US20180083930A1 - Reads for dispersed computation jobs - Google Patents

Abstract

A method for execution by one or more processing modules of one or more computing devices of a dispersed storage network (DSN). The method begins by receiving, by a first processing module, an encoded data slice and an associated partial task. The method continues by identifying a data record associated with the encoded data slice. The method continues by generating a slice request for a data record that includes at least one additional encoded data slice not received by the first processing module and outputting the slice request to a second processing module. The method continues by receiving, by the first processing module, the at least one additional encoded data slice from the second processing module and performing the associated partial task on the encoded data slice and the at least one additional encoded data slice to produce at least partial results.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS
• The present U.S. Utility patent application claims priority pursuant to 35 U.S.C. § 120 as a continuation-in-part of U.S. Utility application Ser. No. 15/418,164, entitled “ENCRYPTING SEGMENTED DATA IN A DISTRIBUTED COMPUTING SYSTEM” filed Jan. 27, 2017, which is a continuation of U.S. Utility application Ser. No. 13/917,017, entitled “ENCRYPTING SEGMENTED DATA IN A DISTRIBUTED COMPUTING SYSTEM”, filed Jun. 13, 2013, issued as U.S. Pat. No. 9,674,155 on Jun. 6, 2017, which is a continuation-in-part of U.S. Utility application Ser. No. 13/707,428, entitled “DISTRIBUTED COMPUTING IN A DISTRIBUTED STORAGE AND TASK NETWORK”, filed Dec. 6, 2012, issued as U.S. Pat. No. 9,298,548 on Mar. 29, 2016, which claims priority pursuant to 35 U.S.C. § 119(e) to U.S. Provisional Application No. 61/569,387, entitled “DISTRIBUTED STORAGE AND TASK PROCESSING”, filed Dec. 12, 2011, all of which are hereby incorporated herein by reference in their entirety and made part of the present U.S. Utility patent application for all purposes.
• U.S. Utility application Ser. No. 13/917,017 also claims priority pursuant to 35 U.S.C. § 119(e) to U.S. Provisional Application No. 61/679,007, entitled “TASK PROCESSING IN A DISTRIBUTED STORAGE AND TASK NETWORK”, filed Aug. 2, 2012, which is hereby incorporated herein by reference in its entirety and made part of the present U.S. Utility patent application for all purposes.
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
• Not applicable.
INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC
• Not applicable.
BACKGROUND OF THE INVENTIONTechnical Field of the Invention
• This invention relates generally to computer networks and more particularly to dispersing error encoded data.
Description of Related Art
• Computing devices are known to communicate data, process data, and/or store data. Such computing devices range from wireless smart phones, laptops, tablets, personal computers (PC), work stations, and video game devices, to data centers that support millions of web searches, stock trades, or on-line purchases every day. In general, a computing device includes a central processing unit (CPU), a memory system, user input/output interfaces, peripheral device interfaces, and an interconnecting bus structure.
• As is further known, a computer may effectively extend its CPU by using “cloud computing” to perform one or more computing functions (e.g., a service, an application, an algorithm, an arithmetic logic function, etc.) on behalf of the computer. Further, for large services, applications, and/or functions, cloud computing may be performed by multiple cloud computing resources in a distributed manner to improve the response time for completion of the service, application, and/or function. For example, Hadoop is an open source software framework that supports distributed applications enabling application execution by thousands of computers.
• In addition to cloud computing, a computer may use “cloud storage” as part of its memory system. As is known, cloud storage enables a user, via its computer, to store files, applications, etc. on an Internet storage system. The Internet storage system may include a RAID (redundant array of independent disks) system and/or a dispersed storage system that uses an error correction scheme to encode data for storage.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)
• FIG. 1 is a schematic block diagram of an embodiment of a dispersed or distributed storage network (DSN) in accordance with the present invention;
• FIG. 2 is a schematic block diagram of an embodiment of a computing core in accordance with the present invention;
• FIG. 3 is a schematic block diagram of an example of dispersed storage error encoding of data in accordance with the present invention;
• FIG. 4 is a schematic block diagram of a generic example of an error encoding function in accordance with the present invention;
• FIG. 5 is a schematic block diagram of a specific example of an error encoding function in accordance with the present invention;
• FIG. 6 is a schematic block diagram of an example of a slice name of an encoded data slice (EDS) in accordance with the present invention;
• FIG. 7 is a schematic block diagram of an example of dispersed storage error decoding of data in accordance with the present invention;
• FIG. 8 is a schematic block diagram of a generic example of an error decoding function in accordance with the present invention;
• FIG. 9 is a schematic block diagram of another embodiment of a distributed computing system in accordance with the present invention; and
• FIG. 9A is a flowchart illustrating an example of obtaining a data record in accordance with the present invention.
DETAILED DESCRIPTION OF THE INVENTION
• FIG. 1 is a schematic block diagram of an embodiment of a dispersed, or distributed, storage network (DSN)10 that includes a plurality of computing devices12-16, a managingunit18, anintegrity processing unit20, and aDSN memory22. The components of the DSN10 are coupled to anetwork24, which may include one or more wireless and/or wire lined communication systems; one or more non-public intranet systems and/or public internet systems; and/or one or more local area networks (LAN) and/or wide area networks (WAN).
• The DSNmemory22 includes a plurality ofstorage units36 that may be located at geographically different sites (e.g., one in Chicago, one in Milwaukee, etc.), at a common site, or a combination thereof. For example, if the DSNmemory22 includes eightstorage units36, each storage unit is located at a different site. As another example, if the DSNmemory22 includes eightstorage units36, all eight storage units are located at the same site. As yet another example, if the DSNmemory22 includes eightstorage units36, a first pair of storage units are at a first common site, a second pair of storage units are at a second common site, a third pair of storage units are at a third common site, and a fourth pair of storage units are at a fourth common site. Note that aDSN memory22 may include more or less than eightstorage units36. Further note that eachstorage unit36 includes a computing core (as shown inFIG. 2, or components thereof) and a plurality of memory devices for storing dispersed error encoded data.
• Each of the computing devices12-16, the managingunit18, and theintegrity processing unit20 include acomputing core26, which includes network interfaces30-33. Computing devices12-16 may each be a portable computing device and/or a fixed computing device. A portable computing device may be a social networking device, a gaming device, a cell phone, a smart phone, a digital assistant, a digital music player, a digital video player, a laptop computer, a handheld computer, a tablet, a video game controller, and/or any other portable device that includes a computing core. A fixed computing device may be a computer (PC), a computer server, a cable set-top box, a satellite receiver, a television set, a printer, a fax machine, home entertainment equipment, a video game console, and/or any type of home or office computing equipment. Note that each of the managingunit18 and theintegrity processing unit20 may be separate computing devices, may be a common computing device, and/or may be integrated into one or more of the computing devices12-16 and/or into one or more of thestorage units36.
• Eachinterface30,32, and33 includes software and hardware to support one or more communication links via thenetwork24 indirectly and/or directly. For example,interface30 supports a communication link (e.g., wired, wireless, direct, via a LAN, via thenetwork24, etc.) betweencomputing devices14 and16. As another example,interface32 supports communication links (e.g., a wired connection, a wireless connection, a LAN connection, and/or any other type of connection to/from the network24) betweencomputing devices12 &16 and theDSN memory22. As yet another example,interface33 supports a communication link for each of the managingunit18 and theintegrity processing unit20 to thenetwork24.
• Computing devices12 and16 include a dispersed storage (DS)client module34, which enables the computing device to dispersed storage error encode and decode data as subsequently described with reference to one or more ofFIGS. 3-9A. In this example embodiment,computing device16 functions as a dispersed storage processing agent forcomputing device14. In this role,computing device16 dispersed storage error encodes and decodes data on behalf ofcomputing device14. With the use of dispersed storage error encoding and decoding, the DSN10 is tolerant of a significant number of storage unit failures (the number of failures is based on parameters of the dispersed storage error encoding function) without loss of data and without the need for a redundant or backup copies of the data. Further, the DSN10 stores data for an indefinite period of time without data loss and in a secure manner (e.g., the system is very resistant to unauthorized attempts at accessing the data).
• In operation, the managingunit18 performs DS management services. For example, the managingunit18 establishes distributed data storage parameters (e.g., vault creation, distributed storage parameters, security parameters, billing information, user profile information, etc.) for computing devices12-14 individually or as part of a group of user devices. As a specific example, the managingunit18 coordinates creation of a vault (e.g., a virtual memory block associated with a portion of an overall namespace of the DSN) within theDSTN memory22 for a user device, a group of devices, or for public access and establishes per vault dispersed storage (DS) error encoding parameters for a vault. The managingunit18 facilitates storage of DS error encoding parameters for each vault by updating registry information of the DSN10, where the registry information may be stored in theDSN memory22, a computing device12-16, the managingunit18, and/or theintegrity processing unit20.
