Patent Description:
There are many challenges to backing-up high-performance databases. Some considerations include:.

The instant disclosure, therefore, identifies and addresses a need for systems and methods for backing-up an eventually-consistent database in a production cluster.

<CIT> describes techniques for creating, in a network, a single instance of deduplicated data across a plurality of end user data. A first computing device receives data associated with a plurality of computing devices, the plurality of computing devices being managed by the first computing device. The first computing device aggregates and deduplicates the data associated with each of the plurality of computing devices. The deduplicated aggregated data set is then transmitted to a second computing device for further aggregation and deduplication with one or more additional aggregated data sets generated by other computing devices managing respective sets of computing devices.

<CIT> describes authoritative and non-authoritative restore.

<CIT> describes delta partitions for backup and restore.

<CIT> describes backup, archive and disaster recovery solution with distributed storage over multiple clouds.

<CIT> describes systems and methods for managing distributed database deployments.

<CIT> describes system and method for partitioning backup data streams in a deduplication based storage system.

The invention is set out by the subject-matter of the independent claims; embodiments are provided by the subject-matter of the dependent claims.

Throughout the drawings, identical reference characters and descriptions indicate similar, but not necessarily identical, elements. While the example embodiments described herein are susceptible to various modifications and alternative forms, specific embodiments have been shown by way of example in the drawings and will be described in detail herein. However, the example embodiments described herein are not intended to be limited to the particular forms disclosed. Rather, the instant disclosure covers all modifications, equivalents, and alternatives falling within the scope of the appended claims.

The present disclosure is generally directed to systems and methods for backing-up an eventually-consistent database in a production cluster. The provided systems and methods may perform backup and/or restoration of an eventually-consistent, scale-out database (e.g., like CASSANDRA) in a phased manner that enables repair-less restore, replica removal, and record synthesis. In some examples, the systems and methods described herein may form a stable copy of production data on a production node, transfer information from the stable copy to a backup copy on a backup node, merge record updates into complete backup records while discarding stale and redundant records, and transfer the complete backup records from the backup node to a cloud storage device. In embodiments, the disclosed techniques may be utilized in connection with cloud-based storage devices.

By doing so, in examples, the systems and methods described herein may improve the functioning of computing devices by automatically protecting large datasets within short spans of time, providing authoritative copies, solving deduplication challenges, backing-up records stored in changing architectures, backing-up records of reconfigured nodes, and/or backing-up data files that increase in size over time, thus enabling cost-effective storage management. Also, in examples, the systems and methods described herein may also save power and/or better-manage network bandwidth utilization.

The following will provide, with reference to <FIG>, detailed descriptions of example systems for backing-up an eventually-consistent database in a production cluster. Detailed descriptions of corresponding computer-implemented methods will also be provided in connection with <FIG>.

<FIG> is a block diagram of an example system <NUM> for backing-up an eventually-consistent database in a production cluster. As illustrated in this figure, example system <NUM> may include one or more modules <NUM> for performing one or more tasks. As will be explained in greater detail below, modules <NUM> may include a forming module <NUM>, a provisioning module <NUM>, a first transferring module <NUM>, a performing module <NUM>, an identifying and discarding module <NUM>, and/or a second transferring module <NUM>. Although illustrated as separate elements, one or more of modules <NUM> in <FIG> may represent portions of a single module or application.

In certain embodiments, one or more of modules <NUM> in <FIG> may represent one or more software applications or programs that, when executed by a computing device, may cause the computing device to perform one or more tasks. For example, and as will be described in greater detail below, one or more of modules <NUM> may represent modules stored and configured to run on one or more computing devices, such as the devices illustrated in <FIG> (e.g., computing device <NUM>, server <NUM>, production node <NUM> backup node <NUM>, and/or cloud storage device <NUM>). One or more of modules <NUM> in <FIG> may also represent all or portions of one or more special-purpose computers configured to perform one or more tasks.

As illustrated in <FIG>, example system <NUM> may also include one or more storage devices, such as storage device <NUM>. Storage device <NUM> generally represents any type or form of volatile or non-volatile storage device or medium capable of storing data and/or computer-readable instructions. In one example, storage device <NUM> may store, load, and/or maintain information indicating one or more of an amount of data in stable copy <NUM> and/or replication factor <NUM>. In one example, storage device <NUM> may store, load, and/or maintain information indicating one or more of production data, stable copy of production data <NUM>, backup copy <NUM>, record updates <NUM>, complete backup records <NUM>, redundant records <NUM>, and/or stale records <NUM>. Examples of storage device <NUM> include, without limitation, Random Access Memory (RAM), Read Only Memory (ROM), flash memory, Hard Disk Drives (HDDs), Solid-State Drives (SSDs), optical disk drives, caches, AMAZON ELASTIC BLOCK STORE service, variations or combinations of one or more of the same, and/or any other suitable storage memory.

The term "replication factor," as used herein, generally refers to an integer number of copies of data stored across a cluster of nodes. For example, without limitation, when a cluster's replication factor equals two and data is written to the cluster, two copies of the data are stored (e.g., in different nodes and/or racks) within the cluster. A replication factor greater than one may provide fault tolerance.

As illustrated in <FIG>, example system <NUM> may also include one or more physical processors, such as physical processor <NUM>. Physical processor <NUM> generally represents any type or form of hardware-implemented processing unit capable of interpreting and/or executing computer-readable instructions. In one example, physical processor <NUM> may access and/or modify one or more of modules <NUM> stored in memory <NUM>. Additionally or alternatively, physical processor <NUM> may execute one or more of modules <NUM> to facilitate backing-up an eventually-consistent database in a production cluster. Examples of physical processor <NUM> include, without limitation, microprocessors, microcontrollers, Central Processing Units (CPUs), Field-Programmable Gate Arrays (FPGAs) that implement softcore processors, Application-Specific Integrated Circuits (ASICs), portions of one or more of the same, variations or combinations of one or more of the same, or any other suitable physical processor.

As illustrated in <FIG>, example system <NUM> may also include one or more memory devices, such as memory <NUM>. Memory <NUM> generally represents any type or form of volatile or non-volatile storage device or medium capable of storing data and/or computer-readable instructions. In one example, memory <NUM> may store, load, and/or maintain one or more of modules <NUM>. Examples of memory <NUM> include, without limitation, Random Access Memory (RAM), Read Only Memory (ROM), flash memory, Hard Disk Drives (HDDs), Solid-State Drives (SSDs), optical disk drives, caches, variations or combinations of one or more of the same, or any other suitable storage memory.

Example system <NUM> in <FIG> may be implemented in a variety of ways. For example, all or a portion of example system <NUM> may represent portions of example system <NUM> in <FIG>. As shown in <FIG>, system <NUM> may include a computing device <NUM> in communication (e.g., via a network <NUM>) with a server <NUM>, a production node <NUM>, a backup node <NUM>, and/or a cloud storage device <NUM>. In one example, all or a portion of the functionality of modules <NUM> may be perfonned by computing device <NUM>, server <NUM>, production node <NUM>, backup node <NUM>, cloud storage device <NUM>, and/or any other suitable computing system. In examples, all or a portion of example system <NUM> may be a constituent component of computing device <NUM>, server <NUM>, production node <NUM>, a production cluster, backup node <NUM>, a backup cluster, cloud storage device <NUM>, and/or any other suitable computing system. As will be described in greater detail below, one or more of modules <NUM> from <FIG> may, when executed by at least one processor of computing device <NUM>, server <NUM>, production node <NUM>, backup node <NUM>, and/or cloud storage device <NUM>, enable computing device <NUM>, server <NUM>, production node <NUM>, backup node <NUM>, and/or cloud storage device <NUM> to back-up an eventually-consistent database in a production cluster. In examples, production node <NUM> may be a constituent component of a production cluster. In embodiments, backup node <NUM> may be a constituent component of a backup cluster, such as a CASSANDRA cluster.

Computing device <NUM> generally represents any type or form of computing device capable of reading computer-executable instructions. In some examples, computing device <NUM> may represent a computer running storage management software. Additional examples of computing device <NUM> include, without limitation, laptops, tablets, desktops, servers, cellular phones, Personal Digital Assistants (PDAs), multimedia players, embedded systems, wearable devices (e.g., smart watches, smart glasses, etc.), smart vehicles, so-called Internet-of-Things devices (e.g., smart appliances, etc.), gaming consoles, variations or combinations of one or more of the same, or any other suitable computing device.

Network <NUM> generally represents any medium or architecture capable of facilitating communication or data transfer. In one example, network <NUM> may facilitate communication between computing device <NUM> and server <NUM>. In this example, network <NUM> may facilitate communication or data transfer using wireless and/or wired connections. Examples of network <NUM> include, without limitation, an intranet, a Wide Area Network (WAN), a Local Area Network (LAN), a Personal Area Network (PAN), the Internet, Power Line Communications (PLC), a cellular network (e.g., a Global System for Mobile Communications (GSM) network), portions of one or more of the same, variations or combinations of one or more of the same, or any other suitable network.

Server <NUM> generally represents any type or form of computing device that is capable of reading computer-executable instructions In some examples, computing device <NUM> may represent a computer running storage management software. Additional examples of server <NUM> include, without limitation, storage servers, database servers, application servers, and/or web servers configured to run certain software applications and/or provide various storage, database, and/or web services. Although illustrated as a single entity in <FIG>, server <NUM> may include and/or represent a plurality of servers that work and/or operate in conjunction with one another.

Production node <NUM> generally represents any type or form of computing device that is capable of reading computer-executable instructions and/or storing information. In some examples, production node <NUM> may represent a computer running storage software. Additional examples of production node <NUM> include, without limitation, storage servers, database servers, application servers, and/or web servers configured to run certain software applications and/or provide various storage, database, and/or web services. Although illustrated as a single entity in <FIG>, production node <NUM> may include and/or represent a plurality of servers that work and/or operate in conjunction with one another. In examples, production node <NUM> may be a constituent component of a production cluster. In embodiments, production node <NUM> may be a part of a cluster of nodes storing information for an APACHE CASSANDRA database.

In examples, production node <NUM> may store production information and/or stable copy of production data <NUM>.

Backup node <NUM> generally represents any type or form of computing device that is capable of reading computer-executable instructions and/or storing information. In some examples, backup node <NUM> may represent a computer running storage software. Additional examples of backup node <NUM> include, without limitation, storage servers, database servers, application servers, and/or web servers configured to run certain software applications and/or provide various storage, database, and/or web services. Although illustrated as a single entity in <FIG>, backup node <NUM> may include and/or represent a plurality of servers that work and/or operate in conjunction with one another. In embodiments, backup node <NUM> may be a constituent component of a backup cluster.

In examples, backup node <NUM> may store backup copy <NUM>, record updates <NUM>, complete backup records <NUM>, redundant records <NUM>, and/or stale records <NUM>.

Cloud storage device <NUM> generally represents any type or form of computing device that is capable of reading computer-executable instructions and/or storing information. In some examples, cloud storage device <NUM> may represent a computer running storage management software. Additional examples of cloud storage device <NUM> include, without limitation, storage servers, database servers, application servers, and/or web servers configured to run certain software applications and/or provide various storage, database, and/or web services. In examples, cloud storage device <NUM> may be provided by AMAZON SIMPLE STORAGE SERVICE (S3). Although illustrated as a single entity in <FIG>, cloud storage device <NUM> may include and/or represent a plurality of servers that work and/or operate in conjunction with one another.

In examples, cloud storage device <NUM> may store backup copy <NUM> and/or complete backup records <NUM>.

Many other devices or subsystems may be connected to system <NUM> in <FIG> and/or system <NUM> in <FIG>. Conversely, all of the components and devices illustrated in <FIG> and <FIG> need not be present to practice the embodiments described and/or illustrated herein. The devices and subsystems referenced above may also be interconnected in different ways from that shown in <FIG>. Systems <NUM> and <NUM> may also employ any number of software, firmware, and/or hardware configurations. For example, one or more of the example embodiments disclosed herein may be encoded as a computer program (also referred to as computer software, software applications, computer-readable instructions, and/or computer control logic) on a computer-readable medium.

