Patent Publication Number: US-2023135151-A1

Title: Persistent metafile used to cache slot header metadata for improved read performance of objects within an object store

Description:
RELATED APPLICATIONS 
     This application claims priority to India Patent Application, titled “UNIFIED OBJECT FORMAT FOR A CLOUD BACKUP SYSTEM AND A HYBRID STORAGE SYSTEM”, filed on Oct. 28, 2021 and accorded Indian Application No.: 202141049373, which is incorporated herein by reference. 
     BACKGROUND 
     A device such as a node may store data on behalf of a client within a volume. The volume may be stored within local storage accessible to the device, such as within on-premise storage. The device may implement storage management functions for the client. For example, the device may create backups of the volume by creating snapshots of the volume. A snapshot of the volume may capture a point-in-time representation of a state of the volume. The device may use the snapshot in order to restore the volume back to a state of the volume at which the snapshot was created. A cloud backup system may be configured to back up the snapshots to an object store for low cost long term retention. A hybrid storage system may utilize storage of the device as a performance tier for actively processing client I/O and the object store an external capacity tier for storing infrequently accessed data. 
    
    
     
       DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a block diagram illustrating an example computing environment in which an embodiment of the present technology may be implemented. 
         FIG.  2    is a block diagram illustrating an example of a network environment with exemplary nodes in accordance with an embodiment of the present technology. 
         FIG.  3    is a block diagram illustrating an example of various components that may be present within a node that may be used in accordance with an embodiment of the present technology. 
         FIG.  4    is an example of a unified object format in accordance with an embodiment of the present technology. 
         FIG.  5    is an example of a unified object format in accordance with an embodiment of the present technology. 
         FIG.  6    is an example of a unified object format in accordance with an embodiment of the present technology. 
         FIG.  7    is a block diagram illustrating an example of implementing a unified object format in accordance with an embodiment of the present technology. 
         FIG.  8    is a flow chart illustrating an example of implementing a unified object format in accordance with an embodiment of the present technology. 
         FIG.  9 A  is a component block diagram illustrating an example system for implementing a unified object format in accordance with an embodiment of the present technology. 
         FIG.  9 B  is a component block diagram illustrating an example system for implementing a repair process in accordance with an embodiment of the present technology. 
         FIG.  10    is an example of a computer readable medium in which an embodiment of the present technology may be implemented. 
     
    
    
     DETAILED DESCRIPTION 
     The techniques described herein are directed to implementing a unified object format that is compatible with both a cloud backup system and a hybrid storage system. The present technology provides a cost effective system for storing and retrieving data. Frequently accessed client data is stored within a performance tier that provides low latency access to the client data. In this way, client I/O operations are executed upon the performance tier for reduced latency and improved client performance for access the client data. Infrequently accessed client data is tiered out from the performance tier to an object store for long term, scalable, low cost storage. In order to reduce the cost and storage consumed by the infrequently accessed client data, the unified object format is used to preserve any compression that was performed upon the client data while in the performance tier. The unified object format is also used to perform additional compression for the client data. Such compression greatly reduces the amount of storage used to store this client data within the object store, which provides substantial cost savings to the client than if uncompressed client data was stored in the object store. 
     The present technology also improve the latency and cost of accessing client data in the object store. In order to read data stored within an object of the object store, an offset and length of where the data is stored within the object must be known. Thus, two read operations must be performed to the object store in order to read the data. One read operation to read the offset and length from the slot header metadata and an additional read operation to read the actual data according to the offset and length. This greatly increases the latency of reading the data and increases the cost of reading the data (e.g., the object store may charge on a per operation basis). In order to reduce this read latency and cost, the slot header metadata is cached at the performance tier within a persistent metafile that can be quickly and locally accessed to obtain the offset and length, which is then used to issue a single read operation to read the data from the object according to the offset and length. 
     The present technology also provides a repair subsystem (a slot header repair subsystem) that can recover from failures, data corruption, data loss, and stale data. The repair subsystem may be implemented for the performance tier. If the persistent metafile experiences data corruption, data loss, or becomes stale, then the repair subsystem executes a repair procedure to repair or replace the persistent metafile so that the client does not experience the increased read latency and/or cost that would otherwise be incurred without the benefit of the persistent metafile. 
     A storage environment may be implemented as a hybrid cloud environment. With the hybrid cloud environment, data may be stored across various types of storage hosted by various storage providers, computing devices, services, and/or nodes. For example, the hybrid cloud environment may include on-premise storage that is implemented as a performance tier of the hybrid cloud environment. The on-premise storage may be comprised of primary storage managed by a node (e.g., data storage device  210 ( 1 ) accessible to disk module  216 ( 1 ) of node  202 ( 1 )), such as disks attached to the node, network attached storage, locally attached storage, memory of the node, etc. The hybrid cloud environment may also comprise storage hosted by a 3 rd  party cloud storage provider (e.g., storage provided by cloud storage nodes  236 ). The storage of the 3 rd  party cloud storage provider (e.g., AWS, Azure, a 3 rd  party hyperscaler, a multi-tenant computing environment, etc.) may be implemented as an object store within which clients may store data in objects. It may be appreciated that a variety of other types of storage (e.g., HDDs, SSD, NVMe storage, etc.) and/or storage provided by other services, nodes, and/or computing devices (e.g., a locally implemented object store implemented on-premise by a node) may be used as storage of the hybrid cloud environment. 
     The cloud backup system may create snapshots of volumes (e.g., volumes of data stored within the on-premise storage) and backup up the snapshots into the hybrid cloud environment, such as into the object store as objects. The hybrid storage system may store client data within the various types of storage of the hybrid cloud environment, such as by storing frequently accessed data within the on-premise storage (e.g., storing frequently accessed data within the faster on-premise storage for fast client access) and infrequency accessed data in the storage of the 3 rd  party cloud storage provider such as in the object store (e.g., store infrequently accessed data within the slower object store for reduced storage costs). The hybrid storage system may tier that data between the various types of storage of the hybrid storage system. 
     Within conventional storage environments that do not have a unified object format, a node may store data in a volume that is hosted within storage such as on-premise storage. The cloud backup system may be capable of creating snapshots of the volume and backing up these snapshots into objects within an object store (e.g., a cloud storage environment). A snapshot, whose data is backed up into the objects, may be used to restore the volume to a prior state captured by the snapshot. These objects may be formatted according to an object format that may be proprietary to the cloud backup system. Thus, other systems such as a hybrid storage system may be unable to interpret this object format and thus is unable to provide functionality for the objects. Similarly, the hybrid storage system may provide data tiering functionality that stores frequently accessed data within a performance tier (e.g., the on-premise storage) and infrequently accessed data within a capacity tier of the object store according to a format that is proprietary to the hybrid storage system. Thus, other systems such as the cloud backup system are unable to provide functionality for the data managed by the hybrid storage system. 
     In addition to the hybrid storage system and the cloud backup system being unable to interpret, access, and/or provide functionality for the same objects due to using different object formats, compression may not be retained when the hybrid storage system tiers data from the performance tier to the capacity tier. In particular, the hybrid storage system may implement a temperature sensitive storage efficiency feature that may compress data in the performance tier according to various compression algorithms as the data becomes colder. In particular, as data is accessed less frequently (becomes colder), larger/heavier compression algorithms may be used to compress the cold data such as by using 32 kb compression. This compressed data may reside in the performance tier, such as an on-premise tier, and results in storage savings. 
     Object store storage may be relatively cheaper than storage of the performance tier, and thus the data may be tiered to the object store in order to reduce cost. Unfortunately, the compression that was performed upon the data while in the performance tier may not be maintained when tiering the data to the object store, and thus any storage savings from compression at the performance tier is lost when the data is tiered to the object store. This is because the current object format of objects used by the hybrid storage system to tier data to the object store may need the data to be stored at fixed offsets within the object, otherwise the data cannot be located. When data is compressed into a compression group, there is no longer a fixed offset for the data because the data is being compressed with other data (e.g., eight 4 kb blocks may be compressed together using 32 kb compression). Thus, when tiering the data to the object store, the data must be decompressed, packed into objects according to the object format propriety at the hybrid storage system, and stored to the object store in an uncompressed state. Thus, all storage savings from compression are lost when the data is tiered to the object store. 
     In order to address these deficiencies, the present technology implements a unified object format that is compatible with both the cloud backup system and the hybrid storage system. This unified object format is used to format objects comprising data tiered out to the object store. With this unified object format, an object comprises slot header information that may be self-contained within the object. The slot header information comprises information per slot within the object and has compression group information about how data is grouped within the object. This information may be used to locate particular blocks of data within objects even if the data has been compressed through compression groups. This information may also be used to selectively retrieve or tier out certain data on-demand from the performance tier to the capacity tier. This allows the hybrid storage system to retrieve select data (e.g., frequently accessed) on-demand from the object store, and allows the cloud backup system to merely tier out remaining data not yet tiered out to the object store by the hybrid storage system. 
     Because the unified object format may be compatible with both the cloud backup system and the hybrid storage system, a whole volume copy to cloud backup to an object store may be provided by the cloud backup system for a volume (e.g., a volume comprising user data) managed by the hybrid storage system. The whole volume copy to cloud backup may be performed by merely tiering what blocks of the volume have not already been tiered by the hybrid storage system to the object store. For example, if the hybrid storage system has tiered 80% of the data in the volume (e.g., relatively infrequently accessed user data) to the object store, then the cloud backup system has to merely transfer the remaining 20% of data in the volume (e.g., mainly metadata) in order to perform and complete the whole volume copy to cloud backup of the volume. That is, when data of a volume is partially tiered to the object store by the hybrid storage system, a backup to the object store of the complete volume by the cloud backup system may be incrementally performed (e.g., backing up merely the 20%) as merely the remaining data of the volume needs to be backed up to the object store and the data already stored in the object store in objects can be skipped. This is enabled because of the common unified object format that both systems can interpret and utilize. 
