Patent Publication Number: US-9405473-B2

Title: Dense tree volume metadata update logging and checkpointing

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application is a continuation of U.S. patent application Ser. No. 14/084,137, entitled Dense Tree Volume Metadata Update Logging and Checkpointing, filed on Nov. 19, 2013 by Ling Zheng, et al., now issued as U.S. Pat. No. 9,201,918 on Dec. 1, 2015, and is related to U.S. patent application Ser. No. 14/161,097, filed on Jan. 22, 2014, entitled Dense Tree Volume Metadata Update Logging and Checkpointing, by Ling Zheng et al., now issued as U.S. Pat. No. 8,996,797 on Mar. 31, 2015, which applications are hereby incorporated by reference. The present application is also related to U.S. Pat. No. 8,892,818 entitled Dense Tree Volume Metadata Organization, by Ling Zheng et al., issued on Nov. 18, 2014. 
    
    
     BACKGROUND 
     1. Technical Field 
     The present disclosure relates to storage systems and, more specifically, to efficient logging and checkpointing of metadata in storage systems configured to provide a distributed storage architecture of a cluster. 
     2. Background Information 
     A plurality of storage systems may be interconnected as a cluster and configured to provide storage service relating to the organization of storage containers stored on storage devices coupled to the systems. The storage system cluster may be further configured to operate according to a client/server model of information delivery to thereby allow one or more clients (hosts) to access the storage containers. The storage devices may be embodied as solid-state drives (SSDs), such as flash storage devices, whereas the storage containers may be embodied as files or logical units (LUNs). Each storage container may be implemented as a set of data structures, such as data blocks that store data for the storage container and metadata blocks that describe the data of the storage container. For example, the metadata may describe, e.g., identify, locations of the data throughout the cluster. 
     The data of the storage containers accessed by a host may be stored on any of the storage systems of the cluster; moreover, the locations of the data may change throughout the cluster. Therefore, the storage systems may maintain metadata describing the locations of the storage container data throughout the cluster. However, it may be generally cumbersome to update the metadata every time the location of storage container data changes. One way to avoid such cumbersome updates is to maintain the metadata in a data structure that is efficiently accessed to resolve locations of the data. Accordingly, it is desirable to provide an organization of the metadata that enables efficient determination of the location of storage container data in a storage system cluster. In addition, it is desirable to provide a metadata organization that is “friendly” to, i.e., exploits the performance of, the storage devices configured to store the metadata. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The above and further advantages of the embodiments herein may be better understood by referring to the following description in conjunction with the accompanying drawings in which like reference numerals indicate identically or functionally similar elements, of which: 
         FIG. 1  is a block diagram of a plurality of nodes interconnected as a cluster; 
         FIG. 2  is a block diagram of a node; 
         FIG. 3  is a block diagram of a storage input/output (I/O) stack of the node; 
         FIG. 4  illustrates a write path of the storage I/O stack; 
         FIG. 5  illustrates a read path of the storage I/O stack; 
         FIG. 6  is a block diagram of various volume metadata entries; 
         FIG. 7  is a block diagram of a dense tree metadata structure; 
         FIG. 8  is a block diagram of a top level of the dense tree metadata structure; 
         FIG. 9  illustrates mapping between levels of the dense tree metadata structure; 
         FIG. 10  illustrates a workflow for inserting a volume metadata entry into the dense tree metadata structure in accordance with a write request; 
         FIG. 11  illustrates merging between levels of the dense tree metadata structure; 
         FIG. 12  illustrates batch updating between levels of the dense tree metadata structure; 
         FIG. 13  is an example simplified procedure for merging between levels of the dense tree metadata structure; 
         FIG. 14  illustrates volume logging of the dense tree metadata structure; and 
         FIG. 15  illustrates a workflow for deleting a volume metadata entry from the dense tree metadata structure in accordance with a delete request. 
     
    
    
     DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS 
     The embodiments described herein are directed to efficient logging and checkpointing of metadata, i.e., reducing operations to storage, managed by a volume layer of a storage input/output (I/O) stack executing on one or more nodes of a cluster. The metadata managed by the volume layer, i.e., the volume metadata, is illustratively organized as a multi-level dense tree metadata structure, wherein each level of the dense tree metadata structure (dense tree) includes volume metadata entries for storing the volume metadata. Each volume metadata entry may be a descriptor that embodies one of a plurality of types, including a data entry, an index entry, and a hole (i.e., absence of data) entry. 
     When a level of the dense tree is full, the volume metadata entries of the level are merged with the next lower level of the dense tree. As part of the merge, new index entries are created in the level to point to new lower level metadata pages. A top level (e.g., level 0) of the dense tree is illustratively maintained in-core such that a merge operation to a next lower level (e.g., level 1) facilitates a checkpoint to solid-state drives (SSD) illustratively embodied as flash storage devices (flash). The lower levels (e.g., levels 1 and/or 2) of the dense tree are illustratively maintained on-flash and updated (e.g., merged) as a batch operation when the higher levels are full. The merge operation illustratively includes a sort, e.g., a 2-way merge sort operation, so that the merge result is ordered and dense (i.e., compact) requiring fewer operations on a subsequent merge operation. 
     In an embodiment, the volume layer records changes to the volume metadata in a volume layer log maintained by the volume layer. The volume layer log is illustratively a two level, append-only logging structure, i.e., recording data changes, wherein the first level is non-volatile (NV) random access memory (embodied as a NVLog) and the second level is SSD. New volume metadata entries inserted into level 0 of the dense tree are also recorded in the volume layer log of the NVLogs. When there are sufficient entries in the volume layer log, e.g., when the log is full, the volume metadata entries are flushed (written) from log to SSD as one or more extents. 
     DESCRIPTION 
     Storage Cluster 
       FIG. 1  is a block diagram of a plurality of nodes  200  interconnected as a cluster  100  and configured to provide storage service relating to the organization of information on storage devices. The nodes  200  may be interconnected by a cluster interconnect fabric  110  and include functional components that cooperate to provide a distributed storage architecture of the cluster  100 , which may be deployed in a storage area network (SAN). As described herein, the components of each node  200  include hardware and software functionality that enable the node to connect to one or more hosts  120  over a computer network  130 , as well as to one or more storage arrays  150  of storage devices over a storage interconnect  140 , to thereby render the storage service in accordance with the distributed storage architecture. 
     Each host  120  may be embodied as a general-purpose computer configured to interact with any node  200  in accordance with a client/server model of information delivery. That is, the client (host) may request the services of the node, and the node may return the results of the services requested by the host, by exchanging packets over the network  130 . The host may issue packets including file-based access protocols, such as the Network File System (NFS) protocol over the Transmission Control Protocol/Internet Protocol (TCP/IP), when accessing information on the node in the form of storage containers such as files and directories. However, in an embodiment, the host  120  illustratively issues packets including block-based access protocols, such as the Small Computer Systems Interface (SCSI) protocol encapsulated over TCP (iSCSI) and SCSI encapsulated over FC (FCP), when accessing information in the form of storage containers such as logical units (LUNs). Notably, any of the nodes  200  may service a request directed to a storage container on the cluster  100 . 
