Table of Contents
This chapter will cover operational tools and practices required of a running Apache HBase cluster. The subject of operations is related to the topics of Chapter 15, Troubleshooting and Debugging Apache HBase, Chapter 14, Apache HBase Performance Tuning, and Chapter 2, Apache HBase Configuration but is a distinct topic in itself.
HBase provides several tools for administration, analysis, and debugging of your cluster.
The entry-point to most of these tools is the bin/hbase command, though
some tools are available in the dev-support/ directory.
To see usage instructions for bin/hbase command, run it with no
arguments, or with the -h argument. These are the usage instructions for
HBase 0.98.x. Some commands, such as version, pe,
ltt, clean, are not available in previous
versions.
$ bin/hbase
Usage: hbase [<options>] <command> [<args>]
Options:
--config DIR Configuration direction to use. Default: ./conf
--hosts HOSTS Override the list in 'regionservers' file
Commands:
Some commands take arguments. Pass no args or -h for usage.
shell Run the HBase shell
hbck Run the hbase 'fsck' tool
hlog Write-ahead-log analyzer
hfile Store file analyzer
zkcli Run the ZooKeeper shell
upgrade Upgrade hbase
master Run an HBase HMaster node
regionserver Run an HBase HRegionServer node
zookeeper Run a Zookeeper server
rest Run an HBase REST server
thrift Run the HBase Thrift server
thrift2 Run the HBase Thrift2 server
clean Run the HBase clean up script
classpath Dump hbase CLASSPATH
mapredcp Dump CLASSPATH entries required by mapreduce
pe Run PerformanceEvaluation
ltt Run LoadTestTool
version Print the version
CLASSNAME Run the class named CLASSNAME
Some of the tools and utilities below are Java classes which are passed directly to the
bin/hbase command, as referred to in the last line of the usage
instructions. Others, such as hbase shell (Chapter 4, The Apache HBase Shell),
hbase upgrade (Chapter 3, Upgrading), and hbase
thrift (Chapter 12, Thrift API and Filter Language), are documented elsewhere in this guide.
There is a Canary class can help users to canary-test the HBase cluster status, with
every column-family for every regions or regionservers granularity. To see the usage, use
the --help parameter.
$ ${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.tool.Canary -help
Usage: bin/hbase org.apache.hadoop.hbase.tool.Canary [opts] [table1 [table2]...] | [regionserver1 [regionserver2]..]
where [opts] are:
-help Show this help and exit.
-regionserver replace the table argument to regionserver,
which means to enable regionserver mode
-daemon Continuous check at defined intervals.
-interval <N> Interval between checks (sec)
-e Use region/regionserver as regular expression
which means the region/regionserver is regular expression pattern
-f <B> stop whole program if first error occurs, default is true
-t <N> timeout for a check, default is 600000 (milliseconds)This tool will return non zero error codes to user for collaborating with other monitoring tools, such as Nagios. The error code definitions are:
private static final int USAGE_EXIT_CODE = 1; private static final int INIT_ERROR_EXIT_CODE = 2; private static final int TIMEOUT_ERROR_EXIT_CODE = 3; private static final int ERROR_EXIT_CODE = 4;
Here are some examples based on the following given case. There are two HTable called test-01 and test-02, they have two column family cf1 and cf2 respectively, and deployed on the 3 regionservers. see following table.
| RegionServer | test-01 | test-02 |
|---|---|---|
| rs1 | r1 | r2 |
| rs2 | r2 | |
| rs3 | r2 | r1 |
Following are some examples based on the previous given case.
$ ${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.tool.Canary
3/12/09 03:26:32 INFO tool.Canary: read from region test-01,,1386230156732.0e3c7d77ffb6361ea1b996ac1042ca9a. column family cf1 in 2ms
13/12/09 03:26:32 INFO tool.Canary: read from region test-01,,1386230156732.0e3c7d77ffb6361ea1b996ac1042ca9a. column family cf2 in 2ms
13/12/09 03:26:32 INFO tool.Canary: read from region test-01,0004883,1386230156732.87b55e03dfeade00f441125159f8ca87. column family cf1 in 4ms
13/12/09 03:26:32 INFO tool.Canary: read from region test-01,0004883,1386230156732.87b55e03dfeade00f441125159f8ca87. column family cf2 in 1ms
...