     The unified object format layout may be used to format objects comprising data being tiered to the object store, such as compressed data. The unified object format layout may be leveraged by both the hybrid storage system and the cloud backup system, along with other products and features, to format objects according to the same unified object format. This enables interplay between the hybrid storage system and the cloud backup system. For example, the hybrid storage system may tier infrequently accessed data to the object store in objects formatted according to the unified object format, while leaving some frequently accessed data and metadata in the performance tier. Subsequently, if all of the data is to be archived to the object store, then the cloud backup system merely copies the remaining data from the performance tier to the object store into objects formatted according to the unified object format. Without this unified object format layout, all of the data would have to be retrieved from the object store by the cloud backup system, reformatted into the object format proprietary to the cloud backup system, and then stored back into the object store. In another example, the cloud backup system may be used to archive a volume to the object store so that all data of the volume is located in the object store in objects formatted according to the unified object format. Subsequently, the hybrid storage system may be used to selectively bring back certain data (e.g., frequently accessed data and metadata) from the object store to the performance tier on-demand so that the data may be readily accessible to clients. 
     Because the unified object format layout enables the ability to locate particular blocks of data that have been compressed into compression groups and stored within objects, compression provided by the performance tier may be retained, along with the ability to perform additional compression. 
     This unified object format layout provides the ability to maintain a file system independent/agnostic format so that data managed by the file system may be interpreted by tools/analytics that run on in the object store. The unified object format layout provides seamless data mobility across different engines/modules, such as a logical replication engine, analytical tools, backup functionality, etc. The unified object format layout may be a self-contained format, which is used to preserve compression savings from the file system at the performance tier and provides flexibility to perform additional compression when tiering data out to the object store. Performing compression while tiering data to the object store improves storage savings at the capacity tier of the object store. This compression during tiering can be performed regarding of whether the data was compressed or not compressed by the file system at the performance tier. This self-contained unified object format enables the ability to decompress and recompress to a bigger compression chunk size such as by using a different (heavier) compression algorithm. The unified object format layout may be optimized to have a smaller metadata footprint for representing compression information. With this unified object format layout, slot header information is self-contained within an object. The slot header information comprises information per slot and has compression group information about how data is grouped in the group. This information is used to locate a compression group comprising a particular block of data. In this way, the slot header information can be used to translate from a slot number to an actual location in an object of a corresponding compression group containing a particular block. Accordingly, the compression group (e.g., a 32kb compression group) can be read from the object, decompressed, and the block can be accessed. 
     In some embodiments, the unified object format reduces the amount of metadata that must be cached within the performance tier to 4 kb (e.g., an  8 x reduction) for data tiered to the object store and improves the read performance on a primary workload. In particular, the proprietary object format used by the cloud backup system may be suitable for backup environments for backing up data to a capacity tier but may not be suitable for use by a file system for primary workloads (e.g., processing read and write operations such as client I/O). The proprietary object format also has metadata information used to track compression group information, but the amount of metadata is around 32 kb for a 4 mb object. This metadata may be around 0.8% of the logical data, which is substantial when a large number of objects are stored in the object store. When the proprietary object format is used in a file system, the metadata for every object needs to be cached/stored in the performance tier. This is to avoid double object store reads. Instead of having to perform two reads to the object store (e.g., a read to access the metadata from the object and a read to access the actual data according to the metadata), the first read goes to cached slot metadata information at the performance tier in order to obtain a location of data in the object in the object store. This first read is a local read within the performance tier and is low cost or overhead. The second read is performed to read the actual data from the object store using the location of the object, and thus there is a single read operation targeting the object store. With metadata consumption around 0.8%, there is too much overhead consumption of expensive storage within the performance tier used to cache such metadata. For example, for a 1000 TB amount of data tiered to the capacity tier of the object store,  8 TB of the performance tier may be consumed for caching metadata. Thus, the proprietary object format cannot be used for primary workloads, and this new unified object format overcomes these issues by reducing metadata used to store data in the performance tier to 4 kb (e.g., an  8 x reduction) and improves the read performance on primary workloads. In some embodiments, the slot header information is cached as a 4 kb space optimized metadata entry within the persistent metafile, which consumes 0.1% storage compared to metadata information used by the proprietary object format. 
     In some embodiments, a workflow for independent and layered compression to the object store is provided. This workflow supports the ability to tier already compressed data in a file system to the object store in objects formatted according to the unified object format and/or may also perform additional compression while sending the compressed data to the object store. This multi-layered compression helps to reduce the storage consumption of the object store and has little to no impact for reads from the file system. The additional compression may correspond to compression independent from the compression performed by the file system, such as compression upon uncompressed blocks of the file system. This enables the ability to compress smaller compression groups into to larger compression groups and/or transform a compression group from being compressed by one compression algorithm to being compressed by a different compression algorithm. The additional compression may occur independently and metadata about the compression groups is self-contained and tracked as part of the object that is being written to the object store. In particular, the slot header information within an object comprises information per slot and has compression group information about how data is grouped. This information is used to locate a compression group comprising a particular block of data. In this way, the slot header information can be used to translate from a slot number to an actual location in an object of a corresponding compression group containing a particular block. 
     In some embodiments, performance may be improved by caching metadata information such as slot header information of objects in the performance tier. Reading a block of data from a compression group stored within an object in the object store using the unified object format layout may result in two read operations, which is inefficient and results in large latency when reading data from the object store and increased cost. Two read operations are performed because the slot header information is read first from an object header of an object in order to identify and locate a corresponding compression group comprising the block to read from the object. A second read operation is then performed to read the actual compression group from the object in order to access the block. Instead of performing two read operations to the object store, which increases latency and cost, a technique is provided for reading a block of data from a compression group stored within an object in the object store using the unified object format layout by performing a single read operation as opposed to two read operations. This is enabled by caching slot header information into a persistent metafile at the performance tier for local and quick access. The persistent metafile is first consulted locally at the performance tier in order to identify an {offset, len} used to access a slot of data in an object targeted by a read operation. The offset and length is then used to perform a single read operation to the object store in order to read the data from the slot of the object. In this way, merely a single read operation to the object store (a cloud computing environment) is performed, and thus improving read performance and reducing client latency. 
     The unified object format layout comes in 3 sections. The first section comprises first level metadata (slot header information) that contains compression group information and the index that point to logical block of compression group—{cg_num, cg_index} for all 1024 slots in the object. The second section contains second level metadata compression group information an optimized form—{cg_num, cg_read_offset, cg_read_length} for all different compression groups in an object. cg_num is a compression group number, cg_read_offset is a read offset of the compression group, and cg_read_length is a length of the compression group to read. The third section comprises compressed data information. Without caching the slot header information, a read to a block includes a first cloud read to get metadata that resides in the object that gives {cg_read_offset, cg_read_length}, and a second read on the object to read the actual contents of the slot using this metadata, which is expensive and impacts the latency and also adds to more object store reads. 
     This technique involves replicating the (optimized) metadata information (slot header information) that is self-contained within an object header of the object to the local/performance tier in the form of a persistent metafile. Before data is tiered out from the performance tier to the object store by the hybrid storage system, the slot header information is constructed at the performance tier as part of the creation of objects to store the data and the tiering of the objects. This slot header information (e.g., 4 kb slot header information) is cached as the persistent metafile at the performance tier for quick access. For each object tiered to the object store, this slot header information is populated within the persistent metafile and can be retrieved from the persistent metafile using an object identifier of an object (e.g., each entry within the persistent metafile may map an object identifier of an object to slot header information of the object). The persistent metafile is small, and thus there is little to no impact due to keeping the persistent metafile in the performance tier. 
     In some embodiments, a repair subsystem (a slot header repair subsystem) is provided to repair the persistent metafile in the performance tier in the event the persistent metafile becomes corrupt, has stale information, or is missing, which would result in incorrect data or no data being read from the object store. The repair subsystem provides the ability to repair the persistent metafile (a slot header metafile) that has been cached at the performance tier for quickly identifying offsets and lengths within objects for accessing particular blocks (a block to read from the object in the object store) stored within compression groups in the objects in the object store. Two different repair mechanisms are provided, and different triggers can be defined and used to trigger repair. Repair can be triggered on the fly in response to encountering an issue. For example, a read operation targeting a block of data in an object tiered to the object store may be received at the performance tier. The persistent metafile may be evaluated using an object identifier of the object to identify an entry for the object comprising information used to identify a corresponding compression group stored within the object and comprising the block (an offset and length to read). As part of evaluating the persistent metafile, a sequence number of the object may be compared with a sequence number of the entry to see if the sequence numbers match. If the sequence numbers do not match, then the entry may be determined to be invalid, and thus a repair of the entry may be triggered. As part of the repair, slot header information may be asynchronously retrieved from the object in the object store and used to repair the entry for the object identifier of the object with the slot header information retrieved from the object. In this way, subsequent read operations to the object will use the repaired entry. 
     The repair subsystem can also be triggered through a background scanner path. The persistent metafile is evaluated to determine the sizes of objects to read from the object store as part of resync path since objects do not have fixed sizes due to storing compressed data as part of the unified object format layout. A similar comparison of sequence numbers of the objects is performed in order to identify any entries (an entry for an object ID where the entry has a different sequence number than a sequence number of the object associated with the object ID) that need to be repaired. Repair can also be triggered during performance of other operations, such as a file system Iron operation (e.g., an operation to check file and directory metadata, scan inodes, and fix file system inconsistencies), garbage collection etc. Repair can also be triggered on demand using a separate tool tailored for proactive repair. That is, doing repair on the fly can be very expensive and lead to performance issues since the slot header information must be retrieved from an object head of an object in the object store in order to repair an entry that can then be used to process a pending read operation. Thus, this tool can be used to scan through all entries within the persistent metafile in order to identify and repair any invalid entries. This can be implemented by a user, implemented based upon a threshold number of repairs being performed on the fly, etc. 
     In some embodiments, a technique is provided to track what compression mechanism was used for an object ID of an object in a persistent manner. In particular, if data has been tiered out to the object store with the object ID as part of a normal write operation, then this is tracked. Thus, a resync operation will skip that object ID so that the data is only written out once for the same path (the normal write operation). Even if there is a restart of a write operation or a resync, the path used to write the data to the object store is tracked. This ensures that when data is written to the object store, there will be no instances where two objects have the same name but different data/sizes. This enables the ability to move data between different object stores (cloud providers) that support different object layouts, while still retaining compression and/or storage savings when possible. 
