An implementation of TinkerPop Blueprints using Accumulo

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This is an implementation of the TinkerPop Blueprints 2.6 API using Apache Accumulo as the backend. This combines the many benefits and flexibility of Blueprints with the scalability and performance of Accumulo.

In addition to the basic Blueprints functionality, we provide a number of enhanced features, including:

  • Indexing implementations via IndexableGraph and KeyIndexableGraph
  • Support for mock, mini, and distributed instances of Accumulo
  • Numerous performance tweaks and configuration parameters
  • Support for high speed ingest
  • Hadoop integration

Feel free to contact us with bugs, suggestions, pull requests, or simply how you are leveraging AccumuloGraph in your own work.

Getting Started

First, include AccumuloGraph as a Maven dependency. Releases are deployed to Maven Central.


For non-Maven users, the binary jars can be found in the releases section in this GitHub repository, or you can get them from Maven Central.

Creating an AccumuloGraph involves setting a few parameters in an AccumuloGraphConfiguration object, and opening the graph. The defaults are sensible for using an Accumulo cluster. We provide some simple examples below. Javadocs for AccumuloGraphConfiguration explain all the other parameters in more detail.

First, to instantiate an in-memory graph:

Configuration cfg = new AccumuloGraphConfiguration()

This creates a "Mock" instance which holds the graph in memory. You can now use all the Blueprints and AccumuloGraph-specific functionality with this in-memory graph. This is useful for getting familiar with AccumuloGraph's functionality, or for testing or prototyping purposes.

To use an actual Accumulo cluster, use the following:

Configuration cfg = new AccumuloGraphConfiguration()

This directs AccumuloGraph to use a "Distributed" Accumulo instance, and sets the appropriate ZooKeeper parameters, instance name, and authentication information, which correspond to the usual Accumulo connection settings. The graph name is used to create several backing tables in Accumulo, and the setCreate option tells AccumuloGraph to create the backing tables if they don't already exist.

AccumuloGraph also has limited support for a "Mini" instance of Accumulo.

Improving Performance

This section describes various configuration parameters that greatly enhance AccumuloGraph's performance. Brief descriptions of each option are provided here, but refer to the AccumuloGraphConfiguration Javadoc for fuller explanations.

Disable consistency checks

The Blueprints API specifies a number of consistency checks for various operations, and requires errors if they fail. Some examples of invalid operations include adding a vertex with the same id as an existing vertex, adding edges between nonexistent vertices, and setting properties on nonexistent elements. Unfortunately, checking the above constraints for an Accumulo installation entails significant performance issues, since these require extra traffic to Accumulo using inefficient non-batched access patterns.

To remedy these performance issues, AccumuloGraph exposes several options to disable various of the above checks. These include:

  • setAutoFlush - to disable automatically flushing changes to the backing Accumulo tables
  • setSkipExistenceChecks - to disable element existence checks, avoiding trips to the Accumulo cluster
  • setIndexableGraphDisabled - to disable indexing functionality, which improves performance of element removal

Tweak Accumulo performance parameters

Accumulo itself features a number of performance-related parameters, and we allow configuration of these. Generally, these relate to write buffer sizes, multithreading, etc. The settings include:

  • setMaxWriteLatency - max time prior to flushing element write buffer
  • setMaxWriteMemory - max size for element write buffer
  • setMaxWriteThreads - max threads used for element writing
  • setMaxWriteTimeout - max time to wait before failing element buffer writes
  • setQueryThreads - number of query threads to use for fetching elements, properties etc.

Enable edge and property preloading

As a performance tweak, AccumuloGraph performs lazy loading of properties and edges. This means that an operation such as getVertex does not by default populate the returned vertex object with the associated vertex's properties and edges. Instead, they are initialized only when requested via getProperty, getEdges, etc. These are useful for use cases where you won't be accessing many of these properties. However, if certain properties or edges will be accessed frequently, you can set options for preloading these specific properties and edges, which will be more efficient than on-the-fly loading. These options include:

  • setPreloadedProperties - set property keys to be preloaded
  • setPreloadedEdgeLabels - set edges to be preloaded based on their labels

Enable caching

AccumuloGraph contains a number of caching options that mitigate the need for Accumulo traffic for recently-accessed elements. The following options control caching:

  • setVertexCacheParams - size and expiry for vertex cache
  • setEdgeCacheParams - size and expiry for edge cache
  • setPropertyCacheTimeout - property expiry time, which can be specified globally and/or for individual properties

High Speed Ingest

One of Accumulo's key advantages is its ability for high-speed ingest of huge amounts of data. To leverage this ability, we provide an additional AccumuloBulkIngester class that exchanges consistency guarantees for high speed ingest.

The following is an example of how to use the bulk ingester to ingest a simple graph:

AccumuloGraphConfiguration cfg = ...;
AccumuloBulkIngester ingester = new AccumuloBulkIngester(cfg);
// Add a vertex.
// Add another vertex with properties.
  .add("P1", "V1").add("P2", "V2")
// Add an edge.
ingester.addEdge("A", "B", "edge").finish();
// Shutdown and compact tables.

See the Javadocs for more details. Note that you are responsible for ensuring that data is entered in a consistent way, or the resulting graph will have undefined behavior.

Hadoop Integration

AccumuloGraph features Hadoop integration via custom input and output format implementations. VertexInputFormat and EdgeInputFormat allow vertex and edge inputs to mappers, respectively. Use as follows:

AccumuloGraphConfiguration cfg = ...;

// For vertices:
Job j = new Job();
VertexInputFormat.setAccumuloGraphConfiguration(j, cfg);

// For edges:
Job j = new Job();
EdgeInputFormat.setAccumuloGraphConfiguration(j, cfg);

ElementOutputFormat allows writing to an AccumuloGraph from reducers. Use as follows:

AccumuloGraphConfiguration cfg = ...;

Job j = new Job();
ElementOutputFormat.setAccumuloGraphConfiguration(j, cfg);

Rexster Configuration

Below is a snippet to show an example of AccumuloGraph integration with Rexster. For a complete list of options for configuration, see AccumuloGraphConfiguration$Keys