A data processing system includes one or more nodes, each node including a memory sub-system. The sub-system includes a fine-grained, memory, and a less-fine-grained (e.g., page-based) memory. The fine-grained memory optionally serves as a cache and/or as a write buffer for the page-based memory. Software executing on the system uses a node address space which enables access to the page-based memories of all nodes. Each node optionally provides ACID memory properties for at least a portion of the space. In at least a portion of the space, memory elements are mapped to locations in the page-based memory. In various embodiments, some of the elements are compressed, the compressed elements are packed into pages, the pages are written into available locations in the page-based memory, and a map maintains an association between the some of the elements and the locations.

System including a fine-grained memory and a less-fine-grained memory

Data replication in a distributed node system including one or more nodes. A consensus protocol for failure recovery is implemented. Data items and information relating to consensus protocol roles of participant nodes are stored in at least some of the plurality of nodes. Logical logs stored in at least some of the plurality of nodes are created. The logical logs contain additional consensus protocol information including container metadata and replicated data.

Failure recovery using consensus replication in a distributed flash memory system

A collaborative authoring application provides an authoring environment in which two or more users can edit a document concurrently. Each user edits a copy of the document, sends updates to a master copy of the document, and receives updates from the master copy of the document. The authoring environment may be configured into a public mode of operation, in which content and metadata are synchronized automatically, or into a private mode of operation, in which metadata is synchronized automatically and content is synchronized only at the request of the user. The authoring application may edit documents offline in public or private mode.

Collaborative authoring modes

A distributed database system has multiple compute nodes each running an instance of a database management system (DBMS) program that accesses database records in a local buffer cache. Records are persistently stored in distributed flash memory on multiple storage nodes. A Sharing Data Fabric (SDF) is a middleware layer between the DBMS programs and the storage nodes and has API functions called by the DBMS programs when a requested record is not present in the local buffer cache. The SDF fetches the requested record from flash memory and loads a copy into the local buffer cache. The SDF has threads on a home storage node that locate database records using a node map. A global cache directory locks and pins records to local buffer caches for updating by a node's DBMS program. DBMS operations are grouped into transactions that are committed or aborted together as a unit.

Scalable database management software on a cluster of nodes using a shared-distributed flash memory

In one embodiment, a method includes collecting communications from a plurality of sources. The method further includes selecting a set of topics and a set of users. In addition, the method includes, for each user, identifying, from the collected communications, conversations in which the user has participated. Moreover, the method includes, for each topic, for each user: measuring a proportion of the identified conversations that contain content suggestive of the topic; analyzing timing of the identified conversations; and examining relationships among data attributes of the identified conversations. Further, the method includes generating multidimensional expertise data. The multidimensional expertise data is representative of the user's expertise on the topic. The multidimensional expertise data includes a topical dimension, an expertise-scope dimension, and a timeline dimension. The method also includes, for each topic, ranking the set of users by expertise index. Also, the method includes providing a searchable interface.

System and method for managing and identifying subject matter experts

A plurality of mid-tier databases form a single, consistent cache grid for data in a one or more backend data sources, such as a database system. The mid-tier databases may be standard relational databases. Cache agents at each mid-tier database swap in data from the backend database as needed. Consistency in the cache grid is maintained by ownership locks. Cache agents prevent database operations that will modify cached data in a mid-tier database unless and until ownership of the cached data can be acquired for the mid-tier database. Cache groups define what backend data may be cached, as well as a general structure in which the backend data is to be cached. Metadata for cache groups is shared to ensure that data is cached in the same form throughout the entire grid. Ownership of cached data can then be tracked through a mapping of cached instances of data to particular mid-tier databases.

Distributed consistent grid of in-memory database caches

A replication track is a designated group of transactions that are to be replicated at a destination database in a way that, with respect to any other transaction in the replication track, preserves transactional dependency. Further, transactions in a replication track can be replicated at the destination database without regard to transactional dependency of other transactions in another track. This facilitates concurrent parallel replication of transactions of different tracks. Replicating data in this manner is referred to herein as track replication. An application may request execution of transactions and designate different tracks for transactions.

User-defined parallelization in transactional replication of in-memory database

A system includes an active node and a standby node and zero or more replica nodes. Each of the nodes includes a database system, such as an in-memory database system. Client updates applied to the active node are written through to the standby node, and the standby node writes the updates through to a primary database and updates the replica nodes. Commit ticket numbers tag entries in transaction logs and are used to facilitate recovery if either of the active node or the standby node fails. Updates applied to the primary database are autorefreshed to the active node and written through by the active node to the standby node which propagates the updates to the replica nodes. Bookmarks are used to track updated records of the primary database and are used to facilitate recovery if either of the active node or the standby node fails.

Database system with active standby and nodes

A fully transactional mid-tier database system services database transactions. A cache manager dynamically loads database entries from a fully transactional backend-tier database system into the mid-tier database system according to the received database transactions. Time based aging or usage based aging can be assigned to selected tables in the mid-tier database system. Database entries contained in the selected tables are then automatically removed according to assigned aging constraints.

Database system with dynamic database caching

A database is stored as a plurality of database shards in a distributed database grid comprising a plurality of grid elements, each including a mid-tier database system. A first grid element receives, from an application executing in the same memory as a mid-tier database system of the first grid element, a first database transaction including at least one database operation on specific data stored in a first database shard that belongs to the first grid element. The first grid element performs and commits the first database transaction without participation of another grid element of the plurality of grid elements. The first grid element receives a second database transaction that requires access to another database shard that does not belong to the first grid element. Multiple grid elements of the plurality of grid elements perform the second database transaction and commit the second database transaction using a two-phase commit protocol.

John Ernest Miller

Papers

Distributed consistent grid of in-memory database caches

User-defined parallelization in transactional replication of in-memory database

Database system with active standby and nodes

Database system with dynamic database caching

Direct-connect functionality in a distributed database grid