     As an example, different object store providers (cloud providers) may have different layout requirements, which affects the ability to utilize the same unified object format layout across different object store providers. Some object store providers may have layout requirements that allow for the unified object format layout to be used and shared across the object store providers so that compressed data can still be stored and identified within objects using the previously described techniques regardless of which object store of the object store providers is used to store the objects. But for some cloud providers, data is written out in a different format such as an uncompressed format with defined offsets/boundaries (4 kb boundaries) because better storage savings can be achieved with this fixed format by using compression, deduplication, and/or other storage efficiency functionality provided by a backend of these object store providers. Thus, objects stored through these object store providers have a different format (uncompressed data stored at fixed boundaries) than the unified object format layout, which can be problematic where one of these object store providers is used as either a primary or secondary site and an object store provider that supports the unified object format is used as the other secondary or primary site. 
     Cloud mirroring may be performed from a first cloud provider supporting the unified object format layer (e.g., compressed data in variable sized objects) to a second cloud provider where the unified object format layer is not used (e.g., an object store where only uncompressed data is packaged into objects at defined offsets/boundaries according to a fixed format). In this scenario, the data from the first cloud provider can be retrieved, uncompressed, packaged into the fixed format, and stored into the second cloud provider. However, issues may occur when trying to mirror from the second cloud provider to the first cloud provider. Accordingly, in this reverse scenario, uncompressed data from the second cloud provider may be mirrored to the first cloud provider. When the uncompressed data is read from the second cloud provider, a compression algorithm may be implemented on the fly to compress the data. The compressed data is then stored in objects according to the unified object format layer at the first cloud provider. However, there may not be a way to remember what compression algorithm was used. There could be instances where there is a retry mechanism that retries storing an object to the first cloud provider. If a different compression algorithm is used by the retry mechanism, then there could be two objects with the same name but different data/sizes due to the different compression algorithms. This will violate the invariant that any data written to an object store provider using a same name is to be exactly the same when written out again. If there is no way to ensure that data, written out with the same name from one write to another write, is identical, then data corruption could occur. To address this issue, this technique tracks what compression mechanism was used for an object ID of an object in a persistent manner and what path was used to write the data to the object store. If data has been tiered out to the object store with an object ID as part of a normal write operation, then this normal write path is tracked. Thus, a resync operation will skip that object ID so that the data is only written out once for the same path (the normal write path). Even if there is a restart of a write operation or a resync, the path used to write the data to the object store is tracked. This ensures that when data is written to the object store, there will be no instances where two objects have the same name but different data/sizes. This enables the ability to move data between different object stores (cloud providers) that support different object layouts, while still retaining compression and/or storage savings when possible. 
     Various embodiments of the present technology provide for a wide range of technical effects, advantages, and/or improvements to computing systems and components. For example, various embodiments may include one or more of the following technical effects, advantages, and/or improvements: 1) the ability for both a cloud backup system and a hybrid storage system to interpret a unified object format so that both systems may manage and provide functionality for objects formatted according to the unified object format; 2) the ability to retain compression of data performed by a file system at a performance tier when the data is tiered to an object store in order to reduce storage consumption and cost of storing data in the object store; 3) the ability to provide additional compression for data being tiered to an object store in order to reduce storage consumption and cost of storing data in the object store; 4) caching slot header information within a persistent metafile that can be quickly and locally accessed at the performance tier so that a read for data within an object does not result in 2 read operations to the object store but merely a single read operation that uses information locally acquired from the persistent metafile, thus reducing read latency and improving client performance; 5) a repair subsystem that provides the ability to rebuild and/or the repair the persistent metafile for improved resiliency; and 6) the ability to migrate objects amongst object stores that may support different object format layout requirements, thus allowing clients to migrate data between different object store providers. 
       FIG.  1    is a diagram illustrating an example operating environment  100  in which an embodiment of the techniques described herein may be implemented. In one example, the techniques described herein may be implemented within a client device  128 , such as a laptop, a tablet, a personal computer, a mobile device, a server, a virtual machine, a wearable device, etc. In another example, the techniques described herein may be implemented within one or more nodes, such as a first node  130  and/or a second node  132  within a first cluster  134 , a third node  136  within a second cluster  138 , etc., which may be part of a on-premise, cloud-based, or hybrid storage solution. In some embodiments, the storage provided by the clusters of nodes  134 ,  138  and storage provided by the data storage tier  108  of a distributed computing platform  102  (e.g., a 3 rd  party cloud storage provider that hosts an object storage  124  and/or other types of storage) may be implemented as a hybrid cloud environment used by a cloud backup system  150  and hybrid storage system  158  for store data on behalf of clients, such as the client device  128 . 
     A node may comprise a storage controller, a server, an on-premise device, a virtual machine such as a storage virtual machine, hardware, software, or combination thereof. The one or more nodes may be configured to manage the storage and access to data on behalf of the client device  128  and/or other client devices. In another example, the techniques described herein may be implemented within a distributed computing platform  102  such as a cloud computing environment (e.g., a cloud storage environment, a multi-tenant platform, a hyperscale infrastructure comprising scalable server architectures and virtual networking, etc.) configured to manage the storage and access to data on behalf of client devices and/or nodes. 
     In yet another example, at least some of the techniques described herein are implemented across one or more of the client device  128 , the one or more nodes  130 ,  132 , and/or  136 , and/or the distributed computing platform  102 . For example, the client device  128  may transmit operations, such as data operations to read data and write data and metadata operations (e.g., a create file operation, a rename directory operation, a resize operation, a set attribute operation, etc.), over a network  126  to the first node  130  for implementation by the first node  130  upon storage. 
     The first node  130  may store data associated with the operations within volumes or other data objects/structures hosted within locally attached storage, remote storage hosted by other computing devices accessible over the network  126 , storage provided by the distributed computing platform  102 , etc. The first node  130  may replicate the data and/or the operations to other computing devices, such as to the second node  132 , the third node  136 , a storage virtual machine executing within the distributed computing platform  102 , etc., so that one or more replicas of the data are maintained. For example, the third node  136  may host a destination storage volume that is maintained as a replica of a source storage volume of the first node  130 . Such replicas can be used for disaster recovery and failover. 
     In an embodiment, the techniques described herein are implemented by a storage operating system or are implemented by a separate module that interacts with the storage operating system. The storage operating system may be hosted by the client device,  128 , a node, the distributed computing platform  102 , or across a combination thereof. In some embodiments, the storage operating system may execute within a storage virtual machine, a hyperscaler, or other computing environment. The storage operating system may implement a storage file system to logically organize data within storage devices as one or more storage objects and provide a logical/virtual representation of how the storage objects are organized on the storage devices. 
     A storage object may comprise any logically definable storage element stored by the storage operating system (e.g., a volume stored by the first node  130 , a cloud object stored by the distributed computing platform  102 , etc.). Each storage object may be associated with a unique identifier that uniquely identifies the storage object. For example, a volume may be associated with a volume identifier uniquely identifying that volume from other volumes. The storage operating system also manages client access to the storage objects. 
     The storage operating system may implement a file system for logically organizing data. For example, the storage operating system may implement a write anywhere file layout for a volume where modified data for a file may be written to any available location as opposed to a write-in-place architecture where modified data is written to the original location, thereby overwriting the previous data. In some embodiments, the file system may be implemented through a file system layer that stores data of the storage objects in an on-disk format representation that is block-based (e.g., data is stored within 4 kilobyte blocks and inodes are used to identify files and file attributes such as creation time, access permissions, size and block location, etc.). 
     Deduplication may be implemented by a deduplication module associated with the storage operating system. Deduplication is performed to improve storage efficiency. One type of deduplication is inline deduplication that ensures blocks are deduplicated before being written to a storage device. Inline deduplication uses a data structure, such as an incore hash store, which maps fingerprints of data to data blocks of the storage device storing the data. Whenever data is to be written to the storage device, a fingerprint of that data is calculated and the data structure is looked up using the fingerprint to find duplicates (e.g., potentially duplicate data already stored within the storage device). If duplicate data is found, then the duplicate data is loaded from the storage device and a byte by byte comparison may be performed to ensure that the duplicate data is an actual duplicate of the data to be written to the storage device. If the data to be written is a duplicate of the loaded duplicate data, then the data to be written to disk is not redundantly stored to the storage device. 
     Instead, a pointer or other reference is stored in the storage device in place of the data to be written to the storage device. The pointer points to the duplicate data already stored in the storage device. A reference count for the data may be incremented to indicate that the pointer now references the data. If at some point the pointer no longer references the data (e.g., the deduplicated data is deleted and thus no longer references the data in the storage device), then the reference count is decremented. In this way, inline deduplication is able to deduplicate data before the data is written to disk. This improves the storage efficiency of the storage device. 
     Background deduplication is another type of deduplication that deduplicates data already written to a storage device. Various types of background deduplication may be implemented. In an embodiment of background deduplication, data blocks that are duplicated between files are rearranged within storage units such that one copy of the data occupies physical storage. References to the single copy can be inserted into a file system structure such that all files or containers that contain the data refer to the same instance of the data. 
     Deduplication can be performed on a data storage device block basis. In an embodiment, data blocks on a storage device can be identified using a physical volume block number. The physical volume block number uniquely identifies a particular block on the storage device. Additionally, blocks within a file can be identified by a file block number. The file block number is a logical block number that indicates the logical position of a block within a file relative to other blocks in the file. For example, file block number  0  represents the first block of a file, file block number  1  represents the second block, and the like. File block numbers can be mapped to a physical volume block number that is the actual data block on the storage device. During deduplication operations, blocks in a file that contain the same data are deduplicated by mapping the file block number for the block to the same physical volume block number, and maintaining a reference count of the number of file block numbers that map to the physical volume block number. 
     For example, assume that file block number  0  and file block number  5  of a file contain the same data, while file block numbers  1 - 4  contain unique data. File block numbers  1 - 4  are mapped to different physical volume block numbers. File block number  0  and file block number  5  may be mapped to the same physical volume block number, thereby reducing storage requirements for the file. Similarly, blocks in different files that contain the same data can be mapped to the same physical volume block number. For example, if file block number  0  of file A contains the same data as file block number  3  of file B, file block number  0  of file A may be mapped to the same physical volume block number as file block number  3  of file B. 
     In another example of background deduplication, a changelog is utilized to track blocks that are written to the storage device. Background deduplication also maintains a fingerprint database (e.g., a flat metafile) that tracks all unique block data such as by tracking a fingerprint and other filesystem metadata associated with block data. Background deduplication can be periodically executed or triggered based upon an event such as when the changelog fills beyond a threshold. As part of background deduplication, data in both the changelog and the fingerprint database is sorted based upon fingerprints. This ensures that all duplicates are sorted next to each other. The duplicates are moved to a dup file. 
