CQL

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# Cassandra Query Language (CQL) v3.4.7

1. Cassandra Query Language (CQL) v3.4.7
   1. CQL Syntax
      1. Preamble
      2. Conventions
      3. Identifiers and keywords
      4. Constants
      5. Comments
      6. Statements
      7. Prepared Statement
   2. Data Definition
      1. CREATE KEYSPACE
      2. USE
      3. ALTER KEYSPACE
      4. DROP KEYSPACE
      5. CREATE TABLE
      6. ALTER TABLE
      7. DROP TABLE
      8. TRUNCATE
      9. CREATE INDEX
      10. DROP INDEX
      11. CREATE MATERIALIZED VIEW
      12. ALTER MATERIALIZED VIEW
      13. DROP MATERIALIZED VIEW
      14. CREATE TYPE
      15. ALTER TYPE
      16. DROP TYPE
      17. CREATE TRIGGER
      18. DROP TRIGGER
      19. CREATE FUNCTION
      20. DROP FUNCTION
      21. CREATE AGGREGATE
      22. DROP AGGREGATE
   3. Data Manipulation
      1. INSERT
      2. UPDATE
      3. DELETE
      4. BATCH
   4. Queries
      1. SELECT
   5. Database Roles
      1. CREATE ROLE
      2. ALTER ROLE
      3. DROP ROLE
      4. GRANT ROLE
      5. REVOKE ROLE
      6. CREATE USER
      7. ALTER USER
      8. DROP USER
      9. LIST USERS
   6. Data Control
      1. Permissions
      2. GRANT PERMISSION
      3. REVOKE PERMISSION
   7. Data Types
      1. Working with timestamps
      2. Working with dates
      3. Working with time
      4. Working with durations
      5. Counters
      6. Working with collections
      7. Working with vectors
   8. Arithmetic Operators
      1. Number Arithmetic
      2. Datetime Arithmetic
   9. Functions
      1. Cast
      2. Token
      3. Uuid
      4. Timeuuid functions
      5. Datetime functions
      6. Blob conversion functions
   10. Aggregates
       1. Count
       2. Max and Min
       3. Sum
       4. Avg
   11. User-Defined Functions
   12. User-Defined Aggregates
   13. JSON Support
       1. SELECT JSON
       2. INSERT JSON
       3. JSON Encoding of Cassandra Data Types
       4. The from_json() Function
       5. The to_json() Function
   14. Appendix A: CQL Keywords
   15. Appendix B: CQL Reserved Types
   16. Changes
       1. 3.4.7
       2. 3.4.6
       3. 3.4.5
       4. 3.4.4
       5. 3.4.3
       6. 3.4.2
       7. 3.4.1
       8. 3.4.0
       9. 3.3.1
       10. 3.3.0
       11. 3.2.0
       12. 3.1.7
       13. 3.1.6
       14. 3.1.5
       15. 3.1.4
       16. 3.1.3
       17. 3.1.2
       18. 3.1.1
       19. 3.1.0
       20. 3.0.5
       21. 3.0.4
       22. 3.0.3
       23. 3.0.2
       24. 3.0.1
   17. Versioning

## CQL Syntax

### Preamble

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

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

### Conventions

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

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

    <start> ::= TERMINAL <non-terminal1> <non-terminal1>

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

    SELECT sample_usage FROM cql;

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

### Identifiers and keywords

The CQL language uses identifiers (or names) to identify tables, columns and other objects. An identifier is a token matching the regular expression `[a-zA-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 Appendix A.

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

There is a second kind of identifiers called quoted identifiers defined by enclosing an arbitrary sequence of characters in double-quotes(`"`). Quoted identifiers are never keywords. Thus `"select"` is not a reserved keyword and can be used to refer to a column, while `select` would raise a parse error. Also, contrarily to unquoted identifiers and keywords, quoted identifiers are case sensitive (`"My Quoted Id"` is different from `"my quoted id"`). A fully lowercase quoted identifier that matches `[a-zA-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 allows to declare columns with arbitrary names, and those can sometime clash with specific names used by the server. For instance, when using conditional update, the server will respond with a result-set containing a special result named `"[applied]"`. If you’ve declared a column with such a name, this could potentially confuse some tools and should be avoided. In general, unquoted identifiers should be preferred but if you use quoted identifiers, it is strongly advised to avoid any name enclosed by squared brackets (like `"[applied]"`) and any name that looks like a function call (like `"f(x)"`).

### Constants

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

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

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

### Comments

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

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

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

### Statements

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

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

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

    <identifier> ::= any quoted or unquoted identifier, excluding reserved keywords
     <tablename> ::= (<identifier> '.')? <identifier>

        <string> ::= a string constant
       <integer> ::= an integer constant
         <float> ::= a float constant
        <number> ::= <integer> | <float>
          <uuid> ::= a uuid constant
       <boolean> ::= a boolean constant
           <hex> ::= a blob constant

      <constant> ::= <string>
                   | <number>
                   | <uuid>
                   | <boolean>
                   | <hex>
      <variable> ::= '?'
                   | ':' <identifier>
          <term> ::= <constant>
                   | <collection-literal>
                   | <variable>
                   | <function> '(' (<term> (',' <term>)*)? ')'
                   | CAST '(' <term> AS <type> ')'

      <collection-literal> ::= <map-literal>
                             | <set-literal>
                             | <list-literal>
             <map-literal> ::= '{' ( <term> ':' <term> ( ',' <term> ':' <term> )* )? '}'
             <set-literal> ::= '{' ( <term> ( ',' <term> )* )? '}'
            <list-literal> ::= '[' ( <term> ( ',' <term> )* )? ']'

      <vector-literal> ::= '[' ( <term> ( ',' <term> )* )? ']'

      <function> ::= <identifier>

      <properties> ::= <property> (AND <property>)*
        <property> ::= <identifier> '=' ( <identifier> | <constant> | <map-literal> )

Please note that not every possible productions of the grammar above will be valid in practice. Most notably, `<variable>` and nested `<collection-literal>` are currently not allowed inside `<collection-literal>`.

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

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

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

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

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

Sample:

      'some string value'

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

### Prepared Statement

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

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

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

## Data Definition

### CREATE KEYSPACE

Syntax:

    <create-keyspace-stmt> ::= CREATE KEYSPACE (IF NOT EXISTS)? <identifier> WITH <properties>

Sample:

    CREATE KEYSPACE Excelsior
               WITH replication = {'class': 'SimpleStrategy', 'replication_factor' : 3};

    CREATE KEYSPACE Excalibur
               WITH replication = {'class': 'NetworkTopologyStrategy', 'DC1' : 1, 'DC2' : 3}
                AND durable_writes = false;

The `CREATE KEYSPACE` statement creates a new top-level keyspace. A keyspace is a namespace that defines a replication strategy and some options for a set of tables. Valid keyspaces names are identifiers composed exclusively of alphanumerical characters and whose length is lesser or equal to 32. Note that as identifiers, keyspace names are case insensitive: use a quoted identifier for case sensitive keyspace names.

The supported `<properties>` for `CREATE KEYSPACE` are:

name | kind | mandatory | default | description
--- | --- | --- | --- | ---
`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.

