Abstract: A database system uses byte ordering for keys and a trie index to reference stored data. The keys of a database are converted into byte-comparable sequences of byte values. The trie index is generated including nodes connected by edges defining paths from a root node to leaf nodes. Each edge is associated with at least one byte value such that each path from the root node to a leaf node through one or more edges defines a unique byte prefix for a byte-comparable sequence of byte values. The leaf node of each path is associated with a database location value. A record is accessed in the database using a database location value determined from referencing the trie index using a byte-comparable sequence of byte values of the record generated from a key of the record. A trie structure and byte ordered keys may be used for partition or row indices.

Abstract: In partitioning a graph database, a plurality of vertices of the graph database is assigned to a plurality of nodes. The vertices of the graph database are connected by edges that indicate relationships between the vertices. A vertex of the graph database is designated as a super-vertex that is split into a truncated vertex and at least one vertex representative.

Abstract: A database system uses byte ordering for keys and a trie index to reference stored data. The keys of a database are converted into byte-comparable sequences of byte values. The trie index is generated including nodes connected by edges defining paths from a root node to leaf nodes. Each edge is associated with at least one byte value such that each path from the root node to a leaf node through one or more edges defines a unique byte prefix for a byte-comparable sequence of byte values. The leaf node of each path is associated with a database location value. A record is accessed in the database using a database location value determined from referencing the trie index using a byte-comparable sequence of byte values of the record generated from a key of the record. A trie structure and byte ordered keys may be used for partition or row indices.

Abstract: Embodiments relate to a compacting datafiles generated by a database node using a compaction processing node with separate compute resources. The database node generates datafiles and stores the datafiles in a data store. To perform compacting of the datafiles, a snapshot of the data store is created and stored in a snapshot store separate from the data store. The compaction processing node is initiated and attached with the snapshot store. The compaction processing node generates a compacted datafile that is stored in the snapshot store. The database node replaces the data store with the snapshot store, and writes additional datafiles using the snapshot store as a new data store. The compaction processing node may be an instance of a cloud compute infrastructure that is initiated to perform the compaction to reduce compute resource usage by the database node.

Abstract: A distributed system that manages resources of the distributed system without the need for complex time synchronization systems is described. The distributed system includes a resource manager that manages the resources of the distributed system. The resource manager assigns leases and renews leases of resources of the distributed system to clients in the distributed system. The leases specify the duration of time that the lease is awarded to clients.

Abstract: At least a portion of a graph database having a plurality of vertex-centric indices is stored. A virtual edge to be generated is identified based on a plurality of edges of the graph database. The virtual edge connecting at least a pair of vertices that were not previously directly connected is generated. The plurality of vertex-centric indices is updated to include information about the virtual edge.

Abstract: In partitioning a graph database, a plurality of vertices of the graph database is assigned to a plurality of nodes. The vertices of the graph database are connected by edges that indicate relationships between the vertices. One or more abstract paths between one or more vertices of the graph database are identified. Each abstract path is weighted based on a likelihood of a database query following the abstract path. The vertices of the graph database are assigned to the nodes according to the abstract paths between the vertices.

Abstract: In partitioning a graph database, a plurality of vertices of the graph database is assigned to a plurality of nodes. The vertices of the graph database are connected by edges that indicate relationships between the vertices. A vertex of the graph database is designated as a super-vertex that is split into a truncated vertex and at least one vertex representative.

Abstract: A distributed system that manages resources of the distributed system without the need for complex time synchronization systems is described. The distributed system includes a resource manager that manages the resources of the distributed system. The resource manager assigns leases and renews leases of resources of the distributed system to clients in the distributed system. The leases specify the duration of time that the lease is awarded to clients.

Abstract: Described embodiments provide systems and methods for building client server applications. The application server code is deployed within a distributed datastore and utilizes the datastore's data locality information to fulfill requests with minimal remote procedure calls (RPC), reduced transfer of data between servers, and shared data caches. The application server inherits the properties of the distributed datastore such as statefulness, replication and failover. Described embodiments also enable the fetching and processing of data in a “big data” environment—a system that contains multiple servers' worth of data, with improved time and bandwidth considerations.

Abstract: Described embodiments provide systems and methods for building client server applications. The application server code is deployed within a distributed datastore and utilizes the datastore's data locality information to fulfill requests with minimal remote procedure calls (RPC), reduced transfer of data between servers, and shared data caches. The application server inherits the properties of the distributed datastore such as statefulness, replication and failover. Described embodiments also enable the fetching and processing of data in a “big data” environment—a system that contains multiple servers' worth of data, with improved time and bandwidth considerations.