RE: Shuffle phase replication factor
Ling, Thanks for the response! I could use more clarification on item 1. Specifically * mapred.reduce.parallel.copies limits the number of outbound connections for a reducer, but not the inbound connections for a mapper. Does tasktracker.http.threads limit the number of simultaneous inbound connections for a mapper, or only the size of the thread pool servicing the connections? (i.e. is it one thread per inbound connection?). * Who actually creates the listen port for serving up the mapper files? The mapper task? Or something more persistent in MapReduce? Thanks, John From: erlv5...@gmail.com [mailto:erlv5...@gmail.com] On Behalf Of Kun Ling Sent: Wednesday, May 22, 2013 7:50 PM To: user Subject: Re: Shuffle phase replication factor Hi John, 1. for the number of simultaneous connection limitations. You can configure this using the mapred.reduce.parallel.copies flag. the default is 5. 2. For the aggressively disconnect implication, I am afraid it is only a little. Normally, each reducer will connect to each mapper task, and asking for the partions of the map output file. Because there are about 5 simultaneous connections to fetch the map output for each reducer. For a large MR cluster with 1000 node, and a Huge MR job with 1000 Mapper, and 1000 reducer, for each node, there are only about 5 connections. So the imply is only a little. 3. What happens to the pending/ failing coonection, the short answer is: just try to reconnect.There is a List, which maintain all the output of the Mapper that need to copied, and the element will be removed iff the map output is successfully copied. A forever loop will keep on look into the List, and fetch the corrsponding map output. All the above answer is based on the Hadoop 1.0.4 source code, especially the ReduceTask.java file. yours, Ling Kun On Wed, May 22, 2013 at 10:57 PM, John Lilley john.lil...@redpoint.netmailto:john.lil...@redpoint.net wrote: U, is that also the limit for the number of simultaneous connections? In general, one does not need a 1:1 map between threads and connections. If this is the connection limit, does it imply that the client or server side aggressively disconnects after a transfer? What happens to the pending/failing connection attempts that exceed the limit? Thanks! john From: Rahul Bhattacharjee [mailto:rahul.rec@gmail.commailto:rahul.rec@gmail.com] Sent: Wednesday, May 22, 2013 8:52 AM To: user@hadoop.apache.orgmailto:user@hadoop.apache.org Subject: Re: Shuffle phase replication factor There are properties/configuration to control the no. of copying threads for copy. tasktracker.http.threads=40 Thanks, Rahul On Wed, May 22, 2013 at 8:16 PM, John Lilley john.lil...@redpoint.netmailto:john.lil...@redpoint.net wrote: This brings up another nagging question I've had for some time. Between HDFS and shuffle, there seems to be the potential for every node connecting to every other node via TCP. Are there explicit mechanisms in place to manage or limit simultaneous connections? Is the protocol simply robust enough to allow a server-side to disconnect at any time to free up slots and the client-side will retry the request? Thanks john From: Shahab Yunus [mailto:shahab.yu...@gmail.commailto:shahab.yu...@gmail.com] Sent: Wednesday, May 22, 2013 8:38 AM To: user@hadoop.apache.orgmailto:user@hadoop.apache.org Subject: Re: Shuffle phase replication factor As mentioned by Bertrand, Hadoop, The Definitive Guide, is well... really definitive :) place to start. It is pretty thorough for starts and once you are gone through it, the code will start making more sense too. Regards, Shahab On Wed, May 22, 2013 at 10:33 AM, John Lilley john.lil...@redpoint.netmailto:john.lil...@redpoint.net wrote: Oh I see. Does this mean there is another service and TCP listen port for this purpose? Thanks for your indulgence... I would really like to read more about this without bothering the group but not sure where to start to learn these internals other than the code. john From: Kai Voigt [mailto:k...@123.orgmailto:k...@123.org] Sent: Tuesday, May 21, 2013 12:59 PM To: user@hadoop.apache.orgmailto:user@hadoop.apache.org Subject: Re: Shuffle phase replication factor The map output doesn't get written to HDFS. The map task writes its output to its local disk, the reduce tasks will pull the data through HTTP for further processing. Am 21.05.2013 um 19:57 schrieb John Lilley john.lil...@redpoint.netmailto:john.lil...@redpoint.net: When MapReduce enters shuffle to partition the tuples, I am assuming that it writes intermediate data to HDFS. What replication factor is used for those temporary files? john -- Kai Voigt k...@123.orgmailto:k...@123.org -- http://www.lingcc.com
