The New and Improved SQL:2016 Standard Jan Michels Keith Hare Krishna Kulkarni Calisto Zuzarte Oracle Corporation JCC Consulting SQL Consultant IBM Corporation [email protected] [email protected] [email protected] [email protected]
Zhen Hua Liu Beda Hammerschmidt Fred Zemke Oracle Corporation Oracle Corporation Oracle Corporation [email protected] [email protected] [email protected]
ABSTRACT A high-level theme in SQL:2016 is expanding the SQL SQL:2016 (officially called ISO/IEC 9075:2016, language to support new data storage and retrieval Information technology — Database languages — paradigms that are emerging from the NoSQL and Big SQL) was published in December of 2016, replacing Data worlds. The major new features in SQL:2016 are: SQL:2011 as the most recent revision of the SQL • Support for Java Script Object Notation (JSON) standard. This paper gives an overview of the most data important new features in SQL:2016. • Polymorphic Table Functions • Row Pattern Recognition 1. INTRODUCTION SQL:2016 also includes a number of smaller features, The database query language SQL has been around for such as additional built-in functions. more than 30 years. SQL was first standardized in 1986 by ANSI as a US standard and a year later by In addition to the formal SQL standard, the SQL ISO as an international standard, with the first committee has developed a series of Technical Reports implementations of SQL preceding the standard by a (TRs). TRs, while non-normative, contain information few years. Ever since first published, the SQL standard that is useful for understanding how the SQL standard has been a tremendous success. This is evidenced not works. The SQL Technical Report series contains only by the many relational database systems seven TRs [10] – [16] that are available at no charge (RDBMSs) that implement SQL as their primary from the ISO/IEC JTC1 “Freely available standards” query1 language but also by the so-called “NoSQL” web page4. databases that more and more see the requirement (and The remainder of this paper is structured as follows: value) to add an SQL interface to their systems. One of section 2 discusses the support for JSON data, section the success factors of SQL and the standard is that it 3 discusses polymorphic table functions, section 4 evolves as new requirements and technologies emerge. discusses the row pattern recognition functionality, and Be it the procedural [23], active database, or object- section 5 showcases a select few of the smaller relational extensions that were added in the 1990s [22], enhancements. the XML capabilities in the 2000s [19], temporal tables in the early 2010s [17], or the many other features 2. SUPPORT FOR JSON DATA described in previous papers [18], [20], [21], and not JSON [24] is a simple, semi-structured data format that the least the features described in this paper, the SQL is popular in developer communities because it is well standard has always kept up with the latest trends in suited as a data serialization format for data the database world. interchange. JSON data is annotated and the format SQL:2016 consists of nine parts [1]-[9], all of which allows nesting making it easy-to-read by both humans were published together. However, with the exception and machines. Many database applications that would of Part 2, “Foundation” [2], the other parts did not benefit from JSON also need to access “traditional” significantly change from their previous versions, tabular data. Thus, there is great value in storing, containing mostly bug fixes and changes required to querying, and manipulating of JSON data inside an align with the new functionality in Part 2. As with the RDBMS as well as providing bi-directional conversion previous revisions, SQL:2016 is available for purchase between relational data and JSON data. from the ANSI2 and ISO3 web stores. To minimize the overhead of a new SQL data type, SQL:2016 uses the existing SQL string types (i.e., either character strings like VARCHAR and CLOB, or 1 “Query” in this context is not restricted to retrieval-only operations but also includes, among others, DML and DDL statements. 3 https://www.iso.org/store.html 2 http://webstore.ansi.org/ 4 http://standards.iso.org/ittf/PubliclyAvailableStandards/
