Accumulo 2.0 documentation  >>  Troubleshooting  >>  Advanced Troubleshooting

Advanced Troubleshooting

Tablet server locks

My tablet server lost its lock. Why?

The primary reason a tablet server loses its lock is that it has been pushed into swap.

A large java program (like the tablet server) may have a large portion of its memory image unused. The operation system will favor pushing this allocated, but unused memory into swap so that the memory can be re-used as a disk buffer. When the java virtual machine decides to access this memory, the OS will begin flushing disk buffers to return that memory to the VM. This can cause the entire process to block long enough for the zookeeper lock to be lost.

Configure your system to reduce the kernel parameter swappiness from the default (60) to zero.

My tablet server lost its lock, and I have already set swappiness to zero. Why?

Be careful not to over-subscribe memory. This can be easy to do if your accumulo processes run on the same nodes as hadoop’s map-reduce framework. Remember to add up:

  • size of the JVM for the tablet server
  • size of the in-memory map, if using the native map implementation
  • size of the JVM for the data node
  • size of the JVM for the task tracker
  • size of the JVM times the maximum number of mappers and reducers
  • size of the kernel and any support processes

If a 16G node can run 2 mappers and 2 reducers, and each can be 2G, then there is only 8G for the data node, tserver, task tracker and OS.

Reduce the memory footprint of each component until it fits comfortably.

My tablet server lost its lock, swappiness is zero, and my node has lots of unused memory!

The JVM memory garbage collector may fall behind and cause a “stop-the-world” garbage collection. On a large memory virtual machine, this collection can take a long time. This happens more frequently when the JVM is getting low on free memory. Check the logs of the tablet server. You will see lines like this:

2013-06-20 13:43:20,607 [tabletserver.TabletServer] DEBUG: gc ParNew=0.00(+0.00) secs
    ConcurrentMarkSweep=0.00(+0.00) secs freemem=1,868,325,952(+1,868,325,952) totalmem=2,040,135,680

When freemem becomes small relative to the amount of memory needed, the JVM will spend more time finding free memory than performing work. This can cause long delays in sending keep-alive messages to zookeeper.

Ensure the tablet server JVM is not running low on memory.

I’m seeing errors in tablet server logs that include the words “MutationsRejectedException” and “# constraint violations: 1”. Moments after that the server died.

The error you are seeing is part of a failing tablet server scenario. This is a bit complicated, so name two of your tablet servers A and B.

Tablet server A is hosting a tablet, let’s call it a-tablet.

Tablet server B is hosting a metadata tablet, let’s call it m-tablet.

m-tablet records the information about a-tablet, for example, the names of the files it is using to store data.

When A ingests some data, it eventually flushes the updates from memory to a file.

Tablet server A then writes this new information to m-tablet, on Tablet server B.

Here’s a likely failure scenario:

Tablet server A does not have enough memory for all the processes running on it. The operating system sees a large chunk of the tablet server being unused, and swaps it out to disk to make room for other processes. Tablet server A does a java memory garbage collection, which causes it to start using all the memory allocated to it. As the server starts pulling data from swap, it runs very slowly. It fails to send the keep-alive messages to zookeeper in a timely fashion, and it looses its zookeeper session.

But, it’s running so slowly, that it takes a moment to realize it should no longer be hosting tablets.

The thread that is flushing a-tablet memory attempts to update m-tablet with the new file information.

Fortunately there’s a constraint on m-tablet. Mutations to the metadata table must contain a valid zookeeper session. This prevents tablet server A from making updates to m-tablet when it no long has the right to host the tablet.

The “MutationsRejectedException” error is from tablet server A making an update to tablet server B’s m-tablet. It’s getting a constraint violation: tablet server A has lost its zookeeper session, and will fail momentarily.

Ensure that memory is not over-allocated. Monitor swap usage, or turn swap off.

My accumulo client is getting a MutationsRejectedException. The monitor is displaying “No Such SessionID” errors.

When your client starts sending mutations to accumulo, it creates a session. Once the session is created, mutations are streamed to accumulo, without acknowledgement, against this session. Once the client is done, it will close the session, and get an acknowledgement.

