Patent Publication Number: US-11651005-B2

Title: Intelligent datastore determination for microservice

Description:
FIELD 
     The field relates generally to information processing systems, and more particularly to data storage associated with microservices in information processing systems. 
     BACKGROUND 
     The concept of microservices is based on service-oriented software development wherein a software application program (application) is created as a set of loosely-coupled services. One example of a microservice is a CRUD microservice. CRUD stands for “Create, Read, Update, Delete” and is a data driven microservice wherein each operation (create, read, update, and delete) is a basic operation associated with persistent data storage. In current practice, a CRUD microservice is single microservice performed for a single data object in a single database (datastore). That is, create, read, update and delete operations of the microservice are performed in accordance with the same database, i.e., the data object is accessed in the given database, CRUD operations are performed as needed, and the result is effectuated in the given database (e.g., data object updated or deleted). Since the CRUD microservice is performed for a single data object in a single database, as the data object grows, the read, update and delete operations will experience performance issues, and hence the performance of the CRUD microservice will be compromised. 
     SUMMARY 
     Illustrative embodiments provide techniques for improved microservice management in an information processing system. 
     In one embodiment, a method comprises dividing a plurality of operations of a microservice between a plurality of databases, and synchronizing data corresponding to the plurality of operations between the plurality of databases. The microservice is a create, read, update, delete (CRUD) microservice, and the plurality of operations comprise creating, reading, updating and deleting the data. 
     Further illustrative embodiments are provided in the form of a non-transitory computer-readable storage medium having embodied therein executable program code that when executed by a processor causes the processor to perform the above steps. Still further illustrative embodiments comprise an apparatus with a processor and a memory configured to perform the above steps. 
     These and other features and advantages of embodiments described herein will become more apparent from the accompanying drawings and the following detailed description. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    depicts details of an information processing system with an intelligent connection framework for assigning different databases to microservice operations according to an illustrative embodiment. 
         FIG.  2    depicts details of an information processing system with intelligent datastore determination for microservice management according to an illustrative embodiment. 
         FIG.  3    depicts details of a CRUD microservice management methodology with intelligent database determination according to an illustrative embodiment. 
         FIG.  4    depicts a graph of hits and response time to determine problematic time periods when assigning microservice operations to databases according to an illustrative embodiment. 
         FIG.  5    depicts a process for using artificial intelligence/machine learning (AI/ML) to assign microservice operations to databases according to an illustrative embodiment. 
         FIG.  6    depicts a process for assigning microservice operations to different databases according to an illustrative embodiment. 
         FIGS.  7  and  8    show examples of processing platforms that may be utilized to implement at least a portion of an information processing system in illustrative embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     Illustrative embodiments will be described herein with reference to exemplary information processing systems and associated computers, servers, storage devices and other processing devices. It is to be appreciated, however, that embodiments are not restricted to use with the particular illustrative system and device configurations shown. Accordingly, the term “information processing system” as used herein is intended to be broadly construed, so as to encompass, for example, processing systems comprising cloud computing and storage systems, as well as other types of processing systems comprising various combinations of physical and virtual processing resources. An information processing system may therefore comprise, for example, at least one data center or other type of cloud-based system that includes one or more clouds hosting tenants that access cloud resources. Such systems are considered examples of what are more generally referred to herein as cloud-based computing environments. Some cloud infrastructures are within the exclusive control and management of a given enterprise, and therefore are considered “private clouds.” The term “enterprise” as used herein is intended to be broadly construed, and may comprise, for example, one or more businesses, one or more corporations or any other one or more entities, groups, or organizations. An “entity” as illustratively used herein may be a person or system. On the other hand, cloud infrastructures that are used by multiple enterprises, and not necessarily controlled or managed by any of the multiple enterprises but rather respectively controlled and managed by third-party cloud providers, are typically considered “public clouds.” Enterprises can choose to host their applications or services on private clouds, public clouds, and/or a combination of private and public clouds (hybrid clouds) with a vast array of computing resources attached to or otherwise a part of the infrastructure. Numerous other types of enterprise computing and storage systems are also encompassed by the term “information processing system” as that term is broadly used herein. 
     As used herein, “real-time” refers to output within strict time constraints. Real-time output can be understood to be instantaneous or on the order of milliseconds or microseconds. Real-time output can occur when the connections with a network are continuous and a user device receives messages without any significant time delay. Of course, it should be understood that depending on the particular temporal nature of the system in which an embodiment is implemented, other appropriate timescales that provide at least contemporaneous performance and output can be achieved. 
     As used herein, “application programming interface (API)” refers to a set of subroutine definitions, protocols, and/or tools for building software. Generally, an API defines communication between software components. APIs permit programmers to write software applications consistent with an operating environment or website. 
