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1Z0-450 - Oracle Application Express 3.2-(R) Developing Web Applications - Dump Information

Vendor : Oracle
Exam Code : 1Z0-450
Exam Name : Oracle Application Express 3.2-(R) Developing Web Applications
Questions and Answers : 49 Q & A
Updated On : November 14, 2018
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1Z0-450 Questions and Answers

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1Z0-450 Oracle Application Express 3.2-(R) Developing Web Applications

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1Z0-450 exam Dumps Source : Oracle Application Express 3.2-(R) Developing Web Applications

Test Code : 1Z0-450
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)

birth-up values run deep in our DNA, says Steve Daheb, Senior vice-president, Oracle Cloud | killexams.com Real Questions and Pass4sure dumps

India is a key boom financial system and a essential marketplace for Oracle. “we're dedicated to India and are helping a number of clients of their adventure to cloud,” Steve Daheb, senior vice-president, Oracle Cloud, tells Sudhir Chowdhary in a recent interview. Excerpts:

What were your observations on the evolution of cloud in the closing 2-three years?

instead of ‘why cloud’, customers at the moment are asking ‘how’. They have an interest in greater understanding a way to do the mission-vital migration seamlessly. The dialog has shifted to being facts centric—analytics, securing the statistics, placing information to more desirable use with laptop gaining knowledge of. I accept as true with we are in a very respectable position to pressure the next wave of business cloud adoption.

inform us concerning the updates on your ordinary cloud enterprise.

For us it's vital to tackle all layers of the stack, from purposes which are tied intently to our structures, which are linked to these purposes to run analytics round them, to comfy them and prolong their capabilities to the underlying infrastructure. This makes it possible for our shoppers to run excessive performance workloads easily, whereas also decreasing cost.

What kind of momentum do you are expecting going ahead?

we are already seeing lots of cloud adoption, and we anticipate greater big scale adoption in the coming months. independent database and infrastructure are very important to us. With loads of our shoppers trying to find an integrated answer, we're bundling in infrastructure, analytics, integration, and so on.

How are CIOs reacting to the independent database?

CIOs are very attracted to it, given the efficiency merits. With our autono-mous database, IT groups can flow faraway from protection and as a substitute focal point on greater value projects. From a value discount viewpoint, reduction of risk and the potential to focal point on innovation, agencies can installation data-warehouses in seconds now, automatically.

Cyber safety has become a key subject for companies. What can they expect next from Oracle?

At Oracle, security is embedded in every little thing we do, as we analyze it holistically. no matter if it is identification entry administration, the purposes layer, application protecting, records layer or encryption layer, we now have multiple layers of defence. we've introduced Gen 2 cloud, with an impenetrable barrier and self reliant robots to nullify threats.

How crucial is India for Oracle?

India is a key growth economic climate and a vital marketplace for Oracle. we are dedicated to India and are helping a few shoppers in their event to cloud. we're partnering a few government businesses for a lot of vital digitisation and transformation initiatives, together with the domain of better citizen service/ engagement. we've a sturdy associate ecosystem as smartly to aid clients radically change into digital-first agencies quicker.

How does Oracle see India’s birth-up economy which has unfolded opportunities for cloud answer providers?

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No effect found, are attempting new key phrase!when it comes to ecosystems, GAAP purposes complete revenues were $ ... I’ll talk a bit bit about an organization known as Federal specific. FedEx is -- in the FedEx aspect of the condo, a traditional Oracle consumer ...

10 SQL hints that you simply didn’t suppose had been feasible | killexams.com Real Questions and Pass4sure dumps

This put up was at first published over at jooq.org, a weblog focusing on all issues open supply, Java and application building from the perspective of jOOQ.

Listicles like these do work – now not handiest do they attract attention, if the content is additionally useful (and in this case it is, have faith me), the article format can also be extremely unique.

this article will bring you 10 SQL tricks that lots of you might not have idea had been feasible. The article is a abstract of my new, extremely fast-paced, ridiculously childish-humored talk, which I’m giving at conferences (currently at JAX, and Devoxx France). You can also quote me on this:

the total slides can also be seen on SlideShare:

… and i’m bound there’ll be a recording on video quickly. listed below are 10 SQL tricks that you just Didn’t feel had been feasible:

Introduction

in an effort to have in mind the value of these 10 SQL hints, it is first crucial to have in mind the context of the SQL language. Why do I talk about SQL at Java conferences? (and that i’m continually the only one!) here's why:

sql-tricks-slide-006

From early days onwards, programming language designers had this need to design languages during which you tell the machine WHAT you need in consequence, no longer a way to achieve it. as an example, in SQL, you inform the machine that you just need to “connect” (be a part of) the person desk and the address desk and locate the clients that reside in Switzerland. You don’t care HOW the database will retrieve this assistance (e.g. may still the users desk be loaded first, or the tackle desk? may still the two tables be joined in a nested loop or with a hashmap? should still all statistics be loaded in reminiscence first and then filtered for Swiss users, or should still we handiest load Swiss addresses within the first place? and so on.)

As with every abstraction, you are going to nevertheless should recognize the fundamentals of what’s happening in the back of the scenes in a database to help the database make the appropriate decisions if you query it. as an instance, it makes sense to:

  • establish a proper foreign key relationship between the tables (this tells the database that every tackle is guaranteed to have a corresponding consumer)
  • Add an index on the search field: The country (this tells the database that particular countries may also be present in O(log N) instead of O(N))
  • but as soon as your database and your software matures, you can have put all of the essential meta information in area and you can focus in your enterprise logic only. here 10 tricks show unbelievable performance written in exactly a few traces of declarative SQL, producing basic and also complicated output.

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    1. everything is a desk

    this is probably the most trivial of hints, and not even truly a trick, but it surely is simple to an intensive figuring out of SQL: every little thing is a table! in case you see a SQL remark like this:

    choose * FROM person

    … you are going to promptly spot the desk grownup sitting appropriate there in the FROM clause. That’s cool, it truly is a table. but did you realize that the entire statement is additionally a desk? as an instance, that you may write:

    choose * FROM ( opt for * FROM adult ) t

    And now, you've got created what is referred to as a “derived table” – i.e. a nestedSELECT statement in a FROM clause.

    That’s trivial, but if you consider of it, rather elegant. that you would be able to also create advert-hoc, in-memory 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 effortlessly yields:

    If that clause isn't supported, you can revert to derived tables, e.g. in Oracle:

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

    Now that you just’re on account that VALUES() and derived tables are definitely the same thing, conceptually, let’s evaluate the INSERT observation, which is available 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) select 1 AS a FROM twin UNION ALL opt for 2 AS a FROM dual UNION ALL opt for 3 AS a FROM dual

    In SQL every thing is a table. for those who’re inserting rows right into a table, you’re not really inserting individual rows. You’re in fact inserting complete tables. Most people just turn up to insert a single-row-desk many of the time, and consequently don’t recognize what INSERT in reality does.

    every little thing is a desk. In PostgreSQL, even functions are tables:

    opt for * FROM substring('abcde', 2, 3)

    The above yields:

    if you’re programming in Java, that you would be able to use the analogy of the Java eight stream API to take this one step extra. believe here equal ideas:

    table : circulate<Tuple<..>> select : map() diverse : diverse() be a part of : flatMap() where / HAVING : filter() community with the aid of : assemble() ORDER by using : sorted() UNION ALL : concat()

    With Java 8, “everything is a flow” (as soon as you delivery working with Streams, at least). No count the way you transform a stream, e.g. with map() or filter(), the ensuing type is at all times a flow again.

