5 ms·

1.1B taxi rides benchmarked on distributed GPU-powered MapD

53 points by tmostak 9y ago

Bedon292 9y ago
Amazing to see the improvements that MapD has made over the past few years. I have been following them for a long time, and was excited to catch wind of 3.0 this morning. Then I get on here to see someone already benchmarking and working with it.
arnon 9y ago
1.1B records = 500GB of raw CSV data. This fits into RAM quite easily on a machine like the P2.8xlarge, especially when compression is used (like MapD uses). I'd like to see how well this performs on a dataset that doesn't fit in the RAM.
- tmostak 9y ago
  Its honestly a pretty small dataset for us. MapD can easily do sub-100ms queries on 100B+ records with a single rack. You can fit a lot of GPU + CPU memory on a small cluster. That RAM goes even further when you use compression like you mentioned. We're fast at pulling data off of disk but we've never really aimed to be a traditional disk-based data warehouse. There are already great systems for that.
  - menegattig 9y ago
    At what point do you think starts making sense cost-wise to use MapD (or GPU in general) instead of Redshift or BigQuery?
    - kornish 9y ago
      My guess is that it makes more sense when interactive query latency (hundreds of ms) is of the utmost concern.
      - menegattig 9y ago
        Right, I agree with you. I was just wondering what kind of companies (except from financial sector) would be willing to spend hundreds of thousands to get their latency from hundreds of ms to dozens of ms. I'm saying that because if you have a very well-tuned Redshift cluster, you can easily get dozens of ms for your queries, spending thousands of dollars, not hundreds of thousands.
        tmostak 9y ago
        You might be surprised. Telcos need to troubleshoot network problems in real time, automakers and insurance companies need to track cars in real-time, oil companies need to interactively query and visualize geological data, and the infosec industry needs real-time packet analysis. We have customers almost in every vertical, all united by their need for real-time analytics. Some want to use MapD us visualization, others for programatic querying for things like fraud detection, and others still to feed into machine learning algorithms. I'm also curious how you envisage paying thousands of dollars per year to get queries in dozens of ms on datasets this size, much less 10-100X larger (which customers would often use MapD for). Mark benchmarked a 6-node ds2.8xlarge cluster of Redshift (> $40/hour) and found it up to 70X slower than MapD on this dataset. That's similar to our price on Amazon for this 2-node cluster. Not saying Redshift isn't a great system, just that I don't buy the price/performance numbers you are quoting (for real workloads, not for some specific query that can be indexed well, etc)
        menegattig 9y ago
        Very clear, thanks so much for the detailed explanation. I'm still curious about how much it would cost for scenario where you have 1 billion user and 200 billion events for a year of data and keep adding 10 billion monthly (a very real DMP or Telco scenario) and you have to make a query like this one below on top of all this data (200 billion records). I'm wondering how many MapD servers/infrastructure I would need to have in order to get results under 100-ms. Count UNIQUE Users that from "San Francisco" OR "New York" AND accessed the pages "/sports" OR "/news" more than 3 times in the past 12 months.
  - gorodetsky 9y ago
    Do you have any plans on offering MapD on Google Cloud Platform? (They have their own "Marketplace".)
    - tmostak 9y ago
      Yes absolutely. We were part of their launch and are just going through the process now of adding MapD to the marketplace.
      - gorodetsky 9y ago
        Awesome! Can't wait to try it out, hopefully with K100 at some point.
        arnon 9y ago
        P100 probably?
        tmostak 9y ago
        Yes. :)
mmrezaie 9y ago
Is there a bridge for using MapD with Spark interface or somehow combining them? This can be interesting for the clusters with a lot of GPUs and a lot of data to do data manipulations.
- Bedon292 9y ago
  That actually sounds amazing, I would totally use that. I am actually hoping that they will create GDAL bindings for geospatial data.
  - tmostak 9y ago
    We actually already can import geospatial formats via GDAL (shapefiles, geojson, kml). We can render points and polygon data. More geospatial abilities to come!
    - Bedon292 9y ago
      Awesome! Thanks for the update, super stoked for that. Can you also export through GDAL?
trafficlight 9y ago
Found elsewhere on the internet: 'On a system with eight Tesla K80s, which might cost somewhere between $60,000 to $70,000, the license for the MapD stack would be “a small multiple” of this hardware cost.' I guess I'm not playing with this anytime soon.
- Bedon292 9y ago
  Still not cheap, but you could play with it on an p2.xlarge AWS instance for $4 an hour: https://aws.amazon.com/marketplace/pp/B01M0ZY2OV https://aws.amazon.com/marketplace/pp/B01M0ZY2OV
menegattig 9y ago
Price comparison between Amazon Redshift, Google BigQuery, ElasticSearch and SlicingDice using the same dataset: https://blog.slicingdice.com/slicingdice-pricing-model-and-competitors-comparison-31f1c9f0f076 https://blog.slicingdice.com/slicingdice-pricing-model-and-c...
- marklit 9y ago
  Same dataset? Mind telling me the uncompressed byte count?
baronseng 9y ago
the benchmark page [1] mark did a good summary of how various technologies compare. [1] http://tech.marksblogg.com/benchmarks.html http://tech.marksblogg.com/benchmarks.html

53 points by tmostak 9y ago

Amazing to see the improvements that MapD has made over the past few years. I have been following them for a long time, and was excited to catch wind of 3.0 this morning. Then I get on here to see someone already benchmarking and working with it.

arnon 9y ago

1.1B records = 500GB of raw CSV data. This fits into RAM quite easily on a machine like the P2.8xlarge, especially when compression is used (like MapD uses). I'd like to see how well this performs on a dataset that doesn't fit in the RAM.

tmostak 9y ago
Its honestly a pretty small dataset for us. MapD can easily do sub-100ms queries on 100B+ records with a single rack. You can fit a lot of GPU + CPU memory on a small cluster. That RAM goes even further when you use compression like you mentioned. We're fast at pulling data off of disk but we've never really aimed to be a traditional disk-based data warehouse. There are already great systems for that.
- menegattig 9y ago
  At what point do you think starts making sense cost-wise to use MapD (or GPU in general) instead of Redshift or BigQuery?
  - kornish 9y ago
    My guess is that it makes more sense when interactive query latency (hundreds of ms) is of the utmost concern.
    - menegattig 9y ago
      Right, I agree with you. I was just wondering what kind of companies (except from financial sector) would be willing to spend hundreds of thousands to get their latency from hundreds of ms to dozens of ms. I'm saying that because if you have a very well-tuned Redshift cluster, you can easily get dozens of ms for your queries, spending thousands of dollars, not hundreds of thousands.
      - tmostak 9y ago
        You might be surprised. Telcos need to troubleshoot network problems in real time, automakers and insurance companies need to track cars in real-time, oil companies need to interactively query and visualize geological data, and the infosec industry needs real-time packet analysis. We have customers almost in every vertical, all united by their need for real-time analytics. Some want to use MapD us visualization, others for programatic querying for things like fraud detection, and others still to feed into machine learning algorithms. I'm also curious how you envisage paying thousands of dollars per year to get queries in dozens of ms on datasets this size, much less 10-100X larger (which customers would often use MapD for). Mark benchmarked a 6-node ds2.8xlarge cluster of Redshift (> $40/hour) and found it up to 70X slower than MapD on this dataset. That's similar to our price on Amazon for this 2-node cluster. Not saying Redshift isn't a great system, just that I don't buy the price/performance numbers you are quoting (for real workloads, not for some specific query that can be indexed well, etc)
        menegattig 9y ago
        Very clear, thanks so much for the detailed explanation. I'm still curious about how much it would cost for scenario where you have 1 billion user and 200 billion events for a year of data and keep adding 10 billion monthly (a very real DMP or Telco scenario) and you have to make a query like this one below on top of all this data (200 billion records). I'm wondering how many MapD servers/infrastructure I would need to have in order to get results under 100-ms. Count UNIQUE Users that from "San Francisco" OR "New York" AND accessed the pages "/sports" OR "/news" more than 3 times in the past 12 months.
- gorodetsky 9y ago
  Do you have any plans on offering MapD on Google Cloud Platform? (They have their own "Marketplace".)
  - tmostak 9y ago
    Yes absolutely. We were part of their launch and are just going through the process now of adding MapD to the marketplace.
    - gorodetsky 9y ago
      Awesome! Can't wait to try it out, hopefully with K100 at some point.
      - arnon 9y ago
        P100 probably?
        tmostak 9y ago
        Yes. :)

mmrezaie 9y ago

Is there a bridge for using MapD with Spark interface or somehow combining them? This can be interesting for the clusters with a lot of GPUs and a lot of data to do data manipulations.

Bedon292 9y ago
That actually sounds amazing, I would totally use that. I am actually hoping that they will create GDAL bindings for geospatial data.
- tmostak 9y ago
  We actually already can import geospatial formats via GDAL (shapefiles, geojson, kml). We can render points and polygon data. More geospatial abilities to come!
  - Bedon292 9y ago
    Awesome! Thanks for the update, super stoked for that. Can you also export through GDAL?

trafficlight 9y ago

Found elsewhere on the internet: 'On a system with eight Tesla K80s, which might cost somewhere between $60,000 to $70,000, the license for the MapD stack would be “a small multiple” of this hardware cost.' I guess I'm not playing with this anytime soon.

Bedon292 9y ago
Still not cheap, but you could play with it on an p2.xlarge AWS instance for $4 an hour: https://aws.amazon.com/marketplace/pp/B01M0ZY2OV https://aws.amazon.com/marketplace/pp/B01M0ZY2OV

menegattig 9y ago

Price comparison between Amazon Redshift, Google BigQuery, ElasticSearch and SlicingDice using the same dataset: https://blog.slicingdice.com/slicingdice-pricing-model-and-competitors-comparison-31f1c9f0f076 https://blog.slicingdice.com/slicingdice-pricing-model-and-c...

marklit 9y ago
Same dataset? Mind telling me the uncompressed byte count?

baronseng 9y ago

the benchmark page [1] mark did a good summary of how various technologies compare. [1] http://tech.marksblogg.com/benchmarks.html http://tech.marksblogg.com/benchmarks.html