We can do call transcription, so that supervisors can help with training agents and services that extract meaning and themes out of those calls. We don't talk about the primitive capabilities that power that, we just talk about the capabilities to transcribe calls and to extract meaning from the calls.

It's really important that we provide solutions for customers at all levels of the stack. Given the economic challenges that customers are facing, how is AWS ensuring that enterprises are getting better returns on their cloud investments?

Now's the time to lean into the cloud more than ever, precisely because of the uncertainty. We saw it during the pandemic in early , and we're seeing it again now, which is, the benefits of the cloud only magnify in times of uncertainty.

For example, the one thing which many companies do in challenging economic times is to cut capital expense. For most companies, the cloud represents operating expense, not capital expense. You're not buying servers, you're basically paying per unit of time or unit of storage. That provides tremendous flexibility for many companies who just don't have the CapEx in their budgets to still be able to get important, innovation-driving projects done.

Another huge benefit of the cloud is the flexibility that it provides — the elasticity, the ability to dramatically raise or dramatically shrink the amount of resources that are consumed. You can only imagine if a company was in their own data centers, how hard that would have been to grow that quickly. The ability to dramatically grow or dramatically shrink your IT spend essentially is a unique feature of the cloud. These kinds of challenging times are exactly when you want to prepare yourself to be the innovators … to reinvigorate and reinvest and drive growth forward again.

We've seen so many customers who have prepared themselves, are using AWS, and then when a challenge hits, are actually able to accelerate because they've got competitors who are not as prepared, or there's a new opportunity that they spot. We see a lot of customers actually leaning into their cloud journeys during these uncertain economic times.

Do you still push multi-year contracts, and when there's times like this, do customers have the ability to renegotiate? Many are rapidly accelerating their journey to the cloud. Some customers are doing some belt-tightening. What we see a lot of is folks just being really focused on optimizing their resources, making sure that they're shutting down resources which they're not consuming. You do see some discretionary projects which are being not canceled, but pushed out.

Every customer is free to make that choice. But of course, many of our larger customers want to make longer-term commitments, want to have a deeper relationship with us, want the economics that come with that commitment. We're signing more long-term commitments than ever these days.

We provide incredible value for our customers, which is what they care about. That kind of analysis would not be feasible, you wouldn't even be able to do that for most companies, on their own premises.

So some of these workloads just become better, become very powerful cost-savings mechanisms, really only possible with advanced analytics that you can run in the cloud. In other cases, just the fact that we have things like our Graviton processors and … run such large capabilities across multiple customers, our use of resources is so much more efficient than others. We are of significant enough scale that we, of course, have good purchasing economics of things like bandwidth and energy and so forth.

So, in general, there's significant cost savings by running on AWS, and that's what our customers are focused on. The margins of our business are going to … fluctuate up and down quarter to quarter. It will depend on what capital projects we've spent on that quarter.

Obviously, energy prices are high at the moment, and so there are some quarters that are puts, other quarters there are takes. The important thing for our customers is the value we provide them compared to what they're used to.

And those benefits have been dramatic for years, as evidenced by the customers' adoption of AWS and the fact that we're still growing at the rate we are given the size business that we are. That adoption speaks louder than any other voice. Do you anticipate a higher percentage of customer workloads moving back on premises than you maybe would have three years ago? Absolutely not. We're a big enough business, if you asked me have you ever seen X, I could probably find one of anything, but the absolute dominant trend is customers dramatically accelerating their move to the cloud.

Moving internal enterprise IT workloads like SAP to the cloud, that's a big trend. Creating new analytics capabilities that many times didn't even exist before and running those in the cloud. More startups than ever are building innovative new businesses in AWS. Our public-sector business continues to grow, serving both federal as well as state and local and educational institutions around the world.

It really is still day one. The opportunity is still very much in front of us, very much in front of our customers, and they continue to see that opportunity and to move rapidly to the cloud. In general, when we look across our worldwide customer base, we see time after time that the most innovation and the most efficient cost structure happens when customers choose one provider, when they're running predominantly on AWS. A lot of benefits of scale for our customers, including the expertise that they develop on learning one stack and really getting expert, rather than dividing up their expertise and having to go back to basics on the next parallel stack.

That being said, many customers are in a hybrid state, where they run IT in different environments. In some cases, that's by choice; in other cases, it's due to acquisitions, like buying companies and inherited technology.

We understand and embrace the fact that it's a messy world in IT, and that many of our customers for years are going to have some of their resources on premises, some on AWS. Some may have resources that run in other clouds. We want to make that entire hybrid environment as easy and as powerful for customers as possible, so we've actually invested and continue to invest very heavily in these hybrid capabilities.

A lot of customers are using containerized workloads now, and one of the big container technologies is Kubernetes. We have a managed Kubernetes service, Elastic Kubernetes Service, and we have a … distribution of Kubernetes Amazon EKS Distro that customers can take and run on their own premises and even use to boot up resources in another public cloud and have all that be done in a consistent fashion and be able to observe and manage across all those environments.

So we're very committed to providing hybrid capabilities, including running on premises, including running in other clouds, and making the world as easy and as cost-efficient as possible for customers. Can you talk about why you brought Dilip Kumar, who was Amazon's vice president of physical retail and tech, into AWS as vice president applications and how that will play out?

