Abstract:
Disclosed are various embodiments for distributing data items within a plurality of nodes. A data item update request is replicated from a master node in a plurality of nodes to a plurality of slave nodes within the plurality of nodes. The replicated data item update request is determined to be locality-based durable. Responsive to the determination that the replicated data item update request is locality-based durable, the data item update request is confirmed to a client, wherein the client had originated the data item update request. Upon failover of the master node to another node within the plurality of nodes, a fault-tolerant failover quorum ensures that all previously confirmed updates are found and recognized by the new master node.

Description:
BACKGROUND 
     A data store, such as, for example, a non-relational database, a relational database management system (RDBMS), or other data systems may be implemented as a distributed system. Distributed systems can offer improved reliability and availability, better fault tolerance, increased performance, and easier expansion. Some distributed models employ single-master replication, where data written to a master data store is replicated to one or more secondary stores. Distributed data stores may experience difficulties if the master data store fails. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Many aspects of the present disclosure can be better understood with reference to the following drawings. The components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the disclosure. Moreover, in the drawings, like reference numerals designate corresponding parts throughout the several views. 
         FIG. 1  is a drawing of a networked environment according to various embodiments of the present disclosure. 
         FIG. 2  is another view of the networked environment of  FIG. 1  according to various embodiments of the present disclosure. 
         FIG. 3  is a flowchart illustrating an example of functionality implemented as portions of a data store management application executed in a computing device in the networked environment of  FIG. 1  according to various embodiments of the present disclosure. 
         FIG. 4  is a flowchart illustrating another example of functionality implemented as portions of a data store management application executed in a computing device in the networked environment of  FIG. 1  according to various embodiments of the present disclosure. 
         FIG. 5  is a flowchart illustrating yet another example of functionality implemented as portions of a data store management application executed in a computing device in the networked environment of  FIG. 1  according to various embodiments of the present disclosure. 
         FIG. 6  is a schematic block diagram that provides one example illustration of a computing device employed in the networked environment of  FIG. 1  according to various embodiments of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     The present disclosure relates to failover recovery in a distributed data store. In one embodiment, a distributed data store can employ a single-master replication model that provides for a master data store and one or more slave data stores. The master data store can receive updates to data items stored in the distributed data store received from client systems and propagate the updates to the slave data stores. Upon propagating the update to a requisite number of slave data stores, the master data store can then consider the update as successful, durable, and/or committed to the distributed data store. To provide data durability or integrity from a client or user point of view, any update to a data item acknowledged to the user as successful in a distributed data store according to embodiments of the disclosure should be able to survive the failure of the master data store. In such a scenario, a slave data store in the distributed data store can be designated as the new master data store. To provide such failover capability to the distributed data store, the new master data store, previously a slave data store, must be able to determine at least the last successful updates committed to the distributed data store and acknowledge as successful to a client in order to properly assume its role as the master. 
     With reference to  FIG. 1 , shown is a networked environment  100  according to various embodiments. The networked environment  100  includes one or more computing devices  103   a  . . .  103 N in data communication with one or more client devices  106  by way of a network  109 . The network  109  includes, for example, the Internet, intranets, extranets, wide area networks (WANs), local area networks (LANs), wired networks, wireless networks, or other suitable networks, etc., or any combination of two or more such networks. 
     Each of the computing devices  103   a  . . .  103 N may comprise, for example, a server computer or any other system providing computing capability. Alternatively, a plurality of computing devices  103   a  . . .  103 N may be employed that are arranged, for example, in one or more server banks or computer banks or other arrangements. A plurality of computing devices  103   a  . . .  103 N together may comprise, for example, a cloud computing resource, a grid computing resource, and/or any other distributed computing arrangement. Such computing devices  103   a  . . .  103 N may be located in a single installation or may be distributed among many different geographical locations. For purposes of convenience, the computing device  103  is referred to herein in the singular. Even though the computing device  103  is referred to in the singular, it is understood that a plurality of computing devices  103   a  . . .  103 N may be employed in the various arrangements as described above. 
     Various applications and/or other functionality may be executed in the computing device  103  according to various embodiments. Also, various data is stored in a respective data store  112   a  . . .  112 N that is accessible to the computing device  103 . The respective data store  112   a  . . .  112 N may be representative of a plurality of data stores as can be appreciated. The data stored in the data store  112 , for example, is associated with the operation of the various applications and/or functional entities described below. The data stored in a data store  112  includes, for example, replicated data  115  and potentially other data. The replicated data  115  includes any data maintained in the data store  112  that can be durably persisted across a distributed data store implemented by the various computing devices  103  in the system. 
