spark的资源调整参数

野性酷女 2024-03-02 08:52 9阅读 0赞

–基础资源

set spark.driver.memory=15g;
    set spark.driver.cores=3;
    set spark.driver.memoryOverhead=4096;
    set spark.executor.memory=5G;
    set spark.executor.memoryOverhead=1024;
    set spark.executor.cores=2;
    set spark.vcore.boost.ratio=2;

–动态executor申请

set spark.dynamicAllocation.minExecutors=10;
    set spark.dynamicAllocation.maxExecutors=300;

–ae，shuffle partition并行度

set spark.sql.adaptive.minNumPostShufflePartitions=10;
    set spark.sql.adaptive.maxNumPostShufflePartitions=1000;

–268435456;

set spark.sql.adaptive.shuffle.targetPostShuffleInputSize=536870912;

–开启parquet切分

set spark.sql.parquet.adaptiveFileSplit=true;

–初始task调节，合并小文件

set spark.sql.files.maxPartitionBytes=536870912;

中型任务  
目前测试：在不手动添加任何参数、平均时长在90min以内、单个shuffle 量在2T以下的任务可以使用该模版，但实际任务情况还需跟踪观察。  
spark.executor.memoryOverhead 每个executor的堆外内存大小，堆外内存主要用于数据IO，对于报堆外OOM的任务要适当调大，单位Mb，与之配合要调大executor JVM参数，例如：set spark.executor.memoryOverhead=3072  
set spark.executor.extraJavaOptions=-XX:MaxDirectMemorySize=2560m

–基础资源

set spark.driver.memory=25g;
    set spark.driver.cores=4;
    set spark.driver.memoryOverhead=5120;
    set spark.executor.memory=10G;
    set spark.executor.memoryOverhead=4096;
    set spark.executor.cores=3;
    set spark.vcore.boost.ratio=1;

–动态executor申请

set spark.dynamicAllocation.minExecutors=10;
    set spark.dynamicAllocation.maxExecutors=600;

–AQE

set spark.sql.adaptive.minNumPostShufflePartitions=10;
    set spark.sql.adaptive.maxNumPostShufflePartitions=1000;
    set spark.sql.adaptive.shuffle.targetPostShuffleInputSize= 536870912;

–开启parquet切分，初始task调节，合并小文件

set spark.sql.parquet.adaptiveFileSplit=true;
    set spark.sql.files.maxPartitionBytes=536870912;

–推测

set spark.speculation.multiplier=2.5;
    set spark.speculation.quantile=0.8;

–shuffle 落地hdfs

set spark.shuffle.hdfs.enabled=true;
    set spark.shuffle.io.maxRetries=1;
    set spark.shuffle.io.retryWait=0s;

大型任务  
目前测试：在不手动添加任何参数、平均时长在120min以内、单个shuffle 量在10T以下的任务可以使用该模版，但实际任务情况还需跟踪观察。

–基础资源

set spark.driver.memory=25g;
    set spark.driver.cores=4;
    set spark.driver.memoryOverhead=5120;
    set spark.executor.memory=15G;
    set spark.executor.memoryOverhead=3072;
    set spark.executor.cores=3;
    set spark.vcore.boost.ratio=1;

–动态executor申请

set spark.dynamicAllocation.minExecutors=10;
    set spark.dynamicAllocation.maxExecutors=900;

–ae

set spark.sql.adaptive.minNumPostShufflePartitions=10;
    set spark.sql.adaptive.maxNumPostShufflePartitions=3000;
    set spark.sql.adaptive.shuffle.targetPostShuffleInputSize= 536870912;

–shuffle 落地hdfs

set spark.shuffle.hdfs.enabled=true;
    set spark.shuffle.io.maxRetries=1;
    set spark.shuffle.io.retryWait=0s;

–开启parquet切分，合并小文件

set spark.sql.parquet.adaptiveFileSplit=true;
    set spark.sql.files.maxPartitionBytes=536870912;

–推测

set spark.speculation.multiplier=2.5;
    set spark.speculation.quantile=0.9;

超大型任务  
目前测试：在不手动添加任何参数、平均时长大于120min、单个shuffle 量在10T以上的任务可以使用该模版，但实际任务情况还需跟踪观察。

–基础资源

set spark.driver.memory=30g;
    set spark.driver.cores=4;
    set spark.driver.memoryOverhead=5120;
    set spark.executor.memory=20G;
    set spark.executor.memoryOverhead= 5120;
    set spark.executor.cores=5;
    set spark.vcore.boost.ratio=1;

