string(14) "127.0.0.1:9200"
URL: http://127.0.0.1:9200/likecs_art_db/_search<br>REQUEST:<pre>Array
(
    [query] => Array
        (
            [match] => Array
                (
                    [text] => Array
                        (
                            [query] => spark on yarn内存和CPU分配
                        )

                )

        )

    [highlight] => Array
        (
            [fields] => Array
                (
                    [text] => stdClass Object
                        (
                        )

                )

            [pre_tags] => #em#
            [post_tags] => #/em#
        )

    [size] => 8
    [from] => 0
)
</pre>RESPONSE:<pre>string(7473) "{"took":25,"timed_out":false,"_shards":{"total":1,"successful":1,"skipped":0,"failed":0},"hits":{"total":{"value":10000,"relation":"gte"},"max_score":49.521553,"hits":[{"_index":"likecs_art_db","_type":"_doc","_id":"44370","_score":49.521553,"_source":{"id":"44370","text":"spark on yarn\u5185\u5b58\u548cCPU\u5206\u914d","intro":"\u76ee\u5f55\n\nECharts\n\u5f02\u6b65\u52a0\u8f7d\n\n\n\nECharts\r\n\u6570\u636e\u53ef\u89c6\u5316\u5728\u8fc7\u53bb\u51e0\u5e74\u4e2d\u53d6\u5f97\u4e86\u5de8\u5927\u8fdb\u5c55\u3002\u5f00\u53d1\u4eba\u5458\u5bf9\u53ef\u89c6\u5316\u4ea7\u54c1\u7684\u671f\u671b\u4e0d\u518d\u662f\u7b80\u5355\u7684\u56fe\u8868\u521b\u5efa\u5de5\u5177\uff0c\u800c\u662f\u5728\u4ea4\u4e92\u3001\u6027\u80fd\u3001\u6570\u636e\u5904\u7406\u7b49\u65b9\u9762\u6709\u66f4\u9ad8\u7684\u8981\u6c42\u3002\r\nchart.setOption({\r\n    color: [\r\n        ","username":"yesecangqiong","tagsname":"spark on yarn|\u5185\u5b58|CPU|AppMaster|driver|executor","tagsid":"[\"10403\",\"3101\",\"5672\",17513,\"3174\",\"14819\"]","catesname":"","catesid":"[]","createtime":"1544948211"},"highlight":{"text":["#em#spark#/em# #em#on#/em# #em#yarn#/em##em#内#/em##em#存#/em##em#和#/em##em#CPU#/em##em#分#/em##em#配#/em#"]}},{"_index":"likecs_art_db","_type":"_doc","_id":"203312491","_score":42.679817,"_source":{"id":"203312491","text":"Spark On YARN\u5185\u5b58\u5206\u914d","intro":"\u76ee\u5f55\n\nECharts\n\u5f02\u6b65\u52a0\u8f7d\n\n\n\nECharts\r\n\u6570\u636e\u53ef\u89c6\u5316\u5728\u8fc7\u53bb\u51e0\u5e74\u4e2d\u53d6\u5f97\u4e86\u5de8\u5927\u8fdb\u5c55\u3002\u5f00\u53d1\u4eba\u5458\u5bf9\u53ef\u89c6\u5316\u4ea7\u54c1\u7684\u671f\u671b\u4e0d\u518d\u662f\u7b80\u5355\u7684\u56fe\u8868\u521b\u5efa\u5de5\u5177\uff0c\u800c\u662f\u5728\u4ea4\u4e92\u3001\u6027\u80fd\u3001\u6570\u636e\u5904\u7406\u7b49\u65b9\u9762\u6709\u66f4\u9ad8\u7684\u8981\u6c42\u3002\r\nchart.setOption({\r\n    color: [\r\n        ","username":"","tagsname":null,"tagsid":"","catesname":null,"catesid":"","createtime":"1636731701"},"highlight":{"text":["#em#Spark#/em# #em#On#/em# #em#YARN#/em##em#内#/em##em#存#/em##em#分#/em##em#配#/em#"]}},{"_index":"likecs_art_db","_type":"_doc","_id":"203312557","_score":35.57295,"_source":{"id":"203312557","text":"Spark On Yarn \u4e2dExecutor \u5185\u5b58\u5206\u914d\u7684\u673a\u5236","intro":"\u76ee\u5f55\n\nECharts\n\u5f02\u6b65\u52a0\u8f7d\n\n\n\nECharts\r\n\u6570\u636e\u53ef\u89c6\u5316\u5728\u8fc7\u53bb\u51e0\u5e74\u4e2d\u53d6\u5f97\u4e86\u5de8\u5927\u8fdb\u5c55\u3002\u5f00\u53d1\u4eba\u5458\u5bf9\u53ef\u89c6\u5316\u4ea7\u54c1\u7684\u671f\u671b\u4e0d\u518d\u662f\u7b80\u5355\u7684\u56fe\u8868\u521b\u5efa\u5de5\u5177\uff0c\u800c\u662f\u5728\u4ea4\u4e92\u3001\u6027\u80fd\u3001\u6570\u636e\u5904\u7406\u7b49\u65b9\u9762\u6709\u66f4\u9ad8\u7684\u8981\u6c42\u3002\r\nchart.setOption({\r\n    