     The unique changelog entries are moved to the fingerprint database, which will serve as duplicate data for a next deduplication operation. In order to optimize certain filesystem operations needed to deduplicate a block, duplicate records in the dup file are sorted in certain filesystem sematic order (e.g., inode number and block number). Next, the duplicate data is loaded from the storage device and a whole block byte by byte comparison is performed to make sure duplicate data is an actual duplicate of the data to be written to the storage device. After, the block in the changelog is modified to point directly to the duplicate data as opposed to redundantly storing data of the block. 
     In some embodiments, deduplication operations performed by a data deduplication layer of a node can be leveraged for use on another node during data replication operations. For example, the first node  130  may perform deduplication operations to provide for storage efficiency with respect to data stored on a storage volume. The benefit of the deduplication operations performed on first node  130  can be provided to the second node  132  with respect to the data on first node  130  that is replicated to the second node  132 . In some embodiments, a data transfer protocol, referred to as the LRSE (Logical Replication for Storage Efficiency) protocol, can be used as part of replicating consistency group differences from the first node  130  to the second node  132 . 
     In the LRSE protocol, the second node  132  maintains a history buffer that keeps track of data blocks that the second node  132  has previously received. The history buffer tracks the physical volume block numbers and file block numbers associated with the data blocks that have been transferred from first node  130  to the second node  132 . A request can be made of the first node  130  to not transfer blocks that have already been transferred. Thus, the second node  132  can receive deduplicated data from the first node  130 , and will not need to perform deduplication operations on the deduplicated data replicated from first node  130 . 
     In an embodiment, the first node  130  may preserve deduplication of data that is transmitted from first node  130  to the distributed computing platform  102 . For example, the first node  130  may create an object comprising deduplicated data. The object is transmitted from the first node  130  to the distributed computing platform  102  for storage. In this way, the object within the distributed computing platform  102  maintains the data in a deduplicated state. Furthermore, deduplication may be preserved when deduplicated data is transmitted/replicated/mirrored between the client device  128 , the first node  130 , the distributed computing platform  102 , and/or other nodes or devices. 
     In an embodiment, compression may be implemented by a compression module associated with the storage operating system. The compression module may utilize various types of compression techniques to replace longer sequences of data (e.g., frequently occurring and/or redundant sequences) with shorter sequences, such as by using Huffman coding, arithmetic coding, compression dictionaries, etc. For example, an uncompressed portion of a file may comprise “ggggnnnnnnqqqqqqqqqq”, which is compressed to become “4g6n10q”. In this way, the size of the file can be reduced to improve storage efficiency. Compression may be implemented for compression groups. A compression group may correspond to a compressed group of blocks. The compression group may be represented by virtual volume block numbers. The compression group may comprise contiguous or non-contiguous blocks. 
     Compression may be preserved when compressed data is transmitted/replicated/mirrored between the client device  128 , a node, the distributed computing platform  102 , and/or other nodes or devices. For example, an object may be created by the first node  130  to comprise compressed data. The object is transmitted from the first node  130  to the distributed computing platform  102  for storage. In this way, the object within the distributed computing platform  102  maintains the data in a compressed state. 
     In an embodiment, various types of synchronization may be implemented by a synchronization module associated with the storage operating system. In an embodiment, synchronous replication may be implemented, such as between the first node  130  and the second node  132 . It may be appreciated that the synchronization module may implement synchronous replication between any devices within the operating environment  100 , such as between the first node  130  of the first cluster  134  and the third node  136  of the second cluster  138  and/or between a node of a cluster and an instance of a node or virtual machine in the distributed computing platform  102 . 
     As an example, during synchronous replication, the first node  130  may receive a write operation from the client device  128 . The write operation may target a file stored within a volume managed by the first node  130 . The first node  130  replicates the write operation to create a replicated write operation. The first node  130  locally implements the write operation upon the file within the volume. The first node  130  also transmits the replicated write operation to a synchronous replication target, such as the second node  132  that maintains a replica volume as a replica of the volume maintained by the first node  130 . The second node  132  will execute the replicated write operation upon the replica volume so that file within the volume and the replica volume comprises the same data. After, the second node  132  will transmit a success message to the first node  130 . With synchronous replication, the first node  130  does not respond with a success message to the client device  128  for the write operation until both the write operation is executed upon the volume and the first node  130  receives the success message that the second node  132  executed the replicated write operation upon the replica volume. 
     In another example, asynchronous replication may be implemented, such as between the first node  130  and the third node  136 . It may be appreciated that the synchronization module may implement asynchronous replication between any devices within the operating environment  100 , such as between the first node  130  of the first cluster  134  and the distributed computing platform  102 . In an embodiment, the first node  130  may establish an asynchronous replication relationship with the third node  136 . The first node  130  may capture a baseline snapshot of a first volume as a point in time representation of the first volume. The first node  130  may utilize the baseline snapshot to perform a baseline transfer of the data within the first volume to the third node  136  in order to create a second volume within the third node  136  comprising data of the first volume as of the point in time at which the baseline snapshot was created. 
     After the baseline transfer, the first node  130  may subsequently create snapshots of the first volume over time. As part of asynchronous replication, an incremental transfer is performed between the first volume and the second volume. In particular, a snapshot of the first volume is created. The snapshot is compared with a prior snapshot that was previously used to perform the last asynchronous transfer (e.g., the baseline transfer or a prior incremental transfer) of data to identify a difference in data of the first volume between the snapshot and the prior snapshot (e.g., changes to the first volume since the last asynchronous transfer). Accordingly, the difference in data is incrementally transferred from the first volume to the second volume. In this way, the second volume will comprise the same data as the first volume as of the point in time when the snapshot was created for performing the incremental transfer. It may be appreciated that other types of replication may be implemented, such as semi-sync replication. 
     In an embodiment, the first node  130  may store data or a portion thereof within storage hosted by the distributed computing platform  102  by transmitting the data within objects to the distributed computing platform  102 . In one example, the first node  130  may locally store frequently accessed data within locally attached storage. Less frequently accessed data may be transmitted to the distributed computing platform  102  for storage within a data storage tier  108 . The data storage tier  108  may store data within a service data store  120 , and may store client specific data within client data stores assigned to such clients such as a client ( 1 ) data store  122  used to store data of a client ( 1 ) and a client (N) data store  124  used to store data of a client (N). The data stores may be physical storage devices or may be defined as logical storage, such as a virtual volume, LUNs, or other logical organizations of data that can be defined across one or more physical storage devices. In another example, the first node  130  transmits and stores all client data to the distributed computing platform  102 . In yet another example, the client device  128  transmits and stores the data directly to the distributed computing platform  102  without the use of the first node  130 . 
     The management of storage and access to data can be performed by one or more storage virtual machines (SVMs) or other storage applications that provide software as a service (SaaS) such as storage software services. In one example, an SVM may be hosted within the client device  128 , within the first node  130 , or within the distributed computing platform  102  such as by the application server tier  106 . In another example, one or more SVMs may be hosted across one or more of the client device  128 , the first node  130 , and the distributed computing platform  102 . The one or more SVMs may host instances of the storage operating system. 
     In an embodiment, the storage operating system may be implemented for the distributed computing platform  102 . The storage operating system may allow client devices to access data stored within the distributed computing platform  102  using various types of protocols, such as a Network File System (NFS) protocol, a Server Message Block (SMB) protocol and Common Internet File System (CIFS), and Internet Small Computer Systems Interface (iSCSI), and/or other protocols. The storage operating system may provide various storage services, such as disaster recovery (e.g., the ability to non-disruptively transition client devices from accessing a primary node that has failed to a secondary node that is taking over for the failed primary node), backup and archive function, replication such as asynchronous and/or synchronous replication, deduplication, compression, high availability storage, cloning functionality (e.g., the ability to clone a volume, such as a space efficient flex clone), snapshot functionality (e.g., the ability to create snapshots and restore data from snapshots), data tiering (e.g., migrating infrequently accessed data to slower/cheaper storage), encryption, managing storage across various platforms such as between on-premise storage systems and multiple cloud systems, etc. 
     In one example of the distributed computing platform  102 , one or more SVMs may be hosted by the application server tier  106 . For example, a server ( 1 )  116  is configured to host SVMs used to execute applications such as storage applications that manage the storage of data of the client ( 1 ) within the client ( 1 ) data store  122 . Thus, an SVM executing on the server ( 1 )  116  may receive data and/or operations from the client device  128  and/or the first node  130  over the network  126 . The SVM executes a storage application and/or an instance of the storage operating system to process the operations and/or store the data within the client ( 1 ) data store  122 . The SVM may transmit a response back to the client device  128  and/or the first node  130  over the network  126 , such as a success message or an error message. In this way, the application server tier  106  may host SVMs, services, and/or other storage applications using the server ( 1 )  116 , the server (N)  118 , etc. 
     A user interface tier  104  of the distributed computing platform  102  may provide the client device  128  and/or the first node  130  with access to user interfaces associated with the storage and access of data and/or other services provided by the distributed computing platform  102 . In an embodiment, a service user interface  110  may be accessible from the distributed computing platform  102  for accessing services subscribed to by clients and/or nodes, such as data replication services, application hosting services, data security services, human resource services, warehouse tracking services, accounting services, etc. For example, client user interfaces may be provided to corresponding clients, such as a client ( 1 ) user interface  112 , a client (N) user interface  114 , etc. The client ( 1 ) can access various services and resources subscribed to by the client ( 1 ) through the client ( 1 ) user interface  112 , such as access to a web service, a development environment, a human resource application, a warehouse tracking application, and/or other services and resources provided by the application server tier  106 , which may use data stored within the data storage tier  108 . 
     The client device  128  and/or the first node  130  may subscribe to certain types and amounts of services and resources provided by the distributed computing platform  102 . For example, the client device  128  may establish a subscription to have access to three virtual machines, a certain amount of storage, a certain type/amount of data redundancy, a certain type/amount of data security, certain service level agreements (SLAs) and service level objectives (SLOs), latency guarantees, bandwidth guarantees, access to execute or host certain applications, etc. Similarly, the first node  130  can establish a subscription to have access to certain services and resources of the distributed computing platform  102 . 
