Patent Publication Number: US-8127109-B2

Title: Virtual block device

Description:
CLAIM OF PRIORITY 
     This application is a continuation of U.S. patent application Ser. No. 12/414,538 filed Mar. 30, 2009, entitled SYSTEM AND METHOD FOR DATA DEDUPLICATION by John Edward Gerard Matze. 
    
    
     BACKGROUND 
     1. Technical Field 
     The present invention relates to systems and methods for deduplicating data in electronic systems. 
     2. Related Art 
     Disks provide an easy, fast, and convenient way for backing up datacenters. As additional backups are made, including full, incremental, and differential backups, additional disks and disk space are required. However, disks add costs to any backup solution including the costs of the disks themselves, costs associated with powering and cooling the disks, and costs associated with physically storing the disks in the datacenter. 
     Thus, it becomes desirable to maximize the usage of disk storage available on each disk. One method of maximizing storage on a disk is to use some form of data compression. Software-based compression can be slow and processor-intensive, therefore hardware-accelerated compression came to be used. However, using data compression can achieve a nominal compression ratio of 2:1, which only slows the need to add additional disk storage. 
     Data deduplication provides another method of capacity optimization which can reduce the storage capacity required for a given amount of data. This in turn can reduce acquisition, power, heating, and cooling costs. Additionally, management costs can be reduced by reducing the number of physical disks required for data backup. 
     Data deduplication can be performed in-line or in post-processing. In-line data deduplication is performed in real time, as the data is being written. Post-processing occurs after data has been written to a non-deduplicating disk but before the data is committed to a permanent medium. Post-processing requires the full backup to be stored temporarily, thus defeating the storage benefits of deduplication. 
     SUMMARY 
     In one embodiment, a system for deduplicating data comprises a card operable to receive at least one data block and a processor on the card that generates a hash for each data block. The system further comprises a first module that determines a processing status for the hash and a second module that discards duplicate hashes and their data blocks and writes unique hashes and their data blocks to a computer readable medium. In one embodiment, the processor also compresses each data block using a compression algorithm. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Further details of the present invention are explained with the help of the attached drawings in which: 
         FIG. 1  shows a diagram of block level deduplication in accordance with an embodiment. 
         FIG. 2  shows a system for data deduplication in accordance with an embodiment. 
         FIG. 3  shows a method for data deduplication in accordance with an embodiment. 
         FIG. 4  shows a second method for data deduplication in accordance with an embodiment. 
         FIG. 5  shows a file to block mapping in accordance with an embodiment. 
         FIG. 6  shows a networked storage example in accordance with an embodiment. 
         FIG. 7  shows potential deduplication deployment points in accordance with an embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     Data deduplication is a technique that can be used to minimize the amount of storage required for a given amount of data by eliminating redundant or duplicate data patterns within the given amount of data. Unique patterns of data can each be associated with a “fingerprint,” calculated based on each pattern of data. Each fingerprint identifies a unique data pattern and can be used to identify and discard duplicate data patterns. Because only unique data is stored, the total required disk space is reduced. 
     Identifying and assigning a fingerprint to a data pattern may often be accomplished using a cryptographic hash function. The cryptographic hash function can receive a data pattern and produce a unique fingerprint based on the data pattern. Like data compression, hash processing can also be processor intensive. For example, in-line software deduplication (i.e., where deduplication is performed in real time) can accept data streams in the 10s of megabytes per second in stark contrast to non-deduplicating methods that can accept data streams in the 100s of megabytes per second. Therefore it can be beneficial to off-load hash processing to dedicated hardware. This can accelerate hash processing and relieve processing strain on general purpose processors. 
     In one embodiment, a method for deduplication comprises receiving a block of data. The block of data may be received from a file system. The method further comprises generating a hash value for the block of data. The hash value may be generated using a number of different algorithms. The method also comprises determining if the hash value is unique. This can be accomplished by comparing the hash value for the block of data with hash values for other blocks of data that have already been processed and written to disk. The method additionally comprises discarding the hash value and the block of data if the hash value is not unique and writing the block of data to a disk if the hash value is unique. Furthermore, data compression services may be used in conjunction with data deduplication to further minimize the required storage space for a given dataset. In one embodiment, if the hash value is unique the data block is compressed before it is written to the disk. 
     In one embodiment, data deduplication can be performed at the file or at the block (sub-file) level. File level deduplication, also called Single Instance Stores (SIS), eliminates identical files within or across systems. File level deduplication, however, requires the files to be identical to be deduplicated.  FIG. 1  shows a diagram of block level deduplication in accordance with an embodiment. When application data is received at the file system it is broken up into clusters or blocks before being written to disk. In  FIG. 1 , NTFS file  100  comprises one or more clusters as shown by clusters  1  through n. Using file level deduplication, NTFS file  100  would be compared with other files in the system. If it were found to be identical to another file already stored on the system it would be discarded. Using block level deduplication, each cluster (or block) that comprises the file may be deduplicated. For example, two presentation files that have identical content but have different title pages will not be deduplicated at the file level. When data deduplication is performed at the block or sub-file level, the data blocks that comprise each file are deduplicated. Deduplication at the block level may be used to discard the duplicate blocks and store those blocks that are different (e.g., the blocks corresponding to the title pages). 
