Abstract: According to examples, an apparatus may include a processor and a memory on which is stored machine readable instructions that are to cause the processor to receive, from a first node, a client request and a response corresponding to the processed client request, in which the first node is configured to generate the response by processing the client request on a service and processed client request is configured to change a state of the service. The instructions are also to cause the processor to receive, from the first node, a response corresponding to the received request, generate an entry including the received request and response, commit the generated entry into a journal, and send an acknowledgement of the received request and response to the first node after the generated entry is committed into the journal.

Abstract: According to examples, an apparatus may include a processor and a memory on which is stored machine readable instructions that are to cause the processor to receive, from a first node, a client request and a response corresponding to the processed client request, in which the first node is configured to generate the response by processing the client request on a service and processed client request is configured to change a state of the service. The instructions are also to cause the processor to receive, from the first node, a response corresponding to the received request, generate an entry including the received request and response, commit the generated entry into a journal, and send an acknowledgement of the received request and response to the first node after the generated entry is committed into the journal.

Abstract: Described herein are technologies pertaining to migrating state information of operators in a first continuous query plan to a second continuous query plan in an asynchronous manner, such that the first continuous query plan need not cease executing during the migrating of the state information. State information pertaining to stateful operators, such as join operators, is migrated from the first continuous query plan to the second continuous query plan by way of a transformation plan. State matching is utilized to generate the transformation plan.

Abstract: Histograms formed based on samples of a population, such as histograms created from random page-level samples of a data store, are intelligently scaled to histograms estimating distribution of the entire population of the data store. As an optional optimization, where a threshold number of duplicate samples are observed during page-level sampling, the number of distinct values in the overall population data is presumed to be the number of distinct values in the sample data. Also, during estimation of distinct values of an overall population, a “Chao” estimator can optionally be utilized as a lower bound of the estimate. The resulting estimate is then used when scaling, which can take domain knowledge of the data being scaled into account in order to prevent scaled estimates from exceeding the limits of the domain.

Abstract: Described herein are technologies pertaining to migrating state information of operators in a first continuous query plan to a second continuous query plan in an asynchronous manner, such that the first continuous query plan need not cease executing during the migrating of the state information. State information pertaining to stateful operators, such as join operators, is migrated from the first continuous query plan to the second continuous query plan by way of a transformation plan. State matching is utilized to generate the transformation plan.

Abstract: Histograms formed based on samples of a population, such as histograms created from random page-level samples of a data store, are intelligently scaled to histograms estimating distribution of the entire population of the data store. As an optional optimization, where a threshold number of duplicate samples are observed during page-level sampling, the number of distinct values in the overall population data is presumed to be the number of distinct values in the sample data. Also, during estimation of distinct values of an overall population, a “Chao” estimator can optionally be utilized as a lower bound of the estimate. The resulting estimate is then used when scaling, which can take domain knowledge of the data being scaled into account in order to prevent scaled estimates from exceeding the limits of the domain.

Abstract: Histograms formed based on samples of a population, such as histograms created from random page-level samples of a data store, are intelligently scaled to histograms estimating distribution of the entire population of the data store. As an optional optimization, where a threshold number of duplicate samples are observed during page-level sampling, the number of distinct values in the overall population data is presumed to be the number of distinct values in the sample data. Also, during estimation of distinct values of an overall population, a “Chao” estimator can optionally be utilized as a lower bound of the estimate. The resulting estimate is then used when scaling, which can take domain knowledge of the data being scaled into account in order to prevent scaled estimates from exceeding the limits of the domain.

Abstract: A system and method that facilitates and effectuates estimating the result of performing a data analysis operation on a set of data. Employing an approximation of the data analysis operation on a statistically valid random sample view of the data allows for a statistically accurate estimate of the result to be obtained. Sequential sampling in the view enables the approximated operation to evaluate accuracy conditions at intervals during the scan of the sample view and obtain the estimated result without having to scan the entire sample view. Feedback regarding the accuracy of the estimated result can be captured when the data analysis operation is performed against the set of data. Process control techniques can be employed with the feedback to maintain the statistical validity of the sample view.

Abstract: Histograms formed based on samples of a population, such as histograms created from random page-level samples of a data store, are intelligently scaled to histograms estimating distribution of the entire population of the data store. As an optional optimization, where a threshold number of duplicate samples are observed during page-level sampling, the number of distinct values in the overall population data is presumed to be the number of distinct values in the sample data. Also, during estimation of distinct values of an overall population, a “Chao” estimator can optionally be utilized as a lower bound of the estimate.