This document describes the Cassandra Query Language (CQL) version 3. CQL v3 is not backward compatible with CQL v2 and differs from it in numerous ways. Note that this document describes the last version of the languages. However, the changes section provides the diff between the different versions of CQL v3.

CQL v3 offers a model very close to SQL in the sense that data is put in tables containing rows of columns. For that reason, when used in this document, these terms (tables, rows and columns) have the same definition than they have in SQL. But please note that as such, they do not refer to the concept of rows and columns found in the internal implementation of Cassandra and in the thrift and CQL v2 API.

To aid in specifying the CQL syntax, we will use the following conventions in this document:

bc(syntax). ::= TERMINAL

bc(sample). SELECT sample_usage FROM cql;

The CQL language uses identifiers (or names) to identify tables, columns and other objects. An identifier is a token matching the regular expression [a-zA-Z0-9_]*.

A number of such identifiers, like SELECT or WITH, are keywords. They have a fixed meaning for the language and most are reserved. The list of those keywords can be found in Appendix A.

Identifiers and (unquoted) keywords are case insensitive. Thus SELECT is the same than select or sElEcT, and myId is the same than myid or MYID for instance. A convention often used (in particular by the samples of this documentation) is to use upper case for keywords and lower case for other identifiers.

There is a second kind of identifiers called quoted identifiers defined by enclosing an arbitrary sequence of characters in double-quotes("). Quoted identifiers are never keywords. Thus "select" is not a reserved keyword and can be used to refer to a column, while select would raise a parse error. Also, contrarily to unquoted identifiers and keywords, quoted identifiers are case sensitive ("My Quoted Id" is different from "my quoted id"). A fully lowercase quoted identifier that matches [a-zA-Z0-9_]* is equivalent to the unquoted identifier obtained by removing the double-quote (so "myid" is equivalent to myid and to myId but different from "myId"). Inside a quoted identifier, the double-quote character can be repeated to escape it, so "foo "" bar" is a valid identifier.

Warning: quoted identifiers allows to declare columns with arbitrary names, and those can sometime clash with specific names used by the server. For instance, when using conditional update, the server will respond with a result-set containing a special result named "[applied]". If you’ve declared a column with such a name, this could potentially confuse some tools and should be avoided. In general, unquoted identifiers should be preferred but if you use quoted identifiers, it is strongly advised to avoid any name enclosed by squared brackets (like "[applied]") and any name that looks like a function call (like "f(x)").

CQL defines the following kind of constants: strings, integers, floats, booleans, uuids and blobs:

For how these constants are typed, see the data types section.

A comment in CQL is a line beginning by either double dashes (--) or double slash (//).

Multi-line comments are also supported through enclosure within / and / (but nesting is not supported).

bc(sample).
— This is a comment
/* This is
a multi-line comment */

CQL consists of statements. As in SQL, these statements can be divided in 3 categories:

All statements end with a semicolon (;) but that semicolon can be omitted when dealing with a single statement. The supported statements are described in the following sections. When describing the grammar of said statements, we will reuse the non-terminal symbols defined below:

bc(syntax)..
::= any quoted or unquoted identifier, excluding reserved keywords
::= ( `.')?

::= a string constant
::= an integer constant
::= a float constant
::= |
::= a uuid constant
::= a boolean constant
::= a blob constant

::=
|
|
|
|
::= ?'
| `:'
::=
|
|
| `(' ( (,' )*)? `)'

::=
|
|
::= `\{' ( `:' ( `,' `:' )* )? `}'
::= `\{' ( ( `,' )* )? `}'
::= `[' ( ( `,' )* )? `]'

::=

::= (AND )*
::= =' ( | | )
p.
Please note that not every possible productions of the grammar above will be valid in practice. Most notably, `<variable> and nested <collection-literal> are currently not allowed inside <collection-literal>.

A <variable> can be either anonymous (a question mark (?)) or named (an identifier preceded by :). Both declare a bind variables for prepared statements. The only difference between an anymous and a named variable is that a named one will be easier to refer to (how exactly depends on the client driver used).

The <properties> production is use by statement that create and alter keyspaces and tables. Each <property> is either a simple one, in which case it just has a value, or a map one, in which case it’s value is a map grouping sub-options. The following will refer to one or the other as the kind (simple or map) of the property.

A <tablename> will be used to identify a table. This is an identifier representing the table name that can be preceded by a keyspace name. The keyspace name, if provided, allow to identify a table in another keyspace than the currently active one (the currently active keyspace is set through the USE statement).

For supported <function>, see the section on functions.

Strings can be either enclosed with single quotes or two dollar characters. The second syntax has been introduced to allow strings that contain single quotes. Typical candidates for such strings are source code fragments for user-defined functions.

Sample:

bc(sample)..
`some string value'

double-dollar string can contain single ’ quotes
p.

CQL supports prepared statements. Prepared statement is an optimization that allows to parse a query only once but execute it multiple times with different concrete values.

In a statement, each time a column value is expected (in the data manipulation and query statements), a <variable> (see above) can be used instead. A statement with bind variables must then be prepared. Once it has been prepared, it can executed by providing concrete values for the bind variables. The exact procedure to prepare a statement and execute a prepared statement depends on the CQL driver used and is beyond the scope of this document.