       FIG. 2  is a block diagram of a node  200  that is illustratively embodied as a storage system having one or more central processing units (CPUs)  210  coupled to a memory  220  via a memory bus  215 . The CPU  210  is also coupled to a network adapter  230 , one or more storage controllers  240 , a cluster interconnect interface  250  and a non-volatile random access memory (NVRAM  280 ) via a system interconnect  270 . The network adapter  230  may include one or more ports adapted to couple the node  200  to the host(s)  120  over computer network  130 , which may include point-to-point links, wide area networks, virtual private networks implemented over a public network (Internet) or a local area network. The network adapter  230  thus includes the mechanical, electrical and signaling circuitry needed to connect the node to the network  130 , which illustratively embodies an Ethernet or Fibre Channel (FC) network. 
     The memory  220  may include memory locations that are addressable by the CPU  210  for storing software programs and data structures associated with the embodiments described herein. The CPU  210  may, in turn, include processing elements and/or logic circuitry configured to execute the software programs, such as a storage input/output (I/O) stack  300 , and manipulate the data structures. Illustratively, the storage I/O stack  300  may be implemented as a set of user mode processes that may be decomposed into a plurality of threads. An operating system kernel  224 , portions of which are typically resident in memory  220  (in-core) and executed by the processing elements (i.e., CPU  210 ), functionally organizes the node by, inter alia, invoking operations in support of the storage service implemented by the node and, in particular, the storage I/O stack  300 . A suitable operating system kernel  224  may include a general-purpose operating system, such as the UNIX® series or Microsoft Windows® series of operating systems, or an operating system with configurable functionality such as microkernels and embedded kernels. However, in an embodiment described herein, the operating system kernel is illustratively the Linux® operating system. It will be apparent to those skilled in the art that other processing and memory means, including various computer readable media, may be used to store and execute program instructions pertaining to the embodiments herein. 
     Each storage controller  240  cooperates with the storage I/O stack  300  executing on the node  200  to access information requested by the host  120 . The information is preferably stored on storage devices such as solid state drives (SSDs)  260 , illustratively embodied as flash storage devices, of storage array  150 . In an embodiment, the flash storage devices may be based on NAND flash components, e.g., single-layer-cell (SLC) flash, multi-layer-cell (MLC) flash or triple-layer-cell (TLC) flash, although it will be understood to those skilled in the art that other block-oriented, non-volatile, solid-state electronic devices (e.g., drives based on storage class memory components) may be advantageously used with the embodiments described herein. Accordingly, the storage devices may or may not be block-oriented (i.e., accessed as blocks). The storage controller  240  includes one or more ports having I/O interface circuitry that couples to the SSDs  260  over the storage interconnect  140 , illustratively embodied as a serial attached SCSI (SAS) topology. Alternatively, other point-to-point I/O interconnect arrangements such as a conventional serial ATA (SATA) topology or a PCI topology, may be used. The system interconnect  270  may also couple to local storage  248 , such as persistent memory, configured to locally store cluster-related configuration information, e.g., as cluster database (DB)  244 , which may be replicated to the other nodes  200  in the cluster  100 . 
     The cluster interconnect interface  250  may include one or more ports adapted to couple the node  200  to the other node(s) of the cluster  100 . In an embodiment, Ethernet may be used as the clustering protocol and interconnect fabric media, although it will be apparent to those skilled in the art that other types of protocols and interconnects, such as Infiniband, may be utilized within the embodiments described herein. The NVRAM  280  may include a back-up battery or other built-in last-state retention capability (e.g., non-volatile semiconductor memory such as storage class memory) that is capable of maintaining data in light of a failure to the node and cluster environment. Illustratively, a portion of the NVRAM  280  may be configured as one or more non-volatile logs (NVLogs  285 ) configured to temporarily record (“log”) I/O requests, such as write requests, received from the host  120 . 
     Storage I/O Stack 
       FIG. 3  is a block diagram of the storage I/O stack  300  that may be advantageously used with one or more embodiments described herein. The storage I/O stack  300  includes a plurality of software modules or layers that cooperate with other functional components of the nodes  200  to provide the distributed storage architecture of the cluster  100 . In an embodiment, the distributed storage architecture presents an abstraction of a single storage container, i.e., all of the storage arrays  150  of the nodes  200  for the entire cluster  100  organized as one large pool of storage. In other words, the architecture consolidates storage, i.e., the SSDs  260  of the arrays  150 , throughout the cluster (retrievable via cluster-wide keys) to enable storage of the LUNs. Both storage capacity and performance may then be subsequently scaled by adding nodes  200  to the cluster  100 . 
     Illustratively, the storage I/O stack  300  includes an administration layer  310 , a protocol layer  320 , a persistence layer  330 , a volume layer  340 , an extent store layer  350 , a Redundant Array of Independent Disks (RAID) layer  360 , a storage layer  365  and a NVRAM (storing NVLogs) “layer” interconnected with a messaging kernel  370 . The messaging kernel  370  may provide a message-based (or event-based) scheduling model (e.g., asynchronous scheduling) that employs messages as fundamental units of work exchanged (i.e., passed) among the layers. Suitable message-passing mechanisms provided by the messaging kernel to transfer information between the layers of the storage I/O stack  300  may include, e.g., for intra-node communication: i) messages that execute on a pool of threads, ii) messages that execute on a single thread progressing as an operation through the storage I/O stack, iii) messages using an Inter Process Communication (IPC) mechanism and, e.g., for inter-node communication: messages using a Remote Procedure Call (RPC) mechanism in accordance with a function shipping implementation. Alternatively, the I/O stack may be implemented using a thread-based or stack-based execution model. In one or more embodiments, the messaging kernel  370  allocates processing resources from the operating system kernel  224  to execute the messages. Each storage I/O stack layer may be implemented as one or more instances (i.e., processes) executing one or more threads (e.g., in kernel or user space) that process the messages passed between the layers such that the messages provide synchronization for blocking and non-blocking operation of the layers. 
     In an embodiment, the protocol layer  320  may communicate with the host  120  over the network  130  by exchanging discrete frames or packets configured as I/O requests according to pre-defined protocols, such as iSCSI and FCP. An I/O request, e.g., a read or write request, may be directed to a LUN and may include I/O parameters such as, inter alia, a LUN identifier (ID), a logical block address (LBA) of the LUN, a length (i.e., amount of data) and, in the case of a write request, write data. The protocol layer  320  receives the I/O request and forwards it to the persistence layer  330 , which records the request into a persistent write-back cache  380 , illustratively embodied as a log whose contents can be replaced randomly, e.g., under some random access replacement policy rather than only in serial fashion, and returns an acknowledgement to the host  120  via the protocol layer  320 . In an embodiment only I/O requests that modify the LUN, e.g., write requests, are logged. Notably, the I/O request may be logged at the node receiving the I/O request, or in an alternative embodiment in accordance with the function shipping implementation, the I/O request may be logged at another node. 
