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The Date fomats can change as well [Red][= 12 Sept; displayed as 12 September 11. Hello Hui, I have a data validation cell (A1) with a dropdown list for 'Qty' and '$$$'. My data set is values that I plot asa Pie Chart (In Column B1). These values are conditionally read from 2 different tables depending on the drop down list selection for $$$$ or Qty. I have conditionlly formatted all cells in B1 to display number format as Number (0 decimal places) or Currency $ again dependent on selection made in A1. Now my pie chart is updating correctly based on my selections and data but the labels do not get formatted to Number or Currency automatically. How can I conditionally format the labels based on selection in A1? Hi Annie, I think this might work for you.basically what Hui said but a couple small tweaks. Use this custom format General;[. I need to do something like your highlight thousands as K, but to this degree: 1? 0.0001 1m 0.001 10m 0.010 100m 0.100 1 1.000 10 10.000 100 100.000 1k 1000.000 10k 10000.000 100k 100000.000 1M 1000000.000 10M 10000000.000 100M 100000000.000 1G.000 10G 0.000 100G 00.000 From what i've been told i can not express all of that as a chart label number format, so i was looking at other options. Within VBA and Excel, how can i apply a NumberFormat like this to a chart? 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The most used version is 5.2.2.304, with over 98% of all installations currently using this version. Querying like in Sql Server possible in DocumentDB? - Stack Overflow SQL Doc SQL Doc Learn about SQL syntax, database concepts, and SQL queries for DocumentDB, a NoSQL database. SQL can used as a JSON query language in DocumentDB. SQL query and SQL syntax in DocumentDB Microsoft Azure DocumentDB supports querying documents using SQL (Structured Query Language) as a JSON query language. DocumentDB is truly schema-free. By virtue of its commitment to the JSON data model directly within the database engine, it provides automatic indexing of JSON documents without requiring explicit schema or creation of secondary indexes. While designing the query language for DocumentDB we had two goals in mind: • Instead of inventing a new JSON query language, we wanted to support SQL. ![]() SQL is one of the most familiar and popular query languages. DocumentDB SQL provides a formal programming model for rich queries over JSON documents. • As a JSON document database capable of executing JavaScript directly in the database engine, we wanted to use JavaScript's programming model as the foundation for our query language. The DocumentDB SQL is rooted in JavaScript's type system, expression evaluation, and function invocation. ![]() This in-turn provides a natural programming model for relational projections, hierarchical navigation across JSON documents, self joins, spatial queries, and invocation of user defined functions (UDFs) written entirely in JavaScript, among other features. We believe that these capabilities are key to reducing the friction between the application and the database and are crucial for developer productivity. We recommend getting started by watching the following video, where Aravind Ramachandran shows DocumentDB's querying capabilities, and by visiting our Query Playground. Where you can try out DocumentDB and run SQL queries against our dataset. Then, return to this article, where we'll start with a SQL query tutorial that walks you through some simple JSON documents and SQL commands. Getting started with SQL commands in DocumentDB To see DocumentDB SQL at work, let's begin with a few simple JSON documents and walk through some simple queries against it. Consider these two JSON documents about two families. Note that with DocumentDB, we do not need to create any schemas or secondary indices explicitly. We simply need to insert the JSON documents to a DocumentDB collection and subsequently query. Here we have a simple JSON document for the Andersen family, the parents, children (and their pets), address and registration information. The document has strings, numbers, booleans, arrays and nested properties. Here's a second document with one subtle difference – givenName and familyName are used instead of firstName and lastName. Now let's try a few queries against this data to understand some of the key aspects of DocumentDB SQL. For example, the following query will return the documents where the id field matches AndersenFamily. Since it's a SELECT *. The output of the query is the complete JSON document: Now consider the case where we need to reformat the JSON output in a different shape. This query projects a new JSON object with two selected fields, Name and City, when the address' city has the same name as the state. In this case, 'NY, NY' matches. The next query returns all the given names of children in the family whose id matches WakefieldFamily ordered by the city of residence. We would like to draw attention to a few noteworthy aspects of the DocumentDB query language through the examples we've seen so far: • Since DocumentDB SQL works on JSON values, it deals with tree shaped entities instead of rows and columns. Therefore, the language lets you refer to nodes of the tree at any arbitrary depth, like Node1.Node2.Node3.Nodem. Similar to relational SQL referring to the two part reference of.. • The structured query language works with schema-less data. Therefore, the type system needs to be bound dynamically. The same expression could yield different types on different documents. The result of a query is a valid JSON value, but is not guaranteed to be of a fixed schema. • DocumentDB only supports strict JSON documents. This means the type system and expressions are restricted to deal only with JSON types. Please refer to the JSON specification for more details. • A DocumentDB collection is a schema-free container of JSON documents. The relations in data entities within and across documents in a collection are implicitly captured by containment and not by primary key and foreign key relations. This is an important aspect worth pointing out in light of the intra-document joins discussed later in this article. DocumentDB indexing Before we get into the DocumentDB SQL syntax, it is worth exploring the indexing design in DocumentDB. The purpose of database indexes is to serve queries in their various forms and shapes with minimum resource consumption (like CPU and input/output) while providing good throughput and low latency. Often, the choice of the right index for querying a database requires much planning and experimentation. This approach poses a challenge for schema-less databases where the data doesn’t conform to a strict schema and evolves rapidly. Therefore, when we designed the DocumentDB indexing subsystem, we set the following goals: Index documents without requiring schema: The indexing subsystem does not require any schema information or make any assumptions about schema of the documents. Support for efficient, rich hierarchical, and relational queries: The index supports the DocumentDB query language efficiently, including support for hierarchical and relational projections. Support for consistent queries in face of a sustained volume of writes: For high write throughput workloads with consistent queries, the index is updated incrementally, efficiently, and online in the face of a sustained volume of writes. The consistent index update is crucial to serve the queries at the consistency level in which the user configured the document service. Support for multi-tenancy: Given the reservation based model for resource governance across tenants, index updates are performed within the budget of system resources (CPU, memory, and input/output operations per second) allocated per replica. Storage efficiency: For cost effectiveness, the on-disk storage overhead of the index is bounded and predictable. This is crucial because DocumentDB allows the developer to make cost based tradeoffs between index overhead in relation to the query performance. Refer to the DocumentDB samples on MSDN for samples showing how to configure the indexing policy for a collection. Let’s now get into the details of the DocumentDB SQL syntax. Basics of a DocumentDB SQL query Every query consists of a SELECT clause and optional FROM and WHERE clauses per ANSI-SQL standards. Typically, for each query, the source in the FROM clause is enumerated. Then the filter in the WHERE clause is applied on the source to retrieve a subset of JSON documents. Finally, the SELECT clause is used to project the requested JSON values in the select list. FROM clause The FROM clause is optional unless the source is filtered or projected later in the query. The purpose of this clause is to specify the data source upon which the query must operate. Commonly the whole collection is the source, but one can specify a subset of the collection instead. A query like SELECT * FROM Families indicates that the entire Families collection is the source over which to enumerate. A special identifier ROOT can be used to represent the collection instead of using the collection name. The following list contains the rules that are enforced per query: The collection can be aliased, such as SELECT f.id FROM Families AS f or simply SELECT f.id FROM Families f. Here f is the equivalent of Families. AS is an optional keyword to alias the identifier. Note that once aliased, the original source cannot be bound. For example, SELECT Families.id FROM Families f is syntactically invalid since the identifier 'Families' cannot be resolved anymore. All properties that need to be referenced must be fully qualified. In the absence of strict schema adherence, this is enforced to avoid any ambiguous bindings. Therefore, SELECT id FROM Families f is syntactically invalid since the property id is not bound. Sub-documents The source can also be reduced to a smaller subset. For instance, to enumerating only a sub-tree in each document, the sub-root could then become the source, as shown in the following example. While the above example used an array as the source, an object could also be used as the source, which is what's shown in the following example. Any valid JSON value (not undefined) that can be found in the source will be considered for inclusion in the result of the query. If some families don’t have an address.state value, they will be excluded in the query