Row Pattern Recognition

December 4, 2025About 18 min

Row Pattern Recognition

1. Overview

IoTDB supports Row Pattern Recognition. This feature enables capturing a segment of continuous data by defining the recognition logic of pattern variables and regular expressions, and performing analysis and calculation on each captured data segment. It is suitable for business scenarios such as identifying specific patterns in time-series data and detecting specific events. If we regard Row Pattern Recognition as grouping processing of data, the core process is roughly as follows:

Perform group capture through the PATTERN, DEFINE, and SUBSET clauses
Conduct computational processing on the captured groups through the MEASURES clause
Set the output format of groups through the ROWS PER MATCH clause
Specify how to locate the start position of the next group through the AFTER MATCH SKIP clause

Note: This feature is available starting from version V2.0.5.

2. Function Introduction

2.1 Syntax Format

MATCH_RECOGNIZE (
  [ PARTITION BY column [, ...] ]
  [ ORDER BY column [, ...] ]
  [ MEASURES measure_definition [, ...] ]
  [ ROWS PER MATCH ]
  [ AFTER MATCH skip_to ]
  PATTERN ( row_pattern )
  [ SUBSET subset_definition [, ...] ]
  DEFINE variable_definition [, ...]
)

Note:

PARTITION BY: Optional. Used to group the input table, and each group can perform pattern matching independently. If this clause is not specified, the entire input table will be processed as a single unit.
ORDER BY: Optional. Used to ensure that input data is processed in a specific order during matching.
MEASURES: Optional. Used to specify which information to extract from the matched segment of data.
ROWS PER MATCH: Optional. Used to specify the output method of the result set after successful pattern matching.
AFTER MATCH SKIP: Optional. Used to specify which row to resume from for the next pattern match after identifying a non-empty match.
PATTERN: Used to define the row pattern to be matched.
SUBSET: Optional. Used to merge rows matched by multiple basic pattern variables into a single logical set.
DEFINE: Used to define the basic pattern variables for the row pattern.

2.2 DEFINE Clause

Used to specify the judgment condition for each basic pattern variable in pattern recognition. These variables are usually represented by identifiers (e.g., A, B), and the Boolean expressions in this clause precisely define which rows meet the requirements of the variable.

During pattern matching execution, a row is only marked as the variable (and thus included in the current matching group) if the Boolean expression returns TRUE.

-- A row can only be identified as B if its totalprice value is less than the totalprice value of the previous row.
DEFINE B AS totalprice < PREV(totalprice)

Variables not explicitly defined in this clause have an implicitly set condition of always true (TRUE), meaning they can be successfully matched on any input row.

2.3 SUBSET Clause

Used to merge rows matched by multiple basic pattern variables (e.g., A, B) into a combined pattern variable (e.g., U), allowing these rows to be treated as a single logical set for operations. It can be used in the MEASURES, DEFINE, and AFTER MATCH SKIP clauses.

SUBSET U = (A, B)

For example, for the pattern PATTERN ((A | B){5} C+), it is impossible to determine whether the 5th repetition matches the basic pattern variable A or B during matching. Therefore:

In the MEASURES clause, if you need to reference the last row matched in this phase, you can do so by defining the combined pattern variable SUBSET U = (A, B). At this point, the expression RPR_LAST(U.totalprice) will directly return the totalprice value of the target row.
In the AFTER MATCH SKIP clause, if the matching result does not include the basic pattern variable A or B, executing AFTER MATCH SKIP TO LAST B or AFTER MATCH SKIP TO LAST A will fail to jump due to missing anchors. However, by introducing the combined pattern variable SUBSET U = (A, B), using AFTER MATCH SKIP TO LAST U is always valid.

2.4 PATTERN Clause

Used to define the row pattern to be matched, whose basic building block is a row pattern variable.

PATTERN ( row_pattern )

2.4.1 Pattern Types

Row Pattern	Syntax Format	Description
Pattern Concatenation	`A B+ C+ D+`	Composed of subpatterns without any operators, matching all subpatterns in the declared order sequentially.
Pattern Alternation	`A \| B \| C`	Composed of multiple subpatterns separated by `\|`, matching only one of them. If multiple subpatterns can be matched, the leftmost one is selected.
Pattern Permutation	`PERMUTE(A, B, C)`	Equivalent to performing alternation matching on all different orders of the subpattern elements. It requires that A, B, and C must all be matched, but their order of appearance is not fixed. If multiple matching orders are possible, the priority is determined by the lexicographical order based on the definition sequence of elements in the PERMUTE list. For example, A B C has the highest priority, while C B A has the lowest.
Pattern Grouping	`(A B C)`	Encloses subpatterns in parentheses to treat them as a single unit, which can be used with other operators. For example, `(A B C)+` indicates a pattern where a group of `(A B C)` appears consecutively.
Empty Pattern	`()`	Represents an empty match that does not contain any rows.
Pattern Exclusion	`{- row_pattern -}`	Used to specify the matched part to be excluded from the output. Usually used with the `ALL ROWS PER MATCH` option to output rows of interest. For example, `PATTERN (A {- B+ C+ -} D+)` with ALL ROWS PER MATCH will only output the first row `(corresponding to A)` and the trailing rows `(corresponding to D+)` of the match.

2.4.2 Partition Start/End Anchor

^A indicates matching a pattern that starts with A as the partition beginning
- When the value of the PATTERN clause is ^A, the match must start from the first row of the partition, and this row must satisfy the definition of A.
- When the value of the PATTERN clause is ^A^ or A^, the output result is empty.
A$ indicates matching a pattern that ends with A as the partition end
- When the value of the PATTERN clause is A$, the match must end at the end of the partition, and this row must satisfy the definition of A.
- When the value of the PATTERN clause is $A or $A$ , the output result is empty.

For example illustrations, seeSection 3.1

2.4.3 Quantifiers

Quantifiers are used to specify the number of times a subpattern repeats, placed after the corresponding subpattern (e.g., (A | B)*).

Common quantifiers are as follows:

Quantifier	Description
`*`	Zero or more repetitions
`+`	One or more repetitions
`?`	Zero or one repetition
`{n}`	Exactly n repetitions
`{m, n}`	Repetitions between m and n times (m and n are non-negative integers). * If the left bound is omitted, the default starts from 0; * If the right bound is omitted, there is no upper limit on the number of repetitions (e.g., {5,} is equivalent to "at least five times"); * If both left and right bounds are omitted (i.e., {,}), it is equivalent to `*`.

The matching preference can be changed by adding ? after the quantifier.
- {3,5}: Prefers 5 times, least prefers 3 times; {3,5}?: Prefers 3 times, least prefers 5 times.
- ?: Prefers 1 time; ??: Prefers 0 times.

2.5 AFTER MATCH SKIP Clause

Used to specify which row to start the next pattern match from after identifying a non-empty match.

Jump Strategy	Description	Allows Overlapping Matches?
`AFTER MATCH SKIP PAST LAST ROW`	Default behavior. Starts from the row after the last row of the current match.	No
`AFTER MATCH SKIP TO NEXT ROW`	Starts from the second row in the current match.	Yes
`AFTER MATCH SKIP TO [ FIRST \| LAST ] pattern_variable`	Jumps to start from the [ first row	last row ] of a pattern variable.

Among all possible configurations, only when ALL ROWS PER MATCH WITH UNMATCHED ROWS is used in combination with AFTER MATCH SKIP PAST LAST ROW can the system ensure that exactly one output record is generated for each input row.

For example illustrations, see Section 3.2

2.6 ROWS PER MATCH Clause

Used to specify the output method of the result set after a successful pattern match, including the following two main options:

Output Method	Rule Description	Output Result	Handling Logic for Empty Matches/Unmatched Rows
ONE ROW PER MATCH	Generates one output row for each successful match.	* Columns in the PARTITION BY clause* Expressions defined in the MEASURES clause.	Outputs empty matches; skips unmatched rows.
ALL ROWS PER MATCH	Each row in a match generates an output record, unless the row is excluded via exclusion syntax.	* Columns in the PARTITION BY clause* Columns in the ORDER BY clause* Expressions defined in the MEASURES clause* Remaining columns in the input table	* Default: Outputs empty matches; skips unmatched rows.* ALL ROWS PER MATCHSHOW EMPTY MATCHES: Outputs empty matches by default; skips unmatched rows.* ALL ROWS PER MATCHOMIT EMPTY MATCHES: Does not output empty matches; skips unmatched rows.* ALL ROWS PER MATCHWITH UNMATCHED ROWS: Outputs empty matches and generates an additional output record for each unmatched row.

2.7 MEASURES Clause

Used to specify which information to extract from a matched set of data. This clause is optional; if not explicitly specified, some input columns will become the output results of pattern recognition based on the settings of the ROWS PER MATCH clause.

SQL

MEASURES measure_expression AS measure_name [, ...]

A measure_expression is a scalar value calculated from the matched set of data.

Usage Example	Description
`A.totalprice AS starting_price`	Returns the price from the first row in the matched group (i.e., the only row associated with variable A) as the starting price.
`RPR_LAST(B.totalprice) AS bottom_price`	Returns the price from the last row associated with variable B, representing the lowest price in the "V" shape pattern (corresponding to the end of the downward segment).
`RPR_LAST(U.totalprice) AS top_price`	Returns the highest price in the matched group, corresponding to the last row associated with variable C or D (i.e., the end of the entire matched group). [Assuming SUBSET U = (C, D)]

Each measure_expression defines an output column, which can be referenced by its specified measure_name.

2.8 Row Pattern Recognition Expressions

Expressions used in the MEASURES and DEFINE clauses are scalar expressions, evaluated in the row-level context of the input table. In addition to supporting standard SQL syntax, scalar expressions also support special extended functions for row pattern recognition.