13/12/09 03:26:32 INFO tool.Canary: read from region test-02,,1386559511167.aa2951a86289281beee480f107bb36ee. column family cf1 in 5ms
13/12/09 03:26:32 INFO tool.Canary: read from region test-02,,1386559511167.aa2951a86289281beee480f107bb36ee. column family cf2 in 3ms
13/12/09 03:26:32 INFO tool.Canary: read from region test-02,0004883,1386559511167.cbda32d5e2e276520712d84eaaa29d84. column family cf1 in 31ms
13/12/09 03:26:32 INFO tool.Canary: read from region test-02,0004883,1386559511167.cbda32d5e2e276520712d84eaaa29d84. column family cf2 in 8ms
So you can see, table test-01 has two regions and two column families, so the Canary tool will pick 4 small piece of data from 4 (2 region * 2 store) different stores. This is a default behavior of the this tool does.
You can also test one or more specific tables.
$ ${HBASE_HOME}/bin/hbase orghapache.hadoop.hbase.tool.Canary test-01 test-02This will pick one small piece of data from each regionserver, and can also put your resionserver name as input options for canary-test specific regionservers.
$ ${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.tool.Canary -regionserver
13/12/09 06:05:17 INFO tool.Canary: Read from table:test-01 on region server:rs2 in 72ms
13/12/09 06:05:17 INFO tool.Canary: Read from table:test-02 on region server:rs3 in 34ms
13/12/09 06:05:17 INFO tool.Canary: Read from table:test-01 on region server:rs1 in 56msThis will test both table test-01 and test-02.
$ ${HBASE_HOME}/bin/hbase orghapache.hadoop.hbase.tool.Canary -e test-0[1-2]Run repeatedly with interval defined in option -interval whose default value is 6 seconds. This daemon will stop itself and return non-zero error code if any error occurs, due to the default value of option -f is true.
$ ${HBASE_HOME}/bin/hbase orghapache.hadoop.hbase.tool.Canary -daemonRun repeatedly with internal 5 seconds and will not stop itself even error occurs in the test.
$ ${HBASE_HOME}/bin/hbase orghapache.hadoop.hbase.tool.Canary -daemon -interval 50000 -f falseIn some cases, we suffered the request stucked on the regionserver and not response back to the client. The regionserver in problem, would also not indicated to be dead by Master, which would bring the clients hung. So we provide the timeout option to kill the canary test forcefully and return non-zero error code as well. This run sets the timeout value to 60 seconds, the default value is 600 seconds.
$ ${HBASE_HOME}/bin/hbase orghapache.hadoop.hbase.tool.Canary -t 600000You can configure HBase to run a script on a period and if it fails N times (configurable), have the server exit. See HBASE-7351 Periodic health check script for configurations and detail.
Several frequently-accessed utilities are provided as Driver classes, and executed by
the bin/hbase command. These utilities represent MapReduce jobs which
run on your cluster. They are run in the following way, replacing
UtilityName with the utility you want to run. This command
assumes you have set the environment variable HBASE_HOME to the directory
where HBase is unpacked on your server.
${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.mapreduce.UtilityName
The following utilities are available:
Complete a bulk data load.
Export a table from the local cluster to a peer cluster.
Write table data to HDFS.
Import data written by a previous Export operation.
Import data in TSV format.
Count rows in an HBase table.
Compare the data from tables in two different clusters. WARNING: It doesn't work for incrementColumnValues'd cells since the timestamp is changed. Note that this command is in a different package than the others.
Each command except RowCounter accepts a single
--help argument to print usage instructions.
To run hbck against your HBase cluster run $
./bin/hbase hbck At the end of the command's output it prints
OK or INCONSISTENCY. If your cluster reports
inconsistencies, pass -details to see more detail emitted. If
inconsistencies, run hbck a few times because the inconsistency may be
transient (e.g. cluster is starting up or a region is splitting). Passing
-fix may correct the inconsistency (This latter is an experimental
feature).
