The library is build such that it can support multiple storage backends. However, the tested storage system being used is ROOT. The following explains the requirements for a user-provided storage back-end.
There is no interface a writing class has to fulfill. It only needs to take advantage of the interfaces provided in PODIO. To persistify a collection, three pieces of information have to be stored:
- the ID of the collection,
- the vector of PODs in the collection, and
- the relation information in the collection
Before writing out a collection, the data need to be put into the proper structure. For this, the method prepareForWrite
needs to be invoked. In the case of trivial POD members this results in a no-op. In case, the collection contains references to other collections, the pointers are being translated into collID:objIndex
pairs. A serialization solution would - in principle - then look like this:
collection->prepareForWrite();
void* buffer = collection->getBufferAddress();
auto refCollections = collection->referenceCollections();
// ...
// write buffer, collection ID, and refCollections
// ...
The main requirement for a reading backend is its capability of reading back all
the necessary data from which a collection can be constructed in the form of
podio::CollectionReadBuffers
. From these buffers collections can then be
constructed. Each instance has to contain the (type erased) POD buffers (as a
std::vector
), the (possibly empty) vectors of podio::ObjectID
s that contain
the relation information as well the (possibly empty) vectors for the vector
member buffers, which are currently stored as pairs of the type (as a
std::string
) and (type erased) data buffers in the form of std::vector
s.
It is possible to turn on an automatic conversion to JSON for podio generated datamodels using the nlohmann/json library.
To enable this feature the core datamodel library has to be compiled with the PODIO_JSON_OUTPUT
and linked against the nlohmann/json library (needs at least version 3.10).
In cmake the necessary steps are (assuming that datamodel
is the datamodel core library)
find_library(nlohmann_json 3.10 REQUIRED)
target_link_library(datamodel PUBLIC nlohmann_json::nlohmann_json)
target_compile_definitions(datamodel PUBLIC PODIO_JSON_OUTPUT)
With JSON support enabled it is possible to convert collections (or single objects) to JSON simply via
#include "nlohmann/json.hpp"
auto collection = // get collection from somewhere
nlohmann::json json{
{"collectionName", collection}
};
Each element of the collection will be converted to a JSON object, where the keys are the same as in the datamodel definition. Components contained in the objects will similarly be similarly converted.
JSON is not foreseen as a mode for persistency, i.e. there is no plan to add the conversion from JSON to the in memory representation of the datamodel.
PODIO was written with thread-safety in mind and avoids the usage of globals and statics. However, a few assumptions about user code and use-patterns were made. The following lists the caveats of the library when it comes to parallelization.
As explained in the section about mutability of data, thread-safety is only guaranteed if data are considered read-only after creation.
During the calls of prepareForWriting
and prepareAfterReading
on collections other operations like object creation or addition will lead to an inconsistent state.
The Readers and Writers that ship with podio are assumed to run on a single thread only (more precisely we assume that each Reader or Writer doesn't have to synchronize with any other for file operations).
-
Install pre-commit
$ pip install pre-commit
-
Run pre-commit manually
$ pre-commit run --all-files
It is possible to get the EDM definition(s) that was used to generate the
datatypes that are stored in a data file. This makes it possible to re-generate
the necessary code and build all libraries again in case they are not easily
available otherwise. To see which EDM definitions are available in a data file
use the podio-dump
utility
podio-dump <data-file>
which will give an (exemplary) output like this
input file: <data-file>
EDM model definitions stored in this file: edm4hep
[...]
To actually dump the model definition to stdout use the --dump-edm
option
and the name of the datamodel you want to dump:
podio-dump --dump-edm edm4hep <data-file> > dumped_edm4hep.yaml
Here we directly redirected the output to a yaml file that can then again be
used by the podio_class_generator.py
to generate the corresponding c++ code
(or be passed to the cmake macros).
Note that the dumped EDM definition is equivalent but not necessarily exactly
the same as the original EDM definition. E.g. all the datatypes will have all
their fields (Members
, OneToOneRelations
, OneToManyRelations
,
VectorMembers
) defined, and defaulted to empty lists in case they were not
present in the original EDM definition. The reason for this is that the embedded
EDM definition is the pre-processed and validated one as described
below
The EDM definition can also be accessed programmatically via the
[ROOT|SIO]FrameReader::getEDMDefinition
method. It takes an EDM name as its
single argument and returns the EDM definition as a JSON string. Most likely
this has to be decoded into an actual JSON structure in order to be usable (e.g.
via json.loads
in python to get a dict
).
The EDM definition is embedded into the core EDM library as a raw string literal
in JSON format. This string is generated into the DatamodelDefinition.h
file as
namespace <package_name>::meta {
static constexpr auto <package_name>__JSONDefinition = R"EDMDEFINITION(<json encoded definition>)EDMDEFINITION";
}
where <package_name>
is the name of the EDM as passed to the
podio_class_generator.py
(or the cmake macro). The <json encoded definition>
is obtained from the pre-processed EDM definition that is read from the yaml
file. During this pre-processing the EDM definition is validated, and optional
fields are filled with empty defaults. Additionally, the includeSubfolder
option will be populated with the actual include subfolder, in case it has been
set to True
in the yaml file. Since the json encoded definition is generated
right before the pre-processed model is passed to the class generator, this
definition is equivalent, but not necessarily equal to the original definition.
To make access to information about currently loaded and available datamodels a
bit easier the DatamodelRegistry
(singleton) keeps a map of all loaded
datamodels and provides access to this information possible. In this context we
refer to an EDM as the shared library (and the corresponding public headers)
that have been compiled from code that has been generated from a datamodel
definition in the original YAML file. In general whenever we refer to a
datamodel in this context we mean the entity as a whole, i.e. its definition
in a YAML file, the concrete implementation as an EDM, as well as other related
information that is related to it.
Currently the DatamodelRegistry
provides mainly access to the original
definition of available datamodels via two methods:
const std::string_view getDatamodelDefinition(const std::string& edmName) const;
const std::string_view getDatamodelDefinition(size_t index) const;
where index
can be obtained from each collection via
getDatamodelRegistryIndex
. That in turn simply calls
<package_name>::meta::DatamodelRegistryIndex::value()
, another singleton like object
that takes care of registering an EDM definition to the DatamodelRegistry
during its static initialization. It is also defined in the
DatamodelDefinition.h
header.
Since the datamodel definition is embedded as a raw string literal into the core EDM shared library, it is in principle also relatively straight forward to retrieve it from this library by inspecting the binary, e.g. via
readelf -p .rodata libedm4hep.so | grep options
which will result in something like
[ 300] {"options": {"getSyntax": true, "exposePODMembers": false, "includeSubfolder": "edm4hep/"}, "components": {<...>}, "datatypes": {<...>}}
The podio/utilities/DatamodelRegistryIOHelpers.h
header defines two utility
classes, that help with instrumenting readers and writers with functionality to
read and write all the necessary EDM definitions.
- The
DatamodelDefinitionCollector
is intended for usage in writers. It essentially collects the datamodel definitions of all the collections it encounters. TheregisterDatamodelDefinition
method it provides should be called with every collection that is written. ThegetDatamodelDefinitionsToWrite
method returns a vector of all datamodel names and their definition that were encountered during writing. It is then the writers responsibility to actually store this information into the file. - The
DatamodelDefinitionHolder
is intended to be used by readers. It provides thegetDatamodelDefinition
andgetAvailableDatamodels
methods. It is again the readers property to correctly populate it with the data it has read from file. Currently theSIOReader
,ROOTReader
and theROOTRNTupleReader
, use it and also offer the same functionality as public methods with the help of it.