2.8.1 Pattern Variable References

SQL

A.totalprice  
U.orderdate  
orderstatus

When a column name is prefixed with a basic pattern variable or a combined pattern variable, it refers to the corresponding column values of all rows matched by that variable.
If a column name has no prefix, it is equivalent to using the "global combined pattern variable" (i.e., the union of all basic pattern variables) as the prefix, referring to the column values of all rows in the current match.

Using table names as column name prefixes in pattern recognition expressions is not allowed.

2.8.2 Extended Functions

Function Name	Function Syntax	Description
`MATCH_NUMBER` Function	`MATCH_NUMBER()`	Returns the sequence number of the current match within the partition, starting from 1. Empty matches occupy match sequence numbers just like non-empty matches.
`CLASSIFIER` Function	`CLASSIFIER(option)`	1. Returns the name of the basic pattern variable mapped by the current row. 2. `option` is an optional parameter: a basic pattern variable `CLASSIFIER(A)` or a combined pattern variable `CLASSIFIER(U)` can be passed in to limit the function's scope; for rows outside the scope, NULL is returned directly. When used with a combined pattern variable, it can be used to distinguish which basic pattern variable in the union the row is mapped to.
Logical Navigation Functions	`RPR_FIRST(expr, k)`	1. Indicates locating the first row satisfying `expr` in the current match group, then searching for the k-th occurrence of the row corresponding to the same pattern variable towards the end of the group, and returning the specified column value of that row. If the k-th matching row is not found in the specified direction, the function returns NULL. 2. `k` is an optional parameter, defaulting to 0 (only locating the first row satisfying the condition); if explicitly specified, it must be a non-negative integer.
Logical Navigation Functions	`RPR_LAST(expr, k)`	1. Indicates locating the last row satisfying `expr` in the current match group, then searching for the k-th occurrence of the row corresponding to the same pattern variable towards the start of the group, and returning the specified column value of that row. If the k-th matching row is not found in the specified direction, the function returns NULL. 2. `k` is an optional parameter, defaulting to 0 (only locating the last row satisfying the condition); if explicitly specified, it must be a non-negative integer.
Physical Navigation Functions	`PREV(expr, k)`	1. Indicates offsetting k rows towards the start from the last row matched to the given pattern variable, and returning the corresponding column value. If navigation exceeds the partition boundary, the function returns NULL. 2. `k` is an optional parameter, defaulting to 1; if explicitly specified, it must be a non-negative integer.
Physical Navigation Functions	`NEXT(expr, k)`	1. Indicates offsetting k rows towards the end from the last row matched to the given pattern variable, and returning the corresponding column value. If navigation exceeds the partition boundary, the function returns NULL. 2. `k` is an optional parameter, defaulting to 1; if explicitly specified, it must be a non-negative integer.
Aggregate Functions	COUNT, SUM, AVG, MAX, MIN Functions	Can be used to calculate data in the current match. Aggregate functions and navigation functions are not allowed to be nested within each other. (Supported from version V2.0.6)
Nested Functions	`PREV/NEXT(CLASSIFIER())`	Nesting of physical navigation functions and the CLASSIFIER function. Used to obtain the pattern variables corresponding to the previous and next matching rows of the current row.
Nested Functions	`PREV/NEXT(RPR_FIRST/RPR_LAST(expr, k)`）	Logical functions are allowed to be nested inside physical functions; physical functions are not allowed to be nested inside logical functions. Used to perform logical offset first, then physical offset.

For example illustrations, see Section 3.3

2.8.3 RUNNING and FINAL Semantics

Definition

RUNNING: Indicates the calculation scope is from the start row of the current match group to the row currently being processed (i.e., up to the current row).
FINAL: Indicates the calculation scope is from the start row of the current match group to the final row of the group (i.e., the entire match group).

Scope of Application

The DEFINE clause uses RUNNING semantics by default.
The MEASURES clause uses RUNNING semantics by default and supports specifying FINAL semantics. When using the ONE ROW PER MATCH output mode, all expressions are calculated from the last row position of the match group, and at this time, RUNNING semantics are equivalent to FINAL semantics.

Syntax Constraints

RUNNING and FINAL need to be written before logical navigation functions or aggregate functions, and cannot directly act on column references.
- Valid: RUNNING RPP_LAST(A.totalprice), FINAL RPP_LAST(A.totalprice)
- Invalid: RUNNING A.totalprice, FINAL A.totalprice, RUNNING PREV(A.totalprice)

3. Syntax Examples

Original Data

SQL

IoTDB:database3> select * from t
+-----------------------------+------+----------+
|                         time|device|totalprice|
+-----------------------------+------+----------+
|2025-01-01T00:01:00.000+08:00|    d1|        90|
|2025-01-01T00:02:00.000+08:00|    d1|        80|
|2025-01-01T00:03:00.000+08:00|    d1|        70|
|2025-01-01T00:04:00.000+08:00|    d1|        80|
|2025-01-01T00:05:00.000+08:00|    d1|        70|
|2025-01-01T00:06:00.000+08:00|    d1|        80|
+-----------------------------+------+----------+

