Abstract: A data management services architecture includes architectural components that run in both a storage and compute domains. The architectural components redirect storage requests from the storage domain to the compute domain, manage resources allocated from the compute domain, ensure compliance with a policy that governs resource consumption, deploy program code for data management services, dispatch service requests to deployed services, and monitor deployed services. The architectural components also include a service map to locate program code for data management services, and service instance information for monitoring deployed services and dispatching requests to deployed services. Since deployed services can be stateless or stateful, the services architecture also includes state data for the stateful services, with supporting resources that can expand or contract based on policy and/or service demand. The architectural components also include containers for the deployed services.

Abstract: A data management services architecture includes architectural components that run in both a storage and compute domains. The architectural components redirect storage requests from the storage domain to the compute domain, manage resources allocated from the compute domain, ensure compliance with a policy that governs resource consumption, deploy program code for data management services, dispatch service requests to deployed services, and monitor deployed services. The architectural components also include a service map to locate program code for data management services, and service instance information for monitoring deployed services and dispatching requests to deployed services. Since deployed services can be stateless or stateful, the services architecture also includes state data for the stateful services, with supporting resources that can expand or contract based on policy and/or service demand. The architectural components also include containers for the deployed services.

Abstract: A deduplication service can be provided to a storage domain from a services framework that expands and contracts to both meet service demand and to conform to resource management of a compute domain. The deduplication service maintains a fingerprint database and reference count data in compute domain resources, but persists these into the storage domain for use in the case of a failure or interruption of the deduplication service in the compute domain. The deduplication service responds to service requests from the storage domain with indications of paths in a user namespace and whether or not a piece of data had a fingerprint match in the fingerprint database. The indication of a match guides the storage domain to either store the piece of data into the storage backend or to reference another piece of data. The deduplication service uses the fingerprints to define paths for corresponding pieces of data.

Abstract: A deduplication service can be provided to a storage domain from a services framework that expands and contracts to both meet service demand and to conform to resource management of a compute domain. The deduplication service maintains a fingerprint database and reference count data in compute domain resources, but persists these into the storage domain for use in the case of a failure or interruption of the deduplication service in the compute domain. The deduplication service responds to service requests from the storage domain with indications of paths in a user namespace and whether or not a piece of data had a fingerprint match in the fingerprint database. The indication of a match guides the storage domain to either store the piece of data into the storage backend or to reference another piece of data. The deduplication service uses the fingerprints to define paths for corresponding pieces of data.

Abstract: A data management services architecture includes architectural components that run in both a storage and compute domains. The architectural components redirect storage requests from the storage domain to the compute domain, manage resources allocated from the compute domain, ensure compliance with a policy that governs resource consumption, deploy program code for data management services, dispatch service requests to deployed services, and monitor deployed services. The architectural components also include a service map to locate program code for data management services, and service instance information for monitoring deployed services and dispatching requests to deployed services. Since deployed services can be stateless or stateful, the services architecture also includes state data for the stateful services, with supporting resources that can expand or contract based on policy and/or service demand. The architectural components also include containers for the deployed services.

Abstract: A data management services architecture includes architectural components that run in both a storage and compute domains. The architectural components redirect storage requests from the storage domain to the compute domain, manage resources allocated from the compute domain, ensure compliance with a policy that governs resource consumption, deploy program code for data management services, dispatch service requests to deployed services, and monitor deployed services. The architectural components also include a service map to locate program code for data management services, and service instance information for monitoring deployed services and dispatching requests to deployed services. Since deployed services can be stateless or stateful, the services architecture also includes state data for the stateful services, with supporting resources that can expand or contract based on policy and/or service demand. The architectural components also include containers for the deployed services.

Abstract: A data management services architecture includes architectural components that run in both a storage and compute domains. The architectural components redirect storage requests from the storage domain to the compute domain, manage resources allocated from the compute domain, ensure compliance with a policy that governs resource consumption, deploy program code for data management services, dispatch service requests to deployed services, and monitor deployed services. The architectural components also include a service map to locate program code for data management services, and service instance information for monitoring deployed services and dispatching requests to deployed services. Since deployed services can be stateless or stateful, the services architecture also includes state data for the stateful services, with supporting resources that can expand or contract based on policy and/or service demand. The architectural components also include containers for the deployed services.

