forked from D-Net/dnet-hadoop
Resolve conflicts
This commit is contained in:
Serafeim Chatzopoulos
commit
82e2a96f51
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@ -46,6 +46,7 @@
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<!-- Script name goes here -->
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<jar>create_openaire_ranking_graph.py</jar>
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<!-- spark configuration options: I've taken most of them from an example from dhp workflows / Master value stolen from sandro -->
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<<<<<<< HEAD
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<spark-opts>
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--executor-memory=${sparkHighExecutorMemory}
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--executor-cores=${sparkExecutorCores}
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@ -56,10 +57,20 @@
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--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
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--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}
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</spark-opts>
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=======
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<spark-opts>--executor-memory ${sparkHighExecutorMemory} --executor-cores ${sparkExecutorCores} --driver-memory ${sparkHighDriverMemory}
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--master yarn
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--deploy-mode cluster
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--conf spark.sql.shuffle.partitions=7680
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--conf spark.extraListeners=${spark2ExtraListeners}
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--conf spark.sql.queryExecutionListeners=${spark2SqlQueryExecutionListeners}
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--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
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--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}</spark-opts>
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>>>>>>> 4a905932a3db36c61570c24b9aa54283cd30abba
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<!-- Script arguments here -->
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<!-- The openaire graph data from which to read relations and objects -->
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<arg>${openaireDataInput}</arg>
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<!-- Year for filtering entries w/ larger values / empty -->
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<!-- Year for filtering entries w/ larger values / empty -->
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<arg>${currentYear}</arg>
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<!-- number of partitions to be used on joins -->
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<arg>${sparkShufflePartitions}</arg>
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@ -68,14 +79,14 @@
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<!-- This needs to point to the file on the hdfs i think -->
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<file>${wfAppPath}/create_openaire_ranking_graph.py#create_openaire_ranking_graph.py</file>
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</spark>
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<!-- Do this after finishing okay -->
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<ok to="end" />
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<!-- Go there if we have an error -->
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<error to="openaire-graph-error" />
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</action>
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</action>
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<!-- Citation Count and RAM are calculated in parallel-->
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<!-- Impulse Requires resources and will be run after-->
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<fork name="non-iterative-rankings">
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@ -83,7 +94,7 @@
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<!-- <path start="spark-impulse"/> -->
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<path start="spark-ram"/>
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</fork>
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<!-- CC here -->
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<action name="spark-cc">
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<!-- This is required as a tag for spark jobs, regardless of programming language -->
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@ -96,12 +107,13 @@
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<!-- using configs from an example on openaire -->
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<master>yarn-cluster</master>
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<mode>cluster</mode>
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<!-- This is the name of our job -->
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<name>Spark CC</name>
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<!-- Script name goes here -->
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<jar>CC.py</jar>
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<!-- spark configuration options: I've taken most of them from an example from dhp workflows / Master value stolen from sandro -->
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<<<<<<< HEAD
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<spark-opts>
