Re: hadoop input/output format advanced control
my personal preference would be something like a Map[String, String] that only reflects the changes you want to make the Configuration for the given input/output format (so system wide defaults continue to come from sc.hadoopConfiguration), similarly to what cascading/scalding did, but am arbitrary Configuration will work too. i will make a jira and pullreq when i have some time. On Wed, Mar 25, 2015 at 1:23 AM, Patrick Wendell pwend...@gmail.com wrote: I see - if you look, in the saving functions we have the option for the user to pass an arbitrary Configuration. https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala#L894 It seems fine to have the same option for the loading functions, if it's easy to just pass this config into the input format. On Tue, Mar 24, 2015 at 3:46 PM, Koert Kuipers ko...@tresata.com wrote: the (compression) codec parameter that is now part of many saveAs... methods came from a similar need. see SPARK-763 hadoop has many options like this. you either going to have to allow many more of these optional arguments to all the methods that read from hadoop inputformats and write to hadoop outputformats, or you force people to re-create these methods using HadoopRDD, i think (if thats even possible). On Tue, Mar 24, 2015 at 6:40 PM, Koert Kuipers ko...@tresata.com wrote: i would like to use objectFile with some tweaks to the hadoop conf. currently there is no way to do that, except recreating objectFile myself. and some of the code objectFile uses i have no access to, since its private to spark. On Tue, Mar 24, 2015 at 2:59 PM, Patrick Wendell pwend...@gmail.com wrote: Yeah - to Nick's point, I think the way to do this is to pass in a custom conf when you create a Hadoop RDD (that's AFAIK why the conf field is there). Is there anything you can't do with that feature? On Tue, Mar 24, 2015 at 11:50 AM, Nick Pentreath nick.pentre...@gmail.com wrote: Imran, on your point to read multiple files together in a partition, is it not simpler to use the approach of copy Hadoop conf and set per-RDD settings for min split to control the input size per partition, together with something like CombineFileInputFormat? On Tue, Mar 24, 2015 at 5:28 PM, Imran Rashid iras...@cloudera.com wrote: I think this would be a great addition, I totally agree that you need to be able to set these at a finer context than just the SparkContext. Just to play devil's advocate, though -- the alternative is for you just subclass HadoopRDD yourself, or make a totally new RDD, and then you could expose whatever you need. Why is this solution better? IMO the criteria are: (a) common operations (b) error-prone / difficult to implement (c) non-obvious, but important for performance I think this case fits (a) (c), so I think its still worthwhile. But its also worth asking whether or not its too difficult for a user to extend HadoopRDD right now. There have been several cases in the past week where we've suggested that a user should read from hdfs themselves (eg., to read multiple files together in one partition) -- with*out* reusing the code in HadoopRDD, though they would lose things like the metric tracking preferred locations you get from HadoopRDD. Does HadoopRDD need to some refactoring to make that easier to do? Or do we just need a good example? Imran (sorry for hijacking your thread, Koert) On Mon, Mar 23, 2015 at 3:52 PM, Koert Kuipers ko...@tresata.com wrote: see email below. reynold suggested i send it to dev instead of user -- Forwarded message -- From: Koert Kuipers ko...@tresata.com Date: Mon, Mar 23, 2015 at 4:36 PM Subject: hadoop input/output format advanced control To: u...@spark.apache.org u...@spark.apache.org currently its pretty hard to control the Hadoop Input/Output formats used in Spark. The conventions seems to be to add extra parameters to all methods and then somewhere deep inside the code (for example in PairRDDFunctions.saveAsHadoopFile) all these parameters get translated into settings on the Hadoop Configuration object. for example for compression i see codec: Option[Class[_ : CompressionCodec]] = None added to a bunch of methods. how scalable is this solution really? for example i need to read from a hadoop dataset and i dont want the input (part) files to get split up. the way to do this is to set mapred.min.split.size. now i dont want to set this at the level of the SparkContext (which can be done), since i dont want it to apply to input formats in general. i want it to apply to just this one specific input dataset i need to read. which leaves me with no options
Re: hadoop input/output format advanced control
Yeah I agree that might have been nicer, but I think for consistency with the input API's maybe we should do the same thing. We can also give an example of how to clone sc.hadoopConfiguration and then set some new values: val conf = sc.hadoopConfiguration.clone() .set(k1, v1) .set(k2, v2) val rdd = sc.objectFile(..., conf) I have no idea if that's the correct syntax, but something like that seems almost as easy as passing a hashmap with deltas. - Patrick On Wed, Mar 25, 2015 at 6:34 AM, Koert Kuipers ko...@tresata.com wrote: my personal preference would be something like a Map[String, String] that only reflects the changes you want to make the Configuration for the given input/output format (so system wide defaults continue to come from sc.hadoopConfiguration), similarly to what cascading/scalding did, but am arbitrary Configuration will work too. i will make a jira and pullreq when i have some time. On Wed, Mar 25, 2015 at 1:23 AM, Patrick Wendell pwend...@gmail.com wrote: I see - if you look, in the saving functions we have the option for the user to pass an arbitrary Configuration. https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala#L894 It seems fine to have the same option for the loading functions, if it's easy to just pass this config into the input format. On Tue, Mar 24, 2015 at 3:46 PM, Koert Kuipers ko...@tresata.com wrote: the (compression) codec parameter that is now part of many saveAs... methods came from a similar need. see SPARK-763 hadoop has many options like this. you either going to have to allow many more of these optional arguments to all the methods that read from hadoop inputformats and write to hadoop outputformats, or you force people to re-create these methods using HadoopRDD, i think (if thats even possible). On Tue, Mar 24, 2015 at 6:40 PM, Koert Kuipers ko...@tresata.com wrote: i would like to use objectFile with some tweaks to the hadoop conf. currently there is no way to do that, except recreating objectFile myself. and some of the code objectFile uses i have no access to, since its private to spark. On Tue, Mar 24, 2015 at 2:59 PM, Patrick Wendell pwend...@gmail.com wrote: Yeah - to Nick's point, I think the way to do this is to pass in a custom conf when you create a Hadoop RDD (that's AFAIK why the conf field is there). Is there anything you can't do with that feature? On Tue, Mar 24, 2015 at 11:50 AM, Nick Pentreath nick.pentre...@gmail.com wrote: Imran, on your point to read multiple files together in a partition, is it not simpler to use the approach of copy Hadoop conf and set per-RDD settings for min split to control the input size per partition, together with something like CombineFileInputFormat? On Tue, Mar 24, 2015 at 5:28 PM, Imran Rashid iras...@cloudera.com wrote: I think this would be a great addition, I totally agree that you need to be able to set these at a finer context than just the SparkContext. Just to play devil's advocate, though -- the alternative is for you just subclass HadoopRDD yourself, or make a totally new RDD, and then you could expose whatever you need. Why is this solution better? IMO the criteria are: (a) common operations (b) error-prone / difficult to implement (c) non-obvious, but important for performance I think this case fits (a) (c), so I think its still worthwhile. But its also worth asking whether or not its too difficult for a user to extend HadoopRDD right now. There have been several cases in the past week where we've suggested that a user should read from hdfs themselves (eg., to read multiple files together in one partition) -- with*out* reusing the code in HadoopRDD, though they would lose things like the metric tracking preferred locations you get from HadoopRDD. Does HadoopRDD need to some refactoring to make that easier to do? Or do we just need a good example? Imran (sorry for hijacking your thread, Koert) On Mon, Mar 23, 2015 at 3:52 PM, Koert Kuipers ko...@tresata.com wrote: see email below. reynold suggested i send it to dev instead of user -- Forwarded message -- From: Koert Kuipers ko...@tresata.com Date: Mon, Mar 23, 2015 at 4:36 PM Subject: hadoop input/output format advanced control To: u...@spark.apache.org u...@spark.apache.org currently its pretty hard to control the Hadoop Input/Output formats used in Spark. The conventions seems to be to add extra parameters to all methods and then somewhere deep inside the code (for example in PairRDDFunctions.saveAsHadoopFile) all these parameters get translated into settings on the Hadoop Configuration object. for example for compression i see codec:
Re: hadoop input/output format advanced control
Regarding Patrick's question, you can just do new Configuration(oldConf) to get a cloned Configuration object and add any new properties to it. -Sandy On Wed, Mar 25, 2015 at 4:42 PM, Imran Rashid iras...@cloudera.com wrote: Hi Nick, I don't remember the exact details of these scenarios, but I think the user wanted a lot more control over how the files got grouped into partitions, to group the files together by some arbitrary function. I didn't think that was possible w/ CombineFileInputFormat, but maybe there is a way? thanks On Tue, Mar 24, 2015 at 1:50 PM, Nick Pentreath nick.pentre...@gmail.com wrote: Imran, on your point to read multiple files together in a partition, is it not simpler to use the approach of copy Hadoop conf and set per-RDD settings for min split to control the input size per partition, together with something like CombineFileInputFormat? On Tue, Mar 24, 2015 at 5:28 PM, Imran Rashid iras...@cloudera.com wrote: I think this would be a great addition, I totally agree that you need to be able to set these at a finer context than just the SparkContext. Just to play devil's advocate, though -- the alternative is for you just subclass HadoopRDD yourself, or make a totally new RDD, and then you could expose whatever you need. Why is this solution better? IMO the criteria are: (a) common operations (b) error-prone / difficult to implement (c) non-obvious, but important for performance I think this case fits (a) (c), so I think its still worthwhile. But its also worth asking whether or not its too difficult for a user to extend HadoopRDD right now. There have been several cases in the past week where we've suggested that a user should read from hdfs themselves (eg., to read multiple files together in one partition) -- with*out* reusing the code in HadoopRDD, though they would lose things like the metric tracking preferred locations you get from HadoopRDD. Does HadoopRDD need to some refactoring to make that easier to do? Or do we just need a good example? Imran (sorry for hijacking your thread, Koert) On Mon, Mar 23, 2015 at 3:52 PM, Koert Kuipers ko...@tresata.com wrote: see email below. reynold suggested i send it to dev instead of user -- Forwarded message -- From: Koert Kuipers ko...@tresata.com Date: Mon, Mar 23, 2015 at 4:36 PM Subject: hadoop input/output format advanced control To: u...@spark.apache.org u...@spark.apache.org currently its pretty hard to control the Hadoop Input/Output formats used in Spark. The conventions seems to be to add extra parameters to all methods and then somewhere deep inside the code (for example in PairRDDFunctions.saveAsHadoopFile) all these parameters get translated into settings on the Hadoop Configuration object. for example for compression i see codec: Option[Class[_ : CompressionCodec]] = None added to a bunch of methods. how scalable is this solution really? for example i need to read from a hadoop dataset and i dont want the input (part) files to get split up. the way to do this is to set mapred.min.split.size. now i dont want to set this at the level of the SparkContext (which can be done), since i dont want it to apply to input formats in general. i want it to apply to just this one specific input dataset i need to read. which leaves me with no options currently. i could go add yet another input parameter to all the methods (SparkContext.textFile, SparkContext.hadoopFile, SparkContext.objectFile, etc.). but that seems ineffective. why can we not expose a Map[String, String] or some other generic way to manipulate settings for hadoop input/output formats? it would require adding one more parameter to all methods to deal with hadoop input/output formats, but after that its done. one parameter to rule them all then i could do: val x = sc.textFile(/some/path, formatSettings = Map(mapred.min.split.size - 12345)) or rdd.saveAsTextFile(/some/path, formatSettings = Map(mapred.output.compress - true, mapred.output.compression.codec - somecodec))
Re: hadoop input/output format advanced control
Great - that's even easier. Maybe we could have a simple example in the doc. On Wed, Mar 25, 2015 at 7:06 PM, Sandy Ryza sandy.r...@cloudera.com wrote: Regarding Patrick's question, you can just do new Configuration(oldConf) to get a cloned Configuration object and add any new properties to it. -Sandy On Wed, Mar 25, 2015 at 4:42 PM, Imran Rashid iras...@cloudera.com wrote: Hi Nick, I don't remember the exact details of these scenarios, but I think the user wanted a lot more control over how the files got grouped into partitions, to group the files together by some arbitrary function. I didn't think that was possible w/ CombineFileInputFormat, but maybe there is a way? thanks On Tue, Mar 24, 2015 at 1:50 PM, Nick Pentreath nick.pentre...@gmail.com wrote: Imran, on your point to read multiple files together in a partition, is it not simpler to use the approach of copy Hadoop conf and set per-RDD settings for min split to control the input size per partition, together with something like CombineFileInputFormat? On Tue, Mar 24, 2015 at 5:28 PM, Imran Rashid iras...@cloudera.com wrote: I think this would be a great addition, I totally agree that you need to be able to set these at a finer context than just the SparkContext. Just to play devil's advocate, though -- the alternative is for you just subclass HadoopRDD yourself, or make a totally new RDD, and then you could expose whatever you need. Why is this solution better? IMO the criteria are: (a) common operations (b) error-prone / difficult to implement (c) non-obvious, but important for performance I think this case fits (a) (c), so I think its still worthwhile. But its also worth asking whether or not its too difficult for a user to extend HadoopRDD right now. There have been several cases in the past week where we've suggested that a user should read from hdfs themselves (eg., to read multiple files together in one partition) -- with*out* reusing the code in HadoopRDD, though they would lose things like the metric tracking preferred locations you get from HadoopRDD. Does HadoopRDD need to some refactoring to make that easier to do? Or do we just need a good example? Imran (sorry for hijacking your thread, Koert) On Mon, Mar 23, 2015 at 3:52 PM, Koert Kuipers ko...@tresata.com wrote: see email below. reynold suggested i send it to dev instead of user -- Forwarded message -- From: Koert Kuipers ko...@tresata.com Date: Mon, Mar 23, 2015 at 4:36 PM Subject: hadoop input/output format advanced control To: u...@spark.apache.org u...@spark.apache.org currently its pretty hard to control the Hadoop Input/Output formats used in Spark. The conventions seems to be to add extra parameters to all methods and then somewhere deep inside the code (for example in PairRDDFunctions.saveAsHadoopFile) all these parameters get translated into settings on the Hadoop Configuration object. for example for compression i see codec: Option[Class[_ : CompressionCodec]] = None added to a bunch of methods. how scalable is this solution really? for example i need to read from a hadoop dataset and i dont want the input (part) files to get split up. the way to do this is to set mapred.min.split.size. now i dont want to set this at the level of the SparkContext (which can be done), since i dont want it to apply to input formats in general. i want it to apply to just this one specific input dataset i need to read. which leaves me with no options currently. i could go add yet another input parameter to all the methods (SparkContext.textFile, SparkContext.hadoopFile, SparkContext.objectFile, etc.). but that seems ineffective. why can we not expose a Map[String, String] or some other generic way to manipulate settings for hadoop input/output formats? it would require adding one more parameter to all methods to deal with hadoop input/output formats, but after that its done. one parameter to rule them all then i could do: val x = sc.textFile(/some/path, formatSettings = Map(mapred.min.split.size - 12345)) or rdd.saveAsTextFile(/some/path, formatSettings = Map(mapred.output.compress - true, mapred.output.compression.codec - somecodec)) - To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
Re: hadoop input/output format advanced control