Re: Shuffle phase replication factor
In MR1, the tasktracker serves the mapper files (so that tasks don't have to stick around taking up resources). In MR2, the shuffle service, which lives inside the nodemanager, serves them. -Sandy On Thu, May 23, 2013 at 10:22 AM, John Lilley john.lil...@redpoint.netwrote: Ling, Thanks for the response! I could use more clarification on item 1. Specifically **· **mapred.reduce.parallel.copies limits the number of outbound connections for a reducer, but not the inbound connections for a mapper. Does tasktracker.http.threads limit the number of simultaneous inbound connections for a mapper, or only the size of the thread pool servicing the connections? (i.e. is it one thread per inbound connection?). **· **Who actually creates the listen port for serving up the mapper files? The mapper task? Or something more persistent in MapReduce? Thanks, John ** ** *From:* erlv5...@gmail.com [mailto:erlv5...@gmail.com] *On Behalf Of *Kun Ling *Sent:* Wednesday, May 22, 2013 7:50 PM *To:* user *Subject:* Re: Shuffle phase replication factor ** ** Hi John, ** ** ** ** 1. for the number of simultaneous connection limitations. You can configure this using the mapred.reduce.parallel.copies flag. the default is 5. ** ** 2. For the aggressively disconnect implication, I am afraid it is only a little. Normally, each reducer will connect to each mapper task, and asking for the partions of the map output file. Because there are about 5 simultaneous connections to fetch the map output for each reducer. For a large MR cluster with 1000 node, and a Huge MR job with 1000 Mapper, and 1000 reducer, for each node, there are only about 5 connections. So the imply is only a little. ** ** ** ** 3. What happens to the pending/ failing coonection, the short answer is: just try to reconnect.There is a List, which maintain all the output of the Mapper that need to copied, and the element will be removed iff the map output is successfully copied. A forever loop will keep on look into the List, and fetch the corrsponding map output. ** ** ** ** All the above answer is based on the Hadoop 1.0.4 source code, especially the ReduceTask.java file. ** ** yours, Ling Kun ** ** On Wed, May 22, 2013 at 10:57 PM, John Lilley john.lil...@redpoint.net wrote: U, is that also the limit for the number of simultaneous connections? In general, one does not need a 1:1 map between threads and connections.** ** If this is the connection limit, does it imply that the client or server side aggressively disconnects after a transfer? What happens to the pending/failing connection attempts that exceed the limit? Thanks! john *From:* Rahul Bhattacharjee [mailto:rahul.rec@gmail.com] *Sent:* Wednesday, May 22, 2013 8:52 AM *To:* user@hadoop.apache.org *Subject:* Re: Shuffle phase replication factor There are properties/configuration to control the no. of copying threads for copy. tasktracker.http.threads=40 Thanks, Rahul On Wed, May 22, 2013 at 8:16 PM, John Lilley john.lil...@redpoint.net wrote: This brings up another nagging question I’ve had for some time. Between HDFS and shuffle, there seems to be the potential for “every node connecting to every other node” via TCP. Are there explicit mechanisms in place to manage or limit simultaneous connections? Is the protocol simply robust enough to allow a server-side to disconnect at any time to free up slots and the client-side will retry the request? Thanks john *From:* Shahab Yunus [mailto:shahab.yu...@gmail.com] *Sent:* Wednesday, May 22, 2013 8:38 AM *To:* user@hadoop.apache.org *Subject:* Re: Shuffle phase replication factor As mentioned by Bertrand, Hadoop, The Definitive Guide, is well... really definitive :) place to start. It is pretty thorough for starts and once you are gone through it, the code will start making more sense too. Regards, Shahab On Wed, May 22, 2013 at 10:33 AM, John Lilley john.lil...@redpoint.net wrote: Oh I see. Does this mean there is another service and TCP listen port for this purpose? Thanks for your indulgence… I would really like to read more about this without bothering the group but not sure where to start to learn these internals other than the code. john *From:* Kai Voigt [mailto:k...@123.org] *Sent:* Tuesday, May 21, 2013 12:59 PM *To:* user@hadoop.apache.org *Subject:* Re: Shuffle phase replication factor The map output doesn't get written to HDFS. The map task writes its output to its local disk, the reduce tasks will pull the data through HTTP for further processing. Am 21.05.2013 um 19:57 schrieb John Lilley john.lil...@redpoint.net
RE: Shuffle phase replication factor
Oh I see. Does this mean there is another service and TCP listen port for this purpose? Thanks for your indulgence... I would really like to read more about this without bothering the group but not sure where to start to learn these internals other than the code. john From: Kai Voigt [mailto:k...@123.org] Sent: Tuesday, May 21, 2013 12:59 PM To: user@hadoop.apache.org Subject: Re: Shuffle phase replication factor The map output doesn't get written to HDFS. The map task writes its output to its local disk, the reduce tasks will pull the data through HTTP for further processing. Am 21.05.2013 um 19:57 schrieb John Lilley john.lil...@redpoint.netmailto:john.lil...@redpoint.net: When MapReduce enters shuffle to partition the tuples, I am assuming that it writes intermediate data to HDFS. What replication factor is used for those temporary files? john -- Kai Voigt k...@123.orgmailto:k...@123.org
RE: Shuffle phase replication factor
This brings up another nagging question I've had for some time. Between HDFS and shuffle, there seems to be the potential for every node connecting to every other node via TCP. Are there explicit mechanisms in place to manage or limit simultaneous connections? Is the protocol simply robust enough to allow a server-side to disconnect at any time to free up slots and the client-side will retry the request? Thanks john From: Shahab Yunus [mailto:shahab.yu...@gmail.com] Sent: Wednesday, May 22, 2013 8:38 AM To: user@hadoop.apache.org Subject: Re: Shuffle phase replication factor As mentioned by Bertrand, Hadoop, The Definitive Guide, is well... really definitive :) place to start. It is pretty thorough for starts and once you are gone through it, the code will start making more sense too. Regards, Shahab On Wed, May 22, 2013 at 10:33 AM, John Lilley john.lil...@redpoint.netmailto:john.lil...@redpoint.net wrote: Oh I see. Does this mean there is another service and TCP listen port for this purpose? Thanks for your indulgence... I would really like to read more about this without bothering the group but not sure where to start to learn these internals other than the code. john From: Kai Voigt [mailto:k...@123.orgmailto:k...@123.org] Sent: Tuesday, May 21, 2013 12:59 PM To: user@hadoop.apache.orgmailto:user@hadoop.apache.org Subject: Re: Shuffle phase replication factor The map output doesn't get written to HDFS. The map task writes its output to its local disk, the reduce tasks will pull the data through HTTP for further processing. Am 21.05.2013 um 19:57 schrieb John Lilley john.lil...@redpoint.netmailto:john.lil...@redpoint.net: When MapReduce enters shuffle to partition the tuples, I am assuming that it writes intermediate data to HDFS. What replication factor is used for those temporary files? john -- Kai Voigt k...@123.orgmailto:k...@123.org
Re: Shuffle phase replication factor
There are properties/configuration to control the no. of copying threads for copy. tasktracker.http.threads=40 Thanks, Rahul On Wed, May 22, 2013 at 8:16 PM, John Lilley john.lil...@redpoint.netwrote: This brings up another nagging question I’ve had for some time. Between HDFS and shuffle, there seems to be the potential for “every node connecting to every other node” via TCP. Are there explicit mechanisms in place to manage or limit simultaneous connections? Is the protocol simply robust enough to allow a server-side to disconnect at any time to free up slots and the client-side will retry the request? Thanks john ** ** *From:* Shahab Yunus [mailto:shahab.yu...@gmail.com] *Sent:* Wednesday, May 22, 2013 8:38 AM *To:* user@hadoop.apache.org *Subject:* Re: Shuffle phase replication factor ** ** As mentioned by Bertrand, Hadoop, The Definitive Guide, is well... really definitive :) place to start. It is pretty thorough for starts and once you are gone through it, the code will start making more sense too. ** ** Regards, Shahab ** ** On Wed, May 22, 2013 at 10:33 AM, John Lilley john.lil...@redpoint.net wrote: Oh I see. Does this mean there is another service and TCP listen port for this purpose? Thanks for your indulgence… I would really like to read more about this without bothering the group but not sure where to start to learn these internals other than the code. john *From:* Kai Voigt [mailto:k...@123.org] *Sent:* Tuesday, May 21, 2013 12:59 PM *To:* user@hadoop.apache.org *Subject:* Re: Shuffle phase replication factor The map output doesn't get written to HDFS. The map task writes its output to its local disk, the reduce tasks will pull the data through HTTP for further processing. Am 21.05.2013 um 19:57 schrieb John Lilley john.lil...@redpoint.net: ** ** When MapReduce enters “shuffle” to partition the tuples, I am assuming that it writes intermediate data to HDFS. What replication factor is used for those temporary files? john -- Kai Voigt k...@123.org ** ** ** **
RE: Shuffle phase replication factor
U, is that also the limit for the number of simultaneous connections? In general, one does not need a 1:1 map between threads and connections. If this is the connection limit, does it imply that the client or server side aggressively disconnects after a transfer? What happens to the pending/failing connection attempts that exceed the limit? Thanks! john From: Rahul Bhattacharjee [mailto:rahul.rec@gmail.com] Sent: Wednesday, May 22, 2013 8:52 AM To: user@hadoop.apache.org Subject: Re: Shuffle phase replication factor There are properties/configuration to control the no. of copying threads for copy. tasktracker.http.threads=40 Thanks, Rahul On Wed, May 22, 2013 at 8:16 PM, John Lilley john.lil...@redpoint.netmailto:john.lil...@redpoint.net wrote: This brings up another nagging question I’ve had for some time. Between HDFS and shuffle, there seems to be the potential for “every node connecting to every other node” via TCP. Are there explicit mechanisms in place to manage or limit simultaneous connections? Is the protocol simply robust enough to allow a server-side to disconnect at any time to free up slots and the client-side will retry the request? Thanks john From: Shahab Yunus [mailto:shahab.yu...@gmail.commailto:shahab.yu...@gmail.com] Sent: Wednesday, May 22, 2013 8:38 AM To: user@hadoop.apache.orgmailto:user@hadoop.apache.org Subject: Re: Shuffle phase replication factor As mentioned by Bertrand, Hadoop, The Definitive Guide, is well... really definitive :) place to start. It is pretty thorough for starts and once you are gone through it, the code will start making more sense too. Regards, Shahab On Wed, May 22, 2013 at 10:33 AM, John Lilley john.lil...@redpoint.netmailto:john.lil...@redpoint.net wrote: Oh I see. Does this mean there is another service and TCP listen port for this purpose? Thanks for your indulgence… I would really like to read more about this without bothering the group but not sure where to start to learn these internals other than the code. john From: Kai Voigt [mailto:k...@123.orgmailto:k...@123.org] Sent: Tuesday, May 21, 2013 12:59 PM To: user@hadoop.apache.orgmailto:user@hadoop.apache.org Subject: Re: Shuffle phase replication factor The map output doesn't get written to HDFS. The map task writes its output to its local disk, the reduce tasks will pull the data through HTTP for further processing. Am 21.05.2013 um 19:57 schrieb John Lilley john.lil...@redpoint.netmailto:john.lil...@redpoint.net: When MapReduce enters “shuffle” to partition the tuples, I am assuming that it writes intermediate data to HDFS. What replication factor is used for those temporary files? john -- Kai Voigt k...@123.orgmailto:k...@123.org
Re: Shuffle phase replication factor