### USE

Syntax:

    <use-stmt> ::= USE <identifier>

Sample:

    USE myApp;

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

### ALTER KEYSPACE

Syntax:

    <create-keyspace-stmt> ::= ALTER KEYSPACE (IF EXISTS)? <identifier> WITH <properties>

Sample:

    ALTER KEYSPACE Excelsior
              WITH replication = {'class': 'SimpleStrategy', 'replication_factor' : 4};

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

### DROP KEYSPACE

Syntax:

    <drop-keyspace-stmt> ::= DROP KEYSPACE ( IF EXISTS )? <identifier>

Sample:

    DROP KEYSPACE myApp;

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

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

### CREATE TABLE

Syntax:

    <create-table-stmt> ::= CREATE ( TABLE | COLUMNFAMILY ) ( IF NOT EXISTS )? <tablename>
                              '(' <column-definition> ( ',' <column-definition> )* ')'
                              ( WITH <option> ( AND <option>)* )?

    <column-definition> ::= <identifier> <type> ( STATIC )? ( <column_mask> )? ( PRIMARY KEY )?
                          | PRIMARY KEY '(' <partition-key> ( ',' <identifier> )* ')'

    <column-mask> ::= MASKED WITH ( DEFAULT | <function> '(' ( <term> (',' <term>)* )?  ')' )

    <partition-key> ::= <identifier>
                      | '(' <identifier> (',' <identifier> )* ')'

    <option> ::= <property>
               | COMPACT STORAGE
               | CLUSTERING ORDER

Sample:

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

    CREATE TABLE timeline (
        userid uuid,
        posted_month int,
        posted_time uuid,
        body text,
        posted_by text,
        PRIMARY KEY (userid, posted_month, posted_time)
    ) WITH compaction = { 'class' : 'LeveledCompactionStrategy' };

The `CREATE TABLE` statement creates a new table. Each such table is a set of rows (usually representing related entities) for which it defines a number of properties. A table is defined by a name, it defines the columns composing rows of the table and have a number of options. Note that the `CREATE COLUMNFAMILY` syntax is supported as an alias for `CREATE TABLE` (for historical reasons).

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

#### `<tablename>`

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

#### `<column-definition>`

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

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

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

is equivalent to

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

#### Partition key and clustering columns

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

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

#### `STATIC` columns

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

    CREATE TABLE test (
        pk int,
        t int,
        v text,
        s text static,
        PRIMARY KEY (pk, t)
    );
    INSERT INTO test(pk, t, v, s) VALUES (0, 0, 'val0', 'static0');
    INSERT INTO test(pk, t, v, s) VALUES (0, 1, 'val1', 'static1');
    SELECT * FROM test WHERE pk=0 AND t=0;

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

A few restrictions applies to when static columns are allowed:

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

#### `<option>`

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

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

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

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

option | kind | default | description
--- | --- | --- | ---
`comment` | simple | none | A free-form, human-readable comment.
`gc_grace_seconds` | simple | 864000 | Time to wait before garbage collecting tombstones (deletion markers).
`bloom_filter_fp_chance` | simple | 0.00075 | The target probability of false positive of the sstable bloom filters. Said bloom filters will be sized to provide the provided probability (thus lowering this value impact the size of bloom filters in-memory and on-disk)
`default_time_to_live` | simple | 0 | The default expiration time (“TTL”) in seconds for a table.
`compaction` | map | see below | Compaction options, see below.
`compression` | map | see below | Compression options, see below.
`caching` | map | see below | Caching options, see below.

#### Compaction options

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

option | supported compaction strategy | default | description
--- | --- | --- | ---
`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 gcable 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 gcable 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`.

#### Compression options

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

option | default | description
--- | --- | ---
`class` | LZ4Compressor | The compression algorithm to use. Default compressor are: LZ4Compressor, SnappyCompressor and DeflateCompressor. Use `'enabled' : false` to disable compression. Custom compressor can be provided by specifying the full class name as a string constant.
`enabled` | true | By default compression is enabled. To disable it, set `enabled` to `false`
`chunk_length_in_kb` | 64KB | On disk SSTables are compressed by block (to allow random reads). This defines the size (in KB) of said block. Bigger values may improve the compression rate, but increases the minimum size of data to be read from disk for a read
`crc_check_chance` | 1.0 | When compression is enabled, each compressed block includes a checksum of that block for the purpose of detecting disk bitrot and avoiding the propagation of corruption to other replica. This option defines the probability with which those checksums are checked during read. By default they are always checked. Set to 0 to disable checksum checking and to 0.5 for instance to check them every other read

#### Caching options

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

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 inserting / updating a given row, not all columns needs to be defined (except for those part of the key), and missing columns occupy no space on disk. Furthermore, adding new columns (see <a href=#alterStmt>`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.

### ALTER TABLE

Syntax:

    <alter-table-stmt> ::= ALTER (TABLE | COLUMNFAMILY) (IF NOT EXISTS)? <tablename> <instruction>

    <instruction> ::= ADD (IF NOT EXISTS)? ( <identifier> <type> ( <column-mask> )?
                                           ( , <identifier> <type> ( <column-mask> )? )* )
                    | DROP  (IF EXISTS)? ( <identifier> ( , <identifier> )* )
                    | ALTER <identifier> ( <column-mask> | DROP MASKED )
                    | RENAME (IF EXISTS)? <identifier> to <identifier> (AND <identifier> to <identifier>)*
                    | DROP COMPACT STORAGE
                    | WITH  <option> ( AND <option> )*

    <column-mask> ::= MASKED WITH ( DEFAULT | <function> '(' ( <term> (',' <term>)* )?  ')' )

Sample:

    ALTER TABLE addamsFamily

    ALTER TABLE addamsFamily
    ADD gravesite varchar;

    ALTER TABLE addamsFamily
    WITH comment = 'A most excellent and useful column family';

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 readded, 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 readd 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.
- `ALTER`: Alters an existing column. It can be used to set its data mask, or to set it as unmasked. The data mask is any function meant to obscure the real values of the column.
- `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 secondary 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.
- `DROP COMPACT STORAGE`: Removes Thrift compatibility mode from the table.
- `WITH`: Allows to update the options of the table. The supported `<option>` (and syntax) are the same as for the `CREATE TABLE` statement except that `COMPACT STORAGE` is not supported. Note that setting any `compaction` sub-options has the effect of erasing all previous `compaction` options, so you need to re-specify all the sub-options if you want to keep them. The same note applies to the set of `compression` sub-options.

#### CQL type compatibility:

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

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

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

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

### DROP TABLE

Syntax:

    <drop-table-stmt> ::= DROP TABLE ( IF EXISTS )? <tablename>

Sample:

    DROP TABLE worldSeriesAttendees;

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

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

### TRUNCATE

Syntax:

    <truncate-stmt> ::= TRUNCATE ( TABLE | COLUMNFAMILY )? <tablename>

Sample:

    TRUNCATE superImportantData;

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

### CREATE INDEX

Syntax:

    <create-index-stmt> ::= CREATE ( CUSTOM )? INDEX ( IF NOT EXISTS )? ( <indexname> )?
                                ON <tablename> '(' <index-identifier> ')'
                                ( USING <string> ( WITH OPTIONS = <map-literal> )? )?