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--executor-memory=${sparkHighExecutorMemory}
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--executor-cores=${sparkExecutorCores}
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@ -112,6 +124,16 @@
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--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
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--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}
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</spark-opts>
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=======
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<spark-opts>--executor-memory ${sparkHighExecutorMemory} --executor-cores ${sparkExecutorCores} --driver-memory ${sparkNormalDriverMemory}
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--master yarn
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--deploy-mode cluster
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--conf spark.sql.shuffle.partitions=7680
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--conf spark.extraListeners=${spark2ExtraListeners}
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--conf spark.sql.queryExecutionListeners=${spark2SqlQueryExecutionListeners}
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--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
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--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}</spark-opts>
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>>>>>>> 4a905932a3db36c61570c24b9aa54283cd30abba
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<!-- Script arguments here -->
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<arg>${openaireGraphInputPath}</arg>
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<!-- number of partitions to be used on joins -->
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@ -119,13 +141,13 @@
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<!-- This needs to point to the file on the hdfs i think -->
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<file>${wfAppPath}/CC.py#CC.py</file>
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</spark>
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<!-- Do this after finishing okay -->
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<ok to="join-non-iterative-rankings" />
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<!-- Go there if we have an error -->
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<error to="cc-fail" />
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</action>
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</action>
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<!-- IMPULSE here -->
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<action name="spark-ram">
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@ -135,16 +157,17 @@
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<job-tracker>${jobTracker}</job-tracker>
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<!-- This should give the machine/root of the hdfs, serafeim has provided a link with the required job properties -->
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<name-node>${nameNode}</name-node>
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<!-- using configs from an example on openaire -->
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<!-- using configs from an example on openaire -->
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<master>yarn-cluster</master>
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<mode>cluster</mode>
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<!-- This is the name of our job -->
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<name>Spark RAM</name>
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<!-- Script name goes here -->
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<jar>TAR.py</jar>
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<!-- spark configuration options: I've taken most of them from an example from dhp workflows / Master value stolen from sandro -->
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<<<<<<< HEAD
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<spark-opts>
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--executor-memory=${sparkHighExecutorMemory}
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--executor-cores=${sparkExecutorCores}
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@ -155,6 +178,16 @@
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--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
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--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}
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</spark-opts>
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=======
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<spark-opts>--executor-memory ${sparkHighExecutorMemory} --executor-cores ${sparkExecutorCores} --driver-memory ${sparkNormalDriverMemory}
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--master yarn
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--deploy-mode cluster
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--conf spark.sql.shuffle.partitions=7680
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--conf spark.extraListeners=${spark2ExtraListeners}
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--conf spark.sql.queryExecutionListeners=${spark2SqlQueryExecutionListeners}
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--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
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--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}</spark-opts>
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>>>>>>> 4a905932a3db36c61570c24b9aa54283cd30abba