     As shown, a variety of clients, such as the client device  128  and the first node  130 , incorporating and/or incorporated into a variety of computing devices may communicate with the distributed computing platform  102  through one or more networks, such as the network  126 . For example, a client may incorporate and/or be incorporated into a client application (e.g., software) implemented at least in part by one or more of the computing devices. 
     Examples of suitable computing devices include personal computers, server computers, desktop computers, nodes, storage servers, nodes, laptop computers, notebook computers, tablet computers or personal digital assistants (PDAs), smart phones, cell phones, and consumer electronic devices incorporating one or more computing device components, such as one or more electronic processors, microprocessors, central processing units (CPU), or controllers. Examples of suitable networks include networks utilizing wired and/or wireless communication technologies and networks operating in accordance with any suitable networking and/or communication protocol (e.g., the Internet). In use cases involving the delivery of customer support services, the computing devices noted represent the endpoint of the customer support delivery process, i.e., the consumer&#39;s device. 
     The distributed computing platform  102 , such as a multi-tenant business data processing platform or cloud computing environment, may include multiple processing tiers, including the user interface tier  104 , the application server tier  106 , and a data storage tier  108 . The user interface tier  104  may maintain multiple user interfaces, including graphical user interfaces and/or web-based interfaces. The user interfaces may include the service user interface  110  for a service to provide access to applications and data for a client (e.g., a “tenant”) of the service, as well as one or more user interfaces that have been specialized/customized in accordance with user specific requirements (e.g., as discussed above), which may be accessed via one or more APIs. 
     The service user interface  110  may include components enabling a tenant to administer the tenant&#39;s participation in the functions and capabilities provided by the distributed computing platform  102 , such as accessing data, causing execution of specific data processing operations, etc. Each processing tier may be implemented with a set of computers, virtualized computing environments such as a storage virtual machine or storage virtual server, and/or computer components including computer servers and processors, and may perform various functions, methods, processes, or operations as determined by the execution of a software application or set of instructions. 
     The data storage tier  108  may include one or more data stores, which may include the service data store  120  and one or more client data stores  122 - 124 . Each client data store may contain tenant-specific data that is used as part of providing a range of tenant-specific business and storage services or functions, including but not limited to ERP, CRM, eCommerce, Human Resources management, payroll, storage services, etc. Data stores may be implemented with any suitable data storage technology, including structured query language (SQL) based relational database management systems (RDBMS), file systems hosted by operating systems, object storage, etc. 
     In accordance with one embodiment of the present technology, the distributed computing platform  102  may be a multi-tenant and service platform operated by an entity in order to provide multiple tenants with a set of business-related applications, data storage, and functionality. These applications and functionality may include ones that a business uses to manage various aspects of its operations. For example, the applications and functionality may include providing web-based access to business information systems, thereby allowing a user with a browser and an Internet or intranet connection to view, enter, process, or modify certain types of business information or any other type of information. 
     A clustered network environment  200  that may implement one or more aspects of the techniques described and illustrated herein is shown in  FIG.  2   . The clustered network environment  200  includes data storage apparatuses  202 ( 1 )- 202 ( n ) that are coupled over a cluster or cluster fabric  204  that includes one or more communication network(s) and facilitates communication between the data storage apparatuses  202 ( 1 )- 202 ( n ) (and one or more modules, components, etc. therein, such as, nodes  206 ( 1 )- 206 ( n ), for example), although any number of other elements or components can also be included in the clustered network environment  200  in other examples. This technology provides a number of advantages including methods, non-transitory computer readable media, and computing devices that implement the techniques described herein. 
     In this example, nodes  206 ( 1 )- 206 ( n ) can be primary or local storage controllers or secondary or remote storage controllers that provide client devices  208 ( 1 )- 208 ( n ) with access to data stored within data storage devices  210 ( 1 )- 210 ( n ) and cloud storage device(s)  236  (also referred to as cloud storage node(s)). The nodes  206 ( 1 )- 206 ( n ) may be implemented as hardware, software (e.g., a storage virtual machine), or combination thereof. 
     In some embodiments, the data storage devices  210 ( 1 )- 210 ( n ) and the cloud storage device(s)  236  (e.g., storage of a 3 rd  party cloud storage provider used to host an object storage  124 ) may be implemented as a hybrid cloud environment used by a cloud backup system  150  and hybrid storage system  158  for store data on behalf of clients, such as client devices  212 ( 1 )- 212 ( n ). 
     The data storage apparatuses  202 ( 1 )- 202 ( n ) and/or nodes  206 ( 1 )- 206 ( n ) of the examples described and illustrated herein are not limited to any particular geographic areas and can be clustered locally and/or remotely via a cloud network, or not clustered in other examples. Thus, in one example the data storage apparatuses  202 ( 1 )- 202 ( n ) and/or node computing device  206 ( 1 )- 206 ( n ) can be distributed over a plurality of storage systems located in a plurality of geographic locations (e.g., located on-premise, located within a cloud computing environment, etc.); while in another example a clustered network can include data storage apparatuses  202 ( 1 )- 202 ( n ) and/or node computing device  206 ( 1 )- 206 ( n ) residing in a same geographic location (e.g., in a single on-site rack). 
     In the illustrated example, one or more of the client devices  208 ( 1 )- 208 ( n ), which may be, for example, personal computers (PCs), computing devices used for storage (e.g., storage servers), or other computers or peripheral devices, are coupled to the respective data storage apparatuses  202 ( 1 )- 202 ( n ) by network connections  212 ( 1 )- 212 ( n ). Network connections  212 ( 1 )- 212 ( n ) may include a local area network (LAN) or wide area network (WAN) (i.e., a cloud network), for example, that utilize TCP/IP and/or one or more Network Attached Storage (NAS) protocols, such as a Common Internet Filesystem (CIFS) protocol or a Network Filesystem (NFS) protocol to exchange data packets, a Storage Area Network (SAN) protocol, such as Small Computer System Interface (SCSI) or Fiber Channel Protocol (FCP), an object protocol, such as simple storage service (S 3 ), and/or non-volatile memory express (NVMe), for example. 
     Illustratively, the client devices  208 ( 1 )- 208 ( n ) may be general-purpose computers running applications and may interact with the data storage apparatuses  202 ( 1 )- 202 ( n ) using a client/server model for exchange of information. That is, the client devices  208 ( 1 )- 208 ( n ) may request data from the data storage apparatuses  202 ( 1 )- 202 ( n ) (e.g., data on one of the data storage devices  210 ( 1 )- 210 ( n ) managed by a network storage controller configured to process I/O commands issued by the client devices  208 ( 1 )- 208 ( n )), and the data storage apparatuses  202 ( 1 )- 202 ( n ) may return results of the request to the client devices  208 ( 1 )- 208 ( n ) via the network connections  212 ( 1 )- 212 ( n ). 
     The nodes  206 ( 1 )- 206 ( n ) of the data storage apparatuses  202 ( 1 )- 202 ( n ) can include network or host nodes that are interconnected as a cluster to provide data storage and management services, such as to an enterprise having remote locations, cloud storage (e.g., a storage endpoint may be stored within cloud storage device(s)  236 ), etc., for example. Such nodes  206 ( 1 )- 206 ( n ) can be attached to the cluster fabric  204  at a connection point, redistribution point, or communication endpoint, for example. One or more of the nodes  206 ( 1 )- 206 ( n ) may be capable of sending, receiving, and/or forwarding information over a network communications channel, and could comprise any type of device that meets any or all of these criteria. 
     In an embodiment, the nodes  206 ( 1 ) and  206 ( n ) may be configured according to a disaster recovery configuration whereby a surviving node provides switchover access to the data storage devices  210 ( 1 )- 210 ( n ) in the event a disaster occurs at a disaster storage site (e.g., the node computing device  206 ( 1 ) provides client device  212 ( n ) with switchover data access to data storage devices  210 ( n ) in the event a disaster occurs at the second storage site). In other examples, the node computing device  206 ( n ) can be configured according to an archival configuration and/or the nodes  206 ( 1 )- 206 ( n ) can be configured based on another type of replication arrangement (e.g., to facilitate load sharing). Additionally, while two nodes are illustrated in  FIG.  2   , any number of nodes or data storage apparatuses can be included in other examples in other types of configurations or arrangements. 
     As illustrated in the clustered network environment  200 , nodes  206 ( 1 )- 206 ( n ) can include various functional components that coordinate to provide a distributed storage architecture. For example, the nodes  206 ( 1 )- 206 ( n ) can include network modules  214 ( 1 )- 214 ( n ) and disk modules  216 ( 1 )- 216 ( n ). Network modules  214 ( 1 )- 214 ( n ) can be configured to allow the nodes  206 ( 1 )- 206 ( n ) (e.g., network storage controllers) to connect with client devices  208 ( 1 )- 208 ( n ) over the storage network connections  212 ( 1 )- 212 ( n ), for example, allowing the client devices  208 ( 1 )- 208 ( n ) to access data stored in the clustered network environment  200 . 
     Further, the network modules  214 ( 1 )- 214 ( n ) can provide connections with one or more other components through the cluster fabric  204 . For example, the network module  214 ( 1 ) of node computing device  206 ( 1 ) can access the data storage device  210 ( n ) by sending a request via the cluster fabric  204  through the disk module  216 ( n ) of node computing device  206 ( n ) when the node computing device  206 ( n ) is available. Alternatively, when the node computing device  206 ( n ) fails, the network module  214 ( 1 ) of node computing device  206 ( 1 ) can access the data storage device  210 ( n ) directly via the cluster fabric  204 . The cluster fabric  204  can include one or more local and/or wide area computing networks (i.e., cloud networks) embodied as Infiniband, Fibre Channel (FC), or Ethernet networks, for example, although other types of networks supporting other protocols can also be used. 
     Disk modules  216 ( 1 )- 216 ( n ) can be configured to connect data storage devices  210 ( 1 )- 210 ( n ), such as disks or arrays of disks, SSDs, flash memory, or some other form of data storage, to the nodes  206 ( 1 )- 206 ( n ). Often, disk modules  216 ( 1 )- 216 ( n ) communicate with the data storage devices  210 ( 1 )- 210 ( n ) according to the SAN protocol, such as SCSI or FCP, for example, although other protocols can also be used. Thus, as seen from an operating system on nodes  206 ( 1 )- 206 ( n ), the data storage devices  210 ( 1 )- 210 ( n ) can appear as locally attached. In this manner, different nodes  206 ( 1 )- 206 ( n ), etc. may access data blocks, files, or objects through the operating system, rather than expressly requesting abstract files. 