     The components executed on the computing device  103 , for example, include a data store management application  118 , and other applications, services, processes, systems, engines, or functionality not discussed in detail herein. When a computing device  103  is designated as a master data store for a distributed data store implemented by computing devices  103   a  . . .  103 N, the data store management application  118  takes on a master role and is thus executed to manage the data store  112  and to facilitate replication of data to one or more data stores  112  accessible to computing devices  103  that are designated as slave data stores. In a master role, the data store management application  118  may obtain data item update requests  121  from the client device  106  and respond with data item update confirmations  124 . The updates may take the form of writes to the data store  112 , for example. The master data store management application  118  may also generate and send data item replication requests to the slave data store management applications  118  and obtain data item replication confirmations from the slave data store management applications  118 . 
     When a computing device  103  is designated as a slave data store for a distributed data store implemented by computing devices  103   a  . . .  103 N, the data store management application  118  takes on a slave role and is thus executed to receive data item replication requests from a master data store management application  118  and cause the corresponding data item to be stored in the data store  112  managed by the slave data store management applications  118 . In other words, the slave data store management applications  118  are each configured to obtain data item replication requests from the master data store management application  118 . In response to the data item replication requests, the slave data store management application  118  is configured to commit data item updates to its respective data store  112   a  . . .  112 N and then generate and send data item replication confirmations to the master data store management application  118 . 
     The client device  106  is representative of a plurality of client devices that may be coupled to the network  109 . The client device  106  may comprise, for example, a processor-based system such as a computer system. Such a computer system may be embodied in the form of a desktop computer, a laptop computer, a personal digital assistant, a cellular telephone, a set-top box, a music player, a video player, a media player, a web pad, a tablet computer system, a game console, or other devices with like capability. 
     The client device  106  may be configured to execute various applications such as a data store client application  127  and other applications. The data store client application  127  may be executed in a client device  106 , for example, to facilitate interaction with the data store management application  118 . In one embodiment, the data store client application  127  may be configured, for example, to access and render network pages, such as web pages, or other network content served up by the computing device  103 , a web server, a page server, or other servers for the purpose of interfacing with the data store management application  118 . The client device  106  may be configured to execute applications beyond the data store client application  127  such as, for example, browser applications, email applications, instant message applications, and/or other applications. 
     In various embodiments, the data store client application  127  may comprise a thin client application, a thick client application, or another type of client application. Some embodiments may include a graphical user interface and/or a command-line interface. In some embodiments, the client device  106  can be configured to interact with a distributed data store provided by the computing devices  103   a  . . .  103 N via an application programming interface (API) provided by the data store management application  118  executed in a master data store or slave data store. 
     A data item update request  121  is generated by a data store client application  127 . Although the data store client application  127  is described as executed in a client device  106 , it is understood that the client device  106  may correspond to a server computer that processes business logic, generates network pages, and/or performs other tasks. Thus, although a user may generate a data item update request  121  through a user interface, a data item update request  121  may also be generated automatically by business logic applications, workflow engines, network page generation applications, and/or other applications. 
     The data item update request  121  may correspond to a portion of another application, such as, for example, a module, a library, etc. in various embodiments. The data item update request  121  may be sent over the network  109  to the data store management application  118  using hypertext transfer protocol (HTTP), simple object access protocol (SOAP), remote procedure call (RPC), remote method invocation (RMI), representational state transfer (REST), Windows Communication Foundation, and/or other frameworks and protocols. In various embodiments, the data item update request  121  may describe updates to data items by using, for example, structured query language (SQL), extensible markup language (XML), JavaScript object notation (JSON), yet another markup language (YAML), and/or other formats. 
     Turning now to  FIG. 2 , shown is another view of the networked environment  100  ( FIG. 1 ). Where  FIG. 1  focused on structure of the components,  FIG. 2  focuses on how the computing devices  103   a  . . .  103 N are distributed among physical locations. The computing devices  103   a  . . .  103 N may be referred to herein as nodes  103  or replicated nodes. Together, nodes  103  function as a distributed data store  200 . Each computing device  103  resides at a particular physical location, and these locations can be grouped into availability zones. A collection of computing devices  103  which all reside at the same physical location (e.g., building, campus, etc.) is commonly referred to as “data center.” The example networked environment  100  of  FIG. 2  includes three data centers  203   a ,  203   b ,  203   c . Availability zones and/or data centers are geographically separated to some degree, but the degree of separation may vary. That is, availability zones and/or data centers can be distributed across a town, across a city, across a country, across the world, etc. Such distribution provides for greater stability of a distributed data store  200  so that if a catastrophic event occurs in one location and may affect a subset of the nodes  103  in the distributed data store  200 , the catastrophic event does not jeopardize the system as a whole. 
