SELECT

LIMIT {[offset,] row_count | row_count OFFSET offset}

SELECT * FROM hrRec;
****
+-----------+-----------+----------+--------+
| FirstName | LastName  | City     | Tenure |
+-----------+-----------+----------+--------+
| Adam      | Gale      | Brooklyn |     40 |
| Samantha  | Beck      | New York |     44 |
| Clara     | Wakefield | DC       |     24 |
| Todd      | Bridges   | DC       |     30 |
| Ron       | Fletcher  | New York |     23 |
+-----------+-----------+----------+--------+

SELECT * FROM hrRec LIMIT 2;
****
+-----------+----------+----------+--------+
| FirstName | LastName | City     | Tenure |
+-----------+----------+----------+--------+
| Adam      | Gale     | Brooklyn |     40 |
| Samantha  | Beck     | New York |     44 |
+-----------+----------+----------+--------+

SELECT * FROM hrRec LIMIT 1,2;
****
+-----------+-----------+----------+--------+
| FirstName | LastName  | City     | Tenure |
+-----------+-----------+----------+--------+
| Samantha  | Beck      | New York |     44 |
| Clara     | Wakefield | DC       |     24 |
+-----------+-----------+----------+--------+

SELECT * FROM hrRec LIMIT 2 OFFSET 1;
****
+-----------+-----------+----------+--------+
| FirstName | LastName  | City     | Tenure |
+-----------+-----------+----------+--------+
| Samantha  | Beck      | New York |     44 |
| Clara     | Wakefield | DC       |     24 |
+-----------+-----------+----------+--------+

The following example uses the Orders table:

DESCRIBE Orders;
****
+--------------+-------------+------+------+---------+-------+
| Field        | Type        | Null | Key  | Default | Extra |
+--------------+-------------+------+------+---------+-------+
| OrderNumber  | int(11)     | NO   | PRI  | NULL    |       |
| Status       | varchar(20) | NO   |      | NULL    |       |
| CustomerName | char(20)    | YES  |      | NULL    |       |
+--------------+-------------+------+------+---------+-------+

SELECT * FROM Orders;
****
+-------------+------------+--------------+
| OrderNumber | Status     | CustomerName |
+-------------+------------+--------------+
|           1 | Delivered  | John         |
|           3 | In Transit | Bon          |
|           2 | Delivered  | Kerry        |
|           4 | Delivered  | Tom          |
+-------------+------------+--------------+

The following transaction locks the row where OrderNumber is 3, using the FOR UPDATE clause. The row will remain locked as long as the transaction is open.

BEGIN;
SELECT * FROM Orders WHERE OrderNumber = 3 FOR UPDATE;
****
+-------------+------------+--------------+
| OrderNumber | Status     | CustomerName |
+-------------+------------+--------------+
|           3 | In Transit | Bon          |
+-------------+------------+--------------+

Now, execute the following query in a different connection:

UPDATE Orders SET Status = "Delivered" WHERE OrderNumber=3;
****
ERROR 1205 (HY000): Leaf Error (127.0.0.1:3307): Lock wait timeout exceeded; try restarting transaction.  Lock owned by connection id 77, query `open idle transaction`

The above query returns an error since the rows are locked by the previous transaction. To remove the lock on the rows, commit or rollback the open transaction. See the Query Errors topic for more information on locked rows.

SingleStoreDB supports the following JOIN and subquery syntax for the table_reference part of SELECT statements:

join_table:
           table_reference {LEFT | RIGHT | FULL} [OUTER] JOIN table_factor join_condition
         | table_reference [INNER | CROSS] JOIN table_factor [join_condition]
         | table_reference NATURAL {LEFT | RIGHT} [OUTER] JOIN table_factor
         | table_reference STRAIGHT_JOIN table_factor [join_condition]
         | table_reference, table_factor

join_condition:
         ON conditional_expr

SELECT * FROM my_MemSQL_table WHERE col = 1;


SELECT COUNT(*), user_name, page_url from clicks, users, pages
 ->     WHERE clicks.user_id = users.user_id AND pages.page_id = clicks.page_id
 ->     GROUP BY users.user_id, pages.page_id
 ->     ORDER BY COUNT(*) DESC;
 +- ---------+- ----------+- -------------------------------+
 | COUNT(*) | user_name | page_url                       |
 +- ---------+- ----------+- -------------------------------+
 |        5 | jake      | memsql.com                     |
 |        2 | john      | http://www.singlestore.com/download |
 |        1 | jake      | docs.singlestore.com                |
 |        1 | jake      | memsql.com                     |
 |        1 | jake      | http://www.singlestore.com/download |
 +- ---------+- ----------+- -------------------------------+
 5 rows in set (0.00 sec)

SELECT t1.*, t2.* FROM t1 FULL OUTER JOIN t2 ON t1.a = t2.a;

Using a JOIN clause with the USING clause will match only one column when there are more than one columns that match.

SELECT <table1_name>.<column_name> AS <column_name> 
    FROM <table_name> JOIN <table_name> USING (<column_name>);

TABLE 1:

CREATE TABLE assets(
asset_id int,
asset_type varchar(50),
asset_desc varchar(50),
emp_id numeric (5)
);

TABLE 2:

CREATE TABLE
employees(
emp_id numeric (5),
emp_name varchar(75)
);

SELECT employees.emp_name AS employees, asset_desc as assets, assets.asset_type
    FROM assets JOIN employees USING (emp_id);
****
+------------------------------------+
|employees |assets      |asset_type  |
+------------------------------------+
|T_Willams |macbook pro | laptop     |
+------------------------------------+
|A_Young   |iphone 12   | cell phone |
+------------------------------------+
|D_Karras  |macbook air | laptop     |
+------------------------------------+

Note: The following examples use the hrRec table.

DESC hrRec;
****
+-----------+-------------+------+------+---------+-------+
| Field     | Type        | Null | Key  | Default | Extra |
+-----------+-------------+------+------+---------+-------+
| FirstName | varchar(20) | YES  |      | NULL    |       |
| LastName  | varchar(20) | YES  |      | NULL    |       |
| City      | varchar(20) | YES  |      | NULL    |       |
| Tenure    | int(11)     | YES  |      | NULL    |       |
+-----------+-------------+------+------+---------+-------+

DELIMITER //

CREATE OR REPLACE PROCEDURE into_var ()
AS
DECLARE
fname VARCHAR(20); lname VARCHAR(20); city VARCHAR(20); ten INT;
BEGIN
SELECT FirstName, LastName, City, Tenure
 INTO fname, lname, city, ten
FROM hrRec
WHERE Tenure > 40;
ECHO SELECT CONCAT(fname, " ", lname) AS "Name", city AS "City", ten AS "Tenure";
END //

DELIMITER ;

CALL into_var();
****
+---------------+----------+--------+
| Name          | City     | Tenure |
+---------------+----------+--------+
| Samantha Beck | New York |     44 |
+---------------+----------+--------+

DELIMITER //

CREATE OR REPLACE PROCEDURE into_var () AS
DECLARE
  row_c INT; sum_t INT;
BEGIN
  SELECT COUNT(*), SUM(Tenure) INTO row_c, sum_t FROM hrRec;
  ECHO SELECT row_c AS "Row Count", sum_t AS "Tenure";
END //

DELIMITER ;

CALL into_var();
****
+-----------+--------+
| Row Count | Tenure |
+-----------+--------+
|         5 |    170 |
+-----------+--------+

DELIMITER //

CREATE OR REPLACE PROCEDURE into_var () AS
DECLARE
  row_c INT; sum_t INT; qry VARCHAR(50);
BEGIN
  qry = "SELECT COUNT(*), SUM(Tenure) FROM hrRec";
  EXECUTE IMMEDIATE qry INTO row_c, sum_t;
  ECHO SELECT row_c AS "Row Count", sum_t AS "Tenure";
END //

DELIMITER ;

CALL into_var();
****
+-----------+--------+
| Row Count | Tenure |
+-----------+--------+
|         5 |    170 |
+-----------+--------+

DELIMITER //

CREATE OR REPLACE PROCEDURE cityCount () AS
DECLARE cnum INT;
BEGIN
  SELECT City INTO cnum FROM hrRec WHERE Tenure > 50;
  ECHO SELECT cnum;
END //

DELIMITER ;

CALL cityCount();
****
ERROR 2439 (HY000): Unhandled exception
Type: ER_INTO_VARIABLES_NO_ROWS (2439)
Message: Query with 'INTO VARIABLES' clause returned zero rows whereas expected 1 row

DELIMITER //

CREATE OR REPLACE PROCEDURE cityCount () AS
DECLARE cnum INT;
BEGIN
  SELECT City INTO cnum FROM hrRec WHERE Tenure > 50;
  ECHO SELECT cnum;
EXCEPTION
  WHEN ER_INTO_VARIABLES_NO_ROWS THEN
  cnum = 'No city found!';
  ECHO SELECT cnum;
END //

DELIMITER ;

CALL cityCount();
****
+----------------+
| cnum           |
+----------------+
| No city found! |
+----------------+

EXCEPTION
  WHEN ER_SUBQUERY_NO_1_ROW THEN
  ECHO SELECT 'SELECT ... INTO statement returned more than 1 row.';

The following examples show the use of nested sub-selects.

SELECT cust_id FROM customers
WHERE EXISTS
  ( SELECT order_id FROM orders
    WHERE order_id IN
      ( SELECT id FROM transaction WHERE count > 5)
  );

DELETE FROM records
WHERE
  id = ( SELECT order_id
         FROM orders
         WHERE order_date > ( SELECT CURRENT_DATE() + 30)
       );

SELECT [ALL | DISTINCT | DISTINCTROW] FROM table_reference

SELECT * FROM Product;
+------------+------------+----------+
| Product_id | Brand_name | City     |
+------------+------------+----------+
|          1 | Nike       | London   |
|          3 | Nike       | New York |
|          2 | Adidas     | Paris    |
|          3 | Puma       | Spain    |
+------------+------------+----------+

SELECT ALL Brand_name FROM Product;
+------------+
| Brand_name |
+------------+
| Nike       |
| Nike       |
| Adidas     |
| Puma       |
+------------+

SELECT DISTINCT Brand_name FROM Product;
+------------+
| Brand_name |
+------------+
| Nike       |
| Adidas     |
| Puma       |
+------------+

The following query writes the contents of table_name to a file in the home directory of username. The file is formatted as a comma-separated values (CSV) file.

SELECT * FROM table_name INTO OUTFILE '/home/username/file_name.csv'
        FIELDS TERMINATED BY ','
        LINES TERMINATED BY '\n'

The following example demonstrates how to write the contents of a table to HDFS at a specified path.

SELECT * FROM stockTo
INTO HDFS 'hdfs://hadoop-namenode:8020/stock_dir/records_file.csv'

The following example writes the contents of the table t1, to the S3 bucket at the specified path, using the S3 connection link S3con stored in the db1 database:

USE db1;
SELECT * FROM t1 INTO LINK S3con 'testing/output';

Note: The connection link S3Con should already exist in db1. Refer to the CREATE LINK topic for details on creating a connection link.

The following query writes the contents of table_name to a set of files in the /tmp directory on each leaf node (/tmp/a_0, /tmp/a_1, /tmp/a_2, etc.). The files are comma-separated value (CSV) with one output file per partition (a_0 is for partition 0, a_1 is for partition 1, etc.).

SELECT * FROM table_name INTO FS '/tmp/a'
FIELDS TERMINATED BY ','
LINES TERMINATED BY '\n'

The following simple example shows how to save all rows in table t1 to an S3 bucket using an AWS access key.

