anchor.diff:  
   === Using high resolution timings across machines.
   
  -On most systems `System.nanoTime()` is roughly the number of nano-second since the system last reboot (although different JVMs may behave differently)  This is the same across JVM on the same amchine, but wildly different between machines.  The absolute difference machines is meaningless, however the information can be used to detect outliners. i.e. you can't determine what the best latency is, but you can determine how far off the best latencies you are.  This is useful if you are focussing on the 99%ile latencies for example.  We have a class called `RunningMinimum` to obtain timings from different machines while compensating for a drift in the nanoTime between machines. The more often you take measurements the more accurate this running minimum is.
  +On most systems `System.nanoTime()` is roughly the number of nano-second since the system last rebooted (although different JVMs may behave differently)  This is the same across JVM on the same machine, but wildly different between machines.  The absolute difference machines is meaningless, however the information can be used to detect outliners. i.e. you can't determine what the best latency is, but you can determine how far off the best latencies you are.  This is useful if you are focusing on the 99%ile latencies for example.  We have a class called `RunningMinimum` to obtain timings from different machines while compensating for a drift in the nanoTime between machines. The more often you take measurements the more accurate this running minimum is.
   
   === Compacting logs
   
  -Chronicle Queue manages storage by cycle. You can add a `StoreFileListener` which will notify you when a file is added and when it is no longer retained.  You can move, compress or delete all the message for a day at once.
  +Chronicle Queue manages storage by cycle. You can add a `StoreFileListener` which will notify you when a file is added and when it is no longer retained.  You can move, compress or delete all the messages for a day at once.
   
   == Chronicle Queue vs Kafka
   
  -Chronicle Queue is designed to support over an order of magnitude the throughput with an order of magnitude lower latency of Kafka.  While Karfa is faster than many of the alternatives it doesn't support both throughputs over million events per second and low latency e.g. 1 - 20 micro-seconds at the same time.
  +Chronicle Queue is designed to support over an order of magnitude of throughput with an order of magnitude lower latency of Kafka.  While Karfa is faster than many of the alternatives it doesn't support both throughputs over million events per second and low latency e.g. 1 - 20 micro-seconds at the same time.
   
   Chronicle Queue attempts to handle more volume from a single thread, to a single partition and thus avoid the need for the complexity and downsides of having partitions.  Note: Chronicle Engine supports partitioning of queues across machines, though not the partitioning of a queue.
   
  -Unlike Karfa which uses a broker which uses the operating system's file system and cache.  Chronicle Queue relies entirely on the file system and cache.
  +Unlike Karfka which uses a broker which uses the operating system's file system and cache.  Chronicle Queue relies entirely on the file system and cache.
   
   === Similar product guides
   
  
positive.code: 
  === Lambda Stream Processing
  
  Streaming components are highly performant, deterministic and have reproducable.
  You can reproduce bugs which only show up after a million events played in a particular order, with accelerated realistic timings.
  
  This makes using Stream Processing attractive for systems which need a high degree of quality outcomes.
  
  == Using Chronicle Queue
  
  Chronicle Queue is designed to be driven from code.  You can easily add an interface which suits you needs.
  
  === Writing to a Queue
  
  
negative.code: 
  Chronicle Queue is most often use for "Producer Centric" systems where you need to retain a lot of data for days or years.
  
  === What is a Producer Centric System?
  
  Most messaging systems are "Consumer Centric", a common example is a user GUI.  
  You can have multiple users on different machines, different quality of networks, doing a variety of other things for the user at different times.
  