In addition to providing column values, bind markers may be used to provide values for LIMIT, TIMESTAMP, and TTL clauses. If anonymous bind markers are used, the names for the query parameters will be [limit], [timestamp], and [ttl], respectively.

Syntax:

bc(syntax)..
::= CREATE KEYSPACE (IF NOT EXISTS)? WITH
p.
Sample:

bc(sample)..
CREATE KEYSPACE Excelsior
WITH replication = \{’class’: `SimpleStrategy', `replication_factor' : 3};

CREATE KEYSPACE Excalibur
WITH replication = \{’class’: NetworkTopologyStrategy', `DC1' : 1, `DC2' : 3}
AND durable_writes = false;
p.
The `CREATE KEYSPACE statement creates a new top-level keyspace. A keyspace is a namespace that defines a replication strategy and some options for a set of tables. Valid keyspaces names are identifiers composed exclusively of alphanumerical characters and whose length is lesser or equal to 32. Note that as identifiers, keyspace names are case insensitive: use a quoted identifier for case sensitive keyspace names.

The supported <properties> for CREATE KEYSPACE are:

The replication <property> is mandatory. It must at least contains the 'class' sub-option which defines the replication strategy class to use. The rest of the sub-options depends on that replication strategy class. By default, Cassandra support the following 'class':

Attempting to create an already existing keyspace will return an error unless the IF NOT EXISTS option is used. If it is used, the statement will be a no-op if the keyspace already exists.

Syntax:

bc(syntax). ::= USE

Sample:

bc(sample). USE myApp;

The USE statement takes an existing keyspace name as argument and set it as the per-connection current working keyspace. All subsequent keyspace-specific actions will be performed in the context of the selected keyspace, unless otherwise specified, until another USE statement is issued or the connection terminates.

Syntax:

bc(syntax)..
::= ALTER KEYSPACE WITH
p.
Sample:

bc(sample)..
ALTER KEYSPACE Excelsior
WITH replication = \{’class’: `SimpleStrategy', `replication_factor' : 4};

The ALTER KEYSPACE statement alters the properties of an existing keyspace. The supported <properties> are the same as for the CREATE KEYSPACE statement.

Syntax:

bc(syntax). ::= DROP KEYSPACE ( IF EXISTS )?

Sample:

bc(sample). DROP KEYSPACE myApp;

A DROP KEYSPACE statement results in the immediate, irreversible removal of an existing keyspace, including all column families in it, and all data contained in those column families.

If the keyspace does not exists, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op.

Syntax:

bc(syntax)..
::= CREATE ( TABLE | COLUMNFAMILY ) ( IF NOT EXISTS )?
`(' ( `,' )* `)'
( WITH ( AND )* )?

::= ( STATIC )? ( PRIMARY KEY )?
| PRIMARY KEY `(' ( `,' )* `)'

::=
| (' (,' )* `)'

::=
| COMPACT STORAGE
| CLUSTERING ORDER
p.
Sample:

bc(sample)..
CREATE TABLE monkeySpecies (
species text PRIMARY KEY,
common_name text,
population varint,
average_size int
) WITH comment=`Important biological records';

CREATE TABLE timeline (
userid uuid,
posted_month int,
posted_time uuid,
body text,
posted_by text,
PRIMARY KEY (userid, posted_month, posted_time)
) WITH compaction = \{ class' : `LeveledCompactionStrategy' };
p.
The `CREATE TABLE statement creates a new table. Each such table is a set of rows (usually representing related entities) for which it defines a number of properties. A table is defined by a name, it defines the columns composing rows of the table and have a number of options. Note that the CREATE COLUMNFAMILY syntax is supported as an alias for CREATE TABLE (for historical reasons).

Attempting to create an already existing table will return an error unless the IF NOT EXISTS option is used. If it is used, the statement will be a no-op if the table already exists.

Syntax:

bc(syntax)..
::= ALTER (TABLE | COLUMNFAMILY)

::= ADD
| ADD ( ( , )* )
| DROP
| DROP ( ( , )* )
| WITH ( AND )*
p.
Sample:

bc(sample)..
ALTER TABLE addamsFamily

ALTER TABLE addamsFamily
ADD gravesite varchar;

ALTER TABLE addamsFamily
WITH comment = A most excellent and useful column family';
p.
The `ALTER statement is used to manipulate table definitions. It allows for adding new columns, dropping existing ones, or updating the table options. As with table creation, ALTER COLUMNFAMILY is allowed as an alias for ALTER TABLE.

The <tablename> is the table name optionally preceded by the keyspace name. The <instruction> defines the alteration to perform:

Syntax:

bc(syntax). ::= DROP TABLE ( IF EXISTS )?

Sample:

bc(sample). DROP TABLE worldSeriesAttendees;

The DROP TABLE statement results in the immediate, irreversible removal of a table, including all data contained in it. As for table creation, DROP COLUMNFAMILY is allowed as an alias for DROP TABLE.

If the table does not exist, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op.

Syntax:

bc(syntax). ::= TRUNCATE ( TABLE | COLUMNFAMILY )?

Sample:

bc(sample). TRUNCATE superImportantData;

The TRUNCATE statement permanently removes all data from a table.

Syntax:

bc(syntax)..
::= CREATE ( CUSTOM )? INDEX ( IF NOT EXISTS )? ( )?
ON `(' `)'
( USING ( WITH OPTIONS = )? )?

