Patent Publication Number: US-11657027-B2

Title: Systems and methods for logically copying data from a source database to one or more target databases

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is a continuation of U.S. application Ser. No. 16/947,136, filed Jul. 20, 2020, the contents of which are hereby incorporated by reference. 
    
    
     TECHNICAL FIELD 
     Embodiments of the subject matter described herein relate generally to cloud-based computing. More particularly, embodiments of the subject matter relate to logically copying data from a source database to target database(s). 
     BACKGROUND 
     Today many enterprises now use cloud-based computing platforms that allow services and data to be accessed over the Internet (or via other networks). Infrastructure providers of these cloud-based computing platforms offer network-based processing systems that often support multiple enterprises (or tenants) using common computer hardware and data storage. This “cloud” computing model allows applications to be provided over a platform “as a service” supplied by the infrastructure provider. 
     A database server instance may contain multiple databases. A database is a logical container for data and related objects. A relational database stores data in tables, and can include any number of tables. Each table includes columns and rows. In other words, a table can be viewed as a collection of rows, all with a similar set of columns, that are used to store data in an organized format. When a table is created, columns are created, where each column has a data type associated with it. A data type represents the type of data that each column can hold. Columns describe the specific pieces of information in the table (e.g., names of fields). A row is a collection of attributes that describe an entity, and therefore, a table can be viewed as a collection of rows with the same attribute types. Each row stores the actual, corresponding data. The intersection of a column and a row can be referred to as a record. 
     Databases sizes in many cloud computing platforms can be very large (e.g., on the order of terabytes or more). In some cases, it is necessary to migrate or replicate data from a source database (e.g., at one platform/pod) to other target databases (e.g., at another platform/pod). Conventional recovery manager (RMAN) physical copy methodologies are one example of a replication technology that can be used for this purpose. 
     Current methodologies for copying data from a source database to target database(s) are slow and/or inefficient. As such, it would be desirable to provide technologies for logically copying data from a source database to target database(s) in a fast and efficient manner. The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       A more complete understanding of the subject matter may be derived by referring to the detailed description and claims when considered in conjunction with the following figures, wherein like reference numbers refer to similar elements throughout the figures. 
         FIG.  1    is a block diagram that illustrates a conventional recovery manager (RMAN) physical copy methodology that is part of an instance refresh process. 
         FIG.  2 A  is a block diagram that illustrates a logical layout or logical structure of a database in accordance with the disclosed embodiments. 
         FIG.  2 B  is a block diagram that illustrates partitioning of a table in accordance with the disclosed embodiments. 
         FIG.  3 A  is a system that illustrates a logical copy processor for copying data from a source database to two or more target databases in accordance with the disclosed embodiments. 
         FIG.  3 B  is a flowchart that illustrates a logical copy method performed by the logical copy processor of  FIG.  3 A  for copying data from a source database to two or more target databases in accordance with the disclosed embodiments. 
         FIG.  4    is a flowchart that illustrates a method performed by the logical copy processor of  FIG.  3 A  for collecting partition information based on the partition input arguments in accordance with the disclosed embodiments. 
         FIG.  5    is a database logical structure that illustrates a RowID format that includes some of the partition metadata information in accordance with the disclosed embodiments. 
         FIG.  6    is a method for creating RowID ranges in accordance with the disclosed embodiments. 
         FIG.  7    is a flowchart that illustrates a RowID copy method performed by a parallel executor of the logical copy processor of  FIG.  3 A  in accordance with the disclosed embodiments. 
         FIG.  8    shows a block diagram of an example of an environment in which an on-demand database service can be used in accordance with some implementations. 
         FIG.  9    shows a block diagram of example implementations of elements of  FIG.  8    and example interconnections between these elements according to some implementations. 
         FIG.  10 A  shows a system diagram illustrating example architectural components of an on-demand database service environment according to some implementations. 
         FIG.  10 B  shows a system diagram further illustrating example architectural components of an on-demand database service environment according to some implementations. 
         FIG.  11    illustrates a diagrammatic representation of a machine in the exemplary form of a computer system within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed. 
     
    
    
     DETAILED DESCRIPTION 
     As noted above, current methodologies for copying data from a source database to target database(s) are slow and/or inefficient.  FIG.  1    is a block diagram  100  that illustrates a conventional recovery manager (RMAN) physical copy methodology that is part of an instance refresh process. An instance refresh process can refer to splitting/migrating a source database (e.g., of Salesforce customer data) into one or more target databases. As two non-limiting examples of conditions for initiating the instance refresh process could be, for example, when source database hardware has reached its end-of-life, or when the source database instance has run out of compute or storage capacity. 
     A processor  115  executes the RMAN physical copy method to copy data from a source database  110  (e.g., at one pod) to two or more target databases  120  (e.g., at other pods). The RMAN physical copy method executed by the processor  215  includes preparing the databases, and then initiating the RMAN physical copy process. For example, the source database  110  and target databases  120  are prepared by confirming that a connection between the source database  110  to target databases  120  is working acceptably, and then the RMAN physical copy method is used to copy the files from source database  110  to both targets databases  120 . 
     One drawback of the RMAN physical copy technology is that it employs a two-way instance refresh mechanism in which, close or near to 100% of data, is physically copied from a source database to two or more respective target databases using partition mapping. For instance, in the case where data is being copied to two target databases, this process happens even though only 50% of the data needs to be copied to each of the respective target databases. As such, too much storage space is consumed at the target databases. In addition, there is no way to filter the data that is copied to each target database in a fast and efficient manner. To explain further, when the RMAN physical copy methodologies are used to copy data, there is no way to filter out the data from the source database at the table/partition level. As such, even though all of the data from the source database  110  does not need to be copied to both of the target databases  120 , all of the data from the source database  110  is copied to both of the target databases  120 . As such, both of the target databases  120  must have at least the same amount of storage space allocated as the source database  110 . 
     Another disadvantage is fragmentation. The RMAN physical copy method is a file-based copy method in which datafiles will copy to the target databases without any change (e.g., meaning that they are the same size as they are at the source database). As such, whatever fragmented data exists on the source database will be same as that at the target databases. For example, tables and indexes that are fragmented in datafiles at source database will also be fragmented in datafiles at the target databases. As a result, using the RMAN physical copy method will result in each of the target databases being the same size as the source database. 
     Yet another disadvantage of this approach is that copying results in downtime for customers while all data from the source database  110  is being copied to both of the target databases  120 , and during that maintenance window unwanted data must be maintained thus increasing down time for customers. 
     As such, it would be desirable to provide technologies for logically copying data from a source database to target database(s) in a fast and efficient manner. It would be desirable to provide technologies for logically copying only “necessary” partition data from the source database  110  to the target databases  120  (e.g., where the necessary partition data is actual database data from the source database  110  such as customer data from the source database  110 ). 
     The exemplary embodiments presented herein relate to logically copying data from a source database to one or more target databases. For example, the described subject matter can be implemented in the context of any cloud-based computing environment including, for example, a multi-tenant database system. In one embodiment, a database migration system and related methodologies are provided for logically copying data from a source database to one or more target databases. The source database comprises a table having a plurality of partitions (e.g., each table can be split or partitioned into a number (1 . . . N) of partitions). Each partition can include multiple extents within that partition. In one non-limiting embodiment, the target databases, such as, a first target database and a second target database, a processor of the database migration system can be provided with partition information that specifies, for example, a first set of partitions from the source database that are to be copied to the first target database, and a second set of partitions from the source database that are to be copied to the second target database. The processor can collect, based on partition input arguments, table partition information that indicates the first set of partitions are to be copied to the first target database and the second set of partitions are to be copied to the second target database. The partition input arguments can include, for example, a first set of partition numbers that will be assigned to the first target database; and a second set of partition numbers that will be assigned to the second target database. 
     Based on the table partition information, the processor can query the source database to collect partition metadata information for the first set of partitions and the second set of partitions, and store a first set of the partition metadata information for the first set of partitions at the first target database, and a second set of the partition metadata information for the second set of partitions at the second target database. In one embodiment, each instance of the partition metadata information can include, for example, a table name, a partition name, an object ID, a file number, a block number, and a row number. 
     Based on the partition metadata information, at least one extent chunk can be created for each partition of a table. Each extent chunk can include a set or group of extents. In one embodiment, each extent chunk can be computed by computing the value of the starting row identifier for that extent chunk, and computing the value of the ending row identifier for that extent chunk. 
     For example, in one embodiment, for each of the first set of partitions, the first set of extent chunks can be created based on the first set of the partition metadata information, and for each of the second set of partitions, the second set of extent chunks can be created based on the second set of the partition metadata information. 
     Each extent chunk can include a group of extents specified by a range of row identifiers (IDs) that is specified, for example, by a value of a starting row identifier and a value of an ending row identifier for that extent chunk. In one embodiment, each row identifier has a format comprising: an object ID, a file number, a block number, and a row number. 
     In one embodiment, the first target database can query the source database, based on a first set of extent chunks, for a first set of data from the source database, and write the first set of data to the first target database. Similarly, the second target database can query the source database, based on a second set of extent chunks, for a second set of data from the source database, and write the second set of data to the second target database. For example, in one implementation, each table of a first set of tables has a subset of related extent chunks from the first set of extent chunks, and for each table of the first set of tables, a thread of a multi-thread processor can process each related extent chunk (of the subset of related extent chunks associated with that table) by: querying the source database for particular data in a range of row identifiers specified by that related extent chunk; and writing the particular data to the first target database. Collectively the multi-thread processor can multi-thread process all of the first set of extent chunks in parallel to copy the first set of data from the source database to the first target database. The first set of data from the source database comprises all of the particular data in the range of row identifiers specified by each related extent chunk of the subset of related extent chunks. 
     The disclosed systems and methods for logically copying data from a source database to one or more target databases can provide a number of improvements in contrast to physical copy methods. For example, one advantage of the disclosed systems and methods for logically copying data is that only “necessary” data is copied from a source database to each target database (based on partition input arguments). As such, the amount of data copied to each target database is less, and storage space is drastically reduced. Yet another advantage of the disclosed systems and methods for logically copying data is that it fully supports partition input arguments to copy customer data. The partition input arguments (e.g., partition numbers) can be configured and specified by whoever runs the automation. Another advantage of the disclosed systems and methods for logically copying data is that target databases (that data is being migrated to) can be smaller in size making it easy to migrate data from large database platforms to smaller database platforms. 
     Another advantage of the disclosed systems and methods for logically copying data is that since it is a logical copy, fragmentation at the target databases is not an issue. To explain further, by logically copying data, fresh data blocks, which are allocated in contiguous manner, are written to target databases so there is no chance of fragmentation at the target databases. Hence database performance will be improved. 
     Still another advantage of the disclosed systems and methods for logically copying data is that data is chunked into separate parts and copied in parallel. As such, less data is copied from the source database to each target database (e.g., only necessary data for each target database), and the rate at which it is copied is faster because extent chunks are processed in parallel using multi-thread processing, which helps increase speed. Thus, this method is very fast when compare to other copy methodologies. 
     Another advantage of the disclosed systems and methods for logically copying data is that they are resumable. To explain further, all successes/failures are tracked. As such, in case of abnormal termination or exit during the automated execution of the logical data copy, the logical data copy can resume or restart from where it left off. In other words, the logical data copy process is “resumable” in that it can start from any point it terminates at and does not need to start from beginning. 
       FIG.  2 A  is a block diagram that illustrates a logical layout or logical structure  200  of a database  210  in accordance with one non-limiting implementation. In some database systems, data is stored logically in one or more tablespaces and physically in datafiles associated with the corresponding tablespaces. The tablespaces collectively store all of the database&#39;s data. In other words, a database includes one or more tablespaces. As shown in  FIG.  2 A , one-possible non-limiting implementation of a database  210  that can be logically divided into one or more tablespaces  220 , where each tablespace  220  is a container for one or more datafiles  230  that physically store data of the database. Each datafile  230  is assigned to a tablespace. A tablespace can refer to a logical space or “division” within the database required to store data (e.g., a logical unit of database storage). Stated differently one or more datafiles form a logical unit of database storage called a tablespace. A tablespace links the physical storage layer (e.g., files on disks) and the logical storage layer (e.g., tables, indexes). 
     A datafile  230  is an operating system file that conforms to an operating system, and is used to store data (e.g., a datafile is a physical structure/entity where the data is stored physically). For example, the data of logical data structures, such as tables and indexes, is stored in datafiles  230  of the database. Collectively, the database&#39;s data is stored in the datafiles  230 . 
     Each datafile  230  can have one or more table segments  240 . A table segment  240  can be a partitioned table or a non-partitioned table. Each table segment  240  is a set of extents  250  that contains all the data for a specific logical storage structure within a tablespace (e.g., that have been allocated for a certain logical data structure, and that all are stored in the same tablespace). For example, for each table  220 , one or more extents  250  can be allocated to form a table segment  240  (also referred to as a data segment) for that table, and for each index, one or more extents can be allocated to form an index segment. For example, data for each table (or each partition of a table) can be stored in its own data table segment  240 , while each data for each index can be stored in its own index table segment  240 . An extent represents continuous data blocks  260  that are used to store specific data information. A data block  260  represents specific number of bytes of physical database space  270  on disk or OS blocks. 
       FIG.  2 B  is a block diagram that illustrates partitioning of a table  280  in accordance with an embodiment. Partitioning allows the table  280  to be subdivided into smaller pieces, where each piece of such a database object is called a partition  290 - 1  . . .  290 -N. Each partition  290 - 1  . . .  290 -N has its own name, and may optionally have its own storage characteristics. Within each partition, there can be multiple extents within that partition (as illustrated by smaller squares under each of the partitions  290 - 1  . . .  290 -N). 
     In accordance with the disclosed embodiments, a logical copy system and methodologies are provided for copying data from a source database  110  to two or more target databases  120 . In one embodiment, a RowID based or logical copy methodology is provided that allows for data to be copied to multiple targets in a fast and efficient way by copying only “necessary” data (e.g., actual database data from the source database  110 ). By doing so, the overall migration time (including customer downtime) can be drastically reduced. In general terms, the RowID based copy method is a purely logical copy that: splits all table data into extent chunks and then copies them in parallel to target using multi-threading. This way only “necessary” partition data (as defined using partition input arguments) needs to be copied, as opposed to all data. This solution will work for all table objects (e.g., Large OBjects (LOBs) that can contain both complex structured and unstructured data, NON-LOBs or normal tables other than LOB tables, etc.). 
       FIG.  3 A  is a system that illustrates a logical copy processor  310  for copying data from a source database  110  to two or more target databases  120  in accordance with the disclosed embodiments.  FIG.  3 B  is a flowchart that illustrates a logical copy method  320  performed by the logical copy processor  310  of  FIG.  3 A  for copying data from a source database  110  to two or more target databases  120  in accordance with the disclosed embodiments.  FIG.  3 A  will be described below in conjunction with  FIG.  3 B . 
     In one embodiment, the logical copy method  320  starts at step  330 , where the logical copy processor  310  is provided with connectivity information for the source database  110 , the first target database  120 - 1  (target database 1) and the second target database  120 - 2  (target database 2). This connectivity information is used to establish a connection between the source database  110  and the target databases  120 . For each database, this connectivity information can include: the database name, the database hostname, the database port, the database username and the database password. In addition, the logical copy processor  310  is also provided with partition information regarding which sets of partitions from the source database  110  are to be copied to the first target database  120 - 1  (target database 1), and which sets of partitions from the source database  110  are to be copied to the second target database  120 - 2  (target database 2). 
     The logical copy method  320  then proceeds to step  340 , where the logical copy processor  310  can collect partition information based on table partition input arguments. For example, in one implementation, at step  340 , table partition information is collected based on partition input arguments for the RowID Copy automation (e.g., partition numbers input by human such as an administrator). The logical copy processor  310  can query the source database  110  and collect partition metadata information. The logical copy processor  310  can store the partition metadata information in the target databases  120  (target database 1  120 - 1  and target database 2  120 - 2 ). Hence it only contains required partition metadata information on each target database  120 . This partition metadata information can include at least: table name, partition name, object ID, file number, block number, row number, etc. One implementation of step  340  will be described below with reference to  FIG.  4   . 
     The logical copy method  320  then proceeds to step  350 , where the logical copy processor  310  can split table data in extent chunks (also referred to herein as RowID ranges). Each extent chunk can include a set or group of extents specified by a range of row identifiers (RowIDs). For example, in one implementation of step  350 , extent chunks (RowID ranges) are created using metadata information collected (at step  340 ) on the target database side. For instance, in one implementation, the extent chunks (RowID ranges) can be created using partition metadata information that was collected and stored in the target databases  120  (e.g., target database 1  120 - 1  and target database 2  120 - 2 ) at step  340 . One implementation of step  350  will be described below with reference to  FIG.  6   . 
     At step  360 , a RowID copy process is performed, where data is copied per the extent chunks (RowID ranges) that were created as part of step  350 . For example, in one implementation of step  360 , multi-thread processing can be performed, where each thread of a multi-thread processor processes an extent chunk and copies corresponding data for that extent chunk to an appropriate one of the target databases. Each extent chunk can include a set or group of extents (described above with reference to  FIGS.  2 A and  2 B ) specified by a range of row identifiers (RowIDs). This way, the corresponding data for each extent chunk can be copied to an appropriate one of the target database, while other threads copy corresponding data for each of the other extent chunks to an appropriate one of the target databases in parallel. One specific implementation of step  360  will be described below with reference to  FIG.  7   . 
       FIG.  4    is a flowchart that illustrates a method  340  performed by the logical copy processor  310  of  FIG.  3 A  for collecting partition information based on the partition input arguments in accordance with the disclosed embodiments. In this non-limiting example,  FIG.  4    explains details about how partition information is collected based on partition input arguments, and how that partition information can then be used to collect the partition metadata information for target database 1  120 - 1  and target database 2  120 - 2 . In this simplified example, the partition input arguments specify that partition numbers 01, 03, 05, 07 will be assigned to the first target database 1  120 - 1  and that partition numbers 02, 04, 06, 08 will be assigned to the second target database 2  120 - 2 . 
       FIG.  5    is a database logical structure  500  that illustrates a RowID format that includes some of the partition metadata information in accordance with the disclosed embodiments. As shown in  FIG.  5   , the RowID format can include: data object ID (column  510 ), file number (column  520 ), block number (column  530 ) and row number (column  540 ). 
       FIG.  6    is a method  600  for creating RowID ranges in accordance with the disclosed embodiments. In one implementation, a RowID range creation algorithm  600  (e.g., in SQL) is provided for creating extent chunks for each partition of a table (see  FIG.  2 B ). In this non-limiting example, it is assumed that there are a maximum of 10,000 rows (maximum value) in a given block, but the maximum number of rows in a given block can vary depending on the implementation. Each extent chunk is specified by a range of row IDs, or “RowID range,” that can be specified using two values: a Start RowID, which may be created or computed using code  610 , and an End RowID, which may be created or computed using code  620 . An extent chunk can be created for each and every table/partition. As described above with reference to step  360 , the extent chunk values (or RowID Ranges) can then be used query the source database  110  and get the data for each of the target databases  120 . 
       FIG.  7    is a flowchart that illustrates a logical RowID copy method  360  performed by a parallel executor  710  of the logical copy processor  310  of  FIG.  3 A  in accordance with the disclosed embodiments. In this non-limiting example,  FIG.  7    explains how an autonomous parallel executor  710  concurrently copies appropriate data to the first target database 1  120 - 1 ; however, it should be appreciated that another instance of the parallel executor  710  can also copy appropriate data to the second target database 2  120 - 2  at the same time. In this example, the parallel executor  710  can process a set of tables  720 - 1  . . .  720 - 4  and related extent chunks (RowID Ranges)  730 - 1  . . .  730 -N of each table  720 , and start copying them in parallel to the first target database 1  120 - 1  using multi-threading. In this example, each thread  740  of a multi-thread processor can process and copy one of the extent chunks  730  to the first target database 1  120 - 1 . This way, data for each table  720  (e.g., even Terabytes of data) can be copied to the first target database  120 - 1  in a faster and more efficient way. Less data is copied from source database to each target database (e.g., only necessary data for each target database). The rate at which it is copied is faster because extent chunks  730 - 1  . . .  730 -N of each table  720  are processed in parallel using multi-thread processing, which helps increase speed. Thus, this logical RowID copy method  360  is very fast when compared to other copy methodologies. 
     The following description is of one example of a system in which the features described above may be implemented. The components of the system described below are merely one example and should not be construed as limiting. The features described above with respect to  FIGS.  1 - 7    may be implemented in other types of computing environments, such as one with multiple databases, a multi-tenant database system environment, a single-tenant database system environment, or some combination of the above. 
       FIG.  8    shows a block diagram of an example of an environment  810  in which an on-demand database service can be used in accordance with some implementations. The environment  810  includes user systems  812 , a network  814 , a database system  816  (also referred to herein as a “cloud-based system”), a processor system  817 , an application platform  818 , a network interface  820 , tenant database  822  for storing tenant data  823 , system database  824  for storing system data  825 , program code  826  for implementing various functions of the system  816 , and process space  828  for executing database system processes and tenant-specific processes, such as running applications as part of an application hosting service. In some other implementations, environment  810  may not have all of these components or systems, or may have other components or systems instead of, or in addition to, those listed above. 
     In some implementations, the environment  810  is an environment in which an on-demand database service exists. An on-demand database service, such as that which can be implemented using the system  816 , is a service that is made available to users outside of the enterprise(s) that own, maintain or provide access to the system  816 . As described above, such users generally do not need to be concerned with building or maintaining the system  816 . Instead, resources provided by the system  816  may be available for such users&#39; use when the users need services provided by the system  816 ; that is, on the demand of the users. Some on-demand database services can store information from one or more tenants into tables of a common database image to form a multi-tenant database system (MTS). The term “multi-tenant database system” can refer to those systems in which various elements of hardware and software of a database system may be shared by one or more customers or tenants. For example, a given application server may simultaneously process requests for a great number of customers, and a given database table may store rows of data such as feed items for a potentially much greater number of customers. A database image can include one or more database objects. A relational database management system (RDBMS) or the equivalent can execute storage and retrieval of information against the database object(s). 
     Application platform  818  can be a framework that allows the applications of system  816  to execute, such as the hardware or software infrastructure of the system  816 . In some implementations, the application platform  818  enables the creation, management and execution of one or more applications developed by the provider of the on-demand database service, users accessing the on-demand database service via user systems  812 , or third party application developers accessing the on-demand database service via user systems  812 . 
     In some implementations, the system  816  implements a web-based customer relationship management (CRM) system. For example, in some such implementations, the system  816  includes application servers configured to implement and execute CRM software applications as well as provide related data, code, forms, renderable web pages and documents and other information to and from user systems  812  and to store to, and retrieve from, a database system related data, objects, and Web page content. In some MTS implementations, data for multiple tenants may be stored in the same physical database object in tenant database  822 . In some such implementations, tenant data is arranged in the storage medium(s) of tenant database  822  so that data of one tenant is kept logically separate from that of other tenants so that one tenant does not have access to another tenant&#39;s data, unless such data is expressly shared. The system  816  also implements applications other than, or in addition to, a CRM application. For example, the system  816  can provide tenant access to multiple hosted (standard and custom) applications, including a CRM application. User (or third party developer) applications, which may or may not include CRM, may be supported by the application platform  818 . The application platform  818  manages the creation and storage of the applications into one or more database objects and the execution of the applications in one or more virtual machines in the process space of the system  816 . 
     According to some implementations, each system  816  is configured to provide web pages, forms, applications, data and media content to user (client) systems  812  to support the access by user systems  812  as tenants of system  816 . As such, system  816  provides security mechanisms to keep each tenant&#39;s data separate unless the data is shared. If more than one MTS is used, they may be located in close proximity to one another (for example, in a server farm located in a single building or campus), or they may be distributed at locations remote from one another (for example, one or more servers located in city A and one or more servers located in city B). As used herein, each MTS could include one or more logically or physically connected servers distributed locally or across one or more geographic locations. Additionally, the term “server” is meant to refer to a computing device or system, including processing hardware and process space(s), an associated storage medium such as a memory device or database, and, in some instances, a database application (for example, OODBMS or RDBMS) as is well known in the art. It should also be understood that “server system” and “server” are often used interchangeably herein. Similarly, the database objects described herein can be implemented as part of a single database, a distributed database, a collection of distributed databases, a database with redundant online or offline backups or other redundancies, etc., and can include a distributed database or storage network and associated processing intelligence. 
     The network  814  can be or include any network or combination of networks of systems or devices that communicate with one another. For example, the network  814  can be or include any one or any combination of a LAN (local area network), WAN (wide area network), telephone network, wireless network, cellular network, point-to-point network, star network, token ring network, hub network, or other appropriate configuration. The network  814  can include a TCP/IP (Transfer Control Protocol and Internet Protocol) network, such as the global internetwork of networks often referred to as the “Internet” (with a capital “I”). The Internet will be used in many of the examples herein. However, it should be understood that the networks that the disclosed implementations can use are not so limited, although TCP/IP is a frequently implemented protocol. 
     The user systems  812  can communicate with system  816  using TCP/IP and, at a higher network level, other common Internet protocols to communicate, such as HTTP, FTP, AFS, WAP, etc. In an example where HTTP is used, each user system  812  can include an HTTP client commonly referred to as a “web browser” or simply a “browser” for sending and receiving HTTP signals to and from an HTTP server of the system  816 . Such an HTTP server can be implemented as the sole network interface  820  between the system  816  and the network  814 , but other techniques can be used in addition to or instead of these techniques. In some implementations, the network interface  820  between the system  816  and the network  814  includes load sharing functionality, such as round-robin HTTP request distributors to balance loads and distribute incoming HTTP requests evenly over a number of servers. In MTS implementations, each of the servers can have access to the MTS data; however, other alternative configurations may be used instead. 
