A cloudwatch metrics:

  • Represents a time-ordered set of data points published to CloudWatch.
  • It is similar to a variable to monitor, with data points as values of that variable over time.
  • data points can come from any application in AWS
  • AWS services send metrics to CloudWatch
  • Can send custom metrics to CloudWatch also
  • Can add data points in any order or at any rate
  • Retrieve statistics about data points as an ordered set of time-series data.
  • Metrics are specific to a Region of creation
  • Metrics cannot be deleted,
  • Automatically expire after 15 months if no new data published to them.
  • They expire on a rolling basis; as new data points come in, data older than 15 months drops.
  • Metrics are uniquely defined by a name, a namespace, and zero or more dimensions.
  • Each data point in a metric has a time stamp, and (optionally) a unit of measure.

CloudWatch Metrics Time Stamps

  • Each metric data point must link with a time stamp.
  • The time stamp can be up to two weeks in the past
  • up to two hours into the future.
  • If no time stamp is given, CloudWatch creates a time stamp on time data point receive.
  • Time stamps are dateTime objects
  • Coordinated Universal Time (UTC) is recommended
  • When you retrieve statistics from CloudWatch, all times are in UTC
  • CloudWatch alarms check metrics based on the current time in UTC.

CloudWatch Metrics Retention

This retains metric data as follows:

  • Data points with a period of less than 60 seconds are available for 3 hours. Also called as high-resolution custom metrics.
  • Data points with a period of 60 seconds (1 minute) are available for 15 days
  • And, 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)

CloudWatch Metrics Units

  • Each statistic has a unit of measure.
  • Example units – Bytes, Seconds, Count, and Percent.
  • Can specify a unit when you create a custom metric.
  • If not specified, CloudWatch uses None as the unit.
  • CloudWatch attaches no significance to a unit internally
  • Metric data points that specify a unit of measure aggregates separately.
  • Statistics without specifying a unit, CloudWatch aggregates all data points of the same unit together.

CloudWatch Metrics Periods

  • Period is the length of time associate with a specific Amazon CloudWatch statistic.
  • Periods defined in seconds, and valid values for period are 1, 5, 10, 30, or any multiple of 60.
  • For period of six minutes, use 360 as the period value.
  • Can adjust how the data aggregates by varying the length of the period.
  • Only custom metrics that you define with a storage resolution of 1 second support sub-minute periods.
  • To retrieve statistics, specify a period, start time, and end time.
  • The default values for the start time and end time get you the last hour’s worth of statistics.
  • For statistics aggregated over the entire hour, specify a period of 3600.
  • aggregated statistics are stamped with the time corresponding to the beginning of the period.
  • Periods are also important for CloudWatch alarms.

CloudWatch Metrics Aggregation

  • CloudWatch aggregates statistics as per specified period length
  • publish as many data points as needed with same or similar time stamps.
  • CloudWatch aggregates them as per specified period length.
  • CloudWatch does not aggregate data across Regions.
  • For large datasets, insert a pre-aggregated dataset called a statistic set.
  • With statistic sets, gives Min, Max, Sum, and SampleCount for a number of data points.
  • CloudWatch doesn’t differentiate the source of a metric.
  • Treated as single metric, even if metric is published with same namespace and dimensions from different sources

CloudWatch Percentiles

  • A percentile indicates the relative standing of a value in a dataset.
  • example, the 95th percentile means that 95 percent of the data is lower than this value and 5 percent of the data is higher than this value.
  • Used to isolate anomalies.

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