Patent Publication Number: US-11645243-B2

Title: Accessing files in a database stage using a user defined function

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims the benefit of priority to U.S. Provisional Patent Application Ser. No. 63/197,803 filed Jun. 7, 2021, the contents of which are incorporated by reference in their entirety. 
    
    
     TECHNICAL FIELD 
     Embodiments of the disclosure relate generally to a network-based database system or a cloud data platform and, more specifically, to implementing file access to user defined functions of a database. 
     BACKGROUND 
     Cloud-based data systems such as data warehouses provide users an ability to track and manage large amounts of data. Users can implement functions to process the data, such as user defined functions. However, implementing user defined functions on databases is restricted and it is often not practical to scale use of the user defined functions to enable access to a large file (e.g., a one terabyte file) or a large number of files (e.g., 1,000,000 image files). 
    
    
     
       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 data warehouse system in communication with a cloud storage platform, in accordance with some embodiments of the present disclosure. 
         FIG.  2    is a block diagram illustrating 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 an example software architecture executing a user defined function (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 a data architecture for implementing file access to UDFs in a staging location, in accordance with some embodiments of the present disclosure. 
         FIGS.  6 A and  6 B  show example flow diagrams for implementing file access for user defined functions, 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 set forth in the following description in order 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. 
     As discussed it can be difficult to implement database user defined functions (UDFs) in a scalable approach that can access large files. To this end, a function file access system can store one or more UDF signatures specifying a function name and that take a path to files on a stage (e.g., internal storage, external storage). The instructions of the UDF can be uploaded and stored on a database system (e.g., for a Java UDF, Java code is stored). The UDF can be shared and executed in the distributed database between users. For example, a provider account user can create a UDF and share access to it to enable a consumer account user to call the UDF. When the consumer account user calls the UDF, the system determines that the string corresponds to an UDF interface (e.g., such as Java InputStream), which has a pointer to the stage file (e.g., where the stage name is specified in the UDF signature, along with other parameters such as a location in the stream (e.g., “0”, “1000”). An execution node can receive the UDF requests over a network channel and authenticate that the user has access to the files, and if transfers credentials to threads of the execution node. The threads of the execution node can then retrieve and cache the file contents as pages in local memory of the execution node. In some example embodiments, the pages pre-cached such that pages that are later requested are already stored on the execution node. In some example embodiments, the system  230  perform just-in-time retrieval and the pages are pre-cached but are rather downloaded only upon being requested by a given UDF (e.g., one or more streams in a given UDF). 
       FIG.  1    illustrates an example computing environment  100  that includes a database system in the example form of a network-based data warehouse 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. 
     As shown, the computing environment  100  comprises the network-based data warehouse system  102  in communication with a cloud storage platform  104  (e.g., AWS®, Microsoft Azure Blob Storage®, or Google Cloud Storage). The network-based data warehouse 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 data warehouse system  102 . 
     The network-based data warehouse system  102  comprises a compute service manager  108 , an execution platform  110 , and one or more metadata databases  112 . The network-based data warehouse 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 data warehouse 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 data warehouse system  102 . A user may utilize the client device  114  to submit data storage, retrieval, and analysis requests to the compute service manager  108 . 
     The compute service manager  108  is also coupled to one or more metadata databases  112  that store metadata pertaining to various functions and aspects associated with the network-based data warehouse 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. 
     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. The execution platform  110  is coupled to storage platform  104  of the cloud storage platform  104 . 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. 
     The execution platform  110  comprises a plurality of compute nodes. A set of processes on a compute node executes a query plan compiled by the compute service manager  108 . The set of processes can include: a first process to execute the query plan; a second process to monitor and delete cache files using a least recently used (LRU) policy and implement an out of memory ( 00 M) error mitigation process; a third process that extracts health information from process logs and status to send back to the compute service manager  108 ; a fourth process to establish communication with the compute service manager  108  after a system boot; and a fifth process to handle all communication with a compute cluster for a given job provided by the compute service manager  108  and to communicate information back to the compute service manager  108  and other compute nodes of the execution platform  110 . 
     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 data warehouse system  102 . Thus, in the described embodiments, the network-based data warehouse system  102  is dynamic and supports regular changes to meet the current data processing needs. 
     During typical operation, the network-based data warehouse 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 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  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 use of remote 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. The 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. The function file access system  230  is configured to manage file access to files in a stage (e.g., internal or external stage) using user defined function signatures and UDFs that point to the stage, as discussed in further detail below. 
