Patent Publication Number: US-2023139194-A1

Title: Index generation using lazy reassembling of semi-structured data

Description:
PRIORITY CLAIM 
     This application is a continuation of U.S. patent application Ser. No. 17/814,110 entitled “LAZY REASSEMBLING OF SEMI-STRUCTURED DATA,” filed Jul. 21, 2022, which is a continuation-in-part of U.S. Pat. No. 11,494,384 entitled “PROCESSING QUERIES ON SEMI-STRUCTURED DATA COLUMNS,” filed Mar. 16, 2022, which is a continuation of U.S. Pat. No. 11,308,090 entitled “PRUNING INDEX TO SUPPORT SEMI-STRUCTURED DATA TYPES,” filed Aug. 4, 2021, which claims priority to U.S. Provisional Patent Application No. 63/197,750 entitled “PRUNING INDEX TO SUPPORT SEMI-STRUCTURED DATA TYPES,” filed on Jun. 7, 2021 and is a continuation-in-part of U.S. Pat. No. 11,308,089 entitled “PRUNING INDEX MAINTENANCE,” filed Jun. 25, 2021, which is a continuation of U.S. Pat. No. 11,086,875 entitled “DATABASE QUERY PROCESSING USING A PRUNING INDEX,” which is a continuation of U.S. Pat. No. 10,942,925 entitled “DATABASE QUERY PROCESSING USING A PRUNING INDEX,” filed on Jul. 17, 2020 which is a continuation of U.S. Pat. No. 10,769,150, entitled “PRUNING INDEXES TO ENHANCE DATABASE QUERY PROCESSING,” filed on Dec. 26, 2019; the contents of which are incorporated herein by reference. 
    
    
     TECHNICAL FIELD 
     Embodiments of the disclosure relate generally to databases and, more specifically, to lazy reassembling of semi-structured data in a database system. 
     BACKGROUND 
     When certain information is to be extracted from a database, a query statement may be executed against the database data. A database system processes the query and returns certain data according to one or more search conditions that indicate what information should be returned by the query. The database system extracts specific data from the database and formats that data into a readable form. However, it can be challenging to execute queries on a very large table because a significant amount of time and computing resources are required to scan an entire table to identify data that satisfies the query. 
    
    
     
       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 provider system, 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 conceptual diagram illustrating generation of an example blocked bloom filter, which may form part of a pruning index, in accordance with some example embodiments. 
         FIG.  5    illustrates a portion of an example pruning index, in accordance with some embodiments of the present disclosure. 
         FIG.  6    is a conceptual diagram illustrating further details regarding the creation of an example pruning index, in accordance with some embodiments. 
         FIG.  7    is a conceptual diagram illustrating maintenance of a pruning index, in accordance with some embodiments. 
         FIGS.  8 - 13    are flow diagrams illustrating operations of the network-based database system in performing a method for generating and using a pruning index in processing a database query, in accordance with some embodiments of the present disclosure. 
         FIG.  14    is a conceptual diagram illustrating an example reassembly hook object and example residual objects, in accordance with some embodiments of the present disclosure. 
         FIG.  15    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 noted above, processing queries directed to very large tables is challenging because a significant amount of time and computing resources are required to scan an entire table to identify data that satisfies the query. Therefore, it can be desirable to execute a query without scanning the entire table. Aspects of the present disclosure address the above and other challenges in processing queries on large tables by creating a pruning index that may be used to construct a reduced scan set for processing a query. More specifically, a large source table may be organized into a set of batch units such as micro-partitions, and a pruning index can be created for the source table to be used in identifying a subset of the batch units to scan to identify data that satisfies the query. 
     It is common for data to be stored by database systems in semi-structured formats, which can store objects of any kind such as numbers, strings, timestamps, or the like. Accordingly, the pruning indexes described herein are configured to support primitive data types (e.g., STRING, NUMBER, or the like) as well as such semi-structured and complex (e.g., ARRAY and OBJECT) data types. 
     Consistent with some embodiments, a network-based database system generates a pruning index for a source table and uses the pruning index to prune micro-partitions of the source table when processing queries directed to the source table. The pruning index includes a probabilistic data structure that stores fingerprints for all searchable values in a source table. The fingerprints are based on hashes computed based on searchable values in the source table. To support semi-structured data type values, hashes can be computed over indexing transformations of searchable values. That is, for each semi-structured data type value, one or more indexing transformations are generated and the fingerprints are generated based on hashes computed over the one or more indexing transformations. An indexing transformation is generated by converting a semi-structured data value to a primitive data type. Semi-structured data types are automatically parsed to identify all paths that can be indexed. To support partial matching queries, fingerprints can be generated by computing a hash over a set of N-grams generated based on a searchable value, in some embodiments. 
     In generating a pruning index, the network-based database system uses the fingerprints to generate a filter for each micro-partition of the source table that indexes distinct values (or distinct N-grams of searchable values) in each column of the micro-partition of the source table. The filter may, for example, comprise a blocked bloom filter, a bloom filter, a hash filter, or a cuckoo filter. 
     For a given query, the pruning index can be used to quickly disqualify micro-partitions that are certain to not include data that satisfies the query. When a query is received, rather than scanning the entire source table to identify matching data, the network-based database system probes the pruning index to identify a reduced scan set of micro-partitions comprising only a subset of the micro-partitions of the source table, and only the reduced scan set of micro-partitions is scanned when executing the query. 
     The database system can use a pruning index to prune a scan set for queries with equality predicates (e.g., “=”) and queries with pattern matching predicates (e.g., LIKE, ILIKE, CONTAINS, STARTSWITH, ENDSWITH, RLIKE, REGEXP, etc.) on both structured and semi-structured data types. As discussed herein, a “predicate” comprises an expression (e.g., a SQL expression) that evaluates a search condition that is either TRUE, FALSE, or UNKNOWN. For a given equality predicate, the database system uses the pruning index to identify a subset of micro-partitions to scan for data that matches an entire string or other searchable value. For a given pattern matching predicate, the database system uses the pruning index to identify a set of micro-partitions to scan for data that matches a specified search pattern, which can include one or more partial strings and one or more wildcards (e.g., “%” or “_”) used to represent wildcard character positions in the pattern (e.g., character positions whose underlying value is unconstrained by the query). 
     By using a pruning index to prune the set of micro-partitions to scan in executing a query, the database system accelerates the execution of point queries on large tables when compared to conventional methodologies. Using a pruning index in this manner also guarantees a constant overhead for every searchable value on the table. Additional benefits of pruning index utilization include, but are not limited to, an ability to support multiple predicate types, an ability to quickly compute the number of distinct values in a table, and the ability to support join pruning. 
     In addition, by utilizing indexing transformations when building the pruning index, query predicates on semi-structured fields can be supported. Contrary to conventional approaches, this approach does not require manual selection of semi-structured data fields to be indexed. Further, unlike conventional approaches, this approach does not use any additional storage or concepts such as virtual columns or generated columns to store the fields to be indexed. Also, the pruning indexes described herein support indexing and matching against predicates regardless of how nested or evolving the structure of the input data is. Moreover, this approach does not enforce any data type restrictions on the semi-structured data fields and the values in the predicate. Finally, the generation of the pruning index involves cast-sensitive indexing of individual semi-structured data type fields, meaning that each input record can be attempted to be converted to relevant data types to match with casting behavior of semi-structured data type columns. 
     When building a pruning index on a semi-structured data column, a lazy reassembly process may be used rather than reassembling an entire tree structure for the semi-structured data column prior to indexing. For some embodiments, when ingesting semi-structured data from a column of a source table, the database system subcolumnarizes a first portion of the semi-structured data based on a set of rules. As used herein, “subcolumnarization” refers to storing semi-structured data in a columnar form within a column based on a set of rules. Accordingly, the database system extracts the first portion to a columnar form within the column based on the set of rules. The database system generates a reassembly hook object for the semi-structured data that includes information about the subcolumnarized data. The reassembly hook object comprises a data structure such as a tree structure that represents the first portion of semi-structured data. For example, the first portion of semi-structured data can include a first subcolumnarized path and a second subcolumnarized path. In this example, the reassembly hook object includes a root node with a first branch corresponding to the first subcolumnarized path and a second branch corresponding to the second subcolumnarized path. The first branch includes a first leaf node with a pointer to records (primitive values) at the first subcolumnarized path and the second branch includes a second leaf node with a pointer to primitive values for the second subcolumnarized path. 
     In addition to the reassembly hook object, one or more residual data objects are generated. The one or more residual data objects represent the second portion of the semi-structured data that is not subcolumnarized. In building the pruning index for the semi-structured data column in the source table, the database system traverses the reassembly hook object and the one or more residual objects to identify values for indexing, rather than reassembling the entire tree structure for the semi-structured column prior to indexing. 
     By incorporating the lazy reassembly technique for indexing semi-structured data referenced above and described herein, the database system eliminates a need for indexer instances to traverse entire semi-structured data trees for every row in a base table. This approach also improves data locality. Moreover, when subcolumn entries are dictionary encoded, the database system is able to perform memoization on top of the dictionary values such that every distinct value gets traversed and indexed only once. Further, the lazy reassembly approach leads to faster index build performance. In addition, when attempting to reinsert semi-structured data into a materialized table or ingesting it from a column-oriented data storage format such as PARQUET, leaving the data in lazily reassembled format and doing subcolumnarization in the insert path of the lazily reassembled data is more efficient. 
     As discussed herein, a “micro-partition” is a batch unit, and each micro-partition has contiguous units of storage. By way of example, each micro-partition may contain between 50 MB and 500 MB of uncompressed data (note that the actual size in storage may be smaller because data may be stored compressed). Groups of rows in tables may be mapped into individual micro-partitions organized in a columnar fashion. This size and structure allow for extremely granular selection of the micro-partitions to be scanned, which can comprise millions, or even hundreds of millions, of micro-partitions. This granular selection process for micro-partitions to be scanned is referred to herein as “pruning.” Pruning involves using metadata to determine which portions of a table, including which micro-partitions or micro-partition groupings in the table, are not pertinent to a query, and then avoiding those non-pertinent micro-partitions when responding to the query and scanning only the pertinent micro-partitions to respond to the query. Metadata may be automatically gathered about all rows stored in a micro-partition, including: the range of values for each of the columns in the micro-partition; the number of distinct values; and/or additional properties used for both optimization and efficient query processing. In one embodiment, micro-partitioning may be automatically performed on all tables. For example, tables may be transparently partitioned using the ordering that occurs when the data is inserted/loaded. However, it should be appreciated that this disclosure of the micro-partition is exemplary only and should be considered non-limiting. It should be appreciated that the micro-partition may include other database storage devices without departing from the scope of the disclosure. 
       FIG.  1    illustrates an example computing environment  100  that includes a database system  102  in communication with a storage platform  104 , 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. 
     As shown, the computing environment  100  comprises the database system  102  and a storage platform  104  (e.g., AWS®, Microsoft Azure Blob Storage®, or Google Cloud Storage®). The database system  102  is used for reporting and analysis of integrated data from one or more disparate sources including storage devices  106 - 1  to  106 -N within the storage platform  104 . The 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 database system  102 . 
     The database system  102  comprises a compute service manager  108 , an execution platform  110 , and a database  114 . The database system  102  hosts and provides data reporting and analysis services to multiple client accounts. Administrative users can create and manage identities (e.g., users, roles, and groups) and use permissions to allow or deny access to the identities to resources and services. 
     The compute service manager  108  coordinates and manages operations of the database system  102 . The compute service manager  108  also performs query optimization and compilation as well as managing clusters of compute 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 user device  112 . The user device  112  corresponds to a user of one of the multiple client accounts supported by the database system  102 . In some embodiments, the compute service manager  108  does not receive any direct communications from the user device  112  and only receives communications concerning jobs from a queue within the database system  102 . 
     The compute service manager  108  is also coupled to database  114 , which is associated with the data stored in the computing environment  100 . The database  114  stores data pertaining to various functions and aspects associated with the database system  102  and its users. In some embodiments, the database  114  includes a summary of data stored in remote data storage systems as well as data available from a local cache. Additionally, the database  114  may include information regarding how data is organized in remote data storage systems (e.g., the storage platform  104 ) and the local caches. The database  114  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. 
     For example, the database  114  can include one or more pruning indexes. The compute service manager  108  may generate a pruning index for each source table accessed from the storage platform  104  and use a pruning index to prune the set of micro-partitions of a source table to scan for data in executing a query. That is, given a query directed at a source table organized into a set of micro-partitions, the compute service manager  108  can access a pruning index from the database  114  and use the pruning index to identify a reduced set of micro-partitions to scan in executing the query. The set of micro-partitions to scan in executing a query may be referred to herein as a “scan set.” 
     In some embodiments, the compute service manager  108  may determine that a job should be performed based on data from the database  114 . In such embodiments, the compute service manager  108  may scan the data and determine that a job should be performed to improve data organization or database performance. For example, the compute service manager  108  may determine that a new version of a source table has been generated and the pruning index has not been refreshed to reflect the new version of the source table. The database  114  may include a transactional change tracking stream indicating when the new version of the source table was generated and when the pruning index was last refreshed. Based on that transaction stream, the compute service manager  108  may determine that a job should be performed. In some embodiments, the compute service manager  108  determines that a job should be performed based on a trigger event and stores the job in a queue until the compute service manager  108  is ready to schedule and manage the execution of the job. In an embodiment of the disclosure, the compute service manager  108  determines whether a table or pruning index needs to be reclustered based on one or more data manipulation language (DML) commands being performed, wherein one or more of the DML commands constitute the trigger event. 
     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 . The storage platform  104  comprises multiple data storage devices  106 - 1  to  106 -N. In some embodiments, the data storage devices  106 - 1  to  106 -N are cloud-based storage devices located in one or more geographic locations. For example, the data storage devices  106 - 1  to  106 -N may be part of a public cloud infrastructure or a private cloud infrastructure. The data storage devices  106 - 1  to  106 -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 storage platform  104  may include distributed file systems (e.g., 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 micro-partition files using a least recently used (LRU) policy and implement an out of memory (OOM) error mitigation process; a third process that extracts health information from process logs and status 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. 
     As shown in  FIG.  1   , the data storage devices  106 - 1  to  106 -N are decoupled from the computing resources associated with the execution platform  110 . This architecture supports dynamic changes to the database system  102  based on the changing data storage/retrieval needs as well as the changing needs of the users and systems. The support of dynamic changes allows the database system  102  to scale quickly in response to changing demands on the systems and components within the database system  102 . The decoupling of the computing resources from the data storage devices supports the storage of large amounts of data without requiring a corresponding large amount of computing resources. Similarly, this decoupling of resources supports a significant increase in the computing resources utilized at a particular time without requiring a corresponding increase in the available data storage resources. 
     The compute service manager  108 , database  114 , execution platform  110 , and storage platform  104  are shown in  FIG.  1    as individual discrete components. However, each of the compute service manager  108 , database  114 , 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 , database  114 , 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 database system  102 . Thus, in the described embodiments, the database system  102  is dynamic and supports regular changes to meet the current data processing needs. 
     During typical operation, the 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 the database  114  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 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 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  106 - 1  to  106 -N in the storage platform  104 . Thus, the computing resources and cache resources are not restricted to specific data storage devices  106 - 1  to  106 -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 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 key manager  204  coupled to a data storage device  206 . Access manager  202  handles authentication and authorization tasks for the systems described herein. Key manager  204  manages storage and authentication of keys used during authentication and authorization tasks. For example, access manager  202  and key manager  204  manage the keys used to access data stored in remote storage devices (e.g., data storage devices in storage platform  104 ). As used herein, the remote storage devices may also be referred to as “persistent storage devices” or “shared storage devices.” 
     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 necessary 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 . As discussed below, each virtual warehouse includes multiple execution nodes that each include a cache and a processor. 
     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 caches (e.g., the caches in execution platform  110 ). The configuration and metadata manager  222  uses the metadata to determine which data micro-partitions need to be accessed to retrieve data for processing a particular task or job. A monitor and workload analyzer  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 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 . Data storage device  226  in  FIG.  2    represents any data storage device within the database system  102 . For example, data storage device  226  may represent caches in execution platform  110 , storage devices in storage platform  104 , or any other storage device. 
     As shown, the compute service manager  108  further includes a pruning index generator  228 . The pruning index generator  228  is responsible for generating pruning indexes to be used in pruning scan sets for queries directed to tables stored in the storage platform  104 . Each pruning index comprises a set of filters (e.g., blocked bloom filters, bloom filters, hash filter, or cuckoo filters) that encode an existence of unique values in each column of a source table. The pruning index generator  228  generates a filter for each micro-partition of a source table and each filter indicates whether data matching a query is potentially stored on a particular micro-partition of the source table. Further details regarding the generation of pruning indexes are discussed below. 
       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 includes 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 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  106 - 1  to  106 -N shown in  FIG.  1   . Thus, the virtual warehouses are not necessarily assigned to a specific data storage device  106 - 1  to  106 -N and, instead, can access data from any of the data storage devices  106 - 1  to  106 -N within the storage platform  104 . Similarly, each of the execution nodes shown in  FIG.  3    can access data from any of the data storage devices  106 - 1  to  106 -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 N 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 the execution nodes are caching. 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 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 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 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 conceptual diagram illustrating generation of a filter  400 , which forms part of a pruning index generated by the database system  102  based on a source table  402 , in accordance with some example embodiments. As shown, the source table  402  is organized into multiple micro-partitions and each micro-partition comprises multiple columns in which values are stored. 
     In generating a pruning index, the compute service manager  108  generates a filter for each micro-partition of the source table  402 , an example of which is illustrated in  FIG.  4    as blocked bloom filter  400 . Blocked bloom filter  400  comprises multiple bloom filters and encodes the existence of distinct values present in each column of the corresponding micro-partition. When a query is received, rather than scanning the entire source table  402  to evaluate the query, the database system  102  probes the pruning index to identify a reduced scan set of micro-partitions comprising only a subset of the micro-partitions of the source table  402 . 
     As shown, the blocked bloom filter  400  is decomposed into N bloom filters stored as individual columns of the pruning index to leverage columnar scans. In generating the blocked bloom filter  400  for a particular micro-partition of the source table  402 , values of stored values or preprocessed variants thereof are transformed into bit positions in the bloom filters. For example, a set of fingerprints (e.g., hash values) can be generated from stored values (or N-grams generated from stored values) in each column of the micro-partition and the set of fingerprints may be used to set bits in the bloom filters. Each line of the blocked bloom filter  400  is encoded and stored as a single row in the pruning index. Each bloom filter  400  is represented in the pruning index as a two-dimensional array indexed by the fingerprints for the stored column values. 
       FIG.  5    illustrates a portion of an example pruning index  500 , in accordance with some embodiments of the present disclosure. The example pruning index  500  is organized into a plurality of rows and columns. The columns of the pruning index  500  comprise a partition number  502  to store a partition identifier and a blocked bloom filter  504  (e.g., the blocked bloom filter  400 ) that is decomposed into multiple numeric columns; each column in the blocked bloom filter  504  represents a bloom filter. To avoid obscuring the inventive subject matter with unnecessary detail, various additional columns that are not germane to conveying an understanding of the inventive subject matter may have been omitted from the example pruning index  500  in  FIG.  5   . 
       FIG.  6    is a conceptual diagram illustrating creation of an example pruning index, in accordance with some embodiments. The creation of a filter (e.g., a blocked bloom filter) is performed by a specialized operator within the compute service manager  108  that computes the set of rows of the pruning index. This operator obtains all the columns of a particular micro-partition of a source table and populates the filter for that micro-partition. 
     If the total number of distinct values (or distinct N-grams of stored values) in the source table is unknown, the compute service manager  108  allocates a maximum number of levels to the pruning index, populates each filter, and then applies a consolidation phase to merge the different filters in a final representation of the pruning index. The memory allocated to compute this information per micro-partition is constant. In the example illustrated in  FIG.  6   , the memory allocated to compute this information is a two-dimensional array of unsigned integers. The first dimension is indexed by the level (maximum number of levels) and the second dimension is indexed by the number of bloom filters. Since each partition is processed by a single thread, the total memory is bounded by the number of threads (e.g., 8) and the maximum level of levels. 
