Incremental Processing

Data Processing
Updated on:
July 11, 2024

What is incremental processing?

Incremental processing refers to computing systems that incorporate new data continuously over time by updating prior results and computations incrementally, rather than having to recalculate everything from scratch.

It aims to improve efficiency, reduce computational load, and adapt to changing data flows by propagating incremental updates rather than reexecuting entire process batches.

For instance, incrementally updating analytics metrics or machine learning models as new data arrives rather than full rebuilds. Incremental systems maintain state and propagate changes.

Technologies like Apache Arrow and query engines like Apache Arrow DataFusion are optimized for incremental processing across both streaming and batch data using columnar memory formats.

Incremental architectures reduce recomputation needs significantly. They work well with distributed tracing to efficiently update computations across nodes.

How does incremental processing work?

Incremental algorithms maintain state like summaries, indexes and intermediate results that allow incorporating new data and incrementally updating outputs.

Strategies include incremental graph algorithms, nested relational algebra, incremental SAT solving, materialized views, and incremental machine learning models.

Why is incremental processing useful? Where is it applied?

Incremental processing increases efficiency dealing with streaming data that is constantly evolving. Use cases include streaming analytics, continuous queries, dynamic graphs, adaptive control systems, and self-adjusting computations that must react to real-time data flows.

By avoiding full recomputations, incremental approaches enable low-latency processing and greater scalability for dynamic data.


How does incremental processing contrast with batch processing?

It aims to update results continuously vs processing predetermined batches. Latency is lower but complexity is higher.

What are the challenges in incremental processing?

Challenges include algorithm complexity, handling delayed/out-of-order data, debugging, high state management costs, and bounding recomputation needs.

What strategies are used for incremental processing?

Strategies include memoization, caching, change data capture, materialized views, incremental computation graphs, and incremental learning techniques.

When is incremental processing suitable?

It excels for workloads with:

  • Frequently updating dynamic data
  • Need for fast response to new data
  • Queries on rolling or time-windowed aggregates
  • High cost of rebuilding on full dataset


Related Entries

Online Analytical Processing (OLAP)

Online analytical processing (OLAP) refers to the technology that enables complex multidimensional analytical queries on aggregated, historical data for business intelligence and reporting.

Apache Arrow DataFusion

Apache DataFusion is an extensible, high-performance data processing framework in Rust, designed to efficiently execute analytical queries on large datasets. It utilizes the Apache Arrow in-memory data format.

Distributed Tracing

Distributed tracing is a method used to profile and monitor complex distributed systems by instrumenting apps to log timing data across components, letting operators analyze bottlenecks and failures.


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