-- Create Statement
create table t(device tag, totalprice int32 field)

insert into t(time,device,totalprice) values(2025-01-01T00:01:00, 'd1', 90),(2025-01-01T00:02:00, 'd1', 80),(2025-01-01T00:03:00, 'd1', 70),(2025-01-01T00:04:00, 'd1', 80),(2025-01-01T00:05:00, 'd1', 70),(2025-01-01T00:06:00, 'd1', 80)

3.1 PATTERN Clause Partition Anchor

Query SQL

SQL

SELECT m.time, m.match, m.price, m.label
FROM t
MATCH_RECOGNIZE ( 
    ORDER BY time 
    MEASURES 
         MATCH_NUMBER() AS match, 
         RUNNING RPR_LAST(totalprice) AS price, 
         CLASSIFIER() AS label 
    ALL ROWS PER MATCH 
    AFTER MATCH SKIP PAST LAST ROW 
    PATTERN %s -- PATTERN Clause 
    DEFINE A AS true 
) AS m;

Query Results

When the PATTERN clause is PATTERN (^A)

SQL

+-----------------------------+-----+-----+-----+
|                         time|match|price|label|
+-----------------------------+-----+-----+-----+
|2025-01-01T00:01:00.000+08:00|    1|   90|    A|
+-----------------------------+-----+-----+-----+
Total line number = 1

When the PATTERN clause is PATTERN (^A^)

SQL

+----+-----+-----+-----+
|time|match|price|label|
+----+-----+-----+-----+
+----+-----+-----+-----+
Empty set.

When the PATTERN clause is PATTERN (A$)

SQL

+-----------------------------+-----+-----+-----+
|                         time|match|price|label|
+-----------------------------+-----+-----+-----+
|2025-01-01T00:06:00.000+08:00|    1|   80|    A|
+-----------------------------+-----+-----+-----+
Total line number = 1

When the PATTERN clause is PATTERN ($A$)

SQL

+----+-----+-----+-----+
|time|match|price|label|
+----+-----+-----+-----+
+----+-----+-----+-----+
Empty set.

3.2 AFTER MATCH SKIP Clause

Query SQL

SQL

SELECT m.time, m.match, m.price, m.label
FROM t
MATCH_RECOGNIZE (
    ORDER BY time
    MEASURES 
        MATCH_NUMBER() AS match,
        RUNNING RPR_LAST(totalprice) AS price,
        CLASSIFIER() AS label
    ALL ROWS PER MATCH
    %s -- AFTER MATCH SKIP Clause
    PATTERN (A B+ C+ D?)
    SUBSET U = (C, D)
    DEFINE 
        B AS B.totalprice < PREV (B.totalprice),
        C AS C.totalprice > PREV (C.totalprice),
        D AS false -- Never matches successfully
) AS m;

Query Results

When AFTER MATCH SKIP PAST LAST ROW
- First match: Rows 1, 2, 3, 4
- Second match: According to the semantics of AFTER MATCH SKIP PAST LAST ROW, starting from row 5, no valid match can be found
- This pattern will never have overlapping matches

SQL

+-----------------------------+-----+-----+-----+
|                         time|match|price|label|
+-----------------------------+-----+-----+-----+
|2025-01-01T00:01:00.000+08:00|    1|   90|    A|
|2025-01-01T00:02:00.000+08:00|    1|   80|    B|
|2025-01-01T00:03:00.000+08:00|    1|   70|    B|
|2025-01-01T00:04:00.000+08:00|    1|   80|    C|
+-----------------------------+-----+-----+-----+
Total line number = 4

When AFTER MATCH SKIP TO NEXT ROW
- First match: Rows 1, 2, 3, 4
- Second match: According to the semantics of AFTER MATCH SKIP TO NEXT ROW, starting from row 2, matches: Rows 2, 3, 4
- Third match: Attempts to start from row 3, fails
- Fourth match: Attempts to start from row 4, succeeds, matches rows 4, 5, 6
- This pattern allows overlapping matches

SQL

+-----------------------------+-----+-----+-----+
|                         time|match|price|label|
+-----------------------------+-----+-----+-----+
|2025-01-01T00:01:00.000+08:00|    1|   90|    A|
|2025-01-01T00:02:00.000+08:00|    1|   80|    B|
|2025-01-01T00:03:00.000+08:00|    1|   70|    B|
|2025-01-01T00:04:00.000+08:00|    1|   80|    C|
|2025-01-01T00:02:00.000+08:00|    2|   80|    A|
|2025-01-01T00:03:00.000+08:00|    2|   70|    B|
|2025-01-01T00:04:00.000+08:00|    2|   80|    C|
|2025-01-01T00:04:00.000+08:00|    3|   80|    A|
|2025-01-01T00:05:00.000+08:00|    3|   70|    B|
|2025-01-01T00:06:00.000+08:00|    3|   80|    C|
+-----------------------------+-----+-----+-----+
Total line number = 10

When AFTER MATCH SKIP TO FIRST C
- First match: Rows 1, 2, 3, 4
- Second match: Starts from the first C (i.e., row 4), matches rows 4, 5, 6
- This pattern allows overlapping matches

SQL

+-----------------------------+-----+-----+-----+
|                         time|match|price|label|
+-----------------------------+-----+-----+-----+
|2025-01-01T00:01:00.000+08:00|    1|   90|    A|
|2025-01-01T00:02:00.000+08:00|    1|   80|    B|
|2025-01-01T00:03:00.000+08:00|    1|   70|    B|
|2025-01-01T00:04:00.000+08:00|    1|   80|    C|
|2025-01-01T00:04:00.000+08:00|    2|   80|    A|
|2025-01-01T00:05:00.000+08:00|    2|   70|    B|
|2025-01-01T00:06:00.000+08:00|    2|   80|    C|
+-----------------------------+-----+-----+-----+
Total line number = 7

When AFTER MATCH SKIP TO LAST B or AFTER MATCH SKIP TO B
- First match: Rows 1, 2, 3, 4
- Second match: Attempts to start from the last B (i.e., row 3), fails
- Third match: Attempts to start from row 4, successfully matches rows 4, 5, 6
- This pattern allows overlapping matches

SQL

+-----------------------------+-----+-----+-----+
|                         time|match|price|label|
+-----------------------------+-----+-----+-----+
|2025-01-01T00:01:00.000+08:00|    1|   90|    A|
|2025-01-01T00:02:00.000+08:00|    1|   80|    B|
|2025-01-01T00:03:00.000+08:00|    1|   70|    B|
|2025-01-01T00:04:00.000+08:00|    1|   80|    C|
|2025-01-01T00:04:00.000+08:00|    2|   80|    A|
|2025-01-01T00:05:00.000+08:00|    2|   70|    B|
|2025-01-01T00:06:00.000+08:00|    2|   80|    C|
+-----------------------------+-----+-----+-----+
Total line number = 7

When AFTER MATCH SKIP TO U
- First match: Rows 1, 2, 3, 4
- Second match: SKIP TO U means jumping to the last C or D; D can never match successfully, so it jumps to the last C (i.e., row 4), successfully matching rows 4, 5, 6
- This pattern allows overlapping matches

SQL

+-----------------------------+-----+-----+-----+
|                         time|match|price|label|
+-----------------------------+-----+-----+-----+
|2025-01-01T00:01:00.000+08:00|    1|   90|    A|
|2025-01-01T00:02:00.000+08:00|    1|   80|    B|
|2025-01-01T00:03:00.000+08:00|    1|   70|    B|
|2025-01-01T00:04:00.000+08:00|    1|   80|    C|
|2025-01-01T00:04:00.000+08:00|    2|   80|    A|
|2025-01-01T00:05:00.000+08:00|    2|   70|    B|
|2025-01-01T00:06:00.000+08:00|    2|   80|    C|
+-----------------------------+-----+-----+-----+
Total line number = 7

When AFTER MATCH SKIP TO A, you cannot jump to the first row of the match, otherwise it will cause an infinite loop

SQL

Msg: org.apache.iotdb.jdbc.IoTDBSQLException: 701: AFTER MATCH SKIP TO failed: cannot skip to first row of match

When AFTER MATCH SKIP TO B, you cannot jump to a pattern variable that does not exist in the match group

SQL

Msg: org.apache.iotdb.jdbc.IoTDBSQLException: 701: AFTER MATCH SKIP TO failed: pattern variable is not present in match

3.3 Row Pattern Expressions - Extended Functions

3.3.1 CLASSIFIER() Function

Query SQL

SQL

SELECT m.time, m.match, m.price, m.lower_or_higher, m.label
FROM t
MATCH_RECOGNIZE (
    ORDER BY time
    MEASURES 
        MATCH_NUMBER() AS match,
        RUNNING RPR_LAST(totalprice) AS price,
        CLASSIFIER(U) AS lower_or_higher,
        CLASSIFIER(W) AS label
    ALL ROWS PER MATCH
    PATTERN ((L | H) A)
    SUBSET 
        U = (L, H),
        W = (A, L, H)
    DEFINE 
        A AS A.totalprice = 80,
        L AS L.totalprice < 80,
        H AS H.totalprice > 80
) AS m;

Query Results

SQL

+-----------------------------+-----+-----+---------------+-----+
|                         time|match|price|lower_or_higher|label|
+-----------------------------+-----+-----+---------------+-----+
|2025-01-01T00:01:00.000+08:00|    1|   90|              H|    H|
|2025-01-01T00:02:00.000+08:00|    1|   80|              H|    A|
|2025-01-01T00:03:00.000+08:00|    2|   70|              L|    L|
|2025-01-01T00:04:00.000+08:00|    2|   80|              L|    A|
|2025-01-01T00:05:00.000+08:00|    3|   70|              L|    L|
|2025-01-01T00:06:00.000+08:00|    3|   80|              L|    A|
+-----------------------------+-----+-----+---------------+-----+
Total line number = 6

Query SQL

SQL

SELECT m.time, m.measure
FROM t
MATCH_RECOGNIZE ( 
    ORDER BY time 
    MEASURES 
         %s AS measure -- MEASURES Clause
    ALL ROWS PER MATCH 
    PATTERN (A+)
    DEFINE A AS true 
) AS m;

Query Results

When the value is totalprice, RPR_LAST(totalprice), RUNNING RPR_LAST(totalprice)

SQL

+-----------------------------+-------+
|                         time|measure|
+-----------------------------+-------+
|2025-01-01T00:01:00.000+08:00|     90|
|2025-01-01T00:02:00.000+08:00|     80|
|2025-01-01T00:03:00.000+08:00|     70|
|2025-01-01T00:04:00.000+08:00|     80|
|2025-01-01T00:05:00.000+08:00|     70|
|2025-01-01T00:06:00.000+08:00|     80|
+-----------------------------+-------+
Total line number = 6

When the value is FINAL RPR_LAST(totalprice)

SQL

+-----------------------------+-------+
|                         time|measure|
+-----------------------------+-------+
|2025-01-01T00:01:00.000+08:00|     80|
|2025-01-01T00:02:00.000+08:00|     80|
|2025-01-01T00:03:00.000+08:00|     80|
|2025-01-01T00:04:00.000+08:00|     80|
|2025-01-01T00:05:00.000+08:00|     80|
|2025-01-01T00:06:00.000+08:00|     80|
+-----------------------------+-------+
Total line number = 6

When the value is RPR_FIRST(totalprice), RUNNING RPR_FIRST(totalprice), FINAL RPR_FIRST(totalprice)

SQL

+-----------------------------+-------+
|                         time|measure|
+-----------------------------+-------+
|2025-01-01T00:01:00.000+08:00|     90|
|2025-01-01T00:02:00.000+08:00|     90|
|2025-01-01T00:03:00.000+08:00|     90|
|2025-01-01T00:04:00.000+08:00|     90|
|2025-01-01T00:05:00.000+08:00|     90|
|2025-01-01T00:06:00.000+08:00|     90|
+-----------------------------+-------+
Total line number = 6

When the value is RPR_LAST(totalprice, 2)

SQL

+-----------------------------+-------+
|                         time|measure|
+-----------------------------+-------+
|2025-01-01T00:01:00.000+08:00|   null|
|2025-01-01T00:02:00.000+08:00|   null|
|2025-01-01T00:03:00.000+08:00|     90|
|2025-01-01T00:04:00.000+08:00|     80|
|2025-01-01T00:05:00.000+08:00|     70|
|2025-01-01T00:06:00.000+08:00|     80|
+-----------------------------+-------+
Total line number = 6

When the value is FINAL RPP_LAST(totalprice, 2)

SQL

+-----------------------------+-------+
|                         time|measure|
+-----------------------------+-------+
|2025-01-01T00:01:00.000+08:00|     80|
|2025-01-01T00:02:00.000+08:00|     80|
|2025-01-01T00:03:00.000+08:00|     80|
|2025-01-01T00:04:00.000+08:00|     80|
|2025-01-01T00:05:00.000+08:00|     80|
|2025-01-01T00:06:00.000+08:00|     80|
+-----------------------------+-------+
Total line number = 6

When the value is RPR_FIRST(totalprice, 2) and FINAL RPR_FIRST(totalprice, 2)

SQL

+-----------------------------+-------+
|                         time|measure|
+-----------------------------+-------+
|2025-01-01T00:01:00.000+08:00|     70|
|2025-01-01T00:02:00.000+08:00|     70|
|2025-01-01T00:03:00.000+08:00|     70|
|2025-01-01T00:04:00.000+08:00|     70|
|2025-01-01T00:05:00.000+08:00|     70|
|2025-01-01T00:06:00.000+08:00|     70|
+-----------------------------+-------+
Total line number = 6

Query SQL

SQL

SELECT m.time, m.measure
FROM t
MATCH_RECOGNIZE ( 
    ORDER BY time 
    MEASURES 
         %s AS measure -- MEASURES Clause
    ALL ROWS PER MATCH 
    PATTERN (B)
    DEFINE B AS B.totalprice >= PREV(B.totalprice)
) AS m;

Query Results

When the value is PREV(totalprice)

SQL

+-----------------------------+-------+
|                         time|measure|
+-----------------------------+-------+
|2025-01-01T00:04:00.000+08:00|     70|
|2025-01-01T00:06:00.000+08:00|     70|
+-----------------------------+-------+
Total line number = 2

When the value is PREV(B.totalprice, 2)

SQL

+-----------------------------+-------+
|                         time|measure|
+-----------------------------+-------+
|2025-01-01T00:04:00.000+08:00|     80|
|2025-01-01T00:06:00.000+08:00|     80|
+-----------------------------+-------+
Total line number = 2

