SkyWalking内置MQE语法
此文档出自SkyWalking官方git https://github.com/apache/skywalking
docs/en/api/metrics-query-expression.md
Metrics Query Expression(MQE) Syntax
MQE is a string that consists of one or more expressions. Each expression could be a combination of one or more operations.
The expression allows users to do simple query-stage calculation through V3 APIs.
Expression = <Operation> Expression1 <Operation> Expression2 <Operation> Expression3 ...
The following document lists the operations supported by MQE.
Metrics Expression
Metrics Expression will return a collection of time-series values.
Common Value Metrics
Expression:
<metric_name>
For example:
If we want to query the service_sla metric, we can use the following expression:
service_sla
Result Type
The ExpressionResultType of the expression is TIME_SERIES_VALUES.
Labeled Value Metrics
For now, we only have a single anonymous label with multi label values in a labeled metric.
To be able to use it in expressions, define _ as the anonymous label name (key).
Expression:
<metric_name>{_='<label_value_1>,...'}
{_='<label_value_1>,...'} is the selected label value of the metric. If is not specified, all label values of the metric will be selected.
For example:
If we want to query the service_percentile metric with the label values 0,1,2,3,4, we can use the following expression:
service_percentile{_='0,1,2,3,4'}
If we want to rename the label values to P50,P75,P90,P95,P99, see Relabel Operation.
Result Type
The ExpressionResultType of the expression is TIME_SERIES_VALUES and with labels.
Binary Operation
The Binary Operation is an operation that takes two expressions and performs a calculation on their results.
The following table lists the binary operations supported by MQE.
Expression:
Expression1 <Binary-Operator> Expression2
| Operator | Definition |
|---|---|
| + | addition |
| - | subtraction |
| * | multiplication |
| / | division |
| % | modulo |
For example:
If we want to transform the service_sla metric value to percent, we can use the following expression:
service_sla / 100
Result Type
For the result type of the expression, please refer to the following table.
Binary Operation Rules
The following table lists if the different result types of the input expressions could do this operation and the result type after the operation.
The expression could be on the left or right side of the operator.
Note: If the expressions on both sides of the operator are the TIME_SERIES_VALUES with labels, they should have the same labels for calculation.
| Expression | Expression | Yes/No | ExpressionResultType |
|---|---|---|---|
| SINGLE_VALUE | SINGLE_VALUE | Yes | SINGLE_VALUE |
| SINGLE_VALUE | TIME_SERIES_VALUES | Yes | TIME_SERIES_VALUES |
| SINGLE_VALUE | SORTED_LIST/RECORD_LIST | Yes | SORTED_LIST/RECORD_LIST |
| TIME_SERIES_VALUES | TIME_SERIES_VALUES | Yes | TIME_SERIES_VALUES |
| TIME_SERIES_VALUES | SORTED_LIST/RECORD_LIST | no | |
| SORTED_LIST/RECORD_LIST | SORTED_LIST/RECORD_LIST | no |
Compare Operation
Compare Operation takes two expressions and compares their results.
The following table lists the compare operations supported by MQE.
Expression:
Expression1 <Compare-Operator> Expression2
| Operator | Definition |
|---|---|
| > | greater than |
| >= | greater than or equal |
| < | less than |
| <= | less than or equal |
| == | equal |
| != | not equal |
The result of the compare operation is an int value:
- 1: true
- 0: false
For example:
Compare the service_resp_time metric value if greater than 3000, if the service_resp_time result is:
{"data": {"execExpression": {"type": "TIME_SERIES_VALUES","error": null,"results": [{"metric": {"labels": []},"values": [{"id": "1691658000000", "value": "2500", "traceID": null}, {"id": "1691661600000", "value": 3500, "traceID": null}]}]}}
}
we can use the following expression:
service_resp_time > 3000
and get result:
{"data": {"execExpression": {"type": "TIME_SERIES_VALUES","error": null,"results": [{"metric": {"labels": []},"values": [{"id": "1691658000000", "value": "0", "traceID": null}, {"id": "1691661600000", "value": 1, "traceID": null}]}]}}
}
Compare Operation Rules and Result Type
Same as the Binary Operation Rules.