     As noted above, at any point in time one node  103  acts as a master and the other nodes  103  act as slaves. In the example networked environment  100  of  FIG. 2 , node  103   m  is the master node while nodes  103   a ,  103   b  and  103   c  are slave nodes. The master node  103   m  is located at data center  203   a , as is slave node  103   a . Slave node  103   b  is located at data center  203   b  and slave node  203   c  is located at data center  203   c . It should be appreciated that a networked environment  100  can include any number of data centers, a data center  203  can include any number of nodes, and the master node can reside at any data center  203 . 
     An overview of the operation of distributed data store  200  will now be provided. A data store client application  127  executing on a client device  106  generates a data item update request  121 . The data item update request  121  is received by the master node  103   m . The master node  103   m  sends a data item replication request  206  to the slave nodes  103   a ,  103   b  and  103   c . The data item update request  121  may be an actual replica of the originally received data item update request  121 , a separate request including some or all of the information in the originally received data item update request  121 , or other variations as should be appreciated. 
     After processing the data item replication request  206 , the slave nodes  103   a ,  103   b  and  103   c  each send a data item replication confirmation  209  back to the master node  103   m . After receiving a predefined quorum of acknowledgements  209 , the master node  103   m  responds to the data store client application  127  with a data item update confirmation  124 . The quorum required to send out this data item update confirmation  124  is a locality-based durability quorum, described below in connection with  FIG. 3  and  FIG. 4 . Because the durability quorum is defined in terms of location and the location of a node does not change, changes in the node membership does not change the composition of the durability quorum. 
     The distributed data store  200  includes features which facilitate recovery upon failure of the master node  103   m . A failure can be represented by a hardware failure of some kind, an abnormal termination of the data store management application  118 , and/or other failure as can be appreciated. Therefore, the remaining computing devices  103  executing an instance of the data store management application  118  can elect a new master node by employing a consensus algorithm. In some embodiments, the data store management application  118  executed in the various computing devices  103  can be configured to collectively employ a Paxos election scheme in order to determine the identity of the computing device  103  that will serve as the master. The election of a master among the various computing devices  103  in the distributed data store  200  can also be determined by other methods of reaching consensus in a distributed system of peers as can be appreciated. The quorum required in the election of a new master is a locality-based failover quorum, described below in connection with  FIG. 3  and  FIG. 5 . The use of locality-based quorums for durability and for election allows the new master to synchronize with a smaller number of slave nodes as compared to a simple majority quorum, which provides a faster failover that is also fault tolerant. 
     Referring next to  FIG. 3 , shown is a flowchart that provides one example of the operation of a portion of the data store management application  118  ( FIG. 1 ) according to various embodiments. In particular, the flowchart of  FIG. 3  illustrates aspects of a process in which a data write requested by a client is replicated in a distributed data store. It is understood that the flowchart of  FIG. 3  provides merely an example of the many different types of functional arrangements that may be employed to implement the operation of the portion of the data store management application  118  as described herein. As an alternative, the flowchart of  FIG. 3  may be viewed as depicting an example of steps of a method implemented in the computing device  103  ( FIG. 1 ) according to one or more embodiments. 
     Beginning at box  303 , the data store management application  118  ( FIG. 1 ) executing on the master node  103   m  ( FIG. 2 ) replicates a data item update request  121  ( FIG. 1 ) to the slave nodes  103  ( FIG. 2 ). Next at box  306 , the master data store management application  118  waits for the data item update request  121  to obtain a locality-based durability quorum. As used herein, reaching a locality-based durability quorum means that a master action such as an update to the replicated data store is not considered durable until the update is acknowledged by at least one node  103  located in each of K data centers  203 , where K is a configurable durability requirement for the distributed data model. K is less than N, where N is the total number of data centers  203 . Gaining K-data center durability guarantees (barring a double fault scenario) that if the master node  103  fails, the succeeding master will know about, and have processed, the update. 