     Illustratively, dedicated logs may be maintained by the various layers of the storage I/O stack  300 . For example, a dedicated log  335  may be maintained by the persistence layer  330  to record the I/O parameters of an I/O request as equivalent internal, i.e., storage I/O stack, parameters, e.g., volume ID, offset, and length. In the case of a write request, the persistence layer  330  may also cooperate with the NVRAM  280  to implement the write-back cache  380  configured to store the write data associated with the write request. In an embodiment, the write-back cache  380  may be structured as a log. Notably, the write data for the write request may be physically stored in the cache  380  such that the log  335  contains the reference to the associated write data. It will be understood to persons skilled in the art the other variations of data structures may be used to store or maintain the write data in NVRAM including data structures with no logs. In an embodiment, a copy of the write-back cache may also be maintained in the memory  220  to facilitate direct memory access to the storage controllers. In other embodiments, caching may be performed at the host  120  or at a receiving node in accordance with a protocol that maintains coherency between the data stored at the cache and the cluster. 
     In an embodiment, the administration layer  310  may apportion the LUN into multiple volumes, each of which may be partitioned into multiple regions (e.g., allotted as disjoint block address ranges), with each region having one or more segments stored as multiple stripes on the array  150 . A plurality of volumes distributed among the nodes  200  may thus service a single LUN, i.e., each volume within the LUN services a different LBA range (i.e., offset range) or set of ranges within the LUN. Accordingly, the protocol layer  320  may implement a volume mapping technique to identify a volume to which the I/O request is directed (i.e., the volume servicing the offset range indicated by the parameters of the I/O request). Illustratively, the cluster database  244  may be configured to maintain one or more associations (e.g., key-value pairs) for each of the multiple volumes, e.g., an association between the LUN ID and a volume, as well as an association between the volume and a node ID for a node managing the volume. The administration layer  310  may also cooperate with the database  244  to create (or delete) one or more volumes associated with the LUN (e.g., creating a volume ID/LUN key-value pair in the database  244 ). Using the LUN ID and LBA (or LBA range), the volume mapping technique may provide a volume ID (e.g., using appropriate associations in the cluster database  244 ) that identifies the volume and node servicing the volume destined for the request, as well as translate the LBA (or LBA range) into an offset and length within the volume. Specifically, the volume ID is used to determine a volume layer instance that manages volume metadata associated with the LBA or LBA range. As noted, the protocol layer  320  may pass the I/O request (i.e., volume ID, offset and length) to the persistence layer  330 , which may use the function shipping (e.g., inter-node) implementation to forward the I/O request to the appropriate volume layer instance executing on a node in the cluster based on the volume ID. 
     In an embodiment, the volume layer  340  may manage the volume metadata by, e.g., maintaining states of host-visible containers, such as ranges of LUNs, and performing data management functions, such as creation of snapshots and clones, for the LUNs in cooperation with the administration layer  310 . The volume metadata is illustratively embodied as in-core mappings from LUN addresses (i.e., LBAs) to durable extent keys, which are unique cluster-wide IDs associated with SSD storage locations for extents within an extent key space of the cluster-wide storage container. That is, an extent key may be used to retrieve the data of the extent at an SSD storage location associated with the extent key. Alternatively, there may be multiple storage containers in the cluster wherein each container has its own extent key space, e.g., where the administration layer  310  provides distribution of extents among the storage containers. An extent is a variable length block of data that provides a unit of storage on the SSDs and that need not be aligned on any specific boundary, i.e., it may be byte aligned. Accordingly, an extent may be an aggregation of write data from a plurality of write requests to maintain such alignment. Illustratively, the volume layer  340  may record the forwarded request (e.g., information or parameters characterizing the request), as well as changes to the volume metadata, in dedicated log  345  maintained by the volume layer  340 . Subsequently, the contents of the volume layer log  345  may be written to the storage array  150  in accordance with a checkpoint (e.g., synchronization) operation that stores in-core metadata on the array  150 . That is, the checkpoint operation (checkpoint) ensures that a consistent state of metadata, as processed in-core, is committed to (i.e., stored on) the storage array  150 ; whereas the retirement of log entries ensures that the entries accumulated in the volume layer log  345  synchronize with the metadata checkpoints committed to the storage array  150  by, e.g., retiring those accumulated log entries that are prior to the checkpoint. In one or more embodiments, the checkpoint and retirement of log entries may be data driven, periodic or both. 
     In an embodiment, the extent store layer  350  is responsible for storing extents prior to storage on the SSDs  260  (i.e., on the storage array  150 ) and for providing the extent keys to the volume layer  340  (e.g., in response to a forwarded write request). The extent store layer  350  is also responsible for retrieving data (e.g., an existing extent) using an extent key (e.g., in response to a forwarded read request). The extent store layer  350  may be responsible for performing de-duplication and compression on the extents prior to storage. The extent store layer  350  may maintain in-core mappings (e.g., embodied as hash tables) of extent keys to SSD storage locations (e.g., offset on an SSD  260  of array  150 ). The extent store layer  350  may also maintain a dedicated log  355  of entries that accumulate requested “put” and “delete” operations (i.e., write requests and delete requests for extents issued from other layers to the extent store layer  350 ), where these operations change the in-core mappings (i.e., hash table entries). Subsequently, the in-core mappings and contents of the extent store layer log  355  may be written to the storage array  150  in accordance with a “fuzzy” checkpoint  390  (i.e., checkpoint with incremental changes recorded in one or more log files) in which selected in-core mappings, less than the total, are committed to the array  150  at various intervals (e.g., driven by an amount of change to the in-core mappings, size thresholds of log  355 , or periodically). Notably, the accumulated entries in log  355  may be retired once all in-core mappings have been committed to include the changes recorded in those entries prior to the first interval. 
     In an embodiment, the RAID layer  360  may organize the SSDs  260  within the storage array  150  as one or more RAID groups (e.g., sets of SSDs) that enhance the reliability and integrity of extent storage on the array by writing data “stripes” having redundant information, i.e., appropriate parity information with respect to the striped data, across a given number of SSDs  260  of each RAID group. The RAID layer  360  may also store a number of stripes (e.g., stripes of sufficient depth) at once, e.g., in accordance with a plurality of contiguous write operations, so as to reduce data relocation (i.e., internal flash block management) that may occur within the SSDs as a result of the operations. In an embodiment, the storage layer  365  implements storage I/O drivers that may communicate directly with hardware (e.g., the storage controllers and cluster interface) cooperating with the operating system kernel  224 , such as a Linux virtual function I/O (VFIO) driver. 
