Architecture of the OpenQuake Engine v2

The OpenQuake Engine version 2 is a complete rewrite of version 1.0, so a new document describing the overall architecture is needed. Whereas in the past the Engine was database-centric and structured as a Web application with an Object Relational Mapper, now it is calculation-centric and structured as a scientific application: everything is done in memory and in the core engine there is no database, nor ORM. Intermediate results are stored in HDF5 format. In the calculators we work as much as possible in terms of arrays which are efficiently manipulated at C/Fortran speed with a stack of well established scientific libraries (numpy/scipy).

Design principles

The main design principle has been simplicity: everything has to be as simple as possible (but not simplest). The goal has been to keep the engine simple enough that a single person can understand it, can debug it and can extend it without tremendous efforts. All the rest comes from simplicity: transparency, ability to inspect and debug, modularity, adaptability of the code, etc. Even efficiency: in the last three years most of the performance improvements came from free, just from removing complications. When a thing is simple it is easy to make it fast. The battle for simplicity is never ending, so there are still several things in the engine that are more complex than they should: we are working on that.

After simplicity the second design goal has been performance: the engine is a number chrunching application after all, and we need to run massively parallel calculations taking days or weeks of runtime. Efficiency in terms of computation time and memory requirements is of paramount importance, since it makes the difference between being able to run a computation and being unable to do it. Being too slow to be usable should be considered as a bug.

The third requirement is reproducibility, which is the same as testability: it is essential to have a suite of tests checking that the calculators are providing the expected outputs against a set of predefined inputs. With respect to OpenQuake Engine 1.0 we have at least tripled the number of scientific tests. We are testing a lot more corner cases and a lot more functionalities. On the other hand several programming unit tests have been removed: they were testing implementations details of an implementation that has been removed and replaced with a simpler one.

Components of the OpenQuake Engine

The OpenQuake Engine suite is composed of several components:

  • a set of support libraries addressing different concerns like reading the inputs and writing the outputs, implementing basic geometric manipulations, managing distributed computing and generic programming utilities
  • the hazardlib and risklib scientific libraries, providing the building blocks for hazard and risk calculations, notably the GMPEs for hazard and the vulnerability/fragility functions for risk
  • the hazard and risk calculators, implementing the core logic of the engine
  • the datastore, which is an HDF5 file working as a short term storage/cache for a calculation; it is possible to run a calculation starting from an existing datastore, to avoid recomputing everything every time; there is a separate datastore for each calculation
  • the database, which is a SQLite file working as a long term storage for the calculation metadata; the database contains the start/stop times of the computations, the owner of a calculation, the calculation descriptions, the performances, the logs, etc; the bulk scientific data (essentially big arrays) are kept in the datastore
  • the database server, which is a service mediating the interaction between the calculators and the database
  • the Web UI is a web applications that allows to run and monitor computations via a browser; multiple calculations can be run in parallel
  • the oq command-line tool; it allows to run computations and provides an interface to the underlying database and datastores so that it is possible to list and export the results
  • the engine can run on a cluster of machines: in that case you have to start the rabbitmq and celery components which are not required on a single machine installation. In that case a minimal amount of configuration is needed, whereas in single machine installations the engine works out of the box

This is the full stack of internal libraries used by the engine: each of those is a Python package containing several modules or event subpackages. The stack is a dependency tower where the higher levels depend on the lower levels but not viceversa:

  • level 8: commands (commands for oq script)
  • level 7: server (database and Web UI)
  • level 6: engine (command-line tool, export, logs)
  • level 5: calculators (hazard and risk calculators)
  • level 4: commonlib (configuration, logic trees, datastore, I/O)
  • level 3: risklib (risk validation, risk models, risk inputs)
  • level 2: hazardlib (validation, read/write XML, source and site objects, geospatial utilities, GSIM library)
  • level 1: baselib (programming utilities, parallelization, monitoring, Python 3 compatibility)

baselib and hazardlib are very stable and can be used outside of the engine; the other libraries are directly related to the engine and are likely to be affected by backward-incompatible changes in the future, as the code base evolves.

The GMPE library in hazardlib and the calculators are designed to be extensible, so that it is easy to add a new GMPE class or a new calculator. We routinely add several new GMPEs per release; adding new calculators is less common and it requires more expertise, but it is possible and it has been done several times in the past. In particular it is often easier to add a specific calculator optimized for a given use case rather than complicating the current calculators.

The results of a computation are automatically saved in the datastore and can be exported in a portable format, such as XML or CSV. You can assume that the datastore of version X of the engine will not work with version X + 1: on the contrary, the exported files will likely be same across different versions. It is important to export all of the outputs you are interested in before doing an upgrade, otherwise you would be forced to downgrade in order to be able to export the previous results.

The WebUI provides a REST API that can be used in third party applications: for instance a QGIS plugin could download the maps generated by the engine via the WebUI and display them. There is lot of functionality in the API which is documented here: It is possible to build your own user interface for the engine on top of it, since the API is stable and kept backward compatible.