Cassandra Query Language (CQL) v3.4.3

CQL Syntax

Preamble

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 as 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.

Conventions

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

Language rules will be given in a BNF -like notation:

::= TERMINAL

Nonterminal symbols will have <angle brackets>.
As additional shortcut notations to BNF, we’ll use traditional regular expression’s symbols (?, + and *) to signify that a given symbol is optional and/or can be repeated. We’ll also allow parentheses to group symbols and the [<characters>] notation to represent any one of <characters>.
The grammar is provided for documentation purposes and leave some minor details out. For instance, the last column definition in a CREATE TABLE statement is optional but supported if present even though the provided grammar in this document suggest it is not supported.
Sample code will be provided in a code block:

SELECT sample_usage FROM cql;

References to keywords or pieces of CQL code in running text will be shown in a fixed-width font.

Identifiers and keywords

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 as select or sElEcT, and myId is the same as 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 allow for declaring 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)").

Constants

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

A string constant is an arbitrary sequence of characters enclosed by single-quote('). One can include a single-quote in a string by repeating it, e.g. 'It''s raining today'. Those are not to be confused with quoted identifiers that use double-quotes.
An integer constant is defined by '-'?[0-9]+.
A float constant is defined by '-'?[0-9]+('.'[0-9]*)?([eE][+-]?[0-9+])?. On top of that, NaN and Infinity are also float constants.
A boolean constant is either true or false up to case-insensitivity (i.e. True is a valid boolean constant).
A UUID constant is defined by hex{8}-hex{4}-hex{4}-hex{4}-hex{12} where hex is a hexadecimal character, e.g. [0-9a-fA-F] and {4} is the number of such characters.
A blob constant is a hexadecimal number defined by 0[xX](hex)+ where hex is a hexadecimal character, e.g. [0-9a-fA-F].

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

Terms

CQL has the notion of a term, which denotes the kind of values that CQL support. Terms are defined by:

term::= constant | literal | function_call | arithmetic_operation | type_hint | bind_marker
literal::= collection_literal | vector_literal | udt_literal | tuple_literal
function_call::= identifier '(' [ term (',' term)* ] ')'
arithmetic_operation::= '-' term | term ('+' | '-' | '*' | '/' | '%') term
type_hint::= '(' cql_type ')' term
bind_marker::= '?' | ':' identifier

A term is thus one of:

A constant
A literal for either a collection, a vector, a user-defined type or a tuple
A function call, either a native function or a user-defined function
An arithmetic operation between terms
A type hint
A bind marker, which denotes a variable to be bound at execution time. See the section on prepared-statements for details. A bind marker can be either anonymous (?) or named (:some_name). The latter form provides a more convenient way to refer to the variable for binding it and should generally be preferred.

Comments

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).

-- This is a comment
// This is a comment too
/* This is
a multi-line comment */

Statements

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

Data definition statements, that allow to set and change the way data is stored.
Data manipulation statements, that allow to change data
Queries, to look up data

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:

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

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 anonymous 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 its 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:

'some string value'
$$A King's ransom$$

double-dollar string can contain single ’ quotes

Prepared Statement

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 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.

Prepared Statement Caching

Prepared Statements are cached by cassandra in-memory using a Caffeine-managed cache which can be configured using prepared_statements_cache_size. The cache is also persisted to the system.prepared_statements table so it can be preloaded into memory on startup.

To ensure optimal performance, it’s important to use a bind <variable> for all non-constant values in your CQL statements. If you include literal values directly in the query instead, each variation will be treated as a unique statement that must be prepared and cached separately. This will soon overflow the prepared statement cache, which is small by design.

When the cache reaches its maximum size, older or less frequently used statements are evicted, leading to additional overhead as previously prepared statements must be re-prepared.

Data Definition

CREATE KEYSPACE

Syntax:

create_keyspace_statement::= CREATE KEYSPACE [ IF NOT EXISTS ] keyspace_name
	WITH options

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;

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:

name kind mandatory default description

name	kind	mandatory	default	description
`replication`	map	yes		The replication strategy and options to use for the keyspace.
`durable_writes`	simple	no	true	Whether to use the commit log for updates on this keyspace (disable this option at your own risk!).

replication

map

yes

The replication strategy and options to use for the keyspace.

durable_writes

simple

true

Whether to use the commit log for updates on this keyspace (disable this option at your own risk!).

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':

'SimpleStrategy': A simple strategy that defines a simple replication factor for the whole cluster. The only sub-options supported is 'replication_factor' to define that replication factor and is mandatory.
'NetworkTopologyStrategy': A replication strategy that allows to set the replication factor independently for each data-center. The rest of the sub-options are key-value pairs where each time the key is the name of a datacenter and the value the replication factor for that data-center.

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.

USE

Syntax:

use_statement::= USE keyspace_name

Sample:

USE excelsior;

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.

ALTER KEYSPACE

Syntax:

alter_keyspace_statement::= ALTER KEYSPACE [ IF EXISTS ] keyspace_name
	WITH options

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.

DROP KEYSPACE

Syntax:

::= DROP KEYSPACE [ IF EXISTS ]

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 exist, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op.

CREATE TABLE

Syntax:

create_table_statement::= CREATE TABLE [ IF NOT EXISTS ] table_name '('
	column_definition  ( ',' column_definition )*
	[ ',' PRIMARY KEY '(' primary_key ')' ]
	 ')' [ WITH table_options ]
column_definition::= column_name cql_type [ STATIC ] [ column_mask ] [ PRIMARY KEY]
column_mask::= MASKED WITH ( DEFAULT | function_name '(' term ( ',' term )* ')' )
primary_key::= partition_key [ ',' clustering_columns ]
partition_key::= column_name  | '(' column_name ( ',' column_name )* ')'
clustering_columns::= column_name ( ',' column_name )*
table_options::= COMPACT STORAGE [ AND table_options ]
	| CLUSTERING ORDER BY '(' clustering_order ')'
	[ AND table_options ]  | options
clustering_order::= column_name (ASC | DESC) ( ',' column_name (ASC | DESC) )*

Sample:

CREATE TABLE monkey_species (
    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' };

CREATE TABLE loads (
    machine inet,
    cpu int,
    mtime timeuuid,
    load float,
    PRIMARY KEY ((machine, cpu), mtime)
) WITH CLUSTERING ORDER BY (mtime DESC);

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.

`<tablename>`

Valid table names are the same as valid keyspace names (up to 32 characters long alphanumerical identifiers). If the table name is provided alone, the table is created within the current keyspace (see USE), but if it is prefixed by an existing keyspace name (see <tablename> grammar), it is created in the specified keyspace (but does not change the current keyspace).

`<column-definition>`

A CREATE TABLE statement defines the columns that rows of the table can have. A column is defined by its name (an identifier) and its type (see the data types section for more details on allowed types and their properties).

Within a table, a row is uniquely identified by its PRIMARY KEY (or more simply the key), and hence all table definitions must define a PRIMARY KEY (and only one). A PRIMARY KEY is composed of one or more of the columns defined in the table. If the PRIMARY KEY is only one column, this can be specified directly after the column definition. Otherwise, it must be specified by following PRIMARY KEY by the comma-separated list of column names composing the key within parenthesis. Note that:

CREATE TABLE t (
k int PRIMARY KEY,
other text
)

is equivalent to

CREATE TABLE t (
k int,
other text,
PRIMARY KEY (k)
)

Partition key and clustering columns

In CQL, the order in which columns are defined for the PRIMARY KEY matters. The first column of the key is called the partition key. It has the property that all the rows sharing the same partition key (even across table in fact) are stored on the same physical node. Also, insertion/update/deletion on rows sharing the same partition key for a given table are performed atomically and in isolation. Note that it is possible to have a composite partition key, i.e. a partition key formed of multiple columns, using an extra set of parentheses to define which columns forms the partition key.

The remaining columns of the PRIMARY KEY definition, if any, are called __clustering columns. On a given physical node, rows for a given partition key are stored in the order induced by the clustering columns, making the retrieval of rows in that clustering order particularly efficient (see SELECT).

`STATIC` columns

Some columns can be declared as STATIC in a table definition. A column that is static will be ``shared'' by all the rows belonging to the same partition (having the same partition key). For instance, in:

CREATE TABLE t (
    pk int,
    t int,
    v text,
    s text static,
    PRIMARY KEY (pk, t)
);

INSERT INTO t (pk, t, v, s) VALUES (0, 0, 'val0', 'static0');
INSERT INTO t (pk, t, v, s) VALUES (0, 1, 'val1', 'static1');
SELECT * FROM t;

the last query will return 'static1' as value for s, since s is static and thus the 2nd insertion modified this `shared'' value. Note however that static columns are only static within a given partition, and if in the example above both rows where from different partitions (i.e. if they had different value for `pk), then the 2nd insertion would not have modified the value of s for the first row.

A few restrictions applies to when static columns are allowed:

tables with the COMPACT STORAGE option (see below) cannot have them
a table without clustering columns cannot have static columns (in a table without clustering columns, every partition has only one row, and so every column is inherently static).
only non PRIMARY KEY columns can be static

`<option>`

The CREATE TABLE statement supports a number of options that controls the configuration of a new table. These options can be specified after the WITH keyword.

The first of these option is COMPACT STORAGE. This option is mainly targeted towards backward compatibility for definitions created before CQL3 (see www.datastax.com/dev/blog/thrift-to-cql3 for more details). The option also provides a slightly more compact layout of data on disk but at the price of diminished flexibility and extensibility for the table. Most notably, COMPACT STORAGE tables cannot have collections nor static columns and a COMPACT STORAGE table with at least one clustering column supports exactly one (as in not 0 nor more than 1) column not part of the PRIMARY KEY definition (which imply in particular that you cannot add nor remove columns after creation). For those reasons, COMPACT STORAGE is not recommended outside of the backward compatibility reason evoked above.

Another option is CLUSTERING ORDER. It allows to define the ordering of rows on disk. It takes the list of the clustering column names with, for each of them, the on-disk order (Ascending or descending). Note that this option affects what ORDER BY are allowed during SELECT.

Table creation supports the following other <property>:

option kind default description

option	kind	default	description
`comment`	simple	none	A free-form, human-readable comment.
`gc_grace_seconds`	simple	864000	Time to wait before garbage collecting tombstones (deletion markers).
`bloom_filter_fp_chance`	simple	0.00075	The target probability of false positive of the sstable bloom filters. Said bloom filters will be sized to provide the provided probability (thus lowering this value impact the size of bloom filters in-memory and on-disk)
`default_time_to_live`	simple	0	The default expiration time (``TTL'') in seconds for a table.
`compaction`	map	see below	Compaction options, see below.
`compression`	map	see below	Compression options, see below.
`caching`	map	see below	Caching options, see below.
`crc_check_chance`	simple	1.0	This option defines the probability with which checksums should be checked during reads to detect bit rot and prevent the propagation of corruption to other replicas. The default value is 1 to apply a checksum every time a data chunk is read. Set to 0 to disable checksum checking and to 0.5 for instance to check every other read. Due to technical limitations we only currently apply this for compressed files. If compression is not enabled on the table, no checksums will be verified.

comment

simple

none

A free-form, human-readable comment.

gc_grace_seconds

simple

864000

Time to wait before garbage collecting tombstones (deletion markers).

bloom_filter_fp_chance

simple

0.00075

The target probability of false positive of the sstable bloom filters. Said bloom filters will be sized to provide the provided probability (thus lowering this value impact the size of bloom filters in-memory and on-disk)

default_time_to_live

simple

The default expiration time (``TTL'') in seconds for a table.

compaction

map

see below

Compaction options, see below.

compression

map

see below

Compression options, see below.

caching

map

see below

Caching options, see below.

crc_check_chance

simple

1.0

This option defines the probability with which checksums should be checked during reads to detect bit rot and prevent the propagation of corruption to other replicas. The default value is 1 to apply a checksum every time a data chunk is read. Set to 0 to disable checksum checking and to 0.5 for instance to check every other read.

Due to technical limitations we only currently apply this for compressed files. If compression is not enabled on the table, no checksums will be verified.