color: [\r\n        ","username":"","tagsname":null,"tagsid":"","catesname":null,"catesid":"","createtime":"1622892969"},"highlight":{"text":["#em#Spark#/em# #em#On#/em# #em#Yarn#/em# 中Executor #em#内#/em##em#存#/em##em#分#/em##em#配#/em#的机制"]}},{"_index":"likecs_art_db","_type":"_doc","_id":"203312567","_score":35.57295,"_source":{"id":"203312567","text":"Spark On Yarn \u4e2dExecutor \u5185\u5b58\u5206\u914d\u7684\u673a\u5236","intro":"\u76ee\u5f55\n\nECharts\n\u5f02\u6b65\u52a0\u8f7d\n\n\n\nECharts\r\n\u6570\u636e\u53ef\u89c6\u5316\u5728\u8fc7\u53bb\u51e0\u5e74\u4e2d\u53d6\u5f97\u4e86\u5de8\u5927\u8fdb\u5c55\u3002\u5f00\u53d1\u4eba\u5458\u5bf9\u53ef\u89c6\u5316\u4ea7\u54c1\u7684\u671f\u671b\u4e0d\u518d\u662f\u7b80\u5355\u7684\u56fe\u8868\u521b\u5efa\u5de5\u5177\uff0c\u800c\u662f\u5728\u4ea4\u4e92\u3001\u6027\u80fd\u3001\u6570\u636e\u5904\u7406\u7b49\u65b9\u9762\u6709\u66f4\u9ad8\u7684\u8981\u6c42\u3002\r\nchart.setOption({\r\n    color: [\r\n        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","username":"frankzye","tagsname":"","tagsid":"","catesname":"","catesid":"","createtime":"1631375796"},"highlight":{"text":["#em#YARN#/em#的#em#内#/em##em#存#/em##em#和#/em##em#CPU#/em##em#配#/em#置"]}},{"_index":"likecs_art_db","_type":"_doc","_id":"41197","_score":30.157894,"_source":{"id":"41197","text":"Spark-on-yarn","intro":"\u76ee\u5f55\n\nECharts\n\u5f02\u6b65\u52a0\u8f7d\n\n\n\nECharts\r\n\u6570\u636e\u53ef\u89c6\u5316\u5728\u8fc7\u53bb\u51e0\u5e74\u4e2d\u53d6\u5f97\u4e86\u5de8\u5927\u8fdb\u5c55\u3002\u5f00\u53d1\u4eba\u5458\u5bf9\u53ef\u89c6\u5316\u4ea7\u54c1\u7684\u671f\u671b\u4e0d\u518d\u662f\u7b80\u5355\u7684\u56fe\u8868\u521b\u5efa\u5de5\u5177\uff0c\u800c\u662f\u5728\u4ea4\u4e92\u3001\u6027\u80fd\u3001\u6570\u636e\u5904\u7406\u7b49\u65b9\u9762\u6709\u66f4\u9ad8\u7684\u8981\u6c42\u3002\r\nchart.setOption({\r\n    color: [\r\n        ","username":"dzqk","tagsname":"\u5927\u6570\u636e spark on 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","username":"","tagsname":null,"tagsid":"","catesname":null,"catesid":"","createtime":"1626284576"},"highlight":{"text":["#em#Spark#/em#-#em#On#/em#-#em#YARN#/em#"]}},{"_index":"likecs_art_db","_type":"_doc","_id":"203254645","_score":30.157894,"_source":{"id":"203254645","text":"Spark on Yarn","intro":"\u76ee\u5f55\n\nECharts\n\u5f02\u6b65\u52a0\u8f7d\n\n\n\nECharts\r\n\u6570\u636e\u53ef\u89c6\u5316\u5728\u8fc7\u53bb\u51e0\u5e74\u4e2d\u53d6\u5f97\u4e86\u5de8\u5927\u8fdb\u5c55\u3002\u5f00\u53d1\u4eba\u5458\u5bf9\u53ef\u89c6\u5316\u4ea7\u54c1\u7684\u671f\u671b\u4e0d\u518d\u662f\u7b80\u5355\u7684\u56fe\u8868\u521b\u5efa\u5de5\u5177\uff0c\u800c\u662f\u5728\u4ea4\u4e92\u3001\u6027\u80fd\u3001\u6570\u636e\u5904\u7406\u7b49\u65b9\u9762\u6709\u66f4\u9ad8\u7684\u8981\u6c42\u3002\r\nchart.setOption({\r\n    color: [\r\n        ","username":"","tagsname":null,"tagsid":"","catesname":null,"catesid":"","createtime":"1627649527"},"highlight":{"text":["#em#Spark#/em# #em#on#/em# #em#Yarn#/em#"]}}]}}"
</pre>string(14) "127.0.0.1:9200"
URL: http://127.0.0.1:9200/likecs_art_db/_search<br>REQUEST:<pre>Array
(
    [query] => Array
        (
            [match] => Array
                (
                    [text] => Array
                        (
                            [query] => spark on yarn内存和CPU分配
                        )