     In one embodiment, a system for deduplicating data may comprise a dedicated hardware card operable to receive at least one data block. A processor on the card may generate a hash for each data block. The system may also comprise a first module that determines a processing status for the hash. The processing status may indicate whether the data block associated with the hash is unique. Additionally, a second module may discard duplicate hashes and their data blocks and write unique hashes and their data blocks to a computer readable medium. In one embodiment, the dedicated hardware card may include a processor that is also operable to compress each data block using a compression algorithm. 
       FIG. 2  shows a system for data deduplication in accordance with an embodiment. In  FIG. 2 , the system includes a file system comprising an I/O Manager  200 , NT File System (NTFS) driver  202 , Dynamic Disk Manager  204 , Disk Class Driver  206 , and StorPort Driver  208 . The system further includes one or more Virtual Block Devices (VBD)  210  which provide an application interface to capacity optimization services. In one embodiment, the capacity optimization services include data deduplication services. In other embodiments, the capacity optimization services may further include adaptive data compression, thin provisioning, and capacity monitoring services. Applications interact with each VBD as they would any other standard volume (for example, a VBD may be represented as a D: drive, or other typical volume label). 
     Each VBD can be configured to use different deduplication block sizes. For example, the deduplication block size may be set at 4 k, 8 k, 16 k, or 32 k. If the block size is set at 4 k, then a file will be broken into however many 4 k sized blocks are necessary to contain the file. These blocks will then be deduplicated. Block size may be configured to provide improved performance for different applications having different performance requirements. In one embodiment, each VBD is created by the StorPort VMiniport Driver  212  which can manage up to 16 different VBDs. The StorPort driver  208  is further operable to divert data to the deduplication services and filter the data using fingerprints created by the deduplication services. 
       FIG. 2  further includes a Dedupe Block Manager (DBM)  214 . The DBM provides an interface with hardware capacity optimization services which can include deduplication and compression services. During a write operation, the DBM passes blocks of data to a processor on card  218  via Card Driver  216 . The processor generates a fingerprint, also called a hash, for each block of data and returns the hash to the DBM. In one embodiment, the card compresses the data blocks and returns a compressed block along with its corresponding hash to the DBM. The DBM can then use each hash value to determine whether a data block is unique, and therefore should be written to disk  220 , or whether the data block is a duplicate and should be discarded. In one embodiment, the card  218  can be the Hifn DR255 Card available from Hifn Inc., of Los Gatos, Calif. 
     In one embodiment, the hash is generated using a Secure Hash Algorithm (SHA-1). SHA-1 is specified in the Secure Hash Standard developed by the National Institute for Science and Technology and published in 1994. SHA-1 produces a 160-bit fingerprint (hash). It is possible for hash functions to generate the same hash for two different data patterns. When the same hash is assigned to different data patterns, this is called a hash collision. Hash collisions can lead to data corruption. It is therefore beneficial to use an algorithm that makes hash collisions very unlikely. SHA-1&#39;s 160-bit hash has a probability of randomly generating the same hash for different patterns of approximately 1 in 10 24 . This is significantly less likely than many disk errors. For example, the probability of an unrecoverable read error on a typical disk is approximately 1 in 10 14 . 
       FIG. 3  shows a method of data deduplication in accordance with an embodiment. At step  300 , a block of data is sent by the DBM to the processor on the card. At step  302 , the processor generates a hash for the block of data. In one embodiment, the hash is a 160-bit hash generated using the SHA-1 algorithm. The processor then returns the hash to the DBM. At step  304 , the DBM generates a truncated hash based on the full hash. The truncated hash is used to perform a very fast lookup in an array stored in memory or on a solid-state drive (SSD). The array includes an index of all possible truncated hash values. The size of the array will depend based on the length of the truncated hash. Each entry in the array is initially set to a default value. In one embodiment, as shown in  FIG. 3 , the default value can be −1. As hashes are processed, entries in the array are updated to include a pointer to the full hash value corresponding to the truncated hash and the data block. 
     At step  306 , the truncated hash is looked up in the array. At step  308 , the corresponding entry is shown to be −1, the default value. The default value indicates that the datablock is not currently stored. This can be because either this truncated hash has not been processed and the data block is unique or the data block was previously processed and then deleted along with its associated hash. At step  310 , an appropriately sized Extent and Sub-block allocation is found for the full hash and the data block. At step  312 , the data block is written to disk and the array is updated with an appropriate pointer. 