For more information, see Appendix C, hbck In Depth.
The main method on FSHLog offers manual split and dump
facilities. Pass it WALs or the product of a split, the content of the
recovered.edits. directory.
You can get a textual dump of a WAL file content by doing the following:
$ ./bin/hbase org.apache.hadoop.hbase.regionserver.wal.FSHLog --dump hdfs://example.org:8020/hbase/.logs/example.org,60020,1283516293161/10.10.21.10%3A60020.1283973724012
The return code will be non-zero if issues with the file so you can test wholesomeness
of file by redirecting STDOUT to /dev/null and testing the
program return.
Similarly you can force a split of a log file directory by doing:
$ ./bin/hbase org.apache.hadoop.hbase.regionserver.wal.FSHLog --split hdfs://example.org:8020/hbase/.logs/example.org,60020,1283516293161/
CopyTable is a utility that can copy part or of all of a table, either to the same cluster or another cluster. The target table must first exist. The usage is as follows:
$ ./bin/hbase org.apache.hadoop.hbase.mapreduce.CopyTable --help
/bin/hbase org.apache.hadoop.hbase.mapreduce.CopyTable --help
Usage: CopyTable [general options] [--starttime=X] [--endtime=Y] [--new.name=NEW] [--peer.adr=ADR] <tablename>
Options:
rs.class hbase.regionserver.class of the peer cluster,
specify if different from current cluster
rs.impl hbase.regionserver.impl of the peer cluster,
startrow the start row
stoprow the stop row
starttime beginning of the time range (unixtime in millis)
without endtime means from starttime to forever
endtime end of the time range. Ignored if no starttime specified.
versions number of cell versions to copy
new.name new table's name
peer.adr Address of the peer cluster given in the format
hbase.zookeeer.quorum:hbase.zookeeper.client.port:zookeeper.znode.parent
families comma-separated list of families to copy
To copy from cf1 to cf2, give sourceCfName:destCfName.
To keep the same name, just give "cfName"
all.cells also copy delete markers and deleted cells
Args:
tablename Name of the table to copy
Examples:
To copy 'TestTable' to a cluster that uses replication for a 1 hour window:
$ bin/hbase org.apache.hadoop.hbase.mapreduce.CopyTable --starttime=1265875194289 --endtime=1265878794289 --peer.adr=server1,server2,server3:2181:/hbase --families=myOldCf:myNewCf,cf2,cf3 TestTable
For performance consider the following general options:
It is recommended that you set the following to >=100. A higher value uses more memory but
decreases the round trip time to the server and may increase performance.
-Dhbase.client.scanner.caching=100
The following should always be set to false, to prevent writing data twice, which may produce
inaccurate results.
-Dmapred.map.tasks.speculative.execution=false
Caching for the input Scan is configured via hbase.client.scanner.caching
in the job configuration.
By default, CopyTable utility only copies the latest version of row cells unless
--versions=n is explicitly specified in the command.
See Jonathan Hsieh's Online HBase Backups with CopyTable blog post for more on CopyTable.
Export is a utility that will dump the contents of table to HDFS in a sequence file. Invoke via:
$ bin/hbase org.apache.hadoop.hbase.mapreduce.Export <tablename> <outputdir> [<versions> [<starttime> [<endtime>]]]
Note: caching for the input Scan is configured via
hbase.client.scanner.caching in the job configuration.
Import is a utility that will load data that has been exported back into HBase. Invoke via:
$ bin/hbase org.apache.hadoop.hbase.mapreduce.Import <tablename> <inputdir>
To import 0.94 exported files in a 0.96 cluster or onwards, you need to set system property "hbase.import.version" when running the import command as below:
$ bin/hbase -Dhbase.import.version=0.94 org.apache.hadoop.hbase.mapreduce.Import <tablename> <inputdir>
ImportTsv is a utility that will load data in TSV format into HBase. It has two distinct
usages: loading data from TSV format in HDFS into HBase via Puts, and preparing StoreFiles
to be loaded via the completebulkload.
