CREATE MATERIALIZED VIEW

Optimizes read requests and eliminates the need for multiple write requests by duplicating data from a base table.

Optimizes read requests by allowing different partitioning and clustering columns than the base table and eliminates the need for individual write requests to multiple tables. When data is written to the base table, it is also automatically written to all associated materialized views.
Restriction:
  • Use all base table primary keys in the materialized view.
  • Multiple non-primary key columns from the base table are supported when the partition key is the same as in the base table, otherwise only a single non-primary key from the base table is allowed in the materialized view's PRIMARY KEY.
  • Static columns are not supported.

Synopsis

CREATE MATERIALIZED VIEW [ IF NOT EXISTS ] [keyspace_name.]view_name
  AS SELECT [ (column_list) ]
  FROM [keyspace_name.]table_name
  [ WHERE column_name IS NOT NULL
  [ AND column_name IS NOT NULL ... ] ]
  [ AND relation [ AND ... ] ] 
  PRIMARY KEY ( column_list )
  [ WITH [ table_properties ]
  [ [ AND ] CLUSTERING ORDER BY (cluster_column_name order_option) ] ] ;
Table 1. Legend
Syntax conventions Description
UPPERCASE Literal keyword.
Lowercase Not literal.
Italics Variable value. Replace with a user-defined value.
[] Optional. Square brackets ( [] ) surround optional command arguments. Do not type the square brackets.
( ) Group. Parentheses ( ( ) ) identify a group to choose from. Do not type the parentheses.
| Or. A vertical bar ( | ) separates alternative elements. Type any one of the elements. Do not type the vertical bar.
... Repeatable. An ellipsis ( ... ) indicates that you can repeat the syntax element as often as required.
'Literal string' Single quotation ( ' ) marks must surround literal strings in CQL statements. Use single quotation marks to preserve upper case.
{ key : value } Map collection. Braces ( { } ) enclose map collections or key value pairs. A colon separates the key and the value.
<datatype1,datatype2> Set, list, map, or tuple. Angle brackets ( < > ) enclose data types in a set, list, map, or tuple. Separate the data types with a comma.
cql_statement; End CQL statement. A semicolon ( ; ) terminates all CQL statements.
[--] Separate the command line options from the command arguments with two hyphens ( -- ). This syntax is useful when arguments might be mistaken for command line options.
' <schema> ... </schema> ' Search CQL only: Single quotation marks ( ' ) surround an entire XML schema declaration.
@xml_entity='xml_entity_type' Search CQL only: Identify the entity and literal value to overwrite the XML element in the schema and solrConfig files.
IF NOT EXISTS
Optional. Suppresses the error message when attempting to create a materialized view that already exists. Use to continue executing commands, such as a SOURCE command. The option only validates that a materialized view with the same name exists; columns, primary keys, properties, and other settings can differ.
keyspace_name
Optional. When no keyspace is selected or to create the view in another keyspace, set the keyspace name before the materialized view name.
Note: Base tables and materialized views are always in the same keyspace.
view_name
Materialized view names can only contain alpha-numeric characters and underscores. The view name must begin with a number or letter and can be up to 49 characters long.
column_list

Comma-separated list of columns from the base table to include in the materialized view.

Static columns, even when specified, are not supported and not included in the materialized view.

column_name IS NOT NULL

Test all columns for null values in the WHERE clause. Separate each condition with AND. Rows with null values in any column are not inserted into the materialized view table.

AND relation

Other relations that target the specific data needed.

PRIMARY KEY ( column_list )

Comma-separated list of columns used to partition and cluster the data. You can add non-primary key columns from the base table. Reorder the primary keys as needed to query the table more efficiently, including changing the partitioning and clustering keys.

List the partition key first, followed by the clustering keys. Create a compound partition key by enclosing column names in parenthesis, for example:
PRIMARY KEY (
  ( PK_column1 [, PK_column2... ] ),
  clustering_column1 [, clustering_column2 ... ] )
table_properties
Optional. Specify table properties if different than default. Separate table property definitions with an AND. See table properties.
Note: The base table properties are not copied.
Restriction: Change log, CDC, is not available for materialized views. Not all table properties are available when creating a materialized view; for example, default_time_to_live is not available.

