Accumulo 2.x Documentation  >>  Administration  >>  In-depth Installation

In-depth Installation

This document provides detailed instructions for installing Accumulo. For basic instructions, see the quick start.


Because we are running essentially two or three systems simultaneously layered across the cluster: HDFS, Accumulo and MapReduce, it is typical for hardware to consist of 4 to 8 cores, and 8 to 32 GB RAM. This is so each running process can have at least one core and 2 - 4 GB each.

One core running HDFS can typically keep 2 to 4 disks busy, so each machine may typically have as little as 2 x 300GB disks and as much as 4 x 1TB or 2TB disks.

It is possible to do with less than this, such as with 1u servers with 2 cores and 4GB each, but in this case it is recommended to only run up to two processes per machine – i.e. DataNode and TabletServer or DataNode and MapReduce worker but not all three. The constraint here is having enough available heap space for all the processes on a machine.


Accumulo communicates via remote procedure calls over TCP/IP for both passing data and control messages. In addition, Accumulo uses HDFS clients to communicate with HDFS. To achieve good ingest and query performance, sufficient network bandwidth must be available between any two machines.

In addition to needing access to ports associated with HDFS and ZooKeeper, Accumulo will use the following default ports. Please make sure that they are open, or change their value in

Port Description Property Name
4445 Shutdown Port (Accumulo MiniCluster) n/a
4560 Accumulo monitor (for centralized log display) monitor.port.log4j
9995 Accumulo HTTP monitor monitor.port.client
9997 Tablet Server tserver.port.client
9998 Accumulo GC gc.port.client
9999 Master Server master.port.client
12234 Accumulo Tracer trace.port.client
42424 Accumulo Proxy Server n/a
10001 Master Replication service master.replication.coordinator.port
10002 TabletServer Replication service replication.receipt.service.port

In addition, the user can provide 0 and an ephemeral port will be chosen instead. This ephemeral port is likely to be unique and not already bound. Thus, configuring ports to use 0 instead of an explicit value, should, in most cases, work around any issues of running multiple distinct Accumulo instances (or any other process which tries to use the same default ports) on the same hardware. Finally, the *.port.client properties will work with the port range syntax (M-N) allowing the user to specify a range of ports for the service to attempt to bind. The ports in the range will be tried in a 1-up manner starting at the low end of the range to, and including, the high end of the range.

Download Tarball

Download a binary distribution of Accumulo and install it to a directory on a disk with sufficient space:

cd <install directory>
tar xzf accumulo-2.0.0-bin.tar.gz
cd accumulo-2.0.0

Repeat this step on each machine in your cluster. Typically, the same <install directory> is chosen for all machines in the cluster.

There are four scripts in the bin/ directory that are used to manage Accumulo:

  1. accumulo - Runs Accumulo command-line tools and starts Accumulo processes
  2. accumulo-service - Runs Accumulo processes as services
  3. accumulo-cluster - Manages Accumulo cluster on a single node or several nodes
  4. accumulo-util - Accumulo utilities for creating configuration, native libraries, etc.

These scripts will be used in the remaining instructions to configure and run Accumulo.


Accumulo requires HDFS and ZooKeeper to be configured and running before starting. Password-less SSH should be configured between at least the Accumulo master and TabletServer machines. It is also a good idea to run Network Time Protocol (NTP) within the cluster to ensure nodes’ clocks don’t get too out of sync, which can cause problems with automatically timestamped data.


The Accumulo tarball contains a conf/ directory where Accumulo looks for configuration. If you installed Accumulo using downstream packaging, the conf/ could be something else like /etc/accumulo/.

Before starting Accumulo, the configuration files and must exist in conf/ and be properly configured. If you are using accumulo-cluster to launch a cluster, the conf/ directory must also contain host files for Accumulo services (i.e gc, masters, monitor, tservers, tracers). You can either create these files manually or run accumulo-cluster create-config.

