Data Manipulation (DML)¶

`INSERT`¶

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

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

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

`SELECT`¶

[ WITH <alias> AS <query>,... ]
SELECT [ALL|DISTINCT] <expr> [AS [<alias>]], ...
  FROM <table> [ <alias> ], ...
  [WHERE <expr>]
  [GROUP BY <expr>, ...]
  [HAVING <expr>]
  [ORDER BY <expr> [ ASC | DESC ] , ...]
  [LIMIT {<number>|ALL} [OFFSET <number> [ROWS]]]
  [ANY | ALL (subquery) ;

Usage Notes

ORDER BY sort order defaults to ASC.
ORDER BY 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.

`EXPLAIN`¶

Shows generated Intermediate Representation (IR) code, identifying whether it is executed on GPU or CPU. This is primarily used internally by MapD 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. At the top of the listing, though, 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.

Method \| Description
LogicalAggregate	LogicalAggregate is a relational operator that eliminates duplicates and computes totals.
LogicalCalc	A relational expression that computes project expressions and also filters.
LogicalChi	Relational operator that converts a stream to a relation.
LogicalCorrelate	A relational operator that performs nested-loop joins.
LogicalDelta	Relational operator that converts a relation to a stream.
LogicalExchange	Relational expression that imposes a particular distribution on its input without otherwise changing its content.
LogicalFilter	Relational expression that iterates over its input and returns elements for which a condition evaluates to true.
LogicalIntersect	Relational expression that returns the intersection of the rows of its inputs.
LogicalJoin	Relational expression that combines two relational expressions according to some condition.
LogicalMatch	Relational expression that represents a MATCH_RECOGNIZE node.
LogicalMinus	Relational expression that returns the rows of its first input minus any matching rows from its other inputs. Corresponds to the SQL EXCEPT operator.
LogicalProject	Relational expression that computes a set of ‘select expressions’ from its input relational expression.
LogicalSort	Relational expression that imposes a particular sort order on its input without otherwise changing its content.
LogicalTableFunctionScan	Relational expression that calls a table-valued function.
LogicalTableModify	Relational expression that modifies a table. It is similar to TableScan, but represents a request to modify a table rather than read from it.
LogicalTableScan	A LogicalTableScan reads all the rows from a RelOptTable.
LogicalUnion	Relational expression that returns the union of the rows of its inputs, optionally eliminating duplicates.
LogicalValues	Relational expression whose value is a sequence of zero or more literal row values.
LogicalWindow	A relational expression representing a set of window aggregates.

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

mapdql> explain calcite (select * from movies);
Explanation
LogicalProject(movieId=[$0], title=[$1], genres=[$2])
   LogicalTableScan(table=[[CATALOG, mapd, MOVIES]])

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

mapdql> explain calcite (select * from movies order by title);
Explanation
LogicalSort(sort0=[$1], dir0=[ASC])
   LogicalProject(movieId=[$0], title=[$1], genres=[$2])
      LogicalTableScan(table=[[CATALOG, mapd, MOVIES]])

When the SQL statement is simple, the EXPLAIN CALCITE version is actually less “human readable.” The value of EXPLAIN CALCITE becomes clear as 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.

mapdql> 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, mapd, BOOK]])
                    LogicalTableScan(table=[[CATALOG, mapd, BOOK_CUSTOMER]])
                LogicalTableScan(table=[[CATALOG, mapd, BOOK_ORDER]])
            LogicalTableScan(table=[[CATALOG, mapd, SHIPPER]])

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

mapdql> 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, mapd, BOOK_ORDER]])
                  LogicalTableScan(table=[[CATALOG, mapd, BOOK_CUSTOMER]])
                LogicalTableScan(table=[[CATALOG, mapd, BOOK]])
            LogicalTableScan(table=[[CATALOG, mapd, SHIPPER]])

Table Expression and Join Support¶

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

Usage Notes

If join column names or aliases are not unique, they must be prefixed by their table name.
Data types of join columns must be SMALLINT, INTEGER, BIGINT, or TEXT/VARCHAR ENCODING DICT.
Data types of join columns must match exactly. For example a SMALLINT column cannot be joined to a BIGINT column.
For all but the first table list in the from-list, the data values in the join column must be unique. In data warehouse terms, list the “fact” table first, followed by any number of “dimension” tables.