::=
| keys( )
p.
Sample:

bc(sample).
CREATE INDEX userIndex ON NerdMovies (user);
CREATE INDEX ON Mutants (abilityId);
CREATE INDEX ON users (keys(favs));
CREATE CUSTOM INDEX ON users (email) USING `path.to.the.IndexClass';
CREATE CUSTOM INDEX ON users (email) USING `path.to.the.IndexClass' WITH OPTIONS = \{’storage’: `/mnt/ssd/indexes/'};

The CREATE INDEX statement is used to create a new (automatic) secondary index for a given (existing) column in a given table. A name for the index itself can be specified before the ON keyword, if desired. If data already exists for the column, it will be indexed asynchronously. After the index is created, new data for the column is indexed automatically at insertion time.

Attempting to create an already existing index will return an error unless the IF NOT EXISTS option is used. If it is used, the statement will be a no-op if the index already exists.

Syntax:

bc(syntax). ::= DROP INDEX ( IF EXISTS )? ( `.' )?

Sample:

bc(sample)..
DROP INDEX userIndex;

DROP INDEX userkeyspace.address_index;
p.
The DROP INDEX statement is used to drop an existing secondary index. The argument of the statement is the index name, which may optionally specify the keyspace of the index.

If the index does not exists, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op.

Syntax:

bc(syntax)..
::= CREATE MATERIALIZED VIEW ( IF NOT EXISTS )? AS
SELECT ( `(' ( `,' ) * `)' | `' )
FROM
( WHERE )?
PRIMARY KEY `(' ( `,' ) `)'
( WITH ( AND )* )?
p.
Sample:

bc(sample)..
CREATE MATERIALIZED VIEW monkeySpecies_by_population AS
SELECT *
FROM monkeySpecies
WHERE population IS NOT NULL AND species IS NOT NULL
PRIMARY KEY (population, species)
WITH comment=Allow query by population instead of species';
p.
The `CREATE MATERIALIZED VIEW statement creates a new materialized view. Each such view is a set of rows which corresponds to rows which are present in the underlying, or base, table specified in the SELECT statement. A materialized view cannot be directly updated, but updates to the base table will cause corresponding updates in the view.

Attempting to create an already existing materialized view will return an error unless the IF NOT EXISTS option is used. If it is used, the statement will be a no-op if the materialized view already exists.

Syntax:

bc(syntax). ::= ALTER MATERIALIZED VIEW
WITH ( AND )*

The ALTER MATERIALIZED VIEW statement allows options to be update; these options are the same as CREATE TABLE’s options.

Syntax:

bc(syntax). ::= DROP MATERIALIZED VIEW ( IF EXISTS )?

Sample:

bc(sample). DROP MATERIALIZED VIEW monkeySpecies_by_population;

The DROP MATERIALIZED VIEW statement is used to drop an existing materialized view.

If the materialized view does not exists, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op.

Syntax:

bc(syntax)..
::= CREATE TYPE ( IF NOT EXISTS )?
`(' ( `,' )* `)'

::= ( `.' )?

::=

Sample:

bc(sample)..
CREATE TYPE address (
street_name text,
street_number int,
city text,
state text,
zip int
)

CREATE TYPE work_and_home_addresses (
home_address address,
work_address address
)
p.
The CREATE TYPE statement creates a new user-defined type. Each type is a set of named, typed fields. Field types may be any valid type, including collections and other existing user-defined types.

Attempting to create an already existing type will result in an error unless the IF NOT EXISTS option is used. If it is used, the statement will be a no-op if the type already exists.

Syntax:

bc(syntax)..
::= ALTER TYPE

::= ADD
| RENAME TO ( AND TO )*
p.
Sample:

bc(sample)..
ALTER TYPE address ADD country text

ALTER TYPE address RENAME zip TO zipcode AND street_name TO street
p.
The ALTER TYPE statement is used to manipulate type definitions. It allows for adding new fields, renaming existing fields, or changing the type of existing fields.

Syntax:

bc(syntax)..
::= DROP TYPE ( IF EXISTS )?
p.
The DROP TYPE statement results in the immediate, irreversible removal of a type. Attempting to drop a type that is still in use by another type or a table will result in an error.

If the type does not exist, an error will be returned unless IF EXISTS is used, in which case the operation is a no-op.

Syntax:

bc(syntax)..
::= CREATE TRIGGER ( IF NOT EXISTS )? ( )?
ON
USING

Sample:

bc(sample).
CREATE TRIGGER myTrigger ON myTable USING `org.apache.cassandra.triggers.InvertedIndex';

The actual logic that makes up the trigger can be written in any Java (JVM) language and exists outside the database. You place the trigger code in a lib/triggers subdirectory of the Cassandra installation directory, it loads during cluster startup, and exists on every node that participates in a cluster. The trigger defined on a table fires before a requested DML statement occurs, which ensures the atomicity of the transaction.

Syntax:

bc(syntax)..
::= DROP TRIGGER ( IF EXISTS )? ( )?
ON
p.
Sample:

bc(sample).
DROP TRIGGER myTrigger ON myTable;

DROP TRIGGER statement removes the registration of a trigger created using CREATE TRIGGER.

