Patent Grant
11099917
Computing devices can utilize communication networks to exchange data. Companies and organizations operate computer networks that interconnect a number of computing devices to support operations or to provide services to third parties. The computing systems can be located in a single geographic location or located in multiple, distinct geographic locations (e.g., interconnected via private or public communication networks). Specifically, data centers or data processing centers, herein generally referred to as a “data center,” may include a number of interconnected computing systems to provide computing resources to users of the data center. The data centers may be private data centers operated on behalf of an organization or public data centers operated on behalf, or for the benefit of, the general public.
To facilitate increased utilization of data center resources, virtualization technologies allow a single physical computing device to host one or more instances of virtual machines that appear and operate as independent computing devices to users of a data center. With virtualization, the single physical computing device can create, maintain, delete, or otherwise manage virtual machines in a dynamic manner. In turn, users can request computer resources from a data center, including single computing devices or a configuration of networked computing devices, and be provided with varying numbers of virtual machine resources.
In some scenarios, virtual machine instances may be configured according to a number of virtual machine instance types to provide specific functionality. For example, various computing devices may be associated with different combinations of operating systems or operating system configurations, virtualized hardware resources and software applications to enable a computing device to provide different desired functionalities, or to provide similar functionalities more efficiently. These virtual machine instance type configurations are often contained within a device image, which includes static data containing the software (e.g., the OS and applications together with their configuration and data files, etc.) that the virtual machine will run once started. The device image is typically stored on the disk used to create or initialize the instance. Thus, a computing device may process the device image in order to implement the desired software configuration.
One example use of data centers is to process or analyze large data sets, which may be impractical to analyze using a single computing device. Various techniques have been developed to allow for multiple computing devices (or in some instances multiple processors within a single computing device) to process data concurrently. This concurrent data processing is sometimes referred to as “parallelization.” One technique for allowing parallelization in processing data sets is the “MapReduce” programming model. This programming model generally requires a centralized “infrastructure” or “framework,” which controls execution of two functions by individual computing devices within a set of devices. Execution of the first function, a “map” function, causes multiple devices to process portions (or “chunks”) of a full set of raw data to generate a set of intermediate results, such as counts of individual words within a corpus of text. Execution of the second function, a “reduce” function, causes one or more devices to combine multiple sets of intermediate results (from multiple map functions) to produce a set of aggregated results. The reduce functions may be executed multiple times, with each execution further reducing the number of aggregated results, until a single aggregate result record is created. In traditional implementations of the MapReduce programming model, an infrastructure or framework must typically execute continuously to coordinate execution of map and reduce functions until a result is provided.
Generally described, aspects of the present disclosure relate to an on-demand code execution environment that enables clients to request execution of user-defined code in order to process a set of data. More specifically, aspects of the present disclosure enable processing of a set of data statefully, without requiring that state information be maintained in the execution environments in which code executes. In the context of data set processing, stateful processing generally refers to processing in which a result of processing a later data item in a set depends at least partly on retained state information related to previously processed data items, as opposed to stateless processing, which operation does not depend on retained state information. Stateful processing may be utilized, for example, to implement the MapReduce programming model, such as by counting a number of occurrences of a particular data item in a set. While stateful processing is often desirable to end users, successful and resilient implementation of stateful processing can present challenges to distributed systems. Typically, such processing is implemented by code that stores state information locally, such that processing of a later data item can be based on the locally stored state information. However, local storage of state information inhibits portability of code executions, as each data item in a data set must be routed to an environment that contains the correct state information. Local storage of state information also creates additional requirements for resiliency of execution environments, as a loss of the state information can place analysis of a data set into an indeterminate state. For these and other reasons, many systems that might otherwise be desirable to use for stateful processing, such as on-demand code execution systems (sometimes called “serverless” compute systems) do not generally support stateful code execution. The embodiments disclosed herein address these problems, by providing for stateful processing within execution environments without requiring local storage of state information within those environments.
For ease of illustration, embodiments of the present disclosure are described with reference to implementation of the MapReduce programming model, which can be used to analyze large data sets, and which generally relies at least in part on stateful code to “reduce” outputs of an initial “map” stage of the model. As is known in the art, the MapReduce programming model is a logical data processing model used to enable a distributed computing system (e.g., including multiple distinct devices in communication with one another) to process data sets, and particularly large data sets (e.g., on the order of gigabytes, terabytes, petabytes, etc.). Generally, the MapReduce programming model includes at least two data processing stages: a map stage and a reduce stage. During the map stage, individual devices within the distributed computing system collect a portion of the data set (e.g., chunks of n gigabytes) and “map” the content of the portion into a set of outputs. Each set of outputs is then processed by a “reduce” function to aggregate, combine, or otherwise determine a result of processing the content within the output set. A common example of the MapReduce paradigm is that of counting the occurrence of words within a corpus. For example, suppose that a user obtains a multi-terabyte textual data set, and would like to determine what words exist within the data set and how often each word occurs. Utilizing a single device, processing the data set may take excessive amounts of time. However, under the MapReduce model, each device within a distributed system may process a limited portion (or “chunk”) of the data set according to a map function. In this example, the map function may correspond to counting the occurrence of each word within the portion of the data set. By increasing the number of devices within the system, the speed of processing can be increased. The limitation of such processing is that each device would process only a limited portion of the data set, and thus be aware only of a count of words within that portion. To aggregate the counts across devices, a reduce function is used. While it may be possible to use a single device to aggregate the word counts generated at each map function, this configuration once again creates a bottleneck to processing. To address this, the MapReduce model enables multiple reduce functions to be implemented across the distributed system, each function processing a subset of the outputs of the map functions, according to an attribute of the outputs. For example, in the example of a word count, 26 reduce functions may be implemented, each creating a count of words starting with a particular character in the English alphabet. Illustratively, a first reduce function may obtain, from the map functions, all counts of words starting with the letter ‘a,’ a second reduce function may obtain all counts of words starting with ‘b,’ etc. Each reduce function can therefore gain an overall word count for a certain portion of the words in the corpus. The outputs of the reduce function can be combined to obtain the total word count. Often, combining the outputs of a reduce function is a relatively low-cost operation, as those outputs are distinct and non-overlapping. Thus, the MapReduce programming model can be utilized to facilitate rapid, distributed processing of data sets.
As will be appreciated in light of the above, a reduce stage of the MapReduce model is often implemented statefully, as a result of processing an nth data item in a data set generally depends on a result of processing the (n−1)th data item. To provide for stateful reduce stages, the MapReduce model has traditionally been implemented on dedicated distributed systems, configured with framework software that handles the generation and configuration of worker computer nodes within the system. Use of dedicated worker nodes provides, among other potential advantages, an affinity between worker nodes and the portions of the data set on which they implement the reduce stage. Each worker node can be provided with an appropriate portion of the output of a map stage, thus ensuring accurate implementation of the reduce stage with respect to that portion. However, traditional MapReduce implementations also have disadvantages. For example, worker nodes are commonly dedicated solely to implementation of the MapReduce model, limiting their ability to undertake other computing tasks. Establishing such a distributed system can therefore be time consuming and inefficient, particularly where a user does not require a continual, dedicated system for implementation of the MapReduce model.
Embodiments of the present disclosure address the above-noted issues by enabling typically stateful processing, such as the reduce stage of the MapReduce programming model, to be implemented within stateless execution environments, such as those provided by an on-demand code execution system. As will be described in more detail below, embodiments of the present disclosure enable an intermediary device to maintain state information related to iterative data processing (e.g., a reduce stage of the MapReduce model), and to submit the state information in each request to execute code on an on-demand code execution system. The intermediary device can obtain updated state information in response to each call, and include the updated state information in a next call. In this manner, execution environments on the on-demand code execution system are relieved of an obligation to maintain state information, and may continue to operate statelessly. However, state information can nevertheless be maintained while processing a set of data, enabling successful stateful analysis of data sets. As will be described below, the intermediary device may be configured to ensure resiliency of operation, such that failures within a system processing a data set can be identified and corrected. Moreover, the intermediary device can be configured to ensure efficient resiliency, such that providing resiliency does not have a substantial negative impact on the ability of the system to processes the data sets.
In one embodiment, the intermediary device is a poller device, which operates to retrieve a set of calls to invoke code on an on-demand code execution system (e.g., to process a data item included or referenced in the call), and to pass each call to the on-demand code execution system. The set of calls can be illustratively maintained as a queue, such as a message queue on a stream data processing system. Illustratively, a poller device in accordance with embodiments of the present disclosure may determine initial state information for processing data items on the queue (e.g., as a null state), retrieve an initial batch of calls from the queue, and iteratively submit each call to the on-demand code execution system along with current state information representing a state of processing of the queue. The poller device can be configured to receive in response to each call updated state information, which information can be included within subsequent calls. Because the state information for the queue is passed in each call, environments on the on-demand code execution system are not themselves required to maintain state information. For this reason, no affinity is generally required between a poller device and an environment in which a call is processed. Rather, the on-demand code execution system can route calls from the poller device to any suitable environment, increasing flexibility of the on-demand code execution system in executing code corresponding to the call. The poller device can be configured to periodically save the state information to a resilient storage system (e.g., a network storage location with built-in redundancy), and to resume processing based on that saved state information in the event of a failure. Thus, maintaining state information at a poller device provides an efficient mechanism for enabling stateful data processing at an on-demand code execution system.
While other mechanisms for enabling stateful data processing at an on-demand code execution system are contemplated herein, these other mechanisms are generally less desirable than maintaining state information at an intermediary (e.g., poller) device. For example, it is conceivable that an on-demand code execution system is configured to provide affinity for multiple calls to execute a given set of code, such that each call is routed to the same execution environment. It is further conceivable that the on-demand code execution system enables each such environment to maintain local state information, thus enabling stateful execution of code within the environment. However, this approach significantly decreases flexibility of operation of the on-demand code execution system, requiring that the system maintain execution environments for long periods of time. Moreover, this approach may not be well suited to address issues that frequently arise in distributed processing systems, such as the need to provide resiliency of operation or the need to scale up or down a number of environments in response to changing operational load. For example, to address these issues, the on-demand code execution system may be required to frequently save state information of each environment, significantly increasing resource usage of the system. The system may also be required to provide for transferring of state information between environments during scale up or scale down events, increasing the complexity of managing such environments. Another possible mechanism for retaining state information between processing of calls is to configure each execution environment, during processing of a call, to write its state information to a persistent external location, such as network data storage. Thus, subsequent executions may retrieve the state information from the persistent location to facilitate processing of subsequent calls. However, in distributed systems, writing to an external storage location is generally considered a “heavy weight” operation, as it can significantly increase the computing resources used to process a call. For example, writing to a network location may require initiation of a transport control protocol (TCP) session with the network location, a process that can take significant time and resources (in terms of the resources that would otherwise be required to process a single call). Where the number of calls is large (e.g., when processing multi-terabyte data sets), the additional overhead required for such a heavy weight operation can be significant.
Embodiments of the present disclosure enable state information to be maintained between processing of calls without these drawbacks. For example, an intermediary device can pass state information for a call at the same time that the call is submitted to an on-demand code execution system, and can receive updated state information as a response to that call. Thus, no additional network communications are required by the on-demand code execution system. Moreover, the intermediary device can implement provide resiliency by saving state information periodically, at a periodicity that can be adjusted based on the resources available to the device and overhead required to resume operation in the event of a failure. Specifically, because the intermediary device is capable of a “long term” view of processing of a queue of calls, the device is not required to ensure state information is saved after each call, as the on-demand code execution system or an external data store might be.
The on-demand code execution system, as described in detail herein, may provide a network-accessible service enabling users to submit or designate computer-executable code to be executed by virtual machine instances on the on-demand code execution system. Each set of code on the on-demand code execution system may define a “task,” and implement specific functionality corresponding to that task when executed on a virtual machine instance of the on-demand code execution system. Individual implementations of the task on the on-demand code execution system may be referred to as an “execution” of the task (or a “task execution”). The on-demand code execution system can further enable users to trigger execution of a task based on a variety of potential events, such as detecting new data at a network-based storage system, transmission of an application programming interface (“AN”) call to the on-demand code execution system, or transmission of a specially formatted hypertext transport protocol (“HTTP”) packet to the on-demand code execution system. Thus, users may utilize the on-demand code execution system to execute any specified executable code “on-demand,” without requiring configuration or maintenance of the underlying hardware or infrastructure on which the code is executed. Further, the on-demand code execution system may be configured to execute tasks in a rapid manner (e.g., in under 100 milliseconds [ms]), thus enabling execution of tasks in “real-time” (e.g., with little or no perceptible delay to an end user).
Because the on-demand code execution system can provide the ability to execute a task on-demand, without configuration of an underlying device on which to execute the code, the on-demand code execution system can provide an excellent platform on which to implement the MapReduce programming model. For example, as described in U.S. patent application Ser. No. 16/144,997, entitled “MAPREDUCE IMPLEMENTATION IN AN ON-DEMAND NETWORK CODE EXECUTION SYSTEM AND STREAM DATA PROCESSING SYSTEM” and filed Sep. 27, 2018 (the “'997 Application”), which application is hereby incorporated by reference in its entirety, the on-demand code execution system may be used in conjunction with a stream data processing system to implement the MapReduce programming model without requiring a centralized controller. The present disclosure can further improve operation of the on-demand code execution system to implement the MapReduce programming model or other data analysis by providing an efficient way of maintaining state information for such data analysis, without requiring that such state information be maintained within environments of the on-demand code execution system or persisted by such environments to additional external locations.
As will be appreciated by one of skill in the art in light of the present disclosure, the embodiments disclosed herein improves the ability of computing systems, such as on-demand code execution systems, to process and analyze data sets in a parallelizable manner. More specifically, embodiments of the present disclosure enable state information to be efficiently maintained between code executions on on-demand code execution systems, without requiring that such state information be maintained in an execution environment of the systems. Moreover, the presently disclosed embodiments address technical problems inherent within computing systems; specifically, the need to maintain state information when statefully processing data sets, and the difficulty of maintaining such information without increasing computing resources used to process the data set or decreasing flexibility in where such processing occurs. These technical problems are addressed by the various technical solutions described herein, including the use of an intermediary device to retrieve calls to an on-demand code execution system from a queue and to iteratively submit the calls to the on-demand code execution systems while maintaining state information passed with the calls. Thus, the present disclosure represents an improvement on existing data processing systems and computing systems in general.
The general execution of tasks on the on-demand code execution environment will now be discussed. Specifically, to execute tasks, the on-demand code execution environment described herein may maintain a pool of pre-initialized virtual machine instances that are ready for use as soon as a user request is received. Due to the pre-initialized nature of these virtual machines, delay (sometimes referred to as latency) associated with executing the user code (e.g., instance and language runtime startup time) can be significantly reduced, often to sub-100 millisecond levels. Illustratively, the on-demand code execution environment may maintain a pool of virtual machine instances on one or more physical computing devices, where each virtual machine instance has one or more software components (e.g., operating systems, language runtimes, libraries, etc.) loaded thereon. When the on-demand code execution environment receives a request to execute the program code of a user (a “task”), which specifies one or more computing constraints for executing the program code of the user, the on-demand code execution environment may select a virtual machine instance for executing the program code of the user based on the one or more computing constraints specified by the request and cause the program code of the user to be executed on the selected virtual machine instance. The program codes can be executed in isolated containers that are created on the virtual machine instances. Since the virtual machine instances in the pool have already been booted and loaded with particular operating systems and language runtimes by the time the requests are received, the delay associated with finding compute capacity that can handle the requests (e.g., by executing the user code in one or more containers created on the virtual machine instances) is significantly reduced.
The on-demand code execution environment may include a virtual machine instance manager configured to receive user code (threads, programs, etc., composed in any of a variety of programming languages) and execute the code in a highly scalable, low latency manner, without requiring user configuration of a virtual machine instance. Specifically, the virtual machine instance manager can, prior to receiving the user code and prior to receiving any information from a user regarding any particular virtual machine instance configuration, create and configure virtual machine instances according to a predetermined set of configurations, each corresponding to any one or more of a variety of run-time environments. Thereafter, the virtual machine instance manager receives user-initiated requests to execute code, and identifies a pre-configured virtual machine instance to execute the code based on configuration information associated with the request. The virtual machine instance manager can further allocate the identified virtual machine instance to execute the user's code at least partly by creating and configuring containers inside the allocated virtual machine instance. Various embodiments for implementing a virtual machine instance manager and executing user code on virtual machine instances is described in more detail in U.S. Pat. No. 9,323,556, entitled “PROGRAMMATIC EVENT DETECTION AND MESSAGE GENERATION FOR REQUESTS TO EXECUTE PROGRAM CODE” and filed Sep. 30, 2014 (“the '556 Patent”), the entirety of which is hereby incorporated by reference.
As used herein, the term “virtual machine instance” is intended to refer to an execution of software or other executable code that emulates hardware to provide an environment or platform on which software may execute (an “execution environment”). Virtual machine instances are generally executed by hardware devices, which may differ from the physical hardware emulated by the virtual machine instance. For example, a virtual machine may emulate a first type of processor and memory while being executed on a second type of processor and memory. Thus, virtual machines can be utilized to execute software intended for a first execution environment (e.g., a first operating system) on a physical device that is executing a second execution environment (e.g., a second operating system). In some instances, hardware emulated by a virtual machine instance may be the same or similar to hardware of an underlying device. For example, a device with a first type of processor may implement a plurality of virtual machine instances, each emulating an instance of that first type of processor. Thus, virtual machine instances can be used to divide a device into a number of logical sub-devices (each referred to as a “virtual machine instance”). While virtual machine instances can generally provide a level of abstraction away from the hardware of an underlying physical device, this abstraction is not required. For example, assume a device implements a plurality of virtual machine instances, each of which emulate hardware identical to that provided by the device. Under such a scenario, each virtual machine instance may allow a software application to execute code on the underlying hardware without translation, while maintaining a logical separation between software applications running on other virtual machine instances. This process, which is generally referred to as “native execution,” may be utilized to increase the speed or performance of virtual machine instances. Other techniques that allow direct utilization of underlying hardware, such as hardware pass-through techniques, may be used, as well.
While a virtual machine executing an operating system is described herein as one example of an execution environment, other execution environments are also possible. For example, tasks or other processes may be executed within a software “container,” which provides a runtime environment without itself providing virtualization of hardware. Containers may be implemented within virtual machines to provide additional security, or may be run outside of a virtual machine instance.
