Patent Application
20250013453
The present disclosure generally relates to database objects, and in particular, creating, sharing, and using bundles (also referred to as packages) in a multi-tenant database.
As the world becomes more data driven, database systems and other data systems are storing more and more data. Developers therefore are developing applications to access and use the data in more efficient and useful manners. However, developer efforts can be hindered by a number of factors. For example, database systems typically lack modularity. Because of this lack of modularity, developers cannot define modules or groups of primitive objects as schema objects that can be deployed and upgraded atomically.
Database systems can hamper code sharing and distribution. For example, one developer may send a code module to another user, who may have to essentially copy and paste the code to use it. Typically, there is not an easy way to share code modules without relinquishing control of the code. Moreover, handling different versions and upgrades of the code package can further complicate matters.
Various ones of the appended drawings merely illustrate example embodiments of the present disclosure and should not be considered as limiting its scope.
The description that follows includes systems, methods, techniques, instruction sequences, and computing machine program products that embody illustrative embodiments of the disclosure. In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide an understanding of various embodiments of the inventive subject matter. It will be evident, however, to those skilled in the art, that embodiments of the inventive subject matter may be practiced without these specific details. In general, well-known instruction instances, protocols, structures, and techniques are not necessarily shown in detail.
Techniques for creating, sharing, and using bundles (also referred to as packages) in a multi-tenant database are described herein. Bundles can address the challenges of modularity, encapsulation, code sharing, and distribution described above. A bundle is a schema object with associated hidden schemas. A bundle can be created by a provider user and can be shared with a plurality of consumer users. The bundle can be used to enable code sharing and distribution by provider accounts to a plurality of consumer accounts without losing control while maintaining security protocols. Different versioning and upgrading techniques are described herein, which allow for transparent and continuous operation by the consumer accounts during upgrading.
As shown, the shared data processing platform 100 comprises the network-based data system 102, a cloud computing storage platform 104 (e.g., a storage platform, an AWS® service, Microsoft Azure®, or Google Cloud Services®), and a remote computing device 106. The network-based data warehouse system 102 is a cloud database system used for storing and accessing data (e.g., internally storing data, accessing external remotely located data) in an integrated manner, and reporting and analysis of the integrated data from the one or more disparate sources (e.g., the cloud computing storage platform 104). The cloud computing storage platform 104 comprises a plurality of computing machines and provides on-demand computer system resources such as data storage and computing power to the network-based data system 102. While in the embodiment illustrated in
The remote computing device 106 (e.g., a user device such as a laptop computer) comprises one or more computing machines (e.g., a user device such as a laptop computer) that execute a remote software component 108 (e.g., browser accessed cloud service) to provide additional functionality to users of the network-based data system 102. The remote software component 108 comprises a set of machine-readable instructions (e.g., code) that, when executed by the remote computing device 106, cause the remote computing device 106 to provide certain functionality. The remote software component 108 may operate on input data and generates result data based on processing, analyzing, or otherwise transforming the input data. As an example, the remote software component 108 can be a data provider or data consumer that enables database tracking procedures, such as streams on shared tables and views.
The network-based data warehouse system 102 comprises an access management system 110, a compute service manager 112, an execution platform 114, and a database 116. The access management system 110 enables administrative users to manage access to resources and services provided by the network-based data system 102. Administrative users can create and manage users, roles, and groups, and use permissions to allow or deny access to resources and services. The access management system 110 can store shared data that securely manages shared access to the storage resources of the cloud computing storage platform 104 amongst different users of the network-based data system 102, as discussed in further detail below.
The compute service manager 112 coordinates and manages operations of the network-based data system 102. The compute service manager 112 also performs query optimization and compilation as well as managing clusters of computing services that provide compute resources (e.g., virtual warehouses, virtual machines, EC2 clusters). The compute service manager 112 can support any number of client accounts such as end users providing data storage and retrieval requests, system administrators managing the systems and methods described herein, and other components/devices that interact with compute service manager 112.
The compute service manager 112 is also coupled to database 116, which is associated with the entirety of data stored on the shared data processing platform 100. The database 116 stores data pertaining to various functions and aspects associated with the network-based data system 102 and its users.
In some embodiments, database 116 includes a summary of data stored in remote data storage systems as well as data available from one or more local caches. Additionally, database 116 may include information regarding how data is organized in the remote data storage systems and the local caches. Database 116 allows systems and services to determine whether a piece of data needs to be accessed without loading or accessing the actual data from a storage device. The compute service manager 112 is further coupled to an execution platform 114, which provides multiple computing resources (e.g., virtual warehouses) that execute various data storage and data retrieval tasks, as discussed in greater detail below.
