Sap Se

Sap Ag

Transact in Memory, Inc.

Sang Kyun Cha

Juchang Lee

Chanho Jeong

Yong Sik Kwon

Yongsik Yoon

Sung Heun Wi

Jaeyun Noh

Sangyong Hwang

Chang Bin Song

Kihong Kim

Alexander Schroeder

Ki Hong Kim

Michael Muehle

Chulwon Lee

Marco Paskamp

Ki-Hong Kim

Keun-joo Kwon

Sang Yong Hwang

Kyu Hwan Kim

Chul Won Lee

Ji Hoon Jang

Keunjoo Kwon

Keun-Joo Kwon

Ivan Schreter

Chang Gyoo Park

Joo Yeon Lee

Joo Young Yoon

Beomsoo Kim

Hyeong Seog Kim

Eunsang Kim

Jane Jung Lee

Myunggon Park

Ju Chang Lee

Keun Joo Kwon

Devices, methods and systems for processing database queries formatted differently than the database storage model being queried are disclosed. Processing database queries independent of the storage model of the queried database may be performed by receiving a query for one or more data items stored in a database, determining whether to use at least one query operator that uses data having a format different from the storage model format of at least one of one or more data items stored in the database and converting the format of the data used by the at least one query operator to a format that matches the storage model format of at least one of one or more data items stored in the database. Related systems, methods, and articles of manufacture are also described.

Processing database queries using format conversion

Technologies are described for restarting an application while maintaining data in memory (e.g., using shared memory). For example, shared memory can be associated with an application. The shared memory can also be associated with a holder process to maintain the shared memory from the time the application stops to the time the application starts again. When the application starts, the shared memory can be associated with the started application. In addition, restart of in-memory databases can be provided using shared memory. For example, in-memory data can be maintained when a database process or database management system stops and starts (e.g., during a restart).

Fast restart of applications using shared memory

A hybrid database table is stored as both a row and a column store. One or more techniques may be employed alone or in combination to enhance performance of the hybrid table by regulating access to, and/or the size of, the processing-intensive column store data. For example during an insert operation, the column store data may be searched for a uniqueness violation only after certain filtering and/or boundary conditions have been considered. In another technique, a hybrid table manager may control movement of data to the column store based upon considerations such as frequency of access, or underlying business logic. In still another technique, querying of the hybrid table may result in a search of the column store data only after an initial search of row store data fails to return a result.

Hybrid database table stored as both row and column store

Technologies are described for performing proactive memory allocation (e.g., pre-allocation). Proactive memory allocation (e.g., proactive memory page allocation) can be provided for applications, such as database applications. For example, an application can be associated with a free memory pool (e.g., a free memory pool containing free memory pages). A dedicated thread of the application can monitor the free memory pool and perform proactive memory allocation when needed (e.g., when the amount of memory in the pool is low). For example, the dedicated thread can obtain new free memory from the operating system and add it to the pool.

Proactive memory allocation

A mixed join between database column and row tables employs an algorithm that recognizes both row and column store, and is executable upon the data in its native form (row or column) without requiring conversion between orientations. The native mixed join algorithm accesses the column dictionary of the column table for efficient join processing. The native mixed join algorithm may also exploit an inverted index (if present) to search a record (e.g. docid) with a given value. In particular, the native mixed join algorithm looks up a column dictionary for a join condition, while iterating the row table and returning matched records in a pipelined manner.

Mixed join of row and column database tables in native orientation

Total global minimum costs can be determined for multiple sub-plans for completing a multi-operation database process to be performed in a distributed database management system that includes a plurality of nodes. The multiple sub-plans can include different distributions of node locations of a plurality of operators among the plurality of nodes. An optimal plan having a lowest total minimum global cost can be selected from the multiple sub-plans.

Optimal operator placement for distributed query processing

Client-side partition-aware batch insert operations are presented. For example, a server generates partition metadata, which is provided to a client. The client uses the partition metadata to determine the database nodes to which to send batch insert requests. For example, the client divides batch insert data, such as records for a partitioned table, among multiple database nodes having partitions of the table. The client issues batch insert requests to the respective database nodes for execution. When executed by the database nodes, batch insert operations can be performed in parallel.

Client-side partition-aware batching of records for insert operations

Technologies are described for performing replication within a database environment. For example, replication of database data can be performed using data manipulation language (DML) statements and logical logs. A database node can execute a DML statement, create a logical log comprising the DML statement, and insert the logical log into a logical log queue. The logical log can be sent to one or more other database nodes for execution of the DML statement stored within the logical log. Logical logs can be grouped for sending to other nodes. Logical logs can be executed on a first node and one or more other nodes within the same transaction boundary. Execution of DML statements can be decoupled from sending, receiving, grouping, and/or processing of logical logs.

DML replication with logical log shipping

For each data change occurring transaction created as part of a write operation initiated for one or more tables in a main-memory-based DBMS, a transaction log entry can be written to a private log buffer corresponding to the transaction. All transaction log entries in the private log buffer can be flushed to a global log buffer upon completion of the transaction to which the private log buffer corresponds.

Transaction private log buffering for high performance of transaction processing

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Seoul, South Korea

Sang Kyun Cha