Syntax:

bc(syntax)..
::= CREATE ( OR REPLACE )?
FUNCTION ( IF NOT EXISTS )?
( `.' )?
`(' ( `,' )* `)'
( CALLED | RETURNS NULL ) ON NULL INPUT
RETURNS
LANGUAGE
AS

Sample:

bc(sample).
CREATE OR REPLACE FUNCTION somefunction
( somearg int, anotherarg text, complexarg frozen, listarg list )
RETURNS NULL ON NULL INPUT
RETURNS text
LANGUAGE java
AS + ;
CREATE FUNCTION akeyspace.fname IF NOT EXISTS
( someArg int )
CALLED ON NULL INPUT
RETURNS text
LANGUAGE java
AS + ;

CREATE FUNCTION creates or replaces a user-defined function.

Syntax:

bc(syntax)..
::= DROP FUNCTION ( IF EXISTS )?
( `.' )?
( `(' ( `,' )* `)' )?

Sample:

bc(sample).
DROP FUNCTION myfunction;
DROP FUNCTION mykeyspace.afunction;
DROP FUNCTION afunction ( int );
DROP FUNCTION afunction ( text );

DROP FUNCTION statement removes a function created using CREATE FUNCTION.
You must specify the argument types (signature ) of the function to drop if there are multiple functions with the same name but a different signature (overloaded functions).

DROP FUNCTION with the optional IF EXISTS keywords drops a function if it exists.

Syntax:

bc(syntax)..
::= CREATE ( OR REPLACE )?
AGGREGATE ( IF NOT EXISTS )?
( `.' )?
`(' ( `,' )* `)'
SFUNC
STYPE
( FINALFUNC )?
( INITCOND )?
p.
Sample:

bc(sample).
CREATE AGGREGATE myaggregate ( val text )
SFUNC myaggregate_state
STYPE text
FINALFUNC myaggregate_final
INITCOND `foo';

See the section on user-defined aggregates for a complete example.

CREATE AGGREGATE creates or replaces a user-defined aggregate.

CREATE AGGREGATE with the optional OR REPLACE keywords either creates 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.

CREATE AGGREGATE with the optional IF NOT EXISTS keywords either creates an aggregate if it does not already exist.

OR REPLACE and IF NOT EXIST cannot be used together.

Aggregates belong to a keyspace. If no keyspace is specified in <aggregate-name>, the current keyspace is used (i.e. the keyspace specified using the USE statement). It is not possible to create a user-defined aggregate in one of the system keyspaces.

Signatures for user-defined aggregates follow the same rules as for user-defined functions.

STYPE defines the type of the state value and must be specified.

The optional INITCOND defines the initial state value for the aggregate. It defaults to null. A non-null INITCOND must be specified for state functions that are declared with RETURNS NULL ON NULL INPUT.

SFUNC references an existing function to be used as the state modifying function. The type of first argument of the state function must match STYPE. The remaining argument types of the state function must match the argument types of the aggregate function. State is not updated for state functions declared with RETURNS NULL ON NULL INPUT and called with null.

The optional FINALFUNC is called just before the aggregate result is returned. It must take only one argument with type STYPE. 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.

See the section on user-defined aggregates for more information.

Syntax:

bc(syntax)..
::= DROP AGGREGATE ( IF EXISTS )?
( `.' )?
( `(' ( `,' )* `)' )?
p.

Sample:

bc(sample).
DROP AGGREGATE myAggregate;
DROP AGGREGATE myKeyspace.anAggregate;
DROP AGGREGATE someAggregate ( int );
DROP AGGREGATE someAggregate ( text );

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 aggregates with the same name but a different signature (overloaded aggregates).

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

Signatures for user-defined aggregates follow the same rules as for user-defined functions.

Syntax:

bc(syntax)..
::= INSERT INTO
( ( VALUES )
| ( JSON ))
( IF NOT EXISTS )?
( USING ( AND )* )?

::= `(' ( `,' )* `)'

::= `(' ( `,' )* `)'

::= TIMESTAMP
| TTL
p.
Sample:

bc(sample)..
INSERT INTO NerdMovies (movie, director, main_actor, year)
VALUES (`Serenity', `Joss Whedon', `Nathan Fillion', 2005)
USING TTL 86400;

INSERT INTO NerdMovies JSON \{movie'': Serenity'', director'': Joss Whedon'', ``year'': 2005}'
p.
The `INSERT statement writes one or more columns for a given row in a table. Note that since a row is identified by its PRIMARY KEY, at least the columns composing it must be specified. The list of columns to insert to must be supplied when using the VALUES syntax. When using the JSON syntax, they are optional. See the section on INSERT JSON for more details.

Note that unlike in SQL, INSERT does not check the prior existence of the row by default: the row is created if none existed before, and updated otherwise. Furthermore, there is no mean to know which of creation or update happened.

It is however possible to use the IF NOT EXISTS condition to only insert if the row does not exist prior to the insertion. But please note that using IF NOT EXISTS will incur a non negligible performance cost (internally, Paxos will be used) so this should be used sparingly.

All updates for an INSERT are applied atomically and in isolation.

Please refer to the UPDATE section for information on the <option> available and to the collections section for use of <collection-literal>. Also note that INSERT does not support counters, while UPDATE does.

Syntax:

bc(syntax)..
::= UPDATE
( USING ( AND )* )?
SET ( `,' )*
WHERE
( IF ( AND condition )* )?