     As shown in  FIG.  6   , at each partition boundary, the compute service manager  108  combines blocks based on a target bloom filter density. For example, the compute service manager  108  may combine blocks such that the bloom filter density is no more than half. Since the domain of fingerprints (e.g., hashed values) is uniform, this can be done incrementally or globally based on the observed number of distinct values computed above. 
     If the number of distinct values is known, the compute service manager  108  determines the number of levels for the pruning index by dividing the maximum number of distinct values (or distinct N-grams) by the number of distinct values (or distinct N-grams) per level. To combine two levels, the compute service manager  108  performs a logical OR on all the integers representing the filter. 
     For performance reasons, the filter functions (create and check) can combine two hash functions (e.g., two 32-bit hash functions). Both the hash function computation and the filter derivation need to be identical on both the execution platform  110  and compute service manager  108  to allow for pruning in compute service manager  108  and in the scan set initialization in the execution platform  110 . 
       FIG.  7    is a conceptual diagram illustrating maintenance of a pruning index based on changes to a source table, in accordance with some embodiments. As shown, at  700 , a change is made to a source table (e.g., addition of one or more rows or columns). The change to the source table triggers generation of additional rows in the pruning index for each changed or new micro-partition of the source table, at  702 . At a regular interval, the newly produced rows in the pruning index are reclustered, at  704 . 
     The compute service manager  108  uses a deterministic selection algorithm as part of clustering the pruning index. The processing of each micro-partition in the source table creates a bounded (and mostly constant) number of rows based on the number of distinct values (or N-grams of stored values) in the source micro-partition. By construction, those rows are known to be unique and the index domain is non-overlapping for that partition and fully overlapping with already clustered index rows. To minimize the cost of clustering, the compute service manager  108  delays reclustering of rows until a threshold number of rows have been produced to create constant partitions. 
     Although the pruning index is described in some embodiments as being implemented specifically with blocked bloom filters, it shall be appreciated that the pruning index is not limited to blocked bloom filters, and in other embodiments, the pruning index may be implemented using other filters such as bloom filters, hash filters, or cuckoo filters. 
       FIGS.  8 - 13    are flow diagrams illustrating operations of the database system  102  in performing a method  800  for generating and using a pruning index in processing a database query, in accordance with some embodiments of the present disclosure. The method  800  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  800  may be performed by components of database system  102 . Accordingly, the method  800  is described below, by way of example with reference thereto. However, it shall be appreciated that method  800  may be deployed on various other hardware configurations and is not intended to be limited to deployment within the database system  102 . 
     Depending on the embodiment, an operation of the method  800  may be repeated in different ways or involve intervening operations not shown. Though the operations of the method  800  may be depicted and described in a certain order, the order in which the operations are performed may vary among embodiments, including performing certain operations in parallel or performing sets of operations in separate processes. For example, although the use and generation of the pruning index are described and illustrated together as part of the method  800 , it shall be appreciated that the use and generation of the pruning index may be performed as separate processes, consistent with some embodiments. 
     At operation  805 , the compute service manager  108  accesses a source table that is organized into a plurality of micro-partitions. The source table comprises a plurality of cells organized into rows and columns and a data value is included in each cell. 
     At operation  810 , the compute service manager  108  generates a pruning index based on the source table. The pruning index comprises a set of filters (e.g., a set of blocked bloom filters) that index distinct values (or distinct N-grams of stored values) in each column of each micro-partition of the source table. A filter is generated for each micro-partition in the source table and each filter is decomposed into multiple numeric columns (e.g., 32 numeric columns) to enable integer comparisons. Consistent with some embodiments, the pruning index comprises a plurality of rows and each row comprises at least a micro-partition identifier and a set of bloom filters. Consistent with some embodiments, the compute service manager  108  generates the pruning index in an offline process before receiving a query. 
     At operation  815 , the compute service manager  108  receives a query directed at the source table. The query can comprise an equality predicate (e.g., “=”) or a pattern matching predicate (e.g., LIKE, ILIKE, CONTAINS, STARTSWITH, ENDSWITH, REGEXP, RLIKE, etc.). In instances in which the query includes a pattern matching predicate, the query specifies a search pattern for which matching stored data in the source table is to be identified. A query predicate can be directed to primitive data types (e.g., STRING, NUMBER, or the like), complex data types (e.g., ARRAY or OBJECT), semi-structured data types (e.g., JSON, XML, Parquet, and ORC), or combinations thereof. 
     At operation  820 , the compute service manager  108  accesses the pruning index associated with the source table based on the query being directed at the source table. For example, the database  114  may store information describing associations between tables and pruning indexes. 
     At operation  825 , the compute service manager  108  uses the pruning index to prune the set of micro-partitions of the source table to be scanned for data that satisfies the query (e.g., a data value that satisfies the equality predicate or data that matches the search pattern). That is, the compute service manager  108  uses the pruning index to identify a reduced scan set comprising only a subset of the micro-partitions of the source table. The reduced scan set includes one or more micro-partitions in which data that satisfies the query is potentially stored. The subset of micro-partitions of the source table include micro-partitions determined to potentially include data that satisfies the query based on the set of bloom filters in the pruning index. 
     At operation  830 , the execution platform  110  processes the query. In processing the query, the execution platform  110  scans the subset of micro-partitions of the reduced scan set while foregoing a scan of the remaining micro-partitions. In this way, the execution platform  110  searches only micro-partitions where matching data is potentially stored while foregoing an expenditure of additional time and resources to also search the remaining micro-partitions for which it is known, based on the pruning index, that matching data is not stored. 
     Consistent with some embodiments, rather than providing a reduced scan set with micro-partitions of the source table to scan for data, the compute service manager  108  may instead identify and compile a set of non-matching micro-partitions. The compute service manager  108  or the execution platform  110  may remove micro-partitions from the scan set based on the set of non-matching micro-partitions. 
     As shown in  FIG.  9   , the method  800  may, in some embodiments, further include operations  905  and  910 . Consistent with these embodiments, the operations  905  and  910  may be performed as part of the operation  810  where the compute service manager  108  generates the pruning index. The operations  905  and  910  are described below in reference to a single micro-partition of the source table simply for ease of explanation. However, it shall be appreciated that in generating the pruning index, the compute service manager  108  generates a filter for each micro-partition of the source table, and thus the operations  905  and  910  may be performed for each micro-partition of the source table. 
     At operation  905 , the compute service manager  108  generates a filter for a micro-partition of the source table. For example, the compute service manager  108  may generate a blocked bloom filter for the micro-partition that indexes distinct values (or distinct N-grams of values) in each column of the micro-partition of the source table. The generating of the filter can include generating a set of fingerprints for each searchable data value in the micro-partition. 
     Given that values in semi-structured data type columns can be stored as potentially multiple different data types by the network-based database system  102  (referred to herein as “stored data types”), the compute service manager  108  can, in some embodiments, generate fingerprints for a given value in a semi-structured column of the source table based on one or more data type transformations generated for the value, as will be discussed in further detail below. A data type transformation can be generated by converting a data value into a stored data type, for example, using an SQL Cast Function (also referred to simply as a “cast”). By generating fingerprints in this matter, the compute service manager  108  can support indexing of semi-structured data type values included in the source table. 
     In some embodiments, for a given data value in the micro-partition, the compute service manager  108  can generate the set of fingerprints based on a set of N-grams generated for the data value. The set of N-grams can be generated based on the data value or one or more preprocessed variants of the data value. The compute service manager  108  can generate a fingerprint based on a hash that is computed of an N-gram. 
     In computing the hash, the compute service manager  108  may utilize a rolling hash function or other known hashing scheme that allows individual characters to be added or removed from a window of characters. An example hash function used by the compute service manager  108  is the XxHash( ) function, although other known hash functions can be utilized. Each generated fingerprint is used to populate a cell in the filter. 
     At operation  910 , which is optional in some embodiments, the compute service manager  108  merges one or more rows of the filter. The compute service manager  108  can merge rows by performing a logical OR operation. The compute service manager  108  may merge rows of the filter until a density threshold is reached, where the density refers to the ratio of is and Os in a row. The density threshold may be based on a target false positive rate. 
     The source table can include one or more columns of data of a semi-structured data type used to store values of any kind such as primitive data types like numbers, strings, binary data, date, time, and timestamp values, as well as compound data types such as values and arrays that store a nested structure inside. Accordingly, it is important that pruning indexes generated by the database system  102  also support query predicates on semi-structured data types in addition to predicates on primitive data-type fields. As non-limiting examples, a pruning index can be generated to support the following types of predicates:
         . . . where &lt;path_to_semi-structured_data_type_field&gt;=&lt;constant&gt;;   . . . where &lt;path_to_semi-structured_data_type_field&gt;::&lt;cast_to_type&gt;=&lt;constant&gt;   . . . where &lt;semi-structured_data_type_column&gt;=&lt;constant&gt;;   . . . where &lt;path_to_semi-structured_data_type_field&gt; like ‘%pattern%’;   . . . where &lt;semi-structured_data_type_column&gt; like ‘%pattern%’;   . . . where array_contains(&lt;value&gt;, &lt;array&gt;);   . . . where arrays_overlap(&lt;array 1 &gt;, &lt;array 2 &gt;)   . . . where is_&lt;any_data_type&gt;(&lt;path_to_semi-structured_data_type_field&gt;)   . . . where &lt;semi-structured_data_type_column&gt; is null   . . . where &lt;semi-structured_data_type_field&gt; is not null       