     The user systems  812  can be implemented as any computing device(s) or other data processing apparatus or systems usable by users to access the database system  816 . For example, any of user systems  812  can be a desktop computer, a work station, a laptop computer, a tablet computer, a handheld computing device, a mobile cellular phone (for example, a “smartphone”), or any other Wi-Fi-enabled device, wireless access protocol (WAP)-enabled device, or other computing device capable of interfacing directly or indirectly to the Internet or other network. The terms “user system” and “computing device” are used interchangeably herein with one another and with the term “computer.” As described above, each user system  812  typically executes an HTTP client, for example, a web browsing (or simply “browsing”) program, such as a web browser based on the WebKit platform, Microsoft&#39;s Internet Explorer browser, Netscape&#39;s Navigator browser, Opera&#39;s browser, Mozilla&#39;s Firefox browser, or a WAP-enabled browser in the case of a cellular phone, PDA or other wireless device, or the like, allowing a user (for example, a subscriber of on-demand services provided by the system  816 ) of the user system  812  to access, process and view information, pages and applications available to it from the system  816  over the network  814 . 
     Each user system  812  also typically includes one or more user input devices, such as a keyboard, a mouse, a trackball, a touch pad, a touch screen, a pen or stylus or the like, for interacting with a graphical user interface (GUI) provided by the browser on a display (for example, a monitor screen, liquid crystal display (LCD), light-emitting diode (LED) display, among other possibilities) of the user system  812  in conjunction with pages, forms, applications and other information provided by the system  816  or other systems or servers. For example, the user interface device can be used to access data and applications hosted by system  816 , and to perform searches on stored data, and otherwise allow a user to interact with various GUI pages that may be presented to a user. As discussed above, implementations are suitable for use with the Internet, although other networks can be used instead of or in addition to the Internet, such as an intranet, an extranet, a virtual private network (VPN), a non-TCP/IP based network, any LAN or WAN or the like. 
     The users of user systems  812  may differ in their respective capacities, and the capacity of a particular user system  812  can be entirely determined by permissions (permission levels) for the current user of such user system. For example, where a salesperson is using a particular user system  812  to interact with the system  816 , that user system can have the capacities allotted to the salesperson. However, while an administrator is using that user system  812  to interact with the system  816 , that user system can have the capacities allotted to that administrator. Where a hierarchical role model is used, users at one permission level can have access to applications, data, and database information accessible by a lower permission level user, but may not have access to certain applications, database information, and data accessible by a user at a higher permission level. Thus, different users generally will have different capabilities with regard to accessing and modifying application and database information, depending on the users&#39; respective security or permission levels (also referred to as “authorizations”). 
     According to some implementations, each user system  812  and some or all of its components are operator-configurable using applications, such as a browser, including computer code executed using a central processing unit (CPU) such as an Intel Pentium® processor or the like. Similarly, the system  816  (and additional instances of an MTS, where more than one is present) and all of its components can be operator-configurable using application(s) including computer code to run using the processor system  817 , which may be implemented to include a CPU, which may include an Intel Pentium® processor or the like, or multiple CPUs. 
     The system  816  includes tangible computer-readable media having non-transitory instructions stored thereon/in that are executable by or used to program a server or other computing system (or collection of such servers or computing systems) to perform some of the implementation of processes described herein. For example, computer program code  826  can implement instructions for operating and configuring the system  816  to intercommunicate and to process web pages, applications and other data and media content as described herein. In some implementations, the computer code  826  can be downloadable and stored on a hard disk, but the entire program code, or portions thereof, also can be stored in any other volatile or non-volatile memory medium or device as is well known, such as a ROM or RAM, or provided on any media capable of storing program code, such as any type of rotating media including floppy disks, optical discs, digital versatile disks (DVD), compact disks (CD), microdrives, and magneto-optical disks, and magnetic or optical cards, nanosystems (including molecular memory ICs), or any other type of computer-readable medium or device suitable for storing instructions or data. Additionally, the entire program code, or portions thereof, may be transmitted and downloaded from a software source over a transmission medium, for example, over the Internet, or from another server, as is well known, or transmitted over any other existing network connection as is well known (for example, extranet, VPN, LAN, etc.) using any communication medium and protocols (for example, TCP/IP, HTTP, HTTPS, Ethernet, etc.) as are well known. It will also be appreciated that computer code for the disclosed implementations can be realized in any programming language that can be executed on a server or other computing system such as, for example, C, C++, HTML, any other markup language, Java™, JavaScript, ActiveX, any other scripting language, such as VBScript, and many other programming languages as are well known may be used. (Java™ is a trademark of Sun Microsystems, Inc.). 
       FIG.  9    shows a block diagram of example implementations of elements of  FIG.  8    and example interconnections between these elements according to some implementations. That is,  FIG.  9    also illustrates environment  410 , but  FIG.  9   , various elements of the system  816  and various interconnections between such elements are shown with more specificity according to some more specific implementations. Elements from  FIG.  8    that are also shown in  FIG.  9    will use the same reference numbers in  FIG.  9    as were used in  FIG.  8   . Additionally, in  FIG.  9   , the user system  812  includes a processor system  912 A, a memory system  912 B, an input system  912 C, and an output system  912 D. The processor system  912 A can include any suitable combination of one or more processors. The memory system  912 B can include any suitable combination of one or more memory devices. The input system  912 C can include any suitable combination of input devices, such as one or more touchscreen interfaces, keyboards, mice, trackballs, scanners, cameras, or interfaces to networks. The output system  912 D can include any suitable combination of output devices, such as one or more display devices, printers, or interfaces to networks. 
     In  FIG.  9   , the network interface  420  of  FIG.  8    is implemented as a set of HTTP application servers  900   1 - 900   N . Each application server  900 , also referred to herein as an “app server,” is configured to communicate with tenant database  822  and the tenant data  923  therein, as well as system database  824  and the system data  925  therein, to serve requests received from the user systems  912 . The tenant data  923  can be divided into individual tenant storage spaces  913 , which can be physically or logically arranged or divided. Within each tenant storage space  913 , tenant data  914  and application metadata  916  can similarly be allocated for each user. For example, a copy of a user&#39;s most recently used (MRU) items can be stored to user storage  914 . Similarly, a copy of MRU items for an entire organization that is a tenant can be stored to tenant storage space  913 . 
