anchor.diff:  | FlowFile | Information Packet | 
   A FlowFile represents the objects moving through the system and for each one NiFi
   keeps track of a Map of key/value pair attribute strings and its associated 
  -content zero or bytes.
  +content of zero or more bytes.
   
   | FlowFile Processor | Black Box | 
   Processors are what actually performs work.  In <<eip>> terms a processor is 
  
positive.code: NiFi executes within a JVM living within a host operating system.  The primary
  components of NiFi then living within the JVM are as follows:
  
  * Web Server
  ** The purpose of the web server is to host NiFi's HTTP-based command and control API.
  * Flow Controller
  ** The flow controller is the brains of the operation. It provides threads for extensions to run on and manages their schedule of when they'll receive resources to execute.
  * Extensions
  ** There are various types of extensions for NiFi which will be described in other documents.  But the key point here is that extensions operate/execute within the JVM.
  * FlowFile Repository
  ** The FlowFile Repository is where NiFi keeps track of the state of what it knows about a given FlowFile that is presently active in the flow.  The implementation of the repository is pluggable.  The default approach is a persistent Write-Ahead Log that lives on a specified disk partition. 
  * Content Repository
  ** The Content Repository is where the actual content bytes of a given FlowFile live.  The implementation of the repository is pluggable.  The default approach is a fairly simple mechanism which stores blocks of data in the file system.   More than one file system storage location can be specified so as to get different physical partitions engaged to reduce contention on any single volume.
  * Provenance Repository
  ** The Provenance Repository is where all provenance event data is stored.  The repository construct is pluggable with the default implementation being to use  one or more physical disk volumes.  Within each location event data is indexed  and searchable.
  
  NiFi is also able to operate within a cluster.
  
  
negative.code: regard to CPU and disk.  Many more details will
  be provided on best practices and configuration tips in the Administration Guide. 
  
  For IO::
  The throughput or latency
  one can expect to see will vary greatly on how the system is configured.  Given
  that there are pluggable approaches to most of the major NiFi subsystems the
  performance will depend on the implementation.  But, for something concrete and broadly
  applicable lets consider the out of the box default implementations that are used.
  These are all persistent with guaranteed delivery and do so using local disk.  So 
  being conservative assume roughly 50 MB/s read/write rate on modest disks or RAID volumes 
  within a typical server.  NiFi for a large class of data flows then should be able to 
  efficiently reach one hundred or more MB/s of throughput.  That is because linear growth
  is expected for each physical parition and content repository added to NiFi.  This will 
  bottleneck at some point on the FlowFile repository and provenance repository.  
  We plan to provide a benchmarking/performance test template to 
  include in the build which will allow users to easily test their system and 
  to identify where bottlenecks are and at which point they might become a factor.  It 
  should also make it easy for system administrators to make changes and to verity the impact.
  
  For CPU::
  The FlowController acts as the engine dictating when a given processor will be
  given a thread to execute.  Processors should be written to return the thread
  as soon as they're done executing their task.  The FlowController can be given a 
  configuration value indicating how many threads there should be for the various
  thread pools it maintains.  The ideal number of threads to use will depend on the 
  resources of the host system in terms of numbers of cores, whether that system is
  running other services as well, and the nature of the processing in the flow.  For
  typical IO heavy flows though it would be quite reasonable to set many dozens of threads
  to be available if not more.
  
  For RAM::
  NiFi lives within the JVM and is thus generally limited to the memory space it 
  is afforded by the JVM.  Garbage collection of the JVM becomes a very important
  factor to both restricting the total practical size the heap can be as well as
  how well the application will run over time.  
  
  High Level Overview of Key NiFi Features
  ----------------------------------------
  Guaranteed Delivery::
  A core philosophy of NiFi has been that even at very high scale guaranteed delivery
  is a must.  This is achieved through effective use of a purpose-built persistent 
  write-ahead log and content repository.  Together they are designed in such a way
  as to allow for very high transaction rates, effective load-spreading, copy-on-write,
  and play to the strengths of traditional disk read/writes.
  
