Question

这是我用来将数据从SQL Server导入到Hive的sqoop命令 sqoop-import-all-tables --connect "jdbc:sqlserver://ip.ip.ip.ip\MIGERATIONSERVER;port=1433;username=sa;password=blablaq;database=sqlserverdb" --create-hive-table --hive-import --hive-database hivemtdb
问题是sqlserverdb有大约100个表但是当我发出这个命令时，它只是将6或7个随机表导入到hive。这种行为对我来说真的很奇怪。我无法找到我在做错的地方。
编辑：1

Warning: /usr/hdp/2.4.3.0-227/accumulo does not exist! Accumulo imports will fail.
Please set $ACCUMULO_HOME to the root of your Accumulo installation.
16/10/13 13:17:38 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6.2.4.3.0-227
16/10/13 13:17:38 INFO tool.BaseSqoopTool: Using Hive-specific delimiters for output. You can override
16/10/13 13:17:38 INFO tool.BaseSqoopTool: delimiters with --fields-terminated-by, etc.
16/10/13 13:17:38 INFO manager.SqlManager: Using default fetchSize of 1000
16/10/13 13:17:38 INFO tool.CodeGenTool: Beginning code generation
16/10/13 13:17:38 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM [UserMessage] AS t WHERE 1=0
16/10/13 13:17:38 INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /usr/hdp/2.4.3.0-227/hadoop-mapreduce
Note: /tmp/sqoop-sherry/compile/c809ee201c0aec1edf2ed5a1ef4aed4c/UserMessage.java uses or overrides a deprecated API.
Note: Recompile with -Xlint:deprecation for details.
16/10/13 13:17:39 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-sherry/compile/c809ee201c0aec1edf2ed5a1ef4aed4c/UserMessage.jar
16/10/13 13:17:39 INFO mapreduce.ImportJobBase: Beginning import of UserMessage
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/usr/hdp/2.4.3.0-227/hadoop/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/usr/hdp/2.4.3.0-227/zookeeper/lib/slf4j-log4j12-1.6.1.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
16/10/13 13:17:40 INFO impl.TimelineClientImpl: Timeline service address: http://machine-02-xx:8188/ws/v1/timeline/
16/10/13 13:17:40 INFO client.RMProxy: Connecting to ResourceManager at machine-02-xx/xxx.xx.xx.xx:8050
16/10/13 13:17:42 INFO db.DBInputFormat: Using read commited transaction isolation
16/10/13 13:17:42 INFO mapreduce.JobSubmitter: number of splits:1
16/10/13 13:17:42 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1475746531098_0317
16/10/13 13:17:43 INFO impl.YarnClientImpl: Submitted application application_1475746531098_0317
16/10/13 13:17:43 INFO mapreduce.Job: The url to track the job: http://machine-02-xx:8088/proxy/application_1475746531098_0317/
16/10/13 13:17:43 INFO mapreduce.Job: Running job: job_1475746531098_0317
16/10/13 13:17:48 INFO mapreduce.Job: Job job_1475746531098_0317 running in uber mode : false
16/10/13 13:17:48 INFO mapreduce.Job:  map 0% reduce 0%
16/10/13 13:17:52 INFO mapreduce.Job:  map 100% reduce 0%
16/10/13 13:17:52 INFO mapreduce.Job: Job job_1475746531098_0317 completed successfully
16/10/13 13:17:52 INFO mapreduce.Job: Counters: 30
        File System Counters
                FILE: Number of bytes read=0
                FILE: Number of bytes written=156179
                FILE: Number of read operations=0
                FILE: Number of large read operations=0
                FILE: Number of write operations=0
                HDFS: Number of bytes read=87
                HDFS: Number of bytes written=0
                HDFS: Number of read operations=4
                HDFS: Number of large read operations=0
                HDFS: Number of write operations=2
        Job Counters
                Launched map tasks=1
                Other local map tasks=1
                Total time spent by all maps in occupied slots (ms)=3486
                Total time spent by all reduces in occupied slots (ms)=0
                Total time spent by all map tasks (ms)=1743
                Total vcore-seconds taken by all map tasks=1743
                Total megabyte-seconds taken by all map tasks=2677248
        Map-Reduce Framework
                Map input records=0
                Map output records=0
                Input split bytes=87
                Spilled Records=0
                Failed Shuffles=0
                Merged Map outputs=0
                GC time elapsed (ms)=30
                CPU time spent (ms)=980
                Physical memory (bytes) snapshot=233308160
                Virtual memory (bytes) snapshot=3031945216
                Total committed heap usage (bytes)=180879360
        File Input Format Counters
                Bytes Read=0
        File Output Format Counters
                Bytes Written=0
16/10/13 13:17:52 INFO mapreduce.ImportJobBase: Transferred 0 bytes in 12.6069 seconds (0 bytes/sec)
16/10/13 13:17:52 INFO mapreduce.ImportJobBase: Retrieved 0 records.
16/10/13 13:17:52 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM [UserMessage] AS t WHERE 1=0
16/10/13 13:17:52 WARN hive.TableDefWriter: Column SendDate had to be cast to a less precise type in Hive
16/10/13 13:17:52 INFO hive.HiveImport: Loading uploaded data into Hive

