我們采用亞馬遜emr構建的集群，用hive查詢的時候報錯，FAILED: Execution Error, return code 2 from org.apache.hadoop.hive.ql.exec.tez.TezTask，查看了下面的參數，挺有幫助的 我是設置了這個參數set hive.tez.auto.reducer.parallelism=true;
Tez內存優化
1、AM、Container大小設置
tez.am.resource.memory.mb
參數說明：Set tez.am.resource.memory.mb tobe the same as yarn.scheduler.minimum-allocation-mb the YARNminimum container size.
hive.tez.container.size
參數說明：Set hive.tez.container.size to be the same as or a small multiple(1 or 2 times that) of YARN container size yarn.scheduler.minimum-allocation-mb but NEVER more than yarn.scheduler.maximum-allocation-mb.
2、AM、Container JVM參數設置
tez.am.launch.cmd-opts
默認值：80%*tez.am.resource.memory.mb
參數說明：一般不需要調整
hive.tez.java.ops
默認值：80%*hive.tez.container.size
參數說明：Hortonworks建議“–server –Djava.net.preferIPv4Stack=true–XX:NewRatio=8 –XX:+UseNUMA –XX:UseG1G”
tez.container.max.java.heap.fraction
默認值：0.8
參數說明：task\AM占用JVM Xmx的比例，該參數建議調整，需根據具體業務情況修改；
3、Hive內存Map Join參數設置
tez.runtime.io.sort.mb
默認值：100
參數說明：輸出排序需要的內存大小。建議值：40%*hive.tez.container.size，一般不超過2G；
hive.auto.convert.join.noconditionaltask
默認值：true
參數說明：是否將多個mapjoin合並為一個，使用默認值
hive.auto.convert.join.noconditionaltask.size
默認值：
參數說明：多個mapjoin轉換為1個時，所有小表的文件大小總和的最大值，這個值只是限制輸入的表文件的大小，並不代表實際mapjoin時hashtable的大小。 建議值：1/3* hive.tez.container.size
tez.runtime.unordered.output.buffer.size-mb
默認值：100
參數說明：Size of the buffer to use if not writing directly to disk.。 建議值：10%* hive.tez.container.size
4、Container重用設置
tez.am.container.reuse.enabled
默認值：true
參數說明：Container重用開關
Mapper/Reducer優化
1、Mapper數設置
默認值：50*1024*1024
參數說明：Lower bound on thesize (in bytes) of a grouped split, to avoid generating too many small splits.
tez.grouping.max-size
默認值：1024*1024*1024
參數說明：Upper bound on thesize (in bytes) of a grouped split, to avoid generating excessively largesplits.
;
2、Reducer數設置
hive.tez.auto.reducer.parallelism
默認值：false
參數說明：Turn on Tez' autoreducer parallelism feature. When enabled, Hive will still estimate data sizesand set parallelism estimates. Tez will sample source vertices' output sizesand adjust the estimates at runtime as necessary.
建議設置為true.
hive.tex.min.partition.factor
默認值：0.25
參數說明：When auto reducerparallelism is enabled this factor will be used to put a lower limit to thenumber of reducers that Tez specifies.
hive.tez.max.partition.factor
默認值：2.0
參數說明：When auto reducerparallelism is enabled this factor will be used to over-partition data inshuffle edges.
hive.exec.reducers.bytes.per.reducer
默認值：256,000,000
參數說明：Sizeper reducer. The default in Hive 0.14.0 and earlier is 1 GB, that is, if theinput size is 10 GB then 10 reducers will be used. In Hive 0.14.0 and later thedefault is 256 MB, that is, if the input size is 1 GB then 4 reducers willbe used.
以下公式確認Reducer個數：
Max(1, Min(hive.exec.reducers.max [1009], ReducerStage estimate/hive.exec.reducers.bytes.per.reducer))x hive.tez.max.partition.factor [2]
3、Shuffle參數設置
tez.shuffle-vertex-manager.min-src-fraction
默認值：0.25
參數說明：thefraction of source tasks which should complete before tasks for the currentvertex are scheduled.
tez.shuffle-vertex-manager.max-src-fraction
默認值：0.75
參數說明：oncethis fraction of source tasks have completed, all tasks on the current vertexcan be scheduled. Number of tasks ready for scheduling on the current vertexscales linearly between min-fraction and max-fraction.
例子：
hive.exec.reducers.bytes.per.reducer=1073741824;// 1gb
tez.shuffle-vertex-manager.min-src-fraction=0.25；
tez.shuffle-vertex-manager.max-src-fraction=0.75；
This indicates thatthe decision will be made between 25% of mappers finishing and 75% of mappersfinishing, provided there's at least 1Gb of data being output (i.e if 25% ofmappers don't send 1Gb of data, we will wait till at least 1Gb is sent out).
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