    We’ve written a whole article to explain this extra deeply, and to compare the flow API with SQL:standard SQL Clauses and Their Equivalents in Java 8 Streams

    And in case you’re attempting to find “greater streams” (i.e. streams with much more SQL semantics), do check out jOOλ, an open source library that brings SQL window functions to Java.

    2. statistics era with recursive SQL

    general desk Expressions (also: CTE, also referred to as subquery factoring, e.g. in Oracle) are the best solution to declare variables in SQL (apart from the imprecise WINDOW clause that simplest PostgreSQL and Sybase SQL anyplace know).

    this is a powerful theory. extremely powerful. trust right here remark:

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

    It yields

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

    the usage of the basic WITH clause, that you would be able to specify a list of table variables (bear in mind: every little thing is a desk), which may even rely on each and every different.

    it's effortless to keep in mind. This makes CTE (typical desk Expressions) already very valuable, however what’s in fact basically astonishing is that they’re allowed to be recursive! trust the following PostgreSQL instance:

    WITH RECURSIVE t(v) AS ( choose 1 -- Seed Row UNION ALL choose v + 1 -- Recursion FROM t ) select v FROM t restrict 5

    It yields

    v—12345

    How does it work? It’s surprisingly convenient, once you see during the many keywords. You define a standard table expression that has exactly two UNION ALL subqueries.

    the primary UNION ALL subquery is what I continually name the “seed row”. It “seeds” (initialises) the recursion. it may produce one or a couple of rows on which we can recurse afterwards. be aware: every thing is a desk, so our recursion will occur on a whole desk, no longer on a person row/value.

    The second UNION ALL subquery is the place the recursion occurs. in case you appear closely, you will observe that it selects from t. I.e. the 2d subquery is allowed to choose from the very CTE that we’re about to declare. Recursively. It therefore has also access to the column v, which is being declared through the CTE that already uses it.

    In our example, we seed the recursion with the row (1), and then recurse by means of including v + 1. The recursion is then stopped at the use-web page with the aid of atmosphere aLIMIT 5 (beware of doubtlessly endless recursions – just like with Java 8 Streams).

    facet notice: Turing completeness

    Recursive CTE make SQL:1999 turing comprehensive, which capability that any program can also be written in SQL! (if you’re crazy enough)

    One awesome illustration that frequently shows up on blogs: The Mandelbrot Set, e.g. as displayed on http://explainextended.com/2013/12/31/satisfied-new-12 months-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 choose r, i, CASE WHEN abs(rx * rx + ix * ix) &amp;amp;lt;= 2 THEN rx * rx - ix * ix conclusion + r, CASE WHEN abs(rx * rx + ix * ix) &amp;amp;lt;= 2 THEN 2 * rx * ix end + i, g + 1 FROM q the place rx is not NULL AND g &amp;amp;lt; 99 ) select array_to_string(array_agg(s ORDER with the aid of r), '') FROM ( select i, r, substring(' .:-=+*#%@', max(g) / 10 + 1, 1) s FROM q group with the aid of i, r ) q group by means of i ORDER by using i

    Run the above on PostgreSQL, and also you’ll get whatever thing like

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

    incredible, huh?

    three. running total Calculations

    This blog is filled with running complete examples. They’re probably the most most academic examples to study superior SQL, as a result of there are at the least a dozen of ways the way to put in force a operating total.

    A working total is effortless to take note, conceptually.

    eder 1

    In Microsoft Excel, you may readily calculate a sum (or change) of two old (or subsequent) values, after which use the beneficial crosshair cursor to drag that system via your whole spreadsheet. You “run” that total through the spreadsheet. A “running total”.

    In SQL, the greatest solution to try this is through the use of window functions, a further theme that this blog has covered many time and again.

    Window services are an impressive thought – now not so effortless to have in mind at the beginning, however basically, they’re basically actually handy:

    Window services are aggregations / rankings on a subset of rows relative to the current row being transformed by using select

    That’s it.:)

    What it very nearly ability is that a window feature can perform calculations on rows which are “above” or “under” the current row. unlike ordinary aggregations and community with the aid of, although, they don’t transform the rows, which makes them very valuable.

    The syntax can be summarized as follows, with particular person constituents being non-compulsory

    characteristic(...) OVER ( PARTITION by means of ... ORDER by means of ... ROWS BETWEEN ... AND ... )

    So, we've any sort of feature (we’ll see examples for such features later), adopted with the aid of this OVER() clause, which specifies the window. I.e. this OVER()clause defines:

  • The PARTITION: best rows that are in the identical partition as the latest row will be regarded for the window
  • The ORDER: The window can be ordered independently of what we’re deciding on
  • The ROWS (or range) body definition: The window can also be restrained to a set volume of rows “ahead” and “in the back of”
  • That’s all there is to window services.

    Now how does that assist us calculate a working total? accept as true with here records:

    | identity | VALUE_DATE | quantity | balance | |------|------------|--------|------------| | 9997 | 2014-03-18 | 99.17 | 19985.eighty one | | 9981 | 2014-03-sixteen | 71.44 | 19886.64 | | 9979 | 2014-03-sixteen | -ninety four.60 | 19815.20 | | 9977 | 2014-03-16 | -6.ninety six | 19909.80 | | 9971 | 2014-03-15 | -sixty five.95 | 19916.seventy six |

    Let’s anticipate that stability is what we need to calculate from volume

    Intuitively, we will instantly see that right here holds genuine:

    sql-tricks-slide-081

    So, in undeniable English, any steadiness will also be expressed with right here pseudo SQL:

    TOP_BALANCE – SUM(volume) OVER (“all the rows on proper of the current row”)

    In precise SQL, that could then be written as follows:

    SUM(t.amount) OVER ( PARTITION by means of t.account_id ORDER by way of t.value_date DESC, t.id DESC ROWS BETWEEN UNBOUNDED preceding AND 1 preceding )

    rationalization:

  • The partition will calculate the sum for each checking account, now not for the whole information set
  • The ordering will make sure that transactions are ordered (inside the partition) earlier than summing
  • The rows clause will agree with only previous rows (within the partition, given the ordering) ahead of summing
  • All of this may occur in-reminiscence over the information set that has already been chosen through you to your FROM .. where and so on. clauses, and is hence extremely fast.

    Intermezzo

    before we flow on to the entire other dazzling hints, trust this: We’ve considered

  • (Recursive) typical table Expressions (CTE)
  • Window services
  • each of those points are:

  • staggering
  • Exremely potent
  • Declarative
  • a part of the SQL ordinary
  • accessible in most accepted RDBMS (apart from MySQL)
  • Very important constructing blocks
  • If anything can be concluded from this text, it's the proven fact that you'll want to completely know these two constructing blocks of contemporary SQL. Why? as a result of:

    eder 2

    four. finding the largest collection and not using a gaps

    Stack Overflow has this very fine feature to inspire people to dwell on their website for as long as feasible. Badges:

    sql-tricks-slide-090

    For scale, that you could see how many badges I actually have. heaps.

