Patent Publication Number: US-2023153302-A1

Title: Optimizing query performance in virtual database

Description:
TECHNICAL FIELD 
     The present disclosure relates to virtual database performance optimization, and more particularly to methods, computer program products, and systems for optimizing a response time of real time queries based on real time statistics in a virtual database. 
     BACKGROUND 
     A virtual database system, also referred to a federated database or a federated system, is a type of distributed database management system offering a unified logical view in accessing and operating numerous constituent database systems without technical details. The constituent database systems approached by the virtual database are individual database management systems of respective types that can be accessed and operated respectively and can be geographically dispersed. The virtual database system coordinates operations of the constituent database systems that are heterogeneous and autonomous without imposing any single data model on the data. In order to achieve the level of data virtualization for a client upon the heterogeneous database systems in respective technical environments, the virtual database uses various techniques for abstraction and transformation based on semantics of queries to attain query performance. The virtual database system and data virtualization is widely used in the areas of technologies including business intelligence, service-oriented architecture data services, cloud computing, in conjunction with various data service models including online transaction processing and online analytical processing, data warehousing. 
     SUMMARY 
     The shortcomings of the prior art are overcome, and additional advantages are provided, through the provision, in one aspect, of a method. The method includes, for instance: collecting, by one or more processor, real time system statistics on a system environment in a remote platform running a remote data source amongst a plurality of remote data sources of a virtual database, where the remote data sources of the virtual database are heterogeneous database management systems run on respective remote platforms across disparate geographical locations; gathering, by the one or more processor, real time data source statistics on one or more data source objects from the remote data source of the virtual database; updating, by the one or more processor, a global catalog of the virtual database with the real time system statistics from the collecting and the real time data source statistics from the gathering; optimizing, by the one or more processor, an access plan to process a query submitted to the virtual database from a client application based on the real time system statistics and the real time data source statistics in the global catalog; and producing, by the one or more processor, a response corresponding to the query by executing the access plan to thereby improve both a response time of the query and a cost of processing. 
     Additional features are realized through the techniques set forth herein. Other embodiments and aspects, including but not limited to computer program products and systems, are described in detail herein and are considered a part of the claimed invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       One or more aspects of the present invention are particularly pointed out and distinctly claimed as examples in the claims at the conclusion of the specification. The foregoing and other objects, features, and advantages of the invention are apparent from the following detailed description taken in conjunction with the accompanying drawings in which: 
         FIG.  1    depicts a virtual database system for optimizing query performance, in accordance with one or more embodiments set forth herein; 
         FIG.  2    depicts an overall workflow for query processing as performed by the virtual database server of  FIG.  1   , in accordance with one or more embodiments set forth herein; 
         FIG.  3    depicts a real time statistics workflow as performed by the DV engine of  FIG.  2   , in accordance with one or more embodiments set forth herein; 
         FIG.  4    depicts data flows described in the real time statistics workflow of the DV engine in  FIG.  3   , in accordance with one or more embodiments set forth herein; 
         FIG.  5    depicts a cloud computing node according to an embodiment of the present invention; 
         FIG.  6    depicts a cloud computing environment according to an embodiment of the present invention; and 
         FIG.  7    depicts abstraction model layers according to an embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
       FIG.  1    depicts a virtual database system  100  for optimizing query performance, in accordance with one or more embodiments set forth herein. 
     The virtual database system  100  includes a virtual database server  120  that interacts with a client  101  in servicing requests to the virtual database system  100  indicating a plurality of heterogeneous database systems that operates to be perceived by the client  101  as a single database. The client  101  collectively refers to end users and client applications that submits a query  103  to the virtual database server  120  to get a response  109  corresponding to the query  103  based on content of the virtual database system  100 . The client  101  interacts with the virtual database server  120  via a query interface  115  in the virtual database server  120 , often in a specific query language such as Structured Query Language (SQL). 
     Individual database systems of the virtual database system  100 , shown as DS_A  160 , DS_B  170 , and DS_C  180  are referred to as data sources of the virtual database server  120 . Respective data sources DS_A  160 , DS_B  170 , and DS_C  180  are heterogeneous in types of the databases and distributed in many locations. The data sources DS_A  160 , DS_B  170 , and DS_C  180  can be geographically local to the virtual database server  120  or remote across digital communication networks, can be on a private cloud or a public cloud, and can be a relational database or a non-relational database. Examples of the data sources DS_A  160 , DS_B  170 , and DS_C  180  include relational databases such as Oracle, DB2, Microsoft SQL Server, MySQL, PostgreSQL, MS Azure, AWS database, etc., and non-relational databases such as XML data, spreadsheet data, cloud message queue, web services, datacenter-cloud applications by various vendors, etc., running on respective platforms. (Oracle and MySQL are registered trademarks of Oracle and/or its affiliates in the United States and/or other countries; DB2 and db2 are registered trademarks of International Business Machines Corporation in the United States and/or other countries; Microsoft, SQL Server, and Azure are registered trademarks of Microsoft Corporation in the United States and/or other countries; PostgreSQL is a registered trademark of PostgreSQL Community Association of Canada in the United States and/or other countries; and AWS is a registered trademark of Amazon Technologies, Inc., in the United States and/or other countries.) Each of the data sources DS_A  160 , DS_B  170 , and DS_C  180  coupled to the virtual database server  120  is an individual database management system (DBMS) that participates in the virtual database system  100 , and accordingly, the query  103  to the virtual database server  120  can be sent for processing to any of the data sources DS_A  160 , DS_B  170 , and DS_C  180  based on an access plan devised by the virtual database server  120 . 
     The virtual database server  120  includes a virtual database management system (VDMS)  110  for handling the query  103  and the response  109  and for controlling query processing and coordinating results obtained from the data sources DS_A  160 , DS_B  170 , and DS_C  180 . The VDMS  110  is operatively coupled to the data sources DS_A  160 , DS_B  170 , and DS_C  180  via a database connector  117 . 
     The database connector  117  is a standard application programming interface (API) for accessing respective DBMSs for the data sources DS_A  160 , DS_B  170 , and DS_C  180 . Examples of the database connector  117  are Open Database Connectivity (ODBC) and/or Java Database Connectivity (JDBC) in combination with various routines referred to as wrappers that implements interactions between the VDMS  110  and the respective DBMSs for the data sources DS_A  160 , DS_B  170 , and DS_C  180  as a part of a library of the VDMS  110 . (Java is a registered trademarks of Oracle and/or its affiliates in the United States and/or other countries.) 
     The virtual database server  120  communicates with the data sources DS_A  160 , DS_B  170 , and DS_C  180  via a particular type of communication protocols that enable communication between applications accessing the virtual database server  120  and disparate platforms that respectively run the data sources DS_A  160 , DS_B  170 , and DS_C  180 , and enables relational database to be distributed among multiple platforms such as Distributed Relational Database Architecture™ (DRDA). 
     As noted above, the virtual database server  120  is a platform to achieve data virtualization that allows the client  101 , often applications accessing the virtual database server  120 , to retrieve and manipulate data as if the client  101  uses a single database, without concerning any technical details about the data such as table formats or the location and type of the data source. The query interface  115  of the virtual database server  120  provides such a singular view of the virtual database system  100  and the VDMS  110  performs unique functions to support the data virtualization. The singular view of the virtual database system  100  is often referred to as SQL view, as SQL is a commonly used query language for database accesses. Functionalities of the VDMS  110  on the virtual database server  120  are presented in  FIG.  2    and corresponding description, in accordance with embodiments of the present invention. 
     The functionalities supporting the data virtualization can create a lot of overhead and degrade performance in servicing the query  103  if a data transfer of a very large volume of data from one data source to another data source is completed without justification or accessing certain data that do not contribute to processing the query  103 . Particularly if the query  103  requires the response  109  in real time, as in financial transactions, any delay with the response  109  would offset benefits of data virtualization for the client  101 . In this specification, term “query performance degradation” refers to a combination of delayed responses and higher cost of processing, and terms “optimization”, “query performance optimization”, “plan optimization” interchangeably refer to a reduction in a response time for the query  103  and/or the cost of processing the query  103 . 
     In order to optimize query performance in the virtual database system  100 , the virtual database server  120  includes a statistics pushdown analysis process (SPDA)  130  and a system statistics collection agent (SSCA)  150 . 
     The query performance indicating a response time to service the query  103  in combination with a cost of processing the query  103  would measure how efficiently the virtual database system  100  operates. As noted earlier, the virtual database server  120  devises the access plan to optimize processing of the query  103  in the virtual database system  100  addressing many strategic decisions involving issues of, for example, whether the query  103  would be executed locally or remotely, and how to access data source objects relevant to processing the query  103  across platforms processing the query  103 , etc. The data sources DS_A  160 , DS_B  170 , and DS_C  180 , can be local or remote to the virtual database server  120 , and as noted above, each of the data sources DS_A  160 , DS_B  170 , and DS_C  180  includes individual database management system that can process queries. The query  103  may need to retrieve data from two or more distinct data sources for processing, in which case data source objects from one of the data sources should be transferred to another platform regardless of which platform running one of the data sources executes the query  103 . Once the query  103  has been processed, the response  109  is to be generated and delivered to the client  101  by the virtual database server  120 . 
     One of the typical factors affecting the access plan in optimizing the query performance is a relative amount of data source objects in remote or local data sources relevant to processing the query  103 . The virtual database server  120  would determine a remote or local execution of the query  103  in the access plan to minimize the amount of data source objects to transfer for processing. For example, if there are a million records in a remote data source but a few hundred records in a local data source relevant to processing the query  103 , then the virtual database server  120  would devise the access plan to execute the query  103  at the remote database instead of transferring all records from the remote data source to a local platform to process the query  103 . Such technique of moving operations of the query  103  to a location of the data source objects in distributed database systems is referred to as a push down, or pushdown, and pushdown analysis is to determine if query operations can be performed at a data source. In terms of query semantics, applying more selective predicates to the query  103  as early as possible is a common technique referred to as a “predicate pushdown”, which significantly minimizes the amount of data source objects to process the query  103 . 
     For conventional pushdown analysis based on a volume of the data source objects, conventional virtual database servers use a system procedure to learn remote data source statistics information on data source objects such as server name, schema name, column types, index names, etc., on respective remote data sources. The remote data source statistics is often referred to as “nickname statistics”, based on the term “nickname” indicating an identifier of any data source object created in the conventional virtual database servers for access. In conventional virtual database systems, all nicknamed data source objects across all data sources coupled to the conventional virtual database systems are listed for direct check and access in a global catalog. Certain instances of conventional virtual database systems can supply the global catalog with additional metadata information about the nicknamed objects such as values in certain columns of the data source object, which is referred to as nickname column options, to facilitate direct access to a column of a table to streamline the pushdown analysis on the data source objects. 
     In conventional virtual databases, the nickname statistics primarily concerns description and usages of data source objects and the remote data source statistics are rarely updated on the conventional virtual database servers. In conventional virtual databases, there is no automated feedback loop that can be initiated by a remote platform when a third-party application having insert/update/delete (IUD) operations runs on the remote platform and changes the data source objects, which the conventional virtual databases must control and keep track of Conventional virtual database servers do not have much system environment information on remote platforms that can be used for the access plan, which can be caused by restrictions on parameters offered by database connectors used in the conventional virtual database servers. 
     To improve query performance in the virtual database system  100  over conventional virtual databases with drawbacks noted above, the virtual database server  120  includes the SPDA  130  and instances of the SSCA  150  respective to the virtual database server  120  and each of the data sources DS_A  160 , DS_B  170 , and DS_C  180  for collecting real time system statistics across the data sources DS_A  160 , DS_B  170 , and DS_C  180  and processing the real time system statistics in devising the access plan for the query  103 . Further the virtual database system  100  also collects and utilizes real time data source statistics to monitor any change to data source objects by a third-party application that does not participate in the virtual database system  100  running on a remote platform. The virtual database server  120  updates the real time statistics by use of a two-tiered synchronization mechanism such that the real time statistics can contribute to query performance based on a real time statistics update configuration. 
     The SPDA  130  performs statistics mapping between the virtual database server  120  and each of the data sources DS_A  160 , DS_B  170 , and DS_C  180  by converting methods and formats to access desired statistics information into respective methods and formats available from each of the data sources DS_A  160 , DS_B  170 , and DS_C  180  and other functions to facilitate communications between instances of the SSCA  150  on each of the data sources DS_A  160 , DS_B  170 , and DS_C  180 . For example, for a data source running db2 DBMS, the SPDA  130  determines a RUNSTATS command line for desired data source object and communicates with the SSCA  150  running on the db2 platform to execute the RUNSTATS command. Similarly, for another data source running Oracle 19c DBMS, the SPDA  130  determines a job “DBMS_STATS.SET_GLOBAL_PREFS” will collect real time statistics and communicates with the SSCA  150  running on the Oracle platform to execute the job identified. Similarly, for still another data source running MySQL DBMS, the SPDA  130  determines that a query to access INFORMATION_SCHEMA.TABLES for a desired information is necessary and communicates with the SSCA  150  running on the MySQL platform to run the query to retrieve the statistics information. For another data source running SQL Server DBMS, the SPDA  130  determines a procedure “set statistics time” on SQL statement is a proper way to access the desired statistics information and communicates to the SSCA  150  to perform the procedure as noted. For certain remote DBMS that does not provide particular method to fetch real time statistics information, the SPDA  130  queries system information tables, as noted above. 
     The virtual database server  120  and/or the SPDA  130  stores the real time system statistics of the virtual database server  120  and respective platforms running each of the data sources DS_A  160 , DS_B  170 , and DS_C  180  and the real time data source statistics for each of the data sources DS_A  160 , DS_B  170 , and DS_C  180  in a real time statistics table (RTSTAT)  140 . 
     The real time system statistics refers to certain preselected parameters in technical environment for all remote platforms running the data sources DS_A  160 , DS_B  170 , and DS_C  180  and the virtual database server  120 . The real time system statistics includes, but are not limited to, available computing resources, memory spaces, and input/output data speed, on respective platforms running the data sources DS_A  160 , DS_B  170 , and DS_C  180  as well as respective digital communication network bandwidths, or speeds of data transfer, between the virtual database server  120  and each of the data sources DS_A  160 , DS_B  170 , and DS_C  180 . The real time system statistics would determine how fast the data source objects can be transferred between the virtual database server  120  and any of the data sources DS_A  160 , DS_B  170 , and DS_C  180  that run on remote platforms from the virtual database server  120 , and processing time at the remote platforms if the query  103  is processed at the remote platforms. 
     The virtual database system  100  improves query performance by providing a range of statistics information including, but not limited to, real time system statistics including network connectivity of the data sources in the virtual database system  100  and processing, memory, and I/O capacity for respective remote platforms running the data sources. The virtual database system  100  provides real time global data source statistics collection amongst heterogeneous remote data sources. The virtual database system  100  provides a method for real time statistics collection that is initiated by a preexisting nickname statistics collection method such that the system statistic data would be newly collected whenever the nickname statistics is collected. The SPDA  130  of the virtual database system  100  provides mappings of the real time statistics collection method for the respective remote platforms with a pushdown analysis to collect the real time statistics across the remote platforms that are heterogeneous, in disparate system environments. The SSCA  150  of the virtual database system  100  improves the database connectivity to facilitate collection of real time statistics that was not supported in conventional database connectors. 
       FIG.  2    depicts an overall workflow  200  for query processing as performed by the virtual database server  120  of  FIG.  1   , in accordance with one or more embodiments set forth herein. 
     A data virtualization (DV) engine  201  represents a combination of selected components from the VDMS  110  and the SPDA  130 , which improves query performance over the VDMS  110  by use of the real time statistics made available by the SPDA  130  and the SSCA  150 . 
     The DV engine  201  includes a parser  210  compiling the query  103  as provided by the client  101 . The parser  210  determines a meaning of the query  103  and represents the query  103  with a query global semantics  211  and in a query graph model  220 . The query global semantics  211  represents the meaning of the query  103  independent from various representation in query operators and data object formats specific to a particular type of DBMS. Similarly, the query graph model  220  represents the query  103  with a graph describing data source objects as nodes and operations as edges. Based on representations of the query  103  in the query global semantics  211  and the query graph model  220 , the DV engine  201  prepares the query  103  to rewrite for any type of DBMS amongst the data sources coupled to the virtual database server  120  that may execute the query  103  in a query preparation process  212 . 
     The SPDA  130  enhances an SQL pushdown analysis process  230  of the VDMS  110  with real time statistics pushdown analysis in order to achieve query block translation, access plan optimization, parameter generation, etc. The SPDA  130  interacts with the SSCA  150  for collecting real time system statistics from the data sources coupled to the virtual database server  120 . The DV engine  201  also provides pushdown analyses of the data source objects as enhanced by real time data source statistics, in addition to conventionally provided data source statistics by the SQL pushdown analysis process  230 . The SPDA  130  in combination with the SQL pushdown analysis process  230  collect the real time system statistics, the real time data source statistics, and conventional data source statistics, and make available for the query preparation process  212 , a plan optimizer  213 , and a statement generator  214 . 
     The plan optimizer  213  devises the access plan for processing the query  103  based on the statistics data collected by the SPDA  130  and the SQL pushdown analysis process  230 . As noted, the access plan includes decisions on in which platform coupled to the virtual database server  120  the query  103  should be performed and which data source objects would be accessed and/or transferred. The plan optimizer  213  shares the access plan with an access plan manager  240  for execution. The statement generator  214  would generate a query statement corresponding to the query  103  for a platform set for execution, and a threaded code generator  215  generates a series of codes for the platform corresponding to the query statement, as an executable plan  250 . A runtime interpreter  216  interprets the codes in the executable plan  250  as being controlled by the access plan manager  240 , which generate parts of the response  109  corresponding to the section of the codes in the executable plan  250 . A response merger  217  coordinates results generated from the runtime interpreter  216  executing the executable plan  250  and produces the response  109  in accordance with system parameters and available resources. 
     By using a system procedure of the VDMS  110 , the access plan manager  240  may offer a table explanation  249  indicating certain depiction or description on formats of the data source objects in the query statement or table formats in the response  109 . Another system procedure may offer a query explanation  259  with an input of the executable plan  250 . 
       FIG.  3    depicts a workflow  300  for real time statistics feature as performed by the DV engine  201  of  FIG.  2   , in accordance with one or more embodiments set forth herein. 
     In block  310 , the DV engine  201  enables the real time statistics feature for the data sources DS_A  160 , DS_B  170 , and DS_C  180  coupled to the virtual database server  120 . Then, the DV engine  201  proceeds with block  320 . 
     In certain embodiments of the present invention, the DV engine  201  will obtain a system configuration file having the real time statistics option checked for an enabled state based on a predefined set of binary values indicating whether or not the real time statistics feature is operating in the data sources DS_A  160 , DS_B  170 , and DS_C  180  coupled to the virtual database server  120 . The SPDA  130  in the virtual database server  120  and the SSCA  150  on respective platforms of the data sources DS_A  160 , DS_B  170 , and DS_C  180  as well as the virtual database server  120  will be activated or deactivated accordingly. For example, ‘RT_Statistics=[Enabled|Disabled]’ or ‘RT_Statistics=[1|0]’ line in the system configuration file would indicate the real time statistics feature has been enabled or disabled, respectively. 
     In other embodiments of the present invention, the RT_Statistics configuration value would have 4 levels, representing the real time statistics feature enabled or disabled as well as how often the real time statistics would be collected. In the same embodiments of the present invention, the DV engine is configured with ‘RT_Statistics=[0|1|2|3]’, where ‘RT_Statistics=0’ represents that the real time statistics feature is disabled, ‘RT_Statistics=1’ represents that the real time statistics feature is enabled with a level of weekly collection, ‘RT_Statistics=2’ represents that the real time statistics feature is enabled with a level of daily collection, and ‘RT_Statistics=3’ represents that the real time statistics feature is enabled with a level of hourly collection of parameters set for the real time statistics feature. 
     In other embodiments of the present invention, the RT_Statistics configuration for update can be set for each operation on nicknamed data source objects such that the DV engine  201  collects the real time statistics upon executing a query on the nicknamed data source objects, as periodic collection of real time statistics across all data sources might be unnecessary if the data source objects and the system environment have not changed since the latest collection of the real time statistics. 
     In block  320 , the DV engine  201  collects real time system statistics of the remote platforms running the data sources DS_A  160 , DS_B  170 , and DS_C  180  by invoking the SSCA  150  on the respective remote platforms upon nickname creation. The DV engine  201  also collects the real time data source statistics and records the real time statistics in a global catalog of the virtual database server  120 . Data flows between components of the DV engine  201  in block  320  are presented in  FIG.  4    and corresponding description. Then, the DV engine  201  proceeds with block  330 . 
     As noted earlier, the DV engine  201  registers all data source objects across the remote platforms running the data sources DS_A  160 , DS_B  170 , and DS_C  180  to identify the data source objects for access, which is a process referred to as a nickname creation as the identifier for the data source objects is termed as nickname in virtual databases such as db2. Upon creating nicknames and registering the data source objects, the DV engine  201  also collects conventional data source statistics on the nicknamed data source objects, which is referred to as nickname statistics. 
     In certain embodiments of the present invention, the DV engine  201  invokes the SSCA  150  on the remote platforms running the data sources DS_A  160 , DS_B  170 , and DS_C  180  for the real time system statistics when the nickname statistics is collected. In the same embodiments, the DV engine  201  has a method for collecting real time system statistics on parameters of network, remote CPU, remote memory, and remote IO, denoted as RT_Statistics1=F(Network, Remote_CPU, Remote_memory, Remote_IO), where a parameter Network indicates a cost of data transfer across network in the virtual database system  100 , a parameter Remote_CPU indicates a cost of computation on a remote platform from which the SSCA  150  collects the real time system statistics, a parameter Remote_memory indicates a cost of memory on the remote platform from which the SSCA  150  collects the real time system statistics, and a parameter Remote_IO indicates a cost of input/output on the remote platform from which the SSCA  150  collects the real time system statistics. 
     In the same embodiments as above, the SSCA  150  triggers the DV engine  201  to invoke a method for collecting real time data source statistics on parameters of IUD_priority, Index_priority, and Rows_modified, denoted as RT_Statistics2=F(IUD_priority, Index_priority, Rows_modified), where a parameter IUD_priority is to keep track of insert, update, and delete operations (IUD) that had been performed by a remote database system on a remote platform that modify data source objects registered with the DV engine  201  with nicknames, a parameter Index_priority is to keep track of any index alteration on table data source objects in remote platforms by a remote database system regarding data source objects registered with the DV engine  201 , and a parameter Rows_modified is to keep track of the number of rows in remote tables that had been modified by a remote database system regarding data source objects registered with the DV engine  201 . In the same embodiments, as above, the DV engine  201  communicates with the remote platforms running the data sources DS_A  160 , DS_B  170 , and DS_C  180  for the real time data source statistics periodically according to a level set in the real time statistics update configuration. 