The term "computer-readable medium," as used herein, generally refers to any form of device, carrier, or medium capable of storing or carrying computer-readable instructions. Examples of computer-readable media include, without limitation, transmission-type media, such as carrier waves, and non-transitory-type media, such as magnetic-storage media (e.g., hard disk drives, tape drives, and floppy disks), optical-storage media (e.g., Compact Disks (CDs), Digital Video Disks (DVDs), and BLU-RAY disks), electronic-storage media (e.g., solid-state drives and flash media), and other distribution systems.

<FIG> is a flow diagram of an example computer-implemented method <NUM> for backing-up an eventually-consistent database in a production cluster. The steps shown in <FIG> may be performed by any suitable computer-executable code and/or computing system, including system <NUM> in <FIG>, system <NUM> in <FIG>, and/or variations or combinations of one or more of the same. In one example, each of the steps shown in <FIG> may represent an algorithm whose structure includes and/or is represented by multiple sub-steps, examples of which will be provided in greater detail below.

As illustrated in <FIG>, at step <NUM> one or more of the systems described herein may form, on production nodes, stable copies of production data. Forming stable production copies may provide independent instances of production data from which to perform rebase operations and/or enable expiring backups made previous to rebase points in time to enable reclaiming storage space. The systems described herein may perform step <NUM> in a variety of ways. For example, forming module <NUM> may, as part of computing device <NUM> in <FIG>, form, on production node <NUM>, stable copy <NUM> of production data. Producing stable copies establishes copies of rapidly-changing production data at respective instances in time.

In some embodiments, method <NUM> may include identifying a topology of a production cluster of which the production node is a constituent part to identify the production node as requiring backup. For example, system <NUM> and/or computing device <NUM> may identify a topology of a production cluster of which production node <NUM> is a constituent part. System <NUM> and/or computing device <NUM> may also identify at least one production cluster and/or production node (e.g., production node <NUM>) as requiring backup.

In some embodiments, method <NUM> may include determining the amount of data in the stable copy, such as for use in step <NUM>. For example, system <NUM> and/or computing device <NUM> may determine an amount of data in stable copy of production data <NUM>.

As illustrated in <FIG>, at step <NUM> one or more of the systems described herein may provision storage on backup nodes based on amounts of data in the stable copies and/or replication factors. The replication factors may be replication factors of key spaces involved in backups. The systems described herein may perform step <NUM> in a variety of ways. For example, provisioning module <NUM> may, as part of computing device <NUM> in <FIG>, provision storage on backup node <NUM> based on amount of data in stable copy <NUM> and/or replication factor <NUM>. Amount of data in stable copy <NUM> may be an amount of data in stable copy of production data <NUM>.

In an example, method <NUM> may include provisioning the backup node, in a backup cluster, based on the amount of data in the stable copy (i.e., the amount of data to be backed-up) and/or processing power of the backup node. For example, system <NUM> and/or computing device <NUM> may provision backup node <NUM> based on amount of data in stable copy <NUM> and/or processing power of backup node <NUM>.

In examples, a number of production nodes in a production cluster of which the production node is a constituent part may not equal a number of backup nodes in a backup cluster of which the backup node is a constituent part. In some embodiments, a number of production nodes in a production cluster of which the production node is a constituent part may equal a number of backup nodes in a backup cluster of which the backup node is a constituent part.

As illustrated in <FIG>, at step <NUM> one or more of the systems described herein may transfer information from the stable copies to backup copies on the backup nodes. The systems described herein may perform step <NUM> in a variety of ways. For example, first transferring module <NUM> may, as part of computing device <NUM> in <FIG>, transfer information from stable copy of production data <NUM> to backup copy <NUM> on backup node <NUM>. In examples, data from different stable back-up copies (i.e., replicas) may be transferred to the same node in the backup cluster in parallel from different nodes in the production cluster.