    How do you calculate these badges? Let’s have a glance on the “fanatic” and the “Fanatic”. These badges are awarded to any person who spends a given quantity of consecutive days on their platform. inspite of any wedding date or wife’s birthday, you must LOG IN, or the counter begins from zero again.

    Now as we’re doing declarative programming, we don’t care about keeping any state and in-reminiscence counters. We are looking to specific this in the variety of on-line analytic SQL. I.e. consider this statistics:

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

    That doesn’t aid a great deal. Let’s eradicate the hours from the timestamp. That’s effortless:

    opt for distinct solid(login_time AS DATE) AS login_date FROM logins where user_id = :user_id

    Which yields:

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

    Now, that we’ve discovered about window capabilities, let’s just add an easy row number to every of those dates:

    select login_date, row_number() OVER (ORDER by way 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 |

    nonetheless convenient. Now, what occurs, if instead of deciding on these values separately, we subtract them?

    opt for login_date - row_number() OVER (ORDER through login_date) FROM login_dates

    We’re getting some thing like this:

    | LOGIN_DATE | RN | GRP | |------------|----|------------| | 2014-03-18 | four | 2014-03-14 | | 2014-03-sixteen | three | 2014-03-13 | | 2014-03-15 | 2 | 2014-03-13 | | 2014-03-14 | 1 | 2014-03-13 |

    Wow. entertaining. So, 14 – 1 = 13, 15 – 2 = 13, 16 – 3 = 13, however 18 – 4 = 14. no person can say it greater than Doge:

    eder 3

    There’s a simple illustration for this behavior:

  • ROW_NUMBER() in no way has gaps. That’s how it’s described
  • Our records, youngsters, does
  • So when we subtract a “gapless” sequence of consecutive integers from a “gapful” sequence of non-consecutive dates, we are able to get the equal date for each “gapless” subseries of consecutive dates, and we’ll get a brand new date once more the place the date collection had gaps.

    Huh.

    This means we can now without problems community via this arbitrary date value:

    choose min(login_date), max(login_date), max(login_date) - min(login_date) + 1 AS durationFROM login_date_groups community through grp ORDER via size DESC

    And we’re done. The biggest series of consecutive dates with out a gaps has been discovered:

    | MIN | MAX | length | |------------|------------|--------| | 2014-03-14 | 2014-03-16 | three | | 2014-03-18 | 2014-03-18 | 1 |

    With the total question being:

    WITH login_dates AS ( choose distinct cast(login_time AS DATE) login_date FROM logins where user_id = :user_id ), login_date_groups AS ( select login_date, login_date - row_number() OVER (ORDER via login_date) AS grp FROM login_dates ) choose min(login_date), max(login_date), max(login_date) - min(login_date) + 1 AS periodFROM login_date_groups group through grp ORDER through length DESC

    eder 4

    now not that complicated within the end, appropriate? Of course, having the thought makes all of the difference, but the query itself is really very very standard and stylish. No way you might put in force some integral-vogue algorithm in a leaner method than this.

    Whew.

    5. discovering the size of a collection

    previously, we had considered series of consecutive values. That’s convenient to take care of as we can abuse of the consecutiveness of integers. What if the definition of a “series” is less intuitive, and in addition to that, several sequence contain the same values? consider the following facts, the place size is the size of each and every sequence that we are looking to calculate:

    | identification | VALUE_DATE | amount | size | |------|------------|--------|------------| | 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 | 3 | | 9964 | 2014-03-15 | 15.13 | 2 | | 9962 | 2014-03-15 | 17.forty seven | 2 | | 9960 | 2014-03-15 | -three.fifty five | 1 | | 9959 | 2014-03-14 | 32.00 | 1 |

    yes, you’ve guessed appropriate. A “series” is defined through the incontrovertible fact that consecutive (ordered through identification) rows have the identical signal(quantity). assess once again the records formatted as under:

    | identification | VALUE_DATE | amount | size | |------|------------|--------|------------| | 9997 | 2014-03-18 | +99.17 | 2 | | 9981 | 2014-03-sixteen | +71.44 | 2 | | 9979 | 2014-03-16 | -ninety four.60 | 3 | | 9977 | 2014-03-16 | - 6.96 | three | | 9971 | 2014-03-15 | -65.ninety five | three | | 9964 | 2014-03-15 | +15.13 | 2 | | 9962 | 2014-03-15 | +17.47 | 2 | | 9960 | 2014-03-15 | - 3.fifty five | 1 | | 9959 | 2014-03-14 | +32.00 | 1 |

    How can we do it? “easy”😉 First, let’s do away with the entire noise, and add one more row quantity:

    select id, quantity, sign(amount) AS signal, row_number() OVER (ORDER by identification DESC) AS rn FROM trx

    this will give us:

    | id | amount | sign | RN | |------|--------|------|----| | 9997 | ninety nine.17 | 1 | 1 | | 9981 | 71.forty four | 1 | 2 | | 9979 | -94.60 | -1 | 3 | | 9977 | -6.ninety six | -1 | four | | 9971 | -65.ninety five | -1 | 5 | | 9964 | 15.13 | 1 | 6 | | 9962 | 17.47 | 1 | 7 | | 9960 | -three.fifty five | -1 | eight | | 9959 | 32.00 | 1 | 9 |

    Now, the subsequent goal is to produce here desk:

    | identification | quantity | signal | RN | LO | hi | |------|--------|------|----|----|----| | 9997 | 99.17 | 1 | 1 | 1 | | | 9981 | seventy one.forty four | 1 | 2 | | 2 | | 9979 | -ninety four.60 | -1 | 3 | 3 | | | 9977 | -6.ninety six | -1 | 4 | | | | 9971 | -65.ninety five | -1 | 5 | | 5 | | 9964 | 15.13 | 1 | 6 | 6 | | | 9962 | 17.forty seven | 1 | 7 | | 7 | | 9960 | -three.55 | -1 | eight | eight | eight | | 9959 | 32.00 | 1 | 9 | 9 | 9 |

    in this table, we need to replica the row number value into “LO” on the “reduce” end of a sequence, and into “hello” at the “upper” end of a collection. For this we’ll be the use 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 old row from the latest row. for instance:

    opt for lag(v) OVER (ORDER with the aid of v), v, lead(v) OVER (ORDER via v) FROM ( VALUES (1), (2), (three), (four) ) t(v)

    The above question produces:

    eder 4

    That’s staggering! be aware, with window capabilities, that you can function rankings or aggregations on a subset of rows relative to the present row. in the case of LEAD() and LAG(), we comfortably entry a single row relative to the latest row, given its offset. here is effective in so many instances.

    carrying on with with our “LO” and “hi” example, we can without problems write:

    select trx.*, CASE WHEN lag(signal) OVER (ORDER through identity DESC) != sign THEN rn conclusion AS lo, CASE WHEN lead(signal) OVER (ORDER with the aid of identification DESC) != signal THEN rn conclusion AS hi, FROM trx

    … wherein we evaluate the “previous” sign (lag(signal)) with the “current” signal (signal). in the event that they’re distinctive, we put the row number in “LO”, because that’s the lessen sure of our collection.