     Though  FIG. 3  shows a single replicate and acknowledge path in boxes  303  and  306 , some embodiments of master data store management application  118  support handling multiple replicates in parallel. The master data store management application  118  repeats the operation of boxes  303  and  306  until an event  309  indicates that a failure of the master node  103   m  has been detected. The event may take the form of a timeout, a message from the master node, a message from another node, or any other suitable implementation. 
     Upon failure detection, the data store management application  118  executing on a node  103  other than the failed master node  103   m  begins operating at box  312 . At box  312 , a new master candidate is determined by an election among nodes  103  other than the failed master node  103   m . The election employs a consensus algorithm as described above. Next at box  315  the data store management application  118  on the newly elected master node  103  waits for consensus among a locality-based failover quorum before acting as the master (e.g., before receiving data item update requests  121  from clients). As used herein, locality-based failover quorum is defined as participation from all nodes  103  in N−K+1 data centers  203 . Having seen full participation from the N−K+1 quorum of data centers  203  at box  315 , the newly elected master is guaranteed to know about all of the updates  121  that have gained locality-based durability. To this end, at box  318 , the newly elected master ensures that all data discovered during the wait for consensus (box  315 ) is locality-based durable. 
     At box  321 , having ensured that the new master candidate knows about all locality-based durable updates, the data store management application  118  executing on the newly elected master node  103  transitions from a new master candidate to the master. As such, the data store management application  118  receives data item update requests  121  from clients and processes them according to boxes  303  and  306 . The use of a locality-based failover quorum together with a locality-based durability quorum means that the failover at box  318  is guaranteed to occur safely without any loss of client updates  121 . This is true because the newly elected master node  103  is guaranteed to have seen the most recent update  121  that the failed master had successfully completed, as well as all writes prior to that. 
     Turning now to  FIG. 4 , shown is a flowchart that provides additional detail for the write replication operations of  FIG. 3  according to various embodiments. It is understood that the flowchart of  FIG. 4  provides merely an example of the many different types of functional arrangements that may be employed to implement the operation of the portion of the data store management application  118  ( FIG. 1 ) as described herein. As an alternative, the flowchart of  FIG. 4  may be viewed as depicting an example of steps of a method implemented in the computing device  103  ( FIG. 1 ) according to one or more embodiments. 
     Beginning at box  403 , the data store management application  118  ( FIG. 1 ) executing on the master node  103   m  ( FIG. 2 ) receives a data item update request  121  ( FIG. 1 ) from a data store client application  127  ( FIG. 1 ). The data item update request  121  includes data to be updated. Next at box  406 , the master data store management application  118  applies the update by sending a data item replication request  206  ( FIG. 2 ) to each of the slave data store management applications  118 . Having requested replication by the slave data stores, the master data store management application  118  now waits for the write action to be K-data center durable. 
     At box  409 , the master data store management application  118  receives a data item replication confirmation  209  ( FIG. 2 ) from a particular slave data store management application  118 , acknowledging that the slave has committed the data item to its data store. As described above in connection with  FIG. 1  and  FIG. 2 , each slave data store management application  118  executes on a particular node  103 , where that node resides at a particular data center  203 . Next at box  412  the master data store management application  118  determines whether the write is locality-based durable, i.e., whether write acknowledgements have been received at from least one node  103  located in each of K data centers  203 , where K is a value less than the number of data centers  203 , represented as N. If at box  412  it is determined that the write is not K-data center durable, then processing returns to block  409 , where the master data store management application  118  waits for write acknowledgements from additional nodes  103 . 
     If at box  412  it is determined that the write is K-data center  203  durable, then processing moves to block  415 . At box  415 , the master data store management application  118  sends a confirmation to the data store client application  127  which sent the data item update request  121  in box  403 . The confirmation sent in box  415  indicates that the data item update request  121  received in box  403  was successfully performed. The process of  FIG. 4  is then complete. In some embodiments, a durability timeout is used such that if locality-based durability is not achieved in a predetermined amount of time, a failure code is send to the data store client application  127  instead of a confirmation. 
     Moving on to  FIG. 5 , shown is a flowchart that provides additional detail for the new master transition operations of  FIG. 3  according to various embodiments. In particular, the flowchart of  FIG. 5  illustrates aspects of a single master failover in a distributed data store. It is understood that the flowchart of  FIG. 5  provides merely an example of the many different types of functional arrangements that may be employed to implement the operation of the portion of the data store management application  118  ( FIG. 1 ) as described herein. As an alternative, the flowchart of  FIG. 5  may be viewed as depicting an example of steps of a method implemented in the computing device  103  ( FIG. 1 ) according to one or more embodiments. 