To load data via Puts (i.e., non-bulk loading):
$ bin/hbase org.apache.hadoop.hbase.mapreduce.ImportTsv -Dimporttsv.columns=a,b,c <tablename> <hdfs-inputdir>
To generate StoreFiles for bulk-loading:
$ bin/hbase org.apache.hadoop.hbase.mapreduce.ImportTsv -Dimporttsv.columns=a,b,c -Dimporttsv.bulk.output=hdfs://storefile-outputdir <tablename> <hdfs-data-inputdir>
These generated StoreFiles can be loaded into HBase via Section 17.1.12, “CompleteBulkLoad”.
Running ImportTsv with no arguments prints brief usage information:
Usage: importtsv -Dimporttsv.columns=a,b,c <tablename> <inputdir>
Imports the given input directory of TSV data into the specified table.
The column names of the TSV data must be specified using the -Dimporttsv.columns
option. This option takes the form of comma-separated column names, where each
column name is either a simple column family, or a columnfamily:qualifier. The special
column name HBASE_ROW_KEY is used to designate that this column should be used
as the row key for each imported record. You must specify exactly one column
to be the row key, and you must specify a column name for every column that exists in the
input data.
By default importtsv will load data directly into HBase. To instead generate
HFiles of data to prepare for a bulk data load, pass the option:
-Dimporttsv.bulk.output=/path/for/output
Note: the target table will be created with default column family descriptors if it does not already exist.
Other options that may be specified with -D include:
-Dimporttsv.skip.bad.lines=false - fail if encountering an invalid line
'-Dimporttsv.separator=|' - eg separate on pipes instead of tabs
-Dimporttsv.timestamp=currentTimeAsLong - use the specified timestamp for the import
-Dimporttsv.mapper.class=my.Mapper - A user-defined Mapper to use instead of org.apache.hadoop.hbase.mapreduce.TsvImporterMapper
For example, assume that we are loading data into a table called 'datatsv' with a ColumnFamily called 'd' with two columns "c1" and "c2".
Assume that an input file exists as follows:
row1 c1 c2
row2 c1 c2
row3 c1 c2
row4 c1 c2
row5 c1 c2
row6 c1 c2
row7 c1 c2
row8 c1 c2
row9 c1 c2
row10 c1 c2
For ImportTsv to use this imput file, the command line needs to look like this:
HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-server-VERSION.jar importtsv -Dimporttsv.columns=HBASE_ROW_KEY,d:c1,d:c2 -Dimporttsv.bulk.output=hdfs://storefileoutput datatsv hdfs://inputfile
... and in this example the first column is the rowkey, which is why the HBASE_ROW_KEY is used. The second and third columns in the file will be imported as "d:c1" and "d:c2", respectively.
If you have preparing a lot of data for bulk loading, make sure the target HBase table is pre-split appropriately.
For more information about bulk-loading HFiles into HBase, see Section 9.8, “Bulk Loading”
The completebulkload utility will move generated StoreFiles into an HBase
table. This utility is often used in conjunction with output from Section 17.1.11, “ImportTsv”.
There are two ways to invoke this utility, with explicit classname and via the driver:
$ bin/hbase org.apache.hadoop.hbase.mapreduce.LoadIncrementalHFiles <hdfs://storefileoutput> <tablename>
.. and via the Driver..
HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-server-VERSION.jar completebulkload <hdfs://storefileoutput> <tablename>
Data generated via MapReduce is often created with file permissions that are not compatible with the running HBase process. Assuming you're running HDFS with permissions enabled, those permissions will need to be updated before you run CompleteBulkLoad.
For more information about bulk-loading HFiles into HBase, see Section 9.8, “Bulk Loading”.
WALPlayer is a utility to replay WAL files into HBase.
The WAL can be replayed for a set of tables or all tables, and a timerange can be provided (in milliseconds). The WAL is filtered to this set of tables. The output can optionally be mapped to another set of tables.
WALPlayer can also generate HFiles for later bulk importing, in that case only a single table and no mapping can be specified.