When the value is PREV(B.totalprice, 4)

SQL

+-----------------------------+-------+
|                         time|measure|
+-----------------------------+-------+
|2025-01-01T00:04:00.000+08:00|   null|
|2025-01-01T00:06:00.000+08:00|     80|
+-----------------------------+-------+
Total line number = 2

When the value is NEXT(totalprice) or NEXT(B.totalprice, 1)

SQL

+-----------------------------+-------+
|                         time|measure|
+-----------------------------+-------+
|2025-01-01T00:04:00.000+08:00|     70|
|2025-01-01T00:06:00.000+08:00|   null|
+-----------------------------+-------+
Total line number = 2

When the value is NEXT(B.totalprice, 2)

SQL

+-----------------------------+-------+
|                         time|measure|
+-----------------------------+-------+
|2025-01-01T00:04:00.000+08:00|     80|
|2025-01-01T00:06:00.000+08:00|   null|
+-----------------------------+-------+
Total line number = 2

3.3.4 Aggregate Functions

Query SQL

SQL

SELECT m.time, m.count, m.avg, m.sum, m.min, m.max
FROM t
MATCH_RECOGNIZE ( 
    ORDER BY time 
    MEASURES 
         COUNT(*) AS count,
         AVG(totalprice) AS avg,
         SUM(totalprice) AS sum,
         MIN(totalprice) AS min,
         MAX(totalprice) AS max
    ALL ROWS PER MATCH 
    PATTERN (A+)
    DEFINE A AS true 
) AS m;

Query Results

SQL

+-----------------------------+-----+-----------------+-----+---+---+
|                         time|count|              avg|  sum|min|max|
+-----------------------------+-----+-----------------+-----+---+---+
|2025-01-01T00:01:00.000+08:00|    1|             90.0| 90.0| 90| 90|
|2025-01-01T00:02:00.000+08:00|    2|             85.0|170.0| 80| 90|
|2025-01-01T00:03:00.000+08:00|    3|             80.0|240.0| 70| 90|
|2025-01-01T00:04:00.000+08:00|    4|             80.0|320.0| 70| 90|
|2025-01-01T00:05:00.000+08:00|    5|             78.0|390.0| 70| 90|
|2025-01-01T00:06:00.000+08:00|    6|78.33333333333333|470.0| 70| 90|
+-----------------------------+-----+-----------------+-----+---+---+
Total line number = 6

3.3.5 Nested Functions

Example 1

Query SQL

SQL

SELECT m.time, m.match, m.price, m.lower_or_higher, m.label, m.prev_label, m.next_label
FROM t
MATCH_RECOGNIZE (
    ORDER BY time
    MEASURES 
        MATCH_NUMBER() AS match,
        RUNNING RPR_LAST(totalprice) AS price,
        CLASSIFIER(U) AS lower_or_higher,
        CLASSIFIER(W) AS label,
        PREV(CLASSIFIER(W)) AS prev_label,
        NEXT(CLASSIFIER(W)) AS next_label
    ALL ROWS PER MATCH
    PATTERN ((L | H) A)
    SUBSET 
        U = (L, H),
        W = (A, L, H)
    DEFINE 
        A AS A.totalprice = 80,
        L AS L.totalprice < 80,
        H AS H.totalprice > 80
) AS m;

Query Results

SQL

+-----------------------------+-----+-----+---------------+-----+----------+----------+
|                         time|match|price|lower_or_higher|label|prev_label|next_label|
+-----------------------------+-----+-----+---------------+-----+----------+----------+
|2025-01-01T00:01:00.000+08:00|    1|   90|              H|    H|      null|         A|
|2025-01-01T00:02:00.000+08:00|    1|   80|              H|    A|         H|      null|
|2025-01-01T00:03:00.000+08:00|    2|   70|              L|    L|      null|         A|
|2025-01-01T00:04:00.000+08:00|    2|   80|              L|    A|         L|      null|
|2025-01-01T00:05:00.000+08:00|    3|   70|              L|    L|      null|         A|
|2025-01-01T00:06:00.000+08:00|    3|   80|              L|    A|         L|      null|
+-----------------------------+-----+-----+---------------+-----+----------+----------+
Total line number = 6

Example 2

Query SQL

SQL

SELECT m.time, m.prev_last_price, m.next_first_price 
FROM t 
MATCH_RECOGNIZE ( 
    ORDER BY time  
    MEASURES 
        PREV(RPR_LAST(totalprice), 2) AS prev_last_price, 
        NEXT(RPR_FIRST(totalprice), 2) as next_first_price  
    ALL ROWS PER MATCH 
    PATTERN (A+)    
    DEFINE A AS true 
) AS m;

Query Results

SQL

+-----------------------------+---------------+----------------+
|                         time|prev_last_price|next_first_price|
+-----------------------------+---------------+----------------+
|2025-01-01T00:01:00.000+08:00|           null|              70|
|2025-01-01T00:02:00.000+08:00|           null|              70|
|2025-01-01T00:03:00.000+08:00|             90|              70|
|2025-01-01T00:04:00.000+08:00|             80|              70|
|2025-01-01T00:05:00.000+08:00|             70|              70|
|2025-01-01T00:06:00.000+08:00|             80|              70|
+-----------------------------+---------------+----------------+
Total line number = 6

4. Scenario Examples

Using Sample Data as the source data

4.1 Time Segment Query

Segment the data in table1 by time intervals less than or equal to 24 hours, and query the total number of data entries in each segment, as well as the start and end times.

