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: 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: 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 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: 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.

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. A primitive storage layer (storage pools) knits together raw block stores and provides a storage mechanism for containers and transaction logs. Storage pools are manipulated by individual file servers. 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.

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 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 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.