The present application is one of two contemporaneously filed applications that share common inventorship, share a common applicant and assignee, and disclose related subject matter. The two applications are: R. Brosch, Maintaining Data Deduplication Reference Information, filed Jun. 3, 2016, application Ser. No. 15/173,323, and R. Brosch, Maintaining I/O Transaction Metadata in Log-With-Index Structure, filed Jun. 3, 2016, application Ser. No. 15/173,289. The related application is herein incorporated by reference in its entirety.
The present disclosure generally relates to data storage and data storage systems including, but not limited to, data storage systems employing deduplication.
As the value and use of information continue to increase, individuals and businesses seek additional ways to process and store information. One option available to users is information handling systems. An information handling system generally processes, compiles, stores, and/or communicates information or data for business, personal, or other purposes, thereby allowing users to take advantage of the value of the information. Because technology and information handling needs and requirements vary between different users or applications, information handling systems may also vary regarding what information is handled, how the information is handled, how much information is processed, stored, or communicated, and how quickly and efficiently the information may be processed, stored, or communicated. The variations in information handling systems allow for information handling systems to be general or configured for a specific user or specific use such as financial transaction processing, airline reservations, enterprise data storage, or global communications. In addition, an information handling system may include a variety of hardware and software components that may be configured to process, store, and communicate information and may include one or more computer systems, data storage systems, and networking systems.
An information handling system can be configured in several different configurations ranging from a single, stand-alone computer system to a distributed, multi-device computer system, to a networked computer system with remote or cloud-based information handling resources.
Information handling systems for managing large and frequently accessed and modified databases may employ techniques, features, and data structures to achieve various data storage efficiencies. These efficiencies may include, as non-limiting examples, minimizing or reducing the amount and/or cost of storage capacity required to store and manage a dataset, increasing the amount the size of a dataset that can be achieved in a given amount of physical storage, reducing the time required to search for and/or access a particular record, reducing the risk of lost data as well as the risk of lost compute cycles that may occur when, for example, a power failure occurs while data is being archived or otherwise managed, and so forth.
Data deduplication is an example of a technique used to reduce the amount of storage required to store a dataset. Deduplication aims to achieve data storage efficiencies by detecting and eliminating or reducing the number of duplicated data blocks, duplication in a dataset and eliminating storage of duplicate data patterns. While data deduplication may achieve an increase in data density, i.e., the ratio of data represented to storage required, deduplication introduces its own complexities.
For example, whereas a storage system without deduplication exhibits a 1:1 ratio between stored data and references to the stored data, a deduplicated database may be characterized as exhibiting an N:1 ratio between data references and data patterns, where a data pattern refers to the block's content, i.e., the block's pattern of 1's and 0's. Accordingly, when a reference to a deduplicated data pattern is removed, the corresponding data pattern cannot be removed unless the dataset includes no other valid or active references to the data pattern. Similarly, if a particular data pattern becomes corrupted, all references to the data pattern must be identified. In the absence of reverse mapping information, i.e., mappings, for each data pattern, to each of its references, identifying all instances of references to a data pattern is, as a general rule, infeasible or inefficient since one would have to scan every data reference associated with a dataset every time a reference to a data pattern is removed.
More generally, large and/or frequently accessed and updated databases may need to maintain supporting data structures to ensure reasonable performance for basic operations, including inserting, deleting, querying, and archiving data records, as well as more advanced operations for summarizing one or more aspects of a dataset.
In accordance with disclosed subject matter, issues associated with the use of particular types of metadata and particular types of data structures to maintain metadata for large datasets are addressed. Although disclosed in the context of a log-with-index (LWI) data structure that features sequential insertion of new records backed by a tree-based index, various disclosed features including, atomic merging of archived data structures, may be applicable in other contexts not specifically disclosed herein. Similarly, although a data deduplication application suitable for use with disclosed LWI data structures and processes is disclosed, other applications may also employ disclosed features.
In accordance with disclosed subject matter, an information handling system may include a processor and a memory or other storage resource, accessible to the processor, that includes processor-executable program instructions that, when executed by the processor, cause the processor to perform storage management operations including maintaining an LWI structure that includes a log for inserting and deleting records, each of which includes a key field indicating a key. The LWI structure further includes an index tree, e.g., a binary tree, for maintaining an index of records in the log.
The LWI structure may maintain the log in HDD or other nonvolatile storage for persistence while insertions and deletions of records in the log may be performed sequentially to ensure adequate performance. The index tree may be maintained in RAM memory or SSD for performance
The system may perform log clear operations from time to time in accordance with a log full signal asserted, for example, when the log is full or nearly full. The log clear operations may include committing the log records, ordered in accordance with the index, to a tablet stored in a tablet library that includes previously generated tablets generated in conjunction with previous log full signals. The records in the log and the corresponding index tree data may then bee deleted or otherwise released.
The system may maintain a tablet index for accessing records stored in any of the tablets in the tablet library. In accordance with a merge tablet signal asserted periodically or from time to time in response to a tablet merge criteria, the system may merge a first tablet in the tablet library with a second tablet to produce a merged tablet.
Merging the first tablet and the second tablet may include iteratively performing a plurality of atomic merges for each of a plurality of atomic portions of the applicable tablets. Each atomic merge may merge an atomic portion of the first tablet with a corresponding atomic portion of the second tablet to form a corresponding atomic portion of the merged tablet. Each atomic merge may also update a portion of the tablet index corresponding to the atomic portion.
In some embodiments, the size of the atomic portions, i.e., the atomicity of the atomic merges, is defined in terms of keys or key ranges. Key-based atomicity is consistent with the key-ordered structure of the tablets. In some embodiments atomicity may be defined, at least in part, in terms of bytes, e.g., 1 GB or 10 GB atoms. If the tablet is formatted as a plurality of fixed sized pages, e.g., 2 KB pages, the atomic portions may be defined in terms of the number of pages. Atomicity might also be defined as percentage of the tablet, e.g., each atomic portion comprises 1% or 10% of the tablet. A combination of key ranges, number of pages, total bytes, or percentage of table parameters may be used to define the atomic portions in still other embodiments.
To address the risk associated with the potentially massive number of compute cycles required to merge two or more very large tablets, the system may maintain the tablet index nodes in copy-on-write storage wherein updating or otherwise writing to a node preserves the existing node data and creates a copy of the existing data for modification purposes. For example, during an atomic merge covering keys A through F, tablet index nodes associated with nodes A through F or a subset thereof are copy-on-write updated to preserve the existing node data. If the atomic merge does not complete successfully due to a power outage or other cause, the original node data may be restored.
When a merge cycle or an atomic merge cycle is complete, the copy-on-write data for the applicable tablet index nodes may be committed and the pre-existing node data may be released. In addition, the tablet index may include a super root node comprising a parent node of the root nodes for the first, second, and merged tablets. Not until the merge of the entire first and second tablets is complete will the data for the super node be updated to reflect the commitment of the merged data to the tablet library and the corresponding release of the original data.
In some embodiments, each record in the log structure may include a key-value pair comprising a key field indicating a key and a value field containing a value for the indicated key. In other embodiments, the value field may be omitted or may contain a null value.
In addition, each record in the log structure may include a presence bit for distinguishing between insertion transactions and deletion transactions and a sequence field storing a sequence value common to each record in the log. The sequence value may be incremented each time the log is cleared such that the sequence number identifies a particular interval of time defined by the log full signals. The sequence number may be beneficially employed to maintain continuity and/or recognize a disruption in continuity. For example, when the log is cleared and a new tablet is created in the tablet library, the sequence number may be committed to the new tablet. In the event of a power failure or other disruption, the system may use the sequence number to confirm correct status. For example, the sequence number associated with the most recent tablet should exceed the sequence number of the records in the log structure by 1.
In accordance with disclosed data deduplication embodiments, a data deduplication method includes detecting a deduplication transaction comprising a data reference, located at a data reference address, and a data pattern at a data pattern address. A data deduplication key may be determined based on the data reference address and the data pattern address, e.g., by concatenating the data pattern address and the data reference address with the data pattern address as the most significant bits. The resulting data deduplication key may be stored in a key field of a record in the log of a LWI structure. An index of the records in the log may be maintained in a binary index stored in RAM or SSD.
Responsive to detecting a log full signal, the storage controller may perform log clear operations that include: creating, in a tablet library comprising at least one other tablet, a new tablet, storing the logged records, sorted in accordance with the data deduplication keys, to the new tablet, and clearing the log and index tree of all entries. A log sequence number may be maintained by the log, stored in a sequence field of each record in the log, and included in a new tablet when the tablet is written.
From time to time, a tablet merge signal may initiate a merging of a first tablet of the tablet library and a second tablet of the tablet library to form a merged tablet. After the merged tablet is completed and committed to the storage library, the first and second tablets may be released from the tablet library and the corresponding storage locations released.
In at least one embodiment, the data deduplication keys are “value-less”, i.e., the records in the log either: do not include a value field corresponding to the key field or include a null value in the value field. Each record may include a presence bit for distinguishing between insertion transactions and deletion transactions and a sequence field storing the previously described sequence value common to each record in the log.
Merging the first tablet and the second tablet may include iteratively performing a plurality of atomic merges for each of a plurality of atomic portions of the applicable tablets. Each atomic merge may include merging an atomic portion of the first tablet with an atomic portion of the second tablet to form an atomic portion of the merged tablet and updating tablet index nodes corresponding to the atomic portion. Boundaries of the atomic portion may be defined in terms of either: a particular range of the keys or a particular number of fixed size tablet pages.
The tablet index may be maintained as copy-on-write data and, in these embodiments, updating the tablet index nodes preserves existing node data until the atomic merge is committed to the merged tablet. The tablet index may include a super root node configured as a parent node of root nodes for the first, second, and merged tablets. In these embodiments, updating the tablet index nodes during an atomic merge preserves the existing node data until the atomic merge is committed to the merged tablet and the atomic portions of the first and second tablets can be released.
The data deduplication method may support extended log query commands including, in at least one embodiment, a range query and a summarize query. The range query retrieves records within a range defined within the query. The summary query may indicate a range of keys, a key mask, and a maximum count, and a query processor may return a result indicating a number of key values within the range of keys subject to the key mask and the maximum count.
The above summary is not intended as a comprehensive description of the claimed subject matter but, rather, is intended to provide an overview of the applicable subject matter. Other methods, systems, software, functionality, features and advantages of the claimed subject matter will be or will become apparent to one with skill in the art upon examination of the following FIGUREs and detailed written description.
The description of the illustrative embodiments can be read in conjunction with the accompanying FIGUREs. It will be appreciated that, for simplicity and clarity of illustration, elements illustrated in the FIGUREs have not necessarily been drawn to scale. For example, the dimensions of some of the elements may be exaggerated relative to other elements. Embodiments incorporating teachings of the present disclosure are shown and described with respect to the FIGUREs presented herein, in which:
In the following detailed description, specific exemplary embodiments in which disclosed subject matter may be practiced are described in sufficient detail to enable those skilled in the art to practice the disclosed embodiments. For example, details such as specific method orders, structures, elements, and connections have been presented herein. However, it is to be understood that the specific details presented need not be utilized to practice embodiments of disclosed subject matter. It is also to be understood that other embodiments may be utilized and that logical, architectural, programmatic, mechanical, electrical and other changes may be made within the scope of the disclosed subject matter. The following detailed description is, therefore, not to be taken as limiting the scope of the appended claims and equivalents thereof.
References within the specification to “one embodiment,” “an embodiment,” “at least one embodiment”, or “some embodiments” and the like indicate that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. The appearance of such phrases in various places within the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Further, various features may be described which may be exhibited by some embodiments and not by others. Similarly, various requirements may be described which may be requirements for some embodiments but not for other embodiments.
It is understood that the use of specific component, device, and/or parameter names and/or corresponding acronyms thereof, such as those of the executing utility, logic, and/or firmware described herein, are for example only and not meant to imply any limitations on the described embodiments. The embodiments may thus be described with different nomenclature and/or terminology utilized to describe the components, devices, parameters, methods and/or functions herein, without limitation. References to any specific protocol or proprietary name in describing one or more elements, features or concepts of the embodiments are provided solely as examples of one implementation, and such references do not limit the extension of the claimed embodiments to embodiments in which different elements, features, protocols, or concept names are utilized. Thus, each term utilized herein is to be given its broadest interpretation given the context in which that term is utilized.
Disclosed subject matter includes a storage controller with an LWI structure that supports sequential inserts of new transactions into a non-volatile log structure that is backed by a RAM-speed index tree analogous to a log structured merge tree. The LWI structure combines a memory-based index for fast searching and sorting with an NVM-based or HDD-based, sequentially accessed log structure to provide persistence and adequate insert performance. Disclosed LWI structures are tailored for use in one or more data storage and storage system applications including one or more data deduplication applications.
Storage system 100 is an information handling system that receives and processes I/O operations 92. The storage system 100 of
Although storage system 100 encompasses any one or more suitable types of storage media, the storage media types emphasized in the description of the following figures include hard disk drive (HDD) storage, any of various nonvolatile semiconductor-based storage media including, as non-limiting examples, a solid state drive (SSD), and traditional random access memory (RAM). An SSD generally includes a flash memory array and a storage drive interface integrated within a single device. In at least one embodiment, storage system 100 supports nonvolatile memory (NVM) express (NVMe) SSDs, in which the storage interface is a peripheral component interconnect express (PCIe) interface.
The metadata 145 may include substantially any information descriptive of, derived from, or otherwise associated with dataset 140. The metadata 145, illustrated in more detail in
As depicted in
The chip set 102 illustrated in
The memory 110 illustrated in
In conjunction with metadata transaction functionality supported by storage controller 100, the memory 110 illustrated in
Query processor functionality corresponding to query processor instructions 114 may support commands including: INSERT k→v (insert the mapping k→v into the transaction log), DELETE(k) (remove or invalidate an existing k→v mapping from the transaction log), and QUERY(k), (return the value v associated with a key k if k is present). In addition, disclosed embodiments may support extended metadata commands including, as non-limiting examples, RANGE (k1,k2), which may retrieve all keys in dataset 140 between k1 and k2 inclusive, and SUMMARIZE (k1,k2,c,m) for determining key counts including, for example, the count of all keys, as masked by m, from k1 to k2, to a maximum of c.
The LWI structure 150 illustrated in
In at least one embodiment, the transaction log 152 of
The metadata 145 of
Referring now to
In at least some embodiments, the transaction log is maintained in nonvolatile storage and transactions are incorporated into the transaction log sequentially, to insure adequate insert/delete performance. The index tree may be maintained in RAM or in an SSD and is used to maintain an index of the keys stored in the transaction log.
The metadata method 400 of
When a log full signal is generated, the metadata method 400 of
The metadata method 400 illustrated in
Tablet merges may occur periodically or based upon one or more other or additional criteria. The size of a tablet resulting from the merging of two existing tablets can range from a minimum size, equal to the size of the more recent tablet, to a maximum size, equal to the sum of the two tablets. Assuming that any two newly created tablets are of approximately the same size and a tablet merge occurs between two existing tablets each time a new tablet is created, i.e., whenever the number of tablets equals three the two oldest tablets are merged, it can be seen that the size of the merged tablet may grow monotonically over time.
To prevent an unchecked drift of tablet size over time, any one of various suitable operations may be included within metadata method 400. For example, if the size of the tablet library, excluding the oldest tablet, exceeds a particular threshold, merge operations are performed. In another example, a time stamp may be associated with each tablet. If the age of the oldest tablet exceeds a particular threshold, merge operations are performed.
To reduce the risk associated with losing a potentially enormous amount of computational “work-in-progress” during a tablet merge, the tablet merge operation 432 depicted in
Atom sizes may be chosen in accordance with any one or more of various suitable parameters. For example, because the tablets contain records arranged in key sorted order, the atomicity of each merge may be defined according to a range of keys. Alternatively, if the tablets are formatted into fixed-size pages when stored, atomicity might be defined in terms of physical pages, e.g., each atomic merge merges N physical pages. Defining atomicity in terms of physical pages may, however, result in some duplicate entries in the merged tablet since page ranges and key ranges are not inherently aligned.
The tablet merge operation 432 illustrated in
Whenever the distribution of keys within a tablet is non-uniform, a key-defined atomicity may result in some atomic merges that process more records than others. Similarly, if the distribution of keys in a first tablet differs from the distribution of keys in a second tablet, an atomic merge may require more time to complete since fewer keys in the older tablet can be discarded due to the presence of the same key in the newer tablet. Some embodiments may impose page-based or other size-based constraints on the size of an atom to achieve a more uniform atomic risk, i.e., the risk of re-performing an atomic merge. Once the first or next successive ATP is defined, the tablet merge operation 432 of
As described below with respect to
The source tablet ATPs are then merged (operation 510) into the ATP of the merged tablet. The basic merge operation may comprise a prioritized union of the source ATPs in which duplicate records are discarded and any conflicting records are resolved in favor of the more recent record. In this manner, the merged tablet contains all the records included in the newer tablet plus any record in the older tablet for a key not found in the newer tablet. After the entire ATP of the source tablets has been merged into the corresponding ATP of the merged tablet, the tablet index nodes that were previously stored to copy-on-write storage locations can be committed (operation 512) to the tablet index and the original data can be released.
The tablet merge operation 432 illustrated in
The tablet index 171 of
Data deduplication keys 720 may be beneficially used in storage systems 100 that support data deduplication. Data deduplication support may include elements (not depicted in
For at least some purposes useful in maintaining data deduplication metadata, it may be beneficial to configure a key that, when sorted, readily reveals the number of data deduplication references 712 to any particular data pattern 702. The data deduplication keys 720-1 through 720-4 corresponding to data deduplication references 712-1 through 712-4 respectively are produced or obtained by concatenating the applicable data reference address 714 and the applicable data pattern address 704 with the data pattern address 704 stored in the most significant bits and the data reference address 714 stored in the least significant bits. Other embodiments may produce or obtain data deduplication keys 720 differently.
In at least one embodiment, each data deduplication key 720 represents a key 162 in the log 152 of
Accordingly, the data deduplication keys 720 illustrated in
The data deduplication references 720 illustrated in
Any one or more processes or methods described above, including processes and methods associated with the
A computer readable medium, which may also be referred to as computer readable memory or computer readable storage, encompasses volatile and non-volatile media, memory, and storage, whether programmable or not, whether randomly accessible or not, and whether implemented in a semiconductor, ferro-magnetic, optical, organic, or other suitable medium. Information handling systems may include two or more different types of computer readable medium and, in such systems, program code may be stored, in whole or in part, in two or more different types of computer readable medium.
Unless indicated otherwise, operational elements of illustrated or described methods may be combined, performed simultaneously, or performed in a different order than illustrated or described. In this regard, use of the terms first, second, etc. does not necessarily denote any order, importance, or preference, but may instead merely distinguish two or more distinct elements.
Program code for effecting described operations may be written in any appropriate combination of programming languages and encompasses human readable program code including source code as well as machine readable code including object code. Program code may be executed by a general purpose processor, a special purpose processor, including, as non-limiting examples, a graphics processor, a service processor, or an embedded processor or controller.
Disclosed subject matter may be implemented in any appropriate combination of software, firmware, and hardware. Terms including circuit(s), chip(s), processor(s), device(s), computer(s), desktop(s), laptop(s), system(s), and network(s) suggest at least some hardware or structural element(s), but may encompass non-transient intangible elements including program instruction(s) and one or more data structures including one or more databases.
While the disclosure has been described with reference to exemplary embodiments, it will be understood by those skilled in the art that the disclosure encompasses various changes and equivalents substituted for elements. Therefore, the disclosure is not limited to the particular embodiments expressly disclosed, but encompasses all embodiments falling within the scope of the appended claims.
As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification indicate the presence of stated features, operations, elements, and/or components, but does not preclude the presence or addition of one or more other features, operations, elements, components, and/or groups thereof.
Number | Name | Date | Kind |
---|---|---|---|
8200700 | Moore | Jun 2012 | B2 |
8423520 | Rao | Apr 2013 | B2 |
8521705 | Jayaraman | Aug 2013 | B2 |
8862559 | Jayaraman | Oct 2014 | B2 |
9037544 | Zheng | May 2015 | B1 |
9830342 | Dolph | Nov 2017 | B2 |
10102144 | Sundararaman | Oct 2018 | B2 |
20060004840 | Senda | Jan 2006 | A1 |
20080005145 | Worrall | Jan 2008 | A1 |
20090031096 | Smith | Jan 2009 | A1 |
20120072656 | Archak | Mar 2012 | A1 |
20130006997 | Asayama | Jan 2013 | A1 |
20130246372 | Rao | Sep 2013 | A1 |
20130346725 | Lomet | Dec 2013 | A1 |
20140258244 | Rao | Sep 2014 | A1 |
20140337338 | Shinn | Nov 2014 | A1 |
20150134616 | Zheng | May 2015 | A1 |
20160162508 | Brosch | Jun 2016 | A1 |
20160196320 | Borowiec | Jul 2016 | A1 |
20170032013 | Zheng | Feb 2017 | A1 |
20170193041 | Fuchs | Jul 2017 | A1 |
20170235496 | Brosch | Aug 2017 | A1 |
20170351731 | Brosch | Dec 2017 | A1 |
Entry |
---|
Spillane et al.; “An Efficient Multi-Tier Tablet Server Storage Architecture,” 2nd ACM Symposium on Cloud Computing (SOCC 2011 ) (Year: 2011). |
Number | Date | Country | |
---|---|---|---|
20170351697 A1 | Dec 2017 | US |