you can also try using below,   CREATE TABLE my_first_table ( id BIGINT, name STRING, PRIMARY KEY(id) ) PARTITION BY HASH PARTITIONS 16 STORED AS KUDU TBLPROPERTIES ( 'kudu.master_addresses' = '<master1>[:port],<master2>[:port],<master3>[:port]' ); ... View more

hi,adar:     if both the WAL segments and the CFiles are copied duing a tablet copy,then the follower tablet will  alse flushing wal data to disk when growing up to 8M,in my opinion  there has no difference between master tablet and follower tablet during the reading and writing,is that right? ... View more

I have tested the backup/restore solution and seems to be working like charm with spark :  -First, check and record the names as given in the list of the kudu_master (or the primary elected master in case of multi masters )  http://Master1:8051/tables    -Download the kudu-backupX.X.jar in case you can't find it in /opt/cloudera/parcels/CDH-X.Xcdh.XX/lib/ and put it there  -In kuduMasterAddresses you put the name of your Kudu_master or the names of your three masters separated by ',' -Backup :  sudo -u hdfs spark2-submit --class org.apache.kudu.backup.KuduBackup  /opt/cloudera/parcels/CDH-X.Xcdh.XX/lib/kudu-backup2_2.11-1.13.0.jar --kuduMasterAddresses MASTER1(,MASTER2,..) --rootPath hdfs:///PATH_HDFS impala::DB.TABLE -COPY : sudo -u hdfs hadoop distcp -i - hdfs:///PATH_HDFS/DB.TABLE hdfs://XXX:8020/kudu_backups/ -Restore: sudo -u hdfs spark2-submit --class org.apache.kudu.backup.KuduRestore /opt/cloudera/parcels/CDH-X.Xcdh.XX/lib/kudu-backup2_2.11-1.13.0.jar --kuduMasterAddresses MASTER1(,MASTER2,..) --rootPath hdfs:///PATH_HDFS impala::DB.TABLE finally INVALIDATE METADATA  ... View more

Where can I change this 300 limit ... View more

Kudu is often bottlenecked by the speed at which it can flush data to disk. This usually corresponds to the number of data directories (and to maintenance_manager_num_threads). So certainly the more disks (and thus disk bandwidth) that Kudu has access to, the faster it can ingest data. If you reduce the number of partitions, you'll generally be reducing the overall ingest speed because you're reducing write parallelism. If your goal is to reduce ingest speed, then by all means explore reducing the number of partitions. ... View more

You can most certainly project more than one column at a time in an Impala query, be it from a table in Kudu or from HDFS. Based on your problem description, it almost sounds like a problem with your terminal, or with the impala-shell configuration. Have you looked at the impala-shell configuration options? Maybe something there can help solve the problem. ... View more

This error indicates that a Kudu tablet server has closed a scanner because it hadn't been accessed in some time. It could manifest on a particularly slow query that very infrequently fetches new data from Kudu.   Can you share the Impala query profile for one of the failed queries, as well as the Impala daemon coordinator log?   You can also work around the issue by reconfiguring Kudu's --scanner_ttl_ms flag to a much higher value (the default is 60s), though this will come at the potential cost of Kudu memory if any clients are orphaning their scanners. ... View more