     While the clustered network environment  200  illustrates an equal number of network modules  214 ( 1 )- 214 ( n ) and disk modules  216 ( 1 )- 216 ( n ), other examples may include a differing number of these modules. For example, there may be a plurality of network and disk modules interconnected in a cluster that do not have a one-to-one correspondence between the network and disk modules. That is, different nodes can have a different number of network and disk modules, and the same node computing device can have a different number of network modules than disk modules. 
     Further, one or more of the client devices  208 ( 1 )- 208 ( n ) can be networked with the nodes  206 ( 1 )- 206 ( n ) in the cluster, over the storage connections  212 ( 1 )- 212 ( n ). As an example, respective client devices  208 ( 1 )- 208 ( n ) that are networked to a cluster may request services (e.g., exchanging of information in the form of data packets) of nodes  206 ( 1 )- 206 ( n ) in the cluster, and the nodes  206 ( 1 )- 206 ( n ) can return results of the requested services to the client devices  208 ( 1 )- 208 ( n ). In one example, the client devices  208 ( 1 )- 208 ( n ) can exchange information with the network modules  214 ( 1 )- 214 ( n ) residing in the nodes  206 ( 1 )- 206 ( n ) (e.g., network hosts) in the data storage apparatuses  202 ( 1 )- 202 ( n ). 
     In one example, the storage apparatuses  202 ( 1 )- 202 ( n ) host aggregates corresponding to physical local and remote data storage devices, such as local flash or disk storage in the data storage devices  210 ( 1 )- 210 ( n ), for example. One or more of the data storage devices  210 ( 1 )- 210 ( n ) can include mass storage devices, such as disks of a disk array. The disks may comprise any type of mass storage devices, including but not limited to magnetic disk drives, flash memory, and any other similar media adapted to store information, including, for example, data and/or parity information. 
     The aggregates include volumes  218 ( 1 )- 218 ( n ) in this example, although any number of volumes can be included in the aggregates. The volumes  218 ( 1 )- 218 ( n ) are virtual data stores or storage objects that define an arrangement of storage and one or more filesystems within the clustered network environment  200 . Volumes  218 ( 1 )- 218 ( n ) can span a portion of a disk or other storage device, a collection of disks, or portions of disks, for example, and typically define an overall logical arrangement of data storage. In one example, volumes  218 ( 1 )- 218 ( n ) can include stored user data as one or more files, blocks, or objects that may reside in a hierarchical directory structure within the volumes  218 ( 1 )- 218 ( n ). 
     Volumes  218 ( 1 )- 218 ( n ) are typically configured in formats that may be associated with particular storage systems, and respective volume formats typically comprise features that provide functionality to the volumes  218 ( 1 )- 218 ( n ), such as providing the ability for volumes  218 ( 1 )- 218 ( n ) to form clusters, among other functionality. Optionally, one or more of the volumes  218 ( 1 )- 218 ( n ) can be in composite aggregates and can extend between one or more of the data storage devices  210 ( 1 )- 210 ( n ) and one or more of the cloud storage device(s)  236  to provide tiered storage, for example, and other arrangements can also be used in other examples. 
     In one example, to facilitate access to data stored on the disks or other structures of the data storage devices  210 ( 1 )- 210 ( n ), a filesystem may be implemented that logically organizes the information as a hierarchical structure of directories and files. In this example, respective files may be implemented as a set of disk blocks of a particular size that are configured to store information, whereas directories may be implemented as specially formatted files in which information about other files and directories are stored. 
     Data can be stored as files or objects within a physical volume and/or a virtual volume, which can be associated with respective volume identifiers. The physical volumes correspond to at least a portion of physical storage devices, such as the data storage devices  210 ( 1 )- 210 ( n ) (e.g., a Redundant Array of Independent (or Inexpensive) Disks (RAID system)) whose address, addressable space, location, etc. does not change. Typically, the location of the physical volumes does not change in that the range of addresses used to access it generally remains constant. 
     Virtual volumes, in contrast, can be stored over an aggregate of disparate portions of different physical storage devices. Virtual volumes may be a collection of different available portions of different physical storage device locations, such as some available space from disks, for example. It will be appreciated that since the virtual volumes are not “tied” to any one particular storage device, virtual volumes can be said to include a layer of abstraction or virtualization, which allows it to be resized and/or flexible in some regards. 
     Further, virtual volumes can include one or more logical unit numbers (LUNs), directories, Qtrees, files, and/or other storage objects, for example. Among other things, these features, but more particularly the LUNs, allow the disparate memory locations within which data is stored to be identified, for example, and grouped as data storage unit. As such, the LUNs may be characterized as constituting a virtual disk or drive upon which data within the virtual volumes is stored within an aggregate. For example, LUNs are often referred to as virtual drives, such that they emulate a hard drive, while they actually comprise data blocks stored in various parts of a volume. 
     In one example, the data storage devices  210 ( 1 )- 210 ( n ) can have one or more physical ports, wherein each physical port can be assigned a target address (e.g., SCSI target address). To represent respective volumes, a target address on the data storage devices  210 ( 1 )- 210 ( n ) can be used to identify one or more of the LUNs. Thus, for example, when one of the nodes  206 ( 1 )- 206 ( n ) connects to a volume, a connection between the one of the nodes  206 ( 1 )- 206 ( n ) and one or more of the LUNs underlying the volume is created. 
     Respective target addresses can identify multiple of the LUNs, such that a target address can represent multiple volumes. The I/O interface, which can be implemented as circuitry and/or software in a storage adapter or as executable code residing in memory and executed by a processor, for example, can connect to volumes by using one or more addresses that identify the one or more of the LUNs. 
     Referring to  FIG.  3   , node computing device  206 ( 1 ) in this particular example includes processor(s)  300 , a memory  302 , a network adapter  304 , a cluster access adapter  306 , and a storage adapter  308  interconnected by a system bus  310 . In other examples, the node computing device  206 ( 1 ) comprises a virtual machine, such as a virtual storage machine. The node computing device  206 ( 1 ) also includes a storage operating system  312  installed in the memory  302  that can, for example, implement a RAID data loss protection and recovery scheme to optimize reconstruction of data of a failed disk or drive in an array, along with other functionality such as deduplication, compression, snapshot creation, data mirroring, synchronous replication, asynchronous replication, encryption, etc. In some examples, the node computing device  206 ( n ) is substantially the same in structure and/or operation as node computing device  206 ( 1 ), although the node computing device  206 ( n ) can also include a different structure and/or operation in one or more aspects than the node computing device  206 ( 1 ). In some embodiments, programming code of the cloud backup system  150  and/or the hybrid storage system  158  may be stored within memory  302  and executed by the processors  300 . 
     The network adapter  304  in this example includes the mechanical, electrical and signaling circuitry needed to connect the node computing device  206 ( 1 ) to one or more of the client devices  208 ( 1 )- 208 ( n ) over network connections  212 ( 1 )- 212 ( n ), which may comprise, among other things, a point-to-point connection or a shared medium, such as a local area network. In some examples, the network adapter  304  further communicates (e.g., using TCP/IP) via the cluster fabric  204  and/or another network (e.g., a WAN) (not shown) with cloud storage device(s)  236  to process storage operations associated with data stored thereon. 
     The storage adapter  308  cooperates with the storage operating system  312  executing on the node computing device  206 ( 1 ) to access information requested by one of the client devices  208 ( 1 )- 208 ( n ) (e.g., to access data on a data storage device  210 ( 1 )- 210 ( n ) managed by a network storage controller). The information may be stored on any type of attached array of writeable media such as magnetic disk drives, flash memory, and/or any other similar media adapted to store information. 
     In the exemplary data storage devices  210 ( 1 )- 210 ( n ), information can be stored in data blocks on disks. The storage adapter  308  can include I/O interface circuitry that couples to the disks over an I/O interconnect arrangement, such as a storage area network (SAN) protocol (e.g., Small Computer System Interface (SCSI), Internet SCSI (iSCSI), hyperSCSI, Fiber Channel Protocol (FCP)). The information is retrieved by the storage adapter  308  and, if necessary, processed by the processor(s)  300  (or the storage adapter  308  itself) prior to being forwarded over the system bus  310  to the network adapter  304  (and/or the cluster access adapter  306  if sending to another node computing device in the cluster) where the information is formatted into a data packet and returned to a requesting one of the client devices  208 ( 1 )- 208 ( 2 ) and/or sent to another node computing device attached via the cluster fabric  204 . In some examples, a storage driver  314  in the memory  302  interfaces with the storage adapter to facilitate interactions with the data storage devices  210 ( 1 )- 210 ( n ). 
     The storage operating system  312  can also manage communications for the node computing device  206 ( 1 ) among other devices that may be in a clustered network, such as attached to a cluster fabric  204 . Thus, the node computing device  206 ( 1 ) can respond to client device requests to manage data on one of the data storage devices  210 ( 1 )- 210 ( n ) or cloud storage device(s)  236  (e.g., or additional clustered devices) in accordance with the client device requests. 
     The file system module  318  of the storage operating system  312  can establish and manage one or more filesystems including software code and data structures that implement a persistent hierarchical namespace of files and directories, for example. As an example, when a new data storage device (not shown) is added to a clustered network system, the file system module  318  is informed where, in an existing directory tree, new files associated with the new data storage device are to be stored. This is often referred to as “mounting” a filesystem. 
     In the example node computing device  206 ( 1 ), memory  302  can include storage locations that are addressable by the processor(s)  300  and adapters  304 ,  306 , and  308  for storing related software application code and data structures. The processor(s)  300  and adapters  304 ,  306 , and  308  may, for example, include processing elements and/or logic circuitry configured to execute the software code and manipulate the data structures. 
     The storage operating system  312 , portions of which are typically resident in the memory  302  and executed by the processor(s)  300 , invokes storage operations in support of a file service implemented by the node computing device  206 ( 1 ). Other processing and memory mechanisms, including various computer readable media, may be used for storing and/or executing application instructions pertaining to the techniques described and illustrated herein. For example, the storage operating system  312  can also utilize one or more control files (not shown) to aid in the provisioning of virtual machines. 
