Data Definition

CQL stores data in tables, whose schema defines the layout of the data in the table. Tables are located in keyspaces. A keyspace defines options that apply to all the keyspace’s tables. The replication strategy is an important keyspace option, as is the replication factor. A good general rule is one keyspace per application. It is common for a cluster to define only one keyspace for an actie application.

This section describes the statements used to create, modify, and remove those keyspace and tables.

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 are 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 with a CREATE KEYSPACE statement:

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

For example:

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 contain the 'class' sub-option that defines the desired replication strategy class. The rest of the sub-options depend on which replication strategy is used. By default, Cassandra supports the following 'class' values:

SimpleStrategy

A simple strategy that defines a replication factor for data to be spread across the entire cluster. This is generally not a wise choice for production, as it does not respect datacenter layouts and can lead to wildly varying query latency. For production, use NetworkTopologyStrategy. SimpleStrategy supports a single mandatory argument:

NetworkTopologyStrategy

A production-ready replication strategy that sets the replication factor independently for each data-center. The rest of the sub-options are key-value pairs, with a key set to a data-center name and its value set to the associated replication factor. Options:

When later altering keyspaces and changing the replication_factor, auto-expansion will only add new datacenters for safety, it will not alter existing datacenters or remove any, even if they are no longer in the cluster. If you want to remove datacenters while setting the replication_factor, explicitly zero out the datacenter you want to have zero replicas.

An example of auto-expanding datacenters with two datacenters: DC1 and DC2:

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

will result in:

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

An example of auto-expanding and overriding a datacenter:

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

will result in:

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

An example that excludes a datacenter while using replication_factor:

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

will result in:

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

If transient replication has been enabled, transient replicas can be configured for both SimpleStrategy and NetworkTopologyStrategy by defining replication factors in the format '<total_replicas>/<transient_replicas>'

For instance, this keyspace will have 3 replicas in DC1, 1 of which is transient, and 5 replicas in DC2, 2 of which are transient:

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

USE

The USE statement changes the current keyspace to the specified keyspace. A number of objects in CQL are bound to a keyspace (tables, user-defined types, functions, etc.) and the current keyspace is the default keyspace used when those objects are referred to in a query without a fully-qualified name (without a prefixed keyspace name). A USE statement specifies the keyspace to use as an argument:

use_statement::= USE keyspace_name

Using CQL:

USE excelsior;

ALTER KEYSPACE

An ALTER KEYSPACE statement modifies the options of a keyspace:

alter_keyspace_statement::= ALTER KEYSPACE keyspace_name
    WITH options

For example:

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

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

DROP KEYSPACE

Dropping a keyspace is done with the DROP KEYSPACE statement:

drop_keyspace_statement::= DROP KEYSPACE [ IF EXISTS ] keyspace_name

For example:

DROP KEYSPACE excelsior;

Dropping a keyspace results in the immediate, irreversible removal of that keyspace, including all the tables, user-defined types, user-defined functions, 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 example, here are some CQL statements to create tables:

CREATE TABLE monkey_species (
    species text PRIMARY KEY,
    common_name text,
    population varint,
    average_size int
) WITH comment='Important biological records';
CREATE TABLE timeline (
    userid uuid,
    posted_month int,
    posted_time uuid,
    body text,
    posted_by text,
    PRIMARY KEY (userid, posted_month, posted_time)
) WITH compaction = { 'class' : 'LeveledCompactionStrategy' };
CREATE TABLE loads (
    machine inet,
    cpu int,
    mtime timeuuid,
    load float,
    PRIMARY KEY ((machine, cpu), mtime)
) WITH CLUSTERING ORDER BY (mtime DESC);

A CQL table has a name and is composed of a set of rows. Creating a table amounts to defining which columns each rows will have, which of those columns comprise the primary key, as well as defined 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.

Column definitions

Every row in a CQL table will have the predefined columns defined at table creation. Columns can be added later using an alter statement.

A column_definition is comprised of the name of the column and its type, restricting the values that are accepted for that column. Additionally, a column definition can have the following modifiers:

• STATIC: declares the column as a static column

• PRIMARY KEY: declares the column as the sole component of the primary key of the table

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 example:

• Code

• Results

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 rows in the partition (the partition key being pk, and both rows are in the same partition): the second insertion overrides the value for s.

The use of static columns has the following restrictions:

• 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.

The Primary key

Within a table, a row is uniquely identified by its PRIMARY KEY, and hence all tables must define a single PRIMARY KEY. A PRIMARY KEY is composed of one or more of the defined columns in the table. Syntactically, the primary key is defined with the phrase PRIMARY KEY followed by a comma-separated list of the column names within parenthesis. If the primary key has only one column, you can alternatively add the PRIMARY KEY phrase to that column in the table definition. The order of the columns in the primary key definition defines the partition key and clustering columns.

