Patent Publication Number: US-2023147989-A1

Title: Configuring managed event tables using execution node processes

Description:
PRIORITY CLAIM 
     This application is a Continuation of U.S. patent application Ser. No. 17/649,571, filed Feb. 1, 2022 and entitled “MANAGED EVENT TABLES IN A DATABASE SYSTEM,” which claims the benefit of priority to U.S. Provisional Patent Application 63/263,737, filed Nov. 8, 2021, and entitled “MANAGED EVENT TABLES,” which applications are incorporated herein by reference in their entireties. 
    
    
     TECHNICAL FIELD 
     Embodiments of the disclosure relate generally to databases and, more specifically, to managed event tables for logging data from User-Defined Functions (UDFs) executing in a secure environment. 
     BACKGROUND 
     Databases are widely used for data storage and access in computing applications. A goal of database storage is to provide enormous sums of information in an organized manner so that it can be accessed, managed, updated, and shared. In a database, data may be organized into rows, columns, and tables. Databases are used by various entities and companies for storing information that may need to be accessed or analyzed. 
     Cloud-based data warehouses and other cloud database systems or data platforms sometimes provide support for UDFs that enable such systems to perform operations that are not available through the built-in, system-defined functions. However, for mitigating security risks, security mechanisms associated with user code executing on such systems can be used. 
     Some programs execute on multi-tenant cloud environments, and their programs include UDFs. To provide security, the programs can be executed in safe environments. However, by executing in the safe environments, the UDFs are not able to log data. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present disclosure will be understood more fully from the detailed description given below and from the accompanying drawings of various embodiments of the disclosure. 
         FIG.  1    illustrates an example computing environment that includes a network-based database system in communication with a cloud storage platform, in accordance with some embodiments of the present disclosure. 
         FIG.  2    is a block diagram illustrating the components of a compute service manager, in accordance with some embodiments of the present disclosure. 
         FIG.  3    is a block diagram illustrating components of an execution platform, in accordance with some embodiments of the present disclosure. 
         FIG.  4    is a computing environment conceptually illustrating a computing architecture executing a UDF by a process running on a given execution node of the execution platform, in accordance with some embodiments of the present disclosure. 
         FIG.  5    shows an example of a computing architecture for providing managed event tables for logging data from UDFs executing in a secure environment, in accordance with some embodiments of the present disclosure. 
         FIG.  6    is a flow diagram illustrating operations of a database system in performing a method for configuring managed event tables, in accordance with some embodiments of the present disclosure. 
         FIG.  7    illustrates a diagrammatic representation of a machine in the form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed herein, in accordance with some embodiments of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Reference will now be made in detail to specific example embodiments for carrying out the inventive subject matter. Examples of these specific embodiments are illustrated in the accompanying drawings, and specific details are outlined in the following description to provide a thorough understanding of the subject matter. It will be understood that these examples are not intended to limit the scope of the claims to the illustrated embodiments. On the contrary, they are intended to cover such alternatives, modifications, and equivalents as may be included within the scope of the disclosure. 
     In the present disclosure, physical units of data that are stored in a data platform and that make up the content of, e.g., database tables in customer accounts are referred to as micro-partitions. In different implementations, a data platform may store metadata in micro-partitions as well. The term “micro-partitions” is distinguished in this disclosure from the term “files,” which, as used herein, refers to data units such as image files (e.g., Joint Photographic Experts Group (JPEG) files, Portable Network Graphics (PNG) files, etc.), video files (e.g., Moving Picture Experts Group (MPEG) files, MPEG-4 (MP4) files, Advanced Video Coding High Definition (AVCHD) files, etc.), Portable Document Format (PDF) files, documents that are formatted to be compatible with one or more word-processing applications, documents that are formatted to be compatible with one or more spreadsheet applications, and/or the like. If stored internal to the data platform, a given file is referred to herein as an “internal file” and may be stored in (or at, or on, etc.) what is referred to herein as an “internal storage location.” If stored external to the data platform, a given file is referred to herein as an “external file” and is referred to as being stored in (or at, or on, etc.) what is referred to herein as an “external storage location.” These terms are further discussed below. 
     Computer-readable files come in several varieties, including unstructured files, semi-structured files, and structured files. These terms may mean different things to different people. As used herein, examples of unstructured files include image files, video files, PDFs, audio files, and the like; examples of semi-structured files include JavaScript Object Notation (JSON) files, eXtensible Markup Language (XML) files, and the like; and examples of structured files include Variant Call Format (VCF) files, Keithley Data File (KDF) files, Hierarchical Data Format version 5 (HDF5) files, and the like. As known to those of skill in the relevant arts, VCF files are often used in the bioinformatics field for storing, e.g., gene-sequence variations, KDF files are often used in the semiconductor industry for storing, e.g., semiconductor-testing data, and HDF5 files are often used in industries such as the aeronautics industry, in that case for storing data such as aircraft-emissions data. Numerous other example unstructured-file types, semi-structured-file types, and structured-file types, as well as example uses thereof, could certainly be listed here as well and will be familiar to those of skill in the relevant arts. Different people of skill in the relevant arts may classify types of files differently among these categories and may use one or more different categories instead of or in addition to one or more of these. 
     Data platforms are widely used for data storage and data access in computing and communication contexts. Concerning architecture, a data platform could be an on-premises data platform, a network-based data platform (e.g., a cloud-based data platform), a combination of the two, and/or include another type of architecture. Concerning the type of data processing, a data platform could implement online analytical processing (OLAP), online transactional processing (OLTP), a combination of the two, and/or another type of data processing. Moreover, a data platform could be or include a relational database management system (RDBMS) and/or one or more other types of database management systems. 
     In a typical implementation, a data platform may include one or more databases that are respectively maintained in association with any number of customer accounts (e.g., accounts of one or more data providers), as well as one or more databases associated with a system account (e.g., an administrative account) of the data platform, one or more other databases used for administrative purposes, and/or one or more other databases that are maintained in association with one or more other organizations and/or for any other purposes. A data platform may also store metadata (e.g., account object metadata) in association with the data platform in general and in association with, for example, particular databases and/or particular customer accounts as well. Users and/or executing processes that are associated with a given customer account may, via one or more types of clients, be able to cause data to be ingested into the database, and may also be able to manipulate the data, add additional data, remove data, run queries against the data, generate views of the data, and so forth. As used herein, the terms “account object metadata” and “account object” are used interchangeably. 
     In an implementation of a data platform, a given database (e.g., a database maintained for a customer account) may reside as an object within, e.g., a customer account, which may also include one or more other objects (e.g., users, roles, grants, shares, warehouses, resource monitors, integrations, network policies, and/or the like). Furthermore, a given object such as a database may itself contain one or more objects such as schemas, tables, materialized views, and/or the like. A given table may be organized as a collection of records (e.g., rows) so that each includes a plurality of attributes (e.g., columns). In some implementations, database data is physically stored across multiple storage units, which may be referred to as files, blocks, partitions, micro-partitions, and/or by one or more other names. In many cases, a database on a data platform serves as a backend for one or more applications that are executing on one or more application servers. 