::= ='
| `=' (+' | `-') ( | | )
| `=' `+'
| `[' `]' `='
| `.' `='

::=
| IN
| `[' `]'
| `[' `]' IN
| `.'
| `.' IN

::= `<' | `⇐' | `=' | `!=' | `>=' | `>'
::= ( | `(' ( ( `,' )* )? `)')

::= ( AND )*

::= ='
| `(' (,' )* )' `='
| IN `(' ( ( `,' )* )? `)'
| IN
| `(' (,' )* )' IN `(' ( ( `,' )* )? `)'
| `(' (,' )* `)' IN

::= TIMESTAMP
| TTL
p.
Sample:

bc(sample)..
UPDATE NerdMovies USING TTL 400
SET director = `Joss Whedon',
main_actor = `Nathan Fillion',
year = 2005
WHERE movie = `Serenity';

UPDATE UserActions SET total = total + 2 WHERE user = B70DE1D0-9908-4AE3-BE34-5573E5B09F14 AND action = click';
p.
The `UPDATE statement writes one or more columns for a given row in a table. The <where-clause> is used to select the row to update and must include all columns composing the PRIMARY KEY. Other columns values are specified through <assignment> after the SET keyword.

Note that unlike in SQL, UPDATE does not check the prior existence of the row by default (except through the use of <condition>, see below): the row is created if none existed before, and updated otherwise. Furthermore, there are no means to know whether a creation or update occurred.

It is however possible to use the conditions on some columns through IF, in which case the row will not be updated unless the conditions are met. But, please note that using IF conditions will incur a non-negligible performance cost (internally, Paxos will be used) so this should be used sparingly.

In an UPDATE statement, all updates within the same partition key are applied atomically and in isolation.

The c = c + 3 form of <assignment> is used to increment/decrement counters. The identifier after the `=' sign must be the same than the one before the `=' sign (Only increment/decrement is supported on counters, not the assignment of a specific value).

The id = id + <collection-literal> and id[value1] = value2 forms of <assignment> are for collections. Please refer to the relevant section for more details.

The id.field = <term> form of <assignemt> is for setting the value of a single field on a non-frozen user-defined types.

Syntax:

bc(syntax)..
::= DELETE ( ( `,' )* )?
FROM
( USING TIMESTAMP )?
WHERE
( IF ( EXISTS | ( ( AND )*) ) )?

::=
| `[' `]'
| `.'

::= ( AND )*

::=
| (' (,' )* )'
| IN `(' ( ( `,' )* )? `)'
| IN
| `(' (,' )* )' IN `(' ( ( `,' )* )? `)'
| `(' (,' )* `)' IN

::= `=' | `<' | `>' | `⇐' | `>='
::= ( | `(' ( ( `,' )* )? `)')

::= ( | `!=')
| IN
| `[' `]' ( | `!=')
| `[' `]' IN
| `.' ( | `!=')
| `.' IN

Sample:

bc(sample)..
DELETE FROM NerdMovies USING TIMESTAMP 1240003134 WHERE movie = `Serenity';

DELETE phone FROM Users WHERE userid IN (C73DE1D3-AF08-40F3-B124-3FF3E5109F22, B70DE1D0-9908-4AE3-BE34-5573E5B09F14);
p.
The DELETE statement deletes columns and rows. If column names are provided directly after the DELETE keyword, only those columns are deleted from the row indicated by the <where-clause>. The id[value] syntax in <selection> is for non-frozen collections (please refer to the collection section for more details). The id.field syntax is for the deletion of non-frozen user-defined types. Otherwise, whole rows are removed. The <where-clause> specifies which rows are to be deleted. Multiple rows may be deleted with one statement by using an IN clause. A range of rows may be deleted using an inequality operator (such as >=).

DELETE supports the TIMESTAMP option with the same semantics as the UPDATE statement.

In a DELETE statement, all deletions within the same partition key are applied atomically and in isolation.

A DELETE operation can be conditional through the use of an IF clause, similar to UPDATE and INSERT statements. However, as with INSERT and UPDATE statements, this will incur a non-negligible performance cost (internally, Paxos will be used) and so should be used sparingly.

Syntax:

bc(syntax)..
::= BEGIN ( UNLOGGED | COUNTER ) BATCH
( USING ( AND )* )?
( `;' )*
APPLY BATCH

::=
|
|

::= TIMESTAMP
p.
Sample:

bc(sample).
BEGIN BATCH
INSERT INTO users (userid, password, name) VALUES (`user2', `ch@ngem3b', `second user');
UPDATE users SET password = `ps22dhds' WHERE userid = `user3';
INSERT INTO users (userid, password) VALUES (`user4', `ch@ngem3c');
DELETE name FROM users WHERE userid = `user1';
APPLY BATCH;

The BATCH statement group multiple modification statements (insertions/updates and deletions) into a single statement. It serves several purposes:

Note that:

Syntax:

bc(syntax)..
::= SELECT ( JSON )?
FROM
( WHERE )?
( GROUP BY )?
( ORDER BY )?
( PER PARTITION LIMIT )?
( LIMIT )?
( ALLOW FILTERING )?

::= DISTINCT?

