= Cassandra Query Language (CQL) v3.4.3

:toc:
:toclevels: 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 link:#changes[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
http://en.wikipedia.org/wiki/Backus%E2%80%93Naur_Form[BNF] -like
notation:

[source,bnf]
::= 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:

[source,sql]
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]]
=== 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-Z]``[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 link:#appendixA[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-Z]``[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 http://en.wikipedia.org/wiki/Universally_unique_identifier[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 link:#types[data types
section].

== Terms

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

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

A term is thus one of:

* A xref:cassandra:developing/cql/definitions.adoc#constants[constant]
* A literal for either a xref:cassandra:developing/cql/types.adoc#collections[collection], a xref:cassandra:developing/cql/types.adoc#vectors[vector],
a xref:cassandra:developing/cql/types.adoc#udts[user-defined type] or a xref:cassandra:developing/cql/types.adoc#tuples[tuple]
* A xref:cassandra:developing/cql/functions.adoc#cql-functions[function] call, either a xref:cassandra:developing/cql/functions.adoc#scalar-native-functions[native function]
or a xref:cassandra:developing/cql/functions.adoc#user-defined-scalar-functions[user-defined function]
* An xref:cassandra:developing/cql/operators.adoc#arithmetic_operators[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).

[source,sql]
----
-- 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:

[source,bnf]
----
::= 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
link:#preparedStatement[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
link:#functions[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:_

[source,text]
----
'some string value'
$$A King's ransom$$
----

$$double-dollar string can contain single ’ quotes$$

[[preparedStatement]]
=== 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
https://github.com/ben-manes/caffeine[Caffeine]-managed cache which
can be configured using
xref:managing/configuration/cass_yaml_file.adoc#_prepared_statements_cache_size[`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
https://github.com/ben-manes/caffeine/wiki/Eviction[evicted],
leading to additional overhead as previously prepared statements must
be re-prepared.

[[dataDefinition]]
== Data Definition

[[createKeyspaceStmt]]
=== CREATE KEYSPACE

_Syntax:_

[source,bnf]
include::cassandra:example$BNF/create_ks.bnf[]

_Sample:_

[source,sql]
----
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:

[cols=",,,,",options="header",]
|===
|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!).
|===

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.

[[useStmt]]
=== USE

_Syntax:_

[source,bnf]
include::cassandra:example$BNF/use_ks.bnf[]

_Sample:_

[source,sql]
include::cassandra:example$CQL/use_ks.cql[]

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 link:#statements[otherwise specified], until
another USE statement is issued or the connection terminates.

[[alterKeyspaceStmt]]
=== ALTER KEYSPACE

_Syntax:_

[source,bnf]
include::cassandra:example$BNF/alter_ks.bnf[]

_Sample:_

[source,sql]
include::cassandra:example$CQL/alter_ks.cql[]

The `ALTER KEYSPACE` statement alters the properties of an existing
keyspace. The supported `<properties>` are the same as for the
link:#createKeyspaceStmt[`CREATE KEYSPACE`] statement.

[[dropKeyspaceStmt]]
=== DROP KEYSPACE

_Syntax:_

[source,bnf]
::= DROP KEYSPACE [ IF EXISTS ]

_Sample:_

[source,sql]
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.

[[createTableStmt]]
=== CREATE TABLE

_Syntax:_

[source,bnf]
include::cassandra:example$BNF/create_table.bnf[]

_Sample:_

[source,sql]
----
include::cassandra:example$CQL/create_table.cql[]
----

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
link:#createTableName[name], it defines the columns composing rows of
the table and have a number of link:#createTableOptions[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.

[[createTableName]]
==== `<tablename>`

Valid table names are the same as valid
link:#createKeyspaceStmt[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
link:#statements[`<tablename>`] grammar), it is created in the specified
keyspace (but does *not* change the current keyspace).

[[createTableColumn]]
==== `<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 link:#types[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:

[source,sql]
----
CREATE TABLE t (
k int PRIMARY KEY,
other text
)
----

is equivalent to

[source,sql]
----
CREATE TABLE t (
k int,
other text,
PRIMARY KEY (k)
)
----

[[createTablepartitionClustering]]
==== 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`).

[[createTableStatic]]
==== `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:

[source,sql]
----
include::cassandra:example$CQL/create_static_column.cql[]

include::cassandra:example$CQL/insert_static_data.cql[]
include::cassandra:example$CQL/select_static_data.cql[]
----

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

[[createTableOptions]]
==== `<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
http://www.datastax.com/dev/blog/thrift-to-cql3[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 link:#selectOrderBy[what `ORDER BY` are allowed
during `SELECT`].

