Monitor 9 Matematika

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Jesper Sahu

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Aug 3, 2024, 4:15:00 PM8/3/24

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Metric math enables you to query multiple CloudWatch metrics and use math expressions to create new time series based on these metrics. You can visualize the resulting time series on the CloudWatch console and add them to dashboards. Using AWS Lambda metrics as an example, you could divide the Errors metric by the Invocations metric to get an error rate. Then add the resulting time series to a graph on your CloudWatch dashboard.

You can add a math expression to a graph on your CloudWatch dashboard. Each graph is limited to using a maximum of 500 metrics and expressions, so you can add a math expression only if the graph has 499 or fewer metrics. This applies even if not all the metrics are displayed on the graph.

If the result of an expression is an array of time series, each of those time series is displayed on the graph with a separate line, with different colors. Immediately under the graph is a legend for each line in the graph. For a single expression that produces multiple time series, the legend captions for those time series are in the format Expression-Label Metric-Label. For example, if the graph includes a metric with a label of Errors and an expression FILL(METRICS(), 0) that has a label of Filled With 0:, one line in the legend would be Filled With 0: Errors. To have the legend show only the original metric labels, set Expression-Label to be empty.

After you have added the desired expressions, you can simplify the graph by hiding some of the original metrics. To hide a metric or expression, clear the check box to the left of the Id field.

The following sections explain the functions available for metric math. All functions must be written in uppercase letters (such as AVG), and the Id field for all metrics and math expressions must start with a lowercase letter.

The final result of any math expression must be a single time series or an array of time series. Some functions produce a scalar number. You can use these functions within a larger function that ultimately produces a time series. For example, taking the AVG of a single time series produces a scalar number, so it can't be the final expression result. But you could use it in the function m1-AVG(m1) to display a time series of the difference between each individual data point and the average value in the time series.

The following table lists the basic arithmetic functions that are supported. Missing values in a time series are treated as 0. If the value of a data point causes a function to attempt to divide by zero, the data point is dropped.

You can use comparison and logical operators with either a pair of time series or a pair of single scalar values. When you use a comparison operator with a pair of time series, the operators return a time series where each data point is either 0 (false) or 1 (true). If you use a comparison operator on a pair of scalar values, a single scalar value is returned, either 0 or 1.

When comparison operators are used between two time series, and only one of the time series has a value for a particular time stamp, the function treats the missing value in the other time series as 0.

To see how these operators are used, suppose we have two time series: metric1 has values of [30, 20, 0, 0] and metric2 has values of [20, -, 20, -] where - indicates that there is no value for that timestamp.

The final result of any math expression must be a single time series or an array of time series. Some functions in tables in the following sections produce a scalar number. You can use these functions within a larger function that ultimately produces a time series. For example, taking the AVG of a single time series produces a scalar number, so it can't be the final expression result. But you could use it in the function m1-AVG(m1) to display a time series of the difference between each individual data point and the average value of that data point.

In the following table, every example in the Examples column is an expression that results in a single time series or an array of time series. These examples show how functions that return scalar numbers can be used as part of a valid expression that produces a single time series.

Returns an anomaly detection band for the specified metric. The band consists of two time series, one representing the upper limit of the "normal" expected value of the metric, and the other representing the lower limit. The function can take two arguments. The first is the ID of the metric to create the band for. The second argument is the number of standard deviations to use for the band. If you don't specify this argument, the default of 2 is used. For more information, see Using CloudWatch anomaly detection.

The AVG of a single time series returns a scalar representing the average of all the data points in the metric. The AVG of an array of time series returns a single time series. Missing values are treated as 0.

We recommend that you do not use this function in CloudWatch alarms if you want the function to return a scalar. For example, AVG(m2). Whenever an alarm evaluates whether to change state, CloudWatch attempts to retrieve a higher number of data points than the number specified as Evaluation Periods. This function acts differently when extra data is requested.

We recommend that you do not use this function in CloudWatch alarms. Whenever an alarm evaluates whether to change state, CloudWatch attempts to retrieve a higher number of data points than the number specified as Evaluation Periods. This function acts differently when extra data is requested.

Returns Performance Insights Counter metrics for databases such as Amazon Relational Database Service and Amazon DocumentDB (with MongoDB compatibility). This function returns the same amount of data that you can get by directly querying the Performance Insights APIs. You can use these metrics in CloudWatch for graphing and creating alarms.

When you use this function, you must specify the Unique Database Resource ID of the database. This is different than the database identifier. To find the database resource ID in the Amazon RDS console, choose the DB instance to see its details. Then choose the Configuration tab. The Resource ID is displayed in the Configuration section.

Performance Insights metrics retrieved with this function are not stored in CloudWatch. Therefore, some CloudWatch features such as cross-account observability, anomaly detection, metric streams, metrics explorer, and Metric Insights don't work with Performance Insights metrics that you retrieve with DB_PERF_INSIGHTS.

High-resolution metrics with sub-minute granularity retrieved by DB_PERF_INSIGHTS are only applicable to the DBLoad metric, or for operating system metrics if you have enabled Enhanced Monitoring at a higher resolution. For more information about Amazon RDS enhanced monitoring, see Monitoring OS metrics with Enhanced Monitoring.

You can create a high-resolution alarm using the DB_PERF_INSIGHTS function for a maximum time range of three hours. You can use the CloudWatch console to graph metrics retrieved with the DB_PERF_INSIGHTS function for any time range.

When you use this function in an alarm, you can encounter an issue if your metrics are being published with a slight delay, and the most recent minute never has data. In this case, FILL replaces that missing data point with the requested value. That causes the latest data point for the metric to always be the FILL value, which can result in the alarm being stuck in either OK state or ALARM state. You can work around this by using a M out of N alarm. For more information, see Evaluating an alarm.

Returns the first or last time series from an array of time series. This is useful when used with the SORT function. It can also be used to get the high and low thresholds from the ANOMALY_DETECTION_BAND function.

Use IF along with a comparison operator to filter out data points from a time series, or create a mixed time-series composed of multiple collated time series. For more information, see Using IF expressions.

If you input an array of time series, the MAX function creates and returns a time series that consists of the highest value for each data point, among the time series that were used as the input.

We recommend that you do not use this function in CloudWatch alarms if you want the function to return a scalar. For example, MAX(m2) Whenever an alarm evaluates whether to change state, CloudWatch attempts to retrieve a higher number of data points than the number specified as Evaluation Periods. This function acts differently when extra data is requested.

If you input an array of time series, the MIN function creates and returns a time series that consists of the lowest value for each data point, among the time series that were used as the input.

We recommend that you do not use this function in CloudWatch alarms if you want the function to return a scalar. For example, MIN(m2) Whenever an alarm evaluates whether to change state, CloudWatch attempts to retrieve a higher number of data points than the number specified as Evaluation Periods. This function acts differently when extra data is requested.

DAY returns a non-sparse time series of integers between 1 and 7 that represent the UTC day of the week of each timestamp in the original time series. 1 represents Monday and 7 represents Sunday.

Returns the rate of change of the metric per second. This is calculated as the difference between the latest data point value and the previous data point value, divided by the time difference in seconds between the two values.

Setting alarms on expressions that use the RATE function on metrics with sparse data can behave unpredictably, because the range of data points fetched when evaluating the alarm can vary based on when the data points were last published.