     In computer security, a sandbox (e.g., sandbox environment) is a security mechanism for separating running programs, usually to prevent system failures or prevent exploitation of software vulnerabilities. A sandbox can be used to execute untested or untrusted programs or code, possibly from unverified or untrusted third parties, suppliers, users, or web sites, without risking harm to the host machine or operating system. A sandbox can provide a tightly controlled set of resources for guest programs to run in, such as storage and memory scratch space. Network access, the ability to inspect the host system or read from input devices can be disallowed or restricted. UDFs typically can run in a sandbox environment. 
     Logging from UDFs is important for debuggability during development and troubleshooting issues in production. Some existing solutions recommend users to log to tables from the UDF, but this approach is not scalable and a UDF typically does not allow running SQL queries. Moreover, logging to tables is not the normal procedure that programmers are accustomed to when using programming languages (e.g., Java, Python, etc.). 
     A log may be a record of transactions or activities that take place on a computer system. In one aspect, systems and methods are presented to provide managed event tables for generating log data from UDFs executing in a secure sandbox environment. Log data is treated as sensitive and handled with appropriate security policies, as with any other user data in the system. As used herein, the terms “event table” (ET) and “managed event table” (MET) are interchangeable and indicate a table used as a destination for logs/metrics associated with UDF execution. For example, a MET is a type of table in an event-driven architecture that is provided as a destination for log data generated from UDFs executing in a sandboxed environment. Users can query a MET in the same way as a regular table. In some embodiments, a MET can be attached to an object (e.g., an Account object, a Database object, a Schema object, etc.) and can be used as the destination for logs/metrics originating from objects under that parent object. In some embodiments, a MET can be attached to an Account object, with the parent object of the MET being a schema object. Additional configurations associated with a MET are further discussed at least in connection with  FIGS.  4 - 6   . 
     The various embodiments that are described herein are described with reference where appropriate to one or more of the various figures. An example computing environment including a compute service manager with a MET manager configured to perform MET configuration functions are discussed in connection with  FIGS.  1 - 3   . An example MET manager and MET configuration functions are discussed in connection with  FIGS.  4 - 6   . A more detailed discussion of example computing devices that may be used with the disclosed techniques is provided in connection with  FIG.  7   . 
       FIG.  1    illustrates an example computing environment  100  that includes a database system in the example form of a network-based database system  102 , in accordance with some embodiments of the present disclosure. To avoid obscuring the inventive subject matter with unnecessary detail, various functional components that are not germane to conveying an understanding of the inventive subject matter have been omitted from  FIG.  1   . However, a skilled artisan will readily recognize that various additional functional components may be included as part of the computing environment  100  to facilitate additional functionality that is not specifically described herein. In other embodiments, the computing environment may comprise another type of network-based database system or a cloud data platform. For example, in some aspects, the computing environment  100  may include a cloud computing platform  101  with the network-based database system  102 , and a storage platform  104  (also referred to as a cloud storage platform). The cloud computing platform  101  provides computing resources and storage resources that may be acquired (purchased) or leased and configured to execute applications and store data. 
     The cloud computing platform  101  may host a cloud computing service  103  that facilitates storage of data on the cloud computing platform  101  (e.g., data management and access) and analysis functions (e.g. SQL queries, analysis), as well as other processing capabilities (e.g., configuring replication group objects as described herein). The cloud computing platform  101  may include a three-tier architecture: data storage (e.g., storage platforms  104  and  122 ), an execution platform  110  (e.g., providing query processing), and a compute service manager  108  providing cloud services. 
     It is often the case that organizations that are customers of a given data platform also maintain data storage (e.g., a data lake) that is external to the data platform (i.e., one or more external storage locations). For example, a company could be a customer of a particular data platform and also separately maintain storage of any number of files—be they unstructured files, semi-structured files, structured files, and/or files of one or more other types—on, as examples, one or more of their servers and/or on one or more cloud-storage platforms such as AMAZON WEB SERVICES™ (AWS™), MICROSOFT® AZURE®, GOOGLE CLOUD PLATFORM™, and/or the like. The customer&#39;s servers and cloud-storage platforms are both examples of what a given customer could use as what is referred to herein as an external storage location. The cloud computing platform  101  could also use a cloud-storage platform as what is referred to herein as an internal storage location concerning the data platform. 
     From the perspective of the network-based database system  102  of the cloud computing platform  101 , one or more files that are stored at one or more storage locations are referred to herein as being organized into one or more of what is referred to herein as either “internal stages” or “external stages.” Internal stages are stages that correspond to data storage at one or more internal storage locations, and where external stages are stages that correspond to data storage at one or more external storage locations. In this regard, external files can be stored in external stages at one or more external storage locations, and internal files can be stored in internal stages at one or more internal storage locations, which can include servers managed and controlled by the same organization (e.g., company) that manages and controls the data platform, and which can instead or in addition include data-storage resources operated by a storage provider (e.g., a cloud-storage platform) that is used by the data platform for its “internal” storage. The internal storage of a data platform is also referred to herein as the “storage platform” of the data platform. It is further noted that a given external file that given customer stores at a given external storage location may or may not be stored in an external stage in the external storage location—i.e., in some data-platform implementations, it is a customer&#39;s choice whether to create one or more external stages (e.g., one or more external-stage objects) in the customer&#39;s data-platform account as an organizational and functional construct for conveniently interacting via the data platform with one or more external files. 
     As shown, the network-based database system  102  of the cloud computing platform  101  is in communication with the cloud storage platforms  104  and  122  (e.g., AWS®, Microsoft Azure Blob Storage®, or Google Cloud Storage). The network-based database system  102  is a network-based system used for reporting and analysis of integrated data from one or more disparate sources including one or more storage locations within the cloud storage platform  104 . The cloud 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 database system  102 . 
     The network-based database system  102  comprises a compute service manager  108 , an execution platform  110 , and one or more metadata databases  112 . The network-based database system  102  hosts and provides data reporting and analysis services to multiple client accounts. 
     The compute service manager  108  coordinates and manages operations of the network-based database system  102 . The compute service manager  108  also performs query optimization and compilation as well as managing clusters of computing services that provide compute resources (also referred to as “virtual warehouses”). The compute service manager  108  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  108 . 
     The compute service manager  108  is also in communication with a client device  114 . The client device  114  corresponds to a user of one of the multiple client accounts supported by the network-based database system  102 . A user may utilize the client device  114  to submit data storage, retrieval, and analysis requests to the compute service manager  108 . Client device  114  (also referred to as remote computing device or user device  114 ) may include one or more of a laptop computer, a desktop computer, a mobile phone (e.g., a smartphone), a tablet computer, a cloud-hosted computer, cloud-hosted serverless processes, or other computing processes or devices may be used (e.g., by a data provider) to access services provided by the cloud computing platform  101  (e.g., cloud computing service  103 ) by way of a network  106 , such as the Internet or a private network. A data consumer  115  can use another computing device to access data of the data provider (e.g., data obtained via the client device  114 ). 
     In the description below, actions are ascribed to users, particularly consumers and providers. Such actions shall be understood to be performed concerning client device (or devices)  114  operated by such users. For example, notification to a user may be understood to be a notification transmitted to client device  114 , input or instruction from a user may be understood to be received by way of the client device  114 , and interaction with an interface by a user shall be understood to be interaction with the interface on the client device  114 . In addition, database operations (joining, aggregating, analysis, etc.) ascribed to a user (consumer or provider) shall be understood to include performing such actions by the cloud computing service  103  in response to an instruction from that user. 
     In some embodiments, the client device  114  is configured with an application connector  128 , which may be configured to perform managed event table (MET) configuration functions  130 . For example, client device  114  can be associated with a data provider using the cloud computing service  103  of the network-based database system  102 . In some embodiments, MET configuration functions  130  include generating MET configurations  138  for communication to the network-based database system  102  via the network  106 . For example, MET configurations  138  can be communicated to the MET manager  132  within the compute service manager  108 . The MET manager  132  is configured to perform MET configuration functions which can be based (at least partially) on one or more of the MET configurations  138 . For example, the MET manager  132  can configure MET  134  based on the MET configurations  138 , with the MET  134  used in connection with logging data associated with a UDF execution. In some aspects, the UDF execution can be configured using the UDF client  136  which is part of the execution platform  110 . 
     The compute service manager  108  is also coupled to one or more metadata databases  112  that store metadata about various functions and aspects associated with the network-based database system  102  and its users. For example, a metadata database  112  may include a summary of data stored in remote data storage systems as well as data available from a local cache. Additionally, a metadata database  112  may include information regarding how data is organized in remote data storage systems (e.g., the cloud storage platform  104 ) and the local caches. Information stored by a metadata database  112  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. In some embodiments, metadata database  112  is configured to store account object metadata (e.g., account objects used in connection with a replication group object). Additionally, the metadata database  112  can also store the MET  134 . In some embodiments, MET  134  can be stored in storage platform  104  or cloud-storage platforms  122 . 
     The compute service manager  108  is further coupled to the execution platform  110 , which provides multiple computing resources that execute various data storage and data retrieval tasks. As illustrated in  FIG.  3   , the execution platform  110  comprises a plurality of compute nodes. The execution platform  110  is coupled to storage platform  104  and cloud storage platforms  122 . The storage platform  104  comprises multiple data storage devices  120 - 1  to  120 -N. In some embodiments, the data storage devices  120 - 1  to  120 -N are cloud-based storage devices located in one or more geographic locations. For example, the data storage devices  120 - 1  to  120 -N may be part of a public cloud infrastructure or a private cloud infrastructure. The data storage devices  120 - 1  to  120 -N may be hard disk drives (HDDs), solid-state drives (SSDs), storage clusters, Amazon S3™ storage systems, or any other data-storage technology. Additionally, the cloud storage platform  104  may include distributed file systems (such as Hadoop Distributed File Systems (HDFS)), object storage systems, and the like. In some embodiments, at least one internal stage  126  may reside on one or more of the data storage devices  120 - 1 - 120 -N, and at least one external stage  124  may reside on one or more of the cloud storage platforms  122 . 
     In some embodiments, as mentioned above, the compute service manager  108  includes the MET manager  132 . The MET manager  132  comprises suitable circuitry, interfaces, logic, and/or code and is configured to perform the disclosed functionalities associated with configuration and use of METs in connection with log data (or another type of data) associated with UDF execution. For example and as discussed in connection with  FIGS.  4 - 5   , MET manager  132  can configure generation, processing (e.g., via MET components  413 ), and ingestion (e.g., via ingestion component  502 ) of log data (e.g., log data  508 ) into MET  134 . Additional functionalities associated with MET configuration are discussed in connection with  FIGS.  4 - 6   . 
     In some embodiments, communication links between elements of the computing environment  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 alternate embodiments, these communication links are implemented using any type of communication medium and any communication protocol. 
     The compute service manager  108 , metadata database(s)  112 , execution platform  110 , and storage platform  104 , are shown in  FIG.  1    as individual discrete components. However, each of the compute service manager  108 , metadata database(s)  112 , execution platform  110 , and storage platform  104  may be implemented as a distributed system (e.g., distributed across multiple systems/platforms at multiple geographic locations). Additionally, each of the compute service manager  108 , metadata database(s)  112 , execution platform  110 , and storage platform  104  can be scaled up or down (independently of one another) depending on changes to the requests received and the changing needs of the network-based database system  102 . Thus, in the described embodiments, the network-based database system  102  is dynamic and supports regular changes to meet the current data processing needs. 
     During a typical operation, the network-based database system  102  processes multiple jobs determined by the compute service manager  108 . These jobs are scheduled and managed by the compute service manager  108  to determine when and how to execute the job. For example, the compute service manager  108  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  108  may assign each of the multiple discrete tasks to one or more nodes of the execution platform  110  to process the task. The compute service manager  108  may determine what data is needed to process a task and further determine which nodes within the execution platform  110  are best suited to process the task. Some nodes may have already cached the data needed to process the task and, therefore, be a good candidate for processing the task. Metadata stored in a metadata database  112  assists the compute service manager  108  in determining which nodes in the execution platform  110  have already cached at least a portion of the data needed to process the task. One or more nodes in the execution platform  110  process the task using data cached by the nodes and, if necessary, data retrieved from the cloud storage platform  104 . It is desirable to retrieve as much data as possible from caches within the execution platform  110  because the retrieval speed is typically much faster than retrieving data from the cloud storage platform  104 . 
     As shown in  FIG.  1   , the cloud computing platform  101  of the computing environment  100  separates the execution platform  110  from the storage platform  104 . In this arrangement, the processing resources and cache resources in the execution platform  110  operate independently of the data storage devices  120 - 1  to  120 -N in the cloud storage platform  104 . Thus, the computing resources and cache resources are not restricted to specific data storage devices  120 - 1  to  120 -N. Instead, all computing resources and all cache resources may retrieve data from, and store data to, any of the data storage resources in the cloud storage platform  104 . 
       FIG.  2    is a block diagram illustrating components of the compute service manager  108 , in accordance with some embodiments of the present disclosure. As shown in  FIG.  2   , the compute service manager  108  includes an access manager  202  and a credential management system (or key manager)  204  coupled to an access metadata database  206 , which is an example of the metadata database(s)  112 . Access manager  202  handles authentication and authorization tasks for the systems described herein. The credential management system  204  facilitates the use of remotely stored credentials to access external resources such as data resources in a remote storage device. As used herein, the remote storage devices may also be referred to as “persistent storage devices” or “shared storage devices.” For example, the credential management system  204  may create and maintain remote credential store definitions and credential objects (e.g., in the access metadata database  206 ). A remote credential store definition identifies a remote credential store and includes access information to access security credentials from the remote credential store. A credential object identifies one or more security credentials using non-sensitive information (e.g., text strings) that are to be retrieved from a remote credential store for use in accessing an external resource. When a request invoking an external resource is received at run time, the credential management system  204  and access manager  202  use information stored in the access metadata database  206  (e.g., a credential object and a credential store definition) to retrieve security credentials used to access the external resource from a remote credential store. 
     A request processing service  208  manages received data storage requests and data retrieval requests (e.g., jobs to be performed on database data). For example, the request processing service  208  may determine the data to process a received query (e.g., a data storage request or data retrieval request). The data may be stored in a cache within the execution platform  110  or in a data storage device in storage platform  104 . 
     A management console service  210  supports access to various systems and processes by administrators and other system managers. Additionally, the management console service  210  may receive a request to execute a job and monitor the workload on the system. 