Logical Operator Support¶

Operator	Description
AND	logical AND
NOT	negates value
OR	logical OR

Comparison Operator Support¶

Operator	Description
=	equals
<>	not equals
>	greater than
>=	greater than or equal to
<	less than
<=	less than or equal to
BETWEEN x AND y	is a value within a range
NOT BETWEEN x AND y	is a value not within a range
IS NULL	is a value null
IS NOT NULL	is a value not null

Mathematical Function Support¶

Function	Description
ABS(x)	returns the absolute value of x
CEIL(x)	returns the smallest integer not less than the argument
DEGREES(x)	converts radians to degrees
EXP(x)	returns the value of e to the power of x
FLOOR(x)	returns the largest integer not greater than the argument
LN(x)	returns the natural logarithm of x
LOG(x)	returns the natural logarithm of x
LOG10(x)	returns the base-10 logarithm of the specified float expression x
MOD(x, y)	returns the remainder of x divided by y
PI()	returns the value of pi
POWER(x, y)	returns the value of x raised to the power of y
RADIANS(x)	converts degrees to radians
ROUND(x)	rounds x to the nearest integer value, but does not change the data type. For example, the double value 4.1 rounds to the double value 4.
ROUND_TO_DIGIT (x, y)	rounds x to y decimal places
SIGN(x)	returns the sign of x as -1, 0, 1 if x is negative, zero, or positive
TRUNCATE(x, y)	truncates x to y decimal places

Statistical Function Support¶

In addition to standard double-precision floating point functions, single-precision floating point functions are also provided. Single-precision functions can run faster on graphics cards but might cause overflow errors.

Double-precision FP Function	Single-precision FP Function	Description
CORRELATION(x, y)	CORRELATION_FLOAT(x, y)	Alias of CORR. Returns the coefficient of correlation of a set of number pairs.
CORR(x, y)	CORR_FLOAT(x, y)	Returns the coefficient of correlation of a set of number pairs.
COVAR_POP(x, y)	COVAR_POP_FLOAT(x, y)	Returns the population covariance of a set of number pairs.
COVAR_SAMP(x, y)	COVAR_SAMP_FLOAT(x, y)	Returns the sample covariance of a set of number pairs.
STDDEV(x)	STDDEV_FLOAT(x)	Alias of STDDEV_SAMP. Returns sample standard deviation of the value.
STDDEV_POP(x)	STDDEV_POP_FLOAT(x)	Returns the population standard the standard deviation of the value.
STDDEV_SAMP(x)	STDDEV_SAMP_FLOAT(x)	Returns the sample standard deviation of the value.
VARIANCE(x)	VARIANCE_FLOAT(x)	Alias of VAR_SAMP. Returns the sample variance of the value.
VAR_POP(x)	VAR_POP_FLOAT(x)	Returns the population variance sample variance of the value.
VAR_SAMP(x)	VAR_SAMP_FLOAT(x)	Returns the sample variance of the value.

Trigonometric Function Support¶

Function	Description
ACOS(x)	returns the arc cosine of x
ASIN(x)	returns the arc sine of x
ATAN(x)	returns the arc tangent of x
ATAN2(x, y)	returns the arc tangent of x and y
COS(x)	returns the cosine of x
COT(x)	returns the cotangent of x
SIN(x)	returns the sine of x
TAN(x)	returns the tangent of x

Geometric Function Support¶

Function	Description
DISTANCE_IN_METERS(fromLon, fromLat, toLon, toLat)	Calculate distance in meters between two WGS-84 positions.
CONV_4326_900913_X(x)	Convert WGS-84 latitude to WGS-84 Web Mercator x coordinate.
CONV_4326_900913_Y(y)	Convert WGS-84 longitude to WGS-84 Web Mercator y coordinate.

