Sap Se

Stefan Noll

Paul Willems

Norman May

Jens Thilo Teubner

Alexander Boehm

Alexander Martin Böhm

Jens Teubner

Alexander Martin BÖHM

A method for processing non-standardized data in a relational database may include identifying, within a sequence of operations forming a query pipeline for executing a query, a first operation ingesting a non-standardized data. In response to identifying the first operation ingesting the non-standardized data, a second operation may be inserted before the first operation. The non-standardized data may be output by a third operation preceding the first operation or a source external to the query pipeline. The second operation may serialize the non-standardized data for ingestion by the first operation, for example, by generating a relational table populated by the non-standardized data. The query may be executed by performing the sequence of operations included in the query pipeline. Related systems and computer program products are also provided.

Transport of non-standardized data between relational database operations

In some implementations, there is provided a method that includes detecting in a query plan a pipeline that includes a last restart query operator that can cause a retry of the query plan during execution of the query plan; configuring the pipeline to execute the pipeline using an open call configuration, configuring at least one subsequent pipeline to execute in a fetch call configuration; executing the query plan including the pipeline in the open call configuration; sending, by the send operator, a message indicating the last restart query operator cannot cause a retry of the execution of the query plan; and causing execution of at least one operator in the subsequent pipeline to execute in the fetch call configuration, in which result streaming of partial results is allowed for the at least one operator.

Result streaming for distributed query processing

The present disclosure involves systems, software, and computer implemented methods for efficient shared bulk loading into optimized storage. One example method includes identifying a data set on a client to load to a server. Load steps that can be performed either on the client or the server are identified. Values for load factors are determined. Load rules are determined that are based on load factors and are for determining processing locations for the load steps. A processing location of either the client or the server is determined for each load step, based on the load rules. Load steps that have a processing location of the client are executed on the data set, on the client. The data set is transferred from the client to the server, over a network. Load steps that have a processing location of the server are executed on the data set, at the server.

Efficient shared bulk loading into optimized storage

Disclosed herein are system, method, and computer program product embodiments for cache partitioning to accelerate concurrent workload performance of in-memory databases. An embodiment operates by storing a first bitmask, associating the first bitmask with a first processor core, setting a subset of the bits of the first bitmask, wherein the subset of the bits of the first bitmask represents a first portion of shared last-level cache, and wherein any part of the first bitmask excluding the subset of the bits of the first bitmask represents a second portion of the lowest-level cache, and disallowing eviction of any cache line in the second portion of the lowest-level cache by the first processor core.

Growing community of inventors

Sandhausen, Germany

Stefan Noll