Summary

    This concludes my tutorial on how to containerize the Oracle Database using Docker. Note that Oracle has furthermore provided build files for other Oracle Database versions and editions. The steps described above are largely the selfsame but you should always refer to the README.md that comes with the build files. In there, you will furthermore find more options for how to avoid your Oracle Database containers.

    You can find the GitHub repository here.


    rapid application progress (RAD) | killexams.com true questions and Pass4sure dumps

    In software development, RAD (rapid application development) is a concept that was born out of frustration with the waterfall software design approach which too often resulted in products that were out of date or inefficient by the time they were actually released. The term was inspired by James Martin, who worked with colleagues to develop a original mode called Rapid Iterative Production Prototyping (RIPP). In 1991, this approach became the premise of the bespeak Rapid Application Development.

    Martin's progress philosophy focused on speed and used strategies such as prototyping, iterative progress and time boxing. He believed that software products can subsist developed faster and of higher attribute through:

  • Gathering requirements using workshops or focus groups
  • Prototyping and early, reiterative user testing of designs
  • The re-use of software components
  • A rigidly paced schedule that defers design improvements to the next product version
  • Less formality in reviews and other team communication
  • Rapid application progress is noiseless in consume today and some companies offer products that provide some or everything of the tools for RAD software development. (The concept can subsist applied to hardware progress as well.) These products include requirements gathering tools, prototyping tools, computer-aided software engineering tools, language progress environments such as those for the Java platform, groupware for communication among progress members, and testing tools.

    RAD usually embraces object-oriented programming methodology, which inherently fosters software re-use. The most Popular object-oriented programming languages, C++ and Java, are offered in visual programming packages often described as providing rapid application development.



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