    Then we evaluate the “next” signal (lead(sign)) with the “current” sign (signal). in the event that they’re diverse, we put the row quantity in “hello”, because that’s the higher sure of our collection.

    ultimately, a bit boring NULL handling to get every little thing appropriate, and we’re achieved:

    choose -- With NULL handling... trx.*, CASE WHEN coalesce(lag(sign) OVER (ORDER by identity DESC), 0) != signal THEN rn end AS lo, CASE WHEN coalesce(lead(signal) OVER (ORDER by identity DESC), 0) != signal THEN rn end AS hi, FROM trx

    subsequent step. We desire “LO” and “hello” to appear in ALL rows, no longer just at the “lessen” and “higher” bounds of a collection. E.g. like this:

    | id | amount | signal | RN | LO | hi | |------|--------|------|----|----|----| | 9997 | 99.17 | 1 | 1 | 1 | 2 | | 9981 | 71.forty four | 1 | 2 | 1 | 2 | | 9979 | -94.60 | -1 | 3 | three | 5 | | 9977 | -6.ninety six | -1 | four | three | 5 | | 9971 | -sixty five.95 | -1 | 5 | three | 5 | | 9964 | 15.13 | 1 | 6 | 6 | 7 | | 9962 | 17.47 | 1 | 7 | 6 | 7 | | 9960 | -3.55 | -1 | eight | eight | 8 | | 9959 | 32.00 | 1 | 9 | 9 | 9 |

    We’re using a function that is available at the least in Redshift, Sybase SQL anywhere, DB2, Oracle. We’re the use of the “IGNORE NULLS” clause that may also be passed to a few window capabilities:

    opt for trx.*, last_value (lo) IGNORE NULLS OVER ( ORDER via identification DESC ROWS BETWEEN UNBOUNDED preceding AND current ROW) AS lo, first_value(hi) IGNORE NULLS OVER ( ORDER by using identification DESC ROWS BETWEEN latest ROW AND UNBOUNDED FOLLOWING) AS hello FROM trx

    lots of key terms! however the essence is at all times the identical. From any given “present” row, we look at all of the “previous values” (ROWS BETWEEN UNBOUNDED preceding AND latest ROW), however ignoring all the nulls. From those outdated values, we take the last price, and that’s our new “LO” price. In other phrases, we take the “closest preceding” “LO” price.

    The same with “hi”. From any given “existing” row, we examine all of the “subsequent values” (ROWS BETWEEN existing ROW AND UNBOUNDED FOLLOWING), but ignoring all of the nulls. From the subsequent values, we take the first price, and that’s our new “hello” cost. In different words, we take the “closest following” “hi” price.

    explained in Powerpoint:

    eder 4

    Getting it a hundred% suitable, with a little boring NULL fiddling:

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

    finally, we’re simply doing a trivial last step, preserving in mind off-via-1 blunders:

    select trx.*, 1 + hi - lo AS periodFROM trx

    And we’re done. here’s our result:

    | identity | amount | signal | RN | LO | hi | length| |------|--------|------|----|----|----|-------| | 9997 | 99.17 | 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 | 3 | | 9971 | -sixty five.ninety five | -1 | 5 | three | 5 | three | | 9964 | 15.13 | 1 | 6 | 6 | 7 | 2 | | 9962 | 17.forty seven | 1 | 7 | 6 | 7 | 2 | | 9960 | -3.fifty five | -1 | eight | 8 | eight | 1 | | 9959 | 32.00 | 1 | 9 | 9 | 9 | 1 |

    And the whole query here:

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

    eder 4

    Huh. This SQL factor does start getting pleasing!

    competent for greater?

    6. The subset sum problem with SQL

    here's my common!

    what is the subset sum issue? discover a fun explanation here:https://xkcd.com/287

    And a humdrum one right here:https://en.wikipedia.org/wiki/Subset_sum_problem

    well-nigh, for each of these totals…

    | id | total | |----|-------| | 1 | 25150 | | 2 | 19800 | | 3 | 27511 |

    … we want to locate the “most beneficial” (i.e. the closest) sum possible, such as any combination of those items:

    | identity | merchandise | |------|-------| | 1 | 7120 | | 2 | 8150 | | 3 | 8255 | | 4 | 9051 | | 5 | 1220 | | 6 | 12515 | | 7 | 13555 | | eight | 5221 | | 9 | 812 | | 10 | 6562 |

    As you’re all brief along with your mental mathemagic processing, you've got instantly calculated these to be the optimal sums:

    | complete | most efficient | 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? easy. simply create a CTE that consists of the entire 2n *possible* sums after which find the closest one for every complete:

    -- all the feasible 2N sums WITH sums(sum, max_id, calc) AS (...) -- discover the greatest sum per “total” select totals.complete, something_something(complete - sum) AS ideal, something_something(total - sum) AS calc FROM draw_the_rest_of_the_*bleep*_owl

    As you’re analyzing this, you could be like my pal here:

    eder 4

    however don’t be concerned, the answer is – once more – not all that challenging (although it doesn’t operate as a result of the nature of the algorithm):

    WITH sums(sum, id, calc) AS ( select item, identity, to_char(merchandise) FROM items UNION ALL opt for item + sum, objects.identification, calc || ' + ' || item FROM sums join gadgets ON sums.identification &lt; items.identification ) select totals.identity, totals.total, min (sum) retain ( DENSE_RANK FIRST ORDER by abs(total - sum) ) AS most appropriate, min (calc) maintain ( DENSE_RANK FIRST ORDER with the aid of abs(total - sum) ) AS calc, FROM totals pass be part of sums neighborhood by way of totals.identity, totals.complete

    in this article, I gained’t clarify the particulars of this answer, since the illustration has been taken from a outdated article so that you can discover right here:

    the way to discover the closest subset sum with SQL

    appreciate studying the particulars, but make certain to come back lower back here for the closing four tricks:

    7. Capping a working complete

    to this point, we’ve seen how to calculate an “average” working complete with SQL using window functions. That become effortless. Now, how about if we cap the operating complete such that it never goes under zero? virtually, we want to calculate this:

    | DATE | amount | total | |------------|--------|-------| | 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 big terrible quantity -20000 turned into subtracted, instead of exhibiting the precise complete of -15550, we without difficulty screen 0. In other words (or facts units):

    | DATE | amount | complete | |------------|--------|-------| | 2012-01-01 | 800 | 800 | most useful(0, 800) | 2012-02-01 | 1900 | 2700 | top of the line(0, 2700) | 2012-03-01 | 1750 | 4450 | most fulfilling(0, 4450) | 2012-04-01 | -20000 | 0 | most reliable(0, -15550) | 2012-05-01 | 900 | 900 | most beneficial(0, 900) | 2012-06-01 | 3900 | 4800 | most efficient(0, 4800) | 2012-07-01 | -2600 | 2200 | most appropriate(0, 2200) | 2012-08-01 | -2600 | 0 | most beneficial(0, -400) | 2012-09-01 | 2100 | 2100 | most effective(0, 2100) | 2012-10-01 | -2400 | 0 | choicest(0, -300) | 2012-eleven-01 | 1100 | 1100 | most beneficial(0, 1100) | 2012-12-01 | 1300 | 2400 | ideal(0, 2400)

