Whisper is a fixed-size database, similar in design and purpose to RRD (round-robin-database). It provides fast, reliable storage of numeric data over time. Whisper allows for higher resolution (seconds per point) of recent data to degrade into lower resolutions for long-term retention of historical data.
Data points in Whisper are stored on-disk as big-endian double-precision floats. Each value is paired with a timestamp in seconds since the UNIX Epoch (01-01-1970). The data value is parsed by the Python float() function and as such behaves in the same way for special strings such as 'inf'. Maximum and minimum values are determined by the Python interpreter’s allowable range for float values which can be found by executing:
python -c 'import sys; print sys.float_info'
Whisper databases contain one or more archives, each with a specific data resolution and retention (defined in number of points or max timestamp age). Archives are ordered from the highest-resolution and shortest retention archive to the lowest-resolution and longest retention period archive.
To support accurate aggregation from higher to lower resolution archives, the number of points in a longer retention archive must be divisible by its next lower retention archive. For example, an archive with 1 data points every 60 seconds and retention of 120 points (2 hours worth of data) can have a lower-resolution archive following it with a resolution of 1 data point every 300 seconds for 1200 points, while the same resolution but for only 1000 points would be invalid since 1000 is not evenly divisible by 120.
The total retention time of the database is determined by the archive with the highest retention as the time period covered by each archive is overlapping (see Multi-Archive Storage and Retrieval Behavior). That is, a pair of archives with retentions of 1 month and 1 year will not provide 13 months of data storage. Instead, it will provide 1 year of storage.
Whisper databases with more than a single archive need a strategy to collapse multiple data points for when the data rolls up a lower precision archive. By default, an average function is used. Available aggregation methods are: * average * sum * last * max * min
When Whisper writes to a database with multiple archives, the incoming data point is written to all archives at once. The data point will be written to the lowest resolution archive as-is, and will be aggregated by the configured aggregation method (see Rollup Aggregation) and placed into each of the higher-retention archives.
When data is retrieved (scoped by a time range), the first archive which can satisfy the entire time period is used. If the time period overlaps an archive boundary, the lower-resolution archive will be used. This allows for a simpler behavior while retrieving data as the data’s resolution is consistent through an entire returned series.
Whisper is somewhat inefficient in its usage of disk space because of certain design choices:
Whisper is fast enough for most purposes. It is slower than RRDtool primarily as a consequence of Whisper being written in Python, while RRDtool is written in C. The speed difference between the two in practice is quite small as much effort was spent to optimize Whisper to be as close to RRDtool’s speed as possible. Testing has shown that update operations take anywhere from 2 to 3 times as long as RRDtool, and fetch operations take anywhere from 2 to 5 times as long. In practice the actual difference is measured in hundreds of microseconds (10^-4) which means less than a millisecond difference for simple cases.
Data types in Python’s struct format: