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Oracle Oracle Application Express 3.2-(R)

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

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

targets
  • reveal an light formulation to function records mining via scraping information from a web market.
  • exhibit the Oracle statistics Visualization desktop and spend it to determine expense drivers inside the mined facts.
  • Produce a predictive mannequin the spend 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 scrawny 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 simultaneous 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 pile within the Oracle toolset can multiply consumer journey, accelerate pile timelines
  • Co-Presenters

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    Do you've got EBS clients in the box, the air, or at sea? in that case these clients may additionally believe drugs or telephones, however more commonly than no longer they’re the spend of a desktop or other computing platform that requires offline use. during this session they explore an light spend 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 reveal 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
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  • 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 possible | killexams.com real Questions and Pass4sure dumps

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

    Listicles love these enact travail – no longer most efficient enact they appeal to attention, if the content is furthermore positive (and in this case it's, confidence me), the article structure will furthermore live extraordinarily entertaining.

    this text will carry you 10 SQL hints that many of you may now not believe conception 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 live viewed on SlideShare:

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

    Introduction

    in order to remember the cost of these 10 SQL tricks, it's first crucial to capture into account the context of the SQL language. Why enact 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 declare 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 portion of) the user table and the ply table and determine the clients that live in Switzerland. You don’t care HOW the database will retrieve this suggestions (e.g. should the users table live loaded first, or the address desk? may quiet the two tables live joined in a nested loop or with a hashmap? should quiet shameful records live 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 quiet should comprehend the basics of what’s happening in the back of the scenes in a database to assist the database manufacture the correct 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 believe a corresponding user)
  • Add an index on the search field: The country (this tells the database that particular international locations may furthermore live present in O(log N) as an alternative of O(N))
  • however as soon as your database and your software matures, you will believe establish shameful of the crucial meta records in vicinity and you may seat of attention to your industry expedient judgment most effective. right here 10 tricks demonstrate astonishing performance written in barely a couple of traces of declarative SQL, producing primary and furthermore advanced output.

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    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 happen to discern a SQL observation love this:

    opt for * FROM adult

    … you're going to directly spot the desk person sitting correct 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 ( select * FROM person ) t

    And now, you believe 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 select 3 AS a FROM dual ) t

    Now that you just’re when you deem 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 right 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 minuscule 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 spend the analogy of the Java 8 plod API to capture this one step further. deem the following equal ideas:

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

    With Java 8, “every minuscule 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 type is shameful 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), enact 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 efficient manner to declare variables in SQL (apart from the imprecise WINDOW clause that simplest PostgreSQL and Sybase SQL anywhere live vigilant of).

    here's an impressive thought. extraordinarily potent. accept as honest 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 spend of the fundamental WITH clause, that you may specify a listing of table variables (remember: every minuscule thing is a desk), which may additionally even depend upon each and every different.

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

    WITH RECURSIVE t(v) AS ( select 1 -- Seed Row UNION ALL select 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 discern in the course of the many key phrases. You define a typical table expression that has precisely two UNION shameful subqueries.

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

    The 2nd UNION shameful subquery is where the recursion happens. if you appear intently, you will celebrate 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 manufacture SQL:1999 turing finished, which potential that any program will furthermore live 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 discontinuance + 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 regain anything like

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

    stunning, huh?

    three. operating total Calculations

    This blog is plenary 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 implement a working complete.

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

    eder 1

    In Microsoft Excel, you may effortlessly cipher a sum (or change) of two previous (or subsequent) values, after which spend the positive crosshair cursor to drag that manner 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 spend of window services, a different matter that this weblog has covered many repeatedly.

    Window capabilities are an impressive conception – now not so handy to retain in judgement 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 accomplish calculations on rows which are “above” or “below” the existing row. not love 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 live summarized as follows, with particular person parts being not obligatory

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

    So, they now believe any type of function (we’ll discern 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 live considered for the window
  • The ORDER: The window can furthermore live ordered independently of what we’re making a selection on
  • The ROWS (or range) corpse definition: The window will furthermore live confined to a set quantity of rows “ahead” and “in the back of”
  • That’s shameful there is to window features.