    How will we do it?

    eder 4

    exactly. With obscure, dealer-certain SQL. in this case, we’re using Oracle SQL

    eder 4

    How does it work? especially effortless!

    just add mannequin after any desk, and also you’re opening up a can of staggering SQL worms!

    opt for ... FROM some_table -- Put this after any table model ...

    as soon as we put mannequin there, we will enforce spreadsheet good judgment without delay in our SQL statements, simply as with Microsoft Excel.

    the following three clauses are probably the most effective and widely used (i.e. 1-2 per 12 months by any individual on this planet):

    mannequin -- The spreadsheet dimensions DIMENSION via ... -- The spreadsheet cellphone type MEASURES ... -- The spreadsheet formulation suggestions ...

    The that means of each and every of those three extra clauses is premiere defined with slides once again.

    The DIMENSION by using clause specifies the scale of your spreadsheet. unlike in MS Excel, that you can have any number of dimensions in Oracle:

    eder 4

    The MEASURES clause specifies the values that are available in each cell of your spreadsheet. in contrast to in MS Excel, that you could have an entire tuple in every phone in Oracle, not only a single cost.

    eder 4

    The rules clause specifies the formulas that follow to every telephone in your spreadsheet. in contrast to in MS Excel, these rules / formulas are centralized at a single region, as an alternative of being put inside of each telephone:

    eder 4

    This design makes mannequin a bit of harder to use than MS Excel, but tons extra potent, if you dare. The entire query will then be “trivially”:

    choose * FROM ( opt for date, amount, 0 AS total FROM quantities ) mannequin DIMENSION by using (row_number() OVER (ORDER via date) AS rn) MEASURES (date, quantity, complete) rules ( complete[any] = superior(0, coalesce(total[cv(rn) - 1], 0) + volume[cv(rn)]) )

    This entire element is so potent, it ships with its own white paper via Oracle, so instead of explaining issues extra right here in this article, please do study the mind-blowing white paper:

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

    8. Time sequence sample recognition

    if you’re into fraud detection or some other container that runs real time analytics on large records sets, time series pattern awareness is not at all a brand new term to you.

    If we assessment the “size of a collection” records set, we could wish to generate triggers on advanced pursuits over our time series as such:

    | identity | VALUE_DATE | volume | LEN | set off |------|------------|---------|-----|-------- | 9997 | 2014-03-18 | + 99.17 | 1 | | 9981 | 2014-03-sixteen | - 71.forty four | four | | 9979 | 2014-03-16 | - 94.60 | 4 | x | 9977 | 2014-03-sixteen | - 6.ninety six | four | | 9971 | 2014-03-15 | - 65.ninety five | four | | 9964 | 2014-03-15 | + 15.13 | three | | 9962 | 2014-03-15 | + 17.forty seven | three | | 9960 | 2014-03-15 | + 3.fifty five | three | | 9959 | 2014-03-14 | - 32.00 | 1 |

    the guideline of the above set off is:

    trigger on the 3rd repetition of an event if the experience occurs extra than 3 times.

    comparable to the old model clause, we can do this with an Oracle-certain clause that turned into added to Oracle 12c:

    opt for ... FROM some_table -- Put this after any desk to pattern-suit -- the desk’s contents MATCH_RECOGNIZE (...)

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

    opt for * FROM seriesMATCH_RECOGNIZE ( -- pattern matching is executed in this order ORDER by means of ... -- These are the columns produced by way of matches MEASURES ... -- a brief specification of what rows are -- lower back from each in shape ALL ROWS PER fit -- «ordinary expressions» of hobbies to healthy sample (...) -- The definitions of «what's an adventure» define ... )

    That sounds crazy. Let’s examine some example clause implementations

    select * FROM collectionMATCH_RECOGNIZE ( ORDER by way of identification MEASURES classifier() AS trg ALL ROWS PER suit sample (S (R X R+)?) outline R AS signal(R.volume) = prev(sign(R.volume)), X AS signal(X.quantity) = prev(signal(X.volume)) )

    What will we do right here?

  • We order the desk by means of id, which is the order wherein we want to fit pursuits. convenient.
  • We then specify the values that we need in consequence. We desire the “MEASURE” trg, which is described because the classifier, i.e. the literal that we’ll use within the pattern afterwards. Plus we need all of the rows from a match.
  • We then specify an everyday expression-like sample. The pattern is an adventure “S” for delivery, followed optionally by using “R” for Repeat, “X” for our particular event X, followed with the aid of one or more “R” for repeat once again. If the complete pattern fits, we get SRXR or SRXRR or SRXRRR, i.e. X might be on the third place of a sequence of length >= 4
  • ultimately, we outline R and X as being the same issue: The event whenSIGN(quantity) of the latest row is a similar as signal(quantity) of the outdated row. We don’t should outline “S”. “S” is simply another row.
  • This question will magically produce here output:

    | identity | VALUE_DATE | volume | TRG | |------|------------|---------|-----| | 9997 | 2014-03-18 | + ninety nine.17 | S | | 9981 | 2014-03-sixteen | - 71.forty four | R | | 9979 | 2014-03-16 | - ninety four.60 | X | | 9977 | 2014-03-sixteen | - 6.96 | R | | 9971 | 2014-03-15 | - sixty five.95 | S | | 9964 | 2014-03-15 | + 15.13 | S | | 9962 | 2014-03-15 | + 17.47 | S | | 9960 | 2014-03-15 | + three.55 | S | | 9959 | 2014-03-14 | - 32.00 | S |

    we can see a single “X” in our event circulation. exactly where we had expected it. on the third repetition of an experience (identical signal) in a sequence of size > 3.

    increase!

    As we don’t truly care about “S” and “R” pursuits, let’s just get rid of them as such:

    opt for id, value_date, volume, CASE trg WHEN 'X' THEN 'X' end trg FROM seriesMATCH_RECOGNIZE ( ORDER through identity MEASURES classifier() AS trg ALL ROWS PER suit pattern (S (R X R+)?) define R AS signal(R.volume) = prev(sign(R.amount)), X AS sign(X.amount) = prev(sign(X.volume)) )

    to supply:

    | id | VALUE_DATE | amount | TRG | |------|------------|---------|-----| | 9997 | 2014-03-18 | + ninety nine.17 | | | 9981 | 2014-03-sixteen | - 71.44 | | | 9979 | 2014-03-sixteen | - ninety four.60 | X | | 9977 | 2014-03-sixteen | - 6.ninety six | | | 9971 | 2014-03-15 | - 65.95 | | | 9964 | 2014-03-15 | + 15.13 | | | 9962 | 2014-03-15 | + 17.forty seven | | | 9960 | 2014-03-15 | + 3.fifty five | | | 9959 | 2014-03-14 | - 32.00 | |

    thanks Oracle!

    eder 4

    once again, don’t are expecting me to clarify this any improved than the miraculous Oracle white paper already did, which I strongly suggest analyzing in case you’re using Oracle 12c anyway:

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

    9. Pivoting and Unpivoting

    in case you’ve examine this a long way, the following will be almost too embarassingly fundamental:

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

    | name | TITLE | score | |-----------|-----------------|--------| | A. provide | ANNIE id | G | | A. provide | DISCIPLE mom | PG | | A. furnish | 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:

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

    look at how we kinda grouped by way of the actors after which “pivoted” the quantity films per rating each and every actor performed in. in its place of exhibiting this in a “relational” manner, (i.e. each and every neighborhood is a row) we pivoted the whole factor to produce a column per group. we are able to try this, as a result of we understand all the viable groups in improve.