       FIG. 4  shows a second method of data deduplication in accordance with an embodiment. Steps  400 - 406  proceed as in  FIG. 3 . At step  408 , the truncated hash is found to have been previously processed. The corresponding entry is shown to be a pointer to the address of the full hash and data block. This indicates that the truncated hash value has been previously processed. At step  410 , the full hash is retrieved from the metadata as indicated in the pointer. If the full hash values match, then the data block has been previously processed. The data block is discarded and a metadata counter is incremented. At step  412 , if the full hash values do not match then it is determined whether there is a pointer to a second metadata location including a second full hash and data block corresponding to the truncated hash. Each previously processed full hash corresponding to the truncated hash is checked against the full hash. If no match is found, then a new metadata location is calculated for the hash and data block and they are written to disk. Additionally, a pointer to the new metadata location is added to the last hash checked. If a match is found, then the data is discarded and the metadata counter is incremented. 
     Using a truncated hash array is more efficient than checking each full hash as only a fraction of hashes require a full check. The length of the truncated hashes can be varied based on performance. If too many truncated hash collisions occur, the length of the truncated hash can be extended. 
     In one embodiment, data deduplication can be performed at the block level. File systems, such as NTFS, operate at the file level. Accordingly, mappings must be used between files in the file system and blocks used for deduplication.  FIG. 5  shows a file to block mapping in accordance with an embodiment. NTFS allocates disk space using clusters. Cluster size is set during formatting and can range from 4 kilobytes to 64 kilobytes. There are two types of clusters in NTFS: Logical Clusters and Virtual Clusters. Logical clusters are referred to by their Logical Cluster Number (LCN). The LCNs are directly mapped to a physical disk address or RAID logical address by multiplying the cluster size of the partition by a sequential LCN. Virtual clusters are referred to by their Virtual Cluster Number (VCN). Files are mapped to LCNs by VCNs using a series of sequential numbers incremented for as many clusters as are needed to contain the file. As shown in  FIG. 4 , in one embodiment, each LCN is then mapped to addresses in the Virtual Block Device. 
     Block alignment can impact both capacity optimization and performance. In one embodiment, deduplication compares blocks that start on fixed boundaries against other blocks which start on fixed boundaries. If the blocks are not properly aligned on fixed boundaries, the effectiveness of the deduplication services can be negatively affected. In one embodiment, the block size used for deduplication is chosen to match the cluster or block size of the system it is associated with to minimize block misalignment. Some backup software creates output datasets that are not aligned at fixed boundaries. In embodiments designed for use with such datasets, an additional software layer is used to realign the data blocks from the backup format before deduplication. 
     Deduplication services can be added to a system at a number of different points, depending on network configuration and the needs of the user.  FIG. 6  shows a networked storage example in accordance with an embodiment. Network Attached Storage (NAS) servers provide data storage services to other devices attached to the network. NAS servers can be used in a variety of networks including home networks, small office/home office (SOHO) networks, and business networks. In  FIG. 5 , two NAS servers are attached to a network and provide shared data storage to several workstations, connected to the network. Here, deduplication services are added to the file servers to offer capacity savings for the entire network. In one embodiment, deduplication services may be used with Microsoft SharePoint, available from Microsoft Corporation of Redmond, Wash. 
       FIG. 7  shows potential deduplication deployment points in accordance with an embodiment. Depending on network configuration details, deduplication services can be added at a variety of points in a network.  FIG. 6  shows some of these points. These points include the FC array, iSCSI array, SCSI DAS array, or Windows Unified Data Storage Server (WUDSS). 
     In one embodiment, data compression services are provided in addition to data deduplication services. Data compression looks for repetitive data sequences in a data stream. Different data compression algorithms can be used as are known in the art. Adaptive data compression techniques that adapt dynamically to different types of data being compressed can also be used. In one embodiment, the data compression algorithm used is a variant of the Lempel-Ziv compression algorithm called eLZS. 
     In one embodiment, thin provisioning may be used in conjunction with data deduplication and/or data compression to increase efficiency and cost-effectiveness. Thin provisioning enables storage managers to allocate volumes of any size to servers without physically installing the storage. Thus, additional capacity can be added “just in time,” preventing storage from being acquired and idled. 
     In one embodiment, a physical capacity monitor is used to determine the utilization of physical storage devices underlying the capacity optimization services, including data compression, data deduplication, and thin provisioning. Capacity optimization services reduce, but do not eliminate, the need for additional storage capacity. The physical capacity monitor can notify the user that additional storage capacity is required. 
     In one embodiment, a user or system administrator may configure the capacity optimization services using a graphical user interface (GUI). Alerts may be configured using the GUI to notify users via email. The alerts may include informational, warning, or error messages. 
     In one embodiment, capacity optimization services may be applied generally to any application data. However, many backup implementations are application-specific and therefore include primarily data of a single type from a specific application. Therefore, it is of note, that capacity optimization services are not uniformly effective across many data types. For example, video files, such as .avi and .wmv, may experience very high data reduction when stored on a system featuring capacity optimization services. However, some data, such as video surveillance data may not lend itself to capacity optimization services at all. Video surveillance data is often compressed at the camera, limiting the effectiveness of additional compression. Additionally, time stamp information is generally added to each frame of the video surveillance data, limiting the effectiveness of data deduplication, even at the block level. 
     Although the present invention has been described above with particularity, this was merely to teach one of ordinary skill in the art how to make and use the invention. Many modifications will fall within the scope of the invention, as that scope is defined by the following claims.