Querying

Selectors

The SELECT statement is used to query data from a table in CQL. The results are returned as a set of rows. Selectors are used to specify which columns are queried and returned. The SELECT statement can also transform the data on return using functions. Table data also has related metadata that can be returned using metadata functions.

Return all columns

To return all columns from a table, use the wildcard * selector.

SELECT * FROM cycling.cyclist_name;
Results 
 id                                   | firstname | lastname
--------------------------------------+-----------+-----------------
 e7ae5cf3-d358-4d99-b900-85902fda9bb0 |      Alex |           FRAME
 fb372533-eb95-4bb4-8685-6ef61e994caa |   Michael |        MATTHEWS
 5b6962dd-3f90-4c93-8f61-eabfa4a803e2 |  Marianne |             VOS
 220844bf-4860-49d6-9a4b-6b5d3a79cbfb |     Paolo |       TIRALONGO
 6ab09bec-e68e-48d9-a5f8-97e6fb4c9b47 |    Steven |      KRUIKSWIJK
 e7cd5752-bc0d-4157-a80f-7523add8dbcd |      Anna | VAN DER BREGGEN

(6 rows)

Since the * selector returns all columns, it is not necessary to specify the column names. The more columns a table has, the more resources are required to return the data.

Return specific columns

Because the number of columns returned can affect performance, it is recommended to specify the columns to return, if possible.

For example, to return only the lastname column from the cycling.cyclist_name table:

SELECT lastname FROM cycling.cyclist_name;
Results 
 lastname
-----------------
           FRAME
        MATTHEWS
             VOS
       TIRALONGO
      KRUIKSWIJK
 VAN DER BREGGEN

(6 rows)

To return multiple columns, separate the column names with a comma:

SELECT lastname, firstname FROM cycling.cyclist_name;
Results 
 lastname        | firstname
-----------------+-----------
           FRAME |      Alex
        MATTHEWS |   Michael
             VOS |  Marianne
       TIRALONGO |     Paolo
      KRUIKSWIJK |    Steven
 VAN DER BREGGEN |      Anna

(6 rows)

Columns can be renamed on return using an alias:

SELECT id, cyclist_age AS age FROM cycling.cyclist_alt_stats LIMIT 3;
Results 
 id                                   | age
--------------------------------------+------
 e0953617-07eb-4c82-8f91-3b2757981625 | null
 1ba0417d-62da-4103-b710-de6fb222db6f |   34
 4ceb495c-55ab-4f71-83b9-81117252bf13 |   34

(3 rows)

A column alias cannot be used in the WHERE clause; use the original column name.

Return transformed data

The SELECT statement can transform the data on return using functions. The functions are varied and can be used to manipulate the data in different ways. For example, JSON can be used to return all columns as JSON, toJson to return selected column as JSON, or CAST can be used to change the data type of a column. Distinct column values can be returned using the DISTINCT function. The number of rows with a particular column can be returned using the COUNT function. Any native or user-defined functions can be used to transform the data, such as the SUM or AVG function. Any user-defined aggregate function can be used to transform the data, such as the average function. The TOKEN function can be used to return the token value of a column.

To output selected data from a table in JSON format, use the JSON function:

SELECT JSON * FROM cycling.birthday_list;
Results 
 [json]
----------------------------------------------------------------------------------------------------------
    {"cyclist_name": "Claudio HEINEN", "blist": {"age": "23", "bday": "27/07/1992", "nation": "GERMANY"}}
    {"cyclist_name": "Claudio VANDELLI", "blist": {"age": "54", "bday": "27/07/1961", "nation": "ITALY"}}
 {"cyclist_name": "Luc HAGENAARS", "blist": {"age": "28", "bday": "27/07/1987", "nation": "NETHERLANDS"}}
   {"cyclist_name": "Toine POELS", "blist": {"age": "52", "bday": "27/07/1963", "nation": "NETHERLANDS"}}
     {"cyclist_name": "Allan DAVIS", "blist": {"age": "35", "bday": "27/07/1980", "nation": "AUSTRALIA"}}
   {"cyclist_name": "Laurence BOURQUE", "blist": {"age": "23", "bday": "27/07/1992", "nation": "CANADA"}}

(6 rows)

Note the nested JSON structure in the output, with the column name as the key and the column value as the value.

To specify the JSON format for a particular column, enclose its name in the toJson() function:

SELECT id, commenter, toJson(created_at) FROM cycling.comments_vs;
Results 
 id                                   | commenter | system.tojson(created_at)
--------------------------------------+-----------+----------------------------
 e8ae5cf3-d358-4d99-b900-85902fda9bb0 |      John | "2017-04-01 14:33:02.160Z"
 e7ae5cf3-d358-4d99-b900-85902fda9bb0 |      Alex | "2017-04-01 14:33:02.160Z"
 e7ae5cf3-d358-4d99-b900-85902fda9bb0 |      Alex | "2017-03-21 21:11:09.999Z"
 e7ae5cf3-d358-4d99-b900-85902fda9bb0 |      Alex | "2017-02-14 20:43:20.000Z"
 c7fceba0-c141-4207-9494-a29f9809de6f |       Amy | "2024-07-02 22:39:30.758Z"
 c7fceba0-c141-4207-9494-a29f9809de6f |       Amy | "2017-04-01 13:43:08.030Z"
 c7fceba0-c141-4207-9494-a29f9809de6f |       Amy | "2017-03-22 01:16:59.001Z"
 c7fceba0-c141-4207-9494-a29f9809de6f |       Amy | "2017-02-17 08:43:20.234Z"
 c7fceba0-c141-3207-9494-a29f9809de6f |       Amy | "2017-02-17 08:43:20.234Z"
 c7fceba0-c141-7207-9494-a29f9809de6f |       Amy | "2024-07-02 22:39:30.763Z"
 e8ae5df3-d358-4d99-b900-85902fda9bb0 |      Jane | "2017-04-01 14:33:02.160Z"
 c7fceba0-c141-4207-9594-a29f9809de6f |      Jane | "2017-02-17 08:43:20.234Z"

(12 rows)

Note that the JSON-formatted timestamp column can include complete time zone information.

To cast a column to a different data type, use the CAST function:

  SELECT CAST(created_at AS date) FROM cycling.comments;
Results 
 cast(created_at as date)
--------------------------
               2023-04-01
               2024-06-07

(2 rows)

If a table has duplicate values in a column, use the DISTINCT function to return only the unique values:

SELECT DISTINCT country FROM cycling.country_flag;
Results 
 country
---------
 Belgium
  France

(2 rows)

You may want to count the number of rows in a column. Use the COUNT function to return the count:

SELECT start_month, MAX(start_day), COUNT(*) FROM cycling.events
  WHERE year = 2017 AND discipline = 'Cyclo-cross';
Results 
 start_month | system.max(start_day) | count
-------------+-----------------------+-------
           1 |                    28 |    11

(1 rows)

To use the COUNT function with a WHERE clause, include the COUNT function in the SELECT list and the WHERE clause in the query. For example, to get the number of cyclists from Belgium:

SELECT count(cyclist_name) FROM cycling.country_flag 
 WHERE country='Belgium';
Results 
 system.count(cyclist_name)
----------------------------
                          2

(1 rows)

A number of native functions can be used to transform the data. The data is transformed only on return, rather than stored in the table. These are general functions that are familiar to most people, such as the average, or AVG function:

SELECT AVG(race_points) FROM cycling.cyclist_points 
  WHERE id = e3b19ec4-774a-4d1c-9e5a-decec1e30aac;
Results 
 system.avg(race_points)
-------------------------
                      67

(1 rows)

User-defined functions can also be used to transform the data. For examples, the 'left` function can be used to return a column that is left-justified:

SELECT left(firstname, 1), lastname FROM cycling.cyclist_name;
Results

User-defined aggregates can also be used to transform the data. For examples, the 'average` function can be used to retrieve the average of the column cyclist_time_sec from a table:

SELECT average(cyclist_time_sec) AS Average FROM cycling.team_average
  WHERE team_name = 'UnitedHealthCare Pro Cycling Womens Team';
Results 
 average
-------------
 11474.66667

(1 rows)

Warnings :
User defined function cycling.average_state : (tuple<int, bigint>, int) -> tuple<int, bigint> consumed more than 500µs CPU time (2120µs)

The TOKEN function can be used to return the token value of a column. The token value is the hashed value of the partition key, thus it can only be used on the partition key column or columns. This function is useful when using the token value in a query to filter data with a WHERE clause.

To return the token value of the race_id column in the cycling.race table:

SELECT race_name AS name,race_year AS year FROM cycling.rank_by_year_and_name
  WHERE TOKEN(race_year,race_name) >= 4582455970709790046;
Results 
 name                                       | year
--------------------------------------------+------
 Tour of Japan - Stage 4 - Minami > Shinshu | 2015
 Tour of Japan - Stage 4 - Minami > Shinshu | 2015
 Tour of Japan - Stage 4 - Minami > Shinshu | 2015

(3 rows)

Return column metadata

Each non-primary key column in a table has metadata associated with it. The WRITETIME function returns the timestamp of the last write to a column. The TTL function returns the time to live of a column.

It is important to note that the metadata functions are not available for all columns. For example, the metadata functions are not available for columns with a data type of counter or primary key columns. Additionally, the metadata functions are not available for columns that are part of a collection or a user-defined type that is not frozen.