    Unpivoting is the opposite, when from the above, we need to get lower back to the “row per community” representation:

    | identify | score | count number | |-----------|--------|-------| | A. provide | NC-17 | 3 | | A. supply | PG | 6 | | A. provide | G | 5 | | A. provide | PG-13 | three | | A. grant | R | 6 | | A. HUDSON | NC-17 | 12 | | A. HUDSON | PG | 4 |

    It’s definitely in reality effortless. here's how we’d do it in PostgreSQL:

    choose first_name, last_name, count number(*) FILTER (the place score = 'NC-17') AS "NC-17", count(*) FILTER (where ranking = 'PG' ) AS "PG", count number(*) FILTER (the place rating = 'G' ) AS "G", count number(*) FILTER (where rating = 'PG-13') AS "PG-13", count(*) FILTER (where score = 'R' ) AS "R" FROM actor AS a be part of film_actor AS fa using (actor_id) join movie AS f the usage of (film_id) neighborhood by actor_id

    we can append an easy FILTER clause to an combination feature as a way to count number only probably the most statistics.

    In all other databases, we’d do it like this:

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

    The fine issue here is that combination services usually simplest accept as true with non-NULL values, so if we make the entire values NULL that aren't pleasing per aggregation, we’ll get the same effect.

    Now, in case you’re the usage of either SQL Server, or Oracle, that you would be able to use the constructed-in PIVOT or UNPIVOT clauses as a substitute. once again, as with mannequin or MATCH_RECOGNIZE, simply append this new keyword after a desk and get the equal effect:

    -- PIVOTING select whatever, some thing 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 FOR score IN ( "NC-17" AS 'NC-17', "PG" AS 'PG', "G" AS 'G', "PG-13" AS 'PG-13', "R" AS 'R' ) )

    handy. next.

    10. Abusing XML and JSON

    First off

    eder 4

    JSON is simply XML with less facets and fewer syntax

    Now, everybody knows that XML is incredible. The corollary is for that reason:

    JSON is much less superb

    Don’t use JSON.

    Now that we’ve settled this, we can safely ignore the ongoing JSON-in-the-database-hype (which most of you are going to regret in five years anyway), and flow on to the final instance. the way to do XML within the database.

    here is what we wish to do:

    eder 4

    Given the customary XML document, we are looking to parse that document, unnest the comma-separated list of movies per actor, and produce a denormalized representation of actors/movies in a single relation.

    in a position. Set. Go. here's the idea. we have three CTE:

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

    within the first one, we conveniently parse the XML. right here with PostgreSQL:

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

    effortless.

    Then, we do some XPath magic to extract the particular person values from the XML constitution and put these into columns:

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

    nonetheless easy.

    at last, just somewhat of recursive usual expression pattern matching magic, and we’re executed!

    WITH RECURSIVE x(v) AS (select '...'::xml), actors(actor_id, first_name, last_name, films) AS (...), films(actor_id, first_name, last_name, film_id, film) AS ( opt for actor_id, first_name, last_name, 1, regexp_replace(movies, ',.+', '') FROM actors UNION ALL opt for actor_id, a.first_name, a.last_name, f.film_id + 1, regexp_replace(a.movies, '.*' || f.film || ', ?(.*?)(,.+)?', '\1') FROM movies AS f join actors AS a using (actor_id) the place a.films now not LIKE '%' || f.film ) select * FROM movies

    Let’s conclude:

    eder 4

    Conclusion

    All of what this article has proven became declarative. and relatively effortless. Of route, for the enjoyable effect that I’m trying to obtain during this talk, some exaggerated SQL become taken and i expressly called everything “easy”. It’s no longer at all easy, you should practice SQL. Like many other languages, however somewhat harder as a result of:

  • The syntax is a little awkward every now and then
  • Declarative pondering is not easy. at least, it’s very distinctive
  • however once you get a cling of it, declarative programming with SQL is completely worth it as that you may specific complicated relationships between your statistics in very very little code with the aid of simply describing the effect you are looking to get from the database.

    Isn’t that mind-blowing?

    And if that changed into a little over the top, do note that I’m satisfied to seek advice from your JUG / convention to give this talk (simply contact us), or if you are looking to get in reality down into the details of those things, we also present this speak as a public or in-condominium workshop. Do get in touch! We’re searching forward.

    See once again the complete set of slides here:


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    Introducing Force.com | killexams.com real questions and Pass4sure dumps

    This chapter introduces the concepts, terminology, and technology components of the Force.com platform and its context in the broader Platform as a Service (PaaS) landscape. The goal is to provide context for exploring Force.com within a corporate software development organization.

    This chapter is from the book 

    If any of the following sentences describe you, this chapter is intended to help:

  • You have read about cloud computing or PaaS and want to learn how Force.com compares to other technologies.
  • You want to get started with Force.com but need to select a suitable first project.
  • You have a project in mind to build on Force.com and want to learn how you can leverage existing development skills and process.
  • This chapter consists of three sections:

  • Force.com in the Cloud Computing Landscape: Learn about PaaS and Force.com's unique features as a PaaS solution.
  • Inside a Force.com Project: Examine how application development with Force.com differs from other technologies in terms of project selection, technical roles, and tools.
  • Sample Application: A sample business application is referenced throughout this book to provide a concrete basis for discussing technical problems and their solutions. In this chapter, the sample application's requirements and use cases are outlined, as well as a development plan, mapped to chapters of the book.
  • Phrases like "cloud computing" and "Platform as a Service" have many meanings put forth by many vendors. This section provides definitions of the terms to serve as a basis for understanding Force.com and comparing it with other products in the market. With this background, you can make the best choice for your projects, whether that is Force.com, another PaaS product, or your own in-house infrastructure.

    Platform as a Service (PaaS)

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    PaaS is a category within the umbrella of cloud computing. "Cloud computing" is a phrase to describe the movement of computing resources away from physical data centers or servers in a closet in your company and into the network, where they can be provisioned, accessed, and deprovisioned instantly. You plug a lamp into an electrical socket to use the electrons in your region's power grid. Running a diesel generator in your basement is usually not necessary. You trust that the power company is going to provide that service, and you pay the company as you use the service.

    Cloud computing as a general concept spans every conceivable configuration of infrastructure, well outside the scope of this book. The potential benefits are reduced complexity and cost versus a traditional approach. The traditional approach is to invest in infrastructure by acquiring new infrastructure assets and staff or redeploying or optimizing existing investments. Cloud computing provides an alternative.