     Write Path 
       FIG. 4  illustrates an I/O (e.g., write) path  400  of the storage I/O stack  300  for processing an I/O request, e.g., a SCSI write request  410 . The write request  410  may be issued by host  120  and directed to a LUN stored on the storage array  150  of the cluster  100 . Illustratively, the protocol layer  320  receives and processes the write request by decoding  420  (e.g., parsing and extracting) fields of the request, e.g., LUN ID, LBA and length (shown at  413 ), as well as write data  414 . The protocol layer may use the results  422  from decoding  420  for a volume mapping technique  430  (described above) that translates the LUN ID and LBA range (i.e., equivalent offset and length) of the write request to an appropriate volume layer instance, i.e., volume ID (volume  445 ), in the cluster  100  that is responsible for managing volume metadata for the LBA range. In an alternative embodiment, the persistence layer  330  may implement the above described volume mapping technique  430 . The protocol layer then passes the results  432 , e.g., volume ID, offset, length (as well as write data), to the persistence layer  330 , which records the request in the persistent layer log  335  and returns an acknowledgement to the host  120  via the protocol layer  320 . The persistence layer  330  may aggregate and organize write data  414  from one or more write requests into a new extent  470  and perform a hash computation, i.e., a hash function, on the new extent to generate a hash value  472  in accordance with an extent hashing technique  474 . 
     The persistent layer  330  may then pass the write request with aggregated write date including, e.g., the volume ID, offset and length, as parameters  434  of a message to the appropriate volume layer instance. In an embodiment, message passing of the parameters  434  (received by the persistent layer) may be redirected to another node via the function shipping mechanism, e.g., RPC, for inter-node communication. Alternatively, message passing of parameters  434  may be via the IPC mechanism, e.g., message threads, for intra-node communication. 
     In one or more embodiments, a bucket mapping technique  476  is provided that translates the hash value  472  to an instance of an appropriate extent store layer (e.g., extent store instance  478 ) that is responsible for storing the new extent  470 . Note that the bucket mapping technique may be implemented in any layer of the storage I/O stack above the extent store layer. In an embodiment, for example, the bucket mapping technique may be implemented in the persistence layer  330 , the volume layer  340 , or a layer that manages cluster-wide information, such as a cluster layer (not shown). Accordingly, the persistence layer  330 , the volume layer  340 , or the cluster layer may contain computer executable instructions executed by the CPU  210  to perform operations that implement the bucket mapping technique  476 . The persistence layer  330  may then pass the hash value  472  and the new extent  470  to the appropriate volume layer instance and onto the appropriate extent store instance via an extent store put operation. The extent hashing technique  474  may embody an approximately uniform hash function to ensure that any random extent to be written may have an approximately equal chance of falling into any extent store instance  478 , i.e., hash buckets are distributed across extent store instances of the cluster  100  based on available resources. As a result, the bucket mapping technique  476  provides load-balancing of write operations (and, by symmetry, read operations) across nodes  200  of the cluster, while also leveling flash wear in the SSDs  260  of the cluster. 
     In response to the put operation, the extent store instance may process the hash value  472  to perform an extent metadata selection technique  480  that (i) selects an appropriate hash table  482  (e.g., hash table  482   a ) from a set of hash tables (illustratively in-core) within the extent store instance  478 , and (ii) extracts a hash table index  484  from the hash value  472  to index into the selected hash table and lookup a table entry having an extent key  618  identifying a storage location  490  on SSD  260  for the extent. Accordingly, the extent store layer  350  contains computer executable instructions executed by the CPU  210  to perform operations that implement the extent metadata selection technique  480  described herein. If a table entry with a matching extent key is found, then the SSD location  490  mapped from the extent key  618  is used to retrieve an existing extent (not shown) from SSD. The existing extent is then compared with the new extent  470  to determine whether their data is identical. If the data is identical, the new extent  470  is already stored on SSD  260  and a de-duplication opportunity (denoted de-duplication  452 ) exists such that there is no need to write another copy of the data. Accordingly, a reference count (not shown) in the table entry for the existing extent is incremented and the extent key  618  of the existing extent is passed to the appropriate volume layer instance for storage within an entry (denoted as volume metadata entry  600 ) of a dense tree metadata structure (e.g., dense tree  700   a ), such that the extent key  618  is associated an offset range  440  (e.g., offset range  440   a ) of the volume  445 . 
     However, if the data of the existing extent is identical to the data of the new extent  470 , a collision occurs and a deterministic algorithm is invoked to sequentially generate as many new candidate extent keys (not shown) mapping to the same bucket as needed to either provide de-duplication  452  or produce an extent key that is not already stored within the extent store instance. Notably, another hash table (e.g. hash table  482   n ) may be selected by a new candidate extent key in accordance with the extent metadata selection technique  480 . In the event that no de-duplication opportunity exists (i.e., the extent is not already stored) the new extent  470  is compressed in accordance with compression technique  454  and passed to the RAID layer  360 , which processes the new extent  470  for storage on SSD  260  within one or more stripes  464  of RAID group  466 . The extent store instance may cooperate with the RAID layer  360  to identify a storage segment  460  (i.e., a portion of the storage array  150 ) and a location on SSD  260  within the segment  460  in which to store the new extent  470 . Illustratively, the identified storage segment is a segment with a large contiguous free space having, e.g., location  490  on SSD  260   b  for storing the extent  470 . 
     In an embodiment, the RAID layer  360  then writes the stripe  464  across the RAID group  466 , illustratively as one or more full write stripes  462 . The RAID layer  360  may write a series of stripes  464  of sufficient depth to reduce data relocation that may occur within the flash-based SSDs  260  (i.e., flash block management). The extent store instance then (i) loads the SSD location  490  of the new extent  470  into the selected hash table  482   n  (i.e., as selected by the new candidate extent key), (ii) passes a new extent key (denoted as extent key  618 ) to the appropriate volume layer instance for storage within an entry (also denoted as volume metadata entry  600 ) of a dense tree  700  managed by that volume layer instance, and (iii) records a change to extent metadata of the selected hash table in the extent store layer log  355 . Illustratively, the volume layer instance selects dense tree  700   a  spanning an offset range  440   a  of the volume  445  that encompasses the LBA range of the write request. As noted, the volume  445  (e.g., an offset space of the volume) is partitioned into multiple regions (e.g., allotted as disjoint offset ranges); in an embodiment, each region is represented by a dense tree  700 . The volume layer instance then inserts the volume metadata entry  600  into the dense tree  700   a  and records a change corresponding to the volume metadata entry in the volume layer log  345 . Accordingly, the I/O (write) request is sufficiently stored on SSD  260  of the cluster. 