• The DSN managingunit18 creates and stores user profile information (e.g., an access control list (ACL)) in local memory and/or within memory of theDSN memory22. The user profile information includes authentication information, permissions, and/or the security parameters. The security parameters may include encryption/decryption scheme, one or more encryption keys, key generation scheme, and/or data encoding/decoding scheme.
• TheDSN managing unit18 creates billing information for a particular user, a user group, a vault access, public vault access, etc. For instance, theDSTN managing unit18 tracks the number of times a user accesses a non-public vault and/or public vaults, which can be used to generate per-access billing information. In another instance, theDSTN managing unit18 tracks the amount of data stored and/or retrieved by a user device and/or a user group, which can be used to generate per-data-amount billing information.
• As another example, the managingunit18 performs network operations, network administration, and/or network maintenance. Network operations includes authenticating user data allocation requests (e.g., read and/or write requests), managing creation of vaults, establishing authentication credentials for user devices, adding/deleting components (e.g., user devices, storage units, and/or computing devices with a DS client module34) to/from theDSN10, and/or establishing authentication credentials for thestorage units36. Network administration includes monitoring devices and/or units for failures, maintaining vault information, determining device and/or unit activation status, determining device and/or unit loading, and/or determining any other system level operation that affects the performance level of theDSN10. Network maintenance includes facilitating replacing, upgrading, repairing, and/or expanding a device and/or unit of theDSN10.
• Theintegrity processing unit20 performs rebuilding of ‘bad’ or missing encoded data slices. At a high level, theintegrity processing unit20 performs rebuilding by periodically attempting to retrieve/list encoded data slices, and/or slice names of the encoded data slices, from theDSN memory22. For retrieved encoded slices, they are checked for errors due to data corruption, outdated version, etc. If a slice includes an error, it is flagged as a ‘bad’ slice. For encoded data slices that were not received and/or not listed, they are flagged as missing slices. Bad and/or missing slices are subsequently rebuilt using other retrieved encoded data slices that are deemed to be good slices to produce rebuilt slices. The rebuilt slices are stored in theDSTN memory22.
• FIG. 2 is a schematic block diagram of an embodiment of acomputing core26 that includes aprocessing module50, amemory controller52,main memory54, a videographics processing unit55, an input/output (TO)controller56, a peripheral component interconnect (PCI)interface58, anIO interface module60, at least one IO device interface module62, a read only memory (ROM) basic input output system (BIOS)64, and one or more memory interface modules. The one or more memory interface module(s) includes one or more of a universal serial bus (USB) interface module66, a host bus adapter (HBA)interface module68, a network interface module70, aflash interface module72, a harddrive interface module74, and aDSN interface module76.
• TheDSN interface module76 functions to mimic a conventional operating system (OS) file system interface (e.g., network file system (NFS), flash file system (FFS), disk file system (DFS), file transfer protocol (FTP), web-based distributed authoring and versioning (WebDAV), etc.) and/or a block memory interface (e.g., small computer system interface (SCSI), internet small computer system interface (iSCSI), etc.). TheDSN interface module76 and/or the network interface module70 may function as one or more of the interface30-33 ofFIG. 1. Note that the IO device interface module62 and/or the memory interface modules66-76 may be collectively or individually referred to as IO ports.
• FIG. 3 is a schematic block diagram of an example of dispersed storage error encoding of data. When acomputing device12 or16 has data to store it disperse storage error encodes the data in accordance with a dispersed storage error encoding process based on dispersed storage error encoding parameters. The dispersed storage error encoding parameters include an encoding function (e.g., information dispersal algorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding, non-systematic encoding, on-line codes, etc.), a data segmenting protocol (e.g., data segment size, fixed, variable, etc.), and per data segment encoding values. The per data segment encoding values include a total, or pillar width, number (T) of encoded data slices per encoding of a data segment i.e., in a set of encoded data slices); a decode threshold number (D) of encoded data slices of a set of encoded data slices that are needed to recover the data segment; a read threshold number (R) of encoded data slices to indicate a number of encoded data slices per set to be read from storage for decoding of the data segment; and/or a write threshold number (W) to indicate a number of encoded data slices per set that must be accurately stored before the encoded data segment is deemed to have been properly stored. The dispersed storage error encoding parameters may further include slicing information (e.g., the number of encoded data slices that will be created for each data segment) and/or slice security information (e.g., per encoded data slice encryption, compression, integrity checksum, etc.).