     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 in 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  oversee 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 data warehouse 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 . Data storage device  226  in  FIG.  2    represents any data storage device within the network-based data warehouse 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. 
       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 , virtual warehouse  2 , and virtual warehouse 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 the 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 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, alternate 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 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. 
     Although virtual warehouses  1 ,  2 , and n are associated with the same execution platform  110 , the virtual warehouses may be implemented using multiple computing systems at multiple geographic locations. For example, virtual warehouse  1  can be implemented by a computing system at a first geographic location, while virtual warehouses  2  and n are implemented by another computing system at a second geographic location. In some embodiments, these different computing systems are cloud-based computing systems maintained by one or more different entities. 
     Additionally, each virtual warehouse is shown in  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 implements 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 cloud storage platform  104 , but each virtual warehouse has its own execution nodes with independent processing and caching resources. This configuration allows requests on different virtual warehouses to be processed independently and with no interference between the requests. This independent processing, combined with the ability to dynamically add and remove virtual warehouses, supports the addition of new processing capacity for new users without impacting the performance observed by the existing users. 
       FIG.  4    is a computing environment  400  conceptually illustrating an example software architecture executing a user defined function (UDF) by a process running on a given execution node of the execution platform  110 , in accordance with some embodiments of the present disclosure. 
     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 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 which 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., JAVA based UDF) can be performed by a user code runtime  424  executing within a sandbox process  420  (e.g., UDF server). 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 a 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 at 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 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 a lightweight process 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 in a secure manner within the sandbox environment. 
     In an embodiment, the sandbox process  420  can utilize a virtual network connection in order to communicate with other components within the subject system. A specific set of rules can be configured for the virtual network connection with respect to 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 . 
       FIG.  5    shows a data architecture  500  for implementing file access to UDFs in a staging location, according to some example embodiments. The UDF server  515  (e.g., a child process of the XP process that runs a given execution node) can manage multiple UDFs  520  (e.g., UDF 1, UDF2, and UDF3), each of which can be defined using a UDF signature or definition, discussed in further detail below in  FIG.  6   . In accordance with some example embodiments, each UDF manages one or more streams  525  (e.g., Java InputStream), where the number of streams can be specified as a parameter of the function or application being specified by the end-user defining the UDF signature(s). For example, if a given UDF is a difference comparing function to compare two different files (e.g., File 3 Page 2; and File 3 page 4), then two streams (e.g., Stream1 and Stream2) can be created to process the two files (e.g., via the threads). 
     The UDF server  515  can open, read, or close requests to the execution node  510  (e.g., execution node  302 - 1 ) via a network interface channel  530 , such as RPC (e.g., gRPC, Google Remote Procedure Call). In the execution node  510 , multiple threads  535  serve the requests received from the network interface channel  530  (e.g., to perform concurrent processing of requested data). In some example embodiments, each of the threads goes through a compute service manager access point to access the compute service manager  505  (e.g., compute service manager  108 ) to confirm the user (e.g., the user calling the UDF) has access rights for a given file handled by the thread. If the thread has access, the compute service manager  505  passes the credential and access information to the thread and the thread access the storage platform  104  (e.g., internal stage, external object storage, external tables, etc.) and downs the files, which are stored as pages  540  on local memory  545  (e.g., disk) of the execution node  510 . 
       FIG.  6 A  shows a flow diagram of an example method  600  for implementing file access to user defined functions on a distributed database, according to some example environments. At operation  605 , the function file access system  230  generates one or more user defined function definitions. For example, an end-user defines a UDF function signature as follows: 
     
       
         
           
               
             
               
                   
               
             
            
               
                 :::::::::::::::::CODE BEGIN::::::::::::::::: 
               
               
                 public static int Func_123(InputStream s1, int pos1, InputStream s2, int 
               
               
                 pos2) 
               
               
                 :::::::::::::::::CODE END::::::::::::::::: 
               
               
                   
               
            
           
         
       
     
     At operation  610 , the function fife access system  230  stores one or more user defined functions (UDFs) that correspond with the definitions of operation  605 . An example UDF (e.g., a Java program) that can be uploaded and stored at operation  610  includes: 
     
       
         
           
               
               
             
               
                   
               
             
            
               
                   
                 :::::::::::::::::CODE BEGIN::::::::::::::::: 
               
               
                   
                 import java.io.*; 
               
               
                   
                 import com.berryworks.edireader.json.fromedi. EditoJson; 
               
               
                   
                 public class Func_123{ 
               
               
                   
                  public static String Parse(InputStream in) 
               
               
                   
                  { 
               
               
                   
                   final EditoJson editoJson = new EditoJson( ); 
               
               
                   
                   editoJson.setFormatting(true); //format 
               
               
                   
                   editoJson.setAnnotated(false); //annotate 
               
               
                   
                   editoJson.setSummarize(false); //summarize 
               
               
                   
                   StringWriter sw = new StringWriter( ); 
               
               
                   
                   try (Reader reader = new BufferedReader(new 
               
               
                   
                 InputStreamReader(in, “UTF-8”)); 
               
               
                   
                   Writer writer = new BufferedWriter(sw)) { 
               
               
                   
                   editoJson.asJson(reader, writer); 
               
               
                   
                   } catch (Exception e) { 
               
               
                   
                    throw new RuntimeException(e.getMessage( )); 
               
               
                   
                   } 
               
               
                   
                   return sw.toString( ); 
               
               
                   
                 } 
               
               
                   
                 :::::::::::::::::CODE END::::::::::::::::: 
               
               
                   
               
            
           
         
       
     
     At operation  615 , the function file access system  230  receives a call for one of the UDFs. For example, an end-user having access to call the function inputs SQL to call the function: 
     
       
         
           
               
             
               
                   
               
             
            
               
                 :::::::::::::::::CODE BEGIN::::::::::::::::: 
               
               
                 SELECT Func_123(‘@stage1/path/file1’, 0, ‘@stage2/path/file2’, 1000); 
               
               
                 :::::::::::::::::CODE END::::::::::::::::: 
               
               
                   
               
            
           
         
       
     
