Converts ROOT trees into different formats to make them accessible in Big Data applications.
There are several projects here, three of which are complete. They all belong in the same git repository because they share code.
root2avrois a C++ program that converts ROOT TTree data into an equivalent Avro data (which may be saved to a file on disk or streamed into another application.scaroot-readeris a hybrid Scala/C++ (through JNA) library that streams ROOT TTree data directly into the JVM. Data representation is controlled with (possibly) user-supplied callbacks.Spark examplesshows how to use ScaROOT-Reader in Spark.
Click on the links to go to specific documentation for each.
Rough performance statistics for 1000 Event.root entries on a single machine (my laptop). Take these numbers as relative.
- 1.8 sec: read TTree, discard data.
- 1.8 sec: read TTree, create Scala objects with ScaROOT-Reader (negligible difference from above). However, repeating this test eventually produced some 3 second spikes, presumably due to garbage collector pauses.
- 5.7 sec: convert to uncompressed Avro file and save. Reading from Avro file in Java: about 1 sec. Avro file is 2.0 times as large as the original ROOT file.
- 6.1 sec: convert to Snappy-compressed Avro file and save. Avro file is 1.4 times as large as the original ROOT file.
- 29 sec: convert to Avro with any other compression method. Avro file is 1.0 times as large as the original ROOT file (suggesting that ROOT uses something like deflate).
- 18 sec: convert to JSON file and save. The JSON file is huge.
- 29 sec: abandoned
scaroot-oldreaderversion (see old branch).
Unfortunately, file-reading cannot be parallelized in the same process: you immediately get libRIO segmentation faults. Adding a "micro-batch" strategy of copying several entries from C++ to Scala at a time does nothing for performance.