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1Z1-450 - Oracle Application Express 3.2-(R) Developing Web Applications - Dump Information

Vendor : Oracle
Exam Code : 1Z1-450
Exam Name : Oracle Application Express 3.2-(R) Developing Web Applications
Questions and Answers : 49 Q & A
Updated On : December 7, 2018
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1Z1-450 Oracle Application Express 3.2-(R) Developing Web Applications

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

Test Code : 1Z1-450
Test Name : Oracle Application Express 3.2-(R) Developing Web Applications
Vendor Name : Oracle
Q&A : 49 Real Questions

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Oracle Oracle Application Express 3.2-(R)

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

No outcomes found, are attempting new key phrase!when it comes to ecosystems, GAAP applications complete revenues have 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 show hall entrance for IOUG & OAUG Collaborate17

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

Your statistics has a story to inform, and if advised appropriately, your data can predict the future. during this session we simplify the complicated task of asset valuation with an exciting story the use of a strategy that you can observe to any asset you want to purchase — be it your subsequent car, boat, home, or airplane. We use 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, use Oracle information Visualization computing device to identify the expense drivers of this market, and categorical a simple R commentary in order to predict the price of any asset out there.

targets
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  • exhibit the Oracle statistics Visualization desktop and use it to determine expense drivers inside the mined facts.
  • Produce a predictive mannequin the use 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 lean 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 contemporary 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.

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  • 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 beginning published over at jooq.org, a weblog specializing in all issues open source, Java and application construction from the viewpoint of jOOQ.

    Listicles like these do work – no longer most effective do they appeal to attention, if the content is also positive (and in this case it's, trust me), the article structure will also be extraordinarily entertaining.

    this text will carry you 10 SQL hints that many of you may now not have idea 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 also be viewed on SlideShare:

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

    Introduction

    in order to remember the cost of these 10 SQL tricks, it's first crucial to take into account the context of the SQL language. Why do 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 tell 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 part of) the user table and the handle table and discover the clients that live in Switzerland. You don’t care HOW the database will retrieve this suggestions (e.g. should the users table be loaded first, or the address desk? may still the two tables be joined in a nested loop or with a hashmap? should still all records be loaded in memory first after which filtered for Swiss clients, or should we only load Swiss addresses in the first location? and many others.)

    As with every abstraction, you are going to still should comprehend the basics of what’s happening in the back of the scenes in a database to assist the database make 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 certain to have a corresponding user)
  • Add an index on the search field: The country (this tells the database that particular international locations may also be present in O(log N) as an alternative of O(N))
  • however as soon as your database and your software matures, you will have put all of the crucial meta records in vicinity and you may center of attention to your business good judgment most effective. right here 10 tricks show astonishing performance written in barely a couple of traces of declarative SQL, producing primary and also advanced output.

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    1. everything is a desk

    this is the most trivial 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 see a SQL observation like 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 also a table? for instance, that you can write:

    select * FROM ( choose * FROM person ) t

    And now, you have 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 also 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 choose 3 AS a FROM dual ) t

    Now that you just’re when you consider 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 whole tables. Most americans just turn up to insert a single-row-desk most of the time, and for that reason don’t know what INSERT really does.

    every little 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 use the analogy of the Java 8 move API to take this one step further. consider the following equal ideas:

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

    With Java 8, “every little 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 all the time a movement once again.

    We’ve written a whole 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), do 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 method to declare variables in SQL (apart from the imprecise WINDOW clause that simplest PostgreSQL and Sybase SQL anywhere be aware of).

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

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

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

    the primary UNION ALL subquery is what I usually call the “seed row”. It “seeds” (initialises) the recursion. it could produce one or several rows on which we will recurse afterwards. bear in mind: every little thing is a table, so our recursion will turn up on an entire table, not on an individual row/value.

    The 2nd UNION ALL 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 reason has additionally access to the column v, which is being declared by using the CTE that already uses it.

    In our instance, we seed the recursion with the row (1), after which recurse by way 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 make SQL:1999 turing finished, which potential that any program will also be 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 end + 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 also you’ll get anything like

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

    stunning, huh?

    three. operating total Calculations

    This blog is full 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 way to enforce a working complete.

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

    eder 1

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

    Window capabilities are an impressive idea – now not so handy to keep in mind 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 perform calculations on rows which are “above” or “below” the existing row. not like standard aggregations and neighborhood by using, despite the fact, they don’t seriously change the rows, which makes them very useful.

    The syntax will also be summarized as follows, with particular person parts being not obligatory

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

    So, we now have any type of function (we’ll see 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 be considered for the window
  • The ORDER: The window can also be ordered independently of what we’re making a choice on
  • The ROWS (or range) body definition: The window will also be confined to a set quantity of rows “ahead” and “in the back of”
  • That’s all there is to window features.

    Now how does that assist us calculate a running total? accept as true 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 count on that steadiness is what we want to calculate from quantity

    Intuitively, we can immediately see that here holds true:

    sql-tricks-slide-081

    So, in undeniable English, any steadiness can be 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 be 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 calculate the sum for each and every checking account, no longer for the whole facts set
  • The ordering will make certain that transactions are ordered (in the partition) in advance of summing
  • The rows clause will agree with most effective 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 we movement on to all of the other extraordinary tricks, agree with this: We’ve seen

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

  • dazzling
  • Exremely effective
  • Declarative
  • a part of the SQL typical
  • purchasable in most universal RDBMS (apart from MySQL)
  • Very crucial constructing blocks
  • If the rest may also be 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 quality characteristic to inspire individuals to live on their website for provided that possible. Badges:

    sql-tricks-slide-090

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

    How do you calculate these badges? Let’s have a look 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, we don’t care about holding any state and in-memory counters. We want to express this in the form of on-line analytic SQL. I.e. agree 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 support 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 place 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 easy 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, we subtract them?

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

    We’re getting whatever thing like 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 say it better than Doge:

    eder 3

    There’s a simple illustration for this conduct:

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

    Huh.

    This capacity we can now quite simply group by way of this arbitrary 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 place 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 idea makes the entire difference, however the question itself is in fact very very simple and chic. No approach you may put in force some imperative-fashion algorithm in a leaner way than this.

    Whew.

    5. discovering the length of a series

    previously, we had considered collection of consecutive values. That’s handy to deal with as we will abuse of the consecutiveness of integers. What if the definition of a “series” is much less intuitive, and also to that, a couple of series contain the identical values? believe here statistics, the place size is the size of each sequence that we 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 have the same 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 do we do it? “convenient”😉 First, let’s do 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 produce 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, we are looking to reproduction the row quantity price into “LO” at the “lower” end of a series, and into “hello” on the “higher” conclusion of a series. For this we’ll be 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 way 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 perform rankings or aggregations on a subset of rows relative to the present row. in the case of LEAD() and LAG(), we with no trouble access a single row relative to the existing row, given its offset. here's helpful in so many situations.

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

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

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

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

    ultimately, a little boring NULL coping with to get 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 way of identity DESC), 0) != signal THEN rn conclusion AS hello, FROM trx

    subsequent step. We need “LO” and “hello” to seem in ALL rows, no longer just on the “reduce” and “upper” bounds of a series. E.g. like 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 use 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 be handed to a few window capabilities:

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

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

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

    defined in Powerpoint:

    eder 4

    Getting it 100% proper, with a bit boring 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 trivial last step, conserving in intellect off-by way of-1 blunders:

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

    And we’re completed. right here’s our 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 full question here:

    WITH trx1(identification, amount, signal, rn) AS ( choose 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 way 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 run 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 |

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

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

    As you’re all brief together with your intellectual mathemagic processing, you have instantly calculated these to be the most beneficial 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 do it with SQL? effortless. simply create a CTE that carries the entire 2n *viable* sums and then discover the closest one for each and every total:

    -- the entire possible 2N sums WITH sums(sum, max_id, calc) AS (...) -- locate the most appropriate 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 be like my friend right here:

    eder 4

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

    WITH sums(sum, id, calc) AS ( choose merchandise, id, to_char(merchandise) FROM gadgets UNION ALL choose item + sum, gadgets.identity, calc || ' + ' || item FROM sums be a part 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 be part 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 make sure to come back back right here for the last 4 tricks:

    7. Capping a running total

    to date, we’ve considered how to calculate an “normal” running complete with SQL the use of window functions. That became effortless. Now, how about if we cap the working complete such that it certainly not goes below zero? almost, we wish to calculate 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 large poor volume -20000 changed into subtracted, as an alternative of displaying the true complete of -15550, we without difficulty 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 we do it?

    eder 4

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

    as soon as we put mannequin there, we can implement spreadsheet logic without delay in our 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 have any variety of dimensions in Oracle:

    eder 4

    The MEASURES clause specifies the values that are available in each telephone of your spreadsheet. not like in MS Excel, that you can have 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 put inside of each cellphone:

    eder 4

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

    opt for * FROM ( choose 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, please do 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 all a new time period to you.

    If we evaluate the “length of a series” information set, we could wish to generate triggers on complicated routine over our 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, we are able to do this with an Oracle-certain clause that become introduced to Oracle 12c:

    opt for ... FROM some_table -- Put 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 ALL ROWS PER match -- «ordinary expressions» of events to in shape pattern (...) -- The definitions of «what is an experience» define ... )

    That sounds loopy. Let’s look at some example clause implementations

    select * FROM seriesMATCH_RECOGNIZE ( ORDER by identity MEASURES classifier() AS trg ALL 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 we do right here?

  • We order the desk with the aid of id, which is the order by which we are looking to healthy movements. convenient.
  • We then specify the values that we desire as a result. We want the “MEASURE” trg, which is defined because the classifier, i.e. the literal that we’ll use within the pattern afterwards. Plus we 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 our particular event X, followed by one or extra “R” for repeat once again. If the total pattern suits, we get SRXR or SRXRR or SRXRRR, i.e. X will be on the third position of a collection of size >= 4
  • eventually, we define R and X as being the equal issue: The adventure whenSIGN(amount) of the existing row is the same as signal(volume) of the previous row. We don’t have to define “S”. “S” is only another row.
  • This question will magically produce 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 see a single “X” in our adventure flow. exactly the place we had anticipated it. on the third repetition of an event (identical sign) in a series of size > three.

    boom!

    As we don’t definitely care 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 ALL 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 explain 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 be nearly too embarassingly essential:

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

    | name | TITLE | ranking | |-----------|-----------------|--------| | A. provide | ANNIE id | G | | A. grant | 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 we name pivoting:

    | name | NC-17 | PG | G | PG-13 | R | |-----------|-------|-----|-----|-------|-----| | A. grant | 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 look at how we 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) we pivoted the entire issue to provide a column per neighborhood. we are able to do this, because we recognize all of the viable corporations in strengthen.

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

    | identify | rating | count number | |-----------|--------|-------| | A. grant | NC-17 | 3 | | A. furnish | PG | 6 | | A. furnish | 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 do it in PostgreSQL:

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

    In all different databases, we’d do it like 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 part of movie AS f using (film_id) neighborhood through actor_id

    The great aspect right here is that combination functions usually only trust non-NULL values, so if we make all the values NULL that don't seem to be interesting per aggregation, we’ll get the equal result.

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

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

    JSON is much less astounding

    Don’t use JSON.

    Now that we’ve settled this, we 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 way to do XML within the database.

    here is what we wish to do:

    eder 4

    Given the customary XML doc, we wish to parse that doc, unnest the comma-separated checklist of movies per actor, and produce 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, we 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, we do some XPath magic to extract the particular person values from the XML constitution and put 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 ( choose actor_id, first_name, last_name, 1, regexp_replace(films, ',.+', '') FROM actors UNION ALL choose actor_id, a.first_name, a.last_name, f.film_id + 1, regexp_replace(a.films, '.*' || f.movie || ', ?(.*?)(,.+)?', '\1') FROM films AS f be a part of actors AS a the use of (actor_id) the place a.films not LIKE '%' || 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 effect 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 all handy, you should observe SQL. Like many different languages, but a little more durable as a result of:

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

    Isn’t that marvelous?

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

    See once again the complete set of slides here:


    1Z1-450 Oracle Application Express 3.2-(R) Developing Web Applications

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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 cool 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, 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 have 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, all I have to do 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 be 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 be updated ---> Package selinux-policy-targeted.noarch 0:3.13.1-166.0.2.el7 will be 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 be updated ---> Package selinux-policy.noarch 0:3.13.1-166.0.2.el7 will be 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 Dependent 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 have to configure my environment appropriately.

    Enable Non-Root User

    The first thing I want to do 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 new 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 run as the root user), all you have to do 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 Base Image Size

    Before I go ahead and run the image build, I want to double-check one important parameter: the default base 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 be 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 be more than enough for the Oracle Database image. The setting can be 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 be 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 do it below. # Example: Use 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 base 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. Use `--storage-opt dm.thinpooldev` to specify a custom block 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 need to get the Docker build files and the Oracle install binaries. Both are easy to obtain, as shown below. Note that I use the oracle Linux user for all the following steps, which I have 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 have to download the Docker build files. There are various ways to do this. I can, for example, clone the Git repository directly. But for simplicity and for the people who aren’t familiar with Git, I will just use the download option on GitHub itself. If you go to the main repository URL, you will see a green button saying Clone or download. By clicking on it, you will have 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 place that most people go to. Once you have 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 have all 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 also 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 pull the Oracle Linux slim base image and execute a yum install as well as a yum upgrade inside the container. For it to succeed, it needs to have 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. Use `--storage-opt dm.thinpooldev` to specify a custom block 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 be 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 run the Oracle Database inside a Docker container. All I have to do is to issue the docker run command and pass in the appropriate parameters. One important parameter is the -p for the mapping of ports inside the container to the outside world. This is required so that I can also connect to the database from outside the Docker container. Another important parameter is the -v parameter, which allows me to keep 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 use the -v parameter or create a named Docker volume! The last useful parameter that I’m going to use is the --name parameter which specifies the name of the Docker container itself. If omitted, a random name will be generated. However, passing on a name will allow me to refer to the container via that name later on:

    [oracle@localhost dockerfiles]$ cd ~ [oracle@localhost ~]$ mkdir oradata [oracle@localhost ~]$ chmod a+w oradata [oracle@localhost ~]$ docker run --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. All rights reserved. Starting /opt/oracle/product/12.2.0.1/dbhome_1/bin/tnslsnr: please 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 Level 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 be accessible. CAUSE: The database should be registered with a listener in order to access the EM DB Express URL. ACTION: Select a listener to be 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. All 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 new database is being created. Subsequent startups of the same container or newly created containers pointing to the same volume will just start up the database again. Once the database is created and or started the container will run 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 see 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 also 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 use 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 new shell, just execute the following command against the running container:

    [oracle@localhost ~]$ docker exec oracle-ee ./setPassword.sh LetsDocker The Oracle base 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. All 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. All 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> grant 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 stop the Docker container, you can just do so via the docker stop command.  All you will have to do is to issue the command and pass on the container name 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 be 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 do that via the -t option, which allows you to pass on a new timeout in seconds for the container to shut down successfully.

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

    Just keep that in mind when specifying a timeout for busy database containers:

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

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

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

    The docker start command will put 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 also see that this time, the database was just restarted rather than created.

    Note: A docker logs -f will follow the log output, i.e. keep on printing new 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. All rights reserved. Connected to an idle instance. SQL> ORACLE instance started. Total System Global Area 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 plan 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. All 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 also provided build files for other Oracle Database versions and editions. The steps described above are largely the same but you should always refer to the README.md that comes with the build files. In there, you will also find more options for how to run 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 new method 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 speed and used strategies such as prototyping, iterative development and time boxing. He believed that software products can be developed faster and of higher quality 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 still in use today and some companies offer products that provide some or all of the tools for RAD software development. (The concept can be applied to hardware development as well.) These products include 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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    Oracle 1Z1-450 Exam (Oracle Application Express 3.2-(R) Developing Web Applications) Detailed Information



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