He's a longtime, tenured Amazonian with many, many different roles — important roles — in the company over a many-year period. Dilip has come over to AWS to report directly to me, running an applications group. We do have more and more customers who want to interact with the cloud at a higher level — higher up the stack or more on the application layer. We talked about Connect, our contact center solution, and we've also built services specifically for the healthcare industry like a data lake for healthcare records called Amazon HealthLake.

We've built a lot of industrial services like IoT services for industrial settings, for example, to monitor industrial equipment to understand when it needs preventive maintenance.

We have a lot of capabilities we're building that are either for … horizontal use cases like Amazon Connect or industry verticals like automotive, healthcare, financial services. We see more and more demand for those, and Dilip has come in to really coalesce a lot of teams' capabilities, who will be focusing on those areas. You can expect to see us invest significantly in those areas and to come out with some really exciting innovations.

Would that include going into CRM or ERP or other higher-level, run-your-business applications? I don't think we have immediate plans in those particular areas, but as we've always said, we're going to be completely guided by our customers, and we'll go where our customers tell us it's most important to go next.

It's always been our north star. Correction: This story was updated Nov. Bennett Richardson bennettrich is the president of Protocol. Prior to joining Protocol in , Bennett was executive director of global strategic partnerships at POLITICO, where he led strategic growth efforts including POLITICO's European expansion in Brussels and POLITICO's creative agency POLITICO Focus during his six years with the company.

Prior to POLITICO, Bennett was co-founder and CMO of Hinge, the mobile dating company recently acquired by Match Group. Bennett began his career in digital and social brand marketing working with major brands across tech, energy, and health care at leading marketing and communications agencies including Edelman and GMMB. Bennett is originally from Portland, Maine, and received his bachelor's degree from Colgate University. Prior to joining Protocol in , he worked on the business desk at The New York Times, where he edited the DealBook newsletter and wrote Bits, the weekly tech newsletter.

He has previously worked at MIT Technology Review, Gizmodo, and New Scientist, and has held lectureships at the University of Oxford and Imperial College London. He also holds a doctorate in engineering from the University of Oxford. We launched Protocol in February to cover the evolving power center of tech.

It is with deep sadness that just under three years later, we are winding down the publication. As of today, we will not publish any more stories. All of our newsletters, apart from our flagship, Source Code, will no longer be sent. Source Code will be published and sent for the next few weeks, but it will also close down in December.

Building this publication has not been easy; as with any small startup organization, it has often been chaotic. But it has also been hugely fulfilling for those involved. We could not be prouder of, or more grateful to, the team we have assembled here over the last three years to build the publication. They are an inspirational group of people who have gone above and beyond, week after week.

Today, we thank them deeply for all the work they have done. We also thank you, our readers, for subscribing to our newsletters and reading our stories. We hope you have enjoyed our work. As companies expand their use of AI beyond running just a few machine learning models, and as larger enterprises go from deploying hundreds of models to thousands and even millions of models, ML practitioners say that they have yet to find what they need from prepackaged MLops systems.

As companies expand their use of AI beyond running just a few machine learning models, ML practitioners say that they have yet to find what they need from prepackaged MLops systems. Kate Kaye is an award-winning multimedia reporter digging deep and telling print, digital and audio stories.

She covers AI and data for Protocol. Her reporting on AI and tech ethics issues has been published in OneZero, Fast Company, MIT Technology Review, CityLab, Ad Age and Digiday and heard on NPR. Kate is the creator of RedTailMedia. org and is the author of "Campaign ' A Turning Point for Digital Media," a book about how the presidential campaigns used digital media and data.

On any given day, Lily AI runs hundreds of machine learning models using computer vision and natural language processing that are customized for its retail and ecommerce clients to make website product recommendations, forecast demand, and plan merchandising. And he said that while some MLops systems can manage a larger number of models, they might not have desired features such as robust data visualization capabilities or the ability to work on premises rather than in cloud environments.

As companies expand their use of AI beyond running just a few ML models, and as larger enterprises go from deploying hundreds of models to thousands and even millions of models, many machine learning practitioners Protocol interviewed for this story say that they have yet to find what they need from prepackaged MLops systems. Companies hawking MLops platforms for building and managing machine learning models include tech giants like Amazon, Google, Microsoft, and IBM and lesser-known vendors such as Comet, Cloudera, DataRobot, and Domino Data Lab.

It's actually a complex problem. Intuit also has constructed its own systems for building and monitoring the immense number of ML models it has in production, including models that are customized for each of its QuickBooks software customers.

The model must recognize those distinctions. For instance, Hollman said the company built an ML feature management platform from the ground up.

For companies that have been forced to go DIY, building these platforms themselves does not always require forging parts from raw materials.

DBS has incorporated open-source tools for coding and application security purposes such as Nexus, Jenkins, Bitbucket, and Confluence to ensure the smooth integration and delivery of ML models, Gupta said. Intuit has also used open-source tools or components sold by vendors to improve existing in-house systems or solve a particular problem, Hollman said.

However, he emphasized the need to be selective about which route to take. I think that the best AI will be a build plus buy. However, creating consistency through the ML lifecycle from model training to deployment to monitoring becomes increasingly difficult as companies cobble together open-source or vendor-built machine learning components, said John Thomas, vice president and distinguished engineer at IBM.