If the client fails to communicate with accumulo, it will release the session, assuming that the client has died. If the client then attempts to send more mutations against the session, you will see “No Such SessionID” errors on the server, and MutationRejectedExceptions in the client.

The client library should be either actively using the connection to the tablet servers, or closing the connection and sessions. If the session times out, something is causing your client to pause.

The most frequent source of these pauses are java garbage collection pauses due to the JVM running out of memory, or being swapped out to disk.

Ensure your client has adequate memory and is not being swapped out to disk.

HDFS Failures

I had disastrous HDFS failure. After bringing everything back up, several tablets refuse to go online.

Data written to tablets is written into memory before being written into indexed files. In case the server is lost before the data is saved into a an indexed file, all data stored in memory is first written into a write-ahead log (WAL). When a tablet is re-assigned to a new tablet server, the write-ahead logs are read to recover any mutations that were in memory when the tablet was last hosted.

If a write-ahead log cannot be read, then the tablet is not re-assigned. All it takes is for one of the blocks in the write-ahead log to be missing. This is unlikely unless multiple data nodes in HDFS have been lost.

Get the WAL files online and healthy. Restore any data nodes that may be down.

How do find out which tablets are offline?

Use accumulo admin checkTablets

$ accumulo admin checkTablets

I lost three data nodes, and I’m missing blocks in a WAL. I don’t care about data loss, how can I get those tablets online?

See the system metadata table page which shows a typical metadata table listing. The entries with a column family of log are references to the WAL for that tablet. If you know what WAL is bad, you can find all the references with a grep in the shell:

shell> grep 0cb7ce52-ac46-4bf7-ae1d-acdcfaa97995
3< log:127.0.0.1+9997/0cb7ce52-ac46-4bf7-ae1d-acdcfaa97995 []    127.0.0.1+9997/0cb7ce52-ac46-4bf7-ae1d-acdcfaa97995|6

You can remove the WAL references in the metadata table.

shell> grant -u root Table.WRITE -t accumulo.metadata
shell> delete 3< log 127.0.0.1+9997/0cb7ce52-ac46-4bf7-ae1d-acdcfaa97995

Note: the colon (:) is omitted when specifying the row cf cq for the delete command.

The master will automatically discover the tablet no longer has a bad WAL reference and will assign the tablet. You will need to remove the reference from all the tablets to get them online.

The metadata (or root) table has references to a corrupt WAL.

This is a much more serious state, since losing updates to the metadata table will result in references to old files which may not exist, or lost references to new files, resulting in tablets that cannot be read, or large amounts of data loss.

The best hope is to restore the WAL by fixing HDFS data nodes and bringing the data back online. If this is not possible, the best approach is to re-create the instance and bulk import all files from the old instance into a new tables.

A complete set of instructions for doing this is outside the scope of this guide, but the basic approach is:

  • Use tables -l in the shell to discover the table name to table id mapping
  • Stop all accumulo processes on all nodes
  • Move the accumulo directory in HDFS out of the way: $ hadoop fs -mv /accumulo /corrupt
  • Re-initialize accumulo
  • Recreate tables, users and permissions
  • Import the directories under /corrupt/tables/<id> into the new instance

One or more HDFS Files under /accumulo/tables are corrupt

Accumulo maintains multiple references into the tablet files in the metadata tables and within the tablet server hosting the file, this makes it difficult to reliably just remove those references.

The directory structure in HDFS for tables will follow the general structure:

/accumulo
/accumulo/tables/
/accumulo/tables/!0
/accumulo/tables/!0/default_tablet/A000001.rf
/accumulo/tables/!0/t-00001/A000002.rf
/accumulo/tables/1
/accumulo/tables/1/default_tablet/A000003.rf
/accumulo/tables/1/t-00001/A000004.rf
/accumulo/tables/1/t-00001/A000005.rf
/accumulo/tables/2/default_tablet/A000006.rf
/accumulo/tables/2/t-00001/A000007.rf

If files under /accumulo/tables are corrupt, the best course of action is to recover those files in hdsf see the section on HDFS. Once these recovery efforts have been exhausted, the next step depends on where the missing file(s) are located. Different actions are required when the bad files are in Accumulo data table files or if they are metadata table files.