As illustrated in <FIG>, at step <NUM> one or more of the systems described herein may optimize records by performing record synthesis on the backup copies to merge record updates into complete backup records. Record synthesis may merge updates to different columns at different times to recreate complete records. Step <NUM> may be performed locally at backup nodes to process backup copies in-parallel and/or substantially without inter-node communication. The systems described herein may perform step <NUM> in a variety of ways. For example, performing module <NUM> may, as part of computing device <NUM> in <FIG>, perform record synthesis on backup copy <NUM> to merge record updates <NUM> into complete backup records <NUM>.

As illustrated in <FIG>, at step <NUM> one or more of the systems described herein may optimize records by identifying and/or discarding at least one stale record and/or at least one redundant record in the complete backup records and/or backup copy. The systems described herein may perform step <NUM> in a variety of ways. For example, identifying and discarding module <NUM> may, as part of computing device <NUM> in <FIG>, identify and/or discard at least one stale record <NUM> and/or at least one redundant record <NUM> in complete backup records <NUM> and/or backup copy <NUM>. Deduplicating may provide consistent data to reduce a probability that CASSANDRA will automatically perform repairs to fix inconsistencies in restored backups.

In examples, method <NUM> may include optimizing records to reclaim storage space. A common problem with a forever incrementally back-up strategy is reclaiming space because of backups expiring. In embodiments, the provided systems and methods may rebase backups by creating synthetic full backups from independent instances of production clusters. This may effectively create an independent chain, expire backups previous to a rebase point, and reclaim storage space previously used by expired backups. For example, method <NUM> may rebase at least a portion of backup copy <NUM>, such as to form complete backup records <NUM>.

In examples, method <NUM> may perform rebasing on backup copies in a backup node and not on data on production nodes. In an example, backup copies are partitioned among different backup nodes so each backup node may perform rebase operations independently. Rebasing may occur during backup expiry to delete backups and recover the associated storage space and/or to enable faster restores. Rebasing may be performed periodically (e.g., to consistently reduce a number of incremental backups. Overwritten data may be removed to produce a smaller resultant dataset. Following rebase operations, subsequent backups may be re-parented to create new backup chains. In examples, method <NUM> may delete backup copies may prior to a rebase point and reclaim storage space previously used by the deleted backups.

<FIG> depicts an example of a rebase operation <NUM>. In <FIG>, "F" represents a full backup, "I" represents an incremental backup, "R" represents a rebase backup, and the arrows represent dependency of a backup on a prior backup (e.g., an incremental backup being dependent on a full backup).

At time <NUM>, full backup F1 has six incremental backups dependent thereon (I11, I12, I13, I21, I22, I23).

At time <NUM>, a rebase operation creates rebase backup R2 from full backup F1 and incremental backups I11, I12, and I13.

At time <NUM>, incremental backup I21 is reparented from I13 to R2.

At time <NUM>, full backup F1 and incremental backups I11, I12, and I13 are expired, as represented in <FIG> by strikethrough. In examples, any of full backup F1 and incremental backups I11, I12, and I13 may be deleted to save storage space.

At time <NUM>, a rebase operation creates rebase backup R3 from rebase backup R2 and incremental backups I21, I22, and I23.

At time <NUM>, rebase backup R2 and incremental backups I21, I22, and <NUM> may be expired, as represented in <FIG> by strikethrough. In examples, any of rebase backup R2 and incremental backups <NUM>, I22, and I23 may be deleted to save storage space.

At time <NUM>, incremental backup I31 is created and parented to rebase backup R3.

Returning to <FIG>, at step <NUM> one or more of the systems described herein may transfer (i.e., upload) the complete backup records from the backup nodes to cloud storage devices. Backup data may be transferred in parallel from backup nodes in the backup cluster to backup media. The systems described herein may perform step <NUM> in a variety of ways. For example, second transferring module <NUM> may, as part of computing device <NUM> in <FIG>, transfer complete backup records <NUM> from backup node <NUM> to cloud storage device <NUM>.