String Function Support¶

Function	Description
CHAR_LENGTH(str)	returns the number of characters in a string
LENGTH(str)	returns the length of a string in bytes

Pattern Matching Support¶

Name	Example	Description
str LIKE pattern	`'ab' LIKE 'ab'`	returns true if the string matches the pattern
str NOT LIKE pattern	`'ab' NOT LIKE 'cd'`	returns true if the string does not match the pattern
str ILIKE pattern	`'AB' ILIKE 'ab'`	case-insensitive LIKE
str REGEXP POSIX pattern	`'^[a-z]+r$'`	lower case string ending with r
REGEXP_LIKE ( str , POSIX pattern )	`'^[hc]at'`	cat or hat

Wildcard characters supported by LIKE and ILIKE:

% matches any number of characters, including zero characters

_ matches exactly one character

Date/Time Function Support¶

Function	Description
DATE_TRUNC(date_part, timestamp)	Truncates the timestamp to the specified date_part.
EXTRACT(date_part FROM timestamp)	Returns the specified date_part from timestamp.
INTERVAL count date_part	Adds or Subtracts count date_part units from a timestamp. Examples: DATE ‘2008-1-31’ + INTERVAL ‘1’ YEAR DATE ‘2008-03-01’ - INTERVAL ‘1’ DAY
NOW()	Returns the current timestamp.
TIMESTAMPADD(date_part, count, timestamp \| date)	Adds an interval of count date_part to timestamp or date and returns signed date_part units in the provided timestamp or date form.
TIMESTAMPDIFF(date_part, timestamp1, timestamp2)	Subtracts timestamp1 from timestamp2 and returns the result in signed date_part units.

Supported date_part types:

DATE_TRUNC [YEAR, QUARTER, MONTH, DAY, HOUR, MINUTE, SECOND,
            MILLENNIUM, CENTURY, DECADE, WEEK, QUARTERDAY]
EXTRACT    [YEAR, QUARTER, MONTH, DAY, HOUR, MINUTE, SECOND,
            DOW, ISODOW, DOY, EPOCH, QUARTERDAY, WEEK]

Accepted Date, Time, and Timestamp formats¶

Datatype	Formats	Examples
DATE	YYYY-MM-DD	2013-10-31
DATE	MM/DD/YYYY	10/31/2013
DATE	DD-MON-YY	31-Oct-13
DATE	DD/Mon/YYYY	31/Oct/2013
TIME	HH:MM	23:49
TIME	HHMMSS	234901
TIME	HH:MM:SS	23:49:01
TIMESTAMP	DATE TIME	31-Oct-13 23:49:01
TIMESTAMP	DATETTIME	31-Oct-13T23:49:01
TIMESTAMP	DATE:TIME	11/31/2013:234901
TIMESTAMP	DATE TIME ZONE	31-Oct-13 11:30:25 -0800
TIMESTAMP	DATE HH.MM.SS PM	31-Oct-13 11.30.25pm
TIMESTAMP	DATE HH:MM:SS PM	31-Oct-13 11:30:25pm
TIMESTAMP		1383262225

Usage Notes

For two-digit years, years 69-99 are assumed to be previous century (e.g. 1969), and 0-68 are assumed to be current century (016).
For four-digit years, negative years (e.g. BC) are not supported.
Hours are expressed in 24-hour format.
When time components are separated by colons, you can write them as one or two digits.
Months are case insensitive. You can spell them out or abbreviate to three characters.
For timestamps, decimal seconds are ignored. Time zone offsets are written as +/-HHMM.
For timestamps, a numeric string is converted to +/- seconds since January 1, 1970.
On output, dates are formatted as YYYY-MM-DD. Times are formatted as HH:MM:SS.

