Re: Single threaded laptop implementation beating a 128 node GraphX cluster on a 1TB data set (128 billion nodes) - What is a use case for GraphX then? when is it worth the cost?
Note that even the Facebook four degrees of separation paper went down to a single machine running WebGraph (http://webgraph.di.unimi.it/) for the final steps, after running jobs in there Hadoop cluster to build the dataset for that final operation. The computations were performed on a 24-core machine with 72 GiB of memory and 1 TiB of disk space.6 The first task was to import the Facebook graph(s) into a compressed form for WebGraph [4], so that the multiple scans required by HyperANF’s diffusive process could be carried out relatively quickly. Some toolkits/libraries are optimised for that single dedicated use —yet are downstream of the raw data; where memory reads $L1-$L3 cache locality becomes the main performance problem, and where synchronisation techniques like BSP aren't necessarily needed. On 29 Mar 2015, at 23:18, Eran Medan ehrann.meh...@gmail.commailto:ehrann.meh...@gmail.com wrote: Hi Sean, I think your point about the ETL costs are the wining argument here. but I would like to see more research on the topic. What I would like to see researched - is ability to run a specialized set of common algorithms in fast-local-mode just like a compiler optimizer can decide to inline some methods, or rewrite a recursive function as a for loop if it's in tail position, I would say that the future of GraphX can be that if a certain algorithm is a well known one (e.g. shortest paths) and can be run locally faster than on a distributed set (taking into account bringing all the data locally) then it will do so. Thanks! On Sat, Mar 28, 2015 at 1:34 AM, Sean Owen so...@cloudera.commailto:so...@cloudera.com wrote: (I bet the Spark implementation could be improved. I bet GraphX could be optimized.) Not sure about this one, but in core benchmarks often start by assuming that the data is local. In the real world, data is unlikely to be. The benchmark has to include the cost of bringing all the data to the local computation too, since the point of distributed computation is bringing work to the data. Specialist implementations for a special problem should always win over generalist, and Spark is a generalist. Likewise you can factor matrices way faster in a GPU than in Spark. These aren't entirely either/or propositions; you can use Rust or GPU in a larger distributed program. Typically a real-world problem involves more than core computation: ETL, security, monitoring. Generalists are more likely to have an answer to hand for these. Specialist implementations do just one thing, and they typically have to be custom built. Compare the cost of highly skilled developer time to generalist computing resources; $1m buys several dev years but also rents a small data center. Speed is an important issue but by no means everything in the real world, and these are rarely mutually exclusive options in the OSS world. This is a great piece of work, but I don't think it's some kind of argument against distributed computing. On Fri, Mar 27, 2015 at 6:32 PM, Eran Medan ehrann.meh...@gmail.commailto:ehrann.meh...@gmail.com wrote: Remember that article that went viral on HN? (Where a guy showed how GraphX / Giraph / GraphLab / Spark have worse performance on a 128 cluster than on a 1 thread machine? if not here is the article -http://www.frankmcsherry.org/graph/scalability/cost/2015/01/15/COST.html) Well as you may recall, this stirred up a lot of commotion in the big data community (and Spark/GraphX in particular) People (justly I guess) blamed him for not really having “big data”, as all of his data set fits in memory, so it doesn't really count. So he took the challenge and came with a pretty hard to argue counter benchmark, now with a huge data set (1TB of data, encoded using Hilbert curves to 154GB, but still large). see at - http://www.frankmcsherry.org/graph/scalability/cost/2015/02/04/COST2.html He provided the source here https://github.com/frankmcsherry/COST as an example His benchmark shows how on a 128 billion edges graph, he got X2 to X10 faster results on a single threaded Rust based implementation So, what is the counter argument? it pretty much seems like a blow in the face of Spark / GraphX etc, (which I like and use on a daily basis) Before I dive into re-validating his benchmarks with my own use cases. What is your opinion on this? If this is the case, then what IS the use case for using Spark/GraphX at all?
Re: Single threaded laptop implementation beating a 128 node GraphX cluster on a 1TB data set (128 billion nodes) - What is a use case for GraphX then? when is it worth the cost?
