Hewlett Packard Enterprise Development LP

Hewlett-packard Development Company, L.p.

Micro Focus Lcc

QiFan Chen

Awny Kayed Al-Omari

Choudur K Lakshminarayan

Ramakumar Kosuru

Hansjorg Zeller

Kashif Abdullah Siddiqui

Gregory S Battas

Michael James Hanlon

Robert Matthew Wehrmeister

Ahmed Kamal Ezzat

Tom C Reyes

Hao Qin

Taoufik Ben Abdellatif

Ahmed K Ezzat

Robert M Wehrmeister

A technique includes determining a statistic for values associated with at least one column of a database based at least in part on a first sample of the values; and determining a degree of change in a second sample of the values relative to the first sample; and estimating a count of unique values for the column(s). The estimation of the count of unique values includes selectively incrementally updating the statistic using the second sample based at least in part on the determined degree of change; and basing estimation of the count at least in part on the updated statistic. The technique further includes processing a query to the database based at least in part on the count.

Incrementally updating a database statistic

A method of managing a database including creating an initial counting bloom filter (CBF) instance having an array of counters and hash functions that map an inserted value to the array of counters, and designating the initial CBF instance as a current CBF instance, and sequentially inserting each value of a sample data set of a table column into the hash functions of the current CBF instance and incrementing counters of the array of counters to which the value is mapped. The method further includes, prior to inserting each value into the hash functions of the current CBF instance, when a number of counters of the array of counters having non-zero values is at least at a threshold level, designating the current CBF instance as an old CBF instance, creating a new CBF instance having an array of counters and hash functions that map an inserted value to the array counters, and designating the new CBF instance as the current CBF instance.

Managing database with counting bloom filters

Incrementally updating statistics includes sampling rows from a database column in a database to generate a first sample, sampling a subset of modified rows from the database column after generating the first sample to generate a second sample, determining whether distribution changes occurred to the database column based on the first and second samples, and updating a database statistic about the database column in response to determining that a distribution change exists.

Incrementally updating statistics

In one example in accordance with the present disclosure, a method for determining query plans includes obtaining a current query and generating a first literal and a first signature key from the current query. The method includes determining that the first signature key matches a second signature key stored in a parser cache and obtaining a literals data structure corresponding to the second signature key. The literals data structure comprises a group of literals, a list of high and low boundary intervals and a parameterization indicator. The method includes identifying, using the parameterization indicator, a parameterized literal in the group of literals. The method includes determining that a high and low boundary interval for the second parameterized literal contains the first literal and executing a stored query plan on the current query.

Query plan determination

A method of estimating a number of unique entry counts of an attribute in a database comprises, with a processor: identifying a sample of entries from an attribute database, determining frequencies of a number of input observations of the sample of entries, determining a number of high frequency values of the sample of entries, and estimating a number of unique entry counts of an attribute within the attribute database using a counting Bloom filter and based on the frequencies of the input observations and the high frequency values.

Estimating unique entry counts using a counting bloom filter

A method for creating a joined data set from a join input data set is disclosed. The method starts by categorizing the join input data set into a high-skew data set and a low-skew data set. The low-skew data set is distributed to the plurality of CPUs using a first distribution method. The high-skew data set is distributed to the plurality of CPUs using a second distribution method. The plurality of CPUs process the high-skew data set and the low-skew data set to create the joined data set.

Data skew insensitive parallel join scheme

There is provided a computer-executable method of executing an outer join on a parallel database management system. An exemplary method comprises receiving an outer skewed values list (SVL). The outer SVL may comprise values that are indicated to be skewed. The exemplary method further comprises receiving an inner SVL. The inner SVL may comprise values that are indicated to be skewed. Additionally, the exemplary method comprises partitioning the outer table and the inner table across a plurality of join instances, based on the outer SVL and the inner SVL. A missing skew value is identified. The missing skewed value may be a value of the inner SVL that is not found in the inner table. The outer join is performed using the plurality of join instances, based on the missing skewed value.

System and method for outer joins on a parallel database management system

Optimized query plans are partitioned into equivalence groups that each include equivalence classes. Each equivalence group corresponds to a particular compiled, normalized, and parameterized query plan prior to optimization. Each equivalence class within an equivalence group corresponds to a different query plan corresponding to the particular compiled, normalized, and parameterized query plan represented by the equivalence group that has been optimized with respect to the selectivity of one or more predicate clauses of the query that is compiled to produce the particular compiled, normalized, and parameterized query plan. Optimized query plans are cached according to their respective equivalence groups and equivalence classes. When a query, similar to a query already compiled, optimized, and cached, is subsequently received and compiled, a selectivity for a predicate of the compiled query is computed, allowing the database management system to retrieve a cached query plan optimized for a similar query with similar selectivity.

Growing community of inventors

Austin, TX, United States of America

QiFan Chen