     Beginning at box  503 , a failure of the master node  103  ( FIG. 1 ) is detected. Such a failure can be represented by a hardware failure of some kind, an abnormal termination of the master data store management application  118 , and/or other failure as can be appreciated. At box  506 , the remaining computing devices  103  that are executing an instance of the data store management application  118  vote in an election for a new master node  103 , and a new master is elected. 
     At this point, the distributed data store is not yet consistent if the previous (now failed) master had received one or more data item update request from a clients and had sent corresponding replication request to some slaves, but not to the slave which is now the new master. Accordingly, at box  509 , the data store management application  118  executing on the newly elected master node  103  recovers replicated client data as necessary from other slave nodes  103  to maintain data consistency. Notably, during this recovery process the newly elected master node  103  does not begin acting as master (e.g., receiving update requests from clients) until the distributed system can guarantee that the failover can occur without losing any previous writes from the failed master. To this end, at box  512  the newly elected master node  103  determines whether the data writes recovered in box  509  include participation from all nodes  103  in a predetermined quorum. Specifically, at box  512  the newly elected master node  103  determines whether recovered data has been received from a locality-based failover quorum. As noted above, the locality-based failover quorum is defined as N−K+1 data centers. 
     If at box  512  it is determined that the locality-based failover quorum has been reached, processing continues at block  515 . At box  515 , the data store management application  118  executing on the newly elected master node  103  ensures that data recovered in box  509  is locality-based durable. Having determined that the recovered data is locality-based durable, at box  518  the data store management application  118  executing on the newly elected master node  103  transitions to a state in which the application is ready to receive data item update requests  121  from clients. Determining that the locality-based failover quorum has been reached and that recovered data is locality-based durable guarantees that the failover can occur safely, by guaranteeing that the newly elected master node  103  has recovered the most recent write that the failed master had successfully completed, as well as all writes prior to that. The process of  FIG. 5  is then complete. 
     If at box  512  it is instead determined that the locality-based failover quorum has not been reached, the data store management application  118  executing on the newly elected master node  103  returns to box  509  to wait for additional client data to be recovered from other slave nodes  103 . As described above, when the locality-based failover quorum is reached, the master node  103  transitions in box  515  and the process ends. 
     Moving on to  FIG. 6 , shown is a schematic block diagram of the computing device  103  according to an embodiment of the present disclosure. The computing device  103  includes at least one processor circuit, for example, having a processor  603  and a memory  606 , both of which are coupled to a local interface  609 . To this end, the computing device  103  may comprise, for example, at least one server computer or like device. The local interface  609  may comprise, for example, a data bus with an accompanying address/control bus or other bus structure as can be appreciated. 
     Stored in the memory  606  are both data and several components that are executable by the processor  603 . In particular, stored in the memory  606  and executable by the processor  603  are the data store management application  118  and potentially other applications. Also stored in the memory  606  may be a data store  112  and other data. In addition, an operating system may be stored in the memory  606  and executable by the processor  603 . While not illustrated, the client device  106  also includes components like those shown in  FIG. 6 , whereby data store management application  118  is stored in a memory and executable by a processor. 
     It is understood that there may be other applications that are stored in the memory  606  and are executable by the processors  603  as can be appreciated. Where any component discussed herein is implemented in the form of software, any one of a number of programming languages may be employed such as, for example, C, C++, C#, Objective C, Java, Javascript, Perl, PHP, Visual Basic, Python, Ruby, Delphi, Flash, or other programming languages. 
     A number of software components are stored in the memory  606  and are executable by the processor  603 . In this respect, the term “executable” means a program file that is in a form that can ultimately be run by the processor  603 . Examples of executable programs may be, for example, a compiled program that can be translated into machine code in a format that can be loaded into a random access portion of the memory  606  and run by the processor  603 , source code that may be expressed in proper format such as object code that is capable of being loaded into a random access portion of the memory  606  and executed by the processor  603 , or source code that may be interpreted by another executable program to generate instructions in a random access portion of the memory  606  to be executed by the processor  603 , etc. An executable program may be stored in any portion or component of the memory  606  including, for example, random access memory (RAM), read-only memory (ROM), hard drive, solid-state drive, USB flash drive, memory card, optical disc such as compact disc (CD) or digital versatile disc (DVD), floppy disk, magnetic tape, or other memory components. 