     Read Path 
       FIG. 5  illustrates an I/O (e.g., read) path  500  of the storage I/O stack  300  for processing an I/O request, e.g., a SCSI read request  510 . The read request  510  may be issued by host  120  and received at the protocol layer  320  of a node  200  in the cluster  100 . Illustratively, the protocol layer  320  processes the read request by decoding  420  (e.g., parsing and extracting) fields of the request, e.g., LUN ID, LBA, and length (shown at  513 ), and uses the results  522 , e.g., LUN ID, offset, and length, for the volume mapping technique  430 . That is, the protocol layer  320  may implement the volume mapping technique  430  (described above) to translate the LUN ID and LBA range (i.e., equivalent offset and length) of the read request to an appropriate volume layer instance, i.e., volume ID (volume  445 ), in the cluster  100  that is responsible for managing volume metadata for the LBA (i.e., offset) range. The protocol layer then passes the results  532  to the persistence later  330 , which may search the write cache  380  to determine whether some or all of the read request can be serviced from its cached data. If the entire request cannot be serviced from the cached data, the persistence layer  330  may then pass the remaining portion of the request including, e.g., the volume ID, offset and length, as parameters  534  to the appropriate volume layer instance in accordance with the function shipping mechanism (e.g., RPC for inter-node communication) or the IPC mechanism (e.g., message threads, for intra-node communication). 
     The volume layer instance may process the read request to access a dense tree metadata structure (e.g., dense tree  700   a ) associated with a region (e.g., offset range  440   a ) of a volume  445  that encompasses the requested offset range (specified by parameters  534 ). The volume layer instance may further process the read request to search for (lookup) one or more volume metadata entries  600  of the dense tree  700   a  to obtain one or more extent keys  618  associated with one or more extents  470  within the requested offset range. As described further herein, each dense tree  700  may be embodied as multiple levels of a search structure with possibly overlapping offset range entries at each level. The entries, i.e., volume metadata entries  600 , provide mappings from host-accessible LUN addresses, i.e., LBAs, to durable extent keys. The various levels of the dense tree may have volume metadata entries  600  for the same offset, in which case the higher level has the newer entry and is used to service the read request. A top level of the dense tree  700  is illustratively resident in-core and a page cache  448  may be used to access lower levels of the tree. If the requested range or portion thereof is not present in the top level, a metadata page associated with an index entry at the next lower tree level is accessed. The metadata page (i.e., in the page cache  448 ) at the next level is then searched (e.g., a binary search) to find any overlapping entries. This process is then iterated until one or more volume metadata entries  600  of a level are found to ensure that the extent key(s)  618  for the entire requested read range are found. If no metadata entries exist for the entire or portions of the requested read range, then the missing portion(s) are zero filled. 
     Once found, each extent key  618  is processed by the volume layer  340  to, e.g., implement the bucket mapping technique  476  that translates the extent key to an appropriate extent store instance  478  responsible for storing the requested extent  470 . Note that, in an embodiment, each extent key  618  is substantially identical to hash value  472  associated with the extent  470 , i.e., the hash value as calculated during the write request for the extent, such that the bucket mapping  476  and extent metadata selection  480  techniques may be used for both write and read path operations. Note also that the extent key  618  may be derived from the hash value  472 . The volume layer  340  may then pass the extent key  618  (i.e., the hash value  472  from a previous write request for the extent) to the appropriate extent store instance  478  (via an extent store get operation), which performs an extent key-to-SSD mapping to determine the location on SSD  260  for the extent. 
     In response to the get operation, the extent store instance may process the extent key  618  (i.e., hash value  472 ) to perform the extent metadata selection technique  480  that (i) selects an appropriate hash table (e.g., hash table  482   a ) from a set of hash tables within the extent store instance  478 , and (ii) extracts a hash table index  484  from the extent key  618  (i.e., hash value  472 ) to index into the selected hash table and lookup a table entry having a matching extent key  618  that identifies a storage location  490  on SSD  260  for the extent  470 . That is, the SSD location  490  mapped to the extent key  618  may be used to retrieve the existing extent (denoted as extent  470 ) from SSD  260  (e.g., SSD  260   b ). The extent store instance then cooperates with the RAID storage layer  360  to access the extent on SSD  260   b  and retrieve the data contents in accordance with the read request. Illustratively, the RAID layer  360  may read the extent in accordance with an extent read operation  468  and pass the extent  470  to the extent store instance. The extent store instance may then decompress the extent  470  in accordance with a decompression technique  456 , although it will be understood to those skilled in the art that decompression can be performed at any layer of the storage I/O stack  300 . The extent  470  may be stored in a buffer (not shown) in memory  220  and a reference to that buffer may be passed back through the layers of the storage I/O stack. The persistence layer may then load the extent into a read cache  580  (or other staging mechanism) and may extract appropriate read data  512  from the read cache  580  for the LBA range of the read request  510 . Thereafter, the protocol layer  320  may create a SCSI read response  514 , including the read data  512 , and return the read response to the host  120 . 
     Dense Tree Volume Metadata 
     As noted, a host-accessible LUN may be apportioned into multiple volumes, each of which may be partitioned into one or more regions, wherein each region is associated with a disjoint offset range, i.e., a LBA range, owned by an instance of the volume layer  340  executing on a node  200 . For example, assuming a maximum volume size of 64 terabytes (TB) and a region size of 16 gigabytes (GB), a volume may have up to 4096 regions (i.e., 16 GB×4096=64 TB). In an embodiment, region  1  may be associated with an offset range of, e.g., 0-16 GB, region  2  may be associated with an offset range of 16 GB-32 GB, and so forth. Ownership of a region denotes that the volume layer instance manages metadata, i.e., volume metadata, for the region, such that I/O requests destined to an offset range within the region are directed to the owning volume layer instance. Thus, each volume layer instance manages volume metadata for, and handles I/O requests to, one or more regions. A basis for metadata scale-out in the distributed storage architecture of the cluster  100  includes partitioning of a volume into regions and distributing of region ownership across volume layer instances of the cluster. 
     Volume metadata, as well as data storage, in the distributed storage architecture is illustratively extent based. The volume metadata of a region that is managed by the volume layer instance is illustratively embodied as in memory (in-core) and on SSD (on-flash) volume metadata configured to provide mappings from host-accessible LUN addresses, i.e., LBAs, of the region to durable extent keys. In other words, the volume metadata maps LBA (i.e., offset) ranges of the LUN to data of the LUN (via extent keys) within the respective LBA range. In an embodiment, the volume layer organizes the volume metadata (embodied as volume metadata entries  600 ) as a data structure, i.e., a dense tree metadata structure (dense tree  700 ), which maps an offset range within the region to one or more extent keys. That is, LUN data (user data) stored as extents (accessible via extent keys) is associated with LUN offset (i.e., LBA) ranges represented as volume metadata (also stored as extents). Accordingly, the volume layer  340  contains computer executable instructions executed by the CPU  210  to perform operations that organize and manage the volume metadata entries of the dense tree metadata structure described herein. 
       FIG. 6  is a block diagram of various volume metadata entries  600  of the dense tree metadata structure. Each volume metadata entry  600  of the dense tree  700  may be a descriptor that embodies one of a plurality of types, including a data entry (D)  610 , an index entry (I)  620 , and a hole entry (H)  630 . The data entry (D)  610  is configured to map (offset, length) to an extent key for an extent (user data) and includes the following content: type  612 , offset  614 , length  616  and extent key  618 . The index entry (I)  620  is configured to map (offset, length) to a page key (e.g., an extent key) of a metadata page (stored as an extent), i.e., a page containing one or more volume metadata entries, at a next lower level of the dense tree; accordingly, the index entry  620  includes the following content: type  622 , offset  624 , length  626  and page key  628 . Illustratively, the index entry  620  manifests as a pointer from a higher level to a lower level, i.e., the index entry  620  essentially serves as linkage between the different levels of the dense tree. The hole entry (H)  630  represents absent data as a result of a hole punching operation at (offset, length) and includes the following content: type  632 , offset  634 , and length  636 . 