Logging initialized using configuration in jar:file:/usr/hdp/2.4.3.0-227/hive/lib/hive-common-1.2.1000.2.4.3.0-227.jar!/hive-log4j.properties
OK
Time taken: 1.286 seconds
Loading data to table sqlcmc.usermessage
Table sqlcmc.usermessage stats: [numFiles=1, totalSize=0]
OK
Time taken: 0.881 seconds
Note: /tmp/sqoop-sherry/compile/c809ee201c0aec1edf2ed5a1ef4aed4c/DadChMasConDig.java uses or overrides a deprecated API.
Note: Recompile with -Xlint:deprecation for details.

Logging initialized using configuration in jar:file:/usr/hdp/2.4.3.0-227/hive/lib/hive-common-1.2.1000.2.4.3.0-227.jar!/hive-log4j.properties
OK

Answer 1

首先，import-all-tables将为所有表运行导入表。

如果您没有定义作业中的映射器数量，Sqoop将默认选择4个映射器。因此，它需要表具有主键或您指定--split-by列名称。

如果是这种情况，您会看到如下错误：

错误tool.ImportAllTablesTool：导入时出错：无法找到表测试的主键。请使用--split-by指定一个，或使用'-m 1'执行顺序导入。

因此，您可以使用1个映射器，这将使您的导入过程变慢。

更好的方法是添加--autoreset-to-one-mapper，它将使用主键导入具有命令中提到的映射器数量的表，并且它将自动为没有主键的表使用1个映射器。

来你的问题，

表格DadChMasConDig 的

sqoop导入失败。

我不知道它为什么没有登录控制台。

在导入此表时，可能会出现例如
的异常

运行导入作业遇到IOException：java.io.IOException：Hive不支持列<somecolumn>的SQL类型

例如，不支持varbinary。

如果仅在HDFS中导入数据，那应该不是问题。你可以尝试：

sqoop-import-all-tables --connect "jdbc:sqlserver://ip.ip.ip.ip\MIGERATIONSERVER;port=1433;username=sa;password=blablaq;database=sqlserverdb"

Answer 2

我遇到了同样的问题，以下内容对我有用。尽管通常--create-hive-table和--hive-overwrite不能一起使用，也不能一起使用。但是没有其他组合有效，每次仅导入10个表格中的3个或一部分表格

 sqoop import-all-tables \
       --connect jdbc:mysql://<mysql-url>/my_database \
       --username sql_user \
       --password sql_pwd \
       --hive-import \
       --hive-database test_hive \
       --hive-overwrite \
       --create-hive-table \
       --warehouse-dir /apps/hive/warehouse/test_hive.db \
       -m 1

Sqoop“import-all-tables”无法导入所有表

2 个答案: