SQL Developer Guide - Kinetica Docs

The following guide provides step-by-step instructions to get started interacting with Kinetica using SQL. This guide demonstrates some of the most common and useful SQL commands, but a detailed look into the full SQL support is available on the SQL page.

Prerequisites

For a description of how to configure various clients for issuing SQL commands, see Connecting.

Data File

The tutorial script makes reference to a data file in the current directory. This path can be updated to point to a different path local to where the script is being run:

Upload Data File

UPLOAD FILE '../data/taxi_trip_data.csv'
INTO 'data'

The data file will be uploaded into KiFS and then loaded into the table from there:

Insert Data from File

LOAD INTO tutorial_sql.taxi_trip_data
FROM FILE PATHS 'kifs://data/taxi_trip_data.csv'

Creating a Type and Table

Before creating any database objects for this tutorial, a schema needs to be created to contain them:

Create Tutorial Schema

CREATE SCHEMA tutorial_sql

Tables can be created as replicated using the REPLICATED specification. The OR REPLACE specification can be added to ensure the table is replaced if it already exists in the database. Adding a schema name before the table name in the CREATE TABLE command, e.g., CREATE TABLE my_schema.mytable will create the table under the given schema; however, the schema must first exist. For this tutorial, three tables will be created:

A base table containing a sample of NYC taxi cab ride data
A lookup table containing sample cab vendor information
A 1-to-1 relation table containing payment information for a subset of the cab-ride transactions

Create Vendor Table

CREATE OR REPLACE REPLICATED TABLE tutorial_sql.vendor
(
  vendor_id VARCHAR(4) NOT NULL,
  vendor_name VARCHAR(32) NOT NULL,
  phone VARCHAR(10),
  email VARCHAR(32),
  hq_street VARCHAR(32) NOT NULL,
  hq_city VARCHAR(8) NOT NULL,
  hq_state VARCHAR(2) NOT NULL,
  hq_zip INT NOT NULL,
  num_emps INT NOT NULL,
  num_cabs INT NOT NULL,
  PRIMARY KEY (vendor_id)
)

Create Payment Table

CREATE OR REPLACE TABLE tutorial_sql.payment
(
  payment_id LONG NOT NULL,
  payment_type VARCHAR(16),
  credit_type VARCHAR(16),
  payment_timestamp TYPE_TIMESTAMP,
  fare_amount DECIMAL(7,2),
  surcharge DECIMAL(7,2),
  mta_tax DECIMAL(5,2),
  tip_amount DECIMAL(7,2),
  tolls_amount DECIMAL(7,2),
  total_amount DECIMAL(7,2),
  PRIMARY KEY (payment_id)
)

Create Taxi Trip Table

CREATE OR REPLACE TABLE tutorial_sql.taxi_trip_data
(
  transaction_id LONG NOT NULL,
  payment_id LONG(SHARD_KEY) NOT NULL,
  vendor_id VARCHAR(4) NOT NULL,
  pickup_datetime TYPE_TIMESTAMP,
  dropoff_datetime TYPE_TIMESTAMP,
  passenger_count TINYINT,
  trip_distance REAL,
  pickup_longitude REAL,
  pickup_latitude REAL,
  dropoff_longitude REAL,
  dropoff_latitude REAL,
  PRIMARY KEY (transaction_id, payment_id)
)

Inserting Data

The INSERT INTO … VALUES statement is used to insert records into a table. At a minimum, each record must have values specified for all of the NOT NULL columns in the table. The values specified in the VALUES clause must match the number and order of the columns specified, or they must align with the ordering of the columns in the table if no columns were specified:

Insert Data into Specified Columns

-- Use explicit column name syntax when the ordering of values for the set of
-- records doesn't match the natural ordering of all of the columns defined
-- within the Vendor table; here, num_emps and num_cabs are reversed
INSERT INTO tutorial_sql.vendor (vendor_id, vendor_name, phone, email, hq_street, hq_city, hq_state, hq_zip, num_cabs, num_emps)
VALUES
  ('VTS','Vine Taxi Service','9998880001','admin@vtstaxi.com','26 Summit St.','Flushing','NY',11354,450,400),
  ('YCAB','Yes Cab','7895444321',null,'97 Edgemont St.','Brooklyn','NY',11223,445,425),
  ('NYC','New York City Cabs',null,'support@nyc-taxis.com','9669 East Bayport St.','Bronx','NY',10453,505,500),
  ('DDS','Dependable Driver Service',null,null,'8554 North Homestead St.','Bronx','NY',10472,200,124),
  ('CMT','Crazy Manhattan Taxi','9778896500','admin@crazymanhattantaxi.com','950 4th Road Suite 78','Brooklyn','NY',11210,500,468),
  ('TNY','Taxi New York',null,null,'725 Squaw Creek St.','Bronx','NY',10458,315,305),
  ('NYMT','New York Metro Taxi',null,null,'4 East Jennings St.','Brooklyn','NY',11228,166,150),
  ('5BTC','Five Boroughs Taxi Co.','4566541278','mgmt@5btc.com','9128 Lantern Street','Brooklyn','NY',11229,193,175)

If each record in the VALUES clause specifies values for all columns in their natural table ordering (the order in which they appeared in the original CREATE TABLE statement) no column names need to be specified, like so:

Insert Data into All Columns

-- Use shorthand syntax, where each record of values matches the natural
-- ordering of all of the columns defined within the Payment table
INSERT INTO tutorial_sql.payment
VALUES
  (136,'Cash',null,'2015-04-11 01:42:01',4,0.5,0.5,1,0,6.3),
  (148,'Cash',null,'2015-04-27 08:49:41',9.5,0,0.5,1,0,11.3),
  (114,'Cash',null,'2015-04-05 18:47:53',5.5,0,0.5,1.89,0,8.19),
  (180,'Cash',null,'2015-04-13 22:57:03',6.5,0.5,0.5,1,0,8.8),
  (109,'Cash',null,'2015-04-13 18:08:33',22.5,0.5,0.5,4.75,0,28.55),
  (132,'Cash',null,'2015-04-19 19:46:19',6.5,0.5,0.5,1.55,0,9.35),
  (134,'Cash',null,'2015-04-19 19:44:28',33.5,0.5,0.5,0,0,34.8),
  (176,'Cash',null,'2015-04-07 10:52:42',9,0.5,0.5,2.06,0,12.36),
  (100,'Cash',null,null,9,0,0.5,2.9,0,12.7),
  (193,'Cash',null,null,3.5,1,0.5,1.59,0,6.89),
  (140,'Credit','Visa',null,28,0,0.5,0,0,28.8),
  (161,'Credit','Visa',null,7,0,0.5,0,0,7.8),
  (199,'Credit','Visa',null,6,1,0.5,1,0,8.5),
  (159,'Credit','Visa','2015-04-10 14:01:27',7,0,0.5,0,0,7.8),
  (156,'Credit','MasterCard','2015-04-10 13:32:33',12.5,0.5,0.5,0,0,13.8),
  (198,'Credit','MasterCard','2015-04-19 19:43:56',9,0,0.5,0,0,9.8),
  (107,'Credit','MasterCard','2015-04-11 01:56:17',5,0.5,0.5,0,0,6.3),
  (166,'Credit','American Express','2015-04-12 03:18:43',17.5,0,0.5,0,0,18.3),
  (187,'Credit','American Express','2015-04-10 12:49:41',14,0,0.5,0,0,14.8),
  (125,'Credit','Discover','2015-04-24 10:01:13',8.5,0.5,0.5,0,0,9.8),
  (119,null,null,'2015-04-30 22:04:31',9.5,0,0.5,0,0,10.3),
  (150,null,null,'2015-04-30 22:20:47',7.5,0,0.5,0,0,8.3),
  (170,'No Charge',null,'2015-04-30 22:05:02',28.6,0,0.5,0,0,28.6),
  (123,'No Charge',null,'2015-04-27 12:10:49',20,0.5,0.5,0,0,21.3),
  (181,null,null,'2015-04-27 11:51:01',6.5,0.5,0.5,0,0,7.8),
  (189,'No Charge',null,null,6.5,0,0.5,0,0,7)

As mentioned in the Prerequisites section, the data file is uploaded into Kifs and then inserted into the target table from there:

Insert Data from KiFS File

LOAD INTO tutorial_sql.taxi_trip_data
FROM FILE PATHS 'kifs://data/taxi_trip_data.csv'

Retrieving Data

A SELECT statement will retrieve the records from the given table. You can reduce the amount of records returned by using a TOP n clause. This will select the first n records returned by the query. Use * to select all columns in the table.

Retrieve 10 Records

SELECT TOP 10 *
FROM tutorial_sql.payment
ORDER BY payment_id

Retrieve All Records

SELECT *
FROM tutorial_sql.vendor
ORDER BY vendor_id

Updating Records

The UPDATE statement is used to update matching key values for all records in a table. A WHERE clause can be used to specify the records to update.

Update Records

-- Update the e-mail of, and add two employees and one cab to, the DDS vendor
UPDATE tutorial_sql.vendor
SET
  email = 'management@ddstaxico.com',
  num_emps = num_emps + 2,
  num_cabs = num_cabs + 1
WHERE vendor_id = 'DDS'

Deleting Records

The DELETE statement is used to delete records from a table. A WHERE clause can be used to specify the records to delete.

Delete Records

-- Delete payment 189
DELETE
FROM tutorial_sql.payment
WHERE payment_id = 189

Alter Table

Some properties can be altered or added after table creation, including indexes and dictionary encoding. Use the ALTER TABLE command to specify the table and property you want to alter.

Indexes

ADD INDEX will add a column index to a table column.

Add Index on Column

ALTER TABLE tutorial_sql.payment
ADD INDEX (fare_amount)

Dictionary Encoding

Use ALTER COLUMN to add column properties like dictionary encoding.

Dictionary Encode Column

ALTER TABLE tutorial_sql.taxi_trip_data
ALTER COLUMN vendor_id VARCHAR(4, DICT) NOT NULL

When adding a new property, all existing column properties must be listed along with any new properties; those not listed will be removed.

Filters & Aggregates

A simple filter can be performed using the SELECT statement’s WHERE clause.

Filter by Value

-- Select all payments with a fare amount greater than 8
SELECT payment_id, fare_amount
FROM tutorial_sql.payment
WHERE fare_amount > 8
ORDER BY payment_id

This query makes use of the column index created in the preceding Indexes section.

BETWEEN can be used to filter values within a range, inclusively.

Filter by Range

-- Select trips with passenger counts between 6 and 10
SELECT pickup_datetime, dropoff_datetime, trip_distance, passenger_count
FROM tutorial_sql.taxi_trip_data
WHERE passenger_count BETWEEN 6 AND 10
ORDER BY pickup_datetime

The CASE ... WHEN statement is a logical function that allows you to categorize records based on given filters.

Filter by Multiple Conditions

-- Select the top 30 records where the pickup is between April 20th and
-- April 26th, then order them from longest trip to shortest trip.  A trip
-- description will also be returned, designating any trip of over six miles as
-- a "long trip", between three and six miles as a "medium trip", and three or
-- shorter as a "short trip".
SELECT TOP 30
  vendor_id,
  pickup_datetime,
  dropoff_datetime,
  passenger_count,
  CASE
    WHEN trip_distance > 6 THEN 'long trip'
    WHEN trip_distance > 3 THEN 'medium trip'
    ELSE 'short trip'
  END AS trip_description
FROM tutorial_sql.taxi_trip_data
WHERE pickup_datetime BETWEEN '2015-04-20 00:00:00.000' AND '2015-04-27 00:00:00.000'
ORDER BY trip_distance DESC

Aggregate functions are just a small offering of the available SQL functions. Aggregate functions are paired with a GROUP BY clause that groups the returned records by values in a given column. The HAVING clause filters records after they’ve been aggregated.