     In an embodiment, the volume metadata entry types are of a fixed size (e.g., 12 bytes including a type field of 1 byte, an offset of 4 bytes, a length of 1 byte, and a key of 6 bytes) to facilitate search of the dense tree metadata structure as well as storage on metadata pages. Thus, some types may have unused portions, e.g., the hole entry  630  includes less information than the data entry  610  and so may have one or more unused bytes. In an alternative embodiment, the entries may be variable in size to avoid unused bytes. Advantageously, the volume metadata entries may be sized for in-core space efficiency (as well as alignment on metadata pages), which improves both read and write amplification for operations. For example, the length field ( 616 ,  626 ,  636 ) of the various volume metadata entry types may represent a unit of sector size, such as 512 bytes or 520 bytes, such that a 1 byte length may represent a range of 255×512 bytes=128K bytes. 
       FIG. 7  is a block diagram of the dense tree metadata structure that may be advantageously used with one or more embodiments described herein. The dense tree metadata structure  700  is configured to provide mappings of logical offsets within a LUN (or volume) to extent keys managed by one or more extent store instances. Illustratively, the dense tree metadata structure is organized as a multi-level dense tree  700 , where a top level  800  represents recent volume metadata changes and subsequent descending levels represent older changes. Specifically, a higher level of the dense tree  700  is updated first and, when that level fills, an adjacent lower level is updated, e.g., via a merge operation. A latest version of the changes may be searched starting at the top level of the dense tree and working down to the descending levels. Each level of the dense tree  700  includes fixed size records or entries, i.e., volume metadata entries  600 , for storing the volume metadata. A volume metadata process  710  illustratively maintains the top level  800  of the dense tree in memory (in-core) as a balanced tree that enables indexing by offsets. The volume metadata process  710  also maintains a fixed sized (e.g., 4 KB) in-core buffer as a staging area (i.e., an in-core staging buffer  715 ) for volume metadata entries  600  inserted into the balanced tree (i.e., top level  800 ). Each level of the dense tree is further maintained on-flash as a packed array of volume metadata entries, wherein the entries are stored as extents illustratively organized as fixed sized (e.g., 4 KB) metadata pages  720 . Notably, the staging buffer  715  is de-staged to SSD upon a trigger, e.g., the staging buffer is full. Each metadata page  720  has a unique identifier (ID), which guarantees that no two metadata pages can have the same content. Illustratively, metadata may not be de-duplicated by the extent store layer  350 . 
     In an embodiment, the multi-level dense tree  700  includes three (3) levels, although it will be apparent to those skilled in the art that additional levels N of the dense tree may be included depending on parameters (e.g., size) of the dense tree configuration. Illustratively, the top level  800  of the tree is maintained in-core as level 0 and the lower levels are maintained on-flash as levels 1 and 2. In addition, copies of the volume metadata entries  600  stored in staging buffer  715  may also be maintained on-flash as, e.g., a level 0 linked list. A leaf level, e.g., level 2, of the dense tree contains data entries  610 , whereas a non-leaf level, e.g., level 0 or 1, may contain both data entries  610  and index entries  620 . Each index entry (I)  620  at level N of the tree is configured to point to (reference) a metadata page  720  at level N+1 of the tree. Each level of the dense tree  600  also includes a header (e.g., level 0 header  730 , level 1 header  740  and level 2 header  750 ) that contains per level information, such as reference counts associated with the extents. Each upper level header contains a header key (an extent key for the header, e.g., header key  732  of level 0 header  730 ) to a corresponding lower level header. A region key  762  to a root, e.g., level 0 header  730  (and top level  800 ), of the dense tree  700  is illustratively stored on-flash and maintained in a volume root extent, e.g., a volume superblock  760 . Notably, the volume superblock  760  contains region keys to the roots of the dense tree metadata structures for all regions in a volume. 
       FIG. 8  is a block diagram of the top level  800  of the dense tree metadata structure. As noted, the top level (level 0) of the dense tree  700  is maintained in-core as a balanced tree, which is illustratively embodied as a B+ tree data structure. However, it will be apparent to those skilled in the art that other data structures, such as AVL trees, Red-Black trees, and heaps (partially sorted trees), may be advantageously used with the embodiments described herein. The B+ tree (top level  800 ) includes a root node  810 , one or more internal nodes  820  and a plurality of leaf nodes (leaves)  830 . The volume metadata stored on the tree is preferably organized in a manner that is efficient both to search, in order to service read requests and to traverse (walk) in ascending order of offset to accomplish merges to lower levels of the tree. The B+ tree has certain properties that satisfy these requirements, including storage of all data (i.e., volume metadata entries  600 ) in leaves  830  and storage of the leaves as sequentially accessible, e.g., as one or more linked lists. Both of these properties make sequential read requests for write data (i.e., extents) and read operations for dense tree merge more efficient. Also, since it has a much higher fan-out than a binary search tree, the illustrative B+ tree results in more efficient lookup operations. As an optimization, the leaves  830  of the B+ tree may be stored in a page cache  448 , making access of data more efficient than other trees. In addition, resolution of overlapping offset entries in the B+ tree optimizes read requests of extents. Accordingly, the larger the fraction of the B+ tree (i.e., volume metadata) maintained in-core, the less loading (reading) of metadata from SSD is required so as to reduce read amplification. 
       FIG. 9  illustrates mappings  900  between levels of the dense tree metadata structure. Each level of the dense tree  700  includes one or more metadata pages  720 , each of which contains multiple volume metadata entries  600 . As noted, each volume metadata entry  600  has a fixed size, e.g., 12 bytes, such that a predetermined number of entries may be packed into each metadata page  720 . The data entry (D)  610  is a map of (offset, length) to an address of (user) data which is retrievable using an extent key  618  (i.e., from an extent store instance). The (offset, length) illustratively specifies an offset range of a LUN. The index entry (I)  620  is a map of (offset, length) to a page key  628  of a metadata page  720  at the next lower level. Illustratively, the offset in the index entry (I)  620  is the same as the offset of the first entry in the metadata page  720  at the next lower level. The length  626  in the index entry  620  is illustratively the cumulative length of all entries in the metadata page  720  at the next lower level (including gaps between entries). 