Hi John, 1. for the number of simultaneous connection limitations. You can configure this using the mapred.reduce.parallel.copies flag. the default is 5. 2. For the aggressively disconnect implication, I am afraid it is only a little. Normally, each reducer will connect to each mapper task, and asking for the partions of the map output file. Because there are about 5 simultaneous connections to fetch the map output for each reducer. For a large MR cluster with 1000 node, and a Huge MR job with 1000 Mapper, and 1000 reducer, for each node, there are only about 5 connections. So the imply is only a little. 3. What happens to the pending/ failing coonection, the short answer is: just try to reconnect.There is a List, which maintain all the output of the Mapper that need to copied, and the element will be removed iff the map output is successfully copied. A forever loop will keep on look into the List, and fetch the corrsponding map output. All the above answer is based on the Hadoop 1.0.4 source code, especially the ReduceTask.java file. yours, Ling Kun On Wed, May 22, 2013 at 10:57 PM, John Lilley john.lil...@redpoint.netwrote: U, is that also the limit for the number of simultaneous connections? In general, one does not need a 1:1 map between threads and connections. If this is the connection limit, does it imply that the client or server side aggressively disconnects after a transfer? What happens to the pending/failing connection attempts that exceed the limit? Thanks! john ** ** *From:* Rahul Bhattacharjee [mailto:rahul.rec@gmail.com] *Sent:* Wednesday, May 22, 2013 8:52 AM *To:* user@hadoop.apache.org *Subject:* Re: Shuffle phase replication factor ** ** There are properties/configuration to control the no. of copying threads for copy. tasktracker.http.threads=40 Thanks, Rahul ** ** On Wed, May 22, 2013 at 8:16 PM, John Lilley john.lil...@redpoint.net wrote: This brings up another nagging question I’ve had for some time. Between HDFS and shuffle, there seems to be the potential for “every node connecting to every other node” via TCP. Are there explicit mechanisms in place to manage or limit simultaneous connections? Is the protocol simply robust enough to allow a server-side to disconnect at any time to free up slots and the client-side will retry the request? Thanks john *From:* Shahab Yunus [mailto:shahab.yu...@gmail.com] *Sent:* Wednesday, May 22, 2013 8:38 AM *To:* user@hadoop.apache.org *Subject:* Re: Shuffle phase replication factor As mentioned by Bertrand, Hadoop, The Definitive Guide, is well... really definitive :) place to start. It is pretty thorough for starts and once you are gone through it, the code will start making more sense too. Regards, Shahab On Wed, May 22, 2013 at 10:33 AM, John Lilley john.lil...@redpoint.net wrote: Oh I see. Does this mean there is another service and TCP listen port for this purpose? Thanks for your indulgence… I would really like to read more about this without bothering the group but not sure where to start to learn these internals other than the code. john *From:* Kai Voigt [mailto:k...@123.org] *Sent:* Tuesday, May 21, 2013 12:59 PM *To:* user@hadoop.apache.org *Subject:* Re: Shuffle phase replication factor The map output doesn't get written to HDFS. The map task writes its output to its local disk, the reduce tasks will pull the data through HTTP for further processing. Am 21.05.2013 um 19:57 schrieb John Lilley john.lil...@redpoint.net: When MapReduce enters “shuffle” to partition the tuples, I am assuming that it writes intermediate data to HDFS. What replication factor is used for those temporary files? john -- Kai Voigt k...@123.org ** ** -- http://www.lingcc.com
Re: Shuffle phase replication factor
The map output doesn't get written to HDFS. The map task writes its output to its local disk, the reduce tasks will pull the data through HTTP for further processing. Am 21.05.2013 um 19:57 schrieb John Lilley john.lil...@redpoint.net: When MapReduce enters “shuffle” to partition the tuples, I am assuming that it writes intermediate data to HDFS. What replication factor is used for those temporary files? john -- Kai Voigt k...@123.org
Re: Shuffle phase replication factor
Intermediate data is written to local disk, not to HDFS. Ian. On May 21, 2013, at 1:57 PM, John Lilley john.lil...@redpoint.net wrote: When MapReduce enters “shuffle” to partition the tuples, I am assuming that it writes intermediate data to HDFS. What replication factor is used for those temporary files? john --- Ian Wrigley Sr. Curriculum Manager Cloudera, Inc Cell: (323) 819 4075
