[jira] [Updated] (SPARK-6728) Improve performance of py4j for large bytearray
[ https://issues.apache.org/jira/browse/SPARK-6728?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Davies Liu updated SPARK-6728: -- Target Version/s: (was: 1.6.0) > Improve performance of py4j for large bytearray > --- > > Key: SPARK-6728 > URL: https://issues.apache.org/jira/browse/SPARK-6728 > Project: Spark > Issue Type: Improvement > Components: PySpark >Affects Versions: 1.3.0 >Reporter: Davies Liu >Priority: Critical > > PySpark relies on py4j to transfer function arguments and return between > Python and JVM, it's very slow to pass a large bytearray (larger than 10M). > In MLlib, it's possible to have a Vector with more than 100M bytes, which > will need few GB memory, may crash. > The reason is that py4j use text protocol, it will encode the bytearray as > base64, and do multiple string concat. > Binary will help a lot, create a issue for py4j: > https://github.com/bartdag/py4j/issues/159 -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-6728) Improve performance of py4j for large bytearray
[ https://issues.apache.org/jira/browse/SPARK-6728?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Yin Huai updated SPARK-6728: Priority: Critical (was: Major) Target Version/s: 1.4.0 > Improve performance of py4j for large bytearray > --- > > Key: SPARK-6728 > URL: https://issues.apache.org/jira/browse/SPARK-6728 > Project: Spark > Issue Type: Improvement > Components: PySpark >Affects Versions: 1.3.0 >Reporter: Davies Liu >Priority: Critical > > PySpark relies on py4j to transfer function arguments and return between > Python and JVM, it's very slow to pass a large bytearray (larger than 10M). > In MLlib, it's possible to have a Vector with more than 100M bytes, which > will need few GB memory, may crash. > The reason is that py4j use text protocol, it will encode the bytearray as > base64, and do multiple string concat. > Binary will help a lot, create a issue for py4j: > https://github.com/bartdag/py4j/issues/159 -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-6728) Improve performance of py4j for large bytearray
[ https://issues.apache.org/jira/browse/SPARK-6728?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Yin Huai updated SPARK-6728: Affects Version/s: 1.3.0 > Improve performance of py4j for large bytearray > --- > > Key: SPARK-6728 > URL: https://issues.apache.org/jira/browse/SPARK-6728 > Project: Spark > Issue Type: Improvement > Components: PySpark >Affects Versions: 1.3.0 >Reporter: Davies Liu > > PySpark relies on py4j to transfer function arguments and return between > Python and JVM, it's very slow to pass a large bytearray (larger than 10M). > In MLlib, it's possible to have a Vector with more than 100M bytes, which > will need few GB memory, may crash. > The reason is that py4j use text protocol, it will encode the bytearray as > base64, and do multiple string concat. > Binary will help a lot, create a issue for py4j: > https://github.com/bartdag/py4j/issues/159 -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-6728) Improve performance of py4j for large bytearray
[ https://issues.apache.org/jira/browse/SPARK-6728?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Sean Owen updated SPARK-6728: - Target Version/s: 1.5.0 (was: 1.4.0) > Improve performance of py4j for large bytearray > --- > > Key: SPARK-6728 > URL: https://issues.apache.org/jira/browse/SPARK-6728 > Project: Spark > Issue Type: Improvement > Components: PySpark >Affects Versions: 1.3.0 >Reporter: Davies Liu >Priority: Critical > > PySpark relies on py4j to transfer function arguments and return between > Python and JVM, it's very slow to pass a large bytearray (larger than 10M). > In MLlib, it's possible to have a Vector with more than 100M bytes, which > will need few GB memory, may crash. > The reason is that py4j use text protocol, it will encode the bytearray as > base64, and do multiple string concat. > Binary will help a lot, create a issue for py4j: > https://github.com/bartdag/py4j/issues/159 -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-6728) Improve performance of py4j for large bytearray
[ https://issues.apache.org/jira/browse/SPARK-6728?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Reynold Xin updated SPARK-6728: --- Target Version/s: 1.6.0 (was: 1.5.0) > Improve performance of py4j for large bytearray > --- > > Key: SPARK-6728 > URL: https://issues.apache.org/jira/browse/SPARK-6728 > Project: Spark > Issue Type: Improvement > Components: PySpark >Affects Versions: 1.3.0 >Reporter: Davies Liu >Priority: Critical > > PySpark relies on py4j to transfer function arguments and return between > Python and JVM, it's very slow to pass a large bytearray (larger than 10M). > In MLlib, it's possible to have a Vector with more than 100M bytes, which > will need few GB memory, may crash. > The reason is that py4j use text protocol, it will encode the bytearray as > base64, and do multiple string concat. > Binary will help a lot, create a issue for py4j: > https://github.com/bartdag/py4j/issues/159 -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
