RRD Data Storage Mechanism

TODO: collapasable, copyable, download, etc

How does Round Robin Database store data and what happens when it rolls up to each time period like 1 min to 5 min to 1 hour, etc?A Round Robin Database (RRD) is a type of fixed-size database that is designed specifically to handle time series data, like network bandwidth, CPU load, or temperatures. RRD is designed to save data in a way that allows for efficient storage, querying, and graphing of time series data. Here's how it works and how it handles roll-ups:

1. Fixed Size: One of the defining features of an RRD is that it has a fixed size. This means that once the database is created, it doesn't grow in size, even if you continue to add new data points.

2. Round Robin Archives: The data is stored in what's called a "Round Robin Archive" (RRA). Each RRA is designed to hold data for a specific time period at a particular granularity or resolution. For example, you might have an RRA that stores data for the last 24 hours at a 1-minute resolution.

3. Data Roll-ups: As time progresses and the resolution of older data becomes less important, the data can be rolled up into a more coarse resolution to save space. For instance, after 24 hours, you might no longer need per-minute data, but you'd like to keep hourly averages for the past month. This is achieved by defining multiple RRAs with varying resolutions and durations. When data is added to the RRD, it is saved in all RRAs that it applies to.

   Let's take an example. Assume you have three RRAs:

   • 1-minute resolution for 24 hours.
   • 5-minute average for 7 days.
   • 1-hour average for 1 year.

   Every minute, you store a data point in the RRD. For the first RRA, each data point is stored directly. For the second RRA, every five data points are averaged and then stored as a single data point. For the third RRA, every 60 data points (from an hour) are averaged and stored as a single point.

4. Round Robin Mechanism: When an RRA is full (after 24 hours for the 1-minute resolution RRA in the above example), the oldest data point is overwritten by the newest one in a circular manner, thus the name "Round Robin". This ensures the fixed size of the database.

5. Consolidation: As data moves from a higher-resolution RRA to a lower one, it undergoes a process called consolidation. In our example, every 5 data points from the 1-minute RRA would be averaged (or consolidated) into a single data point for the 5-minute RRA. The same happens for the 1-hour RRA.

6. Handling Missing Data: If for some reason data isn't collected, RRD can handle this by marking the data as "Unknown" or by using the last known data point, depending on configuration.

RRD offers a very space-efficient way of storing time series data, as the storage requirements are known in advance and do not grow over time. This makes it particularly well-suited for applications where storage space is at a premium, like in embedded systems or when dealing with vast amounts of monitoring data.What algorithm is used to average the data?In a Round Robin Database (RRD), the most common method of consolidation or aggregation when transitioning from a higher-resolution RRA (Round Robin Archive) to a lower one is simple averaging, but other methods can also be used.

Here's a brief overview of the consolidation functions that RRD supports:

1. AVERAGE: This is the method you're asking about. It computes the average of the data points. For instance, if you're consolidating from a 1-minute resolution to a 5-minute resolution, it will take the average of five 1-minute data points to produce a single 5-minute data point.

   Formula: [ \text{Average} = \frac{\text{Sum of data points}}{\text{Number of data points}} ]

2. MIN: This method selects the smallest value from the set of data points being consolidated.

3. MAX: Opposite to the MIN function, this selects the largest value from the set of data points being consolidated.

4. LAST: This simply takes the last data point from the set of data points being consolidated. It's useful in certain situations where you just want the most recent data point, regardless of the values of the earlier data points.

When you define an RRA, you specify the consolidation function to use. Depending on your specific use-case and the nature of your data, you might choose one function over another. For instance, if you're tracking CPU utilization, using the AVERAGE function would provide a representative overview. However, if you're tracking maximum temperatures for a system, you'd be more interested in using the MAX function.

It's worth noting that a single RRD can have multiple RRAs, each with its own consolidation function. This allows for versatile querying and reporting based on the same dataset.Does this system support these data types? Counter, Gauge, Histogram, and Exponential HistogramRRDtool, which is the primary software package for creating and managing Round Robin Databases (RRDs), supports a limited set of data types that can be used to interpret the input data. Here's how they relate to the types you mentioned:

1. Counter: RRDtool supports the COUNTER data type. This is used for continuously increasing counters like the number of bytes sent on an interface. When you feed a COUNTER value into RRDtool, it will store the rate of change of the counter, not the absolute value. If the counter wraps (like if it hits its maximum value and starts over), RRDtool can handle it.

2. Gauge: RRDtool also supports the GAUGE data type. This is for things that have a particular value at a given point in time, like the current number of running processes or the current temperature. It doesn't assume that the value is always increasing or decreasing, unlike COUNTER.

3. Histogram: RRDtool doesn't directly support histograms in the same way some other monitoring systems might. However, with some ingenuity, you could use a combination of RRAs or multiple RRDs to track histogram-like data. But it would require custom tooling and wouldn't be as straightforward as using a system designed with native histogram support.

4. Exponential Histogram: Same as the regular histogram, RRDtool doesn't natively support exponential histograms. You'd need to manage the data and buckets yourself and perhaps just store the bucket counts as GAUGE values in RRD.

If you need native support for more complex data types like histograms or exponential histograms, you might look into other monitoring and metric systems. Tools and platforms like Prometheus, StatsD, and OpenTelemetry provide more versatile metric types beyond the basic counter and gauge.How does Prometheus support histogram and exponential histogram?Prometheus is a popular open-source monitoring and alerting toolkit that has built-in support for various metric types, including histograms and summaries (which are somewhat akin to exponential histograms). Here's how Prometheus handles these:

1. Histogram:

   • Prometheus's histogram metric type allows you to measure the distribution of a set of values.
   • When you define a histogram, you also define a set of "buckets" with specific upper bounds. Each time a new value is observed, Prometheus will increment the counter for the appropriate bucket.
   • For instance, if you have buckets with bounds "5, 10, 15", and you observe a value of "7", the counters for both the "5" and "10" buckets would be incremented (since 7 is more than 5 but less than 10).
   • In addition to the bucketed values, histograms also expose a _sum (total sum of observed values) and a _count (count of observations) metric.
   • With the _count, _sum, and bucket values, you can calculate various percentiles over your data.

2. Summary:

   • Summaries in Prometheus are similar to histograms but allow you to calculate configurable quantiles over sliding time windows.
   • A summary metric type tracks the _sum and _count like a histogram. But instead of predefined buckets, it exposes configurable quantiles (e.g., 0.50, 0.95, 0.99, representing the 50th, 95th, and 99th percentiles).
   • Summaries calculate these quantiles based on a sliding time window. This is where the "exponential" part comes into play. Prometheus's client libraries usually implement this by using exponentially decaying reservoirs or other techniques that give more weight to recent observations.
   • However, since quantile calculation is done on the client side, summaries cannot be aggregated across multiple instances (like histograms can).

When to use histograms vs. summaries:

• If you need a precise and aggregatable distribution of your data, use a histogram.
• If you want to calculate quantiles over sliding time windows and do not need to aggregate the data across multiple instances, use a summary.

It's worth noting that both histograms and summaries, especially when used with high cardinality or high resolution, can be expensive in terms of storage and computational resources. Always be mindful of the bounds, quantiles, and the resolution you choose to avoid overwhelming your Prometheus instance.

RRD Data Storage Mechanism created at Tue Jan 20 1970 15:12:30 GMT+0000 (Coordinated Universal Time)