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Accumulo Clients

Creating Client Code

If you are using Maven to create Accumulo client code, add the following to your pom:



Before writing Accumulo client code, you will need the following information.

  • Accumulo instance name
  • Zookeeper connection string
  • Accumulo username & password

The Connector object is the main entry point for Accumulo clients. It can be created using one of the following methods:

  1. Using the file (a template can be found in the conf/ directory of the tarball distribution):
     Connector conn = Connector.builder()
  2. Using the builder methods of Connector:
     Connector conn = Connector.builder().forInstance("myinstance", "zookeeper1,zookeper2")
                         .usingPassword("myuser", "mypassword").build();
  3. Using a Java Properties object.
     Properties props = new Properties()
     props.put("", "myinstance")
     props.put("instance.zookeepers", "zookeeper1,zookeeper2")
     props.put("auth.method", "password")
     props.put("auth.username", "myuser")
     props.put("auth.password", "mypassword")
     Connector conn = Connector.builder().usingProperties(props).build();

If a file or a Java Properties object is used to create a Connector, the following client properties must be set:


When creating a Connector, the user must be authenticated using one of the following implementations of AuthenticationToken below:

  1. PasswordToken is the must commonly used implementation.
  2. CredentialProviderToken leverages the Hadoop CredentialProviders (new in Hadoop 2.6). For example, the CredentialProviderToken can be used in conjunction with a Java KeyStore to alleviate passwords stored in cleartext. When stored in HDFS, a single KeyStore can be used across an entire instance. Be aware that KeyStores stored on the local filesystem must be made available to all nodes in the Accumulo cluster.
  3. KerberosToken can be provided to use the authentication provided by Kerberos. Using Kerberos requires external setup and additional configuration, but provides a single point of authentication through HDFS, YARN and ZooKeeper and allowing for password-less authentication with Accumulo.

     KerberosToken token = new KerberosToken();
     Connector conn = Connector.builder().forInstance("myinstance", "zookeeper1,zookeper2")
                         .usingToken(token.getPrincipal(), token).build();

Writing Data

With a Connector created, it can be used to create objects (like the BatchWriter) for reading and writing from Accumulo:

BatchWriter writer = conn.createBatchWriter("table");

Data is written to Accumulo by creating Mutation objects that represent all the changes to the columns of a single row. The changes are made atomically in the TabletServer. Clients then add Mutations to a BatchWriter which submits them to the appropriate TabletServers.

Mutations can be created thus:

Text rowID = new Text("row1");
Text colFam = new Text("myColFam");
Text colQual = new Text("myColQual");
ColumnVisibility colVis = new ColumnVisibility("public");
long timestamp = System.currentTimeMillis();

Value value = new Value("myValue".getBytes());

Mutation mutation = new Mutation(rowID);
mutation.put(colFam, colQual, colVis, timestamp, value);


The BatchWriter is highly optimized to send Mutations to multiple TabletServers and automatically batches Mutations destined for the same TabletServer to amortize network overhead. Care must be taken to avoid changing the contents of any Object passed to the BatchWriter since it keeps objects in memory while batching.

Mutations are added to a BatchWriter thus:

// BatchWriterConfig has reasonable defaults
BatchWriterConfig config = new BatchWriterConfig();
config.setMaxMemory(10000000L); // bytes available to batchwriter for buffering mutations

BatchWriter writer = conn.createBatchWriter("table", config)

For more example code, see the batch writing and scanning example.


The ConditionalWriter enables efficient, atomic read-modify-write operations on rows. The ConditionalWriter writes special Mutations which have a list of per column conditions that must all be met before the mutation is applied. The conditions are checked in the tablet server while a row lock is held (Mutations written by the BatchWriter will not obtain a row lock). The conditions that can be checked for a column are equality and absence. For example a conditional mutation can require that column A is absent inorder to be applied. Iterators can be applied when checking conditions. Using iterators, many other operations besides equality and absence can be checked. For example, using an iterator that converts values less than 5 to 0 and everything else to 1, its possible to only apply a mutation when a column is less than 5.

In the case when a tablet server dies after a client sent a conditional mutation, its not known if the mutation was applied or not. When this happens the ConditionalWriter reports a status of UNKNOWN for the ConditionalMutation. In many cases this situation can be dealt with by simply reading the row again and possibly sending another conditional mutation. If this is not sufficient, then a higher level of abstraction can be built by storing transactional information within a row.

See the reservations example for example code that uses the ConditionalWriter.


By default, Accumulo writes out any updates to the Write-Ahead Log (WAL). Every change goes into a file in HDFS and is sync’d to disk for maximum durability. In the event of a failure, writes held in memory are replayed from the WAL. Like all files in HDFS, this file is also replicated. Sending updates to the replicas, and waiting for a permanent sync to disk can significantly write speeds.

Accumulo allows users to use less tolerant forms of durability when writing. These levels are:

  • none - no durability guarantees are made, the WAL is not used
  • log - the WAL is used, but not flushed; loss of the server probably means recent writes are lost
  • flush - updates are written to the WAL, and flushed out to replicas; loss of a single server is unlikely to result in data loss.
  • sync - updates are written to the WAL, and synced to disk on all replicas before the write is acknowledge. Data will not be lost even if the entire cluster suddenly loses power.

Durability can be set in multiple ways:

  1. The default durability of a table can be set in the Accumulo shell
  2. When creating a Connector, the default durability can be overriden using withBatchWriterConfig() or by setting batch.writer.durability in
  3. When a BatchWriter or ConditionalWriter is created, the durability settings above will be overriden by the BatchWriterConfig that is passed in.

     BatchWriterConfig cfg = new BatchWriterConfig();
     // We don't care about data loss with these writes:
     // This is DANGEROUS:
     Connection conn = ... ;
     BatchWriter bw = conn.createBatchWriter(table, cfg);

Reading Data

Accumulo is optimized to quickly retrieve the value associated with a given key, and to efficiently return ranges of consecutive keys and their associated values.


To retrieve data, Clients use a Scanner, which acts like an Iterator over keys and values. Scanners can be configured to start and stop at particular keys, and to return a subset of the columns available.

// specify which visibilities we are allowed to see
Authorizations auths = new Authorizations("public");

Scanner scan = conn.createScanner("table", auths);
scan.setRange(new Range("harry","john"));
scan.fetchColumnFamily(new Text("attributes"));

for (Entry<Key,Value> entry : scan) {
  Text row = entry.getKey().getRow();
  Value value = entry.getValue();

Isolated Scanner

Accumulo supports the ability to present an isolated view of rows when scanning. There are three possible ways that a row could change in Accumulo :

  • a mutation applied to a table
  • iterators executed as part of a minor or major compaction
  • bulk import of new files

Isolation guarantees that either all or none of the changes made by these operations on a row are seen. Use the IsolatedScanner to obtain an isolated view of an Accumulo table. When using the regular scanner it is possible to see a non isolated view of a row. For example if a mutation modifies three columns, it is possible that you will only see two of those modifications. With the isolated scanner either all three of the changes are seen or none.

The IsolatedScanner buffers rows on the client side so a large row will not crash a tablet server. By default rows are buffered in memory, but the user can easily supply their own buffer if they wish to buffer to disk when rows are large.

See the isolation example for example code that uses the IsolatedScanner.


For some types of access, it is more efficient to retrieve several ranges simultaneously. This arises when accessing a set of rows that are not consecutive whose IDs have been retrieved from a secondary index, for example.

The BatchScanner is configured similarly to the Scanner; it can be configured to retrieve a subset of the columns available, but rather than passing a single Range, BatchScanners accept a set of Ranges. It is important to note that the keys returned by a BatchScanner are not in sorted order since the keys streamed are from multiple TabletServers in parallel.

ArrayList<Range> ranges = new ArrayList<Range>();
// populate list of ranges ...

BatchScanner bscan = conn.createBatchScanner("table", auths, 10);

for (Entry<Key,Value> entry : bscan) {

For more example code, see the batch writing and scanning example.

At this time, there is no client side isolation support for the BatchScanner. You may consider using the WholeRowIterator with the BatchScanner to achieve isolation. The drawback of this approach is that entire rows are read into memory on the server side. If a row is too big, it may crash a tablet server.

Running Client Code

There are multiple ways to run Java code that use Accumulo. Below is a list of the different ways to execute client code.

  • build and execute an uber jar
  • add accumulo classpath to your Java classpath
  • use the accumulo command
  • use the accumulo-util hadoop-jar command

Build and execute an uber jar

If you have included accumulo-core as dependency in your pom, you can build an uber jar using the Maven assembly or shade plugin and use it to run Accumulo client code. When building an uber jar, you should set the versions of any Hadoop dependencies in your pom to match the version running on your cluster.

Add ‘accumulo classpath’ to your Java classpath

To run Accumulo client code using the java command, use the accumulo classpath command to include all of Accumulo’s dependencies on your classpath:

java -classpath /path/to/my.jar:/path/to/dep.jar:$(accumulo classpath) arg1 arg2

If you would like to review which jars are included, the accumulo classpath command can output a more human readable format using the -d option which enables debugging:

accumulo classpath -d

Use the accumulo command

Another option for running your code is to use the Accumulo script which can execute a main class (if it exists on its classpath):

accumulo arg1 arg2

While the Accumulo script will add all of Accumulo’s dependencies to the classpath, you will need to add any jars that your create or depend on beyond what Accumulo already depends on. This can be accomplished by either adding the jars to the lib/ext directory of your Accumulo installation or by adding jars to the CLASSPATH variable before calling the accumulo command.

export CLASSPATH=/path/to/my.jar:/path/to/dep.jar; accumulo arg1 arg2

Use the ‘accumulo-util hadoop-jar’ command

If you are writing map reduce job that accesses Accumulo, then you can use accumulo-util hadoop-jar to run those jobs. See the MapReduce example for more information.

Additional Documentation

This page covers Accumulo client basics. Below are links to additional documentation that may be useful when creating Accumulo clients:

  • Iterators - Server-side programming mechanism that can modify key/value pairs at various points in data management process
  • Proxy - Documentation for interacting with Accumulo using non-Java languages through a proxy server
  • MapReduce - Documentation for reading and writing to Accumulo using MapReduce.
Find documentation for all releases in the archive