Re: How are intermediate key/value pairs materialized between map and reduce?
As you noticed, your map tasks are spilling three times as many records as they are outputting. In general, if the map output buffer is large enough to hold all records in memory, these values will be equal. If there isn't enough room, as was the case with your job, the buffer makes additional intermediate spills. To fix this, you can try tuning the per-job configurables io.sort.mb and io.sort.record.percent. Look at the counters of a few map tasks to get an idea of how much data (io.sort.mb) and how many records (io.sort.record.percent) they produce. Ed On Wed, Feb 24, 2010 at 2:45 AM, Tim Kiefer tim-kie...@gmx.de wrote: Sure, I see: Map input eecords: 10,000 Map output records: 600,000 Map output bytes: 307,216,800,000 (each reacord is about 500kb - that fits the application and is to be expected) Map spilled records: 1,802,965 (ahhh... now that you ask for it - here there also is a factor of 3 between output and spilled). So - question now is: why are three times as many records spilled than actually produced by the mappers? In my map function, I do not perform any additional file writing besides the context.write() for the intermediate records. Thanks, Tim Am 24.02.2010 05:28, schrieb Amogh Vasekar: Hi, Can you let us know what is the value for : Map input records Map spilled records Map output bytes Is there any side effect file written? Thanks, Amogh On 2/23/10 8:57 PM, Tim Kiefertim-kie...@gmx.de wrote: No... 900GB is in the map column. Reduce adds another ~70GB of FILE_BYTES_WRITTEN and the total column consequently shows ~970GB. Am 23.02.2010 16:11, schrieb Ed Mazur: Hi Tim, I'm guessing a lot of these writes are happening on the reduce side. On the JT web interface, there are three columns: map, reduce, overall. Is the 900GB figure from the overall column? The value in the map column will probably be closer to what you were expecting. There are writes on the reduce side too during the shuffle and multi-pass merge. Ed 2010/2/23 Tim Kiefertim-kie...@gmx.de: Hi Gang, thanks for your reply. To clarify: I look at the statistics through the job tracker. In the webinterface for my job I have columns for map, reduce and total. What I was refering to is map - i.e. I see FILE_BYTES_WRITTEN = 3 * Map Output Bytes in the map column. About the replication factor: I would expect the exact same thing - changing to 6 has no influence on FILE_BYTES_WRITTEN. About the sorting: I have io.sort.mb = 100 and io.sort.factor = 10. Furthermore, I have 40 mappers and map output data is ~300GB. I can't see how that ends up in a factor 3? - tim Am 23.02.2010 14:39, schrieb Gang Luo: Hi Tim, the intermediate data is materialized to local file system. Before it is available for reducers, mappers will sort them. If the buffer (io.sort.mb) is too small for the intermediate data, multi-phase sorting happen, which means you read and write the same bit more than one time. Besides, are you looking at the statistics per mapper through the job tracker, or just the information output when a job finish? If you look at the information given out at the end of the job, note that this is an overall statistics which include sorting at reduce side. It also include the amount of data written to HDFS (I am not 100% sure). And, the FILE-BYTES_WRITTEN has nothing to do with the replication factor. I think if you change the factor to 6, FILE_BYTES_WRITTEN is still the same. -Gang Hi there, can anybody help me out on a (most likely) simple unclarity. I am wondering how intermediate key/value pairs are materialized. I have a job where the map phase produces 600,000 records and map output bytes is ~300GB. What I thought (up to now) is that these 600,000 records, i.e., 300GB, are materialized locally by the mappers and that later on reducers pull these records (based on the key). What I see (and cannot explain) is that the FILE_BYTES_WRITTEN counter is as high as ~900GB. So - where does the factor 3 come from between Map output bytes and FILE_BYTES_WRITTEN??? I thought about the replication factor of 3 in the file system - but that should be HDFS only?! Thanks - tim
Re: How are intermediate key/value pairs materialized between map and reduce?