The reality is most people are not there, so you have a whole bunch of different tools. Companies struggling to find suitable off-the-shelf MLops platforms are up against another major challenge, too: finding engineering talent. Many companies do not have software engineers on staff with the level of expertise necessary to architect systems that can handle large numbers of models or accommodate millions of split-second decision requests, said Abhishek Gupta, founder and principal researcher at Montreal AI Ethics Institute and senior responsible AI leader and expert at Boston Consulting Group.

For one thing, smaller companies are competing for talent against big tech firms that offer higher salaries and better resources. For companies with less-advanced AI operations, shopping at the existing MLops platform marketplace may be good enough, Hollman said. To give you the best possible experience, this site uses cookies. If you continue browsing. you accept our use of cookies. You can review our privacy policy to find out more about the cookies we use.

Workplace Enterprise Fintech China Policy Newsletters Braintrust Podcast Events Careers About Us. Source Code. Cloud Computing. CX in the Enterprise. Enterprise Power Index. Driver over speeding car Highway car over speeding Suspect taken over dose Suspect arrested. By: ThisIsButter1 By: brrrtmn What is ItemFix? Let's look at what you can do with ItemFix. Tutorial: ItemFix Meme Generator A quick look at using our MEME generator here on Itemfix You can find the meme generator here: www.

Tutorial: Text to Speech Taking a look at the text to speech engine on ItemFix. It was at this moment a meme tutorial Learn how to use the "It was at this moment Declared local variable, input or output parameter of a PSQL module stored procedure, trigger, unnamed PSQL block in DSQL. A member of in an ordered group of one or more unnamed parameters passed to a stored procedure or prepared query. A SELECT statement enclosed in parentheses that returns a single scalar value or, when used in existential predicates, a set of values.

Operations inside the parentheses are performed before operations outside them. When nested parentheses are used, the most deeply nested expressions are evaluated first and then the evaluations move outward through the levels of nesting. Clause applied to CHAR and VARCHAR types to specify the character-set-specific collation sequence to use in string comparisons.

Expression for obtaining the next value of a specified generator sequence. A constant is a value that is supplied directly in an SQL statement, not derived from an expression, a parameter, a column reference nor a variable. It can be a string or a number. The maximum length of a string is 32, bytes; the maximum character count will be determined by the number of bytes used to encode each character.

Double quotes are NOT VALID for quoting strings. SQL reserves a different purpose for them. Care should be taken with the string length if the value is to be written to a VARCHAR column. The maximum length for a VARCHAR is 32, bytes. The character set of a string constant is assumed to be the same as the character set of its destined storage. Each pair of hex digits defines one byte in the string. Strings entered this way will have character set OCTETS by default, but the introducer syntax can be used to force a string to be interpreted as another character set.

The client interface determines how binary strings are displayed to the user. The isql utility, for example, uses upper case letters A-F, while FlameRobin uses lower case letters. Other client programs may use other conventions, such as displaying spaces between the byte pairs: '4E 65 72 76 65 6E'.

The hexadecimal notation allows any byte value including 00 to be inserted at any position in the string. However, if you want to coerce it to anything other than OCTETS, it is your responsibility to supply the bytes in a sequence that is valid for the target character set.

This is known as introducer syntax. Its purpose is to inform the engine about how to interpret and store the incoming string. In SQL, for numbers in the standard decimal notation, the decimal point is always represented by period. Inclusion of commas, blanks, etc.

will cause errors. Exponential notation is supported. For example, 0. Hexadecimal notation is supported by Firebird 2. Numbers with hex digits will be interpreted as type INTEGER ; numbers with hex digits as type BIGINT.

Hex numbers in the range To coerce a number to BIGINT , prepend enough zeroes to bring the total number of hex digits to nine or above. That changes the type but not the value. When written with eight hex digits, as in 0x9E44F9A8 , a value is interpreted as bit INTEGER. Since the leftmost bit sign bit is set, it maps to the negative range With one or more zeroes prepended, as in 0x09E44F9A8 , a value is interpreted as bit BIGINT in the range The sign bit is not set now, so they map to the positive range This is something to be aware of.

Hex numbers between FFFF FFFF FFFF FFFF are all negative BIGINT. A SMALLINT cannot be written in hex, strictly speaking, since even 0x1 is evaluated as INTEGER. However, if you write a positive integer within the bit range 0x decimal zero to 0x7FFF decimal it will be converted to SMALLINT transparently. It is possible to write to a negative SMALLINT in hex, using a 4-byte hex number within the range 0xFFFF decimal to 0xFFFFFFFF decimal SQL operators comprise operators for comparing, calculating, evaluating and concatenating values.

SQL Operators are divided into four types. Each operator type has a precedence , a ranking that determines the order in which operators and the values obtained with their help are evaluated in an expression. The higher the precedence of the operator type is, the earlier it will be evaluated. Each operator has its own precedence within its type, that determines the order in which they are evaluated in an expression. Operators with the same precedence are evaluated from left to right.

To force a different evaluation order, operations can be grouped by means of parentheses. Arithmetic operations are performed after strings are concatenated, but before comparison and logical operations.

Comparison operations take place after string concatenation and arithmetic operations, but before logical operations. Character strings can be constants or values obtained from columns or other expressions. Combines two or more predicates, each of which must be true for the entire predicate to be true. Combines two or more predicates, of which at least one predicate must be true for the entire predicate to be true.

NEXT VALUE FOR returns the next value of a sequence. SEQUENCE is an SQL-compliant term for a generator in Firebird and its ancestor, InterBase.