Compaction options

The compaction property must at least define the 'class' sub-option, that defines the compaction strategy class to use. The default supported class are 'SizeTieredCompactionStrategy', 'LeveledCompactionStrategy' and 'TimeWindowCompactionStrategy'. Custom strategy can be provided by specifying the full class name as a string constant. The rest of the sub-options depends on the chosen class. The sub-options supported by the default classes are:

option supported compaction strategy default description

option	supported compaction strategy	default	description
`enabled`	all	true	A boolean denoting whether compaction should be enabled or not.
`tombstone_threshold`	all	0.2	A ratio such that if a sstable has more than this ratio of GC eligable tombstones over all contained columns, the sstable will be compacted (with no other sstables) for the purpose of purging those tombstones.
`tombstone_compaction_interval`	all	1 day	The minimum time to wait after an sstable creation time before considering it for `tombstone compaction'', where`tombstone compaction'' is the compaction triggered if the sstable has more GC eligible tombstones than `tombstone_threshold`.
`unchecked_tombstone_compaction`	all	false	Setting this to true enables more aggressive tombstone compactions - single sstable tombstone compactions will run without checking how likely it is that they will be successful.
`min_sstable_size`	SizeTieredCompactionStrategy	50MB	The size tiered strategy groups SSTables to compact in buckets. A bucket groups SSTables that differs from less than 50% in size. However, for small sizes, this would result in a bucketing that is too fine-grained. `min_sstable_size` defines a size threshold (in bytes) below which all SSTables belong to one unique bucket
`min_threshold`	SizeTieredCompactionStrategy	4	Minimum number of SSTables needed to start a minor compaction.
`max_threshold`	SizeTieredCompactionStrategy	32	Maximum number of SSTables processed by one minor compaction.
`bucket_low`	SizeTieredCompactionStrategy	0.5	Size tiered consider sstables to be within the same bucket if their size is within [average_size * `bucket_low`, average_size * `bucket_high` ] (i.e the default groups sstable whose sizes diverges by at most 50%)
`bucket_high`	SizeTieredCompactionStrategy	1.5	Size tiered consider sstables to be within the same bucket if their size is within [average_size * `bucket_low`, average_size * `bucket_high` ] (i.e the default groups sstable whose sizes diverges by at most 50%).
`sstable_size_in_mb`	LeveledCompactionStrategy	5MB	The target size (in MB) for sstables in the leveled strategy. Note that while sstable sizes should stay less or equal to `sstable_size_in_mb`, it is possible to exceptionally have a larger sstable as during compaction, data for a given partition key are never split into 2 sstables
`timestamp_resolution`	TimeWindowCompactionStrategy	MICROSECONDS	The timestamp resolution used when inserting data, could be MILLISECONDS, MICROSECONDS etc. (should be understandable by Java TimeUnit) - don’t change this unless you do mutations with USING TIMESTAMP (or equivalent directly in the client)
`compaction_window_unit`	TimeWindowCompactionStrategy	DAYS	The Java TimeUnit used for the window size, set in conjunction with `compaction_window_size`. Must be one of DAYS, HOURS, MINUTES
`compaction_window_size`	TimeWindowCompactionStrategy	1	The number of `compaction_window_unit` units that make up a time window.
`unsafe_aggressive_sstable_expiration`	TimeWindowCompactionStrategy	false	Expired sstables will be dropped without checking its data is shadowing other sstables. This is a potentially risky option that can lead to data loss or deleted data re-appearing, going beyond what `unchecked_tombstone_compaction` does for single sstable compaction. Due to the risk the jvm must also be started with `-Dcassandra.unsafe_aggressive_sstable_expiration=true`.

enabled

all

true

A boolean denoting whether compaction should be enabled or not.

tombstone_threshold

all

0.2

A ratio such that if a sstable has more than this ratio of GC eligable tombstones over all contained columns, the sstable will be compacted (with no other sstables) for the purpose of purging those tombstones.

tombstone_compaction_interval

all

1 day

The minimum time to wait after an sstable creation time before considering it for tombstone compaction'', wheretombstone compaction'' is the compaction triggered if the sstable has more GC eligible tombstones than tombstone_threshold.

unchecked_tombstone_compaction

all

false

Setting this to true enables more aggressive tombstone compactions - single sstable tombstone compactions will run without checking how likely it is that they will be successful.

min_sstable_size

SizeTieredCompactionStrategy

50MB

The size tiered strategy groups SSTables to compact in buckets. A bucket groups SSTables that differs from less than 50% in size. However, for small sizes, this would result in a bucketing that is too fine-grained. min_sstable_size defines a size threshold (in bytes) below which all SSTables belong to one unique bucket

min_threshold

SizeTieredCompactionStrategy

Minimum number of SSTables needed to start a minor compaction.

max_threshold

SizeTieredCompactionStrategy

Maximum number of SSTables processed by one minor compaction.

bucket_low

SizeTieredCompactionStrategy

0.5

Size tiered consider sstables to be within the same bucket if their size is within [average_size * bucket_low, average_size * bucket_high ] (i.e the default groups sstable whose sizes diverges by at most 50%)

bucket_high

SizeTieredCompactionStrategy

1.5

sstable_size_in_mb

LeveledCompactionStrategy

5MB

The target size (in MB) for sstables in the leveled strategy. Note that while sstable sizes should stay less or equal to sstable_size_in_mb, it is possible to exceptionally have a larger sstable as during compaction, data for a given partition key are never split into 2 sstables

timestamp_resolution

TimeWindowCompactionStrategy

MICROSECONDS

The timestamp resolution used when inserting data, could be MILLISECONDS, MICROSECONDS etc. (should be understandable by Java TimeUnit) - don’t change this unless you do mutations with USING TIMESTAMP (or equivalent directly in the client)

compaction_window_unit

TimeWindowCompactionStrategy

DAYS

The Java TimeUnit used for the window size, set in conjunction with compaction_window_size. Must be one of DAYS, HOURS, MINUTES

compaction_window_size

TimeWindowCompactionStrategy

The number of compaction_window_unit units that make up a time window.

unsafe_aggressive_sstable_expiration

TimeWindowCompactionStrategy

false

Expired sstables will be dropped without checking its data is shadowing other sstables. This is a potentially risky option that can lead to data loss or deleted data re-appearing, going beyond what unchecked_tombstone_compaction does for single sstable compaction. Due to the risk the jvm must also be started with -Dcassandra.unsafe_aggressive_sstable_expiration=true.

Compression options

For the compression property, the following sub-options are available:

option default description

option	default	description
`class`	LZ4Compressor	The compression algorithm to use. Default compressor are: LZ4Compressor, SnappyCompressor and DeflateCompressor. Use `'enabled' : false` to disable compression. Custom compressor can be provided by specifying the full class name as a string constant.
`enabled`	true	By default, compression is enabled. To disable it, set `enabled` to `false`	`chunk_length_in_kb`	64KB	On disk SSTables are compressed by block (to allow random reads). This defines the size (in KB) of said block. Bigger values may improve the compression rate, but increases the minimum size of data to be read from disk for a read

class

LZ4Compressor

The compression algorithm to use. Default compressor are: LZ4Compressor, SnappyCompressor and DeflateCompressor. Use 'enabled' : false to disable compression. Custom compressor can be provided by specifying the full class name as a string constant.

enabled

true

By default, compression is enabled. To disable it, set enabled to false

chunk_length_in_kb

64KB

On disk SSTables are compressed by block (to allow random reads). This defines the size (in KB) of said block. Bigger values may improve the compression rate, but increases the minimum size of data to be read from disk for a read

Caching options

For the caching property, the following sub-options are available:

option default description

option	default	description
`keys`	ALL	Whether to cache keys (`key cache'') for this table. Valid values are: `ALL and `NONE`.
`rows_per_partition`	NONE	The amount of rows to cache per partition (`row cache''). If an integer `n is specified, the first `n` queried rows of a partition will be cached. Other possible options are `ALL`, to cache all rows of a queried partition, or `NONE` to disable row caching.

keys

ALL

Whether to cache keys (`key cache'') for this table. Valid values are: `ALL and NONE.

rows_per_partition

NONE

The amount of rows to cache per partition (`row cache''). If an integer `n is specified, the first n queried rows of a partition will be cached. Other possible options are ALL, to cache all rows of a queried partition, or NONE to disable row caching.

Other considerations:

When inserting / updating a given row, not all columns needs to be defined (except for those part of the key), and missing columns occupy no space on disk. Furthermore, adding new columns (see ALTER TABLE) is a constant time operation. There is thus no need to try to anticipate future usage (or to cry when you haven’t) when creating a table.

CREATE TABLE LIKE

Syntax:

create_table_statement::= CREATE TABLE [ IF NOT EXISTS ] new_table_name LIKE old_table_name
	 [ WITH like_options ]
like_options::= INDEXES | options [ AND like_options ]

Sample:

CREATE TABLE ks.newtb1 LIKE ks.oldtb;

CREATE TABLE ks1.newtb1 LIKE ks.oldtb;

USE ks;

CREATE TABLE newtb1 LIKE oldtb;

CREATE TABLE IF NOT EXISTS newtb2 LIKE oldtb;

CREATE TABLE newtb3 LIKE oldtb WITH compaction = { 'class' : 'LeveledCompactionStrategy' }
                               AND compression = { 'class' : 'SnappyCompressor', 'chunk_length_in_kb' : 32 }
                               AND cdc = true;

CREATE TABLE newtb4 LIKE oldtb WITH INDEXES;

CREATE TABLE newtb6 LIKE oldtb WITH INDEXES AND compaction = { 'class' : 'LeveledCompactionStrategy' };

The CREATE TABLE LIKE statement creates a new empty table based on the definition of an already existing table. By default, the new table will have the same schema as the existing table.

The statement allows changing the new table’s options using the WITH keyword. Additionally, the new table can create indexes with the same schema as the existing table’s indexes if the INDEXES keyword is specified. However, only SAI and legacy secondary indexes are supported. TRIGGER and MATERIALIZED VIEW are not supported at this time.

Attempting to create a table that already exists will return an error unless the IF NOT EXISTS option is used. If this option is used, the statement will have no effect if the table already exists.

`<tablename>`

Valid the newly created table names are the same as valid table names (up to 32 characters long alphanumerical identifiers). If the table name is provided alone, the table is created within the current keyspace (see USE), but if it is prefixed by an existing keyspace name (see <tablename> grammar), it is created in the specified keyspace (but does not change the current keyspace).

`<like option>`

The table options specified by CREATE TABLE LIKE statement are the same as the supported supported <option> in the CREATE TABLE statement. And both are set throught keyword WITH.

Indexs will be created together with table if INDEXES keyword is used.

ALTER TABLE

Syntax:

alter_table_statement::= ALTER TABLE [ IF EXISTS ] table_name alter_table_instruction
alter_table_instruction::= ADD [ IF NOT EXISTS ] column_definition ( ',' column_definition)*
	| DROP [ IF EXISTS ] column_name ( ',' column_name )*
	| RENAME [ IF EXISTS ] column_name to column_name (AND column_name to column_name)*
	| ALTER [ IF EXISTS ] column_name ( column_mask | DROP MASKED )
	| WITH options
column_definition::= column_name cql_type [ column_mask]
column_mask::= MASKED WITH ( DEFAULT | function_name '(' term ( ',' term )* ')' )

Sample:

ALTER TABLE addamsFamily ADD gravesite varchar;
ALTER TABLE addamsFamily
   WITH comment = 'A most excellent and useful table';

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. 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.

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

ADD: Adds a new column to the table. The <identifier> for the new column must not conflict with an existing column. Moreover, columns cannot be added to tables defined with the COMPACT STORAGE option. If the new column already exists, the statement will return an error, unless IF NOT EXISTS is used in which case the operation is a no-op.
DROP: Removes a column from the table. Dropped columns will immediately become unavailable in the queries and will not be included in compacted sstables in the future. If a column is read, queries won’t return values written before the column was last dropped. It is assumed that timestamps represent actual time, so if this is not your case, you should NOT read previously dropped columns. Columns can’t be dropped from tables defined with the COMPACT STORAGE option. If the dropped column does not already exist, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op.
RENAME a primary key column of a table. Non-primary key columns cannot be renamed. Furthermore, renaming a column to another name which already exists isn’t allowed. It’s important to keep in mind that renamed columns shouldn’t have dependent seconday indexes. If the renamed column does not already exist, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op.
WITH: Allows to update the options of the table. The supported <option> (and syntax) are the same as for the CREATE TABLE statement except that COMPACT STORAGE is not supported. Note that setting any compaction sub-options has the effect of erasing all previous compaction options, so you need to re-specify all the sub-options if you want to keep them. The same note applies to the set of compression sub-options.

CQL type compatibility:

CQL data types may be converted only as the following table.

Data type may be altered to:	Data type
timestamp	bigint
ascii, bigint, boolean, date, decimal, double, float, inet, int, smallint, text, time, timestamp, timeuuid, tinyint, uuid, varchar, varint	blob
int	date
ascii, varchar	text
bigint	time
bigint	timestamp
timeuuid	uuid
ascii, text	varchar
bigint, int, timestamp	varint

Data type may be altered to:

Data type

timestamp

bigint

ascii, bigint, boolean, date, decimal, double, float, inet, int, smallint, text, time, timestamp, timeuuid, tinyint, uuid, varchar, varint

blob

int

date

ascii, varchar

text

bigint

time

bigint

timestamp

timeuuid

uuid

ascii, text

varchar

bigint, int, timestamp

varint

Clustering columns have stricter requirements, only the below conversions are allowed.

Data type may be altered to:	Data type
ascii, text, varchar	blob
ascii, varchar	text
ascii, text	varchar

Data type may be altered to:

Data type

ascii, text, varchar

blob

ascii, varchar

text

ascii, text

varchar

DROP TABLE

Syntax:

drop_table_statement::= DROP TABLE [ IF EXISTS ] table_name

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.

TRUNCATE

Syntax:

truncate_statement::= TRUNCATE [ TABLE ] table_name

Sample:

TRUNCATE superImportantData;

The TRUNCATE statement permanently removes all data from a table.

CREATE INDEX

The CREATE INDEX statement is used to create a new 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.

Syntax:

create_index_statement::= CREATE [ CUSTOM ] INDEX [ IF NOT EXISTS ] [ index_name ]
	ON table_name '(' index_identifier ')'
	[ USING index_type [ WITH OPTIONS = map_literal ] ]
index_identifier::= column_name
	| ( KEYS | VALUES | ENTRIES | FULL ) '(' column_name ')'
index_type::= 'sai' | 'legacy_local_table' | fully_qualified_class_name

Sample:

CREATE INDEX userIndex ON NerdMovies (user);
CREATE INDEX ON Mutants (abilityId);
CREATE INDEX ON users (KEYS(favs));
CREATE INDEX ON users (age) USING 'sai';
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/'};

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.

Index Types

The USING keyword optionally specifies an index type. There are two built-in types:

legacy_local_table - (default) legacy secondary index, implemented as a hidden local table
sai - "storage-attached" index, implemented via optimized SSTable/Memtable-attached indexes

To create a custom index, a fully qualified class name must be specified.

Indexes on Map Keys

When creating an index on a map column, you may index either the keys or the values. If the column identifier is placed within the keys() function, the index will be on the map keys, allowing you to use CONTAINS KEY in WHERE clauses. Otherwise, the index will be on the map values.

DROP INDEX

Syntax:

drop_index_statement::= DROP INDEX [ IF EXISTS ] index_name

Sample:

DROP INDEX userIndex;
DROP INDEX userkeyspace.address_index;

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 exist, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op.

