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03-15-2016
02:45 PM
2 Kudos
http://repo.hortonworks.com/content/groups/public/org/apache/hive/hive-hbase-handler/1.2.1.2.3.4.0-3485/hive-hbase-handler-1.2.1.2.3.4.0-3485.jar
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03-09-2016
04:06 PM
5 Kudos
Yes, this should work fine.
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03-03-2016
05:51 PM
3 Kudos
HDFS does not have any Thrift services. You can find the HBase Thrift definitions at https://github.com/hortonworks/hbase-release/tree/HDP-2.3.0.0-tag/hbase-thrift/src/main/resources/org/apache/hadoop/hbase
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02-22-2016
09:52 PM
1 Kudo
It looks like SQuirreL is trying to instantiate the Phoenix JDBC driver, but I'm not sure with the information you provided why it's hanging. Is it possible to increase the log level of SQuirreL to see if there is some useful messages coming out of the HBase/Phoenix libraries?
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02-10-2016
12:16 AM
5 Kudos
As long as the networks are routable, this should be functional to not co-locate HDFS and HBase (ZooKeeper gets scary, consider all of the following to apply only to HDFS and HBase). I believe writes will only be slower with respect to the underlying network connection. Each write will still be bound by the sync of the slowest of the three datanodes hosting the replicas for the block, so, I don't think you'll pay a much larger penalty here. Reads, however, will likely be noticeably slower. HBase largely expects to take advantage of a feature in HDFS known as "Short Circuit Reads". This features takes advantage of the local resources that the Datanode and the RegionServer share with a shared memory segment and a Unix domain socket. This avoids a TCP socket when the local RegionServer can read data from the local DataNode. I'm sure there are performance numbers out there floating around (I don't recall specifics), but this is a noticeable performance gain when short-circuit reads can be used (and is why many metrics often consider RegionServer locality to the Regions' data it is hosting).
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02-08-2016
09:26 PM
1 Kudo
Ah, sorry, I missed that detail. I thought you were just doing a normal get on the column.
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02-08-2016
08:18 PM
1 Kudo
Enis was saying that Phoenix expects the value of your FLOAT column to be serialized using https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/util/Bytes.html#toBytes%28float%29. The "ascii" representation of the float which you have in the value is not what Phoenix is expecting and, thus, is parsing it to a value that you do not expect.
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02-08-2016
03:27 PM
1 Kudo
Lots of good comments here too, but also consider: 1. Reducing the swappiness value (10 is a safe bet) http://askubuntu.com/questions/103915/how-do-i-configure-swappiness 2. Disabling transparent huge pages (THP) https://access.redhat.com/solutions/46111 Both of these OS-level properties can exhibit the same symptoms as you would see when running out of memory in the JVM.
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02-03-2016
09:42 PM
3 Kudos
HBase regions are defined by a start and end rowkey. Only data that falls within that range is stored in that region. I would guess that you are constantly inserting new data into the beginning of each "bucket" (defined by your 2 salt characters). Eventually, this region will get so large, that it will split into two daughters. One will contain the "top" half of the data and the other the "bottom". After that, all of the new data will be inserted into the "upper" daughter region. Then, the process repeats. (the same analogy works if you're appending data into the "buckets" instead of inserting at the head). You likely will want to use the `merge_region` HBase shell command to merge away some of the older regions. You could also set the split threshold very high and prevent any future splits, but this would likely have a negative impact on performance (or you would need to drastically increase the number of buckets -- more salt digits -- to spread out the load evenly).
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02-01-2016
07:38 PM
Right, sorry about that, but that's near certainly where the error is coming from. The question on my mind is why sqlline isn't picking up the configuration value. @Terry 's suggestion is a good one. Depending on the version of HDP you're running, HBASE_CONF_DIR would also work.
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