Just the same as spark was disrupting the hadoop ecosystem by changing the assumption that you can't rely on memory in distributed analytics...now maybe we are challenging the assumption that big data analytics need to distributed? I've been asking the same question lately and seen similarly that spark performs quite reliably and well on local single node system even for an app which I ran for a streaming app which I ran for ten days in a row... I almost felt guilty that I never put it on a cluster! On Mar 30, 2015 5:51 AM, Steve Loughran ste...@hortonworks.com wrote: Note that even the Facebook four degrees of separation paper went down to a single machine running WebGraph (http://webgraph.di.unimi.it/) for the final steps, after running jobs in there Hadoop cluster to build the dataset for that final operation. The computations were performed on a 24-core machine with 72 GiB of memory and 1 TiB of disk space.6 The first task was to import the Facebook graph(s) into a compressed form for WebGraph [4], so that the multiple scans required by HyperANF’s diffusive process could be carried out relatively quickly. Some toolkits/libraries are optimised for that single dedicated use —yet are downstream of the raw data; where memory reads $L1-$L3 cache locality becomes the main performance problem, and where synchronisation techniques like BSP aren't necessarily needed. On 29 Mar 2015, at 23:18, Eran Medan ehrann.meh...@gmail.com wrote: Hi Sean, I think your point about the ETL costs are the wining argument here. but I would like to see more research on the topic. What I would like to see researched - is ability to run a specialized set of common algorithms in fast-local-mode just like a compiler optimizer can decide to inline some methods, or rewrite a recursive function as a for loop if it's in tail position, I would say that the future of GraphX can be that if a certain algorithm is a well known one (e.g. shortest paths) and can be run locally faster than on a distributed set (taking into account bringing all the data locally) then it will do so. Thanks! On Sat, Mar 28, 2015 at 1:34 AM, Sean Owen so...@cloudera.com wrote: (I bet the Spark implementation could be improved. I bet GraphX could be optimized.) Not sure about this one, but in core benchmarks often start by assuming that the data is local. In the real world, data is unlikely to be. The benchmark has to include the cost of bringing all the data to the local computation too, since the point of distributed computation is bringing work to the data. Specialist implementations for a special problem should always win over generalist, and Spark is a generalist. Likewise you can factor matrices way faster in a GPU than in Spark. These aren't entirely either/or propositions; you can use Rust or GPU in a larger distributed program. Typically a real-world problem involves more than core computation: ETL, security, monitoring. Generalists are more likely to have an answer to hand for these. Specialist implementations do just one thing, and they typically have to be custom built. Compare the cost of highly skilled developer time to generalist computing resources; $1m buys several dev years but also rents a small data center. Speed is an important issue but by no means everything in the real world, and these are rarely mutually exclusive options in the OSS world. This is a great piece of work, but I don't think it's some kind of argument against distributed computing. On Fri, Mar 27, 2015 at 6:32 PM, Eran Medan ehrann.meh...@gmail.com wrote: Remember that article that went viral on HN? (Where a guy showed how GraphX / Giraph / GraphLab / Spark have worse performance on a 128 cluster than on a 1 thread machine? if not here is the article - http://www.frankmcsherry.org/graph/scalability/cost/2015/01/15/COST.html) Well as you may recall, this stirred up a lot of commotion in the big data community (and Spark/GraphX in particular) People (justly I guess) blamed him for not really having “big data”, as all of his data set fits in memory, so it doesn't really count. So he took the challenge and came with a pretty hard to argue counter benchmark, now with a huge data set (1TB of data, encoded using Hilbert curves to 154GB, but still large). see at - http://www.frankmcsherry.org/graph/scalability/cost/2015/02/04/COST2.html He provided the source here https://github.com/frankmcsherry/COST as an example His benchmark shows how on a 128 billion edges graph, he got X2 to X10 faster results on a single threaded Rust based implementation So, what is the counter argument? it pretty much seems like a blow in the face of Spark / GraphX etc, (which I like and use on a daily basis) Before I dive into re-validating his benchmarks with my own use cases. What is your opinion on this? If this is the case, then what IS the use case for
Re: Single threaded laptop implementation beating a 128 node GraphX cluster on a 1TB data set (128 billion nodes) - What is a use case for GraphX then? when is it worth the cost?
On 30 Mar 2015, at 13:27, jay vyas jayunit100.apa...@gmail.commailto:jayunit100.apa...@gmail.com wrote: Just the same as spark was disrupting the hadoop ecosystem by changing the assumption that you can't rely on memory in distributed analytics...now maybe we are challenging the assumption that big data analytics need to distributed? I've been asking the same question lately and seen similarly that spark performs quite reliably and well on local single node system even for an app which I ran for a streaming app which I ran for ten days in a row... I almost felt guilty that I never put it on a cluster! Modern machines can be pretty powerful: 16 physical cores HT'd to 32, 384+MB, GPU, giving you lots of compute. What you don't get is the storage capacity to match, and especially, the IO bandwidth. RAID-0 striping 2-4 HDDs gives you some boost, but if you are reading, say, a 4 GB file from HDFS broken in to 256MB blocks, you have that data replicated into (4*4*3) blocks: 48. Algorithm and capacity permitting, you've just massively boosted your load time. Downstream, if data can be thinned down, then you can start looking more at things you can do on a single host : a machine that can be in your Hadoop cluster. Ask YARN nicely and you can get a dedicated machine for a couple of days (i.e. until your Kerberos tokens expire).
Re: Single threaded laptop implementation beating a 128 node GraphX cluster on a 1TB data set (128 billion nodes) - What is a use case for GraphX then? when is it worth the cost?
One issue is that 'big' becomes 'not so big' reasonably quickly. A couple of TeraBytes is not that challenging (depending on the algorithm) these days where as 5 years ago it was a big challenge. We have a bit over a PetaByte (not using Spark) and using a distributed system is the only viable way to get reasonable performance for reasonable cost cheers On Tue, Mar 31, 2015 at 4:55 AM, Steve Loughran ste...@hortonworks.com wrote: On 30 Mar 2015, at 13:27, jay vyas jayunit100.apa...@gmail.com wrote: Just the same as spark was disrupting the hadoop ecosystem by changing the assumption that you can't rely on memory in distributed analytics...now maybe we are challenging the assumption that big data analytics need to distributed? I've been asking the same question lately and seen similarly that spark performs quite reliably and well on local single node system even for an app which I ran for a streaming app which I ran for ten days in a row... I almost felt guilty that I never put it on a cluster! Modern machines can be pretty powerful: 16 physical cores HT'd to 32, 384+MB, GPU, giving you lots of compute. What you don't get is the storage capacity to match, and especially, the IO bandwidth. RAID-0 striping 2-4 HDDs gives you some boost, but if you are reading, say, a 4 GB file from HDFS broken in to 256MB blocks, you have that data replicated into (4*4*3) blocks: 48. Algorithm and capacity permitting, you've just massively boosted your load time. Downstream, if data can be thinned down, then you can start looking more at things you can do on a single host : a machine that can be in your Hadoop cluster. Ask YARN nicely and you can get a dedicated machine for a couple of days (i.e. until your Kerberos tokens expire). -- *Franc Carter* I Systems ArchitectI RoZetta Technology [image: Description: Description: Description: cid:image003.jpg@01D02903.9B540580] L4. 55 Harrington Street, THE ROCKS, NSW, 2000 PO Box H58, Australia Square, Sydney NSW, 1215, AUSTRALIA *T* +61 2 8355 2515 Iwww.rozettatechnology.com [image: cid:image002.jpg@01D02903.0B41B280] DISCLAIMER: The contents of this email, inclusive of attachments, may be legally privileged and confidential. Any unauthorised use of the contents is expressly prohibited.
Re: Single threaded laptop implementation beating a 128 node GraphX cluster on a 1TB data set (128 billion nodes) - What is a use case for GraphX then? when is it worth the cost?
