Sap Se

International Business Machines Corporation

Sap Ag

Wolfgang Lehner

Michael Rudolf

Christof Bornhoevd

Marcus Paradies

Norman May

Peter Haas

Ismail Oukid

Rainer Gemulla

Philipp Grosse

Thomas Willhalm

Peter Bumbulis

Lars Fricke

Guido Moerkotte

Florian Wolf

David Kernert

Christof Bornhövd

Frank Koehler

Axel Hertzschuch

Mir Hamid Pirahesh

Richard Sefton Sidle

Stefan A Baeuerle

Anil Kumar Goel

Roberta Jo Cochrane

Robert Schulze

Daniel Booss

Anisoara Nica

Lars Dannecker

Frank Renkes

Matthias Boehm

Hannes Voigt

Daniel Dos Santos Bossle

Joydeep Das

Elena Vasilyeva

Maik Thiele

A system includes determination of a plurality of queries of a workload, determination of a data source comprising a plurality of data rows, and determination of a sample data source based on a cardinality of each of the plurality of queries with respect to the data source and an estimated cardinality of each of the plurality of queries with respect to the data source, wherein the estimated cardinality of a query with respect to the data source is determined based on the sample data source.

Workload-based sampling

A system includes determination of a query on a table, the query including a first predicate and a second predicate, determination of a sample of the table, the sample comprising a plurality of tuples of the table, determination of a first conditional probability of selecting a tuple of the sample satisfying the first predicate and the second predicate, determination of a second conditional probability of selecting a tuple of the sample satisfying the first predicate and not satisfying the second predicate, adjustment of the first conditional probability based on the second conditional probability, a number of tuples of the sample satisfying the second predicate, a number of tuples of the sample not satisfying the second predicate, and a number of tuples of the sample satisfying the first predicate and not satisfying the second predicate, and determination of a selectivity of the query on the table based on the first adjusted conditional probability.

Selectivity estimation using non-qualifying tuples

Annotations can be placed in source code to indicate properties for user-defined functions. A wide variety of properties can be implemented to provide information that can be leveraged when constructing a query execution plan for the user-defined function and associated core database relational operations. A flexible range of permitted partition arrangements can be specified via the annotations. Other supported properties include expected sorting and grouping arrangements, ensured post-conditions, and behavior of the user-defined function.

Annotations for parallelization of user-defined functions with flexible partitioning

Disclosed herein are system, method, and computer program product embodiments for performing ad-hoc analytical queries of graph data. An embodiment operates by receiving a graph pattern for a subgraph of interest. The facts of interest are then selected from graph data based on the received graph pattern. Dimensions are then defined based on a dimension seed pattern and a set of level expressions, and measures are defined based on a computation function and an aggregation function. A graph cube is formed based on the selected facts and the defined dimensions and measures. Because the facts, dimensions, and measures of interest are defined at the time of an analytical query, a user does not have to define such facts, dimensions, and measures, or know which analytical queries will be of interest, at the time of data collection.

Ad-hoc analytical query of graph data

Disclosed herein are system, method, and computer program product embodiments for interactive exploration of graph data. An embodiment operates by compiling a data query in any database-readable language based upon an input parameter. A data query of a graph data set is then conducted but is halted when a breakpoint generated from the input parameter is reached. Halting the data query includes partitioning a data subset from the graph data set. The data subsets is then displayed.

Interactive exploration of large graphs

Disclosed herein are embodiments for performing multi-version concurrency control (MVCC) in non-volatile memory. An embodiment operates by determining that an event occurred, wherein one or more write transactions to one or more records of a multi-version database that were pending prior to the event did not commit. The one or more write transactions are identified based on a commit value that was stored in the non-volatile memory prior to the event. A particular one of the identified uncommitted write transactions is selected. From the multi-version database, a first version of a record corresponding to the selected uncommitted write transaction that was not committed, and an earlier version of the record that was committed prior to the event are identified. A visibility of the record is set to indicate that the earlier version of the record is visible and the first version of the record is not visible.

Multi-version concurrency control (MVCC) in non-volatile memory

Traversal hooks are based on an event-oriented programming model and provide an expressive mechanism to extend a graph traversal operator with domain-specific coding. A traversal operator can visit (i.e., traversal events) vertices and edges of a graph in an ordered manner. Related apparatus, systems, techniques and articles are also described.

