Batch processing

The term "batch processing" originates in the traditional classification of methods of production as job production (one-off production), batch production (production of a "batch" of multiple items at once, one stage at a time), and flow production (mass production, all stages in process at once).

Later history

CDC NOS batch file to get the file STARTRK and output it to the card punch

The first general purpose time sharing system, Compatible Time-Sharing System (CTSS), was compatible with batch processing. This facilitated transitioning from batch processing to interactive computing.[5]

From the late 1960s onwards, interactive computing such as via text-based computer terminal interfaces (as in Unix shells or read-eval-print loops), and later graphical user interfaces became common. Non-interactive computation, both one-off jobs such as compilation, and processing of multiple items in batches, became retrospectively referred to as batch processing, and the term batch job (in early use often "batch of jobs") became common. Early use is particularly found at the University of Michigan, around the Michigan Terminal System (MTS). [6]

Although timesharing did exist, its use was not robust enough for corporate data processing; none of this was related to the earlier unit record equipment, which was human-operated.

Batch applications are still critical in most organizations in large part because many common business processes are amenable to batch processing. While online systems can also function when manual intervention is not desired, they are not typically optimized to perform high-volume, repetitive tasks. Therefore, even new systems usually contain one or more batch applications for updating information at the end of the day, generating reports, printing documents, and other non-interactive tasks that must complete reliably within certain business deadlines.

Some applications are amenable to flow processing, namely those that only need data from a single input at once (not totals, for instance): start the next step for each input as it completes the previous step. In this case flow processing lowers latency for individual inputs, allowing them to be completed without waiting for the entire batch to finish. However, many applications require data from all records, notably computations such as totals. In this case the entire batch must be completed before one has a usable result: partial results are not usable.

Modern batch applications make use of modern batch frameworks such as Jem The Bee, Spring Batch[8] or implementations of JSR 352[9] written for Java, and other frameworks for other programming languages, to provide the fault tolerance and scalability required for high-volume processing. In order to ensure high-speed processing, batch applications are often integrated with grid computing solutions to partition a batch job over a large number of processors, although there are significant programming challenges in doing so. High volume batch processing places particularly heavy demands on system and application architectures as well. Architectures that feature strong input/output performance and vertical scalability, including modern mainframe computers, tend to provide better batch performance than alternatives.

Scripting languages became popular as they evolved along with batch processing.[10]

A batch window is "a period of less-intensive online activity",[11] when the computer system is able to run batch jobs without interference from, or with, interactive online systems.

A bank's end-of-day (EOD) jobs require the concept of cutover, where transaction and data are cut off for a particular day's batch activity ("deposits after 3 PM will be processed the next day").

As requirements for online systems uptime expanded to support globalization, the Internet, and other business needs, the batch window shrank[12][13] and increasing emphasis was placed on techniques that would require online data to be available for a maximum amount of time.

The batch size refers to the number of work units to be processed within one batch operation. Some examples are:

• The number of lines from a file to load into a database before committing the transaction.
• The number of messages to dequeue from a queue.
• The number of requests to send within one payload.

• Efficient bulk database updates and automated transaction processing, as contrasted to interactive online transaction processing (OLTP) applications. The extract, transform, load (ETL) step in populating data warehouses is inherently a batch process in most implementations.
• Performing bulk operations on digital images such as resizing, conversion, watermarking, or otherwise editing a group of image files.
• Converting computer files from one format to another. For example, a batch job may convert proprietary and legacy files to common standard formats for end-user queries and display.
• Training Machine Learning models. For example, an e-commerce website might want to process customer transactions in a hourly batch to update the model that produces related product recommendations, in order to save computational resources.[14]

The IBM mainframe z/OS operating system or platform has arguably the most highly refined and evolved set of batch processing facilities owing to its origins, long history, and continuing evolution. Today such systems commonly support hundreds or even thousands of concurrent online and batch tasks within a single operating system image. Technologies that aid concurrent batch and online processing include Job Control Language (JCL), scripting languages such as REXX, Job Entry Subsystem (JES2 and JES3), Workload Manager (WLM), Automatic Restart Manager (ARM), Resource Recovery Services (RRS), IBM Db2 data sharing, Parallel Sysplex, unique performance optimizations such as HiperDispatch, I/O channel architecture, and several others.

The Unix programs cron, at, and batch (today batch is a variant of at) allow for complex scheduling of jobs. Windows has a job scheduler. Most high-performance computing clusters use batch processing to maximize cluster usage.[15]

• Background process
• Batch file
• Batch renaming - to rename lots of files automatically without human intervention, in order to save time and effort
• BatchPipes - for utility that increases batch performance
• Processing modes
• Production support - for batch job/schedule/stream support
• High-throughput computing