Data Definition¶

Common definitions¶

The names of the keyspaces and tables are defined by the following grammar:

keyspace_name ::=  name
table_name    ::=  [ keyspace_name '.' ] name
name          ::=  unquoted_name | quoted_name
unquoted_name ::=  re('[a-zA-Z_0-9]{1, 48}')
quoted_name   ::=  '"' unquoted_name '"'

Both keyspace and table name should be comprised of only alphanumeric characters, cannot be empty and are limited in size to 48 characters (that limit exists mostly to avoid filenames (which may include the keyspace and table name) to go over the limits of certain file systems). By default, keyspace and table names are case insensitive (myTable is equivalent to mytable) but case sensitivity can be forced by using double-quotes ("myTable" is different from mytable).

Further, a table is always part of a keyspace and a table name can be provided fully-qualified by the keyspace it is part of. If is is not fully-qualified, the table is assumed to be in the current keyspace (see USE statement).

Further, the valid names for columns is simply defined as:

column_name ::=  identifier

We also define the notion of statement options for use in the following section:

options ::=  option ( AND option )*
option  ::=  identifier '=' ( identifier | constant | map_literal )

CREATE KEYSPACE¶

A keyspace is created using a CREATE KEYSPACE statement:

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

For instance:

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;

Attempting to create a keyspace that already exists 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.

The supported options are:

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

CREATE KEYSPACE excalibur
    WITH replication = {'class': 'NetworkTopologyStrategy', 'replication_factor' : 3}

DESCRIBE KEYSPACE excalibur
    CREATE KEYSPACE excalibur WITH replication = {'class': 'NetworkTopologyStrategy', 'DC1': '3', 'DC2': '3'} AND durable_writes = true;

CREATE KEYSPACE excalibur
    WITH replication = {'class': 'NetworkTopologyStrategy', 'replication_factor' : 3, 'DC2': 2}

DESCRIBE KEYSPACE excalibur
    CREATE KEYSPACE excalibur WITH replication = {'class': 'NetworkTopologyStrategy', 'DC1': '3', 'DC2': '2'} AND durable_writes = true;

CREATE KEYSPACE excalibur
    WITH replication = {'class': 'NetworkTopologyStrategy', 'replication_factor' : 3, 'DC2': 0} ;

DESCRIBE KEYSPACE excalibur
    CREATE KEYSPACE excalibur WITH replication = {'class': 'NetworkTopologyStrategy', 'DC1': '3'} AND durable_writes = true;

CREATE KEYSPACE some_keysopace
           WITH replication = {'class': 'NetworkTopologyStrategy', 'DC1' : '3/1'', 'DC2' : '5/2'};

USE¶

The USE statement allows to change the current keyspace (for the connection on which it is executed). A number of objects in CQL are bound to a keyspace (tables, user-defined types, functions, ...) and the current keyspace is the default keyspace used when those objects are referred without a fully-qualified name (that is, without being prefixed a keyspace name). A USE statement simply takes the keyspace to use as current as argument:

use_statement ::=  USE keyspace_name

ALTER KEYSPACE¶

An ALTER KEYSPACE statement allows to modify the options of a keyspace:

alter_keyspace_statement ::=  ALTER KEYSPACE keyspace_name WITH options

For instance:

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

The supported options are the same than for creating a keyspace.

DROP KEYSPACE¶

Dropping a keyspace can be done using the DROP KEYSPACE statement:

drop_keyspace_statement ::=  DROP KEYSPACE [ IF EXISTS ] keyspace_name

For instance:

DROP KEYSPACE Excelsior;

Dropping a keyspace results in the immediate, irreversible removal of that keyspace, including all the tables, UTD and functions in it, and all the data contained in those tables.

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¶

Creating a new table uses the CREATE TABLE statement:

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

For instance:

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' };

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

A CQL table has a name and is composed of a set of rows. Creating a table amounts to defining which columns the rows will be composed, which of those columns compose the primary key, as well as optional options for the table.

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

Every rows in a CQL table has a set of predefined columns defined at the time of the table creation (or added later using an alter statement).

A column_definition is primarily comprised of the name of the column defined and it’s type, which restrict which values are accepted for that column. Additionally, a column definition can have the following modifiers:

STATIC

it declares the column as being a static column.

PRIMARY KEY

it declares the column as being the sole component of the primary key of the table.

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:

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

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

SELECT * FROM t;
   pk | t | v      | s
  ----+---+--------+-----------
   0  | 0 | 'val0' | 'static1'
   0  | 1 | 'val1' | 'static1'

As can be seen, the s value is the same (static1) for both of the row in the partition (the partition key in that example being pk, both rows are in that same partition): the 2nd insertion has overridden the value for s.

The use of static columns as the following restrictions:

• tables with the COMPACT STORAGE option (see below) cannot use 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.

Within a table, a row is uniquely identified by its PRIMARY KEY, and hence all table must define a PRIMARY KEY (and only one). A PRIMARY KEY definition is composed of one or more of the columns defined in the table. Syntactically, the primary key is defined the keywords PRIMARY KEY followed by comma-separated list of the column names composing it within parenthesis, but if the primary key has only one column, one can alternatively follow that column definition by the PRIMARY KEY keywords. The order of the columns in the primary key definition matter.

A CQL primary key is composed of 2 parts:

• the partition key part. It is the first component of the primary key definition. It can be a single column or, using additional parenthesis, can be multiple columns. A table always have at least a partition key, the smallest possible table definition is:

  CREATE TABLE t (k text PRIMARY KEY);
  

• the clustering columns. Those are the columns after the first component of the primary key definition, and the order of those columns define the clustering order.

Some example of primary key definition are:

• PRIMARY KEY (a): a is the partition key and there is no clustering columns.
• PRIMARY KEY (a, b, c) : a is the partition key and b and c are the clustering columns.
• PRIMARY KEY ((a, b), c) : a and b compose the partition key (this is often called a composite partition key) and c is the clustering column.

Within a table, CQL defines the notion of a partition. A partition is simply the set of rows that share the same value for their partition key. Note that if the partition key is composed of multiple columns, then rows belong to the same partition only they have the same values for all those partition key column. So for instance, given the following table definition and content:

CREATE TABLE t (
    a int,
    b int,
    c int,
    d int,
    PRIMARY KEY ((a, b), c, d)
);

SELECT * FROM t;
   a | b | c | d
  ---+---+---+---
   0 | 0 | 0 | 0    // row 1
   0 | 0 | 1 | 1    // row 2
   0 | 1 | 2 | 2    // row 3
   0 | 1 | 3 | 3    // row 4
   1 | 1 | 4 | 4    // row 5

row 1 and row 2 are in the same partition, row 3 and row 4 are also in the same partition (but a different one) and row 5 is in yet another partition.

Note that a table always has a partition key, and that if the table has no clustering columns, then every partition of that table is only comprised of a single row (since the primary key uniquely identifies rows and the primary key is equal to the partition key if there is no clustering columns).

The most important property of partition is that all the rows belonging to the same partition are guarantee to be stored on the same set of replica nodes. In other words, the partition key of a table defines which of the rows will be localized together in the Cluster, and it is thus important to choose your partition key wisely so that rows that needs to be fetch together are in the same partition (so that querying those rows together require contacting a minimum of nodes).

Please note however that there is a flip-side to this guarantee: as all rows sharing a partition key are guaranteed to be stored on the same set of replica node, a partition key that groups too much data can create a hotspot.

Another useful property of a partition is that when writing data, all the updates belonging to a single partition are done atomically and in isolation, which is not the case across partitions.

The proper choice of the partition key and clustering columns for a table is probably one of the most important aspect of data modeling in Cassandra, and it largely impact which queries can be performed, and how efficiently they are.