Execution platform 114 is coupled to multiple data storage devices 124-1 to 124-N that are part of a cloud computing storage platform 104. In some embodiments, data storage devices 124-1 to 124-N are cloud-based storage devices located in one or more geographic locations. For example, data storage devices 124-1 to 124-N may be part of a public cloud infrastructure or a private cloud infrastructure. Data storage devices 124-1 to 124-N may be hard disk drives (HDDs), solid state drives (SSDs), storage clusters, Amazon S3 storage systems or any other data storage technology. Additionally, cloud computing storage platform 104 may include distributed file systems (such as Hadoop Distributed File Systems (HDFS)), object storage systems, and the like.
The execution platform 114 comprises a plurality of compute nodes (e.g., virtual warehouses). A set of processes on a compute node executes a query plan compiled by the compute service manager 112. The set of processes can include: a first process to execute the query plan; a second process to monitor and delete micro-partition files using a least recently used (LRU) policy, and implement an out of memory (OOM) error mitigation process; a third process that extracts health information from process logs and status information to send back to the compute service manager 112; a fourth process to establish communication with the compute service manager 112 after a system boot; and a fifth process to handle all communication with a compute cluster for a given job provided by the compute service manager 112 and to communicate information back to the compute service manager 112 and other compute nodes of the execution platform 114.
The cloud computing storage platform 104 also comprises an access management system 118 and a web proxy 120. As with the access management system 110, the access management system 118 allows users to create and manage users, roles, and groups, and use permissions to allow or deny access to cloud services and resources. The access management system 110 of the network-based data system 102 and the access management system 118 of the cloud computing storage platform 104 can communicate and share information so as to enable access and management of resources and services shared by users of both the network-based data system 102 and the cloud computing storage platform 104. The web proxy 120 handles tasks involved in accepting and processing concurrent API calls, including traffic management, authorization and access control, monitoring, and API version management. The web proxy 120 provides HTTP proxy service for creating, publishing, maintaining, securing, and monitoring APIs (e.g., REST APIs).
In some embodiments, communication links between elements of the shared data processing platform 100 are implemented via one or more data communication networks. These data communication networks may utilize any communication protocol and any type of communication medium. In some embodiments, the data communication networks are a combination of two or more data communication networks (or sub-Networks) coupled to one another. In alternative embodiments, these communication links are implemented using any type of communication medium and any communication protocol.
As shown in
Compute service manager 112, database 116, execution platform 114, cloud computing storage platform 104, and remote computing device 106 are shown in
During typical operation, the network-based data system 102 processes multiple jobs (e.g., queries) determined by the compute service manager 112. These jobs are scheduled and managed by the compute service manager 112 to determine when and how to execute the job. For example, the compute service manager 112 may divide the job into multiple discrete tasks and may determine what data is needed to execute each of the multiple discrete tasks. The compute service manager 112 may assign each of the multiple discrete tasks to one or more nodes of the execution platform 114 to process the task. The compute service manager 112 may determine what data is needed to process a task and further determine which nodes within the execution platform 114 are best suited to process the task. Some nodes may have already cached the data needed to process the task (due to the nodes having recently downloaded the data from the cloud computing storage platform 104 for a previous job) and, therefore, be a good candidate for processing the task. Metadata stored in the database 116 assists the compute service manager 112 in determining which nodes in the execution platform 114 have already cached at least a portion of the data needed to process the task. One or more nodes in the execution platform 114 process the task using data cached by the nodes and, if necessary, data retrieved from the cloud computing storage platform 104. It is desirable to retrieve as much data as possible from caches within the execution platform 114 because the retrieval speed is typically much faster than retrieving data from the cloud computing storage platform 104.
As shown in
The compute service manager 112 also includes a job compiler 206, a job optimizer 208, and a job executor 210. The job compiler 206 parses a job into multiple discrete tasks and generates the execution code for each of the multiple discrete tasks. The job optimizer 208 determines the best method to execute the multiple discrete tasks based on the data that needs to be processed. The job optimizer 208 also handles various data pruning operations and other data optimization techniques to improve the speed and efficiency of executing the job. The job executor 210 executes the execution code for jobs received from a queue or determined by the compute service manager 112.
A job scheduler and coordinator 212 sends received jobs to the appropriate services or systems for compilation, optimization, and dispatch to the execution platform 114. For example, jobs may be prioritized and processed in that prioritized order. In an embodiment, the job scheduler and coordinator 212 determines a priority for internal jobs that are scheduled by the compute service manager 112 with other “outside” jobs such as user queries that may be scheduled by other systems in the database but may utilize the same processing resources in the execution platform 114. In some embodiments, the job scheduler and coordinator 212 identifies or assigns particular nodes in the execution platform 114 to process particular tasks. A virtual warehouse manager 214 manages the operation of multiple virtual warehouses implemented in the execution platform 114. As discussed below, each virtual warehouse includes multiple execution nodes that each include a cache and a processor (e.g., a virtual machine, an operating system level container execution environment).
Additionally, the compute service manager 112 includes a configuration and metadata manager 216, which manages the information related to the data stored in the remote data storage devices and in the local caches (i.e., the caches in execution platform 114). The configuration and metadata manager 216 uses the metadata to determine which data micro-partitions need to be accessed to retrieve data for processing a particular task or job. A monitor and workload analyzer 218 oversees processes performed by the compute service manager 112 and manages the distribution of tasks (e.g., workload) across the virtual warehouses and execution nodes in the execution platform 114. The monitor and workload analyzer 218 also redistributes tasks, as needed, based on changing workloads throughout the network-based data system 102 and may further redistribute tasks based on a user (e.g., “external”) query workload that may also be processed by the execution platform 114. The configuration and metadata manager 216 and the monitor and workload analyzer 218 are coupled to a data storage device 220. Data storage device 220 in
Although each virtual warehouse shown in
Each virtual warehouse is capable of accessing any of the data storage devices 124-1 to 124-N shown in
In the example of
Similar to virtual warehouse 1 discussed above, virtual warehouse 2 includes three execution nodes 312-1, 312-2, and 312-N. Execution node 312-1 includes a cache 314-1 and a processor 316-1. Execution node 312-2 includes a cache 314-2 and a processor 316-2. Execution node 312-N includes a cache 314-N and a processor 316-N. Additionally, virtual warehouse 3 includes three execution nodes 322-1, 322-2, and 322-N. Execution node 322-1 includes a cache 324-1 and a processor 326-1. Execution node 322-2 includes a cache 324-2 and a processor 326-2. Execution node 322-N includes a cache 324-N and a processor 326-N.
In some embodiments, the execution nodes shown in
Although the execution nodes shown in
To improve cache hits and avoid overlapping redundant data stored in the node caches, the job optimizer 208 assigns input file sets to the nodes using a consistent hashing scheme to hash over table file names of the data accessed (e.g., data in database 116 or database 122). Subsequent or concurrent queries accessing the same table file will therefore be performed on the same node, according to some example embodiments.
As discussed, the nodes and virtual warehouses may change dynamically in response to environmental conditions (e.g., disaster scenarios), hardware/software issues (e.g., malfunctions), or administrative changes (e.g., changing from a large cluster to smaller cluster to lower costs). In some example embodiments, when the set of nodes changes, no data is reshuffled immediately. Instead, the least recently used replacement policy is implemented to eventually replace the lost cache contents over multiple jobs. Thus, the caches reduce or eliminate the bottleneck problems occurring in platforms that consistently retrieve data from remote storage systems. Instead of repeatedly accessing data from the remote storage devices, the systems and methods described herein access data from the caches in the execution nodes, which is significantly faster and avoids the bottleneck problem discussed above. In some embodiments, the caches are implemented using high-speed memory devices that provide fast access to the cached data. Each cache can store data from any of the storage devices in the cloud computing storage platform 104.
Further, the cache resources and computing resources may vary between different execution nodes. For example, one execution node may contain significant computing resources and minimal cache resources, making the execution node useful for tasks that require significant computing resources. Another execution node may contain significant cache resources and minimal computing resources, making this execution node useful for tasks that require caching of large amounts of data. Yet another execution node may contain cache resources providing faster input-output operations, useful for tasks that require fast scanning of large amounts of data. In some embodiments, the execution platform 114 implements skew handling to distribute work amongst the cache resources and computing resources associated with a particular execution, where the distribution may be further based on the expected tasks to be performed by the execution nodes. For example, an execution node may be assigned more processing resources if the tasks performed by the execution node become more processor-intensive. Similarly, an execution node may be assigned more cache resources if the tasks performed by the execution node require a larger cache capacity. Further, some nodes may be executing much slower than others due to various issues (e.g., virtualization issues, network overhead). In some example embodiments, the imbalances are addressed at the scan level using a file stealing scheme. In particular, whenever a node process completes scanning its set of input files, it requests additional files from other nodes. If the one of the other nodes receives such a request, the node analyzes its own set (e.g., how many files are left in the input file set when the request is received), and then transfers ownership of one or more of the remaining files for the duration of the current job (e.g., query). The requesting node (e.g., the file stealing node) then receives the data (e.g., header data) and downloads the files from the cloud computing storage platform 104 (e.g., from data storage device 124-1), and does not download the files from the transferring node. In this way, lagging nodes can transfer files via file stealing in a way that does not worsen the load on the lagging nodes.
Although virtual warehouses 1, 2, and n are associated with the same execution platform 114, the virtual warehouses may be implemented using multiple computing systems at multiple geographic locations. For example, virtual warehouse 1 can be implemented by a computing system at a first geographic location, while virtual warehouses 2 and n are implemented by another computing system at a second geographic location. In some embodiments, these different computing systems are cloud-based computing systems maintained by one or more different entities.
Additionally, each virtual warehouse is shown in
Execution platform 114 is also fault tolerant. For example, if one virtual warehouse fails, that virtual warehouse is quickly replaced with a different virtual warehouse at a different geographic location.
A particular execution platform 114 may include any number of virtual warehouses. Additionally, the number of virtual warehouses in a particular execution platform is dynamic, such that new virtual warehouses are created when additional processing and/or caching resources are needed. Similarly, existing virtual warehouses may be deleted when the resources associated with the virtual warehouse are no longer necessary.
In some embodiments, the virtual warehouses may operate on the same data in cloud computing storage platform 104, but each virtual warehouse has its own execution nodes with independent processing and caching resources. This configuration allows requests on different virtual warehouses to be processed independently and with no interference between the requests. This independent processing, combined with the ability to dynamically add and remove virtual warehouses, supports the addition of new processing capacity for new users without impacting the performance observed by the existing users.
Next, techniques for creating, sharing, and using bundles (also referred to as packages) will be described. Bundles may address the challenges of modularity, encapsulation, code sharing, and distribution described above. A bundle is a schema object with associated hidden schemas, as described in further detail below. A bundle can be created by a provider user and can be shared with a plurality of consumer users.
Bundles can be used for code modularity and encapsulation. Bundles can be implemented as code-only bundles or as code-and-state bundles. Code-only bundles (also referred to as module) can include packages of code modules such as stored procedures and/or functions. Code-only bundles can be used for packages of geospatial functions, global data sharing stored procedures, etc.
Code-and-state bundles can be used for more advanced functionality such as anonymization, machine learning (ML) models, alerts, budgets. Code-and-state bundles can be referred to as factory (also referred to as class) and instance bundles, where factory refers to a place to share code and instance refers to non-code objects such as tables, stages, etc. For example, a code-and-state bundle (class) can be used to create a ML model to predict rent in a particular city based on data stored in a database system, as described above. The ML model can include a procedure that trains using stored data, and then the ML model can be used to generate a predicted rent output based on a set of inputs. Here, the procedure would be classified as the factory and the trained model would be classified as the state stored in an instance, which can be represented by a table, stage, file, etc. As described in further detail below, the bundles can provide ease of management, versioning, and name spacing.
Bundle Schema B1416 here includes code, such as stored procedure P1( ) 418 and function F1( ) 420, and also includes states, such as stage S1422 and Table T1424. The bundle schema B1416 is nested underneath the bundle B1410 object itself (e.g., parentBundleID). The bundle schema B1416 is provided outside of the parent-child hierarchy of objects that the user can interact directly as compared to objects on the other side of the security barrier 404. Hence, the consumer end user cannot directly name or reference the underlying objects in the bundle schema B1416 that are not exposed.
The security barrier 404 provides a two-way protection. The security barrier 404 prevents consumer end users from directly accessing non-exposed objects in the bundle schema B1416, while also preventing objects inside the bundle schema B1416 from accessing objects outside the bundle schema B1416. For example, while function F1( ) 420 can access table T1424 because both are inside bundle schema B1416, function F1( ) 420 may not be able to access table T20414, which resides outside the bundle schema B1416. Hence, the bundle schema B1416 provides a self-contained schema that is only accessible by a consumer via the interface and cannot access other objects outside the bundle schema. For example, a consumer may not be able to directly reference the bundle schema or objects within a bundle schema using a dot (⋅) notation. Instead, the bundle schema objects can be referenced using the bundle name and a bang (!) notation, indicating its protection.
To provide this level of protection, processes are separated when interacting with the bundle schema using a hidden bundle role 426 (e.g., using bundleRoleID). The hidden bundle role 426 is not viewable or directly accessible by consumer end users. The hidden bundle role 426 is nested underneath the bundle object and effectively owns the objects inside the bundle schema B1416. The hidden bundle role 426 is not directly accessible by consumer end users, but it is used to execute exposed functionality of the bundle B1410. For example, consider that the provider has exposed some functionality of stored procedure P1( ) 418 and/or function F1( ) 420 to be accessible by consumers. When a consumer end user goes to invoke the exposed functionality of P1( ) 418 and/or function F10420, execution is performed on the right side of the security barrier 404 and the context is switched to the hidden bundle role 426. For example, when the invocation by the consumer is received and it is determined that the invocation is allowed based on the end user package role (e.g., reader, writer, etc., which is different type of role than the hidden bundle role 426 and described in further detail below), execution of the invocation is performed in the context of the hidden bundle role 426 inside the bundle schema B1416. The results of the execution are then delivered to consumer end user, who is unaware of the switching of contexts to execute the exposed functionality of the bundle.
In response to receiving a command referencing the bundle from the consumer account role. the system may determine that command invokes exposed functionality of the bundle which is allowed for the consumer account role based on the privileges granted to the consumer account role. To execute the command, the hidden bundle schema is engaged. For example, the command may need to reference hidden procedures, functions, tables, stages, etc. included in the hidden bundle schema. Portions of the command that needs to engage with the hidden bundle schema can be executed in the context of a hidden bundle role (e.g., hidden bundle role 426 described above). The hidden bundle role is not viewable or directly accessible by consumer account role. The hidden bundle role is nested underneath the bundle object and effectively owns the objects inside the hidden bundle schema. The hidden bundle role is not directly accessible by the consumer account role, but it is used to execute exposed functionality of the bundle. The results of the command execution may be delivered to the consumer account role. The consumer account role may not be aware that the command was executed in the context of the hidden bundle role.
A package/bundle version may be defined. For example, an “alter” command may be used to add package version. The package version properties may include a name, description (comment), and body. Versions can be added, modified, and dropped. The version name/identifier (e.g., “0.0.0”) may be a type string and may include a sequence, such as <major>.<minor>.<patch>. Each sequence number is a non-negative integer. Hence, providers can convey the significance of changes to consumers between releases. For example, <major> is changed for most significant changes, and changes to <minor> and <patch> represent changes of decreasing significance. Major and minor versions can typically include functionality changes, and path versions typically can include bug fixes and incremental code changes. Providers can release versions in increasing order and use ordering-based utility functions. Consumers can thus get consistent semantics across providers in the marketplace.
By using packages/bundles as described herein, the package/bundle can be shared with consumers while the provider retains control of the objects and code in the bundle. A bundle can be shared by granting privileges to consumers (e.g., package roles). Package roles may be granted to roles in the provider account. Package roles may be granted to other consumers to share and to DB roles (which are granted to share).
Versioning bundles provides more control to the provider. Alter commands can be used to set versions. In a first alter command, an “ADMIN” role can be added to the package roles. In a second alter command, a new version of the package can be added, e.g., version 1.0.0. The new version includes a body defining the new package version objects and defines privilege grants to different package roles of reader and admin. Each new version of bundle creates a new nested schema. This allows sharing of different versions with different consumers, such as rollout by region.
Version selection can be used to designate which version a particular consumer is designated to use. The provider can control which versions are shared with different consumers. While roles can be defined at the package-level, privilege grants to specific functions can be defined at the version-level. Thus, when a new version is rolled out by a provider, consumers do not have to change their package role designations. For example, a consumer user can grant different people in their account the package role of reader. Then, depending on which version the consumer is designated, the readers will have access to possibly different functionality. For example, if the consumer is designated version 1, which includes only functionality A granted to readers, then the reader roles will have access to only functionality A. If version 2 adds functionality B granted to readers and if the consumer is designated version 2, the previously granted readers will now automatically have access to functionality B.
Version control can enable a slow rollout by region of a new version of a package. The version selector command can be used to designate bundle versions for consumers. For example, consumers can be defined by different regions. All regions except one (e.g., “Southeast”) can be designated to use version 0.0.0 of the bundle. The “Southeast” region can be designated to use version 1.0.0.
After a package or bundle is shared, the consumer can explore the exposed contents of the bundle. For example, show commands can be used to list bundle specifications as a consumer and provider. In some embodiments, a single command can be used to show all functions and procedures of a bundle. A consumer cannot specify which version in the show command because the provider designates the version to the consumer, and the consumer cannot switch to a different version by itself.
Bundles or packages described herein can be used in different platforms. Bundles can be used with native applications. Native applications are applications built by third parties using objects from the data system, such as user defined functions (UDFs), UDTFs, external functions, stored procedures, tasks, streams, etc. These native applications can be distributed in a marketplace to other consumers. Consumers can then discover these applications on the marketplace and install them inside their accounts. The native applications may contain several discrete modules of functionality. Bundles, for example, can be included in these discrete modules.
Next, techniques for bundle version creation, version selection logic, and version upgrades are described.
Version selection logic 504 is provided on the provider side and includes logic to decide which version of C1502 to use for instance creation and upgrade on the different consumer sides. The version selection logic 504 can be a programmable parameter to map between bundle name and which version to select for a given consumer.
One consumer instance (i1) 506 is shown. The consumer instance i1506 includes an internal schema with local states, such as table 508 (state_table) and stage 510 (i_stage). The consumer instance i1506 is created by invoking the setup and construct procedure, which create the table 508 and stage 510 accordingly. Version selection logic 504 directs the consumer instance i1506 to the selected version of the bundle C1502, and its respective setup and construction procedures.
Rules are used to enforce transparent upgrades on the provider side and consumer side, respectively. The system (e.g., network-based data system 102) can maintain a list of versions of respective bundles.
At operation 708, the system may check if the non-small change is backward compatible with at least the immediately preceding minor version. The new minor or major version should be backward compatible to the last minor version preceding it, but the new minor or major version does not need to be backward compatible to older minor versions. Consider the example of adding new minor version 5.4.2 in the example of
If the new version is not backward compatible to the last preceding minor version, then, at operation 710, an error message may be generated and transmitted to the provider account. At operation 712, the new version will be given a new major/minor version number. The added version identifier is larger than the latest minor version in the chain. At operation 714, the new version with its new version identifier (whether a new patch version number or new major or minor version number) is released. The new version can be released to a subset of consumer accounts for upgrade to effectuate, for example, a slow rollout or testing.