A CQL primary key is composed of two parts:

partition key

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

  CREATE TABLE t (k text PRIMARY KEY);

clustering columns

• The columns are the columns that follow the partition key in the primary key definition. The order of those columns define the clustering order.

Some examples of primary key definition are:

• PRIMARY KEY (a): a is the single partition key and there are no clustering columns

• PRIMARY KEY (a, b, c) : a is the single partition key and b and c are the clustering columns

• PRIMARY KEY ((a, b), c) : a and b compose the composite partition key and c is the clustering column

Partition key

Within a table, CQL defines the notion of a partition that defines the location of data within a Cassandra cluster. A partition is 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 when they have the same values for all those partition key columns. A hash is computed from the partition key columns and that hash value defines the partition location. 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)
);
INSERT INTO t (a, b, c, d) VALUES (0,0,0,0);
INSERT INTO t (a, b, c, d) VALUES (0,0,1,1);
INSERT INTO t (a, b, c, d) VALUES (0,1,2,2);
INSERT INTO t (a, b, c, d) VALUES (0,1,3,3);
INSERT INTO t (a, b, c, d) VALUES (1,1,4,4);
SELECT * FROM t;

will result in

 a | b | c | d
---+---+---+---
 0 | 0 | 0 | 0     (1)
 0 | 0 | 1 | 1
 0 | 1 | 2 | 2    (2)
 0 | 1 | 3 | 3
 1 | 1 | 4 | 4  (3)
(5 rows)

Note that a table always has a partition key, and that if the table has no clustering columns, then every partition of that table has a single row. because the partition key, compound or otherwise, identifies a single location.

The most important property of partition is that all the rows belonging to the same partition are guaranteed to be stored on the same set of replica nodes. In other words, the partition key of a table defines which rows will be localized on the same node in the cluster. The localization of data is important to the efficient retrieval of data, requiring the Cassandra coordinator to contact as few nodes as possible. However, there is a flip-side to this guarantee, and all rows sharing a partition key will be stored on the same node, creating a hotspot for both reading and writing. While selecting a primary key that groups table rows assists batch updates and can ensure that the updates are atomic and done in isolation, the partitions must be sized “just right, not too big nor too small”.

Data modeling that considers the querying patterns and assigns primary keys based on the queries will have the lowest latency in fetching data.

Clustering columns

The clustering columns of a table define the clustering order for the partition of that table. For a given partition, all rows are ordered by that clustering order. Clustering columns also add uniqueness to a row in a table.

For instance, given:

CREATE TABLE t2 (
    a int,
    b int,
    c int,
    d int,
    PRIMARY KEY (a, b, c)
);
INSERT INTO t2 (a, b, c, d) VALUES (0,0,0,0);
INSERT INTO t2 (a, b, c, d) VALUES (0,0,1,1);
INSERT INTO t2 (a, b, c, d) VALUES (0,1,2,2);
INSERT INTO t2 (a, b, c, d) VALUES (0,1,3,3);
INSERT INTO t2 (a, b, c, d) VALUES (1,1,4,4);
SELECT * FROM t2;

will result in

 a | b | c | d
---+---+---+---
 1 | 1 | 4 | 4    (1)
 0 | 0 | 0 | 0
 0 | 0 | 1 | 1
 0 | 1 | 2 | 2
 0 | 1 | 3 | 3
(5 rows)

Looking more closely at the four rows in the same partition, the b clustering column defines the order in which those rows are displayed. Whereas the partition key of the table groups rows on the same node, the clustering columns control how those rows are stored on the node.

That sorting allows the very efficient retrieval of a range of rows within a partition:

SELECT * FROM t2 WHERE a = 0 AND b > 0 and b <= 3;

will result in

 a | b | c | d
---+---+---+---
 0 | 1 | 2 | 2
 0 | 1 | 3 | 3
(2 rows)

Table options

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.

One important option that cannot be changed after creation, CLUSTERING ORDER BY, influences how queries can be done against the table. It is worth discussing in more detail here.

Clustering order

The clustering order of a table is defined by the clustering columns. By default, the clustering order is ascending for the clustering column’s data types. For example, integers order from 1, 2, …​ n, while text orders from A to Z.

The CLUSTERING ORDER BY table option uses a comma-separated list of the clustering columns, each set for either ASC (for ascending order) or DESC (for _descending order). The default is ascending for all clustering columns if the CLUSTERING ORDER BY option is not set.

This option is basically a hint for the storage engine that changes the order in which it stores the row. Beware of the consequences of setting this option:

• It changes the default ascending order of results when queried with a SELECT statement with no ORDER BY clause.

• It limits how the ORDER BY clause is used in SELECT statements on that table. Results can only be ordered with either the original clustering order or the reverse clustering order. Suppose you create a table with two clustering columns a and b, defined WITH CLUSTERING ORDER BY (a DESC, b ASC). Queries on the table can use ORDER BY (a DESC, b ASC) or ORDER BY (a ASC, b DESC). Mixed order, such as ORDER BY (a ASC, b ASC) or ORDER BY (a DESC, b DESC) will not return expected order.

• It has a performance impact on queries. Queries in reverse clustering order are slower than the default ascending order. If you plan to query mostly in descending order, declare the clustering order in the table schema using WITH CLUSTERING ORDER BY (). This optimization is common for time series, to retrieve the data from newest to oldest.

Other table options

A table supports the following options:

Speculative retry 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, a useful action when replicas are slow or unresponsive. Speculative retries reduce the latency. The speculative_retry option configures rapid read protection, where a coordinator sends more requests than needed to satisfy the consistency level.

Pre-Cassandra 4.0 speculative retry policy takes a single string as a parameter:

• NONE

• ALWAYS

• 99PERCENTILE (PERCENTILE)

• 50MS (CUSTOM)

An example of setting speculative retry sets a custom value:

ALTER TABLE users WITH speculative_retry = '10ms';

This example uses a percentile for the setting:

ALTER TABLE users WITH speculative_retry = '99PERCENTILE';

A percentile setting can backfire. If a single host becomes unavailable, it can force up the percentiles. A value of p99 will not speculate as intended because the value at the specified percentile has increased too much. If the consistency level is set to ALL, all replicas are queried regardless of the speculative retry setting.

Cassandra 4.0 supports case-insensitivity for speculative retry values (CASSANDRA-14293). For example, assigning the value as none, None, or NONE has the same effect.

Additionally, the following values are added:

Cassandra 4.0 adds support for hybrid MIN() and MAX() speculative retry policies, with a mix and match of either MIN(), MAX(), MIN(), MIN(), or MAX(), MAX() (CASSANDRA-14293). The hybrid mode will still speculate if the normal p99 for the table is < 50ms, the minimum value. But if the p99 level goes higher than the maximum value, then that value can be used. In a hybrid value, one value must be a fixed time (ms) value and the other a percentile value.

To illustrate variations, the following examples are all valid:

min(99percentile,50ms)
max(99p,50MS)
MAX(99P,50ms)
MIN(99.9PERCENTILE,50ms)
max(90percentile,100MS)
MAX(100.0PERCENTILE,60ms)

The additional_write_policy setting specifies the threshold at which a cheap quorum write will be upgraded to include transient replicas.

Compaction options

The compaction options must minimally define the 'class' sub-option, to specify the compaction strategy class to use. The supported classes are:

• 'SizeTieredCompactionStrategy', STCS (Default)

• 'LeveledCompactionStrategy', LCS

• 'TimeWindowCompactionStrategy', TWCS

The 'DateTieredCompactionStrategy' is also supported but deprecated; 'TimeWindowCompactionStrategy' should be used. If a custom strategies is required, specify 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, TWCS.

Compression options

The compression options define if and how the SSTables of the table are compressed. Compression is configured on a per-table basis as an optional argument to CREATE TABLE or ALTER TABLE. The following sub-options are available:

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

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

Caching options

Caching optimizes the use of cache memory of a table. The cached data is weighed by size and access frequency. The caching options can 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 cached per partition:

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

Read Repair options

The read_repair options configure the read repair behavior, tuning for various performance and consistency behaviors.

The values are:

Two consistency properties are affected by read repair behavior.

• Monotonic quorum reads: 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, the read values may be visible in one read, and then disappear in a subsequent read. BLOCKING provides this behavior.

• Write atomicity: 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. NONE provides this behavior.

Other considerations:

• Adding new columns (see ALTER TABLE below) is a constant time operation. Thus, there is no need to anticipate future usage while initially 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 example:

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

The ALTER TABLE statement can:

• ADD a new column to a table. The primary key of a table cannot ever be altered. A new column, thus, cannot be part of the primary key. Adding a column is a constant-time operation based on the amount of data in the table.

• DROP a column from a table. This command drops both the column and all its content. Be aware that, while the column becomes immediately unavailable, its content are removed lazily during compaction. Because of this lazy removal, the command is a constant-time operation based on the amount of data in the table. Also, it is important to know that once a column is dropped, a column with the same name can be re-added, unless the dropped column was a non-frozen column like a collection.

• Use WITH to change a table option. The supported options are the same as those used when creating a table, with the exception of CLUSTERING ORDER. However, setting any compaction sub-options will erase ALL previous compaction options, so you need to re-specify all the sub-options you wish to keep. The same is true for 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, when the operation is a no-op.

TRUNCATE

A table can be truncated using the TRUNCATE statement:

truncate_statement::= TRUNCATE [ TABLE ] table_name

TRUNCATE TABLE foo is the preferred syntax for consistency with other DDL statements. However, tables are the only object that can be truncated currently, and the TABLE keyword can be omitted.

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