SELECT

Returns data from a table.

Returns data from a single table. A SELECT statement without a WHERE clause is not recommended because all rows from all partitions are returned.

CAUTION: DataStax recommends limiting queries to a single partition using the WHERE clause. Queries across multiple partitions can impact performance.

Synopsis

SELECT [ JSON ] selectors 
  FROM [keyspace_name.]table_name 
  [ WHERE [ primary_key_conditions ] [ AND ] [ index_conditions ]
  [ GROUP BY column_name [ , ... ] ]
  [ ORDER BY PK_column_name [ , ... ] ( ASC | DESC ) ] 
  [ ( LIMIT N | PER PARTITION LIMIT N ) ]
  [ ALLOW FILTERING ] ;
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.

selectors

Determines the columns returned in the results set.
column_list | DISTINCT partition_key [ AS output_name ]
Restriction: Use either a column list or DISTINCT partition_key.
column_list
Determines the columns and column order returned in the result set. Specify a comma-separated list of columns or use an asterisk to return all columns in the stored order.
column_name | function_name( argument_list )
DISTINCT partition_key

Returns unique values for the full partition key. Use a comma-separated list of columns for a composite partition key.

Tip: Run DESC TABLE table_name to get the PRIMARY KEY definition and then SELECT DISTINCT partition_key FROM table_name to get the table partition values.
AS output_name
Renames the column to the new output name in the result set; for example:
COUNT(id) AS "Cyclist Count"
Note: If the name contains special characters, spaces, or to retain capitalization, surround the new name with double quotes.

keyspace_name.table_name

The keyspace name is required to identify a table in a different keyspace or if no keyspace is set for the session. If the keyspace or table name contain uppercase letters, enclose the name in double quotation marks; for example:
FROM "TestTable"

primary_key_conditions

Improves the efficiency of the query using logic statements to identify the data location and allows filtering on the last clustering column.
partition_conditions
[ AND clustering_conditions ] | [ AND index_conditions ]
Tip: To return all the data stored on a partition specify just the partition key values.

Logical statement syntax

To create logic statements that test the column value, use the syntax:

column_name operator value
Separate multiple statements with AND. Rows that meet all the conditions are returned. For example:
SELECT
  rank, cyclist_name AS name
FROM
  cycling.rank_by_year_and_name
WHERE
  "race_name"  = 'Tour of Japan - Stage 4 - Minami > Shinshu' 
  AND race_year = 2014;
Tip: The database does not support queries with logical disjunctions (OR).
column_name
Enclose column names that have uppercase or special characters in double quotes.
Note: Enclose string values in single quotes.
operators
Operator Description
= Column value exactly matches the specified value.
IN Equal to any value in a comma-separated list of values
>= Greater than or equal to the value.
<= Less than or equal to the value.
> Greater than the value.
< Less than the value.
CONTAINS Matches a value in any type of collection. Only use on indexed collections.
CONTAINS KEY Matches a key name in a map. Only use on maps with indexed keys.
value
Enclose string values in single quotes.
Note: Enclose column names that have uppercase or special characters in double quotes.

Identifying the data location and filtering by clustering columns

Use WHERE clauses to maximize read efficiency by identifying the location of the data. The database evaluates the WHERE logical statements hierarchically:
  1. Partition key columns: Use the equals operator to identify all partition key values (or none). Ensure that the data model supports single partition queries to avoid performance issues.
    Note: Partitions are typically large sets of data. The partitioner distributes the data by creating a hash of the partition key columns and stores all the rows with the same hash on the same node. Similar or like data, such as partition key date column values 7/01/2017 and 7/02/2017, may not be located on the same node.
  2. Clustering columns determine the sort order within the partition. Data is sorted by the first clustering column, the second clustering column, and so on.
Note: ALLOW FILTERING overrides restrictions on filtering partition columns, clustering columns, and regular columns, but can negatively impact performance, causing read latencies. Avoid ALLOW FILTERING in a production environment. In test environments, use cqlsh TRACING to analyze performance problems.
partition_conditions

The database requires that all partitions are restricted except when querying a secondary or search index. Use logic statements that identify the partition key columns with these operators:

  • Equals (=): Any partition key column.
  • IN: Restricted to the last column of the partition key to search multiple partitions.
  • Range (>=, <=, >, and <) on tokens: Fully tokenized partition key (all partition key columns specified in order as arguments of the token function). Use token ranges to scan data stored on a particular node.
Note: For secondary index queries, equals is the only operator supported for partition key logical statements.

