// Need some intro for UDF and native functions in general and point those to it.  
// [[cql-functions]][[native-functions]] 

== Functions

CQL supports 2 main categories of functions:

* xref:cql/functions.adoc#scalar-functions[scalar functions] that take a number of values and produce an output
* xref:cql/functions.adoc#aggregate-functions[aggregate functions] that aggregate multiple rows resulting from a `SELECT` statement

In both cases, CQL provides a number of native "hard-coded" functions as
well as the ability to create new user-defined functions.

[NOTE]
.Note
====
By default, the use of user-defined functions is disabled by default for
security concerns (even when enabled, the execution of user-defined
functions is sandboxed and a "rogue" function should not be allowed to
do evil, but no sandbox is perfect so using user-defined functions is
opt-in). See the `enable_user_defined_functions` in `cassandra.yaml` to
enable them.
====

A function is identifier by its name:

[source, bnf]
----
include::example$BNF/function.bnf[]
----

=== Scalar functions

[[scalar-native-functions]]
==== Native functions

===== Cast

The `cast` function can be used to converts one native datatype to
another.

The following table describes the conversions supported by the `cast`
function. Cassandra will silently ignore any cast converting a datatype
into its own datatype.

[cols=",",options="header",]
|===
|From |To

| `ascii` | `text`, `varchar`

| `bigint` | `tinyint`, `smallint`, `int`, `float`, `double`, `decimal`, `varint`,
`text`, `varchar`

| `boolean` | `text`, `varchar`

| `counter` | `tinyint`, `smallint`, `int`, `bigint`, `float`, `double`, `decimal`,
`varint`, `text`, `varchar`

| `date` | `timestamp`

| `decimal` | `tinyint`, `smallint`, `int`, `bigint`, `float`, `double`, `varint`,
`text`, `varchar`

| `double` | `tinyint`, `smallint`, `int`, `bigint`, `float`, `decimal`, `varint`,
`text`, `varchar`

| `float` | `tinyint`, `smallint`, `int`, `bigint`, `double`, `decimal`, `varint`,
`text`, `varchar`

| `inet` | `text`, `varchar`

| `int` | `tinyint`, `smallint`, `bigint`, `float`, `double`, `decimal`, `varint`,
`text`, `varchar`

| `smallint` | `tinyint`, `int`, `bigint`, `float`, `double`, `decimal`, `varint`,
`text`, `varchar`

| `time` | `text`, `varchar`

| `timestamp` | `date`, `text`, `varchar`

| `timeuuid` | `timestamp`, `date`, `text`, `varchar`

| `tinyint` | `tinyint`, `smallint`, `int`, `bigint`, `float`, `double`, `decimal`,
`varint`, `text`, `varchar`

| `uuid` | `text`, `varchar`

| `varint` | `tinyint`, `smallint`, `int`, `bigint`, `float`, `double`, `decimal`,
`text`, `varchar`
|===

The conversions rely strictly on Java's semantics. For example, the
double value 1 will be converted to the text value '1.0'. For instance:

[source,cql]
----
SELECT avg(cast(count as double)) FROM myTable
----

===== Token

The `token` function computes the token for a given partition key. 
The exact signature of the token function depends on the table concerned and the partitioner used by the cluster.

The type of the arguments of the `token` depend on the partition key column type. The returned type depends on the defined partitioner:

[cols=",",options="header",]
|===
|Partitioner | Returned type
| Murmur3Partitioner | `bigint`
| RandomPartitioner | `varint`
| ByteOrderedPartitioner | `blob`
|===

For example, consider the following table:

[source,cql]
----
include::example$CQL/create_table_simple.cql[]
----

The table uses the default Murmur3Partitioner.
The `token` function uses the single argument `text`, because the partition key is `userid` of text type.
The returned type will be `bigint`.

===== Uuid

The `uuid` function takes no parameters and generates a random type 4
uuid suitable for use in `INSERT` or `UPDATE` statements.

===== Timeuuid functions

====== `now`

The `now` function takes no arguments and generates, on the coordinator
node, a new unique timeuuid at the time the function is invoked. Note
that this method is useful for insertion but is largely non-sensical in
`WHERE` clauses. 