     The process space  828  includes system process space  902 , individual tenant process spaces  904  and a tenant management process space  910 . The application platform  818  includes an application setup mechanism  938  that supports application developers&#39; creation and management of applications. Such applications and others can be saved as metadata into tenant database  822  by save routines  936  for execution by subscribers as one or more tenant process spaces  904  managed by tenant management process  910 , for example. Invocations to such applications can be coded using PL/SOQL  934 , which provides a programming language style interface extension to API  932 . A detailed description of some PL/SOQL language implementations is discussed in commonly assigned U.S. Pat. No. 7,730,478, titled METHOD AND SYSTEM FOR ALLOWING ACCESS TO DEVELOPED APPLICATIONS VIA A MULTI-TENANT ON-DEMAND DATABASE SERVICE, by Craig Weissman, issued on Jun. 1, 2010, and hereby incorporated by reference in its entirety and for all purposes. Invocations to applications can be detected by one or more system processes, which manage retrieving application metadata  816  for the subscriber making the invocation and executing the metadata as an application in a virtual machine. 
     The system  816  of  FIG.  9    also includes a user interface (UI)  930  and an application programming interface (API)  932  to system  816  resident processes to users or developers at user systems  912 . In some other implementations, the environment  810  may not have the same elements as those listed above or may have other elements instead of, or in addition to, those listed above. 
     Each application server  900  can be communicably coupled with tenant database  822  and system database  824 , for example, having access to tenant data  923  and system data  925 , respectively, via a different network connection. For example, one application server  900   1  can be coupled via the network  814  (for example, the Internet), another application server  900   N  can be coupled via a direct network link, and another application server (not illustrated) can be coupled by yet a different network connection. Transfer Control Protocol and Internet Protocol (TCP/IP) are examples of typical protocols that can be used for communicating between application servers  900  and the system  816 . However, it will be apparent to one skilled in the art that other transport protocols can be used to optimize the system  816  depending on the network interconnections used. 
     In some implementations, each application server  900  is configured to handle requests for any user associated with any organization that is a tenant of the system  816 . Because it can be desirable to be able to add and remove application servers  900  from the server pool at any time and for various reasons, in some implementations there is no server affinity for a user or organization to a specific application server  900 . In some such implementations, an interface system implementing a load balancing function (for example, an F5 Big-IP load balancer) is communicably coupled between the application servers  900  and the user systems  912  to distribute requests to the application servers  900 . In one implementation, the load balancer uses a least-connections algorithm to route user requests to the application servers  900 . Other examples of load balancing algorithms, such as round robin and observed-response-time, also can be used. For example, in some instances, three consecutive requests from the same user could hit three different application servers  900 , and three requests from different users could hit the same application server  900 . In this manner, by way of example, system  816  can be a multi-tenant system in which system  816  handles storage of, and access to, different objects, data and applications across disparate users and organizations. 
     In one example storage use case, one tenant can be a company that employs a sales force where each salesperson uses system  816  to manage aspects of their sales. A user can maintain contact data, leads data, customer follow-up data, performance data, goals and progress data, etc., all applicable to that user&#39;s personal sales process (for example, in tenant database  822 ). In an example of a MTS arrangement, because all of the data and the applications to access, view, modify, report, transmit, calculate, etc., can be maintained and accessed by a user system  912  having little more than network access, the user can manage his or her sales efforts and cycles from any of many different user systems. For example, when a salesperson is visiting a customer and the customer has Internet access in their lobby, the salesperson can obtain critical updates regarding that customer while waiting for the customer to arrive in the lobby. 
     While each user&#39;s data can be stored separately from other users&#39; data regardless of the employers of each user, some data can be organization-wide data shared or accessible by several users or all of the users for a given organization that is a tenant. Thus, there can be some data structures managed by system  816  that are allocated at the tenant level while other data structures can be managed at the user level. Because an MTS can support multiple tenants including possible competitors, the MTS can have security protocols that keep data, applications, and application use separate. Also, because many tenants may opt for access to an MTS rather than maintain their own system, redundancy, up-time, and backup are additional functions that can be implemented in the MTS. In addition to user-specific data and tenant-specific data, the system  816  also can maintain system level data usable by multiple tenants or other data. Such system level data can include industry reports, news, postings, and the like that are sharable among tenants. 
     In some implementations, the user systems  912  (which also can be client systems) communicate with the application servers  900  to request and update system-level and tenant-level data from the system  816 . Such requests and updates can involve sending one or more queries to tenant database  822  or system database  824 . The system  816  (for example, an application server  900  in the system  816 ) can automatically generate one or more SQL statements (for example, one or more SQL queries) designed to access the desired information. System database  824  can generate query plans to access the requested data from the database. The term “query plan” generally refers to one or more operations used to access information in a database system. 
     Each database can generally be viewed as a collection of objects, such as a set of logical tables, containing data fitted into predefined or customizable categories. A “table” is one representation of a data object, and may be used herein to simplify the conceptual description of objects and custom objects according to some implementations. It should be understood that “table” and “object” may be used interchangeably herein. Each table generally contains one or more data categories logically arranged as columns or fields in a viewable schema. Each row or element of a table can contain an instance of data for each category defined by the fields. For example, a CRM database can include a table that describes a customer with fields for basic contact information such as name, address, phone number, fax number, etc. Another table can describe a purchase order, including fields for information such as customer, product, sale price, date, etc. In some MTS implementations, standard entity tables can be provided for use by all tenants. For CRM database applications, such standard entities can include tables for case, account, contact, lead, and opportunity data objects, each containing pre-defined fields. As used herein, the term “entity” also may be used interchangeably with “object” and “table.” 
     In some MTS implementations, tenants are allowed to create and store custom objects, or may be allowed to customize standard entities or objects, for example by creating custom fields for standard objects, including custom index fields. Commonly assigned U.S. Pat. No. 7,779,039, titled CUSTOM ENTITIES AND FIELDS IN A MULTI-TENANT DATABASE SYSTEM, by Weissman et al., issued on Aug. 17, 2010, and hereby incorporated by reference in its entirety and for all purposes, teaches systems and methods for creating custom objects as well as customizing standard objects in a multi-tenant database system. In some implementations, for example, all custom entity data rows are stored in a single multi-tenant physical table, which may contain multiple logical tables per organization. It is transparent to customers that their multiple “tables” are in fact stored in one large table or that their data may be stored in the same table as the data of other customers. 