Table creation supports the following other `<property>`:

[cols=",,,",options="header",]
|===
|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
link:#compactionOptions[below].

|`compression` |_map_ |_see below_ |Compression options, see
link:#compressionOptions[below].

|`caching` |_map_ |_see below_ |Caching options, see
link:#cachingOptions[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.
|===

[[compactionOptions]]
==== 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 link:#constants[string constant].
The rest of the sub-options depends on the chosen class. The sub-options
supported by the default classes are:

[cols=",,,",options="header",]
|===
|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`.
|===

[[compressionOptions]]
===== Compression options

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

[cols=",,,,,",options="header",]
|===
|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 link:#constants[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
|===

[[cachingOptions]]
===== Caching options

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

[cols=",,",options="header",]
|===
|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.
|===

===== Other considerations:

* When link:#insertStmt[inserting] / link:#updateStmt[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.

[[copyTableStmt]]
=== CREATE TABLE LIKE

_Syntax:_

[source,bnf]
include::cassandra:example$BNF/create_table_like.bnf[]

_Sample:_

[source,sql]
----
include::cassandra:example$CQL/create_table_like.cql[]
----

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.

[[copyTableStmt]]
==== `<tablename>`

Valid the newly created table names are the same as valid
link:#createTableStmt[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
link:#copyTableStmt[`<tablename>`] grammar), it is created in the specified
keyspace (but does *not* change the current keyspace).

[[createTableLikeOptions]]
==== `<like option>`

The table options specified by `CREATE TABLE LIKE` statement are the same
as the supported link:#createTableOptions[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.

[[alterTableStmt]]
==== ALTER TABLE

_Syntax:_

[source,bnf]
include::cassandra:example$BNF/alter_table.bnf[]


_Sample:_

[source,sql]
----
include::cassandra:example$CQL/alter_table_add_column.cql[]
include::cassandra:example$CQL/alter_table_with_comment.cql[]
----

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
link:#createTableOptions[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.

[cols=",",options="header",]
|===
|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.

[cols=",",options="header",]
|===
|Data type may be altered to: |Data type
|ascii, text, varchar |blob
|ascii, varchar |text
|ascii, text |varchar
|===

[[dropTableStmt]]
==== DROP TABLE

_Syntax:_

[source,bnf]
include::cassandra:example$BNF/drop_table.bnf[]

_Sample:_

[source,sql]
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.

[[truncateStmt]]
==== TRUNCATE

_Syntax:_

[source,bnf]
include::cassandra:example$BNF/truncate_table.bnf[]

_Sample:_

[source,sql]
TRUNCATE superImportantData;

The `TRUNCATE` statement permanently removes all data from a table.

[[createIndexStmt]]
==== 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:_

[source,bnf]
include::cassandra:example$BNF/create_index_statement.bnf[]

_Sample:_

[source,sql]
include::cassandra:example$CQL/create_index.cql[]

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.

[[indexTypes]]
===== 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.

[[keysIndex]]
===== Indexes on Map Keys

When creating an index on a link:#map[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.

[[dropIndexStmt]]
==== DROP INDEX

_Syntax:_

[source,bnf]
include::cassandra:example$BNF/drop_index_statement.bnf[]

_Sample:_

[source,sql]
----
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.

[[createMVStmt]]
==== CREATE MATERIALIZED VIEW

_Syntax:_

[source,bnf]
include::cassandra:example$BNF/create_mv_statement.bnf[]

_Sample:_

[source,sql]
include::cassandra:example$CQL/create_mv_statement.cql[]

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.

[[createMVWhere]]
===== `WHERE` Clause

The `<where-clause>` is similar to the link:#selectWhere[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.)

[[alterMVStmt]]
==== ALTER MATERIALIZED VIEW

_Syntax:_

[source,bnf]
::= 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.

[[dropMVStmt]]
==== DROP MATERIALIZED VIEW

_Syntax:_

[source,bnf]
include::cassandra:example$BNF/drop_mv_statement.bnf[]

_Sample:_

[source,sql]
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.

[[createTypeStmt]]
==== CREATE TYPE

_Syntax:_

[source,bnf]
include::cassandra:example$BNF/create_type.bnf[]

_Sample:_

[source,sql]
----
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.

[[createTypeName]]
===== `<typename>`

Valid type names are identifiers. The names of existing CQL types and
link:#appendixB[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.

[[alterTypeStmt]]
==== ALTER TYPE

_Syntax:_

[source,bnf]
----
::= ALTER TYPE [ IF EXISTS ]

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

_Sample:_

[source,sql]
----
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.

