Extensible streams on data sources

The subject technology determines a set of shards corresponding to an external data source accessible via a network, the external data source being hosted by an external system separate from a network-based database system. The subject technology determines, using a stream object, a set of offsets of each shard of the set of shards. The subject technology identifies an operation to perform on the set of shards, the operation comprising a read operation or a write operation. The subject technology, based on the set of shards and the set of offsets, performs the operation on the external data source.

TECHNICAL FIELD

Embodiments of the disclosure relate generally to databases and, more specifically, to utilizing data stream platforms in order to access data for storage in the databases.

BACKGROUND

Databases are widely used for data storage and access in computing applications. A goal of database storage is to provide enormous sums of information in an organized manner so that it can be accessed, managed, and updated. In a database, data may be organized into rows, columns, and tables. Databases are used by various entities and companies for storing information that may need to be accessed or analyzed.

A cloud data warehouse (also referred to as a “network-based data warehouse” or simply as a “data warehouse”) is one type of network-based data system used for data analysis and reporting that comprises a central repository of integrated data from one or more disparate sources. A cloud data warehouse is commonly an online analytical processing (OLAP) database that can store current and historical data that can be used for creating analytical reports for an enterprise, based on data stored within databases of the enterprise. To this end, data warehouses typically provide business intelligence tools, tools to extract, transform, and load data into the repository, and tools to manage and retrieve metadata. There are other types of network-based data systems, such as online transaction processing (OLTP) databases, as well as data systems that operate with characteristics of multiple types of traditional database systems.

When certain information is to be extracted from a database, a query statement may be executed against the database data. A cloud data warehouse system processes the query and returns certain data according to one or more query predicates that indicate what information should be returned by the query. The data warehouse 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 at least because a significant amount of time and computing resources are required to scan an entire table to identify data that satisfies the query.

DETAILED DESCRIPTION

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, executing a query without scanning the entire table can result in performance improvements to a data warehouse system and improve latency for returning a result of the query. In some existing data warehouse systems, one approach is to provide support for table streams, which in an example can be implemented as objects that expose Change Data Capture (CDC) information from tables, views, and materialized views, and the like. Such CDC information represents salient changes to data including inserts, updates, and deletes, as well as metadata about each change. In particular, an individual table stream tracks the changes made to rows in a source table. A table stream (referred to herein as a “stream”) generates a “change table” with information indicating changes, at the row level, between two transactional points of time in a table. When executing a query corresponding to a current transaction, the data warehouse system can utilize the stream to determine changes since a prior transaction (e.g., prior query). In this manner, the stream enables querying and consuming a sequence of change records in a transactional fashion, which provide a convenient way to continuously process new or changed data.

In today's expanding cloud computing environment, however, many users may process queries in different data sources (e.g., for extracting, transforming, and loading data into a new host source to perform data analytics on the data), sometimes across various storage platforms, which may be hosted by third parties. In some existing data warehouse systems, although streams for data may be provided, access to external data sources via streams is not supported. Aspects of the present disclosure address the above and other challenges in processing queries, in external data sources, by advantageously enabling the subject system to leverage streams of data provided in external data stream platforms. More specifically, as described in embodiments herein, an extensible stream, using a stream object, is implemented that facilitates performing an operation (e.g., read or write), in response to a query, on an external data stream platform in a transactional manner and to track the delta of changes in the external data source.

FIG. 1illustrates an example computing environment100that includes a network-based data warehouse system102in communication with a storage platform104, 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 fromFIG. 1. However, a skilled artisan will readily recognize that various additional functional components may be included as part of the computing environment100to facilitate additional functionality that is not specifically described herein.

As shown, the computing environment100comprises the network-based data warehouse system102and a storage platform104(e.g., AWS®, Microsoft Azure Blob Storage®, or Google Cloud Storage®). The network-based data warehouse system102is used for reporting and analysis of integrated data from one or more disparate sources including storage devices106-1to106-N within the storage platform104. The storage platform104comprises a plurality of computing machines and provides on-demand computer system resources such as data storage and computing power to the network-based data warehouse system102.

The network-based data warehouse system102comprises a compute service manager108, an execution platform110, and a database114. The network-based data warehouse system102hosts and provides 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 manager108coordinates and manages operations of the network-based data warehouse system102. The compute service manager108also performs query optimization and compilation as well as managing clusters of computing services that provide compute resources (also referred to as “virtual warehouses”). The compute service manager108can 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 manager108.

The compute service manager is also in communication with a user device112. The user device112corresponds to a user of one of the multiple client accounts supported by the network-based data-warehouse102. In some embodiments, the compute service manager108does not receive any direct communications from the user device112and only receives communications concerning jobs from a queue within the network-based data warehouse system102.

The compute service manager108is also coupled to database114, which is associated with the data stored the computing environment100. The database114stores data pertaining to various functions and aspects associated with the network-based data warehouse system102and its users. In some embodiments, the database114includes a summary of data stored in remote data storage systems as well as data available from a local cache. Additionally, the database114may include information regarding how data is organized in remote data storage systems (e.g., the storage platform104) and the local caches. The database114allows 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 database114can include information corresponding to a set of micro-partitions. 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 be comprised of 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.

In some embodiments, the compute service manager108may determine that a job should be performed based on data from the database114. In such embodiments, the compute service manager108may scan the data and determine that a job should be performed to improve data organization or database performance. For example, the compute service manager108may 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 database114may 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 manager108may determine that a job should be performed. In some embodiments, the compute service manager108determines that a job should be performed based on a trigger event and stores the job in a queue until the compute service manager108is ready to schedule and manage the execution of the job. In an embodiment of the disclosure, the compute service manager108determines whether a table or pruning index needs to be reclustered based on one or more DML commands being performed, wherein one or more of DML commands constitute the trigger event.