SIGMOD Record, June 2018 (Vol. 47, No. 2) 51 binary strings like BLOB) to carry JSON values. Since Square brackets [ ] enclose JSON arrays. A JSON there is no standard JSON query language yet, the SQL array is an ordered, comma-separated list of JSON standard defines a path language for navigation within values. In the first and second rows, the key called JSON data and a set of SQL built-in functions and "phoneNumber" has a value which is a JSON array. predicates for querying within a JSON document. This illustrates how JSON objects and arrays can be We will illustrate the SQL/JSON5 path language and nested arbitrarily. SQL/JSON operators in the following sections. Due to Scalar JSON values are character strings, numbers, and space restrictions, we cannot cover all SQL/JSON the literals true, false and null. features. For a detailed description of the SQL/JSON The sample data is fairly homogeneous, but this is not functionality the interested reader is referred to [15]. a requirement of JSON. For example, the elements of an array do not need to be of the same type, and 2.1 Querying JSON in SQL objects in different rows do not have to have the same 2.1.1 Sample data keys. Our examples will use the table T shown below: 2.1.2 IS JSON predicate ID JCOL The IS JSON predicate is used to verify that an SQL 111 { "Name" : "John Smith", value contains a syntactically correct JSON value. For "address" : { example, this predicate can be used in a column check "streetAddress": "21 2nd Street", constraint, like this: "city": "New York", CREATE TABLE T ( "state" : "NY", Id INTEGER PRIMARY KEY, "postalCode" : 10021 }, Jcol CHARACTER VARYING ( 5000 ) "phoneNumber" : [ CHECK ( Jcol IS JSON ) ) { "type" : "home", "number" : "212 555-1234" }, The preceding might have been used to create the table { "type" : "fax", T, insuring that the value of Jcol is valid JSON in all "number" : "646 555-4567" } ] } rows of T. 222 { "Name" : "Peter Walker", In the absence of such a constraint, one could use IS "address" : { JSON as a filter to locate valid JSON data, like this: "streetAddress": "111 Main Street", SELECT * FROM T WHERE Jcol IS JSON "city": "San Jose", "state" : "CA", 2.1.3 SQL/JSON path expressions "postalCode" : 95111 }, The remaining SQL/JSON operators to query JSON "phoneNumber" : [ use the SQL/JSON path language. It is used to { "type" : "home", navigate within a JSON value to its components. It is "number" : "408 555-9876" }, similar to XPath for XML and also somewhat similar { "type" : "office", to object/array navigation in the JavaScript language. "number" : "650 555-2468" } ] } A path expression is composed of a sequence of path 333 {"Name":"JamesLee"} steps; each step can be associated with a set of predicates. In T, the column JCOL contains JSON data stored in a character string. 2.1.4 JSON_EXISTS predicate Curly braces { } enclose JSON objects. A JSON JSON_EXISTS is used to determine if an SQL/JSON object has zero or more comma-separated key/value path expression has any matches in a JSON document. pairs, called members. The key is a character string For example, this query finds the IDs of the rows with before a colon; the value is a JSON value placed after a a key called “address”: colon. For example, in each row, the outermost JSON SELECT Id object has a key called "Name" with varying values in FROM T each row. WHERE JSON_EXISTS ( Jcol, 'strict $.address' )
5 The support for JSON is specified in Foundation [2] and The example works as follows. The first argument to not in a separate part (as is done, e.g., for SQL/XML [9]). JSON_EXISTS, Jcol, specifies the context item Still, the moniker SQL/JSON is associated with the JSON- (JSON value) on which JSON_EXISTS operates. The specific functionality in SQL. keyword strict selects the strict mode; the
52 SIGMOD Record, June 2018 (Vol. 47, No. 2) alternative is lax. As its name implies, strict mode Or suppose the task is to find all fax phone numbers. expects that the JSON document conforms strictly to The query to solve this is the path expression, whereas lax mode relaxes some of SELECT Id, JSON_VALUE ( Jcol, these expectations, as will be seen in later examples. 'lax $.phoneNumber $.address is the path expression that is applied to ?(@.type=="fax").number') the context item. In the path expression, $ is a variable AS Fax referencing the context item, the period is an operator FROM T used to navigate to a key/value pair within a JSON This query illustrates a filter, introduced by a question object, and address is the name of the desired key. mark and enclosed within parentheses. The filter is The JSON_EXISTS predicate will be true if this path processed as follows: since the query is in lax mode, expression successfully finds one or more such the array $.phoneNumber is unwrapped into a key/value pairs. With the sample data, the query will sequence of items. Each item is tested against the find the IDs 111 and 222 but not 333. predicate within the parentheses. In this predicate, the at-sign @ is a variable bound to the item being tested. 