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https://issues.apache.org/jira/browse/CALCITE-4864?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Jing Zhang updated CALCITE-4864:
--------------------------------
Description:
h1. Background
h2. What is Polymorphic Table Function
A Polymorphic Table Function (abbreviated PTF) is a function that returns a
table whose row type is not declared when the function is created. It is
introduced in SQL 2017 standard.
h2. When y
PTF is the evolution of the table function. It has the following features
compare with normal table function:
# may have generic table parameters (i.e., no row type of table parameters
declared when the PTF is created)
# returns a table whose row type is not declared when the function is created,
it may depend on the function arguments.
PTF could have generic table parameters. And input tables are classified by
three characteristics:
# Input tables have either row semantics or set semantics)
## Row semantics means that the the result of the PTF is determined only by
looking at the current row
## Set semantics means that the result of the PTF can be determined by looking
at the current row and some state “summarized” from previously processed rows.
# The second characteristic, which applies only to input tables with set
semantics, is whether the PTF can generate a result row even if the input table
is empty.
# The third characteristic is whether the input table supports pass-through
columns or not. Pass-through columns is a mechanism enabling the PTF to copy
every column of an input row into columns of an output row.
h2. Examples
There are many interesting examples of PTF in SQL standard. Here I only give
one case.
We often need to read a CSV file, generally, the first line of the file
contains a list of column names, and subsequent lines of the file contain data.
The data in general can be treated as a large VARCHAR. However, some of the
fields may be numeric or datetime.
A PTF named CSVreader is designed to read a file of comma-separated values and
interpret this file as a table.
CSVreader function has three parameters:
# The first parameter, File, is the name of a file on the query author's
system. This file must contain the comma-separated values that are to be
converted to a table. The first line of the file contains the names of the
resulting columns. Succeeding lines contain the data. Each line after the first
will result in one row of output, with column names as determined by the first
line of the input.
# Floats is a PTF descriptor area, which should provide a list of the column
names that are to be interpreted numerically. These columns will be output with
the data type FLOAT.
# Dates is a PTF descriptor area, which provides a list of the column names
that are to be interpreted as datetimes. These columns will be output with the
data type DATE.
How to use this PTF in query?
For example, suppose that the contents of abc.csv are
{code:java}
docno,name,due_date,principle,interest
123,Mary,01/01/2014,234.56,345.67
234,Edgar,01/01/2014,654.32,543.21 {code}
the query author may write a query such as the following:
{code:java}
SELECT *
FROM TABLE (
CSVreader ( File => 'abc.csv',
Floats => DESCRIPTOR ("principle", "interest")
Dates => DESCRIPTOR ("due_date") ) ) AS S {code}
The result will be
||docno||name||due_date||principle||interest||
|123|Mary|01/01/2014|234.56|345.67|
|234|Edgar|01/01/2014|654.32|543.21|
was:
A Polymorphic Table Function (abbreviated PTF) is a function that returns a
table whose row type is not declared when the function is created. It is
introduced in SQL 2017 standard.
PTF is the evolution of the table function. It has the following features
compare with normal table function:
# may have generic table parameters (i.e., no row type of table parameters
declared when the PTF is created)
# returns a table whose row type is not declared when the function is created,
it may depend on the function arguments.
There are many interesting examples of PTF in SQL standard. Here I only give
one case.
We often need to read a CSV file, generally, the first line of the file
contains a list of column names, and subsequent lines of the file contain data.
The data in general can be treated as a large VARCHAR. However, some of the
fields may be numeric or datetime.
A PTF named CSVreader is designed to read a file of comma-separated values and
interpret this file as a table.
CSVreader function has three parameters:
# The first parameter, File, is the name of a file on the query author's
system. This file must contain the comma-separated values that are to be
converted to a table. The first line of the file contains the names of the
resulting columns. Succeeding lines contain the data. Each line after the first
will result in one row of output, with column names as determined by the first
line of the input.
# Floats is a PTF descriptor area, which should provide a list of the column
names that are to be interpreted numerically. These columns will be output with
the data type FLOAT.
# Dates is a PTF descriptor area, which provides a list of the column names
that are to be interpreted as datetimes. These columns will be output with the
data type DATE.
How to use this PTF in query?
For example, suppose that the contents of abc.csv are
{code:java}
docno,name,due_date,principle,interest
123,Mary,01/01/2014,234.56,345.67
234,Edgar,01/01/2014,654.32,543.21 {code}
the query author may write a query such as the following:
{code:java}
SELECT *
FROM TABLE (
CSVreader ( File => 'abc.csv',
Floats => DESCRIPTOR ("principle", "interest")
Dates => DESCRIPTOR ("due_date") ) ) AS S {code}
The result will be
||docno||name||due_date||principle||interest||
|123|Mary|01/01/2014|234.56|345.67|
|234|Edgar|01/01/2014|654.32|543.21|
A mentioned earlier, PTF could have generic table parameters. And input tables
are classified by three characteristics:
# Input tables have either row semantics or set semantics)
## Row semantics means that the the result of the PTF is determined only by
looking at the current row
## Set semantics means that the result of the PTF can be determined by looking
at the current row and some state “summarized” from previously processed rows.
# The second characteristic, which applies only to input tables with set
semantics, is whether the PTF can generate a result row even if the input table
is empty.
# The third characteristic is whether the input table supports pass-through
columns or not. Pass-through columns is a mechanism enabling the PTF to copy
every column of an input row into columns of an output row.
> Supports Polymorphic Table function
> -----------------------------------
>
> Key: CALCITE-4864
> URL: https://issues.apache.org/jira/browse/CALCITE-4864
> Project: Calcite
> Issue Type: New Feature
> Components: core
> Reporter: Jing Zhang
> Priority: Major
>
> h1. Background
> h2. What is Polymorphic Table Function
> A Polymorphic Table Function (abbreviated PTF) is a function that returns a
> table whose row type is not declared when the function is created. It is
> introduced in SQL 2017 standard.
>
> h2. When y
> PTF is the evolution of the table function. It has the following features
> compare with normal table function:
> # may have generic table parameters (i.e., no row type of table parameters
> declared when the PTF is created)
> # returns a table whose row type is not declared when the function is
> created, it may depend on the function arguments.
> PTF could have generic table parameters. And input tables are classified by
> three characteristics:
> # Input tables have either row semantics or set semantics)
> ## Row semantics means that the the result of the PTF is determined only by
> looking at the current row
> ## Set semantics means that the result of the PTF can be determined by
> looking at the current row and some state “summarized” from previously
> processed rows.
> # The second characteristic, which applies only to input tables with set
> semantics, is whether the PTF can generate a result row even if the input
> table is empty.
> # The third characteristic is whether the input table supports pass-through
> columns or not. Pass-through columns is a mechanism enabling the PTF to copy
> every column of an input row into columns of an output row.
> h2. Examples
> There are many interesting examples of PTF in SQL standard. Here I only give
> one case.
> We often need to read a CSV file, generally, the first line of the file
> contains a list of column names, and subsequent lines of the file contain
> data. The data in general can be treated as a large VARCHAR. However, some of
> the fields may be numeric or datetime.
> A PTF named CSVreader is designed to read a file of comma-separated values
> and interpret this file as a table.
> CSVreader function has three parameters:
> # The first parameter, File, is the name of a file on the query author's
> system. This file must contain the comma-separated values that are to be
> converted to a table. The first line of the file contains the names of the
> resulting columns. Succeeding lines contain the data. Each line after the
> first will result in one row of output, with column names as determined by
> the first line of the input.
> # Floats is a PTF descriptor area, which should provide a list of the column
> names that are to be interpreted numerically. These columns will be output
> with the data type FLOAT.
> # Dates is a PTF descriptor area, which provides a list of the column names
> that are to be interpreted as datetimes. These columns will be output with
> the data type DATE.
> How to use this PTF in query?
> For example, suppose that the contents of abc.csv are
> {code:java}
> docno,name,due_date,principle,interest
> 123,Mary,01/01/2014,234.56,345.67
> 234,Edgar,01/01/2014,654.32,543.21 {code}
>
> the query author may write a query such as the following:
> {code:java}
> SELECT *
> FROM TABLE (
> CSVreader ( File => 'abc.csv',
> Floats => DESCRIPTOR ("principle", "interest")
> Dates => DESCRIPTOR ("due_date") ) ) AS S {code}
>
> The result will be
> ||docno||name||due_date||principle||interest||
> |123|Mary|01/01/2014|234.56|345.67|
> |234|Edgar|01/01/2014|654.32|543.21|
>
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