In an example, method <NUM> may include reverting the backup node to a pre-transfer state. For example, at least a portion of backup copy <NUM> may be deleted from backup node <NUM>.

In an embodiment, method <NUM> may include restoring the backup copy from the cloud storage device to the production node. For example, at least a portion of backup copy <NUM> and/or at least a portion of complete backup records <NUM> may be restored from cloud storage device <NUM> to computing device <NUM>, server <NUM>, production node <NUM>, backup node <NUM>, a different production node, and/or a device coupled to network <NUM>.

In an embodiment, restoring may include determining amounts of data to process from backup copies. Keyspaces that need to be restored may be identified and chains of incremental backups are identified until full backups are identified for the keyspaces. Restoring may include computing an amount of data that needs to be transferred for each of the keyspaces being restored. In embodiments, restoring may include determining amounts of data to process from backup copies on cloud storage device <NUM>. Restoring may include computing an amount of data that needs to be transferred from cloud storage device <NUM> for each of the keyspaces being restored.

In an example, restoring may include preparing backup clusters. Having identified amounts of data to be restored, nodes in backup clusters may be provisioned and storage on the backup nodes in the backup clusters may be provisioned. The number of nodes that need to be provisioned may depend on amounts of data that need to be processed and/or processing power of the backup nodes. Restoring may include distributing responsibilities of processing individual column families to the nodes in the backup cluster such that no two nodes process the same columns family and/or the data is equally distributed in each of the nodes. For example, restoring may include preparing backup clusters including backup node <NUM> and/or provisioning storage on backup node <NUM>.

In examples, restoring may include downloading the data in parallel to each of the nodes in the backup cluster from the backup media (e_g. , cloud storage device). For example, restoring may include downloading backup copy <NUM> from cloud storage device <NUM> to backup node <NUM>.

In an embodiment, restoring may optionally include performing record synthesis that merges updates to different columns at different times to recreate complete records. Savings of storage space may be gained by performing record synthesis, such as when restoring from an incremental backup. For example, restoring may include performing record synthesis that merges record updates <NUM> to different columns at different times to recreate complete backup records <NUM>. Optimizing backup data in this manner may reduce an amount of data to be restored.

In an embodiment, restoring may include preparing production clusters. Restoring may include creating schemas for the keyspaces that need to be restored and/or preparing production clusters to receive data from backup clusters. For example, restoring may include preparing a production cluster including production node <NUM> to receive data from backup node <NUM>.

In examples, restoring may include transferring data. Data may be transferred (e.g., scattered) in-parallel from backup nodes in the backup clusters to production nodes in production clusters. Restoring may include multiply writing records to different production nodes depending on replication factors. In examples, restoring may include transferring data from backup node <NUM> to production node <NUM>.

In embodiments, restoring may include removing temporary data structures on the backup nodes in the backup clusters. In examples, restoring may include reverting backup nodes to a pre-transfer state. For example, restoring may include reverting backup node <NUM> to a pre-transfer state.

As detailed above, the steps outlined in method <NUM> in <FIG> may provide methods for backing-up an eventually-consistent database in a production cluster and/or restoring an eventually-consistent database from a cloud storage device. In examples, the provided systems and methods may be used with scale-out high-performance databases. By doing so, in examples, the systems and methods described herein may improve the functioning of computing devices by automatically protecting large datasets within short spans of time, providing authoritative copies, solving deduplication challenges, backing-up records stored in changing architectures, backing-up records of reconfigured nodes, and/or backing-up data files that increase in size over time, thus enabling cost-effective storage management. Also, in examples, the systems and methods described herein may also save power by reducing a quantity of data to be transferred.

While the foregoing disclosure sets forth various embodiments using specific block diagrams, flowcharts, and examples, each block diagram component, flowchart step, operation, and/or component described and/or illustrated herein may be implemented, individually and/or collectively, using a wide range of hardware, software, or firmware (or any combination thereof) configurations. In addition, any disclosure of components contained within other components should be considered example in nature since many other architectures can be implemented to achieve the same functionality.

In some examples, all or a portion of example system <NUM> in <FIG> may represent portions of a cloud-computing or network-based environment. Cloud-computing environments may provide various services and applications via the Internet. These cloud-based services (e.g., software as a service, platform as a service, infrastructure as a service, etc.) may be accessible through a web browser or other remote interface. Various functions described herein may be provided through a remote desktop environment or any other cloud-based computing environment.

In various embodiments, all or a portion of example system <NUM> in <FIG> may facilitate multi-tenancy within a cloud-based computing environment. In other words, the modules described herein may configure a computing system (e.g., a server) to facilitate multi-tenancy for one or more of the functions described herein. For example, one or more of the modules described herein may program a server to enable two or more clients (e.g., customers) to share an application that is running on the server. A server programmed in this manner may share an application, operating system, processing system, and/or storage system among multiple customers (i.e., tenants). One or more of the modules described herein may also partition data and/or configuration information of a multi-tenant application for each customer such that one customer cannot access data and/or configuration information of another customer.

According to various embodiments, all or a portion of example system <NUM> in <FIG> may be implemented within a virtual environment. For example, the modules and/or data described herein may reside and/or execute within a virtual machine. As used herein, the term "virtual machine" generally refers to any operating system environment that is abstracted from computing hardware by a virtual machine manager (e.g., a hypervisor).

In some examples, all or a portion of example system <NUM> in <FIG> may represent portions of a mobile computing environment. Mobile computing environments may be implemented by a wide range of mobile computing devices, including mobile phones, tablet computers, e-book readers, personal digital assistants, wearable computing devices (e.g., computing devices with a head-mounted display, smartwatches, etc.), variations or combinations of one or more of the same, or any other suitable mobile computing devices. In some examples, mobile computing environments may have one or more distinct features, including, for example, reliance on battery power, presenting only one foreground application at any given time, remote management features, touchscreen features, location and movement data (e.g., provided by Global Positioning Systems, gyroscopes, accelerometers, etc.), restricted platforms that restrict modifications to system-level configurations and/or that limit the ability of third-party software to inspect the behavior of other applications, controls to restrict the installation of applications (e.g., to only originate from approved application stores), etc. Various functions described herein may be provided for a mobile computing environment and/or may interact with a mobile computing environment.

While various embodiments have been described and/or illustrated herein in the context of fully functional computing systems, one or more of these example embodiments may be distributed as a program product in a variety of forms, regardless of the particular type of computer-readable media used to actually carry out the distribution. The embodiments disclosed herein may also be implemented using modules that perform certain tasks. These modules may include script, batch, or other executable files that may be stored on a computer-readable storage medium or in a computing system. In some embodiments, these modules may configure a computing system to perform one or more of the example embodiments disclosed herein.

The preceding description has been provided to enable others skilled in the art to best utilize various aspects of the example embodiments disclosed herein. This example description is not intended to be exhaustive or to be limited to any precise form disclosed. Many modifications and variations are possible without departing from the scope of the instant disclosure. The embodiments disclosed herein should be considered in all respects illustrative and not restrictive. Reference should be made to the appended claims and their equivalents in determining the scope of the instant disclosure.

Claim 1:
A computer-implemented method for backing-up an eventually-consistent database in a production cluster, at least a portion of the method being performed by a computing device (<NUM>) comprising at least one processor (<NUM>), the method comprising:
forming, on a production node (<NUM>), a stable copy of production data (<NUM>);
provisioning storage on a backup node (<NUM>) based on an amount of data in the stable copy and a replication factor;
transferring information from the stable copy to a backup copy (<NUM>) on the backup node;
performing record synthesis on the backup copy to merge record updates (<NUM>) into complete backup records (<NUM>), comprising performing a rebase operation to create a synthetic full backup (R2) from a previous full backup (F1) and one or more incremental backups (I11, I12, I13) of the backup copy;
identifying and discarding any stale records (<NUM>) and any redundant records (<NUM>) in the complete backup records, including the previous full backup (F1) and the one or more incremental backups (I11, I12, I13) used to create the synthetic full backup (R2); and
transferring the complete backup records from the backup node to a cloud storage device (<NUM>).