result. WHERE clause The WHERE clause ( WHERE ) is optional. It specifies the condition(s) that the JSON documents provided by the source must satisfy in order to be included as part of the result. Any JSON document must evaluate the specified conditions to 'true' to be considered for the result. The WHERE clause is used by the index layer in order to determine the absolute smallest subset of source documents that can be part of the result. The following query requests documents that contain a name property whose value is AndersenFamily. Any other document that does not have a name property, or where the value does not match AndersenFamily is excluded. The previous example showed a simple equality query. DocumentDB SQL also supports a variety of scalar expressions. The most commonly used are binary and unary expressions. Property references from the source JSON object are also valid expressions. The following binary operators are currently supported and can be used in queries as shown in the following examples: Let’s take a look at some queries using binary operators. The unary operators +,-, and NOT are also supported, and can be used inside queries as shown in the following example: In addition to binary and unary operators, property references are also allowed. For example, SELECT * FROM Families f WHERE f.isRegistered returns the JSON document containing the property isRegistered where the property's value is equal to the JSON true value. Any other values (false, null, Undefined,.... Etc.) leads to the source document being excluded from the result. Equality and comparison operators The following table shows the result of equality comparisons in DocumentDB SQL between any two JSON types. For other comparison operators such as >, >=. =, ) is mandatory and specifies what values will be retrieved from the query, just like in ANSI-SQL. The subset that's been filtered on top of the source documents are passed onto the projection phase, where the specified JSON values are retrieved and a new JSON object is constructed, for each input passed onto it. The following example shows a typical SELECT query. Nested properties In the following example, we are projecting two nested properties f.address.state and f.address.city. Projection also supports JSON expressions as shown in the following example. Let's look at the role of $1 here. The SELECT clause needs to create a JSON object and since no key is provided, we use implicit argument variable names starting with $1. For example, this query returns two implicit argument variables, labeled $1 and $2. Aliasing Now let's extend the example above with explicit aliasing of values. AS is the keyword used for aliasing. Note that it's optional as shown while projecting the second value as NameInfo. In case a query has two properties with the same name, aliasing must be used to rename one or both of the properties so that they are disambiguated in the projected result. Scalar expressions In addition to property references, the SELECT clause also supports scalar expressions like constants, arithmetic expressions, logical expressions, etc. For example, here's a simple 'Hello World' query. Here's a more complex example that uses a scalar expression. In the following example, the result of the scalar expression is a Boolean. Object and array creation Another key feature of DocumentDB SQL is array/object creation. In the previous example, note that we created a new JSON object. Similarly, one can also construct arrays as shown in the following examples. VALUE keyword The VALUE keyword provides a way to return JSON value. For example, the query shown below returns the scalar 'Hello World' instead of. The following query returns the JSON value without the 'address' label in the results. The following example extends this to show how to return JSON primitive values (the leaf level of the JSON tree). * Operator The special operator (*) is supported to project the document as-is. When used, it must be the only projected field. While a query like SELECT * FROM Families f is valid, SELECT VALUE * FROM Families f and SELECT *, f.id FROM Families f are not valid. TOP Operator The TOP keyword can be used to limit the number of values from a query. When TOP is used in conjunction with the ORDER BY clause, the result set is limited to the first N number of ordered values; otherwise, it returns the first N number of results in an undefined order. As a best practice, in a SELECT statement, always use an ORDER BY clause with the TOP clause. This is the only way to predictably indicate which rows are affected by TOP. TOP can be used with a constant value (as shown above) or with a variable value using parameterized queries. For more details, please see parameterized queries below. ORDER BY clause Like in ANSI-SQL, you can include an optional Order By clause while querying. The clause can include an optional ASC/DESC argument to specify the order in which results must be retrieved. For a more detailed look at Order By, refer to DocumentDB Order By Walkthrough. For example, here's a query that retrieves families in order of the resident city's name. And here's a query that retrieves families in order of creation date, which is