       FIG.  10 A  shows a system diagram illustrating example architectural components of an on-demand database service environment  1000  according to some implementations. A client machine communicably connected with the cloud  1004 , generally referring to one or more networks in combination, as described herein, can communicate with the on-demand database service environment  1000  via one or more edge routers  1008  and  1012 . A client machine can be any of the examples of user systems  12  described above. The edge routers can communicate with one or more core switches  1020  and  1024  through a firewall  1016 . The core switches can communicate with a load balancer  1028 , which can distribute server load over different pods, such as the pods  1040  and  1044 . The pods  1040  and  1044 , which can each include one or more servers or other computing resources, can perform data processing and other operations used to provide on-demand services. Communication with the pods can be conducted via pod switches  1032  and  1036 . Components of the on-demand database service environment can communicate with database storage  1056  through a database firewall  1048  and a database switch  1052 . 
     As shown in  FIGS.  10 A and  10 B , accessing an on-demand database service environment can involve communications transmitted among a variety of different hardware or software components. Further, the on-demand database service environment  1000  is a simplified representation of an actual on-demand database service environment. For example, while only one or two devices of each type are shown in  FIGS.  10 A and  10 B , some implementations of an on-demand database service environment can include anywhere from one to several devices of each type. Also, the on-demand database service environment need not include each device shown in  FIGS.  10 A and  10 B , or can include additional devices not shown in  FIGS.  10 A and  10 B . 
     Additionally, it should be appreciated that one or more of the devices in the on-demand database service environment  1000  can be implemented on the same physical device or on different hardware. Some devices can be implemented using hardware or a combination of hardware and software. Thus, terms such as “data processing apparatus,” “machine,” “server” and “device” as used herein are not limited to a single hardware device, rather references to these terms can include any suitable combination of hardware and software configured to provide the described functionality. 
     The cloud  1004  is intended to refer to a data network or multiple data networks, often including the Internet. Client machines communicably connected with the cloud  1004  can communicate with other components of the on-demand database service environment  1000  to access services provided by the on-demand database service environment. For example, client machines can access the on-demand database service environment to retrieve, store, edit, or process information. In some implementations, the edge routers  1008  and  1012  route packets between the cloud  1004  and other components of the on-demand database service environment  1000 . For example, the edge routers  1008  and  1012  can employ the Border Gateway Protocol (BGP). The BGP is the core routing protocol of the Internet. The edge routers  1008  and  1012  can maintain a table of IP networks or ‘prefixes’, which designate network reachability among autonomous systems on the Internet. 
     In some implementations, the firewall  1016  can protect the inner components of the on-demand database service environment  1000  from Internet traffic. The firewall  1016  can block, permit, or deny access to the inner components of the on-demand database service environment  1000  based upon a set of rules and other criteria. The firewall  1016  can act as one or more of a packet filter, an application gateway, a stateful filter, a proxy server, or any other type of firewall. 
     In some implementations, the core switches  1020  and  1024  are high-capacity switches that transfer packets within the on-demand database service environment  1000 . The core switches  1020  and  1024  can be configured as network bridges that quickly route data between different components within the on-demand database service environment. In some implementations, the use of two or more core switches  1020  and  1024  can provide redundancy or reduced latency. 
     In some implementations, the pods  1040  and  1044  perform the core data processing and service functions provided by the on-demand database service environment. Each pod can include various types of hardware or software computing resources. An example of the pod architecture is discussed in greater detail with reference to  FIG.  10 B . In some implementations, communication between the pods  1040  and  1044  is conducted via the pod switches  1032  and  1036 . The pod switches  1032  and  1036  can facilitate communication between the pods  1040  and  1044  and client machines communicably connected with the cloud  1004 , for example via core switches  1020  and  1024 . Also, the pod switches  1032  and  1036  may facilitate communication between the pods  1040  and  1044  and the database storage  1056 . In some implementations, the load balancer  1028  can distribute workload between the pods  1040  and  1044 . Balancing the on-demand service requests between the pods can assist in improving the use of resources, increasing throughput, reducing response times, or reducing overhead. The load balancer  1028  may include multilayer switches to analyze and forward traffic. 
     In some implementations, access to the database storage  1056  is guarded by a database firewall  1048 . The database firewall  1048  can act as a computer application firewall operating at the database application layer of a protocol stack. The database firewall  1048  can protect the database storage  1056  from application attacks such as structure query language (SQL) injection, database rootkits, and unauthorized information disclosure. In some implementations, the database firewall  1048  includes a host using one or more forms of reverse proxy services to proxy traffic before passing it to a gateway router. The database firewall  1048  can inspect the contents of database traffic and block certain content or database requests. The database firewall  1048  can work on the SQL application level atop the TCP/IP stack, managing applications&#39; connection to the database or SQL management interfaces as well as intercepting and enforcing packets traveling to or from a database network or application interface. 
     In some implementations, communication with the database storage  1056  is conducted via the database switch  1052 . The multi-tenant database storage  1056  can include more than one hardware or software components for handling database queries. Accordingly, the database switch  1052  can direct database queries transmitted by other components of the on-demand database service environment (for example, the pods  1040  and  1044 ) to the correct components within the database storage  1056 . In some implementations, the database storage  1056  is an on-demand database system shared by many different organizations as described above with reference to  FIG.  8    and  FIG.  9   . 
       FIG.  10 B  shows a system diagram further illustrating example architectural components of an on-demand database service environment according to some implementations. The pod  1044  can be used to render services to a user of the on-demand database service environment  1000 . In some implementations, each pod includes a variety of servers or other systems. The pod  1044  includes one or more content batch servers  1064 , content search servers  1068 , query servers  1082 , file force servers  1086 , access control system (ACS) servers  1080 , batch servers  1084 , and app servers  1088 . The pod  1044  also can include database instances  1090 , quick file systems (QFS)  1092 , and indexers  1094 . In some implementations, some or all communication between the servers in the pod  1044  can be transmitted via the switch  1036 . 
     In some implementations, the app servers  1088  include a hardware or software framework dedicated to the execution of procedures (for example, programs, routines, scripts) for supporting the construction of applications provided by the on-demand database service environment  1000  via the pod  1044 . In some implementations, the hardware or software framework of an app server  1088  is configured to execute operations of the services described herein, including performance of the blocks of various methods or processes described herein. In some alternative implementations, two or more app servers  1088  can be included and cooperate to perform such methods, or one or more other servers described herein can be configured to perform the disclosed methods. 