Abstract: A deduplication service can be provided to a storage domain from a services framework that expands and contracts to both meet service demand and to conform to resource management of a compute domain. The deduplication service maintains a fingerprint database and reference count data in compute domain resources, but persists these into the storage domain for use in the case of a failure or interruption of the deduplication service in the compute domain. The deduplication service responds to service requests from the storage domain with indications of paths in a user namespace and whether or not a piece of data had a fingerprint match in the fingerprint database. The indication of a match guides the storage domain to either store the piece of data into the storage backend or to reference another piece of data. The deduplication service uses the fingerprints to define paths for corresponding pieces of data.

Abstract: A deduplication service can be provided to a storage domain from a services framework that expands and contracts to both meet service demand and to conform to resource management of a compute domain. The deduplication service maintains a fingerprint database and reference count data in compute domain resources, but persists these into the storage domain for use in the case of a failure or interruption of the deduplication service in the compute domain. The deduplication service responds to service requests from the storage domain with indications of paths in a user namespace and whether or not a piece of data had a fingerprint match in the fingerprint database. The indication of a match guides the storage domain to either store the piece of data into the storage backend or to reference another piece of data. The deduplication service uses the fingerprints to define paths for corresponding pieces of data.

Abstract: A data management services architecture includes architectural components that run in both a storage and compute domains. The architectural components redirect storage requests from the storage domain to the compute domain, manage resources allocated from the compute domain, ensure compliance with a policy that governs resource consumption, deploy program code for data management services, dispatch service requests to deployed services, and monitor deployed services. The architectural components also include a service map to locate program code for data management services, and service instance information for monitoring deployed services and dispatching requests to deployed services. Since deployed services can be stateless or stateful, the services architecture also includes state data for the stateful services, with supporting resources that can expand or contract based on policy and/or service demand. The architectural components also include containers for the deployed services.

Abstract: A data management services architecture includes architectural components that run in both a storage and compute domains. The architectural components redirect storage requests from the storage domain to the compute domain, manage resources allocated from the compute domain, ensure compliance with a policy that governs resource consumption, deploy program code for data management services, dispatch service requests to deployed services, and monitor deployed services. The architectural components also include a service map to locate program code for data management services, and service instance information for monitoring deployed services and dispatching requests to deployed services. Since deployed services can be stateless or stateful, the services architecture also includes state data for the stateful services, with supporting resources that can expand or contract based on policy and/or service demand. The architectural components also include containers for the deployed services.

Abstract: Systems and techniques for managing data storage are disclosed. In some aspects, a front-end node responds to a request to write an object by dividing the object into multiple source data segments. The front-end node generates redundancy data for the multiple source data segments using a rateless erasure encoding. The front-end node associates a respective subset of the redundancy data with each of the multiple source data segments, wherein each subset of redundancy data and associated source data segment form an encoded segment. The rateless erasure encoding further includes defining multiple segment-level fragments within each of the encoded segments. The front-end node transmits each of the encoded segments to a selected one of multiple storage nodes, wherein each of the selected storage nodes are selected based on a determined storage layout of the encoded segments across the multiple storage nodes.

Abstract: Technology is disclosed for accessing data fragments of data objects. The method receives a request for storing a data fragment of a data object in the storage server. The request includes an object identifier of the data object. The method further extracts a first string from the object identifier. The method then determines whether there is an existing file system object having a file system name that matches the first string. If there is no file system object that has a file system name that matches the first string, the method stores the data fragment as a fragment file with a file system name matching the first string.