stored as a number representing the epoch time, i.e, elapsed time since Jan 1, 1970 in seconds. Advanced database concepts and SQL queries Iteration A new construct was added via the IN keyword in DocumentDB SQL to provide support for iterating over JSON arrays. The FROM source provides support for iteration. Let's start with the following example: Now let's look at another query that performs iteration over children in the collection. Note the difference in the output array. This example splits children and flattens the results into a single array. This can be further used to filter on each individual entry of the array as shown in the following example. Joins In a relational database, the need to join across tables is very important. It's the logical corollary to designing normalized schemas. Contrary to this, DocumentDB deals with the denormalized data model of schema-free documents. This is the logical equivalent of a 'self-join'. The syntax that the language supports is JOIN JOIN. Overall, this returns a set of N -tuples (tuple with N values). Each tuple has values produced by iterating all collection aliases over their respective sets. In other words, this is a full cross product of the sets participating in the join. The following examples show how the JOIN clause works. In the following example, the result is empty since the cross product of each document from source and an empty set is empty. In the following example, the join is between the document root and the children sub-root. It's a cross product between two JSON objects. The fact that children is an array is not effective in the JOIN since we are dealing with a single root that is the children array. Hence the result contains only two results, since the cross product of each document with the array yields exactly only one document. The following example shows a more conventional join: The first thing to note is that the from_source of the JOIN clause is an iterator. So, the flow in this case is as follows: • Expand each child element c in the array. • Apply a cross product with the root of the document f with each child element c that was flattened in the first step. • Finally, project the root object f name property alone. The first document ( AndersenFamily ) contains only one child element, so the result set contains only a single object corresponding to this document. The second document ( WakefieldFamily ) contains two children. So, the cross product produces a separate object for each child, thereby resulting in two objects, one for each child corresponding to this document. Note that the root fields in both these documents will be same, just as you would expect in a cross product. The real utility of the JOIN is to form tuples from the cross-product in a shape that's otherwise difficult to project. Furthermore, as we will see in the example below, you could filter on the combination of a tuple that lets' the user chose a condition satisfied by the tuples overall. This example is a natural extension of the preceding example, and performs a double join. So, the cross product can be viewed as the following pseudo-code. AndersenFamily has one child who has one pet. So, the cross product yields one row (1*1*1) from this family. WakefieldFamily however has two children, but only one child 'Jesse' has pets. Jesse has 2 pets though. Hence the cross product yields 1*1*2 = 2 rows from this family. In the next example, there is an additional filter on pet. This excludes all the tuples where the pet name is not 'Shadow'. Notice that we are able to build tuples from arrays, filter on any of the elements of the tuple, and project any combination of the elements. JavaScript integration DocumentDB provides a programming model for executing JavaScript based application logic directly on the collections in terms of stored procedures and triggers. This allows for both: • Ability to do high performance transactional CRUD operations and queries against documents in a collection by virtue of the deep integration of JavaScript runtime directly within the database engine. • A natural modeling of control flow, variable scoping, and assignment and integration of exception handling primitives with database transactions. For more details about DocumentDB support for JavaScript integration, please refer to the JavaScript server side programmability documentation. User Defined Functions (UDFs) Along with the types already defined in this article, DocumentDB SQL provides support for User Defined Functions (UDF). In particular, scalar UDFs are supported where the developers can pass in zero or many arguments and return a single argument result back. Each of these arguments are checked for being legal JSON values. The DocumentDB SQL syntax is extended to support custom application logic using these User Defined Functions. UDFs can be registered with DocumentDB and then be referenced as part of a SQL query. In fact, the UDFs are exquisitely designed to be invoked by queries. As a corollary to this choice, UDFs do not have access to the context object which the other JavaScript types (stored procedures and triggers) have. Since queries execute as read-only, they can run either on primary or on secondary