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Most operating systems , such as Unix and Windows , provide basic job scheduling capabilities, notably by at and batch , cron , and the Windows Task Scheduler. Web hosting services provide job scheduling capabilities through a control panel or a webcron solution. Operating system "OS" or point program supplied job-scheduling will not usually provide the ability to schedule beyond a single OS instance or outside the remit of the specific program.

Organizations needing to automate unrelated IT workload may also leverage further advanced features from a job scheduler, such as:. These advanced capabilities can be written by in-house developers but are more often provided by suppliers who specialize in systems-management software.

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There are many concepts that are central to almost every job scheduler implementation and that are widely recognized with minimal variations:. Beyond the basic, single OS instance scheduling tools there are two major architectures that exist for Job Scheduling software. An important niche for job schedulers is managing the job queue for a cluster of computers. Typically, the scheduler will schedule jobs from the queue as sufficient resources cluster nodes become idle.

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Some widely used cluster batch systems are. Mathematical Formulation 4. Extensions and Computational Enhancements 5. Scheduling Formulation 6. Examples 7. Computational Results 8. Conclusions Acknowledgment Supporting Information Available. Cited By. This article is cited by 12 publications.

Towards Low-Latency Batched Stream Processing by Pre-Scheduling - IEEE Journals & Magazine

DOI: Andres F. Merchan and Christos T. Sara Velez and Christos T. Pablo A. Marchetti, Carlos A. Songsong Liu, Jose M. Pinto and Lazaros G.

Production Scheduling Approaches for Operations Management

If you target different tables or databases, it is possible to see some performance gain with this strategy. Database sharding or federations would be a scenario for this approach. Sharding uses multiple databases and routes different data to each database. If each small batch is going to a different database, then performing the operations in parallel can be more efficient.

However, the performance gain is not significant enough to use as the basis for a decision to use database sharding in your solution. In some designs, parallel execution of smaller batches can result in improved throughput of requests in a system under load. In this case, even though it is quicker to process a single larger batch, processing multiple batches in parallel might be more efficient. If you do use parallel execution, consider controlling the maximum number of worker threads.

A smaller number might result in less contention and a faster execution time. Also, consider the additional load that this places on the target database both in connections and transactions. Typical guidance on database performance also affects batching.

For example, insert performance is reduced for tables that have a large primary key or many nonclustered indexes. This statement suppresses the return of the count of the affected rows in the procedure. It is possible that more complex stored procedures would benefit from this statement. The following sections describe how to use table-valued parameters in three application scenarios.

The first scenario shows how buffering and batching can work together. The second scenario improves performance by performing master-detail operations in a single stored procedure call.

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Although there are some scenarios that are obvious candidate for batching, there are many scenarios that could take advantage of batching by delayed processing. However, delayed processing also carries a greater risk that the data is lost in the event of an unexpected failure.

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It is important to understand this risk and consider the consequences. For example, consider a web application that tracks the navigation history of each user.

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For example, a rule could specify that the batch should be processed after 20 seconds or when the buffer reaches items. The following code example uses Reactive Extensions - Rx to process buffered events raised by a monitoring class. When the buffer fills or a timeout is reached, the batch of user data is sent to the database with a table-valued parameter. The following NavHistoryData class models the user navigation details.

It contains basic information such as the user identifier, the URL accessed, and the access time. The NavHistoryDataMonitor class is responsible for buffering the user navigation data to the database. The following code shows the constructor logic that uses Rx to create an observable collection based on the event.

It then subscribes to this observable collection with the Buffer method. The overload specifies that the buffer should be sent every 20 seconds or entries. The handler converts all of the buffered items into a table-valued type and then passes this type to a stored procedure that processes the batch. To use this buffering class, the application creates a static NavHistoryDataMonitor object. Each time a user accesses a page, the application calls the NavHistoryDataMonitor. RecordUserNavigationEntry method. The buffering logic proceeds to take care of sending these entries to the database in batches.

However, it can be more challenging to batch inserts that involve more than one table. The master table identifies the primary entity. One or more detail tables store more data about the entity. In this scenario, foreign key relationships enforce the relationship of details to a unique master entity. Consider a simplified version of a PurchaseOrder table and its associated OrderDetail table. Each order contains one or more product purchases.

This information is captured in the PurchaseOrderDetail table. The following definition of a foreign key enforces this constraint. In order to use table-valued parameters, you must have one user-defined table type for each target table. Then define a stored procedure that accepts tables of these types. This procedure allows an application to locally batch a set of orders and order details in a single call. The following Transact-SQL provides the complete stored procedure declaration for this purchase order example.

In this example, the locally defined IdentityLink table stores the actual OrderID values from the newly inserted rows. These order identifiers are different from the temporary OrderID values in the orders and details table-valued parameters. After this step, the IdentityLink table can facilitate inserting the order details with the actual OrderID that satisfies the foreign key constraint. This stored procedure can be used from code or from other Transact-SQL calls.

Job scheduler

See the table-valued parameters section of this paper for a code example. This solution allows each batch to use a set of OrderID values that begin at 1. These temporary OrderID values describe the relationships in the batch, but the actual OrderID values are determined at the time of the insert operation. You can run the same statements in the previous example repeatedly and generate unique orders in the database. For this reason, consider adding more code or database logic that prevents duplicate orders when using this batching technique.

This example demonstrates that even more complex database operations, such as master-detail operations, can be batched using table-valued parameters. Another batching scenario involves simultaneously updating existing rows and inserting new rows. First, create the user-defined table type:. Next, create a stored procedure or write code that uses the MERGE statement to perform the update and insert.

The contents of the employees table are not shown here.

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  • How to use batching to improve SQL Database application performance?
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This article focused on how database design and coding techniques related to batching can improve your application performance and scalability. But this is just one factor in your overall strategy. For more ways to improve performance and scalability, see Azure SQL Database performance guidance for single databases and Price and performance considerations for an elastic pool.

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