Hi, Map spilled records: 1,802,965 (ahhh... now that you ask for it - here there also is a factor of 3 between output and spilled). Exactly what I suspected :) Ed has already provided some pointers as to why this is the case. You should try to minimize this number as much as possible, since this along with the Reduce Shuffle Bytes degrades your job performance by considerable amount. To understand the internals and what Ed said, I would strongly recommend going through http://www.slideshare.net/gnap/berkeley-performance-tuning By a few fellow Yahoos. There is detailed explanation on why map side spills occur and how one can minimize that :) Thanks, Amogh On 2/24/10 1:15 PM, Tim Kiefer tim-kie...@gmx.de wrote: Sure, I see: Map input eecords: 10,000 Map output records: 600,000 Map output bytes: 307,216,800,000 (each reacord is about 500kb - that fits the application and is to be expected) Map spilled records: 1,802,965 (ahhh... now that you ask for it - here there also is a factor of 3 between output and spilled). So - question now is: why are three times as many records spilled than actually produced by the mappers? In my map function, I do not perform any additional file writing besides the context.write() for the intermediate records. Thanks, Tim Am 24.02.2010 05:28, schrieb Amogh Vasekar: Hi, Can you let us know what is the value for : Map input records Map spilled records Map output bytes Is there any side effect file written? Thanks, Amogh
How are intermediate key/value pairs materialized between map and reduce?
Hi there, can anybody help me out on a (most likely) simple unclarity. I am wondering how intermediate key/value pairs are materialized. I have a job where the map phase produces 600,000 records and map output bytes is ~300GB. What I thought (up to now) is that these 600,000 records, i.e., 300GB, are materialized locally by the mappers and that later on reducers pull these records (based on the key). What I see (and cannot explain) is that the FILE_BYTES_WRITTEN counter is as high as ~900GB. So - where does the factor 3 come from between Map output bytes and FILE_BYTES_WRITTEN??? I thought about the replication factor of 3 in the file system - but that should be HDFS only?! Thanks - tim
Re: How are intermediate key/value pairs materialized between map and reduce?
Hi Tim, the intermediate data is materialized to local file system. Before it is available for reducers, mappers will sort them. If the buffer (io.sort.mb) is too small for the intermediate data, multi-phase sorting happen, which means you read and write the same bit more than one time. Besides, are you looking at the statistics per mapper through the job tracker, or just the information output when a job finish? If you look at the information given out at the end of the job, note that this is an overall statistics which include sorting at reduce side. It also include the amount of data written to HDFS (I am not 100% sure). And, the FILE-BYTES_WRITTEN has nothing to do with the replication factor. I think if you change the factor to 6, FILE_BYTES_WRITTEN is still the same. -Gang - 原始邮件 发件人： Tim Kiefer tim-kie...@gmx.de 收件人： common-user@hadoop.apache.org common-user@hadoop.apache.org 发送日期： 2010/2/23 (周二) 6:44:28 上午 主 题： How are intermediate key/value pairs materialized between map and reduce? Hi there, can anybody help me out on a (most likely) simple unclarity. I am wondering how intermediate key/value pairs are materialized. I have a job where the map phase produces 600,000 records and map output bytes is ~300GB. What I thought (up to now) is that these 600,000 records, i.e., 300GB, are materialized locally by the mappers and that later on reducers pull these records (based on the key). What I see (and cannot explain) is that the FILE_BYTES_WRITTEN counter is as high as ~900GB. So - where does the factor 3 come from between Map output bytes and FILE_BYTES_WRITTEN??? I thought about the replication factor of 3 in the file system - but that should be HDFS only?! Thanks - tim ___ 好玩贺卡等你发，邮箱贺卡全新上线！ http://card.mail.cn.yahoo.com/
Re: How are intermediate key/value pairs materialized between map and reduce?
Hi Gang, thanks for your reply. To clarify: I look at the statistics through the job tracker. In the webinterface for my job I have columns for map, reduce and total. What I was refering to is map - i.e. I see FILE_BYTES_WRITTEN = 3 * Map Output Bytes in the map column. About the replication factor: I would expect the exact same thing - changing to 6 has no influence on FILE_BYTES_WRITTEN. About the sorting: I have io.sort.mb = 100 and io.sort.factor = 10. Furthermore, I have 40 mappers and map output data is ~300GB. I can't see how that ends up in a factor 3? - tim Am 23.02.2010 14:39, schrieb Gang Luo: Hi Tim, the intermediate data is materialized to local file system. Before it is available for reducers, mappers will sort them. If the buffer (io.sort.mb) is too small for the intermediate data, multi-phase sorting happen, which means you read and write the same bit more than one time. Besides, are you looking at the statistics per mapper through the job tracker, or just the information output when a job finish? If you look at the information given out at the end of the job, note that this is an overall statistics which include sorting at reduce side. It also include the amount of data written to HDFS (I am not 100% sure). And, the FILE-BYTES_WRITTEN has nothing to do with the replication factor. I think if you change the factor to 6, FILE_BYTES_WRITTEN is still the same. -Gang - 原始邮件 发件人： Tim Kiefer tim-kie...@gmx.de 收件人： common-user@hadoop.apache.org common-user@hadoop.apache.org 发送日期： 2010/2/23 (周二) 6:44:28 上午 主 题： How are intermediate key/value pairs materialized between map and reduce? Hi there, can anybody help me out on a (most likely) simple unclarity. I am wondering how intermediate key/value pairs are materialized. I have a job where the map phase produces 600,000 records and map output bytes is ~300GB. What I thought (up to now) is that these 600,000 records, i.e., 300GB, are materialized locally by the mappers and that later on reducers pull these records (based on the key). What I see (and cannot explain) is that the FILE_BYTES_WRITTEN counter is as high as ~900GB. So - where does the factor 3 come from between Map output bytes and FILE_BYTES_WRITTEN??? I thought about the replication factor of 3 in the file system - but that should be HDFS only?! Thanks - tim ___ 好玩贺卡等你发，邮箱贺卡全新上线！ http://card.mail.cn.yahoo.com/
Re: How are intermediate key/value pairs materialized between map and reduce?
Hi Tim, I'm guessing a lot of these writes are happening on the reduce side. On the JT web interface, there are three columns: map, reduce, overall. Is the 900GB figure from the overall column? The value in the map column will probably be closer to what you were expecting. There are writes on the reduce side too during the shuffle and multi-pass merge. Ed 2010/2/23 Tim Kiefer tim-kie...@gmx.de: Hi Gang, thanks for your reply. To clarify: I look at the statistics through the job tracker. In the webinterface for my job I have columns for map, reduce and total. What I was refering to is map - i.e. I see FILE_BYTES_WRITTEN = 3 * Map Output Bytes in the map column. About the replication factor: I would expect the exact same thing - changing to 6 has no influence on FILE_BYTES_WRITTEN. About the sorting: I have io.sort.mb = 100 and io.sort.factor = 10. Furthermore, I have 40 mappers and map output data is ~300GB. I can't see how that ends up in a factor 3? - tim Am 23.02.2010 14:39, schrieb Gang Luo: Hi Tim, the intermediate data is materialized to local file system. Before it is available for reducers, mappers will sort them. If the buffer (io.sort.mb) is too small for the intermediate data, multi-phase sorting happen, which means you read and write the same bit more than one time. Besides, are you looking at the statistics per mapper through the job tracker, or just the information output when a job finish? If you look at the information given out at the end of the job, note that this is an overall statistics which include sorting at reduce side. It also include the amount of data written to HDFS (I am not 100% sure). And, the FILE-BYTES_WRITTEN has nothing to do with the replication factor. I think if you change the factor to 6, FILE_BYTES_WRITTEN is still the same. -Gang - 原始邮件 发件人： Tim Kiefer tim-kie...@gmx.de 收件人： common-user@hadoop.apache.org common-user@hadoop.apache.org 发送日期： 2010/2/23 (周二) 6:44:28 上午 主 题： How are intermediate key/value pairs materialized between map and reduce? Hi there, can anybody help me out on a (most likely) simple unclarity. I am wondering how intermediate key/value pairs are materialized. I have a job where the map phase produces 600,000 records and map output bytes is ~300GB. What I thought (up to now) is that these 600,000 records, i.e., 300GB, are materialized locally by the mappers and that later on reducers pull these records (based on the key). What I see (and cannot explain) is that the FILE_BYTES_WRITTEN counter is as high as ~900GB. So - where does the factor 3 come from between Map output bytes and FILE_BYTES_WRITTEN??? I thought about the replication factor of 3 in the file system - but that should be HDFS only?! Thanks - tim ___ 好玩贺卡等你发，邮箱贺卡全新上线！ http://card.mail.cn.yahoo.com/
Re: How are intermediate key/value pairs materialized between map and reduce?
No... 900GB is in the map column. Reduce adds another ~70GB of FILE_BYTES_WRITTEN and the total column consequently shows ~970GB. Am 23.02.2010 16:11, schrieb Ed Mazur: Hi Tim, I'm guessing a lot of these writes are happening on the reduce side. On the JT web interface, there are three columns: map, reduce, overall. Is the 900GB figure from the overall column? The value in the map column will probably be closer to what you were expecting. There are writes on the reduce side too during the shuffle and multi-pass merge. Ed 2010/2/23 Tim Kiefer tim-kie...@gmx.de: Hi Gang, thanks for your reply. To clarify: I look at the statistics through the job tracker. In the webinterface for my job I have columns for map, reduce and total. What I was refering to is map - i.e. I see FILE_BYTES_WRITTEN = 3 * Map Output Bytes in the map column. About the replication factor: I would expect the exact same thing - changing to 6 has no influence on FILE_BYTES_WRITTEN. About the sorting: I have io.sort.mb = 100 and io.sort.factor = 10. Furthermore, I have 40 mappers and map output data is ~300GB. I can't see how that ends up in a factor 3? - tim Am 23.02.2010 14:39, schrieb Gang Luo: Hi Tim, the intermediate data is materialized to local file system. Before it is available for reducers, mappers will sort them. If the buffer (io.sort.mb) is too small for the intermediate data, multi-phase sorting happen, which means you read and write the same bit more than one time. Besides, are you looking at the statistics per mapper through the job tracker, or just the information output when a job finish? If you look at the information given out at the end of the job, note that this is an overall statistics which include sorting at reduce side. It also include the amount of data written to HDFS (I am not 100% sure). And, the FILE-BYTES_WRITTEN has nothing to do with the replication factor. I think if you change the factor to 6, FILE_BYTES_WRITTEN is still the same. -Gang - 原始邮件 发件人： Tim Kiefer tim-kie...@gmx.de 收件人： common-user@hadoop.apache.org common-user@hadoop.apache.org 发送日期： 2010/2/23 (周二) 6:44:28 上午 主 题： How are intermediate key/value pairs materialized between map and reduce? Hi there, can anybody help me out on a (most likely) simple unclarity. I am wondering how intermediate key/value pairs are materialized. I have a job where the map phase produces 600,000 records and map output bytes is ~300GB. What I thought (up to now) is that these 600,000 records, i.e., 300GB, are materialized locally by the mappers and that later on reducers pull these records (based on the key). What I see (and cannot explain) is that the FILE_BYTES_WRITTEN counter is as high as ~900GB. So - where does the factor 3 come from between Map output bytes and FILE_BYTES_WRITTEN??? I thought about the replication factor of 3 in the file system - but that should be HDFS only?! Thanks - tim ___ 好玩贺卡等你发，邮箱贺卡全新上线！ http://card.mail.cn.yahoo.com/
Re: How are intermediate key/value pairs materialized between map and reduce?
Hi, Can you let us know what is the value for : Map input records Map spilled records Map output bytes Is there any side effect file written? Thanks, Amogh On 2/23/10 8:57 PM, Tim Kiefer tim-kie...@gmx.de wrote: No... 900GB is in the map column. Reduce adds another ~70GB of FILE_BYTES_WRITTEN and the total column consequently shows ~970GB. Am 23.02.2010 16:11, schrieb Ed Mazur: Hi Tim, I'm guessing a lot of these writes are happening on the reduce side. On the JT web interface, there are three columns: map, reduce, overall. Is the 900GB figure from the overall column? The value in the map column will probably be closer to what you were expecting. There are writes on the reduce side too during the shuffle and multi-pass merge. Ed 2010/2/23 Tim Kiefer tim-kie...@gmx.de: Hi Gang, thanks for your reply. To clarify: I look at the statistics through the job tracker. In the webinterface for my job I have columns for map, reduce and total. What I was refering to is map - i.e. I see FILE_BYTES_WRITTEN = 3 * Map Output Bytes in the map column. About the replication factor: I would expect the exact same thing - changing to 6 has no influence on FILE_BYTES_WRITTEN. About the sorting: I have io.sort.mb = 100 and io.sort.factor = 10. Furthermore, I have 40 mappers and map output data is ~300GB. I can't see how that ends up in a factor 3? - tim Am 23.02.2010 14:39, schrieb Gang Luo: Hi Tim, the intermediate data is materialized to local file system. Before it is available for reducers, mappers will sort them. If the buffer (io.sort.mb) is too small for the intermediate data, multi-phase sorting happen, which means you read and write the same bit more than one time. Besides, are you looking at the statistics per mapper through the job tracker, or just the information output when a job finish? If you look at the information given out at the end of the job, note that this is an overall statistics which include sorting at reduce side. It also include the amount of data written to HDFS (I am not 100% sure). And, the FILE-BYTES_WRITTEN has nothing to do with the replication factor. I think if you change the factor to 6, FILE_BYTES_WRITTEN is still the same. -Gang Hi there, can anybody help me out on a (most likely) simple unclarity. I am wondering how intermediate key/value pairs are materialized. I have a job where the map phase produces 600,000 records and map output bytes is ~300GB. What I thought (up to now) is that these 600,000 records, i.e., 300GB, are materialized locally by the mappers and that later on reducers pull these records (based on the key). What I see (and cannot explain) is that the FILE_BYTES_WRITTEN counter is as high as ~900GB. So - where does the factor 3 come from between Map output bytes and FILE_BYTES_WRITTEN??? I thought about the replication factor of 3 in the file system - but that should be HDFS only?! Thanks - tim
Re: How are intermediate key/value pairs materialized between map and reduce?
Sure, I see: Map input eecords: 10,000 Map output records: 600,000 Map output bytes: 307,216,800,000 (each reacord is about 500kb - that fits the application and is to be expected) Map spilled records: 1,802,965 (ahhh... now that you ask for it - here there also is a factor of 3 between output and spilled). So - question now is: why are three times as many records spilled than actually produced by the mappers? In my map function, I do not perform any additional file writing besides the context.write() for the intermediate records. Thanks, Tim Am 24.02.2010 05:28, schrieb Amogh Vasekar: Hi, Can you let us know what is the value for : Map input records Map spilled records Map output bytes Is there any side effect file written? Thanks, Amogh On 2/23/10 8:57 PM, Tim Kiefertim-kie...@gmx.de wrote: No... 900GB is in the map column. Reduce adds another ~70GB of FILE_BYTES_WRITTEN and the total column consequently shows ~970GB. Am 23.02.2010 16:11, schrieb Ed Mazur: Hi Tim, I'm guessing a lot of these writes are happening on the reduce side. On the JT web interface, there are three columns: map, reduce, overall. Is the 900GB figure from the overall column? The value in the map column will probably be closer to what you were expecting. There are writes on the reduce side too during the shuffle and multi-pass merge. Ed 2010/2/23 Tim Kiefertim-kie...@gmx.de: Hi Gang, thanks for your reply. To clarify: I look at the statistics through the job tracker. In the webinterface for my job I have columns for map, reduce and total. What I was refering to is map - i.e. I see FILE_BYTES_WRITTEN = 3 * Map Output Bytes in the map column. About the replication factor: I would expect the exact same thing - changing to 6 has no influence on FILE_BYTES_WRITTEN. About the sorting: I have io.sort.mb = 100 and io.sort.factor = 10. Furthermore, I have 40 mappers and map output data is ~300GB. I can't see how that ends up in a factor 3? - tim Am 23.02.2010 14:39, schrieb Gang Luo: Hi Tim, the intermediate data is materialized to local file system. Before it is available for reducers, mappers will sort them. If the buffer (io.sort.mb) is too small for the intermediate data, multi-phase sorting happen, which means you read and write the same bit more than one time. Besides, are you looking at the statistics per mapper through the job tracker, or just the information output when a job finish? If you look at the information given out at the end of the job, note that this is an overall statistics which include sorting at reduce side. It also include the amount of data written to HDFS (I am not 100% sure). And, the FILE-BYTES_WRITTEN has nothing to do with the replication factor. I think if you change the factor to 6, FILE_BYTES_WRITTEN is still the same. -Gang Hi there, can anybody help me out on a (most likely) simple unclarity. I am wondering how intermediate key/value pairs are materialized. I have a job where the map phase produces 600,000 records and map output bytes is ~300GB. What I thought (up to now) is that these 600,000 records, i.e., 300GB, are materialized locally by the mappers and that later on reducers pull these records (based on the key). What I see (and cannot explain) is that the FILE_BYTES_WRITTEN counter is as high as ~900GB. So - where does the factor 3 come from between Map output bytes and FILE_BYTES_WRITTEN??? I thought about the replication factor of 3 in the file system - but that should be HDFS only?! Thanks - tim