To return the writetime of the firstname column in the cycling.cyclist_points table:

SELECT WRITETIME (firstname) FROM cycling.cyclist_points
  WHERE id = e3b19ec4-774a-4d1c-9e5a-decec1e30aac;
Results 
 writetime(firstname)
----------------------
     1719960338817118
     1719960338823276
     1719960338827071

(3 rows)

The TTL function returns the time to live of a column. This function is useful when using Time to Live (TTL) to expire data in a table. If a TTL is set on a column, the data is automatically deleted after the specified time has elapsed.

To return the TTL of the race_points column in the cycling.cyclist_points table:

SELECT TTL(race_name) FROM cycling.calendar WHERE race_id = 200;
Results 
 ttl(race_name)
----------------
            199

(1 rows)

WHERE clause

The SELECT statement is used to query data from a table in CQL. The results are returned as a set of rows. Selectors are used to specify which columns are queried and returned.

The WHERE clause is used to filter the rows that are returned. Using WHERE, you can specify a condition that must be met for a row to be included in the result set. The condition can include multiple columns and multiple conditions linked with AND. A subtle variation to the use of AND is using a tuple to group columns to meet a condition. The TOKEN function can be used to retrieve data from a specific node in the cluster.

Several arithmetic and non-arithmetic operators are used in the WHERE clause to specify the condition.

Operator Description Usage

=

Equal

Any column type

>

Greater than

Numeric, text, date, time, timestamp

<

Less than

Numeric, text, date, time, timestamp

>=

Greater than or equal

Numeric, text, date, time, timestamp

⇐

Less than or equal

Numeric, text, date, time, timestamp

IN (value1, value2)

Supply a list of values to match

Any column type

CONTAINS (value)

Any collection contains value

Any column type within a non-frozen collection, used with SAI and 2i

CONTAINS KEY (key)

Map collection contains key

Any column type within a non-frozen map collection, used with SAI and 2i

LIKE 'pattern'

Fuzzy text pattern matching

Text column type, used with SASI

Note that the != operator is not supported in CQL SELECT statements.

Arithmetic operators

To return a result set that uses an equality condition, use the = operator. Partition key columns always use the equality operator in the WHERE clause. The exception is if the partition key is retrieved using the TOKEN function, inequality operators can be used. Clustering columns can use the equality operator in the WHERE clause.

The equality operator will result in the quickest query response time, provided the query avoids multiple partitions.

  SELECT * FROM cycling.cyclist_base WHERE age = 28;
Results 
 cid                                  | age | birthday   | country     | name
--------------------------------------+-----+------------+-------------+-------------------
 6ab09bec-e68e-48d9-a5f8-97e6fb4c9b47 |  28 | 1987-06-07 | Netherlands | Steven KRUIKSWIJK

(1 rows)

Only tokenized partition key columns and the last clustering columns in a WHERE clause can use the inequality operators.

To return a result set that uses an inequality condition, use one of the the operators: <, >, ⇐, or >=.

  SELECT * FROM cycling.cyclist_base WHERE age >= 20;
Results 
 cid                                  | age | birthday   | country       | name
--------------------------------------+-----+------------+---------------+-------------------
 e7ae5cf3-d358-4d99-b900-85902fda9bb0 |  22 | 1993-06-18 |   New Zealand |        Alex FRAME
 c9c9c484-5e4a-4542-8203-8d047a01b8a8 |  27 | 1987-09-04 |        Brazil |   Cristian EGIDIO
 862cc51f-00a1-4d5a-976b-a359cab7300e |  20 | 1994-09-04 |       Denmark |     Joakim BUKDAL
 220844bf-4860-49d6-9a4b-6b5d3a79cbfb |  38 | 1977-07-08 |         Italy |   Paolo TIRALONGO
 6ab09bec-e68e-48d9-a5f8-97e6fb4c9b47 |  28 | 1987-06-07 |   Netherlands | Steven KRUIKSWIJK
 95fed1a4-db7e-4c8d-8b2d-f7f9340f2c0b |  20 | 1993-04-09 | United States |        Zam WEATON

(6 rows)

Non-arithmetic operators

Non-arithmetic operators are used to specify conditions that are not based on numerical values.

Partition key columns always use the equality operator in the WHERE clause, with the exception being the use of the IN operator if the column is the last partition key column. Clustering columns can use the IN operator in the WHERE clause.

To return a result set that uses the IN condition, use the IN operator.

Note that using both IN and ORDER BY require turning off paging with the PAGING OFF command in cqlsh.

 
SELECT * FROM cycling.calendar WHERE race_id IN (100, 101, 102)
ORDER BY race_start_date DESC;
Results

There are cases in which the IN operator should not be used. A good blogpost on this topic is Cassandra Query Patterns: Not using the “in” query for multiple partitions.

The CONTAINS and CONTAINS KEY operators are used with collections (set, list, map) to specify conditions that are based on the contents of the collection. In order to facilitate the use of these operators, a secondary index is often required, as the collection column is usually a non-primary key column.

To return a result set that uses the CONTAINS condition, use the CONTAINS operator. This particular example uses the CONTAINS operator to return a result set that contains a specific value for a column in a map collection.

SELECT firstname,lastname,teams FROM cycling.cyclist_teams 
  WHERE teams CONTAINS 'Team Garmin - Cervelo';
Results 
 firstname | lastname   | teams
-----------+------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
     Jamie |    BENNETT |                                                                     {2011: 'Team Garmin - Cervelo', 2013: 'Boels:Dolmans Cycling Team', 2014: 'Boels:Dolmans Cycling Team'}
 Elizabeth | ARMITSTEAD | {2011: 'Team Garmin - Cervelo', 2012: 'AA Drink - Leontien.nl', 2013: 'Boels:Dolmans Cycling Team', 2014: 'Boels:Dolmans Cycling Team', 2015: 'Boels:Dolmans Cycling Team'}

(2 rows)

Another example demonstrates that frozen collections can also be used with the CONTAINS operator:

SELECT * FROM cycling.race_results WHERE race_history CONTAINS 1984;
Results 
InvalidRequest: Error from server: code=2200 [Invalid query] 
message="Column race_history has an index but does not support the operators 
specified in the query. If you want to execute this query despite the performance 
unpredictability, use ALLOW FILTERING"

Remember that the CONTAINS KEY is used only with a map collection. This example uses the CONTAINS KEY operator to return a result set that contains a specific key of a key-value pair in a map collection.

SELECT firstname,lastname,teams FROM cycling.cyclist_teams 
  WHERE teams CONTAINS KEY 2015;
Results 
 firstname | lastname   | teams
-----------+------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 Elizabeth | ARMITSTEAD | {2011: 'Team Garmin - Cervelo', 2012: 'AA Drink - Leontien.nl', 2013: 'Boels:Dolmans Cycling Team', 2014: 'Boels:Dolmans Cycling Team', 2015: 'Boels:Dolmans Cycling Team'}
  Marianne |        VOS |                                                                                                                                   {2015: 'Rabobank-Liv Woman Cycling Team'}

(2 rows)

Multiple conditions

Multiple conditions can be specified in the WHERE clause using the AND operator. Using more than one condition in the WHERE clause allows you to filter the result set based on multiple criteria. In the following example, the AND operator is used to return a result set that meets two conditions.

  SELECT * FROM cycling.cyclist_base WHERE age >= 28 AND age < 20;
Results 
 cid | age | birthday | country | name
-----+-----+----------+---------+------

(0 rows)
Fails because of the disjoint ranges

Using a tuple for multiple conditions

A grouping of columns can be used to meet a condition. In this example, a tuple is used to group two columns, race_start_date and race_end_date, to meet a condition. Not that searching for race_start_date = <value> AND race_end_date = <value> may return a different result set than searching for the tuple of values.

SELECT * FROM cycling.calendar WHERE race_id IN (100, 101, 102)
  AND (race_start_date, race_end_date) >= ('2015-05-09', '2015-05-24');
Results 
 race_id | race_start_date                 | race_end_date                   | race_name
---------+---------------------------------+---------------------------------+-----------------------
     100 | 2015-05-09 00:00:00.000000+0000 | 2015-05-31 00:00:00.000000+0000 |         Giro d'Italia
     101 | 2015-06-07 00:00:00.000000+0000 | 2015-06-14 00:00:00.000000+0000 | Criterium du Dauphine
     102 | 2015-06-13 00:00:00.000000+0000 | 2015-06-21 00:00:00.000000+0000 |        Tour de Suisse

(3 rows)

Clustering columns

Clustering columns can make filtering with a WHERE clause uniquely responsive for some data. The clustering columns are used to sort the data within a partition, and the data is stored in the sorted order. This quality means that slices, where a range of rows are retrieved, can be very efficient. A particular row can be found using the clustering columns in the order that they are defined in the primary key. The rows that come before or after that row can be retrieved quickly. In fact, even a slice of a slice can be retrieved, by narrowing down the range of rows with conditions on the first clustering columns. A tuple can be handy for sorting on multiple clustering columns.

Because the database uses the clustering columns to determine the location of the data on the partition, you must identify the higher level clustering columns definitively using the equals (=) or IN operators. In a query, you can only restrict the lowest level using the range operators (>, >=, <, or <=).

When a query contains no restrictions on clustering or index columns, all the data from the partition is returned.

How order impacts clustering restrictions

Because the database uses the clustering columns to determine the location of the data on the partition, you must identify the higher level clustering columns definitively using the equals (=) or IN operators. In a query, you can only restrict the lowest level using the range operators (>, >=, <, or <=).

How data is stored

The following table is used to illustrate how clustering works:

CREATE TABLE cycling.numbers (
   key int,
   col_1 int,
   col_2 int,
   col_3 int,
   col_4 int,
   PRIMARY KEY ((key), col_1, col_2, col_3, col_4)
);

The example table contains the following data:

 key | col_1 | col_2 | col_3 | col_4
-----+-------+-------+-------+-------
 100 |     1 |     1 |     1 |     1
 100 |     1 |     1 |     1 |     2
 100 |     1 |     1 |     1 |     3
 100 |     1 |     1 |     2 |     1
 100 |     1 |     1 |     2 |     2
 100 |     1 |     1 |     2 |     3
 100 |     1 |     2 |     2 |     1
 100 |     1 |     2 |     2 |     2
 100 |     1 |     2 |     2 |     3
 100 |     2 |     1 |     1 |     1
 100 |     2 |     1 |     1 |     2
 100 |     2 |     1 |     1 |     3
 100 |     2 |     1 |     2 |     1
 100 |     2 |     1 |     2 |     2
 100 |     2 |     1 |     2 |     3
 100 |     2 |     2 |     2 |     1
 100 |     2 |     2 |     2 |     2
 100 |     2 |     2 |     2 |     3

(18 rows)

The database stores and locates the data using a nested sort order. The data is stored in hierarchy that the query must traverse:

 [json]
--------------------------------------------------------------
 {"key": 100, "col_1": 1, "col_2": 1, "col_3": 1, "col_4": 1}
 {"key": 100, "col_1": 1, "col_2": 1, "col_3": 1, "col_4": 2}
 {"key": 100, "col_1": 1, "col_2": 1, "col_3": 1, "col_4": 3}
 {"key": 100, "col_1": 1, "col_2": 1, "col_3": 2, "col_4": 1}
 {"key": 100, "col_1": 1, "col_2": 1, "col_3": 2, "col_4": 2}
 {"key": 100, "col_1": 1, "col_2": 1, "col_3": 2, "col_4": 3}
 {"key": 100, "col_1": 1, "col_2": 2, "col_3": 2, "col_4": 1}
 {"key": 100, "col_1": 1, "col_2": 2, "col_3": 2, "col_4": 2}
 {"key": 100, "col_1": 1, "col_2": 2, "col_3": 2, "col_4": 3}
 {"key": 100, "col_1": 2, "col_2": 1, "col_3": 1, "col_4": 1}
 {"key": 100, "col_1": 2, "col_2": 1, "col_3": 1, "col_4": 2}
 {"key": 100, "col_1": 2, "col_2": 1, "col_3": 1, "col_4": 3}
 {"key": 100, "col_1": 2, "col_2": 1, "col_3": 2, "col_4": 1}
 {"key": 100, "col_1": 2, "col_2": 1, "col_3": 2, "col_4": 2}
 {"key": 100, "col_1": 2, "col_2": 1, "col_3": 2, "col_4": 3}
 {"key": 100, "col_1": 2, "col_2": 2, "col_3": 2, "col_4": 1}
 {"key": 100, "col_1": 2, "col_2": 2, "col_3": 2, "col_4": 2}
 {"key": 100, "col_1": 2, "col_2": 2, "col_3": 2, "col_4": 3}

(18 rows)

To avoid full scans of the partition and to make queries more efficient, the database requires that the higher level columns in the sort order (col_1, col_2, and col_3) are identified using the equals or IN operators. Ranges are allowed on the last column (col_4).

Selecting data from a clustering segment

For example, to find only values in column 4 that are less than or equal to 2:

SELECT * FROM cycling.numbers
  WHERE key = 100 AND col_1 = 1 AND col_2 = 1 AND col_3 = 1 AND col_4 <= 2;
Results

The results contain the first two rows:

 key | col_1 | col_2 | col_3 | col_4
-----+-------+-------+-------+-------
 100 |     1 |     1 |     1 |     1
 100 |     1 |     1 |     1 |     2

(2 rows)

The IN operator can impact performance on medium-large datasets. When selecting multiple segments, the database loads and filters all the specified segments.

For example, to find all values less than or equal to 2 in both col_1 segments 1 and 2:

SELECT * FROM cycling.numbers WHERE key = 100 AND col_1 = 1 AND col_2 > 1
Results

The following visualization shows all the segments the database must load to filter multiple segments:

cqlQueryClusteringColumnSegments

The results return the range from both segments.

 key | col_1 | col_2 | col_3 | col_4
-----+-------+-------+-------+-------
 100 |     1 |     2 |     2 |     1
 100 |     1 |     2 |     2 |     2
 100 |     1 |     2 |     2 |     3

(3 rows)

Use TRACING to analyze the impact of various queries in your environment.
Invalid restrictions

Queries that attempt to return ranges without identifying any of the higher level segments are rejected:

SELECT * FROM cycling.numbers WHERE key = 100 AND col_4 <= 2;
Results

The request is invalid:

InvalidRequest: Error from server: code=2200 [Invalid query] message="PRIMARY KEY column "col_4"
cannot be restricted as preceding column "col_1" is not restricted"

You can force the query using the ALLOW FILTERING option; however, this loads the entire partition and negatively impacts performance by causing long READ latencies.
Only restricting top level clustering columns

Unlike partition columns, a query can omit lower level clustering column in logical statements.

