Database Supercomputing in the Cloud

This post will combine the information in the last two posts to show how the cloud might be used as a database supercomputer at no extra cost. 

To quickly recap: in the post here, you saw a simple model that shows how a cluster dedicated to a single query uses the on-demand resources of cloud computing to provide a 10X-100X performance boost over a multitasking cluster running many queries at once. Note that this model did not account for the start-up time of spinning up a new cluster instance for each query, but we assume that there is still a significant saving for queries that run for several minutes. Further, we believe that queries can be queued and run one at a time on clusters to reduce the start-up costs whenever there is work queued. 

The next post here reminded us of the cost of populating cache from DRAM whenever there is a multitask context switch from one query to the next. You also saw how supercomputing SIMD instructions further accelerate processing. Finally, we discussed how modern columnar databases store data as vectors in a way that allows compressed columnar data to be loaded directly into cache to optimize the use of that memory. 

One last note: all the cores share the L3 cache in a chip. On a 4-CPU processor, this means that you could run 4 parallel processes, all on compressed vectorized data. 

So how might the cloud help here? First, if we dedicate a cluster to query processing where “dedicate” means that the hardware is dedicated to a single queue of queries, then once the query data is loaded in memory and the cache is primed, the performance can be 1000X the speed of an alternative configuration where the hardware is shared both by a multitasking DBMS and that DBMS is sharing the cluster with other cloud workloads dispatched by the cloud operating system. Better still, since in the cloud using fine-grained billing, you pay only for what you use, so this extra performance is nearly free. 

When SAP HANA was introduced, SAP published amazing 1000X benchmarks using in-memory supercomputing instructions. Unfortunately, the technology did not quite exist to deploy queries on-demand on tens or hundreds or thousands of dedicated cloud computers. I imagine that we are not too far from this today. 

The bottom line is that multitasking has never been efficient. When you swap context in and out of cache, you are just wasting resources. It made sense when computing was expensive, and you swapped context to take advantage of the time it took to perform I/O to disk or tape. For analytic workloads where the data is columnar and vectorized, you can fit multiple terabytes of compressed data in memory and stream it to supercomputing instructions quickly.  

I imagine a time when simple, very high performance, single-threaded database processing units, similar in architecture to GPUs, will handle queries in a dedicated manner, fetching vectorized data from products like Apache Arrow, and 1000X speed-up at a very low cost will be the standard. 

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