     In the same embodiments as above, the DV engine  201  has two classes RT_Stats and Non_RT_Stats defined to use the same method RT_Statistics2( ) interface on remote data source statistics. The DV engine  201  defines class RT_Stats for data sources that support real time statistics collection such as Oracle 19c and db2, and class Non_RT_Stats for data sources that do not support real time statistics collection such as MySQL. As noted above, respective data sources are mapped to the desired real time statistics collection by the SPDA  130 , such that the DV engine  201  would access information equivalent to the parameters of the real time statistics in respective remote data sources. For example, for a remote data source that does not support real time statistics collection, as in MySQL above, the DV engine  201  sends a query such as “select*from information_schema.tables where table_name=‘table’;” to retrieve the information for the real time data source statistics from the remote data source. Similarly, the DV engine  201  can run a command such as “runstats on table &lt;tab_schema&gt;.&lt;table&gt;[on all column];” for a db2 remote data source, use a predefined job “dbms_stats.set_global_prefs” offering real time statistics for a Oracle 19c remote data source, set timestamp option on SQL statement inquiring statistics for a SQL Server remote data source, and use a query such as “select*from pg_stats where tablename=‘tb1’” for a PostgreSQL remote data source. 
     In block  330 , the DV engine  201  creates a query statement based on an access plan to optimize query performance according to information available in the global catalog, including the real time statistics collected from block  320 . Then, the DV engine  201  proceeds with block  340 . 
     Conventionally, the access plan in virtual databases is affected most by the type of table operations and volume of the records, that is, a number of rows in a table, affected by the table operations. For example, as a common query will include inner join operation and substring operation, if the number of rows in a remote table is much greater than the number of rows in a local table, then the access plan would generate a remote statement to push down the inner join operation and the substring operation to the remote table and performs the operations on a remote platform. Conversely, if the number of rows in a remote table is much less than the number of rows in a local table, then the access plan would generate a remote statement to fetch the remote table to a local platform to perform the operations on the local platform where the local table that is much greater than the remote table is located. If the number of rows in a remote table and the number of rows in a local table are substantially similar without much difference, then the access plan can decide to either perform the operations on a local platform or on a remote platform, as there is not much difference in performance either way. 
     In the same embodiments as noted in block  320 , the DV engine  201  uses the real time system statistics collected and stored in the global catalog in devising the access plan and generating the query statement in block  330 . For example, if the real time system statistics on Network parameter indicates that the network cost in the virtual database system  100  is great to the extent that bulk transfers of data source objects are not recommended, then the access plan would avoid any bulk transfer for processing the query  103 . Also, if the real time system statistics on Remote_CPU parameter indicates that the cost of computation in a specific remote platform is very high, then the access plan would avoid any computation-bound operations on the specific remote platform. The real time system statistics made available from block  320  can contribute to further optimize the access plan, in cases where seemingly similar access plans based on conventional data source statistics can incur distinctly differentiated costs of processing based on the real time system statistics. 
     In block  340 , the DV engine  201  scans local catalogs of all remote platforms for any changes with the parameters subject to real time statistics according to a real time statistics update configuration. Then, the DV engine  201  proceeds with block  350 . 
     Upon registering data source objects from remote data sources with nicknames, the DV engine  201  collects data source statistics regarding the data source objects registered with the respective nickname and stores the data source statistics as nickname statistics in the global catalog, as a part of conventional virtual database operation. Conventionally the nickname statistics includes, for example, a server name, a schema name, column types, index names, etc. Remote platforms will not update the global catalog on the virtual database server  120  with any changes with the nickname statistics on the remote platforms. Data flows regarding the nickname statistics are presented in  FIG.  4    and corresponding description. 
     Due to the lack of automated update on the nickname statistics with the global catalog for changes to the remote data source objects, the DV engine  201  collects real time data source statistics by use of a customary method for collecting data source statistics in real time. The real time statistics update configuration of the DV engine  201  indicates whether the real time statistics feature is enabled in the DV engine  201  and how often the real time data source statistics should be updated, as described in block  310 . 
     In block  350 , the DV engine  201  determines whether global statistic, as discovered in all local catalogs on respective remote platforms from block  340 , has been changed since the latest collection. If the DV engine  201  determines that the global statistic has been changed, then the DV engine  201  proceeds with block  360 . If the DV engine  201  determines that the global statistic has not been changed, then the DV engine  201  loops back to block  330  to continue with query processing based on the current data of the global catalog. 
     In certain embodiments of the present invention, any remote platform running the data sources DS_A  160 , DS_B  170 , and DS_C  180  returns any changes on nicknamed data source objects to the DV engine  201  upon inquiry. As the DV engine  201  registered the real time statistics parameters regarding IUD operations, rows modified, index priority in any of the data source objects, and system environments of network, CPU, memory, I/O specification on respective remote platforms running the data sources with respective nicknames, the DV engine  201  can check for any changes on the real time statistics via the nickname statistics collection method. 
     In block  360 , the DV engine  201  collects the real time system statistics and the real time data sources statistics again from the data sources DS_A  160 , DS_B  170 , and DS_C  180 , as the global statistics on nicknamed data source objects subject to real time statistics as discovered in block  350 . Then, the DV engine  201  proceeds with block  370 . 
     In the same embodiments as above in block  320 , the DV engine  201  invokes the method RT_Statistics1( ), via the SPDA  130  and the SSCA  150 , to collect the real time system statistics for values of respective parameters Network, CPU, Memory, and IO from the remote platforms running the data sources DS_A  160 , DS_B  170 , and DS_C  180 . The DV engine  201  invokes the method RT_Statistics2( ) to collect real time data source statistics from the remote platforms running the data sources DS_A  160 , DS_B  170 , and DS_C  180 , for values of respective parameters IUD_priority, Index_priority, and Rows_modified. 