2.1.5 JSON_VALUE function The predicate @.type == "fax" is true if the value The JSON_VALUE function is used to extract a scalar of the "type" member equals "fax". The result of value from a given JSON value. For example, to find the filter is the sequence of just those items that the value of the "Name" key/value pair in each row, satisfied the predicate. Finally, the member accessor one could use this query: .number is applied, to obtain the value of the SELECT JSON_VALUE ( Jcol, member whose key is "number". The result of the 'lax $.Name' ) AS Name query is: FROM T ID FAX This example uses lax mode, which is more forgiving than strict mode. For example, it is common to use a 111 646 555-4567 singleton JSON value interchangeably with an array of 222 length one. To accommodate that convention, in lax 333 mode, if a path step requires an array but does not find one, the data is implicitly wrapped in a JSON array. All of these examples returned character string data, Conversely, if a path step expects a non-array but the default return type of JSON_VALUE. Optional encounters an array, the array is unwrapped into a syntax can be used to specify other return types, as sequence of items, and the path step operates on each well as various options to handle empty or error item in the sequence. results. The following query might be used to find the first 2.1.6 JSON_QUERY function phone number in each row. JSON_VALUE can only extract scalars from a JSON SELECT Id, JSON_VALUE ( Jcol, value. The JSON_QUERY function, on the other hand, 'lax $.phoneNumber[0].number' ) is used to extract a fragment (i.e., an SQL/JSON AS Firstphone object, array, or scalar, possibly wrapped in an FROM T SQL/JSON array, if the user specifies this) from a JSON arrays are 0-relative, so the first element is given JSON value. For example, to obtain the addressed [0]. The last row of sample data has no complete value of the "address" key, this query might such data; in that case, lax mode produces an empty be used: sequence (instead of an error) and JSON_VALUE will SELECT Id, JSON_QUERY ( Jcol, return a null value. The result of the query is: 'lax $.address' ) AS Address ID FIRSTPHONE FROM T 111 212 555-1234 With the following results6: 222 408 555-9876 333
In the last row above, the FIRSTPHONE cell is blank, 6 The result shows some insignificant pretty-printing indicating an SQL null value, a convention we will use whitespace. The SQL standard does not prescribe this. A throughout this paper. conforming implementation is allowed to either add or omit insignificant whitespace. Here it is only shown for readability.
SIGMOD Record, June 2018 (Vol. 47, No. 2) 53 ID ADDRESS SELECT T.Id, Jt.Name, Jt.Type, Jt.Number 111 { "streetAddress": "21 2nd Street", FROM T, "city": "New York", JSON_TABLE ( T.Jcol, 'lax $' "state" : "NY", COLUMNS "postalCode" : 10021 } (NameVARCHAR(30) 222 { "streetAddress": "111 Main Street", PATH 'lax $.Name', "city": "San Jose", NESTED PATH "state" : "CA", 'lax $.phoneNumber[*]' "postalCode" : 95111 } COLUMNS 333 (TypeVARCHAR(10) In the last row, ADDRESS is null because the data does PATH 'lax $.type', not match the path expression. There are options to Number VARCHAR ( 12 ) obtain other behaviors for empty and error conditions. PATH 'lax $.number' ) ) 2.1.7 JSON_TABLE function )ASJt JSON_TABLE is a table function that is invoked in the The preceding example has an outer row pattern 'lax FROM clause of a query to generate a relational table $' and within that, a nested row pattern 'lax from a JSON value. As a simple example, to extract $.phoneNumber[*]'. The nested row pattern uses the scalar values of name and ZIP code from each the wildcard array element accessor [*] to iterate over JSON document, the following query can be used: all elements of the phoneNumber array. Thus it is SELECT T.Id, Jt.Name, Jt.Zip possible to flatten hierarchical data. The first column FROM T, Name is found at the outer level of the hierarchy, JSON_TABLE ( T.Jcol, 'lax $' whereas the nested columns Type and Number are COLUMNS ( found in the inner level of the hierarchy. The result on Name VARCHAR ( 30 ) the sample data is: PATH 'lax $.Name' Zip VARCHAR ( 5 ) PATH ID NAME TYPE NUMBER 'lax $.address.postalCode' 111 JohnSmith home 212555-1234 ) 111 John Smith fax 646 555-4567 )ASJt 222 Peter Walker home 408 555-9876 In this example, the first path expression 'lax $' is the “row pattern” used to locate rows. The path 222 Peter Walker office 650 555-2468 expression here is the simplest possible, just $, 333 James Lee meaning that there is no navigation within a JSON document; however, if the row data were deeply nested The last row has no phone number. If one wanted to within a JSON document, then the row pattern would include only those JSON documents that have a be more complicated. phoneNumber member, the following WHERE clause could be appended to the previous query: The example defines two output columns, NAME and ZIP. Each output column has its own PATH clause, WHERE JSON_EXISTS ( T.Jcol, specifying the “column pattern” that is used to 'lax $.phoneNumber' ) navigate within a row to locate the data for a column. In the sample data, this WHERE clause would filter out The example has the following results on the sample the row whose ID is 333. data: 2.1.8 