    <index-identifier> ::= <identifier>
                         | keys( <identifier> )

Sample:

    CREATE INDEX userIndex ON NerdMovies (user);
    CREATE INDEX ON Mutants (abilityId);
    CREATE INDEX ON users (KEYS(favs));
    CREATE INDEX ON users (age) USING 'sai';
    CREATE CUSTOM INDEX ON users (email) USING 'path.to.the.IndexClass';
    CREATE CUSTOM INDEX ON users (email) USING 'path.to.the.IndexClass' WITH OPTIONS = {'storage': '/mnt/ssd/indexes/'};

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

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

#### 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-attched” index, implemented via optimized SSTable/Memtable-attached indexes

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

#### Indexes on Map Keys

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

### DROP INDEX

Syntax:

    <drop-index-stmt> ::= DROP INDEX ( IF EXISTS )? ( <keyspace> '.' )? <identifier>

Sample:

    DROP INDEX userIndex;

    DROP INDEX userkeyspace.address_index;

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

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

### CREATE MATERIALIZED VIEW

Syntax:

    <create-table-stmt> ::= CREATE MATERIALIZED VIEW ( IF NOT EXISTS )? <viewname> AS
                              SELECT ( '(' <identifier> ( ',' <identifier> ) * ')' | '*' )
                              FROM <tablename>
                              ( WHERE <where-clause> )?
                              PRIMARY KEY '(' <partition-key> ( ',' <identifier> )* ')'
                              ( WITH <option> ( AND <option>)* )?

Sample:

    CREATE MATERIALIZED VIEW monkeySpecies_by_population AS
        SELECT *
        FROM monkeySpecies
        WHERE population IS NOT NULL AND species IS NOT NULL
        PRIMARY KEY (population, species)
        WITH comment='Allow query by population instead of species';

The `CREATE MATERIALIZED VIEW` statement creates a new materialized view. Each such view is a set of rows which corresponds to rows which are present in the underlying, or base, table specified in the `SELECT` statement. A materialized view cannot be directly updated, but updates to the base table will cause corresponding updates in the view.

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

#### `WHERE` Clause

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

### ALTER MATERIALIZED VIEW

Syntax:

    <alter-materialized-view-stmt> ::= ALTER MATERIALIZED VIEW (IF EXISTS)? <viewname>
                                                     WITH <option> ( AND <option> )*

The `ALTER MATERIALIZED VIEW` statement allows options to be update; these options are the same as `CREATE TABLE`'s options.
If the view does not exist, the statement will return an error, unless `IF EXISTS` is used in which case the operation is a no-op.

### DROP MATERIALIZED VIEW

Syntax:

    <drop-materialized-stmt> ::= DROP MATERIALIZED VIEW ( IF EXISTS )? <tablename>

Sample:

    DROP MATERIALIZED VIEW monkeySpecies_by_population;

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

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

### CREATE TYPE

Syntax:

    <create-type-stmt> ::= CREATE TYPE ( IF NOT EXISTS )? <typename>
                             '(' <field-definition> ( ',' <field-definition> )* ')'

    <typename> ::= ( <keyspace-name> '.' )? <identifier>

    <field-definition> ::= <identifier> <type>

Sample:

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

    CREATE TYPE work_and_home_addresses (
        home_address address,
        work_address address
    )

The `CREATE TYPE` statement creates a new user-defined type. Each type is a set of named, typed fields. Field types may be any valid type, including collections and other existing user-defined types.

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

#### `<typename>`

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

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

### ALTER TYPE

Syntax:

    <alter-type-stmt> ::= ALTER TYPE (IF EXISTS)? <typename> <instruction>

    <instruction> ::= ADD (IF NOT EXISTS)? <field-name> <type>
                    | RENAME (IF EXISTS)? <field-name> TO <field-name> ( AND <field-name> TO <field-name> )*

Sample:

    ALTER TYPE address ADD country text

    ALTER TYPE address RENAME zip TO zipcode AND street_name TO street

The `ALTER TYPE` statement is used to manipulate type definitions. It allows for adding new fields, renaming existing fields, or changing the type of existing fields.
If the type does not exist, the statement will return an error, unless `IF EXISTS` is used in which case the operation is a no-op.

### DROP TYPE

Syntax:

    <drop-type-stmt> ::= DROP TYPE ( IF EXISTS )? <typename>

The `DROP TYPE` statement results in the immediate, irreversible removal of a type. Attempting to drop a type that is still in use by another type or a table will result in an error.

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

### CREATE TRIGGER

Syntax:

    <create-trigger-stmt> ::= CREATE TRIGGER ( IF NOT EXISTS )? ( <triggername> )?
                                ON <tablename>
                                USING <string>

Sample:

    CREATE TRIGGER myTrigger ON myTable USING 'org.apache.cassandra.triggers.InvertedIndex';

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

### DROP TRIGGER

Syntax:

    <drop-trigger-stmt> ::= DROP TRIGGER ( IF EXISTS )? ( <triggername> )?
                                ON <tablename>

Sample:

    DROP TRIGGER myTrigger ON myTable;

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

### CREATE FUNCTION

Syntax:

    <create-function-stmt> ::= CREATE ( OR REPLACE )?
                                FUNCTION ( IF NOT EXISTS )?
                                ( <keyspace> '.' )? <function-name>
                                '(' <arg-name> <arg-type> ( ',' <arg-name> <arg-type> )* ')'
                                ( CALLED | RETURNS NULL ) ON NULL INPUT
                                RETURNS <type>
                                LANGUAGE <language>
                                AS <body>

Sample:

    CREATE OR REPLACE FUNCTION somefunction
        ( somearg int, anotherarg text, complexarg frozen<someUDT>, listarg list<bigint> )
        RETURNS NULL ON NULL INPUT
        RETURNS text
        LANGUAGE java
        AS $$
           // some Java code
        $$;
    CREATE FUNCTION akeyspace.fname IF NOT EXISTS
        ( someArg int )
        CALLED ON NULL INPUT
        RETURNS text
        LANGUAGE java
        AS $$
           // some Java code
        $$;

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

#### Function Signature

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

1. The fully qualified function name – i.e keyspace plus function-name
2. 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:

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

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

`OR REPLACE` and `IF NOT EXIST` cannot be used together.

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

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

### DROP FUNCTION

Syntax:

    <drop-function-stmt> ::= DROP FUNCTION ( IF EXISTS )?
                             ( <keyspace> '.' )? <function-name>
                             ( '(' <arg-type> ( ',' <arg-type> )* ')' )?

Sample:

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

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

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

### CREATE AGGREGATE

Syntax:

    <create-aggregate-stmt> ::= CREATE ( OR REPLACE )?
                                AGGREGATE ( IF NOT EXISTS )?
                                ( <keyspace> '.' )? <aggregate-name>
                                '(' <arg-type> ( ',' <arg-type> )* ')'
                                SFUNC <state-functionname>
                                STYPE <state-type>
                                ( FINALFUNC <final-functionname> )?
                                ( INITCOND <init-cond> )?

Sample:

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

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

`CREATE AGGREGATE` creates or replaces a user-defined aggregate.

`CREATE AGGREGATE` with the optional `OR REPLACE` keywords either creates an aggregate or replaces an existing one with the same signature. A `CREATE AGGREGATE` without `OR REPLACE` fails if an aggregate with the same signature already exists.

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

`OR REPLACE` and `IF NOT EXIST` cannot be used together.

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

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

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

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

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

The optional `FINALFUNC` is called just before the aggregate result is returned. It must take only one argument with type `STYPE`. The return type of the `FINALFUNC` may be a different type. A final function declared with `RETURNS NULL ON NULL INPUT` means that the aggregate’s return value will be `null`, if the last state is `null`.

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

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

### DROP AGGREGATE

Syntax:

    <drop-aggregate-stmt> ::= DROP AGGREGATE ( IF EXISTS )?
                             ( <keyspace> '.' )? <aggregate-name>
                             ( '(' <arg-type> ( ',' <arg-type> )* ')' )?

Sample:

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

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

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

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

## Data Manipulation

### INSERT

Syntax:

    <insertStatement> ::= INSERT INTO <tablename>
                          ( ( <name-list> VALUES <value-list> )
                          | ( JSON <string> (DEFAULT ( NULL | UNSET ))?))
                          ( IF NOT EXISTS )?
                          ( USING <option> ( AND <option> )* )?

    <names-list> ::= '(' <identifier> ( ',' <identifier> )* ')'

    <value-list> ::= '(' <term> ( ',' <term> )* ')'

    <option> ::= TIMESTAMP <integer>
               | TTL <integer>

Sample:

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

    INSERT INTO NerdMovies JSON '{"movie": "Serenity", "director": "Joss Whedon", "year": 2005}'

The `INSERT` statement writes one or more columns for a given row in a table. Note that since a row is identified by its `PRIMARY KEY`, at least the columns composing it must be specified. The list of columns to insert to must be supplied when using the `VALUES` syntax. When using the `JSON` syntax, they are optional. See the section on `INSERT JSON` for more details.

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

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

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

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

### UPDATE

Syntax:

    <update-stmt> ::= UPDATE <tablename>
                      ( USING <option> ( AND <option> )* )?
                      SET <assignment> ( ',' <assignment> )*
                      WHERE <where-clause>
                      ( IF <condition> ( AND condition )* )?

    <assignment> ::= <identifier> '=' <term>
                   | <identifier> '=' <identifier> ('+' | '-') (<int-term> | <set-literal> | <list-literal>)
                   | <identifier> '=' <identifier> '+' <map-literal>
                   | <identifier> '[' <term> ']' '=' <term>
                   | <identifier> '.' <field> '=' <term>

    <condition> ::= <identifier> <op> <term>
                  | <identifier> IN <in-values>
                  | <identifier> '[' <term> ']' <op> <term>
                  | <identifier> '[' <term> ']' IN <in-values>
                  | <identifier> '.' <field> <op> <term>
                  | <identifier> '.' <field> IN <in-values>

    <op> ::= '<' | '<=' | '=' | '!=' | '>=' | '>' | CONTAINS ( KEY )?
    <in-values> ::= (<variable> | '(' ( <term> ( ',' <term> )* )? ')')

    <where-clause> ::= <relation> ( AND <relation> )*

    <relation> ::= <identifier> '=' <term>
                 | '(' <identifier> (',' <identifier>)* ')' '=' <term-tuple>
                 | <identifier> IN '(' ( <term> ( ',' <term>)* )? ')'
                 | <identifier> IN <variable>
                 | '(' <identifier> (',' <identifier>)* ')' IN '(' ( <term-tuple> ( ',' <term-tuple>)* )? ')'
                 | '(' <identifier> (',' <identifier>)* ')' IN <variable>

    <option> ::= TIMESTAMP <integer>
               | TTL <integer>

Sample:

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

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

The `UPDATE` statement writes one or more columns for a given row in a table. The `<where-clause>` is used to select the row to update and must include all columns composing the `PRIMARY KEY`. Other columns values are specified through `<assignment>` after the `SET` keyword.

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

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

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

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

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

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

#### `<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. A TTL of `null` is equivalent to inserting with a TTL of 0.

### DELETE

Syntax:

    <delete-stmt> ::= DELETE ( <selection> ( ',' <selection> )* )?
                      FROM <tablename>
                      ( USING TIMESTAMP <integer>)?
                      WHERE <where-clause>
                      ( IF ( EXISTS | ( <condition> ( AND <condition> )*) ) )?

    <selection> ::= <identifier>
                  | <identifier> '[' <term> ']'
                  | <identifier> '.' <field>

    <where-clause> ::= <relation> ( AND <relation> )*

    <relation> ::= <identifier> <op> <term>
                 | '(' <identifier> (',' <identifier>)* ')' <op> <term-tuple>
                 | <identifier> IN '(' ( <term> ( ',' <term>)* )? ')'
                 | <identifier> IN <variable>
                 | '(' <identifier> (',' <identifier>)* ')' IN '(' ( <term-tuple> ( ',' <term-tuple>)* )? ')'
                 | '(' <identifier> (',' <identifier>)* ')' IN <variable>

    <op> ::= '=' | '<' | '>' | '<=' | '>=' | CONTAINS ( KEY )?
    <in-values> ::= (<variable> | '(' ( <term> ( ',' <term> )* )? ')')

    <condition> ::= <identifier> (<op> | '!=') <term>
                  | <identifier> IN <in-values>
                  | <identifier> '[' <term> ']' (<op> | '!=') <term>
                  | <identifier> '[' <term> ']' IN <in-values>
                  | <identifier> '.' <field> (<op> | '!=') <term>
                  | <identifier> '.' <field> IN <in-values>

Sample:

    DELETE FROM NerdMovies USING TIMESTAMP 1240003134 WHERE movie = 'Serenity';

    DELETE phone FROM Users WHERE userid IN (C73DE1D3-AF08-40F3-B124-3FF3E5109F22, B70DE1D0-9908-4AE3-BE34-5573E5B09F14);

The `DELETE` statement deletes columns and rows. If column names are provided directly after the `DELETE` keyword, only those columns are deleted from the row indicated by the `<where-clause>`. The `id[value]` syntax in `<selection>` is for non-frozen collections (please refer to the collection section for more details). The `id.field` syntax is for the deletion of non-frozen user-defined types. Otherwise, whole rows are removed. The `<where-clause>` specifies which rows are to be deleted. Multiple rows may be deleted with one statement by using an `IN` clause. A range of rows may be deleted using an inequality operator (such as `>=`).

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

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

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

### BATCH

Syntax:

    <batch-stmt> ::= BEGIN ( UNLOGGED | COUNTER ) BATCH
                     ( USING <option> ( AND <option> )* )?
                        <modification-stmt> ( ';' <modification-stmt> )*
                     APPLY BATCH

    <modification-stmt> ::= <insert-stmt>
                          | <update-stmt>
                          | <delete-stmt>

    <option> ::= TIMESTAMP <integer>

Sample:

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

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

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

Note that:

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

#### `UNLOGGED`

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

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

#### `COUNTER`

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

#### `<option>`

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

## Queries

### SELECT

Syntax:

    <select-stmt> ::= SELECT ( JSON )? <select-clause>
                      FROM <tablename>
                      ( WHERE <where-clause> )?
                      ( GROUP BY <group-by>)?
                      ( ORDER BY <order-by> )?
                      ( PER PARTITION LIMIT <integer> )?
                      ( LIMIT <integer> )?
                      ( ALLOW FILTERING )?

    <select-clause> ::= DISTINCT? <selection-list>

    <selection-list> ::= <selector> (AS <identifier>)? ( ',' <selector> (AS <identifier>)? )*
                       | '*'

    <selector> ::= <identifier>
                 | <term>
                 | WRITETIME '(' <selector> ')'
                 | MAXWRITETIME '(' <selector> ')'
                 | COUNT '(' '*' ')'
                 | TTL '(' <selector> ')'
                 | CAST '(' <selector> AS <type> ')'
                 | <function> '(' (<selector> (',' <selector>)*)? ')'
                 | <selector> '.' <identifier>
                 | <selector> '[' <term> ']'
                 | <selector> '[' <term>? .. <term>? ']'

    <where-clause> ::= <relation> ( AND <relation> )*

    <relation> ::= <identifier> <op> <term>
                 | '(' <identifier> (',' <identifier>)* ')' <op> <term-tuple>
                 | <identifier> IN '(' ( <term> ( ',' <term>)* )? ')'
                 | '(' <identifier> (',' <identifier>)* ')' IN '(' ( <term-tuple> ( ',' <term-tuple>)* )? ')'
                 | TOKEN '(' <identifier> ( ',' <identifer>)* ')' <op> <term>

    <op> ::= '=' | '<' | '>' | '<=' | '>=' | CONTAINS | CONTAINS KEY
    <group-by> ::= (<identifier>,)* (<identifier> | <function>)
    <order-by> ::= <ordering> ( ',' <odering> )*
    <ordering> ::= <identifer> ( ASC | DESC )?
    <term-tuple> ::= '(' <term> (',' <term>)* ')'

Sample:

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

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

    SELECT name AS user_name, occupation AS user_occupation FROM users;

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

    SELECT COUNT(*) FROM users;

    SELECT COUNT(*) AS user_count FROM users;

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

#### `<select-clause>`

The `<select-clause>` determines which columns needs 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 @@s. The function allowed are the same as for `<term>` and are described in the function section. In addition to these generic functions, the `WRITETIME` and `MAXWRITETIME` (resp. `TTL`) function allows to select the timestamp of when the column was inserted (resp. the time to live (in seconds) for the column (or null if the column has no expiration set)) and the `CAST` function can be used to convert one data type to another.

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 @[ ... @], where both bound can be omitted.

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

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

#### `<where-clause>`

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

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

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

The following query is allowed:

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

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

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

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

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

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

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

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

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

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

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

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

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

#### `<order-by>`

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

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

#### `<group-by>`

The `GROUP BY` option allows to condense 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. The last argument can be a monotonic function on the primary key column.

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

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

#### `LIMIT` and `PER PARTITION LIMIT`

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

#### `ALLOW FILTERING`

By default, CQL only allows select queries that don’t involve “filtering” server side, i.e. queries where we know that all (live) record read will be returned (maybe partly) in the result set. The reasoning is that 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:

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

    CREATE INDEX ON users(birth_year);

Then the following queries are valid:

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

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 of the number of user profile stored in the database (not directly at least: due to secondary index implementation consideration, this query may still depend on the number of node in the cluster, which indirectly depends on the amount of data stored. Nevertheless, the number of nodes will always be multiple number of magnitude lower than the number of user profile stored). Of course, both query may return very large result set in practice, but the amount of data returned can always be controlled by adding a `LIMIT`.

However, the following query will be rejected:

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

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

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

## Database Roles

### CREATE ROLE

Syntax:

    <create-role-stmt> ::= CREATE ROLE ( IF NOT EXISTS )? <identifier> ( WITH <option> ( AND <option> )* )?

    <option> ::= ("HASHED")? PASSWORD = <string>
               | LOGIN = <boolean>
               | SUPERUSER = <boolean>
               | OPTIONS = <map_literal>
               | ACCESS TO DATACENTERS { dcPermission (, dcPermission)* }
               | ACCESS TO ALL DATACENTERS
               | ACCESS FROM CIDRS { cidrPermission (, cidrPermission)* }
               | ACCESS FROM ALL CIDRS

Sample:

    CREATE ROLE new_role;
    CREATE ROLE alice WITH PASSWORD = 'password_a' AND LOGIN = true;
    CREATE ROLE bob WITH PASSWORD = 'password_b' AND LOGIN = true AND SUPERUSER = true;
    CREATE ROLE carlos WITH OPTIONS = { 'custom_option1' : 'option1_value', 'custom_option2' : 99 };
    CREATE ROLE rob WITH LOGIN = true and PASSWORD = 'password_c' AND ACCESS FROM ALL CIDRS;
    CREATE ROLE hob WITH LOGIN = true and PASSWORD = 'password_d' AND ACCESS FROM CIDRS { 'region1' };

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

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

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

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

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

#### Setting credentials for internal authentication

Use the `WITH PASSWORD` clause to set a password for internal authentication, enclosing the password in single quotation marks.
If internal authentication has not been set up or the role does not have `LOGIN` privileges, the `WITH PASSWORD` clause is not necessary.

#### Creating a role conditionally

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

    CREATE ROLE other_role;
    CREATE ROLE IF NOT EXISTS other_role;

### ALTER ROLE

Syntax:

    <alter-role-stmt> ::= ALTER ROLE (IF EXISTS)? <identifier> ( WITH <option> ( AND <option> )* )?

    <option> ::= PASSWORD = <string>
               | LOGIN = <boolean>
               | SUPERUSER = <boolean>
               | OPTIONS = <map_literal>
               | ACCESS TO DATACENTERS { dcPermission (, dcPermission)* }
               | ACCESS TO ALL DATACENTERS
               | ACCESS FROM CIDRS { cidrPermission (, cidrPermission)* }
               | ACCESS FROM ALL CIDRS

Sample:

    ALTER ROLE bob WITH PASSWORD = 'PASSWORD_B' AND SUPERUSER = false;
    ALTER ROLE rob WITH LOGIN = true and PASSWORD = 'password_c' AND ACCESS FROM ALL CIDRS;
    ALTER ROLE hob WITH LOGIN = true and PASSWORD = 'password_d' AND ACCESS FROM CIDRS { 'region1' };

Conditions on executing `ALTER ROLE` statements:

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

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

### DROP ROLE

Syntax:

    <drop-role-stmt> ::= DROP ROLE ( IF EXISTS )? <identifier>

Sample:

    DROP ROLE alice;
    DROP ROLE IF EXISTS bob;

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

### GRANT ROLE

Syntax:

    <grant-role-stmt> ::= GRANT <identifier> TO <identifier>

Sample:

    GRANT report_writer TO alice;

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

    GRANT role_a TO role_b;
    GRANT role_b TO role_a;

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

### REVOKE ROLE

Syntax:

    <revoke-role-stmt> ::= REVOKE <identifier> FROM <identifier>

Sample:

    REVOKE report_writer FROM alice;

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

#### LIST ROLES

Syntax:

    <list-roles-stmt> ::= LIST ROLES ( OF <identifier> )? ( NORECURSIVE )?

Sample:

    LIST ROLES;

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

    LIST ROLES OF @alice@;

Enumerate all roles granted to `alice`, including those transitively aquired.

    LIST ROLES OF @bob@ NORECURSIVE

List all roles directly granted to `bob`.

### CREATE USER

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

Syntax:

    <create-user-statement> ::= CREATE USER ( IF NOT EXISTS )? <identifier> ( WITH ("HASHED")? PASSWORD <string> )? (<option>)?

    <option> ::= SUPERUSER
               | NOSUPERUSER

Sample:

    CREATE USER alice WITH PASSWORD 'password_a' SUPERUSER;
    CREATE USER bob WITH PASSWORD 'password_b' NOSUPERUSER;

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

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

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

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

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

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

### ALTER USER

Syntax:

    <alter-user-statement> ::= ALTER USER (IF EXISTS)? <identifier> ( WITH PASSWORD <string> )? ( <option> )?

    <option> ::= SUPERUSER
               | NOSUPERUSER

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

    ALTER USER alice WITH PASSWORD 'PASSWORD_A';
    ALTER USER bob SUPERUSER;

### DROP USER

Syntax:

    <drop-user-stmt> ::= DROP USER ( IF EXISTS )? <identifier>

Sample:

    DROP USER alice;
    DROP USER IF EXISTS bob;

### LIST USERS

Syntax:

    <list-users-stmt> ::= LIST USERS;

Sample:

    LIST USERS;

This statement is equivalent to

    LIST ROLES;

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

## Data Control

### Permissions

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

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

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

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

The full set of available permissions is:

- `CREATE`
- `ALTER`
- `DROP`
- `SELECT`
- `MODIFY`
- `AUTHORIZE`
- `DESCRIBE`
- `EXECUTE`
- `UNMASK`
- `SELECT_MASKED`

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

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

### GRANT PERMISSION

Syntax:

    <grant-permission-stmt> ::= GRANT ( ALL ( PERMISSIONS )? | <permission> ( PERMISSION )? (, PERMISSION)* ) ON <resource> TO <identifier>

    <permission> ::= CREATE | ALTER | DROP | SELECT | MODIFY | AUTHORIZE | DESCRIBE | EXECUTE | UNMASK | SELECT_MASKED

    <resource> ::= ALL KEYSPACES
                 | KEYSPACE <identifier>
                 | ALL TABLES IN KEYSPACE <identifier>
                 | ( TABLE )? <tablename>
                 | ALL ROLES
                 | ROLE <identifier>
                 | ALL FUNCTIONS ( IN KEYSPACE <identifier> )?
                 | FUNCTION <functionname>
                 | ALL MBEANS
                 | ( MBEAN | MBEANS ) <objectName>

Sample:

    GRANT SELECT ON ALL KEYSPACES TO data_reader;

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

    GRANT MODIFY ON KEYSPACE keyspace1 TO data_writer;

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

    GRANT DROP ON keyspace1.table1 TO schema_owner;

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

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

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

    GRANT DESCRIBE ON ALL ROLES TO role_admin;

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

#### GRANT ALL

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

#### Automatic Granting

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

### REVOKE PERMISSION

Syntax:

    <revoke-permission-stmt> ::= REVOKE ( ALL ( PERMISSIONS )? | <permission> ( PERMISSION )? (, PERMISSION)* ) ON <resource> FROM <identifier>

    <permission> ::= CREATE | ALTER | DROP | SELECT | MODIFY | AUTHORIZE | DESCRIBE | EXECUTE | UNMASK | SELECT_MASKED

    <resource> ::= ALL KEYSPACES
                 | KEYSPACE <identifier>
                 | ALL TABLES IN KEYSPACE <identifier>
                 | ( TABLE )? <tablename>
                 | ALL ROLES
                 | ROLE <identifier>
                 | ALL FUNCTIONS ( IN KEYSPACE <identifier> )?
                 | FUNCTION <functionname>
                 | ALL MBEANS
                 | ( MBEAN | MBEANS ) <objectName>

Sample:

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

#### LIST PERMISSIONS

Syntax:

    <list-permissions-stmt> ::= LIST ( ALL ( PERMISSIONS )? | <permission> )
                                     ( ON <resource> )?
                                     ( OF <identifier> ( NORECURSIVE )? )?

    <resource> ::= ALL KEYSPACES
                 | KEYSPACE <identifier>
                 | ALL TABLES IN KEYSPACE <identifier>
                 | ( TABLE )? <tablename>
                 | ALL ROLES
                 | ROLE <identifier>
                 | ALL FUNCTIONS ( IN KEYSPACE <identifier> )?
                 | FUNCTION <functionname>
                 | ALL MBEANS
                 | ( MBEAN | MBEANS ) <objectName>

Sample:

    LIST ALL PERMISSIONS OF alice;

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

    LIST ALL PERMISSIONS ON keyspace1.table1 OF bob;

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

    LIST SELECT PERMISSIONS OF carlos;

Show any permissions granted to `carlos` or any of `carlos`'s roles, limited to `SELECT` permissions on any resource.

## Data Types

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

    <type> ::= <native-type>
             | <collection-type>
             | <tuple-type>
             | <vector-type>
             | <string>       // Used for custom types. The fully-qualified name of a JAVA class

    <native-type> ::= ascii
                    | bigint
                    | blob
                    | boolean
                    | counter
                    | date
                    | decimal
                    | double
                    | duration
                    | float
                    | inet
                    | int
                    | smallint
                    | text
                    | time
                    | timestamp
                    | timeuuid
                    | tinyint
                    | uuid
                    | varchar
                    | varint

    <collection-type> ::= list '<' <native-type> '>'
                        | set  '<' <native-type> '>'
                        | map  '<' <native-type> ',' <native-type> '>'

    <tuple-type> ::= tuple '<' <type> (',' <type>)* '>'

    <vector-type> ::= vector '<' <native-type> ',' <number> '>'

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

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

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

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

For more information on how to use the vector type, see the Working with vectors section below.

### Working with timestamps

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

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

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

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

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

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

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

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

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

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

### Working with dates

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

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

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

- `2014-01-01`

### Working with time

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

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

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

- `08:12:54`
- `08:12:54.123`
- `08:12:54.123456`
- `08:12:54.123456789`

### Working with durations

Values of the `duration` type are encoded as 3 signed integers of variable lengths. The first integer represents the
number of months, the second the number of days and the third the number of nanoseconds. This is due to the fact that
the number of days in a month can change, and a day can have 23 or 25 hours depending on the daylight saving.

A duration can be input as:

- (quantity unit)+ like `12h30m` where the unit can be:
  - `y`: years (12 months)
  - `mo`: months (1 month)
  - `w`: weeks (7 days)
  - `d`: days (1 day)
  - `h`: hours (3,600,000,000,000 nanoseconds)
  - `m`: minutes (60,000,000,000 nanoseconds)
  - `s`: seconds (1,000,000,000 nanoseconds)
  - `ms`: milliseconds (1,000,000 nanoseconds)
  - `us` or `µs` : microseconds (1000 nanoseconds)
  - `ns`: nanoseconds (1 nanosecond)
- ISO 8601 format: `P[n]Y[n]M[n]DT[n]H[n]M[n]S` or `P[n]W`
- ISO 8601 alternative format: `P[YYYY]-[MM]-[DD]T[hh]:[mm]:[ss]`

For example:

        INSERT INTO RiderResults (rider, race, result) VALUES ('Christopher Froome', 'Tour de France', 89h4m48s);
        INSERT INTO RiderResults (rider, race, result) VALUES ('BARDET Romain', 'Tour de France', PT89H8M53S);
        INSERT INTO RiderResults (rider, race, result) VALUES ('QUINTANA Nairo', 'Tour de France', P0000-00-00T89:09:09);

#### duration-limitation:

Duration columns cannot be used in a table’s `PRIMARY KEY`. This limitation is due to the fact that
durations cannot be ordered. It is effectively not possible to know if `1mo` is greater than `29d` without a date
context.

A `1d` duration is not equals to a `24h` one as the duration type has been created to be able to support daylight
saving.

### 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 on which 2 operations are supported: incrementation and decrementation (see `UPDATE` for syntax). Note the value of a counter cannot be set. A counter doesn’t exist until first incremented/decremented, and the first incrementation/decrementation is made as if the previous value was 0. Deletion of counter columns is supported but have some limitations (see the Cassandra Wiki for more information).

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

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

### Working with collections

#### Noteworthy characteristics

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

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

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

#### Maps

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

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

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

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

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

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

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

    // 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:

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

#### Sets

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

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

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

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

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

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

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

#### Lists

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

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

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

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

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

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

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

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

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

    UPDATE plays SET scores[1] = 7 WHERE id = '123-afde';                // sets the 2nd element of scores to 7 (raises an error is scores has less than 2 elements)
    DELETE scores[1] FROM plays WHERE id = '123-afde';                   // deletes the 2nd element of scores (raises an error is scores has less than 2 elements)
    UPDATE plays SET scores = scores - [ 12, 21 ] WHERE id = '123-afde'; // removes all occurrences of 12 and 21 from scores

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

### Working with vectors

Vectors are fixed-size sequences of non-null values of a certain data type. They use the same literals as lists and it isn’t possible to select individual elements of a vector.

To create a column of type `vector`, use the `vector` keyword suffixed with the value type and the number of dimensions enclosed in angle brackets. For example:

bc(sample).
CREATE TABLE plays (
id text PRIMARY KEY,
game text,
players int,
scores vector<int, 3>
)

That would create a `vector` of 3 `int` values.

To write a record using `INSERT`, specify the entire vector as a JSON array:

bc(sample).
INSERT INTO plays (id, game, players, scores) VALUES (‘123-afde’, ‘quake’, 3, [17, 4, 2]);

To update a vector using `UPDATE`, specify the entire vector as a JSON array:

bc(sample).
UPDATE plays SET players = 5, scores = [ 14, 4, 3 ] WHERE id = ‘123-afde’;

It isn’t possible to change the individual values of a vector.

## Arithmetic Operators

### Number Arithmetic

CQL supports the following operators:

Operator | Description
--- | ---
`-` (unary) | Negates operand
`+` | Addition
`-` | Substraction
`*` | Multiplication
`/` | Division
`%` | Returns the remainder of a division

Arithmetic operations are only supported on numeric types or counters.

The return type of the operation will be based on the operand types:

left/right | `tinyint` | `smallint` | `int` | `bigint` | `counter` | `float` | `double` | `varint` | `decimal`
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
`tinyint` | `tinyint` | `smallint` | `int` | `bigint` | `bigint` | `float` | `double` | `varint` | `decimal`
`smallint` | `smallint` | `smallint` | `int` | `bigint` | `bigint` | `float` | `double` | `varint` | `decimal`
`int` | `int` | `int` | `int` | `bigint` | `bigint` | `float` | `double` | `varint` | `decimal`
`bigint` | `bigint` | `bigint` | `bigint` | `bigint` | `bigint` | `double` | `double` | `varint` | `decimal`
`counter` | `bigint` | `bigint` | `bigint` | `bigint` | `bigint` | `double` | `double` | `varint` | `decimal`
`float` | `float` | `float` | `float` | `double` | `double` | `float` | `double` | `decimal` | `decimal`
`double` | `double` | `double` | `double` | `double` | `double` | `double` | `double` | `decimal` | `decimal`
`varint` | `varint` | `varint` | `varint` | `decimal` | `decimal` | `decimal` | `decimal` | `decimal` | `decimal`
`decimal` | `decimal` | `decimal` | `decimal` | `decimal` | `decimal` | `decimal` | `decimal` | `decimal` | `decimal`

`*`, `/` and `%` operators have a higher precedence level than `+` and `-` operator. By consequence, they will be evaluated before. If two operator in an expression have the same precedence level, they will be evaluated left to right based on their position in the expression.

### Datetime Arithmetic

A `duration` can be added (+) or substracted (-) from a `timestamp` or a `date` to create a new `timestamp` or `date`. So for instance:

    SELECT * FROM myTable WHERE t = '2017-01-01' - 2d

will select all the records with a value of `t` which is in the last 2 days of 2016.

## Functions

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

### Cast

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

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

from | to
--- | ---
`ascii` | `text`, `varchar`
`bigint` | `tinyint`, `smallint`, `int`, `float`, `double`, `decimal`, `varint`, `text`, `varchar`
`boolean` | `text`, `varchar`
`counter` | `tinyint`, `smallint`, `int`, `bigint`, `float`, `double`, `decimal`, `varint`, `text`, `varchar`
`date` | `timestamp`
`decimal` | `tinyint`, `smallint`, `int`, `bigint`, `float`, `double`, `varint`, `text`, `varchar`
`double` | `tinyint`, `smallint`, `int`, `bigint`, `float`, `decimal`, `varint`, `text`, `varchar`
`float` | `tinyint`, `smallint`, `int`, `bigint`, `double`, `decimal`, `varint`, `text`, `varchar`
`inet` | `text`, `varchar`
`int` | `tinyint`, `smallint`, `bigint`, `float`, `double`, `decimal`, `varint`, `text`, `varchar`
`smallint` | `tinyint`, `int`, `bigint`, `float`, `double`, `decimal`, `varint`, `text`, `varchar`
`time` | `text`, `varchar`
`timestamp` | `date`, `text`, `varchar`
`timeuuid` | `timestamp`, `date`, `text`, `varchar`
`tinyint` | `tinyint`, `smallint`, `int`, `bigint`, `float`, `double`, `decimal`, `varint`, `text`, `varchar`
`uuid` | `text`, `varchar`
`varint` | `tinyint`, `smallint`, `int`, `bigint`, `float`, `double`, `decimal`, `text`, `varchar`

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

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

### Token

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

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

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

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

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

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

### Uuid

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

### Timeuuid functions

#### `now`

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

    SELECT * FROM myTable WHERE t = now()

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

#### `min_timeuuid` and `max_timeuuid`

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

bc(sample).
SELECT * FROM myTable WHERE t > max_timeuuid(‘2013-01-01 00:05+0000’) AND t < min_timeuuid(‘2013-02-02 10:00+0000’)

will select all rows where the `timeuuid` column `t` is strictly older than ‘2013-01-01 00:05+0000’ but strictly younger than ‘2013-02-02 10:00+0000’. Please note that `t >= max_timeuuid('2013-01-01 00:05+0000')` would still not select a `timeuuid` generated exactly at ‘2013-01-01 00:05+0000’ and is essentially equivalent to `t > maxTimeuuid('2013-01-01 00:05+0000')`.

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

### Datetime functions

#### Retrieving the current date/time

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

function name | output type
--- | ---
`current_timestamp` | `timestamp`
`current_date` | `date`
`current_time` | `time`
`current_timeuuid` | `timeUUID`

For example the last 2 days of data can be retrieved using:

bc(sample).
SELECT * FROM myTable WHERE date >= current_date() - 2d

#### Time conversion functions

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

function name | input type | description
--- | --- | ---
`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

#### Floor function

Rounds date and time to the nearest value.

type | function |
--- | --- | ---
`timestamp` | floor(timestamp, duration [, start_timestamp]) | If the start_timestamp is not used, then the start timestamp is January 1, 1970 00:00:00.000 GMT
`timeuuid` | floor(timeuuid, duration [, start_timestamp]) | If the start_timestamp is not used, then the start timestamp is January 1, 1970 00:00:00.000 GMT
`date` | floor(date, duration [, start_date]) | If the start_date is not used, then the start date is January 1, 1970 GMT
`time` | floor(time, duration [, start_time]) | If the start_time is not used, then the start time is 00:00:00[000000000]

### Blob conversion functions

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

## Aggregates

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

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

### Count

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

    SELECT COUNT(*) FROM plays;
    SELECT COUNT(1) FROM plays;

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

    SELECT COUNT(scores) FROM plays;

### Max and Min

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

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

### Sum

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

    SELECT SUM(players) FROM plays;

### Avg

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

    SELECT AVG(players) FROM plays;

## User-Defined Functions

User-defined functions allow execution of user-provided code in Cassandra. By default, Cassandra supports defining functions in Java and JavaScript. Support for other JSR 223 compliant scripting languages (such as Python, Ruby, and Scala) has been removed in 3.0.11.

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

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

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

User-defined functions are susceptible to all of the normal problems with the chosen programming language. Accordingly, implementations should be safe against null pointer exceptions, illegal arguments, or any other potential source of exceptions. An exception during function execution will result in the entire statement failing.

It is valid to use complex types like collections, tuple types and user-defined types as argument and return types. Tuple types and user-defined types are handled by the conversion functions of the DataStax Java Driver. Please see the documentation of the Java Driver for details on handling tuple types and user-defined types.

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

Note that you can use the double-quoted string syntax to enclose the UDF source code. For example:

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

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

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

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

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

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

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

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

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

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

See `CREATE FUNCTION` and `DROP FUNCTION`.

## User-Defined Aggregates

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

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

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

User-defined aggregates can be used in `SELECT` statement.

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

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

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

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

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

See `CREATE AGGREGATE` and `DROP AGGREGATE`.

## JSON Support

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

### SELECT JSON

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

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

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

### INSERT JSON

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

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

By default (or if `DEFAULT NULL` is explicitly used), a column omitted from the `JSON` map will be set to `NULL`, meaning that any pre-existing value for that column will be removed (resulting in a tombstone being created). Alternatively, if the `DEFAULT UNSET` directive is used after the value, omitted column values will be left unset, meaning that pre-existing values for those column will be preserved.

### JSON Encoding of Cassandra Data Types

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

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

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

### The from_json() Function

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

### The to_json() Function

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

## Appendix A: CQL Keywords

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

Keyword | Reserved?
--- | ---
`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
`CIDRS` | 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
`MAXWRITETIME` | no

## Appendix B: CQL Reserved Types

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

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

## Changes

The following describes the changes in each version of CQL.

### 3.4.7

- Remove deprecated functions `dateOf` and `unixTimestampOf`, replaced by `to_timestamp` and `to_unixtimestamp` (see CASSANDRA-18328).
- Adopt snake_case function names, deprecating all previous camelCase or alltogetherwithoutspaces function names (see CASSANDRA-18037).

### 3.4.6

- Add support for `IF EXISTS` and `IF NOT EXISTS` in `ALTER` statements (see CASSANDRA-16916).
- Allow `GRANT/REVOKE` multiple permissions in a single statement (see CASSANDRA-17030).
- Pre hashed passwords in CQL (see CASSANDRA-17334).
- Add support for type casting in `WHERE` clause components and in the values of `INSERT/UPDATE` statements (see CASSANDRA-14337).
- Add support for `CONTAINS` and `CONTAINS KEY` in conditional `UPDATE` and `DELETE` statement (see CASSANDRA-10537).
- Allow to grant permission for all tables in a keyspace (see CASSANDRA-17027).
- Allow to use pure monotonic functions on the last attribute of the `GROUP BY` clause (see CASSANDRA-11871).
- Add floor function to allow grouping by time range (see CASSANDRA-11871).
- Add support for adding and updating CIDR permissions using `CREATE ROLE` and `ALTER ROLE` statements

### 3.4.5

- Adds support for arithmetic operators. See Number Arithmetic (see CASSANDRA-11935).
- Adds support for + and – operations on dates. See Datetime Arithmetic (see CASSANDRA-11936).
- Adds `currentTimestamp`, `currentDate`, `currentTime` and `currentTimeUUID` functions (see CASSANDRA-13132).

### 3.4.4

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

### 3.4.3

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

### 3.4.2

- Support for selecting elements and slices of a collection (CASSANDRA-7396).
- `INSERT/UPDATE options` for tables having a default_time_to_live specifying a TTL of 0 will remove the TTL from the inserted or updated values
- `ALTER TABLE` `ADD` and `DROP` now allow mutiple columns to be added/removed
- New `PER PARTITION LIMIT` option (see CASSANDRA-7017).
- User-defined functions can now instantiate `UDTValue` and `TupleValue` instances via the new `UDFContext` interface (see CASSANDRA-10818).
- “User-defined types”#createTypeStmt may now be stored in a non-frozen form, allowing individual fields to be updated and deleted in `UPDATE` statements and `DELETE` statements, respectively. (CASSANDRA-7423)

### 3.4.1

- Adds `CAST` functions. See `Cast`.

### 3.4.0

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

### 3.3.1

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

### 3.3.0

- Adds new aggregates
- User-defined functions are now supported through `CREATE FUNCTION` and `DROP FUNCTION`.
- User-defined aggregates are now supported through `CREATE AGGREGATE` and `DROP AGGREGATE`.
- Allows double-dollar enclosed strings literals as an alternative to single-quote enclosed strings.
- Introduces Roles to supercede user based authentication and access control
- `Date` and `Time` data types have been added
- `JSON` support has been added
- `Tinyint` and `Smallint` data types have been added
- Adds new time conversion functions and deprecate `dateOf` and `unixTimestampOf`. See `Time conversion functions`

### 3.2.0

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

### 3.1.7

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

### 3.1.6

- A new `uuid` method has been added.
- Support for `DELETE ... IF EXISTS` syntax.

### 3.1.5

- It is now possible to group clustering columns in a relatiion, see SELECT WHERE clauses.
- Added support for `STATIC` columns, see static in CREATE TABLE.

### 3.1.4

- `CREATE INDEX` now allows specifying options when creating CUSTOM indexes (see CREATE INDEX reference ).

### 3.1.3

- Millisecond precision formats have been added to the timestamp parser (see working with dates ).

### 3.1.2

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

### 3.1.1

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

### 3.1.0

- ALTER TABLE `DROP` option has been reenabled for CQL3 tables and has new semantics now: the space formerly used by dropped columns will now be eventually reclaimed (post-compaction). You should not readd previously dropped columns unless you use timestamps with microsecond precision (see CASSANDRA-3919 for more details).
- `SELECT` statement now supports aliases in select clause. Aliases in WHERE and ORDER BY clauses are not supported. See the section on select for details.
- `CREATE` statements for `KEYSPACE`, `TABLE` and `INDEX` now supports an `IF NOT EXISTS` condition. Similarly, `DROP` statements support a `IF EXISTS` condition.
- `INSERT` statements optionally supports a `IF NOT EXISTS` condition and `UPDATE` supports `IF` conditions.

### 3.0.5

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

### 3.0.4

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

### 3.0.3

- Support for custom secondary indexes has been added.

### 3.0.2

- Type validation for the constants has been fixed. For instance, the implementation used to allow `'2'` as a valid value for an `int` column (interpreting it has the equivalent of `2`), or `42` as a valid `blob` value (in which case `42` was interpreted as an hexadecimal representation of the blob). This is no longer the case, type validation of constants is now more strict. See the data types section for details on which constant is allowed for which type.
- The type validation fixed of the previous point has lead to the introduction of blobs constants to allow inputing blobs. Do note that while inputing blobs as strings constant is still supported by this version (to allow smoother transition to blob constant), it is now deprecated (in particular the data types section does not list strings constants as valid blobs) and will be removed by a future version. If you were using strings as blobs, you should thus update your client code ASAP to switch blob constants.
- A number of functions to convert native types to blobs have also been introduced. Furthermore the token function is now also allowed in select clauses. See the section on functions for details.

### 3.0.1

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

## Versioning

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

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