Examples

This section shows example scenarios that illustrate the use of materialized views.

Basic examples of materialized views

These tables are used in the first example scenario:
CREATE TABLE IF NOT EXISTS cycling.cyclist_base (
  cid UUID PRIMARY KEY,
  name text,
  age int,
  birthday date,
  country text
);
CREATE TABLE IF NOT EXISTS cycling.cyclist_base_ext (
  cid UUID, 
  name text, 
  age int, 
  birthday date, 
  country text,
  PRIMARY KEY (cid, age, birthday)
);
The following materialized view cyclist_by_age uses the base table cyclist_base. The WHERE clause ensures that only rows whose age and cid columns are non-NULL are added to the materialized view. In the materialized view, age is the partition key, and cid is the clustering column. In the base table, cid is the partition key.
CREATE MATERIALIZED VIEW cycling.cyclist_by_age AS
  SELECT age, cid, birthday, country, name
  FROM cycling.cyclist_base 
  WHERE age IS NOT NULL
    AND cid IS NOT NULL
  PRIMARY KEY (age, cid)
  WITH CLUSTERING ORDER BY (cid ASC)
  AND caching = { 'keys' : 'ALL', 'rows_per_partition' : '100' }
  AND comment = 'Based on table cyclist';
The results of this query:
SELECT *
FROM cycling.cyclist_by_age;
are:
 age | cid                                  | birthday   | country       | name
-----+--------------------------------------+------------+---------------+---------------------
  28 | 6ab09bec-e68e-48d9-a5f8-97e6fb4c9b47 | 1987-06-07 |   Netherlands |   Steven KRUIKSWIJK
  19 | 1c526849-d3a2-42a3-bcf9-7903c80b3d16 | 1998-12-23 |     Australia |       Kanden GROVES
  19 | 410919ef-bd1b-4efa-8256-b0fd8ab67029 | 1999-01-04 |    Uzbekistan | Iskandarbek SHODIEV
  18 | 15a116fc-b833-4da6-ab9a-4a7775752836 | 1997-08-19 | United States |        Adrien COSTA
  18 | 18f471bf-f631-4bc4-a9a2-d6f6cf5ea503 | 1997-03-29 |   Netherlands |         Bram WELTEN
  18 | ffdfa2a7-5fc6-49a7-bfdc-3fcdcfdd7156 | 1997-02-08 |   Netherlands |    Pascal EENKHOORN
  22 | e7ae5cf3-d358-4d99-b900-85902fda9bb0 | 1993-06-18 |   New Zealand |          Alex FRAME
  27 | c9c9c484-5e4a-4542-8203-8d047a01b8a8 | 1987-09-04 |        Brazil |     Cristian EGIDIO
  27 | d1aad83b-be60-47a4-bd6e-069b8da0d97b | 1987-09-04 |       Germany |     Johannes HEIDER
  20 | 862cc51f-00a1-4d5a-976b-a359cab7300e | 1994-09-04 |       Denmark |       Joakim BUKDAL
  38 | 220844bf-4860-49d6-9a4b-6b5d3a79cbfb | 1977-07-08 |         Italy |     Paolo TIRALONGO
  29 | 96c4c40d-58c8-4710-b73f-681e9b1f70ae | 1989-04-20 |     Australia |     Benjamin DYBALL

(12 rows)
The following materialized view cyclist_by_birthday_and_age uses the base table cyclist_base. The WHERE clause ensures that only rows whose age, birthday, and cid columns are non-NULL are added to the materialized view. In the materialized view, cid is the partition key, birthday is the first clustering column, and age is the second clustering column. In the base table, cid is the partition key.
CREATE MATERIALIZED VIEW cycling.cyclist_by_birthday_and_age AS
  SELECT age, cid, birthday, country, name
  FROM cycling.cyclist_base
  WHERE age IS NOT NULL
    AND birthday IS NOT NULL
    AND cid IS NOT NULL
  PRIMARY KEY (cid, birthday, age);
The results of this query:
SELECT *
FROM cycling.cyclist_by_birthday_and_age;
are:
 cid                                  | birthday   | age | country       | name
--------------------------------------+------------+-----+---------------+---------------------
 ffdfa2a7-5fc6-49a7-bfdc-3fcdcfdd7156 | 1997-02-08 |  18 |   Netherlands |    Pascal EENKHOORN
 15a116fc-b833-4da6-ab9a-4a7775752836 | 1997-08-19 |  18 | United States |        Adrien COSTA
 e7ae5cf3-d358-4d99-b900-85902fda9bb0 | 1993-06-18 |  22 |   New Zealand |          Alex FRAME
 c9c9c484-5e4a-4542-8203-8d047a01b8a8 | 1987-09-04 |  27 |        Brazil |     Cristian EGIDIO
 410919ef-bd1b-4efa-8256-b0fd8ab67029 | 1999-01-04 |  19 |    Uzbekistan | Iskandarbek SHODIEV
 d1aad83b-be60-47a4-bd6e-069b8da0d97b | 1987-09-04 |  27 |       Germany |     Johannes HEIDER
 862cc51f-00a1-4d5a-976b-a359cab7300e | 1994-09-04 |  20 |       Denmark |       Joakim BUKDAL
 1c526849-d3a2-42a3-bcf9-7903c80b3d16 | 1998-12-23 |  19 |     Australia |       Kanden GROVES
 18f471bf-f631-4bc4-a9a2-d6f6cf5ea503 | 1997-03-29 |  18 |   Netherlands |         Bram WELTEN
 220844bf-4860-49d6-9a4b-6b5d3a79cbfb | 1977-07-08 |  38 |         Italy |     Paolo TIRALONGO
 6ab09bec-e68e-48d9-a5f8-97e6fb4c9b47 | 1987-06-07 |  28 |   Netherlands |   Steven KRUIKSWIJK
 96c4c40d-58c8-4710-b73f-681e9b1f70ae | 1989-04-20 |  29 |     Australia |     Benjamin DYBALL

(12 rows)
The following materialized view cyclist_by_country_and_birthday uses the base table cyclist_base. The WHERE clause ensures that only rows whose birthday and cid columns are non-NULL and country equals Australia are added to the materialized view.
CREATE MATERIALIZED VIEW cycling.cyclist_by_country_and_birthday AS
  SELECT age, cid, birthday, country, name
  FROM cycling.cyclist_base
  WHERE birthday IS NOT NULL
    AND cid IS NOT NULL
    AND country = 'Australia'
  PRIMARY KEY (cid, country, birthday);
The results of this query:
SELECT *
FROM cycling.cyclist_by_country_and_birthday;
are:
 cid                                  | country   | birthday   | age | name
--------------------------------------+-----------+------------+-----+-----------------
 1c526849-d3a2-42a3-bcf9-7903c80b3d16 | Australia | 1998-12-23 |  19 |   Kanden GROVES
 96c4c40d-58c8-4710-b73f-681e9b1f70ae | Australia | 1989-04-20 |  29 | Benjamin DYBALL

(2 rows)
The following materialized view cyclist_by_birthday_and_age19 uses the base table cyclist_base. The WHERE clause ensures that only rows whose birthday and cid columns are non-NULL and age equals the value 19 are added to the materialized view.
CREATE MATERIALIZED VIEW cycling.cyclist_by_birthday_and_age19 AS
  SELECT age, cid, birthday, country, name
  FROM cycling.cyclist_base
  WHERE birthday IS NOT NULL
    AND cid IS NOT NULL
    AND age = 19
  PRIMARY KEY (cid, birthday, age);
The results of this query:
SELECT *
FROM cycling.cyclist_by_birthday_and_age19;
are:
 cid                                  | birthday   | age | country    | name
--------------------------------------+------------+-----+------------+---------------------
 410919ef-bd1b-4efa-8256-b0fd8ab67029 | 1999-01-04 |  19 | Uzbekistan | Iskandarbek SHODIEV
 1c526849-d3a2-42a3-bcf9-7903c80b3d16 | 1998-12-23 |  19 |  Australia |       Kanden GROVE

(2 rows)
The following materialized view cyclist_by_age_birthday_cid uses the base table cyclist_base_ext. The WHERE clause ensures that only rows whose age, birthday, and cid columns are non-NULL are added to the materialized view.
CREATE MATERIALIZED VIEW cycling.cyclist_by_age_birthday_cid AS
  SELECT age, cid, birthday, country, name
  FROM cycling.cyclist_base_ext
  WHERE age IS NOT NULL
    AND birthday IS NOT NULL
    AND cid IS NOT NULL
  PRIMARY KEY (age, birthday, cid);
The results of this query:
SELECT *
FROM cycling.cyclist_by_age_birthday_cid
WHERE age = 19;
are:
 cid                                  | birthday   | age | country    | name
--------------------------------------+------------+-----+------------+---------------------
 410919ef-bd1b-4efa-8256-b0fd8ab67029 | 1999-01-04 |  19 | Uzbekistan | Iskandarbek SHODIEV
 1c526849-d3a2-42a3-bcf9-7903c80b3d16 | 1998-12-23 |  19 |  Australia |       Kanden GROVE

(2 rows)
The results of this query using a WHERE clause for two values:
SELECT *
FROM cycling.cyclist_by_age_birthday_cid
WHERE age = 19
  AND birthday = '1998-12-23';
are:
 age | birthday   | cid                                  | country   | name
-----+------------+--------------------------------------+-----------+---------------
  19 | 1998-12-23 | 1c526849-d3a2-42a3-bcf9-7903c80b3d16 | Australia | Kanden GROVES

(1 rows)
Notice that clustering columns must still be included in order. This query violates the rule:
SELECT *
FROM cycling.cyclist_by_age_birthday_cid
WHERE birthday = '1998-12-23';
Result:
cyclist_by_age-mv.cql:195:InvalidRequest: Error from server: code=2200 [Invalid query] message="Cannot 
execute this query as it might involve data filtering and thus may have unpredictable performance. If you 
want to execute this query despite the performance unpredictability, use ALLOW FILTERING.

Using a materialized view to perform queries that are not possible on a base table

The following scenario shows how to use a materialized view to perform queries that are not possible on a base table unless ALLOW FILTERING is used. ALLOW FILTERING is not recommended because of the performance degradation. This table stores the cycling team mechanic information:
CREATE TABLE IF NOT EXISTS cycling.mechanic (
  emp_id int,
  dept_id int,
  name text,
  age int,
  birthdate date,
  PRIMARY KEY (emp_id, dept_id)
);
The table contains these rows:
 emp_id | dept_id | age | birthdate  | name
--------+---------+-----+------------+------------
      5 |       3 |  25 | 1996-10-04 | Lisa SMITH
      1 |       1 |  21 | 1992-06-18 | Fred GREEN
      2 |       1 |  22 | 1993-01-15 | John SMITH
      4 |       2 |  24 | 1995-08-19 | Jack JONES
      3 |       2 |  23 | 1994-02-07 |   Jane DOE

(5 rows)
This materialized view selects the columns from the previous table and contains a different primary key from the table:
CREATE MATERIALIZED VIEW cycling.mechanic_view AS
  SELECT emp_id, dept_id, name, age, birthdate
  FROM cycling.mechanic
  WHERE emp_id IS NOT NULL
    AND dept_id IS NOT NULL
    AND name IS NOT NULL
    AND age IS NOT NULL
    AND birthdate IS NOT NULL
  PRIMARY KEY (age, emp_id, dept_id);
This query retrieves the rows where the age is 21:
SELECT *
FROM cycling.mechanic_view
WHERE age = 21;
The previous query cannot be run on the base table without ALLOW FILTERING. The output from the previous query is as follows:
 age | emp_id | dept_id | birthdate  | name
-----+--------+---------+------------+------------
  21 |      1 |       1 | 1992-06-18 | Fred GREEN

(1 rows)