Abstract: The present application discloses a method, system, and computer system for parsing files. The method includes receiving an indication that a first file is to be processed, determining to begin processing the first file using a first processing engine based at least in part on one or more predefined heuristics, indicating to process the first file using a first processing engine, determining whether a particular error in processing the first file using the first processing engine has been detected, in response to determining that the particular error has been detected, indicate to stop processing the first file using the first processing engine and indicate to continue processing using a second processing engine, and storing in memory information obtained based on processing the first file by one or more of the first processing engine and the second processing engine.

Abstract: The present application discloses a method, system, and computer system for parsing files. The method includes receiving an indication that a first file is to be processed, determining to begin processing the first file using a first processing engine based at least in part on one or more predefined heuristics, indicating to process the first file using a first processing engine, determining whether a particular error in processing the first file using the first processing engine has been detected, in response to determining that the particular error has been detected, indicate to stop processing the first file using the first processing engine and indicate to continue processing using a second processing engine, and storing in memory information obtained based on processing the first file by one or more of the first processing engine and the second processing engine.

Abstract: A system comprises an interface, a processor, and a memory. The interface is configured to receive a query. The processor is configured to: determine a set of nodes for the query; determine whether a node of the set of nodes comprises a first engine node type or a second engine node type, wherein determining whether the node of the set of nodes comprises the first engine node type or the second engine node type is based at least in part on determining whether the node is able to be executed in a second engine; and generate a plan based at least in part on the set of nodes. The memory is coupled to the processor and is configured to provide the processor with instructions.

Abstract: The problem of distinct value estimation has many applications, but is particularly important in the field of database technology where such information is utilized by query planners to generate and optimize query plans. Introduced is a novel technique for estimating the number of distinct values in a given dataset without scanning all of the values in the dataset. In an example embodiment, the introduced technique includes gathering multiple intermediate probabilistic estimates based on varying samples of the dataset, 2) plotting the multiple intermediate probabilistic estimates against indications of sample size, 3) fitting a function to the plotted data points, and 4) determining an overall distinct value estimate by extrapolating the objective function to an estimated or known total number of values in the dataset.

Abstract: A system includes a plurality of computing units. A first computing unit of the plurality of computing units comprises: a communication interface configured to receive an indication to roll up data in a data table; and a processor coupled to the communication interface and configured to: build a preaggregation hash table based at least in part on a set of columns and the data table by aggregating input rows of the data table; for each preaggregated hash table entry of the preaggregated hash table: provide the preaggregated hash table entry to a second computing unit of the plurality of computing units based at least in part on a distribution hash value; receive a set of received entries from computing units of the plurality of computing units; and build an aggregation hash table based at least in part on the set of received entries by aggregating the set of received entries.

Abstract: The problem of distinct value estimation has many applications, but is particularly important in the field of database technology where such information is utilized by query planners to generate and optimize query plans. Introduced is a novel technique for estimating the number of distinct values in a given dataset without scanning all of the values in the dataset. In an example embodiment, the introduced technique includes gathering multiple intermediate probabilistic estimates based on varying samples of the dataset, 2) plotting the multiple intermediate probabilistic estimates against indications of sample size, 3) fitting a function to the plotted data points, and 4) determining an overall distinct value estimate by extrapolating the objective function to an estimated or known total number of values in the dataset.

Abstract: A system comprises an interface, a processor, and a memory. The interface is configured to receive a query. The processor is configured to: determine a set of nodes for the query; determine whether a node of the set of nodes comprises a first engine node type or a second engine node type, wherein determining whether the node of the set of nodes comprises the first engine node type or the second engine node type is based at least in part on determining whether the node is able to be executed in a second engine; and generate a plan based at least in part on the set of nodes. The memory is coupled to the processor and is configured to provide the processor with instructions.

Abstract: A system for spilling comprises an interface and a processor. The interface is configured to receive an indication to perform a GROUP BY operation, wherein the indication comprises an input table and a grouping column. The processor is configured to: for each input table entry of the input table, determine a key, wherein the key is based at least in part on the input table entry and the grouping column; add the key to a grouping hash table, wherein adding the key to the grouping hash table comprises last-in, first-out (LIFO) spilling when necessary; create an output table based at least in part on the grouping hash table; and provide the output table.