     The compute service manager  108  also includes a job compiler  212 , a job optimizer  214 , and a job executor  216 . The job compiler  212  parses a job into multiple discrete tasks and generates the execution code for each of the multiple discrete tasks. The job optimizer  214  determines the best method to execute the multiple discrete tasks based on the data that needs to be processed. Job optimizer  214  also handles various data pruning operations and other data optimization techniques to improve the speed and efficiency of executing the job. The job executor  216  executes the execution code for jobs received from a queue or determined by the compute service manager  108 . 
     A job scheduler and coordinator  218  sends received jobs to the appropriate services or systems for compilation, optimization, and dispatch to the execution platform  110 . For example, jobs may be prioritized and then processed in that prioritized order. In an embodiment, the job scheduler and coordinator  218  determines a priority for internal jobs that are scheduled by the compute service manager  108  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  110 . In some embodiments, the job scheduler and coordinator  218  identifies or assigns particular nodes in the execution platform  110  to process particular tasks. A virtual warehouse manager  220  manages the operation of multiple virtual warehouses implemented in the execution platform  110 . For example, the virtual warehouse manager  220  may generate query plans for executing received queries. 
     Additionally, the compute service manager  108  includes a configuration and metadata manager  222 , which manages the information related to the data stored in the remote data storage devices and the local buffers (e.g., the buffers in execution platform  110 ). The configuration and metadata manager  222  uses metadata to determine which data files need to be accessed to retrieve data for processing a particular task or job. A monitor and workload analyzer  224  oversees processes performed by the compute service manager  108  and manages the distribution of tasks (e.g., workload) across the virtual warehouses and execution nodes in the execution platform  110 . The monitor and workload analyzer  224  also redistributes tasks, as needed, based on changing workloads throughout the network-based database 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  110 . The configuration and metadata manager  222  and the monitor and workload analyzer  224  are coupled to a data storage device  226 . The data storage device  226  in  FIG.  2    represents any data storage device within the network-based database system  102 . For example, data storage device  226  may represent buffers in execution platform  110 , storage devices in storage platform  104 , or any other storage device. 
     As described in embodiments herein, the compute service manager  108  validates all communication from an execution platform (e.g., the execution platform  110 ) to validate that the content and context of that communication are consistent with the task(s) known to be assigned to the execution platform. For example, an instance of the execution platform executing a query A should not be allowed to request access to data-source D (e.g., data storage device  226 ) that is not relevant to query A. Similarly, a given execution node (e.g., execution node  302 - 1 ) may need to communicate with another execution node (e.g., execution node  302 - 2 ), and should be disallowed from communicating with a third execution node (e.g., execution node  312 - 1 ) and any such illicit communication can be recorded (e.g., in a log or other location). Also, the information stored on a given execution node is restricted to data relevant to the current query and any other data is unusable, rendered so by destruction or encryption where the key is unavailable. 
     As previously mentioned, the compute service manager  108  includes the MET manager  132  configured to perform the disclosed functionalities associated with the configuration (e.g., using MET configurations  138 ) and use of one or more METS (e.g., MET  134 ) for storage and access of log data associated with UDF execution. Additional functionalities performed by the MET manager  132  are discussed in connection with at least  FIGS.  4 - 7   . 
       FIG.  3    is a block diagram illustrating components of the execution platform  110 , in accordance with some embodiments of the present disclosure. As shown in  FIG.  3   , the execution platform  110  includes multiple virtual warehouses, including virtual warehouse  1  (or  301 - 1 ), virtual warehouse  2  (or  301 - 2 ), and virtual warehouse N (or  301 -N). Each virtual warehouse includes multiple execution nodes that each include a data cache and a processor. The virtual warehouses can execute multiple tasks in parallel by using multiple execution nodes. As discussed herein, the execution platform  110  can add new virtual warehouses and drop existing virtual warehouses in real-time based on the current processing needs of the systems and users. This flexibility allows the execution platform  110  to quickly deploy large amounts of computing resources when needed without being forced to continue paying for those computing resources when they are no longer needed. All virtual warehouses can access data from any data storage device (e.g., any storage device in the cloud storage platform  104 ). 
     Although each virtual warehouse shown in  FIG.  3    includes three execution nodes, a particular virtual warehouse may include any number of execution nodes. Further, the number of execution nodes in a virtual warehouse is dynamic, such that new execution nodes are created when additional demand is present, and existing execution nodes are deleted when they are no longer necessary. 
     Each virtual warehouse is capable of accessing any of the data storage devices  120 - 1  to  120 -N shown in  FIG.  1   . Thus, the virtual warehouses are not necessarily assigned to a specific data storage device  120 - 1  to  120 -N and, instead, can access data from any of the data storage devices  120 - 1  to  120 -N within the cloud storage platform  104 . Similarly, each of the execution nodes shown in  FIG.  3    can access data from any of the data storage devices  120 - 1  to  120 -N. In some embodiments, a particular virtual warehouse or a particular execution node may be temporarily assigned to a specific data storage device, but the virtual warehouse or execution node may later access data from any other data storage device. 
     In the example of  FIG.  3   , virtual warehouse  1  includes three execution nodes  302 - 1 ,  302 - 2 , and  302 -N. Execution node  302 - 1  includes a cache  304 - 1  and a processor  306 - 1 . Execution node  302 - 2  includes a cache  304 - 2  and a processor  306 - 2 . Execution node  302 -N includes a cache  304 -N and a processor  306 -N. Each execution node  302 - 1 ,  302 - 2 , and  302 -N is associated with processing one or more data storage and/or data retrieval tasks. For example, a virtual warehouse may handle data storage and data retrieval tasks associated with an internal service, such as a clustering service, a materialized view refresh service, a file compaction service, a storage procedure service, or a file upgrade service. In other implementations, a particular virtual warehouse may handle data storage and data retrieval tasks associated with a particular data storage system or a particular category of data. 
     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  FIG.  3    are stateless with respect to the data being cached by the execution nodes. For example, these execution nodes do not store or otherwise maintain state information about the execution node or the data being cached by a particular execution node. Thus, in the event of an execution node failure, the failed node can be transparently replaced by another node. Since there is no state information associated with the failed execution node, the new (replacement) execution node can easily replace the failed node without concern for recreating a particular state. 
     Although the execution nodes shown in  FIG.  3    each includes one data cache and one processor, alternative embodiments may include execution nodes containing any number of processors and any number of caches. Additionally, the caches may vary in size among the different execution nodes. The caches shown in  FIG.  3    store, in the local execution node, data that was retrieved from one or more data storage devices in the cloud storage platform  104 . 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 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 cache resources and computing resources associated with a particular execution node are determined when the execution node is created, based on the expected tasks to be performed by the execution node. 
     Additionally, the cache resources and computing resources associated with a particular execution node may change over time based on changing tasks performed by the execution node. 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 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  120 - 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  110 , virtual warehouses  1 , N 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  FIG.  3    as having multiple execution nodes. The multiple execution nodes associated with each virtual warehouse may be implemented using multiple computing systems at multiple geographic locations. For example, an instance of virtual warehouse  1  implements execution nodes  302 - 1  and  302 - 2  on one computing platform at a geographic location, and execution node  302 -N at a different computing platform at another geographic location. Selecting particular computing systems to implement an execution node may depend on various factors, such as the level of resources needed for a particular execution node (e.g., processing resource requirements and cache requirements), the resources available at particular computing systems, communication capabilities of networks within a geographic location or between geographic locations, and which computing systems are already implementing other execution nodes in the virtual warehouse. 