     In block  370 , the DV engine  201  updates the global catalog with the real time statistics that has been newly collected from block  360 . The real time statistics in the global catalog is synchronized with the global statistics across all platforms of the virtual database system  100  after performing block  370 . Accordingly, the DV engine  201  uses the global catalog to optimize the access plan and to generate query statements for the remote platforms running the data sources DS_A  160 , DS_B  170 , and DS_C  180 . Then, the DV engine  201  loops back to block  330  to continue with query processing based on the current data of the global catalog. 
       FIG.  4    depicts data flows  400  described in the workflow  300  of the DV engine  201  in  FIG.  3   , in accordance with one or more embodiments set forth herein. 
     In the same embodiments noted in block  320  of  FIG.  3   , the DV engine  201  invokes a method Nickname Statistics Collection( )  403 , defined as SYSPROC.NNSTAT in db2, for example, upon registering the remote data source objects by creating nicknames. 
     The DV engine  201  invokes a method for collecting real time system statistics RT_Statistics1( )  410  concurrently with the method Nickname Statistics Collection( )  403 . The method for collecting real time system statistics RT_Statistics1( )  410  sends a command to the SSCA  150  on the virtual database server  120  to communicate with the SSCA  150  on the remote data source DS_A  160  and to collect the real time system statistics of Net_latency, Remote_CPU, Remote_memory, and Remote_IO from the remote data source DS_A  160 , and the SSCA  150  on the remote data source DS_A  160  collects the real time system statistics of Net_latency, Remote_CPU, Remote_memory, and Remote_IO and returns to the DV engine  201 , as indicated with bidirectional solid links. The method RT_Statistics1( )  410  stores real time system statistics  450  of Net_latency, Remote_CPU, Remote_memory, and Remote_IO from the remote data source DS_A  160  in the real time statistics table (RTSTAT)  140  in a global catalog  440 , as indicated with a solid link to the real time system statistics  450  in the global catalog  440 . The DV engine  201  stores nickname statistics  443  including conventional remote data source statistics in the global catalog  440 . 
     As noted above in  FIG.  3   , upon registering data source objects from remote data sources with nicknames, the DV engine  201  collects data source statistics regarding the data source objects registered with the respective nickname by invoking a method Nickname statistics collection( )  403  and stores the data source statistics as nickname statistics  443  in the global catalog  440 , indicated by short-dashed connections amongst the data source DS_A  160 , the method Nickname statistics collection( )  403 , and the nickname statistics  443  in the global catalog  440 . The DV engine  201  uses conventional database connector interface in communicating with the data source DS_A  160  without going through the SSCA  150 . 
     As noted in block  320 , the DV engine  201  triggered by the SSCA  150  invokes a method RT_Statistics2( )  430  for collecting real time data source statistics on parameters of IUD_priority, Index_priority, and Rows_modified, as described in block  320  of  FIG.  3   . In the same embodiments as above, the DV engine  201  collects real time data source statistics by communicating with the remote platform running the data source DS_A  160  periodically at an interval according to a level set in the real time statistics update configuration. The DV engine  201  subsequently stores real time data source statistics  470  in the global catalog  440 , as indicated by long-dashed links amongst the data source DS_A  160 , the method RT_Statistics2( )  430 , and the real time data source statistics  470  in the global catalog  440 . 
     The DV engine  201  invokes a method for collecting real time system statistics RT_Statistics1( )  410  concurrently with the method Nickname Statistics Collection( )  403 . The method for collecting real time system statistics RT_Statistics1( )  410  sends a command to the SSCA  150  on the virtual database server  120  to communicate with the SSCA  150  on the remote data source DS_A  160  and to collect the real time system statistics of Net_latency, Remote_CPU, Remote_memory, and Remote_IO from the remote data source DS_A  160 , and the SSCA  150  on the remote data source DS_A  160  collects the real time system statistics of Net_latency, Remote_CPU, Remote_memory, and Remote_IO and returns to the DV engine  201 , as indicated with bidirectional solid links. The method RT_Statistics1( )  410  stores real time system statistics  450  of Net_latency, Remote_CPU, Remote_memory, and Remote_IO from the remote data source DS_A  160  in the real time statistics table (RTSTAT)  140  in a global catalog  440 , as indicated with a solid link to the real time system statistics  450  in the global catalog  440 . The DV engine  201  stores nickname statistics  443  including conventional remote data source statistics in the global catalog  440 . 
     Certain embodiments of the present invention improve query performance in virtual database by providing a range of statistics information including, but not limited to, real time system statistics of network connectivity across all data sources, processing, memory and I/O capacities on respective remote platforms running the data sources. Certain embodiments of the present invention provide real time global statistics collection methods and interfaces amongst heterogeneous remote data sources. Certain embodiments of the present invention improve preexisting database connectors with real time statistics collection function that is initiated by existing data source statistics mechanism. Certain embodiments of the present invention provide mappings of the real time statistics collection method for the respective remote platforms per respective support on statistics collection. Certain embodiments of the present invention improve a query pushdown analysis with statistics pushdown as being mapped for the respective remote platforms in the virtual database. Certain embodiments of the present invention improve database connectors with real time statistics collection method. Certain embodiments of the present invention for the virtual database can be implemented by use of a cloud platform/data center/server farm in various types including a Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS), Database-as-a-Service (DBaaS), and combinations thereof. The virtual database system can be offered for and delivered to any service providers/business entities/vendors of software applications from any location in the world in need of data virtualization with optimal query performance based on real time statistics across the virtual database. 