CREATE MATERIALIZED VIEW

Syntax:

create_materialized_view_statement::= CREATE MATERIALIZED VIEW [ IF NOT EXISTS ] view_name
	AS select_statement
	PRIMARY KEY '(' primary_key')'
	WITH table_options

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';

The CREATE MATERIALIZED VIEW statement creates a new materialized view. Each such view is a set of rows corresponding 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.

`WHERE` Clause

The <where-clause> is similar to the where clause of a SELECT statement, with a few differences. First, the where clause must contain an expression that disallows NULL values in columns in the view’s primary key. If no other restriction is desired, this can be accomplished with an IS NOT NULL expression. Second, only columns which are in the base table’s primary key may be restricted with expressions other than IS NOT NULL. (Note that this second restriction may be lifted in the future.)

ALTER MATERIALIZED VIEW

Syntax:

::= ALTER MATERIALIZED VIEW WITH ( AND )*

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

DROP MATERIALIZED VIEW

Syntax:

drop_materialized_view_statement::= DROP MATERIALIZED VIEW [ IF EXISTS ] view_name;

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 exist, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op.

CREATE TYPE

Syntax:

create_type_statement::= CREATE TYPE [ IF NOT EXISTS ] udt_name
        '(' field_definition ( ',' field_definition)* ')'
field_definition::= identifier cql_type

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
)

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.

`<typename>`

Valid type names are identifiers. The names of existing CQL types and reserved type names may not be used.

If the type name is provided alone, the type is created with the current keyspace (see USE). If it is prefixed by an existing keyspace name, the type is created within the specified keyspace instead of the current keyspace.

ALTER TYPE

Syntax:

::= ALTER TYPE [ IF EXISTS ]

::= ADD [ IF NOT EXISTS ]
| RENAME [ IF EXISTS ] TO ( AND TO )*

Sample:

ALTER TYPE address ADD country text
ALTER TYPE address RENAME zip TO zipcode AND street_name TO street

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

DROP TYPE

Syntax:

::= DROP TYPE [ IF EXISTS ]

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.

CREATE TRIGGER

Syntax:

create_trigger_statement ::= CREATE TRIGGER [ IF NOT EXISTS ] trigger_name
	ON table_name
	USING string

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.

DROP TRIGGER

Syntax:

drop_trigger_statement ::= DROP TRIGGER [ IF EXISTS ] trigger_nameON table_name

Sample:

DROP TRIGGER myTrigger ON myTable;

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

CREATE FUNCTION

Syntax:

create_function_statement::= CREATE [ OR REPLACE ] FUNCTION [ IF NOT EXISTS]
	function_name '(' arguments_declaration ')'
	[ CALLED | RETURNS NULL ] ON NULL INPUT
	RETURNS cql_type
	LANGUAGE identifier
	AS string arguments_declaration: identifier cql_type ( ',' identifier cql_type )*

Sample:

CREATE OR REPLACE FUNCTION somefunction(somearg int, anotherarg text, complexarg frozen<someUDT>, listarg list)
    RETURNS NULL ON NULL INPUT
    RETURNS text
    LANGUAGE java
    AS $$
        // some Java code
    $$;

CREATE FUNCTION IF NOT EXISTS akeyspace.fname(someArg int)
    CALLED ON NULL INPUT
    RETURNS text
    LANGUAGE java
    AS $$
        // some Java code
    $$;

CREATE FUNCTION creates or replaces a user-defined function.

Function Signature

Signatures are used to distinguish individual functions. The signature consists of:

The fully qualified function name - i.e keyspace plus function-name
The concatenated list of all argument types

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

CREATE FUNCTION with the optional OR REPLACE keywords either creates 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.

Behavior on invocation with null values must be defined for each function. There are two options:

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.

If the optional IF NOT EXISTS keywords are used, the function will only be created if another function with the same signature does not exist.

OR REPLACE and IF NOT EXIST cannot be used together.

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

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

DROP FUNCTION

Syntax:

drop_function_statement::= DROP FUNCTION [ IF EXISTS ] function_name [ '(' arguments_signature ')' ]
arguments_signature::= cql_type ( ',' cql_type )*

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.

CREATE AGGREGATE

Syntax:

create_aggregate_statement ::= CREATE [ OR REPLACE ] AGGREGATE [ IF NOT EXISTS ]
                                function_name '(' arguments_signature')'
                                SFUNC function_name
                                STYPE cql_type:
                                [ FINALFUNC function_name]
                                [ INITCOND term ]

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.

DROP AGGREGATE

Syntax:

drop_aggregate_statement::= DROP AGGREGATE [ IF EXISTS ] function_name[ '(' arguments_signature ')'
]

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.

Data Manipulation

INSERT

Syntax:

insert_statement::= INSERT INTO table_name ( names_values | json_clause )
	[ IF NOT EXISTS ]
	[ USING insert_parameter ( AND insert_parameter )* ]
names_values::= names VALUES tuple_literal
json_clause::= JSON string [ DEFAULT ( NULL | UNSET ) ]
names::= '(' column_name ( ',' column_name )* ')'
insert_parameter ::= ( TIMESTAMP | TTL ) ( integer | bind_marker )

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}';

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.

UPDATE

Syntax:

update_statement ::=    UPDATE table_name
                        [ USING update_parameter ( AND update_parameter )* ]
                        SET assignment( ',' assignment )*
                        WHERE where_clause
                        [ IF ( EXISTS | condition ( AND condition)*) ]
update_parameter ::= ( TIMESTAMP | TTL ) ( integer | bind_marker )
assignment: simple_selection'=' term
                `| column_name'=' column_name ( '+' | '-' ) term
                | column_name'=' list_literal'+' column_name
simple_selection ::= column_name
                        | column_name '[' term']'
                        | column_name'.' field_name
condition ::= `simple_selection operator term

Sample:

UPDATE NerdMovies
SET director   = 'Joss Whedon',
WHERE movie = 'Serenity';

UPDATE NerdMovies USING TTL 400
   SET director   = 'Joss Whedon',
       main_actor = 'Nathan Fillion',
       year       = 2005
 WHERE movie = 'Serenity';

UPDATE UserActions USING TIMESTAMP 1735689600
   SET total = total + 2
   WHERE user = B70DE1D0-9908-4AE3-BE34-5573E5B09F14
     AND action = 'click';

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 as 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.

`<options>`

The UPDATE and INSERT statements support the following options:

TIMESTAMP: sets the timestamp for the operation. If not specified, the coordinator will use the current time (in microseconds) at the start of statement execution as the timestamp. This is usually a suitable default.
TTL: specifies an optional Time To Live (in seconds) for the inserted values. If set, the inserted values are automatically removed from the database after the specified time. Note that the TTL concerns the inserted values, not the columns themselves. This means that any subsequent update of the column will also reset the TTL (to whatever TTL is specified in that update). By default, values never expire. A TTL of 0 is equivalent to no TTL. If the table has a default_time_to_live, a TTL of 0 will remove the TTL for the inserted or updated values.

DELETE

Syntax:

delete_statement::= DELETE [ simple_selection ( ',' simple_selection ) ]
	FROM table_name
	[ USING update_parameter ( AND update_parameter# )* ]
	WHERE where_clause
	[ IF ( EXISTS | condition ( AND condition)*) ]

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);

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.

BATCH

Syntax:

batch_statement ::=     BEGIN [ UNLOGGED | COUNTER ] BATCH
                        [ USING update_parameter( AND update_parameter)* ]
                        modification_statement ( ';' modification_statement )*
                        APPLY BATCH
modification_statement ::= insert_statement | update_statement | delete_statement

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:

It saves network round-trips between the client and the server (and sometimes between the server coordinator and the replicas) when batching multiple updates.
All updates in a BATCH belonging to a given partition key are performed in isolation.
By default, all operations in the batch are performed as LOGGED, to ensure all mutations eventually complete (or none will). See the notes on UNLOGGED for more details.

Note that:

BATCH statements may only contain UPDATE, INSERT and DELETE statements.
Batches are not a full analogue for SQL transactions.
If a timestamp is not specified for each operation, then all operations will be applied with the same timestamp. Due to Cassandra’s conflict resolution procedure in the case of timestamp ties, operations may be applied in an order that is different from the order they are listed in the BATCH statement. To force a particular operation ordering, you must specify per-operation timestamps.

`UNLOGGED`

By default, Cassandra uses a batch log to ensure all operations in a batch eventually complete or none will (note however that operations are only isolated within a single partition).

There is a performance penalty for batch atomicity when a batch spans multiple partitions. If you do not want to incur this penalty, you can tell Cassandra to skip the batchlog with the UNLOGGED option. If the UNLOGGED option is used, a failed batch might leave the patch only partly applied.

`COUNTER`

Use the COUNTER option for batched counter updates. Unlike other updates in Cassandra, counter updates are not idempotent.

`<option>`

BATCH supports both the TIMESTAMP option, with similar semantic to the one described in the UPDATE statement (the timestamp applies to all the statement inside the batch). However, if used, TIMESTAMP must not be used in the statements within the batch.

Queries

SELECT

Syntax:

select_statement::= SELECT [ JSON | DISTINCT ] ( select_clause | '*' )
	FROM `table_name`
	[ WHERE `where_clause` ]
	[ GROUP BY `group_by_clause` ]
	[ ORDER BY `ordering_clause` ]
	[ PER PARTITION LIMIT (`integer` | `bind_marker`) ]
	[ LIMIT (`integer` | `bind_marker`) ]
	[ ALLOW FILTERING ]
	[ WITH `select_options` ]
select_clause::= `selector` [ AS `identifier` ] ( ',' `selector` [ AS `identifier` ] )
selector::== `column_name`
	| `term`
	| CAST '(' `selector` AS `cql_type` ')'
	| `function_name` '(' [ `selector` ( ',' `selector` )_ ] ')'
	| COUNT '(' '_' ')'
where_clause::= `relation` ( AND `relation` )*
relation::= column_name operator term
	'(' column_name ( ',' column_name )* ')' operator tuple_literal
	TOKEN '(' column_name# ( ',' column_name )* ')' operator term
operator::= '=' | '<' | '>' | '<=' | '>=' | '!=' | IN | NOT IN | CONTAINS | NOT CONTAINS | CONTAINS KEY | NOT CONTAINS KEY
group_by_clause::= column_name ( ',' column_name )*
ordering_clause::= column_name [ ASC | DESC ] ( ',' column_name [ ASC | DESC ] )*
select_options::= `select_option` ( AND `select_option` )*
select_option::= included_indexes  '=' `index_names`
	           | excluded_indexes  '=' `index_names`
index_names::= '{' index_name ( ',' index_name )* '}'

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 (*) 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.

`<select-clause>`

The <select-clause> determines which columns need to be queried and returned in the result-set. It consists of either the comma-separated list of or the wildcard character (*) to select all the columns defined for the table. Please note that for wildcard SELECT queries the order of columns returned is not specified and is not guaranteed to be stable between Cassandra versions.

A <selector> is either a column name to retrieve or a <function> of one or more <term>`s. The function allowed are the same as for `<term> and are described in the function section. In addition to these generic functions, the WRITETIME and MAXWRITETIME (resp. TTL) function allows to select the timestamp of when the column was inserted (resp. the time to live (in seconds) for the column (or null if the column has no expiration set)) and the CAST function can be used to convert one data type to another. The WRITETIME and TTL functions can’t be used on multi-cell columns such as non-frozen collections or non-frozen user-defined types.

Additionally, individual values of maps and sets can be selected using [ <term> ]. For maps, this will return the value corresponding to the key, if such entry exists. For sets, this will return the key that is selected if it exists and is thus mainly a way to check element existence. It is also possible to select a slice of a set or map with `[ <term> … <term> `], where both bound can be omitted.

Any <selector> can be aliased using AS keyword (see examples). Please note that <where-clause> and <order-by> clause should refer to the columns by their original names and not by their aliases.

The COUNT keyword can be used with parenthesis enclosing *. If so, the query will return a single result: the number of rows matching the query. Note that COUNT(1) is supported as an alias.

`<where-clause>`

The <where-clause> specifies which rows must be queried. It is composed of relations on the columns that are part of the PRIMARY KEY and/or have a secondary index defined on them.

Not all relations are allowed in a query. For instance, non-equal relations (where IN is considered as an equal relation) on a partition key are not supported (but see the use of the TOKEN method below to do non-equal queries on the partition key). Moreover, for a given partition key, the clustering columns induce an ordering of rows and relations on them is restricted to the relations that allow to select a contiguous (for the ordering) set of rows. For instance, given

CREATE TABLE posts (
userid text,
blog_title text,
posted_at timestamp,
entry_title text,
content text,
category int,
PRIMARY KEY (userid, blog_title, posted_at)
)

The following query is allowed:

SELECT entry_title, content FROM posts WHERE userid='john doe' AND
blog_title='John''s Blog' AND posted_at >= '2012-01-01' AND posted_at <
'2012-01-31'

But the following one is not, as it does not select a contiguous set of rows (and we suppose no secondary indexes are set):

// Needs a blog_title to be set to select ranges of posted_at
SELECT entry_title, content FROM posts WHERE userid='john doe' AND
posted_at >= '2012-01-01' AND posted_at < '2012-01-31'

When specifying relations, the TOKEN function can be used on the PARTITION KEY column to query. In that case, rows will be selected based on the token of their PARTITION_KEY rather than on the value. Note that the token of a key depends on the partitioner in use, and that in particular the RandomPartitioner won’t yield a meaningful order. Also note that ordering partitioners always order token values by bytes (so even if the partition key is of type int, token(-1) > token(0) in particular). Example:

SELECT * FROM posts WHERE token(userid) > token('tom') AND token(userid)
< token('bob')

Moreover, the IN relation is only allowed on the last column of the partition key and on the last column of the full primary key.