Logging is configured in to use three log4j configuration files in conf/. The file used depends on the Accumulo command or service being run. Logging for most Accumulo services (i.e Master, TabletServer, Garbage Collector) is configured by except for the Monitor which is configured by All Accumulo commands (i.e init, shell, etc) are configured by


Accumulo needs to know where to find the software it depends on. Edit and specify the following:

  1. Enter the location of Hadoop for $HADOOP_HOME
  2. Enter the location of ZooKeeper for $ZOOKEEPER_HOME
  3. Optionally, choose a different location for Accumulo logs using $ACCUMULO_LOG_DIR

Accumulo uses HADOOP_HOME and ZOOKEEPER_HOME to locate Hadoop and Zookeeper jars and add them the CLASSPATH variable. If you are running a vendor-specific release of Hadoop or Zookeeper, you may need to change how your CLASSPATH is built in If Accumulo has problems later on finding jars, run accumulo classpath to print Accumulo’s classpath.

You may want to change the default memory settings for Accumulo’s TabletServer which are by set in the JAVA_OPTS settings for ‘tservers’ in Note the syntax is that of the Java JVM command line options. This value should be less than the physical memory of the machines running TabletServers.

There are similar options for the master’s memory usage and the garbage collector process. Reduce these if they exceed the physical RAM of your hardware and increase them, within the bounds of the physical RAM, if a process fails because of insufficient memory.

Note that you will be specifying the Java heap space in You should make sure that the total heap space used for the Accumulo tserver and the Hadoop DataNode and TaskTracker is less than the available memory on each worker node in the cluster. On large clusters, it is recommended that the Accumulo master, Hadoop NameNode, secondary NameNode, and Hadoop JobTracker all be run on separate machines to allow them to use more heap space. If you are running these on the same machine on a small cluster, likewise make sure their heap space settings fit within the available memory.

Native Map

The tablet server uses a data structure called a MemTable to store sorted key/value pairs in memory when they are first received from the client. When a minor compaction occurs, this data structure is written to HDFS. The MemTable will default to using memory in the JVM but a JNI version, called the native map, can be used to significantly speed up performance by utilizing the memory space of the native operating system. The native map also avoids the performance implications brought on by garbage collection in the JVM by causing it to pause much less frequently.


32-bit and 64-bit Linux and Mac OS X versions of the native map can be built by executing accumulo-util build-native. If your system’s default compiler options are insufficient, you can add additional compiler options to the command line, such as options for the architecture. These will be passed to the Makefile in the environment variable USERFLAGS.


accumulo-util build-native
accumulo-util build-native -m32

After building the native map from the source, you will find the artifact in lib/native. Upon starting up, the tablet server will look in this directory for the map library. If the file is renamed or moved from its target directory, the tablet server may not be able to find it. The system can also locate the native maps shared library by setting LD_LIBRARY_PATH (or DYLD_LIBRARY_PATH on Mac OS X) in

Native Maps Configuration

As mentioned, Accumulo will use the native libraries if they are found in the expected location and tserver.memory.maps.native.enabled is set to true (which is the default). Using the native maps over JVM Maps nets a noticeable improvement in ingest rates; however, certain configuration variables are important to modify when increasing the size of the native map.

To adjust the size of the native map, modify the value of tserver.memory.maps.max. When increasing this value, it is also important to adjust the values below:

The maximum size of the native maps for a server should be less than the product of the write-ahead log maximum size and minor compaction threshold for log files:

$table.compaction.minor.logs.threshold * $tserver.walog.max.size >= $tserver.memory.maps.max

This formula ensures that minor compactions won’t be automatically triggered before the native maps can be completely saturated.

Subsequently, when increasing the size of the write-ahead logs, it can also be important to increase the HDFS block size that Accumulo uses when creating the files for the write-ahead log. This is controlled via tserver.wal.blocksize. A basic recommendation is that when tserver.walog.max.size is larger than 2GB in size, set tserver.wal.blocksize to 2GB. Increasing the block size to a value larger than 2GB can result in decreased write performance to the write-ahead log file which will slow ingest.