replicas. Therefore, UDFs are designed to run on secondary replicas unlike other JavaScript types. Below is an example of how a UDF can be registered at the DocumentDB database, specifically under a document collection. The preceding example creates a UDF whose name is REGEX_MATCH. It accepts two JSON string values input and pattern and checks if the first matches the pattern specified in the second using JavaScript's string.match() function. We can now use this UDF in a query in a projection. UDFs must be qualified with the case-sensitive prefix 'udf.' When called from within queries. Note: Prior to 3/17/2015, DocumentDB supported UDF calls without the 'udf.' Prefix like SELECT REGEX_MATCH(). This calling pattern has been deprecated. The UDF can also be used inside a filter as shown in the example below, also qualified with the 'udf.' Prefix: In essence, UDFs are valid scalar expressions and can be used in both projections and filters. To expand on the power of UDFs, let's look at another example with conditional logic: Below is an example that exercises the UDF. As the preceding examples showcase, UDFs integrate the power of JavaScript language with the DocumentDB SQL to provide a rich programmable interface to do complex procedural, conditional logic with the help of inbuilt JavaScript runtime capabilities. DocumentDB SQL provides the arguments to the UDFs for each document in the source at the current stage (WHERE clause or SELECT clause) of processing the UDF. The result is incorporated in the overall execution pipeline seamlessly. If the properties referred to by the UDF parameters are not available in the JSON value, the parameter is considered as undefined and hence the UDF invocation is entirely skipped. Similarly if the result of the UDF is undefined, it's not included in the result. In summary, UDFs are great tools to do complex business logic as part of the query. Operator evaluation DocumentDB, by the virtue of being a JSON database, draws parallels with JavaScript operators and its evaluation semantics. While DocumentDB tries to preserve JavaScript semantics in terms of JSON support, the operation evaluation deviates in some instances. In DocumentDB SQL, unlike in traditional SQL, the types of values are often not known until the values are actually retrieved from database. In order to efficiently execute queries, most of the operators have strict type requirements. DocumentDB SQL doesn't perform implicit conversions, unlike JavaScript. For instance, a query like SELECT * FROM Person p WHERE p.Age = 21 matches documents which contain an Age property whose value is 21. Any other document whose Age property matches string '21', or other possibly infinite variations like '021', '21.0', '0021', '00021', etc. Will not be matched. This is in contrast to the JavaScript where the string values are implicitly casted to numbers (based on operator, ex: ==). This choice is crucial for efficient index matching in DocumentDB SQL. Parameterized SQL queries DocumentDB supports queries with parameters expressed with the familiar @ notation. Parameterized SQL provides robust handling and escaping of user input, preventing accidental exposure of data through SQL injection. For example, you can write a query that takes last name and address state as parameters, and then execute it for various values of last name and address state based on user input. This request can then be sent to DocumentDB as a parameterized JSON query like shown below. The argument to TOP can be set using parameterized queries like shown below. Parameter values can be any valid JSON (strings, numbers, Booleans, null, even arrays or nested JSON). Also since DocumentDB is schema-less, parameters are not validated against any type. Built-in functions DocumentDB also supports a number of built-in functions for common operations, that can be used inside queries like user defined functions (UDFs). ABS, CEILING, EXP, FLOOR, LOG, LOG10, POWER, ROUND, SIGN, SQRT, SQUARE, TRUNC, ACOS, ASIN, ATAN, ATN2, COS, COT, DEGREES, PI, RADIANS, SIN, and TAN Type checking functions IS_ARRAY, IS_BOOL, IS_NULL, IS_NUMBER, IS_OBJECT, IS_STRING, IS_DEFINED, and IS_PRIMITIVE CONCAT, CONTAINS, ENDSWITH, INDEX_OF, LEFT, LENGTH, LOWER, LTRIM, REPLACE, REPLICATE, REVERSE, RIGHT, RTRIM, STARTSWITH, SUBSTRING, and UPPER SQL Doc. Easy Card Creator Enterprise is easy to operate software solution that allows you - and anyone else for that matter - to design and export ID cards. The application displays a user-friendly interface which makes it a cinch to use. With it you can create ID, post and business cards, badges and labels in a few simple steps. Easy Card Creator Enterprise’s main window contains multiple sections that offer you everything you need for your designs. 