–动态executor申请

set spark.dynamicAllocation.minExecutors=10;
    set spark.dynamicAllocation.maxExecutors=1500;

–ae

set spark.sql.adaptive.minNumPostShufflePartitions=10;
    set spark.sql.adaptive.maxNumPostShufflePartitions=7000;
    set spark.sql.adaptive.shuffle.targetPostShuffleInputSize= 536870912;

–开启parquet切分,合并小文件

set spark.sql.parquet.adaptiveFileSplit=true;
    set spark.sql.files.maxPartitionBytes=536870912;

– shuffle 落地 hdfs，shuffle文件上传hdfs

set spark.shuffle.hdfs.enabled=true;
    set spark.shuffle.io.maxRetries=1;
    set spark.shuffle.io.retryWait=0s;

–推测

set spark.speculation.multiplier=2.5;
    set spark.speculation.quantile=0.9;

其他常用参数  
–ae hash join

set spark.sql.adaptive.hashJoin.enabled=true;
    set spark.sql.adaptiveHashJoinThreshold=52428800;

–输出文件合并 byBytes,该功能会生成两个stage，  
–第一个stage shuffle的数据量来预估最后生成到hdfs上的文件数据量大小，  
–并通过预估的文件数据量大小计算第二个stage的并行度，即最后生成的文件个数。  
–该功能只能控制生成的文件大小尽量接近spark.merge.files.byBytes.fileBytes，且有一定的性能损耗，需根据实测情况选择使用。  
– 最终文件数量：(totalBytes / fileBytes / compressionRatio).toInt + 1

set spark.merge.files.byBytes.enabled=true;
    set spark.merge.files.byBytes.repartitionNumber=100;

–第一个stage的并行读  
set spark.merge.files.byBytes.fileBytes=134217728;  
– 预期的文件大小  
set spark.merge.files.byBytes.compressionRatio=3;  
– 压缩比，shuffle文件和最后生成的文件格式和压缩格式都不相同，因此通过该参数调节  
–输出文件合并 该功能会在原来job的最后一个stage后面增加1个stage来控制最后生成的文件数量,  
–对于动态分区，每个分区生成spark.merge.files.number个文件。

spark.merge.files.enabled=true            
    spark.merge.files.number=512

–skew\_join 解析绕过tqs

set tqs.analysis.skip.hint=true;

–初始task上限

set spark.sql.files.openCostInBytes=4194304;
    set spark.datasource.splits.max=20000;

–broadcast时间

set spark.sql.broadcastTimeout = 3600;

–（防止get json报错）

set spark.sql.mergeGetMapValue.enabled=true;

–ae 倾斜处理 HandlingSkewedJoin OptimizeSkewedJoin

set spark.sql.adaptive.allowBroadcastExchange.enabled=true;
    set spark.sql.adaptive.hashJoin.enabled=false;
    set spark.sql.adaptive.skewedPartitionFactor=3;

– 某partition数据量大于中位数的3倍，判定为倾斜

set spark.sql.adaptive.skewedPartitionMaxSplits=20;

– 限制某一partition最多拆分多少分，spark3已失效

set spark.sql.adaptive.skewedJoin.enabled=true;

– Normal Join Pattern的优化开关

set spark.sql.adaptive.skewedJoinWithAgg.enabled=true;

– JoinWithAgg Pattern的优化开关，非开源版

set spark.sql.adaptive.multipleSkewedJoin.enabled=true;

– MultipleJoin Pattern的优化开关，非开源版

set spark.shuffle.highlyCompressedMapStatusThreshold=20000;

– 分区数大于20000时 使用HighlyCompressedMapStatus统计每个partition数据量，会降低数据统计进度

–并发读文件

set spark.sql.concurrentFileScan.enabled=true;

–filter按比例读取文件  
set spark.sql.files.tableSizeFactor=\{table\_name\}:\{filter 比例\};

set spark.sql.files.tableSizeFactor=dm_content.tcs_task_dict:10;

–AM failed 时长

set spark.yarn.am.waitTime=200s;

–shuffle service 超时设置

set spark.shuffle.registration.timeout=12000;
    set spark.shuffle.registration.maxAttempts=5;

–parquet index 特性生效，in 条件的个数

set spark.sql.parquet.pushdown.inFilterThreshold=30;

–设置engine

set tqs.query.engine.type=sparkcli;

–hive metastore 超时

spark.hadoop.hive.metastore.client.socket.timeout=600

–manta备用

spark.sql.adaptive.maxNumPostShufflePartitions 5000
    spark.executor.memoryOverhead 8000
    spark.sql.adaptive.shuffle.targetPostShuffleInputSize 536870912