     Extending the pruning index to support such semi-structured predicate types can present a number of challenges. For example, semi-structured data type schemas such as JSON schemas can be highly nested (e.g., a field can contain an ARRAY object, which in turn holds values of heterogeneous types, including other complex types such as OBJECT or ARRAY). As another example, semi-structured data can evolve in time (e.g., new fields can be added or existing fields can be removed). As another example, the data type for the same field in one row can be different from the one in another row (e.g., an ID field can be represented as a NUMBER and STRING in different rows). As yet another example of the challenges posed by semi-structured data types, a single value might correspond to multiple data types (e.g., a STRING value can contain a valid DATE, TIME, TIMESTAMP, NUMBER, etc. data types). In still another example, the same value might match against several semi-structured data types due to the presence of a cast function (e.g., a TIMESTAMP value can be stored inside semi-structured data object of any TIMESTAMP version, as a NUMBER, as a STRING of TIMESTAMP, as a STRING of INTEGER). 
     To extend the pruning index to support predicates on semi-structured data fields while addressing the foregoing challenges, operations  1005 ,  1010 ,  1015 ,  1020 ,  1025 , and  1030  can be performed as part of the method  800 , as shown in  FIG.  10   . Consistent with these embodiments, the operations  1005 ,  1010 ,  1015 ,  1020 ,  1025 , and  1030  may be performed as part of operation  810  where the compute service manager  108  generates the pruning index for the source table. 
     At operation  1005 , the compute service manager  108  accesses a reassembly hook object that includes information about semi-structured data in a column of the source table (also referred to as a “semi-structured data type column”). More specifically, the reassembly hook object includes information about a first portion of the semi-structured data that is subcolumnarized within the column of the source table. That is, the first portion of the semi-structured data is stored in columnar form within the column of the source table. The reassembly hook object comprises a first data structure that represents the first portion of the semi-structured data (the subcolumnarized portion). For example, the first data structure may include a tree structure comprising a root node and one or more branches, each of which include at least one child node that branches off the root node. Each branch corresponds to a subcolumnarized path extracted from the semi-structured data and a leaf node in each branch comprises a pointer to a set of primitive values (also referred to as a “primitive node”) for the subcolumnarized path. In an example, the first portion of the semi-structured data includes a subcolumnarized path and the first tree structure includes a branch with a leaf node that includes a pointer to primitive values corresponding to the subcolumnarized path. 
     At operation  1010 , the compute service manager  108  traverses the reassembly hook object to identify a first set of values corresponding to the first portion of the semi-structured data (the subcolumnarized portion of the semi-structured data). For example, the compute service manager  108  may traverse the first data structure to identify the primitive values for each subcolumnarized path in the first portion of semi-structured data. 
     At operation  1015 , the compute service manager  108  accesses one or more residual objects comprising information about a second portion of the semi-structured data that is not subcolumnarized. Each residual object comprises information about at least a portion of the second portion of the semi-structured data. More specifically, each residual object comprises a second data structure representing at least a portion of the second portion of the semi-structured data. The second data structure may include a tree data structure or linear data structure with one or more nodes, each of which corresponds to an element from the second portion of the semi-structured data. The second data structure includes a node with a value from the second portion of the semi-structured data. 
     At operation  1020 , the compute service manager  108  traverses the one or more residual objects to identify a second set of values corresponding to the second portion of the semi-structured data (the non-subcolumnarized portion of the semi-structured data). 
     At operation  1025 , the compute service manager  108  generates one or more indexing transformations for values from the semi-structured data type column of the source table (the first set of values corresponding to the first portion of the semi-structured data and the second set of values corresponding to the second portion of semi-structured data). The compute service manager  108  can generate an indexing transformation for a given value using a SQL cast function. Invocation of a cast function on a value is also referred to herein as “casting.” The compute service manager  108  uses the cast function to convert the value to a stored data type. That is, the compute service manager  108  can cast the value from an input data type to a stored data type to generate an indexing transformation. In some instances, the compute service manager  108  generates an indexing transformation by casting the value from a first logical data type (e.g., FIXED) to a second logical data type (e.g., REAL). In some instances, the compute service manager  108  generates an indexing transformation by casting the value to the same logical data type with a different scale and/or precision (e.g., a FIXED→FIXED (PRECISION, SCALE) transformation). 
     In instances of ARRAY and OBJECT data types, the compute service manager  108  generates an indexing transformation based on a path (e.g., an SQL path) of the data. More specifically, the compute service manager  108  generates a token for the indexing transformation that indicates that a complex path (corresponding to an ARRAY or OBJECT data type) is not indexed specifically. In an example of the forgoing, input data includes: 
     {“id”: 45, “name”: “John Appleseed”, “age”: 45} 
     and a received query predicate includes: 
     src:id=45 
     In this example, if the path (i.e., “/id/” or “/age/”) is not used when indexing and matching, the same values will be treated in the same way and will result in the same hashes. This can be problematic in example instances in which there are sender-receiving IP addresses or the same numeric values in multiple fields. Even if the “id” was different from 45, a pruning index look-up would still identify “45” because it is present in the “age” field. To address the challenges illustrated by this example, a token is generated based on a full absolute path of the data, as mentioned above. 
     Table 1, presented below, lists example indexing transformations that can be generated for multiple input data types. 
     
