摘要:
Certain example embodiments relate to an intelligent messaging grid for Big Data ingestion and/or associated methods. Each node in a network of nodes is dynamically configurable to send and/or receive messages using one of brokered and brokerless communication models. At least some of the nodes have a complex event processing (CEP) engine deployed thereto, the CEP engines being configured to operate on messages received by the respective nodes and being classified as one of at least two different types of CEP engines. For each message received by a given node that is to be forwarded to a further node along one of multiple possible paths, the given node is configured to route the message to be forwarded to an intermediate node in one of the possible paths. The intermediate node is selected by the CEP engine of the given node based on metadata associated with the message to be forwarded.
摘要:
Certain example embodiments provide efficient policy-based access to data stored in memory tiers, including volatile local in-process (L1) cache memory of an application and at least one managed (e.g., non-volatile) in-memory (L2) cache. Operations include receiving an access request for access to a data element in L2; detecting whether a copy of the data element is in L1; if so, copying the data element and the access policy from L2 to L1 and providing the user with access to the copy of data element from L1 if the access policy allows access to the user; and if not, determining, by referring to a copy of the access policy stored in L1, whether the user is allowed to access the data element, and, if the user is allowed to access the data element, providing the user with access to the copy of the data element from the L1 cache memory.
摘要:
Certain example embodiments relate to a computer system for performing a map reduce sequence. Nodes therein include at least one processor and memory and are divided into at least mapper and reducer nodes. Each mapper node executes a map function on input to generate intermediate output elements. Each said intermediate output element includes a first key-value pair. Each element key includes associated map and reduce task identifiers. Each element value includes substantive data, organized as another key-value pair. The intermediate output elements are stored to memory. Each reducer node: retrieves at least intermediate output element values from the memory of a given mapper node, using specified map and reduce task identifiers; stores the retrieved element values to its memory; executes a reduce function on the retrieved element values, in order; and outputs a result from the reduce function for the map reduce sequence. Disk operations advantageously are reduced or eliminated.