Abstract: Techniques are provided for using decentralized lock synchronization to increase network throughput. In an embodiment, a first computer sends, to a second computer comprising a lock, a request to acquire the lock. In response to receiving the lock acquisition request, the second computer detects whether the lock is available. If the lock is unavailable, then the second computer replies by sending a denial to the first computer. Otherwise, the second computer sends an exclusive grant of the lock to the first computer. While the first computer has acquired the lock, the first computer sends data to the second computer. Afterwards, the first computer sends a request to release the lock to the second computer. This completes one duty cycle of the lock, and the lock is again available for acquisition.

Abstract: A method, apparatus, and system for efficiently re-partitioning data using scheduled network communication are provided. Given re-partitioning data defining the data blocks to be sent amongst a plurality of server nodes, a corresponding network schedule is determined to send the data blocks in a coordinated manner. The network schedule is divided into time slots, wherein each of the plurality of server nodes can send up to one data block and receive up to one data block in each time slot. By using a greedy selection algorithm that prioritizes by largest senders and largest receivers, a near optimal schedule can be determined even in the presence of heavy skew. The greedy selection algorithm can be implemented with a O(T*N^2) time complexity, enabling scaling to large multi-node clusters with many server nodes. The network schedule is of particular interest for database execution plans requiring re-partitioning on operators with different keys.

Abstract: A method, apparatus, and system for efficiently re-partitioning data using scheduled network communication are provided. Given re-partitioning data defining the data blocks to be sent amongst a plurality of server nodes, a corresponding network schedule is determined to send the data blocks in a coordinated manner. The network schedule is divided into time slots, wherein each of the plurality of server nodes can send up to one data block and receive up to one data block in each time slot. By using a greedy selection algorithm that prioritizes by largest senders and largest receivers, a near optimal schedule can be determined even in the presence of heavy skew. The greedy selection algorithm can be implemented with a O(T*N?2) time complexity, enabling scaling to large multi-node clusters with many server nodes. The network schedule is of particular interest for database execution plans requiring re-partitioning on operators with different keys.

Abstract: A method and apparatus for adaptive data repartitioning and adaptive data replication is provided. A data set stored in a distributed data processing system is partitioned by a first partitioning key. A live workload comprising a plurality of data processing commands is processed. While processing the live workload, statistical properties of the live workload are maintained. Based on the statistical properties of the live workload with respect to the data set, it is determined to replicate and/or repartition the data set by a second partitioning key. The replicated and/or repartitioned data set is partitioned by the second partitioning key.

Abstract: A method, apparatus, and system for determining a data distribution is provided by using an adaptive resolution histogram. In an embodiment, the adaptive resolution histogram is created using a trie, wherein node values in the trie represent frequency distributions and node positions define associated keys or key prefixes. Keys are derived from input data such as database records that are streamed from a record source. These keys may be processed as received to build the trie in parallel with the production of the input data. To provide adaptive resolution, new child nodes may only be created in the trie when a node value is incremented beyond a predetermined threshold. In this manner, the histogram adjusts the allocation of nodes according to the actual distribution of the data. The completed adaptive resolution histogram may be used for various tasks such as partitioning for balanced parallel processing of the input data.

Abstract: Combining system fault and performance monitoring using distributed data collection and storage of performance data. Storage requirements are relaxed and real-time performance monitoring is possible. Data collection and storage elements can be easily configured via a central configuration database. The configuration database can be easily updated and changed. A federated user model allows normal end users to monitor devices relevant to the part of a service they are responsible for, while allowing administrative users to view the fault and performance of a service in an end-to-end manner.

Abstract: Combining system fault and performance monitoring using distributed data collection and storage of performance data. Storage requirements are relaxed and real-time performance monitoring is possible. Data collection and storage elements can be easily configured via a central configuration database. The configuration database can be easily updated and changed. A federated user model allows normal end users to monitor devices relevant to the part of a service they are responsible for, while allowing administrative users to view the fault and performance of a service in an end-to-end manner.

Abstract: Combining system fault and performance monitoring using distributed data collection and storage of performance data. Storage requirements are relaxed and real-time performance monitoring is possible. Data collection and storage elements can be easily configured via a central configuration database. The configuration database can be easily updated and changed. A federated user model allows normal end users to monitor devices relevant to the part of a service they are responsible for, while allowing administrative users to view the fault and performance of a service in an end-to-end manner.

Abstract: Combining system fault and performance monitoring using distributed data collection and storage of performance data. Storage requirements are relaxed and real-time performance monitoring is possible. Data collection and storage elements can be easily configured via a central configuration database. The configuration database can be easily updated and changed. A federated user model allows normal end users to monitor devices relevant to the part of a service they are responsible for, while allowing administrative users to view the fault and performance of a service in an end-to-end manner.

Abstract: Combining system fault and performance monitoring using distributed data collection and storage of performance data. Storage requirements are relaxed and real-time performance monitoring is possible. Data collection and storage elements can be easily configured via a central configuration database. The configuration database can be easily updated and changed. A federated user model allows normal end users to monitor devices relevant to the part of a service they are responsible for, while allowing administrative users to view the fault and performance of a service in an end-to-end manner.

Abstract: Combining system fault and performance monitoring using distributed data collection and storage of performance data. Storage requirements are relaxed and real-time performance monitoring is possible. Data collection and storage elements can be easily configured via a central configuration database. The configuration database can be easily updated and changed. A federated user model allows normal end users to monitor devices relevant to the part of a service they are responsible for, while allowing administrative users to view the fault and performance of a service in an end-to-end manner.