Cluster Specification

If you are using accumulo-cluster to start a cluster, configure the following on the machine that will serve as the Accumulo master:

  1. Run accumulo-cluster create-config to create the masters and tservers files.
  2. Write the IP address or domain name of the Accumulo Master to the masters file in conf/.
  3. Write the IP addresses or domain name of the machines that will be TabletServers to the tservers file in conf/, one per line.

Note that if using domain names rather than IP addresses, DNS must be configured properly for all machines participating in the cluster. DNS can be a confusing source of errors.


Specify appropriate values for the following properties in

Some settings can be modified via the Accumulo shell and take effect immediately, but some settings require a process restart to take effect. See the configuration overview documentation for details.

Hostnames in configuration files

Accumulo has a number of configuration files which can contain references to other hosts in your network. All of the “host” configuration files for Accumulo (gc, masters, tservers, monitor, tracers) as well as instance.volumes in must contain some host reference.

While IP address, short hostnames, or fully qualified domain names (FQDN) are all technically valid, it is good practice to always use FQDNs for both Accumulo and other processes in your Hadoop cluster. Failing to consistently use FQDNs can have unexpected consequences in how Accumulo uses the FileSystem.

A common way for this problem can be observed is via applications that use Bulk Ingest. The Accumulo Master coordinates moving the input files to Bulk Ingest to an Accumulo-managed directory. However, Accumulo cannot safely move files across different Hadoop FileSystems. This is problematic because Accumulo also cannot make reliable assertions across what is the same FileSystem which is specified with different names. Naively, while might be a valid identifier for an HDFS instance, Accumulo identifies localhost:8020 as a different HDFS instance than

Deploy Configuration

Copy and from the conf/ directory on the master to all Accumulo tablet servers. The “host” configuration files files accumulo-cluster only need to be on servers where that command is run.

Sensitive Configuration Values

Accumulo has a number of properties that can be specified via the file which are sensitive in nature, instance.secret and are two common examples. Both of these properties, if compromised, have the ability to result in data being leaked to users who should not have access to that data.

In Hadoop-2.6.0, a new CredentialProvider class was introduced which serves as a common implementation to abstract away the storage and retrieval of passwords from plaintext storage in configuration files. Any Property marked with the Sensitive annotation is a candidate for use with these CredentialProviders. For version of Hadoop which lack these classes, the feature will just be unavailable for use.

A comma separated list of CredentialProviders can be configured using the Accumulo Property Each configured URL will be consulted when the Configuration object for is accessed.

Using a JavaKeyStoreCredentialProvider for storage

One of the implementations provided in Hadoop-2.6.0 is a Java KeyStore CredentialProvider. Each entry in the KeyStore is the Accumulo Property key name. For example, to store the instance.secret, the following command can be used:

  hadoop credential create instance.secret --provider jceks://file/etc/accumulo/conf/accumulo.jceks

The command will then prompt you to enter the secret to use and create a keystore in:


Then, must be configured to use this KeyStore as a CredentialProvider:

This configuration will then transparently extract the instance.secret from the configured KeyStore and alleviates a human readable storage of the sensitive property.

A KeyStore can also be stored in HDFS, which will make the KeyStore readily available to all Accumulo servers. If the local filesystem is used, be aware that each Accumulo server will expect the KeyStore in the same location.

Client Configuration

Accumulo clients are configured in a different way than Accumulo servers. Accumulo clients are created using Java builder methods or a file containing client properties.

Custom Table Tags

Accumulo has the ability for users to add custom tags to tables. This allows applications to set application-level metadata about a table. These tags can be anything from a table description, administrator notes, date created, etc. This is done by naming and setting a property with a prefix table.custom.*.

Currently, table properties are stored in ZooKeeper. This means that the number and size of custom properties should be restricted on the order of 10’s of properties at most without any properties exceeding 1MB in size. ZooKeeper’s performance can be very sensitive to an excessive number of nodes and the sizes of the nodes. Applications which leverage the user of custom properties should take these warnings into consideration. There is no enforcement of these warnings via the API.

Configuring the ClassLoader

Accumulo builds its Java classpath in This classpath can be viewed by running accumulo classpath.

After an Accumulo application has started, it will load classes from the locations specified in the deprecated general.classpaths property. Additionally, Accumulo will load classes from the locations specified in the general.dynamic.classpaths property and will monitor and reload them if they change. The reloading feature is useful during the development and testing of iterators as new or modified iterator classes can be deployed to Accumulo without having to restart the database.

Accumulo also has an alternate configuration for the classloader which will allow it to load classes from remote locations. This mechanism uses Apache Commons VFS which enables locations such as http and hdfs to be used. This alternate configuration also uses the general.classpaths property in the same manner described above. It differs in that you need to configure the general.vfs.classpaths property instead of the general.dynamic.classpaths property. As in the default configuration, this alternate configuration will also monitor the vfs locations for changes and reload if necessary.

ClassLoader Contexts

With the addition of the VFS based classloader, we introduced the notion of classloader contexts. A context is identified by a name and references a set of locations from which to load classes and can be specified in the file or added using the config command in the shell. Below is an example for specify the app1 context in the file:

# Application A classpath, loads jars from HDFS and local file system

The default behavior follows the Java ClassLoader contract in that classes, if they exists, are loaded from the parent classloader first. You can override this behavior by delegating to the parent classloader after looking in this classloader first. An example of this configuration is:


To use contexts in your application you can set the table.classpath.context on your tables or use the setClassLoaderContext() method on Scanner and BatchScanner passing in the name of the context, app1 in the example above. Setting the property on the table allows your minc, majc, and scan iterators to load classes from the locations defined by the context. Passing the context name to the scanners allows you to override the table setting to load only scan time iterators from a different location.


Accumulo must be initialized to create the structures it uses internally to locate data across the cluster. HDFS is required to be configured and running before Accumulo can be initialized.

Once HDFS is started, initialization can be performed by executing accumulo init. This script will prompt for a name for this instance of Accumulo. The instance name is used to identify a set of tables and instance-specific settings. The script will then write some information into HDFS so Accumulo can start properly.

The initialization script will prompt you to set a root password. Once Accumulo is initialized it can be started.


Starting Accumulo

Make sure Hadoop is configured on all of the machines in the cluster, including access to a shared HDFS instance. Make sure HDFS and ZooKeeper are running. Make sure ZooKeeper is configured and running on at least one machine in the cluster. Start Accumulo using accumulo-cluster start.

To verify that Accumulo is running, check the Accumulo monitor. In addition, the Shell can provide some information about the status of tables via reading the metadata tables.

Stopping Accumulo

To shutdown cleanly, run accumulo-cluster stop and the master will orchestrate the shutdown of all the tablet servers. Shutdown waits for all minor compactions to finish, so it may take some time for particular configurations.

Adding a Tablet Server

Update your conf/tservers file to account for the addition.

Next, ssh to each of the hosts you want to add and run:

accumulo-service tserver start

Make sure the host in question has the new configuration, or else the tablet server won’t start; at a minimum this needs to be on the host(s) being added, but in practice it’s good to ensure consistent configuration across all nodes.

Decommissioning a Tablet Server

If you need to take a node out of operation, you can trigger a graceful shutdown of a tablet server. Accumulo will automatically rebalance the tablets across the available tablet servers.

accumulo admin stop <host(s)> {<host> ...}

Alternatively, you can ssh to each of the hosts you want to remove and run:

accumulo-service tserver stop

Be sure to update your conf/tservers file to account for the removal of these hosts. Bear in mind that the monitor will not re-read the tservers file automatically, so it will report the decommissioned servers as down; it’s recommended that you restart the monitor so that the node list is up to date.

