Netapp, Inc.

Asif Imtiyaz Pathan

Nikhil Mattankot

Parag Sarfare

Ananthan Subramanian

Santhosh Selvaraj

Szu-Wen Kuo

Stephen Wu

Amit Borase

Sudheer Kumar Vavilapalli

Kevin Daniel Varghese

Manan Dahyabhai Patel

Tabriz Holtz

Daniel Ting

Aditya Kulkarni

Manish Katiyar

Farshid Eslami Sarab

Prahlad Purohit

Afshin Salek Ardakani

Neha Kapoor

Tara Faith Olson

Bharadwaj V R

Travis Ryan Grusecki

Techniques are provided for implementing a persistent key-value store for caching client data, journaling, and/or crash recovery. The persistent key-value store may be hosted as a primary cache that provides read and write access to key-value record pairs stored within the persistent key-value store. The key-value record pairs are stored within multiple chains in the persistent key-value store. Journaling is provided for the persistent key-value store such that incoming key-value record pairs are stored within active chains, and data within frozen chains is written in a distributed manner across distributed storage of a distributed cluster of nodes. If there is a failure within the distributed cluster of nodes, then the persistent key-value store may be reconstructed and used for crash recovery.

Persistent key-value store and journaling system

Techniques are provided for journal replay optimization. A distributed storage architecture can implement a journal within memory for logging write operations into log records. Latency of executing the write operations is improved because the write operations can be responded back to clients as complete once logged within the journal without having to store the data to higher latency disk storage. If there is a failure, then a replay process is performed to replay the write operations logged within the journal in order to bring a file system up-to-date. The time to complete the replay of the write operations is significantly reduced by caching metadata (e.g., indirect blocks, checksums, buftree identifiers, file block numbers, and consistency point counts) directly into log records. Replay can quickly access this metadata for replaying the write operations because the metadata does not need to be retrieved from the higher latency disk storage into memory.

Journal replay optimization

Failover methods and systems for a networked storage environment are provided. In one aspect, a read request associated with a first storage object is received, during a replay of entries of a log stored in a non-volatile memory of a second storage node for a failover operation initiated in response to a failure at a first storage node. The second storage node operates as a partner node of the first storage node. The read request is processed using a filtering data structure that is generated from the log prior to the replay and identifies each log entry. The read request is processed when the log does not have an entry associated with the read request, and when the filtering data structure includes an entry associated with the read request, the requested data is located at the non-volatile memory.

Network storage failover systems and associated methods

Techniques are provided for a recovery process with selective ordering and concurrent operations in order to recover from a failure. Representations of active log structures are rebuilt within memory according to ordering values assigned to I/O operations logged within the active log structures. Representation of certain active log structures may be concurrently rebuilt based upon the active log structures comprising I/O operations that are non-overlapping within a distributed file system, have no dependencies, relate to different services, and/or target independent files. Representation of stale log structures are concurrently rebuilt within memory. While rebuilding the log structures and executing the I/O operations, a key value map is concurrently rebuilt within the memory for locating data of the I/O operations. Concurrent operations during the recovery process reduces the time to complete the recovery process, and thus reduces client downtime during the recovery process.

Recovery mechanism with selective ordering and concurrent operations

Failover methods and systems for a networked storage environment are provided. A filtering data structure and a metadata data structure are generated before starting a replay of a log stored in a non-volatile memory of a second storage node, during a failover operation initiated in response to a failure at a first storage node. The second storage node operates as a partner node of the first storage node to mirror at the log one or more write requests received by the first storage node prior to the failure, and data associated with the one or more write requests. The filtering data structure identifies each log entry and the metadata structure stores a metadata attribute of each log entry. The filtering data structure and the metadata structure are used for providing access to a logical storage object during the log replay from the second storage node.

Failover methods and systems for a networked storage environment are provided. A metadata data structure is generated, before starting a replay of entries at a log stored in a non-volatile memory of a second storage node, during a failover operation initiated in response to a failure at a first storage node. The second storage node operates as a partner node of the first storage node, and the metadata structure stores a metadata attribute of each log entry. Furthermore, the metadata attribute of each log entry is persistently stored. The persistently stored metadata attribute is used to respond to a read request received during the replay by the second storage node, while a write request metadata attribute of a write request is used for executing the write request received by the second storage node during the replay.

Methods, non-transitory computer readable media, and computing devices that accelerate data access requests. With this technology, a hierarchy of a plurality of objects is inserted into a location database. Each of at least a subset of the plurality of objects comprises a physical storage location for data stored in a filesystem. One or more of the plurality of objects includes an object version number and a parent version number of a parent one of the plurality of objects. A determination is made when an invalidation event has occurred in the filesystem. The invalidation event is associated with one of the plurality of objects. The object version number for the one of the plurality of objects is modified to invalidate one or more of the subset of the objects, when the determining indicates that the invalidation event has occurred in the filesystem.

Methods for accelerating storage media access and devices thereof

A recovery consumer framework provides for execution of recovery actions by one or more recovery consumers to enable efficient recovery of information (e.g., data and metadata) in a storage system after a failure event (e.g., a power failure). The recovery consumer framework permits concurrent execution of recovery actions so as to reduce recovery time (i.e., duration) for the storage system. The recovery consumer framework may coordinate (e.g., notify) the recovery consumers to serialize execution of the recovery actions by those recovery consumers having a dependency while allowing concurrent execution between recovery consumers having no dependency relationship. Each recovery consumer may register with the framework to associate a dependency on one or more of the other recovery consumers. The dependency association may be represented as a directed graph where each vertex of the graph represents a recovery consumer and each directed edge of the graph represents a dependency. The framework may traverse (i.e., walk) the framework graph and for each vertex encountered, notify the associated recovery consumer to initiate its respective recovery actions.

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Sunnyvale, CA, United States of America

Asif Imtiyaz Pathan