::= (AS )? ( `,' (AS )? )*
| `*'

::=
|
| WRITETIME (' `)'
| COUNT `(' `' `)'
| TTL `(' `)'
| CAST `(' AS `)'
| `(' ( (,' ))? `)'
| `.'
| `[' `]'
| `[' ? .. ? `]'

::= ( AND )*

::=
| (' (,' )* )'
| IN `(' ( ( `,' )* )? `)'
| `(' (,' )* `)' IN `(' ( ( `,' )* )? `)'
| TOKEN `(' ( `,' )* `)'

::= =' | `<' | `>' | `⇐' | `>=' | CONTAINS | CONTAINS KEY
::= (,' )*
::= ( ,' )*
::= ( ASC | DESC )?
::= `(' (,' )* `)'
p.
Sample:

bc(sample)..
SELECT name, occupation FROM users WHERE userid IN (199, 200, 207);

SELECT JSON name, occupation FROM users WHERE userid = 199;

SELECT name AS user_name, occupation AS user_occupation FROM users;

SELECT time, value
FROM events
WHERE event_type = `myEvent'
AND time > `2011-02-03'
AND time ⇐ `2012-01-01'

SELECT COUNT (*) FROM users;

SELECT COUNT (*) AS user_count FROM users;

The SELECT statements reads one or more columns for one or more rows in a table. It returns a result-set of rows, where each row contains the collection of columns corresponding to the query. If the JSON keyword is used, the results for each row will contain only a single column named `json''. See the section on `SELECT JSON for more details.

Syntax:

bc(syntax)..
::= CREATE ROLE ( IF NOT EXISTS )? ( WITH ( AND )* )?

::= PASSWORD =
| LOGIN =
| SUPERUSER =
| OPTIONS =
p.

Sample:

bc(sample).
CREATE ROLE new_role;
CREATE ROLE alice WITH PASSWORD = `password_a' AND LOGIN = true;
CREATE ROLE bob WITH PASSWORD = `password_b' AND LOGIN = true AND SUPERUSER = true;
CREATE ROLE carlos WITH OPTIONS = \{ `custom_option1' : `option1_value', `custom_option2' : 99 };

By default roles do not possess LOGIN privileges or SUPERUSER status.

Permissions on database resources are granted to roles; types of resources include keyspaces, tables, functions and roles themselves. Roles may be granted to other roles to create hierarchical permissions structures; in these hierarchies, permissions and SUPERUSER status are inherited, but the LOGIN privilege is not.

If a role has the LOGIN privilege, clients may identify as that role when connecting. For the duration of that connection, the client will acquire any roles and privileges granted to that role.

Only a client with with the CREATE permission on the database roles resource may issue CREATE ROLE requests (see the relevant section below), unless the client is a SUPERUSER. Role management in Cassandra is pluggable and custom implementations may support only a subset of the listed options.

Role names should be quoted if they contain non-alphanumeric characters.

Syntax:

bc(syntax)..
::= ALTER ROLE ( WITH ( AND )* )?

::= PASSWORD =
| LOGIN =
| SUPERUSER =
| OPTIONS =
p.

Sample:

bc(sample).
ALTER ROLE bob WITH PASSWORD = `PASSWORD_B' AND SUPERUSER = false;

Conditions on executing ALTER ROLE statements:

Syntax:

bc(syntax)..
::= DROP ROLE ( IF EXISTS )?
p.

Sample:

bc(sample).
DROP ROLE alice;
DROP ROLE IF EXISTS bob;

DROP ROLE requires the client to have DROP permission on the role in question. In addition, client may not DROP the role with which it identified at login. Finaly, only a client with SUPERUSER status may DROP another SUPERUSER role.
Attempting to drop a role which does not exist results in an invalid query condition unless the IF EXISTS option is used. If the option is used and the role does not exist the statement is a no-op.

Syntax:

bc(syntax).
::= GRANT TO

Sample:

bc(sample).
GRANT report_writer TO alice;

This statement grants the report_writer role to alice. Any permissions granted to report_writer are also acquired by alice.
Roles are modelled as a directed acyclic graph, so circular grants are not permitted. The following examples result in error conditions:

bc(sample).
GRANT role_a TO role_b;
GRANT role_b TO role_a;

bc(sample).
GRANT role_a TO role_b;
GRANT role_b TO role_c;
GRANT role_c TO role_a;

Syntax:

bc(syntax).
::= REVOKE FROM

Sample:

bc(sample).
REVOKE report_writer FROM alice;

This statement revokes the report_writer role from alice. Any permissions that alice has acquired via the report_writer role are also revoked.

Prior to the introduction of roles in Cassandra 2.2, authentication and authorization were based around the concept of a USER. For backward compatibility, the legacy syntax has been preserved with USER centric statments becoming synonyms for the ROLE based equivalents.

Syntax:

bc(syntax)..
::= CREATE USER ( IF NOT EXISTS )? ( WITH PASSWORD )? ()?

::= SUPERUSER
| NOSUPERUSER
p.