Structural-inspection methods ID NAME ZIP Because the structure of JSON data may not be known 111 John Smith 10021 a priori and/or vary from one JSON value to the next, the SQL/JSON path language provides methods that 222 Peter Walker 95111 allow for the inspection of the structural aspects of a 333 James Lee JSON value. These methods are: JSON_TABLE also allows unnesting of (even deeply) — keyvalue, which returns an SQL/JSON object nested JSON objects/arrays in one invocation by using containing three members for the key, the bound value, a nested COLUMNS clause, as the next example and an ID uniquely identifying the containing input illustrates. This query will return the name and phone object for each member of the input SQL/JSON object. number and type for each person:
54 SIGMOD Record, June 2018 (Vol. 47, No. 2) — type, which returns “object”, “array”, “string”, 3. POLYMORPHIC TABLE “number”, etc. corresponding to the actual type of the FUNCTIONS SQL/JSON item. The SQL standard prior to the 2016 release had only — size, which returns the number of elements, if support for monomorphic table functions, i.e.,the the input SQL/JSON item is an array; otherwise it definition of both the output table and the set of input returns 1. parameters were fixed at function creation time. With the specification of polymorphic table functions For example, to retain only arrays of size 2 or more, (PTFs), SQL:2016 includes a very powerful one might use: enhancement to table functions. With this feature, the strict $.* ? ( @.type() == "array" RDBMS is able to evaluate custom functionality closer && @.size() > 1 ) to the data. For example, the MapReduce paradigm could be implemented using PTFs. 2.2 SQL/JSON constructor functions A polymorphic table function is a function that may SQL/JSON constructor functions use values of SQL have generic table input parameter(s) whose row types and produce JSON (either JSON objects or type(s) may be unknown at creation time. The PTF JSON arrays) represented in SQL character or binary may return a table whose row type also may be string types. unknown when the function is created. The row type of The functions are: JSON_OBJECT, JSON_ARRAY, the result may depend on the function arguments or on JSON_OBJECTAGG,andJSON_ARRAYAGG.The the row type(s) of the input table(s) in the invocation first two are scalar functions, whereas the latter two are of the PTF. When a PTF is invoked in a query, the aggregate functions. As with other SQL RDBMS and the PTF interact through a family of one functions/expressions, these can be arbitrarily nested. to four SQL-invoked procedures. These procedures are This supports the construction of JSON data of called the PTF component procedures7. arbitrary complexity. In the next sections, we describe these four component For example, given the well-known Employees and procedures and give two examples of user-defined Departments tables, one can construct a JSON PTFs. Due to space restrictions, we cannot cover all object for each department that contains all employees PTF features. For a detailed description of all aspects and their salary, sorted by increasing salary using this of PTFs (including the different perspectives of query query: author, PTF author, and RDBMS developer) the SELECT JSON_OBJECT interested reader is referred to [16]. (KEY'department'VALUED.Name, KEY 'employees' 3.1 PTF Component Procedures VALUE JSON_ARRAYAGG There are one to four PTF component procedures: (JSON_OBJECT 1. “describe”: The PTF describe component procedure (KEY'employee' is called once during compilation of the query that VALUE E.Name, invokes the PTF. The primary task of the PTF describe KEY 'salary' component procedure is to determine the row type of VALUE E.Salary ) the output table. This component procedure receives a ORDER BY E.Salary ASC ) description of the input tables and their ordering (if )ASDepartment any) as well as any scalar input arguments that are FROM Departments D, Employees E compile-time constants. This component procedure is WHERE D.Dept_id = E.Dept_id optional if all result columns are defined statically in GROUP BY D.Name the CREATE FUNCTION statement or if the PTF has with results that might look like this: only result columns that are passed through unchanged DEPARTMENT from the input table(s); otherwise it is mandatory. { "department" : "Sales", 2. “start”: The PTF start component procedure is "employees" : [ { "employee" : "James", called at the start of the execution of the PTF to "salary" : 7000 }, allocate any resources that the RDBMS does not { "employee" : "Rachel", provide. This procedure is optional. "salary" : 9000 }, { "employee" : "Logan", 7 SQL:2016 uses the four PTF component procedures as a "salary" : 10000 } ] } specification vehicle. A conforming implementation may substitute this interface with an implementation-defined … API.