Query SQL

SQL

SELECT start_time, end_time, cnt 
FROM table1 
MATCH_RECOGNIZE (
    ORDER BY time 
    MEASURES 
        RPR_FIRST(A.time) AS start_time, 
        RPR_LAST(time) AS end_time, 
        COUNT() AS cnt 
    PATTERN (A B*)  
    DEFINE  B AS (cast(B.time as INT64) - cast(PREV(B.time) as INT64)) <= 86400000
) AS m

Query Results

SQL

+-----------------------------+-----------------------------+---+
|                   start_time|                     end_time|cnt|
+-----------------------------+-----------------------------+---+
|2024-11-26T13:37:00.000+08:00|2024-11-26T13:38:00.000+08:00|  2|
|2024-11-27T16:38:00.000+08:00|2024-11-30T14:30:00.000+08:00| 16|
+-----------------------------+-----------------------------+---+
Total line number = 2

4.2 Difference Segment Query

Segment the data in table2 by humidity value differences less than 0.1, and query the total number of data entries in each segment, as well as the start and end times.

Query SQL

SQL

SELECT start_time, end_time, cnt 
FROM table2 
MATCH_RECOGNIZE (
    ORDER BY time 
    MEASURES 
        RPR_FIRST(A.time) AS start_time, 
        RPR_LAST(time) AS end_time, 
        COUNT() AS cnt 
    PATTERN (A B*)  
    DEFINE  B AS (B.humidity - PREV(B.humidity )) <=0.1
) AS m;

Query Results

SQL

+-----------------------------+-----------------------------+---+
|                   start_time|                     end_time|cnt|
+-----------------------------+-----------------------------+---+
|2024-11-26T13:37:00.000+08:00|2024-11-27T00:00:00.000+08:00|  2|
|2024-11-28T08:00:00.000+08:00|2024-11-29T00:00:00.000+08:00|  2|
|2024-11-29T11:00:00.000+08:00|2024-11-30T00:00:00.000+08:00|  2|
+-----------------------------+-----------------------------+---+
Total line number = 3

4.3 Event Statistics Query

Group the data in table1 by device ID, and count the start and end times and maximum humidity value where the humidity in the Shanghai area is greater than 35.

Query SQL

SQL

SELECT m.device_id, m.match, m.event_start, m.event_end, m.max_humidity 
FROM table1
MATCH_RECOGNIZE (
  PARTITION BY device_id
  ORDER BY time 
  MEASURES
    MATCH_NUMBER() AS match,
    RPR_FIRST(A.time) AS event_start,
    RPR_LAST(A.time) AS event_end,
    MAX(A.humidity) AS max_humidity 
  ONE ROW PER MATCH
  PATTERN (A+)
  DEFINE
    A AS A.region= 'Shanghai' AND A.humidity> 35
) AS m

Query Results

SQL

+---------+-----+-----------------------------+-----------------------------+------------+
|device_id|match|                  event_start|                    event_end|max_humidity|
+---------+-----+-----------------------------+-----------------------------+------------+
|      100|    1|2024-11-28T09:00:00.000+08:00|2024-11-29T18:30:00.000+08:00|        45.1|
|      101|    1|2024-11-30T09:30:00.000+08:00|2024-11-30T09:30:00.000+08:00|        35.2|
+---------+-----+-----------------------------+-----------------------------+------------+
Total line number = 2

Row Pattern Recognition

Row Pattern Recognition

1. Overview

2. Function Introduction

2.1 Syntax Format

2.2 DEFINE Clause

2.3 SUBSET Clause

2.4 PATTERN Clause

2.4.1 Pattern Types

2.4.2 Partition Start/End Anchor

2.4.3 Quantifiers

2.5 AFTER MATCH SKIP Clause

2.6 ROWS PER MATCH Clause

2.7 MEASURES Clause

2.8 Row Pattern Recognition Expressions

2.8.1 Pattern Variable References

2.8.2 Extended Functions

2.8.3 RUNNING and FINAL Semantics

3. Syntax Examples

3.1 PATTERN Clause Partition Anchor

3.2 AFTER MATCH SKIP Clause

3.3 Row Pattern Expressions - Extended Functions

3.3.1 CLASSIFIER() Function

3.3.2 Logical Navigation Functions

3.3.3 Physical Navigation Functions

3.3.4 Aggregate Functions

3.3.5 Nested Functions

4. Scenario Examples

4.1 Time Segment Query

4.2 Difference Segment Query

4.3 Event Statistics Query