anchor.diff:  The limit on the size of a blob written during the test, equal to the
   `?max_blob_size` parameter.
   
  +`max_blob_size_bytes`::
  +(long)
  +The limit, in bytes, on the size of a blob written during the test, equal to
  +the `?max_blob_size` parameter.
  +
   `max_total_data_size`::
   (string)
   The limit on the total size of all blob written during the test, equal to the
   `?max_total_data_size` parameter.
  +
  +`max_total_data_size_bytes`::
  +(long)
  +The limit, in bytes, on the total size of all blob written during the test,
  +equal to the `?max_total_data_size` parameter.
   
   `seed`::
   (long)
  
positive.code: The name of the writer node
  =====
  
  `write_elapsed_nanos`::
  (object)
  The elapsed time spent writing this blob, in nanoseconds.
  
  `overwrite_elapsed_nanos`::
  (object)
  The elapsed time spent overwriting this blob, in nanoseconds. Omitted if the
  blob was not overwritten.
  
  `write_throttled_nanos`::
  (object)
  The length of time spent waiting for the `max_snapshot_bytes_per_sec` throttle
  while writing this blob, in nanoseconds.
  
  
negative.code: measure the performance characteristics of your storage system.
  
  The default values for the parameters to this API are deliberately low to
  reduce the impact of running an analysis inadvertently. A realistic experiment
  should set `blob_count` to at least `2000`, `max_blob_size` to at least `2gb`,
  and `max_total_data_size` to at least `1tb`, and will almost certainly need to
  increase the `timeout` to allow time for the process to complete successfully.
  You should run the analysis on a multi-node cluster of a similar size to your
  production cluster so that it can detect any problems that only arise when the
  