5 Common Myths About Massively Parallel Processing, Debunked
What is MPP? Separating MPP Facts from Fiction for Data Pros
As a data professional, you need a clear picture of the tools and technologies available to you. You want to base your decisions on something other than myths and half-truths.
You and I will tackle five common misconceptions about MPP in this article. I'll break down what MPP is, how it works when it shines, and when it might not be the best fit. By the end, you will have a solid understanding of MPP's capabilities and limitations.
You might be a seasoned data engineer looking to optimise your pipelines. Or an aspiring analytics engineer seeking faster query results. This article has something for you.
Grab a cup of coffee. Get comfortable. You and I will separate fact from fiction.
Read time: 6 minutes
What is Massively Parallel Processing, And Why Should You Care
Massively Parallel Processing, or MPP for short, is the technology behind most modern data tools. MPP makes it possible to crunch large amounts of data and produce reports quickly.
In a nutshell, it is a way of distributing data and computation across multiple processors or nodes. This allows you to accelerate processing and handle massive datasets.
Do you remember our Snowflake storage article? MPP is like having a dozen workers counting bottles together to get the result faster.
One key aspect of MPP is the "shared-nothing" philosophy. In an MPP system, each processor has dedicated memory and storage, and they do not share resources. This allows for excellent scalability - adding more nodes without worrying about resource conflicts.
But enough about the technical details. You are not here to dive deep into how Massively Parallel Processing works (although if you are curious, let me know, and we can discuss it further).
Now, let's focus on shattering some common myths about MPP!
Myth #1: MPP Is Just About Using More Processors
Indeed, MPP involves using a large number of processors to process data in parallel. But it is more than adding more CPUs to solve the problem.
MPP is a specific architecture and approach to processing data. In an MPP system, data is distributed across many independent nodes, each with memory and storage. The nodes work together to process chunks of the data in parallel but don't share resources.
So again, it’s not like having one worker with many hands. It is like cloning this worker and splitting the task among the clones.
This "shared-nothing" architecture allows MPP systems to scale out by adding more nodes as needed. And some platforms, like Snowflake, take it even further - each virtual warehouse you create is essentially an entire cluster of nodes ready to process your data.
But wait — there is more!
Myth #2: More Nodes Always Means Faster Processing
Adding nodes to an MPP cluster can improve performance, but it is not a guaranteed solution. You can't simply keep adding more nodes and expect unlimited speedup.
Distributing data across nodes takes time. The nodes need to communicate to coordinate processing and combine results. The overhead of adding more nodes can outweigh the benefits at a certain point.
It is like having too many individuals flooding the beer warehouse. At some point, they start hindering each other’s progress.
Finding the right balance is essential and not just unthinkingly scaling out. Effective data partitioning, minimising data movement between nodes, and optimising queries play a role in MPP performance.
It's a delicate balance of resource allocation and efficiency.
Now, to the next misconception.
Myth #3: MPP is Only For Huge Datasets
While MPP is great for processing massive amounts of data, you don’t necessarily need enormous datasets to benefit from it. Parallel processing can improve performance even for smaller datasets.
The key is to have a workload that can be parallelised effectively. Analytical queries, machine learning training, and ETL jobs are all suitable candidates for MPP. If your data can be partitioned and processed independently by multiple nodes, MPP can help accelerate the process.
So don't feel excluded if you are not dealing with billions of rows. Massively Parallel Processing can still be beneficial and streamline your data processing tasks.
You may be getting excited about MPP’s capabilities by now, but you are still concerned about the costs.
Myth #4: MPP Systems Are Expensive and Not Cost-Effective
Think MPP will break the bank?
Not necessarily! While MPP systems might seem pricey upfront, they can save you money in the long run.
With MPP, you can process more data in less time. This means faster insights and quicker decision-making. Plus, you can scale your system as your needs grow without breaking the budget.
And here's the kicker: Many MPP solutions now offer cloud-based pricing models. You only pay for what you use and when you use it. No more shelling out for idle resources!
You are almost there. Let’s debunk the last misconception.
Myth #5: Massively Parallel Processing Is Too Complex For Most Organisations
MPP systems can be complex to manage, especially when dealing with clusters of hundreds or thousands of nodes. You know the challenge if you have ever had to manage an EMR cluster (and those are considered good!).
However, platforms like Databricks, BigQuery, and Snowflake have come to the rescue, abstracting away a lot of the underlying complexity of MPP. They handle the details of provisioning nodes, distributing data, and optimising queries behind the scenes. This allows data engineers to focus on the data rather than getting bogged down in infrastructure complexities.
Of course, a solid understanding of MPP concepts is still valuable. But you don't need to be a distributed systems expert to leverage Massively Parallel Processing. These platforms have made it much more accessible to various organisations.
Wrapping Up
Let's recap what you learned about MPP and dispel those misconceptions once and for all:
MPP is not just about using more processors. It's a specific distributed architecture.
MPP can accelerate various workloads, not just massive datasets. Even more minor data can benefit.
Scaling out is only sometimes the answer to better performance. It's about finding the right balance.
MPP doesn’t need to be expensive. It allows you to do more in less time.
While MPP can be complex, cloud platforms have made it much more approachable for organisations of all sizes.
Hopefully, clarifying these common misconceptions has given you a better understanding of how MPP fits into the data landscape. But don't just take my word for it - get hands-on experience!
Set up a cluster, load some data, and start running queries. You will learn and witness firsthand how MPP can enhance your data processing capabilities. It's a transformative technology.
Embrace the power of parallel processing, and let MPP be your ally in the world of data! And if you have any questions along the way, feel free to reach out. Happy data processing!
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Love the third one.
If you already have infra setup, a single node MPP can be great for smaller files.