     Embodiments of the present invention present a computer implemented method including, for instance: collecting, by one or more processor, real time system statistics on a system environment in a remote platform running a remote data source amongst a plurality of remote data sources of a virtual database, where the remote data sources of the virtual database are heterogeneous database management systems run on respective remote platforms across disparate geographical locations; gathering, by the one or more processor, real time data source statistics on one or more data source objects from the remote data source of the virtual database; updating, by the one or more processor, a global catalog of the virtual database with the real time system statistics from the collecting and the real time data source statistics from the gathering; optimizing, by the one or more processor, an access plan to process a query submitted to the virtual database from a client application based on the real time system statistics and the real time data source statistics in the global catalog; and producing, by the one or more processor, a response corresponding to the query by executing the access plan to thereby improve both a response time of the query and a cost of processing. 
     Embodiments of the present invention present a computer implemented method also including, for instance: the collecting including: fetching, upon registering one or more data source objects from the remote data source of the virtual database for use, the real time system statistics on the system environment of the remote data source of the virtual database, on parameters regarding network capacity, processing capacity, memory capacity, and input/output capacity of the remote platform running the remote data source. 
     Embodiments of the present invention present a computer implemented method also including, for instance: the gathering including: fetching, by use of a real time data source statistics collection method mapped for the remote data source, the real time data source statistics on the one or more data source objects from the remote data source that are registered with the virtual database, on parameters regarding priorities for insert-update-delete operations, index priority, and modified rows on the one or more data source objects from the remote data source of the virtual database that have been registered with the virtual database. 
     Embodiments of the present invention present a computer implemented method also including, for instance: ascertaining, prior to the collecting, that the real time system statistics and the real time data source statistics stored in the global catalog of the virtual database has been changed since a latest iteration of the collecting, regarding the one or more data source objects registered with the virtual database; where the collecting including: fetching the real time system statistics on the system environment of the remote data source of the virtual database, on system parameters regarding network capacity, processing capacity, memory capacity, and input/output capacity of the remote platform running the remote data source; and where the gathering including: fetching, by use of a real time data source statistics collection method mapped for the remote data source, the real time data source statistics on the one or more data source objects from the remote data source that are registered with the virtual database, on data source parameters regarding priorities for insert-update-delete operations, index priority, and modified rows on the one or more data source objects from the remote data source of the virtual database that have been registered with the virtual database. 
     Embodiments of the present invention present a computer implemented method also including, for instance: the collecting including: fetching the real time system statistics on the system environment of the remote data source of the virtual database, on parameters regarding network capacity, processing capacity, memory capacity, and input/output capacity of the remote platform running the remote data source, by use of a real time system statistics collection method mapped for the remote platform, including: sending, by a system statistics collection agent running on a server of the virtual database, the real time system statistics collection method mapped for the remote platform to a system statistics collection agent running on the remote platform that forms a direct interface to communicate system statistics information without going through a database connector in the server of the virtual database and one of the database management systems running on the remote platform; and receiving, by the system statistics collection agent running on the server of the virtual database, values respective to the parameters of the real time system statistics collection method from another system statistics collection agent running on the remote platform through the direct interface. 
     Embodiments of the present invention present a computer implemented method also including, for instance: the collecting including: fetching the real time system statistics on the system environment of the remote data source of the virtual database, on parameters regarding network capacity, processing capacity, memory capacity, and input/output capacity of the remote platform running the remote data source, by use of a real time system statistics collection method mapped for the remote platform, including: sending, by a system statistics collection agent running on a server of the virtual database, the real time system statistics collection method mapped for the remote platform to another system statistics collection agent running on the remote platform that forms a direct interface to communicate system statistics information without going through a database connector in the server of the virtual database and one of the database management systems running on the remote platform; and receiving, by the system statistics collection agent running on the server of the virtual database, values respective to the parameters of the real time system statistics collection method from another system statistics collection agent running on the remote platform through the direct interface; and ascertaining, prior to the gathering the real time data source statistics, that the system statistics collection agent running on the server of the virtual database activated the server of the virtual database to execute a real time data source statistics collection method. 
     Embodiments of the present invention present a computer implemented method also including, for instance: mapping, prior to the gathering, a real time data source statistics collection method on parameters regarding priorities for insert-update-delete operations, index priority, and modified rows on the one or more data source objects to a routine that can retrieve the parameters of the real time data source statistics collection method from the remote data source, regardless of real time statistics support on the remote data source. 
       FIGS.  5 - 7    depict various aspects of computing, including a cloud computing system, in accordance with one or more aspects set forth herein. 
     It is to be understood that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed. 
     Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models. 
     Characteristics are as Follows: 
     On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service&#39;s provider. 
     Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs). 
     Resource pooling: the provider&#39;s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter). 
     Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time. 
     Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service. 
     Service Models are as Follows: 
     Software as a Service (SaaS): the capability provided to the consumer is to use the provider&#39;s applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings. 
     Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations. 
     Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls). 
     Deployment Models are as Follows: 
     Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises. 
     Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises. 
     Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services. 
     Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds). 
     A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes. 
     Referring now to  FIG.  5   , a schematic of an example of a computer system/cloud computing node is shown. Cloud computing node  10  is only one example of a suitable cloud computing node and is not intended to suggest any limitation as to the scope of use or functionality of embodiments of the invention described herein. Regardless, cloud computing node  10  is capable of being implemented and/or performing any of the functionality set forth hereinabove. 
     In cloud computing node  10  there is a computer system  12 , which is operational with numerous other general purposes or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with computer system  12  include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments that include any of the above systems or devices, and the like. 
     Computer system  12  may be described in the general context of computer system-executable instructions, such as program processes, being executed by a computer system. Generally, program processes may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. Computer system  12  may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program processes may be located in both local and remote computer system storage media including memory storage devices. 
     As shown in  FIG.  5   , computer system  12  in cloud computing node  10  is shown in the form of a general-purpose computing device. The components of computer system  12  may include, but are not limited to, one or more processors  16 , a system memory  28 , and a bus  18  that couples various system components including system memory  28  to processor  16 . 
