SQL Capabilities

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'; 

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. For more information, see DELETE.

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]])

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

There are two ways of creating joins in a distributed environment.

1. Replicate small dimension tables that are used in the join.
2. Create a shard key on the column used in the join (note that there is a limit of one shard key per table).
   1. 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.

Scenario 1: one table (Customers) is a very small table.

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;

Scenario 2: both tables are large and are joined using a shard key.

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=);

select c.id, c.name from sales s inner join customers c on c.id = s.customerid limit 10;

Scenario 2a: both tables are large and are joined on a TEXT ENCODED column.

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 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.

omnisql> \d arr
CREATE TABLE arr (
sia SMALLINT[])
omnisql> select sia, CARDINALITY(sia) from arr;
sia|EXPR$0
NULL|NULL
{}|0
{NULL}|1
{1}|1
{2,2}|2
{3,3,3}|3

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|{}

The following queries use arrays in an INTEGER field:

omnisql> SELECT name, qty FROM test_array WHERE qty[2] >1;
name|qty
Banana|{1, 2}
Pepper|{1, 2, 3}

omnisql> SELECT name, qty FROM test_array WHERE 15< ALL qty;
No rows returned.

omnisql> SELECT name, qty FROM test_array WHERE 2 = ANY qty;
name|qty
Banana|{1, 2}
Pepper|{1, 2, 3}

omnisql> SELECT COUNT(*) FROM test_array WHERE qty IS NOT NULL;
EXPR$0
8

omnisql> SELECT COUNT(*) FROM test_array WHERE CARDINALITY(qty)>0;
EXPR$0
6

LIKELY/UNLIKELY

Usage Notes

SQL normally assumes that terms in the WHERE clause that cannot be used by indices are usually true. If this assumption is incorrect, it could lead to a suboptimal query plan. Use the LIKELY(X) and UNLIKELY(X) SQL functions to provide hints to the query planner about clause terms that are probably not true, which helps the query planner to select the best possible plan.

Use LIKELY/UNLIKELY to optimize evaluation of OR/AND logical expressions. LIKELY/UNLIKELY causes the left side of an expression to be evaluated first. This allows the right side of the query to be skipped when possible. For example, in the clause UNLIKELY(A) AND B, if A evaluates to FALSE, B does not need to be evaluated.

Consider the following:

SELECT COUNT(*) FROM test WHERE UNLIKELY(x IN (7, 8, 9, 10)) AND y > 42;

If x is one of the values 7, 8, 9, or 10, the filter y > 42 is applied. If x is not one of those values, the filter y > 42 is not applied.