The foregoing aspects and many of the attendant advantages of this disclosure will become more readily appreciated as the same become better understood by reference to the following description, when taken in conjunction with the accompanying drawings.
The illustrative environment 100 further includes one or more auxiliary services 106, which can interact with the on-demand code execution system 110 to implement desired functionality on behalf of a user. Auxiliary services 106 can correspond to network-connected computing devices, such as servers, which generate data accessible to the on-demand code execution system 110 or otherwise communicate to the on-demand code execution system 110. For example, the auxiliary services 106 can include web services (e.g., associated with the user computing devices 102, with the on-demand code execution system 110, or with third parties), databases, really simple syndication (“RSS”) readers, social networking sites, or any other source of network-accessible service or data source. In some instances, auxiliary services 106 may be associated with the on-demand code execution system 110, e.g., to provide billing or logging services to the on-demand code execution system 110. In some instances, auxiliary services 106 actively transmit information, such as API calls or other task-triggering information, to the on-demand code execution system 110. In other instances, auxiliary services 106 may be passive, such that data is made available for access by the on-demand code execution system 110. As described below, components of the on-demand code execution system 110 may periodically poll such passive data sources, and trigger execution of tasks within the on-demand code execution system 110 based on the data provided. While depicted in
The illustrative environment 100 further includes one or more network-based data storage services 108, configured to enable the on-demand code execution system 110 to store and retrieve data from one or more persistent or substantially persistent data sources. Illustratively, the network-based data storage services 108 may enable the on-demand code execution system 110 to retrieve a set of data to be analyzed, and store information (e.g., results) regarding that analysis. The network-based data storage services 108 may represent, for example, a relational or non-relational database. In another example, the network-based data storage services 108 may represent a network-attached storage (NAS), configured to provide access to data arranged as a file system. Various other functionalities may be included within network-based data storage services 108 usable within embodiments of the present disclosure. The network-based data storage services 108 may further enable the on-demand code execution system 110 to query for and retrieve information regarding data stored within the on-demand code execution system 110, such as by querying for a number of relevant files or records, sizes of those files or records, file or record names, file or record creation times, etc. In some instances, the network-based data storage services 108 may provide additional functionality, such as the ability to separate data into logical groups (e.g., groups associated with individual accounts, etc.). While shown as distinct from the auxiliary services 106, the network-based data storage services 108 may in some instances also represent a type of auxiliary service 106.
The illustrative environment 100 further includes a stream data processing system 170. As discussed above, the stream data processing system can provides the ability for upstream devices to place data onto a message stream 172, such as by publishing “messages” onto the stream 172, which may be designated based on a specific “topic.” While a single stream 172 is shown in
The user computing devices 102, auxiliary services 106, network-based data storage services 108, and stream data processing system 170 may communicate with the on-demand code execution system 110 via network 104, which may include any wired network, wireless network, or combination thereof. For example, the network 104 may be a personal area network, local area network, wide area network, over-the-air broadcast network (e.g., for radio or television), cable network, satellite network, cellular telephone network, or combination thereof. As a further example, the network 104 may be a publicly accessible network of linked networks, possibly operated by various distinct parties, such as the Internet. In some embodiments, the network 104 may be a private or semi-private network, such as a corporate or university intranet. The network 104 may include one or more wireless networks, such as a Global System for Mobile Communications (GSM) network, a Code Division Multiple Access (CDMA) network, a Long Term Evolution (LTE) network, or any other type of wireless network. The network 104 can use protocols and components for communicating via the Internet or any of the other aforementioned types of networks. For example, the protocols used by the network 104 may include Hypertext Transfer Protocol (HTTP), HTTP Secure (HTTPS), Message Queue Telemetry Transport (MQTT), Constrained Application Protocol (CoAP), and the like. Protocols and components for communicating via the Internet or any of the other aforementioned types of communication networks are well known to those skilled in the art and, thus, are not described in more detail herein.
The on-demand code execution system 110 and stream data processing system 170 are depicted in
Further, the on-demand code execution system 110 and stream data processing system 170 may be implemented directly in hardware or software executed by hardware devices and may, for instance, include one or more physical or virtual servers implemented on physical computer hardware configured to execute computer executable instructions for performing various features that will be described herein. The one or more servers may be geographically dispersed or geographically co-located, for instance, in one or more data centers. In some instances, the one or more servers may operate as part of a system of rapidly provisioned and released computing resources, often referred to as a “cloud computing environment.”
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To enable interaction with the on-demand code execution system 110, the system 110 includes one or more frontends 120, which enable interaction with the on-demand code execution system 110. In an illustrative embodiment, the frontends 120 serve as a “front door” to the other services provided by the on-demand code execution system 110, enabling users (via user computing devices 102) to provide, request execution of, and view results of computer executable code. The frontends 120 include a variety of components to enable interaction between the on-demand code execution system 110 and other computing devices. For example, each frontend 120 may include a request interface providing user computing devices 102 with the ability to upload or otherwise communication user-specified code to the on-demand code execution system 110 and to thereafter request execution of that code. In one embodiment, the request interface communicates with external computing devices (e.g., user computing devices 102, auxiliary services 106, etc.) via a graphical user interface (GUI), CLI, or API. The frontends 120 process the requests and makes sure that the requests are properly authorized. For example, the frontends 120 may determine whether the user associated with the request is authorized to access the user code specified in the request.
References to user code as used herein may refer to any program code (e.g., a program, routine, subroutine, thread, etc.) written in a specific program language. In the present disclosure, the terms “code,” “user code,” and “program code,” may be used interchangeably. Such user code may be executed to achieve a specific function, for example, in connection with a particular web application or mobile application developed by the user. As noted above, individual collections of user code (e.g., to achieve a specific function) are referred to herein as “tasks,” while specific executions of that code are referred to as “task executions” or simply “executions.” Tasks may be written, by way of non-limiting example, in JavaScript (e.g., node.js), Java, Python, and/or Ruby (and/or another programming language). Tasks may be “triggered” for execution on the on-demand code execution system 110 in a variety of manners. In one embodiment, a user or other computing device may transmit a request to execute a task may, which can generally be referred to as “call” to execute of the task. Such calls may include the user code (or the location thereof) to be executed and one or more arguments to be used for executing the user code. For example, a call may provide the user code of a task along with the request to execute the task. In another example, a call may identify a previously uploaded task by its name or an identifier. In yet another example, code corresponding to a task may be included in a call for the task, as well as being uploaded in a separate location (e.g., storage of an auxiliary service 106 or a storage system internal to the on-demand code execution system 110) prior to the request being received by the on-demand code execution system 110. The on-demand code execution system 110 may vary its execution strategy for a task based on where the code of the task is available at the time a call for the task is processed. A request interface of the frontend 120 may receive calls to execute tasks as Hypertext Transfer Protocol Secure (HTTPS) requests from a user. Also, any information (e.g., headers and parameters) included in the HTTPS request may also be processed and utilized when executing a task. As discussed above, any other protocols, including, for example, HTTP, MQTT, and CoAP, may be used to transfer the message containing a task call to the request interface.
A call to execute a task may specify one or more third-party libraries (including native libraries) to be used along with the user code corresponding to the task. In one embodiment, the call may provide to the on-demand code execution system 110 a ZIP file containing the user code and any libraries (and/or identifications of storage locations thereof) corresponding to the task requested for execution. In some embodiments, the call includes metadata that indicates the program code of the task to be executed, the language in which the program code is written, the user associated with the call, and/or the computing resources (e.g., memory, etc.) to be reserved for executing the program code. For example, the program code of a task may be provided with the call, previously uploaded by the user, provided by the on-demand code execution system 110 (e.g., standard routines), and/or provided by third parties. In some embodiments, such resource-level constraints (e.g., how much memory is to be allocated for executing a particular user code) are specified for the particular task, and may not vary over each execution of the task. In such cases, the on-demand code execution system 110 may have access to such resource-level constraints before each individual call is received, and the individual call may not specify such resource-level constraints. In some embodiments, the call may specify other constraints such as permission data that indicates what kind of permissions or authorities that the call invokes to execute the task. Such permission data may be used by the on-demand code execution system 110 to access private resources (e.g., on a private network).
In some embodiments, a call may specify the behavior that should be adopted for handling the call. In such embodiments, the call may include an indicator for enabling one or more execution modes in which to execute the task referenced in the call. For example, the call may include a flag or a header for indicating whether the task should be executed in a debug mode in which the debugging and/or logging output that may be generated in connection with the execution of the task is provided back to the user (e.g., via a console user interface). In such an example, the on-demand code execution system 110 may inspect the call and look for the flag or the header, and if it is present, the on-demand code execution system 110 may modify the behavior (e.g., logging facilities) of the container in which the task is executed, and cause the output data to be provided back to the user. In some embodiments, the behavior/mode indicators are added to the call by the user interface provided to the user by the on-demand code execution system 110. Other features such as source code profiling, remote debugging, etc. may also be enabled or disabled based on the indication provided in a call.
In accordance with embodiments of the present disclosure, user-submitted code may correspond to an analysis function for a set of data. The function may be embodied in computer-executable code, represented by one or more analysis tasks 164 on the on-demand code execution system 110. In one embodiment, an analysis task 164 implements a reduce stage of the MapReduce programming model. The specific functionalities of a reduce stage in such a model may vary according to the data to be processed. However, in general terms, a reduce function corresponds to code to “reduce” or aggregate multiple intermediate results to an aggregate result. For example, an initial map function may correspond to code to process a corpus of words (e.g., a book or a collection of books) and generate a count of individual words (or, in some instances, specific classes of words, such as surnames) within the corpus. A corresponding reduce function can correspond to code that aggregates the counts of individual words, as produced by individual executions of the map function. Illustratively, a reduce function may take one thousand intermediate count data files, as produced by one thousand instances of the map function, and generate a single data file aggregating the counts of each word within the one thousand intermediate count data files. Where only a single reduce function is executed, the single data file can represent the result of the analysis. Where multiple reduce functions are executed, resulting in multiple outputs (each aggregating counts of words within a set of inputs), an additional reduce function can process those multiple outputs to further aggregate word counts. This process can continue until single reduce function is called, resulting in a single output file as the result of the analysis. Alternatively, each of multiple reduce functions may write output to a common location (e.g., a database) as a final output of the analysis.
Because analysis functions are often specific to the data to be processed, code corresponding to these functions may be provided by an end user requesting analysis of a corresponding data set. In some instances, the on-demand code execution system 110 may also provide one or more sets of corresponding analysis functions (e.g., corresponding to commonly desired analysis types and configured to process data within an expected format).
To enable storage of tasks, the on-demand code execution system 110 may include a task data store 160, which may correspond to a persistent or substantially persistent data store, such as a hard drive (HDD), a solid state drive (SDD), network attached storage (NAS), a tape drive, or any combination thereof. In accordance with embodiments of the present disclosure, the task data store may include one or more analysis tasks 164.
To manage requests for code execution, the frontend 120 can include an execution queue (not shown in
In addition to tasks executed based on explicit user calls and data from auxiliary services 106, the on-demand code execution system 110 may in some instances operate to trigger execution of tasks independently. For example, the on-demand code execution system 110 may operate (based on instructions from a user) to trigger execution of a task at each of a number of specified time intervals (e.g., every 10 minutes).
The frontend 120 can further includes an output interface (not shown in
In some embodiments, the on-demand code execution system 110 may include multiple frontends 120. In such embodiments, a load balancer (not shown in
The on-demand code execution system 110 further includes one or more worker managers 140 that manage the execution environments, such as virtual machine instances 150 (shown as VM instance 150A and 150B, generally referred to as a “VM”), used for servicing incoming calls to execute tasks, and that manage the memory states of execution environments. While the following will be described with reference to virtual machine instances 150 as examples of such environments, embodiments of the present disclosure may utilize other environments, such as software containers. In the example illustrated in
Although the virtual machine instances 150 are described here as being assigned to a particular task, in some embodiments, the instances may be assigned to a group of tasks, such that the instance is tied to the group of tasks and any tasks of the group can be executed within the instance. For example, the tasks in the same group may belong to the same security group (e.g., based on their security credentials) such that executing one task in a container on a particular instance 150 after another task has been executed in another container on the same instance does not pose security risks. As another example, the tasks of the group may share common dependencies, such that an environment used to execute one task of the group can be rapidly modified to support execution of another task within the group.