     In this particular example, the memory  302  also includes a module configured to implement the techniques described herein, as discussed above and further below. 
     The examples of the technology described and illustrated herein may be embodied as one or more non-transitory computer or machine readable media, such as the memory  302 , having machine or processor-executable instructions stored thereon for one or more aspects of the present technology, which when executed by processor(s), such as processor(s)  300 , cause the processor(s) to carry out the steps necessary to implement the methods of this technology, as described and illustrated with the examples herein. In some examples, the executable instructions are configured to perform one or more steps of a method described and illustrated later. 
     In some embodiments of implementing a unified object format, the unified object format overcomes the following disadvantage of the proprietary object formats used by the hybrid storage system and/or the cloud backup system. A proprietary object format used by a file system at a performance tier to tier blocks (e.g., a propriety object format of the hybrid storage system) has the following limitations: 1) the data that is compressed in the file system may not be preserved and requires decompression while sending the data to the object store as objects; and 2) there is no ability to improve storage consumption savings by otherwise being able to do additional compression on the data during tier and/or recompressing already compressed blocks with a different heavier compression algorithm. These limitations of the proprietary object format used by the file system reduces the scope on the amount of capacity tier storage consumption savings in the object store. These issues occur because the block information of the data in the performance tier is stored at the fixed offsets. When the data is compressed, there is no fixed offset for the data, which would be needed for compression with other blocks. Accordingly, the compressed blocks of the file system must be uncompressed, thus losing the storage savings when being sent to capacity tier. As an example, v 1 , v 2 , v 3 , v 4  are blocks that are not compressed by file system (e.g., virtual volume block number  1  (v 1 ), virtual volume block number  2  (v 2 ), etc.). These blocks are written in an uncompressed form to physical blocks P 1 , P 2 , P 3  and P 4  respectively (e.g., physical volume block number  1  (P 1 ), physical volume block number  2  (P 2 ), etc.). In an example, v 5  and v 6  are compressed to a P 5  block, and v 7  and v 8  are compressed to a P 7  block. v 9 , v 10 , v 11 , v 12 , v 13 , v 14 , v 15 , and v 16  are compressed to  2  blocks P 9  and P 10 . As the proprietary object format does not represent the compressed data, the data is sent to the object store in an uncompressed form. 
     In order to overcome these limitations, this unified object format  400  is introduced, an example of which is illustrated by  FIG.  4   . The unified object format  400  may be divided into  3  sections, such as a first section  402 , a second section  404 , and a third section. The first section  402  comprises a first level metadata section information. The first level metadata section information of the first section  402  (first metadata part) comprises slot information  408  that gives information about a block, such as to which compression group ‘number’  410  a slot comprising the block belongs and a logical index  412  in that compression group ‘number’ of the block. The first level metadata section information of the first section  402  (first metadata part) may be of the format of &lt;cg_num, cg_index&gt;. The second section comprises a second metadata part. The second section  404  (second metadata part) may comprise all the compression group&#39;s information  414  and maintains a mapping or location (location information  416 ) to the data portion  418  of the object in optimized form (e.g., an offset and a length of the data portion  418 ). The second section  404  (second metadata part) may be in a format of &lt;offset, len&gt;. The third section  406  is a data section that contains the actual data portion  418 , such as the actual compressed data. 
     The first level metadata section information of the first section  402  (first metadata part) may be part of the on-disk format layout that tracks a list of all the object blocks that belongs to the object. The first level metadata section information of the first section  402  (first metadata part) contains the mapping of the object block number to the compression group to which the object block number belongs and a logical block number in that compression group. For example, first level metadata section information of the first section  402  (first metadata part) may comprise &lt;cg_num= 1 , cg_index= 1 &gt;, &lt;cg_num= 1 , cg_index= 2 &gt;, &lt;cg_num= 2 , cg_index= 1 &gt;, . . . . The second section  404  (second metadata part) may contain information  414  about the compression group, which has location information  416  such &lt;offset, len&gt;for the data section that needs to be read. For example, the second section  404  (second metadata part) may comprise &lt;offset=10K, len=2K&gt;, &lt;offset=12, len=2K&gt;, . . . . The independent compression group metadata information within the object layout helps to perform independent compression and is transparent to the file system of the performance tier. In this way,  FIG.  4    illustrates an example of the unified object format  400  comprising the first section  402  (first metadata part), the second section  404  (second metadata part), and the third section  406  (data section). 
     Referring to example  500  of  FIG.  5   , the unified object format  506  has the ability to retain the compressed data of the file system. The file system may maintain file system logical blocks  502 , such as V 1 -V 16 . Data of the file system logical blocks may be physically stored as file system physical blocks  504  on storage, such as P 9  and P 10 . The unified object format  506  is used to copy of the compressed data of the file system to the object store according to an object layout (without any decompression and recompression) and update the metadata sections of the object layout. The file system logical blocks V 9  to V 16  may be compressed into file system physical blocks P 9  and P 10 . In the unified object format  506 , the data from file system physical blocks P 9  and P 10  may be copied into an object&#39;s data section  507 . A second section  508  (second metadata part) of “compression group” information is updated with &lt;offset, len=8K&gt;. Each object slot information  509  is updated with corresponding compression group number (cg_num) and logical index in the compression group number. Assuming cg_num is 1 and represents 32 KB&#39;s compressed data which is 8 KB, and all the file system logical blocks V 9  to V 16  are in that order for compression. In this example, the object slot information  509  will be &lt;1,1&gt;,&lt;1,2&gt;&lt;1,3&gt;,&lt;1,4&gt;,&lt;1,5&gt;,&lt;1,6&gt;&lt;1,7&gt;,&lt;1,8&gt;. 
     Referring to example  600  of  FIG.  6   , unified object format  608  has ability to perform additional compression of the file system data, such as upon the file system logical blocks  602  and/or the file system physical blocks  604 . The additional compression comes in 2 forms: 1) performing compression on blocks that are not tiered or were not compressed and 2) performing recompression of small compression groups to create bigger compression groups, which can be done with a different heavier compression algorithm. The different types of blocks of the file system are: file system logical blocks V 1  to V 4  that are uncompressed blocks; file system logical blocks V 5  and V 6  that are compressed to file system physical blocks P 5 , and file system logical blocks V 7  and V 8  that are compressed to file system physical blockP 7 . In this way, file system logical blocks  602  are stored within file system physical blocks  604 . In the unified object format  608 , the data of file system physical blocks P 5  and P 7  is decompressed and recompressed with the data of the uncompressed blocks of file system logical blocks V 1  to V 4  to create file system physical blocks  606  P 11  and P 12  that have additional compression. A second section  609  (second metadata part) of “compression group” information is updated with &lt;offset, len&gt;. Each object slot information  610  is updated with corresponding compression group number (cg_num) and logical index in the compression group number. Assuming cg_num is 2 and all file system logical blocks V 1  to V 8  are in that order for compression, then the object slot information  610  will be &lt;2,1&gt;,&lt;2,2&gt;&lt;2,3&gt;,&lt;2,4&gt;,&lt;2,5&gt;,&lt;2,6&gt;&lt;2,7&gt;,&lt;2,8&gt;. 
     The unified object format supports the ability to retain the compressed data of the file system (and also perform additional compression) by providing the additional metadata section in the layout along with the data section. However, this forces the file system to perform two read operations to the object store (cloud) in order to process a single read operation for data stored within an object in the object store. In the first cloud read operation, the metadata section (slot header information) is checked to identify a location of the data within the object. In the second cloud read operation, the actual data is read based on the location information obtained by the first cloud read operation. The double cloud read operations result in performance issues and impacts the latency of the read operation. To address this and improve performance, the metadata information may be stored locally in the performance tier. So during a read operation, the file system first consults the slot object metadata information stored locally at the performance tier to find the object location information. Once the object location information is obtained, a single cloud read is performed to get the actual data. During the single cloud read to get the actual data, the data may be decompressed if the data is part of a compression group.  FIG.  7    illustrates a read workflow  700  for a file system read  702  to a capacity tier  704  of the object store. A local read  706  for an offset and length is performed to slot metadata information  708  that may be cached within the performance tier. Next, a cloud read  710  is performed using the offset and length to the capacity tier  704  to read data within an object in the capacity tier  704 . If the data is to be decompressed, then decompression  712  is performed and the data is returned  714  for the file system read  702 . 
     One embodiment of implementing a unified object format is illustrated by an exemplary method  800  of  FIG.  8   , which is further described in conjunction with system  900  of  FIG.  9 A . A performance tier  902  (e.g., on-premise storage such as hard disk drives, solid state drives, or any other type of storage) may host a file system  906  used to store data accessible to clients and/or applications, such as through a volume. A cloud backup system  150  may create snapshots of the volume. The cloud backup system  150  may be configured to create objects formatted according to the unified object format. The cloud backup system  150  may store snapshot data (backup data) of the snapshots into the objects. The cloud backup system  150  may store the objects into a first object store  914  as objects  916 . A hybrid storage system  158  may be configured to tier data between the performance tier  902 , the first object store  914 , a second object store  918 , and/or other storage locations. For example, frequently accessed data may be stored within the performance tier  902  such as for access through the file system  906  because the performance tier  902  is comprised of relatively faster and/or lower latency compared to the object stores. Infrequently accessed data may be stored within an object store for low cost, scalable, longer term storage, such as by storing the data into objects formatted according to the unified object format and stored within the first object store  914  as the objects  916 . In this way, the hybrid storage system  158  may be configured to interpret the unified object format and to tier data between the object stores and the performance tier  902 . 
     During operation  802  of  FIG.  8   , data  904  with the performance tier  902  may be identified for being tiered out to a capacity tier of the first object store  914 . During operation  804  of  FIG.  8   , an object may be created according to the unified object format. The data  904  may be stored within slots of the object (e.g., the object may comprise  1024  slots that may store 4 kb of data each). During operation  806  of  FIG.  8   , slot header metadata may be attached to the object. The slot header metadata may comprise a data section comprising the data  904 . The slot header metadata may comprise a first metadata portion. The first metadata portion may comprise a compression group number of a slot of the object within which a block of the data  904  is to be stored. The first metadata portion may comprise a compression group logical index in the compression group number at which the slot is located. The slot header metadata may comprise a second metadata portion. The second metadata portion may comprise offset and length location information for the data  904  within the data section. During operation  808  of  FIG.  8   , the object may be stored into the capacity tier of the first object store  914 . 
