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 ] , ...] [ NULLS FIRST | NULLS LAST ]
  [LIMIT {<number>|ALL} [OFFSET <number> [ROWS]]]
  [ANY | ALL (subquery) ;

Usage Notes

  • 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.

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 Relational operator that eliminates duplicates and computes totals
LogicalCalc Relational expression that computes project expressions and also filters
LogicalChi Relational operator that converts a stream to a relation
LogicalCorrelate 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 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 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.” 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.

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 a join column name or alias is not unique, it must be prefixed by its table name.
  • The data type 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
NULLIF(x, y) Compare expressions x and y. If different, return x. If they are the same, return null. For example, if a dataset uses ‘NA’ for null values, you can use this statement to return null using SELECT NULLIF(field_name,'NA').

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

Both double-precision (standard) and single-precision floating point functions are provided. Single-precision functions run faster on GPUs 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) Calculates distance in meters between two WGS-84 positions.
CONV_4326_900913_X(x) Converts WGS-84 latitude to WGS-84 Web Mercator x coordinate.
CONV_4326_900913_Y(y) Converts 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$' Lowercase string ending with r
REGEXP_LIKE ( str , POSIX pattern ) '^[hc]at' cat or hat

Usage Notes

The following wildcard characters are 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.
DATEDIFF(date_part, timestamp, timestamp) returns the difference between two timestamps, calculated to the lowest level of the date_part you specify. For example, if you set the date_part as day, only the year, month, and day are used to calculate the result. Other fields, such as hour and minute, are ignored.
DATEADD(date_part, interval, timestamp) returns a date after a specified time/date interval has been added.
DATEPART(interval, timestamp) Returns a specified part of a given date as an integer value.

Usage Notes

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]
DATEDIFF   [YEAR, QUARTER, MONTH, DAY, HOUR, MINUTE, SECOND,
            MILLENNIUM, CENTURY, DECADE, WEEK, QUARTERDAY]

Supported interval types:

DATEADD     [YEAR, QUARTER, MONTH, DAYOFYEAR, DAY, WEEK, WEEKDAY, HOUR,
             MINUTE, SECOND, MILLISECOND]
DATEPART    [YEAR, QUARTER, MONTH, DAYOFYEAR, DAY, WEEK, WEEKDAY, HOUR,
             MINUTE, SECOND, MILLISECOND]

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 (for example, 1969), and 0-68 are assumed to be current century (for example, 2016).
  • For four-digit years, negative years (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, e) Returns the approximate count of distinct values of x with defined expected error rate e
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 cardinalities. To avoid this large overhead, use APPROX_COUNT_DISTINCT.

  • APPROX_COUNT_DISTINCT(x, e) gives an approximate count of the value x, based on an expected error rate defined in e. The error rate is an integer value from 1 to 100. The lower the value of e, the higher the precision, and the higher the memory cost. Select a value for e based on the level of precision required. On large tables with large cardinalities, consider using APPROX_COUNT_DISTINCT when possible to preserve memory.

    Set the default error rate using the -hll-precision-bits configuration paramenter.

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

  • 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

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 <array_column>
ANY compares a scalar value with a single set of values (in the text array <array_column>), 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 <array_column>
ALL compares a scalar value with a single set of values (in the text array <array_column>), 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) 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 results more efficiently.
UNLIKELY(X) 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 results more efficiently.

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.