Once a triggering event to execute a task has been successfully processed by a frontend 120, the frontend 120 passes a request to a worker manager 140 to execute the task. In one embodiment, each frontend 120 may be associated with a corresponding worker manager 140 (e.g., a worker manager 140 co-located or geographically nearby to the frontend 120) and thus, the frontend 120 may pass most or all requests to that worker manager 140. In another embodiment, a frontend 120 may include a location selector configured to determine a worker manager 140 to which to pass the execution request. In one embodiment, the location selector may determine the worker manager 140 to receive a call based on hashing the call, and distributing the call to a worker manager 140 selected based on the hashed value (e.g., via a hash ring). Various other mechanisms for distributing calls between worker managers 140 will be apparent to one of skill in the art.
Thereafter, the worker manager 140 may modify a virtual machine instance 150 (if necessary) and execute the code of the task within the instance 150. As shown in
As noted above, tasks may be triggered for execution at the on-demand code execution system 110 based on explicit calls from user computing devices 102 (e.g., as received at the request interface 122). Alternatively or additionally, tasks may be triggered for execution at the on-demand code execution system 110 based on data retrieved from one or more auxiliary services 106, network-based data storage services 108, or the stream data processing system 170.
To facilitate interaction with auxiliary services 106, the system 110 including a polling fleet 130, which operates to poll auxiliary services 106, data storage services 108, or the stream data processing system 170 for data. Illustratively, the polling fleet 130 may include one or more computing devices (shown in
In accordance with embodiments of the present disclosure, a message stream 172 can be configured to store calls to process data items within a data set. Illustratively, each message on the messages stream may correspond to a call to execute an analysis task 164 with respect to an individual data item within the data set. Messages may be distributed among the shards 174A-N based on a partition key for the message stream 172, which key may be represented by or included within the data item of the message. In one embodiment, messages are placed onto the message stream 172 via operation of a map stage of a MapReduce model. Example embodiments for operation of such a map stage are discussed in the '997 Application, incorporated by reference above. In another embodiment, messages are placed on the stream 172 via other mechanisms, such as during operation of devices generating the data to be analyzed (e.g., in “real time”).
As shown in
While some functionalities are generally described herein with reference to an individual component of the on-demand code execution system 110 or the stream data processing system 170, other components or a combination of components may additionally or alternatively implement such functionalities. For example, while a poller device 132A may operate to poll a message shard 174 for messages, the message shards 174 may additionally or alternatively be configured to notify the on-demand code execution system 110 (e.g., the frontend) of new messages on the shard 174.
The memory 180 may contain computer program instructions (grouped as modules in some embodiments) that the processing unit 190 executes in order to implement one or more aspects of the present disclosure. The memory 180 generally includes random access memory (RAM), read only memory (ROM) and/or other persistent, auxiliary or non-transitory computer readable media. The memory 180 may store an operating system 184 that provides computer program instructions for use by the processing unit 190 in the general administration and operation of the worker manager 140. The memory 180 may further include computer program instructions and other information for implementing aspects of the present disclosure. For example, in one embodiment, the memory 180 includes a user interface unit 182 that generates user interfaces (and/or instructions therefor) for display upon a computing device, e.g., via a navigation and/or browsing interface such as a browser or application installed on the computing device. In addition, the memory 180 may include and/or communicate with one or more data repositories (not shown), for example, to access user program codes and/or libraries.
In addition to and/or in combination with the user interface unit 182, the memory 180 may include an instance allocation unit 186 and a user code execution unit 188 that may be executed by the processing unit 190. In one embodiment, the user interface unit 182, instance allocation unit 186, and user code execution unit 188 individually or collectively implement various aspects of the present disclosure, e.g., finding compute capacity (e.g., a container) to be used for executing user code, causing the user code to be loaded and executed on the container, etc. as described further below.
The instance allocation unit 186 finds the compute capacity to be used for servicing a request to execute user code. For example, the instance allocation unit 186 identifies a virtual machine instance and/or a container that satisfies any constraints specified by the request and assigns the identified virtual machine instance and/or container to the user or the request itself. The instance allocation unit 186 may perform such identification based on the programming language in which the user code is written. For example, if the user code is written in Python, and the instance allocation unit 186 may find n virtual machine instance (e.g., in the active pool 140A of
The user code execution unit 188 manages the execution of the program code specified by the request of the user once a particular virtual machine instance has been assigned to the user associated with the request and a container on the particular virtual machine instance has been assigned to the request. If the code is pre-loaded in a container on the virtual machine instance assigned to the user, the code is simply executed in the container. If the code is available via a network storage (e.g., storage service 108 of
While the instance allocation unit 186 and the user code execution unit 188 are shown in
In some embodiments, the worker manager 140 may further include components other than those illustrated in
With reference to
The interactions of
At (2), the message stream 172 (e.g., the stream data processing system 170 hosting the message stream 172) enqueues the call onto an appropriate shard 174 of the stream 172. As discussed above, the particular shard onto which the call is placed may depend for example on a partition key for the stream 172 as applied to the data item of the call. For example, the stream data processing system 170 may implement a consistent hashing mechanism (examples of which are known in the art) to select a shard 174 onto which to place the call based on an attribute of the data item which has been designated as a partition key for the stream 172. At (3), the stream 172 acknowledges to the submitting device 301 that the call has been enqueued.
In
Interactions (4) through (10) are illustrative of those which might occur to process calls from an individual shard 174 of the message stream 172 via submission of those calls to the on-demand code execution system 110. While interactions (4) through (10) are described with respect to a particular shard 174 and poller device 132, these interactions may be repeated (e.g., by different poller devices 132 operating at least partially in parallel) to process calls of each shard 174.
Specifically, at (4), a poller device 132 obtains one or more next calls from a shard 174 of the stream 172. In some instances, the poller device 132 may obtain a single call from the shard 174. In other instances, the poller device 132 may obtain multiple calls, such as a next 10, 100, or 500 calls of the shard 174. For ease of reference, the one or more next calls are referred to herein as a “batch” of calls. Thus, interactions (4) through (10) are designated in
As noted above, the shards 174 illustratively operated in a generally (and potentially strictly) first-in, first-out (“FIFO”) manner. Thus, while interactions (1) through (3) conceptually enqueue calls in a “back” of a shard 174, interaction (4) obtains calls from the “front” of the shard 174.