Data File Corruption

When an Accumulo data file is corrupt, the most reliable way to restore Accumulo operations is to replace the missing file with an ``empty’’ file so that references to the file in the METADATA table and within the tablet server hosting the file can be resolved by Accumulo. An empty file can be created using the CreateEmpty utility:

$ accumulo org.apache.accumulo.core.file.rfile.CreateEmpty /path/to/empty/file/empty.rf

The process is to delete the corrupt file and then move the empty file into its place (The generated empty file can be copied and used multiple times if necessary and does not need to be regenerated each time)

$ hadoop fs –rm /accumulo/tables/corrupt/file/thename.rf; \
hadoop fs -mv /path/to/empty/file/empty.rf /accumulo/tables/corrupt/file/thename.rf

Metadata File Corruption

If the corrupt files are metadata files, read the system metadata tables (under the path /accumulo/tables/!0). Then, you will need to rebuild the metadata table by initializing a new instance of Accumulo and then importing all of the existing data into the new instance. This is the same procedure as recovering from a zookeeper failure (see next section), except that you will have the benefit of having the existing user and table authorizations that are maintained in zookeeper.

You can use the DumpZookeeper utility to save this information for reference before creating the new instance. You will not be able to use RestoreZookeeper because the table names and references are likely to be different between the original and the new instances, but it can serve as a reference.

If the files cannot be recovered, replace corrupt data files with a empty rfiles to allow references in the metadata table and in the tablet servers to be resolved. Rebuild the metadata table if the corrupt files are metadata files.

Write-Ahead Log(WAL) File Corruption

In certain versions of Accumulo, a corrupt WAL file (caused by HDFS corruption or a bug in Accumulo that created the file) can block the successful recovery of one to many Tablets. Accumulo can be stuck in a loop trying to recover the WAL file, never being able to succeed.

In the cases where the WAL file’s original contents are unrecoverable or some degree of data loss is acceptable (beware if the WAL file contains updates to the Accumulo metadata table!), the following process can be followed to create an valid, empty WAL file. Run the following commands as the Accumulo unix user (to ensure that the proper file permissions in HDFS)

$ echo -n -e '--- Log File Header (v2) ---\x00\x00\x00\x00' > empty.wal

The above creates a file with the text “— Log File Header (v2) —” and then four bytes. You should verify the contents of the file with a hexdump tool.

Then, place this empty WAL in HDFS and then replace the corrupt WAL file in HDFS with the empty WAL.

$ hdfs dfs -moveFromLocal empty.wal /user/accumulo/empty.wal
$ hdfs dfs -mv /user/accumulo/empty.wal /accumulo/wal/tserver-4.example.com+10011/26abec5b-63e7-40dd-9fa1-b8ad2436606e

After the corrupt WAL file has been replaced, the system should automatically recover. It may be necessary to restart the Accumulo Master process as an exponential backup policy is used which could lead to a long wait before Accumulo will try to re-load the WAL file.

Zookeeper Failures

I lost my ZooKeeper quorum (hardware failure), but HDFS is still intact. How can I recover my Accumulo instance?

ZooKeeper, in addition to its lock-service capabilities, also serves to bootstrap an Accumulo instance from some location in HDFS. It contains the pointers to the root tablet in HDFS which is then used to load the Accumulo metadata tablets, which then loads all user tables. ZooKeeper also stores all namespace and table configuration, the user database, the mapping of table IDs to table names, and more across Accumulo restarts.

Presently, the only way to recover such an instance is to initialize a new instance and import all of the old data into the new instance. The easiest way to tackle this problem is to first recreate the mapping of table ID to table name and then recreate each of those tables in the new instance. Set any necessary configuration on the new tables and add some split points to the tables to close the gap between how many splits the old table had and no splits.

