Use the bulk load tool if you can. See Section 9.8, “Bulk Loading”. Otherwise, pay attention to the below.
Tables in HBase are initially created with one region by default. For bulk imports, this means that all clients will write to the same region until it is large enough to split and become distributed across the cluster. A useful pattern to speed up the bulk import process is to pre-create empty regions. Be somewhat conservative in this, because too-many regions can actually degrade performance.
There are two different approaches to pre-creating splits. The first approach is to rely on the default
(which is implemented in
byte startKey = ...; // your lowest keuy byte endKey = ...; // your highest key int numberOfRegions = ...; // # of regions to create admin.createTable(table, startKey, endKey, numberOfRegions);
And the other approach is to define the splits yourself...
byte splits = ...; // create your own splits admin.createTable(table, splits);
See Section 6.3.6, “Relationship Between RowKeys and Region Splits” for issues related to understanding your keyspace and pre-creating regions.
The default behavior for Puts using the Write Ahead Log (WAL) is that
HLog edits will be written immediately. If deferred log flush is used,
WAL edits are kept in memory until the flush period. The benefit is aggregated and asynchronous
HLog- writes, but the potential downside is that if
the RegionServer goes down the yet-to-be-flushed edits are lost. This is safer, however, than not using WAL at all with Puts.
Deferred log flush can be configured on tables via HTableDescriptor. The default value of
hbase.regionserver.optionallogflushinterval is 1000ms.
When performing a lot of Puts, make sure that setAutoFlush is set
to false on your HTable
instance. Otherwise, the Puts will be sent one at a time to the
RegionServer. Puts added via
htable.add( <List> Put)
wind up in the same write buffer. If
autoFlush = false,
these messages are not sent until the write-buffer is filled. To
explicitly flush the messages, call
close on the
instance will invoke
A frequently discussed option for increasing throughput on
Puts is to call
writeToWAL(false). Turning this off means
that the RegionServer will not write the
Put to the Write Ahead Log,
only into the memstore, HOWEVER the consequence is that if there
is a RegionServer failure there will be data loss.
writeToWAL(false) is used, do so with extreme caution. You may find in actuality that
it makes little difference if your load is well distributed across the cluster.
In general, it is best to use WAL for Puts, and where loading throughput is a concern to use bulk loading techniques instead.
In addition to using the writeBuffer, grouping
Puts by RegionServer can reduce the number of client RPC calls per writeBuffer flush.
There is a utility
HTableUtil currently on TRUNK that does this, but you can either copy that or implement your own verison for
those still on 0.90.x or earlier.
When writing a lot of data to an HBase table from a MR job (e.g., with TableOutputFormat), and specifically where Puts are being emitted from the Mapper, skip the Reducer step. When a Reducer step is used, all of the output (Puts) from the Mapper will get spooled to disk, then sorted/shuffled to other Reducers that will most likely be off-node. It's far more efficient to just write directly to HBase.
For summary jobs where HBase is used as a source and a sink, then writes will be coming from the Reducer step (e.g., summarize values then write out result). This is a different processing problem than from the the above case.
If all your data is being written to one region at a time, then re-read the section on processing timeseries data.
Also, if you are pre-splitting regions and all your data is still winding up in a single region even though your keys aren't monotonically increasing, confirm that your keyspace actually works with the split strategy. There are a variety of reasons that regions may appear "well split" but won't work with your data. As the HBase client communicates directly with the RegionServers, this can be obtained via HTable.getRegionLocation.