Data Types¶

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: incrementing and decrementing (see the UPDATE statement for syntax). Note that the value of a counter cannot be set: a counter does not exist until first incremented/decremented, and that first increment/decrement is made as if the prior value was 0.

Counters have a number of important limitations:

• They cannot be used for columns 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.
• Counters do not support expiration.
• The deletion of counters is supported, but is only guaranteed to work the first time you delete a counter. In other words, you should not re-update a counter that you have deleted (if you do, proper behavior is not guaranteed).
• Counter updates are, by nature, not idemptotent. An important consequence is that if a counter update fails unexpectedly (timeout or loss of connection to the coordinator node), the client has no way to know if the update has been applied or not. In particular, replaying the update may or may not lead to an over count.

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 messages sent by a user”, “events registered by a sensor”...), then collections are not appropriate and a specific table (with clustering columns) should be used. Concretely, (non-frozen) collections have the following noteworthy characteristics and limitations:

• Individual collections are not indexed internally. Which means that even to access a single element of a collection, the while collection has to be read (and reading one is not paged internally).
• While insertion operations on sets and maps never incur a read-before-write internally, some operations on lists do. Further, some lists operations are not idempotent by nature (see the section on lists below for details), making their retry in case of timeout problematic. It is thus advised to prefer sets over lists when possible.

Please note that while some of those limitations may or may not be removed/improved upon in the future, it is a anti-pattern to use a (single) collection to store large amounts of data.

Maps¶

A map is a (sorted) set of key-value pairs, where keys are unique and the map is sorted by its keys. You can define and insert a map with:

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

INSERT INTO users (id, name, favs)
           VALUES ('jsmith', 'John Smith', { 'fruit' : 'Apple', 'band' : 'Beatles' });

// Replace the existing map entirely.
UPDATE users SET favs = { 'fruit' : 'Banana' } WHERE id = 'jsmith';

Further, maps support:

• Updating or inserting one or more elements:

  UPDATE users SET favs['author'] = 'Ed Poe' WHERE id = 'jsmith';
  UPDATE users SET favs = favs + { 'movie' : 'Cassablanca', 'band' : 'ZZ Top' } WHERE id = 'jsmith';
  

• Removing one or more element (if an element doesn’t exist, removing it is a no-op but no error is thrown):

  DELETE favs['author'] FROM users WHERE id = 'jsmith';
  UPDATE users SET favs = favs - { 'movie', 'band'} WHERE id = 'jsmith';
  

  Note that for removing multiple elements in a map, you remove from it a set of keys.

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

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

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

Sets¶

A set is a (sorted) collection of unique values. You can define and insert a map with:

CREATE TABLE images (
    name text PRIMARY KEY,
    owner text,
    tags set<text> // A set of text values
);

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

// Replace the existing set entirely
UPDATE images SET tags = { 'kitten', 'cat', 'lol' } WHERE name = 'cat.jpg';

Further, sets support:

• Adding one or multiple elements (as this is a set, inserting an already existing element is a no-op):

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

• Removing one or multiple elements (if an element doesn’t exist, removing it is a no-op but no error is thrown):

  UPDATE images SET tags = tags - { 'cat' } WHERE name = 'cat.jpg';
  

Lastly, as for maps, TTLs if used only apply to the newly inserted values.

Lists¶

A list is a (sorted) collection of non-unique values where elements are ordered by there position in the list. You can define and insert a list with:

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

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

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

Further, lists support:

• Appending and prepending values to a list:

  UPDATE plays SET players = 5, scores = scores + [ 14, 21 ] WHERE id = '123-afde';
  UPDATE plays SET players = 6, scores = [ 3 ] + scores WHERE id = '123-afde';
  

• Setting the value at a particular position in the list. This imply that the list has a pre-existing element for that position or an error will be thrown that the list is too small:

  UPDATE plays SET scores[1] = 7 WHERE id = '123-afde';
  

• Removing an element by its position in the list. This imply that the list has a pre-existing element for that position or an error will be thrown that the list is too small. Further, as the operation removes an element from the list, the list size will be diminished by 1, shifting the position of all the elements following the one deleted:

  DELETE scores[1] FROM plays WHERE id = '123-afde';
  

• Deleting all the occurrences of particular values in the list (if a particular element doesn’t occur at all in the list, it is simply ignored and no error is thrown):

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

Lastly, as for maps, TTLs when used only apply to the newly inserted values.

Creating a UDT¶

Creating a new user-defined type is done using a CREATE TYPE statement defined by:

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

A UDT has a name (used to declared columns of that type) and is a set of named and typed fields. Fields name can be any type, including collections or other UDT. For instance:

CREATE TYPE phone (
    country_code int,
    number text,
)

CREATE TYPE address (
    street text,
    city text,
    zip text,
    phones map<text, phone>
)

CREATE TABLE user (
    name text PRIMARY KEY,
    addresses map<text, frozen<address>>
)

Note that:

• 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.
• A type is intrinsically bound to the keyspace in which it is created, and can only be used in that keyspace. At creation, if the type name is prefixed by a keyspace name, it is created in that keyspace. Otherwise, it is created in the current keyspace.
• As of Cassandra 4.0, UDT have to be frozen in most cases, hence the frozen<address> in the table definition above. Please see the section on frozen for more details.

UDT literals¶

Once a used-defined type has been created, value can be input using a UDT literal:

udt_literal ::=  '{' identifier ':' term ( ',' identifier ':' term )* '}'

In other words, a UDT literal is like a map literal but its keys are the names of the fields of the type. For instance, one could insert into the table define in the previous section using:

INSERT INTO user (name, addresses)
          VALUES ('z3 Pr3z1den7', {
              'home' : {
                  street: '1600 Pennsylvania Ave NW',
                  city: 'Washington',
                  zip: '20500',
                  phones: { 'cell' : { country_code: 1, number: '202 456-1111' },
                            'landline' : { country_code: 1, number: '...' } }
              },
              'work' : {
                  street: '1600 Pennsylvania Ave NW',
                  city: 'Washington',
                  zip: '20500',
                  phones: { 'fax' : { country_code: 1, number: '...' } }
              }
          })

To be valid, a UDT literal should only include fields defined by the type it is a literal of, but it can omit some field (in which case those will be null).

Altering a UDT¶

An existing user-defined type can be modified using an ALTER TYPE statement:

alter_type_statement    ::=  ALTER TYPE udt_name alter_type_modification
alter_type_modification ::=  ADD field_definition
                             | RENAME identifier TO identifier ( identifier TO identifier )*

You can:

• add a new field to the type (ALTER TYPE address ADD country text). That new field will be null for any values of the type created before the addition.
• rename the fields of the type (ALTER TYPE address RENAME zip TO zipcode).

Dropping a UDT¶

You can drop an existing user-defined type using a DROP TYPE statement:

drop_type_statement ::=  DROP TYPE [ IF EXISTS ] udt_name

Dropping a type results in the immediate, irreversible removal of that type. However, attempting to drop a type that is still in use by another type, table or function will result in an error.

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