It is also possible to `group'' `CLUSTERING COLUMNS together in a relation using the tuple notation. For instance:

SELECT * FROM posts WHERE userid='john doe' AND (blog_title, posted_at)
> ('John''s Blog', '2012-01-01')

will request all rows that sort after the one having `John’s Blog'' as `blog_tile and 2012-01-01' for `posted_at in the clustering order. In particular, rows having a post_at ⇐ '2012-01-01' will be returned as long as their blog_title > 'John''s Blog', which wouldn’t be the case for:

SELECT * FROM posts WHERE userid='john doe' AND blog_title > 'John''s
Blog' AND posted_at > '2012-01-01'

The tuple notation may also be used for IN clauses on CLUSTERING COLUMNS:

SELECT * FROM posts WHERE userid='john doe' AND (blog_title, posted_at)
IN (('John''s Blog', '2012-01-01'), ('Extreme Chess', '2014-06-01'))

The CONTAINS operator may only be used on collection columns (lists, sets, and maps). In the case of maps, CONTAINS applies to the map values. The CONTAINS KEY operator may only be used on map columns and applies to the map keys.

`<order-by>`

The ORDER BY option allows to select the order of the returned results. It takes as argument a list of column names along with the order for the column (ASC for ascendant and DESC for descendant, omitting the order being equivalent to ASC). Currently, the possible orderings are limited (which depends on the table CLUSTERING ORDER ):

if the table has been defined without any specific CLUSTERING ORDER, then allowed orderings are the order induced by the clustering columns and the reverse of that one.
otherwise, the orderings allowed are the order of the CLUSTERING ORDER option and the reversed one.

`<group-by>`

The GROUP BY option allows aggregating values into a single row all selected rows that share the same values for a set of columns.

Using the GROUP BY option, it is only possible to group rows at the partition key level or at a clustering column level. By consequence, the GROUP BY option only accept as arguments primary key column names in the primary key order. If a primary key column is restricted by an equality restriction it is not required to be present in the GROUP BY clause.

Aggregate functions will produce a separate value for each group. If no GROUP BY clause is specified, aggregates functions will produce a single value for all the rows.

If a column is selected without an aggregate function, in a statement with a GROUP BY, the first value encounter in each group will be returned.

`LIMIT` and `PER PARTITION LIMIT`

The LIMIT option to a SELECT statement limits the number of rows returned by a query, while the PER PARTITION LIMIT option limits the number of rows returned for a given partition by the query. Note that both type of limits can be used in the same statement.

`ALLOW FILTERING`

By default, CQL only allows select queries that don’t involve filtering'' server side, i.e. queries where we know that all (live) record read will be returned (maybe partly) in the result set. The reasoning is that thosenon filtering'' queries have predictable performance in the sense that they will execute in a time that is proportional to the amount of data returned by the query (which can be controlled through LIMIT).

The ALLOW FILTERING option allows to explicitly allow (some) queries that require filtering. Please note that a query using ALLOW FILTERING may thus have unpredictable performance (for the definition above), i.e. even a query that selects a handful of records may exhibit performance that depends on the total amount of data stored in the cluster.

For instance, considering the following table holding user profiles with their year of birth (with a secondary index on it) and country of residence:

CREATE TABLE users (
    username text PRIMARY KEY,
    firstname text,
    lastname text,
    birth_year int,
    country text
);

CREATE INDEX ON users(birth_year);

Then the following queries are valid:

SELECT * FROM users;
SELECT firstname, lastname FROM users WHERE birth_year = 1981 ALLOW FILTERING;

because in both case, Cassandra guarantees that these queries performance will be proportional to the amount of data returned. In particular, if no users are born in 1981, then the second query performance will not depend on the number of user profile stored in the database (not directly at least: due to secondary index implementation consideration, this query may still depend on the number of node in the cluster, which indirectly depends on the amount of data stored. Nevertheless, the number of nodes will always be multiple number of magnitude lower than the number of user profile stored). Of course, both query may return very large result set in practice, but the amount of data returned can always be controlled by adding a LIMIT.

However, the following query will be rejected:

SELECT firstname, lastname FROM users WHERE birth_year = 1981 AND
country = 'FR';

because Cassandra cannot guarantee that it won’t have to scan large amount of data even if the result to those query is small. Typically, it will scan all the index entries for users born in 1981 even if only a handful are actually from France. However, if you `know what you are doing'', you can force the execution of this query by using `ALLOW FILTERING and so the following query is valid:

SELECT firstname, lastname FROM users WHERE birth_year = 1981 AND
country = 'FR' ALLOW FILTERING;

Database Roles

CQL uses database roles to represent users and group of users. Syntactically, a role is defined by:

role_name ::= identifier | string

CREATE ROLE

Creating a role uses the CREATE ROLE statement:

create_role_statement ::= CREATE ROLE [ IF NOT EXISTS ] role_name
                          [ WITH role_options# ]
role_options ::= role_option ( AND role_option)*
role_option ::= PASSWORD = <string>
                | HASHED PASSWORD = <string>
                | GENERATED PASSWORD
                | LOGIN = <boolean>
                | SUPERUSER = <boolean>
                | OPTIONS = <map_literal>
                | ACCESS TO DATACENTERS <set_literal>
                | ACCESS TO ALL DATACENTERS
                | ACCESS FROM CIDRS <set_literal>
                | ACCESS FROM ALL CIDRS

For instance:

CREATE ROLE new_role;
CREATE ROLE alice WITH PASSWORD = 'password_a' AND LOGIN = true;
CREATE ROLE alice WITH HASHED PASSWORD = '$2a$10$JSJEMFm6GeaW9XxT5JIheuEtPvat6i7uKbnTcxX3c1wshIIsGyUtG' 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 };
CREATE ROLE alice WITH PASSWORD = 'password_a' AND LOGIN = true AND ACCESS TO DATACENTERS {'DC1', 'DC3'};
CREATE ROLE alice WITH PASSWORD = 'password_a' AND LOGIN = true AND ACCESS TO ALL DATACENTERS;
CREATE ROLE bob WITH LOGIN = true AND PASSWORD = 'password_d' AND ACCESS FROM CIDRS { 'region1', 'region2' };
CREATE ROLE hob WITH LOGIN = true AND PASSWORD = 'password_c' AND ACCESS FROM ALL CIDRS;
CREATE ROLE tom WITH LOGIN = true AND GENERATED PASSWORD;

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), 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.

Setting credentials for internal authentication

Use the WITH PASSWORD clause to set a password for internal authentication, enclosing the password in single quotation marks.

If internal authentication has not been set up or the role does not have LOGIN privileges, the WITH PASSWORD clause is not necessary.

USE WITH HASHED PASSWORD to provide the jBcrypt hashed password directly. See the hash_password tool.

Restricting connections to specific datacenters

If a network_authorizer has been configured, you can restrict login roles to specific datacenters with the ACCESS TO DATACENTERS clause followed by a set literal of datacenters the user can access. Not specifiying datacenters implicitly grants access to all datacenters. The clause ACCESS TO ALL DATACENTERS can be used for explicitness, but there’s no functional difference.

Restricting connections from specific CIDR groups

If a cidr_authorizer has been configured, you can restrict roles to login only from specific regions, aka CIDR groups, with the ACCESS FROM CIDRS clause followed by a set literal of CIDR groups the user can access from. Not specifying CIDR groups implicitly grants access from all CIDR groups. The clause ACCESS FROM ALL CIDRS can be used for explicitness, but there’s no functional difference. This clause can be also be used to remove any CIDR groups restrictions. Valid CIDR groups should be used with ACCESS FROM CIDRS clause. nodetool list-cidrgroups command can be used to see available CIDR groups in the Cluster.

Creating a role conditionally

Attempting to create an existing role results in an invalid query condition unless the IF NOT EXISTS option is used. If the option is used and the role exists, the statement is a no-op:

CREATE ROLE other_role;
CREATE ROLE IF NOT EXISTS other_role;

ALTER ROLE

Altering a role options uses the ALTER ROLE statement:

alter_role_statement ::= ALTER ROLE [ IF EXISTS ] role_name ( WITH <option> ( AND <option> )* )?

<option> ::= PASSWORD = <string>
           | HASHED PASSWORD = <string>
           | GENERATED PASSWORD
           | LOGIN = <boolean>
           | SUPERUSER = <boolean>
           | OPTIONS = <map_literal>
           | ACCESS TO DATACENTERS <set_literal>
           | ACCESS TO ALL DATACENTERS
           | ACCESS FROM CIDRS <set_literal>
           | ACCESS FROM ALL CIDRS

For example:

ALTER ROLE bob WITH PASSWORD = 'PASSWORD_B' AND SUPERUSER = false;
ALTER ROLE bob WITH HASHED PASSWORD = '$2a$10$JSJEMFm6GeaW9XxT5JIheuEtPvat6i7uKbnTcxX3c1wshIIsGyUtG' AND SUPERUSER = false;
ALTER ROLE rob WITH LOGIN = true AND PASSWORD = 'password_c' AND ACCESS FROM ALL CIDRS;
ALTER ROLE IF EXISTS hob WITH LOGIN = true AND PASSWORD = 'password_d' AND ACCESS FROM CIDRS { 'region1' };
ALTER ROLE IF EXISTS hob WITH LOGIN = true AND GENERATED PASSWORD;

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

USE WITH HASHED PASSWORD to provide the jBcrypt hashed password directly. See the hash_password tool.

Restricting connections to specific datacenters

If a network_authorizer has been configured, you can restrict login roles to specific datacenters with the ACCESS TO DATACENTERS clause followed by a set literal of datacenters the user can access. To remove any data center restrictions, use the ACCESS TO ALL DATACENTERS clause.

Restricting connections from specific CIDR groups

Conditions on executing `ALTER ROLE` statements:

a client must have SUPERUSER status to alter the SUPERUSER status of another role
a client cannot alter the SUPERUSER status of any role it currently holds
a client can only modify certain properties of the role with which it identified at login (e.g. PASSWORD)
to modify properties of a role, the client must be granted ALTER permission <cql-permissions> on that role

DROP ROLE

Dropping a role uses the DROP ROLE statement:

drop_role_statement ::= DROP ROLE [ IF EXISTS ] role_name

DROP ROLE requires the client to have DROP permission <cql-permissions> on the role in question. In addition, client may not DROP the role with which it identified at login. Finally, 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.

DROP ROLE intentionally does not terminate any open user sessions. Currently connected sessions will remain connected and will retain the ability to perform any database actions which do not require authorization. However, if authorization is enabled, permissions of the dropped role are also revoked, subject to the caching options configured in cassandra-yaml file. Should a dropped role be subsequently recreated and have new permissions or roles granted to it, any client sessions still connected will acquire the newly granted permissions and roles.

GRANT ROLE

Granting a role to another uses the GRANT ROLE statement:

grant_role_statement ::= GRANT role_name TO role_name

For example:

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:

GRANT role_a TO role_b;
GRANT role_b TO role_a;

GRANT role_a TO role_b;
GRANT role_b TO role_c;
GRANT role_c TO role_a;

REVOKE ROLE

Revoking a role uses the REVOKE ROLE statement:

revoke_role_statement ::= REVOKE role_name FROM role_name

For example:

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.

LIST ROLES

All the known roles (in the system or granted to specific role) can be listed using the LIST ROLES statement:

list_roles_statement ::= LIST ROLES [ OF role_name] [ NORECURSIVE ]

For instance:

LIST ROLES;

returns all known roles in the system, this requires DESCRIBE permission on the database roles resource.

This example enumerates all roles granted to alice, including those transitively acquired:

LIST ROLES OF alice;

This example lists all roles directly granted to bob without including any of the transitively acquired ones:

LIST ROLES OF bob NORECURSIVE;

LIST SUPERUSERS

All the known roles (including transitively acquired) with superuser privilege can be listed using the LIST SUPERUSERS statement:

list_superusers_statement ::= LIST SUPERUSERS

This command requires DESCRIBE permission on all roles of the database.

Users

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 statements becoming synonyms for the ROLE based equivalents. In other words, creating/updating a user is just a different syntax for creating/updating a role.

CREATE USER

Creating a user uses the CREATE USER statement:

create_user_statement ::= CREATE USER [ IF NOT EXISTS ] role_name ( WITH <option> ( AND <option> )* )?
<option> ::= PASSWORD = <string>
           | HASHED PASSWORD = <string>
           | GENERATED PASSWORD
           | SUPERUSER
           | NOSUPERUSER

For example:

CREATE USER alice WITH PASSWORD 'password_a' SUPERUSER;
CREATE USER bob WITH PASSWORD 'password_b' NOSUPERUSER;
CREATE USER bob WITH HASHED PASSWORD '$2a$10$JSJEMFm6GeaW9XxT5JIheuEtPvat6i7uKbnTcxX3c1wshIIsGyUtG' NOSUPERUSER;
CREATE USER tom WITH GENERATED PASSWORD;

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

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;

CREATE ROLE rob WITH LOGIN = true AND PASSWORD = 'password_c' AND ACCESS FROM ALL CIDRS;
CREATE ROLE hob WITH LOGIN = true AND PASSWORD = 'password_d' AND ACCESS FROM CIDRS { 'region1' };

ALTER USER

Altering the options of a user uses the ALTER USER statement:

alter_user_statement ::= ALTER USER [ IF EXISTS ] role_name ( WITH <option> ( AND <option> )* )?

<option> ::= PASSWORD = <string>
           | HASHED PASSWORD = <string>
           | GENERATED PASSWORD
           | SUPERUSER
           | NOSUPERUSER

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

ALTER USER alice WITH PASSWORD 'PASSWORD_A';
ALTER USER alice WITH HASHED PASSWORD '$2a$10$JSJEMFm6GeaW9XxT5JIheuEtPvat6i7uKbnTcxX3c1wshIIsGyUtG';
ALTER USER bob SUPERUSER;

DROP USER

Dropping a user uses the DROP USER statement:

drop_user_statement ::= DROP USER [ IF EXISTS ] role_name

LIST USERS

Existing users can be listed using the LIST USERS statement:

list_users_statement::= LIST USERS

Note that this statement is equivalent to LIST ROLES, but only roles with the LOGIN privilege are included in the output.