SIGMOD Record, June 2018 (Vol. 47, No. 2) 55 3. “fulfill”: The PTF fulfill component procedure is value is a table. The row type of the table is not called during the execution to deliver the output table specified, and may vary depending on the invocation by “piping” rows to the RDBMS. This is the of the PTF. Here, the TABLE specification specifies a component procedure that reads the contents of the generic table output of CSVreader. The row type is input table(s) and generates the output table. This unknown at creation time and this is characteristic of a procedure is mandatory. PTF. 4. “finish”: The PTF finish component procedure is A user reference guide accompanying a PTF will need called at the end of the execution to deallocate any to describe the semantics of the input parameters and resources allocated by the PTF start component what the output will be. For example, here File is the procedure. This procedure is optional. name of a file that contains the comma-separated values which are to be converted to a table. The first 3.2 Execution model line of the file contains the names of the resulting The SQL standard defines the run-time execution of a columns. Succeeding lines contain the data. Each line PTF using an abstraction called a virtual processor, after the first will result in one row of output, with defined as a processing unit capable of executing a column names as determined by the first line of the sequential algorithm. Using techniques such as input. In the example above, Floats and Dates are multiprocessing, a single physical processor might host PTF descriptor areas, which provide a list of the several virtual processors. Virtual processors may column names that are to be interpreted numerically execute independently and concurrently, either on a and as dates, respectively; the data types of all other single physical processor or distributed across multiple columns will be VARCHAR. With that information a physical processors. There is no communication query such as the following can be written: between virtual processors. The RDBMS is responsible for collecting the output on each virtual processor; the SELECT * union of the output from all virtual processors is the FROM TABLE (CSVreader( result of the PTF. The virtual processor abstraction is File => 'abc.csv', the standard’s way of permitting but not requiring Floats => DESCRIPTOR parallelization of PTF execution. ("principal","interest") Dates => DESCRIPTOR 3.3 Examples ("due_date")))ASS This section illustrates the value of PTFs using a couple of examples. The first one is a simple example In the FROM clause, the TABLE operator introduces the introducing the polymorphic nature of a PTF. The invocation of a table function. A table function might second one illustrates a variety of options including be either a conventional (monomorphic) table function multiple input tables with different input semantics. or a PTF. In this case, because CSVreader is declared with return type TABLE, this is a PTF 3.3.1 CSV reader table function invocation. This invocation says that CSVreader Consider a file with a comma-separated list of values should open the file called abc.csv. The list of output (CSV file). The first line of the file contains a list of column names is found in the first line of the file. column names, and subsequent lines of the file contain Among these column names, there must be columns the actual data. A PTF called CSVreader was created named principal and interest, which should be to read a CSV file and provide its data as a table in the interpreted as numeric values, and a column named FROM clause of a query. The effective signature of the due_date which should be interpreted as a date. PTF is: During the compilation of this query, the RDBMS will FUNCTION CSVreader ( call the PTF describe component procedure and File VARCHAR ( 1000 ), provide this information to the component procedure. Floats DESCRIPTOR DEFAULT NULL, In return, the component procedure will provide the Dates DESCRIPTOR DEFAULT NULL ) RDBMS with the row type of the result table. RETURNS TABLE The component procedures are SQL procedures that NOT DETERMINISTIC are specified as a part of the definition of a PTF. For CONTAINS SQL example: This signature has two parameter types that are CREATE FUNCTION CSVreader ( distinctive to PTFs. (a) DESCRIPTOR is a type that is File VARCHAR(1000), capable of describing a list of column names, and Floats DESCRIPTOR DEFAULT NULL, optionally for each column name, a data type. (b) Dates DESCRIPTOR DEFAULT NULL ) TABLE denotes the generic table type, a type whose RETURNS TABLE