                )

        )

    [highlight] => Array
        (
            [fields] => Array
                (
                    [text] => stdClass Object
                        (
                        )

                )

            [pre_tags] => #em#
            [post_tags] => #/em#
        )

    [size] => 8
    [from] => 8
)
</pre>RESPONSE:<pre>string(6818) "{"took":22,"timed_out":false,"_shards":{"total":1,"successful":1,"skipped":0,"failed":0},"hits":{"total":{"value":10000,"relation":"gte"},"max_score":49.521553,"hits":[{"_index":"likecs_art_db","_type":"_doc","_id":"203312287","_score":30.157894,"_source":{"id":"203312287","text":"Spark on Yarn","intro":"\u76ee\u5f55\n\nECharts\n\u5f02\u6b65\u52a0\u8f7d\n\n\n\nECharts\r\n\u6570\u636e\u53ef\u89c6\u5316\u5728\u8fc7\u53bb\u51e0\u5e74\u4e2d\u53d6\u5f97\u4e86\u5de8\u5927\u8fdb\u5c55\u3002\u5f00\u53d1\u4eba\u5458\u5bf9\u53ef\u89c6\u5316\u4ea7\u54c1\u7684\u671f\u671b\u4e0d\u518d\u662f\u7b80\u5355\u7684\u56fe\u8868\u521b\u5efa\u5de5\u5177\uff0c\u800c\u662f\u5728\u4ea4\u4e92\u3001\u6027\u80fd\u3001\u6570\u636e\u5904\u7406\u7b49\u65b9\u9762\u6709\u66f4\u9ad8\u7684\u8981\u6c42\u3002\r\nchart.setOption({\r\n    color: [\r\n        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#em#yarn#/em#"]}},{"_index":"likecs_art_db","_type":"_doc","_id":"203312293","_score":30.157894,"_source":{"id":"203312293","text":"spark on yarn","intro":"\u76ee\u5f55\n\nECharts\n\u5f02\u6b65\u52a0\u8f7d\n\n\n\nECharts\r\n\u6570\u636e\u53ef\u89c6\u5316\u5728\u8fc7\u53bb\u51e0\u5e74\u4e2d\u53d6\u5f97\u4e86\u5de8\u5927\u8fdb\u5c55\u3002\u5f00\u53d1\u4eba\u5458\u5bf9\u53ef\u89c6\u5316\u4ea7\u54c1\u7684\u671f\u671b\u4e0d\u518d\u662f\u7b80\u5355\u7684\u56fe\u8868\u521b\u5efa\u5de5\u5177\uff0c\u800c\u662f\u5728\u4ea4\u4e92\u3001\u6027\u80fd\u3001\u6570\u636e\u5904\u7406\u7b49\u65b9\u9762\u6709\u66f4\u9ad8\u7684\u8981\u6c42\u3002\r\nchart.setOption({\r\n    color: [\r\n        ","username":"","tagsname":null,"tagsid":"","catesname":null,"catesid":"","createtime":"1617864299"},"highlight":{"text":["#em#spark#/em# #em#on#/em# #em#yarn#/em#"]}},{"_index":"likecs_art_db","_type":"_doc","_id":"203312296","_score":30.157894,"_source":{"id":"203312296","text":"Spark on YARN","intro":"\u76ee\u5f55\n\nECharts\n\u5f02\u6b65\u52a0\u8f7d\n\n\n\nECharts\r\n\u6570\u636e\u53ef\u89c6\u5316\u5728\u8fc7\u53bb\u51e0\u5e74\u4e2d\u53d6\u5f97\u4e86\u5de8\u5927\u8fdb\u5c55\u3002\u5f00\u53d1\u4eba\u5458\u5bf9\u53ef\u89c6\u5316\u4ea7\u54c1\u7684\u671f\u671b\u4e0d\u518d\u662f\u7b80\u5355\u7684\u56fe\u8868\u521b\u5efa\u5de5\u5177\uff0c\u800c\u662f\u5728\u4ea4\u4e92\u3001\u6027\u80fd\u3001\u6570\u636e\u5904\u7406\u7b49\u65b9\u9762\u6709\u66f4\u9ad8\u7684\u8981\u6c42\u3002\r\nchart.setOption({\r\n    color: [\r\n        ","username":"","tagsname":null,"tagsid":"","catesname":null,"catesid":"","createtime":"1617350443"},"highlight":{"text":["#em#Spark#/em# #em#on#/em# #em#YARN#/em#"]}},{"_index":"likecs_art_db","_type":"_doc","_id":"203312300","_score":30.157894,"_source":{"id":"203312300","text":"Spark On 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                        <h1 class="page-title"><a href="/show-44370.html" title="spark on yarn内存和CPU分配">spark on yarn内存和CPU分配</a></h1>
            