Database Identities

ADD IDENTITY

Syntax:

::= ADD IDENTITY [ IF NOT EXISTS ] id_name TO ROLE role_name

Sample:

ADD IDENTITY 'id1' TO ROLE 'role1';

Only a user with privileges to add roles can add identities.

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

Adding an identity conditionally

Attempting to add an existing identity results in an invalid query condition unless the IF NOT EXISTS option is used. If the option is used and the identity exists, the statement is a no-op.

ADD IDENTITY [ IF NOT EXISTS ] 'id1' TO ROLE 'role1';

DROP IDENTITY

Syntax:

::= DROP IDENTITY [ IF EXISTS ]

Sample:

DROP IDENTITY 'testIdentity';
DROP IDENTITY IF EXISTS 'testIdentity';

Only a user with privileges to drop roles can remove identities

Attempting to drop an identity which does not exist results in an invalid query condition unless the IF EXISTS option is used. If the option is used and the identity does not exist the statement is a no-op.

Data Control

Permissions

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

The hierarchy of Data resources, Keyspaces and Tables has the structure ALL KEYSPACES → KEYSPACE → TABLE.
Function resources have the structure ALL FUNCTIONS → KEYSPACE → FUNCTION
Resources representing roles have the structure ALL ROLES → ROLE
Resources representing JMX ObjectNames, which map to sets of MBeans/MXBeans, have the structure ALL MBEANS → MBEAN

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:

CREATE
ALTER
DROP
SELECT
MODIFY
AUTHORIZE
DESCRIBE
EXECUTE
UNMASK
SELECT_MASKED

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.

Permission Resource Operations

Permission	Resource	Operations
`CREATE`	`ALL KEYSPACES`	`CREATE KEYSPACE` and `CREATE TABLE` in any keyspace
`CREATE`	`KEYSPACE`	`CREATE TABLE` in specified keyspace
`CREATE`	`ALL FUNCTIONS`	`CREATE FUNCTION` in any keyspace and `CREATE AGGREGATE` in any keyspace
`CREATE`	`ALL FUNCTIONS IN KEYSPACE`	`CREATE FUNCTION` and `CREATE AGGREGATE` in specified keyspace
`CREATE`	`ALL ROLES`	`CREATE ROLE`
`ALTER`	`ALL KEYSPACES`	`ALTER KEYSPACE` and `ALTER TABLE` in any keyspace
`ALTER`	`KEYSPACE`	`ALTER KEYSPACE` and `ALTER TABLE` in specified keyspace
`ALTER`	`TABLE`	`ALTER TABLE`
`ALTER`	`ALL FUNCTIONS`	`CREATE FUNCTION` and `CREATE AGGREGATE`: replacing any existing
`ALTER`	`ALL FUNCTIONS IN KEYSPACE`	`CREATE FUNCTION` and `CREATE AGGREGATE`: replacing existing in specified keyspace
`ALTER`	`FUNCTION`	`CREATE FUNCTION` and `CREATE AGGREGATE`: replacing existing
`ALTER`	`ALL ROLES`	`ALTER ROLE` on any role
`ALTER`	`ROLE`	`ALTER ROLE`
`DROP`	`ALL KEYSPACES`	`DROP KEYSPACE` and `DROP TABLE` in any keyspace
`DROP`	`KEYSPACE`	`DROP TABLE` in specified keyspace
`DROP`	`TABLE`	`DROP TABLE`
`DROP`	`ALL FUNCTIONS`	`DROP FUNCTION` and `DROP AGGREGATE` in any keyspace
`DROP`	`ALL FUNCTIONS IN KEYSPACE`	`DROP FUNCTION` and `DROP AGGREGATE` in specified keyspace
`DROP`	`FUNCTION`	`DROP FUNCTION`
`DROP`	`ALL ROLES`	`DROP ROLE` on any role
`DROP`	`ROLE`	`DROP ROLE`
`SELECT`	`ALL KEYSPACES`	`SELECT` on any table
`SELECT`	`KEYSPACE`	`SELECT` on any table in specified keyspace
`SELECT`	`TABLE`	`SELECT` on specified table
`SELECT`	`ALL MBEANS`	Call getter methods on any mbean
`SELECT`	`MBEANS`	Call getter methods on any mbean matching a wildcard pattern
`SELECT`	`MBEAN`	Call getter methods on named mbean
`MODIFY`	`ALL KEYSPACES`	`INSERT`, `UPDATE`, `DELETE` and `TRUNCATE` on any table
`MODIFY`	`KEYSPACE`	`INSERT`, `UPDATE`, `DELETE` and `TRUNCATE` on any table in specified keyspace
`MODIFY`	`TABLE`	`INSERT`, `UPDATE`, `DELETE` and `TRUNCATE` on specified table
`MODIFY`	`ALL MBEANS`	Call setter methods on any mbean
`MODIFY`	`MBEANS`	Call setter methods on any mbean matching a wildcard pattern
`MODIFY`	`MBEAN`	Call setter methods on named mbean
`AUTHORIZE`	`ALL KEYSPACES`	`GRANT PERMISSION` and `REVOKE PERMISSION` on any table
`AUTHORIZE`	`KEYSPACE`	`GRANT PERMISSION` and `REVOKE PERMISSION` on any table in specified keyspace
`AUTHORIZE`	`TABLE`	`GRANT PERMISSION` and `REVOKE PERMISSION` on specified table
`AUTHORIZE`	`ALL FUNCTIONS`	`GRANT PERMISSION` and `REVOKE PERMISSION` on any function
`AUTHORIZE`	`ALL FUNCTIONS IN KEYSPACE`	`GRANT PERMISSION` and `REVOKE PERMISSION` in specified keyspace
`AUTHORIZE`	`FUNCTION`	`GRANT PERMISSION` and `REVOKE PERMISSION` on specified function
`AUTHORIZE`	`ALL MBEANS`	`GRANT PERMISSION` and `REVOKE PERMISSION` on any mbean
`AUTHORIZE`	`MBEANS`	`GRANT PERMISSION` and `REVOKE PERMISSION` on any mbean matching a wildcard pattern
`AUTHORIZE`	`MBEAN`	`GRANT PERMISSION` and `REVOKE PERMISSION` on named mbean
`AUTHORIZE`	`ALL ROLES`	`GRANT ROLE` and `REVOKE ROLE` on any role
`AUTHORIZE`	`ROLES`	`GRANT ROLE` and `REVOKE ROLE` on specified roles
`DESCRIBE`	`ALL ROLES`	`LIST ROLES` on all roles or only roles granted to another, specified role
`DESCRIBE`	`ALL MBEANS`	Retrieve metadata about any mbean from the platform’s MBeanServer
`DESCRIBE`	`MBEANS`	Retrieve metadata about any mbean matching a wildcard patter from the platform’s MBeanServer
`DESCRIBE`	`MBEAN`	Retrieve metadata about a named mbean from the platform’s MBeanServer
`EXECUTE`	`ALL FUNCTIONS`	`SELECT`, `INSERT` and `UPDATE` using any function, and use of any function in `CREATE AGGREGATE`
`EXECUTE`	`ALL FUNCTIONS IN KEYSPACE`	`SELECT`, `INSERT` and `UPDATE` using any function in specified keyspace and use of any function in keyspace in `CREATE AGGREGATE`
`EXECUTE`	`FUNCTION`	`SELECT`, `INSERT` and `UPDATE` using specified function and use of the function in `CREATE AGGREGATE`
`EXECUTE`	`ALL MBEANS`	Execute operations on any mbean
`EXECUTE`	`MBEANS`	Execute operations on any mbean matching a wildcard pattern
`EXECUTE`	`MBEAN`	Execute operations on named mbean
`UNMASK`	`ALL KEYSPACES`	See the clear contents of masked columns on any table
`UNMASK`	`KEYSPACE`	See the clear contents of masked columns on any table in keyspace
`UNMASK`	`TABLE`	See the clear contents of masked columns on the specified table
`SELECT_MASKED`	`ALL KEYSPACES`	`SELECT` restricting masked columns on any table
`SELECT_MASKED`	`KEYSPACE`	`SELECT` restricting masked columns on any table in specified keyspace
`SELECT_MASKED`	`TABLE`	`SELECT` restricting masked columns on the specified table

CREATE

ALL KEYSPACES

CREATE KEYSPACE and CREATE TABLE in any keyspace

CREATE

KEYSPACE

CREATE TABLE in specified keyspace

CREATE

ALL FUNCTIONS

CREATE FUNCTION in any keyspace and CREATE AGGREGATE in any keyspace

CREATE

ALL FUNCTIONS IN KEYSPACE

CREATE FUNCTION and CREATE AGGREGATE in specified keyspace

CREATE

ALL ROLES

CREATE ROLE

ALTER

ALL KEYSPACES

ALTER KEYSPACE and ALTER TABLE in any keyspace

ALTER

KEYSPACE

ALTER KEYSPACE and ALTER TABLE in specified keyspace

ALTER

TABLE

ALTER TABLE

ALTER

ALL FUNCTIONS

CREATE FUNCTION and CREATE AGGREGATE: replacing any existing

ALTER

ALL FUNCTIONS IN KEYSPACE

CREATE FUNCTION and CREATE AGGREGATE: replacing existing in specified keyspace

ALTER

FUNCTION

CREATE FUNCTION and CREATE AGGREGATE: replacing existing

ALTER

ALL ROLES

ALTER ROLE on any role

ALTER

ROLE

ALTER ROLE

DROP

ALL KEYSPACES

DROP KEYSPACE and DROP TABLE in any keyspace

DROP

KEYSPACE

DROP TABLE in specified keyspace

DROP

TABLE

DROP TABLE

DROP

ALL FUNCTIONS

DROP FUNCTION and DROP AGGREGATE in any keyspace

DROP

ALL FUNCTIONS IN KEYSPACE

DROP FUNCTION and DROP AGGREGATE in specified keyspace

DROP

FUNCTION

DROP FUNCTION

DROP

ALL ROLES

DROP ROLE on any role

DROP

ROLE

DROP ROLE

SELECT

ALL KEYSPACES

SELECT on any table

SELECT

KEYSPACE

SELECT on any table in specified keyspace

SELECT

TABLE

SELECT on specified table

SELECT

ALL MBEANS

Call getter methods on any mbean

SELECT

MBEANS

Call getter methods on any mbean matching a wildcard pattern

SELECT

MBEAN

Call getter methods on named mbean

MODIFY

ALL KEYSPACES

INSERT, UPDATE, DELETE and TRUNCATE on any table

MODIFY

KEYSPACE

INSERT, UPDATE, DELETE and TRUNCATE on any table in specified keyspace

MODIFY

TABLE

INSERT, UPDATE, DELETE and TRUNCATE on specified table

MODIFY

ALL MBEANS

Call setter methods on any mbean

MODIFY

MBEANS

Call setter methods on any mbean matching a wildcard pattern

MODIFY

MBEAN

Call setter methods on named mbean

AUTHORIZE

ALL KEYSPACES

GRANT PERMISSION and REVOKE PERMISSION on any table

AUTHORIZE

KEYSPACE

GRANT PERMISSION and REVOKE PERMISSION on any table in specified keyspace

AUTHORIZE

TABLE

GRANT PERMISSION and REVOKE PERMISSION on specified table

AUTHORIZE

ALL FUNCTIONS

GRANT PERMISSION and REVOKE PERMISSION on any function

AUTHORIZE

ALL FUNCTIONS IN KEYSPACE

GRANT PERMISSION and REVOKE PERMISSION in specified keyspace

AUTHORIZE

FUNCTION

GRANT PERMISSION and REVOKE PERMISSION on specified function

AUTHORIZE

ALL MBEANS

GRANT PERMISSION and REVOKE PERMISSION on any mbean

AUTHORIZE

MBEANS

GRANT PERMISSION and REVOKE PERMISSION on any mbean matching a wildcard pattern

AUTHORIZE

MBEAN

GRANT PERMISSION and REVOKE PERMISSION on named mbean

AUTHORIZE

ALL ROLES

GRANT ROLE and REVOKE ROLE on any role

AUTHORIZE

ROLES

GRANT ROLE and REVOKE ROLE on specified roles

DESCRIBE

ALL ROLES

LIST ROLES on all roles or only roles granted to another, specified role

DESCRIBE

ALL MBEANS

Retrieve metadata about any mbean from the platform’s MBeanServer

DESCRIBE

MBEANS

Retrieve metadata about any mbean matching a wildcard patter from the platform’s MBeanServer

DESCRIBE

MBEAN

Retrieve metadata about a named mbean from the platform’s MBeanServer

EXECUTE

ALL FUNCTIONS

SELECT, INSERT and UPDATE using any function, and use of any function in CREATE AGGREGATE

EXECUTE

ALL FUNCTIONS IN KEYSPACE

SELECT, INSERT and UPDATE using any function in specified keyspace and use of any function in keyspace in CREATE AGGREGATE

EXECUTE

FUNCTION

SELECT, INSERT and UPDATE using specified function and use of the function in CREATE AGGREGATE

EXECUTE

ALL MBEANS

Execute operations on any mbean

EXECUTE

MBEANS

Execute operations on any mbean matching a wildcard pattern

EXECUTE

MBEAN

Execute operations on named mbean

UNMASK

ALL KEYSPACES

See the clear contents of masked columns on any table

UNMASK

KEYSPACE

See the clear contents of masked columns on any table in keyspace

UNMASK

TABLE

See the clear contents of masked columns on the specified table

SELECT_MASKED

ALL KEYSPACES

SELECT restricting masked columns on any table

SELECT_MASKED

KEYSPACE

SELECT restricting masked columns on any table in specified keyspace

SELECT_MASKED

TABLE

SELECT restricting masked columns on the specified table

GRANT PERMISSION

Granting a permission uses the GRANT PERMISSION statement:

grant_permission_statement ::= GRANT permissions ON resource TO role_name
permissions ::= ALL [ PERMISSIONS ] | permission [ PERMISSION ]
permission ::= CREATE | ALTER | DROP | SELECT | MODIFY | AUTHORIZE | DESCRIBE | EXECUTE | UNMASK | SELECT_MASKED
resource ::=    ALL KEYSPACES
                | KEYSPACE keyspace_name
                | [ TABLE ] table_name
                | ALL ROLES
                | ROLE role_name
                | ALL FUNCTIONS [ IN KEYSPACE keyspace_name ]
                | FUNCTION function_name '(' [ cql_type( ',' cql_type )* ] ')'
                | ALL MBEANS
                | ( MBEAN | MBEANS ) string

For example:

GRANT SELECT ON ALL KEYSPACES TO data_reader;

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

GRANT MODIFY ON KEYSPACE keyspace1 TO data_writer;

To 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:

GRANT DROP ON keyspace1.table1 TO schema_owner;

To give any user with the schema_owner role permissions to DROP a specific keyspace1.table1:

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

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

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.