     In some embodiments, the first metadata portion and the second metadata portion is populated with compression group information used to preserve compression performed upon the data  904  by the performance tier  902 . In this way, the data  904  stored within the object retains the compression. In some embodiments, additional compression is performed upon the data  904  to create additionally compressed data stored within the object. The first metadata portion and the second metadata portion may be populated with compression group information used to preserve the additional compression. In this way, the additionally compressed data stored within the object retains the compression. In some embodiments where the data  904  was compressed by the file system  906  using a first compression algorithm, the data  904  may be recompressed using a second compression algorithm different than the first compression algorithm to create additionally compressed data stored within the object. The first metadata portion and the second metadata portion may be populated with compression group information used to preserve the recompression performed by the second compression algorithm. In this way, the additionally compressed data stored within the object retains the compression. In some embodiments where the data  904  was compressed by the file system  906  as a first compression group, the data  904  may be recompressed into a second compression group larger than the first compression group. The first metadata portion and the second metadata portion may be populated with compression group information used to preserve the second compression group. In this way, the second compression group stored within the object retains the compression. 
     In some embodiments, a request to read data from the object within the first object store  914  is received. The offset and length location information in the slot header metadata may be read to identify an offset and a length of the data within the object. An object store read operation maybe performed using the offset and length to read the data from the object within the first object store  914 . The data may be provided in response to the request. In some embodiments, the data may be decompressed and provided in response to the request. 
     In some embodiments, the slot header metadata of the object may be cached within a persistent metafile  912  stored within the performance tier  902 . Slot header metadata may be stored within entries for each object stored within the first object store, such as a first entry for a first object, a second entry for a second object, etc. Each entry may comprise 4 kb or any other amount of information. An entry for an object may comprise a sequence number of the object, an object identifier of the object, and/or other information. When a request to read data from the object is received, the offset and length location information in the slot header metadata cached within the persistent metafile  912  may be locally read from the persistent metafile  912  within the performance tier  902  without having to read such information from the first object store  914 . After the local read operation to the persistent metafile  912  is performed, an object store read operation is performed using the offset and length to read the data from the object within the first object store  914 . 
     In some embodiments, persistent metafile  912  may be determined to be corrupt, comprising stale information, or missing. Accordingly, a repair process  950  may be executed by a repair subsystem  949  (a slot header repair subsystem) to repair the persistent metafile  912  by reading  952  the slot header metadata from the object in the first object store  914  and repairing the persistent metafile  912  using the slot header metadata, as illustrated by  FIG.  9 B . In an example, repair of the persistent metafile  912  may be triggered based upon a determination that a sequence number read from the object in the first object store  914  does not match a sequence number within an entry in the persistent metafile  912  for the object. The entry is assigned an object identifier of the object and comprises the slot header metadata of the object. 
     In some embodiments of triggering the repair process  950 , a resync operation may be performed to evaluate the persistent metafile  912  for identifying sizes of objects to read from the first object store  914  for resync. During the resync operation, sequence numbers of the objects read from the first object store  914  are compared to sequence numbers of entries within the persistent metafile  912  for the objects. A repair of the persistent metafile  912  may be performed for entries with sequence numbers not matching sequence numbers of corresponding objects. 
     In some embodiments of triggering the repair process  950 , a scanner is implemented to evaluate entries within the persistent metafile  912  for objects to determine whether sequence numbers within the entries match sequence numbers assigned to the objects in the first object store  914 . A repair of the persistent metafile  912  is triggered for entries with sequence numbers not matching sequence numbers of corresponding objects. 
     In some embodiments, mirroring of objects between object stores may be performed such to transition a client from using and store objects within the first object store  914  to using and storing objects (e.g., mirrored objects  920 ) within the second object store  918  (or other object stores), as illustrated by  FIG.  9 A . In some embodiments, an object may be retrieved from the first object store  914 , such as being retrieved into the performance tier  902 . The object is written to the second object store  918  in a network and space optimized manner such that decompression and recompression of the object is avoided. 
     Still another embodiment involves a computer-readable medium  1000  comprising processor-executable instructions configured to implement one or more of the techniques presented herein. An example embodiment of a computer-readable medium or a computer-readable device that is devised in these ways is illustrated in  FIG.  10   , wherein the implementation comprises a computer-readable medium  1008 , such as a compact disc-recordable (CD-R), a digital versatile disc-recordable (DVD-R), flash drive, a platter of a hard disk drive, etc., on which is encoded computer-readable data  1006 . This computer-readable data  1006 , such as binary data comprising at least one of a zero or a one, in turn comprises processor-executable computer instructions  1004  configured to operate according to one or more of the principles set forth herein. In some embodiments, the processor-executable computer instructions  1004  are configured to perform a method  1002 , such as at least some of the exemplary method  800  of  FIG.  8   , for example. In some embodiments, the processor-executable computer instructions  1004  are configured to implement a system for implementing a unified object format such as at least some of the exemplary system  900  of  FIG.  9   , for example. Many such computer-readable media are contemplated to operate in accordance with the techniques presented herein. 
     In an embodiment, the described methods and/or their equivalents may be implemented with computer executable instructions. Thus, in an embodiment, a non-transitory computer readable/storage medium is configured with stored computer executable instructions of an algorithm/executable application that when executed by a machine(s) cause the machine(s) (and/or associated components) to perform the method. Example machines include but are not limited to a processor, a computer, a server operating in a cloud computing system, a server configured in a Software as a Service (SaaS) architecture, a smart phone, and so on. In an embodiment, a computing device is implemented with one or more executable algorithms that are configured to perform any of the disclosed methods. 
     It will be appreciated that processes, architectures and/or procedures described herein can be implemented in hardware, firmware and/or software. It will also be appreciated that the provisions set forth herein may apply to any type of special-purpose computer (e.g., file host, storage server and/or storage serving appliance) and/or general-purpose computer, including a standalone computer or portion thereof, embodied as or including a storage system. Moreover, the teachings herein can be configured to a variety of storage system architectures including, but not limited to, a network-attached storage environment and/or a storage area network and disk assembly directly attached to a client or host computer. Storage system should therefore be taken broadly to include such arrangements in addition to any subsystems configured to perform a storage function and associated with other equipment or systems. 
     In some embodiments, methods described and/or illustrated in this disclosure may be realized in whole or in part on computer-readable media. Computer readable media can include processor-executable instructions configured to implement one or more of the methods presented herein, and may include any mechanism for storing this data that can be thereafter read by a computer system. Examples of computer readable media include (hard) drives (e.g., accessible via network attached storage (NAS)), Storage Area Networks (SAN), volatile and non-volatile memory, such as read-only memory (ROM), random-access memory (RAM), electrically erasable programmable read-only memory (EEPROM) and/or flash memory, compact disk read only memory (CD-ROM)s, CD-Rs, compact disk re-writeable (CD-RW)s, DVDs, cassettes, magnetic tape, magnetic disk storage, optical or non-optical data storage devices and/or any other medium which can be used to store data. 
     Some examples of the claimed subject matter have been described with reference to the drawings, where like reference numerals are generally used to refer to like elements throughout. In the description, for purposes of explanation, numerous specific details are set forth in order to provide an understanding of the claimed subject matter. It may be evident, however, that the claimed subject matter may be practiced without these specific details. Nothing in this detailed description is admitted as prior art. 
     Although the subject matter has been described in language specific to structural features or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing at least some of the claims. 
     Various operations of embodiments are provided herein. The order in which some or all of the operations are described should not be construed to imply that these operations are necessarily order dependent. Alternative ordering will be appreciated given the benefit of this description. Further, it will be understood that not all operations are necessarily present in each embodiment provided herein. Also, it will be understood that not all operations are necessary in some embodiments. 
     Furthermore, the claimed subject matter is implemented as a method, apparatus, or article of manufacture using standard application or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computer to implement the disclosed subject matter. The term “article of manufacture” as used herein is intended to encompass a computer application accessible from any computer-readable device, carrier, or media. Of course, many modifications may be made to this configuration without departing from the scope or spirit of the claimed subject matter. 
     As used in this application, the terms “component”, “module,” “system”, “interface”, and the like are generally intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution. For example, a component includes a process running on a processor, a processor, an object, an executable, a thread of execution, an application, or a computer. By way of illustration, both an application running on a controller and the controller can be a component. One or more components residing within a process or thread of execution and a component may be localized on one computer or distributed between two or more computers. 
     Moreover, “exemplary” is used herein to mean serving as an example, instance, illustration, etc., and not necessarily as advantageous. As used in this application, “or” is intended to mean an inclusive “or” rather than an exclusive “or”. In addition, “a” and “an” as used in this application are generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form. Also, at least one of A and B and/or the like generally means A or B and/or both A and B. Furthermore, to the extent that “includes”, “having”, “has”, “with”, or variants thereof are used, such terms are intended to be inclusive in a manner similar to the term “comprising”. 
     Many modifications may be made to the instant disclosure without departing from the scope or spirit of the claimed subject matter. Unless specified otherwise, “first,” “second,” or the like are not intended to imply a temporal aspect, a spatial aspect, an ordering, etc. Rather, such terms are merely used as identifiers, names, etc. for features, elements, items, etc. For example, a first set of information and a second set of information generally correspond to set of information A and set of information B or two different or two identical sets of information or the same set of information. 
     Also, although the disclosure has been shown and described with respect to one or more implementations, equivalent alterations and modifications will occur to others skilled in the art based upon a reading and understanding of this specification and the annexed drawings. The disclosure includes all such modifications and alterations and is limited only by the scope of the following claims. In particular regard to the various functions performed by the above described components (e.g., elements, resources, etc.), the terms used to describe such components are intended to correspond, unless otherwise indicated, to any component which performs the specified function of the described component (e.g., that is functionally equivalent), even though not structurally equivalent to the disclosed structure. In addition, while a particular feature of the disclosure may have been disclosed with respect to only one of several implementations, such feature may be combined with one or more other features of the other implementations as may be desired and advantageous for any given or particular application.