Amazon CloudWatch Concepts
The following terminology and concepts are central to your understanding and use of Amazon CloudWatch:
A namespace is a container for CloudWatch metrics. Metrics in different namespaces are isolated from each other, so that metrics from different applications are not mistakenly aggregated into the same statistics.
There is no default namespace. You must specify a namespace for each data point you publish to CloudWatch. You can specify a namespace name when you create a metric. These names must contain valid XML characters, and be fewer than 256 characters in length. Possible characters are: alphanumeric characters (0-9A-Za-z), period (.), hyphen (-), underscore (_), forward slash (/), hash (#), and colon (:).
The AWS namespaces use the following naming convention:
For example, Amazon EC2 uses the
AWS/EC2 namespace. For the list of AWS namespaces,
see AWS Namespaces.
Metrics are the fundamental concept in CloudWatch. A metric represents a time-ordered set of data points that are published to CloudWatch. Think of a metric as a variable to monitor, and the data points represent the values of that variable over time. For example, the CPU usage of a particular EC2 instance is one metric provided by Amazon EC2. The data points themselves can come from any application or business activity from which you collect data.
AWS services send metrics to CloudWatch, and you can send your own custom metrics to CloudWatch. You can add the data points in any order, and at any rate you choose. You can retrieve statistics about those data points as an ordered set of time-series data.
Metrics exist only in the region in which they are created. Metrics cannot be deleted, but they automatically expire after 15 months if no new data is published to them. Data points older than 15 months expire on a rolling basis; as new data points come in, data older than 15 months is dropped.
Metrics are uniquely defined by a name, a namespace, and one or more dimensions. Each data point has a time stamp, and (optionally) a unit of measure. When you request statistics, the returned data stream is identified by namespace, metric name, dimension, and (optionally) the unit.
Each metric data point must be marked with a time stamp. The time stamp can be up to two weeks in the past and up to two hours into the future. If you do not provide a time stamp, CloudWatch creates a time stamp for you based on the time the data point was received.
Time stamps are
dateTime objects, with the complete date plus hours,
minutes, and seconds (for example, 2016-10-31T23:59:59Z). For more information, see
Although it is not required, we recommend that you use Coordinated Universal Time (UTC).
When you retrieve statistics from CloudWatch, all times are in UTC.
Note that CloudWatch alarms check metrics based on the current time in UTC. Custom metrics sent to CloudWatch with time stamps other than the current UTC time can cause alarms to display the Insufficient Data state or result in delayed alarms.
CloudWatch retains your metric data as follows:
Data points with a period of 60 seconds (1 minute) are available for 15 days
Data points with a period of 300 seconds (5 minute) are available for 63 days
Data points with a period of 3600 seconds (1 hour) are available for 455 days (15 months)
Note that CloudWatch started retaining 5-minute and 1-hour metric data as of 9 July 2016.
A dimension is a name/value pair that uniquely identifies a metric. You can assign up to ten dimensions to a metric.
Every metric has specific characteristics that describe it, and you can think of dimensions as categories for those characteristics. Dimensions help you design a structure for your statistics plan. Because dimensions are part of the unique identifier for a metric, whenever you add a unique name/value pair to one of your metrics, you are creating a new metric.
AWS services that send data to CloudWatch attach dimensions to each metric. You can use
dimensions to filter the results that CloudWatch returns. For example, you can get statistics
for a specific EC2 instance by specifying the
InstanceId dimension when
you search for metrics.
For metrics produced by certain AWS services, such as Amazon EC2, CloudWatch can aggregate data
across dimensions. For example, search for metrics in the
but do not specify any dimensions, CloudWatch aggregates all data for the specified metric
to create the statistic that you requested. Note that CloudWatch does not aggregate across
dimensions for your custom metrics.
CloudWatch treats each unique combination of dimensions as a separate metric, even if the metrics have the same metric name. You can't retrieve statistics using combinations of dimensions that you did not specifically publish. When you retrieve statistics, specify the same values for the namespace, metric name, and dimension parameters that were used when the metrics were created. You can also specify the start and end times for CloudWatch to use for aggregation.
For example, suppose that you publish four distinct metrics named ServerStats in the DataCenterMetric namespace with the following properties:
Dimensions: Server=Prod, Domain=Frankfurt, Unit: Count, Timestamp: 2016-10-31T12:30:00Z, Value: 105 Dimensions: Server=Beta, Domain=Frankfurt, Unit: Count, Timestamp: 2016-10-31T12:31:00Z, Value: 115 Dimensions: Server=Prod, Domain=Rio, Unit: Count, Timestamp: 2016-10-31T12:32:00Z, Value: 95 Dimensions: Server=Beta, Domain=Rio, Unit: Count, Timestamp: 2016-10-31T12:33:00Z, Value: 97
If you publish only those four metrics, you can retrieve statistics for these combinations of dimensions:
You can't retrieve statistics for the following dimensions or if you specify no dimensions:
Statistics are metric data aggregations over specified periods of time. CloudWatch provides statistics based on the metric data points provided by your custom data or provided by other services in AWS to CloudWatch. Aggregations are made using the namespace, metric name, dimensions, and the data point unit of measure, within the time period you specify. The following table describes the available statistics.
The lowest value observed during the specified period. You can use this value to determine low volumes of activity for your application.
The highest value observed during the specified period. You can use this value to determine high volumes of activity for your application.
All values submitted for the matching metric added together. This statistic can be useful for determining the total volume of a metric.
The value of
The count (number) of data points used for the statistical calculation.
The value of the specified percentile. You can specify any percentile, using up to two decimal places (for example, p95.45). For more information, see Percentiles.