    Now how does that assist us cipher a running total? accept as honest 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 signify on that steadiness is what they want to cipher from quantity

    Intuitively, they can immediately discern that here holds true:

    sql-tricks-slide-081

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

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

    In real SQL, that might then live 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 cipher the sum for each and every checking account, no longer for the entire facts set
  • The ordering will manufacture inescapable that transactions are ordered (in the partition) in promote of summing
  • The rows clause will accord with most efficient previous rows (in the partition, given the ordering) ahead of summing
  • All of this could happen 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 shameful 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 portion of the SQL typical
  • purchasable in most universal RDBMS (apart from MySQL)
  • Very crucial constructing blocks
  • If the repose may furthermore live 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 discern what number of badges I actually have. tons.

    How enact you cipher these badges? Let’s believe a survey 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 care about holding any state and in-memory counters. They want to express this in the profile 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 site 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 light 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 love 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 sing 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” train of consecutive integers from a “gapful” train of non-consecutive dates, they can regain the identical date for each “gapless” subseries of consecutive dates, and we’ll regain a brand novel 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 site 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 conception makes the entire difference, however the question itself is in fact very very simple and chic. No approach you may establish in obligate 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 couple of train accommodate the identical values? believe here statistics, the site size is the size of each sequence that they need 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 believe the identical 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 enact they enact it? “convenient”😉 First, let’s enact 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 | sign | 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 yield right here desk:

    | id | quantity | sign | 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 charge into “LO” at the “lower” discontinuance of a series, and into “hello” on the “higher” conclusion of a series. For this we’ll live 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 accomplish rankings or aggregations on a subset of rows relative to the present row. in the case of LEAD() and LAG(), they with no distress access a sole 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) != sign THEN rn discontinuance AS lo, CASE WHEN lead(signal) OVER (ORDER with the aid of id DESC) != signal THEN rn discontinuance AS hello, FROM trx

    … by which they examine the “old” signal (lag(sign)) with the “existing” sign (sign). if they’re distinctive, they establish 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” sign (sign). in the event that they’re distinct, they establish the row quantity in “hi”, as a result of that’s the upper inescapable of their series.

    ultimately, a minuscule tiresome NULL coping with to regain 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 need “LO” and “hello” to appear in shameful rows, no longer just on the “reduce” and “upper” bounds of a series. E.g. love this:

    | id | amount | sign | 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 spend 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 live 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 shameful times the same. From any given “current” row, they study shameful the “outdated values” (ROWS BETWEEN UNBOUNDED previous AND latest ROW), but ignoring the entire nulls. From these outdated values, they capture the closing cost, and that’s their novel “LO” price. In other phrases, they capture the “closest previous” “LO” cost.

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

    defined in Powerpoint:

    eder 4

    Getting it 100% proper, with a bit tiresome 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 final step, conserving in intellect off-by course of-1 blunders:

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

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

    | id | volume | sign | 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 plenary question here:

    WITH trx1(identification, amount, signal, rn) AS ( select 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) != sign THEN rn conclusion, CASE WHEN coalesce(lead(signal) OVER (ORDER with the aid of id DESC), 0) != sign 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 right here:https://xkcd.com/287

    And a dash 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 | particular | |------|-------| | 1 | 7120 | | 2 | 8150 | | three | 8255 | | four | 9051 | | 5 | 1220 | | 6 | 12515 | | 7 | 13555 | | 8 | 5221 | | 9 | 812 | | 10 | 6562 |

    As you’re shameful brief together with your intellectual mathemagic processing, you believe instantly calculated these to live 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 enact 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 possible 2N sums WITH sums(sum, max_id, calc) AS (...) -- locate the most usurp 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 live love my friend right here:

    eder 4

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

    WITH sums(sum, id, calc) AS ( select merchandise, id, to_char(merchandise) FROM gadgets UNION ALL select particular + sum, gadgets.identity, calc || ' + ' || item FROM sums live a portion 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 live portion 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 manufacture certain to arrive back back right here for the final 4 tricks:

    7. Capping a running total

    to date, we’ve considered how to cipher an “normal” running complete with SQL the spend 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 cipher 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 poor volume -20000 changed into subtracted, as an alternative of displaying the honest complete of -15550, they without hardship reveal 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 enact it?

    eder 4

    exactly. With obscure, seller-certain SQL. in this case, we’re the spend 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 -- establish this after any table mannequin ...

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

    the following three clauses are probably the most valuable and universal (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 believe any variety of dimensions in Oracle:

    eder 4

    The MEASURES clause specifies the values that are available in each telephone of your spreadsheet. not love in MS Excel, that you can believe an entire tuple in every cell in Oracle, not only a sole 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 sole location, as an alternative of being establish inside of each cellphone:

    eder 4

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

    opt for * FROM ( select 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 right here listed here, tickle enact 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 real time analytics on tremendous facts units, time sequence pattern recognition is not at shameful a novel 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 experience happens extra than 3 times.

