I'm wondering if there is expected performance increases with using the
Also, I've been getting lock contention errors during export, and I'm wondering if these are less likely using --direct mode? E.g. I'm getting these sorts of exceptions on the sqoop console:
14/03/18 14:44:15 INFO mapred.JobClient: Task Id : attempt_201403180842_0202_m_000002_1, Status : FAILED java.io.IOException: Can't export data, please check failed map task logs at org.apache.sqoop.mapreduce.TextExportMapper.map(TextExportMapper.java:112) at org.apache.sqoop.mapreduce.TextExportMapper.map(TextExportMapper.java:39) at org.apache.hadoop.mapreduce.Mapper.run(Mapper.java:140) at org.apache.sqoop.mapreduce.AutoProgressMapper.run(AutoProgressMapper.java:64) at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:672) at org.apache.hadoop.mapred.MapTask.run(MapTask.java:330) at org.apache.hadoop.mapred.Child$4.run(Child.java:268) at java.security.AccessController.doPrivileged(Native Method) at javax.security.auth.Subject.doAs(Subject.java:415) at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1408) at org.apache.hadoop.mapred.Child.main(Child.java:262) Caused by: java.io.IOException: java.sql.BatchUpdateException: Deadlock found when trying to get lock; try restarting transaction at org.apache.sqoop.mapreduce.AsyncSqlRecordWriter.write(AsyncSqlRecordWr Thanks,
Rather than using the JDBC interface for transferring data, the direct mode delegates the job of transferring data to the native utilities provided by the database vendor. In the case of MySQL, the mysqldump and mysqlimport will be used for retrieving data from the database server or moving data back. Using native utilities will greatly improve performance, as they are optimized to provide the best possible transfer speed while putting less burden on the database server. That said, there are several limitations that come with this faster import. In the case of MySQL, each node hosting a TaskTracker service needs to have both mysqldump and mysqlimport utilities installed. Another limitation of the direct mode is that not all parameters are supported. As the native utilities usually produce text output, binary formats like SequenceFile or Avro won't work. Also, parameters that customize the escape characters, type mapping, column and row delimiters, or the NULL substitution string might not be supported in all cases.
Can you share your entire Sqoop command and the contents of failed task attempt attempt_201403180842_0202_m_000002_1?
On Thu, Mar 20, 2014 at 8:24 AM, Jason Rosenberg <[EMAIL PROTECTED]> wrote:
The lock contention exceptions seem to be retried, up to a point. If they happen too often, the hadoop job tracker eventually decides to kill the job. Even when the job succeeds, there are usually a few of these exceptions. I originally had much more mappers configured, but here, I've reduced it to 8, and this seems to help (although it's not a guarantee). Reducing the num-mappers also makes it less likely that the target mysql db machine will get overloaded (e.g. was getting maxed out on cpu usage with 64 mappers, etc.).
The number of records being sqooped is on the order of 1-5M at a time.
Sounds like I should try --direct mode (but not sure if the null/delimiters we're using will work with it?).
On Thu, Mar 20, 2014 at 12:46 PM, Kathleen Ting <[EMAIL PROTECTED]> wrote:
Generally speaking, you'll want to decrease num-mappers (default is 4) to lessen the load on the db (but ingest rate will decline) and you'll want to increase num-mappers to improve the ingest rate (but db load will be negatively impacted). Beyond that we can't give any recommendation with regards to the number of mappers as every environment is different. The main bottleneck in a Sqoop job is the shared database system. Sqoop can scale only to the extent that is allowed by the particular database. Furthermore, this can differ from table to table even within a single database system depending on what disks the particular import table uses.
The MySQL direct connector uses a native utility called mysqldump to perform a highly efficient data transfer between the MySQL server and Hadoop cluster. This utility unfortunately does not support using custom NULL substitution strings and will always import missing values as a string constant NULL. This is very confusing on the Hive side, as the Hive shell will display the value as NULL as well. It won't be perceived as a missing value, but as a valid string constant. You need to turn off direct mode (by omitting the --direct option) in order to override the default NULL substitution string.
On Tue, Mar 25, 2014 at 8:26 AM, Jason Rosenberg <[EMAIL PROTECTED]> wrote: