Apache Spark 2.1 Benchmarking

Apache Spark is the poster child of Big Data processing. Speeding up Big Data applications is the top priority project at the university lab I work for (Sizing Servers Lab of the University College of West-Flanders), so we produced a benchmark that uses many of the Spark features and is based upon real world usage.

The test is described in the graph above. We first start with 300 GB of compressed data gathered from the CommonCrawl. These compressed files are a large amount of web archives. We decompress the data on the fly to avoid a long wait that is mostly storage related. We then extract the meaningful text data out of the archives by using the Java library "BoilerPipe". Using the Stanford CoreNLP Natural Language Processing Toolkit, we extract entities ("words that mean something") out of the text, and then count which URLs have the highest occurrence of these entities. The Alternating Least Square algorithm is then used to recommend which URLs are the most interesting for a certain subject.

In previous articles, we tested with Spark 1.5 in standalone mode (non-clustered). That worked out well enough, but we saw diminishing returns as core counts went up. In hindsight, just dumping 300 GB of compressed data in one JVM was not optimal for 30+ core systems. The high core counts of the Xeon 8176 and EPYC 7601 caused serious performance issues when we first continued to test this way. The 64 core EPYC 7601 performed like a 16-core Xeon, the Skylake-SP system with 56 cores was hardly better than a 24-core Xeon E5 v4.

So we decided to turn our newest servers into virtual clusters. Our first attempt is to run with 4 executors. Researcher Esli Heyvaert also upgraded our Spark benchmark so it could run on the latest and greatest version: Apache Spark 2.1.1.

Here are the results:

Apache Spark 2.1.1

If you wonder who needs such server behemoths besides the people who virtualize a few dozen virtual machines, the answer is Big Data. Big Data crunching has an unsatisfiable hunger for – mostly integer – processing power. Even on our fastest machine, this test needs about 4 hours to finish. It is nothing less than a killer app.

Our Spark benchmark needs about 120 GB of RAM to run. The time spent on storage I/O is negligible. Data processing is very parallel, but the shuffle phases require a lot of memory interaction. The ALS phase does not scale well over many threads, but is less than 4% of the total testing time.

Given the higher clockspeed in lightly threaded and single threaded parts, the faster shuffle phase probably gives the Intel chip an edge of only about 5%.

Java Performance Floating Point performance
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  • TheOriginalTyan - Tuesday, July 11, 2017 - link

    Another nicely written article. This is going to be a very interesting next couple of months. Reply
  • coder543 - Tuesday, July 11, 2017 - link

    I'm curious about the database benchmarks. It sounds like the database is tiny enough to fit into L3? That seems like a... poor benchmark. Real world databases are gigabytes _at best_, and AMD's higher DRAM bandwidth would likely play to their favor in that scenario. It would be interesting to see different sizes of transactional databases tested, as well as some NoSQL databases. Reply
  • psychobriggsy - Tuesday, July 11, 2017 - link

    I wrote stuff about the active part of a larger database, but someone's put a terrible spam blocker on the comments system.

    Regardless, if you're buying 64C systems to run a DB on, you likely will have a dataset larger than L3, likely using a lot of the actual RAM in the system.
    Reply
  • roybotnik - Wednesday, July 12, 2017 - link

    Yea... we use about 120GB of RAM on the production DB that runs our primary user-facing app. The benchmark here is useless. Reply
  • haplo602 - Thursday, July 13, 2017 - link

    I do hope they elaborate on the DB benchmarks a bit more or do a separate article on it. Since this is a CPU article, I can see the point of using a small DB to fit into the cache, however that is useless as an actual DB test. It's more an int/IO test.

    I'd love to see a larger DB tested that can fit into the DRAM but is larger than available caches (32GB maybe ?).
    Reply
  • ddriver - Tuesday, July 11, 2017 - link

    We don't care about real world workloads here. We care about making intel look good. Well... at this point it is pretty much damage control. So let's lie to people that intel is at least better in one thing.

    Let me guess, the databse size was carefully chosen to NOT fit in a ryzen module's cache, but small enough to fit in intel's monolithic die cache?

    Brought to you by the self proclaimed "Most Trusted in Tech Since 1997" LOL
    Reply
  • Ian Cutress - Tuesday, July 11, 2017 - link

    I'm getting tweets saying this is a severely pro AMD piece. You are saying it's anti-AMD. ¯\_(ツ)_/¯ Reply
  • ddriver - Tuesday, July 11, 2017 - link

    Well, it is hard to please intel fanboys regardless of how much bias you give intel, considering the numbers.

    I did not see you deny my guess on the database size, so presumably it is correct then?
    Reply
  • ddriver - Tuesday, July 11, 2017 - link

    In the multicore 464.h264ref test we have 2670 vs 2680 for the xeon and epyc respectively. Considering that the epyc score is mathematically higher, howdoes it yield a negative zero?

    Granted, the difference is a mere 0.3% advantage for epyc, but it is still a positive number.
    Reply
  • Headley - Friday, July 14, 2017 - link

    I thought the exact same thing Reply

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