The compute service manager108is further coupled to the execution platform110, which provides multiple computing resources that execute various data storage and data retrieval tasks. The execution platform110is coupled to storage platform104of the storage platform104. The storage platform104comprises multiple data storage devices106-1to106-N. In some embodiments, the data storage devices106-1to106-N are cloud-based storage devices located in one or more geographic locations. For example, the data storage devices106-1to106-N may be part of a public cloud infrastructure or a private cloud infrastructure. The data storage devices106-1to106-N may be hard disk drives (HDDs), solid state drives (SSDs), storage clusters, Amazon S3TM storage systems or any other data storage technology. Additionally, the storage platform104may include distributed file systems (such as Hadoop Distributed File Systems (HDFS)), object storage systems, and the like.

The execution platform110comprises a plurality of compute nodes. A set of processes on a compute node executes a query plan compiled by the compute service manager108. 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 manager108; a fourth process to establish communication with the compute service manager108after a system boot; and a fifth process to handle all communication with a compute cluster for a given job provided by the compute service manager108and to communicate information back to the compute service manager108and other compute nodes of the execution platform110.

In some embodiments, communication links between elements of the computing environment100are 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 inFIG. 1, the data storage devices106-1to106-N are decoupled from the computing resources associated with the execution platform110. This architecture supports dynamic changes to the network-based data warehouse system102based 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 network-based data warehouse system102to scale quickly in response to changing demands on the systems and components within the network-based data warehouse system102. 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 manager108, database114, execution platform110, storage platform104, and authentication and identity management system118are shown inFIG. 1as individual discrete components. However, each of the compute service manager108, database114, execution platform110, and storage platform104may be implemented as a distributed system (e.g., distributed across multiple systems/platforms at multiple geographic locations). Additionally, each of the compute service manager108, database114, execution platform110, and storage platform104can be scaled up or down (independently of one another) depending on changes to the requests received and the changing needs of the network-based data warehouse system102. Thus, in the described embodiments, the network-based data warehouse system102is dynamic and supports regular changes to meet the current data processing needs.

During typical operation, the network-based data warehouse system102processes multiple jobs determined by the compute service manager108. These jobs are scheduled and managed by the compute service manager108to determine when and how to execute the job. For example, the compute service manager108may 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 manager108may assign each of the multiple discrete tasks to one or more nodes of the execution platform110to process the task. The compute service manager108may determine what data is needed to process a task and further determine which nodes within the execution platform110are 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 database114assists the compute service manager108in determining which nodes in the execution platform110have already cached at least a portion of the data needed to process the task. One or more nodes in the execution platform110process the task using data cached by the nodes and, if necessary, data retrieved from the storage platform104. It is desirable to retrieve as much data as possible from caches within the execution platform110because the retrieval speed is typically much faster than retrieving data from the storage platform104.

As shown inFIG. 1, the computing environment100separates the execution platform110from the storage platform104. In this arrangement, the processing resources and cache resources in the execution platform110operate independently of the data storage devices106-1to106-N in the storage platform104. Thus, the computing resources and cache resources are not restricted to specific data storage devices106-1to106-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 platform104.

As further illustrated, the network-based data warehouse system102is enabled to communicate with an external data stream platform120. In this example, the external data stream platform may be provided by a third party and accessible by one or more components of the network-based data warehouse system102(e.g., the compute service manager108and/or the execution platform110). Some examples of such a data stream platform include Apache Flink®, Apache Kafka®, Amazon Kinesis®, Apache Pulsar® and the like. Such external data stream platforms provide external data sources which are then accessible by the components of the network-based data warehouse system102. It is appreciated, however, that the subject technology can also implement aspects that utilize internal data sources for streams.

As mentioned herein, a stream object tracks data manipulation language (DML) changes made to tables, including inserts, updates, and deletes, as well as metadata about each change, so that actions can be taken using the changed data. This process is referred to as change data capture (CDC). An individual table stream tracks the changes made to rows in a source table. As mentioned before, a stream provides a “change table” indicating such changes, at the row level, between two transactional points of time in a table, thereby enabling querying and consuming a sequence of change records in a transactional manner.

In an embodiment, a stream maintains a point of time into the transactional versioned timeline of the source table, called an offset, which starts at the transactional point when the stream contents were last consumed using a DML statement. The stream can provide the set of changes from the current offset to the current transactional time of the source table (e.g., the current version of the table). In an example, the stream maintains the delta of the changes; if multiple DML statements change a row, the stream contains only the latest action taken on that row. In an embodiment, the offset is advanced (e.g., updated) when utilized in a transaction.

FIG. 2is a block diagram illustrating components of the compute service manager108, in accordance with some embodiments of the present disclosure. As shown inFIG. 2, the compute service manager108includes an access manager202and a key manager204coupled to a data storage device206. Access manager202handles authentication and authorization tasks for the systems described herein. Key manager204manages storage and authentication of keys used during authentication and authorization tasks. For example, access manager202and key manager204manage the keys used to access data stored in remote storage devices (e.g., data storage devices in storage platform104). As used herein, the remote storage devices may also be referred to as “persistent storage devices” or “shared storage devices.” Additionally, the access manager202handles authorization and authentication tasks for stream objects as discussed further herein.

A request processing service208manages received data storage requests and data retrieval requests (e.g., jobs to be performed on database data). For example, the request processing service208may 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 platform110or in a data storage device in storage platform104.

A management console service210supports access to various systems and processes by administrators and other system managers. Additionally, the management console service210may receive a request to execute a job and monitor the workload on the system.

The compute service manager108also includes a job compiler212, a job optimizer214and a job executor216. The job compiler212parses a job into multiple discrete tasks and generates the execution code for each of the multiple discrete tasks. The job optimizer214determines the best method to execute the multiple discrete tasks based on the data that needs to be processed. The job optimizer214also handles various data pruning operations and other data optimization techniques to improve the speed and efficiency of executing the job. The job executor216executes the execution code for jobs received from a queue or determined by the compute service manager108.

A job scheduler and coordinator218sends received jobs to the appropriate services or systems for compilation, optimization, and dispatch to the execution platform110. For example, jobs may be prioritized and processed in that prioritized order. In an embodiment, the job scheduler and coordinator218determines a priority for internal jobs that are scheduled by the compute service manager108with 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 platform110. In some embodiments, the job scheduler and coordinator218identifies or assigns particular nodes in the execution platform110to process particular tasks. A virtual warehouse manager220manages the operation of multiple virtual warehouses implemented in the execution platform110. As discussed below, each virtual warehouse includes multiple execution nodes that each include a cache and a processor.

Additionally, the compute service manager108includes a configuration and metadata manager222, 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 platform110). The configuration and metadata manager222uses 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 analyzer224oversee processes performed by the compute service manager108and manages the distribution of tasks (e.g., workload) across the virtual warehouses and execution nodes in the execution platform110. The monitor and workload analyzer224also redistribute tasks, as needed, based on changing workloads throughout the data warehouse system102and may further redistribute tasks based on a user (e.g., “external”) query workload that may also be processed by the execution platform110. The configuration and metadata manager222and the monitor and workload analyzer224are coupled to a data storage device226. Data storage device226inFIG. 2represent any data storage device within the data warehouse system102. For example, data storage device226may represent caches in execution platform110, storage devices in storage platform104, or any other storage device.

As shown, the compute service manager108further includes a stream processing engine228. In an embodiment, the stream processing engine228is responsible for generating and managing streams, which are implemented as objects that, in an example, expose change data capture (CDC) information from tables, views, materialized views (e.g., database objects that contain results of respective queries), and/or external tables. Further, the stream processing engine228is configured to support streams on external sources, such as the external data stream platform120. Further details regarding the processing of streams with an external source are discussed further below.

FIG. 3is a block diagram illustrating components of the execution platform110, in accordance with some embodiments of the present disclosure. As shown inFIG. 3, the execution platform110includes multiple virtual warehouses, including virtual warehouse1, virtual warehouse2, and virtual warehouse n. Each virtual warehouse includes multiple execution nodes that each include a data cache and a processor. The virtual warehouses can execute multiple tasks in parallel by using the multiple execution nodes. As discussed herein, the execution platform110can 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 platform110to 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 platform104).

Each virtual warehouse is capable of accessing any of the data storage devices106-1to106-N shown inFIG. 1. Thus, the virtual warehouses are not necessarily assigned to a specific data storage device124-1to124-nand, instead, can access data from any of the data storage devices106-1to106-N within the storage platform104. Similarly, each of the execution nodes shown inFIG. 3can access data from any of the data storage devices106-1to106-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 ofFIG. 3, virtual warehouse1includes three execution nodes302-1,302-2, and302-n. Execution node302-1includes a cache304-1and a processor306-1. Execution node302-2includes a cache304-2and a processor306-2. Execution node302-nincludes a cache304-nand a processor306-n. Each execution node302-1,302-2, and302-nis 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 warehouse1discussed above, virtual warehouse2includes three execution nodes312-1,312-2, and312-n. Execution node312-1includes a cache314-1and a processor316-1. Execution node312-2includes a cache314-2and a processor316-2. Execution node312-nincludes a cache314-nand a processor316-n. Additionally, virtual warehouse3includes three execution nodes322-1,322-2, and322-n. Execution node322-1includes a cache324-1and a processor326-1. Execution node322-2includes a cache324-2and a processor326-2. Execution node322-nincludes a cache324-nand a processor326-n.

Although the execution nodes shown inFIG. 3each 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 inFIG. 3store, in the local execution node, data that was retrieved from one or more data storage devices in storage platform104. 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 platform104.

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 warehouses1,2, and n are associated with the same execution platform110, the virtual warehouses may be implemented using multiple computing systems at multiple geographic locations. For example, virtual warehouse1can be implemented by a computing system at a first geographic location, while virtual warehouses2and 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 inFIG. 3as 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 warehouse1implements execution nodes302-1and302-2on one computing platform at a geographic location and implements execution node302-nat 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 platform110is 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.

The following discussion relates to generating an extensible stream, in accordance to some embodiments. To provide interoperability with external sources, such as the external data stream platform120, the stream processing engine228is configured to generate an extensible stream, which is utilized for responding to a query on the external data stream platform120as discussed further below. As discussed further below, a stream object may be utilized to represent an extensible stream and provides functions and/or methods for performing operations on an external data source, such as the external data stream platform120.

In an embodiment, the following example syntax (e.g., pseudocode statements) enables the stream processing engine228to generate an extensible stream and perform an additional operation(s) using the extensible stream:CREATE [OR REPLACE] EXTENSIBLE STREAM STREAM_NAME LOAD_OFFSETS=load_offset_function(streamObj, action) LOAD_DATA=load_data_function(streamObj, partition_offset_pair, rowwriter)STORE_DATA=store_data_function(streamObject, rows, shard) [USING EXTERNAL INTEGRATION MY_EXTERNAL_INTEGRATION]

It is appreciated that above example syntax is one example and could be, in another example, be represented by a JAVA class implementing a certain interface contract corresponding to the following data contract:The extensible stream implementation is able to retrieve the conceptual offset information from the stream data source. In the case of a Kafka® topic, this is a set of partition/offset pairs representing the point in the topic the information was retrieved last time. In addition, the user of the stream may specify this offset information with the AT clause illustrated below.The extensible stream implementation has to be able to use this offset information and retrieve the data from the stream data source using the offset information. In addition to the streaming data, the new offset information has to be also returned which indicates how “far” the stream was read from. The extensible stream implementation has to be able to write data to the stream data source and return the success or failure of that operation.

In some embodiments, to provide further extensibility for streams, a JAVA JAR (JAVA Archive for aggregating files) providing a class implementing an interface, or a Python® library with a class with appropriate methods, may be provided and associated with the extensible stream.

