DML allows you to update and query data stored in OmniSci.

See Using Geospatial Objects: Geospatial Functions for details on geospatial functions.

INSERT

Use for single-row ad hoc inserts. (When inserting many rows, use the more efficient COPY command.)

INSERT INTO <destination_table> VALUES (<value>, ...);

INSERT INTO <table> (<column>, ...) VALUES (value, ...);

Examples

CREATE TABLE foo (a INT, b FLOAT, c TEXT, d TIMESTAMP);
INSERT INTO foo VALUES (NULL, 3.1415, 'xyz', '2015-05-11 211720');

You can also insert into a table as SELECT, as shown in the following examples:

INSERT INTO destination_table SELECT * FROM source_table;

INSERT INTO destination_table (id, name, age, gender) SELECT * FROM source_table;

INSERT INTO destination_table (name, gender, age, id) SELECT name, gender, age, id  FROM source_table;

INSERT INTO votes_summary (vote_id, vote_count) SELECT vote_id, sum(*) FROM votes GROUP_BY vote_id;

You can insert array literals into array columns. The inserts in the following example each have three array values, and demonstrate how you can:

• Create a table with variable-length and fixed-length array columns.

• Insert NULL arrays into these colums.

• Specify and inserty array literals using {...} or ARRAY[...] syntax.

• Insert empty variable-length arrays using{} and ARRAY[] syntax.

• Insert array values that contain NULL elements.

CREATE TABLE ar (ai INT[], af FLOAT[], ad2 DOUBLE[2]); 
INSERT INTO ar VALUES ({1,2,3},{4.0,5.0},{1.2,3.4}); 
INSERT INTO ar VALUES (ARRAY[NULL,2],NULL,NULL); 
INSERT INTO ar VALUES (NULL,{},{2.0,NULL});

SELECT

query:
  |   WITH withItem [ , withItem ]* query
  |   {
          select
      }
      [ ORDER BY orderItem [, orderItem ]* ]
      [ LIMIT [ start, ] { count | ALL } ]
      [ OFFSET start { ROW | ROWS } ]
​
withItem:
      name
      [ '(' column [, column ]* ')' ]
      AS '(' query ')'
​
orderItem:
      expression [ ASC | DESC ] [ NULLS FIRST | NULLS LAST ]
​
select:
      SELECT [ DISTINCT ] [/*+ hints */]
          { * | projectItem [, projectItem ]* }    
      FROM tableExpression
      [ WHERE booleanExpression ]
      [ GROUP BY { groupItem [, groupItem ]* } ]
      [ HAVING booleanExpression ]
      [ WINDOW window_name AS ( window_definition ) [, ...] ]
​
projectItem:
      expression [ [ AS ] columnAlias ]
  |   tableAlias . *
​
tableExpression:
      tableReference [, tableReference ]*
  |   tableExpression [ ( LEFT ) [ OUTER ] ] JOIN tableExpression [ joinCondition ]
​
joinCondition:
      ON booleanExpression
  |   USING '(' column [, column ]* ')'
​
tableReference:
      tablePrimary
      [ [ AS ] alias ]
​
tablePrimary:
      [ catalogName . ] tableName
  |   '(' query ')'
​
groupItem:
      expression
  |   '(' expression [, expression ]* ')'

For more information, see SELECT.

ORDER BY

• Sort order defaults to ascending (ASC).

• Sorts null values after non-null values by default in an ascending sort, before non-null values in a descending sort. For any query, you can use NULLS FIRST to sort null values to the top of the results or NULLS LAST to sort null values to the bottom of the results.

• Allows you to use a positional reference to choose the sort column. For example, the command SELECT colA,colB FROM table1 ORDER BY 2 sorts the results on colB because it is in position 2.

SELECT Hints

Currently, the only supported SELECT hint is /*+ cpu_mode */ , which forces query execution on CPU, even if a GPU is available. You can use the cpu_mode hint when:

• You know in advance that that running on CPU is more efficient.

• You want to avoid overhead related to a query being punted from GPU to CPU because of a potential runtime failure.

Syntax

Enable cpu_mode hint for a SELECT statement:

SELECT /*+ cpu_mode */ FROM ...;

All SQL hints affect the entire query. For example, if you define the cpu_mode hint in a subquery SELECT clause, it affects to the entire query.

For example, both the WITH clause and main SELECT clause are run on the CPU.

WITH CTE AS (SELECT ... FROM ...) SELECT /*+ cpu_mode */ FROM CTE;

Here, both the subquery and outer query are run on the CPU.