• In the present example, Cauchy Reed-Solomon has been selected as the encoding function (a generic example is shown inFIG. 4 and a specific example is shown inFIG. 5); the data segmenting protocol is to divide the data object into fixed sized data segments; and the per data segment encoding values include: a pillar width of 5, a decode threshold of 3, a read threshold of 4, and a write threshold of 4. In accordance with the data segmenting protocol, thecomputing device12 or16 divides the data (e.g., a file (e.g., text, video, audio, etc.), a data object, or other data arrangement) into a plurality of fixed sized data segments (e.g., 1 through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more). The number of data segments created is dependent of the size of the data and the data segmenting protocol.
• Thecomputing device12 or16 then disperse storage error encodes a data segment using the selected encoding function (e.g., Cauchy Reed-Solomon) to produce a set of encoded data slices.FIG. 4 illustrates a generic Cauchy Reed-Solomon encoding function, which includes an encoding matrix (EM), a data matrix (DM), and a coded matrix (CM). The size of the encoding matrix (EM) is dependent on the pillar width number (T) and the decode threshold number (D) of selected per data segment encoding values. To produce the data matrix (DM), the data segment is divided into a plurality of data blocks and the data blocks are arranged into D number of rows with Z data blocks per row. Note that Z is a function of the number of data blocks created from the data segment and the decode threshold number (D). The coded matrix is produced by matrix multiplying the data matrix by the encoding matrix.
• FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encoding with a pillar number (T) of five and decode threshold number of three. In this example, a first data segment is divided into twelve data blocks (D1-D12). The coded matrix includes five rows of coded data blocks, where the first row of X11-X14 corresponds to a first encoded data slice (EDS 1_1), the second row of X21-X24 corresponds to a second encoded data slice (EDS 2_1), the third row of X31-X34 corresponds to a third encoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to a fourth encoded data slice (EDS 4_1), and the fifth row of X51-X54 corresponds to a fifth encoded data slice (EDS 5_1). Note that the second number of the EDS designation corresponds to the data segment number.
• Returning to the discussion ofFIG. 3, the computing device also creates a slice name (SN) for each encoded data slice (EDS) in the set of encoded data slices. A typical format for aslice name60 is shown inFIG. 6. As shown, the slice name (SN)60 includes a pillar number of the encoded data slice (e.g., one of 1-T), a data segment number (e.g., one of 1-Y), a vault identifier (ID), a data object identifier (ID), and may further include revision level information of the encoded data slices. The slice name functions as, at least part of, a DSN address for the encoded data slice for storage and retrieval from theDSN memory22.
• As a result of encoding, thecomputing device12 or16 produces a plurality of sets of encoded data slices, which are provided with their respective slice names to the storage units for storage. As shown, the first set of encoded data slices includes EDS 1_1 through EDS 5_1 and the first set of slice names includes SN 1_1 through SN 5_1 and the last set of encoded data slices includes EDS 1_Y through EDS 5_Y and the last set of slice names includes SN 1_Y through SN 5_Y.
• FIG. 7 is a schematic block diagram of an example of dispersed storage error decoding of a data object that was dispersed storage error encoded and stored in the example ofFIG. 4. In this example, thecomputing device12 or16 retrieves from the storage units at least the decode threshold number of encoded data slices per data segment. As a specific example, the computing device retrieves a read threshold number of encoded data slices.
• To recover a data segment from a decode threshold number of encoded data slices, the computing device uses a decoding function as shown inFIG. 8. As shown, the decoding function is essentially an inverse of the encoding function ofFIG. 4. The coded matrix includes a decode threshold number of rows (e.g., three in this example) and the decoding matrix in an inversion of the encoding matrix that includes the corresponding rows of the coded matrix. For example, if the coded matrix includesrows 1, 2, and 4, the encoding matrix is reduced torows 1, 2, and 4, and then inverted to produce the decoding matrix.
• In one embodiment, during a dispersed computation job, a data record may cross boundaries of a slice stream, and require reading from adjacent DST units. To limit potential harm, a DST may enforce restrictions regarding how much adjacent data a neighboring DST may read of its data. For example, if records are known to never exceed 1 MB, a DST may limit the ability of an adjacent DST to read more than 1 MB of data.FIGS. 9 and 9A, describe a system and method for handling these adjacent reads (proxied reads) to complete tasks (e.g., partial tasks).
• FIG. 9 is a schematic block diagram of another embodiment of a distributed computing system that includes a user device14 (computing device), a distributed storage and task (DST) processing unit16 (computing device), and at least two DST execution units36 (storage units). EachDST execution unit36 and the at least twoDST execution units36 includes aslice memory700, acomputing task memory702, and a distributed task (DT)execution module90. The system functions to generate data slices for partial task execution to producepartial results708.