GRANT ALL

When the GRANT ALL form is used, the appropriate set of permissions is determined automatically based on the target resource.

Automatic Granting

When a resource is created, via a CREATE KEYSPACE, CREATE TABLE, CREATE FUNCTION, CREATE AGGREGATE or CREATE ROLE statement, the creator (the role the database user who issues the statement is identified as), is automatically granted all applicable permissions on the new resource.

REVOKE PERMISSION

Revoking a permission from a role uses the REVOKE PERMISSION statement:

revoke_permission_statement ::= REVOKE permissions ON resource FROM role_name

For example:

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;

Because of their function in normal driver operations, certain tables cannot have their SELECT permissions revoked. The following tables will be available to all authorized users regardless of their assigned role:

* system_schema.keyspaces
* system_schema.columns
* system_schema.tables
* system.local
* system.peers

LIST PERMISSIONS

Listing granted permissions uses the LIST PERMISSIONS statement:

list_permissions_statement ::= LIST permissions [ ON resource] [ OF role_name[ NORECURSIVE ] ]

For example:

LIST ALL PERMISSIONS OF alice;

Show all permissions granted to alice, including those acquired transitively from any other roles:

LIST ALL PERMISSIONS ON keyspace1.table1 OF bob;

Show all permissions on keyspace1.table1 granted to bob, including those acquired transitively from any other roles. This also includes any permissions higher up the resource hierarchy which can be applied to keyspace1.table1. For example, should bob have ALTER permission on keyspace1, that would be included in the results of this query. Adding the NORECURSIVE switch restricts the results to only those permissions which were directly granted to bob or one of `bob’s roles:

LIST SELECT PERMISSIONS OF carlos;

Show any permissions granted to carlos or any roles assigned to carlos, limited to SELECT permissions on any resource.

Data Types

CQL supports a rich set of data types for columns defined in a table, including collection types. On top of those native
and collection types, users can also provide custom types (through a JAVA class extending AbstractType loadable by
Cassandra). The syntax of types is thus:

::=
|
|
| // Used for custom types. The fully-qualified name of a JAVA class

::= ascii
| bigint
| blob
| boolean
| counter
| date
| decimal
| double
| float
| inet
| int
| smallint
| text
| time
| timestamp
| timeuuid
| tinyint
| uuid
| varchar
| varint

::= list `<' `>'
| set `<' `>'
| map `<' `,' `>'
::= tuple `<' (`,' )* `>'

Note that the native types are keywords and as such are case-insensitive. They are however not reserved ones.

The following table gives additional information on the native data types, and on which kind of constants each type supports:

type constants supported description

type	constants supported	description
`ascii`	strings	ASCII character string
`bigint`	integers	64-bit signed long
`blob`	blobs	Arbitrary bytes (no validation)
`boolean`	booleans	true or false
`counter`	integers	Counter column (64-bit signed value). See Counters for details
`date`	integers, strings	A date (with no corresponding time value). See Working with dates below for more information.
`decimal`	integers, floats	Variable-precision decimal
`double`	integers	64-bit IEEE-754 floating point
`float`	integers, floats	32-bit IEEE-754 floating point
`inet`	strings	An IP address. It can be either 4 bytes long (IPv4) or 16 bytes long (IPv6). There is no `inet` constant, IP address should be inputted as strings
`int`	integers	32-bit signed int
`smallint`	integers	16-bit signed int
`text`	strings	UTF8 encoded string
`time`	integers, strings	A time with nanosecond precision. See Working with time below for more information.
`timestamp`	integers, strings	A timestamp. Strings constant are allow to input timestamps as dates, see Working with timestamps below for more information.
`timeuuid`	uuids	Type 1 UUID. This is generally used as a ``conflict-free'' timestamp. Also see the functions on Timeuuid
`tinyint`	integers	8-bit signed int
`uuid`	uuids	Type 1 or type 4 UUID
`varchar`	strings	UTF8 encoded string
`varint`	integers	Arbitrary-precision integer

ascii

strings

ASCII character string

bigint

integers

64-bit signed long

blob

blobs

Arbitrary bytes (no validation)

boolean

booleans

true or false

counter

integers

Counter column (64-bit signed value). See Counters for details

date

integers, strings

A date (with no corresponding time value). See Working with dates below for more information.

decimal

integers, floats

Variable-precision decimal

double

integers

64-bit IEEE-754 floating point

float

integers, floats

32-bit IEEE-754 floating point

inet

strings

An IP address. It can be either 4 bytes long (IPv4) or 16 bytes long (IPv6). There is no inet constant, IP address should be inputted as strings

int

integers

32-bit signed int

smallint

integers

16-bit signed int

text

strings

UTF8 encoded string

time

integers, strings

A time with nanosecond precision. See Working with time below for more information.

timestamp

integers, strings

A timestamp. Strings constant are allow to input timestamps as dates, see Working with timestamps below for more information.

timeuuid

uuids

Type 1 UUID. This is generally used as a ``conflict-free'' timestamp. Also see the functions on Timeuuid

tinyint

integers

8-bit signed int

uuid

uuids

Type 1 or type 4 UUID

varchar

strings

UTF8 encoded string

varint

integers

Arbitrary-precision integer

For more information on how to use the collection types, see the Working with collections section below.

Working with timestamps

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.:

2011-02-03 04:05+0000
2011-02-03 04:05:00+0000
2011-02-03 04:05:00.000+0000
2011-02-03T04:05+0000
2011-02-03T04:05:00+0000
2011-02-03T04:05:00.000+0000

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.

2011-02-03 04:05
2011-02-03 04:05:00
2011-02-03 04:05:00.000
2011-02-03T04:05
2011-02-03T04:05:00
2011-02-03T04:05:00.000

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:

2011-02-03
2011-02-03+0000

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

Working with dates

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:

2014-01-01

Working with time

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:

08:12:54
08:12:54.123
08:12:54.123456
08:12:54.123456789

Counters

The counter type is used to define counter columns. A counter column is a column whose value is a 64-bit signed integer and which can be incremented or decremented (see UPDATE for syntax). Note the value of a counter cannot be set. A counter doesn’t exist until first increment or decrement operation, which uses the initial value of 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:

It cannot be used for column that is part of the PRIMARY KEY of a table.
A table that contains a counter can only contain counters. In other words, either all the columns of a table outside the PRIMARY KEY have the counter type, or none of them have it.

Working with collections

Noteworthy characteristics

Collections are meant for storing/denormalizing relatively small amount of data. They work well for things like the phone numbers of a given user'',labels applied to an email'', etc. But when items are expected to grow unbounded (all the messages sent by a given user'', events registered by a sensor'', …), then collections are not appropriate anymore and a specific table (with clustering columns) should be used. Concretely, collections have the following limitations:

Collections are always read in their entirety (and reading one is not paged internally).
Collections cannot have more than 65535 elements. More precisely, while it may be possible to insert more than 65535 elements, it is not possible to read more than the 65535 first elements (see CASSANDRA-5428 for details).
While insertion operations on sets and maps never incur a read-before-write internally, some operations on lists do (see the section on lists below for details). It is thus advised to prefer sets to lists when possible.

Please note that while some of those limitations may or may not be loosened in the future, the general rule that collections are for denormalizing small amount of data is meant to stay.

Maps

A map is a typed set of key-value pairs, where keys are unique. Furthermore, note that the map are internally sorted by their keys and will thus always be returned in that order. To create a column of type map, use the map keyword suffixed with comma-separated key and value types, enclosed in angle brackets. For example:

CREATE TABLE users (
id text PRIMARY KEY,
given text,
surname text,
favs map<text, text>  -- A map of text keys, and text values
)

Writing map data is accomplished with a JSON-inspired syntax. To write a record using INSERT, specify the entire map as a JSON-style associative array. Note: This form will always replace the entire map.

// Inserting (or Updating)
INSERT INTO users (id, given, surname, favs)
VALUES ('jsmith', 'John', 'Smith', { 'fruit' : 'apple', 'band' :
'Beatles' })

Adding or updating key-values of a (potentially) existing map can be accomplished either by subscripting the map column in an UPDATE statement or by adding a new map literal:

// Updating (or inserting)
UPDATE users SET favs['author'] = 'Ed Poe' WHERE id = 'jsmith'
UPDATE users SET favs = favs + {'`movie' : 'Cassablanca' } WHERE id =
'jsmith'

Note that TTLs are allowed for both INSERT and UPDATE, but in both case the TTL set only apply to the newly inserted/updated values. In other words,

UPDATE users USING TTL 10 SET favs['color'] = 'green' WHERE id =
'jsmith'

will only apply the TTL to the { 'color' : 'green' } record, the rest of the map remaining unaffected.

Deleting a map record is done with:

DELETE favs['author'] FROM users WHERE id = 'jsmith'

Sets

A set is a typed collection of unique values. Sets are ordered by their values. To create a column of type set, use the set keyword suffixed with the value type enclosed in angle brackets. For example:

CREATE TABLE images (
name text PRIMARY KEY,
owner text,
date timestamp,
tags set
);

Writing a set is accomplished by comma separating the set values, and enclosing them in curly braces. Note: An INSERT will always replace the entire set.

INSERT INTO images (name, owner, date, tags)
VALUES ('cat.jpg', 'jsmith', 'now', { 'kitten', 'cat', 'pet' });

Adding and removing values of a set can be accomplished with an UPDATE by adding/removing new set values to an existing set column.

UPDATE images SET tags = tags + { 'cute', 'cuddly' } WHERE name =
'cat.jpg';
UPDATE images SET tags = tags - { 'lame' } WHERE name = 'cat.jpg';

As with maps, TTLs if used only apply to the newly inserted/updated values.

Lists

A list is a typed collection of non-unique values where elements are ordered by there position in the list. To create a column of type list, use the list keyword suffixed with the value type enclosed in angle brackets. For example:

CREATE TABLE plays (
id text PRIMARY KEY,
game text,
players int,
scores list
)

Do note that as explained below, lists have some limitations and performance considerations to take into account, and it is advised to prefer sets over lists when this is possible.

Writing list data is accomplished with a JSON-style syntax. To write a record using INSERT, specify the entire list as a JSON array. Note: An INSERT will always replace the entire list.

INSERT INTO plays (id, game, players, scores)
VALUES ('123-afde', 'quake', 3, [17, 4, 2]);

Adding (appending or prepending) values to a list can be accomplished by adding a new JSON-style array to an existing list column.

UPDATE plays SET players = 5, scores = scores + [ 14, 21 ] WHERE id =
'123-afde';
UPDATE plays SET players = 5, scores = [ 12 ] + scores WHERE id =
'123-afde';

It should be noted that append and prepend are not idempotent operations. This means that if an append or prepend operation timesout, it is not always safe to retry the operation (as this could result in the record appended or prepended twice).

Lists also provides the following operation: setting an element by its position in the list, removing an element by its position in the list and remove all the occurrence of a given value in the list. However, and contrarily to all the other collection operations, these three operations induce an internal read before the update, and will thus typically have slower performance characteristics. Those operations have the following syntax:

UPDATE plays SET scores[1] = 7 WHERE id = '123-afde'; // sets the 2nd
element of scores to 7 (raises an error is scores has less than 2
elements)

DELETE scores[1] FROM plays WHERE id = '123-afde'; // deletes the 2nd
element of scores (raises an error is scores has less than 2 elements)

UPDATE plays SET scores = scores - [ 12, 21 ] WHERE id = '123-afde'; // removes all occurrences of 12 and 21 from scores

As with maps, TTLs if used only apply to the newly inserted/updated values.

Working with vectors

Vectors are fixed-size sequences of non-null values of a certain data type. They use the same literals as lists.

You can define, insert and update a vector with:

CREATE TABLE plays (
    id text PRIMARY KEY,
    game text,
    players int,
    scores vector<int, 3> // A vector of 3 integers
)

INSERT INTO plays (id, game, players, scores)
           VALUES ('123-afde', 'quake', 3, [17, 4, 2]);

// Replace the existing vector entirely
UPDATE plays SET scores = [ 3, 9, 4] WHERE id = '123-afde';

Note that it isn’t possible to change the individual values of a vector, and it isn’t possible to select individual elements of a vector.

Functions

CQL3 distinguishes between built-in functions (so called `native functions') and user-defined functions. CQL3 includes several native functions, described below:

Cast

The cast function can be used to convert 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.

from to

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`

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'.

SELECT avg(cast(count as double)) FROM myTable

Token

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 Murmur3Partitioner, the return type is bigint.
For RandomPartitioner, the return type is varint.
For ByteOrderedPartitioner, the return type is blob.

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

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 as the primary key)), and the return type will be bigint.

Uuid

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

Timeuuid functions

`now`

The now function takes no arguments and generates, on the coordinator node, a new unique timeuuid (at the time where the statement using it is executed). Note that this method is useful for insertion but is largely non-sensical in WHERE clauses. For instance, a query of the form

SELECT * FROM myTable WHERE t = now();

will never return any result by design, since the value returned by now() is guaranteed to be unique.