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Warning: Media section contains flashy and loud videos may cause epileptic seizure. About Montage Parodies (or “ MLG Montages”) are a series of remix videos parodying the “video game montage” subgenre on YouTube, which is characterized by quick-paced edits and looped footage, as well as heavy use of loud music and sounds in the background. While this style of editing initially rose to popularity through highlight reels of impressive gameplay released by e-sport teams in the, it quickly became known as a cliche as amateur gamers began creating their own, usually with in-game footage of unremarkable quality. Origin As early as September 2010, YouTubers were uploading montages containing footage from first-person shooter games like (shown below, left). ![]() On March 10th, 2011, YouTuber Jamal Nigrumz (also known as jmcxs) uploaded the earliest known montage parody video featuring clips from the massively multiplayer online role-playing game (shown below, right). Common Tropes Images that are often featured in montage parodies include those mocking the controversy, the GIF and the default “Sample Text” captions from the Sony Vegas video editing software. Additionally, references to the first-person shooter trick shot, the phrase and the secret society are often included in the videos. Music / Sound Sample Usage Montages are often uses loud musics from and genres and various music remixes, mashups or comedy tracks. Sometimes sourcing those samples from the sound sharing site, these samples are mostly being refered as Soundclown. Ironic conspiracy theory parts of the videos are usually including the or themes on the background. They can also be remixed with the blasts. As well as the usage to parody drama. There is also largely usage of the hitmarker sound and trigger from the series. Spread On October 18th, 2011, YouTuber souqil released a montage featuring footage from the game Woodcutter Simulator 2011 (shown below, left). On December 23rd, YouTuber motdef uploaded a montage parody for the game Train Simulator inspired by souqil’s video (shown below, right). GAME OF THE YEAR: 420BLAZEIT vs. XxXilluminatiXxx [wow/10 #rekt edition] Montage Parody The Game. Any game I have played in a while Gud one m8 8/8 would play. In the first three years, the videos gained over 310,000 and 2.47 million views respectively. On May 18th, 2012, the /r/montageparodies subreddit was launched for users to share notable montage parody videos. On May 31st, YouTuber xX420KuShxX uploaded a montage featuring shaky Flash browser game footage (shown below, left). On December 14th, 2013, YouTuber QwertySkill uploaded a montage parody containing clips of a woman demonstrating how to properly flip a pancake (shown below, right). • • Rules • No reposting! Reposts will be nuked on sight. All images will be removed, please post these over at • No low quality submissions. Put effort into your submission, we hope this rule goes without saying • Music is fine • If your video took less than thirty minutes to make, don't post it. If you see a post breaking any of these rules please report it and If your post is removed but you didn't receive a message, it is because you weren't approved. If you would like an explanation as to why you weren't approved then please message the mods. AIDS IN MAKING MONTAGE PARODIES CAN BE FOUND IN THE INFO What exactly is a Montage Parody? Well our official definition is:'Any video that edits footage in a comedic way'. Look up the definitions for 'montage' and 'parody' and see whether it fits. We're accepting all sorts of videos now, not just MLG stuff. IRC Our official IRC channel is #MontageParodies on irc.snoonet.org, you can also connect using the web-based kiwiirc. No i just wanted to make comment m10. Here is a apology.i hope it helps: Dear StarCraft Fans: I want to personally apologize to you on behalf of all of us at MLG for the mistakes we made with the WCS America Qualifier this weekend. As many of you know, we have been working on a fast timeline with Blizzard and all of our partners to make the WCS a success and bring you as much competitive StarCraft content as possible. Unfortunately we made some errors that rippled throughout the weekend. First, the demand to enter the WCS Qualifier was much higher than we had anticipated which resulted in many excellent players not getting a chance to enter. We featured a 512-player, double elimination, best of three bracket to maximize individual players opportunity for success, however that impacted the scale of the tournament. Additionally, we experienced strains on both our online product and administrative staffing that should have been avoided. Finally, we admittedly made a poor decision to try to hold back some results of the tournament in order to make spoiler-free viewing a possibility. We will not do that again for any online qualifier. We have immediately opened the final bracket to the public which can be found here. We have also created a feedback email address specifically for WCS America. Going forward, please email your feedback, suggestions and complaints to. Our staff and I will monitor every email and aim to incorporate constructive feedback into our future plans. Again, I want to apologize personally and on behalf of everyone at MLG, to the StarCraft community, the players and to Blizzard for failing to uphold the level of tournament execution we pride ourselves on. I assure you that we are making every effort to rectify this for all future WCS America operations. Sincerely, Sundance DiGiovanni CEO Major League Gaming • • • •. |
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