Aggregate Function Support¶

Function	Description
AVG(x)	returns the average value of x
COUNT()	returns the count of the number of rows returned
COUNT(DISTINCT x)	returns the count of distinct values of x
APPROX_COUNT_DISTINCT(x)	returns the approximate count of distinct values of x
MAX(x)	returns the maximum value of x
MIN(x)	returns the minimum value of x
SUM(x)	returns the sum of the values of x

Usage Notes

COUNT(DISTINCT x ), especially when used in conjunction with GROUP BY, can require a very large amount of memory to keep track of all distinct values in large tables with large cardinality. To avoid this large overhead APPROX_COUNT_DISTINCT (x) gives a very close approximation (within 4%) of the distinct values for a column while keeping performance and memory usage reasonable. It is recommended on large table with large cardinalities to use APPROX_COUNT_DISTINCT when possible.

Conditional Expression Support¶

Expression	Description
CASE WHEN condition THEN result ELSE default	Case operator
COALESCE(val1, val2, ..)	returns the first non-null value in the list

Subquery Expression Support¶

Expression	Example	Description
IN	expr IN (subquery or list of values)	evaluates whether expr equals any value of the IN list
NOT IN	expr NOT IN (subquery or list of values)	evaluates whether expr does not equal any value of the IN list

Usage Notes

A subquery may be used anywhere an expression may be used, subject to any run-time constraints of that expression. For example, a subquery in a CASE statement must return exactly one row, but a subquery may return multiple values to an IN expression.
A subquery may be used anywhere a table is allowed (e.g. FROM subquery), making use of aliases to name any reference to the table and columns returned by the subquery.

Type Cast Support¶

Expression	Example	Description
CAST(expr AS type)	CAST(1.25 AS FLOAT)	converts an expression to another data type

Array Support¶

Expression	Description
SELECT <ArrayCol>[n] ...	Query array elements n of column `ArrayCol`
UNNEST(<ArrayCol>) ...	Expand the array `ArrayCol` to a set of rows.
SELECT <some_column> FROM <your_table> WHERE <test> = ANY <hash_tags>	ANY compares a scalar value with a single set of values (in the text array <hash_tags>), and returns TRUE when the result contains at least one item. ANY must be preceded by a comparison operator.
SELECT <some_column> FROM <your_table> WHERE <test> = ALL <hash_tags>	ALL compares a scalar value with a single set of values (in the text array <hash_tags>), and returns TRUE when the result specified is TRUE for all items in the array. ALL must be preceded by a comparison operator.
ARRAYINDEX(row_index)	Returns a value from a specific location in an array.

`LIKELY/UNLIKELY`¶

Expression	Description
LIKELY(X)	The `LIKELY(X)` function provides a hint to the query planner that argument X is a boolean value that is usually true. The planner can prioritize filters on the value X earlier in the execution cycle and return significant results more efficiently.
UNLIKELY(X)	The `UNLIKELY(X)` function provides a hint to the query planner that argument X is a boolean value that is usually not true. The planner can prioritize filters on the value X later in the execution cycle and return significant results more efficiently.

Usage Notes

SQL normally assumes that terms in the WHERE clause that cannot be used by indices have a strong probability of being true. If this assumption is incorrect, it could lead to a suboptimal query plan. You can 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.

Data Manipulation (DML)¶

INSERT¶

SELECT¶

EXPLAIN¶

EXPLAIN CALCITE¶

Table Expression and Join Support¶

Logical Operator Support¶

Comparison Operator Support¶

Mathematical Function Support¶

Statistical Function Support¶

Trigonometric Function Support¶

Geometric Function Support¶

String Function Support¶

Pattern Matching Support¶

Date/Time Function Support¶

Accepted Date, Time, and Timestamp formats¶

Aggregate Function Support¶

Conditional Expression Support¶

Subquery Expression Support¶

Type Cast Support¶

Array Support¶

LIKELY/UNLIKELY¶

`INSERT`¶

`SELECT`¶

`EXPLAIN`¶

`EXPLAIN CALCITE`¶

`LIKELY/UNLIKELY`¶