The steps described to decommission a node can also be used (without removal of the host from the conf/tservers file) to gracefully stop a node. This will ensure that the tabletserver is cleanly stopped and recovery will not need to be performed when the tablets are re-hosted.

Restarting process on a node

Occasionally, it might be necessary to restart the processes on a specific node. In addition to the accumulo-cluster script, Accumulo has a accumulo-service script that can be use to start/stop processes on a node.

A note on rolling restarts

For sufficiently large Accumulo clusters, restarting multiple TabletServers within a short window can place significant load on the Master server. If slightly lower availability is acceptable, this load can be reduced by globally setting table.suspend.duration to a positive value.

With table.suspend.duration set to, say, 5m, Accumulo will wait for 5 minutes for any dead TabletServer to return before reassigning that TabletServer’s responsibilities to other TabletServers. If the TabletServer returns to the cluster before the specified timeout has elapsed, Accumulo will assign the TabletServer its original responsibilities.

It is important not to choose too large a value for table.suspend.duration, as during this time, all scans against the data that TabletServer had hosted will block (or time out).

Running multiple TabletServers on a single node

With very powerful nodes, it may be beneficial to run more than one TabletServer on a given node. This decision should be made carefully and with much deliberation as Accumulo is designed to be able to scale to using 10’s of GB of RAM and 10’s of CPU cores.

Accumulo TabletServers bind certain ports on the host to accommodate remote procedure calls to/from other nodes. Running more than one TabletServer on a host requires that you set the environment variable ACCUMULO_SERVICE_INSTANCE to an instance number (i.e 1, 2) for each instance that is started. Also, set the these properties in

Multiple TabletServers cannot be started using the accumulo-cluster or accumulo-service commands at this time. The accumulo command must be used:

ACCUMULO_SERVICE_INSTANCE=1; ./bin/accumulo tserver &> ./logs/tserver1.out &
ACCUMULO_SERVICE_INSTANCE=2; ./bin/accumulo tserver &> ./logs/tserver2.out &


Accumulo processes each write to a set of log files. By default, these logs are found at directory set by ACCUMULO_LOG_DIR in

Audit Logging

Accumulo logs many user-initiated actions, and whether they succeeded or failed, to an slf4j logger named org.apache.accumulo.audit. This logger can be configured in the user’s logging framework (such as log4j or logback). In the tarball, the configuration file conf/ demonstrates basic audit logging with example configuration options for log4j.


In the event of TabletServer failure or error on shutting Accumulo down, some mutations may not have been minor compacted to HDFS properly. In this case, Accumulo will automatically reapply such mutations from the write-ahead log either when the tablets from the failed server are reassigned by the Master (in the case of a single TabletServer failure) or the next time Accumulo starts (in the event of failure during shutdown).

Recovery is performed by asking a tablet server to sort the logs so that tablets can easily find their missing updates. The sort status of each file is displayed on Accumulo monitor status page. Once the recovery is complete any tablets involved should return to an online state. Until then those tablets will be unavailable to clients.

The Accumulo client library is configured to retry failed mutations and in many cases clients will be able to continue processing after the recovery process without throwing an exception.

Migrating Accumulo from non-HA Namenode to HA Namenode

The following steps will allow a non-HA instance to be migrated to an HA instance. Consider an HDFS URL hdfs:// which is going to be moved to hdfs://nameservice1.

Before moving HDFS over to the HA namenode, use accumulo admin volumes to confirm that the only volume displayed is the volume from the current namenode’s HDFS URL.

Listing volumes referenced in zookeeper
        Volume : hdfs://

Listing volumes referenced in accumulo.root tablets section
        Volume : hdfs://
Listing volumes referenced in accumulo.root deletes section (volume replacement occurs at deletion time)

Listing volumes referenced in accumulo.metadata tablets section
        Volume : hdfs://

Listing volumes referenced in accumulo.metadata deletes section (volume replacement occurs at deletion time)

After verifying the current volume is correct, shut down the cluster and transition HDFS to the HA nameservice.

Edit to notify accumulo that a volume is being replaced. First, add the new nameservice volume to the instance.volumes property. Next, add the instance.volumes.replacements property in the form of old new. It’s important to not include the volume that’s being replaced in instance.volumes, otherwise it’s possible accumulo could continue to write to the volume.

# instance.dfs.uri and instance.dfs.dir should not be set
instance.volumes.replacements=hdfs:// hdfs://nameservice1/accumulo

Run accumulo init --add-volumes and start up the accumulo cluster. Verify that the new nameservice volume shows up with accumulo admin volumes.

Listing volumes referenced in zookeeper
        Volume : hdfs://
        Volume : hdfs://nameservice1/accumulo

Listing volumes referenced in accumulo.root tablets section
        Volume : hdfs://
        Volume : hdfs://nameservice1/accumulo
Listing volumes referenced in accumulo.root deletes section (volume replacement occurs at deletion time)

Listing volumes referenced in accumulo.metadata tablets section
        Volume : hdfs://
        Volume : hdfs://nameservice1/accumulo
Listing volumes referenced in accumulo.metadata deletes section (volume replacement occurs at deletion time)

Some erroneous GarbageCollector messages may still be seen for a small period while data is transitioning to the new volumes. This is expected and can usually be ignored.

Achieving Stability in a VM Environment

For testing, demonstration, and even operation uses, Accumulo is often installed and run in a virtual machine (VM) environment. The majority of long-term operational uses of Accumulo are on bare-metal cluster. However, the core design of Accumulo and its dependencies do not preclude running stably for long periods within a VM. Many of Accumulo’s operational robustness features to handle failures like periodic network partitioning in a large cluster carry over well to VM environments. This guide covers general recommendations for maximizing stability in a VM environment, including some of the common failure modes that are more common when running in VMs.

Known failure modes: Setup and Troubleshooting

In addition to the general failure modes of running Accumulo, VMs can introduce a couple of environmental challenges that can affect process stability. Clock drift is something that is more common in VMs, especially when VMs are suspended and resumed. Clock drift can cause Accumulo servers to assume that they have lost connectivity to the other Accumulo processes and/or lose their locks in Zookeeper. VM environments also frequently have constrained resources, such as CPU, RAM, network, and disk throughput and capacity. Accumulo generally deals well with constrained resources from a stability perspective (optimizing performance will require additional tuning, which is not covered in this section), however there are some limits.

Physical Memory

One of those limits has to do with the Linux out of memory killer. A common failure mode in VM environments (and in some bare metal installations) is when the Linux out of memory killer decides to kill processes in order to avoid a kernel panic when provisioning a memory page. This often happens in VMs due to the large number of processes that must run in a small memory footprint. In addition to the Linux core processes, a single-node Accumulo setup requires a Hadoop Namenode, a Hadoop Secondary Namenode a Hadoop Datanode, a Zookeeper server, an Accumulo Master, an Accumulo GC and an Accumulo TabletServer. Typical setups also include an Accumulo Monitor, an Accumulo Tracer, a Hadoop ResourceManager, a Hadoop NodeManager, provisioning software, and client applications. Between all of these processes, it is not uncommon to over-subscribe the available RAM in a VM. We recommend setting up VMs without swap enabled, so rather than performance grinding to a halt when physical memory is exhausted the kernel will randomly select processes to kill in order to free up memory.

Calculating the maximum possible memory usage is essential in creating a stable Accumulo VM setup. Safely engineering memory allocation for stability is a matter of then bringing the calculated maximum memory usage under the physical memory by a healthy margin. The margin is to account for operating system-level operations, such as managing process, maintaining virtual memory pages, and file system caching. When the java out-of-memory killer finds your process, you will probably only see evidence of that in /var/log/messages. Out-of-memory process kills do not show up in Accumulo or Hadoop logs.

