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', '20150511 211720`);
SELECT
¶
[ WITH <alias> AS <query>,... ]
SELECT [ALLDISTINCT] <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 nestedloop 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 tablevalued 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 fromlist, 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 base10 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 doubleprecision floating point functions, singleprecision floating point functions are also provided. Singleprecision functions can run faster on graphics cards but might cause overflow errors.
Doubleprecision FP Function  Singleprecision 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 WGS84 positions. 
CONV_4326_900913_X(x)  Convert WGS84 latitude to WGS84 Web Mercator x coordinate. 
CONV_4326_900913_Y(y)  Convert WGS84 longitude to WGS84 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' 
caseinsensitive LIKE 
str REGEXP POSIX pattern  '^[az]+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 ‘2008131’ + INTERVAL ‘1’ YEAR DATE ‘20080301’  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  YYYYMMDD  20131031 
DATE  MM/DD/YYYY  10/31/2013 
DATE  DDMONYY  31Oct13 
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  31Oct13 23:49:01 
TIMESTAMP  DATETTIME  31Oct13T23:49:01 
TIMESTAMP  DATE:TIME  11/31/2013:234901 
TIMESTAMP  DATE TIME ZONE  31Oct13 11:30:25 0800 
TIMESTAMP  DATE HH.MM.SS PM  31Oct13 11.30.25pm 
TIMESTAMP  DATE HH:MM:SS PM  31Oct13 11:30:25pm 
TIMESTAMP  1383262225 
Usage Notes
 For twodigit years, years 6999 are assumed to be previous century (e.g. 1969), and 068 are assumed to be current century (016).
 For fourdigit years, negative years (e.g. BC) are not supported.
 Hours are expressed in 24hour 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 YYYYMMDD. 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 operator 
COALESCE(val1, val2, ..)  returns the first nonnull 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 runtime 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. 

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. 

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.