Abstract:
A system, computer-implemented method, and a computer-readable storage medium for a data graph traversal are provided. The input parameters for traversing the data graph are received. The data graph having a set of vertices and a set of edges are stored in a column based format in a memory cache of a computer device based on the input parameters is traversed. The traversal generates a set of traversed vertices that are the result of the graph traversal.
Abstract:
An execution framework allows developers to write sequential computational logic, constrained for the runtime system to efficiently parallelize execution of custom business logic. The framework can be leveraged to overcome limitations in executing low level procedural code, by empowering the system runtime environment to parallelize this code. Embodiments employ algorithmic skeletons in the realm of optimizing/executing data flow graphs of database management systems. By providing an extensible set of algorithmic skeletons the developer of custom logic can select the skeleton appropriate for new custom logic, and then fill in the corresponding computation logic according to the structural template of the skeleton. The skeleton provides a set of constraints known to the execution environment, that can be leveraged by the optimizer and the execution environment to generate parallel optimized execution plans containing custom logic, without the developer having to explicitly describe parallelization of the logic.
Abstract:
An execution framework allows developers to write sequential computational logic, constrained for the runtime system to efficiently parallelize execution of custom business logic. The framework can be leveraged to overcome limitations in executing low level procedural code, by empowering the system runtime environment to parallelize this code. Embodiments employ algorithmic skeletons in the realm of optimizing/executing data flow graphs of database management systems. By providing an extensible set of algorithmic skeletons the developer of custom logic can select the skeleton appropriate for new custom logic, and then fill in the corresponding computation logic according to the structural template of the skeleton. The skeleton provides a set of constraints known to the execution environment, that can be leveraged by the optimizer and the execution environment to generate parallel optimized execution plans containing custom logic, without the developer having to explicitly describe parallelization of the logic.
Abstract:
In some implementations, a method includes receiving a first data set that is stored using a first format, generating an info item based on the first data set, the info item representing an entity extracted from the first data set, generating a delta item based on the first data set, the delta item including a reference to the info item and defining a context-based modification of the info item, generating a second data set in a second format comprising the info item and the delta item, and storing the second data set to the computer-readable storage medium.