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Accumulo tables can be used as the source and destination of MapReduce jobs. To use an Accumulo table with a MapReduce job, configure the job parameters to use the AccumuloInputFormat and AccumuloOutputFormat. Accumulo specific parameters can be set via these two format classes to do the following:

  • Authenticate and provide user credentials for the input
  • Restrict the scan to a range of rows
  • Restrict the input to a subset of available columns

Mapper and Reducer classes

To read from an Accumulo table create a Mapper with the following class parameterization and be sure to configure the AccumuloInputFormat.

class MyMapper extends Mapper<Key,Value,WritableComparable,Writable> {
    public void map(Key k, Value v, Context c) {
        // transform key and value data here

To write to an Accumulo table, create a Reducer with the following class parameterization and be sure to configure the AccumuloOutputFormat. The key emitted from the Reducer identifies the table to which the mutation is sent. This allows a single Reducer to write to more than one table if desired. A default table can be configured using the AccumuloOutputFormat, in which case the output table name does not have to be passed to the Context object within the Reducer.

class MyReducer extends Reducer<WritableComparable, Writable, Text, Mutation> {
    public void reduce(WritableComparable key, Iterable<Text> values, Context c) {
        Mutation m;
        // create the mutation based on input key and value
        c.write(new Text("output-table"), m);

The Text object passed as the output should contain the name of the table to which this mutation should be applied. The Text can be null in which case the mutation will be applied to the default table name specified in the AccumuloOutputFormat options.

AccumuloInputFormat options

The following code shows how to set up Accumulo

Job job = new Job(getConf());
ConnectionInfo info = Connector.builder().forInstance("myinstance","zoo1,zoo2")
                        .usingPassword("user", "passwd").info()
AccumuloInputFormat.setConnectionInfo(job, info);
AccumuloInputFormat.setInputTableName(job, table);
AccumuloInputFormat.setScanAuthorizations(job, new Authorizations());

Optional Settings:

To restrict Accumulo to a set of row ranges:

ArrayList<Range> ranges = new ArrayList<Range>();
// populate array list of row ranges ...
AccumuloInputFormat.setRanges(job, ranges);

To restrict Accumulo to a list of columns:

ArrayList<Pair<Text,Text>> columns = new ArrayList<Pair<Text,Text>>();
// populate list of columns
AccumuloInputFormat.fetchColumns(job, columns);

To use a regular expression to match row IDs:

IteratorSetting is = new IteratorSetting(30, RexExFilter.class);
RegExFilter.setRegexs(is, ".*suffix", null, null, null, true);
AccumuloInputFormat.addIterator(job, is);

AccumuloMultiTableInputFormat options

The AccumuloMultiTableInputFormat allows the scanning over multiple tables in a single MapReduce job. Separate ranges, columns, and iterators can be used for each table.

InputTableConfig tableOneConfig = new InputTableConfig();
InputTableConfig tableTwoConfig = new InputTableConfig();

To set the configuration objects on the job:

Map<String, InputTableConfig> configs = new HashMap<String,InputTableConfig>();
configs.put("table1", tableOneConfig);
configs.put("table2", tableTwoConfig);
AccumuloMultiTableInputFormat.setInputTableConfigs(job, configs);

Optional settings:

To restrict to a set of ranges:

ArrayList<Range> tableOneRanges = new ArrayList<Range>();
ArrayList<Range> tableTwoRanges = new ArrayList<Range>();
// populate array lists of row ranges for tables...

To restrict Accumulo to a list of columns:

ArrayList<Pair<Text,Text>> tableOneColumns = new ArrayList<Pair<Text,Text>>();
ArrayList<Pair<Text,Text>> tableTwoColumns = new ArrayList<Pair<Text,Text>>();
// populate lists of columns for each of the tables ...

To set scan iterators:

List<IteratorSetting> tableOneIterators = new ArrayList<IteratorSetting>();
List<IteratorSetting> tableTwoIterators = new ArrayList<IteratorSetting>();
// populate the lists of iterator settings for each of the tables ...

The name of the table can be retrieved from the input split:

class MyMapper extends Mapper<Key,Value,WritableComparable,Writable> {
    public void map(Key k, Value v, Context c) {
        RangeInputSplit split = (RangeInputSplit)c.getInputSplit();
        String tableName = split.getTableName();
        // do something with table name

AccumuloOutputFormat options

ConnectionInfo info = Connector.builder().forInstance("myinstance","zoo1,zoo2")
                        .usingPassword("user", "passwd").info()
AccumuloOutputFormat.setConnectionInfo(job, info);
AccumuloOutputFormat.setDefaultTableName(job, "mytable");

Optional Settings:

AccumuloOutputFormat.setMaxLatency(job, 300000); // milliseconds
AccumuloOutputFormat.setMaxMutationBufferSize(job, 50000000); // bytes

The MapReduce example contains a complete example of using MapReduce with Accumulo.

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