A step value of 0 returns the current sequence value. A conditional expression is one that returns different values according to how a certain condition is met. It is composed by applying a conditional function construct, of which Firebird supports several. This section describes only one conditional expression construct: CASE. All other conditional expressions apply internal functions derived from CASE and are described in Conditional Functions.

The CASE construct returns a single value from a number of possible ones. Two syntactic variants are supported:. The simple CASE , comparable to a case construct in Pascal or a switch in C. When this variant is used, test-expr is compared expr 1, expr 2 etc. If no match is found, defaultresult from the optional ELSE clause is returned. If there are no matches and no ELSE clause, NULL is returned. That is, if test-expr is NULL , it does not match any expr , not even an expression that resolves to NULL.

The returned result does not have to be a literal value: it might be a field or variable name, compound expression or NULL literal. A short form of the simple CASE construct is the DECODE function. The first expression to return TRUE determines the result. If no expressions return TRUE , defaultresult from the optional ELSE clause is returned as the result.

If no expressions return TRUE and there is no ELSE clause, the result will be NULL. As with the simple CASE construct, the result need not be a literal value: it might be a field or variable name, a compound expression, or be NULL. NULL is not a value in SQL, but a state indicating that the value of the element either is unknown or it does not exist. When you use NULL in logical Boolean expressions, the result will depend on the type of the operation and on other participating values.

When you compare a value to NULL , the result will be unknown. NULL means NULL but, in Firebird, the logical result unknown is also represented by NULL. It has already been shown that NOT NULL results in NULL. The interaction is a bit more complicated for the logical AND and logical OR operators:.

Up to and including Firebird 2. However, there are logical expressions predicates that can return true, false or unknown. A subquery is a special form of expression that is actually a query embedded within another query. Subqueries are written in the same way as regular SELECT queries, but they must be enclosed in parentheses. Subquery expressions can be used in the following ways:. To obtain values or conditions for search predicates the WHERE , HAVING clauses. To produce a set that the enclosing query can select from, as though were a regular table or view.

Subqueries like this appear in the FROM clause derived tables or in a Common Table Expression CTE. A subquery can be correlated. A query is correlated when the subquery and the main query are interdependent.

To process each record in the subquery, it is necessary to fetch a record in the main query; i. When subqueries are used to get the values of the output column in the SELECT list, a subquery must return a scalar result. Subqueries used in search predicates, other than existential and quantified predicates, must return a scalar result; that is, not more than one column from not more than one matching row or aggregation. Although it is reporting a genuine error, the message can be slightly misleading.

If P resolves as TRUE, it succeeds. If it resolves to FALSE or NULL UNKNOWN , it fails. A trap lies here, though: suppose the predicate, P , returns FALSE. In this case NOT P will return TRUE. On the other hand, if P returns NULL unknown , then NOT P returns NULL as well. In SQL, predicates can appear in CHECK constraints, WHERE and HAVING clauses, CASE expressions, the IIF function and in the ON condition of JOIN clauses. An assertion is a statement about the data that, like a predicate, can resolve to TRUE, FALSE or NULL.

Assertions consist of one or more predicates, possibly negated using NOT and connected by AND and OR operators. Parentheses may be used for grouping predicates and controlling evaluation order. A predicate may embed other predicates. Evaluation sequence is in the outward direction, i.

A comparison predicate consists of two expressions connected with a comparison operator. There are six traditional comparison operators:. For the complete list of comparison operators with their variant forms, see Comparison Operators. If one of the sides left or right of a comparison predicate has NULL in it, the value of the predicate will be UNKNOWN.

The following query will return no data, even if there are printers with no type specified for them, because a predicate that compares NULL with NULL returns NULL :. On the other hand, ptrtype can be tested for NULL and return a result: it is just that it is not a comparison test:.

When CHAR and VARCHAR fields are compared for equality, trailing spaces are ignored in all cases. The BETWEEN predicate tests whether a value falls within a specified range of two values. NOT BETWEEN tests whether the value does not fall within that range. The operands for BETWEEN predicate are two arguments of compatible data types. The search is inclusive the values represented by both arguments are included in the search.

In other words, the BETWEEN predicate could be rewritten:. When BETWEEN is used in the search conditions of DML queries, the Firebird optimizer can use an index on the searched column, if it is available. The LIKE predicate compares the character-type expression with the pattern defined in the second expression. Case- or accent-sensitivity for the comparison is determined by the collation that is in use.

A collation can be specified for either operand, if required. If the tested value matches the pattern, taking into account wildcard symbols, the predicate is TRUE. If the search string contains either of the wildcard symbols, the ESCAPE clause can be used to specify an escape character. Actually, the LIKE predicate does not use an index. So, if you need to search for the beginning of a string, it is recommended to use the STARTING WITH predicate instead of the LIKE predicate.

Search for tables containing the underscore character in their names. The STARTING WITH predicate searches for a string or a string-like type that starts with the characters in its value argument. The search is case-sensitive. When STARTING WITH is used in the search conditions of DML queries, the Firebird optimizer can use an index on the searched column, if it exists.

It can be used for an alphanumeric string-like search on numbers and dates. However, if an accent-sensitive collation is in use then the search will be accent-sensitive. Search for changes in salaries with the date containing number 84 in this case, it means changes that took place in :. SIMILAR TO matches a string against an SQL regular expression pattern.