See Partition keys for examples and instructions.

clustering_conditions

Use logic statements that identify the clustering segment. Clustering columns set the sort order of the stored data, which is nested when there are multiple clustering columns. After evaluating the partition key, the database evaluates the clustering statements in the nested order, the first (top level), second, third, and so on.

All operators are supported in logical statements if the table has only one clustering column. To efficiently locate the data within the partition for tables with multiple clustering columns, the following restrictions apply:
  • All clustering columns excluding the last clustering column:
    • Equals (=)
    • IN
  • Last clustering column: All equality and inequality operators, and multi-column comparisons

Clustering column logic statements also support returning slices across clustering segments:

( column1, column2, ... ) operator ( value1, value2, ... )
[ AND ( column1, column2, ... ) operator ( value1, value2, ... ) ]

The slice identifies the row that has the corresponding values and allows you to return all rows before, after, or between (when two slice statements are included).

See Clustering columns for examples and instructions.

index_conditions

DSE supports these index types:

Secondary index
Logical statements on secondary index columns support these operators:
  • =
  • CONTAINS on index collection types
  • CONTAINS KEY on index map types
Solr query
Filter the query using the solr_query option by creating a Solr expression. See Search index filter syntax.
SASI index
To retrieve data using a SSTable Attached Secondary Index, see Using SASI.

Additional options

Change the scope and order of the data returned by the query.

GROUP BY column_name | function_name( argument_list )
Condenses the selected rows that share the same values for a set of columns or values returned by a function into a group.
ORDER BY ( ASC | DESC )
Sorts the result set in either ascending (ASC) or descending (DESC) order.
Note: When no order is specified, the results are returned in the order that they are stored.
ALLOW FILTERING
Enables filtering without applying logic statements that identify the primary key. Avoid ALLOW FILTERING in a production environment because a full scan of the cluster is performed.
LIMIT N | PER PARTITION LIMIT N
Limits the number of records returned in the result set.

Examples

Using a column alias

When your selection list includes functions or other complex expressions, use aliases to make the output more readable. This query uses the alias best_rank for MIN(rank):
SELECT
  MIN(rank) AS best_rank,
  cyclist_name
FROM
  cycling.rank_by_year_and_name
WHERE
  "race_name" = 'Tour of Japan - Stage 4 - Minami > Shinshu' 
  AND race_year = 2014;
Output:
 best_rank | cyclist_name
-----------+---------------
         1 | Daniel MARTIN

(1 rows)

Specifying the source table using FROM

The following example SELECT statement returns the number of rows in the rank_by_year_and_name table:

SELECT COUNT(*)
FROM cycling.rank_by_year_and_name;

Controlling the number of rows returned using LIMIT

The LIMIT option sets the maximum number of rows that the query returns:

SELECT lastname 
FROM cycling.cyclist_name 
LIMIT 50000;

Even if the query matches 105,291 rows, the database only returns the first 50,000.

The cqlsh shell has a default row limit of 10,000. The DSE server and native protocol do not limit the number of returned rows, but they apply a timeout to prevent malformed queries from causing system instability.

Selecting partitions

Simple partition key, select a single partition:
WHERE partition_column = value
Simple partition key, select multiple partitions:
WHERE partition_column IN ( value1, value2 [ ,... ] )
For a composite partition key, create a condition for each partition key column separated by AND:
WHERE partition_column1 = value1 
AND partition_column2 = value2 [ AND ... ] )

Controlling the number of rows returned using PER PARTITION LIMIT

The PER PARTITION LIMIT option sets the maximum number of rows that the query returns from each partition.

For example, the cycling keyspace contains this table:

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

The table contains these rows:

 race_year | race_name                                  | rank | cyclist_name
-----------+--------------------------------------------+------+----------------------
      2014 |                        4th Tour of Beijing |    1 |    Phillippe GILBERT
      2014 |                        4th Tour of Beijing |    2 |        Daniel MARTIN
      2014 |                        4th Tour of Beijing |    3 | Johan Esteban CHAVES
      2014 | Tour of Japan - Stage 4 - Minami > Shinshu |    1 |        Daniel MARTIN
      2014 | Tour of Japan - Stage 4 - Minami > Shinshu |    2 | Johan Esteban CHAVES
      2014 | Tour of Japan - Stage 4 - Minami > Shinshu |    3 |      Benjamin PRADES
      2015 |   Giro d'Italia - Stage 11 - Forli > Imola |    1 |        Ilnur ZAKARIN
      2015 |   Giro d'Italia - Stage 11 - Forli > Imola |    2 |      Carlos BETANCUR
      2015 | Tour of Japan - Stage 4 - Minami > Shinshu |    1 |      Benjamin PRADES
      2015 | Tour of Japan - Stage 4 - Minami > Shinshu |    2 |          Adam PHELAN
      2015 | Tour of Japan - Stage 4 - Minami > Shinshu |    3 |         Thomas LEBAS

(11 rows)

This query returns the top two racers for each race year and race name combination using PER PARTITION LIMIT 2:

SELECT * 
FROM cycling.rank_by_year_and_name 
PER PARTITION LIMIT 2;

Output:

 race_year | race_name                                  | rank | cyclist_name
-----------+--------------------------------------------+------+----------------------
      2014 |                        4th Tour of Beijing |    1 |    Phillippe GILBERT
      2014 |                        4th Tour of Beijing |    2 |        Daniel MARTIN
      2014 | Tour of Japan - Stage 4 - Minami > Shinshu |    1 |        Daniel MARTIN
      2014 | Tour of Japan - Stage 4 - Minami > Shinshu |    2 | Johan Esteban CHAVES
      2015 |   Giro d'Italia - Stage 11 - Forli > Imola |    1 |        Ilnur ZAKARIN
      2015 |   Giro d'Italia - Stage 11 - Forli > Imola |    2 |      Carlos BETANCUR
      2015 | Tour of Japan - Stage 4 - Minami > Shinshu |    1 |      Benjamin PRADES
      2015 | Tour of Japan - Stage 4 - Minami > Shinshu |    2 |          Adam PHELAN

(8 rows)

Filtering data using WHERE

The WHERE clause contains one or more relations that filter the rows returned by SELECT.

Column specifications

The column specification of the relation must be one of these:
  • One or more members of the partition key of the table.
  • A clustering column, only if the relation is preceded by other relations that specify all columns in the partition key.
  • A column that is indexed using CREATE INDEX.
Restriction: In the WHERE clause, refer to a column using the actual name, not an alias.

Filtering on the partition key

This table has id as the partition key (it is the only column in the primary key, and is therefore also the partition key by default):
CREATE TABLE cycling.cyclist_career_teams (
  id UUID PRIMARY KEY,
  lastname text,
  teams set<text>
);
This query includes the partition key id value in the WHERE clause:
SELECT id, lastname, teams 
FROM cycling.cyclist_career_teams 
WHERE id = 5b6962dd-3f90-4c93-8f61-eabfa4a803e2;
Restriction: A relation that references the partition key can only use an equality operator = or IN. For more details about the IN operator, see the Examples below.

This example table contains a more complex primary key:

CREATE TABLE 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)
);
This query contains a WHERE clause that provides values for the primary key columns that precede the race column:
SELECT *
FROM cycling.events
WHERE
  year = 2017
  AND discipline = 'Cyclo-cross'
  AND start_month = 1
  AND start_day = 1;

Output:

 year | discipline  | start_month | start_day | race                                   | end_day | end_month | location | uci_code
------+-------------+-------------+-----------+----------------------------------------+---------+-----------+----------+----------
 2017 | Cyclo-cross |           1 |         1 | DVV verzekeringen trofee - GP Sven Nys |    null |      null |     Baal |      C1 

(1 rows)

Filtering on a clustering column

Use a relation on a clustering column only if it is preceded by relations that reference all the elements of the partition key.

Example table in the cycling keyspace (the partition key is the id column, the clustering column is race_points):

CREATE TABLE cycling.cyclist_points (
  id UUID, 
  race_points int, 
  firstname text, 
  lastname text, 
  race_title text, 
  PRIMARY KEY (id, race_points)
);

Example query:

SELECT SUM(race_points) 
FROM cycling.cyclist_points 
WHERE id = e3b19ec4-774a-4d1c-9e5a-decec1e30aac
  AND race_points > 7;

Output:

 system.sum(race_points)
-------------------------
                     195

(1 rows)

Add ALLOW FILTERING to filter only on a non-indexed cluster column. Avoid ALLOW FILTERING in a production environment. The following table definition contains a clustering column named race_start_date and does not have a secondary index.

CREATE TABLE cycling.calendar (
  race_id int,
  race_name text,
  race_start_date timestamp,
  race_end_date timestamp,
  PRIMARY KEY (race_id, race_start_date, race_end_date))
  WITH CLUSTERING ORDER BY (race_start_date DESC, race_end_date DESC
);
Example query with ALLOW FILTERING:
SELECT * 
FROM cycling.calendar 
WHERE race_start_date = '2015-06-13' 
ALLOW FILTERING; 

Output:

 race_id | race_start_date                 | race_end_date                   | race_name
---------+---------------------------------+---------------------------------+----------------
     102 | 2015-06-13 00:00:00.000000+0000 | 2015-06-21 00:00:00.000000+0000 | Tour de Suisse

(1 rows)

Filtering on indexed columns

A WHERE clause in a SELECT on a table with a secondary indexed column must include at least one equality relation to the indexed column. See Indexing a column.

Using the IN operator

Use IN, an equals condition operator, to list multiple values for a column in a WHERE clause.

This example selects the rows where race_id is in a list of values:

SELECT * 
FROM cycling.calendar 
WHERE race_id IN (101, 102, 103); 

The values in the list are separated by commas.

Using IN to filter on a compound primary key

Use an IN condition on the last column of a compound primary key only when it is preceded by equality conditions for all preceding columns of the primary key.

For example, examine the primary key in this table:
CREATE TABLE cycling.cyclist_id (
  lastname text,
  firstname text,
  age int,
  id UUID,
  PRIMARY KEY ((lastname, firstname), age)
);

This query contains the appropriate WHERE clause containing equality conditions for the first two columns of the primary key and an IN condition for the last column of the primary key:

SELECT * 
FROM cycling.cyclist_id 
WHERE lastname = 'EENKHOORN'
  AND firstname = 'Pascal'
  AND age IN (17, 18); 

When using IN, you can omit the equality test for clustering columns other than the last clustering column. This may require ALLOW FILTERING and should not be used in a production environment.

This table shows an example in which the race column is the last clustering column:
CREATE TABLE 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)
);
This query contains a WHERE clause with the equality condition for the race column (the last clustering column), an IN clause for the start_month column, and ALLOW FILTERING:
SELECT *
FROM cycling.events
WHERE race = 'Superprestige - Hoogstraten -2017'
  AND start_month IN (1, 2)
ALLOW FILTERING;

CQL supports an empty list of values in the IN clause, which can be useful in driver applications when passing empty arrays as arguments for the IN clause. See Connecting to DSE clusters using DSE drivers.

When not to use IN

Typically, using IN in relations on the partition key is not recommended. To process a list of values, the SELECT may have to query many nodes, which degrades performance.

For example, consider a single local datacenter cluster with 30 nodes, a replication factor of 3, and a consistency level of LOCAL_QUORUM. A query on a single partition key query goes out to two nodes. But if the SELECT uses the IN condition, the operation can involve more nodes — up to 20, depending on where the keys fall in the token range.

Using IN for clustering columns will cause less performance latency because all query actions are performed in a single partition.

See Cassandra Query Patterns: Not using the “in” query for multiple partitions.

Filtering on collections

A query can retrieve a collection in its entirety. You can also index the collection column, and then use the CONTAINS condition in the WHERE clause to filter the data for a particular value in the collection, or use CONTAINS KEY to filter by key.

This example features a set of text values named teams in the cyclist_career_teams table. This query filters on a value in the teams set.

SELECT *
FROM cycling.cyclist_career_teams
WHERE teams CONTAINS 'Rabobank-Liv Giant';
Output:
 id                                   | lastname | teams
--------------------------------------+----------+----------------------------------------------------------------------------------------------------
 1c9ebc13-1eab-4ad5-be87-dce433216d40 |    BRAND | {'AA Drink - Leontien.nl', 'Leontien.nl', 'Rabobank-Liv Giant', 'Rabobank-Liv Woman Cycling Team'}

(1 rows)
The cyclist_teams table contains a map of int keys and text values named teams. The teams map keys are indexed:
CREATE INDEX team_year_idx
ON cycling.cyclist_teams ( KEYS (teams) );
The index allows a query to filter the map keys:
SELECT *
FROM cycling.cyclist_teams
WHERE teams CONTAINS KEY 2015;

See Indexing a collection and cqlCreateIndex.html#cqlCreateIndex__CreatIdxCollKey.

Filtering map entries

This example adds an index for map entries.
CREATE INDEX blist_idx 
ON cycling.birthday_list ( ENTRIES(blist) );
Note: This method only works for maps.
This query finds all cyclists who are 23 years old based on their entry in the blist map in the birthday_list table.
SELECT *
FROM cycling.birthday_list
WHERE blist['age'] = '23';

Filtering a full frozen collection

The example in this section uses a table containing a FROZEN list collection named rnumbers. This statement creates an index, which is required for the query:
CREATE INDEX rnumbers_idx
ON cycling.race_starts ( FULL(rnumbers) );
This query retrieves the row that fully matches the collection's values, specifically a cyclist who has 39 Pro wins, 7 Grand Tour starts, and 14 Classic starts in rnumbers:
SELECT *
FROM cycling.race_starts
WHERE rnumbers = [39, 7, 14];

Range relations

DataStax Enterprise supports greater-than and less-than comparisons. But, for a given partition key, the conditions on the clustering column are restricted to the filters that allow selection of a contiguous set of rows.

This query constructs a filter that selects cycling calendar data whose start date is within a specified range and the race_id is 101. (If race_id were not a component of the primary key, you would need to create an index on race_id to use this query.)
SELECT * 
FROM cycling.calendar 
WHERE race_id = 101
  AND race_start_date >= '2014-05-27' 
  AND race_start_date < '2017-06-16';
Output:
 race_id | race_start_date                 | race_end_date                   | race_name
---------+---------------------------------+---------------------------------+-----------------------
     101 | 2015-06-07 00:00:00.000000+0000 | 2015-06-14 00:00:00.000000+0000 | Criterium du Dauphine
     101 | 2014-06-06 00:00:00.000000+0000 | 2014-06-13 00:00:00.000000+0000 | Criterium du Dauphine

(2 rows)

To allow selection of a contiguous set of rows, the WHERE clause must apply an equality condition to the race_id component of the primary key.

Using compound primary keys and sorting results

These restrictions apply when using an ORDER BY clause with a compound primary key:
  1. Only include clustering columns in the ORDER BY clause.
  2. In the WHERE clause, provide all the partition key values and clustering column values that precede the column(s) in the ORDER BY clause.
  3. When sorting multiple columns, the columns must be listed in the same order in the ORDER BY clause as they are listed in the PRIMARY KEY clause of the table definition.
  4. Sort ordering is limited. For example, if your table definition uses CLUSTERING ORDER BY (start_month ASC, start_day ASC), then you can use ORDER BY start_day, race in your query (ASC is the default). You can also reverse the sort ordering if you apply it to all of the columns; for example, ORDER BY start_day DESC, race DESC.
  5. Refer to a column using the actual name, not an alias.
See the cyclist_category table, which uses a compound primary key. This query retrieves the cyclist categories, in descending order by points.
SELECT *
FROM cycling.cyclist_category
WHERE category = 'Time-trial'
ORDER BY points DESC;

Output:

 category   | points | id                                   | lastname
------------+--------+--------------------------------------+------------
 Time-trial |    182 | 220844bf-4860-49d6-9a4b-6b5d3a79cbfb |  TIRALONGO
 Time-trial |      3 | 6ab09bec-e68e-48d9-a5f8-97e6fb4c9b47 | KRUIJSWIJK

(2 rows)
The following example shows a table with a more complex compound primary key.
CREATE TABLE 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)
);

This query contains a WHERE clause that provides values for all primary key columns that precede the race column and orders the results by race:

SELECT *
FROM cycling.events
WHERE
  year = 2017
  AND discipline = 'Cyclo-cross'
  AND start_month = 1
  AND start_day = 1
ORDER BY race;

Output:

 year | discipline  | start_month | start_day | race                                   | end_day | end_month | location | uci_code
------+-------------+-------------+-----------+----------------------------------------+---------+-----------+----------+----------
 2017 | Cyclo-cross |           1 |         1 | DVV verzekeringen trofee - GP Sven Nys |    null |      null |     Baal |      C1 

(1 rows)
This query has multiple clustering columns in the ORDER BY clause:
SELECT *
FROM cycling.events
WHERE year = 2017
  AND discipline = 'Cyclo-cross'
  AND start_month = 1
ORDER BY start_day, race;

Grouping results

The GROUP BY clause condenses the selected rows that share the same values for a set of columns into a group. A GROUP BY clause can contain:

  • Partition key columns and clustering columns.
  • A monotonic function, including a user-defined function (UDF), on the last clustering column specified in the GROUP BY clause. The FLOOR() function is monotonic when the duration and start time parameters are constants.
The examples in this section use the race_times_summary table:
CREATE TABLE cycling.race_times_summary (
  race_date date,
  race_time time,
  PRIMARY KEY (race_date, race_time)
);
The table contains these rows:
 race_date  | race_time
------------+--------------------
 2018-04-14 | 17:15:18.000000000
 2018-04-14 | 17:15:20.000000000
 2018-04-14 | 17:15:38.000000000
 2018-04-14 | 17:15:40.000000000
 2018-04-14 | 19:15:18.000000000
 2018-04-14 | 19:15:20.000000000
 2018-04-14 | 19:15:38.000000000
 2018-04-14 | 19:15:40.000000000
 2017-04-14 | 19:15:18.000000000
 2017-04-14 | 19:15:20.000000000
 2017-04-14 | 19:15:38.000000000
 2017-04-14 | 19:15:40.000000000

(12 rows)
This query groups the rows by the race_date column values:
SELECT
  race_date, race_time 
FROM
  cycling.race_times_summary
GROUP BY
  race_date;
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.
 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)
This query groups the rows by race_date and FLOOR(race_time, 1h), which rounds the race_time column values to the nearest hour:
SELECT
  race_date, FLOOR(race_time, 1h) 
FROM
  cycling.race_times_summary
GROUP BY
  race_date, FLOOR(race_time, 1h);
Nine rows are returned because there are nine groups of rows with the same race_date and FLOOR(race_time, 1h) values:
 race_date  | system.floor(race_time, 1h)
------------+-----------------------------
 2019-03-21 |          10:00:00.000000000
 2019-03-21 |          11:00:00.000000000
 2019-03-21 |          12:00:00.000000000
 2018-07-26 |          10:00:00.000000000
 2018-07-26 |          11:00:00.000000000
 2018-07-26 |          12:00:00.000000000
 2017-04-14 |          10:00:00.000000000
 2017-04-14 |          11:00:00.000000000
 2017-04-14 |          12:00:00.000000000

(9 rows)

Computing aggregates

DSE provides the built-in functions COUNT(), MIN(), MAX(), SUM(), and AVG() that return aggregate values to SELECT statements. You can also create user-defined aggregates (UDAs). The following sections show examples.

Using COUNT() to get the non-NULL value count for a column

A SELECT using COUNT(column_name) returns the number of non-NULL values in a column.

This query counts the number of last names in the cyclist_name table:

SELECT COUNT(lastname)
FROM cycling.cyclist_name;

Getting the number of matching rows and aggregate values with COUNT()

A SELECT using COUNT(*) returns the number of rows that matched the query. Use COUNT(1) to get the same result. COUNT(*) or COUNT(1) can be used in conjunction with other aggregate functions or columns.

This query returns the number of rows in the cyclist_name table:

SELECT COUNT(*)
FROM cycling.cyclist_name;

This query counts the number of rows and calculates the maximum value for start_day in the events table:

SELECT start_month, MAX(start_day), COUNT(*)
FROM cycling.events
WHERE year = 2017
  AND discipline = 'Cyclo-cross';

Getting maximum and minimum values in a column

A SELECT using MAX(column_name) returns the maximum value in a column. If the column's data type is numeric (bigint, decimal, double, float, int, or smallint), MAX(column_name) returns the maximum value.

For example:
SELECT MAX(race_points) 
FROM cycling.cyclist_points
WHERE id = e3b19ec4-774a-4d1c-9e5a-decec1e30aac;
Output:
 system.max(race_points)
-------------------------
                     120

(1 rows)
Note: If you do not include a WHERE clause, a warning message is displayed:
Warnings :
Aggregation query used without partition key
The MIN function returns the minimum value:
 system.min(race_points)
-------------------------
                       6

(1 rows)

If the column referenced by MAX or MIN has an ascii or text data type, these functions return the last or first item in an alphabetic sort of the column values. If the specified column has data type date or timestamp, these functions return the most recent or least recent times/dates. If the specified column has null values, the MIN function ignores it.

DSE does not return a null value as the MIN value. If the query includes a WHERE clause (recommended), MAX or MIN returns the largest or smallest value from the rows that satisfy the WHERE condition.

Getting the average or sum of a column of numbers

DSE computes the average of all values in a column when AVG is used in the query:

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

(1 rows)

Use SUM to get a total:

SELECT SUM(race_points) 
FROM cycling.cyclist_points 
WHERE id = e3b19ec4-774a-4d1c-9e5a-decec1e30aac
  AND race_points > 7;
Output:
 system.sum(race_points)
-------------------------
                     195

(1 rows)

If any of the rows returned have a null value for the column referenced for AVG aggregation, DSE includes that row in the row count, but uses a zero value to calculate the average. The SUM and AVG functions do not work with text, uuid, or date fields.

This query returns the cyclist team average time using a user-defined aggregate (UDA).

SELECT cycling.average(cyclist_time_sec)
FROM cycling.team_average 
WHERE team_name = 'UnitedHealthCare Pro Cycling Womens Team' 
  AND race_title = 'Amgen Tour of California Women''s Race presented by SRAM - Stage 1 - Lake Tahoe > Lake Tahoe';
See Creating a user-defined aggregate function (UDA) and CREATE AGGREGATE.

Retrieving the date/time a write occurred

The WRITETIME function applied to a column returns the date/time in microseconds at which the column was written to the database.

This query retrieves the date/time that writes occurred to the firstname column of a cyclist:

SELECT WRITETIME (firstname)
FROM cycling.cyclist_points
WHERE id = e3b19ec4-774a-4d1c-9e5a-decec1e30aac;
Output:
 writetime(firstname)
----------------------
     1538688876521481
     1538688876523973
     1538688876525239

The WRITETIME output of the last write 1538688876525239 in microseconds converts to Thursday, October 4, 2018 4:34:36.525 PM GMT-05:00 DST.

Retrieving the time-to-live of a column

The time-to-live (TTL) of a column value in a row is the number of seconds before the value is marked with a tombstone.

This example INSERT sets the TTL of the column values to 200 seconds:
INSERT INTO cycling.calendar (
  race_id, race_name, race_start_date, race_end_date
) VALUES (
  200, 'placeholder', '2015-05-27', '2015-05-27'
)
USING TTL 200;

This example UPDATE sets the TTL of a single race_name column value to 200 seconds:

UPDATE cycling.calendar 
USING TTL 300 
SET race_name = 'dummy' 
WHERE race_id = 200 
  AND race_start_date = '2015-05-27' 
  AND race_end_date = '2015-05-27';
This query retrieves the current TTL of the specified race_name column value:
SELECT TTL(race_name) 
FROM cycling.calendar 
WHERE race_id = 200;
Output:
 ttl(race_name)
----------------
            276

(1 rows)

Retrieving values in JSON format

See Retrieval using JSON