Apache Druid supports two query languages: Druid SQL and native queries.
This document describes the SQL language.

Druid SQL is a built-in SQL layer and an alternative to Druid’s native JSON-based query language, and is powered by a
parser and planner based on Apache Calcite. Druid SQL translates SQL into native Druid
queries on the query Broker (the first process you query), which are then passed down to data processes as native Druid
queries. Other than the (slight) overhead of translating SQL on the Broker, there isn’t an
additional performance penalty versus native queries.

Query syntax

Druid SQL supports SELECT queries with the following structure:

[ EXPLAIN PLAN FOR ]
[ WITH tableName [ ( column1, column2, ... ) ] AS ( query ) ]
SELECT [ ALL | DISTINCT ] { * | exprs }
FROM { <table> | (<subquery>) | <o1> [ INNER | LEFT ] JOIN <o2> ON condition }
[ WHERE expr ]
[ GROUP BY [ exprs | GROUPING SETS ( (exprs), ... ) | ROLLUP (exprs) | CUBE (exprs) ] ]
[ HAVING expr ]
[ ORDER BY expr [ ASC | DESC ], expr [ ASC | DESC ], ... ]
[ LIMIT limit ]
[ UNION ALL <another query> ]

FROM

The FROM clause can refer to any of the following:

• Table datasources from the druid schema. This is the default schema, so Druid table
  datasources can be referenced as either druid.dataSourceName or simply dataSourceName.
• Lookups from the lookup schema, for example lookup.countries. Note that lookups can
  also be queried using the LOOKUP function.
• Subqueries.
• Joins between anything in this list, except between native datasources (table, lookup,
  query) and system tables. The join condition must be an equality between expressions from the left- and right-hand side
  of the join.
• Metadata tables from the INFORMATION_SCHEMA or sys schemas. Unlike the other options for the
  FROM clause, metadata tables are not considered datasources. They exist only in the SQL layer.

For more information about table, lookup, query, and join datasources, refer to the Datasources
documentation.

WHERE

The WHERE clause refers to columns in the FROM table, and will be translated to native filters. The
WHERE clause can also reference a subquery, like WHERE col1 IN (SELECT foo FROM ...). Queries like this are executed
as a join on the subquery, described below in the Query translation section.

GROUP BY

The GROUP BY clause refers to columns in the FROM table. Using GROUP BY, DISTINCT, or any aggregation functions will
trigger an aggregation query using one of Druid’s three native aggregation query types. GROUP BY
can refer to an expression or a select clause ordinal position (like GROUP BY 2 to group by the second selected
column).

The GROUP BY clause can also refer to multiple grouping sets in three ways. The most flexible is GROUP BY GROUPING SETS,
for example GROUP BY GROUPING SETS ( (country, city), () ). This example is equivalent to a GROUP BY country, city
followed by GROUP BY () (a grand total). With GROUPING SETS, the underlying data is only scanned one time, leading to
better efficiency. Second, GROUP BY ROLLUP computes a grouping set for each level of the grouping expressions. For
example GROUP BY ROLLUP (country, city) is equivalent to GROUP BY GROUPING SETS ( (country, city), (country), () )
and will produce grouped rows for each country / city pair, along with subtotals for each country, along with a grand
total. Finally, GROUP BY CUBE computes a grouping set for each combination of grouping expressions. For example,
GROUP BY CUBE (country, city) is equivalent to GROUP BY GROUPING SETS ( (country, city), (country), (city), () ).
Grouping columns that do not apply to a particular row will contain NULL. For example, when computing
GROUP BY GROUPING SETS ( (country, city), () ), the grand total row corresponding to () will have NULL for the
“country” and “city” columns.

When using GROUP BY GROUPING SETS, GROUP BY ROLLUP, or GROUP BY CUBE, be aware that results may not be generated in the
order that you specify your grouping sets in the query. If you need results to be generated in a particular order, use
the ORDER BY clause.

HAVING

The HAVING clause refers to columns that are present after execution of GROUP BY. It can be used to filter on either
grouping expressions or aggregated values. It can only be used together with GROUP BY.

ORDER BY

The ORDER BY clause refers to columns that are present after execution of GROUP BY. It can be used to order the results
based on either grouping expressions or aggregated values. ORDER BY can refer to an expression or a select clause
ordinal position (like ORDER BY 2 to order by the second selected column). For non-aggregation queries, ORDER BY
can only order by the __time column. For aggregation queries, ORDER BY can order by any column.

LIMIT

The LIMIT clause can be used to limit the number of rows returned. It can be used with any query type. It is pushed down
to Data processes for queries that run with the native TopN query type, but not the native GroupBy query type. Future
versions of Druid will support pushing down limits using the native GroupBy query type as well. If you notice that
adding a limit doesn’t change performance very much, then it’s likely that Druid didn’t push down the limit for your
query.

UNION ALL

The “UNION ALL” operator can be used to fuse multiple queries together. Their results will be concatenated, and each
query will run separately, back to back (not in parallel). Druid does not currently support “UNION” without “ALL”.
UNION ALL must appear at the very outer layer of a SQL query (it cannot appear in a subquery or in the FROM clause).

Note that despite the similar name, UNION ALL is not the same thing as as union datasource.
UNION ALL allows unioning the results of queries, whereas union datasources allow unioning tables.

EXPLAIN PLAN

Add “EXPLAIN PLAN FOR” to the beginning of any query to get information about how it will be translated. In this case,
the query will not actually be executed. Refer to the Query translation documentation for help
interpreting EXPLAIN PLAN output.

Identifiers and literals

Identifiers like datasource and column names can optionally be quoted using double quotes. To escape a double quote
inside an identifier, use another double quote, like "My ""very own"" identifier". All identifiers are case-sensitive
and no implicit case conversions are performed.

Literal strings should be quoted with single quotes, like 'foo'. Literal strings with Unicode escapes can be written
like U&'fo\00F6', where character codes in hex are prefixed by a backslash. Literal numbers can be written in forms
like 100 (denoting an integer), 100.0 (denoting a floating point value), or 1.0e5 (scientific notation). Literal
timestamps can be written like TIMESTAMP '2000-01-01 00:00:00'. Literal intervals, used for time arithmetic, can be
written like INTERVAL '1' HOUR, INTERVAL '1 02:03' DAY TO MINUTE, INTERVAL '1-2' YEAR TO MONTH, and so on.

Dynamic parameters

Druid SQL supports dynamic parameters using question mark (?) syntax, where parameters are bound to ? placeholders
at execution time. To use dynamic parameters, replace any literal in the query with a ? character and provide a
corresponding parameter value when you execute the query. Parameters are bound to the placeholders in the order in
which they are passed. Parameters are supported in both the HTTP POST and JDBC APIs.

Data types

Standard types

Druid natively supports five basic column types: “long” (64 bit signed int), “float” (32 bit float), “double” (64 bit
float) “string” (UTF-8 encoded strings and string arrays), and “complex” (catch-all for more exotic data types like
hyperUnique and approxHistogram columns).

Timestamps (including the __time column) are treated by Druid as longs, with the value being the number of
milliseconds since 1970-01-01 00:00:00 UTC, not counting leap seconds. Therefore, timestamps in Druid do not carry any
timezone information, but only carry information about the exact moment in time they represent. See the
Time functions section for more information about timestamp handling.

The following table describes how Druid maps SQL types onto native types at query runtime. Casts between two SQL types
that have the same Druid runtime type will have no effect, other than exceptions noted in the table. Casts between two
SQL types that have different Druid runtime types will generate a runtime cast in Druid. If a value cannot be properly
cast to another value, as in CAST('foo' AS BIGINT), the runtime will substitute a default value. NULL values cast
to non-nullable types will also be substituted with a default value (for example, nulls cast to numbers will be
converted to zeroes).