       
         
           
               
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 Input data type 
                 Indexing transformation 
               
               
                   
               
             
            
               
                 FIXED 
                 FIXED → REAL 
               
               
                   
                 FIXED → FIXED (PRECISION, SCALE) 
               
               
                   
                 TEXT 
               
               
                 REAL 
                 REAL 
               
               
                   
                 REAL → FIXED 
               
               
                   
                 TEXT 
               
               
                 TEXT 
                 TEXT 
               
               
                   
                 TEXT → NUMBER (PRECISION, SCALE) 
               
               
                   
                 DATE 
               
               
                   
                 TIME 
               
               
                   
                 TIMESTAMP 
               
               
                 DATE 
                 DATE 
               
               
                   
                 TEXT 
               
               
                 TIME 
                 TIME 
               
               
                   
                 TEXT 
               
               
                 TIMESTAMP_NTZ 
                 TIMESTAMP_NTZ 
               
               
                 TIMESTAMP_LTZ 
                 TEXT 
               
               
                 TIMESTAMP_TZ 
               
               
                 BOOLEAN 
                 FIXED 
               
               
                   
                 TEXT 
               
               
                 NULL_VALUE 
                 NULL_VALUE 
               
               
                 ARRAY 
                 PATH 
               
               
                 OBJECT 
                 PATH 
               
               
                   
               
            
           
         
       
     
     With specific reference to FIXED and REAL data types, after parsing number input, values can be stored as fixed point (LogicalType::FIXED) or real (LogicalType::REAL) objects. As shown in Table 1, for FIXED input data types, the compute service manager  108  applies a FIXED to REAL transformation. The output value of REAL data type is stabilized and indexed. Additionally, a FIXED to FIXED (precision, scale) transformation is performed, in which the output FIXED data type has a different precision or scale (e.g., 0) than the input. Although this may reduce precision, this enables matching against all valid NUMBER (precision, scale) casts. A FIXED to TEXT transformation is also performed to enable string matching. For REAL input data types, the REAL value is stabilized and indexed if it is in the range of FIXED data type. In instances in which the value is out-of-range (e.g., 1e+50), the value may be discarded from indexing. A REAL to FIXED transformation is also performed and out-of-range REAL values are discarded. As with FIXED data types, a REAL to TEXT transformation is performed to enable exact string matching. 
     With reference to TEXT data types, textual data is indexed as text for exact string matching. Other data types such as DATE, TIME, and TIMESTAMP can be stored as TEXT values. Thus, in order to allow predicates on these data types, the compute service manager  108  may attempt casting textual data to each of these data types and keep successful conversions as indexing transformations. 
     Consistent with some embodiments, the network-based database system  102  can store DATA, TIME, and TIMESTAMP data types in TEXT values. These data types can, however, be present in semi-structured data type columns of tables coming from external scans. When converting valid timestamp strings (and objects) into:
         TIMESTAMP_NTZ=&gt;The output will not contain a time zone. Even if the converted string has its own timezone, it is discarded.   TIMESTAMP_LTZ=&gt;The output will have a local timezone attached. If the converted string does not have a timezone, the compute service manager  108  may add the local timezone. If the string already has a timezone, the compute service manager  108  may first apply the existing timezone, then attach to the local timezone.   TIMESTAMP_TZ=&gt;There is a source local timezone but it is not used during computations. If the converted string does not have a timezone, the compute service manager  108  may add the local timezone. If the string already has a timezone, that timezone is used.       

     Assume STRING=DATE+[TIME]+[TZ] where current local timezone is LTZ. Then: 
     STRING→TIMESTAMP_NTZ=&gt;DATE+[TIME] 
     STRING→TIMESTAMP _TZ=&gt;DATE+[TIME]+(TZ=Ø? LTZ:TZ) 
     STRING→TIMESTAMP_LTZ=&gt;DATE+[TZ=Ø? TIME:TIME+TZ−LTZ]+LTZ 
     TABLES 2 and 3 presented below provide examples of the forgoing formula as applied to GMT-08:00 pacific time. 
     