If any operand is NULL , the result is NULL. Otherwise, the result is TRUE or FALSE. The following syntax defines the SQL regular expression format. It is a complete and correct top-down definition. Feel free to skip it and read the next section, Building Regular Expressions , which uses a bottom-up approach, aimed at the rest of us.

Within regular expressions, most characters represent themselves. The only exceptions are the special characters below:. A regular expression that contains no special or escape characters matches only strings that are identical to itself subject to the collation in use. A bunch of characters enclosed in brackets define a character class.

A character in the string matches a class in the pattern if the character is a member of the class:. Within a class definition, two characters connected by a hyphen define a range. A range comprises the two endpoints and all the characters that lie between them in the active collation. Ranges can be placed anywhere in the class definition without special delimiters to keep them apart from the other elements. Latin letters a.. z and A..

With an accent-insensitive collation, this class also matches accented forms of these characters. Uppercase Latin letters A.. Also matches lowercase with case-insensitive collation and accented forms with accent-insensitive collation.

Lowercase Latin letters a.. Also matches uppercase with case-insensitive collation and accented forms with accent-insensitive collation. Matches horizontal tab ASCII 9 , linefeed ASCII 10 , vertical tab ASCII 11 , formfeed ASCII 12 , carriage return ASCII 13 and space ASCII Including a predefined class has the same effect as including all its members. Predefined classes are only allowed within class definitions. If you need to match against a predefined class and nothing more, place an extra pair of brackets around it.

If a class definition starts with a caret, everything that follows is excluded from the class. All other characters match:. If the caret is not placed at the start of the sequence, the class contains everything before the caret, except for the elements that also occur after the caret:. If the braces contain two numbers separated by a comma, the second number not smaller than the first, then the item must be repeated at least the first number and at most the second number of times in order to match:.

A match is made when the argument string matches at least one of the terms:. A subexpression is a regular expression in its own right. It can contain all the elements allowed in a regular expression, and can also have quantifiers added to it. In order to match against a character that is special in regular expressions, that character has to be escaped.

There is no default escape character; rather, the user specifies one when needed:. Two operands are considered DISTINCT if they have a different value or if one of them is NULL and the other non-null. They are NOT DISTINCT if they have the same value or if both of them are NULL. Since NULL is not a value, these operators are not comparison operators.

The IS [NOT] NULL predicate tests the assertion that the expression on the left side has a value IS NOT NULL or has no value IS NULL. In Firebird 3. This group of predicates includes those that use subqueries to submit values for all kinds of assertions in search conditions.

Existential predicates are so called because they use various methods to test for the existence or non-existence of some assertion, returning TRUE if the existence or non-existence is confirmed or FALSE otherwise.

The EXISTS predicate uses a subquery expression as its argument. It returns TRUE if the subquery result would contain at least one row; otherwise it returns FALSE. NOT EXISTS returns FALSE if the subquery result would contain at least one row; it returns TRUE otherwise. The IN predicate tests whether the value of the expression on the left side is present in the set of values specified on the right side.

The set of values cannot have more than items. The IN predicate can be replaced with the following equivalent forms:. When the IN predicate is used in the search conditions of DML queries, the Firebird optimizer can use an index on the searched column, if a suitable one exists. Queries specified using the IN predicate with a subquery can be replaced with a similar query using the EXISTS predicate. For instance, the following query:.

However, a query using NOT IN with a subquery does not always give the same result as its NOT EXISTS counterpart. The reason is that EXISTS always returns TRUE or FALSE, whereas IN returns NULL in one of these two cases:. when the test value has no match in the IN list and at least one list element is NULL.

It is in only these two cases that IN will return NULL while the corresponding EXISTS predicate will return FALSE 'no matching row found'. But, for the same data, NOT IN will return NULL , while NOT EXISTS will return TRUE , leading to opposite results. Now, assume that the NY celebrities list is not empty and contains at least one NULL birthday.

Then for every citizen who does not share his birthday with a NY celebrity, NOT IN will return NULL , because that is what IN does. The search condition is thereby not satisfied and the citizen will be left out of the SELECT result, which is wrong. non-matches will have a NOT EXISTS result of TRUE and their records will be in the result set. If there is any chance of NULL s being encountered when searching for a non-match, you will want to use NOT EXISTS.

The SINGULAR predicate takes a subquery as its argument and evaluates it as TRUE if the subquery returns exactly one result row; otherwise the predicate is evaluated as FALSE.

The subquery may list several output columns since the rows are not returned anyway. They are only tested for singular existence. The SINGULAR predicate can return only two values: TRUE or FALSE.

A quantifier is a logical operator that sets the number of objects for which this assertion is true. It is not a numeric quantity, but a logical one that connects the assertion with the full set of possible objects.

Such predicates are based on logical universal and existential quantifiers that are recognised in formal logic. In subquery expressions, quantified predicates make it possible to compare separate values with the results of subqueries; they have the following common form:.

When the ALL quantifier is used, the predicate is TRUE if every value returned by the subquery satisfies the condition in the predicate of the main query.

If the subquery returns an empty set, the predicate is TRUE for every left-side value, regardless of the operator. This may appear to be contradictory, because every left-side value will thus be considered both smaller and greater than, both equal to and unequal to, every element of the right-side stream.