SELECT ... FROM (SELECT /*+ cpu_mode */ FROM ...);
SELECT /*+ cpu_mode */ ... FROM (SELECT ... FROM ...) WHERE ...;

UPDATE

UPDATE table_name SET assign [, assign ]* [ WHERE booleanExpression ]

Changes the values of the specified columns based on the assign argument (identifier=expression) in all rows that satisfy the condition in the WHERE clause.

Example

UPDATE UFOs SET shape='ovate' where shape='eggish';

Update Via Subquery

You can update a table via subquery, which allows you to update based on calculations performed on another table.

Examples

UPDATE test_facts SET lookup_id = (SELECT SAMPLE(test_lookup.id) 
FROM test_lookup WHERE test_lookup.val = test_facts.val);

UPDATE test_facts SET val = val+1, lookup_id = (SELECT SAMPLE(test_lookup.id)
FROM test_lookup WHERE test_lookup.val = test_facts.val);

UPDATE test_facts SET lookup_id = (SELECT SAMPLE(test_lookup.id) 
FROM test_lookup WHERE test_lookup.val = test_facts.val) WHERE id < 10;

DELETE

DELETE FROM table_name [ * ] [ [ AS ] alias ]
[ WHERE condition ]

Deletes rows that satisfy the WHERE clause from the specified table. If the WHERE clause is absent, all rows in the table are deleted, resulting in a valid but empty table.

EXPLAIN

Shows generated Intermediate Representation (IR) code, identifying whether it is executed on GPU or CPU. This is primarily used internally by OmniSci to monitor behavior.

EXPLAIN <STMT>

For example, when you use the EXPLAIN command on a basic statement, the utility returns 90 lines of IR code that is not meant to be human readable. However, at the top of the listing, a heading indicates whether it is IR for the CPU or IR for the GPU, which can be useful to know in some situations.

EXPLAIN CALCITE

Returns a relational algebra tree describing the high-level plan to execute the statement.

EXPLAIN CALCITE <STMT>

The table below lists the relational algebra classes used to describe the execution plan for a SQL statement.

For example, a SELECT statement is described as a table scan and projection.

omnisql> EXPLAIN CALCITE (SELECT * FROM movies);
Explanation
LogicalProject(movieId=[$0], title=[$1], genres=[$2])
   LogicalTableScan(TABLE=[[CATALOG, omnisci, MOVIES]])

If you add a sort order, the table projection is folded under a LogicalSort procedure.

omnisql> EXPLAIN calcite (SELECT * FROM movies ORDER BY title);
Explanation
LogicalSort(sort0=[$1], dir0=[ASC])
   LogicalProject(movieId=[$0], title=[$1], genres=[$2])
      LogicalTableScan(TABLE=[[CATALOG, omnisci, MOVIES]])

When the SQL statement is simple, the EXPLAIN CALCITE version is actually less “human readable.” EXPLAIN CALCITE is more useful when you work with more complex SQL statements, like the one that follows. This query performs a scan on the BOOK table before scanning the BOOK_ORDER table.

omnisql> EXPLAIN calcite SELECT bc.firstname, bc.lastname, b.title, bo.orderdate, s.name
FROM book b, book_customer bc, book_order bo, shipper s
WHERE bo.cust_id = bc.cust_id AND b.book_id = bo.book_id AND bo.shipper_id = s.shipper_id
AND s.name = 'UPS';
Explanation
LogicalProject(firstname=[$5], lastname=[$6], title=[$2], orderdate=[$11], name=[$14])
    LogicalFilter(condition=[AND(=($9, $4), =($0, $8), =($10, $13), =($14, 'UPS'))])
        LogicalJoin(condition=[true], joinType=[INNER])
            LogicalJoin(condition=[true], joinType=[INNER])
                LogicalJoin(condition=[true], joinType=[INNER])
                    LogicalTableScan(TABLE=[[CATALOG, omnisci, BOOK]])
                    LogicalTableScan(TABLE=[[CATALOG, omnisci, BOOK_CUSTOMER]])
                LogicalTableScan(TABLE=[[CATALOG, omnisci, BOOK_ORDER]])
            LogicalTableScan(TABLE=[[CATALOG, omnisci, SHIPPER]])

Revising the original SQL command results in a more natural selection order and a more performant query.