    Many companies provide PaaS products. The following subsections introduce the mainstream PaaS products and include brief descriptions of their functionality. Consult the Web sites of each product for further information.

    Amazon Web Services

    Amazon Web Services refers to a family of cloud computing products. The most relevant to PaaS is Elastic Beanstalk, a platform for running Java applications that provides load balancing, auto-scaling, and health monitoring. The platform is actually built on several other Amazon Web Services products that can be independently configured by advanced users, with the most significant being Elastic Compute Cloud (EC2). EC2 is a general-purpose computing platform, not limited to running Java programs. You can provision virtual instances of Windows or Linux machines at will, loading them with your own custom operating-system image or one prebuilt by Amazon or the community. These instances run until you shut them down, and you are billed for usage of resources such as CPU, disk, and network.

    A raw machine with an OS on it is a great start, but to build a business application requires you to install, manage access to, maintain, monitor, patch and upgrade, back up, plan to scale, and generally care and feed in perpetuity an application platform on the EC2 instance. Many of these tasks are still required of Amazon's higher-level Elastic Beanstalk offering. If your organization has the skills to build on .NET, J2EE, LAMP, or other application stacks, plus the OS, database administration, and IT operations experience, Amazon's virtual servers in the cloud could be a strong alternative to running your own servers in-house.

    Amazon provides various other products that complement Elastic Beanstalk and EC2. These include Simple Queue Service for publish-and-subscribe-style integration between applications, Simple DB for managing schemaless data, and Simple Storage Service, a content repository.

    Microsoft Azure

    Azure consists of two products. The first is Windows Azure, an operating system that can utilize Microsoft's data centers for general computation and storage. It is a combination of infrastructure and platform designed to take existing and new .NET-based applications and run them in the cloud, providing similar features for scalability and elasticity as Amazon Web Services. Most Azure applications are developed in C# using Microsoft Visual Studio, although other languages and tools are supported. The second part is SQL Azure, a hosted version of Microsoft SQL Server. The cost of these products is based on resource consumption, defined as a combination of CPU, network bandwidth, storage, and number of transactions.

    Google App Engine

    App Engine is a platform designed for hosting Web applications. App Engine is like having an unlimited number of servers in the cloud working for you, preconfigured with a distributed data store and Python or Java-based application server. It's much like Amazon's Elastic Beanstalk but focused on providing a higher-level application platform. It lacks the configurable lower-level services like EC2 to provide an escape hatch for developers requiring more control over the infrastructure. App Engine includes tools for managing the data store, monitoring your site and its resource consumption, and debugging and logging.

    App Engine is free for a set amount of storage and page views per month. Applications requiring more storage or bandwidth can purchase it by setting a maximum daily dollar amount they're willing to spend, divided into five buckets: CPU time, bandwidth in, bandwidth out, storage, and outbound email.

    Force.com

    Force.com is targeted toward corporate application developers and independent software vendors. Unlike the other PaaS offerings, it does not expose developers directly to its own infrastructure. Developers do not provision CPU time, disk, or instances of running operating systems. Instead, Force.com provides a custom application platform centered around the relational database, one resembling an application server stack you might be familiar with from working with .NET, J2EE, or LAMP.

    Although it integrates with other technologies using open standards such as SOAP and REST, the programming languages and metadata representations used to build applications are proprietary to Force.com. This is unique among the PaaS products but not unreasonable when examined in depth. Force.com operates at a significantly higher level of abstraction than the other PaaS products, promising dramatically higher productivity to developers in return for their investment and trust in a single-vendor solution.

    To extend the reach of Force.com to a larger developer community, Salesforce and VMware provide a product called VMforce. VMforce brings some of the features of the Force.com platform to Java developers. It consists of development tools from the Salesforce community and virtualized computing resources from VMware. With VMforce, you can create hybrid applications that use Force.com for data and services, but are built with Java standard technologies such as Spring. Along the same lines, Salesforce's acquisition of Heroku is expected to extend Force.com features to Ruby developers.

    Force.com is free for developers. Production applications are priced primarily by storage used and number of unique users.

    Facebook

    Facebook is a Web site for connecting with your friends, but it also provides developers with ways to build their own socially aware applications. These applications leverage the Facebook service to create new ways for users to interact while online. The Facebook platform is also accessible to applications not built inside Facebook, exposing the "social graph" (the network of relationships between users) where permitted.

    Much of the value of Facebook as a platform stems from its large user base and consistent yet extensible user experience. It is a set of services for adding social context to applications. Unlike Force.com and App Engine, for example, Facebook has no facility to host custom applications.

    Force.com as a Platform

    Force.com is different from other PaaS solutions in its focus on business applications. Force.com is a part of Salesforce.com, which started as a SaaS Customer Relationship Management (CRM) vendor. But Force.com is not CRM. It provides the infrastructure commonly needed for any business application, customizable for the unique requirements of each business through a combination of code and configuration. This infrastructure is delivered to you as a service on the Internet.

    Because you are reading this book, you have probably developed a few business applications in your time. Consider the features you implemented and reimplemented in multiple applications, the unglamorous plumbing, wiring, and foundation work. Some examples are security, user identity, logging, profiling, integration, data storage, transactions, workflow, collaboration, and reporting. This infrastructure is essential to your applications but expensive to develop and maintain. Business application developers do not code their own relational database kernels, windowing systems, or operating systems. This is basic infrastructure, acquired from software vendors or the open-source community and then configured to meet user requirements. What if you could do the same for your application infrastructure? This is the premise of the Force.com.

    The following subsections list differentiating architectural features of Force.com with brief descriptions.

    Multitenancy

    Multitenancy is an abstract concept, an implementation detail of Force.com, but one with tangible benefits for developers. Figure 1-1 shows a conceptual view of multitenancy. Customers access shared infrastructure, with metadata and data stored in the same logical database.

    The multitenant architecture of Force.com consists of the following features:

  • Shared infrastructure: Every customer (or tenant) of Force.com shares the same infrastructure. They are assigned an independent logical environment within the Force.com platform.

    At first, some might be uncomfortable with the thought of handing their data to a third-party where it is co-mingled with that of competitors. Salesforce's whitepaper on its multitenant technology includes the technical details of how it works and why your data is safe from loss or spontaneous appearance to unauthorized parties.

  • Single version: Only one version of the Force.com platform is in production. The same platform is used to deliver applications of all sizes and shapes, used by 1 to 100,000 users, running everything from dog-grooming businesses to the Japanese national post office.
  • Continuous, zero-cost improvements: When Force.com is upgraded to include new features or bug fixes, the upgrade is enabled in every customer's logical environment with zero to minimal effort required.
  • Salesforce can roll out new releases with confidence because it maintains a single version of its infrastructure and can achieve broad test coverage by leveraging tests, code, and configurations from their production environment. You, the customer, are helping maintain and improve Force.com in a systematic, measurable way as a side effect of simply using it. This deep feedback loop between the Force.com and its users is something impractical to achieve with on-premise software.