     Execution platform  110  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  110  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 the cloud storage platform  104 , but each virtual warehouse has its 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. 
       FIG.  4    is a computing environment  400  conceptually illustrating a computing architecture executing a UDF by a process running on a given execution node of the execution platform  110 , in accordance with some embodiments of the present disclosure. 
     In computer security, a sandbox (e.g., sandbox environment) is a security mechanism for separating running programs, usually to mitigate system failures or software vulnerabilities from spreading. A sandbox can be used to execute untested or untrusted programs or code, possibly from unverified or untrusted third parties, suppliers, users, or websites, without risking harm to the host machine or operating system. A sandbox can provide a tightly controlled set of resources for guest programs to run in, such as storage and memory scratch space. Network access, the ability to inspect the host system, or read from input devices can be disallowed or restricted. 
     As illustrated, the execution node  302 - 1  from the execution platform  110  includes an execution node process  410 , which in an embodiment is running on the processor  306 - 1  and can also utilize memory from the cache  304 - 1  (or another memory device or storage). As mentioned herein, a “process” or “computing process” can refer to an instance of a computer program that is being executed by one or more threads by an execution node or execution platform. 
     As mentioned before, the compute service manager  108  validates all communication from the execution platform  110  to validate that the content and context of that communication are consistent with the task(s) known to be assigned to the execution platform  110 . For example, the execution platform  110  executing a query A is not allowed to request access to a particular data source (e.g., data storage device  226  or any one of the storage devices in the cloud storage platform  104 ) that is not relevant to query A. In an example, the execution node  302 - 1  may need to communicate with a second execution node (e.g., execution node  302 - 2 ), but the security mechanisms described herein can disallow communication with a third execution node (e.g., execution node  312 - 1 ). Moreover, any such illicit communication can be recorded (e.g., in a log  444  or other location). Further, the information stored on a given execution node is restricted to data relevant to the current query and any other data is unusable by destruction or encryption where the key is unavailable. 
     The execution node process  410  is executing a UDF client  412  in the example of  FIG.  4   . In an embodiment, the UDF client  412  is implemented to support UDFs written in a particular programming language such as JAVA, and the like. In an embodiment, the UDF client  412  is implemented in a different programming language (e.g., C or C++) than the user code  430 , which can further improve the security of the computing environment  400  by using a different codebase (e.g., one without the same or fewer potential security exploits). 
     User code  430  may be provided as a package e.g., in the form of a JAR (JAVA archive) file which includes code for one or more UDFs. Server implementation code  432 , in an embodiment, is a JAR file that initiates a server that is responsible for receiving requests from the execution node process  410 , assigning worker threads to execute user code, and returning the results, among other types of server tasks. 
     In an implementation, an operation from a UDF (e.g., a JAVA-based UDF) can be performed by a user code runtime  424  executing within a sandbox process  420  (described further below). In an embodiment, the user code runtime  424  is implemented as a virtual machine, such as a JAVA virtual machine (JVM). Since the user code runtime  424  advantageously executes in a separate process relative to the execution node process  410 , there is a lower risk of manipulating the execution node process  410 . Results of performing the operation, among other types of information or messages, can be stored in log  444  for review and retrieval. In an embodiment, the log  444  can be stored locally in memory at the execution node  302 - 1 , or a separate location such as the storage platform  104 . Moreover, such results can be returned from the user code runtime  424  to the UDF client  412  utilizing a high-performance protocol (e.g., without serialization or deserialization of data, without memory copies; operates on record batches without having to access individual columns, records or cells; utilizes efficient remote procedure call techniques and network protocol(s) for data transfer) for data transfer (e.g., distributed datasets) that further provides authentication and encryption of the data transfer. In an embodiment, the UDF client  412  uses a data transport mechanism that supports a network transfer of columnar data between the user code runtime  424  (and vice-versa) with the aforementioned advantages described above. 
     Security manager  422 , in an example, can prevent completion of an operation from a given UDF by throwing an exception (e.g., if the operation is not permitted), or returns (e.g., doing nothing) if the operation is permitted. In an implementation, the security manager  422  is implemented as a JAVA security manager object that allows applications to implement a security policy such as a security manager policy  442  and enables an application to determine, before performing a possibly unsafe or sensitive operation, what the operation is and whether it is being attempted in a security context that allows the operation to be performed. The security manager policy  442  can be implemented as a file with permissions that the user code runtime  424  is granted. The application (e.g., UDF executed by the user code runtime  424 ) therefore can allow or disallow the operation based at least in part on the security policy. 
     Sandbox process  420 , in an embodiment, is a sub-process (or a separate process) from the execution node process  410 . A sub-process, in an embodiment, refers to a child process of a given parent process (e.g., in this example, the execution node process  410 ). The sandbox process  420 , in an example, is a program that reduces the risk of security breaches by restricting the running environment of untrusted applications using security mechanisms such as namespaces and secure computing modes (e.g., using a system call filter to an executing process and all its descendants, thus reducing the attack surface of the kernel of a given operating system). Moreover, in an example, the sandbox process  420  is lightweight in comparison to the execution node process  410  and is optimized (e.g., closely coupled to security mechanisms of a given operating system kernel) to process a database query securely within the sandbox environment. 
     In an embodiment, the sandbox process  420  can utilize a virtual network connection to communicate with other components within the subject system. A specific set of rules can be configured for the virtual network connection concerning other components of the subject system. For example, such rules for the virtual network connection can be configured for a particular UDF to restrict the locations (e.g., particular sites on the Internet or components that the UDF can communicate) that are accessible by operations performed by the UDF. Thus, in this example, the UDF can be denied access to particular network locations or sites on the Internet. 
     The sandbox process  420  can be understood as providing a constrained computing environment for a process (or processes) within the sandbox, where these constrained processes can be controlled and restricted to limit access to certain computing resources. 
     Examples of security mechanisms can include the implementation of namespaces in which each respective group of processes executing within the sandbox environment has access to respective computing resources (e.g., process IDs, hostnames, user IDs, file names, names associated with network access, and inter-process communication) that are not accessible to another group of processes (which may have access to a different group of resources not accessible by the former group of processes), other container implementations, and the like. By having the sandbox process  420  execute as a sub-process to the execution node process  410 , in some embodiments, latency in processing a given database query can be substantially reduced (e.g., a reduction in latency by a factor of 10× in some instances) in comparison with other techniques that may utilize a virtual machine solution by itself. 
     As further illustrated, the sandbox process  420  can utilize a sandbox policy  440  to enforce a given security policy. The sandbox policy  440  can be a file with information related to a configuration of the sandbox process  420  and details regarding restrictions if any, and permissions for accessing and utilizing system resources. Example restrictions can include restrictions to network access, or file system access (e.g., remapping file system to place files in different locations that may not be accessible, other files can be mounted in different locations, and the like). The sandbox process  420  restricts the memory and processor (e.g., CPU) usage of the user code runtime  424 , ensuring that other operations on the same execution node can execute without running out of resources. 