SQL type	Druid runtime type	Default value	Notes
CHAR	STRING	''
VARCHAR	STRING	''	Druid STRING columns are reported as VARCHAR. Can include multi-value strings as well.
DECIMAL	DOUBLE	0.0	DECIMAL uses floating point, not fixed point math
FLOAT	FLOAT	0.0	Druid FLOAT columns are reported as FLOAT
REAL	DOUBLE	0.0
DOUBLE	DOUBLE	0.0	Druid DOUBLE columns are reported as DOUBLE
BOOLEAN	LONG	false
TINYINT	LONG	0
SMALLINT	LONG	0
INTEGER	LONG	0
BIGINT	LONG	0	Druid LONG columns (except __time) are reported as BIGINT
TIMESTAMP	LONG	0, meaning 1970-01-01 00:00:00 UTC	Druid’s __time column is reported as TIMESTAMP. Casts between string and timestamp types assume standard SQL formatting, e.g. 2000-01-02 03:04:05, not ISO8601 formatting. For handling other formats, use one of the time functions
DATE	LONG	0, meaning 1970-01-01	Casting TIMESTAMP to DATE rounds down the timestamp to the nearest day. Casts between string and date types assume standard SQL formatting, e.g. 2000-01-02. For handling other formats, use one of the time functions
OTHER	COMPLEX	none	May represent various Druid column types such as hyperUnique, approxHistogram, etc

Multi-value strings

Druid’s native type system allows strings to potentially have multiple values. These
multi-value string dimensions will be reported in SQL as VARCHAR typed, and can be
syntactically used like any other VARCHAR. Regular string functions that refer to multi-value string dimensions will be
applied to all values for each row individually. Multi-value string dimensions can also be treated as arrays via special
multi-value string functions, which can perform powerful array-aware operations.

Grouping by a multi-value expression will observe the native Druid multi-value aggregation behavior, which is similar to
the UNNEST functionality available in some other SQL dialects. Refer to the documentation on
multi-value string dimensions for additional details.

NULL values

The druid.generic.useDefaultValueForNull runtime property
controls Druid’s NULL handling mode.

In the default mode (true), Druid treats NULLs and empty strings interchangeably, rather than according to the SQL
standard. In this mode Druid SQL only has partial support for NULLs. For example, the expressions col IS NULL and
col = '' are equivalent, and both will evaluate to true if col contains an empty string. Similarly, the expression
COALESCE(col1, col2) will return col2 if col1 is an empty string. While the COUNT(*) aggregator counts all rows,
the COUNT(expr) aggregator will count the number of rows where expr is neither null nor the empty string. Numeric
columns in this mode are not nullable; any null or missing values will be treated as zeroes.

In SQL compatible mode (false), NULLs are treated more closely to the SQL standard. The property affects both storage
and querying, so for best behavior, it should be set at both ingestion time and query time. There is some overhead
associated with the ability to handle NULLs; see the segment internals
documentation for more details.

Aggregation functions

Aggregation functions can appear in the SELECT clause of any query. Any aggregator can be filtered using syntax like
AGG(expr) FILTER(WHERE whereExpr). Filtered aggregators will only aggregate rows that match their filter. It’s
possible for two aggregators in the same SQL query to have different filters.

Only the COUNT aggregation can accept DISTINCT.

Function	Notes
COUNT(*)	Counts the number of rows.
COUNT(DISTINCT expr)	Counts distinct values of expr, which can be string, numeric, or hyperUnique. By default this is approximate, using a variant of HyperLogLog. To get exact counts set “useApproximateCountDistinct” to “false”. If you do this, expr must be string or numeric, since exact counts are not possible using hyperUnique columns. See also APPROX_COUNT_DISTINCT(expr). In exact mode, only one distinct count per query is permitted.
SUM(expr)	Sums numbers.
MIN(expr)	Takes the minimum of numbers.
MAX(expr)	Takes the maximum of numbers.
AVG(expr)	Averages numbers.
APPROX_COUNT_DISTINCT(expr)	Counts distinct values of expr, which can be a regular column or a hyperUnique column. This is always approximate, regardless of the value of “useApproximateCountDistinct”. This uses Druid’s built-in “cardinality” or “hyperUnique” aggregators. See also COUNT(DISTINCT expr).
APPROX_COUNT_DISTINCT_DS_HLL(expr, [lgK, tgtHllType])	Counts distinct values of expr, which can be a regular column or an HLL sketch column. The lgK and tgtHllType parameters are described in the HLL sketch documentation. This is always approximate, regardless of the value of “useApproximateCountDistinct”. See also COUNT(DISTINCT expr). The DataSketches extension must be loaded to use this function.
APPROX_COUNT_DISTINCT_DS_THETA(expr, [size])	Counts distinct values of expr, which can be a regular column or a Theta sketch column. The size parameter is described in the Theta sketch documentation. This is always approximate, regardless of the value of “useApproximateCountDistinct”. See also COUNT(DISTINCT expr). The DataSketches extension must be loaded to use this function.
DS_HLL(expr, [lgK, tgtHllType])	Creates an HLL sketch on the values of expr, which can be a regular column or a column containing HLL sketches. The lgK and tgtHllType parameters are described in the HLL sketch documentation. The DataSketches extension must be loaded to use this function.
DS_THETA(expr, [size])	Creates a Theta sketch on the values of expr, which can be a regular column or a column containing Theta sketches. The size parameter is described in the Theta sketch documentation. The DataSketches extension must be loaded to use this function.
APPROX_QUANTILE(expr, probability, [resolution])	Computes approximate quantiles on numeric or approxHistogram exprs. The “probability” should be between 0 and 1 (exclusive). The “resolution” is the number of centroids to use for the computation. Higher resolutions will give more precise results but also have higher overhead. If not provided, the default resolution is 50. The approximate histogram extension must be loaded to use this function.
APPROX_QUANTILE_DS(expr, probability, [k])	Computes approximate quantiles on numeric or Quantiles sketch exprs. The “probability” should be between 0 and 1 (exclusive). The k parameter is described in the Quantiles sketch documentation. The DataSketches extension must be loaded to use this function.
APPROX_QUANTILE_FIXED_BUCKETS(expr, probability, numBuckets, lowerLimit, upperLimit, [outlierHandlingMode])	Computes approximate quantiles on numeric or fixed buckets histogram exprs. The “probability” should be between 0 and 1 (exclusive). The numBuckets, lowerLimit, upperLimit, and outlierHandlingMode parameters are described in the fixed buckets histogram documentation. The approximate histogram extension must be loaded to use this function.
DS_QUANTILES_SKETCH(expr, [k])	Creates a Quantiles sketch on the values of expr, which can be a regular column or a column containing quantiles sketches. The k parameter is described in the Quantiles sketch documentation. The DataSketches extension must be loaded to use this function.
BLOOM_FILTER(expr, numEntries)	Computes a bloom filter from values produced by expr, with numEntries maximum number of distinct values before false positive rate increases. See bloom filter extension documentation for additional details.
TDIGEST_QUANTILE(expr, quantileFraction, [compression])	Builds a T-Digest sketch on values produced by expr and returns the value for the quantile. Compression parameter (default value 100) determines the accuracy and size of the sketch. Higher compression means higher accuracy but more space to store sketches. See t-digest extension documentation for additional details.
TDIGEST_GENERATE_SKETCH(expr, [compression])	Builds a T-Digest sketch on values produced by expr. Compression parameter (default value 100) determines the accuracy and size of the sketch Higher compression means higher accuracy but more space to store sketches. See t-digest extension documentation for additional details.
VAR_POP(expr)	Computes variance population of expr. See stats extension documentation for additional details.
VAR_SAMP(expr)	Computes variance sample of expr. See stats extension documentation for additional details.
VARIANCE(expr)	Computes variance sample of expr. See stats extension documentation for additional details.
STDDEV_POP(expr)	Computes standard deviation population of expr. See stats extension documentation for additional details.
STDDEV_SAMP(expr)	Computes standard deviation sample of expr. See stats extension documentation for additional details.
STDDEV(expr)	Computes standard deviation sample of expr. See stats extension documentation for additional details.
EARLIEST(expr)	Returns the earliest value of expr, which must be numeric. If expr comes from a relation with a timestamp column (like a Druid datasource) then “earliest” is the value first encountered with the minimum overall timestamp of all values being aggregated. If expr does not come from a relation with a timestamp, then it is simply the first value encountered.
EARLIEST(expr, maxBytesPerString)	Like EARLIEST(expr), but for strings. The maxBytesPerString parameter determines how much aggregation space to allocate per string. Strings longer than this limit will be truncated. This parameter should be set as low as possible, since high values will lead to wasted memory.
LATEST(expr)	Returns the latest value of expr, which must be numeric. If expr comes from a relation with a timestamp column (like a Druid datasource) then “latest” is the value last encountered with the maximum overall timestamp of all values being aggregated. If expr does not come from a relation with a timestamp, then it is simply the last value encountered.
LATEST(expr, maxBytesPerString)	Like LATEST(expr), but for strings. The maxBytesPerString parameter determines how much aggregation space to allocate per string. Strings longer than this limit will be truncated. This parameter should be set as low as possible, since high values will lead to wasted memory.
ANY_VALUE(expr)	Returns any value of expr including null. expr must be numeric. This aggregator can simplify and optimize the performance by returning the first encountered value (including null)
ANY_VALUE(expr, maxBytesPerString)	Like ANY_VALUE(expr), but for strings. The maxBytesPerString parameter determines how much aggregation space to allocate per string. Strings longer than this limit will be truncated. This parameter should be set as low as possible, since high values will lead to wasted memory.