Upgrading a bundle version on the consumer side is transparent and does not disrupt bundle operation. Upgrading a bundle version on the consumer side uses an in-place, stepped upgrade process with barrier enforcement to ensure smooth, non-disruptive behavior. For example, if upgrading bundle instances on the consumer side would need to create brand new local states to replace old states, data loss could occur during upgrade.
Instead, an in-place, stepped upgrade process is used. Instead of creating new instance schema in an upgrade, the setup procedure on the existing instance will update the state in place. Thus, even during an upgrade, a consumer account can still write to a state (e.g., table) of a bundle instance.
The data system can upgrade instances in an in-place manner without quiescing request to the instance and cloning of state entities. The setup procedure modifies instance local states used by dependency code when jobs using the code are still running. Hence, consumers should not find bundle instances unusable when an upgrade is in progress, which can occur anytime depending on the release schedule of the provider and can last for an extended period of time. The setup procedure is idempotent so that even if it fails in the middle, it can be retried.
At operation 804, the system may check if the target version is a new patch version as compared to the current instance version. If the new version is a patch version, then the system may complete the upgrade to the new patch version at operation 806. The upgraded version may then use the stored procedure of the new version in operation without disruption of bundle operation on the consumer side.
At operation 808, if the target version is a new major or minor version, the system may check if the target version is more than 1 minor version away from the current version. If the target version is not more than 1 minor version than the current version (i.e., next minor version), then the system may directly complete the upgrade to the target major/minor version with job barrier enforcement.
At operation 810, the system may enforce a job barrier to prevent aborting jobs using a potentially non-compatible version in the upgrade process. As mentioned above, backward compatibility can be guaranteed for only the last preceding minor version, and not for other older versions. Therefore, when upgrading an instance from minor version N to N+1, the barrier condition may ensure completion of all jobs running on minor version N−1 or earlier before the upgrade on instance local state can be started. That is, in the
If the target version is more than 1 minor version than the current version, the system may initiate a stepped upgrade with barrier enforcement. As mentioned above, backward compatibility can be guaranteed for only the last preceding minor version, and not for other older versions; therefore, the system may upgrade to the target version in a stepped manner instead of direct manner. That is, the data system may upgrade to the next immediate minor version and then the next (if any) minor version until the target version is reached. For example, in the
At operation 814, the system may also enforce a job barrier to prevent aborting jobs using a non-compatible version in the upgrade process. When upgrading an instance from minor version N to N+1, the barrier condition may ensure completion of all jobs running on minor version N−1 or earlier before the upgrade on instance local state can be started. At operation 816, after the barrier enforcement, the data system completes the migration from the current version to the next intermediate version. The method 800 may then be directed to operation 808 and check if the target version is more than 1 minor version away from the current version. The remaining operations of method 800 may be performed until the upgrade to the target version is completed.
The data system can also handle “bad” versions. Providers may later discover bugs in code or setup procedures, which can corrupt local instance states after a version is released. A patch version in the same minor version can be released to fix the data corruption as well as bugs in code. In addition to releasing patch version, the provider can also mark the corrupted version as “bad.” Marking a “bad” version avoids selecting a “bad” version for steps in an instance upgrade. Also, during upgrade, jobs using code from a “bad” patch version on the same or parent minor version of the upgrade target version can be drained before the upgrade can continue to update instance local state.
The upgrade techniques described herein provide a structure for in-place, stepped upgrades, which provide transparent, seamless operation during upgrade unlike conventional upgrade procedures.
Version 1.1.0 is an initial version. Version 1.1.0 creates table1 with a column for id and a column for name. Version 1.1.0 includes a read method directed to how to read information from table1, and a write method directed at how to write to table1. The write method includes inserting into table1 values (id, first+last) to table1.
Version 1.2.0, which is the next minor version, makes the code forward compatible with future state changes. In particular, version 1.2.0 modifies the write method to specify that values (id, first+last) are inserted into the (id, name) columns, respectively.
Version 1.2.1 is a patch version and included unrelated class code change. Version 1.3.0 is the next minor version and adds new columns “first” and “last” into table1. Note that the read and write methods were not modified in version 1.2.1 to write to these two new columns because that would have mad version 1.2.1 not backwards compatible to version 1.2.0.
Version 1.4.0 adds the “first” column to the insert statement in the write method. The provider account may detect that version 1.4.0 includes an error because it did not include the “last” column. The provider account releases version 1.4.1 as a patch version to also add the “last” column in the insert statement and marks 1.4.0 version as a “bad” version. In version 1.4.1 (as well as version 1.4.0), the code becomes aware of the new columns but not dependent on the new columns.
