Abstract: A map-reduce compatible distributed file system that consists of successive component layers that each provide the basis on which the next layer is built provides transactional read-write-update semantics with file chunk replication and huge file-create rates. Containers provide the fundamental basis for data replication, relocation, and transactional updates. A container location database allows containers to be found among all file servers, as well as defining precedence among replicas of containers to organize transactional updates of container contents. Volumes facilitate control of data placement, creation of snapshots and mirrors, and retention of a variety of control and policy information. Also addressed is the use of distributed transactions in a map-reduce system; the use of local and distributed snapshots; replication, including techniques for reconciling the divergence of replicated data after a crash; and mirroring.

Abstract: The efficiency of a container location database (“CLDB”) that is used for retrieving location information of data containers stored on nodes in a cluster is improved by decreasing the number of key-value store transactions performed during snapshot creation and deletion, thereby decreasing both disk I/O and computing cycles. The CLDB is made more scalable by improving the deletion and creation time of snapshots, allowing one to create and delete more snapshots during a given interval of time. CLDB resilience is improved by decreasing the time required to resync a CLDB node. CLDB data is reduced to decrease IO operations, improve resync times, and to cache most of the data in memory.

Abstract: Approaches for writing data objects to a distributed file system are described. In an example, a size of a data object to be created within a distributed file system may be determined based on a client request. Based on the size of the data object, data content of the data object is inserted into one of the object location table, object data table or a data container within the distributed file system. In an example, metadata corresponding to the data object may be written to an object location table.

Abstract: A map-reduce compatible distributed file system that consists of successive component layers that each provide the basis on which the next layer is built provides transactional read-write-update semantics with file chunk replication and huge file-create rates. Containers provide the fundamental basis for data replication, relocation, and transactional updates. A container location database allows containers to be found among all file servers, as well as defining precedence among replicas of containers to organize transactional updates of container contents. Volumes facilitate control of data placement, creation of snapshots and mirrors, and retention of a variety of control and policy information. Also addressed is the use of distributed transactions in a map-reduce system; the use of local and distributed snapshots; replication, including techniques for reconciling the divergence of replicated data after a crash; and mirroring.

Abstract: A file server receives a request for data from a user device. The data is represented at the file server by a virtual cluster descriptor. The file server queries an identifier map using an identifier of the virtual cluster descriptor. Responsive to the identifier map indicating that the requested data is stored at a location remote from the file server, the file server accesses a cold tier translation table that stores a mapping between an identifier of each of a plurality of virtual cluster descriptors and a storage location of data associated with the respective virtual cluster descriptor. The cold tier translation table is queried using the identifier of the virtual cluster descriptor to identify a storage location of the requested data, and the data is loaded to the file server from the identified storage location.

Abstract: The disclosed embodiments relate to a computer-implemented method for column-oriented access to data. The method can include inserting data into a data store. The data is randomly or sequentially retrievable from the data store by ordering keys for a table in a key-value store and recursively dividing a key space of said table into tablets that each have a range of possible keys. The tablets each contain partitions for key sub-ranges and each partition contains segments. Further, operations on tablets are distributed on different nodes and operations on partitions or segments are handled by using different threads.

Abstract: The present invention also relates to process for the preparation of N-(4-(6,7-dimethoxy quinolin-4-yloxy)phenyl)-N?-(4-fluorophenyl)cyclopropane-1,1-dicarboxamide (S)-malate compound of formula-1a and its polymorphs thereof, represented by the following structural:

Abstract: The present invention also relates to process for the preparation of N-(4-(6,7-dimethoxy quinolin-4-yloxy)phenyl)-N?-(4-fluorophenyl)cyclopropane-1,1-dicarboxamide (S)-malate compound of formula-1a and its polymorphs thereof, represented by the following structural:

Abstract: A map-reduce compatible distributed file system that consists of successive component layers that each provide the basis on which the next layer is built provides transactional read-write-update semantics with file chunk replication and huge file-create rates. Containers provide the fundamental basis for data replication, relocation, and transactional updates. A container location database allows containers to be found among all file servers, as well as defining precedence among replicas of containers to organize transactional updates of container contents. Volumes facilitate control of data placement, creation of snapshots and mirrors, and retention of a variety of control and policy information. Also addressed is the use of distributed transactions in a map-reduce system; the use of local and distributed snapshots; replication, including techniques for reconciling the divergence of replicated data after a crash; and mirroring.

Abstract: A map-reduce compatible distributed file system that consists of successive component layers that each provide the basis on which the next layer is built provides transactional read-write-update semantics with file chunk replication and huge file-create rates. Containers provide the fundamental basis for data replication, relocation, and transactional updates. A container location database allows containers to be found among all file servers, as well as defining precedence among replicas of containers to organize transactional updates of container contents. Volumes facilitate control of data placement, creation of snapshots and mirrors, and retention of a variety of control and policy information. Also addressed is the use of distributed transactions in a map-reduce system; the use of local and distributed snapshots; replication, including techniques for reconciling the divergence of replicated data after a crash; and mirroring.

Abstract: A server computing device is provided, including non-volatile memory and a processor. The processor may receive a plurality of telemetry signals from a plurality of modules executed on a plurality of computing devices. The plurality of modules may be arranged in a dependency hierarchy. The processor may further determine that the plurality of telemetry signals include a plurality of error signals indicating errors at one or more of the modules. Based on the plurality of error signals and a representation of the dependency hierarchy, the processor may further identify an error source module that, among the plurality of modules from which error signals are received, is highest in the dependency hierarchy. The processor may further select a remedial action based on the identification of the error source module. The processor may further output a remedial action notification including an indication of the error source module and/or the remedial action.