[[dropTypeStmt]]
==== DROP TYPE

_Syntax:_

[source,bnf]
::= 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.

[[createTriggerStmt]]
==== CREATE TRIGGER

_Syntax:_

[source,bnf]
include::cassandra:example$BNF/create_trigger_statement.bnf[]

_Sample:_

[source,sql]
include::cassandra:example$CQL/create_trigger.cql[]

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.

[[dropTriggerStmt]]
==== DROP TRIGGER

_Syntax:_

[source,bnf]
include::cassandra:example$BNF/drop_trigger_statement.bnf[]

_Sample:_

[source,sql]
include::cassandra:example$CQL/drop_trigger.cql[]

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

[[createFunctionStmt]]
==== CREATE FUNCTION

_Syntax:_

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

_Sample:_

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

`CREATE FUNCTION` creates or replaces a user-defined function.

[[functionSignature]]
===== 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 link:#useStmt[`USE`] statement). It is not possible
to create a user-defined function in one of the system keyspaces.

See the section on link:#udfs[user-defined functions] for more
information.

[[dropFunctionStmt]]
==== DROP FUNCTION

_Syntax:_

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

_Sample:_

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

`DROP FUNCTION` statement removes a function created using
`CREATE FUNCTION`. +
You must specify the argument types (link:#functionSignature[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.

[[createAggregateStmt]]
==== CREATE AGGREGATE

_Syntax:_

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

_Sample:_

[source,sql]
----
CREATE AGGREGATE myaggregate ( val text )
SFUNC myaggregate_state
STYPE text
FINALFUNC myaggregate_final
INITCOND 'foo';
----

See the section on link:#udas[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 link:#useStmt[`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
link:#functionSignature[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 link:#udas[user-defined aggregates] for more
information.

[[dropAggregateStmt]]
==== DROP AGGREGATE

_Syntax:_

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

_Sample:_

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

The `DROP AGGREGATE` statement removes an aggregate created using
`CREATE AGGREGATE`. You must specify the argument types of the aggregate
to drop if there are multiple 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
link:#functionSignature[same rules] as for user-defined functions.

[[dataManipulation]]
=== Data Manipulation

[[insertStmt]]
==== INSERT

_Syntax:_

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

_Sample:_

[source,sql]
----
include::cassandra:example$CQL/insert_statement.cql[]
----

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
link:#insertJson[`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 link:#updateOptions[`UPDATE`] section for
information on the `<option>` available and to the
link:#collections[collections] section for use of
`<collection-literal>`. Also note that `INSERT` does not support
counters, while `UPDATE` does.

[[updateStmt]]
==== UPDATE

_Syntax:_

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

_Sample:_

[source,sql]
----
include::cassandra:example$CQL/update_statement.cql[]
----

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
link:#collections[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.

[[updateOptions]]
===== `<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.

[[deleteStmt]]
==== DELETE

_Syntax:_

[source,bnf]
include::cassandra:example$BNF/delete_statement.bnf[]

_Sample:_

[source,sql]
----
include::cassandra:example$CQL/delete_statement.cql[]
----

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 link:#collections[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
link:#updateStmt[`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.

[[batchStmt]]
==== BATCH

_Syntax:_

[source,bnf]
include::cassandra:example$BNF/batch_statement.bnf[]

_Sample:_

[source,sql]
include::cassandra:example$CQL/batch_statement.cql[]

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 link:#unloggedBatch[`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
http://wiki.apache.org/cassandra/FAQ#clocktie[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.

[[unloggedBatch]]
===== `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.

[[counterBatch]]
===== `COUNTER`

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

[[batchOptions]]
===== `<option>`

`BATCH` supports both the `TIMESTAMP` option, with similar semantic to
the one described in the link:#updateOptions[`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

[[selectStmt]]
==== SELECT

_Syntax:_

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

_Sample:_

[source,sql]
----
include::cassandra:example$CQL/select_statement.cql[]
----

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 link:#selectJson[`SELECT JSON`] for
more details.

[[selectSelection]]
===== `<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 link:#functions[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 link:#castFun[`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.

[[selectWhere]]
===== `<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 link:#createIndexStmt[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

[source,sql]
----
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:

[source,sql]
----
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):

[source,sql]
----
// 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:

[source,sql]
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:

[source,sql]
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:

[source,sql]
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`:

[source,sql]
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.

[[selectOrderBy]]
===== `<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
link:#createTableOptions[`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.

[[selectGroupBy]]
===== `<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.

[[selectLimit]]
===== `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.