Version 1.5.0, the next minor version, includes a backfill operation to backfill the two new columns “first” and “last.” Version 1.6.0, the next minor version removes code dependency on old column, which included first and last name in the “name” column. Version 1.7.0, the next minor version, removes the old “name” column from table1. If all the changes between version 1.1.0 and version 1.7.0 would have been made in one single minor version change, the in-place upgrade process could not be performed in the consumer accounts causing a disruption in bundle service in the consumer accounts or data loss. Instead, the changes were broken down so that each minor version was backwards compatible to preceding minor version so that in-place, stepped upgrade, as described herein, can be performed allowing transparent, seamless upgrade without disruption or data loss.
In alternative embodiments, the machine 1000 operates as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine 1000 may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine 1000 may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a smart phone, a mobile device, a network router, a network switch, a network bridge, or any machine capable of executing the instructions 1016, sequentially or otherwise, that specify actions to be taken by the machine 1000. Further, while only a single machine 1000 is illustrated, the term “machine” shall also be taken to include a collection of machines 1000 that individually or jointly execute the instructions 1016 to perform any one or more of the methodologies discussed herein.
The machine 1000 includes processors 1010, memory 1030, and input/output (I/O) components 1050 configured to communicate with each other such as via a bus 1002. In an example embodiment, the processors 1010 (e.g., a central processing unit (CPU), a reduced instruction set computing (RISC) processor, a complex instruction set computing (CISC) processor, a graphics processing unit (GPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a radio-frequency integrated circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, a processor 1012 and a processor 1014 that may execute the instructions 1016. The term “processor” is intended to include multi-core processors 1010 that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions 1016 contemporaneously. Although
The memory 1030 may include a main memory 1032, a static memory 1034, and a storage unit 1036, all accessible to the processors 1010 such as via the bus 1002. The main memory 1032, the static memory 1034, and the storage unit 1036 store the instructions 1016 embodying any one or more of the methodologies or functions described herein. The instructions 1016 may also reside, completely or partially, within the main memory 1032, within the static memory 1034, within the storage unit 1036, within at least one of the processors 1010 (e.g., within the processor's cache memory), or any suitable combination thereof, during execution thereof by the machine 1000.
The I/O components 1050 include components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components 1050 that are included in a particular machine 1000 will depend on the type of machine. For example, portable machines such as mobile phones will likely include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components 1050 may include many other components that are not shown in
Communication may be implemented using a wide variety of technologies. The I/O components 1050 may include communication components 1064 operable to couple the machine 1000 to a network 1080 or devices 1070 via a coupling 1082 and a coupling 1072, respectively. For example, the communication components 1064 may include a network interface component or another suitable device to interface with the network 1080. In further examples, the communication components 1064 may include wired communication components, wireless communication components, cellular communication components, and other communication components to provide communication via other modalities. The devices 1070 may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a universal serial bus (USB)). For example, as noted above, the machine 1000 may correspond to any one of the remote computing device 106, the access management system 118, the compute service manager 112, the execution platform 114, the Web proxy 120, and the devices 1070 may include any other of these systems and devices.
The various memories (e.g., 1030, 1032, 1034, and/or memory of the processor(s) 1010 and/or the storage unit 1036) may store one or more sets of instructions 1016 and data structures (e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. These instructions 1016, when executed by the processor(s) 1010, cause various operations to implement the disclosed embodiments.
As used herein, the terms “machine-storage medium,” “device-storage medium,” and “computer-storage medium” mean the same thing and may be used interchangeably in this disclosure. The terms refer to a single or multiple storage devices and/or media (e.g., a centralized or distributed database, and/or associated caches and servers) that store executable instructions and/or data. The terms shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media, including memory internal or external to processors. Specific examples of machine-storage media, computer-storage media, and/or device-storage media include non-volatile memory, including by way of example semiconductor memory devices, e.g., erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), field-programmable gate arrays (FPGAs), and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The terms “machine-storage media,” “computer-storage media,” and “device-storage media” specifically exclude carrier waves, modulated data signals, and other such media, at least some of which are covered under the term “signal medium” discussed below.
In various example embodiments, one or more portions of the network 1080 may be an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local-area network (LAN), a wireless LAN (WLAN), a wide-area network (WAN), a wireless WAN (WWAN), a metropolitan-area network (MAN), the Internet, a portion of the Internet, a portion of the public switched telephone network (PSTN), a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a Wi-Fi® network, another type of network, or a combination of two or more such networks. For example, the network 1080 or a portion of the network 1080 may include a wireless or cellular network, and the coupling 1082 may be a Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or another type of cellular or wireless coupling. In this example, the coupling 1082 may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, third Generation Partnership Project (3GPP) including 3G, fourth generation wireless (4G) networks, Universal Mobile Telecommunications System (UMTS), High-Speed Packet Access (HSPA), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE) standard, others defined by various standard-setting organizations, other long-range protocols, or other data transfer technology.