       
         
           
               
             
               
                 TABLE 2 
               
               
                   
               
               
                 STRING object without timezone information 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
            
               
                 ‘2021-01-01 23:00:00’::variant::timestamp_ntz; 
                 Fri, 1 Jan. 2021 23:00:00 + 0000 
               
               
                 ‘2021-01-01 23:00:00’::variant::timestamp_tz; 
                 Fri, 1 Jan. 2021 23:00:00 − 0800 
               
               
                 ‘2021-01-01 23:00:00’::variant::timestamp_ltz; 
                 Fri, 1 Jan. 2021 23:00:00 − 0800 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 3 
               
               
                   
               
               
                 STRING object with timezone information 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
            
               
                 ‘2021-01-01 23:00:00 − 1200’::variant::timestamp_ntz; 
                 Fri, 1 Jan. 2021 23:00:00 + 0000 
               
               
                 ‘2021-01-01 23:00:00 − 1200’::variant::timestamp_tz; 
                 Fri, 1 Jan. 2021 23:00:00 − 1200 
               
               
                 ‘2021-01-01 23:00:00 − 1200’::variant::timestamp_ltz; 
                 Sat, 2 Jan. 2021 03:00:00 − 0800 
               
               
                   
               
            
           
         
       
     
     Attempting to cast a STRING into all TIMESTAMP types can be very costly. Therefore, it can be more efficient to cast into only one type and gather hashes from that type. For example, it can be more efficient to cast a STRING into TIMESTAMP_TZ type since there are valid STRINGs that are convertible to TIMESTAMP_TZ, but not any other TIMESTAMP type. If the indexed STRING has its own time zone information (e.g., “1970-01-01 03:25:45.000000000Ztz=1440”), casting this STRING into DATE, TIMESTAMP_NTZ or TIMESTAMP_LTZ will fail, but the cast to TIMESTAMP_TZ will succeed. This approach can be advantageous because it does not carry any risk of missing potential successful casts from TEXT to TIMESTAMP types. 
     As noted by TABLE 1, indexing transformations for BOOLEAN data types correspond to FIXED and TEXT data types. For the BOOLEAN to FIXED transformation, the only possible values are ‘0’ and ‘1’. For the BOOLEAN to TEXT transformation, the only possible values are “true” and “false.” 
     With returned reference to  FIG.  10   , at operation  1030 , the compute service manager  108  generates a set of fingerprints for values in the column based on the corresponding indexing transformation(s) generated for the values. That is, the compute service manager  108  generates a set of fingerprints for a given value based on the one or more indexing transformations generated for the value. As noted above in reference to operation  905 , the set of fingerprints generated for each value in the column are used to generate a filter in the pruning index that corresponds to the column. 
     The compute service manager  108  can, in some instances, generate a fingerprint for a given value by computing a hash over an indexing transformation of the value or over the value itself In other words, the set of fingerprints generated for a given value can include one or more fingerprints generated by computing a hash over an indexing transformation and a fingerprint generated by computing a hash over the value itself. 
     As discussed above, for complex data types such as ARRAY and OBJECT, the indexing transformation corresponds to a token that indicates the path (corresponding to an ARRAY or OBJECT data type) is not specifically indexed. In generating a fingerprint for such complex data types, the compute service manager  108  may compute a first hash over the path and compute a second hash over a token using the first hash as a seed to produce the fingerprint for the data. In some example embodiments, the hash function xxHash( ) is used to compute the hashes, though it shall be appreciated that any one of many known hashing techniques and functions can be used. In a first example, the compute service manager  108  can generate a fingerprint for any data type within a semi-structured data object as follows:
         XxHash(&lt;constant&gt;, &lt;seed&gt;=&lt;hash_of_the_path&gt;)       

     In a second example, the compute service manager  108  can generate a fingerprint for a complex data type as follows:
         XxHash(XxHash(&lt;constant&gt;, &lt;hash_of_the_path&gt;), PRIME_NUMBER)       

     In a third example, the compute service manager  108  can generate a fingerprint for a complex data type as follows:
         XxHash(XxHashCombine(&lt;constant&gt;, &lt;seed&gt;=PRIME_NUMBER, &lt;intermediate&gt;=&lt;hash_of_the_path&gt;), PRIME_NUMBER)       

     As shown in  FIG.  11   , the operation  1025  of method  800  may, in some embodiments, include (e.g., as sub-operations or a sub-routine) operations  1105 ,  1110 , and  1115 . At operation  1105 , the compute service manager  108  tries to cast a value in the semi-structured data type column to a stored data type. That is, the compute service manager  108  attempts to convert the value to the stored data type using a SQL cast function. The value can be the first value in the column processed by the compute service manager  108 . 
     If the cast fails, the compute service manager  108  stores an indicator to indicate that values in the column cannot be cast to that particular data type, at operation  1110 . That is, an indicator is stored to indicate that the values in the column are unable to be converted to the data type in response to a failed attempt to convert the value to the data type. In an example, the compute service manager  108  can insert a token in the pruning index that indicates that values in the column cannot be cast to a particular data type. As the compute service manager  108  traverses additional values in the column in generating a filter for the column in the pruning index, the stored token causes the compute service manager  108  to avoid further attempts at casting values in the column to the data type for which the casting failed. If the cast is successful, the compute service manager  108  saves a result of the cast as an indexing transformation for the value, at operation  1115 , and a fingerprint may subsequently be generated based on the indexing transformation, as described above. The compute service manager  108  casts the remaining values in the column to the data types for which the cast is successful. 
     Although only a single data type is addressed above, it shall be appreciated that this process can be repeated for each supported data type. That is, the compute service manager  108  can try to cast the value in the column to each of multiple different supported data types. In this manner, the compute service manager  108  can learn to which data types values in the column can be cast based on which data types the first value in the column can be cast to, and the compute service manager  108  can avoid attempting to cast the remaining values in the column to data types that they cannot be cast to. 
     As shown in  FIG.  12   , the method  800  may further include operations  1205 ,  1210 , and  1215 . As shown, the operations  1205  and  1210  can, in some embodiments, be performed subsequent to the operation  815  where the query directed to the source table is received. Consistent with these embodiments, the query can include a predicate on a semi-structured data type column. At operation  1205 , the compute service manager  108  generates one or more indexing transformations based on the query predicate. Similar to the indexing transformations generated for the values in the semi-structured column, the compute service manager  108  can generate an indexing transformation for the query predicate by executing a cast function over one or more values in the predicate. That is, the compute service manager  108  can use the cast function to convert a value in the query predicate from a first data type to a second data type or to the same data type, but with a different precision and/or scale. In some instances, the value itself can be used as an indexing transformation without casting the value to a different data type. 
     For predicates such as IS_NOT_NULL(&lt;semi-structured_data_type_field&gt;), the argument semi-structured data field can correspond to both internal and leaf nodes, meaning that there is no information about the existence of the path. Thus, the compute service manager  108  cannot simply infer whether this path in fact contains a primitive value or represents an OBJECT or ARRAY data type. Therefore, the compute service manager  108  creates an IN predicate as follows: 
     (&lt;semi-structured_data_type_field&gt; indexed as LEAF node) or 
     (&lt;semi-structured_data_type_field&gt; indexed as INTERNAL node) 
     Accordingly, the compute service manager  108  generates two constants—one for the leaf path and one for the internal path hash. These final constants can be fed into the IN predicate. 
     To address IN predicates in the following form: &lt;semi-structured_data_type_field&gt; in (C 1 , C 2 , . . . , Cn), the semi-structured data type field can be interpreted without any Cast( ) functions when the constants C 1 , C 2 , . . . Cn are numbers. Otherwise, the compute service manager  108  can unwrap the Cast for each constant separately. 
     If the constant to match against is a STRING data type, two constants are created. More specifically, a first constant corresponds to the constant itself without any transformations, and a second constant corresponds to a token that indicates that the input might represent an ARRAY or an OBJECT. In an example, a query predicate includes “ . . . where name=‘[“John”]’;”. In this example, the square bracket can either be part of the input string or can present an ARRAY. Hence, both situations are accounted for in the approach described above. In a general case where there are no bracket symbols present in the input string, only the first constant is created. 
     At operation  1210 , the compute service manager  108  generates a set of search fingerprints based on the one or more indexing transformations. As with the fingerprints generated for the searchable values in the semi-structured column, the compute service manager  108  generates a fingerprint for the query predicate by computing a hash over an indexing transformation generated for the query predicate. 
     As shown, the operation  1215  can be performed as part of the operation  825  where the compute service manager  108  prunes the scan set. At operation  1215 , the compute service manager  108  identifies a subset of partitions to scan based on the pruning index and the search fingerprints. The compute service manager  108  can identify the subset by comparing the set of search fingerprints to values included in the pruning index (e.g., fingerprints of indexing transformations of stored data values in the source table) and identifying one or more values in the pruning index that match one or more search fingerprints. Specifically, the compute service manager  108  identifies one or more micro-partitions that potentially store data that satisfies the query based on fingerprints in the pruning index that match search fingerprint(s). That is, a fingerprint (e.g., hash value computed based on an indexing transformations of semi-structured data value) in the pruning index that matches a search fingerprint generated from an indexing transformation of the query predicate (e.g., a hash value computed based on the indexing transformation) indicates that matching data is potentially stored in a corresponding column of the micro-partition. The corresponding micro-partition can be identified by the compute service manager  108  based on the matching fingerprint in the pruning index. 
     As shown in  FIG.  13   , the method  800  may, in some embodiments, include operations  1305 ,  1310 ,  1315 ,  1320 , and  1325 . Consistent with these embodiments, the operations  1305  and  1310  may be performed prior to operation  810  where the compute service manager  108  generates the pruning index for the source table. At operation  1305 , the compute service manager  108  preprocesses the data values in the cells of the source table. In preprocessing a given data value, the compute service manager  108  generates one or more preprocessed variants of the data value. In performing the preprocessing, the compute service manager performs one or more normalization operations to a given data value. The compute service manager  108  can utilize one of several known normalization techniques to normalize data values. 
     For a given data value, the preprocessing performed by the compute service manager  108  can include, for example, any one or more of: generating a case-agnostic variant (e.g., by converting uppercase characters to lowercase characters), generating one or more misspelled variants based on common or acceptable misspellings of the data value, and generating one or more synonymous variants corresponding to synonyms of the data value. In general, in generating a preprocessed variant (e.g., case-agnostic variant, misspelled variant, a synonymous variant or a variant with special characters to indicate a start and end to a data value), the compute service manager  108  uses a common knowledge base to transform a data value into one or more permutations of the original data value. 
     As an example of the foregoing, the string “Bob” can be transformed into the case-agnostic variant “bob.” As another example, the preprocessed variants of “bob” “bbo” and “obb” can be generated for the string “Bob” to account for misspellings. 
     At operation  1310 , the compute service manager  108  generates a set of N-grams for each preprocessed variant. An N-gram in this context refers to a contiguous sequence of N-items (e.g., characters or words) in a given value. For a given preprocessed variant of a data value in the source table, the compute service manager  108  transforms the value into multiple segments of equal length. For example, for a string, the compute service manager  108  can transform the string into multiple sub-strings of N characters. 
     Depending on the embodiment, the value of N can be predetermined or dynamically computed at the time of generating the pruning index. In embodiments in which the value of N is precomputed, the compute service manager  108  determines an optimal value for N based on a data type of values in the source table. In some embodiments, multiple values of N can be used. That is, a first subset of N-grams can be generated using a first value for N and a second subset of N-grams can be created using a second value of N. 
     Consistent with these embodiments, the operations  1315  and  1320  can be performed prior to operation  820  where the compute service manager  108  prunes the scan set using the pruning index. At operation  1315 , the compute service manager  108  preprocesses a search pattern included in the query. In preprocessing the search pattern, the compute service manager  108  performs the same preprocessing operations that are performed on the data values in the source table at  1305  to ensure that the characters of the search pattern fit the pruning index. Hence, in preprocessing the search pattern, the compute service manager  108  can perform any one or more of: generating a case-agnostic variant of the search pattern (e.g., by converting uppercase characters to lowercase characters), generating one or more misspelled variants based on common or acceptable misspellings of the search pattern, generating one or more synonymous variants corresponding to synonyms of the search pattern, and generating a variant that includes special characters to mark a start and end of the search pattern. In preprocessing a given pattern, the compute service manager  108  can generate one or more preprocessed variants of the search pattern. For example, the compute service manager  108  can generate any one or more of: a case-agnostic variant, misspelled variant, or a synonymous variant for the search pattern. As a further example, the compute service manager  108  can generate a variant that includes special characters to indicate a start and end of a search pattern (e.g., “^testvalue$” for the search pattern “testvalue”). 
     At operation  1320 , the compute service manager  108  generates a set of N-grams for the search pattern based on the one or more preprocessed variants of the search pattern. The compute service manager  108  uses the same value for N that was used to generate the pruning index. In embodiments in which the compute service manager  108  uses multiple values for N in generating the pruning index, the compute service manager  108  uses the same values for generating the set of N-grams for the search pattern. 
     In an example, the query includes the following statement: 
     WHERE a JUKE ‘%LoremIpsum%Dolor%Sit%Amet’ 
     In this example, ‘%LoremIpsum%Dolor%Sit%Amet’ is the search pattern and in preprocessing the search pattern, the compute service manager  108  converts the search pattern to all lower case to create a case-agnostic variant: ‘%loremipsum%dolor%sit%amet’. The compute service manager  108  splits the search pattern into segments at the wild card positions, which, in this example, produces the following sub-strings: “loremipsum,” “dolor,” “sit,” and “amet.” Based on these sub-strings, the compute service manager  108  generates the following set of N-grams: 
     Set [“lorem”, “oremi”, “remip”, “emips”, “mipsu”, “ipsum”, “dolor”] 
     In this example N is 5, and thus the compute service manager  108  discards the sub-strings “sit” and “amet” as their length is less than 5. 
     As shown, consistent with these embodiments, the operation  1325  can be performed as part of the operation  825  where the compute service manager  108  prunes the scan set using the pruning index. At operation  1325 , the compute service manager  108  uses the set of N-grams generated based on the search pattern to identify a subset of micro-partitions of the source table to scan based on the pruning index. The compute service manager  108  may identify the subset of micro-partitions by generating a set of fingerprints based on the set of N-grams (e.g., by computing a hash for each N-gram), comparing the set of fingerprints to values included in the pruning index (e.g., fingerprints of stored data values in the source table), and identifying one or more values in the pruning index that match one or more fingerprints in the set of fingerprints generated based on the N-grams of the search pattern. Specifically, the compute service manager  108  identifies one or more micro-partitions that potentially store data that satisfies the query based on fingerprints of data values in the pruning index that match fingerprints in the set of fingerprints computed for the search pattern. That is, a fingerprint (e.g., hash value computed based on an N-gram of a preprocessed stored data value in the source table) in the pruning index that matches a fingerprint generated from an N-gram of the search pattern (e.g., a hash value computed based on the N-gram) indicates that matching data is potentially stored in a corresponding column of the micro-partition because the N-gram generated from the search pattern is stored in the column of the micro-partition. The corresponding micro-partition can be identified by the compute service manager  108  based on the matching fingerprint in the pruning index. Consistent with some embodiments, in identifying the subset of micro-partitions, the compute service manager  108  uses the pruning index to identify any micro-partitions that contain any one of the fingerprints generated from the search pattern N-grams, and from these micro-partitions, the compute service manager  108  then identifies the micro-partitions that contain all of the N-grams. That is, the compute service manager  108  uses the pruning index to identify a subset of micro-partitions that contain data matching all fingerprints generated based on the N-grams of the search pattern. For example, given fingerprints f 1 , f 2 , and f 3 , the compute service manager  108  uses the pruning index to determine: a first micro-partition and second micro-partition contain data corresponding to f 1 ; the second micro-partition and a third micro-partition contain data corresponding to f 2 ; and the first, second, and third micro-partition contain data corresponding to f 3 . In this example, the compute service manager  108  selects only the second micro-partition for scanning based on the second micro-partition containing data that corresponds to all three fingerprints. 
       FIG.  14    illustrates an example reassembly hook object  1400  and example residual objects  1420 ,  1422 , and  1424  that correspond to semi-structured data in a column  1430  of a table  1440 . In this example, the reassembly hook object  1400  comprises a tree structure that represents a subcolumnarized portion of the semi-structured data in column  1430 . As shown, the tree structure includes a branch  1402  that corresponds to a first subcolumnarized path—“num_path”—and a branch  1404  that corresponds to a second subcolumnarized path—“array_path.” The branch  1402  includes a leaf node  1406  that includes a pointer to primitive values for “num_path” and the branch  1404  includes a leaf node  1408  that includes a pointer to primitive values for “array_path.” 
     The residual objects  1420 ,  1422 , and  1424  correspond to a non-subcolumnarized portion of the semi-structured data. Each of the residual objects  1420 ,  1422 , and  1424  comprise a data structure corresponding to a portion of the non-subcolumnarized portion of the semi-structured data. Each of the residual objects include a leaf node comprising a primitive value corresponding to a portion of the non-subcolumnarized portion of the semi-structured data. For example, as shown, the residual object  1420  includes leaf node  1421 , the residual object  1422  includes leaf node  1423 , and the residual object  1424  includes leaf node  1425 . 
       FIG.  15    illustrates a diagrammatic representation of a machine  1300  in the form of a computer system within which a set of instructions may be executed for causing the machine  1500  to perform any one or more of the methodologies discussed herein, according to an example embodiment. Specifically,  FIG.  15    shows a diagrammatic representation of the machine  1500  in the example form of a computer system, within which instructions  1516  (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine  1500  to perform any one or more of the methodologies discussed herein may be executed. For example, the instructions  1516  may cause the machine  1500  to execute any one or more operations of any one or more of the method  800 . As another example, the instructions  1516  may cause the machine  1500  to implement portions of the functionality illustrated in any one or more of  FIGS.  4 - 8   . In this way, the instructions  1516  transform a general, non-programmed machine into a particular machine  1500  (e.g., the compute service manager  108 , the execution platform  110 , and the data storage devices  206 ) 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  1500  operates as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine  1500  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  1500  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  1516 , sequentially or otherwise, that specify actions to be taken by the machine  1500 . Further, while only a single machine  1500  is illustrated, the term “machine” shall also be taken to include a collection of machines  1500  that individually or jointly execute the instructions  1516  to perform any one or more of the methodologies discussed herein. 
     The machine  1500  includes processors  1510 , memory  1530 , and input/output (I/O) components  1550  configured to communicate with each other such as via a bus  1502 . In an example embodiment, the processors  1510  (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  1512  and a processor  1514  that may execute the instructions  1516 . The term “processor” is intended to include multi-core processors  1510  that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions  1516  contemporaneously. Although  FIG.  15    shows multiple processors  1510 , the machine  1500  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  1530  may include a main memory  1532 , a static memory  1534 , and a storage unit  1536 , all accessible to the processors  1510  such as via the bus  1502 . The main memory  1532 , the static memory  1534 , and the storage unit  1536  store the instructions  1516  embodying any one or more of the methodologies or functions described herein. The instructions  1516  may also reside, completely or partially, within the main memory  1532 , within the static memory  1534 , within the storage unit  1536 , within at least one of the processors  1510  (e.g., within the processor&#39;s cache memory), or any suitable combination thereof, during execution thereof by the machine  1500 . 
     The I/O components  1550  include components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components  1550  that are included in a particular machine  1500  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  1550  may include many other components that are not shown in  FIG.  15   . The I/O components  1550  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  1550  may include output components  1552  and input components  1554 . The output components  1552  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  1554  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  1550  may include communication components  1564  operable to couple the machine  1500  to a network  1580  or devices  1570  via a coupling  1582  and a coupling  1572 , respectively. For example, the communication components  1564  may include a network interface component or another suitable device to interface with the network  1580 . In further examples, the communication components  1564  may include wired communication components, wireless communication components, cellular communication components, and other communication components to provide communication via other modalities. The devices  1570  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  1500  may correspond to any one of the compute service manager  108  and the execution platform  110 , and the devices  1570  may include the data storage device  206  or any other computing device described herein as being in communication with the network-based database system  102  or the storage platform  104 . 
     The various memories (e.g.,  1530 ,  1532 ,  1534 , and/or memory of the processor(s)  1510  and/or the storage unit  1536 ) may store one or more sets of instructions  1516  and data structures (e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. These instructions  1516 , when executed by the processor(s)  1510 , cause various operations to implement the disclosed embodiments. 
     As used herein, the terms “machine-storage medium,” “device-storage medium,” and “computer-storage medium” mean the same thing and may be used interchangeably in this disclosure. The terms refer to a single or multiple storage devices and/or media (e.g., a centralized or distributed database, and/or associated caches and servers) that store executable instructions and/or data. The terms shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media, including memory internal or external to processors. Specific examples of machine-storage media, computer-storage media, and/or device-storage media include non-volatile memory, including by way of example semiconductor memory devices, e.g., erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), field-programmable gate arrays (FPGAs), and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The terms “machine-storage media,” “computer-storage media,” and “device-storage media” specifically exclude carrier waves, modulated data signals, and other such media, at least some of which are covered under the term “signal medium” discussed below. 
     In various example embodiments, one or more portions of the network  1580  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  1580  or a portion of the network  1580  may include a wireless or cellular network, and the coupling  1582  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  1582  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  1516  may be transmitted or received over the network  1580  using a transmission medium via a network interface device (e.g., a network interface component included in the communication components  1564 ) and utilizing any one of a number of well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions  1516  may be transmitted or received using a transmission medium via the coupling  1572  (e.g., a peer-to-peer coupling) to the devices  1570 . 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  1516  for execution by the machine  1500 , and include digital or analog communications signals or other intangible media to facilitate communication of such software. Hence, the terms “transmission medium” and “signal medium” shall be taken to include any form of modulated data signal, carrier wave, and so forth. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. 
     The terms “machine-readable medium,” “computer-readable medium,” and “device-readable medium” mean the same thing and may be used interchangeably in this disclosure. The terms are defined to include both machine-storage media and transmission media. Thus, the terms include both storage devices/media and carrier waves/modulated data signals. 
     The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Similarly, the methods described herein may be at least partially processor-implemented. For example, at least some of the operations of the method  800  may be performed by one or more processors. The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but also deployed across a number of machines. In some example embodiments, the processor or processors may be located in a single location (e.g., within a home environment, an office environment, or a server farm), while in other embodiments the processors may be distributed across a number of locations. 
     Although the embodiments of the present disclosure have been described with reference to specific example embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader scope of the inventive subject matter. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. The accompanying drawings that form a part hereof show, by way of illustration, and not of limitation, specific embodiments in which the subject matter may be practiced. The embodiments illustrated are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed herein. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. This Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled. 
     Such embodiments of the inventive subject matter may be referred to herein, individually and/or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept if more than one is in fact disclosed. Thus, although specific embodiments have been illustrated and described herein, it should be appreciated that any arrangement calculated to achieve the same purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, will be apparent, to those of skill in the art, upon reviewing the above description. 
     In this document, the terms “a” or “an” are used, as is common in patent documents, to include one or more than one, independent of any other instances or usages of “at least one” or “one or more.” In this document, the term “or” is used to refer to a nonexclusive or, such that “A or B” includes “A but not B,” “B but not A,” and “A and B,” unless otherwise indicated. In the appended claims, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.” Also, in the following claims, the terms “including” and “comprising” are open-ended; that is, a system, device, article, or process that includes elements in addition to those listed after such a term in a claim is still deemed to fall within the scope of that claim.