`min_timeuuid` and `max_timeuuid`

The min_timeuuid (resp. max_timeuuid) function takes a timestamp value t (which can be either a timestamp or a date string ) and return a fake timeuuid corresponding to the smallest (resp. biggest) possible timeuuid having for timestamp t. So for instance:

SELECT * FROM myTable
 WHERE t > max_timeuuid('2013-01-01 00:05+0000')
   AND t < min_timeuuid('2013-02-02 10:00+0000');

will select all rows where the timeuuid column t is strictly older than 2013-01-01 00:05+0000' but strictly younger than `2013-02-02 10:00+0000'. Please note that `t >= max_timeuuid('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 > max_timeuuid('2013-01-01 00:05+0000').

Warning: We called the values generated by min_timeuuid and max_timeuuid fake UUID because they do no respect the Time-Based UUID generation process specified by the RFC 4122. In particular, the value returned by these 2 methods will not be unique. This means you should only use those methods for querying (as in the example above). Inserting the result of those methods is almost certainly a bad idea.

Time conversion functions

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

function name input type description

function name	input type	description
`to_date`	`timeuuid`	Converts the `timeuuid` argument into a `date` type
`to_date`	`timestamp`	Converts the `timestamp` argument into a `date` type
`to_timestamp`	`timeuuid`	Converts the `timeuuid` argument into a `timestamp` type
`to_timestamp`	`date`	Converts the `date` argument into a `timestamp` type
`to_unix_timestamp`	`timeuuid`	Converts the `timeuuid` argument into a `bigInt` raw value
`to_unix_timestamp`	`timestamp`	Converts the `timestamp` argument into a `bigInt` raw value
`to_unix_timestamp`	`date`	Converts the `date` argument into a `bigInt` raw value

to_date

timeuuid

Converts the timeuuid argument into a date type

to_date

timestamp

Converts the timestamp argument into a date type

to_timestamp

timeuuid

Converts the timeuuid argument into a timestamp type

to_timestamp

date

Converts the date argument into a timestamp type

to_unix_timestamp

timeuuid

Converts the timeuuid argument into a bigInt raw value

to_unix_timestamp

timestamp

Converts the timestamp argument into a bigInt raw value

to_unix_timestamp

date

Converts the date argument into a bigInt raw value

Blob conversion functions

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 type_as_blob takes a argument of type type and return it as a blob. Conversely, the function blob_as_type takes a 64-bit blob argument and convert it to a bigint value. And so for instance, bigint_as_blob(3) is 0x0000000000000003 and blob_as_bigint(0x0000000000000003) is 3.

Aggregates

Aggregate functions work on a set of rows. They receive values for each row and returns one value for the whole set.
If normal columns, scalar functions, UDT fields, writetime, maxwritetime or ttl are selected together with aggregate functions, the values returned for them will be the ones of the first row matching the query.

CQL3 distinguishes between built-in aggregates (so called `native aggregates') and user-defined aggregates. CQL3 includes several native aggregates, described below:

Count

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

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:

SELECT COUNT (scores) FROM plays;

Max and Min

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

SELECT MIN (players), MAX (players) FROM plays WHERE game = 'quake';

Sum

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

SELECT SUM (players) FROM plays;

Avg

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

SELECT AVG (players) FROM plays;

User-Defined Functions

User-defined functions allow execution of 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) has been removed in 3.0.11.

UDFs are part of the Cassandra schema. As such, they are automatically propagated to all nodes in the cluster.

UDFs can be overloaded - i.e. multiple UDFs with different argument types but the same function name. Example:

CREATE FUNCTION sample ( arg int ) ...;
CREATE FUNCTION sample ( arg text ) ...;

User-defined functions 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.

It is valid to use complex types like collections, tuple types and user-defined types as argument and return types. Tuple types and user-defined types are handled by the conversion functions of the DataStax Java Driver. Please see the documentation of the Java Driver 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 that you can use the double-quoted string syntax to enclose the UDF source code. For example:

CREATE FUNCTION some_function ( arg int )
RETURNS NULL ON NULL INPUT
RETURNS int
LANGUAGE java
AS $$ return arg; $$;

SELECT some_function(column) FROM atable ...;
UPDATE atable SET col = some_function(?) ...;

CREATE TYPE custom_type (txt text, i int);
CREATE FUNCTION fct_using_udt ( udtarg frozen )
RETURNS NULL ON NULL INPUT
RETURNS text
LANGUAGE java
AS $$ return udtarg.getString("txt"); $$;

User-defined functions can be used in SELECT, INSERT and UPDATE statements.

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

CREATE TYPE custom_type (txt text, i int);
CREATE FUNCTION fct_using_udt ( somearg int )
RETURNS NULL ON NULL INPUT
RETURNS custom_type
LANGUAGE java
AS $$
UDTValue udt = udfContext.newReturnUDTValue();
udt.setString("txt", "some string");
udt.setInt("i", 42);
return udt;
$$;

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

public interface UDFContext
{
UDTValue newArgUDTValue(String argName);
UDTValue newArgUDTValue(int argNum);
UDTValue newReturnUDTValue();
UDTValue newUDTValue(String udtName);
TupleValue newArgTupleValue(String argName);
TupleValue newArgTupleValue(int argNum);
TupleValue newReturnTupleValue();
TupleValue newTupleValue(String cqlDefinition);
}

Java UDFs already have some imports for common interfaces and classes defined. These imports are:
Please note, that these convenience imports are not available for script UDFs.

import java.nio.ByteBuffer;
import java.util.List;
import java.util.Map;
import java.util.Set;
import org.apache.cassandra.cql3.functions.UDFContext;
import com.datastax.driver.core.TypeCodec;
import com.datastax.driver.core.TupleValue;
import com.datastax.driver.core.UDTValue;

See CREATE FUNCTION and DROP FUNCTION.

User-Defined Aggregates

User-defined aggregates allow creation of custom aggregate functions using UDFs. Common examples of aggregate functions are count, min, and max.

Each aggregate requires an initial state (INITCOND, which defaults to null) of type STYPE. 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.

STYPE is mandatory in order to be able to distinguish possibly overloaded versions of the state and/or final function (since the overload can appear after creation of the aggregate).

User-defined aggregates can be used in SELECT statement.

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

CREATE OR REPLACE FUNCTION averageState ( state tuple<int,bigint>, val
int )
CALLED ON NULL INPUT
RETURNS tuple<int,bigint>
LANGUAGE java
AS ’
if (val != null) {
state.setInt(0, state.getInt(0)+1);
state.setLong(1, state.getLong(1)+val.intValue());
}
return state;
’;

CREATE OR REPLACE FUNCTION averageFinal ( state tuple<int,bigint> )
CALLED ON NULL INPUT
RETURNS double
LANGUAGE java
AS ’
double r = 0;
if (state.getInt(0) == 0) return null;
r = state.getLong(1);
r /= state.getInt(0);
return Double.valueOf(r);
’;

CREATE OR REPLACE AGGREGATE average ( int )
SFUNC averageState
STYPE tuple<int,bigint>
FINALFUNC averageFinal
INITCOND (0, 0);

CREATE TABLE atable (
pk int PRIMARY KEY,
val int);
INSERT INTO atable (pk, val) VALUES (1,1);
INSERT INTO atable (pk, val) VALUES (2,2);
INSERT INTO atable (pk, val) VALUES (3,3);
INSERT INTO atable (pk, val) VALUES (4,4);
SELECT average(val) FROM atable;

See CREATE AGGREGATE and DROP AGGREGATE.

JSON Support

Cassandra 2.2 introduces JSON support to SELECT and INSERT statements. This support does not fundamentally alter the CQL API (for example, the schema is still enforced), it simply provides a convenient way to work with JSON documents.

SELECT JSON

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.

INSERT JSON

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:

INSERT INTO mytable JSON '{"\"myKey\"": 0, "value": 0}'

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

JSON Encoding of Cassandra Data Types

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 from_json() arguments) as well as the format Cassandra will use when returning data for SELECT JSON statements (and from_json()):

type formats accepted return format notes

type	formats accepted	return format	notes
`ascii`	string	string	Uses JSON’s `\u` character escape
`bigint`	integer, string	integer	String must be valid 64 bit integer
`blob`	string	string	String should be 0x followed by an even number of hex digits
`boolean`	boolean, string	boolean	String must be `true'' or`false''
`date`	string	string	Date in format `YYYY-MM-DD`, timezone UTC
`decimal`	integer, float, string	float	May exceed 32 or 64-bit IEEE-754 floating point precision in client-side decoder
`double`	integer, float, string	float	String must be valid integer or float
`float`	integer, float, string	float	String must be valid integer or float
`inet`	string	string	IPv4 or IPv6 address
`int`	integer, string	integer	String must be valid 32 bit integer
`list`	list, string	list	Uses JSON’s native list representation
`map`	map, string	map	Uses JSON’s native map representation
`smallint`	integer, string	integer	String must be valid 16 bit integer
`set`	list, string	list	Uses JSON’s native list representation
`text`	string	string	Uses JSON’s `\u` character escape
`time`	string	string	Time of day in format `HH-MM-SS[.fffffffff]`
`timestamp`	integer, string	string	A timestamp. Strings constant are allow to input timestamps as dates, see Working with dates below for more information. Datestamps with format `YYYY-MM-DD HH:MM:SS.SSS` are returned.
`timeuuid`	string	string	Type 1 UUID. See Constants for the UUID format
`tinyint`	integer, string	integer	String must be valid 8 bit integer
`tuple`	list, string	list	Uses JSON’s native list representation
`UDT`	map, string	map	Uses JSON’s native map representation with field names as keys
`uuid`	string	string	See Constants for the UUID format
`varchar`	string	string	Uses JSON’s `\u` character escape
`varint`	integer, string	integer	Variable length; may overflow 32 or 64 bit integers in client-side decoder

ascii

string

Uses JSON’s \u character escape

bigint

integer, string

integer

String must be valid 64 bit integer

blob

string

String should be 0x followed by an even number of hex digits

boolean

boolean, string

boolean

String must be true'' orfalse''

date

string

Date in format YYYY-MM-DD, timezone UTC

decimal

integer, float, string

float

May exceed 32 or 64-bit IEEE-754 floating point precision in client-side decoder

double

integer, float, string

float

String must be valid integer or float

float

integer, float, string

float

String must be valid integer or float

inet

string

IPv4 or IPv6 address

int

integer, string

integer

String must be valid 32 bit integer

list

list, string

list

Uses JSON’s native list representation

map

map, string

map

Uses JSON’s native map representation

smallint

integer, string

integer

String must be valid 16 bit integer

set

list, string

list

Uses JSON’s native list representation

text

string

Uses JSON’s \u character escape

time

string

Time of day in format HH-MM-SS[.fffffffff]

timestamp

integer, string

string

A timestamp. Strings constant are allow to input timestamps as dates, see Working with dates below for more information. Datestamps with format YYYY-MM-DD HH:MM:SS.SSS are returned.

timeuuid

string

Type 1 UUID. See Constants for the UUID format

tinyint

integer, string

integer

String must be valid 8 bit integer

tuple

list, string

list

Uses JSON’s native list representation

UDT

map, string

map

Uses JSON’s native map representation with field names as keys

uuid

string

See Constants for the UUID format

varchar

string

Uses JSON’s \u character escape

varint

integer, string

integer

Variable length; may overflow 32 or 64 bit integers in client-side decoder

The from_json() Function

The from_json() 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 to_json() Function

The to_json() 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.

Appendix A: CQL Keywords

CQL distinguishes between reserved and non-reserved keywords. Reserved keywords cannot be used as identifier, they are truly reserved for the language (but one can enclose a reserved keyword by double-quotes to use it as an identifier). Non-reserved keywords however only have a specific meaning in certain context but can used as identifier otherwise. The only raison d’être of these non-reserved keywords is convenience: some keyword are non-reserved when it was always easy for the parser to decide whether they were used as keywords or not.

Keyword Reserved?