     In an illustrative embodiment, an intelligent database (datastore) connection engine is configured to, for a given microservice with multiple operations, bind a given one of the operations of the given microservice to a selected best-fit database. Thus, different operations of the given microservice can be bound to different databases. In illustrative embodiments, determination of the best-fit database for each of the operations of the given microservice is based on one or more of a given user configuration and one or more AI/ML algorithms. 
     Accordingly, in an illustrative embodiment wherein the given microservice is a CRUD microservice, a methodology is provided that intelligently optimizes each CRUD operation in different types of databases as needed. 
       FIG.  1    shows an information processing system  100  configured in accordance with an illustrative embodiment. The information processing system  100  comprises user devices  102 - 1 ,  102 - 2 , . . .  102 -M (collectively “user devices  102 ”). The user devices  102  communicate over a network  104  with an intelligent connection framework  110 . 
     The user devices  102  can comprise, for example, Internet of Things (IoT) devices, desktop, laptop or tablet computers, mobile telephones, or other types of processing devices capable of communicating with the intelligent connection framework  110  over the network  104 . Such devices are examples of what are more generally referred to herein as “processing devices.” Some of these processing devices are also generally referred to herein as “computers.” The user devices  102  may also or alternately comprise virtualized computing resources, such as virtual machines (VMs), containers, etc. The user devices  102  in some embodiments comprise respective computers associated with a particular company, organization or other enterprise. The variable M and other similar index variables herein such as K, L and N are assumed to be arbitrary positive integers greater than or equal to two. 
     The terms “client” or “user” herein are intended to be broadly construed so as to encompass numerous arrangements of human, hardware, software or firmware entities, as well as combinations of such entities. Microservice management services may be provided for users utilizing one or more AWL models, although it is to be appreciated that other types of infrastructure arrangements could be used. At least a portion of the available services and functionalities provided by the intelligent connection framework  110  in some embodiments may be provided under Function-as-a-Service (“FaaS”), Containers-as-a-Service (“CaaS”) and/or Platform-as-a-Service (“PaaS”) models, including cloud-based FaaS, CaaS and PaaS environments. 
     Although not explicitly shown in  FIG.  1   , one or more input-output devices such as keyboards, displays or other types of input-output devices may be used to support one or more user interfaces to the intelligent connection framework  110 , as well as to support communication between the intelligent connection framework  110  and connected devices (e.g., user devices  102 ) and/or other related systems and devices not explicitly shown. 
     In some embodiments, the user devices  102  are assumed to be associated with repair technicians, system administrators, information technology (IT) managers, software developers release management personnel or other authorized personnel configured to access and utilize the intelligent connection framework  110 . 
     The intelligent connection framework  110  in the present embodiment is assumed to be accessible to the user devices  102  over the network  104 . The network  104  is assumed to comprise a portion of a global computer network such as the Internet, although other types of networks can be part of the network  104 , including a wide area network (WAN), a local area network (LAN), a satellite network, a telephone or cable network, a cellular network, a wireless network such as a WiFi or WiMAX network, or various portions or combinations of these and other types of networks. The network  104  in some embodiments therefore comprises combinations of multiple different types of networks each comprising processing devices configured to communicate using Internet Protocol (IP) or other related communication protocols. 
     As a more particular example, some embodiments may utilize one or more high-speed local networks in which associated processing devices communicate with one another utilizing Peripheral Component Interconnect express (PCIe) cards of those devices, and networking protocols such as InfiniBand, Gigabit Ethernet or Fibre Channel. Numerous alternative networking arrangements are possible in a given embodiment, as will be appreciated by those skilled in the art. 
     The intelligent connection framework  110 , on behalf of respective infrastructure tenants each corresponding to one or more users associated with respective ones of the user devices  102  provides a platform for using AI/ML to, for a given microservice with multiple operations, bind a given one of the operations of the given microservice to a selected best-fit database. As explained further herein, different operations of the given microservice can be bound to different databases depending on a variety of factors. 
     Referring to  FIG.  1   , the intelligent connection framework  110  comprises an interface layer  120 , a microservice layer  130 , an intelligent connection manager  140  and a databases layer  150 . The interface layer  120  includes an operational user interface (UI) component  121  and an analytical UI component  122 . The intelligent connection manager  140  includes a plurality of database connection components  141 - 1 ,  141 - 2 , . . .  141 -N (collectively “database connection components  141 ”), and the databases layer  150  includes a plurality of databases  151 - 1 ,  151 - 2 , . . .  151 -N (collectively “databases  151 ”). 
     According to one or more embodiments, the operational and analytical UI components  121  and  122  interface with the microservice layer  130  to call APIs (e.g., representational state transfer (REST) APIs) corresponding to microservices in the microservices layer  130 . The requests of the operational and analytical UI components  121  and  122  are based on different data needs of operational and analytical UIs that may appear on the user devices  102 . For example, an operational UI can display data relating to the monitoring and management of enterprise operations and processes, so that enterprise personnel can visualize real-time data issues that may need to be addressed. In some examples, an operational UI may require data related to the monitoring and analysis of the activities of an enterprise in a given area that is to be viewed by subject matter experts. An analytical UI may display large volumes of data created and used by enterprises to provide comprehensive overviews to, for example, identify trends and generate predictions and targets. Analytical UIs may format the display of large amounts of data in different subject matter areas. 
     Based on the needs of the operational and analytical UIs, the operational and analytical UI components  121  and  122  may request different types of CRUD operations from the microservice layer. For example, in connection with an analytical UI, the analytical UI component  122  may require reads of large volumes of data, requiring a different database than reads for an operational UI, which may have smaller data volume needs than those of an analytical UI. 
     The microservice layer  130  is configured as a microservice-based software architecture composed of multiple different fine-grained services with lightweight protocols, whereby microservices corresponding to different features of an application may be independently developed, deployed and scaled. For example, the operational and analytical UI components  121  and  122  route requests for CRUD operations to the appropriate microservice of the microservice layer  130 . The microservice layer  130  can process user requests received through UIs (e.g., the operational and analytical UIs) on user devices  102  by invoking multiple microservices from one or more back-ends and compiling the results. The UIs on the user devices  102 , such as, for example, operational and analytical UIs, are generated and managed by the interface layer  120 , including UI components, such as, for example, the operational and analytical UI components  121  and  122 . According to an embodiment, the operational and analytical UI components  121  and  122 , and/or the microservice layer  130  may be hosted in a cloud platform server. 
     The microservice layer  130 , through the intelligent connection manager  140 , connects different operations with different databases  151  to retrieve data associated with different microservice operations, such as the CRUD operations. Referring to  FIG.  2   , in some illustrative embodiments, in an information processing system  200 , the databases can comprise a web of datastores  251 - 1 ,  251 - 2 ,  251 - 3  and  251 - 4  (collectively “datastores  251 ”) combined in a blockchain architecture to enable data to be synchronized across the multiple databases. In some embodiments, examples of databases  151  or datastores  251  that are connected as part of a blockchain architecture and selected as a best fit for a given operation of a multi-operation microservice include, but are not limited to, Oracle®, Cassandra®, Mongo®, Elastic, PostgreSQL®, memSQL®, MS SQL, etc. databases. 
     Referring to  FIGS.  1  and  2   , the intelligent connection manager  140  and the intelligent database connection manager  240  divide a plurality of operations of a microservice between a plurality of databases  151  or datastores  251 . The plurality of operations comprises, for example, a data create operation  231 , a data read operation  232 , a data update operation  233  and a data delete operation  234 . As shown in  FIGS.  1  and  2   , the data corresponding to the plurality of operations is synchronized between the plurality of databases  151  or datastores  251 . As noted above, the databases  151  and/or datastores  251  can be combined in a blockchain architecture to enable data to be synchronized across the multiple databases/datastores. The synchronizing may be performed in real-time and/or near real-time with respect to when the plurality of operations are is executed in the databases  151  or datastores  251 . 
     Referring to  FIG.  1   , the database connection components  141  of the intelligent connection manager  140  bind one or more of a plurality of operations of a microservice to a given one of the plurality of databases. For example, in a non-limiting example, the database connection component  141 - 1  may bind a create operation and/or a first type of read operation to a database  151 - 1 , the database connection component  141 - 2  may bind a second type of read operation and/or an update operation to a database  151 - 2 , and the database connection component  141 -N may bind a delete operation to a database  151 -N. To which database an operation is bound can depend on a variety of factors or user configurations, including but not necessarily limited to, the type of operation (e.g., bulk operation, dirty read, etc.), the properties of the database and whether the database is equipped to certain types of operations. For example, some databases may be better equipped to accommodate large volume reads, concurrent operations and/or dirty reads. The number of databases  151  and/or database connection components  141  may vary. 
     In determining to which of a given one of the databases  151  a given one of the plurality of operations is to be bound, the intelligent connection manager  140  is configured to consider one or more configurations that may be specified by a user, an application and/or a service level agreement (SLA). The configurations include, for example, a requirement for concurrency of one or more of the plurality of operations, a requirement for a bulk operation, a requirement for how the data is read, and/or an isolation level requirement. For example, a requirement for concurrency may specify a concurrent read operation by multiple processors from the same memory location, a concurrent write operation by multiple processors from the same memory location, concurrent write and update operations, etc. A requirement for a bulk operation may specify, for example, a bulk read operation, a bulk write operation, etc. A requirement for how data is read may specify, for example, a requirement for a dirty read (e.g., uncommitted dependency), occurring when data is read from a row that has been modified by another running transaction and not yet committed, a non-repeatable read, a phantom read, etc. An isolation level requirement relates to, for example, how and/or when changes made by one operation become visible to certain users and/or systems. 
