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8172_impact_indicators_workflow #284

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miriam.baglioni merged 49 commits from 8172_impact_indicators_workflow into beta 2023-08-14 15:50:48 +02:00
Conversation 7 Commits 49 Files Changed 23 +187 -70
1 changed files with 187 additions and 70 deletions
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dhp-workflows/dhp-impact-indicators/src/main/resources/eu/dnetlib/dhp/oa/graph/impact_indicators/oozie_app/workflow.xml
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@ -46,6 +46,7 @@
<!-- Script name goes here -->
<jar>create_openaire_ranking_graph.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}
@ -56,10 +57,20 @@
--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 -->
<!-- The openaire graph data from which to read relations and objects -->
<arg>${openaireDataInput}</arg>
<!-- Year for filtering entries w/ larger values / empty -->
<!-- Year for filtering entries w/ larger values / empty -->
<arg>${currentYear}</arg>
<!-- number of partitions to be used on joins -->
<arg>${sparkShufflePartitions}</arg>
@ -68,14 +79,14 @@
<!-- This needs to point to the file on the hdfs i think -->
<file>${wfAppPath}/create_openaire_ranking_graph.py#create_openaire_ranking_graph.py</file>
</spark>
<!-- Do this after finishing okay -->
<ok to="end" />
<!-- Go there if we have an error -->
<error to="openaire-graph-error" />
</action>
</action>
<!-- Citation Count and RAM are calculated in parallel-->
<!-- Impulse Requires resources and will be run after-->
<fork name="non-iterative-rankings">
@ -83,7 +94,7 @@
<!-- <path start="spark-impulse"/> -->
<path start="spark-ram"/>
</fork>
<!-- CC here -->
<action name="spark-cc">
<!-- This is required as a tag for spark jobs, regardless of programming language -->
@ -96,12 +107,13 @@
<!-- using configs from an example on openaire -->
<master>yarn-cluster</master>
<mode>cluster</mode>
<!-- This is the name of our job -->
<name>Spark CC</name>
<!-- Script name goes here -->
<jar>CC.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}
@ -112,6 +124,16 @@
--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}
</spark-opts>
=======
<spark-opts>--executor-memory ${sparkHighExecutorMemory} --executor-cores ${sparkExecutorCores} --driver-memory ${sparkNormalDriverMemory}
--master yarn
--deploy-mode cluster
--conf spark.sql.shuffle.partitions=7680
--conf spark.extraListeners=${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>${openaireGraphInputPath}</arg>
<!-- number of partitions to be used on joins -->
@ -119,13 +141,13 @@
<!-- This needs to point to the file on the hdfs i think -->
<file>${wfAppPath}/CC.py#CC.py</file>
</spark>
<!-- Do this after finishing okay -->
<ok to="join-non-iterative-rankings" />
<!-- Go there if we have an error -->
<error to="cc-fail" />
</action>
</action>
<!-- IMPULSE here -->
<action name="spark-ram">
@ -135,16 +157,17 @@
<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 -->
<!-- using configs from an example on openaire -->
<master>yarn-cluster</master>
<mode>cluster</mode>
<!-- This is the name of our job -->
<name>Spark RAM</name>
<!-- Script name goes here -->
<jar>TAR.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}
@ -155,6 +178,16 @@
--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}
</spark-opts>
=======
<spark-opts>--executor-memory ${sparkHighExecutorMemory} --executor-cores ${sparkExecutorCores} --driver-memory ${sparkNormalDriverMemory}
--master yarn
--deploy-mode cluster
--conf spark.sql.shuffle.partitions=7680
--conf spark.extraListeners=${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>${openaireGraphInputPath}</arg>
<arg>${ramGamma}</arg>
@ -166,17 +199,17 @@
<!-- This needs to point to the file on the hdfs i think -->
<file>${wfAppPath}/TAR.py#TAR.py</file>
</spark>
<!-- Do this after finishing okay -->
<ok to="join-non-iterative-rankings" />
<!-- Go there if we have an error -->
<error to="ram-fail" />
</action>
</action>
<!-- JOIN NON-ITERATIVE METHODS AND THEN CONTINUE TO ITERATIVE ONES -->
<join name="join-non-iterative-rankings" to="spark-impulse"/>
<!-- IMPULSE here -->
<action name="spark-impulse">
<!-- This is required as a tag for spark jobs, regardless of programming language -->
@ -189,12 +222,13 @@
<!-- using configs from an example on openaire -->
<master>yarn-cluster</master>
<mode>cluster</mode>
<!-- This is the name of our job -->
<name>Spark Impulse</name>
<!-- Script name goes here -->
<jar>CC.py</jar>
<!-- spark configuration options: I've taken most of them from an example from dhp workflows / Master value stolen from sandro -->
<<<<<<< HEAD
<spark-opts>
--executor-memory=${sparkHighExecutorMemory}
--executor-cores=${sparkExecutorCores}
@ -205,6 +239,16 @@
--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}
</spark-opts>
=======
<spark-opts>--executor-memory ${sparkHighExecutorMemory} --executor-cores ${sparkExecutorCores} --driver-memory ${sparkNormalDriverMemory}
--master yarn
--deploy-mode cluster
--conf spark.sql.shuffle.partitions=7680
--conf spark.extraListeners=${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>${openaireGraphInputPath}</arg>
<!-- number of partitions to be used on joins -->
@ -213,13 +257,13 @@
<!-- This needs to point to the file on the hdfs i think -->
