h1. Online Reconstruction Overview

"Overview talk":https://micewww.pp.rl.ac.uk/attachments/816/20120209-MAUS-SSI-Status.ppt - 09/02/12 - contains a summary of the main concepts and architecture.

h2. Run numbers

Run numbers are assumed to be as follows:

* -N : Monte Carlo simulation of run N

* 0 : pure Monte Carlo simulation

* +N : run N

h2. Transforming spills from an input stream (Input-Transform)

This is the algorithm used to transform spills from an input stream:

<pre>

CLEAR document store

run_number = None

WHILE an input spill is available

  GET next spill

  IF spill does not have a run number

    # Assume pure MC

    spill_run_number = 0

  IF (spill_run_number != run_number)

    # We've changed run.

    IF spill is NOT a start_of_run spill

      WARN user of missing start_of_run spill

    WAIT for current Celery tasks to complete

      WRITE result spills to document store

    run_number = spill_run_number

    CONFIGURE Celery by DEATHing current transforms and BIRTHing new transforms

  TRANSFORM spill using Celery

  WRITE result spill to document store

 DEATH Celery worker transforms

</pre>

If there is no initial @start_of_run@ spill (or no @spill_num@ in the spills) in the input stream (as can occur when using @simple_histogram_example.py@ or @simulate_mice.py@) then spill_run_number will be 0, run_number will be None and a Celery configuration will be done before the first spill needs to be transformed. 

Spills are inserted into the document store in the order of their return from Celery workers. This may not be in synch with the order in which they were originally read from the input stream.

h2. Merging spills and passing results to an output stream (Merge-Output)

This is the algorithm used to merge spills and pass the results to an output stream:

<pre>

run_number = None

end_of_run = None

is_birthed = FALSE

last_time = 01/01/1970

WHILE TRUE

  READ spills added since last time from document store

  IF spill IS "end_of_run"

    end_of_run = spill

  IF spill_run_number != run_number

    IF is_birthed

      IF end_of_run == None

          end_of_run = {"daq_event_type":"end_of_run", "run_num":run_number}

      Send end_of_run to merger

      DEATH merger and outputter

    BIRTH merger and outputter

    run_number = spill_run_number

    end_of_run = None

    is_birthed = TRUE

  MERGE and OUTPUT spill

Send END_OF_RUN block to merger

DEATH merger and outputter

</pre>

The Input-Transform policy of waiting for the processing of spills from a run to complete before starting processing spills from a new run means that all spills from run N-1 are guaranteed to have a time stamp < spills from run N.

is_birthed is used to ensure that there is no BIRTH-DEATH-BIRTH redundancy on receipt of the first spill.

h2. Document store

Spills are stored in documents in a collection in the document store. 

Documents are of form @{"_id":ID, "date":DATE, "doc":SPILL}@ where:

* ID: index of this document in the chain of those successfuly transformed. It has no significance beyond being unique in an execution of Input-Transform loop below. It is not equal to the spill_num (Python @string@)

* DATE: date and time to the milli-second noting when the document was added (Python @timestamp@)

* DOC: spill document. (Python @string@ holding a valid JSON document)

h3. Collection names

For Input-Transform,

* If configuration parameter @doc_collection_name@ is @None@, @""@, or @auto@ then @HOSTNAME_PID@, where @HOSTNAME@ is the machine name and @PID@ the process ID, is used.

* Otherwise the value of @doc_collection_name@ is used.

* @doc_collection_name@ has default value @spills@.

For Merge-Output,

* If configuration parameter @doc_collection_name@ is @None@, @""@, or undefined then an error is raised.

* Otherwise the value of @doc_collection_name@ is used.

h2. Miscellaneous

* Currently Celery timeouts are not used, transforming a spill takes as long as it takes.

* Celery task retries on failure option is not used. If the transformation of a spill fails first time it can't be expected to succeed on a retry.

* If memory leaks arise, e.g. from C++ code, look at Celery rate limitss, which allow the time or number of tasks before sub-process is killed and respawned, to be defined. Soft rate limits would allow @death@ to be run on the transforms first.