Keyword	Reserved?
`ADD`	yes
`AGGREGATE`	no
`ALL`	no
`ALLOW`	yes
`ALTER`	yes
`AND`	yes
`APPLY`	yes
`AS`	no
`ASC`	yes
`ASCII`	no
`AUTHORIZE`	yes
`BATCH`	yes
`BEGIN`	yes
`BIGINT`	no
`BLOB`	no
`BOOLEAN`	no
`BY`	yes
`CALLED`	no
`CAST`	no
`CLUSTERING`	no
`COLUMNFAMILY`	yes
`COMPACT`	no
`CONTAINS`	no
`COUNT`	no
`COUNTER`	no
`CREATE`	yes
`CUSTOM`	no
`DATE`	no
`DECIMAL`	no
`DEFAULT`	yes
`DELETE`	yes
`DESC`	yes
`DESCRIBE`	yes
`DISTINCT`	no
`DOUBLE`	no
`DROP`	yes
`DURATION`	no
`ENTRIES`	yes
`EXECUTE`	yes
`EXISTS`	no
`FILTERING`	no
`FINALFUNC`	no
`FLOAT`	no
`FROM`	yes
`FROZEN`	no
`FULL`	yes
`FUNCTION`	no
`FUNCTIONS`	no
`GRANT`	yes
`GROUP`	no
`IF`	yes
`IN`	yes
`INDEX`	yes
`INET`	no
`INFINITY`	yes
`INITCOND`	no
`INPUT`	no
`INSERT`	yes
`INT`	no
`INTO`	yes
`IS`	yes
`JSON`	no
`KEY`	no
`KEYS`	no
`KEYSPACE`	yes
`KEYSPACES`	no
`LANGUAGE`	no
`LIKE`	no
`LIMIT`	yes
`LIST`	no
`LOGIN`	no
`MAP`	no
`MASKED`	no
`MATERIALIZED`	yes
`MBEAN`	yes
`MBEANS`	yes
`MODIFY`	yes
`NAN`	yes
`NOLOGIN`	no
`NORECURSIVE`	yes
`NOSUPERUSER`	no
`NOT`	yes
`NULL`	yes
`OF`	yes
`ON`	yes
`OPTIONS`	no
`OR`	yes
`ORDER`	yes
`PARTITION`	no
`PASSWORD`	no
`PER`	no
`PERMISSION`	no
`PERMISSIONS`	no
`PRIMARY`	yes
`RENAME`	yes
`REPLACE`	yes
`RETURNS`	no
`REVOKE`	yes
`ROLE`	no
`ROLES`	no
`SCHEMA`	yes
`SELECT`	yes
`SELECT_MASKED`	no
`SET`	yes
`SFUNC`	no
`SMALLINT`	no
`STATIC`	no
`STORAGE`	no
`STYPE`	no
`SUPERUSER`	no
`TABLE`	yes
`TEXT`	no
`TIME`	no
`TIMESTAMP`	no
`TIMEUUID`	no
`TINYINT`	no
`TO`	yes
`TOKEN`	yes
`TRIGGER`	no
`TRUNCATE`	yes
`TTL`	no
`TUPLE`	no
`TYPE`	no
`UNLOGGED`	yes
`UNMASK`	no
`UNSET`	yes
`UPDATE`	yes
`USE`	yes
`USER`	no
`USERS`	no
`USING`	yes
`UUID`	no
`VALUES`	no
`VARCHAR`	no
`VARINT`	no
`VIEW`	yes
`WHERE`	yes
`WITH`	yes
`WRITETIME`	no

ADD

yes

AGGREGATE

ALL

ALLOW

yes

ALTER

yes

AND

yes

APPLY

yes

AS

ASC

yes

ASCII

AUTHORIZE

yes

BATCH

yes

BEGIN

yes

BIGINT

BLOB

BOOLEAN

BY

yes

CALLED

CAST

CLUSTERING

COLUMNFAMILY

yes

COMPACT

CONTAINS

COUNT

COUNTER

CREATE

yes

CUSTOM

DATE

DECIMAL

DEFAULT

yes

DELETE

yes

DESC

yes

DESCRIBE

yes

DISTINCT

DOUBLE

DROP

yes

DURATION

ENTRIES

yes

EXECUTE

yes

EXISTS

FILTERING

FINALFUNC

FLOAT

FROM

yes

FROZEN

FULL

yes

FUNCTION

FUNCTIONS

GRANT

yes

GROUP

IF

yes

IN

yes

INDEX

yes

INET

INFINITY

yes

INITCOND

INPUT

INSERT

yes

INT

INTO

yes

IS

yes

JSON

KEY

KEYS

KEYSPACE

yes

KEYSPACES

LANGUAGE

LIKE

LIMIT

yes

LIST

LOGIN

MAP

MASKED

MATERIALIZED

yes

MBEAN

yes

MBEANS

yes

MODIFY

yes

NAN

yes

NOLOGIN

NORECURSIVE

yes

NOSUPERUSER

NOT

yes

NULL

yes

OF

yes

ON

yes

OPTIONS

OR

yes

ORDER

yes

PARTITION

PASSWORD

PER

PERMISSION

PERMISSIONS

PRIMARY

yes

RENAME

yes

REPLACE

yes

RETURNS

REVOKE

yes

ROLE

ROLES

SCHEMA

yes

SELECT

yes

SELECT_MASKED

SET

yes

SFUNC

SMALLINT

STATIC

STORAGE

STYPE

SUPERUSER

TABLE

yes

TEXT

TIME

TIMESTAMP

TIMEUUID

TINYINT

TO

yes

TOKEN

yes

TRIGGER

TRUNCATE

yes

TTL

TUPLE

TYPE

UNLOGGED

yes

UNMASK

UNSET

yes

UPDATE

yes

USE

yes

USER

USERS

USING

yes

UUID

VALUES

VARCHAR

VARINT

VIEW

yes

WHERE

yes

WITH

yes

WRITETIME

Appendix B: CQL Reserved Types

The following type names are not currently used by CQL, but are reserved for potential future use. User-defined types may not use reserved type names as their name.

type

type
`bitstring`
`byte`
`complex`
`date`
`enum`
`interval`
`macaddr`

bitstring

byte

complex

date

enum

interval

macaddr

CQL Changes

The following describes the changes in each version of CQL.

3.4.8

Add support for the BETWEEN operator in WHERE clauses (19604)
Add support for GENERATED PASSWORD clause (17457)
Add support for NOT operator in WHERE clauses ('18584')

3.4.7

Add vector similarity functions (18640)
Remove deprecated functions dateOf and unixTimestampOf, replaced by toTimestamp and toUnixTimestamp (18328)
Added support for attaching masking functions to table columns (18068)
Add UNMASK permission (18069)
Add SELECT_MASKED permission (18070)
Add support for using UDFs as masking functions (18071)
Adopt snake_case function names, deprecating all previous camelCase or alltogetherwithoutspaces function names (18037)
Add new vector data type (18504)

3.4.6

Add support for IF EXISTS and IF NOT EXISTS in ALTER statements (16916)
Allow GRANT/REVOKE multiple permissions in a single statement (17030)
Pre hashed passwords in CQL (17334)
Add support for type casting in WHERE clause components and in the values of INSERT/UPDATE statements (14337)
Add support for CONTAINS and CONTAINS KEY in conditional UPDATE and DELETE statement (10537)
Allow to grant permission for all tables in a keyspace (17027)
Allow to aggregate by time intervals (11871)

3.4.5

Adds support for arithmetic operators (11935)
Adds support for + and - operations on dates (11936)
Adds currentTimestamp, currentDate, currentTime and currentTimeUUID functions (13132)

3.4.4

ALTER TABLE ALTER has been removed; a column’s type may not be changed after creation (12443).
ALTER TYPE ALTER has been removed; a field’s type may not be changed after creation (12443).

3.4.3

Adds a new duration data types <data-types> (11873).
Support for GROUP BY (10707).
Adds a DEFAULT UNSET option for INSERT JSON to ignore omitted columns (11424).
Allows null as a legal value for TTL on insert and update. It will be treated as equivalent to inserting a 0 (12216).

3.4.2

If a table has a non zero default_time_to_live, then explicitly specifying a TTL of 0 in an INSERT or UPDATE statement will result in the new writes not having any expiration (that is, an explicit TTL of 0 cancels the default_time_to_live). This wasn’t the case before and the default_time_to_live was applied even though a TTL had been explicitly set.
ALTER TABLE ADD and DROP now allow multiple columns to be added/removed.
New PER PARTITION LIMIT option for SELECT statements (see CASSANDRA-7017).
User-defined functions <cql-functions> can now instantiate UDTValue and TupleValue instances via the new UDFContext interface (see CASSANDRA-10818).
User-defined types <udts> may now be stored in a non-frozen form, allowing individual fields to be updated and deleted in UPDATE statements and DELETE statements, respectively. (CASSANDRA-7423).

3.4.1

Adds CAST functions.

3.4.0

Support for materialized views <materialized-views>.
DELETE support for inequality expressions and IN restrictions on any primary key columns.
UPDATE support for IN restrictions on any primary key columns.

3.3.1

The syntax TRUNCATE TABLE X is now accepted as an alias for TRUNCATE X.

3.3.0

User-defined functions and aggregates <cql-functions> are now supported.
Allows double-dollar enclosed strings literals as an alternative to single-quote enclosed strings.
Introduces Roles to supersede user based authentication and access control
New date, time, tinyint and smallint data types <data-types> have been added.
JSON support <cql-json> has been added
Adds new time conversion functions and deprecate dateOf and unixTimestampOf.

3.2.0

User-defined types <udts> supported.
CREATE INDEX now supports indexing collection columns, including indexing the keys of map collections through the keys() function
Indexes on collections may be queried using the new CONTAINS and CONTAINS KEY operators
Tuple types <tuples> were added to hold fixed-length sets of typed positional fields.
DROP INDEX now supports optionally specifying a keyspace.

3.1.7

SELECT statements now support selecting multiple rows in a single partition using an IN clause on combinations of clustering columns.
IF NOT EXISTS and IF EXISTS syntax is now supported by CREATE USER and DROP USER statements, respectively.

3.1.6

A new uuid() method has been added.
Support for DELETE … IF EXISTS syntax.

3.1.5

It is now possible to group clustering columns in a relation, see WHERE <where-clause> clauses.
Added support for static columns <static-columns>.

3.1.4

CREATE INDEX now allows specifying options when creating CUSTOM indexes.

3.1.3

Millisecond precision formats have been added to the timestamp <timestamps> parser.

3.1.2

NaN and Infinity has been added as valid float constants. They are now reserved keywords. In the unlikely case you we using them as a column identifier (or keyspace/table one), you will now need to double quote them.

3.1.1

SELECT statement now allows listing the partition keys (using the DISTINCT modifier). See CASSANDRA-4536.
The syntax c IN ? is now supported in WHERE clauses. In that case, the value expected for the bind variable will be a list of whatever type c is.
It is now possible to use named bind variables (using :name instead of ?).

3.1.0

ALTER TABLE DROP option added.
SELECT statement now supports aliases in select clause. Aliases in WHERE and ORDER BY clauses are not supported.
CREATE statements for KEYSPACE, TABLE and INDEX now supports an IF NOT EXISTS condition. Similarly, DROP statements support a IF EXISTS condition.
INSERT statements optionally supports a IF NOT EXISTS condition and UPDATE supports IF conditions.

3.0.5

SELECT, UPDATE, and DELETE statements now allow empty IN relations (see CASSANDRA-5626).

3.0.4

Updated the syntax for custom secondary indexes <secondary-indexes>.
Non-equal condition on the partition key are now never supported, even for ordering partitioner as this was not correct (the order was not the one of the type of the partition key). Instead, the token method should always be used for range queries on the partition key (see WHERE clauses <where-clause>).

3.0.3

Support for custom secondary indexes <secondary-indexes> has been added.

3.0.2

Type validation for the constants <constants> has been fixed. For instance, the implementation used to allow '2' as a valid value for an int column (interpreting it has the equivalent of 2), or 42 as a valid blob value (in which case 42 was interpreted as an hexadecimal representation of the blob). This is no longer the case, type validation of constants is now more strict. See the data types <data-types> section for details on which constant is allowed for which type.
The type validation fixed of the previous point has lead to the introduction of blobs constants to allow the input of blobs. Do note that while the input of blobs as strings constant is still supported by this version (to allow smoother transition to blob constant), it is now deprecated and will be removed by a future version. If you were using strings as blobs, you should thus update your client code ASAP to switch blob constants.
A number of functions to convert native types to blobs have also been introduced. Furthermore the token function is now also allowed in select clauses. See the section on functions <cql-functions> for details.

3.0.1

Date strings (and timestamps) are no longer accepted as valid timeuuid values. Doing so was a bug in the sense that date string are not valid timeuuid, and it was thus resulting in confusing behaviors. However, the following new methods have been added to help working with timeuuid: now, minTimeuuid, maxTimeuuid , dateOf and unixTimestampOf.
Float constants now support the exponent notation. In other words, 4.2E10 is now a valid floating point value.

Versioning

Versioning of the CQL language adheres to the Semantic Versioning guidelines. Versions take the form X.Y.Z where X, Y, and Z are integer values representing major, minor, and patch level respectively. There is no correlation between Cassandra release versions and the CQL language version.

version	description
Major	The major version must be bumped when backward incompatible changes are introduced. This should rarely occur.
Minor	Minor version increments occur when new, but backward compatible, functionality is introduced.
Patch	The patch version is incremented when bugs are fixed.

version

description

Major

The major version must be bumped when backward incompatible changes are introduced. This should rarely occur.

Minor

Minor version increments occur when new, but backward compatible, functionality is introduced.

Patch

The patch version is incremented when bugs are fixed.

Cassandra Documentation

Version:

Cassandra Query Language (CQL) v3.4.3

CQL Syntax

Preamble

Conventions

Identifiers and keywords

Constants

Terms

Comments

Statements

Prepared Statement

Prepared Statement Caching

Data Definition

CREATE KEYSPACE

USE

ALTER KEYSPACE

DROP KEYSPACE

CREATE TABLE

<tablename>

<column-definition>

Partition key and clustering columns

STATIC columns

<option>

Compaction options

Compression options

Caching options

Other considerations:

CREATE TABLE LIKE

<tablename>

<like option>

ALTER TABLE

CQL type compatibility:

DROP TABLE

TRUNCATE

CREATE INDEX

Index Types

Indexes on Map Keys

DROP INDEX

CREATE MATERIALIZED VIEW

WHERE Clause

ALTER MATERIALIZED VIEW

DROP MATERIALIZED VIEW

CREATE TYPE

<typename>

ALTER TYPE

DROP TYPE

CREATE TRIGGER

DROP TRIGGER

CREATE FUNCTION

Function Signature

DROP FUNCTION

CREATE AGGREGATE

DROP AGGREGATE

Data Manipulation

INSERT

UPDATE

<options>

DELETE

BATCH

UNLOGGED

COUNTER

<option>

Queries

SELECT

<select-clause>

<where-clause>

<order-by>

<group-by>

LIMIT and PER PARTITION LIMIT

ALLOW FILTERING

Database Roles

CREATE ROLE

Setting credentials for internal authentication

Restricting connections to specific datacenters

Restricting connections from specific CIDR groups

Creating a role conditionally

ALTER ROLE

Restricting connections to specific datacenters

Restricting connections from specific CIDR groups

`<tablename>`

`<column-definition>`

`STATIC` columns

`<option>`

`<tablename>`

`<like option>`

`WHERE` Clause

`<typename>`

`<options>`

`UNLOGGED`

`COUNTER`

`<option>`

`<select-clause>`

`<where-clause>`

`<order-by>`

`<group-by>`

`LIMIT` and `PER PARTITION LIMIT`

`ALLOW FILTERING`

Conditions on executing `ALTER ROLE` statements:

`now`

`min_timeuuid` and `max_timeuuid`