<file>${wfAppPath}/CC.py#CC.py</file>
</spark>
<!-- Do this after finishing okay -->
<ok to="iterative-rankings" />
<!-- Go there if we have an error -->
<error to="impulse-fail" />
</action>
</action>
<fork name="iterative-rankings">
<path start="spark-pagerank"/>
@ -234,7 +278,7 @@
<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>
<!-- we could add map-reduce configs here, but I don't know if we need them -->
<!-- This is the type of master-client configuration for running spark -->
<!-- <master>yarn-client</master> -->
@ -242,16 +286,17 @@
<!-- <master>local[*]</master> -->
<!-- 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 -->
<!-- <mode>client</mode> -->
<!-- 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>Spark Pagerank</name>
<!-- Script name goes here -->
<jar>PageRank.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}
@ -262,6 +307,16 @@
--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}
</spark-opts>
=======
<spark-opts>--executor-memory ${sparkHighExecutorMemory} --executor-cores ${sparkExecutorCores} --driver-memory ${sparkNormalDriverMemory}
--master yarn
--deploy-mode cluster
--conf spark.sql.shuffle.partitions=7680
--conf spark.extraListeners=${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>${openaireGraphInputPath}</arg>
<arg>${pageRankAlpha}</arg>
@ -273,14 +328,14 @@
<!-- This needs to point to the file on the hdfs i think -->
<file>${wfAppPath}/PageRank.py#PageRank.py</file>
</spark>
<!-- Do this after finishing okay -->
<ok to="join-iterative-rankings" />
<!-- Go there if we have an error -->
<error to="pagerank-fail" />
</action>
<!-- ATTRANK here -->
<action name="spark-attrank">
<!-- This is required as a tag for spark jobs, regardless of programming language -->
@ -289,16 +344,17 @@
<job-tracker>${jobTracker}</job-tracker>
<!-- This should give the machine/root of the hdfs, serafeim has provided a link with the required job properties -->
<name-node>${nameNode}</name-node>
<!-- using configs from an example on openaire -->
<!-- using configs from an example on openaire -->
<master>yarn-cluster</master>
<mode>cluster</mode>
<!-- This is the name of our job -->
<name>Spark AttRank</name>
<!-- Script name goes here -->
<jar>AttRank.py</jar>
<!-- spark configuration options: I've taken most of them from an example from dhp workflows / Master value stolen from sandro -->
<<<<<<< HEAD
<spark-opts>
--executor-memory=${sparkHighExecutorMemory}
--executor-cores=${sparkExecutorCores}
@ -309,6 +365,16 @@
--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}
</spark-opts>
=======
<spark-opts>--executor-memory ${sparkHighExecutorMemory} --executor-cores ${sparkExecutorCores} --driver-memory ${sparkNormalDriverMemory}
--master yarn
--deploy-mode cluster
--conf spark.sql.shuffle.partitions=7680
--conf spark.extraListeners=${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>${openaireGraphInputPath}</arg>
<arg>${attrankAlpha}</arg>
@ -325,17 +391,17 @@
<!-- This needs to point to the file on the hdfs i think -->
<file>${wfAppPath}/AttRank.py#AttRank.py</file>
</spark>
<!-- Do this after finishing okay -->
<ok to="join-iterative-rankings" />
<!-- Go there if we have an error -->
<error to="attrank-fail" />
</action>
</action>
<!-- JOIN ITERATIVE METHODS AND THEN END -->
<join name="join-iterative-rankings" to="get-file-names"/>
<!-- This will be a shell action that will output key-value pairs for output files -->
<action name="get-file-names">
@ -345,35 +411,35 @@
<job-tracker>${jobTracker}</job-tracker>
<!-- This should give the machine/root of the hdfs -->
<name-node>${nameNode}</name-node>
<!-- Exec is needed for shell commands - points to type of shell command -->
<exec>/usr/bin/bash</exec>
<!-- name of script to run -->
<argument>get_ranking_files.sh</argument>
<!-- We only pass the directory where we expect to find the rankings -->
<argument>/${workingDir}</argument>
<!-- the name of the file run -->
<file>${wfAppPath}/get_ranking_files.sh#get_ranking_files.sh</file>
<!-- Get the output in order to be usable by following actions -->
<capture-output/>
</shell>
<!-- Do this after finishing okay -->
<ok to="format-result-files" />
<!-- Go there if we have an error -->
<error to="filename-getting-error" />
</action>
<!-- Now we will run in parallel the formatting of ranking files for BiP! DB and openaire (json files) -->
<fork name="format-result-files">
<path start="format-bip-files"/>
<path start="format-json-files"/>
</fork>
<!-- Format json files -->
<!-- Two parts: a) format files b) make the file endings .json.gz -->
<action name="format-json-files">
@ -383,16 +449,17 @@
<job-tracker>${jobTracker}</job-tracker>
<!-- This should give the machine/root of the hdfs, serafeim has provided a link with the required job properties -->
<name-node>${nameNode}</name-node>
<!-- using configs from an example on openaire -->
<master>yarn-cluster</master>
<mode>cluster</mode>
<!-- This is the name of our job -->
<name>Format Ranking Results JSON</name>
<!-- Script name goes here -->
<jar>format_ranking_results.py</jar>
<!-- spark configuration options: I've taken most of them from an example from dhp workflows / Master value stolen from sandro -->
<<<<<<< HEAD
<spark-opts>
--executor-memory=${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}