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There are three different ways to get quasi real time information about data taking. They offer different pros and cons and the final choice to decide about which method works best for a given experiments is left to the users.

The approaches are:

1. AMI (Analysis Monitoring Interface) processes running on the instrument dedicated monitoring nodes.
2. Shared memory applications running on the instrument dedicated monitoring nodes.
3. Custom applications running on the experimental hall fast feedback queues and reading the data from disk.

AMI

AMI can display the raw/calibrated detector data in real time and it can calculate and display some simple quantities without writing any code, just with .  Analyses are configured through a GUI with just a few mouse clicks. This is the AMI guide.Note that it's possible to augment the core AMI capabilities by writing a plug-in following a well defined C++ API, but this is not trivial. If you are interested in adding new capabilities to AMI, but you don't have enough C++ expertise in your group to create an AMI plug-in, contact us before coming to LCLS.A guide to using AMI can be found here.

Pros: excellent real time capabilities (< 1s); very simple to use for simple analysis. Cons: non trivial augmenting capabilities through additional C++ code

Cons: it can be tricky/impossible to save&load complicated analyses, need fast X-connection to setup as it is GUI driven.

Shared Memory (Experts only!)

The LCLS data acquisition system can allocate processes, called called shared memory servers, which receive the detector data through the network, build these contributions into events, and then hand over these events to one or more clients running on the same monitoring machine. The clients can be written in any language that can access shared memory. The most common frameworks adopted in LCLS as shared memory clients are are CASS, OnDA and  and psana. 

Pros: excellent  excellent real time capabilities (< 1s); ability to run the same code online and offline.

Cons: competes  if not using pre-existing software, requires very large time investment from both LCLS and the user.  competes with AMI resources (monitoring nodes, data stream); requires setting up the shared memory servers on the DAQ side.

Fast Feedback

This method allows to run the analysis by reading of analysis reads the detector data from a fast , dedicated storage layer called fast feedback (FFB), called fast feedback, dedicated to the running experiment, and runs a prepared analysis. Any framework which can read XTC files can be adopted to run as a fast feedback application. The most common framework used with this method is psana.

Pros: trivial to setup; independent from of the DAQ; ability to run the same code online and offline.

Cons: not quite real time (< 10s); may compete with the translator if the translation services are run against the FFB storage layer.

 ~<1 minutes after the run has ended using the LCLS supported framework).