omnisql> EXPLAIN calcite SELECT bc.firstname, bc.lastname, b.title, bo.orderdate, s.name
FROM book_order bo, book_customer bc, book b, shipper s
WHERE bo.cust_id = bc.cust_id AND bo.book_id = b.book_id AND bo.shipper_id = s.shipper_id
AND s.name = 'UPS';
Explanation
LogicalProject(firstname=[$10], lastname=[$11], title=[$7], orderdate=[$3], name=[$14])
    LogicalFilter(condition=[AND(=($1, $9), =($5, $0), =($2, $13), =($14, 'UPS'))])
        LogicalJoin(condition=[true], joinType=[INNER])
            LogicalJoin(condition=[true], joinType=[INNER])
                LogicalJoin(condition=[true], joinType=[INNER])
                  LogicalTableScan(TABLE=[[CATALOG, omnisci, BOOK_ORDER]])
                  LogicalTableScan(TABLE=[[CATALOG, omnisci, BOOK_CUSTOMER]])
                LogicalTableScan(TABLE=[[CATALOG, omnisci, BOOK]])
            LogicalTableScan(TABLE=[[CATALOG, omnisci, SHIPPER]])

SHOW

Use SHOW commands to get information about databases, tables, and user sessions.

Window Functions

Window functions allow you to work with a subset of rows related to the currently selected row. For a given dimension, you can find the most associated dimension by some other measure (for example, number of records or sum of revenue).

Window functions must always contain an OVER clause. The OVER clause splits up the rows of the query for processing by the window function.

The PARTITION BY list divides the rows into groups that share the same values of the PARTITION BY expression(s). For each row, the window function is computed using all rows in the same partition as the current row.

Rows that have the same value in the ORDER BY clause are considered peers. The ranking functions give the same answer for any two peer rows.

Usage Notes

• OmniSciDB supports the aggregate functions AVG, MIN, MAX, SUM, and COUNT in window functions.

• OmniSciDB does not support empty partitions. For example, the following query triggers an exception because the OVER clause requires a PARTITION BY list:

  SELECT dest, ntile(4) OVER (ORDER BY total_count DESC) AS quartile FROM my_test_data.

• Window functions only work on single fragment datasets. If you want to run window functions over base data in your table, you must ensure there is only one fragment (by increasing the fragment size to be greater than the number of rows expected in the table before import). If you are running the window function on top of an intermediate result (for example, a GROUP BY), the intermediate result is contained in a single fragment, even if the underlying table contains multiple fragments. This happens automatically if a GROUP BY clause is part of the window function query.

Example

This query shows the top airline carrier for each state, based on the number of departures.

select origin_state, carrier_name, n 
   from (select origin_state, carrier_name, row_number() over(
      partition by origin_state order by n desc) as rownum, n 
         from (select origin_state, carrier_name, count(*) as n 
            from flights_2008_7M where extract(year 
               from dep_timestamp) = 2008 
   group by origin_state, carrier_name )) where rownum = 1

Table Expression and Join Support

<table> , <table> WHERE <column> = <column>
<table> [ LEFT ] JOIN <table> ON <column> = <column>

If a join column name or alias is not unique, it must be prefixed by its table name.

You can use BIGINT, INTEGER, SMALLINT, TINYINT, DATE, TIME, TIMESTAMP, or TEXT ENCODING DICT data types. TEXT ENCODING DICT is the most efficient because corresponding dictionary IDs are sequential and span a smaller range than, for example, the 65,535 values supported in a SMALLINT field. Depending on the number of values in your field, you can use TEXT ENCODING DICT(32) (up to approximately 2,150,000,000 distinct values), TEXT ENCODING DICT(16) (up to 64,000 distinct values), or TEXT ENCODING DICT(8) (up to 255 distinct values). For more information, see Data Types and Fixed Encoding.

Geospatial Joins

By default, a join involving a geospatial operator (such as ST_Contains) utilizes the loop join framework.

To allow all loop joins, set the allow-loop-joins flag to true at either the command line when starting OmniSci, or in omnisci.conf. Running geo join queries without allow-loop-joins set to true results in the following error:

Hash join failed: no equijoin expression found.

If you set trivial-loop-join-threshold, loop joins are allowed if the inner table has fewer rows than the trivial join loop threshold you specify. The default value is 1,000 rows.

For geospatial joins, the inner table should always be the more complicated primitive. For example, for ST_Contains(polygon, point), the point table should be the outer table and the polygon table should be the inner table.

Using Joins in a Distributed Environment

You can create joins in a distributed environment in two ways:

• Replicate small dimension tables that are used in the join.

• Create a shard key on the column used in the join (note that there is a limit of one shard key per table). If the column involved in the join is a TEXT ENCODED field, you must create a SHARED DICTIONARY that references the FACT table key you are using to make the join.