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            <footer class="tc"> yesecangqiong 
                <time class="post-date" datetime="2018-12-16">2018-12-16</time>
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<p>以spark1.6为例，使用内存和CPU的无外乎三个:appMaster、driver、executor，下面分别分析spark on yarn的client与cluster模式下的内存和CPU分配<br>一、vcores<br>1、driver核数:<br>	client模式:无<br>	cluster模式:spark.driver.cores=1(默认)<br>2、AppMaster核数:<br>	client模式:spark.yarn.am.cores=1(默认)<br>	cluster模式:spark.driver.cores=1(默认)<br>3、executor核数:<br>	spark.executor.cores=1(默认)<br>4、executor内每个task使用的核数:<br>	spark.task.cpus=1(默认)<br>5、每个executor内能够并行运行的task数:<br>	spark.executor.cores / spark.task.cpus<br>6、yarn上启动executor的个数:<br>	SPARK_EXECUTOR_INSTANCES/spark.executor.instances=2(默认)<br>7、driver核数 + executor核数 * executor个数 + AppMaster核数(client模式) = 要向yarn申请的总的vcores，<br>	具体yarn给多少颗vcores，类似于mapreduce on yarn的情况,参考文章:<br>	https://www.cnblogs.com/yesecangqiong/p/6274427.html<br>注意---------------------------<br>	1、:spark.driver.cores只能在cluster模式下使用<br>	2、spark.executor.instances与spark.dynamicAllocation.enabled不能共同使用，如果两个都做了配置，spark.executor.instances有效<br>	3、要向使spark.task.cpus&gt;1时起作用，还应该保证yarn的配置文件"capacity-scheduler.xml"中的配置选项"yarn.scheduler.capacity.resource-calculator"<br>		的值为:"org.apache.hadoop.yarn.util.resource.DominantResourceCalculator",而不应该是默认的"org.apache.hadoop.yarn.util.resource.DefaultResourceCalculator"<br>		可以参考文章:https://www.cnblogs.com/yesecangqiong/p/10125333.html</p>
<p>二、内存<br>1、driver需要申请的内存 = 基本内存 + 额外内存<br>	基本内存:<br>		spark.driver.memory=1g(默认)<br>	额外内存:<br>		1、有参数配置则直接等于参数配置<br>			spark.yarn.driver.memoryOverhead<br>		2、无参数配置(即默认)<br>			max(driver基本内存 * 0.1,384M)<br>2、appMster需要申请的内存 = 基本内存 + 额外内存(memoryOverhead)<br>	基本内存:<br>		client模式:spark.yarn.am.memory=512MB(默认)<br>		cluster模式:appMaster与driver运行于同一个JVM(yarn上的同一container),决定于:spark.driver.memory=1g(默认)<br>	额外内存:<br>		1、有参数配置则直接等于参数配置<br>			client模式:spark.yarn.am.memoryOverhead<br>			cluster模式:spark.yarn.driver.memoryOverhead<br>		2、没有配置相应参数(即默认):<br>			yarn-client模式:max(AppMaster基本内存 * 0.1,384M)<br>			yarn-cluster模式:max(Driver基本内存 * 0.1,384M)<br>3、executor需要申请的内存 = 基本内存 + 额外内存<br>	基本内存:<br>		spark.executor.memory=1g(默认)<br>	额外内存:<br>		1、有参数配置则直接等于参数配置<br>			spark.yarn.executor.memoryOverhead<br>		2、无参数配置(即默认)<br>			max(executor基本内存 * 0.1,484M)<br>4、driver内存 + executor内存 * executor个数 + AppMaster内存(client模式) = 要向yarn申请的总的内存，<br>	具体yarn给多少内存，类似于mapreduce on yarn的情况,参考文章:<br>	https://www.cnblogs.com/yesecangqiong/p/6274427.html<br>注意--------------------------------:<br>	在client模式下，spark.driver.memory不能由代码中的SparkConf配置指定，只能通过配置文件/运行脚本中指定</p>
<p>三、参考官方文档:<br>	http://spark.apache.org/docs/1.6.0/running-on-yarn.html#configuration<br>	http://spark.apache.org/docs/1.6.0/configuration.html</p>
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