Should we mention that you should synchronize on HadoopRDD.CONFIGURATION_INSTANTIATION_LOCK to avoid a possible race condition in cloning Hadoop Configuration objects prior to Hadoop 2.7.0? :) On Wed, Mar 25, 2015 at 7:16 PM, Patrick Wendell pwend...@gmail.com wrote: Great - that's even easier. Maybe we could have a simple example in the doc. On Wed, Mar 25, 2015 at 7:06 PM, Sandy Ryza sandy.r...@cloudera.com wrote: Regarding Patrick's question, you can just do new Configuration(oldConf) to get a cloned Configuration object and add any new properties to it. -Sandy On Wed, Mar 25, 2015 at 4:42 PM, Imran Rashid iras...@cloudera.com wrote: Hi Nick, I don't remember the exact details of these scenarios, but I think the user wanted a lot more control over how the files got grouped into partitions, to group the files together by some arbitrary function. I didn't think that was possible w/ CombineFileInputFormat, but maybe there is a way? thanks On Tue, Mar 24, 2015 at 1:50 PM, Nick Pentreath nick.pentre...@gmail.com wrote: Imran, on your point to read multiple files together in a partition, is it not simpler to use the approach of copy Hadoop conf and set per-RDD settings for min split to control the input size per partition, together with something like CombineFileInputFormat? On Tue, Mar 24, 2015 at 5:28 PM, Imran Rashid iras...@cloudera.com wrote: I think this would be a great addition, I totally agree that you need to be able to set these at a finer context than just the SparkContext. Just to play devil's advocate, though -- the alternative is for you just subclass HadoopRDD yourself, or make a totally new RDD, and then you could expose whatever you need. Why is this solution better? IMO the criteria are: (a) common operations (b) error-prone / difficult to implement (c) non-obvious, but important for performance I think this case fits (a) (c), so I think its still worthwhile. But its also worth asking whether or not its too difficult for a user to extend HadoopRDD right now. There have been several cases in the past week where we've suggested that a user should read from hdfs themselves (eg., to read multiple files together in one partition) -- with*out* reusing the code in HadoopRDD, though they would lose things like the metric tracking preferred locations you get from HadoopRDD. Does HadoopRDD need to some refactoring to make that easier to do? Or do we just need a good example? Imran (sorry for hijacking your thread, Koert) On Mon, Mar 23, 2015 at 3:52 PM, Koert Kuipers ko...@tresata.com wrote: see email below. reynold suggested i send it to dev instead of user -- Forwarded message -- From: Koert Kuipers ko...@tresata.com Date: Mon, Mar 23, 2015 at 4:36 PM Subject: hadoop input/output format advanced control To: u...@spark.apache.org u...@spark.apache.org currently its pretty hard to control the Hadoop Input/Output formats used in Spark. The conventions seems to be to add extra parameters to all methods and then somewhere deep inside the code (for example in PairRDDFunctions.saveAsHadoopFile) all these parameters get translated into settings on the Hadoop Configuration object. for example for compression i see codec: Option[Class[_ : CompressionCodec]] = None added to a bunch of methods. how scalable is this solution really? for example i need to read from a hadoop dataset and i dont want the input (part) files to get split up. the way to do this is to set mapred.min.split.size. now i dont want to set this at the level of the SparkContext (which can be done), since i dont want it to apply to input formats in general. i want it to apply to just this one specific input dataset i need to read. which leaves me with no options currently. i could go add yet another input parameter to all the methods (SparkContext.textFile, SparkContext.hadoopFile, SparkContext.objectFile, etc.). but that seems ineffective. why can we not expose a Map[String, String] or some other generic way to manipulate settings for hadoop input/output formats? it would require adding one more parameter to all methods to deal with hadoop input/output formats, but after that its done. one parameter to rule them all then i could do: val x = sc.textFile(/some/path, formatSettings = Map(mapred.min.split.size - 12345)) or rdd.saveAsTextFile(/some/path, formatSettings = Map(mapred.output.compress - true, mapred.output.compression.codec - somecodec)) -
Re: hadoop input/output format advanced control
i would like to use objectFile with some tweaks to the hadoop conf. currently there is no way to do that, except recreating objectFile myself. and some of the code objectFile uses i have no access to, since its private to spark. On Tue, Mar 24, 2015 at 2:59 PM, Patrick Wendell pwend...@gmail.com wrote: Yeah - to Nick's point, I think the way to do this is to pass in a custom conf when you create a Hadoop RDD (that's AFAIK why the conf field is there). Is there anything you can't do with that feature? On Tue, Mar 24, 2015 at 11:50 AM, Nick Pentreath nick.pentre...@gmail.com wrote: Imran, on your point to read multiple files together in a partition, is it not simpler to use the approach of copy Hadoop conf and set per-RDD settings for min split to control the input size per partition, together with something like CombineFileInputFormat? On Tue, Mar 24, 2015 at 5:28 PM, Imran Rashid iras...@cloudera.com wrote: I think this would be a great addition, I totally agree that you need to be able to set these at a finer context than just the SparkContext. Just to play devil's advocate, though -- the alternative is for you just subclass HadoopRDD yourself, or make a totally new RDD, and then you could expose whatever you need. Why is this solution better? IMO the criteria are: (a) common operations (b) error-prone / difficult to implement (c) non-obvious, but important for performance I think this case fits (a) (c), so I think its still worthwhile. But its also worth asking whether or not its too difficult for a user to extend HadoopRDD right now. There have been several cases in the past week where we've suggested that a user should read from hdfs themselves (eg., to read multiple files together in one partition) -- with*out* reusing the code in HadoopRDD, though they would lose things like the metric tracking preferred locations you get from HadoopRDD. Does HadoopRDD need to some refactoring to make that easier to do? Or do we just need a good example? Imran (sorry for hijacking your thread, Koert) On Mon, Mar 23, 2015 at 3:52 PM, Koert Kuipers ko...@tresata.com wrote: see email below. reynold suggested i send it to dev instead of user -- Forwarded message -- From: Koert Kuipers ko...@tresata.com Date: Mon, Mar 23, 2015 at 4:36 PM Subject: hadoop input/output format advanced control To: u...@spark.apache.org u...@spark.apache.org currently its pretty hard to control the Hadoop Input/Output formats used in Spark. The conventions seems to be to add extra parameters to all methods and then somewhere deep inside the code (for example in PairRDDFunctions.saveAsHadoopFile) all these parameters get translated into settings on the Hadoop Configuration object. for example for compression i see codec: Option[Class[_ : CompressionCodec]] = None added to a bunch of methods. how scalable is this solution really? for example i need to read from a hadoop dataset and i dont want the input (part) files to get split up. the way to do this is to set mapred.min.split.size. now i dont want to set this at the level of the SparkContext (which can be done), since i dont want it to apply to input formats in general. i want it to apply to just this one specific input dataset i need to read. which leaves me with no options currently. i could go add yet another input parameter to all the methods (SparkContext.textFile, SparkContext.hadoopFile, SparkContext.objectFile, etc.). but that seems ineffective. why can we not expose a Map[String, String] or some other generic way to manipulate settings for hadoop input/output formats? it would require adding one more parameter to all methods to deal with hadoop input/output formats, but after that its done. one parameter to rule them all then i could do: val x = sc.textFile(/some/path, formatSettings = Map(mapred.min.split.size - 12345)) or rdd.saveAsTextFile(/some/path, formatSettings = Map(mapred.output.compress - true, mapred.output.compression.codec - somecodec)) - To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
Re: hadoop input/output format advanced control
I think this would be a great addition, I totally agree that you need to be able to set these at a finer context than just the SparkContext. Just to play devil's advocate, though -- the alternative is for you just subclass HadoopRDD yourself, or make a totally new RDD, and then you could expose whatever you need. Why is this solution better? IMO the criteria are: (a) common operations (b) error-prone / difficult to implement (c) non-obvious, but important for performance I think this case fits (a) (c), so I think its still worthwhile. But its also worth asking whether or not its too difficult for a user to extend HadoopRDD right now. There have been several cases in the past week where we've suggested that a user should read from hdfs themselves (eg., to read multiple files together in one partition) -- with*out* reusing the code in HadoopRDD, though they would lose things like the metric tracking preferred locations you get from HadoopRDD. Does HadoopRDD need to some refactoring to make that easier to do? Or do we just need a good example? Imran (sorry for hijacking your thread, Koert) On Mon, Mar 23, 2015 at 3:52 PM, Koert Kuipers ko...@tresata.com wrote: see email below. reynold suggested i send it to dev instead of user -- Forwarded message -- From: Koert Kuipers ko...@tresata.com Date: Mon, Mar 23, 2015 at 4:36 PM Subject: hadoop input/output format advanced control To: u...@spark.apache.org u...@spark.apache.org currently its pretty hard to control the Hadoop Input/Output formats used in Spark. The conventions seems to be to add extra parameters to all methods and then somewhere deep inside the code (for example in PairRDDFunctions.saveAsHadoopFile) all these parameters get translated into settings on the Hadoop Configuration object. for example for compression i see codec: Option[Class[_ : CompressionCodec]] = None added to a bunch of methods. how scalable is this solution really? for example i need to read from a hadoop dataset and i dont want the input (part) files to get split up. the way to do this is to set mapred.min.split.size. now i dont want to set this at the level of the SparkContext (which can be done), since i dont want it to apply to input formats in general. i want it to apply to just this one specific input dataset i need to read. which leaves me with no options currently. i could go add yet another input parameter to all the methods (SparkContext.textFile, SparkContext.hadoopFile, SparkContext.objectFile, etc.). but that seems ineffective. why can we not expose a Map[String, String] or some other generic way to manipulate settings for hadoop input/output formats? it would require adding one more parameter to all methods to deal with hadoop input/output formats, but after that its done. one parameter to rule them all then i could do: val x = sc.textFile(/some/path, formatSettings = Map(mapred.min.split.size - 12345)) or rdd.saveAsTextFile(/some/path, formatSettings = Map(mapred.output.compress - true, mapred.output.compression.codec - somecodec))
Re: hadoop input/output format advanced control
Imran, on your point to read multiple files together in a partition, is it not simpler to use the approach of copy Hadoop conf and set per-RDD settings for min split to control the input size per partition, together with something like CombineFileInputFormat? On Tue, Mar 24, 2015 at 5:28 PM, Imran Rashid iras...@cloudera.com wrote: I think this would be a great addition, I totally agree that you need to be able to set these at a finer context than just the SparkContext. Just to play devil's advocate, though -- the alternative is for you just subclass HadoopRDD yourself, or make a totally new RDD, and then you could expose whatever you need. Why is this solution better? IMO the criteria are: (a) common operations (b) error-prone / difficult to implement (c) non-obvious, but important for performance I think this case fits (a) (c), so I think its still worthwhile. But its also worth asking whether or not its too difficult for a user to extend HadoopRDD right now. There have been several cases in the past week where we've suggested that a user should read from hdfs themselves (eg., to read multiple files together in one partition) -- with*out* reusing the code in HadoopRDD, though they would lose things like the metric tracking preferred locations you get from HadoopRDD. Does HadoopRDD need to some refactoring to make that easier to do? Or do we just need a good example? Imran (sorry for hijacking your thread, Koert) On Mon, Mar 23, 2015 at 3:52 PM, Koert Kuipers ko...@tresata.com wrote: see email below. reynold suggested i send it to dev instead of user -- Forwarded message -- From: Koert Kuipers ko...@tresata.com Date: Mon, Mar 23, 2015 at 4:36 PM Subject: hadoop input/output format advanced control To: u...@spark.apache.org u...@spark.apache.org currently its pretty hard to control the Hadoop Input/Output formats used in Spark. The conventions seems to be to add extra parameters to all methods and then somewhere deep inside the code (for example in PairRDDFunctions.saveAsHadoopFile) all these parameters get translated into settings on the Hadoop Configuration object. for example for compression i see codec: Option[Class[_ : CompressionCodec]] = None added to a bunch of methods. how scalable is this solution really? for example i need to read from a hadoop dataset and i dont want the input (part) files to get split up. the way to do this is to set mapred.min.split.size. now i dont want to set this at the level of the SparkContext (which can be done), since i dont want it to apply to input formats in general. i want it to apply to just this one specific input dataset i need to read. which leaves me with no options currently. i could go add yet another input parameter to all the methods (SparkContext.textFile, SparkContext.hadoopFile, SparkContext.objectFile, etc.). but that seems ineffective. why can we not expose a Map[String, String] or some other generic way to manipulate settings for hadoop input/output formats? it would require adding one more parameter to all methods to deal with hadoop input/output formats, but after that its done. one parameter to rule them all then i could do: val x = sc.textFile(/some/path, formatSettings = Map(mapred.min.split.size - 12345)) or rdd.saveAsTextFile(/some/path, formatSettings = Map(mapred.output.compress - true, mapred.output.compression.codec - somecodec))
Re: hadoop input/output format advanced control
Yeah - to Nick's point, I think the way to do this is to pass in a custom conf when you create a Hadoop RDD (that's AFAIK why the conf field is there). Is there anything you can't do with that feature? On Tue, Mar 24, 2015 at 11:50 AM, Nick Pentreath nick.pentre...@gmail.com wrote: Imran, on your point to read multiple files together in a partition, is it not simpler to use the approach of copy Hadoop conf and set per-RDD settings for min split to control the input size per partition, together with something like CombineFileInputFormat? On Tue, Mar 24, 2015 at 5:28 PM, Imran Rashid iras...@cloudera.com wrote: I think this would be a great addition, I totally agree that you need to be able to set these at a finer context than just the SparkContext. Just to play devil's advocate, though -- the alternative is for you just subclass HadoopRDD yourself, or make a totally new RDD, and then you could expose whatever you need. Why is this solution better? IMO the criteria are: (a) common operations (b) error-prone / difficult to implement (c) non-obvious, but important for performance I think this case fits (a) (c), so I think its still worthwhile. But its also worth asking whether or not its too difficult for a user to extend HadoopRDD right now. There have been several cases in the past week where we've suggested that a user should read from hdfs themselves (eg., to read multiple files together in one partition) -- with*out* reusing the code in HadoopRDD, though they would lose things like the metric tracking preferred locations you get from HadoopRDD. Does HadoopRDD need to some refactoring to make that easier to do? Or do we just need a good example? Imran (sorry for hijacking your thread, Koert) On Mon, Mar 23, 2015 at 3:52 PM, Koert Kuipers ko...@tresata.com wrote: see email below. reynold suggested i send it to dev instead of user -- Forwarded message -- From: Koert Kuipers ko...@tresata.com Date: Mon, Mar 23, 2015 at 4:36 PM Subject: hadoop input/output format advanced control To: u...@spark.apache.org u...@spark.apache.org currently its pretty hard to control the Hadoop Input/Output formats used in Spark. The conventions seems to be to add extra parameters to all methods and then somewhere deep inside the code (for example in PairRDDFunctions.saveAsHadoopFile) all these parameters get translated into settings on the Hadoop Configuration object. for example for compression i see codec: Option[Class[_ : CompressionCodec]] = None added to a bunch of methods. how scalable is this solution really? for example i need to read from a hadoop dataset and i dont want the input (part) files to get split up. the way to do this is to set mapred.min.split.size. now i dont want to set this at the level of the SparkContext (which can be done), since i dont want it to apply to input formats in general. i want it to apply to just this one specific input dataset i need to read. which leaves me with no options currently. i could go add yet another input parameter to all the methods (SparkContext.textFile, SparkContext.hadoopFile, SparkContext.objectFile, etc.). but that seems ineffective. why can we not expose a Map[String, String] or some other generic way to manipulate settings for hadoop input/output formats? it would require adding one more parameter to all methods to deal with hadoop input/output formats, but after that its done. one parameter to rule them all then i could do: val x = sc.textFile(/some/path, formatSettings = Map(mapred.min.split.size - 12345)) or rdd.saveAsTextFile(/some/path, formatSettings = Map(mapred.output.compress - true, mapred.output.compression.codec - somecodec)) - To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