The instructions 1016 may be transmitted or received over the network 1080 using a transmission medium via a network interface device (e.g., a network interface component included in the communication components 1064) and utilizing any one of a number of well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions 1016 may be transmitted or received using a transmission medium via the coupling 1072 (e.g., a peer-to-peer coupling) to the devices 1070. The terms “transmission medium” and “signal medium” mean the same thing and may be used interchangeably in this disclosure. The terms “transmission medium” and “signal medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying the instructions 1016 for execution by the machine 1000, and include digital or analog communications signals or other intangible media to facilitate communication of such software. Hence, the terms “transmission medium” and “signal medium” shall be taken to include any form of modulated data signal, carrier wave, and so forth. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal.
The terms “machine-readable medium,” “computer-readable medium,” and “device-readable medium” mean the same thing and may be used interchangeably in this disclosure. The terms are defined to include both machine-storage media and transmission media. Thus, the terms include both storage devices/media and carrier waves/modulated data signals.
The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Similarly, the methods described herein may be at least partially processor-implemented. For example, at least some of the operations of the methods described herein may be performed by one or more processors. The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but also deployed across a number of machines. In some example embodiments, the processor or processors may be located in a single location (e.g., within a home environment, an office environment, or a server farm), while in other embodiments the processors may be distributed across a number of locations.
Although the embodiments of the present disclosure have been described with reference to specific example embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader scope of the inventive subject matter. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. The accompanying drawings that form a part hereof show, by way of illustration, and not of limitation, specific embodiments in which the subject matter may be practiced. The embodiments illustrated are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed herein. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. This Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled.
Such embodiments of the inventive subject matter may be referred to herein, individually and/or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept if more than one is in fact disclosed. Thus, although specific embodiments have been illustrated and described herein, it should be appreciated that any arrangement calculated to achieve the same purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, will be apparent, to those of skill in the art, upon reviewing the above description.
In this document, the terms “a” or “an” are used, as is common in patent documents, to include one or more than one, independent of any other instances or usages of “at least one” or “one or more.” In this document, the term “or” is used to refer to a nonexclusive or, such that “A or B” includes “A but not B,” “B but not A,” and “A and B,” unless otherwise indicated. In the appended claims, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.” Also, in the following claims, the terms “including” and “comprising” are open-ended; that is, a system, device, article, or process that includes elements in addition to those listed after such a term in a claim is still deemed to fall within the scope of that claim.
Described implementations of the subject matter can include one or more features, alone or in combination as illustrated below by way of example.
Example 1. A method comprising: providing, from a provider account in a multi-tenant database system, a first version of a bundle to a consumer account, the bundle including an interface directly accessible to the consumer account and a hidden bundle schema not directly accessible by the consumer account; releasing, from the provider account, a second version of the bundle to the consumer account; determining that the second version is more than one minor version from the first version; upgrading, in the consumer account, from the first version to an intermediate version of the bundle based on determining that the second version is more than one minor version from the first version; and upgrading, in the consumer account, from the intermediate version to the second version of the bundle.
Example 2. The method of example 1, wherein the bundle maintains operation by the consumer account during upgrading from the first version to the intermediate version and from intermediate version to the second version.
Example 3. The method of any of examples 1-2, wherein the first version is a n minor version of the bundle, the method further comprising: determining that at least one job is executing on an n−1 or earlier minor version of the bundle in the consumer account; pausing the upgrade to the intermediate version based on determining that the at least one job is running on the n−1 or earlier minor version; detecting that the at least one job is completed; and resuming upgrading to the intermediate version based on detecting that the at least one job is completed.
Example 4. The method of any of examples 1-3, wherein determining that at least one job is running on an n−1 or earlier minor version is based on a first timestamp associated with a start time of the at least one job and a second timestamp associated with an upgrade time of the bundle.
Example 5. The method of any of examples 1-4, further comprising: providing a security barrier between the interface and the hidden bundle schema, wherein the security barrier restricts access of the consumer account to non-exposed objects in the hidden bundle schema, and wherein the security barrier restricts objects in the hidden bundle schema from accessing objects outside the hidden bundle schema.
Example 6. The method of any of examples 1-5, further comprising: receiving, from the consumer account, a command referencing the bundle;
Example 7. The method of any of examples 1-6, wherein the second version is backward compatible with the intermediate version and the intermediate version is backward compatible with the first version, wherein the second version is not backward compatible with the first version.
Example 8. A system comprising: one or more processors of a machine; and a memory storing instructions that, when executed by the one or more processors, cause the machine to perform operations implementing any one of example methods 1 to 7.
Example 9. A machine-readable storage device embodying instructions that, when executed by a machine, cause the machine to perform operations implementing any one of example methods 1 to 7.