In the above example syntax, the “LOAD_OFFSETS” corresponds to a user defined function (e.g., JavaScript® UDF or JS-UDF) that is called to load the offset information from a stream object as well as performing partition discovery on the external data stream platform120to determine new offsets. In an example, the “LOAD_OFFSETS” function accepts an object (e.g., JavaScript® object) representing the stream, and an action as a string (e.g., “STORE_DATA” or “LOAD_DATA”). Further, the aforementioned JavaScript® object provides a function (e.g., “getStreamOffsets”) that provides a set of current offsets as a string, which may be implemented as blob data (e.g., JavaScript® object notation (JSON) blob), a comma delimited string, and the like. In an example, the set of offsets returned from the “getStreamOffsets” function can be specified by a user, or correspond to offsets from a previous time (e.g., the last time) that a read operation was performed using the stream and information related to the stream was stored after reading the stream (e.g., to update a last read offset, etc.). In an embodiment, the aforementioned function can be configured to provide new offsets instead of a current set of offsets.

In another embodiment, the stream processing engine228can determine a range of offsets and divide the offsets into separate sets. For example, when the stream processing engine228discovers a partition and an offset with a value of zero (0), but there is an indication of a high watermark (e.g., the offset of the last message that was successfully copied to all of the log's replicas) with a value of one hundred (100), the stream processing engine228can divide this partition into two (logical) sets, a first set that covers offsets zero (0) to fifty (50) and a second set from fifty one (51) to one hundred (100). As discussed herein, a particular partition can be associated with a particular offset, thus forming a partition/offset pair which is utilized for executing the stream. As referred to herein, execution of a stream corresponds to performing a transaction on the stream. In an example, when the transaction is completed, the offsets can be advanced to reflect the last read offset from the stream.

In another example, the stream processing engine228may not process all 50 available offsets, as discussed above, in each partition/offset pair when loading data. In this instance, the stream processing engine228stores two entries after successfully loading data, in which each entry has the same partition but different offset ranges representing the start and end offset (e.g., 35-50 and 85-100). The stream object behaves as an “out of order” stream in that the records returned when reading from the stream object may be out of order with respect to a logical ordering in the external data stream platform120. In an example, the stream processing engine228may aggregate the two partition/offset pairs such that a new one is generated of partition: [35,50, 85-100], which is intended for one unit of parallelism to process since the total number of offsets to process for that partition is relatively low.

In another example, the stream processing engine228determines that a new partition was created on the external data stream platform120, or that two partitions were merged on the external data stream platform120. In such an instance, the stream processing engine228generates a new partition/offset pair if a new partition was created, or aggregates two partitions into a single partition if the two partitions were merged.

As also mentioned above, the “LOAD_DATA” function corresponds to a function that reads data from the external data stream platform120. In an embodiment, the stream processing engine228can execute an N number of this function in parallel, where N is determined from loading the partition/offset pairs. The LOAD_DATA” function receives the stream object and a shard/offset string that can be used in the function to make an external call. For instance, this shard/offset string could be a Kafka® partition/offset pair, or a Kinesis® shard/sequence number. The LOAD_DATA” function, when executed, provides a rowset (e.g., object that can be used as a table or a view), where the dimensions of this rowset can be based on a user configuration. Additionally, the LOAD_DATA” function, when executed, provides a string which indicates which partition/offset pair that was last read, among other types of information. For example, this could be the last offset that was processed for a partition (e.g., Kafka®), or could be a sequence number (e.g., Kinesis®).

As used herein, the term “shard” refers to a partition of data in a database. For example, in an implementation, a shard can correspond to a horizontal partition of data stored in a given database system. In some database systems, such shards can be referred to as partitions depending on the particular database system implementation (e.g., Kafka®). Further, a given shard can include a sequence of data records.

As also mentioned above, the stream processing engine228executes the “STORE_DATA” function to store the data in the external data stream platform120. In an embodiment, the “STORE_DATA” function receives the stream object, an indication of which rows from the storage platform104to write, and optionally a shard to indicate that a specific shard is to be written in the external data stream platform120. When executed, this function returns a Boolean indicating whether all rows were successfully stored in the external data stream platform120.

As also mentioned above, “USING EXTERNAL INTEGRATION” represents an optional, external integration that can be utilized with initializing a connection to the external data stream platform120, which may include security and configuration information that is different from offset information. Example configuration information may include a retry timeout, maximum number of offsets to process, request timeout, and the like.

In an embodiment, the string “EXTERNAL” is utilized signify that this integration is intended to be used with external sources, and a type of “USER_DEFINED” is utilized to indicate that the user is responsible for defining properties. Two reserved properties include ENABLED and SECRET_PROPERTIES. All options given are passed in and stored as key value pairs. The following pseudocode is an example:CREATE EXTERNAL INTEGRATION MY_INTEGRATIONTYPE=USER_DEFINEDENABLED={TRUE|FALSE}SECRET_PROPERTIES=[PROP1, PROP2]PROP1=VAL1PROP2=VAL2PROPN=VAL2

In the above, ENABLED refers to a requirement in which an integration must be enabled before it can be used. If ENABLED is set to false, then this integration is not usable and reading from the stream fails prior to reaching any execution nodes.

In the above, SECRET_PROPERTIES is a list of secret and/or sensitive properties. For instance, OAuth client information is stored into SECRET_PROPERTIES. The SECRET_PROPERTIES are stored encrypted. Any property in this list will be stored securely, and the network-based data warehouse system102only shows the key name and a redacted property value when describing the integration.

In an embodiment, the network-based data warehouse system102implements a “CREDENTIALS” object that accepts a single strong (e.g., a JSON blob or other blob data), or a set of properties, for enabling an external integration for an extensible stream.

FIG. 4is a conceptual diagram illustrating an example of a stream object410for utilizing with an external data stream platform (e.g., the external data stream platform120), in accordance with some example embodiments.

As illustrated, the stream object410includes methods450, and a set of properties corresponding to offsets452, offsets modified454, and an external integration456. In an embodiment, the stream object410can be implemented as a JavaScript® object that represents a stream, and includes the following methods450:getOffsets( )—returns the currently stored sets of current offsets as a stringstoreOffsets(newOffsetInfo)—receives a string representing offsets and persists the offsets to the streamgetExternalIntegration( )—provides the external integration (e.g., information indicating security and/or configuration information for connecting to the external data stream platform120)

For the purpose of describing an extensible stream, the stream object410is illustrated as including a set of properties as shown inFIG. 4. It is appreciated, however, that in some embodiments such properties may be stored elsewhere in the network-based data warehouse system102, such as in the storage platform104and/or the database114, and associated with the stream object410.

As further illustrated, in an embodiment, the stream object410includes a set of properties. For example, the offsets452corresponds to the stored offsets string as set on the stream. Further, offsets_modified_on454corresponds to the last time that the stream offset was modified. This property is updated each time the offsets on the stream get updated. The external integration456corresponds to a name of the external integration, if set, used by the stream object410. Although not illustrated, in an example, a property indicating a version of the stream object410is provided to differentiate between different stream objects where the version can be incremented each time a successful set of operations is completed (e.g., read or write) using the external data stream platform120.

In an embodiment, one or more properties of the stream object410may be modified. For example, the external integration456can be updated, and/or the offsets452can be overwritten. In an implementation, the offsets452may only be updated when there are no concurrent executions of a stream.

FIG. 5is a conceptual diagram illustrating using an extensible stream for executing a query for reading data from an external data stream platform (e.g., the external data stream platform120), in accordance with some example embodiments.

The following is example pseudocode for executing a read operation on an extensible stream:SELECT . . .FROM STREAM_NAME . . .{<AT({OFFSET=><time_difference>})><USING EXTERNAL INTEGRATION INTEGRATION_NAME>}

In the above example, the “AT” clause is optional since the stream uses the last offset that was advanced to when the stream was consumed (e.g., previously). The “USING” integration clause is also optional since the integration can be associated with the stream at a time when the stream is created.

As illustrated, a query510is received by the request processing service208of the compute service manager108which analyzes the query510and determines that the stream processing engine228should handle the query510as it relates to performing an operation on an extensible stream (e.g., SELECT*FROM STREAM STREAM_NAME). In this example, the query510includes a string corresponding to an extensible (external) stream (e.g., on the external data stream platform120).

In an embodiment, prior to sending the query510to the stream processing engine228, the access manager202performs an authorization check to determine that a corresponding user or client that submitted the query has authorization or sufficient privileges to execute the query510.

After being authorized, the stream processing engine228determines the stream, and a security integration object associated with the query510. In an example, a security integration object is utilized to generate access tokens to enable users to have access to the network-based data warehouse system102.

The stream processing engine228, using a stream object, determines a current shard (or partition in some embodiments)/offsets512. As discussed before, the “LOAD_OFFSETS” function accepts an object (e.g., JavaScript® object) representing the stream in which the JavaScript® object provides a function (e.g., “getStreamOffsets”) that provides a set of current offsets as a string. In an example, the set of offsets returned from the “getStreamOffsets” function can be specified by a user, or correspond to offsets from a previous time (e.g., the last time) that a read operation was performed using the stream object and information related to the stream object was stored to reflect the results of this read operation. Thus, this function determines offsets from a previous time that an extensible stream, corresponding to the stream object, was accessed during a prior transaction and the offsets from the previous time were previously stored upon completion of the prior transaction. Also, the aforementioned getStreamOffsets” function can be configured to provide new offsets instead of a current set of offsets.

Using the current shard/offsets512, the stream processing engine228then invokes a function514(e.g., “LOAD_OFFSETS”) to load offsets on the external data stream platform120to determine a set of partition/offset pairs for loading (e.g., reading into the data warehouse system). In an example, a size of the set can determine a desired degree of parallelism (DoP) for performing the loading of such offsets. The external data stream platform120returns the set of partition/offset pairs to the stream processing engine228.

In an embodiment, the stream processing engine228advantageously performs the loading of the set of partition/offset pairs in a dynamic and scalable manner. For example, the subject system is capable of assigning computing resources and growing/shrinking such resources to handle the partition/offset loading as needed depending on an overall load. At query time, the subject system can perform the aforementioned operations dynamically such that the resources are not fixed.

The stream processing engine228generates a query plan (e.g., an ordered set of steps used to access data) and/or specification and description language (SDL) that indicates the requested DoP, based on the size of the aforementioned set of partitions, to the execution platform110. Further, the stream processing engine228can include, in the query plan, a serialized stream and the security integration object to enable subsequent functions using the object.

In this example, the execution platform110processes the requested DoP and launches a set of execution nodes from the execution nodes302-1,302-2, and302-nbased on the size of the virtual warehouse (e.g., “virtual warehouse1” as shown). In an embodiment, the execution platform110can “slot” the partitions accordingly (e.g., one partition per processor). In an example, the execution platform provides mapping multiple partitions per processor. In this case, this is understood as being a DoP downgrade and the execution platform110then enables partition stealing such that if a processor is finished with processing its partitions and the processor determines that other nodes have un-processed partitions, the processor will grab one from another node that has yet to process the partition. In the case of a multicore processor, the above implementation can also be applied on a per-core basis, where such partition stealing is implemented at the core level for a multicore processor.

The set of execution nodes from the execution nodes302-1,302-2, and302-nthen invokes the external function (e.g., LOAD_DATA function) to retrieve the data accordingly from the external data stream platform120. The set of execution nodes sends rows returned, from the external data stream platform120, to a second set of execution nodes302-X,302-Y, and302-Z, which then stores the rows returned in the storage platform104. For purposes of illustration, three execution nodes are shown in the second set of execution nodes, but it is appreciated that more or fewer execution nodes in this second set can be utilized based on the configuration and/or capacity of the virtual warehouse.

At this stage, the query510completes successfully. The execution platform110executes the “store offsets” function on the second set of execution nodes302-X,302-Y, and302-Z, and sends that information corresponding to new offsets530to the storage platform104as a string for storing in files520, which can correspond to respective micro-partitions provided by the storage platform104. In an embodiment, when committing the transaction, the execution platform110stores the string on the stream as part of the transaction. Alternatively, the new offsets530are stored in an existing stream offset with a new field such as “externalOffset”.

FIG. 6is a conceptual diagram illustrating using an extensible stream for executing a query for storing data on an external data stream platform (e.g., the external data stream platform120), in accordance with some example embodiments.

As illustrated, a command610is received by the request processing service208of the compute service manager108which analyzes the command610and determines that the stream processing engine228should handle the command610as it relates to performing an operation on an extensible stream (e.g., INSERT INTO EXTENSIBLE_EXTERNAL_STREAM_NAME AS SELECT*FROM MY_TABLE). In this example, the command610includes a string corresponding to an extensible stream, and a name of a table stored in the storage platform104.

In an embodiment, prior to sending the command610to the stream processing engine228, the access manager202performs an authorization check to determine that a corresponding user or client that submitted the query has authorization or sufficient privileges to execute the command610. After being authorized, the stream processing engine228determines the stream, and a security integration object612associated with the command610.

The stream processing engine228, using a stream object, executes a LOAD_OFFSETS function614to determine a set of destination shards (e.g., on the external data stream platform120). The external data stream platform120returns the set of destination shards (e.g., partitions) to the stream processing engine228. As DoP can be determined by a number of shards in the set of destination shards. This enables the execution platform110to parallelize data storing on the external data stream platform120(when supported by the external data stream platform120). Given N number of shards, the execution platform110can distribute a set of rows being written to among N number of execution nodes.

The stream processing engine228generates a query plan (e.g., an ordered set of steps used to access data) and/or specification and description language (SDL) that indicates a requested DoP, based on the size of the aforementioned set of destination shards, to the execution platform110. Further, the stream processing engine228can include, in the query plan, a serialized stream and the security integration object to enable subsequent functions using the object.

In this example, the execution platform110processes the requested DoP and launches a set of execution nodes from the execution nodes302-1,302-2, and302-nbased on the size of the virtual warehouse (e.g., “virtual warehouse1” as shown). Based on the command610and/or the aforementioned query plan, the set of execution nodes from the execution nodes302-1,302-2, and302-nreads data (e.g., rows from a table) from the storage platform104. The execution nodes302-1,302-2, and302-nforwards the data to a second set of execution nodes302-X,302-Y, and302-Z, which are responsible for storing the data to the external data stream platform120. For purposes of illustration, three execution nodes are shown in the second set of execution nodes, but it is appreciated that more or fewer execution nodes in this second set can be utilized based on the configuration and/or capacity of the virtual warehouse.

The second set of execution nodes302-X,302-Y, and302-Z then invokes an external function (e.g., STORE_DATA function) for each shard, determined during executing the LOAD_OFFSETS function614, to store the data accordingly to the external data stream platform120. As mentioned before, when executed, the STORE_DATA function returns a Boolean indicating whether all rows were successfully stored in the external data stream platform120. If the Boolean value is true, at this stage, the command610completes successfully, and the transaction is recorded as being committed in the storage platform104. In an example, the command610can be rolled back if it is indicated (e.g., through a false value being returned instead) that the storing operation failed e.g., where the external data stream platform120is currently unavailable at this time, and it would be beneficial to retry the command610at a subsequent time.

Although the above examples described inFIG. 5andFIG. 6involve an external data stream platform (e.g., with external data sources), the subject technology can be applied on internal data sources to advantageously provide streams for such internal data sources. In an embodiment, a user can define a stream on data provided within the network-based data warehouse system102, such as a query history. In this example, a timestamp can be stored from the last time the query history was accessed (e.g., read), and the query history could be run with the timestamp in the stream, the resulting rows then processed, and the last time seen in query history is committed (e.g., stored), which is used as the offset in a subsequent time around. In this example, the system therefore can forgo defining a new stream for every internal catalog entity thereby enabling the user to perform such a task (e.g., defining the stream).

FIG. 7is a flow diagram illustrating operations of the network-based data warehouse system102in performing a method700for reading data from an external data stream platform, in accordance with some embodiments of the present disclosure. The method700may 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 method700may be performed by components of network-based data warehouse system102. Accordingly, the method700is described below, by way of example with reference thereto. However, it shall be appreciated that the method700may be deployed on various other hardware configurations and is not intended to be limited to deployment within the network-based data warehouse system102.

At operation702, the stream processing engine228receives a query for reading data on an external data stream platform (e.g., the external data stream platform120). In an example, as discussed before, the query can include a SELECT statement for reading data from the external data stream platform120, which also specifies an extensible stream and an external integration (e.g., providing information for connecting to and accessing the external data stream platform120).

At operation704, the stream processing engine228determines a current set of shards and offsets based at least in part on the query (e.g., the specified extensible stream. The current set of shards and offsets, in an example, are in the form of respective shard/offset pairs, where a particular shard can be associated with a particular current offset, from a range of offsets. The current offset, in this example, can correspond to a last offset which was read from the extensible stream. Thus, the stream processing engine228can determine changes from this last offset to a current transactional time of a source table provided by the external data stream platform120.

At operation706, the stream processing engine228generates a query plan (e.g., a set of tasks for completing the query) with a requested degree of parallelism (DoP) based on the current set of shards and offsets. As mentioned before, the requested DoP can be based on a number of shards corresponding to the current set of shards and offsets.

At operation708, the execution platform110, based on the current set of shards and offsets, performs the query on the external data stream platform120. As discussed before, the execution platform110processes the requested DoP and launches a set of execution nodes based on the size of the virtual warehouse. In an embodiment, the execution platform110can “slot” the shards accordingly (e.g., one shard per processor). The set of execution nodes invokes an external function (e.g., LOAD_DATA function) to retrieve the data accordingly from the external data stream platform120.

At operation710, the execution platform110receives data from the external data stream platform120. The set of execution nodes sends rows returned, from the external data stream platform120, to a second set of execution nodes, which then stores the rows returned in the storage platform104.

At operation712, the execution platform110indicates that the query is complete. In an embodiment, the execution platform110executes the “store offsets” function on the second set of execution nodes, and sends that information to the storage platform104as a string for storing in the storage platform104. In an embodiment, when committing the transaction, the execution platform110stores the string on the stream as part of the transaction.

FIG. 8is a flow diagram illustrating operations of the network-based data warehouse system102in performing a method800for storing data on an external data stream platform, in accordance with some embodiments of the present disclosure. The method800may 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 method800may be performed by components of network-based data warehouse system102. Accordingly, the method800is described below, by way of example with reference thereto. However, it shall be appreciated that the method800may be deployed on various other hardware configurations and is not intended to be limited to deployment within the network-based data warehouse system102.

At operation802, the stream processing engine228receives a command for storing data on an external data stream platform (e.g., the external data stream platform120). In an example, the command can include a query statement with an INSERT command and a string corresponding to a table stored in the storage platform104for reading the data to be stored.

At operation804, the stream processing engine228determines a set of destination shards on the external data stream platform120. As mentioned before, the stream processing engine228, using a stream object, executes a LOAD_OFFSETS function614to determine a set of destination shards (e.g., on the external data stream platform120). The external data stream platform120returns the set of destination shards (e.g., partitions) to the stream processing engine228.

At operation806, the stream processing engine228generates a query plan (e.g., a set of tasks for completing the command) with a requested degree of parallelism (DoP) based on the set of destination shards. As mentioned before, the requested DoP can be based on a number of shards corresponding to the destination shards.

At operation808, the execution platform110, based on the set of destination shards, performs the command on the external data stream platform120. As discussed before, the execution platform110processes the requested DoP and launches a set of execution nodes based on the size of the virtual warehouse. Based on the command and/or the aforementioned query plan, the set of execution nodes reads data (e.g., rows from a table) from the storage platform104. The execution nodes forwards the data to a second set of execution nodes, which are responsible for storing the data to the external data stream platform120. The second set of execution nodes then invokes an external function (e.g., STORE_DATA function) for each destination shard to store the data accordingly to the external data stream platform120.

At operation810, the execution platform110indicates that the command is complete. As mentioned before, when executed, the STORE_DATA function returns a Boolean indicating whether all rows were successfully stored in the external data stream platform120. If the Boolean value is true, at this stage, the command610completes successfully, and the transaction is recorded as being committed in the storage platform104.

FIG. 9illustrates a diagrammatic representation of a machine900in the form of a computer system within which a set of instructions may be executed for causing the machine900to perform any one or more of the methodologies discussed herein, according to an example embodiment. Specifically,FIG. 9shows a diagrammatic representation of the machine900in the example form of a computer system, within which instructions916(e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine900to perform any one or more of the methodologies discussed herein may be executed. For example, the instructions916may cause the machine900to execute any one or more operations of any one or more of the methods described above. As another example, the instructions916may cause the machine900to implement portions of the functionality illustrated in any one or more ofFIGS. 1-8. In this way, the instructions916transform a general, non-programmed machine into a particular machine900(e.g., the compute service manager108, the execution platform110, and the user device112) that is specially configured to carry out any one of the described and illustrated functions in the manner described herein.

In alternative embodiments, the machine900operates as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine900may 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 machine900may 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 instructions916, sequentially or otherwise, that specify actions to be taken by the machine900. Further, while only a single machine900is illustrated, the term “machine” shall also be taken to include a collection of machines900that individually or jointly execute the instructions916to perform any one or more of the methodologies discussed herein.

The machine900includes processors910, memory930, and input/output (I/O) components950configured to communicate with each other such as via a bus902. In an example embodiment, the processors910(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 processor912and a processor914that may execute the instructions916. The term “processor” is intended to include multi-core processors910that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions916contemporaneously. AlthoughFIG. 9shows multiple processors910, the machine900may 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 memory930may include a main memory932, a static memory934, and a storage unit936, all accessible to the processors910such as via the bus902. The main memory932, the static memory934, and the storage unit936store the instructions916embodying any one or more of the methodologies or functions described herein. The instructions916may also reside, completely or partially, within the main memory932, within the static memory934, within the storage unit936, within at least one of the processors910(e.g., within the processor's cache memory), or any suitable combination thereof, during execution thereof by the machine900.

Communication may be implemented using a wide variety of technologies. The I/O components950may include communication components964operable to couple the machine900to a network980or devices970via a coupling982and a coupling972, respectively. For example, the communication components964may include a network interface component or another suitable device to interface with the network980. In further examples, the communication components964may include wired communication components, wireless communication components, cellular communication components, and other communication components to provide communication via other modalities. The devices970may 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 machine900may correspond to any one of the compute service manager108, the execution platform110, and the devices970may include the user device112or any other computing device described herein as being in communication with the network-based data warehouse system102or the storage platform104.

Executable Instructions and Machine Storage Medium

The various memories (e.g.,1430,1432,1434, and/or memory of the processor(s)1410and/or the storage unit1436) may store one or more sets of instructions1416and data structures (e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. These instructions1416, when executed by the processor(s)1410, cause various operations to implement the disclosed embodiments.

Transmission Medium

EXAMPLES

Example 1 is a network-based database system comprising: at least one hardware processor; and a memory storing instructions that cause the at least one hardware processor to perform operations comprising: determining a set of shards corresponding to an external data source accessible via a network, the external data source being hosted by an external system separate from the network-based database system; determining, using a stream object, a set of offsets of each shard of the set of shards; identifying an operation to perform on the set of shards, the operation comprising a read operation or a write operation; and based on the set of shards and the set of offsets, performing the operation on the external data source.

In Example 2 the subject matter of Example 1 wherein each shard of the set of shards optionally comprises a sequence of data records stored by the external data source

In Example 3, the subject matter of any one of Examples 1 and 2 wherein determining the set of offsets of each shard of the set of shards optionally comprises: executing a user defined function included in the stream object to determine the set of offsets.

In Example 4, the subject matter of any one of Examples 1-3 wherein the user defined function, when executed, optionally determines offsets from a previous time that an extensible stream, corresponding to the stream object, was accessed during a prior transaction and the offsets from the previous time were previously stored upon completion of the prior transaction/

In Example 5, the subject matter of any one of Examples 1-4 wherein the operations optionally further comprise determining a set of new offsets subsequent to the previous time that the extensible stream was accessed.

In Example 6, the subject matter of any one of Examples 1-5 wherein the operations optionally further comprise: determining that a new shard was created subsequent to the previous time that the extensible stream was accessed; determining a new offset corresponding to the new shard; and providing the new offset with the set of new offsets.

In Example 7, the subject matter of any one of Examples 1-6 wherein the operations optionally further comprise: determining that a number of offsets correspond to a particular shard from the set of shards; dividing the particular shard from the set of shards into at least two shards; and accessing a first shard prior to accessing a second shard from the at least two shards.

In Example 8, the subject matter of any one of Examples 1-7 wherein the user defined function, when executed, optionally requests data from the external data source based at least in part on the set of shards and the set of offsets.

In Example 9, the subject matter of any one of Examples 1-8 wherein the user defined function optionally requests data using a set of parallel operations based on the set of shards.

In Example 10, the subject matter of any one of Examples 1-9 wherein the stream object optionally comprises: a first function providing a set of stored offsets; a second function storing a second set of offsets; and a third function providing an integration with an external object that enables communication with the external data source.

In Example 11, the subject matter of any one of Examples 1-10 wherein the external data source optionally comprises an external data stream platform.

Example 12 is a method comprising: determining, using at least one hardware processor, a set of shards corresponding to an external data source accessible via a network, the external data source being hosted by an external system separate from a network-based database system; determining, using a stream object, a set of offsets of each shard of the set of shards; identifying an operation to perform on the set of shards, the operation comprising a read operation or a write operation; and based on the set of shards and the set of offsets, performing the operation on the external data source.

In Example 13, the subject matter of Examiner 12 wherein each shard of the set of shards optionally comprises a sequence of data records stored by the external data source.

In Example 14, the subject matter of Examples 12-13 wherein determining the set of offsets of each shard of the set of shards optionally comprises: executing a user defined function included in the stream object to determine the set of offsets.

In Example 15, the subject matter of any one of Examples 12-14 wherein the user defined function, when executed, optionally determines offsets from a previous time that an extensible stream, corresponding to the stream object, was accessed during a prior transaction and the offsets from the previous time were previously stored upon completion of the prior transaction.

In Example 16, the subject matter of any one of Examples 12-15 further optionally comprising determining a set of new offsets subsequent to the previous time that the extensible stream was accessed.

In Example 17, the subject matter of any one of Examples 12-16 further optionally comprising: determining that a new shard was created subsequent to the previous time that the extensible stream was accessed; determining a new offset corresponding to the new shard; and providing the new offset with the set of new offsets.

In Example 18, the subject matter of any one of Examples 12-17 further optionally comprising determining that a number of offsets correspond to a particular shard from the set of shards; dividing the particular shard from the set of shards into at least two shards; and accessing a first shard prior to accessing a second shard from the at least two shards.

In Example 19, the subject matter of any one of Examples 12-18 wherein the user defined function, when executed, optionally requests data from the external data source based at least in part on the set of shards and the set of offsets, and the user defined function requests data using a set of parallel operations based on the set of shards.

In Example 20 is non-transitory computer-storage medium comprising instructions that, when executed by a processor, configure the processor to perform operations comprising determining a set of shards corresponding to an external data source accessible via an electronic network, the external data source being hosted by an external system separate from a network-based database system; determining, using a stream object, a set of offsets of each shard of the set of shards; identifying an operation to perform on the set of shards, the operation comprising a read operation or a write operation; and based on the set of shards and the set of offsets, performing the operation on the external data source.

In Example 21, the subject matter of Example 20 wherein each shard of the set of shards optionally comprises a sequence of data records stored by the external data source.

In Example 22, the subject matter of Examples 20-21 wherein determining the set of offsets of each shard of the set of shards optionally comprises: executing a user defined function included in the stream object to determine the set of offsets.

In Example 23, the subject matter of Examples 20-22 wherein the user defined function, when executed, optionally determines offsets from a previous time that an extensible stream, corresponding to the stream object, was accessed during a prior transaction and the offsets from the previous time were previously stored upon completion of the prior transaction.

In Example 24, the subject matter of Example 20-23 wherein the operations further optionally comprise: determining a set of new offsets subsequent to the previous time that the extensible stream was accessed.

In Example 25, the subject matter of Example 20-24 wherein the operations optionally further comprise: determining that a new shard was created subsequent to the previous time that the extensible stream was accessed; determining a new offset corresponding to the new shard; and providing the new offset with the set of new offsets.

In Example 26, the subject matter of Example 20-25 wherein the operations further optionally comprise: determining that a number of offsets correspond to a particular shard from the set of shards; dividing the particular shard from the set of shards into at least two shards; and accessing a first shard prior to accessing a second shard from the at least two shards.

In Example 27, the subject matter of Example 20-26 wherein the user defined function, when executed, optionally requests data from the external data source based at least in part on the set of shards and the set of offsets.

In Example 28, the subject matter of Example 20-27 wherein the user defined function optionally requests data using a set of parallel operations based on the set of shards.

In Example 29, the subject matter of Example 20-28 wherein the user defined function, when executed, optionally stores data into the external data source based at least in part on a particular shard from the set of shards, and a set of rows from an internal data source to store in the particular shard.

In Example 30, the subject matter of Example 20-29 wherein the stream object optionally comprises: a first function providing a set of stored offsets; a second function storing a second set of offsets; and a third function providing an integration with an external object that enables communication with the external data source.