12 ms·
Redshift Performance and Cost
- meh02 14y agoA much better comparison would be a self hosted properly tuned PostgreSQL instance vs RedShift. :)
- csarva 14y agoThis is the second article I've seen where the authors forget to multiply by the number of redshift nodes. A single XL node is $0.85/hr so 16 nodes would be $13.60/hr. Still cheaper than their Hive configuration obviously but less than a buck?
- AirbnbNerds 14y agoThanks for pointing this out! We just updated the article to reflect this.
- taf2 14y agodamn yeah, I initially thought wow for $632.4 bucks i'm setting this up next week or even later this week provisioning the redshift - but 16x that at $10118.4 - I'll continue wait... probably a good thing to stay focused on features anyway
- twog 14y agoStill running on posterous. Weird to think that this is probably the last posterous blog post I will read.
- TY 14y agoRedshift is based on ParAccel, not on Postgres. ParAccel uses APIs similar to Postgres due to historical reasons, but not the technology. For a basic overview: http://en.wikipedia.org/wiki/Paraccel http://en.wikipedia.org/wiki/Paraccel As for the rest of the article, it feels like a basic Data Warehousing 101 re-discovered. It should have been titled "Analytics: Back To The Future" :-)
- bloomfilter 14y agoThanks for pointing it out, we have correct it in our post
- meritt 14y agoNo kidding. The amount of startups that have flocked to hadoop for "data analytics" over the past 5 years is extremely disheartening. Almost all of the cases are far more suitable for any off-the-shelf RDBMS much less a column-oriented one. Same thing with MongoDB. How much time and money would have been saved learning Database Theory/SQL/Data Warehousing/Dimensional Modeling instead of cramming everything into an unstructured data-store?
- mixedbit 14y agoWhich off-the-shell RDBMS can handle queries over 3 billion rows?
- jbverschoor 14y agopostgres?
- meritt 14y agoCounter-question: Which startup has a actual data table with over 3 billion rows?
- badgar 14y agoWhen you log every mousedown because the founder misunderstands A/B testing, 3 billion rows is easy to come by. Besides - you're busy changing the world, so you should expect to use the same technology as Facebook and Google.
- PanMan 14y agoWe have just crossed 2 billion items in our datastore. While not 3 billion yet, I expect that to happen later this year. Too bad Redshift can't handle JSON files: Converting everything will be annoying.
- panarky 14y agoThe article mentions this briefly, but it should be emphasized: parallel loading from S3 is MUCH faster. This weekend I loaded 2 billion rows from S3 both ways: - From a single gzipped object: 4 hours 42 minutes - From 2000 gzipped slices of 1M rows each: 17 minutes (Loading from gzipped files is considerably faster, in addition to saving S3 charges.) The article notes that choice of distribution key is critical. I'd add that choice of sort key is equally important. In my testing, a better sort key improved compression from 1.5:1 to 4:1, and also made common queries 5x faster. Unfortunately, you only get one dist key and one sort key per table, so less common queries could get slower.
- fujibee 14y agoAlso if you launch the more instance in a cluster, the faster to load. Our survey: http://www.slideshare.net/Hapyrus/scalability-of-amazon-redshift-data-loading-and-query-speed http://www.slideshare.net/Hapyrus/scalability-of-amazon-reds... We tried much more files (5MB each) to load, but it takes longer time in total.. We're trying to get appropriate size and file numbers.
- monstrado 14y agoWhich storage format did you use for Hive? This is very important to how performance plays out, are you using snappy or LZO compression? Also, this is a relevant comment from a Hive committer (http://news.ycombinator.com/item?id=5248485 http://news.ycombinator.com/item?id=5248485).
- polskibus 14y agoIt's good to see a Redshift evaluation. I'm wondering how does Redshift compare to hadoop airbnb setup when taking data loading and transformation into consideration as well as running aggregate queries? I mean if you want to run analysis fairly often, do you need to reload everything in Redshift? From maintenance point of view, is Hadoop setup more flexible and cheaper than Redshift?
- bloomfilter 14y agoThe hadoop setup we have is actually EMR, and we use s3 for data storage, so for us Hive/Hadoop doesn't save much in terms of data loading. And once you have a process setup to load and update the data in Redshift, you don't have to reload everything
- serbaut 14y agoThe first query seems awfully slow. I have a six node vertica cluster with a 100 column table with 7Bn rows in it and a similar query takes less than 3 seconds.
- mallipeddi 14y agoDisclosure: I work on the Redshift team. The OP's cluster is a 16-node hs1.xlarge cluster (has 3 spindles per node). There's actually a more powerful node-type hs1.8xlarge which has 24 spindles on each node. More info: http://aws.amazon.com/redshift/pricing/ http://aws.amazon.com/redshift/pricing/ So it's not fair to compare Redshift performance to your Vertica cluster unless the hardware is similar.
- jacques_chester 14y agoDumb question: was the data ETL'd into a star schema first? That can make a big difference, especially in columnar stores.
- bloomfilter 14y agoWe sort of made it a partial star schema, but not strictly. since random join can be expensive