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Key Concepts For Processing Data with KNIME: Enhancing Performance
A walkthrough of advanced data management techniques like parallelism, concurrency, streaming, and distributed processing
Introduction
There are many methods and opportunities to enhance your data management practices. From a data analytics standpoint, you want to analyze data quickly and precisely. When you’re constrained by time and resources, achieving speed and performance is crucial for delivering rapid solutions to decision-makers. This article will cover several concepts based on my experience. Its purpose is to inspire those interested in analyzing big data and familiarize them with concepts that can aid in designing optimal solutions while enhancing performance and speed.
Content Insight
- Parallelism
- Concurrency
- Streaming
- Distributing
- In-Memory Databases
Concepts & Examples
1. Task Parallelism
Parallelism involves dividing a main task into multiple sub-tasks, which enhances speed and performance compared to handling the entire task sequentially. In KNIME, you can implement parallel processing by creating a loop that analyzes data using various nodes. By incorporating ‘Parallel Chunk Start’ and ‘Parallel Chunk End’ nodes at the beginning and end of your process, respectively, you can effectively leverage your system’s capabilities. You can manually configure the number of tasks to be created, or you can leave it to KNIME by selecting ‘use automatic chunk count,’ which sets the number based on the exact cores of your computer’s CPU.
First we will set up the number of threads:
After initiating the ‘Parallel Chunk Start’, it creates the specified number of parallel chunks, placing these threads in a new node by generating an additional node named ‘Parallel Chunk’, which contains 3 threads plus the original one, making 4 threads in total.
Inside the “Parallel Chunk” node, you can see that the “Parallel Chunk Start” node has created the same process for each thread:
2. Concurrent Tasks
Concurrency enables the simultaneous handling of multiple tasks, where they do not necessarily start at the same time. In KNIME, you can achieve concurrency by creating different branches using nodes such as the Rule-based Row Filter, triggering them with variable connections, or invoking already existing workflows via the ‘Call Workflow Service’ node. KNIME offers numerous nodes that facilitate the creation of distinct processing pathways.
In the above example, numbers 1 and 2 start simultaneously, while pathways 3 and 4 also start simultaneously, with number 3 waiting for the completion of the two processes before starting. Concurrency can be initiated either via variable connections (shown in red lines) or via standard connections (black lines). Variable connections can also serve as dummy starters and do not need to carry any information.
3. Stream Processing
Streaming allows you to handle data continuously, processing it as it is produced or received without waiting for previous processes to complete. In traditional ETL design, each node waits for the preceding task to finish. KNIME breaks this paradigm by offering streaming capabilities through certain nodes. To enable this functionality, you need to install the ‘KNIME Streaming Execution’ extension. Streamable nodes are marked with additional information, making it easy to identify them, like the picture below where you can see “Row to Column Names” node is marked with “Streamable”.
To utilize this capability, you need to place all your streamable nodes into a Component, which acts as a wrapper and allows you to specify how many rows will be passed to the next node.
You can watch live as your data is processed with each task and forwarded to the next node.
Additionally, you can integrate real-time data sources like Kafka with KNIME.
4. Distributing Workflows and In-Memory Database Support
KNIME can connect to in-memory databases like Exasol and SingleStore to leverage their high-performance capabilities for real-time data processing and analytics. Distributing workflows across multiple KNIME Executors and handling large datasets in a distributed environment is also possible.
Conclusion
Benefits
- Speed and Performance: Implementing parallelism and concurrency allows for faster data analysis and processing. Dividing tasks into sub-tasks (parallelism) and handling multiple tasks simultaneously (concurrency) can significantly reduce processing time.
- Real-time Processing: Streaming capabilities enable continuous data processing, which is crucial for handling data as it arrives, without waiting for previous processes to complete. This is beneficial for real-time analytics and responsiveness.
- Scalability: Distributing workflows across multiple nodes (executors) and leveraging in-memory databases support scalability, enabling efficient handling of large datasets and real-time analytics with high-performance databases like Exasol and SingleStore.
- Flexibility: KNIME’s modular approach and the ability to integrate with tools like Kafka for real-time data integration enhance flexibility in data processing workflows.
Drawbacks
- Complexity in Implementation: Setting up and configuring parallel processing, concurrency, and streaming workflows can be complex, requiring expertise and careful design to optimize performance without introducing errors or inefficiencies.
- Resource Intensiveness: Running parallel tasks and concurrent processes can be resource-intensive, requiring sufficient computational resources (CPU cores, memory) to achieve optimal performance. This can increase infrastructure costs.
- Integration Challenges: Integrating and configuring streaming workflows with external tools like Kafka or setting up distributed environments for large datasets may pose integration challenges and require additional configuration and troubleshooting efforts.
In conclusion, while implementing advanced data management techniques like parallelism, concurrency, streaming, and distributed processing offers significant gains in speed, scalability, and real-time processing capabilities, it also requires careful consideration of complexity, resource management, and integration challenges to effectively optimize data handling processes.