56 SIGMOD Record, June 2018 (Vol. 47, No. 2) NOT DETERMINISTIC CONTAINS SQL The KEEP WHEN EMPTY option implies that the PTF PRIVATE DATA ( FileHandle INTEGER ) could generate result rows even if the input table (in DESCRIBE WITH PROCEDURE this example, T2), is empty. The result rows are based CSVreader_describe on rows from the other input table T1. T1 is specified START WITH PROCEDURE with PRUNE WHEN EMPTY, meaning that there is no CSVreader_start output when the input table is empty. This example is FULFILL WITH PROCEDURE analogous to a left outer join. CSVreader_fulfill FINISH WITH PROCEDURE The UDJoin PTF can be invoked in a query like this: CSVreader_finish SELECT E.*, D.* The procedures CSVreader_describe, FROM TABLE CSVreader_start, CSVreader_fulfill,and (UDJoin( CSVreader_finish are SQL stored procedures T1 => TABLE (Emp) AS E PARTITION BY Deptno, and can take advantage of the existing procedural T2 => TABLE (Dept) AS D language, dynamic SQL, and other existing SQL PARTITION BY Deptno capabilities. ORDER BY Tstamp ) ) 3.3.2 User Defined Join table function In this example, both input tables have set semantics, The following example demonstrates a variety of which permits the use of PARTITION BY and options that can be specified in a PTF. It is a function ORDER BY clauses. PARTITION BY says that the that has input tables and also introduces options related input table is partitioned on a list of columns; each to input table semantics (row or set semantics, keep or partition must be processed on a separate virtual prune when empty) and the use of pass-through processor. In this example, since there are two columns to flow data unaltered to the result table. partitioned tables, the RDBMS must in fact create the The PTF UDJoin performs a custom user-defined cross product of the partitions of the two tables, with a join. It takes two input tables, T1 and T2, and matches virtual processor for each combination of partitions. rows according to some user defined join criterion that (In the absence of PARTITION BY, a table with set may not be built into the database system. The PTF has semantics constitutes a single partition.) The second the following signature: input table is also ordered; the RDBMS must sort the rows of each partition prior to passing them to the CREATE FUNCTION UDJoin fulfill component procedure executing on any virtual (T1TABLEPASSTHROUGH processor. WITH SET SEMANTICS PRUNE WHEN EMPTY, Consider the following variation of the same query: T2 TABLE PASS THROUGH SELECT E.*, D.* WITH SET SEMANTICS FROM TABLE KEEP WHEN EMPTY (UDJoin )RETURNSONLYPASSTHROUGH (T1=>TABLE(Emp)ASE The RETURNS ONLY PASS THROUGH syntax PARTITION BY Deptno, T2 => TABLE (Dept) AS D declares that the PTF does not generate any columns of PARTITION BY Deptno its own; instead the only output columns are passed ORDER BY Tstamp through from input columns. COPARTITION (Emp,Dept))) WITH SET SEMANTICS is specified when the Here, the COPARTITION clause allows each virtual outcome of the function depends on how the data is processor to avoid the cross product as in the earlier partitioned. A table should be given set semantics if all example and collocates the corresponding values in the rows of a partition should be processed on the same virtual processor. In this example, the entire table T2 is Deptno columns from the two tables in the same sent to the virtual processors. virtual processor. WITH ROW SEMANTICS specified on an input table 4. ROW PATTERN RECOGNITION means that the result of the PTF is decided on a row- Row Pattern Recognition (RPR) can be used to search by-row basis for this input table. This is specified if the an ordered partition of rows for matches to a regular PTF does not care how rows are assigned to virtual expression. RPR can be supported in either the FROM processors. Only tables with set semantics may be clause or the WINDOW clause. This article will discuss partitioned and/or ordered.