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<!-- Script arguments here -->
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<arg>${openaireGraphInputPath}</arg>
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<arg>${ramGamma}</arg>
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@ -166,17 +199,17 @@
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<!-- This needs to point to the file on the hdfs i think -->
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<file>${wfAppPath}/TAR.py#TAR.py</file>
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</spark>
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<!-- Do this after finishing okay -->
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<ok to="join-non-iterative-rankings" />
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<!-- Go there if we have an error -->
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<error to="ram-fail" />
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</action>
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</action>
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<!-- JOIN NON-ITERATIVE METHODS AND THEN CONTINUE TO ITERATIVE ONES -->
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<join name="join-non-iterative-rankings" to="spark-impulse"/>
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<!-- IMPULSE here -->
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<action name="spark-impulse">
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<!-- This is required as a tag for spark jobs, regardless of programming language -->
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@ -189,12 +222,13 @@
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<!-- using configs from an example on openaire -->
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<master>yarn-cluster</master>
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<mode>cluster</mode>
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<!-- This is the name of our job -->
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<name>Spark Impulse</name>
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<!-- Script name goes here -->
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<jar>CC.py</jar>
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<!-- spark configuration options: I've taken most of them from an example from dhp workflows / Master value stolen from sandro -->
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<<<<<<< HEAD
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<spark-opts>
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--executor-memory=${sparkHighExecutorMemory}
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--executor-cores=${sparkExecutorCores}
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@ -205,6 +239,16 @@
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--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
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--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}
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</spark-opts>
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=======
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<spark-opts>--executor-memory ${sparkHighExecutorMemory} --executor-cores ${sparkExecutorCores} --driver-memory ${sparkNormalDriverMemory}
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--master yarn
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--deploy-mode cluster
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--conf spark.sql.shuffle.partitions=7680
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--conf spark.extraListeners=${spark2ExtraListeners}
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--conf spark.sql.queryExecutionListeners=${spark2SqlQueryExecutionListeners}
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--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
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--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}</spark-opts>
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>>>>>>> 4a905932a3db36c61570c24b9aa54283cd30abba
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<!-- Script arguments here -->
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<arg>${openaireGraphInputPath}</arg>
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<!-- number of partitions to be used on joins -->
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@ -213,13 +257,13 @@
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<!-- This needs to point to the file on the hdfs i think -->
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<file>${wfAppPath}/CC.py#CC.py</file>
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</spark>
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<!-- Do this after finishing okay -->
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<ok to="iterative-rankings" />
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<!-- Go there if we have an error -->
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<error to="impulse-fail" />
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</action>
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</action>
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<fork name="iterative-rankings">
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<path start="spark-pagerank"/>
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@ -234,7 +278,7 @@
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<job-tracker>${jobTracker}</job-tracker>
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<!-- This should give the machine/root of the hdfs, serafeim has provided a link with the required job properties -->
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<name-node>${nameNode}</name-node>
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<!-- we could add map-reduce configs here, but I don't know if we need them -->
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<!-- This is the type of master-client configuration for running spark -->
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<!-- <master>yarn-client</master> -->
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@ -242,16 +286,17 @@
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<!-- <master>local[*]</master> -->
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<!-- Reference says: The mode element if present indicates the mode of spark, where to run spark driver program. Ex: client,cluster. | In my case I always have a client -->
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<!-- <mode>client</mode> -->
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<!-- using configs from an example on openaire -->
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<!-- using configs from an example on openaire -->
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<master>yarn-cluster</master>
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<mode>cluster</mode>
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<!-- This is the name of our job -->
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<name>Spark Pagerank</name>
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<!-- Script name goes here -->
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<jar>PageRank.py</jar>
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<!-- spark configuration options: I've taken most of them from an example from dhp workflows / Master value stolen from sandro -->
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<<<<<<< HEAD
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<spark-opts>
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--executor-memory=${sparkHighExecutorMemory}
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--executor-cores=${sparkExecutorCores}
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@ -262,6 +307,16 @@
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--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
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--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}
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</spark-opts>
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=======
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<spark-opts>--executor-memory ${sparkHighExecutorMemory} --executor-cores ${sparkExecutorCores} --driver-memory ${sparkNormalDriverMemory}
|
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--master yarn
|
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--deploy-mode cluster
|
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--conf spark.sql.shuffle.partitions=7680
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--conf spark.extraListeners=${spark2ExtraListeners}
|
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--conf spark.sql.queryExecutionListeners=${spark2SqlQueryExecutionListeners}
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--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
|
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--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}</spark-opts>
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>>>>>>> 4a905932a3db36c61570c24b9aa54283cd30abba
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<!-- Script arguments here -->
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<arg>${openaireGraphInputPath}</arg>
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<arg>${pageRankAlpha}</arg>
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@ -273,14 +328,14 @@
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<!-- This needs to point to the file on the hdfs i think -->
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<file>${wfAppPath}/PageRank.py#PageRank.py</file>
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</spark>
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<!-- Do this after finishing okay -->
|
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<ok to="join-iterative-rankings" />
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<!-- Go there if we have an error -->
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<error to="pagerank-fail" />
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</action>
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<!-- ATTRANK here -->
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<action name="spark-attrank">
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<!-- This is required as a tag for spark jobs, regardless of programming language -->
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@ -289,16 +344,17 @@
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<job-tracker>${jobTracker}</job-tracker>
|
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<!-- This should give the machine/root of the hdfs, serafeim has provided a link with the required job properties -->
|
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<name-node>${nameNode}</name-node>
|
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|
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<!-- using configs from an example on openaire -->
|
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|
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<!-- using configs from an example on openaire -->
|
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<master>yarn-cluster</master>
|
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<mode>cluster</mode>
|
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|
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<!-- This is the name of our job -->
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<name>Spark AttRank</name>
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<!-- Script name goes here -->
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<jar>AttRank.py</jar>
|
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<!-- spark configuration options: I've taken most of them from an example from dhp workflows / Master value stolen from sandro -->
|
||||
<<<<<<< HEAD
|
||||
<spark-opts>
|
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--executor-memory=${sparkHighExecutorMemory}
|
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--executor-cores=${sparkExecutorCores}
|
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|
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@ -309,6 +365,16 @@
|
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--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
|
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--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}
|
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</spark-opts>
|
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=======
|
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<spark-opts>--executor-memory ${sparkHighExecutorMemory} --executor-cores ${sparkExecutorCores} --driver-memory ${sparkNormalDriverMemory}
|
||||
--master yarn
|
||||
--deploy-mode cluster
|
||||
--conf spark.sql.shuffle.partitions=7680
|
||||
--conf spark.extraListeners=${spark2ExtraListeners}
|
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--conf spark.sql.queryExecutionListeners=${spark2SqlQueryExecutionListeners}
|
||||
--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
|
||||
--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}</spark-opts>
|
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>>>>>>> 4a905932a3db36c61570c24b9aa54283cd30abba
|
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<!-- Script arguments here -->
|
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<arg>${openaireGraphInputPath}</arg>
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<arg>${attrankAlpha}</arg>
|
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|
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@ -325,17 +391,17 @@
|
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<!-- This needs to point to the file on the hdfs i think -->
|
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<file>${wfAppPath}/AttRank.py#AttRank.py</file>
|
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</spark>
|
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|
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|
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<!-- Do this after finishing okay -->
|
||||
<ok to="join-iterative-rankings" />
|
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<!-- Go there if we have an error -->
|
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<error to="attrank-fail" />
|
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|
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</action>
|
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|
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|
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</action>
|
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|
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<!-- JOIN ITERATIVE METHODS AND THEN END -->
|
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<join name="join-iterative-rankings" to="get-file-names"/>
|
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|
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|
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|
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<!-- This will be a shell action that will output key-value pairs for output files -->
|
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<action name="get-file-names">
|
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|
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@ -345,35 +411,35 @@
|
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<job-tracker>${jobTracker}</job-tracker>
|
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<!-- This should give the machine/root of the hdfs -->
|
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<name-node>${nameNode}</name-node>
|
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|
||||
|
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<!-- Exec is needed for shell commands - points to type of shell command -->
|
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<exec>/usr/bin/bash</exec>
|
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<!-- name of script to run -->
|
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<argument>get_ranking_files.sh</argument>
|
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<!-- We only pass the directory where we expect to find the rankings -->
|
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<argument>/${workingDir}</argument>
|
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|
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|
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<!-- the name of the file run -->
|
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<file>${wfAppPath}/get_ranking_files.sh#get_ranking_files.sh</file>
|
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<!-- Get the output in order to be usable by following actions -->
|
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<capture-output/>
|
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</shell>
|
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|
||||
|
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<!-- Do this after finishing okay -->
|
||||
<ok to="format-result-files" />
|
||||
<!-- Go there if we have an error -->
|
||||
<error to="filename-getting-error" />
|
||||
|
||||
|
||||
</action>
|
||||
|
||||
|
||||
|
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|
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<!-- Now we will run in parallel the formatting of ranking files for BiP! DB and openaire (json files) -->
|
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<fork name="format-result-files">
|
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<path start="format-bip-files"/>
|
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<path start="format-json-files"/>
|
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</fork>
|
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|
||||
|
||||
|
||||
|
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<!-- Format json files -->
|
||||
<!-- Two parts: a) format files b) make the file endings .json.gz -->
|
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<action name="format-json-files">
|
||||
|
|
@ -383,16 +449,17 @@
|
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<job-tracker>${jobTracker}</job-tracker>
|
||||
<!-- This should give the machine/root of the hdfs, serafeim has provided a link with the required job properties -->
|
||||
<name-node>${nameNode}</name-node>
|
||||
|
||||
|
||||
<!-- using configs from an example on openaire -->
|
||||
<master>yarn-cluster</master>
|
||||
<mode>cluster</mode>
|
||||
|
||||
|
||||
<!-- This is the name of our job -->
|
||||
<name>Format Ranking Results JSON</name>
|
||||
<!-- Script name goes here -->
|
||||
<jar>format_ranking_results.py</jar>
|
||||
<!-- spark configuration options: I've taken most of them from an example from dhp workflows / Master value stolen from sandro -->
|
||||
<<<<<<< HEAD
|
||||
<spark-opts>
|
||||
--executor-memory=${sparkNormalExecutorMemory}
|
||||
--executor-cores=${sparkExecutorCores}
|
||||
|
|
@ -403,6 +470,16 @@
|
|||
--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
|
||||
--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}
|
||||
</spark-opts>
|
||||
=======
|
||||
<spark-opts>--executor-memory ${sparkNormalExecutorMemory} --executor-cores ${sparkExecutorCores} --driver-memory ${sparkNormalDriverMemory}
|
||||
--master yarn
|
||||
--deploy-mode cluster
|
||||
--conf spark.sql.shuffle.partitions=7680
|
||||
--conf spark.extraListeners=${spark2ExtraListeners}
|
||||
--conf spark.sql.queryExecutionListeners=${spark2SqlQueryExecutionListeners}
|
||||
--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
|
||||
--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}</spark-opts>
|
||||
>>>>>>> 4a905932a3db36c61570c24b9aa54283cd30abba
|
||||
<!-- Script arguments here -->
|
||||
<arg>json-5-way</arg>
|
||||
<!-- Input files must be identified dynamically -->
|
||||
|
|
@ -417,14 +494,14 @@
|
|||
<arg>openaire</arg>
|
||||
<!-- This needs to point to the file on the hdfs i think -->
|
||||
<file>${wfAppPath}/format_ranking_results.py#format_ranking_results.py</file>
|
||||
</spark>
|
||||
|
||||
</spark>
|
||||
|
||||
<!-- Do this after finishing okay -->
|
||||
<ok to="join-file-formatting" />
|
||||
<!-- Go there if we have an error -->
|
||||
<error to="json-formatting-fail" />
|
||||
</action>
|
||||
|
||||
</action>
|
||||
|
||||
<!-- This is the second line of parallel workflow execution where we create the BiP! DB files -->
|
||||
<action name="format-bip-files">
|
||||
<!-- This is required as a tag for spark jobs, regardless of programming language -->
|
||||
|
|
@ -433,16 +510,17 @@
|
|||
<job-tracker>${jobTracker}</job-tracker>
|
||||
<!-- This should give the machine/root of the hdfs, serafeim has provided a link with the required job properties -->
|
||||
<name-node>${nameNode}</name-node>
|
||||
|
||||
|
||||
<!-- using configs from an example on openaire -->
|
||||
<master>yarn-cluster</master>
|
||||
<mode>cluster</mode>
|
||||
|
||||
|
||||
<!-- This is the name of our job -->
|
||||
<name>Format Ranking Results BiP! DB</name>
|
||||
<!-- Script name goes here -->
|
||||
<jar>format_ranking_results.py</jar>
|
||||
<!-- spark configuration options: I've taken most of them from an example from dhp workflows / Master value stolen from sandro -->
|
||||
<<<<<<< HEAD
|
||||
<spark-opts>
|
||||
--executor-memory=${sparkNormalExecutorMemory}
|
||||
--executor-cores=${sparkExecutorCores}
|
||||
|
|
@ -453,6 +531,16 @@
|
|||
--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
|
||||
--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}
|
||||
</spark-opts>
|
||||
=======
|
||||
<spark-opts>--executor-memory ${sparkNormalExecutorMemory} --executor-cores ${sparkExecutorCores} --driver-memory ${sparkNormalDriverMemory}
|
||||
--master yarn
|
||||
--deploy-mode cluster
|
||||
--conf spark.sql.shuffle.partitions=7680
|
||||
--conf spark.extraListeners=${spark2ExtraListeners}
|
||||
--conf spark.sql.queryExecutionListeners=${spark2SqlQueryExecutionListeners}
|
||||
--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
|
||||
--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}</spark-opts>
|
||||
>>>>>>> 4a905932a3db36c61570c24b9aa54283cd30abba
|
||||
<!-- Script arguments here -->
|
||||
<arg>zenodo</arg>
|
||||
<!-- Input files must be identified dynamically -->
|
||||
|
|
@ -467,16 +555,16 @@
|
|||
<arg>openaire</arg>
|
||||
<!-- This needs to point to the file on the hdfs i think -->
|
||||
<file>${wfAppPath}/format_ranking_results.py#format_ranking_results.py</file>
|
||||
</spark>
|
||||
|
||||
</spark>
|
||||
|
||||
<!-- Do this after finishing okay -->
|
||||
<ok to="join-file-formatting" />
|
||||
<!-- Go there if we have an error -->
|
||||
<error to="bip-formatting-fail" />
|
||||
</action>
|
||||
|
||||
<!-- Finish formatting data and end -->
|
||||