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

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

    The least difficult feasible software of MATCH_RECOGNIZE includes right 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 shameful ROWS PER match -- «ordinary expressions» of events to in shape pattern (...) -- The definitions of «what is an experience» define ... )

    That sounds loopy. Let’s survey at some case clause implementations

    select * FROM seriesMATCH_RECOGNIZE ( ORDER by identity MEASURES classifier() AS trg shameful 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 enact right here?

  • We order the desk with the aid of id, which is the order by which they are looking to healthy 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 spend 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 experience “S” for birth, adopted optionally by “R” for Repeat, “X” for their particular event X, followed by one or extra “R” for restate once again. If the total pattern suits, they regain SRXR or SRXRR or SRXRRR, i.e. X will live 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 identical as signal(volume) of the previous row. They don’t believe to define “S”. “S” is only another row.
  • This question will magically yield right 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 discern a sole “X” in their adventure flow. exactly the site they had anticipated it. on the third repetition of an event (identical sign) in a train of size > three.

    boom!

    As they don’t definitely care about “S” and “R” activities, let’s simply liquidate 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 shameful 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 elaborate 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 live nearly too embarassingly essential:

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

    | title | TITLE | ranking | |-----------|-----------------|--------| | A. provide | ANNIE id | G | | A. concede | 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 title pivoting:

    | title | NC-17 | PG | G | PG-13 | R | |-----------|-------|-----|-----|-------|-----| | A. concede | 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 survey 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 enact this, because they recognize shameful of the viable corporations in strengthen.

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

    | identify | rating | signify number | |-----------|--------|-------| | A. concede | 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 enact it in PostgreSQL:

    choose first_name, last_name, signify number(*) FILTER (where score = 'NC-17') AS "NC-17", signify number(*) FILTER (the site score = 'PG' ) AS "PG", count(*) FILTER (the site rating = 'G' ) AS "G", count(*) FILTER (where score = 'PG-13') AS "PG-13", signify number(*) FILTER (where ranking = 'R' ) AS "R" FROM actor AS a be a portion of film_actor AS fa using (actor_id) be portion 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 signify number most efficient one of the vital statistics.

    In shameful different databases, we’d enact it love 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 portion of movie AS f using (film_id) neighborhood through actor_id

    The worthy aspect right here is that combination functions usually only confidence non-NULL values, so if they manufacture shameful the values NULL that don't appear to live absorbing per aggregation, we’ll regain the equal result.

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

    -- PIVOTING opt for something, whatever FROM some_table PIVOT ( signify 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 ( signify 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 vigilant of that XML is fabulous. The corollary is as a consequence:

    JSON is much less astounding

    Don’t spend 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 enact 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 yield a denormalized representation of actors/movies in a sole 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. right here with PostgreSQL:

    WITH RECURSIVE x(v) AS (choose ' Bud Spencer God Forgives... I Don’t, Double crisis, They call 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 enact some XPath magic to extract the particular person values from the XML constitution and establish 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 ( select actor_id, first_name, last_name, 1, regexp_replace(films, ',.+', '') FROM actors UNION ALL select actor_id, a.first_name, a.last_name, f.film_id + 1, regexp_replace(a.films, '.*' || f.movie || ', ?(.*?)(,.+)?', '\1') FROM films AS f live a portion of actors AS a the spend of (actor_id) the site a.films not love '%' || 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 result 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 shameful handy, you should celebrate SQL. love many different languages, but a minuscule more durable as a result of:

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

    Isn’t that marvelous?

    And if that was a minuscule bit over the top, enact notice that I’m pleased to discuss with your JUG / convention to supply this discourse (simply contact us), or if you wish to regain definitely down into the particulars of those things, they additionally present this talk as a public or in-apartment workshop. enact regain 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 real questions and Pass4sure dumps

    Oracle has released Docker build files for the Oracle Database on GitHub. With those build files, you can Go ahead and build your own Docker image for the Oracle Database. If you don’t know what Docker is, you should Go 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 capture long for people to start looking at containerizing databases, as well, which makes a lot of sense — especially for, but not only, development and test environments. Here is a detailed 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 believe 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, shameful I believe to enact 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 live 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 live updated ---> Package selinux-policy-targeted.noarch 0:3.13.1-166.0.2.el7 will live 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 live updated ---> Package selinux-policy.noarch 0:3.13.1-166.0.2.el7 will live 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 conditional 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 pile an image I first believe to configure my environment appropriately.

