Batch Processing
Batch Processing is a method of running data processing jobs in "batches," where a large volume of data is collected over a period, stored, and then processed all at once as a group. This contrasts with Stream Processing, which processes data continuously as it arrives. Batch processing is typically used for tasks that can tolerate some latency, involve large datasets, and require comprehensive, often complex, computations.
Key Characteristics
- Data Collection: Data is accumulated over time (e.g., hourly, daily, weekly).
- Scheduled Execution: Jobs are typically scheduled to run at specific times or intervals (e.g., overnight).
- Large Data Volumes: Optimized for processing large, bounded datasets.
- High Latency: Results are not available in real-time; there's a delay between data arrival and result availability corresponding to the batch interval.
- Throughput-Oriented: Designed to maximize the amount of data processed over a period rather than minimizing the latency for individual data points.
- Offline Processing: Often performed offline or during off-peak hours to avoid impacting operational systems.
How Batch Processing Works
- Data Ingestion & Staging: Data is collected from various sources and stored in a staging area or data lake.
- Job Execution: A batch job (e.g., a MapReduce job, a Spark application, an ETL script) is initiated.
- Data Processing: The job reads the entire batch of input data, performs the required transformations, calculations, or analyses.
- Output Generation: The processed results are written to a target system, such as a data warehouse, database, file system, or report.
Common Use Cases
- ETL (Extract, Transform, Load): Moving and transforming large datasets from operational systems to data warehouses.
- Reporting and Analytics: Generating daily, weekly, or monthly reports and performing complex historical analyses.
- Billing Systems: Processing customer usage data at the end of a billing cycle.
- Payroll Processing: Calculating salaries and deductions for all employees at set intervals.
- Large-Scale Data Archival and Backups.
- Training Machine Learning Models: Using large historical datasets to train models.
Advantages
- Handles Large Datasets Efficiently: Can process vast amounts of data effectively.
- Simplicity for Certain Tasks: For operations that naturally fit a batch model (e.g., end-of-day reporting), the logic can be straightforward.
- Resource Utilization: Can be scheduled during off-peak hours to optimize resource usage.
- Mature Ecosystem: Many well-established tools and frameworks exist for batch processing (e.g., Apache Hadoop, Apache Spark in batch mode).
Disadvantages
- High Latency: Results are not available in real-time, making it unsuitable for applications requiring immediate insights or actions.
- Stale Data: Decisions are based on data that might be hours or days old.
- Resource Spikes: Can cause significant spikes in resource consumption when batch jobs run.
Batch Processing vs. Stream Processing
| Feature | Batch Processing | Stream Processing |
|---|
| Data Scope | Large, bounded datasets | Unbounded, continuous data streams |
| Latency | High (minutes, hours, days) | Low (milliseconds, seconds) |
| Data Model | Data at rest | Data in motion |
| Computation | On entire dataset or large chunks | On individual events or micro-batches |
| Result Update | Periodic (after batch completion) | Continuous or near real-time |
| Primary Goal | Throughput, comprehensive analysis | Low latency, immediate insights |
Batch Processing in Modern Architectures
While stream processing has gained prominence for real-time needs, batch processing remains essential for many use cases.
- Lambda Architecture: Traditionally, the batch layer in a Lambda architecture handles comprehensive historical processing.
- Kappa Architecture: Aims to replace the batch layer by replaying streams, but some "batch-like" operations might still be performed by configuring stream processors to handle large, bounded inputs.
- Data Lakehouses: Often involve batch ETL/ELT jobs to ingest and transform data into structured formats like Apache Iceberg or Delta Lake, which can then be queried.
- Hybrid Approaches: Many systems use a combination of batch and stream processing to leverage the strengths of both.
RisingWave, while primarily a Streaming Database, can interact with systems that are populated or managed by batch processes (e.g., reading from tables in a data warehouse that are updated nightly) or can sink data that might be consumed by downstream batch jobs. However, its core strength lies in incremental, low-latency processing of streaming data.
Related Glossary Terms