Filter Aggregation by Value

-- Select the longest, shortest, and average trip distance & passenger count for
-- each vendor whose average passenger count is higher than 1.4
SELECT
  vendor_id,
  MAX(trip_distance) max_trip,
  MIN(trip_distance) min_trip,
  ROUND(AVG(trip_distance),2) avg_trip,
  INT(AVG(passenger_count)) avg_passenger_count
FROM tutorial_sql.taxi_trip_data
GROUP BY vendor_id
HAVING AVG(passenger_count) > 1.4
ORDER BY vendor_id

Subqueries

Subqueries allow for nested queries within the clauses of a SQL statement. The below example is a simple SELECT statement that includes subqueries in each of its three clauses.

Subqueries in SELECT, FROM, & WHERE Clauses

-- Show how tips compare between cash and credit card payments: retrieve unique
-- paid-by-cash fare & tip combinations and calculate the relationship between
-- the cash tip percentage and the average credit tip percentage, across all
-- cash fares that were at least as much as the lowest credit card fare.  The
-- tip factor will be the size of the cash tip in terms of the average credit
-- tip; e.g., "10" indicates the cash tip percentage is 10 times higher than the
-- average credit tip
SELECT
  fare_amount,
  tip_amount,
  DECIMAL
  (
    (tip_amount / fare_amount) * 100 /
    (
      SELECT AVG(tip_amount / fare_amount) * 100 as avg_credit_tip_pct
      FROM tutorial_sql.payment
      WHERE payment_type = 'Credit'
    )
  ) as tip_factor_cash_vs_credit_pct
FROM
(
  SELECT DISTINCT
    fare_amount,
    tip_amount
  FROM tutorial_sql.payment
  WHERE payment_type = 'Cash'
) cash_fare_tip
WHERE fare_amount >=
(
  SELECT MIN(fare_amount)
  FROM tutorial_sql.payment
  WHERE payment_type = 'Credit'
)

Common Table Expression / With

The WITH statement can be used to give a subquery an alias for use in a larger query. You can reuse the aliased query as if it were another result set, but only in the query immediately following the WITH statement. The “parameters” to the WITH statement are the aliases that will be given to the result columns returned by the SELECT contained within the WITH; the number of “parameters” and columns in the SELECT clause should match.

Filter Using CTE

-- Retrieve the set of cash payments that fall within the timestamp range of
-- recorded credit payments
WITH credit_pay_ts_min_max (min_pay_ts, max_pay_ts) AS
(
  SELECT
    MIN(payment_timestamp) AS min_pay_ts,
    MAX(payment_timestamp) AS max_pay_ts
  FROM tutorial_sql.payment
  WHERE payment_type = 'Credit'
)
SELECT
  payment_id,
  payment_timestamp,
  total_amount
FROM tutorial_sql.payment
WHERE
  payment_type = 'Cash' AND
  payment_timestamp BETWEEN
    (SELECT min_pay_ts FROM credit_pay_ts_min_max) AND
    (SELECT max_pay_ts FROM credit_pay_ts_min_max)
ORDER BY payment_timestamp

Joins

Joins allow you to link multiple tables together, along their relations, retrieving associated information from any or all of them. Tables can only be joined if they’re sharded similarly or replicated. An inner join returns only records that have matching values in both tables.

Inner Join

-- Retrieve payment information for rides having more than three passengers
SELECT
  t.payment_id,
  payment_type,
  total_amount,
  passenger_count,
  vendor_id,
  trip_distance
FROM tutorial_sql.taxi_trip_data t
INNER JOIN tutorial_sql.payment p ON t.payment_id = p.payment_id
WHERE
  passenger_count > 3
ORDER BY payment_id

A left join returns all of the records an inner join does, but additionally, for each record in the table on the left side of the join that has no match along the relation to a record in the table on the right side of the join, a corresponding record will be returned with “left-side” columns populated with the “left-side” record data and the “right-side” columns populated with nulls.

Left Join

-- Retrieve cab ride transactions and the full name of the associated vendor (if
-- available--blank if vendor name is unknown) for transactions with associated
-- payment data, sorting by increasing values of transaction ID.
SELECT
  transaction_id,
  pickup_datetime,
  trip_distance,
  t.vendor_id,
  vendor_name
FROM tutorial_sql.taxi_trip_data t
LEFT JOIN tutorial_sql.vendor v ON t.vendor_id = v.vendor_id
WHERE payment_id != 0
ORDER BY transaction_id

Full outer joins may require both tables to be replicated or joined on their shard keys. Set merges that perform deduplication of records, like UNION DISTINCT, INTERSECT, and EXCEPT may also need to use replicated tables to ensure the correct results, so a replicated version of the taxi (taxi_trip_data_replicated) table is created at this point in the tutorial.

Create Replicated Table for Full Outer Join

CREATE OR REPLACE REPLICATED TABLE tutorial_sql.taxi_trip_data_replicated (
  transaction_id LONG NOT NULL,
  payment_id LONG NOT NULL,
  vendor_id VARCHAR(4) NOT NULL,
  pickup_datetime TYPE_TIMESTAMP,
  dropoff_datetime TYPE_TIMESTAMP,
  passenger_count DECIMAL(2),
  trip_distance FLOAT,
  pickup_longitude FLOAT,
  pickup_latitude FLOAT,
  dropoff_longitude FLOAT,
  dropoff_latitude FLOAT
)

A full outer join returns all of the records a left join does, but additionally, for each record in the table on the right side of the join that has no match along the relation to a record in the table on the left side of the join, a corresponding record will be returned with “right-side” columns populated with the “right-side” record data and the “left-side” columns populated with nulls.

Full Outer Join

-- Retrieve the vendor IDs of known vendors with no recorded cab ride
-- transactions, as well as the vendor ID and number of transactions for unknown
-- vendors with recorded cab ride transactions
SELECT
  v.vendor_id vend_table_vendors,
  t.vendor_id taxi_table_vendors,
  COUNT(*) as total_records
FROM tutorial_sql.taxi_trip_data_replicated t
FULL OUTER JOIN tutorial_sql.vendor v ON v.vendor_id = t.vendor_id
WHERE
  v.vendor_id IS null OR
  t.vendor_id IS null
GROUP BY
  v.vendor_id,
  t.vendor_id
ORDER BY 1, 2

Create Table As

You can also create a table directly from a query using CREATE TABLE … AS. These can also be created as temporary tables by applying the TEMP specification. Much like regular table creation, you can also specify OR REPLACE to replace the table if it already exists.

Create Temp Table from Query

-- Create a memory-only table containing all payments by credit card
CREATE OR REPLACE TEMP TABLE tutorial_sql.credit_payment AS (
  SELECT *
  FROM tutorial_sql.payment
  WHERE payment_type = 'Credit'
)

Create Table from Query

-- Create a persisted table with cab ride transactions greater than 5 miles
-- whose trip started during lunch hours
CREATE OR REPLACE TABLE tutorial_sql.lunch_time_rides AS (
  SELECT
    HOUR(pickup_datetime) hour_of_day,
    vendor_id,
    passenger_count,
    trip_distance
  FROM tutorial_sql.taxi_trip_data
  WHERE
    HOUR(pickup_datetime) BETWEEN '11' AND '14' AND
    trip_distance > 5
)

Union, Intersect, and Except

A UNION can be used to combine homogeneous data sets into one larger data set. UNION & UNION DISTINCT will both combine data sets but only retain the records that are unique across the chosen columns, removing all duplicates. UNION ALL will combine data sets, retaining all records from the source data sets.

Set Union Retaining Duplicates

-- Calculate the average number of passengers, as well as the shortest, average,
-- and longest trips for all trips in each of the two time periods--from April
-- 1st through the 15th, 2015 and from April 16th through the 23rd, 2015--and
-- return those two sets of statistics in a single result set
SELECT
  '2015-04-01 - 2015-04-15' pickup_window_range,
  INT(AVG(passenger_count)) avg_pass_count,
  ROUND(AVG(trip_distance),2) avg_trip,
  MIN(trip_distance) min_trip,
  MAX(trip_distance) max_trip
FROM tutorial_sql.taxi_trip_data
WHERE pickup_datetime BETWEEN '2015-04-01' AND '2015-04-15 23:59:59.999'
UNION ALL
SELECT
  '2015-04-16 - 2015-04-23',
  INT(AVG(passenger_count)),
  ROUND(AVG(trip_distance),2),
  MIN(trip_distance),
  MAX(trip_distance)
FROM tutorial_sql.taxi_trip_data
WHERE pickup_datetime BETWEEN '2015-04-16' AND '2015-04-23 23:59:59.999'

A INTERSECT will combine data sets but only include the records found in both data sets, removing duplicate result records. INTERSECT ALL will retain duplicate intersecting records.

Set Intersection

-- Retrieve locations (as lat/lon pairs) that were both pick-up and drop-off
-- points
SELECT
  pickup_latitude AS latitude,
  pickup_longitude AS longitude
FROM tutorial_sql.taxi_trip_data_replicated
WHERE
  pickup_latitude <> 0 AND
  pickup_longitude <> 0
INTERSECT
SELECT
  dropoff_latitude,
  dropoff_longitude
FROM tutorial_sql.taxi_trip_data_replicated
ORDER BY latitude, longitude

A EXCEPT will return records that appear in the first data set but not the second data set. Note that the data sets on each side of the EXCEPT will have duplicates removed first, and then the set subtraction will be processed. EXCEPT ALL will retain duplicates from the first set.

Set Exception (Subtraction)

-- Show vendors that operate before noon, but not after noon:  retrieve the
-- unique list of IDs of vendors who provided cab rides between midnight and
-- noon, and remove from that list the IDs of any vendors who provided cab rides
-- between noon and midnight
SELECT vendor_id
FROM tutorial_sql.taxi_trip_data_replicated
WHERE HOUR(pickup_datetime) BETWEEN 0 AND 11
EXCEPT
SELECT vendor_id
FROM tutorial_sql.taxi_trip_data_replicated
WHERE HOUR(pickup_datetime) BETWEEN 12 AND 23

Truncate

Use TRUNCATE TABLE to remove all records from a table without deleting the table.

Truncate Table

TRUNCATE TABLE tutorial_sql.credit_payment

Complete Sample

Included below is a complete example containing all the above queries, the data files, and output.

The script can be run via any SQL client, including KiSQL, as follows:

Run Tutorial

$ kisql --url http://<db.host>:9191 --user <username> --file sql_tutorial.kisql

As this script creates a schema and several database objects within it, system admin permission is required to run it.

​Prerequisites

​Data File

​Creating a Type and Table

​Inserting Data

​Retrieving Data

​Updating Records

​Deleting Records

​Alter Table

​Indexes

​Dictionary Encoding

​Filters & Aggregates

​Subqueries

​Common Table Expression / With

​Joins

​Create Table As

​Union, Intersect, and Except

​Truncate

​Complete Sample

Prerequisites

Data File

Creating a Type and Table

Inserting Data

Retrieving Data

Updating Records

Deleting Records

Alter Table

Indexes

Dictionary Encoding

Filters & Aggregates

Subqueries

Common Table Expression / With

Joins

Create Table As

Union, Intersect, and Except

Truncate

Complete Sample