     As mentioned above, the sandbox process  420  is a sub-process (or separate process) from the execution node process  410 , which in practice means that the sandbox process  420  resides in a separate memory space than the execution node process  410 . In an occurrence of a security breach in connection with the sandbox process  420  (e.g., by errant or malicious code from a given UDF), if arbitrary memory is accessed by a malicious actor, the data or information stored by the execution node process is protected. 
     Although the above discussion of  FIG.  4    describes components that are implemented using JAVA (e.g., object-oriented programming language), it is appreciated that the other programming languages (e.g., interpreted programming languages) are supported by the computing environment  400 . In an embodiment, PYTHON is supported for implementing and executing UDFs in the computing environment  400 . In this example, the user code runtime  424  can be replaced with a PYTHON interpreter for executing operations from UDFs (e.g., written in PYTHON) within the sandbox process  420 . 
     In some embodiments, the sandbox process  420  can be used for configuring one or more MET libraries  421  for executing an event table instance (e.g., as discussed in connection with  FIG.  5   ). Additionally, the execution node process can be configured with a table stage  411  and MET components  413  (e.g., components  510 - 514  in  FIG.  5   ), which can be used in connection with MET-related functions (e.g., as discussed in connection with  FIG.  5   ). 
       FIG.  5    shows an example of a computing architecture  500  for providing managed event tables for logging data from UDFs executing in a secure environment, according to some example embodiments. 
     In some aspects, a MET (e.g., MET  134 ) is configured and managed by the MET manager  132  of the network-based database system  102  and provides functionality that allows users to query the stored log data similar to a regular table. As mentioned above, a MET can be attached to a parent object, such as an account, database, schema, and the like, and provides a destination for log data originating from the parent object and/or any objects under the parent object. 
     In some embodiments, the network-based database system  102  provides users with an API command for creating a new MET. For example, the API command may be provided in an SQL syntax and allows a user to either create or replace an event table, as well as define a name for the event table. A user may therefore use the API (e.g., via client device  114 ) to create an event table, thereby causing log data generated by specified UDFs to be automatically collected and entered into the event table. The user may then simply query the event table (e.g., MET  134 ) to access the stored log data. Implementation and operation of event tables are described in relation to the computing architecture  500  in  FIG.  5   . 
     As shown, the computing architecture  500  includes a client device  114 , a compute service manager  108 , and an execution node  302 - 1 . The execution node  302 - 1  includes a sandbox process  420  and an execution node process  410 . A UDF  504  is executing within the sandbox process  420 . The sandbox process  420  separates the UDF  504  from the other services and processes of the computing architecture  500  and may also limit the resources (e.g., storage, memory, etc.) and functionality available to the UDF  504 . 
     The sandbox process  420  is loaded with event table libraries (e.g., MET libraries  421 ) to facilitate automated logging of the UDF  504  in a MET  134 . The MET libraries  421  are programming libraries that cause the execution of a MET instance  506  within the sandbox process  420 . The MET instance  506  captures log data  508  generated by the UDF  504  executing in the sandbox process  420 . For example, the MET instance  506  may intercept log calls generated by the UDF  504 . 
     The MET instance  506  funnels the captured log data to the execution node process  410 . For example, the MET instance  506  may communicate the captured log data  508  to the execution node process  410  using a remote procedure call, (e.g., a Google Remote Procedure Call (gRPC)) that is used to communicate with the execution node process  410 . 
     As shown, the execution node  302 - 1  is further configured with a table stage  411  and MET components  413 , which can be part of the execution node process  410 . The MET components  413  include a data enrichment component  510 , a data formatting component  512 , and an ingestion scheduling component  514 . A managed event table (e.g., MET  134 ) is automatically assigned a table stage  411  where log data associated with the MET  134  is initially stored. Table stage  411  is a storage location that is internal to the execution node process  410 . The log data stored at table stage  411  may not be accessible to an end-user. The execution node process  410  stores the log data  508  received from the MET instance  506  in table stage  411 . 
     In some embodiments, the data enrichment component  510  enriches the log data  508  with additional metadata. For example, the metadata may include a timestamp value (e.g., a value identifying the time at which the log data  508  was generated or logged), a query identifier, a session identifier, a warehouse identifier (e.g., a warehouse name), a database name, a schema identifier, a source name, a user name, and the like. The metadata that is added by the data enrichment component  510  may be based on the specifications of a user that created the MET  134 , a selected log level, a selected permission level, and the like, which information can be part of the MET configurations  138 . In some aspects, the data enrichment component  510  may enrich the log data  508  before the log data is stored in table stage  411  and/or after the log data  508  is stored in table stage  411 . In some embodiments, the table schema used by the MET  134  may be preconfigured (e.g., via the MET configurations  138 ). Accordingly, the data enrichment component  510  may enrich the log data  508  based on the configured schema. 
     In some embodiments, the execution node process  410  facilitates ingestion of the log data  508  stored in the table stage  411  to the MET  134 . For example, the data formatting component  512  formats the log data  508  into a format that is suitable for ingestion into the MET  134 , and the ingestion scheduling component  514  schedules ingestion of the formatted log data into the MET  134 . In some aspects, formatting the log data includes converting the log data into a file format that is used by the MET  134 . For example, the file format used by the MET  134  may be a JSON object, FDN file, and the like. 
     In some embodiments, the ingestion scheduling component  514  schedules ingestion of the formatted log data by communicating with the ingestion component  502  of the compute service manager  108 . In some aspects, the ingestion component  502  may be part of the MET manager  132 . For example, the ingestion scheduling component  514  notifies ingestion component  502  that formatted log data is available in table stage  411  and is ready to be ingested into the MET  134 . This causes the ingestion component  502  to initiate ingestion of the formatted log data from table stage  411  to the MET  134 . 
     The log data ingested into the MET  134  may be queried by a user. For example, a user may use a remote computing device (e.g., client device  114 ) to communicate with the compute service manager  108  and initiate queries of the MET  134 . In some aspects, MET  134  may provide a limited set of query commands that are available to the user. For example, the query commands may include SHOW, DESC, DROP, TRUNCATE, DELETE, ALTER TABLE ADD/DROP ROW ACCESS POLICY. In some embodiments, other query operations/commands may be disallowed. 
     In some embodiments, the functionality associated with logging data in the MET  134  may be configurable by a user via the MET configurations  138 . For example, the log data that is collected by MET instance  506  and entered into the MET  134  may be configured based on log levels assigned to the MET  134 . As another example, the query functionality and/or log data that is available to a user may be based on privileges that are assigned to the user and/or the MET  134  (e.g., via the MET configurations  138 ). Similarly, the table schema used by the MET  134  may be configurable from a set of predetermined table schemas. 
     In some embodiments, MET configurations  138  can be used for configuring a log level (e.g., which logs are collected as log data by the MET instance  506 ). In some aspects, rules for selecting a log level can include: (a) if a log level is set at object lineage and session parameter is not set then the object lineage value is used; (b) if a session parameter is set and no object lineage parameter is set then the session value is used; and (c) if both session and object lineage parameters are set then the more verbose value is used. 
     In some embodiments, the MET manager  132  can configure the MET  134  based on certain log visibility for certain users (e.g., certain users may be authorized to access only certain logs or log data in the MET  134 ). 
