How does micro-batching affect data latency and throughput?

2 How does micro-batching affect latency?

Latency is the delay between the data arrival and the output. In batch processing, latency depends on the size and frequency of the batches. The larger and less frequent the batches are, the higher the latency is. For example, if you process your data once a day, your latency can be up to 24 hours. In micro-batching, latency depends on the interval and duration of the micro-batches. The shorter and more frequent the micro-batches are, the lower the latency is. For example, if you process your data every minute, your latency can be up to 1 minute.

3 How does micro-batching affect throughput?

Throughput is the rate of data processing per unit of time. In batch processing, throughput depends on the speed and capacity of the system. The faster and larger the system is, the higher the throughput is. However, batch processing also has some overheads, such as loading, scheduling, and committing the batches, which can reduce the throughput. In micro-batching, throughput depends on the balance between the interval and duration of the micro-batches. The optimal balance is when the interval is equal to the duration, which maximizes the utilization of the system. However, if the interval is too short or too long, the throughput can decrease due to underutilization or overloading of the system.

4 What are the benefits of micro-batching?

Micro-batching can offer several advantages when it comes to data processing, such as improved responsiveness and enhanced scalability. It can reduce latency and increase the freshness of the data, which can improve the quality of applications that rely on it. Micro-batching can also leverage the scalability of batch processing, allowing it to handle large volumes of data with high parallelism and fault tolerance. Additionally, it can adapt to variable data rates by adjusting the interval and size of the micro-batches dynamically. Furthermore, micro-batching can simplify the design and implementation of data processing pipelines by using frameworks and tools like Spark, Flink, or Kafka. It also helps to abstract away some of the challenges of streaming processing, such as state management, windowing, or watermarking.

5 What are the challenges of micro-batching?

Micro-batching is not a one-size-fits-all solution for data processing, and it also has some challenges. Finding the optimal balance between the interval and duration of the micro-batches can vary depending on the data characteristics, system performance, and application requirements and may require some trial and error and tuning to achieve the desired results. Additionally, micro-batching can consume more resources than batch processing due to more frequent loading, scheduling, and committing of the micro-batches, which can incur more CPU, memory, disk, and network usage. Furthermore, there is a potential risk of data loss or duplication due to failures or interruptions in the data processing pipeline. To prevent this, mechanisms may be needed to ensure data consistency and idempotency across the micro-batches.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?