apache-spark - 读取配置单元表时spark抛出错误

Question

我正在尝试从 hive 中的 db.abc 中选择 *，这个 hive 表是使用 spark 加载的

它不起作用显示错误:

Error: java.io.IOException: java.lang.IllegalArgumentException: bucketId out of range: -1 (state=,code=0)

当我使用以下属性时，我能够查询配置单元:

set hive.mapred.mode=nonstrict;
set hive.optimize.ppd=true;
set hive.optimize.index.filter=true;
set hive.tez.bucket.pruning=true;
set hive.explain.user=false; 
set hive.fetch.task.conversion=none;

现在，当我尝试使用 spark 读取相同的配置单元表 db.abc 时，我收到如下错误:

Clients can access this table only if they have the following capabilities: CONNECTORREAD,HIVEFULLACIDREAD,HIVEFULLACIDWRITE,HIVEMANAGESTATS,HIVECACHEINVALIDATE,CONNECTORWRITE. This table may be a Hive-managed ACID table, or require some other capability that Spark currently does not implement; at org.apache.spark.sql.catalyst.catalog.CatalogUtils$.throwIfNoAccess(ExternalCatalogUtils.scala:280) at org.apache.spark.sql.hive.HiveTranslationLayerCheck$$anonfun$apply$1.applyOrElse(HiveTranslationLayerStrategies.scala:105) at org.apache.spark.sql.hive.HiveTranslationLayerCheck$$anonfun$apply$1.applyOrElse(HiveTranslationLayerStrategies.scala:85) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:289) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:289) at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70) at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:288) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:286) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:286) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306) at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187) at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304) at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:286) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:286) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:286) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306) at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187) at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304) at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:286) at org.apache.spark.sql.hive.HiveTranslationLayerCheck.apply(HiveTranslationLayerStrategies.scala:85) at org.apache.spark.sql.hive.HiveTranslationLayerCheck.apply(HiveTranslationLayerStrategies.scala:83) at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:87) at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:84) at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:124) at scala.collection.immutable.List.foldLeft(List.scala:84) at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:84) at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:76) at scala.collection.immutable.List.foreach(List.scala:392) at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:76) at org.apache.spark.sql.catalyst.analysis.Analyzer.org$apache$spark$sql$catalyst$analysis$Analyzer$$executeSameContext(Analyzer.scala:124) at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:118) at org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:103) at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:57) at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:55) at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:47) at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:74) at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:642) ... 49 elided

我需要在 spark-submit 或 shell 中添加任何属性吗？或者使用 spark 读取此 hive 表的替代方法是什么

hive 表示例格式:

  CREATE TABLE `hive``(                   |
|   `c_id` decimal(11,0),etc.........       
  ROW FORMAT SERDE                                   |
|   'org.apache.hadoop.hive.ql.io.orc.OrcSerde'      |
| WITH SERDEPROPERTIES (  
 STORED AS INPUTFORMAT                              |
|   'org.apache.hadoop.hive.ql.io.orc.OrcInputFormat'  |
| OUTPUTFORMAT                                       |
|   'org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat' |
 LOCATION                                           |
|  path= 'hdfs://gjuyada/bbts/scl/raw' |
| TBLPROPERTIES (                                    |
|   'bucketing_version'='2',                         |
|   'spark.sql.create.version'='2.3.2.3.1.0.0-78',   |
|   'spark.sql.sources.provider'='orc',              |
|   'spark.sql.sources.schema.numParts'='1',         |
|   'spark.sql.sources.schema.part.0'='{"type":"struct","fields":
[{"name":"Czz_ID","type":"decimal(11,0)","nullable":true,"metadata":{}},
{"name":"DzzzC_CD","type":"string","nullable":true,"metadata":{}},
{"name":"C0000_S_N","type":"decimal(11,0)","nullable":true,"metadata":{}},
{"name":"P_ _NB","type":"decimal(11,0)","nullable":true,"metadata":{}},
{"name":"C_YYYY","type":"string","nullable":true,"metadata":{}},"type":"string","nullable":true,"metadata":{}},{"name":"Cv_ID","type":"string","nullable":true,"metadata":{}},
|   'transactional'='true',                          |
|   'transient_lastDdlTime'='1574817059')  

Answer 1

您试图将 Transactional table (transactional = true) 读入 Spark 的问题。

Officially Spark not yet supported for Hive-ACID table, get a full dump/incremental dump of acid table to regular hive orc/parquet partitioned table then read the data using spark.

有一个 Open Jira saprk-15348 来添加对读取 Hive ACID 表的支持。

如果您在 Acid 表(来自 hive)上运行 major compaction 然后 spark 只能读取 base_XXX 目录，但不能读取此 jira 中提到的 Spark-16996 delta 目录。

有一些解决方法可以使用本链接中提到的 SPARK-LLAP 读取酸表。

我认为从 HDP-3.X HiveWareHouseConnector 开始的 能够支持读取 HiveAcid 表。

你可以创建一个事务表的snapshot non transactional 然后从表中读取数据。

create table <non_trans> stored as orc as select * from <transactional_table>

UPDATE:

1.创建外部hive表:

 CREATE external TABLE `<ext_tab_name>`(  
       <col_name>       <data_type>....etc
           )
    stored as orc
    location '<path>';

2.然后用现有的事务表数据覆盖到上面的外部表。

 insert overwrite table <ext_tab_name> select * from <transactional_tab_name>;