SIGMOD Record, June 2018 (Vol. 47, No. 2) 57 RPR in the FROM clause; RPR in the WINDOW clause matches any row */ uses much the same syntax and semantics. BASB.Price
58 SIGMOD Record, June 2018 (Vol. 47, No. 2) example, the row pattern variable A is undefined, in Total_comp(9000.00) which case any row can be mapped to A. — Passing all arguments by name (in this case the — AS M defines the range variable M to associate with order of the arguments does not need to match the the row pattern output table. order of the parameters in the function signature): Here is some sample data for Ticker, having one Total_comp(Bonus=>1000.00, match to the pattern in the example (the mapping of Base_sal=>9000.00) rows to primary row pattern variables is shown in the — Passing all non-defaulted arguments by name: last column): Total_comp(Base_sal=>9000.00) Symbol Tradeday Price Mapped to All of these invocations return the same result XYZ 2009-06-08 50 (10000.00). It should be clear that the greatest benefit of named and defaulted arguments can be realized XYZ 2009-06-09 60 A when the parameter list is long and many parameters XYZ 2009-06-10 49 B have useful defaults. Specifying some argument values by position and other arguments by name within the XYZ 2009-06-11 40 B same invocation is not supported. XYZ 2009-06-12 35 B 5.2 Additional built-in functions XYZ 2009-06-13 45 C SQL:2016 adds support for additional scalar XYZ 2009-06-14 45 mathematical built-in functions including trigonometric and logarithm functions. The Here is the row of the row pattern output table trigonometric functions are sine, cosine, tangent, and generated by the match shown above: their hyperbolic and inverse counterparts. Besides the Symbol Matchno Startp Bottomp Endp Avgp existing natural logarithm function, SQL:2016 now supports a general logarithm function (where the user XYZ 1 60 35 45 45.8 can specify an arbitrary value for the base) and a Due to space restrictions, we cannot cover all RPR common logarithm function (with the base fixed at 10). features. For a detailed description of the RPR LISTAGG is a new aggregate function that allows functionality the interested reader is referred to [14]. concatenating character strings over a group of rows.
5. ADDITIONAL FUNCTIONALITY 6. FUTURES The SQL standards committee is currently working on 5.1 Default values and named arguments additional expansions in three areas, support for multi- for SQL-invoked functions dimensional arrays, support for streaming data, and SQL:2011 allowed for a parameter of an SQL-invoked support for property graphs. procedure to have a default value and thus be optional The work on multi-dimensional arrays (aka when invoking the procedure. A companion SQL/MDA) is well under way and will be completed enhancement is invoking a procedure using named by the end of 2018. This new incremental part adds a arguments [18]. SQL:2016 extends this functionality to multi-dimensional array type so that instances of a cover SQL-invoked functions as well. For example, a multi-dimensional array can be stored in a column of a function that computes the total compensation as the table and operations can be executed close to the data sum of the base salary and the bonus (where the bonus in the RDBMS. by default is 1000) can be defined like this: The SQL committee has begun investigating CREATE FUNCTION Total_comp ( requirements for streaming data and property graphs in Base_sal DECIMAL(7,2), the context of SQL. Bonus DECIMAL(7,2) DEFAULT 1000.00 )RETURNSDECIMAL(8,2) LANGUAGE SQL 7. ACKNOWLEDGMENTS RETURN Base_sal + Bonus; The authors would like to acknowledge the following people, who at one point or another in one form or This function can now be invoked in different ways: another have contributed to the standardization of the — Passing all arguments by position: functionality presented in this paper: Jim Melton, Total_comp(9000.00, 1000.00) Fatma Ozcan, Hamid Pirahesh, Doug McMahon, Shashaanka Agrawal, Ivan Bowman, Karl Schendel, — Passing all non-defaulted arguments by position: Janhavi Rajagopal, Kathy McKnight, Hans Zeller, Dan
SIGMOD Record, June 2018 (Vol. 47, No. 2) 59 Pasco, Andy Witkowski, Chun-chieh Lin, Lei Sheng, 4: SQL with Routines and types using the JavaTM and Taoufik Abdellatif. programming language, http://standards.iso.org/ittf/PubliclyAvailableStandards/ The authors would like to thank the following people for reviewing earlier drafts of this paper and providing [14] ISO/IEC TR 19075-5:2016, Information technology — Database languages — SQL Technical Reports — Part valuable feedback: Jim Melton, Hermann Baer, Mark 5: Row Pattern Recognition in SQL, Anderson, Andy Witkowski, and Claire McFeely. http://standards.iso.org/ittf/PubliclyAvailableStandards/ [15] ISO/IEC TR 19075-6:2017, Information technology — 8. REFERENCES Database languages — SQL Technical Reports — Part [1] ISO/IEC 9075-1:2016, Information technology — 6: SQL support for JavaScript Object Notation (JSON), Database languages — SQL — Part 1: Framework http://standards.iso.org/ittf/PubliclyAvailableStandards/ (SQL/Framework) [16] ISO/IEC TR 19075-7:2017, Information technology — [2] ISO/IEC 9075-2:2016, Information technology — Database languages — SQL Technical Reports — Part Database languages — SQL — Part 2: Foundation 7: Polymorphic table functions in SQL, (SQL/Foundation) http://standards.iso.org/ittf/PubliclyAvailableStandards/ [3] ISO/IEC 9075-3:2016, Information technology — [17] Krishna Kulkarni and Jan-Eike Michels, “Temporal Database languages — SQL — Part 3: Call-Level features in SQL:2011”, SIGMOD Record Vol. 41 No. 3, Interface (SQL/CLI) September 2012, [4] ISO/IEC 9075-4:2016, Information technology — https://sigmodrecord.org/publications/sigmodRecord/12 Database languages — SQL — Part 4: Persistent stored 09/pdfs/07.industry.kulkarni.pdf modules (SQL/PSM) [18] Fred Zemke, “What’s new in SQL:2011”, SIGMOD [5] ISO/IEC 9075-9:2016, Information technology — Record, Vol. 41, No. 1, March 2012, Database languages — SQL — Part 9: Management of https://sigmodrecord.org/publications/sigmodRecord/12 External Data (SQL/MED) 03/pdfs/10.industry.zemke.pdf [6] ISO/IEC 9075-10:2016,Informationtechnology— [19] Andrew Eisenberg and Jim Melton, “Advancements in Database languages — SQL — Part 10: Object language SQL/XML”, SIGMOD Record Vol. 33 No. 3, bindings (SQL/OLB) September 2004, [7] ISO/IEC 9075-11:2016,Informationtechnology— https://sigmodrecord.org/publications/sigmodRecord/04 Database languages — SQL — Part 11: Information and 09/11.JimMelton.pdf definition schemas (SQL/Schemata) [20] Andrew Eisenberg, Jim Melton, Krishna Kulkarni, Jan- [8] ISO/IEC 9075-13:2016,Informationtechnology— Eike Michels, and Fred Zemke, “SQL:2003 has been Database languages — SQL — Part 13: SQL Routines published”, SIGMOD Record Vol. 33 No. 1, March and types using the Java programming language 2004, (SQL/JRT) https://sigmodrecord.org/publications/sigmodRecord/04 03/E.JimAndrew-standard.pdf [9] ISO/IEC 9075-14:2016,Informationtechnology— Database languages — SQL — Part 14: XML-Related [21] Jim Melton, Jan-Eike Michels, Vanja Josifovski, Specifications (SQL/XML) Krishna Kulkarni, Peter Schwarz, Kathy Zeidenstein, “SQL and Management of External Data”, SIGMOD [10] ISO/IEC TR 19075-1:2011, Information technology — Record Vol. 30 No. 1, March 2001, Database languages — SQL Technical Reports — Part https://sigmodrecord.org/publications/sigmodRecord/01 1: XQuery Regular Expression Support in SQL, 03/JM-Sta.pdf http://standards.iso.org/ittf/PubliclyAvailableStandards/ [22] Andrew Eisenberg and Jim Melton, “SQL:1999, [11] ISO/IEC TR 19075-2:2015, Information technology — formerly known as SQL3”, SIGMOD Record Vol. 28 Database languages — SQL Technical Reports — Part No. 1, March 1999, 2: SQL Support for Time-Related Information, https://sigmodrecord.org/publications/sigmodRecord/99 http://standards.iso.org/ittf/PubliclyAvailableStandards/ 03/standards.pdf.gz [12] ISO/IEC TR 19075-3:2015, Information technology — [23] Andrew Eisenberg, “New Standard for Stored Database languages — SQL Technical Reports — Part Procedures in SQL”, SIGMOD Record Vol 25 No. 4, TM 3: SQL Embedded in Programs using the Java Dec.1996, https://sigmodrecord.org/issues/96- programming language, 12/sqlpsm.ps http://standards.iso.org/ittf/PubliclyAvailableStandards/ [24] Internet Engineering Task Force, RFC 7159, The [13] ISO/IEC TR 19075-4:2015, Information technology — JavaScript Object Notation (JSON) Data Interchange Database languages — SQL Technical Reports — Part Format, March 2014, https://tools.ietf.org/html/rfc7159
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