Sample:

bc(sample).
CREATE USER alice WITH PASSWORD `password_a' SUPERUSER;
CREATE USER bob WITH PASSWORD `password_b' NOSUPERUSER;

CREATE USER is equivalent to CREATE ROLE where the LOGIN option is true. So, the following pairs of statements are equivalent:

bc(sample)..
CREATE USER alice WITH PASSWORD `password_a' SUPERUSER;
CREATE ROLE alice WITH PASSWORD = `password_a' AND LOGIN = true AND SUPERUSER = true;

CREATE USER IF NOT EXISTS alice WITH PASSWORD `password_a' SUPERUSER;
CREATE ROLE IF NOT EXISTS alice WITH PASSWORD = `password_a' AND LOGIN = true AND SUPERUSER = true;

CREATE USER alice WITH PASSWORD `password_a' NOSUPERUSER;
CREATE ROLE alice WITH PASSWORD = `password_a' AND LOGIN = true AND SUPERUSER = false;

CREATE USER alice WITH PASSWORD `password_a' NOSUPERUSER;
CREATE ROLE alice WITH PASSWORD = `password_a' AND LOGIN = true;

CREATE USER alice WITH PASSWORD `password_a';
CREATE ROLE alice WITH PASSWORD = `password_a' AND LOGIN = true;
p.

Syntax:

bc(syntax)..
::= ALTER USER ( WITH PASSWORD )? ( )?

::= SUPERUSER
| NOSUPERUSER
p.

bc(sample).
ALTER USER alice WITH PASSWORD `PASSWORD_A';
ALTER USER bob SUPERUSER;

Syntax:

bc(syntax)..
::= DROP USER ( IF EXISTS )?
p.

Sample:

bc(sample).
DROP USER alice;
DROP USER IF EXISTS bob;

Syntax:

bc(syntax).
::= LIST USERS;

Sample:

bc(sample).
LIST USERS;

This statement is equivalent to

bc(sample).
LIST ROLES;

but only roles with the LOGIN privilege are included in the output.

Permissions on resources are granted to roles; there are several different types of resources in Cassandra and each type is modelled hierarchically:

Permissions can be granted at any level of these hierarchies and they flow downwards. So granting a permission on a resource higher up the chain automatically grants that same permission on all resources lower down. For example, granting SELECT on a KEYSPACE automatically grants it on all TABLES in that KEYSPACE. Likewise, granting a permission on ALL FUNCTIONS grants it on every defined function, regardless of which keyspace it is scoped in. It is also possible to grant permissions on all functions scoped to a particular keyspace.

Modifications to permissions are visible to existing client sessions; that is, connections need not be re-established following permissions changes.

The full set of available permissions is:

Not all permissions are applicable to every type of resource. For instance, EXECUTE is only relevant in the context of functions or mbeans; granting EXECUTE on a resource representing a table is nonsensical. Attempting to GRANT a permission on resource to which it cannot be applied results in an error response. The following illustrates which permissions can be granted on which types of resource, and which statements are enabled by that permission.

Syntax:

bc(syntax)..
::= GRANT ( ALL ( PERMISSIONS )? | ( PERMISSION )? ) ON TO

::= CREATE | ALTER | DROP | SELECT | MODIFY | AUTHORIZE | DESRIBE | EXECUTE

::= ALL KEYSPACES
| KEYSPACE
| ( TABLE )?
| ALL ROLES
| ROLE
| ALL FUNCTIONS ( IN KEYSPACE )?
| FUNCTION
| ALL MBEANS
| ( MBEAN | MBEANS )
p.

Sample:

bc(sample).
GRANT SELECT ON ALL KEYSPACES TO data_reader;

This gives any user with the role data_reader permission to execute SELECT statements on any table across all keyspaces

bc(sample).
GRANT MODIFY ON KEYSPACE keyspace1 TO data_writer;

This give any user with the role data_writer permission to perform UPDATE, INSERT, UPDATE, DELETE and TRUNCATE queries on all tables in the keyspace1 keyspace

bc(sample).
GRANT DROP ON keyspace1.table1 TO schema_owner;

This gives any user with the schema_owner role permissions to DROP keyspace1.table1.

bc(sample).
GRANT EXECUTE ON FUNCTION keyspace1.user_function( int ) TO report_writer;

This grants any user with the report_writer role permission to execute SELECT, INSERT and UPDATE queries which use the function keyspace1.user_function( int )

bc(sample).
GRANT DESCRIBE ON ALL ROLES TO role_admin;

This grants any user with the role_admin role permission to view any and all roles in the system with a LIST ROLES statement

Syntax:

bc(syntax)..
::= REVOKE ( ALL ( PERMISSIONS )? | ( PERMISSION )? ) ON FROM

::= CREATE | ALTER | DROP | SELECT | MODIFY | AUTHORIZE | DESRIBE | EXECUTE

::= ALL KEYSPACES
| KEYSPACE
| ( TABLE )?
| ALL ROLES
| ROLE
| ALL FUNCTIONS ( IN KEYSPACE )?
| FUNCTION
| ALL MBEANS
| ( MBEAN | MBEANS )
p.

Sample:

bc(sample)..
REVOKE SELECT ON ALL KEYSPACES FROM data_reader;
REVOKE MODIFY ON KEYSPACE keyspace1 FROM data_writer;
REVOKE DROP ON keyspace1.table1 FROM schema_owner;
REVOKE EXECUTE ON FUNCTION keyspace1.user_function( int ) FROM report_writer;
REVOKE DESCRIBE ON ALL ROLES FROM role_admin;
p.

Values of the timestamp type are encoded as 64-bit signed integers representing a number of milliseconds since the standard base time known as ``the epoch'': January 1 1970 at 00:00:00 GMT.

Timestamp can be input in CQL as simple long integers, giving the number of milliseconds since the epoch, as defined above.

They can also be input as string literals in any of the following ISO 8601 formats, each representing the time and date Mar 2, 2011, at 04:05:00 AM, GMT.:

The +0000 above is an RFC 822 4-digit time zone specification; +0000 refers to GMT. US Pacific Standard Time is -0800. The time zone may be omitted if desired— the date will be interpreted as being in the time zone under which the coordinating Cassandra node is configured.

There are clear difficulties inherent in relying on the time zone configuration being as expected, though, so it is recommended that the time zone always be specified for timestamps when feasible.

The time of day may also be omitted, if the date is the only piece that matters:

In that case, the time of day will default to 00:00:00, in the specified or default time zone.

Values of the date type are encoded as 32-bit unsigned integers representing a number of days with ``the epoch'' at the center of the range (2^31). Epoch is January 1st, 1970

A date can be input in CQL as an unsigned integer as defined above.

They can also be input as string literals in the following format:

Values of the time type are encoded as 64-bit signed integers representing the number of nanoseconds since midnight.

A time can be input in CQL as simple long integers, giving the number of nanoseconds since midnight.

They can also be input as string literals in any of the following formats:

The counter type is used to define counter columns. A counter column is a column whose value is a 64-bit signed integer and on which 2 operations are supported: incrementation and decrementation (see UPDATE for syntax). Note the value of a counter cannot be set. A counter doesn’t exist until first incremented/decremented, and the first incrementation/decrementation is made as if the previous value was 0. Deletion of counter columns is supported but have some limitations (see the Cassandra Wiki for more information).

The use of the counter type is limited in the following way:

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.

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

bc(sample).
SELECT avg(cast(count as double)) FROM myTable

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

The type of the arguments of the token depend on the type of the partition key columns. The return type depend on the partitioner in use:

For instance, in a cluster using the default Murmur3Partitioner, if a table is defined by

bc(sample).
CREATE TABLE users (
userid text PRIMARY KEY,
username text,
…
)

then the token function will take a single argument of type text (in that case, the partition key is userid (there is no clustering columns so the partition key is the same than the primary key)), and the return type will be bigint.

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

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

A number of functions are provided to `convert'' the native types into binary data (`blob). For every <native-type> type supported by CQL3 (a notable exceptions is blob, for obvious reasons), the function typeAsBlob takes a argument of type type and return it as a blob. Conversely, the function blobAsType takes a 64-bit blob argument and convert it to a bigint value. And so for instance, bigintAsBlob(3) is 0x0000000000000003 and blobAsBigint(0x0000000000000003) is 3.

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

bc(sample).
SELECT COUNT (*) FROM plays;
SELECT COUNT (1) FROM plays;

It also can be used to count the non null value of a given column. Example:

bc(sample).
SELECT COUNT (scores) FROM plays;

The max and min functions can be used to compute the maximum and the minimum value returned by a query for a given column.

bc(sample).
SELECT MIN (players), MAX (players) FROM plays WHERE game = `quake';

The sum function can be used to sum up all the values returned by a query for a given column.

bc(sample).
SELECT SUM (players) FROM plays;

The avg function can be used to compute the average of all the values returned by a query for a given column.

bc(sample).
SELECT AVG (players) FROM plays;

With SELECT statements, the new JSON keyword can be used to return each row as a single JSON encoded map. The remainder of the SELECT statment behavior is the same.

The result map keys are the same as the column names in a normal result set. For example, a statement like SELECT JSON a, ttl(b) FROM …​’' would result in a map with keys `"a" and "ttl(b)". However, this is one notable exception: for symmetry with INSERT JSON behavior, case-sensitive column names with upper-case letters will be surrounded with double quotes. For example, SELECT JSON myColumn FROM …​’' would result in a map key `"\"myColumn\"" (note the escaped quotes).

The map values will JSON-encoded representations (as described below) of the result set values.

With INSERT statements, the new JSON keyword can be used to enable inserting a JSON encoded map as a single row. The format of the JSON map should generally match that returned by a SELECT JSON statement on the same table. In particular, case-sensitive column names should be surrounded with double quotes. For example, to insert into a table with two columns named myKey'' and value'', you would do the following:

bc(sample).
INSERT INTO mytable JSON `\{\''myKey\'': 0, ``value'': 0}'

Any columns which are ommitted from the JSON map will be defaulted to a NULL value (which will result in a tombstone being created).

Where possible, Cassandra will represent and accept data types in their native JSON representation. Cassandra will also accept string representations matching the CQL literal format for all single-field types. For example, floats, ints, UUIDs, and dates can be represented by CQL literal strings. However, compound types, such as collections, tuples, and user-defined types must be represented by native JSON collections (maps and lists) or a JSON-encoded string representation of the collection.

The following table describes the encodings that Cassandra will accept in INSERT JSON values (and fromJson() arguments) as well as the format Cassandra will use when returning data for SELECT JSON statements (and fromJson()):

The fromJson() function may be used similarly to INSERT JSON, but for a single column value. It may only be used in the VALUES clause of an INSERT statement or as one of the column values in an UPDATE, DELETE, or SELECT statement. For example, it cannot be used in the selection clause of a SELECT statement.

The toJson() function may be used similarly to SELECT JSON, but for a single column value. It may only be used in the selection clause of a SELECT statement.