A method for cluster based searching for a value range stored in a storage system, the method may include receiving a request to find a certain value range within a set of information elements that are stored in a storage system; wherein the set of information elements comprises subsets of information elements associated with subset hash based filters; wherein different subsets of information elements are associated with different subset hash based filters; determining a certain cluster value of a certain cluster that comprises the certain value range; applying one or more hush functions on the certain cluster value to provide one or more hash results; and determining whether one or more members of the certain cluster are possibly in a subset of information elements, based on the one or more hash results and on a subset hash based filter of the subset of information elements; and when determining that the one or more members of the certain cluster are possibly in the subset then searching, within the subset, a matching information element that matches the certain value range.

Distributed pseudo-random subset generation includes obtaining a data-query indicating a first table having a first column including unique values, a second table having a second column including unique values, a join clause joining the first table and the second table on the first column and the second column, and a limit value, pseudo-random filtering the first table to obtain left intermediate data and left filtering criteria, pseudo-random filtering the second table to obtain right intermediate data and right filtering criteria, obtaining intermediate results data by full outer joining the left intermediate data and the right intermediate data, obtaining results data by filtering the intermediate results data using most-restrictive filtering criteria among the left filtering criteria and the right filtering criteria, and outputting the results data, wherein outputting the results data includes limiting the cardinality of rows of the results data to be at most the limit value.

A method for presenting output returned by a command-line interface is disclosed. In one embodiment, such a method submits, by way of a command-line interface (CLI), a command to retrieve a list of resources. The method further submits, in association with the command, an indicator to compress the list. In response to receiving the command and indicator, the method retrieves the list of resources and compresses the list such that resources with identical attributes are presented as a single tuple in the list. Such a tuple may, in certain embodiments, identify the resources with identical attributes as a range of resources and/or as a comma delimited list of resources. The tuple may also, in certain embodiments, identify how many resources with identical attributes are represented by the tuple. A corresponding system and computer program product are also disclosed.

A method for providing optimized support for transactional processing and analytical processing with minimal memory footprint may include storing, on a data page in a disk of a database system, a portion of one or more columns of data from a database table. A metadata associated with the data page may be stored on a metadata page in the disk of the database system. The metadata may include one or more byte ranges on the data page at which the portion of the one or more columns of data is stored. The database system may execute one or more queries by accessing, based at least on the metadata associated with the data page, a portion of the data page storing the portion of the one or more columns of data required by the one or more queries. Related systems and articles of manufacture are also provided.

Described are methods, systems and computer readable media for dynamic updating of query result displays.

Described are methods, systems and computer readable media for performance logging of complex query operations.

Described are methods, systems and computer readable media for data source refreshing.

The method may include providing a plurality of synopsis techniques for determining a plurality of attribute value information indicative of the at least one attribute. The method may include determining a data characteristic describing the plurality of data rows of the current data block. The method may include selecting, based on the determined data characteristic, at least one synopsis technique of the provided plurality of synopsis techniques suitable for generating the plurality of attribute value information for the at least one attribute of the current data block. The method may include determining the plurality of attribute value information for the at least one attribute of the plurality of data rows of the current data block using the at least one selected synopsis technique. The method may include storing the determined plurality of attribute value information for the current data block to be used for query processing against the data table.

Provided are methods comprising receiving a query for information from a database, determining particular data element types and data element values that are the subject of the query, instantiating a query data structure containing the data element types and the data element values that are the subject of the query, identifying records within the database that contain one or more data element types and/or data element values that are included in the query data structure, and instantiating a results data structure comprising information relating to the identified records.

A database server stores compressed units in data blocks of a database. A table (or data from a plurality of rows thereof) is first compressed into a “compression unit” using any of a wide variety of compression techniques. The compression unit is then stored in one or more data block rows across one or more data blocks. As a result, a single data block row may comprise compressed data for a plurality of table rows, as encoded within the compression unit. Storage of compression units in data blocks maintains compatibility with existing data block-based databases, thus allowing the use of compression units in preexisting databases without modification to the underlying format of the database. The compression units may, for example, co-exist with uncompressed tables. Various techniques allow a database server to optimize access to data in the compression unit, so that the compression is virtually transparent to the user.