The clustering columns of a table defines the clustering order for the partition of that table. For a given partition, all the rows are physically ordered inside Cassandra by that clustering order. For instance, given:

CREATE TABLE t (
    a int,
    b int,
    c int,
    PRIMARY KEY (a, b, c)
);

SELECT * FROM t;
   a | b | c
  ---+---+---
   0 | 0 | 4     // row 1
   0 | 1 | 9     // row 2
   0 | 2 | 2     // row 3
   0 | 3 | 3     // row 4

then the rows (which all belong to the same partition) are all stored internally in the order of the values of their b column (the order they are displayed above). So where the partition key of the table allows to group rows on the same replica set, the clustering columns controls how those rows are stored on the replica. That sorting allows the retrieval of a range of rows within a partition (for instance, in the example above, SELECT * FROM t WHERE a = 0 AND b > 1 and b <= 3) to be very efficient.

A CQL table has a number of options that can be set at creation (and, for most of them, altered later). These options are specified after the WITH keyword.

Amongst those options, two important ones cannot be changed after creation and influence which queries can be done against the table: the COMPACT STORAGE option and the CLUSTERING ORDER option. Those, as well as the other options of a table are described in the following sections.

A compact table is one defined with the COMPACT STORAGE option. This option is only maintained for backward compatibility for definitions created before CQL version 3 and shouldn’t be used for new tables. Declaring a table with this option creates limitations for the table which are largely arbitrary (and exists for historical reasons). Amongst those limitation:

• a compact table cannot use collections nor static columns.
• if a compact table has at least one clustering column, then it must have exactly one column outside of the primary key ones. This imply you cannot add or remove columns after creation in particular.
• a compact table is limited in the indexes it can create, and no materialized view can be created on it.

The clustering order of a table is defined by the clustering columns of that table. By default, that ordering is based on natural order of those clustering order, but the CLUSTERING ORDER allows to change that clustering order to use the reverse natural order for some (potentially all) of the columns.

The CLUSTERING ORDER option takes the comma-separated list of the clustering column, each with a ASC (for ascendant, e.g. the natural order) or DESC (for descendant, e.g. the reverse natural order). Note in particular that the default (if the CLUSTERING ORDER option is not used) is strictly equivalent to using the option with all clustering columns using the ASC modifier.

Note that this option is basically a hint for the storage engine to change the order in which it stores the row but it has 3 visible consequences:

# it limits which ORDER BY clause are allowed for selects on that table. You can only

order results by the clustering order or the reverse clustering order. Meaning that if a table has 2 clustering column a and b and you defined WITH CLUSTERING ORDER (a DESC, b ASC), then in queries you will be allowed to use ORDER BY (a DESC, b ASC) and (reverse clustering order) ORDER BY (a ASC, b DESC) but not ORDER BY (a ASC, b ASC) (nor ORDER BY (a DESC, b DESC)).

# it also change the default order of results when queried (if no ORDER BY is provided). Results are always returned

in clustering order (within a partition).

# it has a small performance impact on some queries as queries in reverse clustering order are slower than the one in

forward clustering order. In practice, this means that if you plan on querying mostly in the reverse natural order of your columns (which is common with time series for instance where you often want data from the newest to the oldest), it is an optimization to declare a descending clustering order.

A table supports the following options:

By default, Cassandra read coordinators only query as many replicas as necessary to satisfy consistency levels: one for consistency level ONE, a quorum for QUORUM, and so on. speculative_retry determines when coordinators may query additional replicas, which is useful when replicas are slow or unresponsive. additional_write_policy specifies the threshold at which a cheap quorum write will be upgraded to include transient replicas. The following are legal values (case-insensitive):

This setting does not affect reads with consistency level ALL because they already query all replicas.

Note that frequently reading from additional replicas can hurt cluster performance. When in doubt, keep the default 99PERCENTILE.

The compaction options must at least define the 'class' sub-option, that defines the compaction strategy class to use. The default supported class are 'SizeTieredCompactionStrategy' (STCS), 'LeveledCompactionStrategy' (LCS) and 'TimeWindowCompactionStrategy' (TWCS) (the 'DateTieredCompactionStrategy' is also supported but is deprecated and 'TimeWindowCompactionStrategy' should be preferred instead). Custom strategy can be provided by specifying the full class name as a string constant.

All default strategies support a number of common options, as well as options specific to the strategy chosen (see the section corresponding to your strategy for details: STCS, LCS and TWCS).

The compression options define if and how the sstables of the table are compressed. The following sub-options are available:

For instance, to create a table with LZ4Compressor and a chunk_lenth_in_kb of 4KB:

CREATE TABLE simple (
   id int,
   key text,
   value text,
   PRIMARY KEY (key, value)
) with compression = {'class': 'LZ4Compressor', 'chunk_length_in_kb': 4};

The caching options allows to configure both the key cache and the row cache for the table. The following sub-options are available:

For instance, to create a table with both a key cache and 10 rows per partition:

CREATE TABLE simple (
id int,
key text,
value text,
PRIMARY KEY (key, value)
) WITH caching = {'keys': 'ALL', 'rows_per_partition': 10};

The read_repair options configures the read repair behavior to allow tuning for various performance and consistency behaviors. Two consistency properties are affected by read repair behavior.

• Monotonic Quorum Reads: Provided by BLOCKING. Monotonic quorum reads prevents reads from appearing to go back in time in some circumstances. When monotonic quorum reads are not provided and a write fails to reach a quorum of replicas, it may be visible in one read, and then disappear in a subsequent read.
• Write Atomicity: Provided by NONE. Write atomicity prevents reads from returning partially applied writes. Cassandra attempts to provide partition level write atomicity, but since only the data covered by a SELECT statement is repaired by a read repair, read repair can break write atomicity when data is read at a more granular level than it is written. For example read repair can break write atomicity if you write multiple rows to a clustered partition in a batch, but then select a single row by specifying the clustering column in a SELECT statement.

The available read repair settings are:

The default setting. When read_repair is set to BLOCKING, and a read repair is triggered, the read will block on writes sent to other replicas until the CL is reached by the writes. Provides monotonic quorum reads, but not partition level write atomicity

When read_repair is set to NONE, the coordinator will reconcile any differences between replicas, but will not attempt to repair them. Provides partition level write atomicity, but not monotonic quorum reads.

• Adding new columns (see ALTER TABLE below) is a constant time operation. There is thus no need to try to anticipate future usage when creating a table.

ALTER TABLE¶

Altering an existing table uses the ALTER TABLE statement:

alter_table_statement   ::=  ALTER TABLE table_name alter_table_instruction
alter_table_instruction ::=  ADD column_name cql_type ( ',' column_name cql_type )*
                             | DROP column_name ( column_name )*
                             | WITH options

For instance:

ALTER TABLE addamsFamily ADD gravesite varchar;

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

The ALTER TABLE statement can:

• Add new column(s) to the table (through the ADD instruction). Note that the primary key of a table cannot be changed and thus newly added column will, by extension, never be part of the primary key. Also note that compact tables have restrictions regarding column addition. Note that this is constant (in the amount of data the cluster contains) time operation.
• Remove column(s) from the table. This drops both the column and all its content, but note that while the column becomes immediately unavailable, its content is only removed lazily during compaction. Please also see the warnings below. Due to lazy removal, the altering itself is a constant (in the amount of data removed or contained in the cluster) time operation.
• Change some of the table options (through the WITH instruction). The supported options are the same that when creating a table (outside of COMPACT STORAGE and CLUSTERING ORDER that cannot be changed after creation). 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.

DROP TABLE¶

Dropping a table uses the DROP TABLE statement:

drop_table_statement ::=  DROP TABLE [ IF EXISTS ] table_name

Dropping a table results in the immediate, irreversible removal of the table, including all data it contains.

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¶

A table can be truncated using the TRUNCATE statement:

truncate_statement ::=  TRUNCATE [ TABLE ] table_name

Note that TRUNCATE TABLE foo is allowed for consistency with other DDL statements but tables are the only object that can be truncated currently and so the TABLE keyword can be omitted.

Truncating a table permanently removes all existing data from the table, but without removing the table itself.