Rise Algorithms

EasyLambda provides commonly used generic function objects for the rise unit. These objects provide a wide variety of configurability in the form of properties specific to them. These properties are defined only for these function objects and are not to be confused with the properties that apply to units or dataflows.

All the function objects for rise are embellished with the split property which configures how the data generated / loaded by the function object is to be shared among the processes. If it is set, the total data is split among the processes otherwise each process gets a full copy of the data.

Following are some of the common generic function objects for rise unit. Check demoFromFile and demoIO for usage examples of these function objects.


It loads rows from a container or a C++ initializer list. It expects a container and streams rows with multiple columns to the dataflow. If the same list is apriori available to all the processes, splitting the data parallelizes the operation. however, if the list is already split amongst the processes i.e.. each process has different items in the list, then split property is not required.


It loads tabulated data from files in parallel. The types of the columns to be read into the tables can be specified using template parameters which can be either C++ primitives or std::array types. fromFile handles errors in reading and can read in a single file or multiple files simultaneously.

It has a comprehensive set of properties to configure. As one example, it can either impose a strict or a loose schema on the data read in. A strict schema ensures that the rows that have a different number of columns than specified are rejected. A loose schema ensures that extra columns get ignored and default values get filled in for rows that don’t have values for certain columns.

If the data stored in the files is segmented on certain columns and a reduce with key as the same columns needs to be applied, it can be carried out in–process if it is made sure that the same process reads the data unless the value in the key columns change. The fromFile function object provides a segmented property with column selection for the same.

It can also be used for reading in data with header information which needs to be attached to every row. This is a common requirement in physics simulation programs which dump out time–steps at the top of the files and the data rows for the time–step follow the header. The data loaded in such a manner is always segmented on the header column value.


It can be thought of as a lazy range generator function. It calls the next unit with incrementing numbers within a specified range. The values of the generated range can be split among processes controlled by the split property that is by default set.


It calls the next unit a specified number of times with rows having no columns that translate to no parameter in the user function of the next unit. The split property can be used to control whether the number of calls will be total calls over all the processes or number of calls for each process. The split property is by default set.


The function object loads all the file paths from a glob pattern. The file names can be shared among the processes with split property. It is useful in non text files such as images. The function object loads the file paths which then are passed to the next unit which can open the file and return the data.