Abstract: Trace data is received for a first execution of a first query on first tables having first data values. Second data values are generated for second tables from the trace data and without accessing the first data values. The second data values are configured to reproduce, by a second execution of a second query, the evaluation states from the first execution. Evaluation states from the first execution are reproduced without accessing the first data values by performing the second execution of the second query on the second data values. The first execution evaluates only a first subset of the first data values and the generating the second data values from the trace data generates second data values corresponding to the first subset. The second execution only evaluates ones of the second data values that correspond to data values of the first subset.

Abstract: Trace data is received that was captured for an execution of a query on first tables when execution trouble occurs during an execution of the query on the first tables. Trace data includes counts of evaluation states caused by the execution and includes identifications of portions of the query that reference variables of the first tables and that correspond with the evaluation states. Second tables are generated from the evaluation state counts and the identifications of the trace data without accessing data values of the first tables, so that the first tables are kept private. Evaluation states are reproduced from the execution of the query on the first tables by executing the query on the second tables without accessing the data values of the first tables.

Abstract: Trace data is received for a first execution of a first query on first tables having first data values. Second data values are generated for second tables from the trace data and without accessing the first data values. The second data values are configured to reproduce, by a second execution of a second query, the evaluation states from the first execution. Evaluation states from the first execution are reproduced without accessing the first data values by performing the second execution of the second query on the second data values. The first execution evaluates only a first subset of the first data values and the generating the second data values from the trace data generates second data values corresponding to the first subset. The second execution only evaluates ones of the second data values that correspond to data values of the first subset.

Abstract: Trace data is received that was captured for an execution of a query on first tables when execution trouble occurs during an execution of the query on the first tables. Trace data includes counts of evaluation states caused by the execution and includes identifications of portions of the query that reference variables of the first tables and that correspond with the evaluation states. Second tables are generated from the evaluation state counts and the identifications of the trace data without accessing data values of the first tables, so that the first tables are kept private. Evaluation states are reproduced from the execution of the query on the first tables by executing the query on the second tables without accessing the data values of the first tables.

Abstract: Trace data is received that was captured for a execution of a first query on first input data. The trace data associates clauses of the first query with respective evaluation states that arise during the execution. Second input data is generated from the trace data without accessing the first input data. An execution of a second query is performed on the second input data, wherein performing the execution of the second query on the second input data reproduces evaluation states from the execution of the first query on the first input data.

Abstract: Trace data is received that was captured for an execution of a first query on first input data. The trace data associates clauses of the first query with respective evaluation states that arise during the execution. Second input data is generated from the trace data without accessing the first input data. An execution of a second query is performed on the second input data, wherein performing the execution of the second query on the second input data reproduces evaluation states from the execution of the first query on the first input data.

Abstract: Provided are techniques for estimating most frequent values. A sample of values made up of rows is received from each of multiple nodes. The sample of values from each of the multiple nodes are aggregated to generate a sample table storing the rows. A descending list of most frequent values and associated frequencies is obtained using the sample table. Most frequent values are pruned from the descending list whose associated frequencies are below a minimum absolute frequency. The remaining most frequent values are extrapolated to reflect a data set.

Abstract: Trace data is received that was captured for a execution of a first query on first input data. The trace data associates clauses of the first query with respective evaluation states that arise during the execution. Second input data is generated from the trace data without accessing the first input data. An execution of a second query is performed on the second input data, wherein performing the execution of the second query on the second input data reproduces evaluation states from the execution of the first query on the first input data.

Abstract: Trace data is received that was captured for an execution of a first query on first input data. The trace data associates clauses of the first query with respective evaluation states that arise during the execution. Second input data is generated from the trace data without accessing the first input data. An execution of a second query is performed on the second input data, wherein performing the execution of the second query on the second input data reproduces evaluation states from the execution of the first query on the first input data.

Abstract: Provided are techniques for estimating most frequent values. A sample of values made up of rows is received from each of multiple nodes. The sample of values from each of the multiple nodes are aggregated to generate a sample table storing the rows. A descending list of most frequent values and associated frequencies is obtained using the sample table. Most frequent values are pruned from the descending list whose associated frequencies are below a minimum absolute frequency. The remaining most frequent values are extrapolated to reflect a data set.

Abstract: Provided are techniques for estimating most frequent values. A sample of values made up of rows is received from each of multiple nodes. The sample of values from each of the multiple nodes are aggregated to generate a sample table storing the rows. A descending list of most frequent values and associated frequencies is obtained using the sample table. Most frequent values are pruned from the descending list whose associated frequencies are below a minimum absolute frequency. The remaining most frequent values are extrapolated to reflect a data set.

Abstract: Provided are techniques for estimating most frequent values. A sample of values made up of rows is received from each of multiple nodes. The sample of values from each of the multiple nodes are aggregated to generate a sample table storing the rows. A descending list of most frequent values and associated frequencies is obtained using the sample table. Most frequent values are pruned from the descending list whose associated frequencies are below a minimum absolute frequency. The remaining most frequent values are extrapolated to reflect a data set.