Thereafter, the poller device 132 enters a synchronous call loop 306, during which individual calls from a received batch are passed to the on-demand code execution system 100 for processing, and state information reflecting that processing is maintained at the poller device 132. Specifically, at (5), the poller device 132 submits a next, not-yet-processed call within a current batch to the on-demand code execution system (e.g., to the frontend 120). Illustratively, in a first instance of the loop 306, the submitted call may be the first call enqueued into the shard 174. The call may be submitted via any submission process accepted by the system 110, such as via an API call. Thereafter, at (6), the system 110 distributes the call to an execution environment, shown in
To facilitate execution of the analysis task 164, at interaction (5), the poller device 132 submits to the on-demand code execution system 110, in addition to the call, state information reflecting a current state of processing of data items within the shard 174. The poller device 132 may initialize the state information to a null value. Thus, a first call to the on-demand code execution system 110 may include the null value, or may exclude the state information. The result of the task execution, as returned at (8), can include new state information, as updated to reflect the task execution. At (9), the poller device 132 updates its current state information with the new state information (e.g., by replacing it's own state information with the new state information), which updated state information can then be passed in a next call, if interaction (5) is repeated.
As a concrete example of interactions (5) through (10), consider an implementation of the MapReduce model intended to count words within a corpus. In such a scenario, each call within a particular shard 174 may include a word of the corpus as a data item. The poller device 132 may initially set state information to a null value, indicating that no words have been counted for the shard 174. As each call is submitted to the on-demand code execution system 110, an execution environment may return state information reflecting current count values (e.g., initially null) incremented based on a word within the current call. Thus, a first call with a data item of “apple” may result in state information of “{apple: 1}”, indicating that a current count of the word “apple” is “1.” A second call may include that state information, and call for processing of the word “ball.” Execution of the analysis task 164 with respect to this call may result in state information of “{apple: 1; ball: 1}.” This process may continue, such that the state information returned by each task execution is representative of processing each call within a shard 174 of the stream. While a simplistic word count example is provided herein, one skilled in the art will appreciate that data analysis applications, in practice, may encompass a variety of highly complicated analysis.
In
In some embodiments, such as where data analysis depends on an ordering of the calls of the message stream 172, the call loop 360 may occur strictly iteratively, such that each call must be submitted to the system 110, and a result received, before the poller device 132 submits a next call to the system 110. In other instances, a poller device 132 may implement the call loop 306 at least partially in parallel, such as by submitting multiple calls to the system 110 concurrently.
As will be appreciated in view of the above, interactions (5) through (9) generally do not rely on any specific distribution of a call by the on-demand code execution system 110 to a particular execution environment. For example, each instance of call loop 306 may result in a call be distributed to a different execution environment. Beneficially, this configuration enables environments to operate statelessly, such that they need not maintain any information regarding a state of processing of the message stream 172. Stateless operation may enable the on-demand code execution system 110 to operate more efficiently, by distributing calls to environments based, for example, on computing resources available to such an environment, rather than based on whether the environment has appropriate state information. In this manner, overall efficiency is improved relative to alternative state maintenance solutions.
As discussed above, it is generally desirable that distributed systems operate resiliently, such that a failure of one or more devices in the system does not significantly impact operation of the system as a whole. In the present model, it is assumed that the message stream 172 operates resiliently under most failure scenarios, such as by providing redundant storage of messages enqueued on a message stream. It is similarly assumed that the system 110 as a whole operates resiliently under most failure scenarios, such as by providing multiple execution environments on different physical devices. It is generally not necessary that each execution environment guarantee resiliency, as a poller device 132 can be configured to retry any failed calls to execute an analysis task 164, which the system 110 may illustratively distribute to a different (non-failed) environment. Indeed, it may be desirable to make little or no effort to provide resiliency of individual execution environments on the system 110, as providing resiliency generally increases the computing resources required to provide such environments.
Given the assumed resiliency of the message stream 172 and the on-demand code execution system, it may be desirable for the poller device 132 to also operate resiliency, such that a data analysis task can proceed even under a wide variety of failure scenarios. To provide such resiliency, the poller device 132 may periodically or occasionally write current state information, reflecting a state of processing a shard 174 of a stream 172, to a resilient data store, such as a network data storage service 108. It is generally desirable for such writes to occur as infrequently as feasible, as these writes are often “heavy weight” operations. In the illustrative interactions of
Should the poller fleet 130 detect that a poller device 132 has failed, a new poller device 132 may be created, and initialized with the latest state information written to the data storage service 108. The new poller device 132 may then retrieve a next batch of calls (e.g., the next messages of a shard 174 that are not reflected in the state information as having been processed), effectively resuming operation of the failed poller device 132 and providing resilient operation of the poller devices 132. Because the frequency of writes to the data storage service 108 can be controlled based on a number of calls in each instance of the batch loop 304 (a “batch size”), operation of the poller device 132 may be tuned (e.g., by an administrator or end user) based on desired operation. For example, a large batch size might result in less frequent external writes, reducing computing resource usage of the poller device 132, but increasing the amount of calls that would need to be repeated under a failure scenario. Conversely, a small batch size might result in more frequent external writes, but reduce the calls repeated under failures. In either instance, embodiments of the present disclosure enable this tradeoff to be selected independently from operation of other components, such as the on-demand code execution system 110.
A beneficial aspect of the stream data processing system 170 described herein is an ability to scale a capacity of a message stream 172 based on the volume of messages therein, thus enabling variability in the amount of parallelization for a data analysis task, even during operation of the data analysis. For example, an increased amount of calls being enqueued onto a message stream 172 may cause the number of shards 174 of the stream to increase, thus increasing capacity of the stream 172. Conversely, a decreased amount of calls may cause the number of shards 174 to decrease. As poller devices 132 can be configured with a one-to-one correspondence with shards 174, poller devices 132 may be configured to transfer state information appropriately under these conditions to prevent interruption of data analysis. Example illustrations of how state information may be passed between poller devices 132 are show in
Specifically,
In
In
To address this scenario, each poller device 132 can be configured, on halting of the shard 174 corresponding to the device, to make their current state information available to poller devices 132 corresponding to “child” shards 174. Illustratively, each poller device 132 can be configured to write their state information to a data storage service 108. On initializing, each poller device 132 can be configured to determine whether a corresponding shard 174 is a child of a prior shard 174. Illustratively, the stream data processing system 170 may make this information available to the poller fleet 130. If the poller device 132 corresponding to a child shard 174, the poller device 132 can be configured to initialize as its state information the saved state information of the poller device 132 corresponding to the parent shard 174. Thus, in
As may be seen in
This redundancy is addressed by configuring the poller devices 132, or any other device wishing to obtained overall results of processing the calls in a stream 172, to deduplicate the values reflected in state information of various poller devices 132. Specifically, because each data item tuple includes a last location in the stream 172 at which a value for the data item was updated, deduplication can occur by designating a latest location tuple as authoritative for the data item.