Nevertheless, it aligns perfectly with formal logic: if the set is empty, the predicate is true 0 times, i. The quantifiers ANY and SOME are identical in their behaviour. Apparently, both are present in the SQL standard so that they could be used interchangeably in order to improve the readability of operators.

When the ANY or the SOME quantifier is used, the predicate is TRUE if any of the values returned by the subquery satisfies the condition in the predicate of the main query. If the subquery would return no rows at all, the predicate is automatically considered as FALSE. DDL statements are used to create, modify and delete database objects that have been created by users. When a DDL statement is committed, the metadata for the object are created, changed or deleted.

This section describes how to create a database, connect to an existing database, alter the file structure of a database and how to delete one. Optionally includes a port number or service name. Full path and file name including its extension. The file name must be specified according to the rules of the platform file system being used. Database alias previously created in the aliases. conf file. User name of the owner of the new database. It may consist of up to 31 characters.

Password of the user name as the database owner. The maximum length is 31 characters; however only the first 8 characters are considered.

Page size for the database, in bytes. Possible values are the default , and Specifies the character set of the connection available to a client connecting after the database is successfully created. Single quotes are required. The CREATE DATABASE statement creates a new database. You can use CREATE DATABASE or CREATE SCHEMA. They are synonymous. A database may consist of one or several files.

The first main file is called the primary file , subsequent files are called secondary file[s]. Nowadays, multi-file databases are considered an anachronism. It made sense to use multi-file databases on old file systems where the size of any file is limited.

For instance, you could not create a file larger than 4 GB on FAT The primary file specification is the name of the database file and its extension with the full path to it according to the rules of the OS platform file system being used. The database file must not exist at the moment when the database is being created.

If it does exist, you will get an error message and the database will not be created. If the full path to the database is not specified, the database will be created in one of the system directories. The particular directory depends on the operating system. For this reason, unless you have a strong reason to prefer that situation, always specify the absolute path, when creating either the database or an alias for it. You can use aliases instead of the full path to the primary database file.

If you create a database on a remote server, you should specify the remote server specification. The remote server specification depends on the protocol being used. If you use the Named Pipes protocol to create a database on a Windows server, the primary file specification should look like this:.

Clauses for specifying the user name and the password, respectively, of an existing user in the security database security2. The user specified in the process of creating the database will be its owner. This will be important when considering database and object privileges. Clause for specifying the database page size. This size will be set for the primary file and all secondary files of the database.

If you specify the database page size less than 4,, it will be changed automatically to the default page size, 4, Other values not equal to either 4,, 8, or 16, will be changed to the closest smaller supported value.

If the database page size is not specified, it is set to the default value of 4, Clause specifying the maximum size of the primary or secondary database file, in pages. When a database is created, its primary and secondary files will occupy the minimum number of pages necessary to store the system data, regardless of the value specified in the LENGTH clause.

The LENGTH value does not affect the size of the only or last, in a multi-file database file. The file will keep increasing its size automatically when necessary. Clause specifying the character set of the connection available after the database is successfully created. The character set NONE is used by default.

Notice that the character set should be enclosed in a pair of apostrophes single quotes. Clause specifying the default character set for creating data structures of string data types. Character sets are applied to CHAR , VARCHAR and BLOB TEXT data types.

It is also possible to specify the default COLLATION for the default character set, making that collation sequence the default for the default character set. The default will be used for the entire database except where an alternative character set, with or without a specified collation, is used explicitly for a field, domain, variable, cast expression, etc.

Clause that specifies the database page number at which the next secondary database file should start. When the previous file is completely filled with data according to the specified page number, the system will start adding new data to the next database file. For the detailed description of this clause, see ALTER DATABASE. Databases are created in Dialect 3 by default. For the database to be created in SQL dialect 1, you will need to execute the statement SET SQL DIALECT 1 from script or the client application, e.

in isql , before the CREATE DATABASE statement. Creating a database in Windows, located on disk D with a page size of 8, The owner of the database will be the user wizard. The database will be in Dialect 1 and it will use WIN as its default character set. Creating a database in the Linux operating system with a page size of 4, The database will be in Dialect 3 and will use UTF8 as its default character set. Creating a database in Dialect 3 with UTF8 as its default character set.

The primary file will contain up to 10, pages with a page size of 8, As soon as the primary file has reached the maximum number of pages, Firebird will start allocating pages to the secondary file test. If that file is filled up to its maximum as well, test. fdb3 becomes the recipient of all new page allocations. As the last file, it has no page limit imposed on it by Firebird. New allocations will continue for as long as the file system allows it or until the storage device runs out of free space.

If a LENGTH parameter were supplied for this last file, it would be ignored. As far as file size and the use of secondary files are concerned, this database will behave exactly like the one in the previous example. ALTER DATABASE , DROP DATABASE. Multiple ADD FILE clauses are allowed; and an ADD FILE clause that adds multiple files as in the example above can be mixed with others that add only one file.

The statement was documented incorrectly in the old InterBase 6 Language Reference. Only administrators have the authority to use ALTER DATABASE. Adds a secondary file to the database.

It is necessary to specify the full path to the file and the name of the secondary file. The description for the secondary file is similar to the one given for the CREATE DATABASE statement.

This clause does not actually add any file. It just overrides the default name and path of the. delta file. To change the existing settings, you should delete the previously specified description of the.