For example, to filter one of the mid-level columns, restrict the first level column using equals or IN, then specify a range on the second level:

SELECT * FROM cycling.numbers WHERE key = 100 AND col_1 = 1 AND col_2 > 1
Results 
 key | col_1 | col_2 | col_3 | col_4
-----+-------+-------+-------+-------
 100 |     1 |     2 |     2 |     1
 100 |     1 |     2 |     2 |     2
 100 |     1 |     2 |     2 |     3

(3 rows)

Returning ranges that span clustering segments

Slicing provides a way to look at an entire clustering segment and find a row that matches values in multiple columns. The slice logical statement finds a single row location and allows you to return all the rows before, including, between, or after the row.

Slice syntax:

(clustering1, clustering2[, …]) <range_operator> (value1, value2[, …])
[AND (clustering1, clustering2[, …]) <range_operator> (value1, value2[, …])]
Slices across full partition

The slice determines the exact location within the sorted columns; therefore, the highest level is evaluated first, then the second, and so forth in order to drill down to the precise row location. The following statement identifies the row where column 1, 2, and 3 are equal to 2 and column 4 is less than or equal to 1.

SELECT * FROM cycling.numbers
  WHERE key = 100 AND (col_1, col_2, col_3, col_4) <= (2, 2, 2, 1);

The database locates the matching row and then returns every record before the identified row in the results set.

cqlQuerySlicePartition1 
Where col_1 = 1, col_4 contains values 2 and 3 in the results (which are greater than 1).

The database is NOT filtering on all values in column 4, it is finding the exact location shown in dark green. Once it locates the row, the evaluation ends.

The location might be hypothetical, that is the dataset does not contain a row that exactly matches the values. For example, the query specifies slice values of (2, 1, 1, 4).

SELECT * FROM cycling.numbers
  WHERE key = 100 AND (col_1, col_2, col_3, col_4) <= (2, 1, 1, 4);

The query finds where the row would be in the order if a row with those values existed and returns all rows before it:

cqlQuerySlicePartition2

The value of column 4 is only evaluated to locate the row placement within the clustering segment. The database locates the segment and then finds col_4 = 4. After finding the location, it returns the row and all the rows before it in the sort order (which in this case spans all clustering columns).
Slices of clustering segments

The same rules apply to slice restrictions when finding a slice on a lower level segment; identify the higher level clustering segments using equals or IN and specify a range on the lower segments.

For example, to return rows where the value is greater than (1, 3) and less than or equal to (2, 5):

SELECT * FROM cycling.numbers WHERE key = 100 AND col_1 = 1 AND col_2 = 1
  AND (col_3, col_4) >= (1, 2) AND (col_3, col_4) < (2, 3);

When finding a between range, the two slice statements must be on the same columns for lowest columns in the hierarchy.

cqlQuerySliceClusteringSegments
Invalid queries

When returning a slice between two rows, the slice statements must define the same clustering columns. The query is rejected if the columns are different:

SELECT * FROM cycling.numbers WHERE key = 100 AND col_1 = 1
  AND (col_2, col_3, col_4) >= (1, 1, 2)  AND (col_3, col_4) < (2, 3);
Results

The request is invalid:

InvalidRequest: Error from server: code=2200 [Invalid query] 
message="Column "col_3" cannot be restricted by two inequalities not starting 
with the same column"

  • Find the road cycling races that start in 2017 between January 15th and February 14th.

CREATE TABLE IF NOT EXISTS cycling.events (
  year int,
  start_month int,
  start_day int,
  end_month int,
  end_day int,
  race text,
  discipline text,
  location text,
  uci_code text,
  PRIMARY KEY (
    (year, discipline), start_month, start_day, race
  )
);

Limit the start_month and start_day for the range using a slice:

SELECT start_month as month, start_day as day, race FROM cycling.events
  WHERE year = 2017 AND discipline = 'Road'
    AND (start_month, start_day) < (2, 14) 
    AND (start_month, start_day) > (1, 15);
Results

