Dynamic API allocation based on data-tagging

Dynamic API allocation based on data-tagging is provided. Data that is stored in a local system is parsed and normalized. One or more highly used fields is identified and tagged. A counter corresponding to each highly used field is incremented upon each reference. Upon exceeding a threshold, data is migrated to object storage. An index is created for each highly used field. A bi-directional pipeline is created between the local system and the cloud-based system. The data structure is created in object storage in the cloud-based system. Data is dynamically migrated through the pipeline from the local system to cloud-based object storage. Cloud-based system sends an API endpoint to local system. Future data accesses to local data are redirected to object storage using the API endpoint. Local system continues monitoring data utilization. Upon utilization dropping below a threshold, data accesses are redirected to local system, using the local pointer.

BACKGROUND

The present invention generally relates to data storage, and more specifically, to dynamic API allocation based on data-tagging.

As systems evolve, access to system resources, such as data, may be migrated to an Application Programming Interface (API). However, retrieving data from a single stream can lead to performance degradation. Data can be made available as object storage that is dynamically available to the application.

SUMMARY

Embodiments of the present invention are directed to a computer-implemented method for dynamic API allocation based on data-tagging. The method provides that at a local system, data is received and stored in various storage architectures. The local system parses and normalizes the stored data. The local system identifies one or more fields in the data as highly used and creates an index for the one or more fields that are highly used. The local system dynamically migrates all the stored data having the one or more highly used fields from the local system to cloud-based object storage in response to a counter associated with the highly used field exceeding a threshold. The method further provides for creating a bi-directional pipeline from the local system to a cloud-based system and sending to the cloud-based system a data structure corresponding to the stored data along with a request for storage space. The local system receives from the cloud-based system a pointer that establishes addressing between the local system and an object storage location in the cloud-based system. The method further provides for migrating through the bi-directional pipeline the stored data from the local system to the object storage in the cloud-based system. The method further provides for saving the counter associated with the highly used field and setting the counter to zero, and replacing the pointer local system to the pointer from the cloud-based system, whereby requests for data are satisfied by data located by the pointer from the cloud-based system.

Embodiments of the present invention are directed to a system for dynamic API allocation based on data-tagging. arguments of a module in real-time. The system includes a memory having computer readable computer instructions, and a processor for executing the computer readable instructions, that when executed, cause the processor to perform the steps of received and storing data at a local system, whereby the data is stored in various architectures. The computer system further provides that the local system parses and normalizes the stored data. The local system identifies one or more fields in the data as highly used and creates an index for the one or more fields that are highly used. The local system dynamically migrates all the stored data having the one or more highly used fields from the local system to cloud-based object storage in response to a counter associated with the highly used field exceeding a threshold. The computer system further provides for creating a bi-directional pipeline from the local system to a cloud-based system and sending to the cloud-based system a data structure corresponding to the stored data along with a request for storage space. The local system receives from the cloud-based system a pointer that establishes addressing between the local system and an object storage location in the cloud-based system. The computer system further provides for migrating through the bi-directional pipeline the stored data from the local system to the object storage in the cloud-based system. The computer system further provides for saving the counter associated with the highly used field and setting the counter to zero, and replacing the pointer local system to the pointer from the cloud-based system, whereby requests for data are satisfied by data located by the pointer from the cloud-based system.

Embodiments of the invention are directed to a computer program product for dynamic API allocation based on data-tagging, the computer program product comprising a computer readable storage medium having program instructions embodied therewith. The computer readable storage medium is not a transitory signal per se. The program instructions are executable by a processor to cause the processor to receive and store at a local system, data in various architectures. The local system parses and normalizes the stored data. The local system identifies one or more fields in the data as highly used and creates an index for the one or more fields that are highly used. The local system dynamically migrates all the stored data having the one or more highly used fields from the local system to cloud-based object storage in response to a counter associated with the highly used field exceeding a threshold. The computer program product further provides for creating a bi-directional pipeline from the local system to a cloud-based system and sending to the cloud-based system a data structure corresponding to the stored data along with a request for storage space. The local system receives from the cloud-based system a pointer that establishes addressing between the local system and an object storage location in the cloud-based system. The computer program product further provides for migrating through the bi-directional pipeline the stored data from the local system to the object storage in the cloud-based system. The computer program product further provides for saving the counter associated with the highly used field and setting the counter to zero, and replacing the pointer local system to the pointer from the cloud-based system, whereby requests for data are satisfied by data located by the pointer from the cloud-based system.

In the accompanying figures and following detailed description of the disclosed embodiments, the various elements illustrated in the figures are provided with two or three digit reference numbers. Similar reference numbers refer to substantially the same elements.

DETAILED DESCRIPTION

As will be shown inFIGS. 4-6, embodiments of the present invention tend to improve system performance and resource utilization by providing a framework for identifying highly utilized application data. This is done either manually, or automatically by the application at the local system. In this context, “local” refers to the storage location of the data, not the location of the server relative to the cloud-based system. Therefore, the data is referred to as “local” when it is stored on the server (i.e., local system) rather than on the cloud-based system object storage.

Highly utilization application data is dynamically migrated from the local system to object storage on a cloud-based system. Application queries for data that is in the process of migration are queued at the local system until the migration completes. The migrated data is replaced in the local system data storage by a pointer generated by the cloud-based system. Local requests for the migrated data are satisfied by following the pointer to the object storage, while local requests for data that is not highly utilized is still satisfied by the local system. The location of the data is transparent to the application, which does not need modification. The database system includes a pointer, for example “url///data/text.txt”, to where the data in a data structure is actually located. The local pointer in the local system is replaced with a cloud-based pointer, such as “url://cloud.com/text-bucket/txt”. The local pointer in the local system is saved in the event that the data utilization falls below a pre-defined threshold. In that case, application data requests will be redirected to the local system. The storage and computing resources on the local system are conserved by offloading the highly utilized data, thereby improving overall local system performance, particularly I/O requests. As cloud-based systems are typically provisioned for reliability and performance under high volume workloads, the task of data management is also offloaded from the local system. This is possible, in part, because the cloud-based system can be configured to define and apply data retention policies to the migrated data, also providing that only highly utilized data occupies the object storage.

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

FIG. 3depicts an example block diagram of a host computer system/server12(server) which is in communication with one or more components. As shown, multiple servers12may be distributed over a wide geographic area and be in electronic communication with each other, and with the other components shown inFIG. 3, via the network99.

The server12is operational in numerous other computing system environments or configurations. For example, the server12may be a standalone machine, a virtual partition on physical host, a clustered server environment, or a distributed cloud computing environment that include any of the above systems or devices, and the like. When practiced in a distributed cloud computing environment, tasks may be performed by both local and remote servers12that are linked together and communicate through a communications network, such as the network99.

The server12is configured to interact with a cloud-based system, for example, the cloud computing environment described with reference toFIG. 1. The server12can communicate securely with the cloud computing environment through a database direct tunnel, i.e., VPN. This configuration establishes a direct tunnel between the database on the server12and the cloud computing environment, by an exchange of security keys. The secure tunnel allows the exchanging of the database table structure and the database data directly from a database on the server12to an object storage in the cloud computing environment without requiring an Application Programming Interface (API) or interacting with the application front end. Accelerated cloud adoption because of simplified interaction with the cloud computing environment can result.

The server12may be described in the context of executable instructions, such as a program, or more specifically, an operating system (OS)40that is an aggregate of program modules42being executed by the processing unit16to control the operation of the server12. Program modules42perform particular tasks of the OS40, such as process management; memory management; and device management. Specialized program modules42can cooperate with the OS40to perform source code management functions, such as compiling, linking, and preparing the resulting module(s) for execution by the processing unit16. Other specialized program modules can provide a transactional or database environment in which the application program modules execute. Still other specialized program modules42can cooperate with the OS40to tag data for migration to object storage.

The program modules42may be implemented as routines, programs, objects, components, logic, or data structures, for example. The program modules42performing the particular tasks may be grouped by function, according to the server12component that the program modules42control. At least a portion of the program modules42may be specialized to execute the framework ofFIGS. 4-6.

In a distributed computing environment, such as a cloud computing environment, each participating server12may be under the control of an OS40residing on each local and remote server12, respectively. In a virtual machine, also referred to as a virtual server, each instance of the virtual machine is an emulation of a physical computer. A physical computer may host multiple virtual machine instances, each sharing the hardware resources of the physical computer, and each emulating a physical computer. Each of the virtual machine instances is under the control of an OS40.

As shown inFIG. 3, the components of the server12may include, but are not limited to, one or more processors or processing units16, a system memory28, and a bus18that couples various system components, such as the system memory28, to a processor unit16.

System memory28can include computer system readable media in the form of volatile memory, such as random access memory (RAM)30and/or cache memory32. The server12may further include other removable/non-removable, volatile/non-volatile computer system storage media.

By way of example only, a storage system34can be provided as one or more devices for reading from and writing to a non-removable, non-volatile magnetic media, such as a hard disk drive (HDD) or an optical disk drive such as a CD-ROM, DVD-ROM. Each device of the storage system34can be connected to bus18by one or more data media interfaces. The program modules42, the OS40, and one or more application programs, load modules, source code files, and system parameter files (e.g., the input parameters for the testing framework) may be stored on the storage system34and subsequently loaded into memory28for execution, as needed.

The server12may also communicate with one or more external devices14such as a keyboard, a pointing device, a display24, etc.; one or more devices that enable a user to interact with the server12; and/or any devices (e.g., network card, modem, etc.) that enable the server12to communicate with one or more other computing devices. Such communication can occur via I/O interfaces22. Still, the server12can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via a network adapter20. As depicted, the network adapter20communicates with the other components of the server12via bus18.

FIG. 4depicts a flow diagram of a manual process for identifying highly utilized data.

In405, the local system receives and stores a plurality of data. In this embodiment, the plurality of data is stored in one or more database systems. However, other storage architectures or combinations thereof, such as local object storage, an index file, and a linear in-memory file are also included. A pointer to the data is stored with the data on the local system. A cloud-based pointer to the cloud-based object storage can replace that local pointer, thereby dynamically redirecting access to data in either the local or cloud-based locations, depending on utilization of the highly used data. A plurality of applications that can be local or remote to the local system can access the data stored at the local system.

In410, the administrator identifies a particular set of data as highly used. The identification can be made based on system and/or database performance statistics. Alternatively, the administrator can manually identify and pre-configure data as highly used based on input from the application's user. The system then normalizes the data. Normalizing is not limited to a particular method, but includes those methods that produce reduction of data to Canonical form. In this context, “normalizing” refers to parsing the data to learn the field names and the data within them. The field names can be capitalized, lower case, or abbreviated, yet still refer to the same field, because the data in the fields is the same in all cases. Querying the data using the database query language is one way to produce a report of the names of the fields and the data within them. Alternatively, the administrator can create a program in any script or compiled language to extract the names of the fields for analysis.

At415, once the normalization is complete, the database at the local system creates an index based on input from the administrator. Each index represents a field name in the highly used data that an application is likely to use as a search field. The index is created using the language syntax supported by the particular database.

At420, the local system receives an indication from the administrator that one or more index fields is highly used. The indication results from the administrator taking an action to identify the one or more index fields to the database. In the case of a graphical user interface, the administrator can check a box corresponding to the desired index field. In another case, the administrator executes language syntax, script, or other command, to define a tag for each index. In either case, the database responds by setting the indication in the database. How the database tracks and stores tagged fields is dependent upon the schema that the administrator designed and implemented. For example, the database schema may include one or more tables that store and track indexes and whether the index has an associated tag.

At425, the local system migrates the structure (i.e., schema) to the cloud-based system through a pipeline service that the cloud-based system publishes. The cloud-based system creates the cloud-based object storage using the parameters the local system sent through the pipeline service. The parameters include the local structure and the request for the amount of object storage space to reserve for the data. The entire set of data is then migrated into the cloud-based object storage.

FIG. 5is a flow diagram of an automated process for identifying highly used data. Where inFIG. 4, the local system identified and migrated highly used data based on manual administrator input, inFIG. 5, the local system automatically makes the identification and dynamically migrates the data. In bothFIG. 4andFIG. 5, the data structure is only migrated once. The entire set of data (i.e., table, file, etc.), and not just the highly used index fields, is migrated.

At505, the local system receives a search request for data, for example, a query for data in a particular index field.

At510, the local system increments a counter that is associated with the index being searched. Each index field is associated with a counter that is used to monitor references to the data. The counters are stored at the local system.

At515, if the counter associated with the index being searched is not greater than eight times the lowest counter associated with an index, then processing ends until the next search request.

However, if at515the counter is greater than eight times the lowest counter, then at520the local system sets the indication in the database that this is a highly used field, and that the data should be migrated to cloud-based object storage. The counters are calculated and stored at the local system.

At525, the counter associated with the index to be migrated is saved for continued monitoring, and the active counter is reset to zero. The local system continues to monitor and increment the reset counter. A data retention period policy for the cloud-based object storage defines that a counter falling below a user-defined threshold count over a user-defined period of time ages the data off cloud-based object storage. The usage tracking of the data continues after the data has been migrated, if the threshold lowers to less than fifty percent of the saved counter, when the retention period expires, application queries are redirected to the local data instead. This is done by replacing the pointer to the cloud-based object storage that is stored with the data in the local system with the local pointer that was saved during the migration. In this way, the cloud-based object storage is dynamically optimized for applications needing higher performance response times.

FIG. 6is a flow diagram of data movement in the cloud-based system.

At605, once the data to be migrated is identified, the local system creates a pipeline using the protocols provided by the cloud-based system. This establishes bi-directional communication between the local system and the cloud-based system.

At610, the cloud pipeline retrieves the structure, for example the schema, that defines the organization of the data. The cloud pipeline communicates with the cloud-based system to allocate space for the data to be migrated and to create the data structure in the object storage at615. At620, the cloud-based system returns to the cloud pipeline a pointer to the cloud-based object storage. The cloud pipeline uses the pointer to establish addressing between the local system and the cloud-based object storage to migrate the data from the local system at625. At630, once the data is migrated to the cloud-based object storage, the cloud-based system creates an API endpoint which it sends to the local system. The API endpoint represents a hyperlink to the cloud-based object storage, and consists of a combined port and network address. As an example, “http://cloud.com:4434/api/storage-bucket-1/” represents an API address. However, this can vary dependent upon the particular cloud-based system deployment.

The local system receives the API endpoint from the cloud-based system and replaces the local pointer in the local system in the data where the data was previously accessed with the API endpoint. The local data remains stored in the local system. However, to reclaim the space, the local data may be deleted or backed up. At635, future application requests for the migrated data are intercepted by the local system. When the local system accesses the local storage location, instead of finding the actual data, the local system finds the API endpoint. The local system retrieves the API endpoint. The local system sends the query to the object storage, using the API endpoint as the address. In this way, the data is migrated dynamically and transparently to the application. Additionally, modifications to the application are not needed.

FIG. 7is a block diagram of internal and external components of computers and servers depicted inFIG. 1according to at least one embodiment.

It should be appreciated thatFIG. 7provides only an illustration of one implementation and does not imply any limitations regarding the environments in which different embodiments may be implemented. Many modifications to the depicted environments may be made based on design and implementation requirements.

The computer system12may include respective sets of internal components800and external components900illustrated inFIG. 7. Each of the sets of internal components800includes one or more processors820, one or more computer-readable RAMs822and one or more computer-readable ROMs824on one or more buses826, and one or more operating systems828and one or more computer-readable tangible storage devices830. The one or more operating systems828and programs may be stored on one or more computer-readable tangible storage devices830for execution by one or more processors820via one or more RAMs822(which typically include cache memory). In the embodiment illustrated inFIG. 7, each of the computer-readable tangible storage devices830is a magnetic disk storage device of an internal hard drive. Alternatively, each of the computer-readable tangible storage devices830is a semiconductor storage device such as ROM824, EPROM, flash memory or any other computer-readable tangible storage device that can store a computer program and digital information.

Each set of internal components800also includes a R/W drive or interface832to read from and write to one or more portable computer-readable tangible storage devices936such as a CD-ROM, DVD, memory stick, magnetic tape, magnetic disk, optical disk or semiconductor storage device. The testing framework can be stored on one or more of the respective portable computer-readable tangible storage devices936, read via the respective R/W drive or interface832and loaded into the respective hard drive830.

Each set of internal components800may also include network adapters (or switch port cards) or interfaces836such as a TCP/IP adapter cards, wireless Wi-Fi interface cards, or wireless interface cards or other wired or wireless communication links. The software components of the testing framework can be downloaded from an external computer (e.g., server) via a network (for example, the Internet, a local area network or other, wide area network) and respective network adapters or interfaces836. From the network adapters (or switch port adaptors) or interfaces836, the software components of the testing framework are loaded into the respective hard drive830. The network may comprise copper wires, optical fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers.

Each of the sets of external components900can include a computer display monitor920, a keyboard930, and a computer mouse934. External components900can also include touch screens, virtual keyboards, touch pads, pointing devices, and other human interface devices. Each of the sets of internal components800also includes device drivers840to interface to computer display monitor920, keyboard930and computer mouse934. The device drivers840, R/W drive or interface832and network adapter or interface836comprise hardware and software (stored in storage device830and/or ROM824).