If the path and name of the. delta file are not overridden, the file will have the same path and name as the database, but with the. delta file extension. If only a file name is specified, the. delta file will be created in the current directory of the server. This is the clause that deletes the description path and name of the. The file is not actually deleted. delta file from the database header. the same path and name as those of the database, but with the.

delta extension. ALTER DATABASE with this clause freezes the main database file, making it possible to back it up safely using file system tools, even if users are connected and performing operations with data. Until the backup state of the database is reverted to NORMAL , all changes made to the database will be written to the.

delta difference file. Despite its syntax, a statement with the BEGIN BACKUP clause does not start a backup process but just creates the conditions for doing a task that requires the database file to be read-only temporarily.

A statement with this clause merges the. delta file with the main database file and restores the normal operation of the database. Once the END BACKUP process starts, the conditions no longer exist for creating safe backups by means of file system tools. Use of BEGIN BACKUP and END BACKUP and copying the database files with filesystem tools, is not safe with multi-file databases!

Use this method only on single-file databases. Making a safe backup with the gbak utility remains possible at all times, although it is not recommended to run gbak while the database is in LOCKED or MERGE state. Adding a secondary file to the database. As soon as pages are filled in the previous primary or secondary file, the Firebird engine will start adding data to the secondary file test4. CREATE DATABASE , DROP DATABASE.

The DROP DATABASE statement deletes the current database. Before deleting a database, you have to connect to it. The statement deletes the primary file, all secondary files and all shadow files. Only administrators have the authority to use DROP DATABASE. CREATE DATABASE , ALTER DATABASE. A shadow is an exact, page-by-page copy of a database. Once a shadow is created, all changes made in the database are immediately reflected in the shadow.

If the primary database file becomes unavailable for some reason, the DBMS will switch to the shadow. The name of the shadow file and the path to it, in accord with the rules of the operating system. The CREATE SHADOW statement creates a new shadow.

The shadow starts duplicating the database right at the moment it is created. It is not possible for a user to connect to a shadow. Like a database, a shadow may be multi-file. The page size for shadow files is set to be equal to the database page size and cannot be changed. If a calamity occurs involving the original database, the system converts the shadow to a copy of the database and switches to it.

The shadow is then unavailable.

The source of much copied reference material: Paul Vinkenoog Copyright © Firebird Project and all contributing authors, under the Public Documentation License Version 1. Please refer to the License Notice in the Appendix. In , it culminated in a language reference manual, in Russian. At the instigation of Alexey Kovyazin, a campaign was launched amongst Firebird users world-wide to raise funds to pay for a professional translation into English, from which translations into other languages would proceed under the auspices of the Firebird Documentation Project.

This Firebird SQL Language Reference is the first comprehensive manual to cover all aspects of the query language used by developers to communicate, through their applications, with the Firebird relational database management system. It has a long history.

Firebird conforms closely with international standards for SQL, from data type support, data storage structures, referential integrity mechanisms, to data manipulation capabilities and access privileges.

These are the areas addressed in this volume. The material for assembling this Language Reference has been accumulating in the tribal lore of the open source community of Firebird core developers and user-developers for 15 years.

However, it came without rights to existing documentation. Once the code base had been forked by its owners for private, commercial development, it became clear that the open source, non-commercial Firebird community would never be granted right of use. The two important books from the InterBase 6 published set were the Data Definition Guide and the Language Reference. The former covered the data definition language DDL subset of the SQL language, while the latter covered most of the rest.

Fortunately for Firebird users over the years, both have been easy to find on-line as PDF books. From around , Paul, with Firebird Project lead Dmitry Yemanov and a documenter colleague Thomas Woinke, set about the task of designing and assembling a complete SQL language reference for Firebird. They began with the material from the LangRef Updates, which is voluminous. It was going to be a big job but, for all concerned, a spare-time one. They wrote the bulk of the missing DDL section from scratch and wrote, translated or reused DML and PSQL material from the LangRef Updates, Russian language support forums, Firebird release notes, read-me files and other sources.

By the end of , they had the task almost complete, in the form of a Microsoft Word document. The Russian sponsors, recognising that their efforts needed to be shared with the world-wide Firebird community, asked some Project members to initiate a crowd-funding campaign to have the Russian text professionally translated into English.

From there, the source text would be available for translation into other languages for addition to the library. The fund-raising campaign happened at the end of and was successful. In June, , professional translator Dmitry Borodin began translating the Russian text.

Once the DocBook source appears in CVS, we hope the trusty translators will start making versions in German, Japanese, Italian, French, Portuguese, Spanish, Czech. Certainly, we never have enough translators so please, you Firebirders who have English as a second language, do consider translating some sections into your first language.

The first full language reference manual for Firebird would not have eventuated without the funding that finally brought it to fruition. We acknowledge these contributions with gratitude and thank you all for stepping up. Moscow Exchange is the largest exchange holding in Russia and Eastern Europe, founded on December 19, , through the consolidation of the MICEX founded in and RTS founded in exchange groups. IBSurgeon ibase. ru Russia. Distinct subsets of SQL apply to different sectors of activity.

DSQL represents statements passed by client applications through the public Firebird API and processed by the database engine. Procedural SQL augments Dynamic SQL to allow compound statements containing local variables, assignments, conditions, loops and other procedural constructs. Originally, PSQL extensions were available in persistent stored modules procedures and triggers only, but in more recent releases they were surfaced in Dynamic SQL as well see EXECUTE BLOCK.

and preprocess those embedded constructs into the proper Firebird API calls. Interactive ISQL refers to the language that can be executed using Firebird isql , the command-line application for accessing databases interactively. As a regular client application, its native language is DSQL. It also offers a few additional commands that are not available outside its specific environment. Both DSQL and PSQL subsets are completely presented in this reference. Neither ESQL nor ISQL flavours are described here unless mentioned explicitly.

SQL dialect is a term that defines the specific features of the SQL language that are available when accessing a database. SQL dialects can be defined at the database level and specified at the connection level. Three dialects are available:. Dialect 1 is intended solely to allow backward comptibility with legacy databases from very old InterBase versions, v.

Dialect 1 databases retain certain language features that differ from Dialect 3, the default for Firebird databases. Date and time information are stored in a DATE data type. A TIMESTAMP data type is also available, that is identical to this DATE implementation.

Double quotes may be used as an alternative to apostrophes for delimiting string data. Double-quoting strings is therefore to be avoided strenuously. The precision for NUMERIC and DECIMAL data types is smaller than in Dialect 3 and, if the precision of a fixed decimal number is greater than 9, Firebird stores it internally as a long floating point value.

Dialect 2 is available only on the Firebird client connection and cannot be set in the database. It is intended to assist debugging of possible problems with legacy data when migrating a database from dialect 1 to 3. numbers DECIMAL and NUMERIC data types are stored internally as long fixed point values scaled integers when the precision is greater than 9. Double quotes are reserved for delimiting non-regular identifiers, enabling object names that are case-sensitive or that do not meet the requirements for regular identifiers in other ways.

Use of Dialect 3 is strongly recommended for newly developed databases and applications. Both database and connection dialects should match, except under migration conditions with Dialect 2. Processing of every SQL statement either completes successfully or fails due to a specific error condition.

The primary construct in SQL is the statement. A statement defines what the database management system should do with a particular data or metadata object. A clause defines a certain type of directive in a statement.

For instance, the WHERE clause in a SELECT statement and in some other data manipulation statements UPDATE, DELETE specifies criteria for searching one or more tables for the rows that are to be selected, updated or deleted.

Options, being the simplest constructs, are specified in association with specific keywords to provide qualification for clause elements. Where alternative options are available, it is usual for one of them to be the default, used if nothing is specified for that option.

For instance, the SELECT statement will return all of the rows that match the search criteria unless the DISTINCT option restricts the output to non-duplicated rows. All words that are included in the SQL lexicon are keywords.

Some keywords are reserved , meaning their usage as identifiers for database objects, parameter names or variables is prohibited in some or all contexts. Non-reserved keywords can be used as identifiers, although it is not recommended. From time to time, non-reserved keywords may become reserved when some new language feature is introduced. For instance, the following statement will be executed without errors because, although ABS is a keyword, it is not a reserved word.

On the contrary, the following statement will return an error because ADD is both a keyword and a reserved word. Refer to the list of reserved words and keywords in the chapter Reserved Words and Keywords. All database objects have names, often called identifiers. Two types of names are valid as identifiers: regular names, similar to variable names in regular programming languages, and delimited names that are specific to SQL. To be valid, each type of identifier must conform to a set of rules, as follows:.

The name must start with an unaccented, 7-bit ASCII alphabetic character. It may be followed by other 7-bit ASCII letters, digits, underscores or dollar signs. No other characters, including spaces, are valid.

The name is case-insensitive, meaning it can be declared and used in either upper or lower case. It may contain characters from any Latin character set, including accented characters, spaces and special characters.

Delimited identifiers are available in Dialect 3 only. For more details on dialects, see SQL Dialects. A delimited identifier such as "FULLNAME" is the same as the regular identifiers FULLNAME , fullname , FullName , and so on. The reason is that Firebird stores all regular names in upper case, regardless of how they were defined or declared. Delimited identifiers are always stored according to the exact case of their definition or declaration. Thus, "FullName" quoted is different from FullName unquoted, i.

Literals are used to represent data in a direct format. Examples of standard types of literals are:. Details about handling the literals for each data type are discussed in the next chapter, Data Types and Subtypes. Some of these characters, alone or in combinations, may be used as operators arithmetical, string, logical , as SQL command separators, to quote identifiers and to mark the limits of string literals or comments.

Comments may be present in SQL scripts, SQL statements and PSQL modules. A comment can be any text specified by the code writer, usually used to document how particular parts of the code work. The parser ignores the text of comments. Text in block comments may be of any length and can occupy multiple lines. In-line comments start with a pair of hyphen characters, -- and continue up to the end of the current line. define columns in a database table in the CREATE TABLE statement or change columns using ALTER TABLE.

declare or change a domain using the CREATE DOMAIN or ALTER DOMAIN statements. declare local variables in stored procedures, PSQL blocks and triggers and specify parameters in stored procedures.

provide arguments for the CAST function when explicitly converting data from one type to another. The size of a BLOB segment is limited to 64K.

Additionally, on 27 June , the financial watchdog demanded that Binance , the world's largest cryptocurrency exchange, [] cease all regulated activities in the UK. I used Nikon before, then Canon. Including a predefined class has the same effect as including all its members. Archived from the original on April 16, Andes Cocoa bean Mesoamerica Fanery Madagascar Koku rice Manilla W.

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