<join name="join-file-formatting" to="map-openaire-to-doi"/>
|
||||
</action>
|
||||
|
||||
<!-- Finish formatting data and end -->
|
||||
<join name="join-file-formatting" to="map-openaire-to-doi"/>
|
||||
|
||||
<!-- Script here written by Serafeim: maps openaire ids to their synonyms -->
|
||||
<action name="map-openaire-to-doi">
|
||||
|
|
@ -490,16 +578,17 @@
|
|||
<prepare>
|
||||
<delete path="${synonymFolder}"/>
|
||||
</prepare>
|
||||
|
||||
<!-- using configs from an example on openaire -->
|
||||
|
||||
<!-- using configs from an example on openaire -->
|
||||
<master>yarn-cluster</master>
|
||||
<mode>cluster</mode>
|
||||
|
||||
|
||||
<!-- This is the name of our job -->
|
||||
<name>Openaire-DOI synonym collection</name>
|
||||
<!-- Script name goes here -->
|
||||
<jar>map_openaire_ids_to_dois.py</jar>
|
||||
<!-- spark configuration options: I've taken most of them from an example from dhp workflows / Master value stolen from sandro -->
|
||||
<<<<<<< HEAD
|
||||
<spark-opts>
|
||||
--executor-memory=${sparkHighExecutorMemory}
|
||||
--executor-cores=${sparkExecutorCores}
|
||||
|
|
@ -510,6 +599,16 @@
|
|||
--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
|
||||
--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}
|
||||
</spark-opts>
|
||||
=======
|
||||
<spark-opts>--executor-memory ${sparkHighExecutorMemory} --executor-cores ${sparkExecutorCores} --driver-memory ${sparkHighDriverMemory}
|
||||
--master yarn
|
||||
--deploy-mode cluster
|
||||
--conf spark.sql.shuffle.partitions=7680
|
||||
--conf spark.extraListeners=${spark2ExtraListeners}
|
||||
--conf spark.sql.queryExecutionListeners=${spark2SqlQueryExecutionListeners}
|
||||
--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
|
||||
--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}</spark-opts>
|
||||
>>>>>>> 4a905932a3db36c61570c24b9aa54283cd30abba
|
||||
<!-- Script arguments here -->
|
||||
<arg>${openaireDataInput}</arg>
|
||||
<!-- number of partitions to be used on joins -->
|
||||
|
|
@ -517,13 +616,13 @@
|
|||
<!-- This needs to point to the file on the hdfs i think -->
|
||||
<file>${wfAppPath}/map_openaire_ids_to_dois.py#map_openaire_ids_to_dois.py</file>
|
||||
</spark>
|
||||
|
||||
|
||||
<!-- Do this after finishing okay -->
|
||||
<ok to="map-scores-to-dois" />
|
||||
<!-- Go there if we have an error -->
|
||||
<error to="synonym-collection-fail" />
|
||||
|
||||
</action>
|
||||
|
||||
</action>
|
||||
|
||||
|
||||
<!-- Script here written by Serafeim: maps openaire ids to their synonyms -->
|
||||
|
|
@ -535,15 +634,16 @@
|
|||
<!-- This should give the machine/root of the hdfs, serafeim has provided a link with the required job properties -->
|
||||
<name-node>${nameNode}</name-node>
|
||||
|
||||
<!-- using configs from an example on openaire -->
|
||||
<!-- using configs from an example on openaire -->
|
||||
<master>yarn-cluster</master>
|
||||
<mode>cluster</mode>
|
||||
|
||||
|
||||
<!-- This is the name of our job -->
|
||||
<name>Mapping Openaire Scores to DOIs</name>
|
||||
<!-- Script name goes here -->
|
||||
<jar>map_scores_to_dois.py</jar>
|
||||
<!-- spark configuration options: I've taken most of them from an example from dhp workflows / Master value stolen from sandro -->
|
||||
<<<<<<< HEAD
|
||||
<spark-opts>
|
||||
--executor-memory=${sparkHighExecutorMemory}
|
||||
--executor-cores=${sparkExecutorCores}
|
||||
|
|
@ -554,6 +654,16 @@
|
|||
--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
|
||||
--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}
|
||||
</spark-opts>
|
||||
=======
|
||||
<spark-opts>--executor-memory ${sparkHighExecutorMemory} --executor-cores ${sparkExecutorCores} --driver-memory ${sparkHighDriverMemory}
|
||||
--master yarn
|
||||
--deploy-mode cluster
|
||||
--conf spark.sql.shuffle.partitions=7680
|
||||
--conf spark.extraListeners=${spark2ExtraListeners}
|
||||
--conf spark.sql.queryExecutionListeners=${spark2SqlQueryExecutionListeners}
|
||||
--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
|
||||
--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}</spark-opts>
|
||||
>>>>>>> 4a905932a3db36c61570c24b9aa54283cd30abba
|
||||
<!-- Script arguments here -->
|
||||
<arg>${synonymFolder}</arg>
|
||||
<!-- Number of partitions -->
|
||||
|
|
@ -568,13 +678,13 @@
|
|||
<!-- This needs to point to the file on the hdfs i think -->
|
||||
<file>${wfAppPath}/map_scores_to_dois.py#map_scores_to_dois.py</file>
|
||||
</spark>
|
||||
|
||||
|
||||
<!-- Do this after finishing okay -->
|
||||
<ok to="deleteOutputPathForActionSet" />
|
||||
<!-- Go there if we have an error -->
|
||||
<error to="map-scores-fail" />
|
||||
|
||||
</action>
|
||||
|
||||
</action>
|
||||
|
||||
<action name="deleteOutputPathForActionSet">
|
||||
<fs>
|
||||
|
|
@ -629,11 +739,18 @@
|
|||
<!-- Script name goes here -->
|
||||
<jar>projects_impact.py</jar>
|
||||
<!-- spark configuration options: I've taken most of them from an example from dhp workflows / Master value stolen from sandro -->
|
||||
<<<<<<< HEAD
|
||||
<spark-opts>
|
||||
--executor-memory=${sparkHighExecutorMemory}
|
||||
--executor-cores=${sparkExecutorCores}
|
||||
--driver-memory=${sparkNormalDriverMemory}
|
||||
--conf spark.sql.shuffle.partitions=${sparkShufflePartitions}
|
||||
=======
|
||||
<spark-opts>--executor-memory ${sparkHighExecutorMemory} --executor-cores ${sparkExecutorCores} --driver-memory ${sparkNormalDriverMemory}
|
||||
--master yarn
|
||||
--deploy-mode cluster
|
||||
--conf spark.sql.shuffle.partitions=7680
|
||||
>>>>>>> 4a905932a3db36c61570c24b9aa54283cd30abba
|
||||
--conf spark.extraListeners=${spark2ExtraListeners}
|
||||
--conf spark.sql.queryExecutionListeners=${spark2SqlQueryExecutionListeners}
|
||||
--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
|
||||
|
|
|
|||
Loading…
Reference in New Issue