     At operation  620 , the function file access system  230  processes the request for the UDF, as discussed in further detail below with reference to  FIG.  6 B . For example, at runtime the system examines the function signature, and converts the string to the UDF interface (e.g., “InputStream”), where the string input argument must contain a valid stage file handle (e.g., @stage1). Although InputStream is discussed here as an example UDF Jaya interface, it is appreciated that the system likewise other user defined function interfaces, such as Java File. RandomAccessFile, and others. Further, although Java is discussed herein as an example language being implemented in the UDF, it is appreciated that other languages (e.g., PYTHON) and other function interfaces of a given language are implemented by the function file access system  230  in accordance with some example embodiments. 
       FIG.  6 B  shows a flow diagram of the example method  650  processing requests for UDFs, according to some example embodiments. 
     At operation  655 , the UDF server  515  generates requests to a node. For example, the UDF server  515  opens one or more streams according to a requested UDF, and the UDF server  515  sends one or more open requests from streams (e.g., InputStreams) to the execution node  510  via the channel  530 . 
     At operation  660 , the execution node  510  receives the requests. For example, the threads  535  receive open requests from the channel  530 . 
     At operation  665 , the computer service manager  505  authorizes the requests. For example, thread  1  of the threads  535  requests authorization for access to a file in the storage platform  104 , and the computer service manager  505  authorizes the thread&#39;s access (e.g., the user&#39;s access) and gives the thread access data (e.g., credentials) to access the files in the storage platform  104 . 
     At operation  670 , the execution node  510  retrieves file data from the stage. For example, each of the threads  535  concurrently requests authorization and credential data and then downloads data from the storage platform  104  to local memory of the node (e.g., disk  545 ) for further processing according to instructions in the called UDF at operation  675 . 
       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, the instructions  716  may cause the machine  700  to execute any one or more operations of the method  600 . As another example, the instructions  716  may cause the machine  700  to implement portions of the data flows illustrated in at least  FIG.  4   . In this way, the instructions  716  transform a general, non-programmed machine into a particular machine  700  (e.g., 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 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 smart phone, 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. 
     The 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 an example embodiment, 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 devices  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, the machine  700  may correspond to any one of 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 data warehouse system  102  or the cloud storage platform  104 . 
     Executable Instructions and Machine Storage Medium 
     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 a single or multiple storage devices and/or media (e.g., a centralized or distributed database, and/or associated caches and servers) that store executable instructions and/or data. The terms shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media, including memory internal or external to processors. Specific examples of machine-storage media, computer-storage media, and/or device-storage media include non-volatile memory, including by way of example semiconductor memory devices, e.g., erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), field-programmable gate arrays (FPGAs), and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The terms “machine-storage media,” “computer-storage media,” and “device-storage media” specifically exclude carrier waves, modulated data signals, and other such media, at least some of which are covered under the term “signal medium” discussed below. 
     Transmission Medium 
     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 a number of well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions  716  may be transmitted or received using a transmission medium via the coupling  772  (e.g., a peer-to-peer coupling) to the devices  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 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. 
     Computer-Readable Medium 
     The terms “machine-readable medium,” “computer-readable medium,” and “device-readable medium” mean the same thing and may be used interchangeably in this disclosure. The terms are defined to include both machine-storage media and transmission media. Thus, the terms include both storage devices/media and carrier waves/modulated data signals. 
     The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Similarly, the methods described herein may be at least partially processor-implemented. For example, at least some of the operations of the 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 a number of machines. In some example embodiments, the processor or processors may be located in a single location (e.g., within a home environment, an office environment, or a server farm), while in other embodiments the processors may be distributed across a number of locations. In view of the disclosure above, various examples are set forth below. It should be noted that one or more features of an example, taken in isolation or combination, should be considered within the disclosure of this application. 
     Example 1. A method comprising: receiving, in a first computing process of a network database, a plurality of user defined function definitions, each user defined function definition including a user defined function class type and function name for a function to be executed in a second computing process; receiving, by the network database, a user defined function including code related to at least one operation to be performed using the user defined function class type, the user defined function having a function name that matches one of the plurality of user defined function definitions; receiving a request to perform the user defined function, the request generated by the second computing process of the network database; activating one or more objects of the user defined function class type that correspond to the requested user defined function; retrieving, by a node of the networked database, files specified in the request using a path to a file storage stage that correspond to the one or more activated objects of the user defined class type; and storing the files on the node. 
     Example 2. The method of example 1, wherein the file storage stage corresponds to a external storage device that is external to the networked database. 
     Example 3. The method of any one or more of Examples 1 or 2, wherein the one or more objects of the user defined function class type are activated on a user defined function server. 
     Example 4. The method of any one or more of examples 1-3, further comprising: transmitting requests from the one or more objects to a plurality of threads of the node using a network channel within the node. 
     Example 5. The method of any one or more of Examples 1-4, wherein the network channel is a remote procedure call (RPC) channel. 
     Example 6. The method of any one or more of Examples 1-5, further comprising: determining, by the network database, each thread is authorized to access one of the files based on a user of the second computing process being previously granted access to the each of the one of the files by another user of the first computing process. 
     Example 7. The method of any one or more of Examples 1-6, further comprising: processing, by the second computing process, the files according to the at least one operation. 
     Example 8. A system comprising: one or more processors of a machine; and a memory storing instructions that, when executed by the one or more processors, cause the machine to perform operations comprising any one or more of the methods of the Examples 1-7. 
     Example 9. A machine storage medium embodying instructions that, when executed by a machine, cause the machine to perform operations comprising any one or more of the methods of the Examples 1-7. 
     Although the embodiments of the present disclosure have been described with reference to specific example embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader scope of the inventive subject matter. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. The accompanying drawings that form a part hereof show, by way of illustration, and not of limitation, specific embodiments in which the subject matter may be practiced. The embodiments illustrated are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed herein. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. This Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled. 
     Such embodiments of the inventive subject matter may be referred to herein, individually and/or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept if more than one is in fact disclosed. Thus, although specific embodiments have been illustrated and described herein, it should be appreciated that any arrangement calculated to achieve the same purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, will be apparent, to those of skill in the art, upon reviewing the above description. 
     In this document, the terms “a” or “an” are used, as is common in patent documents, to include one or more than one, independent of any other instances or usages of “at least one” or “one or more.” In this document, the term “or” is used to refer to a nonexclusive or, such that “A or B” includes “A but not B,” “B but not A,” and “A and B,” unless otherwise indicated. In the appended claims, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.” Also, in the following claims, the terms “including” and “comprising” are open-ended; that is, a system, device, article, or process that includes elements in addition to those listed after such a term in a claim is still deemed to fall within the scope of that claim.