The results contain events in that time period:

 month | day | race
-------+-----+-----------------------------------------------------------------
     1 |  23 |                      Vuelta Ciclista a la Provincia de San Juan
     1 |  26 | Cadel Evans Great Ocean Road Race - Towards Zero Race Melbourne
     1 |  26 | Challenge Mallorca: Trofeo Porreres-Felanitx-Ses Salines-Campos
     1 |  28 |                               Cadel Evans Great Ocean Road Race
     1 |  28 |          Challenge Mallorca: Trofeo Andratx-Mirador des Colomer
     1 |  28 |            Challenge Mallorca: Trofeo Serra de Tramuntana -2017
     1 |  29 |                               Cadel Evans Great Ocean Road Race
     1 |  29 |                                                      Dubai Tour
     1 |  29 |                             Grand Prix Cycliste la Marseillaise
     1 |  29 |                                Mallorca Challenge: Trofeo Palma
     1 |  31 |                                            Ladies Tour of Qatar
     2 |   1 |                                              Etoile de Besseges
     2 |   1 |                                           Jayco Herald Sun Tour
     2 |   1 |                                 Volta a la Comunitat Valenciana
     2 |   5 |                                       G.P. Costa degli Etruschi
     2 |   6 |                                                   Tour of Qatar
     2 |   9 |                                South African Road Championships
     2 |  11 |                                               Trofeo Laigueglia
     2 |  11 |                           Vuelta Ciclista a la Region de Murcia
     2 |  12 |                                              Clasica de Almeria

(20 rows)

Vector search sorting

This query uses a vector in the ORDER BY clause to get the closest matches to the stored embeddings vector:

SELECT * FROM cycling.comments_vs 
  ORDER BY comment_vector ANN OF [0.15, 0.1, 0.1, 0.35, 0.55] 
  LIMIT 3;
Results 
 id                                   | created_at                      | comment                                | comment_vector                                    | commenter | record_id
--------------------------------------+---------------------------------+----------------------------------------+---------------------------------------------------+-----------+--------------------------------------
 e8ae5cf3-d358-4d99-b900-85902fda9bb0 | 2017-04-01 14:33:02.160000+0000 |              rain, rain,rain, go away! | b'?fff?\\n=q=\\xf5\\xc2\\x8f=\\xcc\\xcc\\xcd?s33' |      John | f25e4fe1-38c3-11ef-bd85-f92c3c7170c3
 e7ae5cf3-d358-4d99-b900-85902fda9bb0 | 2017-04-01 14:33:02.160000+0000 | LATE RIDERS SHOULD NOT DELAY THE START | b'?fff?\\n=q=\\xf5\\xc2\\x8f=\\xcc\\xcc\\xcd?s33' |      Alex | f259bc00-38c3-11ef-bd85-f92c3c7170c3
 e8ae5df3-d358-4d99-b900-85902fda9bb0 | 2017-04-01 14:33:02.160000+0000 |                    Rain like a monsoon | b'?fff?\\n=q=\\xf5\\xc2\\x8f=\\xcc\\xcc\\xcd?s33' |      Jane | f25eec21-38c3-11ef-bd85-f92c3c7170c3

(3 rows)

GROUP BY clause

Group by one or more columns. Condenses the selected rows that share the same values for a set of columns or values returned by a function into a group. Either one or more primary key columns or a deterministic function or aggregate can be used in the GROUP BY clause.

SELECT race_date, race_time FROM cycling.race_times_summary
  GROUP BY race_date;
Results 
 race_date  | race_time
------------+--------------------
 2019-03-21 | 10:01:18.000000000
 2018-07-26 | 10:01:18.000000000
 2017-04-14 | 10:01:18.000000000

(3 rows)

Warnings :
Aggregation query used without partition key

Each set of rows with the same race_date column value are grouped together into one row in the query output. Three rows are returned because there are three groups of rows with the same race_date column value. The value returned is the first value that is found for the group.

This query groups the rows by race_date and FLOOR(race_time, 1h), which returns the hour. The number of rows in each group is returned by COUNT(*).

  SELECT race_date, FLOOR(race_time, 1h), COUNT(*) FROM cycling.race_times_summary
    GROUP BY race_date, FLOOR(race_time, 1h);
Results 
 race_date  | system.floor(race_time, 1h) | count
------------+-----------------------------+-------
 2019-03-21 |          10:00:00.000000000 |     2
 2019-03-21 |          11:00:00.000000000 |     1
 2019-03-21 |          12:00:00.000000000 |     1
 2018-07-26 |          10:00:00.000000000 |     2
 2018-07-26 |          11:00:00.000000000 |     1
 2018-07-26 |          12:00:00.000000000 |     1
 2017-04-14 |          10:00:00.000000000 |     2
 2017-04-14 |          11:00:00.000000000 |     1
 2017-04-14 |          12:00:00.000000000 |     1

(9 rows)

Warnings :
Aggregation query used without partition key

Nine rows are returned because there are nine groups of rows with the same race_date and FLOOR(race_time, 1h) values.

ORDER BY clause

You can fine-tune the display order using the ORDER BY clause. The partition key must be defined in the WHERE clause and then the ORDER BY clause defines one or more clustering columns to use for ordering. The order of the specified columns must match the order of the clustering columns in the PRIMARY KEY definition. The options for ordering are ASC (ascending) and DESC (descending).