Abstract: Technology is disclosed for a data storage architecture for providing enhanced storage resiliency for a data object. The data storage architecture can be implemented in a single-tier configuration and/or a multi-tier configuration. In the single-tier configuration, a data object is encoded, e.g., based on an erasure coding method, to generate many data fragments, which are stored across many storage devices. In the multi-tier configuration, a data object is encoded, e.g., based on an erasure coding method, to generate many data segments, which are sent to one or more tiers of storage nodes. Each of the storage nodes further encodes the data segment to generate many data fragments representing the data segment, which are stored across many storage devices associated with the storage node. The I/O operations for rebuilding the data in case of device failures is spread across many storage devices, which minimizes the wear of a given storage device.

Abstract: A deduplication service can be provided to a storage domain from a services framework that expands and contracts to both meet service demand and to conform to resource management of a compute domain. The deduplication service maintains a fingerprint database and reference count data in compute domain resources, but persists these into the storage domain for use in the case of a failure or interruption of the deduplication service in the compute domain. The deduplication service responds to service requests from the storage domain with indications of paths in a user namespace and whether or not a piece of data had a fingerprint match in the fingerprint database. The indication of a match guides the storage domain to either store the piece of data into the storage backend or to reference another piece of data. The deduplication service uses the fingerprints to define paths for corresponding pieces of data.

Abstract: A data management services architecture includes architectural components that run in both a storage and compute domains. The architectural components redirect storage requests from the storage domain to the compute domain, manage resources allocated from the compute domain, ensure compliance with a policy that governs resource consumption, deploy program code for data management services, dispatch service requests to deployed services, and monitor deployed services. The architectural components also include a service map to locate program code for data management services, and service instance information for monitoring deployed services and dispatching requests to deployed services. Since deployed services can be stateless or stateful, the services architecture also includes state data for the stateful services, with supporting resources that can expand or contract based on policy and/or service demand. The architectural components also include containers for the deployed services.

Abstract: Systems and techniques for managing data storage are disclosed. In some aspects, a front-end node responds to a request to write an object by dividing the object into multiple source data segments. The front-end node generates redundancy data for the multiple source data segments using a rateless erasure encoding. The front-end node associates a respective subset of the redundancy data with each of the multiple source data segments, wherein each subset of redundancy data and associated source data segment form an encoded segment. The rateless erasure encoding further includes defining multiple segment-level fragments within each of the encoded segments. The front-end node transmits each of the encoded segments to a selected one of multiple storage nodes, wherein each of the selected storage nodes are selected based on a determined storage layout of the encoded segments across the multiple storage nodes.

Abstract: Technology is disclosed for a data storage architecture for providing enhanced storage resiliency for a data object. The data storage architecture can be implemented in a single-tier configuration and/or a multi-tier configuration. In the single-tier configuration, a data object is encoded, e.g., based on an erasure coding method, to generate many data fragments, which are stored across many storage devices. In the multi-tier configuration, a data object is encoded, e.g., based on an erasure coding method, to generate many data segments, which are sent to one or more tiers of storage nodes. Each of the storage nodes further encodes the data segment to generate many data fragments representing the data segment, which are stored across many storage devices associated with the storage node. The I/O operations for rebuilding the data in case of device failures is spread across many storage devices, which minimizes the wear of a given storage device.

Abstract: Technology is disclosed for accessing data fragments of data objects. The method receives a request for storing a data fragment of a data object in the storage server. The request includes an object identifier of the data object. The method further extracts a first string from the object identifier. The method then determines whether there is an existing file system object having a file system name that matches the first string. If there is no file system object that has a file system name that matches the first string, the method stores the data fragment as a fragment file with a file system name matching the first string.

Abstract: Technology is disclosed for a data storage architecture for providing enhanced storage resiliency for a data object. The data storage architecture can be implemented in a single-tier configuration and/or a multi-tier configuration. In the single-tier configuration, a data object is encoded, e.g., based on an erasure coding method, to generate many data fragments, which are stored across many storage devices. In the multi-tier configuration, a data object is encoded, e.g., based on an erasure coding method, to generate many data segments, which are sent to one or more tiers of storage nodes. Each of the storage nodes further encodes the data segment to generate many data fragments representing the data segment, which are stored across many storage devices associated with the storage node. The I/O operations for rebuilding the data in case of device failures is spread across many storage devices, which minimizes the wear of a given storage device.