Extending graph traversals with application logic

A data recovery system and method are disclosed. Primary data is stored a database in byte-addressable NVRAM, where the database includes one or more persistent tables of data in a byte-addressable, RAM format, and a persistent memory allocator that maps persistent memory pointers of the persistent memory to virtual memory pointers of a virtual memory associated with the database. Secondary data is stored in volatile DRAM. A failure recovery includes recovering the persistent memory allocator, mapping the persistent memory to the virtual memory to recover primary data using their persistent memory pointers, translating the persistent memory pointers to virtual memory pointers, undoing changes to the primary data made by unfinished transactions of the query execution at the time of failure of one of the one or more queries, and reconstructing the secondary data from the primary data.

Hybrid SCM-DRAM transactional storage engine for fast data recovery

Embodiments relate to storing sparse matrices in an in-memory column-oriented database system. Specifically, recent hardware shifts of primary storage from disc into memory, allow execution of linear algebra queries directly in the database engine. Dynamic matrix manipulation operations (like online insertion or deletion of elements) are not covered by most linear algebra frameworks. Therefore a hybrid architecture comprises a read-optimized main structure, and a write-optimized delta structure. The resulting system layout derived from the Compressed Sparse Row (CSR) representation, integrates well with a columnar database design. Moreover, the resulting architecture is amenable to a wide range of non-numerical use cases when dictionary encoding is used. Performance in specific examples is evaluated for dynamic sparse matrix workloads, by applying work flows of nuclear science and network graphs. Embodiments allow performing linear algebra operations on large, sparse matrices commonly associated with scientific computations and analytical business applications.

Sparse linear algebra in column-oriented in-memory database

According to some embodiments, matrix A data may be loaded into a temporary, unordered starting representation that contains coordinates and values for each element of matrix A. Z-curve ordering of matrix A may be performed to create a two-dimensional density map of matrix A by counting matrix elements that are contained in logical two-dimensional block cells of a given size. A quad-tree recursion may be executed on the two-dimensional density map structure in reduced Z-space to identify areas of different densities in the two dimensional matrix space. An adaptive tile matrix representation of input matrix A may then be created. According to some embodiments, an adaptive tile matrix multiplication operation may perform dynamic tile-granular optimization based on density estimates and a cost model.

Adaptive tile matrix representation and multiplication

A system, computer-implemented method, and computer-readable storage medium for generating a block-based index, are provided. A block index is generated where the block index comprises a plurality of blocks and a block corresponds to a section of a graph column that stores a value. A block range vector is also generated for the index where the block range vector includes range information for the block that corresponds to the section of the graph and where the block-based index facilitates traversal of the graph column that searches for the value by constraining the traversal to the section of the graph.

Index structure to accelerate graph traversal

A system includes determination of a plurality of secondary data structures of a database to be rebuilt, determination, for each of the plurality of secondary data structures, of a current ranking based on a pre-crash workload, a crash-time workload, the post-crash workload, and a rebuild time of the secondary data structure, determination to rebuild one of the plurality of secondary data structures based on the determined rankings, and rebuilding of the one of the plurality of secondary data structures in a dynamic random access memory based on primary data of a database stored in non-volatile random access memory.

Adaptive recovery for SCM-enabled databases

A system, computer-implemented method, and a computer-readable storage medium for a data graph traversal are provided. The input parameters for traversing the data graph are received. The data graph having a set of vertices and a set of edges are stored in a column based format in a memory cache of a computer device based on the input parameters is traversed. The traversal generates a set of traversed vertices that are the result of the graph traversal.

Graph traversal operator inside a column store

A system, computer-implemented method, and a computer-readable storage medium for a traversal of a property graph, are provided. The edge table of the property graph is divided into a plurality of fragments. A first fragment is selected for traversal using a set of selected vertices, where the traversal identifies a set of edges. Based on the set of edges, a set of adjacent vertices is determined during the traversal. A set of discovered vertices in the property graph is determined based on the set of selected vertices and the set of adjacent vertices.

Growing community of inventors

Dresden, Germany

Wolfgang Lehner