Learn where to use the align() function and why the query engine does pre-alignment.

In VMware Aria Operations for Applications (previously known as Tanzu Observability by Wavefront) charts, point buckets represent data that has been summarized over a certain length of time.

Both the Summarize By chart option and the align() function group points into buckets and allow you to specify how those points are aggregated (e.g., averaged, counted, summed, etc.). The align() function allows you to specify the desired bucket size. By default, the summarization method that aggregation functions use is based on the bucket size of the chart resolution.

To support bucketing, align() supports the value bw (bucket window) for the timeWindow parameter.

Why It Makes Sense to Apply align()

Wrapping a query in align() is often a good idea:

  • You can specify the bucket size explicitly.
  • As a result, you can often speed up queries because less rendering is required.

Reducing the Number of Data Points

The speed at which a query is executed and displayed is partly based on how many data points have to be displayed on the chart. Consider a 60-minute chart with data values reported every 60 seconds (assume resolution is ~30s). It takes longer to render and display 60 values than it would take to display 6 values that are aligned at 10-minute buckets. As the time window increases, the effect is more noticeable.

Reducing Unnecessary Interpolation

Using align() can also improve query speed if aggregation functions perform interpolation.

Here’s a simple example:

  • You have 10 series that typically report every 60 seconds and that you want to average over a 2-hour window.
  • 1 of those 10 series is being reported intermittently and often reports only every 120 seconds.
  • Non-raw aggregation functions (e.g., sum() or avg()) interpolate that 1 intermittent series when at least one of the other nine series reported a value. For example, if there were 9 reported values 9:03a, then avg() generates an interpolated value for the 10th series before aggregating.

Interpolated require resources, and that can affect the speed at which the query is displayed. If a 1000+ series that reports every 5 seconds, but not always at the same 5-second interval could require a lot of interpolation and slow down the query speed.

To improve query speed, align to a larger bucket (e.g., align(1m, mean, ts("my.metric")). Then the number of times that an interpolated value can occur might change from 12 times a minute without align() to once a minute with align().

The Pre-Align Warning – When the Query Engine Applies Align

For some queries you see the warning indicator in a chart and a warning like the following:

The expression: ts(<metric>, source=<source>) has been pre-aligned, making it equivalent to
align(120s, mean, ts(<metric>, source=<source>)) in order to improve the performance of the sum()
aggregation operation. You can wrap the expression with align() to explicitly state the periodicity and desired summarization strategy.

where sum(ts(<metric>, source=<source>)) is the original query.

That means that the query engine wrapped your query in align() to avoid unacceptable performance issues. The query engine performs pre-alignment when aggregation functions are applied to more than 100 unique series. In most cases, for instance, where a metric reflects a parameter changing over time, you can ignore the warning.

The pre-alignment is tied to a window of time and not to the actual unique series reporting data at any given moment. For example, if 100+ series are reporting data at the same time for 50% of your chart window but less than 100 series report for the other 50%, then the entire chart window will be pre-aligned if you attempt to aggregate those series into a single series.

Pre-Alignment Side Effects

In most cases, pre-alignment improves query speed and has no undesirable side effects. However, for some very specific use cases, pre-alignment can cause undesirable side effects. If your data set is automatically pre-aligned, you see a warning message below the query.

Here’s an example:

  • You have a http.requests.count metric that reports values once a minute and represents the total number of HTTP requests per minute.
  • You are trying to calculate the number of HTTP requests over the 1-hour sliding window (i.e. msum(1h, rawsum(ts(http.requests.count, source="web*")). If your data set is pre-aligned to a 2-minute (120s) time window, using the default summarization method, average, align(120s, mean) averages the values for every 2-minute window. This pre-alignment cuts the number of values available to msum() in half—which dramatically changes the result of the calculation.

In this case, doing an explicit align() with the sum aggregation method or changing the summarization method to sum() avoids the side effect.