To calculate the max memory usage of all java virtual machine (JVM) processes add the maximum heap size (often limited by a -Xmx… argument, such as in and the off-heap memory usage. Off-heap memory usage includes the following:

  • “Permanent Space”, where the JVM stores Classes, Methods, and other code elements. This can be limited by a JVM flag such as -XX:MaxPermSize:100m, and is typically tens of megabytes.
  • Code generation space, where the JVM stores just-in-time compiled code. This is typically small enough to ignore
  • Socket buffers, where the JVM stores send and receive buffers for each socket.
  • Thread stacks, where the JVM allocates memory to manage each thread.
  • Direct memory space and JNI code, where applications can allocate memory outside of the JVM-managed space. For Accumulo, this includes the native in-memory maps that are allocated with the memory.maps.max parameter in
  • Garbage collection space, where the JVM stores information used for garbage collection.

You can assume that each Hadoop and Accumulo process will use ~100-150MB for Off-heap memory, plus the in-memory map of the Accumulo TServer process. A simple calculation for physical memory requirements follows:

  Physical memory needed
    = (per-process off-heap memory) + (heap memory) + (other processes) + (margin)
    = (number of java processes * 150M + native map) + (sum of -Xmx settings for java process)
        + (total applications memory, provisioning memory, etc.) + (1G)
    = (11*150M +500M) + (1G +1G +1G +256M +1G +256M +512M +512M +512M +512M +512M) + (2G) + (1G)
    = (2150M) + (7G) + (2G) + (1G)
    = ~12GB

These calculations can add up quickly with the large number of processes, especially in constrained VM environments. To reduce the physical memory requirements, it is a good idea to reduce maximum heap limits and turn off unnecessary processes. If you’re not using YARN in your application, you can turn off the ResourceManager and NodeManager. If you’re not expecting to re-provision the cluster frequently you can turn off or reduce provisioning processes such as Salt Stack minions and masters.

Disk Space

Disk space is primarily used for two operations: storing data and storing logs. While Accumulo generally stores all of its key/value data in HDFS, Accumulo, Hadoop, and Zookeeper all store a significant amount of logs in a directory on a local file system. Care should be taken to make sure that (a) limitations to the amount of logs generated are in place, and (b) enough space is available to host the generated logs on the partitions that they are assigned. When space is not available to log, processes will hang. This can cause interruptions in availability of Accumulo, as well as cascade into failures of various processes.

Hadoop, Accumulo, and Zookeeper use log4j as a logging mechanism, and each of them has a way of limiting the logs and directing them to a particular directory. Logs are generated independently for each process, so when considering the total space you need to add up the maximum logs generated by each process. Typically, a rolling log setup in which each process can generate something like 10 100MB files is instituted, resulting in a maximum file system usage of 1GB per process. Default setups for Hadoop and Zookeeper are often unbounded, so it is important to set these limits in the logging configuration files for each subsystem. Consult the user manual for each system for instructions on how to limit generated logs.

Zookeeper Interaction

Accumulo is designed to scale up to thousands of nodes. At that scale, intermittent interruptions in network service and other rare failures of compute nodes become more common. To limit the impact of node failures on overall service availability, Accumulo uses a heartbeat monitoring system that leverages Zookeeper’s ephemeral locks. There are several conditions that can occur that cause Accumulo process to lose their Zookeeper locks, some of which are true interruptions to availability and some of which are false positives. Several of these conditions become more common in VM environments, where they can be exacerbated by resource constraints and clock drift.

Tested Versions

Each release of Accumulo is built with a specific version of Apache Hadoop, Apache ZooKeeper and Apache Thrift. We expect Accumulo to work with versions that are API compatible with those versions. However this compatibility is not guaranteed because Hadoop, ZooKeeper and Thrift may not provide guarantees between their own versions. We have also found that certain versions of Accumulo and Hadoop included bugs that greatly affected overall stability. Thrift is particularly prone to compatibility changes between versions and you must use the same version your Accumulo is built with.

Please check the release notes for your Accumulo version or use the mailing lists at for more info.

Find documentation for all releases in the archive