Invoke via:
$ bin/hbase org.apache.hadoop.hbase.mapreduce.WALPlayer [options] <wal inputdir> <tables> [<tableMappings>]>
For example:
$ bin/hbase org.apache.hadoop.hbase.mapreduce.WALPlayer /backuplogdir oldTable1,oldTable2 newTable1,newTable2
WALPlayer, by default, runs as a mapreduce job. To NOT run WALPlayer as a mapreduce job
on your cluster, force it to run all in the local process by adding the flags
-Dmapreduce.jobtracker.address=local on the command line.
RowCounter is a mapreduce job to count all the rows of a table. This is a good utility to use as a sanity check to ensure that HBase can read all the blocks of a table if there are any concerns of metadata inconsistency. It will run the mapreduce all in a single process but it will run faster if you have a MapReduce cluster in place for it to exploit.
$ bin/hbase org.apache.hadoop.hbase.mapreduce.RowCounter <tablename> [<column1> <column2>...]
Note: caching for the input Scan is configured via
hbase.client.scanner.caching in the job configuration.
HBase ships another diagnostic mapreduce job called CellCounter. Like RowCounter, it gathers more fine-grained statistics about your table. The statistics gathered by RowCounter are more fine-grained and include:
Total number of rows in the table.
Total number of CFs across all rows.
Total qualifiers across all rows.
Total occurrence of each CF.
Total occurrence of each qualifier.
Total number of versions of each qualifier.
The program allows you to limit the scope of the run. Provide a row regex or prefix to
limit the rows to analyze. Use hbase.mapreduce.scan.column.family to specify
scanning a single column family.
$ bin/hbase org.apache.hadoop.hbase.mapreduce.CellCounter <tablename> <outputDir> [regex or prefix]
Note: just like RowCounter, caching for the input Scan is configured via
hbase.client.scanner.caching in the job configuration.
It is possible to optionally pin your servers in physical memory making them less likely to be swapped out in oversubscribed environments by having the servers call mlockall on startup. See HBASE-4391 Add ability to start RS as root and call mlockall for how to build the optional library and have it run on startup.
See the usage for the Compaction Tool. Run it like this ./bin/hbase org.apache.hadoop.hbase.regionserver.CompactionTool
The hbase clean command cleans HBase data from ZooKeeper, HDFS, or both. It is appropriate to use for testing. Run it with no options for usage instructions. The hbase clean command was introduced in HBase 0.98.
$ bin/hbase clean
Usage: hbase clean (--cleanZk|--cleanHdfs|--cleanAll)
Options:
--cleanZk cleans hbase related data from zookeeper.
--cleanHdfs cleans hbase related data from hdfs.
--cleanAll cleans hbase related data from both zookeeper and hdfs.
The hbase pe command is a shortcut provided to run the
org.apache.hadoop.hbase.PerformanceEvaluation tool, which is used for
testing. The hbase pe command was introduced in HBase 0.98.4.
The PerformanceEvaluation tool accepts many different options and commands. For usage instructions, run the command with no options.
To run PerformanceEvaluation prior to HBase 0.98.4, issue the command hbase org.apache.hadoop.hbase.PerformanceEvaluation.
The PerformanceEvaluation tool has received many updates in recent HBase releases, including support for namespaces, support for tags, cell-level ACLs and visibility labels, multiget support for RPC calls, increased sampling sizes, an option to randomly sleep during testing, and ability to "warm up" the cluster before testing starts.
The hbase ltt command is a shortcut provided to run the
org.apache.hadoop.hbase.util.LoadTestTool utility, which is used for
testing. The hbase ltt command was introduced in HBase 0.98.4.
You must specify either -write or -update-read as the
first option. For general usage instructions, pass the -h option.
To run LoadTestTool prior to HBase 0.98.4, issue the command hbase org.apache.hadoop.hbase.util.LoadTestTool.
The LoadTestTool has received many updates in recent HBase releases, including support for namespaces, support for tags, cell-level ACLS and visibility labels, testing security-related features, ability to specify the number of regions per server, tests for multi-get RPC calls, and tests relating to replication.