Abstract: A file server receives a request for data from a user device. The data is represented at the file server by a virtual cluster descriptor. The file server queries an identifier map using an identifier of the virtual cluster descriptor. Responsive to the identifier map indicating that the requested data is stored at a location remote from the file server, the file server accesses a cold tier translation table that stores a mapping between an identifier of each of a plurality of virtual cluster descriptors and a storage location of data associated with the respective virtual cluster descriptor. The cold tier translation table is queried using the identifier of the virtual cluster descriptor to identify a storage location of the requested data, and the data is loaded to the file server from the identified storage location.

Abstract: The disclosed embodiments relate to a computer-implemented method for column-oriented access to data. The method can include inserting data into a data store. The data is randomly or sequentially retrievable from the data store by ordering keys for a table in a key-value store and recursively dividing a key space of said table into tablets that each have a range of possible keys. The tablets each contain partitions for key subranges and each partition contains segments. Further, operations on tablets are distributed on different nodes and operations on partitions or segments are handled by using different threads.

Abstract: This disclosure provides systems, devices, apparatus and methods, including computer programs encoded on storage media, for wireless communication. In one aspect, an access point for wireless communications may include a transceiver; a memory; and a processor communicatively coupled to the transceiver and the memory, the processor being configured to: determine an interference level on a selected bandwidth; selectively transmit, via the transceiver, a clear-to-send (CTS)-to-self frame based on the determined interference level; and initiate a sounding sequence based on the transmitted CTS-to-self frame.

Abstract: A key-value store provides column-oriented access to data in a distributed and fault tolerant manner. Data can be inserted into the data store and data can be retrieved either randomly or sequentially from the data store at high rates. Keys for a table are ordered and the entire table is divided into key ranges. Each key range is handled by a table which itself is divided into key ranges called a partition. Partitions are also divided into segments. Such recursive division into smaller and smaller key ranges provides parallelism. At the highest level, operations on tablets can be distributed to different nodes. At lower levels, different threads can handle operations on individual segments. Large-scale restructuring operations can be decomposed into operations on individual segments so that a global lock on larger objects does not need to be kept across the entire operation.

Abstract: A file server receives a request for data from a user device. The data is represented at the file server by a virtual cluster descriptor. The file server queries an identifier map using an identifier of the virtual cluster descriptor. Responsive to the identifier map indicating that the requested data is stored at a location remote from the file server, the file server accesses a cold tier translation table that stores a mapping between an identifier of each of a plurality of virtual cluster descriptors and a storage location of data associated with the respective virtual cluster descriptor. The cold tier translation table is queried using the identifier of the virtual cluster descriptor to identify a storage location of the requested data, and the data is loaded to the file server from the identified storage location.

Abstract: A map-reduce compatible distributed file system that consists of successive component layers that each provide the basis on which the next layer is built provides transactional read-write-update semantics with file chunk replication and huge file-create rates. Containers provide the fundamental basis for data replication, relocation, and transactional updates. A container location database allows containers to be found among all file servers, as well as defining precedence among replicas of containers to organize transactional updates of container contents. Volumes facilitate control of data placement, creation of snapshots and mirrors, and retention of a variety of control and policy information. Also addressed is the use of distributed transactions in a map-reduce system; the use of local and distributed snapshots; replication, including techniques for reconciling the divergence of replicated data after a crash; and mirroring.

Abstract: A map-reduce compatible distributed file system that consists of successive component layers that each provide the basis on which the next layer is built provides transactional read-write-update semantics with file chunk replication and huge file-create rates. Containers provide the fundamental basis for data replication, relocation, and transactional updates. A container location database allows containers to be found among all file servers, as well as defining precedence among replicas of containers to organize transactional updates of container contents. Volumes facilitate control of data placement, creation of snapshots and mirrors, and retention of a variety of control and policy information. Also addressed is the use of distributed transactions in a map-reduce system; the use of local and distributed snapshots; replication, including techniques for reconciling the divergence of replicated data after a crash; and mirroring.

Abstract: A map-reduce compatible distributed file system that consists of successive component layers that each provide the basis on which the next layer is built provides transactional read-write-update semantics with file chunk replication and huge file-create rates. Containers provide the fundamental basis for data replication, relocation, and transactional updates. A container location database allows containers to be found among all file servers, as well as defining precedence among replicas of containers to organize transactional updates of container contents. Volumes facilitate control of data placement, creation of snapshots and mirrors, and retention of a variety of control and policy information. Also addressed is the use of distributed transactions in a map-reduce system; the use of local and distributed snapshots; replication, including techniques for reconciling the divergence of replicated data after a crash; and mirroring.

Abstract: A map-reduce compatible distributed file system that consists of successive component layers that each provide the basis on which the next layer is built provides transactional read-write-update semantics with file chunk replication and huge file-create rates. Containers provide the fundamental basis for data replication, relocation, and transactional updates. A container location database allows containers to be found among all file servers, as well as defining precedence among replicas of containers to organize transactional updates of container contents. Volumes facilitate control of data placement, creation of snapshots and mirrors, and retention of a variety of control and policy information. Also addressed is the use of distributed transactions in a map-reduce system; the use of local and distributed snapshots; replication, including techniques for reconciling the divergence of replicated data after a crash; and mirroring.