     For example, the metadata page  720  of level 1 includes an index entry “I(2K,10K)” that specifies a starting offset 2K and an ending offset 12K (i.e., 12K=2K+10K); the index entry (I) illustratively points to a metadata page  720  of level 2 covering the specified range. An aggregate view of the data entries (D) packed in the metadata page  720  of level 2 covers the mapping from the smallest offset (e.g., 2K) to the largest offset (e.g., 12K). Thus, each level of the dense tree  700  may be viewed as an overlay of an underlying level. For instance the data entry “D(0,4K)” of level 1 overlaps 2K of the underlying metadata in the page of level 2 (i.e., the range 2K,4K). 
     In one or more embodiments, operations for volume metadata managed by the volume layer  340  include insertion of volume metadata entries, such as data entries  610 , into the dense tree  700  for write requests. As noted, each dense tree  700  may be embodied as multiple levels of a search structure with possibly overlapping offset range entries at each level, wherein each level is a packed array of entries (e.g., sorted by offset) and where leaf entries have an offset range (offset, length) an extent key.  FIG. 10  illustrates a workflow  1000  for inserting a volume metadata entry into the dense tree metadata structure in accordance with a write request. In an embodiment, volume metadata updates (changes) to the dense tree  700  occur first at the top level of the tree, such that a complete, top-level description of the changes is maintained in memory  220 . 
     Operationally, the volume metadata process  710  applies the region key  762  to access the dense tree  700  (i.e., top level  800 ) of an appropriate region (e.g., offset range  440  as determined from the parameters  432  derived from a write request  410 ). Upon completion of a write request, the volume metadata process  710  creates a volume metadata entry, e.g., a new data entry  610 , to record a mapping of offset/length-to-extent key (i.e., offset range-to-user data). Illustratively, the new data entry  610  includes an extent key  618  (i.e., from the extent store layer  350 ) associated with data (i.e., extent  470 ) of the write request  410 , as well as offset  614  and length  616  (i.e., from the write parameters  432 ) and type  612  (i.e., data entry D). The volume metadata process  710  then updates the volume metadata by inserting (adding) the data entry D into the level 0 staging buffer  715 , as well as into the top level  800  of dense tree  700  and the volume layer log  345 , thereby signifying that the write request is stored on the storage array  150 . 
     Dense Tree Volume Metadata Checkpointing 
     When a level of the dense tree  700  is full, volume metadata entries  600  of the level are merged with the next lower level of the dense tree. As part of the merge, new index entries  620  are created in the level to point to new lower level metadata pages  720 , i.e., data entries from the level are merged (and pushed) to the lower level so that they may be “replaced” with an index reference in the level. The top level  800  (i.e., level 0) of the dense tree  700  is illustratively maintained in-core such that a merge operation to level 1 facilitates a checkpoint to SSD  260 . The lower levels (i.e., levels 1 and/or 2) of the dense tree are illustratively maintained on-flash and updated (e.g., merged) as a batch operation (i.e., processing the entries of one level with those of a lower level) when the higher levels are full. The merge operation illustratively includes a sort, e.g., a 2-way merge sort operation. A parameter of the dense tree  700  is the ratio K of the size of level N−1 to the size of level N. Illustratively, the size of the array at level N is K times larger than the size of the array at level N−1, i.e., size of (level N)=K*size of (level N−1). After K merges from level N−1, level N becomes full (i.e., all entries from a new, fully-populated level N−1 are merged with level N, iterated K times.) 
       FIG. 11  illustrates merging  1100  between levels, e.g., levels 0 and 1, of the dense tree metadata structure. In an embodiment, a merge operation is triggered when level 0 is full. When performing the merge operation, the dense tree metadata structure transitions to a “merge” dense tree structure (shown at  1120 ) that merges, while an alternate “active” dense tree structure (shown at  1150 ) is utilized to accept incoming data. Accordingly, two in-core level 0 staging buffers  1130 ,  1160  are illustratively maintained for concurrent merge and active (write) operations, respectively. In other words, an active staging buffer  1160  and active top level  1170  of active dense tree  1150  handle in-progress data flow (i.e., active user read and write requests), while a merge staging buffer  1130  and merge top level  1140  of merge dense tree  1120  handle consistency of the data during a merge operation. That is, a “double buffer” arrangement may be used to handle the merge of data (i.e., entries in the level 0 of the dense tree) while processing active operations. 
     During the merge operation, the merge staging buffer  1130 , as well as the top level  1140  and lower level array (e.g., merge level 1) are read-only and are not modified. The active staging buffer  1160  is configured to accept the incoming (user) data, i.e., the volume metadata entries received from new put operations are loaded into the active staging buffer  1160  and added to the top level  1170  of the active dense tree  1150 . Illustratively, merging from level 0 to level 1 within the merge dense tree  1120  results in creation of a new active level 1 for the active dense tree  1150 , i.e., the resulting merged level 1 from the merge dense tree is inserted as a new level 1 into the active dense tree. A new index entry I is computed to reference the new active level 1 and the new index entry I is loaded into the active staging buffer  1160  (as well as in the active top level  1170 ). Upon completion of the merge, the region key  762  of volume superblock  760  is updated to reference (point to) the root, e.g., active top level  1170  and active level 0 header (not shown), of the active dense tree  1150 , thereby deleting (i.e., rendering inactive) merge level 0 and merge level 1 of the merge dense tree  1120 . The merge staging buffer  1130  (and the top level  1140  of the dense tree) thus becomes an empty inactive buffer until the next merge. The merge data structures (i.e., the merge dense tree  1120  including staging buffer  1130 ) may be maintained in-core and “swapped” as the active data structures at the next merge (i.e., “double buffered”). 
       FIG. 12  illustrates batch updating  1200  between lower levels, e.g., levels 1 and 2, of the dense tree metadata structure. Illustratively, as an example, a metadata page  720  of level 1 includes four data entries D and an index entry I referencing a metadata page  720  of level 2. When full, level 1 batch updates (merges) to level 2, thus emptying the data entries D of level 1, i.e., contiguous data entries are combined (merged) and pushed to the next lower level with a reference inserted in their place in the level. The merge of changes of layer 1 into layer 2 illustratively produces a new set of extents on SSD, i.e., new metadata pages are also stored, illustratively, in an extent store instance. As noted, level 2 is illustratively several times larger, e.g., K times larger, than level 1 so that it can support multiple merges. Each time a merge is performed, some older entries that were previously on SSD may be deleted. Advantageously, use of the multi-level tree structure lowers the overall frequency of volume metadata that is rewritten (and hence reduces write amplification), because old metadata may be maintained on a level while new metadata is accumulated in that level until it is full. Further, when a plurality of upper levels become full, a multi-way merge to a lower level may be performed (e.g., a three-way merge from full levels 0 and 1 to level 2). 