An illustrative example of deduplicating state information is depicted in
The illustration of
To eliminate the redundant information, the poller device 132D is configured to deduplicate the state information received for each parent shard, by taking as authoritative state information with a latest positional value in the message stream 172, as reflected in
While simplified examples are described above, one skilled in the art will appreciate in view of the present disclosure that the sharing of state information between polling devices 132, and deduplication of such information, can facilitate any number of modifications to the number of shards 174 of a stream 172, while enabling a polling device 132 to maintain accurate values at least with respect to data items partitioned to their corresponding shard 174. Moreover, while one technique for deduplicating state information is discussed above, others are contemplated within the present disclosure. For example, in circumstances where a value of a data item is known to only change in a single direction (e.g., a count that can only increase), duplicative values for the data item may be deduplicate by assuming as authoritative a furthest value in that direction (e.g., a largest count). In this way, deduplication can occur without requiring a position for each value to be maintained. As another example, in addition or as an alternative to position information within a shard, other ordering information may be included for individual data items being processed. For example, state information may include an absolute position identifier across a message stream (e.g., across multiple shards), or may include timestamp information corresponding to an ordering messages. The timestamp information may include, for example, a time at which a message was enqueued into the message stream. Thus, a higher timestamp value for state information may identify that the state information represents a state of processing a later message within the message stream, and can be considered authoritative over state information representing a state of processing an earlier message in the message stream.
With reference to
The routine 600 begins at block 602, where the poller device 132 obtains relevant prior state information related to the shard 174. Illustratively, if a prior poller device 132 was processing the shard 174 and failed, the current poller device 132 may obtain the state information of the prior poller device 132, and resume processing the shard 174 from a last position reflected in the state information of the prior poller device 132. As a further illustration, if the shard 174 is a child of one or more prior shards 174, the poller device 132 may obtain and adopt state information for poller devices 132 corresponding to parents of the shard 174. Illustratively, on initialization, the poller device 132 may query the stream data processing system 170 to identify whether the shard 174 has any parent shards 174. If so, the poller device 132 may obtain state information for those parent shards 174, such as from data storage service 108. If no parent shards 174 exist, the poller device 132 can initialize state information to a null value or predefined values established on configuration of the device 132.
At block 604, the poller device 132 deduplicates the state information of the parent shards, if any duplicative information exists. As noted above, deduplication may include taking as authoritative for a given data item a value associated with a furthest position in the message stream 172.
At block 606, the poller device 132 enters batch loop 304, as described above with reference to
At block 608, the poller device 132 enters the synchronous call loop 306, and submits a next call from a current batch to the on-demand code execution system 110, along with the current state information maintained at the poller device 132. As noted above, passing of the state information enables the call to be routed by the system 110 to any appropriate execution environment. The call may be submitted, for example, as a synchronous HTTP request.
At block 610, the poller device 132 obtains a response to the call, including new state information. The new state information illustratively represents the state information passed within the call, as updated by execution of an analysis task 164 on the on-demand code execution system 110. The poller device 132 may thus consider the new state information authoritative, as reflecting a state of processing of data items identified in the shard.
At block 612, the routine 600 returns to block 608 if additional calls exist within the batch that have not been submitted to the on-demand code execution system. Alternatively, the routine 600 proceeds to block 614. At block 614, the poller device 132 checkpoints its state, such as by writing the current state information to a resilient network data storage service. As noted above, should the poller device 132 fail during implementation of the routine 600, a subsequent poller device 132 may utilize the state information to resume processing of the shard 174.
At block 616, the routine 600 returns to block 606 if additional calls exist within the shard 174. Alternatively, if all calls within the shard have been processed, processing of the shard can be considered complete, and the last written state information of the poller device 132 will be representative of processing of all data items on the shard 174. Thus, the routine 600 may end at block 618.
All of the methods and processes described above may be embodied in, and fully automated via, software code modules executed by one or more computers or processors. The code modules may be stored in any type of non-transitory computer-readable medium or other computer storage device. Some or all of the methods may alternatively be embodied in specialized computer hardware.
Conditional language such as, among others, “can,” “could,” “might” or “may,” unless specifically stated otherwise, are otherwise understood within the context as used in general to present that certain embodiments include, while other embodiments do not include, certain features, elements and/or steps. Thus, such conditional language is not generally intended to imply that features, elements and/or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without user input or prompting, whether these features, elements and/or steps are included or are to be performed in any particular embodiment.
Disjunctive language such as the phrase “at least one of X, Y or Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y or Z, or any combination thereof (e.g., X, Y and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y or at least one of Z to each be present.
Unless otherwise explicitly stated, articles such as ‘a’ or ‘an’ should generally be interpreted to include one or more described items. Accordingly, phrases such as “a device configured to” are intended to include one or more recited devices. Such one or more recited devices can also be collectively configured to carry out the stated recitations. For example, “a processor configured to carry out recitations A, B and C” can include a first processor configured to carry out recitation A working in conjunction with a second processor configured to carry out recitations B and C.
Any routine descriptions, elements or blocks in the flow diagrams described herein and/or depicted in the attached figures should be understood as potentially representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or elements in the routine. Alternate implementations are included within the scope of the embodiments described herein in which elements or functions may be deleted, or executed out of order from that shown or discussed, including substantially synchronously or in reverse order, depending on the functionality involved as would be understood by those skilled in the art.
It should be emphasized that many variations and modifications may be made to the above-described embodiments, the elements of which are to be understood as being among other acceptable examples. All such modifications and variations are intended to be included herein within the scope of this disclosure and protected by the following claims.
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| Number | Date | Country | |
|---|---|---|---|
| 20200104198 A1 | Apr 2020 | US |