     The content batch servers  1064  can handle requests internal to the pod. Some such requests can be long-running or not tied to a particular customer. For example, the content batch servers  1064  can handle requests related to log mining, cleanup work, and maintenance tasks. The content search servers  1068  can provide query and indexer functions. For example, the functions provided by the content search servers  1068  can allow users to search through content stored in the on-demand database service environment. The file force servers  1086  can manage requests for information stored in the File force storage  1098 . The File force storage  1098  can store information such as documents, images, and basic large objects (BLOBs). By managing requests for information using the file force servers  1086 , the image footprint on the database can be reduced. The query servers  1082  can be used to retrieve information from one or more file storage systems. For example, the query system  1082  can receive requests for information from the app servers  1088  and transmit information queries to the NFS  1096  located outside the pod. 
     The pod  1044  can share a database instance  1090  configured as a multi-tenant environment in which different organizations share access to the same database. Additionally, services rendered by the pod  1044  may call upon various hardware or software resources. In some implementations, the ACS servers  1080  control access to data, hardware resources, or software resources. In some implementations, the batch servers  1084  process batch jobs, which are used to run tasks at specified times. For example, the batch servers  1084  can transmit instructions to other servers, such as the app servers  1088 , to trigger the batch jobs. 
     In some implementations, the QFS  1092  is an open source file storage system available from Sun Microsystems® of Santa Clara, Calif. The QFS can serve as a rapid-access file storage system for storing and accessing information available within the pod  1044 . The QFS  1092  can support some volume management capabilities, allowing many disks to be grouped together into a file storage system. File storage system metadata can be kept on a separate set of disks, which can be useful for streaming applications where long disk seeks cannot be tolerated. Thus, the QFS system can communicate with one or more content search servers  1068  or indexers  1094  to identify, retrieve, move, or update data stored in the network file storage systems  1096  or other storage systems. 
     In some implementations, one or more query servers  1082  communicate with the NFS  1096  to retrieve or update information stored outside of the pod  1044 . The NFS  1096  can allow servers located in the pod  1044  to access information to access files over a network in a manner similar to how local storage is accessed. In some implementations, queries from the query servers  1082  are transmitted to the NFS  1096  via the load balancer  1028 , which can distribute resource requests over various resources available in the on-demand database service environment. The NFS  1096  also can communicate with the QFS  1092  to update the information stored on the NFS  1096  or to provide information to the QFS  1092  for use by servers located within the pod  1044 . 
     In some implementations, the pod includes one or more database instances  1090 . The database instance  1090  can transmit information to the QFS  1092 . When information is transmitted to the QFS, it can be available for use by servers within the pod  1044  without using an additional database call. In some implementations, database information is transmitted to the indexer  1094 . Indexer  1094  can provide an index of information available in the database  1090  or QFS  1092 . The index information can be provided to file force servers  1086  or the QFS  1092 . 
       FIG.  11    illustrates a diagrammatic representation of a machine in the exemplary form of a computer system  1100  within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed. The system  1100  may be in the form of a computer system within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed. In alternative embodiments, the machine may be connected (e.g., networked) to other machines in a LAN, an intranet, an extranet, or the Internet. The machine may operate in the capacity of a server machine in client-server network environment. The machine may be a personal computer (PC), a set-top box (STB), a server, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. 
     The exemplary computer system  1100  includes a processing device (processor)  1102 , a main memory  1104  (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM)), a static memory  1106  (e.g., flash memory, static random access memory (SRAM)), and a data storage device  1118 , which communicate with each other via a bus  1130 . 
     Processing device  1102  represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device  1102  may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. The processing device  1102  may also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. 
     The computer system  1100  may further include a network interface device  1108 . The computer system  1100  also may include a video display unit  1110  (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), an alphanumeric input device  1112  (e.g., a keyboard), a cursor control device  1114  (e.g., a mouse), and a signal generation device  1116  (e.g., a speaker). 
     The data storage device  1118  may include a computer-readable medium  1128  on which is stored one or more sets of instructions  1122  (e.g., instructions of in-memory buffer service  114 ) embodying any one or more of the methodologies or functions described herein. The instructions  1122  may also reside, completely or at least partially, within the main memory  1104  and/or within processing logic  1126  of the processing device  1102  during execution thereof by the computer system  1100 , the main memory  1104  and the processing device  1102  also constituting computer-readable media. The instructions may further be transmitted or received over a network  1120  via the network interface device  1108 . 
     While the computer-readable storage medium  1128  is shown in an exemplary embodiment to be a single medium, the term “computer-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “computer-readable storage medium” shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present invention. The term “computer-readable storage medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical media, and magnetic media. 
     The preceding description sets forth numerous specific details such as examples of specific systems, components, methods, and so forth, in order to provide a good understanding of several embodiments of the present invention. It will be apparent to one skilled in the art, however, that at least some embodiments of the present invention may be practiced without these specific details. In other instances, well-known components or methods are not described in detail or are presented in simple block diagram format in order to avoid unnecessarily obscuring the present invention. Thus, the specific details set forth are merely exemplary. Particular implementations may vary from these exemplary details and still be contemplated to be within the scope of the present invention. 
     In the above description, numerous details are set forth. It will be apparent, however, to one of ordinary skill in the art having the benefit of this disclosure, that embodiments of the invention may be practiced without these specific details. In some instances, well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring the description. 
     Some portions of the detailed description are presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like. 
     It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the above discussion, it is appreciated that throughout the description, discussions utilizing terms such as “determining,” “identifying,” “adding,” “selecting” or the like, refer to the actions and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (e.g., electronic) quantities within the computer system&#39;s registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices. 
     Embodiments of the invention also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may comprise a general purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium, such as, but not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, and magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any type of media suitable for storing electronic instructions. 
     The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various general purpose systems may be used with programs in accordance with the teachings herein, or it may prove convenient to construct a more specialized apparatus to perform the required method steps. The required structure for a variety of these systems will appear from the description below. In addition, the present invention is not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the invention as described herein. 
     While at least one exemplary embodiment has been presented in the foregoing detailed description, it should be appreciated that a vast number of variations exist. It should also be appreciated that the exemplary embodiment or embodiments described herein are not intended to limit the scope, applicability, or configuration of the claimed subject matter in any way. Rather, the foregoing detailed description will provide those skilled in the art with a convenient road map for implementing the described embodiment or embodiments. It should be understood that various changes can be made in the function and arrangement of elements without departing from the scope defined by the claims, which includes known equivalents and foreseeable equivalents at the time of filing this patent application.