For advice on choosing approximate aggregation functions, check out our approximate aggregations documentation.

Scalar functions

Numeric functions

For mathematical operations, Druid SQL will use integer math if all operands involved in an expression are integers.
Otherwise, Druid will switch to floating point math. You can force this to happen by casting one of your operands
to FLOAT. At runtime, Druid will widen 32-bit floats to 64-bit for most expressions.

Function	Notes
ABS(expr)	Absolute value.
CEIL(expr)	Ceiling.
EXP(expr)	e to the power of expr.
FLOOR(expr)	Floor.
LN(expr)	Logarithm (base e).
LOG10(expr)	Logarithm (base 10).
POWER(expr, power)	expr to a power.
SQRT(expr)	Square root.
TRUNCATE(expr[, digits])	Truncate expr to a specific number of decimal digits. If digits is negative, then this truncates that many places to the left of the decimal point. Digits defaults to zero if not specified.
TRUNC(expr[, digits])	Synonym for TRUNCATE.
ROUND(expr[, digits])	ROUND(x, y) would return the value of the x rounded to the y decimal places. While x can be an integer or floating-point number, y must be an integer. The type of the return value is specified by that of x. y defaults to 0 if omitted. When y is negative, x is rounded on the left side of the y decimal points.
x + y	Addition.
x - y	Subtraction.
x * y	Multiplication.
x / y	Division.
MOD(x, y)	Modulo (remainder of x divided by y).
SIN(expr)	Trigonometric sine of an angle expr.
COS(expr)	Trigonometric cosine of an angle expr.
TAN(expr)	Trigonometric tangent of an angle expr.
COT(expr)	Trigonometric cotangent of an angle expr.
ASIN(expr)	Arc sine of expr.
ACOS(expr)	Arc cosine of expr.
ATAN(expr)	Arc tangent of expr.
ATAN2(y, x)	Angle theta from the conversion of rectangular coordinates (x, y) to polar * coordinates (r, theta).
DEGREES(expr)	Converts an angle measured in radians to an approximately equivalent angle measured in degrees
RADIANS(expr)	Converts an angle measured in degrees to an approximately equivalent angle measured in radians

String functions

String functions accept strings, and return a type appropriate to the function.

Function	Notes
x || y	Concat strings x and y.
CONCAT(expr, expr...)	Concats a list of expressions.
TEXTCAT(expr, expr)	Two argument version of CONCAT.
STRING_FORMAT(pattern[, args...])	Returns a string formatted in the manner of Java’s String.format.
LENGTH(expr)	Length of expr in UTF-16 code units.
CHAR_LENGTH(expr)	Synonym for LENGTH.
CHARACTER_LENGTH(expr)	Synonym for LENGTH.
STRLEN(expr)	Synonym for LENGTH.
LOOKUP(expr, lookupName)	Look up expr in a registered query-time lookup table. Note that lookups can also be queried directly using the lookup schema.
LOWER(expr)	Returns expr in all lowercase.
PARSE_LONG(string[, radix])	Parses a string into a long (BIGINT) with the given radix, or 10 (decimal) if a radix is not provided.
POSITION(needle IN haystack [FROM fromIndex])	Returns the index of needle within haystack, with indexes starting from 1. The search will begin at fromIndex, or 1 if fromIndex is not specified. If the needle is not found, returns 0.
REGEXP_EXTRACT(expr, pattern, [index])	Apply regular expression pattern and extract a capture group, or null if there is no match. If index is unspecified or zero, returns the substring that matched the pattern.
REPLACE(expr, pattern, replacement)	Replaces pattern with replacement in expr, and returns the result.
STRPOS(haystack, needle)	Returns the index of needle within haystack, with indexes starting from 1. If the needle is not found, returns 0.
SUBSTRING(expr, index, [length])	Returns a substring of expr starting at index, with a max length, both measured in UTF-16 code units.
RIGHT(expr, [length])	Returns the rightmost length characters from expr.
LEFT(expr, [length])	Returns the leftmost length characters from expr.
SUBSTR(expr, index, [length])	Synonym for SUBSTRING.
TRIM([BOTH | LEADING | TRAILING] [ FROM] expr)	Returns expr with characters removed from the leading, trailing, or both ends of “expr” if they are in “chars”. If “chars” is not provided, it defaults to " " (a space). If the directional argument is not provided, it defaults to “BOTH”.
BTRIM(expr[, chars])	Alternate form of TRIM(BOTH <chars> FROM <expr>).
LTRIM(expr[, chars])	Alternate form of TRIM(LEADING <chars> FROM <expr>).
RTRIM(expr[, chars])	Alternate form of TRIM(TRAILING <chars> FROM <expr>).
UPPER(expr)	Returns expr in all uppercase.
REVERSE(expr)	Reverses expr.
REPEAT(expr, [N])	Repeats expr N times
LPAD(expr, length[, chars])	Returns a string of “length” from “expr” left-padded with “chars”. If “length” is shorter than the length of “expr”, the result is “expr” which is truncated to “length”. If either “expr” or “chars” are null, the result will be null.
RPAD(expr, length[, chars])	Returns a string of “length” from “expr” right-padded with “chars”. If “length” is shorter than the length of “expr”, the result is “expr” which is truncated to “length”. If either “expr” or “chars” are null, the result will be null.

Time functions

Time functions can be used with Druid’s __time column, with any column storing millisecond timestamps through use
of the MILLIS_TO_TIMESTAMP function, or with any column storing string timestamps through use of the TIME_PARSE
function. By default, time operations use the UTC time zone. You can change the time zone by setting the connection
context parameter “sqlTimeZone” to the name of another time zone, like “America/Los_Angeles”, or to an offset like
“-08:00”. If you need to mix multiple time zones in the same query, or if you need to use a time zone other than
the connection time zone, some functions also accept time zones as parameters. These parameters always take precedence
over the connection time zone.

Literal timestamps in the connection time zone can be written using TIMESTAMP '2000-01-01 00:00:00' syntax. The
simplest way to write literal timestamps in other time zones is to use TIME_PARSE, like
TIME_PARSE('2000-02-01 00:00:00', NULL, 'America/Los_Angeles').

Function	Notes
CURRENT_TIMESTAMP	Current timestamp in the connection’s time zone.
CURRENT_DATE	Current date in the connection’s time zone.
DATE_TRUNC(<unit>, <timestamp_expr>)	Rounds down a timestamp, returning it as a new timestamp. Unit can be ‘milliseconds’, ‘second’, ‘minute’, ‘hour’, ‘day’, ‘week’, ‘month’, ‘quarter’, ‘year’, ‘decade’, ‘century’, or ‘millennium’.
TIME_CEIL(<timestamp_expr>, <period>, [<origin>, [<timezone>]])	Rounds up a timestamp, returning it as a new timestamp. Period can be any ISO8601 period, like P3M (quarters) or PT12H (half-days). The time zone, if provided, should be a time zone name like “America/Los_Angeles” or offset like “-08:00”. This function is similar to CEIL but is more flexible.
TIME_FLOOR(<timestamp_expr>, <period>, [<origin>, [<timezone>]])	Rounds down a timestamp, returning it as a new timestamp. Period can be any ISO8601 period, like P3M (quarters) or PT12H (half-days). The time zone, if provided, should be a time zone name like “America/Los_Angeles” or offset like “-08:00”. This function is similar to FLOOR but is more flexible.
TIME_SHIFT(<timestamp_expr>, <period>, <step>, [<timezone>])	Shifts a timestamp by a period (step times), returning it as a new timestamp. Period can be any ISO8601 period. Step may be negative. The time zone, if provided, should be a time zone name like “America/Los_Angeles” or offset like “-08:00”.
TIME_EXTRACT(<timestamp_expr>, [<unit>, [<timezone>]])	Extracts a time part from expr, returning it as a number. Unit can be EPOCH, SECOND, MINUTE, HOUR, DAY (day of month), DOW (day of week), DOY (day of year), WEEK (week of week year), MONTH (1 through 12), QUARTER (1 through 4), or YEAR. The time zone, if provided, should be a time zone name like “America/Los_Angeles” or offset like “-08:00”. This function is similar to EXTRACT but is more flexible. Unit and time zone must be literals, and must be provided quoted, like TIME_EXTRACT(__time, 'HOUR') or TIME_EXTRACT(__time, 'HOUR', 'America/Los_Angeles').
TIME_PARSE(<string_expr>, [<pattern>, [<timezone>]])	Parses a string into a timestamp using a given Joda DateTimeFormat pattern, or ISO8601 (e.g. 2000-01-02T03:04:05Z) if the pattern is not provided. The time zone, if provided, should be a time zone name like “America/Los_Angeles” or offset like “-08:00”, and will be used as the time zone for strings that do not include a time zone offset. Pattern and time zone must be literals. Strings that cannot be parsed as timestamps will be returned as NULL.
TIME_FORMAT(<timestamp_expr>, [<pattern>, [<timezone>]])	Formats a timestamp as a string with a given Joda DateTimeFormat pattern, or ISO8601 (e.g. 2000-01-02T03:04:05Z) if the pattern is not provided. The time zone, if provided, should be a time zone name like “America/Los_Angeles” or offset like “-08:00”. Pattern and time zone must be literals.
MILLIS_TO_TIMESTAMP(millis_expr)	Converts a number of milliseconds since the epoch into a timestamp.
TIMESTAMP_TO_MILLIS(timestamp_expr)	Converts a timestamp into a number of milliseconds since the epoch.
EXTRACT(<unit> FROM timestamp_expr)	Extracts a time part from expr, returning it as a number. Unit can be EPOCH, MICROSECOND, MILLISECOND, SECOND, MINUTE, HOUR, DAY (day of month), DOW (day of week), ISODOW (ISO day of week), DOY (day of year), WEEK (week of year), MONTH, QUARTER, YEAR, ISOYEAR, DECADE, CENTURY or MILLENNIUM. Units must be provided unquoted, like EXTRACT(HOUR FROM __time).
FLOOR(timestamp_expr TO <unit>)	Rounds down a timestamp, returning it as a new timestamp. Unit can be SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, or YEAR.
CEIL(timestamp_expr TO <unit>)	Rounds up a timestamp, returning it as a new timestamp. Unit can be SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, or YEAR.
TIMESTAMPADD(<unit>, <count>, <timestamp>)	Equivalent to timestamp + count * INTERVAL '1' UNIT.
TIMESTAMPDIFF(<unit>, <timestamp1>, <timestamp2>)	Returns the (signed) number of unit between timestamp1 and timestamp2. Unit can be SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, or YEAR.
timestamp_expr { + | - } <interval_expr>	Add or subtract an amount of time from a timestamp. interval_expr can include interval literals like INTERVAL '2' HOUR, and may include interval arithmetic as well. This operator treats days as uniformly 86400 seconds long, and does not take into account daylight savings time. To account for daylight savings time, use TIME_SHIFT instead.

Reduction functions

Reduction functions operate on zero or more expressions and return a single expression. If no expressions are passed as
arguments, then the result is NULL. The expressions must all be convertible to a common data type, which will be the
type of the result:

• If all argument are NULL, the result is NULL. Otherwise, NULL arguments are ignored.
• If the arguments comprise a mix of numbers and strings, the arguments are interpreted as strings.
• If all arguments are integer numbers, the arguments are interpreted as longs.
• If all arguments are numbers and at least one argument is a double, the arguments are interpreted as doubles.

Function	Notes
GREATEST([expr1, ...])	Evaluates zero or more expressions and returns the maximum value based on comparisons as described above.
LEAST([expr1, ...])	Evaluates zero or more expressions and returns the minimum value based on comparisons as described above.

IP address functions

For the IPv4 address functions, the address argument can either be an IPv4 dotted-decimal string
(e.g., ‘192.168.0.1’) or an IP address represented as an integer (e.g., 3232235521). The subnet
argument should be a string formatted as an IPv4 address subnet in CIDR notation (e.g.,
‘192.168.0.0/16’).

Function	Notes
IPV4_MATCH(address, subnet)	Returns true if the address belongs to the subnet literal, else false. If address is not a valid IPv4 address, then false is returned. This function is more efficient if address is an integer instead of a string.
IPV4_PARSE(address)	Parses address into an IPv4 address stored as an integer . If address is an integer that is a valid IPv4 address, then it is passed through. Returns null if address cannot be represented as an IPv4 address.
IPV4_STRINGIFY(address)	Converts address into an IPv4 address dotted-decimal string. If address is a string that is a valid IPv4 address, then it is passed through. Returns null if address cannot be represented as an IPv4 address.

Comparison operators

Function	Notes
x = y	Equals.
x <> y	Not-equals.
x > y	Greater than.
x >= y	Greater than or equal to.
x < y	Less than.
x <= y	Less than or equal to.
x BETWEEN y AND z	Equivalent to x >= y AND x <= z.
x NOT BETWEEN y AND z	Equivalent to x < y OR x > z.
x LIKE pattern [ESCAPE esc]	True if x matches a SQL LIKE pattern (with an optional escape).
x NOT LIKE pattern [ESCAPE esc]	True if x does not match a SQL LIKE pattern (with an optional escape).
x IS NULL	True if x is NULL or empty string.
x IS NOT NULL	True if x is neither NULL nor empty string.
x IS TRUE	True if x is true.
x IS NOT TRUE	True if x is not true.
x IS FALSE	True if x is false.
x IS NOT FALSE	True if x is not false.
x IN (values)	True if x is one of the listed values.
x NOT IN (values)	True if x is not one of the listed values.
x IN (subquery)	True if x is returned by the subquery. This will be translated into a join; see Query translation for details.
x NOT IN (subquery)	True if x is not returned by the subquery. This will be translated into a join; see Query translation for details.
x AND y	Boolean AND.
x OR y	Boolean OR.
NOT x	Boolean NOT.

Sketch functions

These functions operate on expressions or columns that return sketch objects.

HLL sketch functions

The following functions operate on DataSketches HLL sketches.
The DataSketches extension must be loaded to use the following functions.

Function	Notes
HLL_SKETCH_ESTIMATE(expr, [round])	Returns the distinct count estimate from an HLL sketch. expr must return an HLL sketch. The optional round boolean parameter will round the estimate if set to true, with a default of false.
HLL_SKETCH_ESTIMATE_WITH_ERROR_BOUNDS(expr, [numStdDev])	Returns the distinct count estimate and error bounds from an HLL sketch. expr must return an HLL sketch. An optional numStdDev argument can be provided.
HLL_SKETCH_UNION([lgK, tgtHllType], expr0, expr1, ...)	Returns a union of HLL sketches, where each input expression must return an HLL sketch. The lgK and tgtHllType can be optionally specified as the first parameter; if provided, both optional parameters must be specified.
HLL_SKETCH_TO_STRING(expr)	Returns a human-readable string representation of an HLL sketch for debugging. expr must return an HLL sketch.

Theta sketch functions

The following functions operate on theta sketches.
The DataSketches extension must be loaded to use the following functions.

Function	Notes
THETA_SKETCH_ESTIMATE(expr)	Returns the distinct count estimate from a theta sketch. expr must return a theta sketch.
THETA_SKETCH_ESTIMATE_WITH_ERROR_BOUNDS(expr, errorBoundsStdDev)	Returns the distinct count estimate and error bounds from a theta sketch. expr must return a theta sketch.
THETA_SKETCH_UNION([size], expr0, expr1, ...)	Returns a union of theta sketches, where each input expression must return a theta sketch. The size can be optionally specified as the first parameter.
THETA_SKETCH_INTERSECT([size], expr0, expr1, ...)	Returns an intersection of theta sketches, where each input expression must return a theta sketch. The size can be optionally specified as the first parameter.
THETA_SKETCH_NOT([size], expr0, expr1, ...)	Returns a set difference of theta sketches, where each input expression must return a theta sketch. The size can be optionally specified as the first parameter.

Quantiles sketch functions

The following functions operate on quantiles sketches.
The DataSketches extension must be loaded to use the following functions.

Function	Notes
DS_GET_QUANTILE(expr, fraction)	Returns the quantile estimate corresponding to fraction from a quantiles sketch. expr must return a quantiles sketch.
DS_GET_QUANTILES(expr, fraction0, fraction1, ...)	Returns a string representing an array of quantile estimates corresponding to a list of fractions from a quantiles sketch. expr must return a quantiles sketch.
DS_HISTOGRAM(expr, splitPoint0, splitPoint1, ...)	Returns a string representing an approximation to the histogram given a list of split points that define the histogram bins from a quantiles sketch. expr must return a quantiles sketch.
DS_CDF(expr, splitPoint0, splitPoint1, ...)	Returns a string representing approximation to the Cumulative Distribution Function given a list of split points that define the edges of the bins from a quantiles sketch. expr must return a quantiles sketch.
DS_RANK(expr, value)	Returns an approximation to the rank of a given value that is the fraction of the distribution less than that value from a quantiles sketch. expr must return a quantiles sketch.
DS_QUANTILE_SUMMARY(expr)	Returns a string summary of a quantiles sketch, useful for debugging. expr must return a quantiles sketch.

Other scalar functions

Function	Notes
CAST(value AS TYPE)	Cast value to another type. See Data types for details about how Druid SQL handles CAST.
CASE expr WHEN value1 THEN result1 \[ WHEN value2 THEN result2 ... \] \[ ELSE resultN \] END	Simple CASE.
CASE WHEN boolean_expr1 THEN result1 \[ WHEN boolean_expr2 THEN result2 ... \] \[ ELSE resultN \] END	Searched CASE.
NULLIF(value1, value2)	Returns NULL if value1 and value2 match, else returns value1.
COALESCE(value1, value2, ...)	Returns the first value that is neither NULL nor empty string.
NVL(expr,expr-for-null)	Returns ‘expr-for-null’ if ‘expr’ is null (or empty string for string type).
BLOOM_FILTER_TEST(<expr>, <serialized-filter>)	Returns true if the value is contained in a Base64-serialized bloom filter. See the Bloom filter extension documentation for additional details.

Multi-value string functions

All ‘array’ references in the multi-value string function documentation can refer to multi-value string columns or
ARRAY literals.

Function	Notes
ARRAY(expr1,expr ...)	constructs a SQL ARRAY literal from the expression arguments, using the type of the first argument as the output array type
MV_LENGTH(arr)	returns length of array expression
MV_OFFSET(arr,long)	returns the array element at the 0 based index supplied, or null for an out of range index
MV_ORDINAL(arr,long)	returns the array element at the 1 based index supplied, or null for an out of range index
MV_CONTAINS(arr,expr)	returns 1 if the array contains the element specified by expr, or contains all elements specified by expr if expr is an array, else 0
MV_OVERLAP(arr1,arr2)	returns 1 if arr1 and arr2 have any elements in common, else 0
MV_OFFSET_OF(arr,expr)	returns the 0 based index of the first occurrence of expr in the array, or -1 or null if druid.generic.useDefaultValueForNull=false if no matching elements exist in the array.
MV_ORDINAL_OF(arr,expr)	returns the 1 based index of the first occurrence of expr in the array, or -1 or null if druid.generic.useDefaultValueForNull=false if no matching elements exist in the array.
MV_PREPEND(expr,arr)	adds expr to arr at the beginning, the resulting array type determined by the type of the array
MV_APPEND(arr1,expr)	appends expr to arr, the resulting array type determined by the type of the first array
MV_CONCAT(arr1,arr2)	concatenates 2 arrays, the resulting array type determined by the type of the first array
MV_SLICE(arr,start,end)	return the subarray of arr from the 0 based index start(inclusive) to end(exclusive), or null, if start is less than 0, greater than length of arr or less than end
MV_TO_STRING(arr,str)	joins all elements of arr by the delimiter specified by str
STRING_TO_MV(str1,str2)	splits str1 into an array on the delimiter specified by str2

Query translation

Druid SQL translates SQL queries to native queries before running them, and understanding how this
translation works is key to getting good performance.

Best practices

Consider this (non-exhaustive) list of things to look out for when looking into the performance implications of
how your SQL queries are translated to native queries.

1. If you wrote a filter on the primary time column __time, make sure it is being correctly translated to an
   "intervals" filter, as described in the Time filters section below. If not, you may need to change
   the way you write the filter.

2. Try to avoid subqueries underneath joins: they affect both performance and scalability. This includes implicit
   subqueries generated by conditions on mismatched types, and implicit subqueries generated by conditions that use
   expressions to refer to the right-hand side.

3. Read through the Query execution page to understand how various types of native queries
   will be executed.

4. Be careful when interpreting EXPLAIN PLAN output, and use request logging if in doubt. Request logs will show the
   exact native query that was run. See the next section for more details.

5. If you encounter a query that could be planned better, feel free to
   raise an issue on GitHub. A reproducible test case is always
   appreciated.

Interpreting EXPLAIN PLAN output

The EXPLAIN PLAN functionality can help you understand how a given SQL query will
be translated to native. For simple queries that do not involve subqueries or joins, the output of EXPLAIN PLAN
is easy to interpret. The native query that will run is embedded as JSON inside a “DruidQueryRel” line:

> EXPLAIN PLAN FOR SELECT COUNT(*) FROM wikipedia

DruidQueryRel(query=[{"queryType":"timeseries","dataSource":"wikipedia","intervals":"-146136543-09-08T08:23:32.096Z/146140482-04-24T15:36:27.903Z","granularity":"all","aggregations":[{"type":"count","name":"a0"}]}], signature=[{a0:LONG}])

For more complex queries that do involve subqueries or joins, EXPLAIN PLAN is somewhat more difficult to interpret.
For example, consider this query:

> EXPLAIN PLAN FOR
> SELECT
>     channel,
>     COUNT(*)
> FROM wikipedia
> WHERE channel IN (SELECT page FROM wikipedia GROUP BY page ORDER BY COUNT(*) DESC LIMIT 10)
> GROUP BY channel

DruidJoinQueryRel(condition=[=($1, $3)], joinType=[inner], query=[{"queryType":"groupBy","dataSource":{"type":"table","name":"__join__"},"intervals":{"type":"intervals","intervals":["-146136543-09-08T08:23:32.096Z/146140482-04-24T15:36:27.903Z"]},"granularity":"all","dimensions":["channel"],"aggregations":[{"type":"count","name":"a0"}]}], signature=[{d0:STRING, a0:LONG}])
  DruidQueryRel(query=[{"queryType":"scan","dataSource":{"type":"table","name":"wikipedia"},"intervals":{"type":"intervals","intervals":["-146136543-09-08T08:23:32.096Z/146140482-04-24T15:36:27.903Z"]},"resultFormat":"compactedList","columns":["__time","channel","page"],"granularity":"all"}], signature=[{__time:LONG, channel:STRING, page:STRING}])
  DruidQueryRel(query=[{"queryType":"topN","dataSource":{"type":"table","name":"wikipedia"},"dimension":"page","metric":{"type":"numeric","metric":"a0"},"threshold":10,"intervals":{"type":"intervals","intervals":["-146136543-09-08T08:23:32.096Z/146140482-04-24T15:36:27.903Z"]},"granularity":"all","aggregations":[{"type":"count","name":"a0"}]}], signature=[{d0:STRING}])

Here, there is a join with two inputs. The way to read this is to consider each line of the EXPLAIN PLAN output as
something that might become a query, or might just become a simple datasource. The query field they all have is
called a “partial query” and represents what query would be run on the datasource represented by that line, if that
line ran by itself. In some cases — like the “scan” query in the second line of this example — the query does not
actually run, and it ends up being translated to a simple table datasource. See the Join translation section
for more details about how this works.

We can see this for ourselves using Druid’s request logging feature. After
enabling logging and running this query, we can see that it actually runs as the following native query.

{
  "queryType": "groupBy",
  "dataSource": {
    "type": "join",
    "left": "wikipedia",
    "right": {
      "type": "query",
      "query": {
        "queryType": "topN",
        "dataSource": "wikipedia",
        "dimension": {"type": "default", "dimension": "page", "outputName": "d0"},
        "metric": {"type": "numeric", "metric": "a0"},
        "threshold": 10,
        "intervals": "-146136543-09-08T08:23:32.096Z/146140482-04-24T15:36:27.903Z",
        "granularity": "all",
        "aggregations": [
          { "type": "count", "name": "a0"}
        ]
      }
    },
    "rightPrefix": "j0.",
    "condition": "(\"page\" == \"j0.d0\")",
    "joinType": "INNER"
  },
  "intervals": "-146136543-09-08T08:23:32.096Z/146140482-04-24T15:36:27.903Z",
  "granularity": "all",
  "dimensions": [
    {"type": "default", "dimension": "channel", "outputName": "d0"}
  ],
  "aggregations": [
    { "type": "count", "name": "a0"}
  ]
}

Query types

Druid SQL uses four different native query types.

• Scan is used for queries that do not aggregate (no GROUP BY, no DISTINCT).

• Timeseries is used for queries that GROUP BY FLOOR(__time TO <unit>) or TIME_FLOOR(__time, period), have no other grouping expressions, no HAVING or LIMIT clauses, no nesting, and either no ORDER BY, or an
  ORDER BY that orders by same expression as present in GROUP BY. It also uses Timeseries for “grand total” queries that
  have aggregation functions but no GROUP BY. This query type takes advantage of the fact that Druid segments are sorted
  by time.

• TopN is used by default for queries that group by a single expression, do have ORDER BY and LIMIT
  clauses, do not have HAVING clauses, and are not nested. However, the TopN query type will deliver approximate ranking
  and results in some cases; if you want to avoid this, set “useApproximateTopN” to “false”. TopN results are always
  computed in memory. See the TopN documentation for more details.

• GroupBy is used for all other aggregations, including any nested aggregation queries. Druid’s
  GroupBy is a traditional aggregation engine: it delivers exact results and rankings and supports a wide variety of
  features. GroupBy aggregates in memory if it can, but it may spill to disk if it doesn’t have enough memory to complete
  your query. Results are streamed back from data processes through the Broker if you ORDER BY the same expressions in your
  GROUP BY clause, or if you don’t have an ORDER BY at all. If your query has an ORDER BY referencing expressions that
  don’t appear in the GROUP BY clause (like aggregation functions) then the Broker will materialize a list of results in
  memory, up to a max of your LIMIT, if any. See the GroupBy documentation for details about tuning performance and memory
  use.

Time filters

For all native query types, filters on the __time column will be translated into top-level query “intervals” whenever
possible, which allows Druid to use its global time index to quickly prune the set of data that must be scanned.
Consider this (non-exhaustive) list of time filters that will be recognized and translated to “intervals”:

• __time >= TIMESTAMP '2000-01-01 00:00:00' (comparison to absolute time)
• __time >= CURRENT_TIMESTAMP - INTERVAL '8' HOUR (comparison to relative time)
• FLOOR(__time TO DAY) = TIMESTAMP '2000-01-01 00:00:00' (specific day)

Refer to the Interpreting EXPLAIN PLAN output section for details on confirming
that time filters are being translated as you expect.

Joins

SQL join operators are translated to native join datasources as follows:

1. Joins that the native layer can handle directly are translated literally, to a join datasource
   whose left, right, and condition are faithful translations of the original SQL. This includes any SQL join where
   the right-hand side is a lookup or subquery, and where the condition is an equality where one side is an expression based
   on the left-hand table, the other side is a simple column reference to the right-hand table, and both sides of the
   equality are the same data type.

2. If a join cannot be handled directly by a native join datasource as written, Druid SQL
   will insert subqueries to make it runnable. For example, foo INNER JOIN bar ON foo.abc = LOWER(bar.def) cannot be
   directly translated, because there is an expression on the right-hand side instead of a simple column access. A subquery
   will be inserted that effectively transforms this clause to
   foo INNER JOIN (SELECT LOWER(def) AS def FROM bar) t ON foo.abc = t.def.

3. Druid SQL does not currently reorder joins to optimize queries.

Refer to the Interpreting EXPLAIN PLAN output section for details on confirming
that joins are being translated as you expect.

Refer to the Query execution page for information about how joins are executed.

Subqueries

Subqueries in SQL are generally translated to native query datasources. Refer to the
Query execution page for information about how subqueries are executed.

Note: Subqueries in the WHERE clause, like WHERE col1 IN (SELECT foo FROM ...) are translated to inner joins.

Approximations

Druid SQL will use approximate algorithms in some situations:

• The COUNT(DISTINCT col) aggregation functions by default uses a variant of
  HyperLogLog, a fast approximate distinct counting
  algorithm. Druid SQL will switch to exact distinct counts if you set “useApproximateCountDistinct” to “false”, either
  through query context or through Broker configuration.

• GROUP BY queries over a single column with ORDER BY and LIMIT may be executed using the TopN engine, which uses an
  approximate algorithm. Druid SQL will switch to an exact grouping algorithm if you set “useApproximateTopN” to “false”,
  either through query context or through Broker configuration.

• Aggregation functions that are labeled as using sketches or approximations, such as APPROX_COUNT_DISTINCT, are always
  approximate, regardless of configuration.

Unsupported features

Druid does not support all SQL features. In particular, the following features are not supported.

• JOIN between native datasources (table, lookup, subquery) and system tables.
• JOIN conditions that are not an equality between expressions from the left- and right-hand sides.
• OVER clauses, and analytic functions such as LAG and LEAD.
• OFFSET clauses.
• DDL and DML.
• Using Druid-specific functions like TIME_PARSE and APPROX_QUANTILE_DS on metadata tables.

Additionally, some Druid native query features are not supported by the SQL language. Some unsupported Druid features
include:

• Union datasources
• Inline datasources
• Spatial filters.
• Query cancellation.

Client APIs

HTTP POST

You can make Druid SQL queries using HTTP via POST to the endpoint /druid/v2/sql/. The request should
be a JSON object with a “query” field, like {"query" : "SELECT COUNT(*) FROM data_source WHERE foo = 'bar'"}.

Request

Property	Description	Default
query	SQL query string.	none (required)
resultFormat	Format of query results. See Responses for details.	"object"
header	Whether or not to include a header. See [Responses] for details.	false
context	JSON object containing connection context parameters.	{} (empty)
parameters	List of query parameters for parameterized queries. Each parameter in the list should be a JSON object like {"type": "VARCHAR", "value": "foo"}. The type should be a SQL type; see Data types for a list of supported SQL types.	[] (empty)

You can use curl to send SQL queries from the command-line:

$ cat query.json
{"query":"SELECT COUNT(*) AS TheCount FROM data_source"}

$ curl -XPOST -H'Content-Type: application/json' http://BROKER:8082/druid/v2/sql/ -d @query.json
[{"TheCount":24433}]

There are a variety of connection context parameters you can provide by adding a “context” map,
like:

{
  "query" : "SELECT COUNT(*) FROM data_source WHERE foo = 'bar' AND __time > TIMESTAMP '2000-01-01 00:00:00'",
  "context" : {
    "sqlTimeZone" : "America/Los_Angeles"
  }
}

Parameterized SQL queries are also supported:

{
  "query" : "SELECT COUNT(*) FROM data_source WHERE foo = ? AND __time > ?",
  "parameters": [
    { "type": "VARCHAR", "value": "bar"},
    { "type": "TIMESTAMP", "value": "2000-01-01 00:00:00" }
  ]
}

Metadata is available over HTTP POST by querying metadata tables.

Responses

Druid SQL’s HTTP POST API supports a variety of result formats. You can specify these by adding a “resultFormat”
parameter, like:

{
  "query" : "SELECT COUNT(*) FROM data_source WHERE foo = 'bar' AND __time > TIMESTAMP '2000-01-01 00:00:00'",
  "resultFormat" : "object"
}

The supported result formats are:

Format	Description	Content-Type
object	The default, a JSON array of JSON objects. Each object’s field names match the columns returned by the SQL query, and are provided in the same order as the SQL query.	application/json
array	JSON array of JSON arrays. Each inner array has elements matching the columns returned by the SQL query, in order.	application/json
objectLines	Like “object”, but the JSON objects are separated by newlines instead of being wrapped in a JSON array. This can make it easier to parse the entire response set as a stream, if you do not have ready access to a streaming JSON parser. To make it possible to detect a truncated response, this format includes a trailer of one blank line.	text/plain
arrayLines	Like “array”, but the JSON arrays are separated by newlines instead of being wrapped in a JSON array. This can make it easier to parse the entire response set as a stream, if you do not have ready access to a streaming JSON parser. To make it possible to detect a truncated response, this format includes a trailer of one blank line.	text/plain
csv	Comma-separated values, with one row per line. Individual field values may be escaped by being surrounded in double quotes. If double quotes appear in a field value, they will be escaped by replacing them with double-double-quotes like ""this"". To make it possible to detect a truncated response, this format includes a trailer of one blank line.	text/csv

You can additionally request a header by setting “header” to true in your request, like:

{
  "query" : "SELECT COUNT(*) FROM data_source WHERE foo = 'bar' AND __time > TIMESTAMP '2000-01-01 00:00:00'",
  "resultFormat" : "arrayLines",
  "header" : true
}

In this case, the first result returned will be a header. For the csv, array, and arrayLines formats, the header
will be a list of column names. For the object and objectLines formats, the header will be an object where the
keys are column names, and the values are null.

Errors that occur before the response body is sent will be reported in JSON, with an HTTP 500 status code, in the
same format as native Druid query errors. If an error occurs while the response body is
being sent, at that point it is too late to change the HTTP status code or report a JSON error, so the response will
simply end midstream and an error will be logged by the Druid server that was handling your request.

As a caller, it is important that you properly handle response truncation. This is easy for the “object” and “array”
formats, since truncated responses will be invalid JSON. For the line-oriented formats, you should check the
trailer they all include: one blank line at the end of the result set. If you detect a truncated response, either
through a JSON parsing error or through a missing trailing newline, you should assume the response was not fully
delivered due to an error.

JDBC

You can make Druid SQL queries using the Avatica JDBC driver. Once
you’ve downloaded the Avatica client jar, add it to your classpath and use the connect string
jdbc:avatica:remote:url=http://BROKER:8082/druid/v2/sql/avatica/.

Example code:

// Connect to /druid/v2/sql/avatica/ on your Broker.
String url = "jdbc:avatica:remote:url=http://localhost:8082/druid/v2/sql/avatica/";

// Set any connection context parameters you need here (see "Connection context" below).
// Or leave empty for default behavior.
Properties connectionProperties = new Properties();

try (Connection connection = DriverManager.getConnection(url, connectionProperties)) {
  try (
      final Statement statement = connection.createStatement();
      final ResultSet resultSet = statement.executeQuery(query)
  ) {
    while (resultSet.next()) {
      // Do something
    }
  }
}

Table metadata is available over JDBC using connection.getMetaData() or by querying the
“INFORMATION_SCHEMA” tables.

Connection stickiness

Druid’s JDBC server does not share connection state between Brokers. This means that if you’re using JDBC and have
multiple Druid Brokers, you should either connect to a specific Broker, or use a load balancer with sticky sessions
enabled. The Druid Router process provides connection stickiness when balancing JDBC requests, and can be used to achieve
the necessary stickiness even with a normal non-sticky load balancer. Please see the
Router documentation for more details.

Note that the non-JDBC JSON over HTTP API is stateless and does not require stickiness.

Dynamic Parameters

You can also use parameterized queries in JDBC code, as in this example;

PreparedStatement statement = connection.prepareStatement("SELECT COUNT(*) AS cnt FROM druid.foo WHERE dim1 = ? OR dim1 = ?");
statement.setString(1, "abc");
statement.setString(2, "def");
final ResultSet resultSet = statement.executeQuery();

Connection context

Druid SQL supports setting connection parameters on the client. The parameters in the table below affect SQL planning.
All other context parameters you provide will be attached to Druid queries and can affect how they run. See
Query context for details on the possible options.

Note that to specify an unique identifier for SQL query, use sqlQueryId instead of queryId. Setting queryId for a SQL
request has no effect, all native queries underlying SQL will use auto-generated queryId.

Connection context can be specified as JDBC connection properties or as a “context” object in the JSON API.

Parameter	Description	Default value
sqlQueryId	Unique identifier given to this SQL query. For HTTP client, it will be returned in X-Druid-SQL-Query-Id header.	auto-generated
sqlTimeZone	Sets the time zone for this connection, which will affect how time functions and timestamp literals behave. Should be a time zone name like “America/Los_Angeles” or offset like “-08:00”.	druid.sql.planner.sqlTimeZone on the Broker (default: UTC)
useApproximateCountDistinct	Whether to use an approximate cardinality algorithm for COUNT(DISTINCT foo).	druid.sql.planner.useApproximateCountDistinct on the Broker (default: true)
useApproximateTopN	Whether to use approximate TopN queries when a SQL query could be expressed as such. If false, exact GroupBy queries will be used instead.	druid.sql.planner.useApproximateTopN on the Broker (default: true)

Metadata tables

Druid Brokers infer table and column metadata for each datasource from segments loaded in the cluster, and use this to
plan SQL queries. This metadata is cached on Broker startup and also updated periodically in the background through
SegmentMetadata queries. Background metadata refreshing is triggered by
segments entering and exiting the cluster, and can also be throttled through configuration.

Druid exposes system information through special system tables. There are two such schemas available: Information Schema and Sys Schema.
Information schema provides details about table and column types. The “sys” schema provides information about Druid internals like segments/tasks/servers.

INFORMATION SCHEMA

You can access table and column metadata through JDBC using connection.getMetaData(), or through the
INFORMATION_SCHEMA tables described below. For example, to retrieve metadata for the Druid
datasource “foo”, use the query:

SELECT * FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_SCHEMA = 'druid' AND TABLE_NAME = 'foo'

Note: INFORMATION_SCHEMA tables do not currently support Druid-specific functions like TIME_PARSE and
APPROX_QUANTILE_DS. Only standard SQL functions can be used.

SCHEMATA table

Column	Notes
CATALOG_NAME	Unused
SCHEMA_NAME
SCHEMA_OWNER	Unused
DEFAULT_CHARACTER_SET_CATALOG	Unused
DEFAULT_CHARACTER_SET_SCHEMA	Unused
DEFAULT_CHARACTER_SET_NAME	Unused
SQL_PATH	Unused

TABLES table

Column	Notes
TABLE_CATALOG	Unused
TABLE_SCHEMA
TABLE_NAME
TABLE_TYPE	“TABLE” or “SYSTEM_TABLE”

COLUMNS table

Column	Notes
TABLE_CATALOG	Unused
TABLE_SCHEMA
TABLE_NAME
COLUMN_NAME
ORDINAL_POSITION
COLUMN_DEFAULT	Unused
IS_NULLABLE
DATA_TYPE
CHARACTER_MAXIMUM_LENGTH	Unused
CHARACTER_OCTET_LENGTH	Unused
NUMERIC_PRECISION
NUMERIC_PRECISION_RADIX
NUMERIC_SCALE
DATETIME_PRECISION
CHARACTER_SET_NAME
COLLATION_NAME
JDBC_TYPE	Type code from java.sql.Types (Druid extension)

SYSTEM SCHEMA

The “sys” schema provides visibility into Druid segments, servers and tasks.

Note: “sys” tables do not currently support Druid-specific functions like TIME_PARSE and
APPROX_QUANTILE_DS. Only standard SQL functions can be used.

SEGMENTS table

Segments table provides details on all Druid segments, whether they are published yet or not.

Column	Type	Notes
segment_id	STRING	Unique segment identifier
datasource	STRING	Name of datasource
start	STRING	Interval start time (in ISO 8601 format)
end	STRING	Interval end time (in ISO 8601 format)
size	LONG	Size of segment in bytes
version	STRING	Version string (generally an ISO8601 timestamp corresponding to when the segment set was first started). Higher version means the more recently created segment. Version comparing is based on string comparison.
partition_num	LONG	Partition number (an integer, unique within a datasource+interval+version; may not necessarily be contiguous)
num_replicas	LONG	Number of replicas of this segment currently being served
num_rows	LONG	Number of rows in current segment, this value could be null if unknown to Broker at query time
is_published	LONG	Boolean is represented as long type where 1 = true, 0 = false. 1 represents this segment has been published to the metadata store with used=1. See the Architecture page for more details.
is_available	LONG	Boolean is represented as long type where 1 = true, 0 = false. 1 if this segment is currently being served by any process(Historical or realtime). See the Architecture page for more details.
is_realtime	LONG	Boolean is represented as long type where 1 = true, 0 = false. 1 if this segment is only served by realtime tasks, and 0 if any historical process is serving this segment.
is_overshadowed	LONG	Boolean is represented as long type where 1 = true, 0 = false. 1 if this segment is published and is fully overshadowed by some other published segments. Currently, is_overshadowed is always false for unpublished segments, although this may change in the future. You can filter for segments that “should be published” by filtering for is_published = 1 AND is_overshadowed = 0. Segments can briefly be both published and overshadowed if they were recently replaced, but have not been unpublished yet. See the Architecture page for more details.
payload	STRING	JSON-serialized data segment payload

For example to retrieve all segments for datasource “wikipedia”, use the query:

SELECT * FROM sys.segments WHERE datasource = 'wikipedia'

Another example to retrieve segments total_size, avg_size, avg_num_rows and num_segments per datasource:

SELECT
    datasource,
    SUM("size") AS total_size,
    CASE WHEN SUM("size") = 0 THEN 0 ELSE SUM("size") / (COUNT(*) FILTER(WHERE "size" > 0)) END AS avg_size,
    CASE WHEN SUM(num_rows) = 0 THEN 0 ELSE SUM("num_rows") / (COUNT(*) FILTER(WHERE num_rows > 0)) END AS avg_num_rows,
    COUNT(*) AS num_segments
FROM sys.segments
GROUP BY 1
ORDER BY 2 DESC

Caveat: Note that a segment can be served by more than one stream ingestion tasks or Historical processes, in that case it would have multiple replicas. These replicas are weakly consistent with each other when served by multiple ingestion tasks, until a segment is eventually served by a Historical, at that point the segment is immutable. Broker prefers to query a segment from Historical over an ingestion task. But if a segment has multiple realtime replicas, for e.g… Kafka index tasks, and one task is slower than other, then the sys.segments query results can vary for the duration of the tasks because only one of the ingestion tasks is queried by the Broker and it is not guaranteed that the same task gets picked every time. The num_rows column of segments table can have inconsistent values during this period. There is an open issue about this inconsistency with stream ingestion tasks.

SERVERS table

Servers table lists all discovered servers in the cluster.

Column	Type	Notes
server	STRING	Server name in the form host:port
host	STRING	Hostname of the server
plaintext_port	LONG	Unsecured port of the server, or -1 if plaintext traffic is disabled
tls_port	LONG	TLS port of the server, or -1 if TLS is disabled
server_type	STRING	Type of Druid service. Possible values include: COORDINATOR, OVERLORD, BROKER, ROUTER, HISTORICAL, MIDDLE_MANAGER or PEON.
tier	STRING	Distribution tier see druid.server.tier. Only valid for HISTORICAL type, for other types it’s null
current_size	LONG	Current size of segments in bytes on this server. Only valid for HISTORICAL type, for other types it’s 0
max_size	LONG	Max size in bytes this server recommends to assign to segments see druid.server.maxSize. Only valid for HISTORICAL type, for other types it’s 0

To retrieve information about all servers, use the query:

SELECT * FROM sys.servers;

SERVER_SEGMENTS table

SERVER_SEGMENTS is used to join servers with segments table

Column	Type	Notes
server	STRING	Server name in format host:port (Primary key of servers table)
segment_id	STRING	Segment identifier (Primary key of segments table)

JOIN between “servers” and “segments” can be used to query the number of segments for a specific datasource,
grouped by server, example query:

SELECT count(segments.segment_id) as num_segments from sys.segments as segments
INNER JOIN sys.server_segments as server_segments
ON segments.segment_id  = server_segments.segment_id
INNER JOIN sys.servers as servers
ON servers.server = server_segments.server
WHERE segments.datasource = 'wikipedia'
GROUP BY servers.server;

TASKS table

The tasks table provides information about active and recently-completed indexing tasks. For more information
check out the documentation for ingestion tasks.

Column	Type	Notes
task_id	STRING	Unique task identifier
group_id	STRING	Task group ID for this task, the value depends on the task type. For example, for native index tasks, it’s same as task_id, for sub tasks, this value is the parent task’s ID
type	STRING	Task type, for example this value is “index” for indexing tasks. See tasks-overview
datasource	STRING	Datasource name being indexed
created_time	STRING	Timestamp in ISO8601 format corresponding to when the ingestion task was created. Note that this value is populated for completed and waiting tasks. For running and pending tasks this value is set to 1970-01-01T00:00:00Z
queue_insertion_time	STRING	Timestamp in ISO8601 format corresponding to when this task was added to the queue on the Overlord
status	STRING	Status of a task can be RUNNING, FAILED, SUCCESS
runner_status	STRING	Runner status of a completed task would be NONE, for in-progress tasks this can be RUNNING, WAITING, PENDING
duration	LONG	Time it took to finish the task in milliseconds, this value is present only for completed tasks
location	STRING	Server name where this task is running in the format host:port, this information is present only for RUNNING tasks
host	STRING	Hostname of the server where task is running
plaintext_port	LONG	Unsecured port of the server, or -1 if plaintext traffic is disabled
tls_port	LONG	TLS port of the server, or -1 if TLS is disabled
error_msg	STRING	Detailed error message in case of FAILED tasks

For example, to retrieve tasks information filtered by status, use the query

SELECT * FROM sys.tasks WHERE status='FAILED';

SUPERVISORS table

The supervisors table provides information about supervisors.

Column	Type	Notes
supervisor_id	STRING	Supervisor task identifier
state	STRING	Basic state of the supervisor. Available states: UNHEALTHY_SUPERVISOR, UNHEALTHY_TASKS, PENDING, RUNNING, SUSPENDED, STOPPING. Check Kafka Docs for details.
detailed_state	STRING	Supervisor specific state. (See documentation of the specific supervisor for details, e.g. Kafka or Kinesis)
healthy	LONG	Boolean represented as long type where 1 = true, 0 = false. 1 indicates a healthy supervisor
type	STRING	Type of supervisor, e.g. kafka, kinesis or materialized_view
source	STRING	Source of the supervisor, e.g. Kafka topic or Kinesis stream
suspended	LONG	Boolean represented as long type where 1 = true, 0 = false. 1 indicates supervisor is in suspended state
spec	STRING	JSON-serialized supervisor spec

For example, to retrieve supervisor tasks information filtered by health status, use the query

SELECT * FROM sys.supervisors WHERE healthy=0;

Server configuration

Druid SQL planning occurs on the Broker and is configured by
Broker runtime properties.

Security

Please see Defining SQL permissions in the
basic security documentation for information on what permissions are needed for making SQL queries.