Re: k-means can only run on one executor with one thread?
Hey Xi, Have you tried Spark 1.3.0? The initialization happens on the driver node and we fixed an issue with the initialization in 1.3.0. Again, please start with a smaller k, and increase it gradually, Let us know at what k the problem happens. Best, Xiangrui On Sat, Mar 28, 2015 at 3:11 AM, Xi Shen davidshe...@gmail.com wrote: My vector dimension is like 360 or so. The data count is about 270k. My driver has 2.9G memory. I attache a screenshot of current executor status. I submitted this job with --master yarn-cluster. I have a total of 7 worker node, one of them acts as the driver. In the screenshot, you can see all worker nodes have loaded some data, but the driver is not loaded with any data. But the funny thing is, when I log on to the driver, and check its CPU memory status. I saw one java process using about 18% of CPU, and is using about 1.6 GB memory. [image: Inline image 1] On Sat, Mar 28, 2015 at 7:06 PM Reza Zadeh r...@databricks.com wrote: How many dimensions does your data have? The size of the k-means model is k * d, where d is the dimension of the data. Since you're using k=1000, if your data has dimension higher than say, 10,000, you will have trouble, because k*d doubles have to fit in the driver. Reza On Sat, Mar 28, 2015 at 12:27 AM, Xi Shen davidshe...@gmail.com wrote: I have put more detail of my problem at http://stackoverflow.com/ questions/29295420/spark-kmeans-computation-cannot-be-distributed It is really appreciate if you can help me take a look at this problem. I have tried various settings and ways to load/partition my data, but I just cannot get rid that long pause. Thanks, David [image: --] Xi Shen [image: http://]about.me/davidshen http://about.me/davidshen?promo=email_sig http://about.me/davidshen On Sat, Mar 28, 2015 at 2:38 PM, Xi Shen davidshe...@gmail.com wrote: Yes, I have done repartition. I tried to repartition to the number of cores in my cluster. Not helping... I tried to repartition to the number of centroids (k value). Not helping... On Sat, Mar 28, 2015 at 7:27 AM Joseph Bradley jos...@databricks.com wrote: Can you try specifying the number of partitions when you load the data to equal the number of executors? If your ETL changes the number of partitions, you can also repartition before calling KMeans. On Thu, Mar 26, 2015 at 8:04 PM, Xi Shen davidshe...@gmail.com wrote: Hi, I have a large data set, and I expects to get 5000 clusters. I load the raw data, convert them into DenseVector; then I did repartition and cache; finally I give the RDD[Vector] to KMeans.train(). Now the job is running, and data are loaded. But according to the Spark UI, all data are loaded onto one executor. I checked that executor, and its CPU workload is very low. I think it is using only 1 of the 8 cores. And all other 3 executors are at rest. Did I miss something? Is it possible to distribute the workload to all 4 executors? Thanks, David
Re: k-means can only run on one executor with one thread?
I have put more detail of my problem at http://stackoverflow.com/questions/29295420/spark-kmeans-computation-cannot-be-distributed It is really appreciate if you can help me take a look at this problem. I have tried various settings and ways to load/partition my data, but I just cannot get rid that long pause. Thanks, David [image: --] Xi Shen [image: http://]about.me/davidshen http://about.me/davidshen?promo=email_sig http://about.me/davidshen On Sat, Mar 28, 2015 at 2:38 PM, Xi Shen davidshe...@gmail.com wrote: Yes, I have done repartition. I tried to repartition to the number of cores in my cluster. Not helping... I tried to repartition to the number of centroids (k value). Not helping... On Sat, Mar 28, 2015 at 7:27 AM Joseph Bradley jos...@databricks.com wrote: Can you try specifying the number of partitions when you load the data to equal the number of executors? If your ETL changes the number of partitions, you can also repartition before calling KMeans. On Thu, Mar 26, 2015 at 8:04 PM, Xi Shen davidshe...@gmail.com wrote: Hi, I have a large data set, and I expects to get 5000 clusters. I load the raw data, convert them into DenseVector; then I did repartition and cache; finally I give the RDD[Vector] to KMeans.train(). Now the job is running, and data are loaded. But according to the Spark UI, all data are loaded onto one executor. I checked that executor, and its CPU workload is very low. I think it is using only 1 of the 8 cores. And all other 3 executors are at rest. Did I miss something? Is it possible to distribute the workload to all 4 executors? Thanks, David
Re: k-means can only run on one executor with one thread?
How many dimensions does your data have? The size of the k-means model is k * d, where d is the dimension of the data. Since you're using k=1000, if your data has dimension higher than say, 10,000, you will have trouble, because k*d doubles have to fit in the driver. Reza On Sat, Mar 28, 2015 at 12:27 AM, Xi Shen davidshe...@gmail.com wrote: I have put more detail of my problem at http://stackoverflow.com/questions/29295420/spark-kmeans-computation-cannot-be-distributed It is really appreciate if you can help me take a look at this problem. I have tried various settings and ways to load/partition my data, but I just cannot get rid that long pause. Thanks, David [image: --] Xi Shen [image: http://]about.me/davidshen http://about.me/davidshen?promo=email_sig http://about.me/davidshen On Sat, Mar 28, 2015 at 2:38 PM, Xi Shen davidshe...@gmail.com wrote: Yes, I have done repartition. I tried to repartition to the number of cores in my cluster. Not helping... I tried to repartition to the number of centroids (k value). Not helping... On Sat, Mar 28, 2015 at 7:27 AM Joseph Bradley jos...@databricks.com wrote: Can you try specifying the number of partitions when you load the data to equal the number of executors? If your ETL changes the number of partitions, you can also repartition before calling KMeans. On Thu, Mar 26, 2015 at 8:04 PM, Xi Shen davidshe...@gmail.com wrote: Hi, I have a large data set, and I expects to get 5000 clusters. I load the raw data, convert them into DenseVector; then I did repartition and cache; finally I give the RDD[Vector] to KMeans.train(). Now the job is running, and data are loaded. But according to the Spark UI, all data are loaded onto one executor. I checked that executor, and its CPU workload is very low. I think it is using only 1 of the 8 cores. And all other 3 executors are at rest. Did I miss something? Is it possible to distribute the workload to all 4 executors? Thanks, David
Re: k-means can only run on one executor with one thread?
My vector dimension is like 360 or so. The data count is about 270k. My driver has 2.9G memory. I attache a screenshot of current executor status. I submitted this job with --master yarn-cluster. I have a total of 7 worker node, one of them acts as the driver. In the screenshot, you can see all worker nodes have loaded some data, but the driver is not loaded with any data. But the funny thing is, when I log on to the driver, and check its CPU memory status. I saw one java process using about 18% of CPU, and is using about 1.6 GB memory. [image: Inline image 1] On Sat, Mar 28, 2015 at 7:06 PM Reza Zadeh r...@databricks.com wrote: How many dimensions does your data have? The size of the k-means model is k * d, where d is the dimension of the data. Since you're using k=1000, if your data has dimension higher than say, 10,000, you will have trouble, because k*d doubles have to fit in the driver. Reza On Sat, Mar 28, 2015 at 12:27 AM, Xi Shen davidshe...@gmail.com wrote: I have put more detail of my problem at http://stackoverflow.com/ questions/29295420/spark-kmeans-computation-cannot-be-distributed It is really appreciate if you can help me take a look at this problem. I have tried various settings and ways to load/partition my data, but I just cannot get rid that long pause. Thanks, David [image: --] Xi Shen [image: http://]about.me/davidshen http://about.me/davidshen?promo=email_sig http://about.me/davidshen On Sat, Mar 28, 2015 at 2:38 PM, Xi Shen davidshe...@gmail.com wrote: Yes, I have done repartition. I tried to repartition to the number of cores in my cluster. Not helping... I tried to repartition to the number of centroids (k value). Not helping... On Sat, Mar 28, 2015 at 7:27 AM Joseph Bradley jos...@databricks.com wrote: Can you try specifying the number of partitions when you load the data to equal the number of executors? If your ETL changes the number of partitions, you can also repartition before calling KMeans. On Thu, Mar 26, 2015 at 8:04 PM, Xi Shen davidshe...@gmail.com wrote: Hi, I have a large data set, and I expects to get 5000 clusters. I load the raw data, convert them into DenseVector; then I did repartition and cache; finally I give the RDD[Vector] to KMeans.train(). Now the job is running, and data are loaded. But according to the Spark UI, all data are loaded onto one executor. I checked that executor, and its CPU workload is very low. I think it is using only 1 of the 8 cores. And all other 3 executors are at rest. Did I miss something? Is it possible to distribute the workload to all 4 executors? Thanks, David
Re: k-means can only run on one executor with one thread?
Can you try specifying the number of partitions when you load the data to equal the number of executors? If your ETL changes the number of partitions, you can also repartition before calling KMeans. On Thu, Mar 26, 2015 at 8:04 PM, Xi Shen davidshe...@gmail.com wrote: Hi, I have a large data set, and I expects to get 5000 clusters. I load the raw data, convert them into DenseVector; then I did repartition and cache; finally I give the RDD[Vector] to KMeans.train(). Now the job is running, and data are loaded. But according to the Spark UI, all data are loaded onto one executor. I checked that executor, and its CPU workload is very low. I think it is using only 1 of the 8 cores. And all other 3 executors are at rest. Did I miss something? Is it possible to distribute the workload to all 4 executors? Thanks, David
Re: k-means can only run on one executor with one thread?
Yes, I have done repartition. I tried to repartition to the number of cores in my cluster. Not helping... I tried to repartition to the number of centroids (k value). Not helping... On Sat, Mar 28, 2015 at 7:27 AM Joseph Bradley jos...@databricks.com wrote: Can you try specifying the number of partitions when you load the data to equal the number of executors? If your ETL changes the number of partitions, you can also repartition before calling KMeans. On Thu, Mar 26, 2015 at 8:04 PM, Xi Shen davidshe...@gmail.com wrote: Hi, I have a large data set, and I expects to get 5000 clusters. I load the raw data, convert them into DenseVector; then I did repartition and cache; finally I give the RDD[Vector] to KMeans.train(). Now the job is running, and data are loaded. But according to the Spark UI, all data are loaded onto one executor. I checked that executor, and its CPU workload is very low. I think it is using only 1 of the 8 cores. And all other 3 executors are at rest. Did I miss something? Is it possible to distribute the workload to all 4 executors? Thanks, David
k-means can only run on one executor with one thread?
Hi, I have a large data set, and I expects to get 5000 clusters. I load the raw data, convert them into DenseVector; then I did repartition and cache; finally I give the RDD[Vector] to KMeans.train(). Now the job is running, and data are loaded. But according to the Spark UI, all data are loaded onto one executor. I checked that executor, and its CPU workload is very low. I think it is using only 1 of the 8 cores. And all other 3 executors are at rest. Did I miss something? Is it possible to distribute the workload to all 4 executors? Thanks, David