[[selectAllowFiltering]]
===== `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 those ``non 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:

[source,sql]
include::cassandra:example$CQL/allow_filtering.cql[]

Then the following queries are valid:

[source,sql]
----
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:

[source,sql]
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:

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

include::security.adoc[]

[[types]]
=== 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:

[source,bnf]
----
::=
|
|
| // 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 link:#constants[constants] each type
supports:

[cols=",,",options="header",]
|===
|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
link:#counters[Counters] for details

|`date` |integers, strings |A date (with no corresponding time value).
See link:#usingdates[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
link:#usingtime[Working with time] below for more information.

|`timestamp` |integers, strings |A timestamp. Strings constant are allow
to input timestamps as dates, see link:#usingtimestamps[Working with
timestamps] below for more information.

|`timeuuid` |uuids |Type 1 UUID. This is generally used as a
``conflict-free'' timestamp. Also see the link:#timeuuidFun[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
link:#collections[Working with collections] section below.

[[usingtimestamps]]
==== 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.

[[usingdates]]
==== 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`

[[usingtime]]
==== 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
link:#updateStmt[`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
http://wiki.apache.org/cassandra/Counters[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.

[[collections]]
==== 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
https://issues.apache.org/jira/browse/CASSANDRA-5428[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.

[[map]]
===== Maps

A `map` is a link:#types[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:

[source,sql]
----
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._

[source,sql]
----
// 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:

[source,sql]
----
// 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,

[source,sql]
// Updating (or inserting)
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:

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

[[set]]
===== Sets

A `set` is a link:#types[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:

[source,sql]
----
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._

[source,sql]
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.

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

As with link:#map[maps], TTLs if used only apply to the newly
inserted/updated _values_.

[[list]]
===== Lists

A `list` is a link:#types[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:

[source,sql]
----
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 link:#set[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._

[source,sql]
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.

[source,sql]
----
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:

[source,sql]
----
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 link:#map[maps], TTLs if used only apply to the newly
inserted/updated _values_.

[[vectors]]
==== 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:

[source,sql]
----
include::cassandra:example$CQL/vector.cql[]
----

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 link:#udfs[user-defined functions]. CQL3 includes
several native functions, described below:

[[castFun]]
==== 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.

[cols=",",options="header",]
|===
|from |to
|`ascii` |`text`, `varchar`

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

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

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

|`date` |`timestamp`

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[[tokenFun]]
==== 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

[source,sql]
----
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`.

[[uuidFun]]
==== Uuid

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

[[timeuuidFun]]
==== 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

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

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 link:#usingtimestamps[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:

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

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
http://www.ietf.org/rfc/rfc4122.txt[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_.

[[timeFun]]
==== Time conversion functions

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

[cols=",,",options="header",]
|===
|function name |input type |description
|`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
|===

[[blobFun]]
==== 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 link:#udas[user-defined aggregates]. CQL3 includes
several native aggregates, described below:

[[countFct]]
==== Count

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

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

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

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

[[maxMinFcts]]
==== 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.

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

[[sumFct]]
==== Sum

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

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

[[avgFct]]
==== Avg

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

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

[[udfs]]
=== 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:

[source,sql]
----
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:

[source,sql]
----
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(?) ...;
----

[source,sql]
----
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 link:#selectStmt[`SELECT`],
link:#insertStmt[`INSERT`] and link:#updateStmt[`UPDATE`] statements.

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

[source,sql]
----
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`.

[source,java]
----
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.

[source,java]
----
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 link:#createFunctionStmt[`CREATE FUNCTION`] and
link:#dropFunctionStmt[`DROP FUNCTION`].

[[udas]]
=== User-Defined Aggregates

User-defined aggregates allow creation of custom aggregate functions
using link:#udfs[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 link:#selectStmt[`SELECT`]
statement.

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

[source,sql]
----
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 link:#createAggregateStmt[`CREATE AGGREGATE`] and
link:#dropAggregateStmt[`DROP AGGREGATE`].

[[json]]
=== JSON Support

Cassandra 2.2 introduces JSON support to link:#selectStmt[`SELECT`] and
link:#insertStmt[`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.

[[selectJson]]
==== 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.

[[insertJson]]
==== 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:

[source,sql]
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).

[[jsonEncoding]]
==== 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()`):

[cols=",,,",options="header",]
|===
|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 link:#usingdates[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 link:#constants[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 link:#constants[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
|===

[[from_json]]
==== 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.

[[to_json]]
==== 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.

[[appendixA]]
=== 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.

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

[[appendixB]]
=== 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.

[cols="",options="header",]
|===
|type
|`bitstring`
|`byte`
|`complex`
|`date`
|`enum`
|`interval`
|`macaddr`
|===

include::changes.adoc[]