     Bus  18  represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnects (PCI) bus. 
     Computer system  12  typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system  12 , and it includes both volatile and non-volatile media, removable and non-removable media. 
     System memory  28  can include computer system readable media in the form of volatile memory, such as random access memory (RAM)  30  and/or cache memory  32 . Computer system  12  may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, storage system  34  can be provided for reading from and writing to a non-removable, non-volatile magnetic media (not shown and typically called a “hard drive”). Although not shown, a magnetic disk drive for reading from and writing to a removable, non-volatile memory device (e.g., a “thumb drive”, “external hard drive”), and an optical disk drive for reading from or writing to a removable, non-volatile optical disk such as a CD-ROM, DVD-ROM or other optical media can be provided. In such instances, each can be connected to bus  18  by one or more data media interfaces. As will be further depicted and described below, memory  28  may include at least one program product having a set (e.g., at least one) of program processes that are configured to carry out the functions of embodiments of the invention. 
     One or more program  40 , having a set (at least one) of program processes  42 , may be stored in memory  28  by way of example, and not limitation, as well as an operating system, one or more application programs, other program processes, and program data. Each of the operating system, one or more application programs, other program processes, and program data or some combination thereof, may include an implementation of the SPDA  130 , the SSCA  150  of  FIG.  1   , and the DV engine  201  of  FIG.  2   . Program processes  42 , as in the SPDA  130 , the SSCA  150 , and the DV engine  201 , generally carry out the functions and/or methodologies of embodiments of the invention as described herein. 
     Computer system  12  may also communicate with one or more external devices  14  such as a keyboard, a pointing device, a display  24 , etc.; one or more devices that enable a user to interact with computer system  12 ; and/or any devices (e.g., network card, modem, etc.) that enable computer system  12  to communicate with one or more other computing devices. Such communication can occur via Input/Output (I/O) interfaces  22 . Still yet, computer system  12  can 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 network adapter  20 . As depicted, network adapter  20  communicates with the other components of computer system  12  via bus  18 . 
     In addition to or in place of having external devices  14  and the display  24 , which can be configured to provide user interface functionality, computing node  10  in one embodiment can include another display  25  connected to bus  18 . In one embodiment, the display  25  can be configured as a touch screen render and can be configured to provide user interface functionality, e.g. can facilitate virtual keyboard functionality and input of total data. Computer system  12  in one embodiment can also include one or more sensor device  27  connected to bus  18 . One or more sensor device  27  can alternatively or in addition be connected through I/O interface(s)  22 . The one or more sensor device  27  can include a Global Positioning Sensor (GPS) device in one embodiment and can be configured to provide a location of computing node  10 . In one embodiment, the one or more sensor device  27  can alternatively or in addition include, e.g., one or more of a camera, a gyroscope, a temperature sensor, a humidity sensor, a pulse sensor, a blood pressure (BP) sensor or an audio input device. 
     It should be understood that although not shown, other hardware and/or software components could be used in conjunction with computer system  12 . Examples, include, but are not limited to: microcode, device drivers, redundant processors, external disk drive arrays, Redundant Array of Independent/Inexpensive Disks (RAID) systems, tape drives, and data archival storage systems, etc. 
     Referring now to  FIG.  6   , illustrative cloud computing environment  50  is depicted. As shown, cloud computing environment  50  includes one or more cloud computing nodes  10  running the database decomposition system  120  with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone  54 A, desktop computer  54 B, laptop computer  54 C, and/or automobile computer system  54 N may communicate. Nodes  10  may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment  50  to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices  54 A-N shown in  FIG.  6    are intended to be illustrative only and that computing nodes  10  and cloud computing environment  50  can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser). 
     Referring now to  FIG.  7   , a set of functional abstraction layers provided by cloud computing environment  50  ( FIG.  6   ) is shown. It should be understood in advance that the components, layers, and functions shown in  FIG.  7    are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided: 
     Hardware and software layer  60  includes hardware and software components. Examples of hardware components include: mainframes  61 ; RISC (Reduced Instruction Set Computer) architecture based servers  62 ; servers  63 ; blade servers  64 ; storage devices  65 ; and networks and networking components  66 . In some embodiments, software components include network application server software  67  and database software  68 . 
     Virtualization layer  70  provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers  71 ; virtual storage  72 ; virtual networks  73 , including virtual private networks; virtual applications and operating systems  74 ; and virtual clients  75 . 
     In one example, management layer  80  may provide the functions described below. Resource provisioning  81  provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing  82  provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal  83  provides access to the cloud computing environment for consumers and system administrators. Service level management  84  provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment  85  provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA. 
     Workloads layer  90  provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation  91 ; software development and lifecycle management  92 ; virtual classroom education delivery  93 ; data analytics processing  94 ; transaction processing  95 ; and processing components  96  for various processes in the virtual database system, as described herein. 
     The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprise” (and any form of comprise, such as “comprises” and “comprising”), “have” (and any form of have, such as “has” and “having”), “include” (and any form of include, such as “includes” and “including”), and “contain” (and any form of contain, such as “contains” and “containing”) are open-ended linking verbs. As a result, a method or device that “comprises,” “has,” “includes,” or “contains” one or more steps or elements possesses those one or more steps or elements, but is not limited to possessing only those one or more steps or elements. Likewise, a step of a method or an element of a device that “comprises,” “has,” “includes,” or “contains” one or more features possesses those one or more features, but is not limited to possessing only those one or more features. Furthermore, a device or structure that is configured in a certain way is configured in at least that way, but may also be configured in ways that are not listed. 
     The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below, if any, are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description set forth herein has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the disclosure. The embodiment was chosen and described in order to best explain the principles of one or more aspects set forth herein and the practical application, and to enable others of ordinary skill in the art to understand one or more aspects as described herein for various embodiments with various modifications as are suited to the particular use contemplated.