For example, a query of the form:

[source,cql]
----
include::example$CQL/timeuuid_now.cql[]
----

will not return a result, by design, since the value returned by
`now()` is guaranteed to be unique.

`currentTimeUUID` is an alias of `now`.

====== `minTimeuuid` and `maxTimeuuid`

The `minTimeuuid` function takes a `timestamp` value `t`, either a timestamp or a date string.
It returns a _fake_ `timeuuid` corresponding to the _smallest_ possible `timeuuid` for timestamp `t`. 
The `maxTimeuuid` works similarly, but returns the _largest_ possible `timeuuid`.

For example:

[source,cql]
----
include::example$CQL/timeuuid_min_max.cql[]
----

will select all rows where the `timeuuid` column `t` is later than `'2013-01-01 00:05+0000'` and earlier than `'2013-02-02 10:00+0000'`. 
The clause `t >= maxTimeuuid('2013-01-01 00:05+0000')` would still _not_ select a `timeuuid` generated exactly at '2013-01-01 00:05+0000', and is essentially equivalent to `t > maxTimeuuid('2013-01-01 00:05+0000')`.

[NOTE]
.Note
====
The values generated by `minTimeuuid` and `maxTimeuuid` are called _fake_ UUID because they do no respect the time-based UUID generation process
specified by the http://www.ietf.org/rfc/rfc4122.txt[IETF RFC 4122]. 
In particular, the value returned by these two methods will not be unique.
Thus, only use these methods for *querying*, not for *insertion*, to prevent possible data overwriting. 
====

===== Datetime functions

====== Retrieving the current date/time

The following functions can be used to retrieve the date/time at the
time where the function is invoked:

[cols=",",options="header",]
|===
|Function name |Output type

| `currentTimestamp` | `timestamp`

| `currentDate` | `date`

| `currentTime` | `time`

| `currentTimeUUID` | `timeUUID`
|===

For example the last two days of data can be retrieved using:

[source,cql]
----
include::example$CQL/currentdate.cql[]
----

====== Time conversion functions

A number of functions are provided to convert a `timeuuid`, a `timestamp` or a `date` into another `native` type.

[cols=",,",options="header",]
|===
|Function name |Input type |Description

| `toDate` | `timeuuid` | Converts the `timeuuid` argument into a `date` type

| `toDate` | `timestamp` | Converts the `timestamp` argument into a `date` type

| `toTimestamp` | `timeuuid` | Converts the `timeuuid` argument into a `timestamp` type

| `toTimestamp` | `date` | Converts the `date` argument into a `timestamp` type

| `toUnixTimestamp` | `timeuuid` | Converts the `timeuuid` argument into a `bigInt` raw value

| `toUnixTimestamp` | `timestamp` | Converts the `timestamp` argument into a `bigInt` raw value

| `toUnixTimestamp` | `date` | Converts the `date` argument into a `bigInt` raw value

| `dateOf` | `timeuuid` | Similar to `toTimestamp(timeuuid)` (DEPRECATED)

| `unixTimestampOf` | `timeuuid` | Similar to `toUnixTimestamp(timeuuid)` (DEPRECATED)
|===

===== Blob conversion functions

A number of functions are provided to convert the native types into
binary data, or a `blob`. 
For every xref:cql/types.adoc#native-types[type] supported by CQL, the function `typeAsBlob` takes a argument of type `type` and returns it as a `blob`.
Conversely, the function `blobAsType` takes a 64-bit `blob` argument and converts it to a `bigint` value. 
For example, `bigintAsBlob(3)` returns `0x0000000000000003` and `blobAsBigint(0x0000000000000003)` returns `3`.

[[user-defined-scalar-functions]]
==== User-defined functions

User-defined functions (UDFs) execute user-provided code in Cassandra. 
By default, Cassandra supports defining functions in _Java_ and _JavaScript_. 
Support for other JSR 223 compliant scripting languages, such as Python, Ruby, and Scala, is possible by adding a JAR to the classpath.