SELECT *
FROM t1
INTO S3 'testing/output'
CONFIG '{"region":"us-east-1"}'
CREDENTIALS '{"aws_access_key_id":"your_access_key_id","aws_secret_access_key":"your_secret_access_key"}'

The following example saves the result set of a SELECT query with a GROUP BY clause and sends the file in chunks to an S3 bucket using an Amazon Resource Name (ARN) for AWS Identity and Access Management (IAM).

SELECT t1.a, t2.a
FROM t1, t2
WHERE t1.a = t2.a
GROUP BY t1.a
INTO S3 'bucket_name/file_name'
CONFIG '{"region":"us-east-1", "multipart_chunk_size_mb":100}'
CREDENTIALS '{"role_arn": "arn:aws:iam::<AccountID>:role/EC2AmazonS3FullAccess"}'

The following example uses the format options to output the data in CSV format.

SELECT *
FROM t
INTO S3 'tmp/a'
CONFIG '{"region":"us-east-1"}'
CREDENTIALS '{"aws_access_key_id":"your_access_key_id","aws_secret_access_key":"your_secret_access_key"}'
FIELDS TERMINATED BY ','
LINES TERMINATED BY '\n'

access_id: usually a 24 or 60 character alphanumeric string, which is linked to the Google account, typically all uppercase and starts with GOOG.

secret_key: usually a 40 character Base-64 encoded string that is linked to a specific access_id.

As an alternative to using a SELECT ... INTO GCS statement where you specify the CONFIG and CREDENTIALS clauses, you can use a SELECT ... INTO LINK statement, where you reference a connection link. For more information, see Configuring and Using Connection Links.

The following simple example shows how to save all rows in table table_name to a GCS bucket using a Google access key, and outputs them as a CSV.

CREATE TABLE table_name (column_name INT);
INSERT INTO table_name VALUES (1), (2), (3);
SELECT *
FROM table_name
INTO GCS 'bucket/path'
CREDENTIALS '{"access_id": "replace_with_your_google_access_key", "secret_key": "replace_with_your_google_secret_key"}' FIELDS TERMINATED BY ',' LINES TERMINATED BY '\n';

FORMAT PARQUET

The examples below have been shortened to illustrate the use of the Parquet format. The data ingestion, creation of table2, and start pipeline are not shown.

CREATE TABLE table1 (f_CHAR_2_5 CHAR(2), f_LONGTEXT4 LONGTEXT NOT NULL, 
f_DATETIME_6_3 DATETIME(6), f_NUMERIC_27_13__UNSIGNED2 NUMERIC(27,13) UNSIGNED, f_TINYINT1 TINYINT);

SELECT * FROM table1 into fs ‘/tmp/parquet_files’ FORMAT PARQUET;

CREATE PIPELINE pipe1 AS LOAD DATA fs ‘/tmp/parquet_files’ 
INTO TABLE table2 FORMAT PARQUET (f_CHAR_2_5 <- %::f_CHAR_2_5, f_LONGTEXT4 <- %::f_LONGTEXT4, 
@f_DATETIME_6_3 <- %::f_DATETIME_6_3, f_NUMERIC_27_13__UNSIGNED2 <- %::f_NUMERIC_27_13__UNSIGNED2, 
f_TINYINT1 <- %::f_TINYINT1 )  SET f_DATETIME_6_3=DATE_ADD('1970-01-01', INTERVAL @f_DATETIME_6_3 MICROSECOND);

CREATE TABLE table1 (f_REAL3 REAL NOT NULL, f_TIME2 TIME, f_ENUM__small___medium___large__1 ENUM('small','medium','large'));

SELECT * FROM table1 into fs '/tmp/parquet_files' FORMAT PARQUET;

CREATE  PIPELINE pipe2 AS LOAD DATA fs '/tmp/parquet_files' INTO TABLE table2 FORMAT PARQUET 
(f_REAL3 <- %::f_REAL3, @f_TIME2 <- %::f_TIME2, f_ENUM__small___medium___large__1 <- %::f_ENUM__small___medium___large__1 )
SET f_TIME2=sec_to_time(@f_TIME2/1000);

CREATE ROWSTORE TABLE table1 (f_TEXT5 TEXT NOT NULL, 
f_GEOGRAPHY4 GEOGRAPHY NOT NULL, f_VARCHAR_200_3 VARCHAR(200), 
f_DECIMAL_27_13_2 DECIMAL(27,13), f_INT_UNSIGNED1 INT UNSIGNED);

SELECT * FROM table1 INTO OUTFILE '/tmp/parquet_files3' FORMAT PARQUET;

CREATE LINK p_link_20_56_14_557666 AS fs CREDENTIALS '{}' CONFIG '{}';

CREATE PIPELINE pipe3 AS LOAD DATA LINK p_link_20_56_14_557666
'/tmp/parquet_files3' INTO TABLE table2 FORMAT PARQUET (f_TEXT5 <- %::f_TEXT5,
f_GEOGRAPHY4 <- %::f_GEOGRAPHY4, f_VARCHAR_200_3 <- %::f_VARCHAR_200_3, 
f_DECIMAL_27_13_2 <- %::f_DECIMAL_27_13_2, f_INT_UNSIGNED1 <- %::f_INT_UNSIGNED1);

ratio

A decimal between 0.0 and 1.0 specifying the probability that each row will be retrieved from the table. The probability is applied to each row independently.

Simple SELECT statements

The following example populates a table with the integers 1 to 10 and selects two random samples.

DROP DATABASE IF EXISTS singlestore_docs_example;
CREATE DATABASE singlestore_docs_example;
USE singlestore_docs_example;
CREATE TABLE example_table(num INT);

INSERT INTO example_table VALUES (5), (6), (10), (11), (12),
  (14), (20), (24), (33), (40);

The query below returns a random sample of the num column. Each row in the sample has a seventy percent probability of being returned.

SELECT num FROM example_table WITH (SAMPLE_RATIO = 0.7) ORDER BY num;

Example output follows. You will likely see different results, as the query returns a random sample.

+------+
| num  |
+------+
|    6 |
|   11 |
|   14 |
|   20 |
|   24 |
|   33 |
+------+

The query below returns the average of a random sample of the num column. Each row in the sample has an eighty percent probability being included in the sample and therefore in the AVG computation.

SELECT AVG(num) FROM example_table WITH (SAMPLE_RATIO = 0.8);

Example output follows. You will likely see different results, as the query returns a random sample.

+----------+
| AVG(num) |
+----------+
|  18.1111 |
+----------+

Using WITH (SAMPLE_RATIO = <value>) with SELECT Subqueries

You may use WITH (SAMPLE_RATIO = <value>)as part of a SELECT subquery, as shown in the following example.

CREATE TABLE example_table_2 AS SELECT * FROM example_table
  WITH (SAMPLE_RATIO = 0.6);
SELECT num FROM example_table2 ORDER BY num;

Example output follows. You will likely see different results, as the query returns a random sample.

+------+
| num  |
+------+
|   10 |
|   12 |
|   14 |
|   24 |
|   33 |
|   40 |
+------+

Using WITH (SAMPLE_RATIO = <value>) with Joins

You may use WITH (SAMPLE_RATIO = <value>) in a SELECT statement that contains a join. However, you should do so carefully to ensure the join yields the results you intend. For example, consider joining a CUSTOMER table to an ORDER table using the customer_id field in the ORDER table. The placement of the WITH (SAMPLE_RATIO = <value>) clause within the join will greatly impact the result, as shown below.

The following query selects a sample of customers and returns the orders for those customers only.

SELECT c.name, o.order_id, o.order_total
FROM customer WITH (SAMPLE_RATIO = 0.4) c
JOIN order o
ON c.customer_id = o.customer_id

The following query selects a sample of all orders and returns only those orders along with their associated customers.

SELECT c.name, o.order_id, o.order_total
FROM customer c
JOIN order o WITH (SAMPLE_RATIO = 0.4)
ON c.customer_id = o.customer_id