Netapp, Inc.

Santhosh Selvaraj

Ananthan Subramanian

Anil Paul Thoppil

Parag Sarfare

Nikhil Mattankot

Asif Imtiyaz Pathan

Meera Odugoudar

Szu-Wen Kuo

Wei Sun

Manan Dahyabhai Patel

Kevin Daniel Varghese

Arindam Banerjee

Nikul Y Patel

Vikhyath Rao

Raymond Jordan Go

Sanjay Subramanian Seshadri

Venkata Vijay Chaitanya Challapalli

Kumaran Nagappan

Ravi Lingappa Shamanna

Travis Ryan Grusecki

Techniques are provided for key-value store and file system integration to optimize key value store operations. A key-value store is integrated within a file system of a node. A log structured merge tree of the key-value store may be populated with a key corresponding to a content hash of a value data item stored separate from the key. A random distribution search may be performed upon a sorted log of the log structured merge tree to identify the key for accessing the value data item. A starting location for the random distribution search is derived from key information, a log size of the sorted log, and/or a keyspace size of a keyspace associated with the key.

Key-value store and file system integration

Techniques are provided for implementing a hash building process and an append hash building process. The hash building process builds in-memory hash entries for bins of keys stored within sorted logs of a log structured merge tree used to store keys of a key-value store. The in-memory hash entries can be used to identify the starting locations of bins of keys within the log structured merge tree so that a key within a bin can be searched for from the starting location of the bin as opposed to having to search the entire log structured merge tree. The append hash building process builds two hashes that can be used to more efficiently locate keys and/or ranges of keys within an unsorted append log that would otherwise require a time consuming binary search of the entire append log.

In-memory hash entries and hashes used to improve key search operations for keys of a key value store

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 implementing a garbage collection process and a prediction read ahead mechanism to prefetch keys into memory to improve the efficiency and speed of the garbage collection process. A log structured merge tree is used to store keys of key-value pairs within a key-value store. If a key is no longer referenced by any worker nodes of a distributed storage architecture, then the key can be freed to store other data. Accordingly, garbage collection is performed to identify and free unused keys. The speed and efficiency of garbage collection is improved by dynamically adjusting the amount and rate at which keys are prefetched from disk and cached into faster memory for processing by the garbage collection process.

Prefetching keys for garbage collection

Techniques are provided for implementing a defragmentation process during a merge operation performed by a re-compaction process upon a log structured merge tree. The log structured merge tree is used to store keys of key-value pairs within a key-value store. As the log structured merge tree fills with keys over time, the re-compaction process is performed to merge keys down to lower levels of the log structured merge tree to re-compact the keys. Re-compaction can result in fragmentation because there is a lack of spatial locality of where the re-compaction operations re-writes the keys within storage. Fragmentation increases read and write amplification when accessing the keys stored in different locations within the storage. Accordingly, the defragmentation process is performed during a last merge operation of the re-compaction process in order to store keys together within the storage, thus reducing read and write amplification when accessing the keys.

Defragmentation for log structured merge tree to improve read and write amplification

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 facilitate copy-free data migrations across filesystems. In a first step with this technology, a first set of filesystem metadata associated with a first filesystem is received. At least a portion of the first set of filesystem metadata is retrieved from a first data structure associated with the first filesystem. The first set of filesystem metadata includes a first identifier and a physical location associated with user data. A second identifier, associated with a second filesystem having a different addressing scheme than the first filesystem, is generated from at least the first identifier. A second set of filesystem metadata including the second identifier and the physical location is stored such that at least the second identifier is stored in a second data structure associated with the second filesystem.

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Santhosh Selvaraj