       FIG. 13  is an example simplified procedure  1300  for merging between levels of the dense tree metadata structure. The procedure starts at step  1305  and proceeds to step  1310  where incoming data received at the dense tree metadata structure is inserted into level 0, i.e., top level  800 , of the dense tree. Note that the incoming data is inserted into the top level  800  as a volume metadata entry. At step  1315 , a determination is made as whether level 0, i.e., top level  800 , of the dense tree is rendered full. If not, the procedure returns to step  1310 ; otherwise, if the level 0 is full, the dense tree transitions to a merge dense tree structure at step  1320 . At step  1325 , incoming data is loaded into an active staging buffer of an active dense tree structure and, at step  1330 , the level 0 merges with level 1 of the merge dense tree structure. In response to the merge, a new active level 1 is created for the active dense tree structure at step  1335 . At step  1340 , an index entry is computed to reference the new active level 1 and, at step  1345 , the index entry is loaded into the active dense tree structure. At step  1350 , a region key of a volume superblock is updated to reference the active dense tree structure and, at step  1355 , the level 0 and level 1 of the merge dense tree structure are rendered inactive (alternatively, deleted). The procedure then ends at step  1360 . 
     In an embodiment, as the dense tree fills up, the volume metadata is written out to one or more files on SSD in a sequential format, independent of when the volume layer log  345  is de-staged and written to SSD  260 , i.e., logging operations may be independent of merge operations. When writing volume metadata from memory  220  to SSD, direct pointers to the data, e.g., in-core references to memory locations, may be replaced with pointers to an index block in the file that references a location where the metadata can be found. As the files are accumulated, they are illustratively merged together in a log-structured manner that continually writes the metadata sequentially to SSD. As a result, the lower level files grow and contain volume metadata that may be outdated because updates have occurred to the metadata, e.g., newer entries in the dense tree may overlay older entries, such as a hole entry overlaying an underlying data entry. The updates (i.e., layered LBA ranges) are “folded” into the lower levels, thereby overwriting the outdated metadata. The resulting dense tree structure thus includes newly written metadata and “holes” where outdated metadata has been deleted. 
     Dense Tree Volume Metadata Logging 
     In an embodiment, the volume layer log  345  is a two level, append-only logging structure, wherein the first level is NVRAM  280  (embodied as NVLogs  285 ) and the second level is SSD  260 , e.g., stored as extents. New volume metadata entries  600  inserted into level 0 of the dense tree are also recorded in the volume layer log  345  of NVLogs  285 . When there are sufficient entries in the volume layer log  345 , e.g., when the log  345  is full or exceeds a threshold, the volume metadata entries are flushed (written) from log  345  to SSD  260  as one or more extents  470 . Multiple extents may be linked together with the volume superblock  760  holding a key (i.e., an extent key) to the head of the list. In the case of recovery, the volume layer log  345  is read back to memory  220  to reconstruct the in-core top level  800  (i.e., level 0) of dense tree  700 . Other levels may be demand paged via the page cache  448 , e.g., metadata pages of level 1 are loaded and read as needed. 
       FIG. 14  illustrates volume logging  1400  of the dense tree metadata structure. Copies of the volume metadata entries  600  stored in level 0 of the dense tree are maintained in persistent storage (SSD  260 ) and recorded as volume layer log  345  in, e.g., NVLogs  285 . Specifically, the entries of level 0 are stored in the in-core staging buffer  715 , logged in the append log (volume layer log  345 ) of NVLogs  285  and thereafter flushed to SSD  260  as a linked list of metadata pages  720 . Copies of the level 0 volume metadata are maintained in-core as the active dense tree level 0 so as to service incoming read requests from memory  220 . Illustratively, the in-core top level  800  (e.g., active dense tree level 0  1170 ) may be used as a cache (for hot metadata), whereas the volume metadata stored on the other lower levels of the dense tree are accessed less frequently (cold data) and maintained on SSD. Alternatively, the lower levels also may be cached using the page cache  448 . 
     While there have been shown and described illustrative embodiments directed to logging and checkpointing of metadata managed by a volume layer of a storage I/O stack executing on one or more nodes of a cluster, it is to be understood that various other adaptations and modifications may be made within the spirit and scope of the embodiments herein. For example, embodiments have been shown and described herein with relation to updating of volume metadata changes for write requests at lower levels of a dense tree metadata structure (dense tree) during a merge operation. However, the embodiments in their broader sense are not so limited, and may, in fact, also allow for updating of volume metadata changes for delete requests at the lower dense tree levels during the merge operation. In addition, greater indirection, such as index entries referencing other index entries (e.g., in a lower level) are also expressly contemplated. 
     In an embodiment, deletion of a particular data range, e.g., of a LUN, is represented as a hole punch and manifested as a hole entry (H)  630 .  FIG. 15  illustrates a workflow  1500  for deleting one or more volume metadata entries from the dense tree metadata structure in accordance with a delete request. Assume it is desirable to punch a hole (delete) of a data range 0-12K as represented by hole entry H(0-12K)(offset, length). The entry D(0,2K) is deleted from level 0, with updates to the lower levels occurring in a fashion similar to write requests. That is, the volume layer  340  of the storage I/O stack  300  waits until a merge operation to resolve any overlaps between different levels by, e.g., overwriting the older entries with the newer entries. In this example, the hole entry H for 0-12K range is recent, so when that entry is merged to a lower level, e.g., level 1, the data entries D with corresponding (i.e., overlapping) ranges are deleted. In other words, the hole entry H cancels out any data entries D that happen to previously be in the corresponding range. Thus when level 0 is full and merged with level 1, the data entry D(0,4K)(offset, length) is deleted from level 1, and when level 1 is full and merged with level 2, the data entries D(2K,4K), D(6K,4K) and D(10K,2K) are deleted, i.e., the hole H(0, 12K) overlays the underlying disjoint data entries D(2K, 4K)(offset, length), D(6K, 4K), D(10K, 2K). 
     Advantageously, the update (i.e., merge) and logging operations for the dense tree metadata structure efficiently (i.e., frugally) write in-core metadata to storage so that write amplification resulting from (user) data is reduced. That is, once the in-core dense tree portion (i.e., level 0) is full, operations (e.g., merges) to store that portion to SSD involve writing sorted (i.e., ordered) and dense (i.e., compact) metadata to storage (e.g., SSD). Since such operations relate directly to data (i.e., merger of data entries) as opposed to metadata, necessary metadata changes resulting from other metadata changes are reduced, thus substantially enhancing efficiency. Efficiency is also enhanced as a result of compact in-core metadata structures, i.e., volume metadata entries  600 , being stored in staging buffer  715  prior to de-staging to SSD, while logging operations directly record only write request information, e.g., in volume layer log  345 . Moreover, because it is densely packed irrespective of the I/O requests, e.g., random write requests, the dense tree metadata structure supports large continuous write operations to storage and, thus, is flash friendly with respect to random write operations. 
     The foregoing description has been directed to specific embodiments. It will be apparent, however, that other variations and modifications may be made to the described embodiments, with the attainment of some or all of their advantages. For instance, it is expressly contemplated that the components and/or elements described herein can be implemented as software encoded on a tangible (non-transitory) computer-readable medium (e.g., disks and/or CDs) having program instructions executing on a computer, hardware, firmware, or a combination thereof. Accordingly this description is to be taken only by way of example and not to otherwise limit the scope of the embodiments herein. Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true spirit and scope of the embodiments herein.