• TheDS processing unit16 receivesdata40 and/or atask request38 and encodesdata40 to produce at least two groups of data slices 1-2 and produces at least two groups of partial tasks 1-2 associated with thetask request38. Thedata40 may include a plurality of data records. TheDST processing unit16 may encode a data record of the plurality of data records to produce a last slice of a first group of data slices 1 and a first slice of a second group of data slices 2. A first group ofpartial tasks 1 may include a partial task associated with the data record. TheDST processing unit16 sends the at least two groups of data slices 1-2 and at least two groups of partial tasks 1-2 to a firstDST execution unit36 of the at least twoDST execution units36. The firstDST execution unit36 stores data slices 1 in theslice memory700 of the first DST execution of36 and stores thepartial tasks 1 in thecomputing task memory702 of the firstDST execution unit36.
• TheDT execution module90 of the firstDST execution unit36 retrieves data slices 1 from theslice memory700 and retrievespartial tasks 1 from thecomputing task memory702. TheDT execution module90 determines whether theslice memory700 contains every data slice required to executepartial tasks 1. When theDT execution module90 determines that slice memory does not contain every data slice required to executepartial tasks 1, theDT execution module90 identifies at least one other data slice. For example, the DT execution module identifies a first slice of the data slices 2 when a data record associated with apartial task 1 includes a last slice of the data slices 1 and the first slice of the data slices 2. TheDT execution module90 generates a slice request734 to obtain the at least one other data slice from anotherDST execution unit36. The slice request734 includes one or more of a slice name associated with the at least one other data slice, a requesting entity identifier, a copy of thepartial task 1, or an access credential (e.g., a signature, a signed copy of the partial task 1). The DT execution module sends the slice request734 to the otherDST execution unit36.
• TheDT execution module90 of the otherDST execution unit36 receives the slice request734 and may authorize the slice request734 based on the request. For example, theDT execution module90 of the other DST execution of36 verifies a signature of the slice request734. When the request is authorized, theDT execution module90 of the other DST execution of36 facilitates sending the at least one other data slice to theDST execution unit36. TheDST execution36 stores the at least one other data slice (e.g., data slice 2) in theslice memory700. TheDT execution module90 may determine whether theslice memory700 contains every data slice required to executepartial tasks 1. When theDT execution module90 determines thatslice memory700 contains every data slice required to executepartial tasks 1, theDT execution module90 executes one or more partial tasks ofpartial tasks 1 on data slices retrieved from the slice memory (e.g., data slices 1, data slices 2) to producepartial results708. For example, theDT execution module90 aggregates data slice 1 and data slice 2 to reproduce the data record and executes the one or more partial tasks on the data record to produce thepartial results708. The DT execution module outputs thepartial results708 to theDST processing unit16 and/or theuser device14. Alternatively, or in addition to, theDT execution module90 of the otherDST execution unit36 may perform apartial task 2 on adata slice 2 to producepartial results708.
• FIG. 9A is a flowchart illustrating an example of obtaining a data record. The method begins atstep740 where a processing module (e.g., of a distributed task (DT) execution module) receives a data slice and an associated partial task. The method continues atstep742 where the processing module identifies a data record associated with the data slice. The identifying may be based on one or more obtaining a slice name of the data slice, performing a data record identifier lookup in a slice name to data list, or extracting a data record identifier from the data slice. When the data record includes another encoded data slice (at least one additional encoded data slice), the method continues atstep744 where the processing module generates a slice request. The processing module may determine whether the data record includes the other data slice based on at least one of performing any data record ID to slice name lookup, receiving a list of slice names, or a query. The generating of the slice request includes one or more of identifying a slice name associated with the other data slice, identifying another distributed storage and task (DST) execution unit associated with the other data source, generating a partial task field entry that includes at least a portion of the associated partial task, or generating a credential field entry that includes a signature.