If no order is specified, the results are returned in the stored order.

Note that using both IN and ORDER BY require turning off paging with the PAGING OFF command in cqlsh.

 
SELECT * FROM cycling.calendar WHERE race_id IN (100, 101, 102)
ORDER BY race_start_date ASC;
Results

The ORDER BY clause also supports vector searches of the vector column. The result set is sorted using the Approximate Nearest Neighbor (ANN) algorithm with the supplied array values.

LIMIT clause

If a query returns a large number of rows, you can limit the number of rows returned, to limit the amount of data returned. The default limit is set to 10,000 rows, the number of rows cqlsh allows. This examples limits the rows to 3:

SELECT * FROM cycling.comments_vs 
  ORDER BY comment_vector ANN OF [0.15, 0.1, 0.1, 0.35, 0.55] 
  LIMIT 3;
Results 
 id                                   | created_at                      | comment                                | comment_vector                                    | commenter | record_id
--------------------------------------+---------------------------------+----------------------------------------+---------------------------------------------------+-----------+--------------------------------------
 e8ae5cf3-d358-4d99-b900-85902fda9bb0 | 2017-04-01 14:33:02.160000+0000 |              rain, rain,rain, go away! | b'?fff?\\n=q=\\xf5\\xc2\\x8f=\\xcc\\xcc\\xcd?s33' |      John | f25e4fe1-38c3-11ef-bd85-f92c3c7170c3
 e7ae5cf3-d358-4d99-b900-85902fda9bb0 | 2017-04-01 14:33:02.160000+0000 | LATE RIDERS SHOULD NOT DELAY THE START | b'?fff?\\n=q=\\xf5\\xc2\\x8f=\\xcc\\xcc\\xcd?s33' |      Alex | f259bc00-38c3-11ef-bd85-f92c3c7170c3
 e8ae5df3-d358-4d99-b900-85902fda9bb0 | 2017-04-01 14:33:02.160000+0000 |                    Rain like a monsoon | b'?fff?\\n=q=\\xf5\\xc2\\x8f=\\xcc\\xcc\\xcd?s33' |      Jane | f25eec21-38c3-11ef-bd85-f92c3c7170c3

(3 rows)

PER PARTITION LIMIT clause

The PER PARTITION LIMIT option sets the maximum number of rows that the query returns from each partition. This will only apply to tables that spread across more than one partition. An example of such a table is defined here:

USE cycling;
CREATE TABLE rank_by_year_and_name ( 
  race_year int, 
  race_name text, 
  cyclist_name text, 
  rank int, 
  PRIMARY KEY ((race_year, race_name), rank) 
);

where the partition key is a composite of race_year and race_name.

The following query returns the top two cyclists from each partition stored:

SELECT rank, cyclist_name AS name FROM cycling.rank_by_year_and_name 
  PER PARTITION LIMIT 2;
Results 
 rank | name
------+----------------------
    1 |    Phillippe GILBERT
    2 |        Daniel MARTIN
    1 |        Daniel MARTIN
    2 | Johan Esteban CHAVES
    1 |        Ilnur ZAKARIN
    2 |      Carlos BETANCUR
    1 |      Benjamin PRADES
    2 |          Adam PHELAN

(8 rows)

ALLOW FILTERING

The ALLOW FILTERING clause allows you to perform queries that require scanning all partitions, with no primary key columns specified. It should not be used in production as it can cause severe performance issues! When initially modeling your data, you should avoid using ALLOW FILTERING and instead model your data to avoid it. However, for a small dataset or for testing purposes, it can be useful. It may even help you identify where you need to add indexes to your data model.

For more information, see Allow Filtering explained.

The following query selects the birthday and nationality columns from the cyclist_alt_stats table, with the ALLOW FILTERING clause:

SELECT lastname, birthday, nationality FROM cycling.cyclist_alt_stats
  WHERE birthday = '1991-08-25' AND nationality = 'Ethiopia'
ALLOW FILTERING;
Results 
 lastname | birthday   | nationality
----------+------------+-------------
    GRMAY | 1991-08-25 |    Ethiopia

(1 rows)

Was this helpful?

Give Feedback

How can we improve the documentation?

© 2024 DataStax | Privacy policy | Terms of use

Apache, Apache Cassandra, Cassandra, Apache Tomcat, Tomcat, Apache Lucene, Apache Solr, Apache Hadoop, Hadoop, Apache Pulsar, Pulsar, Apache Spark, Spark, Apache TinkerPop, TinkerPop, Apache Kafka and Kafka are either registered trademarks or trademarks of the Apache Software Foundation or its subsidiaries in Canada, the United States and/or other countries. Kubernetes is the registered trademark of the Linux Foundation.

General Inquiries: +1 (650) 389-6000, info@datastax.com