Data Manipulation

This section describes the statements supported by CQL to insert, update, delete and query data.


Querying data from data is done using a SELECT statement:

select_statement ::=  SELECT [ JSON | DISTINCT ] ( select_clause | '*' )
                      FROM table_name
                      [ WHERE where_clause ]
                      [ GROUP BY group_by_clause ]
                      [ ORDER BY ordering_clause ]
                      [ PER PARTITION LIMIT (integer | bind_marker) ]
                      [ LIMIT (integer | bind_marker) ]
                      [ ALLOW FILTERING ]
select_clause    ::=  selector [ AS identifier ] ( ',' selector [ AS identifier ] )
selector         ::=  column_name
                      | term
                      | CAST '(' selector AS cql_type ')'
                      | function_name '(' [ selector ( ',' selector )* ] ')'
                      | COUNT '(' '*' ')'
where_clause     ::=  relation ( AND relation )*
relation         ::=  column_name operator term
                      '(' column_name ( ',' column_name )* ')' operator tuple_literal
                      TOKEN '(' column_name ( ',' column_name )* ')' operator term
operator         ::=  '=' | '<' | '>' | '<=' | '>=' | '!=' | IN | CONTAINS | CONTAINS KEY
group_by_clause  ::=  column_name ( ',' column_name )*
ordering_clause  ::=  column_name [ ASC | DESC ] ( ',' column_name [ ASC | DESC ] )*

For instance:

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 (*) 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 the rows matching the request, where each row contains the values for the selection corresponding to the query. Additionally, functions including aggregation ones can be applied to the result.

A SELECT statement contains at least a selection clause and the name of the table on which the selection is on (note that CQL does not joins or sub-queries and thus a select statement only apply to a single table). In most case, a select will also have a where clause and it can optionally have additional clauses to order or limit the results. Lastly, queries that require filtering can be allowed if the ALLOW FILTERING flag is provided.

Selection clause

The select_clause determines which columns needs to be queried and returned in the result-set, as well as any transformation to apply to this result before returning. It consists of a comma-separated list of selectors or, alternatively, of the wildcard character (*) to select all the columns defined in the table.


A selector can be one of:

  • A column name of the table selected, to retrieve the values for that column.
  • A term, which is usually used nested inside other selectors like functions (if a term is selected directly, then the corresponding column of the result-set will simply have the value of this term for every row returned).
  • A casting, which allows to convert a nested selector to a (compatible) type.
  • A function call, where the arguments are selector themselves. See the section on functions for more details.
  • The special call COUNT(*) to the COUNT function, which counts all non-null results.


Every top-level selector can also be aliased (using AS). If so, the name of the corresponding column in the result set will be that of the alias. For instance:

// Without alias
SELECT intAsBlob(4) FROM t;

//  intAsBlob(4)
// --------------
//  0x00000004

// With alias
SELECT intAsBlob(4) AS four FROM t;

//  four
// ------------
//  0x00000004


Currently, aliases aren’t recognized anywhere else in the statement where they are used (not in the WHERE clause, not in the ORDER BY clause, …). You must use the orignal column name instead.

WRITETIME and TTL function

Selection supports two special functions (that aren’t allowed anywhere else): WRITETIME and TTL. Both function take only one argument and that argument must be a column name (so for instance TTL(3) is invalid).

Those functions allow to retrieve meta-information that are stored internally for each column, namely:

  • the timestamp of the value of the column for WRITETIME.
  • the remaining time to live (in seconds) for the value of the column if it set to expire (and null otherwise).

The 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:

    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:

 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:

 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 would not be the case for:

 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:

 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.

Grouping results

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.

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.

Ordering results

The ORDER BY clause 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 by the clustering order defined on the table:

  • 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.

Limiting results

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.

Allowing 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:

    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 * 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 * 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 * FROM users WHERE birth_year = 1981 AND country = 'FR' ALLOW FILTERING;


Inserting data for a row is done using an INSERT statement:

insert_statement ::=  INSERT INTO table_name ( names_values | json_clause )
                      [ IF NOT EXISTS ]
                      [ USING update_parameter ( AND update_parameter )* ]
names_values     ::=  names VALUES tuple_literal
json_clause      ::=  JSON string [ DEFAULT ( NULL | UNSET ) ]
names            ::=  '(' column_name ( ',' column_name )* ')'

For instance:

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 JSON support for more detail.

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 informations on the update_parameter.

Also note that INSERT does not support counters, while UPDATE does.


Updating a row is done using an UPDATE statement:

update_statement ::=  UPDATE table_name
                      [ USING update_parameter ( AND update_parameter )* ]
                      SET assignment ( ',' assignment )*
                      WHERE where_clause
                      [ IF ( EXISTS | condition ( AND condition )*) ]
update_parameter ::=  ( TIMESTAMP | TTL ) ( integer | bind_marker )
assignment       ::=  simple_selection '=' term
                     | column_name '=' column_name ( '+' | '-' ) term
                     | column_name '=' list_literal '+' column_name
simple_selection ::=  column_name
                     | column_name '[' term ']'
                     | column_name '.' `field_name
condition        ::=  simple_selection operator term

For instance:

   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. Non primary key columns are then set using the SET keyword.

Note that unlike in SQL, UPDATE does not check the prior existence of the row by default (except through IF, 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.

Regarding the assignment:

  • c = c + 3 is used to increment/decrement counters. The column name after the ‘=’ sign must be the same than the one before the ‘=’ sign. Note that increment/decrement is only allowed on counters, and are the only update operations allowed on counters. See the section on counters for details.
  • id = id + <some-collection> and id[value1] = value2 are for collections, see the relevant section for details.
  • id.field = 3 is for setting the value of a field on a non-frozen user-defined types. see the relevant section for details.

Update parameters

The UPDATE, INSERT (and DELETE and BATCH for the TIMESTAMP) statements support the following parameters:

  • 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.


Deleting rows or parts of rows uses the DELETE statement:

delete_statement ::=  DELETE [ simple_selection ( ',' simple_selection ) ]
                      FROM table_name
                      [ USING update_parameter ( AND update_parameter )* ]
                      WHERE where_clause
                      [ IF ( EXISTS | condition ( AND condition )*) ]

For instance:

 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. 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 operator. A range of rows may be deleted using an inequality operator (such as >=).

DELETE supports the TIMESTAMP option with the same semantics as in updates.

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.


Multiple INSERT, UPDATE and DELETE can be executed in a single statement by grouping them through a BATCH statement:

batch_statement        ::=  BEGIN [ UNLOGGED | COUNTER ] BATCH
                            [ USING update_parameter ( AND update_parameter )* ]
                            modification_statement ( ';' modification_statement )*
                            APPLY BATCH
modification_statement ::=  insert_statement | update_statement | delete_statement

For instance:

   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';

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

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

Note that:

  • BATCH statements may only contain UPDATE, INSERT and DELETE statements (not other batches for instance).
  • 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 (either one generated automatically, or the timestamp provided at the batch level). 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.
  • A LOGGED batch to a single partition will be converted to an UNLOGGED batch as an optimization.

UNLOGGED batches

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 batches

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