• The method continues atstep746 where the processing module outputs the slice request to the other DST execution unit. The method continues atstep748 where the processing module receives the other data slice from the other DST execution unit. The method continues atstep750 where the processing module performs the partial task on the data slice and the other data slice to produce partial results. The performing may include one or more of aggregating at least a portion of the data slice and at least a portion of the other data slice to produce the data record and executing at least a portion of the associated partial task on the data record to produce the partial results.
• The method described above in conjunction with the processing module can alternatively be performed by other modules of the dispersed storage network or by other computing devices. In addition, at least one memory section (e.g., a non-transitory computer readable storage medium) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices of the dispersed storage network (DSN), cause the one or more computing devices to perform any or all of the method steps described above.
• It is noted that terminologies as may be used herein such as bit stream, stream, signal sequence, etc. (or their equivalents) have been used interchangeably to describe digital information whose content corresponds to any of a number of desired types (e.g., data, video, speech, audio, etc. any of which may generally be referred to as ‘data’).
• As may be used herein, the terms “substantially” and “approximately” provides an industry-accepted tolerance for its corresponding term and/or relativity between items. Such an industry-accepted tolerance ranges from less than one percent to fifty percent and corresponds to, but is not limited to, component values, integrated circuit process variations, temperature variations, rise and fall times, and/or thermal noise. Such relativity between items ranges from a difference of a few percent to magnitude differences. As may also be used herein, the term(s) “configured to”, “operably coupled to”, “coupled to”, and/or “coupling” includes direct coupling between items and/or indirect coupling between items via an intervening item (e.g., an item includes, but is not limited to, a component, an element, a circuit, and/or a module) where, for an example of indirect coupling, the intervening item does not modify the information of a signal but may adjust its current level, voltage level, and/or power level. As may further be used herein, inferred coupling (i.e., where one element is coupled to another element by inference) includes direct and indirect coupling between two items in the same manner as “coupled to”. As may even further be used herein, the term “configured to”, “operable to”, “coupled to”, or “operably coupled to” indicates that an item includes one or more of power connections, input(s), output(s), etc., to perform, when activated, one or more its corresponding functions and may further include inferred coupling to one or more other items. As may still further be used herein, the term “associated with”, includes direct and/or indirect coupling of separate items and/or one item being embedded within another item.
• As may be used herein, the term “compares favorably”, indicates that a comparison between two or more items, signals, etc., provides a desired relationship. For example, when the desired relationship is thatsignal1 has a greater magnitude thansignal2, a favorable comparison may be achieved when the magnitude ofsignal1 is greater than that ofsignal2 or when the magnitude ofsignal2 is less than that ofsignal1. As may be used herein, the term “compares unfavorably”, indicates that a comparison between two or more items, signals, etc., fails to provide the desired relationship.
• As may also be used herein, the terms “processing module”, “processing circuit”, “processor”, and/or “processing unit” may be a single processing device or a plurality of processing devices. Such a processing device may be a microprocessor, micro-controller, digital signal processor, microcomputer, central processing unit, field programmable gate array, programmable logic device, state machine, logic circuitry, analog circuitry, digital circuitry, and/or any device that manipulates signals (analog and/or digital) based on hard coding of the circuitry and/or operational instructions. The processing module, module, processing circuit, and/or processing unit may be, or further include, memory and/or an integrated memory element, which may be a single memory device, a plurality of memory devices, and/or embedded circuitry of another processing module, module, processing circuit, and/or processing unit. Such a memory device may be a read-only memory, random access memory, volatile memory, non-volatile memory, static memory, dynamic memory, flash memory, cache memory, and/or any device that stores digital information. Note that if the processing module, module, processing circuit, and/or processing unit includes more than one processing device, the processing devices may be centrally located (e.g., directly coupled together via a wired and/or wireless bus structure) or may be distributedly located (e.g., cloud computing via indirect coupling via a local area network and/or a wide area network). Further note that if the processing module, module, processing circuit, and/or processing unit implements one or more of its functions via a state machine, analog circuitry, digital circuitry, and/or logic circuitry, the memory and/or memory element storing the corresponding operational instructions may be embedded within, or external to, the circuitry comprising the state machine, analog circuitry, digital circuitry, and/or logic circuitry. Still further note that, the memory element may store, and the processing module, module, processing circuit, and/or processing unit executes, hard coded and/or operational instructions corresponding to at least some of the steps and/or functions illustrated in one or more of the Figures. Such a memory device or memory element can be included in an article of manufacture.