  Data Buffering w/ Back Pressure and Pressure Release::
  NiFi supports buffering of all queued data as well as the ability to 
  provide back pressure as those queues reach specified limits or to age off data
  as it reaches a specified age (its value has perished).
  
  Prioritized Queuing::
  NiFi allows the setting of one or more prioritization schemes for how data is
  retrieved from a queue.  The default is oldest first but there are times when
  data should be pulled newest first, largest first, or some other custom scheme.
  
  Flow Specific QoS (latency v throughput, loss tolerance, etc..)::
  There are points of a dataflow where the data is absolutely critical and it is
  loss intolerant.  There are times when it must be processed and delivered within
  seconds to be of any value.  NiFi enables the fine-grained flow specific configuration
  of these concerns.
  
  Data Provenance::
  NiFi automatically records, indexes, and makes available provenance data as
  objects flow through the system even across fan-in, fan-out, transformations, and
  more.  This information becomes extremely critical in supporting compliance, 
  troubleshooting, optimization, and other scenarios.  
  
  Recovery / Recording a rolling buffer of fine-grained history::
  NiFi's content repository is designed to act as a rolling buffer of history.  Data
  is removed only as it ages off the content repository or as space is needed.  This
  combined with the data provenance capability makes for an incredibly useful basis
  to enable click-to-content, download of content, replay, and all at a specific 
  point in and objects lifecycle which can even span generations.
  
  Visual Command and Control::
  Dataflows can become quite complex.  Being able to visualize those flows and express
  them visually can help greatly to reduce that complexity and to identify areas which
  need to be simplified.  NiFi enables not only the visual establishment of dataflows but
  it does so in real-time.  Rather than being 'design and deploy' it is much more like
  molding clay.  If you make a change to the dataflow that change is taking effect.  Changes
  are fine-grained and isolated to the affected components.  You don't need to stop an entire
  flow or set of flows just to make some specific modification.  
  
  Flow Templates::
  Dataflows tend to be highly pattern oriented and while there are often many different
  ways to solve a problem it helps greatly to be able to share those best practices.  Templates
  allow subject matter experts to build and publish their flow designs and for others to benefit
  and collaborate on them.
  
  Security::
      System to system;;
          A dataflow is only as good as it is secure.  NiFi at every point in a dataflow offers secure
          exchange through the use of protocols with encryption such as 2-way SSL.  In addition
          NiFi enables the flow to encrypt and decrypt content and use shared-keys or other mechanisms on 
          either side of the sender/recipient equation.
      User to system;;
          NiFi enables 2-Way SSL authentication and provides pluggable authorization so that it can properly control
          a users access and at particular levels (read-only, dataflow manager, admin).  If a user enters a 
          sensitive property like a password into the flow it is immediately encrypted server side and never again exposed
          on the client side even in its encrypted form.
  
  Designed for Extension::
      NiFi is at its core built for extension and as such it is a platform on which dataflow processes can execute and interact in a predictable and repeatable manner.
      Points of extension;;
          Processors, Controller Services, Reporting Tasks, Prioritizers, Customer User Interfaces
      Classloader Isolation;;
          For any component based system one problem that can quickly occur is dependency nightmares.  NiFi addresses this by providing a custom class loader model
          ensuring that each extension bundle is exposed to a very limited set of dependencies.  As a result extensions can be built with little concern for whether 
          they might conflict with another extension.  The concept of these extension bundles is called 'NiFi Archives' and will be discussed in greater detail 
          in the developers guide.
  Clustering (scale-out)::
      NiFi is designed to scale-out through the use of clustering many nodes together as described above.  If a single node is provisioned and configured
      to handle hundreds of MB/s then a modest cluster could be configured to handle GB/s.  This then brings about interesting challenges of load balancing
      and fail-over between NiFi and the systems from which it gets data.  Use of asynchronous queuing based protocols like messaging services, Kafka, etc.. can
      help.  Use of NiFi's 'site-to-site' feature is also very effective as it is a protocol that allows NiFi and a client (could be another NiFi cluster) to talk to eachother, share information
      about loading, and to exchange data on specific authorized ports.
  
  # References
  [bibliography]
  