UDFs are part of the Cassandra schema, and are automatically propagated to all nodes in the cluster.  
UDFs can be _overloaded_, so that multiple UDFs with different argument types can have the same function name. 

For example:

[source,cql]
----
include::example$CQL/function_overload.cql[]
----

UDFs are susceptible to all of the normal problems with the chosen programming language. 
Accordingly, implementations should be safe against null pointer exceptions, illegal arguments, or any other potential source of exceptions. 
An exception during function execution will result in the entire statement failing.
Valid queries for UDF use are `SELECT`, `INSERT` and `UPDATE` statements.

_Complex_ types like collections, tuple types and user-defined types are valid argument and return types in UDFs. 
Tuple types and user-defined types use the DataStax Java Driver conversion functions.
Please see the Java Driver documentation for details on handling tuple types and user-defined types.

Arguments for functions can be literals or terms. 
Prepared statement placeholders can be used, too.

Note the use the double dollar-sign syntax to enclose the UDF source code. 

For example:

[source,cql]
----
include::example$CQL/function_dollarsign.cql[]
----

The implicitly available `udfContext` field (or binding for script UDFs) provides the necessary functionality to create new UDT and tuple values:

[source,cql]
----
include::example$CQL/function_udfcontext.cql[]
----

The definition of the `UDFContext` interface can be found in the Apache Cassandra source code for `org.apache.cassandra.cql3.functions.UDFContext`.

[source,java]
----
include::example$JAVA/udfcontext.java[]
----

Java UDFs already have some imports for common interfaces and classes defined. These imports are:

[source,java]
----
include::example$JAVA/udf_imports.java[]
----

Please note, that these convenience imports are not available for script UDFs.

[[create-function-statement]]
==== CREATE FUNCTION statement

Creating a new user-defined function uses the `CREATE FUNCTION` statement:

[source,bnf]
----
include::example$BNF/create_function_statement.bnf[]
----

For example:

[source,cql]
----
include::example$CQL/create_function.cql[]
----

`CREATE FUNCTION` with the optional `OR REPLACE` keywords creates either a function or replaces an existing one with the same signature. 
A `CREATE FUNCTION` without `OR REPLACE` fails if a function with the same signature already exists.  
If the optional `IF NOT EXISTS` keywords are used, the function will only be created only if another function with the same signature does not
exist.
`OR REPLACE` and `IF NOT EXISTS` cannot be used together.

Behavior for `null` input values must be defined for each function:

* `RETURNS NULL ON NULL INPUT` declares that the function will always return `null` if any of the input arguments is `null`.
* `CALLED ON NULL INPUT` declares that the function will always be executed.

===== Function Signature

Signatures are used to distinguish individual functions. The signature consists of a fully-qualified function name of the <keyspace>.<function_name> and a concatenated list of all the argument types.

Note that keyspace names, function names and argument types are subject to the default naming conventions and case-sensitivity rules.

Functions belong to a keyspace; if no keyspace is specified, the current keyspace is used. 
User-defined functions are not allowed in the system keyspaces.

[[drop-function-statement]]
==== DROP FUNCTION statement

Dropping a function uses the `DROP FUNCTION` statement:

[source, bnf]
----
include::example$BNF/drop_function_statement.bnf[]
----

For example:

[source,cql]
----
include::example$CQL/drop_function.cql[]
----

You must specify the argument types of the function, the arguments_signature, in the drop command if there are multiple overloaded functions with the same name but different signatures.  
`DROP FUNCTION` with the optional `IF EXISTS` keywords drops a function if it exists, but does not throw an error if it doesn't.

[[aggregate-functions]]
=== Aggregate functions

Aggregate functions work on a set of rows. 
Values for each row are input, to return a single value for the set of rows aggregated.

If `normal` columns, `scalar functions`, `UDT` fields, `writetime`, or `ttl` are selected together with aggregate functions, the values
returned for them will be the ones of the first row matching the query.

==== Native aggregates

[[count-function]]
===== Count

The `count` function can be used to count the rows returned by a query.

