Netapp, Inc.

Meera Odugoudar

Anil Paul Thoppil

Santhosh Selvaraj

Wei Sun

Szu-Wen Kuo

Ananthan Subramanian

Kevin Daniel Varghese

Arindam Banerjee

Manan Dahyabhai Patel

Nikul Y Patel

Vikhyath Rao

Raymond Jordan Go

Sanjay Subramanian Seshadri

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

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

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Milpitas, CA, United States of America

Meera Odugoudar