How would you address performance issues caused by integrating real-time and batch processing systems?

- 🔄 Optimize the data flow by analyzing the data pipeline to identify bottlenecks. - 🛠️ Reformat data for more efficient processing and reduce the size of data batches. - 🚫 Filter out unnecessary data before it enters the system to reduce load. - 📊 Implement data partitioning to divide larger datasets into smaller, manageable chunks for faster processing. - ⚖️ Balance the load between real-time and batch processing systems to prevent overloading any single system. - 🔍 Continuously monitor system performance and adjust configurations to maintain optimal efficiency.

Maybe a slight increase in one pipeline but BIG contribute for SYSTEM. Some point that we can take a look at this ( Pick up easy one or the worst pipeline first ) - Storage Size, Moving Size - I/O bottleneck - Deep understanding storage engine --> Choose Right Methods for storing ( Hash, Round Robin, ... ) - Partitioning, Clustering

Allocate sufficient resources to handle both real-time and batch processing workloads effectively. Ensure that resources such as memory, CPU, and storage are appropriately provisioned to support the combined processing requirements. Implement data partitioning techniques to distribute workload across processing systems efficiently. Partition data based on criteria such as time, key ranges, or data

Partitioning optimizes data storage by addressing I/O bottlenecks. Hash partitioning distributes data across multiple disks, accelerating data loading but potentially slowing queries. Choosing the right partitioning strategy - range, list, or composite - based on specific use cases is crucial. Align your approach with business needs, as most distributed technologies support these methods. Effective partitioning significantly boosts the efficiency and performance of data storage systems.

En el bullicioso centro de datos, nuestro ingeniero se enfrenta a un dragón de dos cabezas: el procesamiento en tiempo real y el procesamiento por lotes. Las chispas vuelan y los sistemas luchan por coexistir. Pero él tiene un plan maestro. Con firmeza, despliega Apache Kafka, un fiel escudero que maneja flujos de datos en tiempo real con gracia. Luego, invoca a AWS Glue, un mago que orquesta ETL, armonizando datos de lotes. Con Amazon Kinesis como aliado, domina el flujo continuo, garantizando que cada dato encuentre su lugar. Así, con astucia y destreza, vence al dragón y trae equilibrio al reino digital.

- Clean Code --> ( Indirect ) Misunderstanding, wrong implementation. - Algorithms: O(n) is big different with O(n^2) - Consider parallel processing - Unit Test include complexity &speed test - Monitoring

To improve the efficiency of data processing, it is essential to examine and optimize the algorithms used in both real-time and batch processing, ensuring maximum efficiency. Refactoring inefficient code can lead to significant improvements by eliminating redundancies and optimizing operations. Adopting parallel processing techniques allows tasks to be executed simultaneously, reducing total processing time. Additionally, using profiling tools helps identify and optimize slow-executing code segments. Implementing load balancing also contributes to more efficient task distribution among available resources, avoiding bottlenecks.

In my point of view, Scaling resources is less important than optimize 1) Increase Cost 2) Increase your stupid code 3) Have to solve bigger problem in the future ++ Scaling with deep understanding what you are doing.

Effective resource allocation is crucial for managing performance issues. When data load overwhelms the system, scaling up resources like computing power or storage may be necessary. However, intelligent use of resources is equally important. Implement auto-scaling solutions to dynamically adjust resources based on demand, ensuring optimal performance without over-provisioning. For instance, using cloud services like AWS Auto Scaling can automatically increase or decrease instances to match current workload requirements.

Streamlining code is essential for optimizing performance in data processes. Evaluate and refactor algorithms in both real-time and batch processing to ensure efficiency. Parallel processing techniques can significantly reduce overall processing time by handling tasks concurrently. Use profiling tools to identify and optimize slow-running code segments, enhancing the overall performance of your data pipeline. For example, converting single-threaded operations to multi-threaded ones can dramatically speed up data processing tasks.

Observability is just as crucial as having multiple data workers in your pipeline. In my most recent experiences I have seen that companies have not had difficulty using computing resources such as clusters or workers, most of the time there are problems related to an unexpected volume of data, for example, which creates memory contention in their processes. , causing your application to become unresponsive and naturally lose data because the TTL of a PubSub, for example, has expired. Some tools that can help with this, DataDog, Kibana, Grafana. With DataDog you can easily increase metrics in real time, in addition to offering several infrastructure resources, while Kibana has helped me handle execution logs that are normally large.

Real time request should be given always higher priority to process faster. Should keep the sufficient Cores and Memory aside for real time queue (preferably separate queue). Now a days storage is cheaper, having separate read only copy storage with right bucketing, indexing and partitioning applied according to expecting incoming real time request makes architecture more robust.

I have 3 peices of advice: 1) Not everything your system does will be important. Sometimes it is best to design some features to only run on free resources. AKA: No requesting monthly reports when the system is almost tapped out. 2) Assume your system will be overwhelmed at some point and design it to stay up "at all costs". It should ALWAYS inform the stakeholders when it's wavering. Plan the most graceful data loss. Even though it is unthinkable to lose any data, it is better than losing all data. 3) Design your system to scale automatically, at a reasonable rate, within budgetary limits. If your system is designed as a cluster, it can use less resources when unloaded and use those savings to spin up additional nodes during peak times.

In my experience, when using Hive external tables for batch execution which is very slow for opening it to real time access. Explored below options, Impala - Separate copy of data stored in cache for faster access HBase - Apply Hive metadata tables on HBase in KeyValue structure MariaDB - Useful if once in a day data gets modified and small data size. Move data to RDBMS daily and enable via RESTFul APIs

There is a lot to consider when talking about performance evaluation on an integration. Anything can happen when we talk about an integration system specially when we talk about integrating Real time and batch processing, because, by definition, there will be bottlenecks on this system. As said before, the best way to address performance issues is to identify the bottlenecks, remove (or reduce) them using appropriate techniques for each problem identified, e.g.: code review, data mapping, update architecture from time to time (when the benefit of change compensates its risks), all the other methods explained on this article and any other that apply for the issue identified. The challenge is to address the issue using the correct technique.

7. Load Balancing: Throttling and Rate Limiting: Implement throttling to prevent real-time systems from being overwhelmed by high data volumes. Load Balancers: Use load balancers to distribute the workload evenly across processing nodes.

As a data engineer, I've overcome performance issues while integrating real-time & batch processing systems. I have followed this approach decoupling, data buffering, load balancing, optimized data processing, real-time monitoring, regular tuning & using cloud services scalability. Holistic integration yields efficient & reliable data processing.