For example:

[source,cql]
----
include::example$CQL/count.cql[]
----

It also can count the non-null values of a given column:

[source,cql]
----
include::example$CQL/count_nonnull.cql[]
----

===== Max and Min

The `max` and `min` functions compute the maximum and the minimum value returned by a query for a given column. 

For example:

[source,cql]
----
include::example$CQL/min_max.cql[]
----

===== Sum

The `sum` function sums up all the values returned by a query for a given column. 

For example:

[source,cql]
----
include::example$CQL/sum.cql[]
----

===== Avg

The `avg` function computes the average of all the values returned by a query for a given column. 

For example:

[source,cql]
----
include::example$CQL/avg.cql[]
----

[[user-defined-aggregates-functions]]
==== User-Defined Aggregates (UDAs)

User-defined aggregates allow the creation of custom aggregate functions. 
User-defined aggregates can be used in `SELECT` statement.

Each aggregate requires an _initial state_ of type `STYPE` defined with the `INITCOND`value (default value: `null`). 
The first argument of the state function must have type `STYPE`. 
The remaining arguments of the state function must match the types of the user-defined aggregate arguments. 
The state function is called once for each row, and the value returned by the state function becomes the new state. 
After all rows are processed, the optional `FINALFUNC` is executed with last state value as its argument.

The `STYPE` value is mandatory in order to distinguish possibly overloaded versions of the state and/or final function, since the
overload can appear after creation of the aggregate.


A complete working example for user-defined aggregates (assuming that a
keyspace has been selected using the `USE` statement):

[source,cql]
----
include::example$CQL/uda.cql[]
----

[[create-aggregate-statement]]
==== CREATE AGGREGATE statement

Creating (or replacing) a user-defined aggregate function uses the
`CREATE AGGREGATE` statement:

[source, bnf]
----
include::example$BNF/create_aggregate_statement.bnf[]
----

See above for a complete example.

The `CREATE AGGREGATE` command with the optional `OR REPLACE` keywords creates either an aggregate or replaces an existing one with the same
signature. 
A `CREATE AGGREGATE` without `OR REPLACE` fails if an aggregate with the same signature already exists.
The `CREATE AGGREGATE` command with the optional `IF NOT EXISTS` keywords creates an aggregate if it does not already exist.
The `OR REPLACE` and `IF NOT EXISTS` phrases cannot be used together.

The `STYPE` value defines the type of the state value and must be specified.
The optional `INITCOND` defines the initial state value for the aggregate; the default value is `null`. 
A non-null `INITCOND` must be specified for state functions that are declared with `RETURNS NULL ON NULL INPUT`.

The `SFUNC` value references an existing function to use as the state-modifying function. 
The first argument of the state function must have type `STYPE`.
The remaining arguments of the state function must match the types of the user-defined aggregate arguments.
The state function is called once for each row, and the value returned by the state function becomes the new state.
State is not updated for state functions declared with `RETURNS NULL ON NULL INPUT` and called with `null`.
After all rows are processed, the optional `FINALFUNC` is executed with last state value as its argument.
It must take only one argument with type `STYPE`, but the return type of the `FINALFUNC` may be a different type. 
A final function declared with `RETURNS NULL ON NULL INPUT` means that the aggregate's return value will be `null`, if the last state is `null`.

If no `FINALFUNC` is defined, the overall return type of the aggregate function is `STYPE`. 
If a `FINALFUNC` is defined, it is the return type of that function.

[[drop-aggregate-statement]]
==== DROP AGGREGATE statement

Dropping an user-defined aggregate function uses the `DROP AGGREGATE`
statement:

[source, bnf]
----
include::example$BNF/drop_aggregate_statement.bnf[]
----

For instance:

[source,cql]
----
include::example$CQL/drop_aggregate.cql[]
----

The `DROP AGGREGATE` statement removes an aggregate created using `CREATE AGGREGATE`. 
You must specify the argument types of the aggregate to drop if there are multiple overloaded aggregates with the same name but a
different signature.

The `DROP AGGREGATE` command with the optional `IF EXISTS` keywords drops an aggregate if it exists, and does nothing if a function with the
signature does not exist.