The directory structure in HDFS for tables will follow the general structure:

/accumulo
/accumulo/tables/
/accumulo/tables/1
/accumulo/tables/1/default_tablet/A000001.rf
/accumulo/tables/1/t-00001/A000002.rf
/accumulo/tables/1/t-00001/A000003.rf
/accumulo/tables/2/default_tablet/A000004.rf
/accumulo/tables/2/t-00001/A000005.rf

For each table, make a new directory that you can move (or copy if you have the HDFS space to do so) all of the rfiles for a given table into. For example, to process the table with an ID of 1, make a new directory, say /new-table-1 and then copy all files from /accumulo/tables/1/\*/*.rf into that directory. Additionally, make a directory, /new-table-1-failures, for any failures during the import process. Then, issue the import command using the Accumulo shell into the new table, telling Accumulo to not re-set the timestamp:

user@instance new_table> importdirectory /new-table-1 /new-table-1-failures false

Any RFiles which were failed to be loaded will be placed in /new-table-1-failures. Rfiles that were successfully imported will no longer exist in /new-table-1. For failures, move them back to the import directory and retry the importdirectory command.

It is extremely important to note that this approach may introduce stale data back into the tables. For a few reasons, RFiles may exist in the table directory which are candidates for deletion but have not yet been deleted. Additionally, deleted data which was not compacted away, but still exists in write-ahead logs if the original instance was somehow recoverable, will be re-introduced in the new instance. Table splits and merges (which also include the deleteRows API call on TableOperations, are also vulnerable to this problem. This process should not be used if these are unacceptable risks. It is possible to try to re-create a view of the accumulo.metadata table to prune out files that are candidates for deletion, but this is a difficult task that also may not be entirely accurate.

Likewise, it is also possible that data loss may occur from write-ahead log (WAL) files which existed on the old table but were not minor-compacted into an RFile. Again, it may be possible to reconstruct the state of these WAL files to replay data not yet in an RFile; however, this is a difficult task and is not implemented in any automated fashion.

The importdirectory shell command can be used to import RFiles from the old instance into a newly created instance, but extreme care should go into the decision to do this as it may result in reintroduction of stale data or the omission of new data.

Upgrade Issues

I upgraded from 1.4 to 1.5 to 1.6 but still have some WAL files on local disk. Do I have any way to recover them?

Yes, you can recover them by running the LocalWALRecovery utility (not available in 1.8 and later) on each node that needs recovery performed. The utility will default to using the directory specified by logger.dir.walog in your configuration, or can be overridden by using the --local-wal-directories option on the tool. It can be invoked as follows:

accumulo org.apache.accumulo.tserver.log.LocalWALRecovery

File Naming Conventions

Why are files named like they are? Why do some start with C and others with F?

The file names give you a basic idea for the source of the file.

The base of the filename is a base-36 unique number. All filenames in accumulo are coordinated with a counter in zookeeper, so they are always unique, which is useful for debugging.

The leading letter gives you an idea of how the file was created:

  • F - Flush: entries in memory were written to a file (Minor Compaction)
  • M - Merging compaction: entries in memory were combined with the smallest file to create one new file
  • C - Several files, but not all files, were combined to produce this file (Major Compaction)
  • A - All files were compacted, delete entries were dropped
  • I - Bulk import, complete, sorted index files. Always in a directory starting with b-

This simple file naming convention allows you to see the basic structure of the files from just their filenames, and reason about what should be happening to them next, just by scanning their entries in the metadata tables.

For example, if you see multiple files with M prefixes, the tablet is, or was, up against its maximum file limit, so it began merging memory updates with files to keep the file count reasonable. This slows down ingest performance, so knowing there are many files like this tells you that the system is struggling to keep up with ingest vs the compaction strategy which reduces the number of files.

HDFS Decommissioning Issues

My Hadoop DataNode is hung for hours trying to decommission.

Write Ahead Logs stay open until they hit the size threshold, which could be many hours or days in some cases. These open files will prevent a DN from finishing its decommissioning process (HDFS-3599) in some versions of Hadoop 2. If you stop the DN, then the WALog file will not be closed and you could lose data. To work around this issue, we now close WALogs on a time period specified by the property tserver.walog.max.age with a default period of 24 hours.

Find documentation for all releases in the archive