     In some embodiments, an event table schema associated with the MET  134  can include one or more of the following: a timestamp, an event type, a query ID, a session ID, a warehouse name, a warehouse ID, a database name, a database ID, a schema name, a schema ID, a source name, a source ID, a line name, a user name, a user ID, an owner role name, an owner role ID, a caller role name, a caller role ID, a log level, an event message, and extra information. 
       FIG.  6    is a flow diagram illustrating operations of a database system in performing a method  600  for configuring managed event tables, in accordance with some embodiments of the present disclosure. Method  600  may be embodied in computer-readable instructions for execution by one or more hardware components (e.g., one or more processors) such that the operations of the method  600  may be performed by components of the network-based database system  102 , such as a network node (e.g., a MET manager  132  executing on a network node of the compute service manager  108 ) or a computing device (e.g., client device  114 ) which may be implemented as machine  700  of  FIG.  7    performing the disclosed functions. Accordingly, method  600  is described below, by way of example with reference thereto. However, it shall be appreciated that method  600  may be deployed on various other hardware configurations and is not intended to be limited to deployment within the network-based database system  102 . 
     At operation  602 , a user code runtime (e.g., user code runtime  424 ) is instantiated within a sandbox process of an execution node (e.g., sandbox process  420  of execution node  302 - 1 ), to execute code of a user-defined function (UDF) (e.g., user code  430  of a UDF  504  can be executed by the sandbox process  420 ). 
     At operation  604 , log data generated by the UDF during the execution of the code is collected using an event table instance within the sandbox process. For example, log data  508  generated during the execution of the UDF code of UDF  504  is collected by the MET instance  506  within the sandbox process  420 . 
     At operation  606 , the log data is provided from the sandbox process to an execution node process of the execution node. For example, log data  508  is provided from the sandbox process  420  to the execution node process  410  of the execution node  302 - 1 . 
     At operation  608 , the log data is formatted using the execution node process, to generate formatted log data. For example, log data  508  is formatted (e.g., by one or more of the MET components  413 ) to generate formatted log data. In some aspects, the formatting is based on a configuration (e.g., MET configurations  138 ) of a managed event table (e.g., MET  134 ) that is external to the execution node (e.g., MET  134  is part of the compute service manager  108  which is external to the execution node  302 - 1 ). 
     At  610 , ingestion of the formatted log data from the execution node process into the managed event table can be caused/initiated. For example, ingestion of the formatted log data from table stage  411  into the MET  134  can be initiated based on communication between the ingestion component  502  and the ingestion scheduling component  514 . 
       FIG.  7    illustrates a diagrammatic representation of a machine  700  in the form of a computer system within which a set of instructions may be executed for causing the machine  700  to perform any one or more of the methodologies discussed herein, according to an example embodiment. Specifically,  FIG.  7    shows a diagrammatic representation of the machine  700  in the example form of a computer system, within which instructions  716  (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine  700  to perform any one or more of the methodologies discussed herein may be executed. For example, instructions  716  may cause machine  700  to execute any one or more operations of method  600  (or any other technique discussed herein, for example in connection with  FIG.  4   - FIG.  6   ). As another example, instructions  716  may cause machine  700  to implement one or more portions of the functionalities discussed herein. In this way, instructions  716  may transform a general, non-programmed machine into a particular machine  700  (e.g., the client device  114 , the compute service manager  108 , or a node in the execution platform  110 ) that is specially configured to carry out any one of the described and illustrated functions in the manner described herein. In yet another embodiment, instructions  716  may configure the client device  114 , the compute service manager  108 , and/or a node in the execution platform  110  to carry out any one of the described and illustrated functions in the manner described herein. 
     In alternative embodiments, the machine  700  operates as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine  700  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  700  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 smartphone, a mobile device, a network router, a network switch, a network bridge, or any machine capable of executing the instructions  716 , sequentially or otherwise, that specify actions to be taken by the machine  700 . Further, while only a single machine  700  is illustrated, the term “machine” shall also be taken to include a collection of machines  700  that individually or jointly execute the instructions  716  to perform any one or more of the methodologies discussed herein. 
     Machine  700  includes processors  710 , memory  730 , and input/output (I/O) components  750  configured to communicate with each other such as via a bus  702 . In some example embodiments, the processors  710  (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  712  and a processor  714  that may execute the instructions  716 . The term “processor” is intended to include multi-core processors  710  that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions  716  contemporaneously. Although  FIG.  7    shows multiple processors  710 , the machine  700  may include a single processor with a single core, a single processor with multiple cores (e.g., a multi-core processor), multiple processors with a single core, multiple processors with multiple cores, or any combination thereof. 
     The memory  730  may include a main memory  732 , a static memory  734 , and a storage unit  736 , all accessible to the processors  710  such as via the bus  702 . The main memory  732 , the static memory  734 , and the storage unit  736  store the instructions  716  embodying any one or more of the methodologies or functions described herein. The instructions  716  may also reside, completely or partially, within the main memory  732 , within the static memory  734 , within machine storage medium  738  of the storage unit  736 , within at least one of the processors  710  (e.g., within the processor&#39;s cache memory), or any suitable combination thereof, during execution thereof by the machine  700 . 
     The I/O components  750  include components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components  750  that are included in a particular machine  700  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  750  may include many other components that are not shown in  FIG.  7   . The I/O components  750  are grouped according to functionality merely for simplifying the following discussion and the grouping is in no way limiting. In various example embodiments, the I/O components  750  may include output components  752  and input components  754 . The output components  752  may include visual components (e.g., a display such as a plasma display panel (PDP), a light-emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), other signal generators, and so forth. The input components  754  may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components), point-based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or another pointing instrument), tactile input components (e.g., a physical button, a touch screen that provides location and/or force of touches or touch gestures or other tactile input components), audio input components (e.g., a microphone), and the like. 
     Communication may be implemented using a wide variety of technologies. The I/O components  750  may include communication components  764  operable to couple the machine  700  to a network  780  or devices  770  via a coupling  782  and a coupling  772 , respectively. For example, the communication components  764  may include a network interface component or another suitable device to interface with the network  780 . In further examples, the communication components  764  may include wired communication components, wireless communication components, cellular communication components, and other communication components to provide communication via other modalities. The device  770  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, machine  700  may correspond to any one of the client device  114 , the compute service manager  108 , or the execution platform  110 , and the devices  770  may include the client device  114  or any other computing device described herein as being in communication with the network-based database system  102  or the cloud storage platform  104 . 
     The various memories (e.g.,  730 ,  732 ,  734 , and/or memory of the processor(s)  710  and/or the storage unit  736 ) may store one or more sets of instructions  716  and data structures (e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. These instructions  716 , when executed by the processor(s)  710 , 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 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  780  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  780  or a portion of the network  780  may include a wireless or cellular network, and the coupling  782  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  782  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  716  may be transmitted or received over the network  780  using a transmission medium via a network interface device (e.g., a network interface component included in the communication components  764 ) and utilizing any one of several well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, instructions  716  may be transmitted or received using a transmission medium via coupling  772  (e.g., a peer-to-peer coupling or another type of wired or wireless network coupling) to the device  770 . 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  716  for execution by the machine  700 , 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 a 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 method  600  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 several 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 several locations. 
     Described implementations of the subject matter can include one or more features, alone or in combination as illustrated below by way of examples. 