     For example, isolation levels may define whether data being operated on in a first operation is visible to other systems and/or users carrying out concurrent operations related to the data being operated on. Lower isolation levels correspond to an increased ability of different users/systems to access the same data at the same time. Conversely, higher isolation levels reduce the types of concurrency effects that users/systems may encounter, but require more system resources and increase the chances that one transaction will block another transaction. 
       FIG.  3    depicts details of a CRUD microservice management methodology  300  with intelligent database determination according to an illustrative embodiment. For example, as shown, for a given operation based on configuration data  315  or AWL input  317  to improve performance, an intelligent database connection engine (manager)  340  (the same or similar to connection managers  140  and  240 ) binds a microservice to a selected best-fit (e.g., optimal) database of a plurality of databases  351 - 1 ,  351 - 2 ,  351 - 3  and  351 - 4  (collectively “databases  351 ”). The databases  351  are the same or similar to the databases  151  and/or datastores  251 . As shown by the double-sided arrows between the databases  351 , the databases  351  can be arranged in a blockchain architecture so that data can be synchronized between the databases  351 . 
     As shown in  FIG.  3   , in a non-limiting operational example, an SLA  360  has time constraint requirements for create, read, update and delete operations  331 ,  332 ,  333  and  334  in connection with employee data. For example, according to the SLA  360 , the create operation  331  should not exceed 600 ms, the read operation  332  should not exceed 400 ms, the update operation  333  should not exceed 800 ms and the delete operation  334  should not exceed 7500 ms. 
     In accordance with the embodiment in  FIG.  3   , the CRUD operations  331 ,  332 ,  333  and  334  are started in database  1   351 - 1  at block  361 . However, upon a determination that the SLA  360  is not being met (block  362 ), the CRUD operations  331 - 334  are divided among database  2   351 - 2 , database  3   351 - 3  and database  4   351 - 4  in order to ensure compliance with the SLA  360 . More specifically, referring to block  363 , database  3   351 - 3  is bound to a bulk write (create) operation, referring to block  364 , database  3   351 - 3  and then database  4   351 - 4  are bound to the read operation, referring to block  365 , database  2   351 - 2  bound to the update operation, and, referring to block  366 , database  3   351 - 3  is bound to a large volume delete operation. 
     The determination of the databases  351  is based on the configuration specifications  315  (e.g., concurrency, type of operation, volume of operation, isolations, SLA, etc. as described herein) and AI/ML determinations  317 . 
     Referring, for example, to  FIGS.  4  and  5   , the intelligent connection managers  140 ,  240  and  340  are configured to use AI/ML techniques to learn how to assign and/or shift/change to the optimal (e.g., best-fit) databases/datastores  151 / 251 / 351  for given operations based on, for example, the type of operation, time of operation, configurations and/or data involved. According to an embodiment, data including one or more of a type of the given one of the plurality of operations and response times of the given one of the plurality of operations over a time period is inputted into one or more AI/ML algorithms. In addition, statistics including one or more of a number of times the data is being accessed and a payload size of the data is inputted into one or more AI/ML algorithms. The AI/ML algorithms include, but are not necessarily limited to, a random forest regressor model, a support vector machine (SVM) and/or neural networks. 
     For example,  FIG.  4    shows a graph  400  of hits and response times to determine problematic time periods when assigning microservice operations to databases. A number of hits refers to, for example, the number of times operations are being performed (e.g., creates (writes), reads, updates and deletions) on data over a given time period. As can be understood from  FIG.  4   , the number of hits and response times are low during generally off-peak time periods of 12 am to 8 am and 7 pm to 11 pm, and the number of hits and response times are higher during the peak time period of 8 am to 7 pm. Due to the higher number of hits and longer response times, the peak time period is identified as a problematic area by the one or more AI/ML algorithms. Accordingly, using the one or more AI/ML algorithms, the intelligent connection manager  140 / 240 / 340  is able to recognize that changing from the off-peak to the peak time period requires a change of database from a first database (DB 1 ) to a second database (DB 2 ) or to a third database (DB 3 ) for the performance of certain microservice operations. For example, based on historical data, the one or more AI/ML algorithms are able to determine that the second database (DB 2 ) is better suited for bulk data operations during the peak time period, and that the third database (DB 3 ) is better suited for operations where dirty reads are not permitted during the peak time period. 
       FIG.  5    depicts a process  500  for using AI/ML to assign microservice operations to databases according to an illustrative embodiment. In connection with  FIG.  4   ,  FIG.  5    shows a process  500  for using one or more AWL algorithms to recognize that changing from the off-peak to the peak time period requires a change of database from the first database (DB 1 ) to the second database (DB 2 ) or to the third database (DB 3 ) for the performance of certain microservice operations. For example, referring to blocks  571 ,  572 ,  573  and  574 , the intelligent connection manager  140 / 240 / 340  collects data on the number of hits (e.g., a number of times the data is being accessed), average response time of one or more of the plurality of operations over a time period, a type (mode) of operation of the one or more of the plurality of operations, and a payload size of the data corresponding to the one or more of the plurality of operations. Referring to block  575  the collected data is inputted into the one or more AI/ML algorithms and statistical analysis is performed. 
     Following the flow in connection with blocks  581 ,  582 ,  583 ,  584 ,  585 ,  586  and  587 , the intelligent connection manager  140 / 240 / 340 , using the results of the statistical analysis and the one or more AI/ML algorithms, determines to which databases certain microservice operations should be bound to given one or more constraints. The flow begins at block  581 . If bulk inserts are required (block  582 ), then database  2  (DB 2 ) is used (block  583 ). If bulk inserts are not a requirement, and normal inserts are used (block  584 ), then a determination is made whether concurrency is a requirement (block  585 ). If concurrency is not a requirement, then database  2  (DB 2 ) is used (block  583 ). If concurrency is a requirement, then database  3  (DB 3 ) is used (block  587 ). In addition, if bulk inserts are not requirement, and normal inserts are used (block  584 ), then a determination is made whether dirty reads are permitted (block  586 ). If dirty reads are permitted, then database  2  (DB 2 ) is used (block  583 ). If dirty reads are not permitted, then database  3  (DB 3 ) is used (block  587 ). As can be seen in  FIG.  5   , the application of the one or more machine learning algorithms includes processes of learning  591  based on collected data, learning and applying results  592 , and re-learning  593  based on feedback. 
     According to one or more embodiments, the databases  151 , datastores  251  and databases  351  can be configured according to a relational database management system (RDBMS) (e.g., PostgreSQL). The databases  151 , datastores  251  and databases  351  in some embodiments are implemented using one or more storage systems or devices associated with the intelligent connection framework  110 . In some embodiments, one or more of the storage systems utilized to implement the databases  151 , datastores  251  and databases  351  comprise a scale-out all-flash content addressable storage array or other type of storage array. 
     The term “storage system” as used herein is therefore intended to be broadly construed, and should not be viewed as being limited to content addressable storage systems or flash-based storage systems. A given storage system as the term is broadly used herein can comprise, for example, network-attached storage (NAS), storage area networks (SANs), direct-attached storage (DAS) and distributed DAS, as well as combinations of these and other storage types, including software-defined storage. 
     Other particular types of storage products that can be used in implementing storage systems in illustrative embodiments include all-flash and hybrid flash storage arrays, software-defined storage products, cloud storage products, object-based storage products, and scale-out NAS clusters. Combinations of multiple ones of these and other storage products can also be used in implementing a given storage system in an illustrative embodiment. 
     Although shown as an element of the intelligent connection framework  110 , the intelligent connection manager  140  in other embodiments can be implemented at least in part externally to the intelligent connection framework  110 , for example, as a stand-alone server, set of servers or other type of system coupled to the network  104 . For example, the intelligent connection manager  140  may be provided as a cloud service accessible by the intelligent connection framework  110 . 
     The intelligent connection manager  140  in the  FIG.  1    embodiment is assumed to be implemented using at least one processing device. Each such processing device generally comprises at least one processor and an associated memory, and implements one or more functional modules for controlling certain features of intelligent connection manager  140 . 
     Although shown as elements of the intelligent connection framework  110 , the interface layer  120 , microservice layer  130 , intelligent connection manager  140  and/or databases layer  150  in other embodiments can be implemented at least in part externally to the intelligent connection framework  110 , for example, as stand-alone servers, sets of servers or other types of systems coupled to the network  104 . For example, the interface layer  120 , microservice layer  130 , intelligent connection manager  140  and/or databases layer  150  may be provided as cloud services accessible by the intelligent connection framework  110 . 
     The interface layer  120 , microservice layer  130 , intelligent connection manager  140  and/or databases layer  150  in the  FIG.  1    embodiment are each assumed to be implemented using at least one processing device. Each such processing device generally comprises at least one processor and an associated memory, and implements one or more functional modules for controlling certain features of the interface layer  120 , microservice layer  130 , intelligent connection manager  140  and/or databases layer  150 . 
     At least portions of the intelligent connection framework  110  and the components thereof may be implemented at least in part in the form of software that is stored in memory and executed by a processor. The intelligent connection framework  110  and the components thereof comprise further hardware and software required for running the intelligent connection framework  110 , including, but not necessarily limited to, on-premises or cloud-based centralized hardware, graphics processing unit (GPU) hardware, virtualization infrastructure software and hardware, Docker containers, networking software and hardware, and cloud infrastructure software and hardware. 
     Although the interface layer  120 , microservice layer  130 , intelligent connection manager  140 , databases layer  150  and other components of the intelligent connection framework  110  in the present embodiment are shown as part of the intelligent connection framework  110 , at least a portion of the interface layer  120 , microservice layer  130 , intelligent connection manager  140 , databases layer  150  and other components of the intelligent connection framework  110  in other embodiments may be implemented on one or more other processing platforms that are accessible to the intelligent connection framework  110  over one or more networks. Such components can each be implemented at least in part within another system element or at least in part utilizing one or more stand-alone components coupled to the network  104 . 
     It is assumed that the intelligent connection framework  110  in the  FIG.  1    embodiment and other processing platforms referred to herein are each implemented using a plurality of processing devices each having a processor coupled to a memory. Such processing devices can illustratively include particular arrangements of compute, storage and network resources. For example, processing devices in some embodiments are implemented at least in part utilizing virtual resources such as virtual machines (VMs) or Linux containers (LXCs), or combinations of both as in an arrangement in which Docker containers or other types of LXCs are configured to run on VMs. 
     The term “processing platform” as used herein is intended to be broadly construed so as to encompass, by way of illustration and without limitation, multiple sets of processing devices and one or more associated storage systems that are configured to communicate over one or more networks. 
     As a more particular example, the interface layer  120 , microservice layer  130 , intelligent connection manager  140 , databases layer  150  and other components of the intelligent connection framework  110 , and the elements thereof can each be implemented in the form of one or more LXCs running on one or more VMs. Other arrangements of one or more processing devices of a processing platform can be used to implement the interface layer  120 , microservice layer  130 , intelligent connection manager  140  and databases layer  150 , as well as other components of the intelligent connection framework  110 . Other portions of the system  100  can similarly be implemented using one or more processing devices of at least one processing platform. 
     Distributed implementations of the system  100  are possible, in which certain components of the system reside in one data center in a first geographic location while other components of the system reside in one or more other data centers in one or more other geographic locations that are potentially remote from the first geographic location. Thus, it is possible in some implementations of the system  100  for different portions of the intelligent connection framework  110  to reside in different data centers. Numerous other distributed implementations of the intelligent connection framework  110  are possible. 
     Accordingly, one or each of the interface layer  120 , microservice layer  130 , intelligent connection manager  140 , databases layer  150  and other components of the intelligent connection framework  110  can each be implemented in a distributed manner so as to comprise a plurality of distributed components implemented on respective ones of a plurality of compute nodes of the intelligent connection framework  110 . 
     It is to be appreciated that these and other features of illustrative embodiments are presented by way of example only, and should not be construed as limiting in any way. 
     Accordingly, different numbers, types and arrangements of system components such as the interface layer  120 , microservice layer  130 , intelligent connection manager  140 , databases layer  150  and other components of the intelligent connection framework  110 , and the elements thereof can be used in other embodiments. 
     It should be understood that the particular sets of modules and other components implemented in the system  100  as illustrated in  FIG.  1    are presented by way of example only. In other embodiments, only subsets of these components, or additional or alternative sets of components, may be used, and such components may exhibit alternative functionality and configurations. 
     For example, as indicated previously, in some illustrative embodiments, functionality for the intelligent connection framework can be offered to cloud infrastructure customers or other users as part of FaaS, CaaS and/or PaaS offerings. 
     The operation of the information processing system  100  and/or  200  will now be described in further detail with reference to the flow diagram of  FIG.  6   . With reference to  FIG.  6   , a process  600  for assigning microservice operations to different databases as shown includes steps  602  and  604 , and is suitable for use in the system  100  and/or  200  but is more generally applicable to other types of information processing systems comprising an intelligent connection framework configured for assigning microservice operations to different databases. 
     In step  602 , a plurality of operations of a microservice are divided between a plurality of databases. The microservice is a CRUD microservice, and the plurality of operations comprise creating, reading, updating and deleting the data. The dividing step comprises binding a given one of the plurality of operations of the microservice to a given one of the plurality of databases. 
     In step  604 , data corresponding to the plurality of operations is synchronized between the plurality of databases. According to one or more embodiments, the synchronizing is performed in at least one of real-time and near real-time with respect to performance of the plurality of operations. The plurality of databases can be combined in a blockchain architecture. 
     It is to be appreciated that the  FIG.  6    process and other features and functionality described above can be adapted for use with other types of information systems configured to execute microservice management services in an intelligent connection framework or other type of framework. 
     The particular processing operations and other system functionality described in conjunction with the flow diagram of  FIG.  6    is therefore presented by way of illustrative example only, and should not be construed as limiting the scope of the disclosure in any way. Alternative embodiments can use other types of processing operations. For example, the ordering of the process steps may be varied in other embodiments, or certain steps may be performed at least in part concurrently with one another rather than serially. Also, one or more of the process steps may be repeated periodically, or multiple instances of the process can be performed in parallel with one another. 
     Functionality such as that described in conjunction with the flow diagram of  FIG.  6    can be implemented at least in part in the form of one or more software programs stored in memory and executed by a processor of a processing device such as a computer or server. As will be described below, a memory or other storage device having executable program code of one or more software programs embodied therein is an example of what is more generally referred to herein as a “processor-readable storage medium.” 
     Illustrative embodiments of systems with an intelligent connection framework as disclosed herein can provide a number of significant advantages relative to conventional arrangements. For example, unlike conventional techniques, instead of using a single database for multiple microservice operations, the embodiments provide a web of more than one database (webbed datastore) that are connected as part of a blockchain architecture and selected as a best fit for a given operation of a multi-operation microservice. 
     Advantageously, the embodiments provide an intelligent database connection manager which binds a given microservice operation to the appropriate (e.g., best-fit) database based on configuration and/or AI/ML techniques to improve performance. For example, when using conventional techniques tied to a single database, as data grows, read, update and delete operations will encounter performance issues, thereby compromising the performance of the microservices. The embodiments advantageously prevent the performance issues of the conventional techniques by binding different operations (e.g., CRUD operations) to different databases based on multiple factors, such as, but not necessarily limited to, a requirement for concurrency of one or more of the plurality of operations, a requirement for a bulk operation, a requirement for how the data is read, and/or an isolation level requirement. The embodiments further advantageously synchronize the data between the plurality of databases that have been used for the plurality of operations. 
     In addition, the embodiments advantageously leverage AI/ML, techniques to learn the optimal databases for given operations based, for example, on the type of operation, time of operation, and data involved, and to generate or shift the connection to the best-fit database as required. 
     It is to be appreciated that the particular advantages described above and elsewhere herein are associated with particular illustrative embodiments and need not be present in other embodiments. Also, the particular types of information processing system features and functionality as illustrated in the drawings and described above are exemplary only, and numerous other arrangements may be used in other embodiments. 
     As noted above, at least portions of the information processing system  100  may be implemented using one or more processing platforms. A given such processing platform comprises at least one processing device comprising a processor coupled to a memory. The processor and memory in some embodiments comprise respective processor and memory elements of a virtual machine or container provided using one or more underlying physical machines. The term “processing device” as used herein is intended to be broadly construed so as to encompass a wide variety of different arrangements of physical processors, memories and other device components as well as virtual instances of such components. For example, a “processing device” in some embodiments can comprise or be executed across one or more virtual processors. Processing devices can therefore be physical or virtual and can be executed across one or more physical or virtual processors. It should also be noted that a given virtual device can be mapped to a portion of a physical one. 
     Some illustrative embodiments of a processing platform that may be used to implement at least a portion of an information processing system comprise cloud infrastructure including virtual machines and/or container sets implemented using a virtualization infrastructure that runs on a physical infrastructure. The cloud infrastructure further comprises sets of applications running on respective ones of the virtual machines and/or container sets. 
     These and other types of cloud infrastructure can be used to provide what is also referred to herein as a multi-tenant environment. One or more system components such as the intelligent connection framework  110  or portions thereof are illustratively implemented for use by tenants of such a multi-tenant environment. 
     As mentioned previously, cloud infrastructure as disclosed herein can include cloud-based systems. Virtual machines provided in such systems can be used to implement at least portions of one or more of a computer system and an intelligent connection framework in illustrative embodiments. These and other cloud-based systems in illustrative embodiments can include object stores. 
     Illustrative embodiments of processing platforms will now be described in greater detail with reference to  FIGS.  7  and  8   . Although described in the context of system  100 , these platforms may also be used to implement at least portions of other information processing systems in other embodiments. 
       FIG.  7    shows an example processing platform comprising cloud infrastructure  700 . The cloud infrastructure  700  comprises a combination of physical and virtual processing resources that may be utilized to implement at least a portion of the information processing system  100 . The cloud infrastructure  700  comprises multiple virtual machines (VMs) and/or container sets  702 - 1 ,  702 - 2 , . . .  702 -L implemented using virtualization infrastructure  704 . The virtualization infrastructure  704  runs on physical infrastructure  705 , and illustratively comprises one or more hypervisors and/or operating system level virtualization infrastructure. The operating system level virtualization infrastructure illustratively comprises kernel control groups of a Linux operating system or other type of operating system. 
     The cloud infrastructure  700  further comprises sets of applications  710 - 1 ,  710 - 2 , . . .  710 -L running on respective ones of the VMs/container sets  702 - 1 ,  702 - 2 , . . .  702 -L under the control of the virtualization infrastructure  704 . The VMs/container sets  702  may comprise respective VMs, respective sets of one or more containers, or respective sets of one or more containers running in VMs. 
     In some implementations of the  FIG.  7    embodiment, the VMs/container sets  702  comprise respective VMs implemented using virtualization infrastructure  704  that comprises at least one hypervisor. A hypervisor platform may be used to implement a hypervisor within the virtualization infrastructure  704 , where the hypervisor platform has an associated virtual infrastructure management system. The underlying physical machines may comprise one or more distributed processing platforms that include one or more storage systems. 
     In other implementations of the  FIG.  7    embodiment, the VMs/container sets  702  comprise respective containers implemented using virtualization infrastructure  704  that provides operating system level virtualization functionality, such as support for Docker containers running on bare metal hosts, or Docker containers running on VMs. The containers are illustratively implemented using respective kernel control groups of the operating system. 
     As is apparent from the above, one or more of the processing modules or other components of system  100  may each run on a computer, server, storage device or other processing platform element. A given such element may be viewed as an example of what is more generally referred to herein as a “processing device.” The cloud infrastructure  700  shown in  FIG.  7    may represent at least a portion of one processing platform. Another example of such a processing platform is processing platform  800  shown in  FIG.  8   . 
     The processing platform  800  in this embodiment comprises a portion of system  100  and includes a plurality of processing devices, denoted  802 - 1 ,  802 - 2 ,  802 - 3 , . . .  802 -K, which communicate with one another over a network  804 . 
     The network  804  may comprise any type of network, including by way of example a global computer network such as the Internet, a WAN, a LAN, a satellite network, a telephone or cable network, a cellular network, a wireless network such as a WiFi or WiMAX network, or various portions or combinations of these and other types of networks. 
     The processing device  802 - 1  in the processing platform  800  comprises a processor  810  coupled to a memory  812 . The processor  810  may comprise a microprocessor, a microcontroller, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a central processing unit (CPU), a graphical processing unit (GPU), a tensor processing unit (TPU), a video processing unit (VPU) or other type of processing circuitry, as well as portions or combinations of such circuitry elements. 
     The memory  812  may comprise random access memory (RAM), read-only memory (ROM), flash memory or other types of memory, in any combination. The memory  812  and other memories disclosed herein should be viewed as illustrative examples of what are more generally referred to as “processor-readable storage media” storing executable program code of one or more software programs. 
     Articles of manufacture comprising such processor-readable storage media are considered illustrative embodiments. A given such article of manufacture may comprise, for example, a storage array, a storage disk or an integrated circuit containing RAM, ROM, flash memory or other electronic memory, or any of a wide variety of other types of computer program products. The term “article of manufacture” as used herein should be understood to exclude transitory, propagating signals. Numerous other types of computer program products comprising processor-readable storage media can be used. 
     Also included in the processing device  802 - 1  is network interface circuitry  814 , which is used to interface the processing device with the network  804  and other system components, and may comprise conventional transceivers. 
     The other processing devices  802  of the processing platform  800  are assumed to be configured in a manner similar to that shown for processing device  802 - 1  in the figure. 
     Again, the particular processing platform  800  shown in the figure is presented by way of example only, and system  100  may include additional or alternative processing platforms, as well as numerous distinct processing platforms in any combination, with each such platform comprising one or more computers, servers, storage devices or other processing devices. 
     For example, other processing platforms used to implement illustrative embodiments can comprise converged infrastructure. 
     It should therefore be understood that in other embodiments different arrangements of additional or alternative elements may be used. At least a subset of these elements may be collectively implemented on a common processing platform, or each such element may be implemented on a separate processing platform. 
     As indicated previously, components of an information processing system as disclosed herein can be implemented at least in part in the form of one or more software programs stored in memory and executed by a processor of a processing device. For example, at least portions of the functionality of one or more components of the intelligent connection framework  110  as disclosed herein are illustratively implemented in the form of software running on one or more processing devices. 
     It should again be emphasized that the above-described embodiments are presented for purposes of illustration only. Many variations and other alternative embodiments may be used. For example, the disclosed techniques are applicable to a wide variety of other types of information processing systems and intelligent connection frameworks. Also, the particular configurations of system and device elements and associated processing operations illustratively shown in the drawings can be varied in other embodiments. Moreover, the various assumptions made above in the course of describing the illustrative embodiments should also be viewed as exemplary rather than as requirements or limitations of the disclosure. Numerous other alternative embodiments within the scope of the appended claims will be readily apparent to those skilled in the art.