• One or more embodiments have been described above with the aid of method steps illustrating the performance of specified functions and relationships thereof. The boundaries and sequence of these functional building blocks and method steps have been arbitrarily defined herein for convenience of description. Alternate boundaries and sequences can be defined so long as the specified functions and relationships are appropriately performed. Any such alternate boundaries or sequences are thus within the scope and spirit of the claims. Further, the boundaries of these functional building blocks have been arbitrarily defined for convenience of description. Alternate boundaries could be defined as long as the certain significant functions are appropriately performed. Similarly, flow diagram blocks may also have been arbitrarily defined herein to illustrate certain significant functionality.
• To the extent used, the flow diagram block boundaries and sequence could have been defined otherwise and still perform the certain significant functionality. Such alternate definitions of both functional building blocks and flow diagram blocks and sequences are thus within the scope and spirit of the claims. One of average skill in the art will also recognize that the functional building blocks, and other illustrative blocks, modules and components herein, can be implemented as illustrated or by discrete components, application specific integrated circuits, processors executing appropriate software and the like or any combination thereof.
• In addition, a flow diagram may include a “start” and/or “continue” indication. The “start” and “continue” indications reflect that the steps presented can optionally be incorporated in or otherwise used in conjunction with other routines. In this context, “start” indicates the beginning of the first step presented and may be preceded by other activities not specifically shown. Further, the “continue” indication reflects that the steps presented may be performed multiple times and/or may be succeeded by other activities not specifically shown. Further, while a flow diagram indicates a particular ordering of steps, other orderings are likewise possible provided that the principles of causality are maintained.
• The one or more embodiments are used herein to illustrate one or more aspects, one or more features, one or more concepts, and/or one or more examples. A physical embodiment of an apparatus, an article of manufacture, a machine, and/or of a process may include one or more of the aspects, features, concepts, examples, etc. described with reference to one or more of the embodiments discussed herein. Further, from figure to figure, the embodiments may incorporate the same or similarly named functions, steps, modules, etc. that may use the same or different reference numbers and, as such, the functions, steps, modules, etc. may be the same or similar functions, steps, modules, etc. or different ones.
• Unless specifically stated to the contra, signals to, from, and/or between elements in a figure of any of the figures presented herein may be analog or digital, continuous time or discrete time, and single-ended or differential. For instance, if a signal path is shown as a single-ended path, it also represents a differential signal path. Similarly, if a signal path is shown as a differential path, it also represents a single-ended signal path. While one or more particular architectures are described herein, other architectures can likewise be implemented that use one or more data buses not expressly shown, direct connectivity between elements, and/or indirect coupling between other elements as recognized by one of average skill in the art.
• The term “module” is used in the description of one or more of the embodiments. A module implements one or more functions via a device such as a processor or other processing device or other hardware that may include or operate in association with a memory that stores operational instructions. A module may operate independently and/or in conjunction with software and/or firmware. As also used herein, a module may contain one or more sub-modules, each of which may be one or more modules.
• As may further be used herein, a computer readable memory includes one or more memory elements. A memory element may be a separate memory device, multiple memory devices, or a set of memory locations within a memory device. Such a memory device may be a read-only memory, random access memory, volatile memory, non-volatile memory, static memory, dynamic memory, flash memory, cache memory, and/or any device that stores digital information. The memory device may be in a form a solid state memory, a hard drive memory, cloud memory, thumb drive, server memory, computing device memory, and/or other physical medium for storing digital information.
• While particular combinations of various functions and features of the one or more embodiments have been expressly described herein, other combinations of these features and functions are likewise possible. The present disclosure is not limited by the particular examples disclosed herein and expressly incorporates these other combinations.