     Example 1 is a system comprising: at least one hardware processor; and at least one memory storing instructions that cause the at least one hardware processor to perform operations comprising: instantiating a user code runtime to execute code of a user-defined function (UDF), the user code runtime instantiated within a sandbox process of an execution node; collecting using an event table instance within the sandbox process, log data generated by the UDF during execution of the code; providing the log data from the sandbox process to an execution node process of the execution node; formatting of the log data using the execution node process, to generate formatted log data, the formatting based on a configuration of a managed event table that is external to the execution node; and causing ingestion of the formatted log data from the execution node process into the managed event table. 
     In Example 2, the subject matter of Example 1 includes subject matter where the instructions further cause the at least one hardware processor to perform operations comprising: configuring the sandbox process with managed event table libraries, the managed event table libraries causing execution of the event table instance. 
     In Example 3, the subject matter of Examples 1-2 includes subject matter where the instructions further cause the at least one hardware processor to perform operations comprising: enriching at the execution node process, the log data with contextual metadata describing the log data, yielding enriched log data. 
     In Example 4, the subject matter of Example 3 includes subject matter where the contextual metadata comprises at least one of a timestamp identifying a time the log data was generated; a query identifier of a query causing the generation of the log data; a session identifier of a session during which the log data was generated; and a warehouse identifier of a virtual warehouse of computing resources that include the execution node. 
     In Example 5, the subject matter of Examples 3-4 includes subject matter where the instructions further cause the at least one hardware processor to perform operations comprising: storing the enriched log data at a table stage storage maintained by the execution node process. 
     In Example 6, the subject matter of Example 5 includes subject matter where the configuration comprises a format that is ingestible by the managed event table. 
     In Example 7, the subject matter of Example 6 includes subject matter where the instructions further cause the at least one hardware processor to perform operations comprising: formatting the enriched log data stored in the table stage storage into the format that is ingestible by the managed event table, yielding the formatted log data. 
     In Example 8, the subject matter of Example 7 includes subject matter where the instructions further cause the at least one hardware processor to perform operations comprising: scheduling the ingestion of the formatted log data into the managed event table, based on an indication the formatted log data is available at the table stage storage. 
     In Example 9, the subject matter of Examples 1-8 includes subject matter where the instructions further cause the at least one hardware processor to perform operations comprising: decoding a query for the log data generated by the UDF during execution of the code; executing the query using the formatted log data stored in the managed event table to generate a query result; and outputting the query result in response to the query. 
     In Example 10, the subject matter of Examples 1-9 includes subject matter where the instructions further cause the at least one hardware processor to perform operations comprising: collecting the log data further based on a data access privilege indicated by the configuration of the managed event table. 
     Example 11 is a method comprising: instantiating within a sandbox process of an execution node, a user code runtime to execute code of a user-defined function (UDF); collecting using an event table instance within the sandbox process, log data generated by the UDF during execution of the code; providing the log data from the sandbox process to an execution node process of the execution node; formatting of the log data using the execution node process, to generate formatted log data, the formatting based on a configuration of a managed event table that is external to the execution node; and causing ingestion of the formatted log data from the execution node process into the managed event table. 
     In Example 12, the subject matter of Example 11 includes, configuring the sandbox process with managed event table libraries, the managed event table libraries causing execution of the event table instance. 
     In Example 13, the subject matter of Examples 11-12 includes, enriching at the execution node process, the log data with contextual metadata describing the log data, yielding enriched log data. 
     In Example 14, the subject matter of Example 13 includes subject matter where the contextual metadata comprises at least one of a timestamp identifying a time the log data was generated; a query identifier of a query causing the generation of the log data; a session identifier of a session during which the log data was generated; and a warehouse identifier of a virtual warehouse of computing resources that include the execution node. 
     In Example 15, the subject matter of Examples 13-14 includes, storing the enriched log data at a table stage storage maintained by the execution node process. 
     In Example 16, the subject matter of Example 15 includes subject matter where the configuration comprises a format that is ingestible by the managed event table. 
     In Example 17, the subject matter of Example 16 includes, formatting the enriched log data stored in the table stage storage into the format that is ingestible by the managed event table, yielding the formatted log data. 
     In Example 18, the subject matter of Example 17 includes, scheduling the ingestion of the formatted log data into the managed event table, based on an indication the formatted log data is available at the table stage storage. 
     In Example 19, the subject matter of Examples 11-18 includes, decoding a query for the log data generated by the UDF during execution of the code; executing the query using the formatted log data stored in the managed event table to generate a query result; and outputting the query result in response to the query. 
     In Example 20, the subject matter of Examples 11-19 includes, collecting the log data further based on a data access privilege indicated by the configuration of the managed event table. 
     Example 21 is a computer-readable medium comprising instructions that, when executed by one or more processors of a machine, configure the machine to perform operations comprising: instantiating within a sandbox process of an execution node, a user code runtime to execute code of a user-defined function (UDF); collecting using an event table instance within the sandbox process, log data generated by the UDF during execution of the code; providing the log data from the sandbox process to an execution node process of the execution node; formatting of the log data using the execution node process, to generate formatted log data, the formatting based on a configuration of a managed event table that is external to the execution node; and causing ingestion of the formatted log data from the execution node process into the managed event table. 
     In Example 22, the subject matter of Example 21 includes, configuring the sandbox process with managed event table libraries, the managed event table libraries causing execution of the event table instance. 
     In Example 23, the subject matter of Examples 21-22 includes, enriching at the execution node process, the log data with contextual metadata describing the log data, yielding enriched log data. 
     In Example 24, the subject matter of Example 23 includes subject matter where the contextual metadata comprises at least one of a timestamp identifying a time the log data was generated; a query identifier of a query causing the generation of the log data; a session identifier of a session during which the log data was generated; and a warehouse identifier of a virtual warehouse of computing resources that include the execution node. 
     In Example 25, the subject matter of Examples 23-24 includes, storing the enriched log data at a table stage storage maintained by the execution node process. 
     In Example 26, the subject matter of Example 25 includes subject matter where the configuration comprises a format that is ingestible by the managed event table. 
     In Example 27, the subject matter of Example 26 includes, formatting the enriched log data stored in the table stage storage into the format that is ingestible by the managed event table, yielding the formatted log data. 
     In Example 28, the subject matter of Example 27 includes, scheduling the ingestion of the formatted log data into the managed event table, based on an indication the formatted log data is available at the table stage storage. 
     In Example 29, the subject matter of Examples 21-28 includes, decoding a query for the log data generated by the UDF during execution of the code; 
     executing the query using the formatted log data stored in the managed event table to generate a query result; and outputting the query result in response to the query. 
     In Example 30, the subject matter of Examples 21-29 includes, the operations further comprising: collecting the log data further based on a data access privilege indicated by the configuration of the managed event table. 
     Example 31 is at least one machine-readable medium including instructions that, when executed by processing circuitry, cause the processing circuitry to perform operations to implement any of Examples 1-30. 
     Example 32 is an apparatus comprising means to implement any of Examples 1-30. 
     Example 33 is a system to implement any of Examples 1-30. 
     Example 34 is a method to implement any of Examples 1-30. 
     Although the embodiments of the present disclosure have been described concerning 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 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 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.