Hi Sean, I think your point about the ETL costs are the wining argument here. but I would like to see more research on the topic. What I would like to see researched - is ability to run a specialized set of common algorithms in fast-local-mode just like a compiler optimizer can decide to inline some methods, or rewrite a recursive function as a for loop if it's in tail position, I would say that the future of GraphX can be that if a certain algorithm is a well known one (e.g. shortest paths) and can be run locally faster than on a distributed set (taking into account bringing all the data locally) then it will do so. Thanks! On Sat, Mar 28, 2015 at 1:34 AM, Sean Owen so...@cloudera.com wrote: (I bet the Spark implementation could be improved. I bet GraphX could be optimized.) Not sure about this one, but in core benchmarks often start by assuming that the data is local. In the real world, data is unlikely to be. The benchmark has to include the cost of bringing all the data to the local computation too, since the point of distributed computation is bringing work to the data. Specialist implementations for a special problem should always win over generalist, and Spark is a generalist. Likewise you can factor matrices way faster in a GPU than in Spark. These aren't entirely either/or propositions; you can use Rust or GPU in a larger distributed program. Typically a real-world problem involves more than core computation: ETL, security, monitoring. Generalists are more likely to have an answer to hand for these. Specialist implementations do just one thing, and they typically have to be custom built. Compare the cost of highly skilled developer time to generalist computing resources; $1m buys several dev years but also rents a small data center. Speed is an important issue but by no means everything in the real world, and these are rarely mutually exclusive options in the OSS world. This is a great piece of work, but I don't think it's some kind of argument against distributed computing. On Fri, Mar 27, 2015 at 6:32 PM, Eran Medan ehrann.meh...@gmail.com wrote: Remember that article that went viral on HN? (Where a guy showed how GraphX / Giraph / GraphLab / Spark have worse performance on a 128 cluster than on a 1 thread machine? if not here is the article - http://www.frankmcsherry.org/graph/scalability/cost/2015/01/15/COST.html) Well as you may recall, this stirred up a lot of commotion in the big data community (and Spark/GraphX in particular) People (justly I guess) blamed him for not really having “big data”, as all of his data set fits in memory, so it doesn't really count. So he took the challenge and came with a pretty hard to argue counter benchmark, now with a huge data set (1TB of data, encoded using Hilbert curves to 154GB, but still large). see at - http://www.frankmcsherry.org/graph/scalability/cost/2015/02/04/COST2.html He provided the source here https://github.com/frankmcsherry/COST as an example His benchmark shows how on a 128 billion edges graph, he got X2 to X10 faster results on a single threaded Rust based implementation So, what is the counter argument? it pretty much seems like a blow in the face of Spark / GraphX etc, (which I like and use on a daily basis) Before I dive into re-validating his benchmarks with my own use cases. What is your opinion on this? If this is the case, then what IS the use case for using Spark/GraphX at all?
Re: Single threaded laptop implementation beating a 128 node GraphX cluster on a 1TB data set (128 billion nodes) - What is a use case for GraphX then? when is it worth the cost?
Hallo, Well all problems you want to solve with technology need to have good justification for a certain technology. So the first thing is that you ask which technology fits to my current and future problems. This is also what the article says. Unfortunately, it does only provide a vague answer why there is this performance gap. Is it a Spark architecture issue? Is it a configuration issue? Is it a design issue of the spark version of the algorithms? Is it an amazon issue? Why did he use a laptop and not a single Amazon machine to compare? Why did he not run multiple threads on a single machine (for some problems single thread might be the fastest solution anyway)? Based on my experience a single machine can be already quiet useful for graph algorithms. There are also different graph systems all for different purposes. Spark Graphx is more general (can be used in combination with the whole Spark Plattform!) and probably less performant than highly specialed graph systems leveraging GPU etc. - These systems have the disadvantage that they are not generally suitable or integrated with other types of processing, such as streaming, mr, rdd, etc. I am always curios for any technology why and where do one looses performance. That's why one does proof-of-concepts and evaluates technology depending on the business case. Maybe the article is right, but it is unclear if it can be generalized or if it really has an impact of your business case for Spark/Graphx. His algorithms can only do graph processing for a very special case and are not suitable for a general all-purpose big data infrastructure. Best regards Le 27 mars 2015 19:33, Eran Medan ehrann.meh...@gmail.com a écrit : Remember that article that went viral on HN? (Where a guy showed how GraphX / Giraph / GraphLab / Spark have worse performance on a 128 cluster than on a 1 thread machine? if not here is the article - http://www.frankmcsherry.org/graph/scalability/cost/2015/01/15/COST.html) Well as you may recall, this stirred up a lot of commotion in the big data community (and Spark/GraphX in particular) People (justly I guess) blamed him for not really having “big data”, as all of his data set fits in memory, so it doesn't really count. So he took the challenge and came with a pretty hard to argue counter benchmark, now with a huge data set (1TB of data, encoded using Hilbert curves to 154GB, but still large). see at - http://www.frankmcsherry.org/graph/scalability/cost/2015/02/04/COST2.html He provided the source here https://github.com/frankmcsherry/COST as an example His benchmark shows how on a 128 billion edges graph, he got X2 to X10 faster results on a single threaded Rust based implementation So, what is the counter argument? it pretty much seems like a blow in the face of Spark / GraphX etc, (which I like and use on a daily basis) Before I dive into re-validating his benchmarks with my own use cases. What is your opinion on this? If this is the case, then what IS the use case for using Spark/GraphX at all?
