Getting TTree clusters as used by TTreeProcessorMT

Dear experts,

I am interested in parallelizing my analysis workflow. My analysis code uses a custom class that whose variables, cuts & weights, and histograms that can be configured in a similar way to RDataFrame, so that running it inside an event loop can be done by a trivial method call, something like:

class Analysis
template <typename T> 
Variable<T>* read( /* ... */ );  // read a branch of type T from ttree
template <typename Tcustom> 
Variable<Tcustom>* define( /* ... */  );  // define a new variable from existing branches
template <typename F> 
Analysis& cut(F callable, /* ... */); // can apply cuts in sequence.
// ....

int main() {
  auto tree = /* ... */;
  // in a PyROOT interface, can be configured on-the-fly
  auto ana = Analysis();
  // running over a TTree is "trivial" at this stage with all actions booked
  for auto (int ientry=0 ; ientry<tree->GetEntries() ; ++ientry) {
  return 0;

Since I can create independent Analysis instances as many times as I want, I can currently simply set ROOT::EnableThreadSafety and use regular std::thread to implement multithreaded analysis runs without using TThreadedObject of e.g. histograms inside my analysis class. In pure C++, parallelizing my code using TTreeProcessorMT would look like:

int main() {
  auto treeProcessor = /* ... */;
  treeProcessor.Process([](TTreeReader& subRange) {
    // instantiate an Analysis() for each subrange
    ana = Analysis();<float>(/*...*/);
    ana.input(&subRange); // <- currently using TTree, will have to use TTreeReader like here instead
    // ...
    while( {
    return 0;

However, since I perform configuration of an Analysis instance to be via external config files and PyROOT interface, I cannot use TTreeProcessorMT directly (as that would mean I need to statically compile the said instance). So instead, I currently have the limitation that my concurrency is bounded by the number of input file/trees that that my ntuples are stored over. I was hoping/wondering how easy it was to access clusters of a TTree corresponding to subranges of a in a thread-safe way as implemented in TTreeProcessorMT?

Edit: I just wanted to add that for the ntuples that I am actually using, all the trees only have one cluster so I am (in theory) getting all the parallel performance I can get by simply running over N files in parallel. However, I am still interested in this for potentially having a more robust code setup where I would always be merging input files in advance (with control over granularity of clusters) and run over its clusters in parallel.

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ROOT Version: 6.20.04

I guess @pcanal can help.

Hi @taehyounpark ,
you can access the cluster boundaries via TTree::GetClusterIterator() and iterating on that. TTreeProcessorMT has extra logic to decide how many clusters to process in the same multi-thread task, and to deal with friend trees or the presence of a TEntryList.

If you add a void operator()(TTreeReader &r) to Analysis you can also directly pass an Analysis object to TTreeProcessorMT::Process.

RDataFrame already solves a lot of these problems for you, maybe you can refactor things so that Analysis builds on top of RDF.


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