This guide aims to provide insights into selecting effective cluster size for data processing and managing Dataflows. We'll talk about key context of data processing like size of your data, complexity of your transformations, and cost management.
We like to think about Dataflows according to their complexity: Low, Normal, and High. Low Dataflows involve minimal to no transformation. You're moving data from source to destination primarily in its original form. An example might be a simple Replication Dataflow that replicates data from the source to the destination as-is.
Normal Dataflows include multiple sources and transformations, handling more varied data. You'll generally see more transformations and a variety of different data sources writing to different locations, but processing logic is still fairly simple. These are your average, day-to-day data pipelines, ingest, some transformation, writing out to one or two destinations. Transformation logic mostly involves things like, data cleansing or reshaping, data enrichment, and/or basic aggregates.
Complex Dataflows are the most intricate, dealing with a high number of sources, transformations, and destinations. These flows require sophisticated processing logic, and have high volume, concurrency, and processing load. Think of a machine learning dataflow where you're training a model or enriching data from an external model.
Low complexity Dataflows such as Replication are like simple, short cargo trains carrying identical goods from one point to another.
Normal complexity Dataflows can be compared to mixed-freight trains with various types of cargo, making a few different stops along the way to shuffle cargo.
Meanwhile, high complexity Dataflows resemble long, specialized trains with different cars for diverse and specific needs, and several different stops along the way.
Within Ascend, each Data Service can support a variety of complexities so it's important to think about the bigger picture for your Dataflows and if you're running several Data Services and Dataflows. For example, you have hundreds of low complexity Dataflows focused on replication. With all of these low complexity Dataflows running simultaneously, we've created a high complexity workload.
The sizes available for selection are dependent upon your environment type/tier. Not all sizes are available for all environment types/tiers. You will see the sizes available that are possible based on your environment type/tier.
Let's delve into the specifics of cluster size for data processing tasks. Small clusters, such as 3X-Small to Medium, are great for development, testing, and handling Low and Normal complexity Dataflows. They are suited for lower data volumes and less complex tasks.
On the other hand, large clusters ranging from Large to 4X-Large are more appropriate for demanding tasks, characterized by high concurrency or parallelization requirements (such as those found in some Normal and most High complexity Dataflows). These are ideal for handling large volumes of data and complex data processing tasks.
Think of small clusters as small train engines pulling a few cars – suitable for lighter loads. Large clusters are like powerful locomotives capable of hauling a long train with diverse and heavy cargo, essential for more complex and voluminous tasks.
When deciding on a cluster size, it's crucial to evaluate the volume and complexity of your dataflow. Understanding the specific demands of your data tasks helps in choosing a cluster that can handle them effectively. Additionally, consider the CPU capabilities and concurrency requirements of your cluster to ensure it can manage the tasks efficiently.
Sizing your cluster is like determining the right engine for your train. The length and weight of the train (dataflow complexity and volume) dictate the power and size of the engine (cluster) needed. Sometimes, the train schedule (how frequently the train runs down the line) also impacts the size of clusters to ensure timely train service!
Start by building a small-scale version of your dataflow and test its performance. Gradually scale up by adding more tasks and complexity as needed. This approach helps in understanding the demands of your Dataflow and scaling your cluster accordingly. Consider the purpose of your cluster, such as development, testing, or production, and size it to match these needs.
Expect that you will need more resources at scale and in production, especially if testing with small/sample datasets in earlier stages of development!
Begin with a small engine and a few cars. Test how well they run together, then add more cars or upgrade to a bigger engine as your train (Dataflow) grows in length and complexity.
Strive for balanced utilization of your cluster's capabilities, typically between 60-90%. Avoid pushing the cluster to its full capacity to maintain flexibility. Additionally, optimize cluster usage based on operational times, scaling up during peak periods and downscaling during off-peak times.
Efficient train operation involves using an engine powerful enough to pull the train without strain, yet not so powerful that it's underutilized. Similarly, manage your cluster to align with the ebb and flow of your data processing needs.
Choosing an effective cluster size and managing Dataflows is akin to running a train operation. It's about finding the right balance between the size of your clusters (engines) and the length and complexity of your Dataflows (trains), ensuring both efficient operations and cost-effectiveness.
Updated about 1 month ago