    Relational Database

    The heart of Force.com is the relational database provided as a service. The relational database is the most well-understood and widely used way to store and manage business data. Business applications typically require reporting, transactional integrity, summarization, and structured search, and implementing those on nonrelational data stores requires significant effort. Force.com provides a relational database to each tenant, one that is tightly integrated with every other feature of the platform. There are no Oracle licenses to purchase, no tablespaces to configure, no JDBC drivers to install, no ORM to wrangle, no DDL to write, no queries to optimize, and no replication and backup strategies to implement. Force.com takes care of all these tasks.

    Application Services

    Force.com provides many of the common services needed for modern business application development. These are the services you might have built or integrated repeatedly in your past development projects. They include logging, transaction processing, validation, workflow, email, integration, testing, reporting, and user interface.

    These services are highly customizable with and without writing code. Although each service can be valued as an individual unit of functionality, their unification offers tremendous value. All the features of Force.com are designed, built, and maintained by a single responsible party, Salesforce. Salesforce provides documentation for these features as well as support staff on-call, training and certification classes, and accountability to its customers for keeping things running smoothly. This is in contrast to many software projects that end up as a patchwork of open-source, best-of-breed tools and libraries glued together by you, the developer, asked to do more with fewer people, shorter timelines, and cheaper, often unsupported tools.

    Declarative Metadata

    Almost every customization configured or coded within Force.com is readily available as simple XML with a documented schema. At any point in time, you can ask Force.com for this metadata via a set of Web services. The metadata can be used to configure an identical environment or managed with your corporate standard source control system. It is also helpful for troubleshooting, allowing you to visually compare the state of two environments. Although a few features of Force.com are not available in this declarative metadata form, Salesforce's stated product direction is to provide full coverage.

    Programming Language

    Force.com has its own programming language, called Apex. It allows developers to script interactions with other platform features, including the user interface. Its syntax is a blend of Java and database stored procedure languages like T/SQL and can be written using a Web browser or a plug-in to the Eclipse IDE.

    Other platforms take a different approach. Google's App Engine simultaneously restricts and extends existing languages such as Python so that they play nicely in a PaaS sandbox. This offers obvious benefits, such as leveraging the development community, ease of migration, and skills preservation. One way to understand Apex is as a domain-specific language. Force.com is not a general-purpose computing platform to run any Java or C# program you want to run. Apex is kept intentionally minimalistic, designed with only the needs of Force.com developers in mind, built within the controlled environment of Salesforce R&D. Although it won't solve every programming problem, Apex's specialized nature leads to some advantages in learning curve, code conciseness, ease of refactoring, and ongoing maintenance costs.

    Force.com Services

    Force.com can be divided into four major services: database, business logic, user interface, and integration. Technically, many more services are provided by Force.com, but these are the high-level categories that are most relevant to new Force.com developers.

    Database

    Force.com is built around a relational database. It allows the definition of custom tables containing up to 800 fields each. Fields contain strongly typed data using any of the standard relational database data types, plus rich types such as currency values, picklists, formatted text, and phone numbers. Fields can contain validation rules to ensure data is clean before being committed, and formulas to derive values, like cells in a spreadsheet. Field history tracking provides an audit log of changes to chosen fields.

    Custom tables can be related to each other, allowing the definition of complex data schemas. Tables, rows, and columns can be configured with security constraints. Data and metadata is protected against accidental deletion through a "recycling bin" metaphor. The database schema is often modifiable instantly, without manual migration. Data is imported from files or other sources with free tools, and APIs are provided for custom data-loading solutions.

    Data is queried via a SQL-like language called SOQL (Salesforce Object Query Language). Full-text search is available through SOSL (Salesforce Object Search Language).

    Business Logic

    Apex is the language used to implement business logic on Force.com. It allows code to be structured into classes and interfaces, and it supports object-oriented behaviors. It has strongly typed collection objects and arrays modeled after Java.

    Data binding is a first-class concept in Apex, with the database schema automatically imported as language constructs. Data manipulation statements, trigger semantics, batch processing, and transaction boundaries are also part of the language.

    The philosophy of test-driven development is hard-wired into the Force.com platform. Methods are annotated as tests and run from a provided test harness or test API calls. Test methods are automatically instrumented by Force.com and output timing information for performance tuning. Force.com prevents code from being deployed into production that does not have adequate unit test coverage.

    User Interface

    Force.com provides two approaches for the development of user interfaces: Page Layouts and Visualforce. Page Layouts are inferred from the data model, including validation rules, and then customized using a WYSIWYG editor. Page Layouts feature the standard Salesforce look-and-feel. For many applications, Page Layouts can deliver some or all of the user interface with no development effort.

    Visualforce allows developers to build custom user interfaces. It consists of a series of XML markup tags called components with their own namespace. As with JSP, ASP.NET, Velocity, and other template processing technologies, the components serve as containers to structure data returned by the Controller, a class written in Apex. To the user, the resulting Web pages might look nothing like Salesforce, or adopt its standard look-and-feel. Visualforce components can express the many types and styles of UIs, including basic entry forms, lists, multistep wizards, Ajax, Adobe Flex, mobile applications, and content management systems. Developers can create their own components to reuse across applications.

    User interfaces in Visualforce are public, private, or some blend of the two. Private user interfaces require a user to log in before gaining access. Public user interfaces, called Sites, can be made available to anonymous users on the Internet.

    Integration

    In the world of integration, more options are usually better, and standards support is essential. Force.com supports a wide array of integration technologies, almost all of them based on industry-standard protocols and message formats. You can integrate other technologies with Force.com using an approach of configuration plus code. Here are some examples:

  • Apex Web Services allows control of data, metadata, and process from any platform supporting SOAP over HTTP, including JavaScript. This makes writing composite applications that combine Force.com with technology from other vendors in many interesting and powerful ways possible. Force.com's Web services API has evolved over many years, spanning more than 20 versions with full backward compatibility.
  • The Force.com database is accessible via Representational State Transfer (REST) calls. This integration method is much lighter weight than Web Services, allowing Web applications to query and modify data in Force.com with simple calls accessible to any development language.
  • Business logic developed in Apex can be exposed as a Web service, accessible with or without a Force.com user identity. Force.com generates the WSDL from your Apex code. Additionally, Force.com converts WSDL to Apex bindings to allow access to external Web services from within the platform.
  • You can create virtual email inboxes on Force.com and write code to process the incoming email. Sending email from Force.com is also supported.
  • Force.com provides an API for making HTTP requests, including support for client-side certificates, SSL, proxies, and HTTP authentication. With this, you can integrate with Web-based resources, everything from static Web pages to REST services returning JSON.
  • Salesforce-to-Salesforce (S2S) is a publish-and-subscribe model of data sharing between multiple Force.com environments. If the company you need to integrate with already uses Force.com and the data is supported by S2S, integration becomes a relatively simple configuration exercise. There is no code or message formats to maintain. Your data is transported within the Force.com environment from one tenant to another.
  • If your requirements dictate a higher-level approach to integration, software vendors like IBM's Cast Iron Systems and Informatica offer adapters to Force.com to read and write data and orchestrate complex transactions spanning disparate systems.



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