    Enable Non-Root User

    The first thing I want to enact 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 novel 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 dash as the root user), shameful you believe to enact 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 Go ahead and dash the image build, I want to double-check one vital 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 live 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 live more than enough for the Oracle Database image. The setting can live create 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 live 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 enact it below. # Example: spend a custom pair of raw rational 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: vigorous (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. spend `--storage-opt dm.thinpooldev` to specify a custom shroud 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 pile the image. First, I need to regain the Docker build files and the Oracle install binaries. Both are light to obtain, as shown below. Note that I spend the oracle Linux user for shameful the following steps, which I believe enabled previously to communicate with the Docker daemon.

    Obtaining the Required Files

    We need the GitHub build files and Oracle installation binaries.

    GitHub Build Files

    First, I believe to download the Docker build files. There are various ways to enact this. I can, for example, clone the Git repository directly. But for simplicity and for the people who aren’t chummy with Git, I will just spend the download option on GitHub itself. If you Go to the main repository URL, you will discern a green button maxim Clone or download. By clicking on it, you will believe 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 site that most people Go to. Once you believe downloaded them, you can proceed with pile 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 believe shameful 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 certain 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 correct version directory. As I’m going to create an Oracle Database 12.2.0.1 image, I need 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 couple of parameters: -v for the version and -e for telling it that I want Enterprise Edition. 

    Note: The build of the image will drag 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 believe 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. spend `--storage-opt dm.thinpooldev` to specify a custom shroud 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: drag 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 live 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 dash the Oracle Database inside a Docker container. shameful I believe to enact is to issue the docker run command and pass in the usurp parameters. One vital 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 vital parameter is the -v parameter, which allows me to retain 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 spend the -v parameter or create a named Docker volume! The final useful parameter that I’m going to spend is the --name parameter which specifies the title of the Docker container itself. If omitted, a random title will live generated. However, passing on a title will allow me to refer to the container via that title later on:

    [oracle@localhost dockerfiles]$ cd ~ [oracle@localhost ~]$ mkdir oradata [oracle@localhost ~]$ chmod a+w oradata [oracle@localhost ~]$ docker dash --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. shameful rights reserved. Starting /opt/oracle/product/12.2.0.1/dbhome_1/bin/tnslsnr: tickle 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 plane 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 live accessible. CAUSE: The database should live registered with a listener in order to access the EM DB Express URL. ACTION: Select a listener to live 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. shameful 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 save STATE Completed: ALTER PLUGGABLE DATABASE ORCLPDB1 save STATE

    On the very first startup of the container, a novel database is being created. Subsequent startups of the identical container or newly created containers pointing to the identical volume will just start up the database again. Once the database is created and or started the container will dash 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 discern 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 spend 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 novel 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. shameful 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. shameful 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> concede 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 discontinue the Docker container, you can just enact so via the docker stop command.  shameful you will believe to enact is to issue the command and pass on the container title 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 live 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 enact that via the -t option, which allows you to pass on a novel timeout in seconds for the container to shut down successfully.

    I will give the database 30 seconds to shut down, but it’s vital 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 shameful the seconds that you believe specified until returning control. So even if you give it ten minutes (600 seconds), it will quiet return as soon as the database is shut down.

    Just retain that in judgement when specifying a timeout for busy database containers:

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

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

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

    The docker start command will establish the container into the background and return 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 discern that this time, the database was just restarted rather than created.

    Note: A docker logs -f will ensue the log output, i.e. retain on printing novel 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. shameful rights reserved. Connected to an idle instance. SQL> ORACLE instance started. Total System Global district 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 plot 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. shameful 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 identical 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 dash your Oracle Database containers.

    You can find the GitHub repository here.


    rapid application development (RAD) | killexams.com real 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 novel manner called Rapid Iterative Production Prototyping (RIPP). In 1991, this approach became the premise of the book Rapid Application Development.

    Martin's development philosophy focused on quicken and used strategies such as prototyping, iterative development and time boxing. He believed that software products can live 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 development is quiet in spend today and some companies offer products that provide some or shameful of the tools for RAD software development. (The concept can live applied to hardware development as well.) These products comprise requirements gathering tools, prototyping tools, computer-aided software engineering tools, language development environments such as those for the Java platform, groupware for communication among development 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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    References :


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