You can add pre-calculated statistics. Instead of data point values, you specify values for
Sum (CloudWatch calculates the average for you). The values you add in
this way are aggregated with any other values associated with the matching metric.
Each statistic has a unit of measure. Example units include
Percent. For the
complete list of the units that CloudWatch supports, see the MetricDatum data type in the
Amazon CloudWatch API Reference.
You can specify a unit when you create a custom metric.
If you do not specify a unit, CloudWatch uses
None as the unit.
Units help provide conceptual meaning to your data.
Though CloudWatch attaches no significance to a unit internally, other applications can
derive semantic information based on the unit.
Metric data points that specify a unit of measure are aggregated separately. When you get statistics without specifying a unit, CloudWatch aggregates all data points of the same unit together. If you have two otherwise identical metrics with different units, two separate data streams are returned, one for each unit.
A period is the length of time associated with a specific Amazon CloudWatch statistic. Each statistic represents an aggregation of the metrics data collected for a specified period of time. Although periods are expressed in seconds, the minimum granularity for a period is one minute. Accordingly, you specify period values as multiples of 60. For example, to specify a period of six minutes, you would use the value 360. You can adjust how the data is aggregated by varying the length of the period. A period can be as short as one minute (60 seconds) or as long as one day (86,400 seconds). The default value is 60 seconds.
When you retrieve statistics, you can specify a period, start time, and end time. These parameters determine the overall length of time associated with the statistics. The default values for the start time and end time get you the last hour's worth of statistics. The values that you specify for the start time and end time determine how many periods CloudWatch will return. For example, retrieving statistics using the default values for the period, start time, and end time returns an aggregated set of statistics for each minute of the previous hour. If you prefer statistics aggregated in ten-minute blocks, specify a period of 600. For statistics aggregated over the entire hour, specify a period of 3600.
Periods are also important for CloudWatch alarms. When you create an alarm to monitor a specific metric, you are asking CloudWatch to compare that metric to the threshold value that you specified. You have extensive control over how CloudWatch makes that comparison. Not only can you specify the period over which the comparison is made, but you can also specify how many evaluation periods are used to arrive at a conclusion. For example, if you specify three evaluation periods, CloudWatch compares a window of three data points. CloudWatch only notifies you if the oldest data point is breaching and the others are breaching or missing. For metrics that are continuously emitted, CloudWatch won't notify you until three failures are found.
Amazon CloudWatch aggregates statistics according to the period length that you specify when retrieving statistics. You can publish as many data points as you want with the same or similar time stamps. CloudWatch aggregates them by period length. Aggregated statistics are only available when using detailed monitoring. In addition, Amazon CloudWatch does not aggregate data across regions.
You can publish data points for a metric that share not only the same time stamp, but also the same namespace and dimensions. CloudWatch will return aggregated statistics for those data points. You can also publish multiple data points for the same or different metrics, with any time stamp.
For large data sets, you can insert a pre-aggregated data set called a statistic set. With statistic sets, you give CloudWatch the Min, Max, Sum, and SampleCount for a number of data points. This is commonly used when you need to collect data many times in a minute. For example, suppose you have a metric for the request latency of a web page. It doesn't make sense to publish data with every web page hit. We suggest that you collect the latency of all hits to that web page, aggregate them once a minute, and send that statistic set to CloudWatch.
Amazon CloudWatch doesn't differentiate the source of a metric. If you publish a metric with the same namespace and dimensions from different sources, CloudWatch treats this as a single metric. This can be useful for service metrics in a distributed, scaled system. For example, all the hosts in a web server application could publish identical metrics representing the latency of requests they are processing. CloudWatch treats these as a single metric, allowing you to get the statistics for minimum, maximum, average, and sum of all requests across your application.
A percentile indicates the relative standing of a value in a data set. For example, the 95th percentile means that 95 percent of the data is below this value and 5 percent of the data is above this value. Percentiles help you get a better understanding of the distribution of your metric data.
Percentiles are often used to isolate anomalies. In a typical distribution, 95 percent of the data is within two standard deviations from the mean and 99.7 percent of the data is within three standard deviations from the mean. Any data that falls outside three standard deviations is often considered to be an anomaly because it differs so greatly from the average value. For example, suppose that you are monitoring the CPU utilization of your EC2 instances to ensure that your customers have a good experience. If you monitor the average, this can hide anomalies. If you monitor the maximum, a single anomaly can skew the results. Using percentiles, you can monitor the 95th percentile of CPU utilization to check for instances with an unusually heavy load.
You can monitor your system and applications using percentiles as you would use the other CloudWatch statistics (Average, Minimum, Maximum, and Sum). For example, when you create an alarm, you can use percentiles as the statistical function. You can specify the percentile with up to two decimal places (for example, p95.45).
You can use an alarm to automatically initiate actions on your behalf. An alarm watches a single metric over a specified time period, and performs one or more specified actions, based on the value of the metric relative to a threshold over time. The action is a notification sent to an Amazon SNS topic or an Auto Scaling policy.
Alarms invoke actions for sustained state changes only. CloudWatch alarms will not invoke actions simply because they are in a particular state. The state must have changed and been maintained for a specified number of periods.
When creating an alarm, select a period that is greater than or equal to the frequency of the metric to be monitored. For example, basic monitoring for Amazon EC2 provides metrics for your instances every 5 minutes. When setting an alarm on a basic monitoring metric, select a period of at least 300 seconds (5 minutes). Detailed monitoring for Amazon EC2 provides metrics for your instances every 1 minute. When setting an alarm on a detailed monitoring metric, select a period of at least 60 seconds (1 minute).