Aggregation Operation
Aggregation Operation takes an expression and performs aggregate calculations on its results.
Expression:
<Aggregation-Operator>(Expression)
| Operator | Definition | ExpressionResultType |
|---|---|---|
| avg | average the result | SINGLE_VALUE |
| count | count number of the result | SINGLE_VALUE |
| latest | select the latest non-null value from the result | SINGLE_VALUE |
| sum | sum the result | SINGLE_VALUE |
| max | select maximum from the result | SINGLE_VALUE |
| min | select minimum from the result | SINGLE_VALUE |
For example:
If we want to query the average value of the service_cpm metric, we can use the following expression:
avg(service_cpm)
Result Type
The different operators could impact the ExpressionResultType, please refer to the above table.
Mathematical Operation
Mathematical Operation takes an expression and performs mathematical calculations on its results.
Expression:
<Mathematical-Operator>(Expression, parameters)
| Operator | Definition | parameters | ExpressionResultType |
|---|---|---|---|
| abs | returns the absolute value of the result | follow the input expression | |
| ceil | returns the smallest integer value that is greater or equal to the result | follow the input expression | |
| floor | returns the largest integer value that is greater or equal to the result | follow the input expression | |
| round | returns result round to specific decimal places | places: a positive integer specific decimal places of the result | follow the input expression |
For example:
If we want to query the average value of the service_cpm metric in seconds,
and round the result to 2 decimal places, we can use the following expression:
round(service_cpm / 60 , 2)
Result Type
The different operators could impact the ExpressionResultType, please refer to the above table.
TopN Operation
TopN Operation takes an expression and performs TopN calculation on its results.
Expression:
top_n(<metric_name>, <top_number>, <order>)
top_number is the number of the top results, should be a positive integer.
order is the order of the top results. The value of order can be asc or des.
For example:
If we want to query the top 10 services with the highest service_cpm metric value, we can use the following expression:
top_n(service_instance_cpm, 10, des)
Result Type
According to the type of the metric, the ExpressionResultType of the expression will be SORTED_LIST or RECORD_LIST.
Relabel Operation
Relabel Operation takes an expression and replaces the label values with new label values on its results.
Expression:
relabel(Expression, _='<new_label_value_1>,...')
_ is the new label of the metric after the label is relabeled, the order of the new label values should be the same as the order of the label values in the input expression result.
For example:
If we want to query the service_percentile metric with the label values 0,1,2,3,4, and rename the label values to P50,P75,P90,P95,P99, we can use the following expression:
relabel(service_percentile{_='0,1,2,3,4'}, _='P50,P75,P90,P95,P99')
Result Type
Follow the input expression.
AggregateLabels Operation
AggregateLabels Operation takes an expression and performs an aggregate calculation on its Labeled Value Metrics results. It aggregates a group of TIME_SERIES_VALUES into a single TIME_SERIES_VALUES.
Expression:
aggregate_labels(Expression, parameter)
| parameter | Definition | ExpressionResultType |
|---|---|---|
| avg | calculate avg value of a Labeled Value Metrics | TIME_SERIES_VALUES |
| sum | calculate sum value of a Labeled Value Metrics | TIME_SERIES_VALUES |
| max | select the maximum value from a Labeled Value Metrics | TIME_SERIES_VALUES |
| min | select the minimum value from a Labeled Value Metrics | TIME_SERIES_VALUES |
For example:
If we want to query all Redis command total rates, we can use the following expression(total_commands_rate is a metric which recorded every command rate in labeled value):
aggregate_labels(total_commands_rate, SUM)
Result Type
The ExpressionResultType of the aggregateLabels operation is TIME_SERIES_VALUES.
Logical Operation
ViewAsSequence Operation
ViewAsSequence operation represents the first not-null metric from the listing metrics in the given prioritized sequence(left to right). It could also be considered as a short-circuit of given metrics for the first value existing metric.
Expression:
view_as_seq([<expression_1>, <expression_2>, ...])
For example:
if the first expression value is empty but the second one is not empty, it would return the result from the second expression.
The following example would return the content of the service_cpm metric.
view_as_seq(not_existing, service_cpm)
Result Type
The result type is determined by the type of selected not-null metric expression.
Expression Query Example
Labeled Value Metrics
service_percentile{_='0,1'}
The example result is:
{"data": {"execExpression": {"type": "TIME_SERIES_VALUES","error": null,"results": [{"metric": {"labels": [{"key": "_", "value": "0"}]},"values": [{"id": "1691658000000", "value": "1000", "traceID": null}, {"id": "1691661600000", "value": 2000, "traceID": null}]},{"metric": {"labels": [{"key": "_", "value": "1"}]},"values": [{"id": "1691658000000", "value": "2000", "traceID": null}, {"id": "1691661600000", "value": 3000, "traceID": null}]}]}}
}
If we want to transform the percentile value unit from ms to s the expression is:
service_percentile{_='0,1'} / 1000
{"data": {"execExpression": {"type": "TIME_SERIES_VALUES","error": null,"results": [{"metric": {"labels": [{"key": "_", "value": "0"}]},"values": [{"id": "1691658000000", "value": "1", "traceID": null}, {"id": "1691661600000", "value": 2, "traceID": null}]},{"metric": {"labels": [{"key": "_", "value": "1"}]},"values": [{"id": "1691658000000", "value": "2", "traceID": null}, {"id": "1691661600000", "value": 3, "traceID": null}]}]}}
}
Get the average value of each percentile, the expression is:
avg(service_percentile{_='0,1'})
{"data": {"execExpression": {"type": "SINGLE_VALUE","error": null,"results": [{"metric": {"labels": [{"key": "_", "value": "0"}]},"values": [{"id": null, "value": "1500", "traceID": null}]},{"metric": {"labels": [{"key": "_", "value": "1"}]},"values": [{"id": null, "value": "2500", "traceID": null}]}]}}
}
Calculate the difference between the percentile and the average value, the expression is:
service_percentile{_='0,1'} - avg(service_percentile{_='0,1'})
{"data": {"execExpression": {"type": "TIME_SERIES_VALUES","error": null,"results": [{"metric": {"labels": [{"key": "_", "value": "0"}]},"values": [{"id": "1691658000000", "value": "-500", "traceID": null}, {"id": "1691661600000", "value": 500, "traceID": null}]},{"metric": {"labels": [{"key": "_", "value": "1"}]},"values": [{"id": "1691658000000", "value": "-500", "traceID": null}, {"id": "1691661600000", "value": 500, "traceID": null}]}]}}
}
Calculate the difference between the service_resp_time and the service_percentile, if the service_resp_time result is:
{"data": {"execExpression": {"type": "TIME_SERIES_VALUES","error": null,"results": [{"metric": {"labels": []},"values": [{"id": "1691658000000", "value": "2500", "traceID": null}, {"id": "1691661600000", "value": 3500, "traceID": null}]}]}}
}
The expression is:
service_resp_time - service_percentile{_='0,1'}
{"data": {"execExpression": {"type": "TIME_SERIES_VALUES","error": null,"results": [{"metric": {"labels": [{"key": "_", "value": "0"}]},"values": [{"id": "1691658000000", "value": "1500", "traceID": null}, {"id": "1691661600000", "value": "1500", "traceID": null}]},{"metric": {"labels": [{"key": "_", "value": "1"}]},"values": [{"id": "1691658000000", "value": "500", "traceID": null}, {"id": "1691661600000", "value": "500", "traceID": null}]}]}}
}