Claims (20)

What is claimed is:

1. A method for execution by one or more processing modules of one or more computing devices of a dispersed storage network (DSN), the method comprises:

receiving, by a first processing module, an encoded data slice and an associated partial task;

identifying a data record associated with the encoded data slice;

generating a slice request for a data record that includes at least one additional encoded data slice not received by the first processing module;

outputting the slice request to a second processing module;

receiving, by the first processing module, the at least one additional encoded data slice from the second processing module; and

performing the associated partial task on the encoded data slice and the at least one additional encoded data slice to produce at least partial results.

2. The method ofclaim 1, wherein the identifying a data record associated with the encoded data slice is based on one or more of: obtaining a slice name of the encoded data slice, performing a data record identifier lookup in a slice name to data list, or extracting a data record identifier from the encoded data slice.

3. The method ofclaim 1, wherein it is determined that the data record includes the at least one additional encoded data slice by at least one of: performing a data record ID to slice name lookup, receiving a list of slice names, or a query.

4. The method ofclaim 1, wherein the generating the slice request includes one or more of: identifying a slice name associated with the at least one additional encoded data slice, identifying the second processing module associated with the at least one additional encoded data slice, generating a partial task field entry that includes at least a portion of the associated partial task, or generating a credential field entry that includes a signature.

5. The method ofclaim 1, wherein the performing the associated partial task on the encoded data slice and the at least one additional encoded data slice to produce at least partial results includes one or more of: aggregating at least a portion of the encoded data slice and at least a portion of the at least one additional encoded data slice to produce the data record and executing at least a portion of the associated partial task on the data record to produce the partial results.

6. The method ofclaim 1, wherein the second processing module includes a distributed storage and task (DST) execution unit.

7. The method ofclaim 1, wherein the second processing module is included as part of a storage unit.

8. A computing device of a group of computing devices of a dispersed storage network (DSN), the computing device comprises:

an interface;

a local memory; and

a processing module operably coupled to the interface and the local memory, wherein the processing module functions to:

receive an encoded data slice and an associated partial task;

identify a data record associated with the encoded data slice;

generate a slice request for a data record that includes at least one additional encoded data slice not received;

output the slice request to another processing module;

receive the at least one additional encoded data slice from the another processing module; and

perform the associated partial task on the encoded data slice and the at least one additional encoded data slice to produce at least partial results.

9. The computing device ofclaim 8, wherein the identify a data record associated with the encoded data slice is based on one or more of: obtaining a slice name of the encoded data slice, performing a data record identifier lookup in a slice name to data list, or extracting a data record identifier from the encoded data slice.

10. The computing device ofclaim 8, wherein it is determined that the data record includes the at least one additional encoded data slice by at least one of: performing a data record ID to slice name lookup, receiving a list of slice names, or a query.

11. The computing device ofclaim 8, wherein the generate a slice request includes one or more of: identifying a slice name associated with the at least one additional encoded data slice, identifying the another processing module associated with the at least one additional encoded data slice, generating a partial task field entry that includes at least a portion of the associated partial task, or generating a credential field entry that includes a signature.

12. The computing device ofclaim 8, wherein the perform the associated partial task on the encoded data slice and the at least one additional encoded data slice to produce at least partial results includes one or more of: aggregating at least a portion of the encoded data slice and at least a portion of the at least one additional encoded data slice to produce the data record and executing at least a portion of the associated partial task on the data record to produce the partial results.

13. The computing device ofclaim 8, wherein the another processing module includes a distributed storage and task (DST) execution unit.

14. The computing device ofclaim 8, wherein the another processing module is included as part of a storage unit.

15. A system, the system comprises:

an interface;

a local memory; and

a processing module operably coupled to the interface and the local memory, wherein the processing module functions to:

receive an encoded data slice and an associated partial task;

identify a data record associated with the encoded data slice;

generate a slice request for a data record that includes at least one additional encoded data slice not received;

output the slice request to another processing module;

receive the at least one additional encoded data slice from the another processing module; and

perform the associated partial task on the encoded data slice and the at least one additional encoded data slice to produce at least partial results.

16. The system ofclaim 15, wherein the identify a data record associated with the encoded data slice is based on one or more of: obtaining a slice name of the encoded data slice, performing a data record identifier lookup in a slice name to data list, or extracting a data record identifier from the encoded data slice.

17. The system ofclaim 15, wherein it is determined that the data record includes the at least one additional encoded data slice by at least one of: performing a data record ID to slice name lookup, receiving a list of slice names, or a query.

18. The system ofclaim 15, wherein the generate a slice request includes one or more of: identifying a slice name associated with the at least one additional encoded data slice, identifying the another processing module associated with the at least one additional encoded data slice, generating a partial task field entry that includes at least a portion of the associated partial task, or generating a credential field entry that includes a signature.

19. The system ofclaim 15, wherein the perform the associated partial task on the encoded data slice and the at least one additional encoded data slice to produce at least partial results includes one or more of: aggregating at least a portion of the encoded data slice and at least a portion of the at least one additional encoded data slice to produce the data record and executing at least a portion of the associated partial task on the data record to produce the partial results.

20. The system ofclaim 15, wherein the another processing module is included as part of a storage unit.

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