     The memory  606  is defined herein as including both volatile and nonvolatile memory and data storage components. Volatile components are those that do not retain data values upon loss of power. Nonvolatile components are those that retain data upon a loss of power. Thus, the memory  606  may comprise, for example, random access memory (RAM), read-only memory (ROM), hard disk drives, solid-state drives, USB flash drives, memory cards accessed via a memory card reader, floppy disks accessed via an associated floppy disk drive, optical discs accessed via an optical disc drive, magnetic tapes accessed via an appropriate tape drive, and/or other memory components, or a combination of any two or more of these memory components. In addition, the RAM may comprise, for example, static random access memory (SRAM), dynamic random access memory (DRAM), or magnetic random access memory (MRAM) and other such devices. The ROM may comprise, for example, a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or other like memory device. 
     Also, the processor  603  may represent multiple processors, and the memory  606  may represent multiple memories that operate in parallel processing circuits, respectively. In such a case, the local interface  609  may be an appropriate network  109  ( FIG. 1 ) that facilitates communication between any two of the multiple processors  603 , between any processor  603  and any of the memories  606 , or between any two of the memories  606 , etc. The local interface  609  may comprise additional systems designed to coordinate this communication, including, for example, performing load balancing. The processor  603  may be of electrical or of some other available construction. 
     Although the data store management application  118  and other various systems described herein may be embodied in software or code executed by general purpose hardware as discussed above, as an alternative the same may also be embodied in dedicated hardware or a combination of software/general purpose hardware and dedicated hardware. If embodied in dedicated hardware, each can be implemented as a circuit or state machine that employs any one of or a combination of a number of technologies. These technologies may include, but are not limited to, discrete logic circuits having logic gates for implementing various logic functions upon an application of one or more data signals, application specific integrated circuits having appropriate logic gates, or other components, etc. Such technologies are generally well known by those skilled in the art and, consequently, are not described in detail herein. 
     The flowcharts of  FIGS. 3 ,  4 , and  5  show the functionality and operation of an implementation of portions of the data store management application  118 . If embodied in software, each block may represent a module, segment, or portion of code that comprises program instructions to implement the specified logical function(s). The program instructions may be embodied in the form of source code that comprises human-readable statements written in a programming language or machine code that comprises numerical instructions recognizable by a suitable execution system such as a processor  603  in a computer system or other system. The machine code may be converted from the source code, etc. If embodied in hardware, each block may represent a circuit or a number of interconnected circuits to implement the specified logical function(s). 
     Although the flowcharts of  FIGS. 3 ,  4 , and  5  show a specific order of execution, it is understood that the order of execution may differ from that which is depicted. For example, the order of execution of two or more blocks may be scrambled relative to the order shown. Also, two or more blocks shown in succession in  FIGS. 3 ,  4 , and  5  may be executed concurrently or with partial concurrence. Further, in some embodiments, one or more of the blocks shown in  FIGS. 3 ,  4 , and  5  may be skipped or omitted. In addition, any number of counters, state variables, warning semaphores, or messages might be added to the logical flow described herein, for purposes of enhanced utility, accounting, performance measurement, or providing troubleshooting aids, etc. It is understood that all such variations are within the scope of the present disclosure. 
     Also, any logic or application described herein, including the data store management application  118 , that comprises software or code can be embodied in any non-transitory computer-readable medium for use by or in connection with an instruction execution system such as, for example, a processor  603  in a computer system or other system. In this sense, the logic may comprise, for example, statements including instructions and declarations that can be fetched from the computer-readable medium and executed by the instruction execution system. In the context of the present disclosure, a “computer-readable medium” can be any medium that can contain, store, or maintain the logic or application described herein for use by or in connection with the instruction execution system. The computer-readable medium can comprise any one of many physical media such as, for example, magnetic, optical, or semiconductor media. More specific examples of a suitable computer-readable medium would include, but are not limited to, magnetic tapes, magnetic floppy diskettes, magnetic hard drives, memory cards, solid-state drives, USB flash drives, or optical discs. Also, the computer-readable medium may be a random access memory (RAM) including, for example, static random access memory (SRAM) and dynamic random access memory (DRAM), or magnetic random access memory (MRAM). In addition, the computer-readable medium may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or other type of memory device. 
     It should be emphasized that the above-described embodiments of the present disclosure are merely possible examples of implementations set forth for a clear understanding of the principles of the disclosure. Many variations and modifications may be made to the above-described embodiment(s) without departing substantially from the spirit and principles of the disclosure. All such modifications and variations are intended to be included herein within the scope of this disclosure and protected by the following claims.