Single threaded laptop implementation beating a 128 node GraphX cluster on a 1TB data set (128 billion nodes) - What is a use case for GraphX then? when is it worth the cost?
Remember that article that went viral on HN? (Where a guy showed how GraphX / Giraph / GraphLab / Spark have worse performance on a 128 cluster than on a 1 thread machine? if not here is the article - http://www.frankmcsherry.org/graph/scalability/cost/2015/01/15/COST.html) Well as you may recall, this stirred up a lot of commotion in the big data community (and Spark/GraphX in particular) People (justly I guess) blamed him for not really having “big data”, as all of his data set fits in memory, so it doesn't really count. So he took the challenge and came with a pretty hard to argue counter benchmark, now with a huge data set (1TB of data, encoded using Hilbert curves to 154GB, but still large). see at - http://www.frankmcsherry.org/graph/scalability/cost/2015/02/04/COST2.html He provided the source here https://github.com/frankmcsherry/COST as an example His benchmark shows how on a 128 billion edges graph, he got X2 to X10 faster results on a single threaded Rust based implementation So, what is the counter argument? it pretty much seems like a blow in the face of Spark / GraphX etc, (which I like and use on a daily basis) Before I dive into re-validating his benchmarks with my own use cases. What is your opinion on this? If this is the case, then what IS the use case for using Spark/GraphX at all?
Re: Single threaded laptop implementation beating a 128 node GraphX cluster on a 1TB data set (128 billion nodes) - What is a use case for GraphX then? when is it worth the cost?
(I bet the Spark implementation could be improved. I bet GraphX could be optimized.) Not sure about this one, but in core benchmarks often start by assuming that the data is local. In the real world, data is unlikely to be. The benchmark has to include the cost of bringing all the data to the local computation too, since the point of distributed computation is bringing work to the data. Specialist implementations for a special problem should always win over generalist, and Spark is a generalist. Likewise you can factor matrices way faster in a GPU than in Spark. These aren't entirely either/or propositions; you can use Rust or GPU in a larger distributed program. Typically a real-world problem involves more than core computation: ETL, security, monitoring. Generalists are more likely to have an answer to hand for these. Specialist implementations do just one thing, and they typically have to be custom built. Compare the cost of highly skilled developer time to generalist computing resources; $1m buys several dev years but also rents a small data center. Speed is an important issue but by no means everything in the real world, and these are rarely mutually exclusive options in the OSS world. This is a great piece of work, but I don't think it's some kind of argument against distributed computing. On Fri, Mar 27, 2015 at 6:32 PM, Eran Medan ehrann.meh...@gmail.com wrote: Remember that article that went viral on HN? (Where a guy showed how GraphX / Giraph / GraphLab / Spark have worse performance on a 128 cluster than on a 1 thread machine? if not here is the article -http://www.frankmcsherry.org/graph/scalability/cost/2015/01/15/COST.html) Well as you may recall, this stirred up a lot of commotion in the big data community (and Spark/GraphX in particular) People (justly I guess) blamed him for not really having “big data”, as all of his data set fits in memory, so it doesn't really count. So he took the challenge and came with a pretty hard to argue counter benchmark, now with a huge data set (1TB of data, encoded using Hilbert curves to 154GB, but still large). see at - http://www.frankmcsherry.org/graph/scalability/cost/2015/02/04/COST2.html He provided the source here https://github.com/frankmcsherry/COST as an example His benchmark shows how on a 128 billion edges graph, he got X2 to X10 faster results on a single threaded Rust based implementation So, what is the counter argument? it pretty much seems like a blow in the face of Spark / GraphX etc, (which I like and use on a daily basis) Before I dive into re-validating his benchmarks with my own use cases. What is your opinion on this? If this is the case, then what IS the use case for using Spark/GraphX at all? - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