# Table customers is very small
CREATE TABLE sales (
id INTEGER,
customerid TEXT ENCODING DICT(32),
saledate DATE ENCODING DAYS(32),
saleamt DOUBLE);
​
CREATE TABLE customers (
id TEXT ENCODING DICT(32),
someid INTEGER,
name TEXT ENCODING DICT(32))
WITH (partitions = 'replicated') #this causes the entire contents of this table to be replicated to each leaf node. Only recommened for small dimension tables.
SELECT c.id, c.name from sales s inner join customers c on c.id = s.customerid limit 10;

CREATE TABLE sales (
id INTEGER,
customerid BIGINT, #note the numeric datatype, so we don't need to specify a shared dictionary on the customer table
saledate DATE ENCODING DAYS(32),
saleamt DOUBLE,
SHARD KEY (customerid))
WITH (SHARD_COUNT = <num gpus in cluster>)
​
CREATE TABLE customers (
id TEXT BIGINT,
someid INTEGER,
name TEXT ENCODING DICT(32)
SHARD KEY (id))
WITH (SHARD_COUNT=<num gpus in cluster>);
​
SELECT c.id, c.name FROM sales s INNER JOIN customers c ON c.id = s.customerid LIMIT 10;

CREATE TABLE sales (
id INTEGER,
customerid TEXT ENCODING DICT(32),
saledate DATE ENCODING DAYS(32),
saleamt DOUBLE,
SHARD KEY (customerid))
WITH (SHARD_COUNT = <num gpus in cluster>)
​
#note the difference when customerid is a text encoded field:
​
CREATE TABLE customers (
id TEXT,
someid INTEGER,
name TEXT ENCODING DICT(32),
SHARD KEY (id),
SHARED DICTIONARY (id) REFERENCES sales(customerid))
WITH (SHARD_COUNT = <num gpus in cluster>)
​
SELECT c.id, c.name FROM sales s INNER JOIN customers c ON c.id = s.customerid LIMIT 10;
​

Logical Operator Support

Conditional Expression Support

Subquery Expression Support

Usage Notes

• You can use a subquery anywhere an expression can be used, subject to any runtime constraints of that expression. For example, a subquery in a CASE statement must return exactly one row, but a subquery can return multiple values to an IN expression.

• You can use a subquery anywhere a table is allowed (for example, FROM subquery), using aliases to name any reference to the table and columns returned by the subquery.

Type Cast Support

The following table shows cast type conversion support.

Array Support

OmniSci supports arrays in dictionary-encoded text and number fields (TINYINT, SMALLINT, INTEGER, BIGINT, FLOAT, and DOUBLE). Data stored in arrays are not normalized. For example, {green,yellow} is not the same as {yellow,green}. As with many SQL-based services, OmniSci array indexes are 1-based.

OmniSci supports NULL variable-length arrays for all integer and floating-point data types, including dictionary-encoded string arrays. For example, you can insert NULL into BIGINT[ ], DOUBLE[ ], or TEXT[ ] columns. OmniSci supports NULL fixed-length arrays for all integer and floating-point data types, but not for dictionary-encoded string arrays. For example, you can insert NULL into BIGINT[2] DOUBLE[3], but not into TEXT[2] columns.

Examples

The following examples show query results based on the table test_array created with the following statement:

CREATE TABLE test_array (name TEXT ENCODING DICT(32),colors TEXT[] ENCODING DICT(32), qty INT[]);

omnisql> SELECT * FROM test_array;
name|colors|qty
Banana|{green, yellow}|{1, 2}
Cherry|{red, black}|{1, 1}
Olive|{green, black}|{1, 0}
Onion|{red, white}|{1, 1}
Pepper|{red, green, yellow}|{1, 2, 3}
Radish|{red, white}|{}
Rutabaga|NULL|{}
Zucchini|{green, yellow}|{NULL}

omnisql> SELECT UNNEST(colors) AS c FROM test_array;
Exception: UNNEST not supported in the projection list yet.

omnisql> SELECT UNNEST(colors) AS c, count(*) FROM test_array group by c;
c|EXPR$1
green|4
yellow|3
red|4
black|2
white|2

omnisql> SELECT name, colors [2] FROM test_array;
name|EXPR$1
Banana|yellow
Cherry|black
Olive|black
Onion|white
Pepper|green
Radish|white
Rutabaga|NULL
Zucchini|yellow

omnisql> SELECT name, colors FROM test_array WHERE colors[1]='green';
name|colors
Banana|{green, yellow}
Olive|{green, black}
Zucchini|{green, yellow}

omnisql> SELECT * FROM test_array WHERE colors IS NULL;
name|colors|qty
Rutabaga|NULL|{}