Abstract: A system comprises an interface, a processor, and a memory. The interface is configured to receive a query. The processor is configured to: determine a set of nodes for the query; determine whether a node of the set of nodes comprises a first engine node type or a second engine node type, wherein determining whether the node of the set of nodes comprises the first engine node type or the second engine node type is based at least in part on determining whether the node is able to be executed in a second engine; and generate a plan based at least in part on the set of nodes. The memory is coupled to the processor and is configured to provide the processor with instructions.

Abstract: The problem of distinct value estimation has many applications, but is particularly important in the field of database technology where such information is utilized by query planners to generate and optimize query plans. Introduced is a novel technique for estimating the number of distinct values in a given dataset without scanning all of the values in the dataset. In an example embodiment, the introduced technique includes gathering multiple intermediate probabilistic estimates based on varying samples of the dataset, 2) plotting the multiple intermediate probabilistic estimates against indications of sample size, 3) fitting a function to the plotted data points, and 4) determining an overall distinct value estimate by extrapolating the objective function to an estimated or known total number of values in the dataset.

Abstract: The problem of distinct value estimation has many applications, but is particularly important in the field of database technology where such information is utilized by query planners to generate and optimize query plans. Introduced is a novel technique for estimating the number of distinct values in a given dataset without scanning all of the values in the dataset. In an example embodiment, the introduced technique includes gathering multiple intermediate probabilistic estimates based on varying samples of the dataset, 2) plotting the multiple intermediate probabilistic estimates against indications of sample size, 3) fitting a function to the plotted data points, and 4) determining an overall distinct value estimate by extrapolating the objective function to an estimated or known total number of values in the dataset.

Abstract: The problem of distinct value estimation has many applications, but is particularly important in the field of database technology where such information is utilized by query planners to generate and optimize query plans. Introduced is a novel technique for estimating the number of distinct values in a given dataset without scanning all of the values in the dataset. In an example embodiment, the introduced technique includes gathering multiple intermediate probabilistic estimates based on varying samples of the dataset, 2) plotting the multiple intermediate probabilistic estimates against indications of sample size, 3) fitting a function to the plotted data points, and 4) determining an overall distinct value estimate by extrapolating the objective function to an estimated or known total number of values in the dataset.

Abstract: A computer process, called VGRAM, improves the performance of these string search algorithms in computers by using a carefully chosen dictionary of variable-length grams based on their frequencies in the string collection. A dynamic programming algorithm for computing a tight lower bound on the number of common grams shared by two similar strings in order to improve query performance is disclosed. A method for automatically computing a dictionary of high-quality grams for a workload of queries. Improvement on query performance is achieved by these techniques by a cost-based quantitative approach to deciding good grams for approximate string queries. An approach for answering approximate queries efficiently based on discarding gram lists, and another is based on combining correlated lists. An indexing structure is reduced to a given amount of space, while retaining efficient query processing by using algorithms in a computer based on discarding gram lists and combining correlated lists.

Abstract: A computer process, called VGRAM, improves the performance of these string search algorithms in computers by using a carefully chosen dictionary of variable-length grams based on their frequencies in the string collection. A dynamic programming algorithm for computing a tight lower bound on the number of common grams shared by two similar strings in order to improve query performance is disclosed. A method for automatically computing a dictionary of high-quality grams for a workload of queries. Improvement on query performance is achieved by these techniques by a cost-based quantitative approach to deciding good grams for approximate string queries. An approach for answering approximate queries efficiently based on discarding gram lists, and another is based on combining correlated lists. An indexing structure is reduced to a given amount of space, while retaining efficient query processing by using algorithms in a computer based on discarding gram lists and combining correlated lists.

Abstract: A method is disclosed for automatically configuring database statistics by: collecting information from a database system, the database information including data query feedback; consolidating and formatting the database information into a plurality of intervals; converting the plurality of intervals into a plurality of non-overlapping buckets; computing frequencies for the buckets by solving a constrained maximum entropy problem to create a proxy data distribution function; and using the proxy data distribution function to determine a set of statistics to maintain for the database information.