HGraphDB is a client layer for using HBase as a graph database. It is an implementation of the Apache TinkerPop 3 interfaces.
Note: For HBase 1.x, use HGraphDB 2.2.2. For HBase 2.x, use HGraphDB 3.0.0.
Releases of HGraphDB are deployed to Maven Central.
<dependency> <groupId>io.hgraphdb</groupId> <artifactId>hgraphdb</artifactId> <version>3.0.0</version> </dependency>
To initialize HGraphDB, create an
HBaseGraphConfiguration instance, and then use a static factory method to create an
Configuration cfg = new HBaseGraphConfiguration() .setInstanceType(InstanceType.DISTRIBUTED) .setGraphNamespace("mygraph") .setCreateTables(true) .setRegionCount(numRegionServers) .set("hbase.zookeeper.quorum", "127.0.0.1") .set("zookeeper.znode.parent", "/hbase-unsecure"); HBaseGraph graph = (HBaseGraph) GraphFactory.open(cfg);
As you can see above, HBase-specific configuration parameters can be passed directly. These will be used when obtaining an HBase connection.
The resulting graph can be used like any other TinkerPop graph instance.
Vertex v1 = graph.addVertex(T.id, 1L, T.label, "person", "name", "John"); Vertex v2 = graph.addVertex(T.id, 2L, T.label, "person", "name", "Sally"); v1.addEdge("knows", v2, T.id, "edge1", "since", LocalDate.now());
A few things to note from the above example :
Two types of indices are supported by HGraphDB:
An index is created as follows:
graph.createIndex(ElementType.VERTEX, "person", "name"); ... graph.createIndex(ElementType.EDGE, "knows", "since");
The above commands should be run before the relevant data is populated. To create an index after data has been populated, first create the index with the following parameters:
graph.createIndex(ElementType.VERTEX, "person", "name", false, /* populate */ true, /* async */ true);
Then run a MapReduce job using the
hbase io.hgraphdb.mapreduce.index.PopulateIndex \ -t vertex -l person -p name -op /tmp -ca gremlin.hbase.namespace=mygraph
Once an index is created and data has been populated, it can be used as follows:
// get persons named John Iterator<Vertex> it = graph.verticesByLabel("person", "name", "John"); ... // get persons first known by John between 2007-01-01 (inclusive) and 2008-01-01 (exclusive) Iterator<Edge> it = johnV.edges(Direction.OUT, "knows", "since", LocalDate.parse("2007-01-01"), LocalDate.parse("2008-01-01"));
Note that the indices support range queries, where the start of the range is inclusive and the end of the range is exclusive.
An index can also be specified as a unique index. For a vertex index, this means only one vertex can have a particular property name-value for the given vertex label. For an edge index, this means only one edge of a specific vertex can have a particular property name-value for a given edge label.
graph.createIndex(ElementType.VERTEX, "person", "name", /* unique */ true);
To drop an index, invoke a MapReduce job using the
hbase io.hgraphdb.mapreduce.index.DropIndex \ -t vertex -l person -p name -op /tmp -ca gremlin.hbase.namespace=mygraph
Once an index is defined, results can be paginated. HGraphDB supports keyset pagination, for both vertex and edge indices.
// get first page of persons (note that null is passed as start key) final int pageSize = 20; Iterator<Vertex> it = graph.verticesWithLimit("person", "name", null, pageSize); ... // get next page using start key of last person from previous page it = graph.verticesWithLimit("person", "name", "John", pageSize + 1); ... // get first page of persons most recently known by John Iterator<Edge> it = johnV.edgesWithLimit(Direction.OUT, "knows", "since", null, pageSize, /* reversed */ true);
Also note that indices can be paginated in descending order by passing
By default HGraphDB does not use a schema. Schema management can be enabled by calling
HBaseGraphConfiguration.useSchema(true). Once schema management is enabled, the schema for vertex and edge labels can be defined.
graph.createLabel(ElementType.VERTEX, "author", /* id */ ValueType.STRING, "age", ValueType.INT); graph.createLabel(ElementType.VERTEX, "book", /* id */ ValueType.STRING, "publisher", ValueType.STRING); graph.createLabel(ElementType.EDGE, "writes", /* id */ ValueType.STRING, "since", ValueType.DATE);
Edge labels must be explicitly connected to vertex labels before edges are added to the graph.
graph.connectLabels("author", "writes", "book");
Additional properties can be added to labels at a later time; otherwise labels cannot be changed.
graph.updateLabel(ElementType.VERTEX, "author", "height", ValueType.DOUBLE);
Whenever vertices or edges are added to the graph, they will first be validated against the schema.
One unique feature of HGraphDB is support for counters. The use of counters requires that schema management is enabled.
graph.createLabel(ElementType.VERTEX, "author", ValueType.STRING, "bookCount", ValueType.COUNTER); HBaseVertex v = (HBaseVertex) graph.addVertex(T.id, "Kierkegaard", T.label, "author"); v.incrementProperty("bookCount", 1L);
One caveat is that indices on counters are not supported.
Counters can be used by clients to materialize the number of edges on a node, for example, which will be more efficient than retrieving all the edges in order to obtain the count. In this case, whenever an edge is added or removed, the client would either increment or decrement the corresponding counter.
Counter updates are atomic as they make use of the underlying support for counters in HBase.
HGraphDB provides integration with Apache Giraph by providing two input formats,
HBaseEdgeInputFormat, that can be used to read from the vertices table and the edges tables, respectively. HGraphDB also provides two abstract output formats,
HBaseEdgeOutputFormat, that can be used to modify the graph after a Giraph computation.
Finally, HGraphDB provides a testing utility,
InternalHBaseVertexRunner, that is similar to
InternalVertexRunner in Giraph, and that can be used to run Giraph computations using a local Zookeeper instance running in another thread.
See this blog post for more details on using Giraph with HGraphDB.
Apache Spark GraphFrames can be used to analyze graphs stored in HGraphDB. First the vertices and edges need to be wrapped with Spark DataFrames using the Spark-on-HBase Connector and a custom SHCDataType. Once the vertex and edge DataFrames are available, obtaining a GraphFrame is as simple as the following:
val g = GraphFrame(verticesDataFrame, edgesDataFrame)
See this blog post for more details on using Spark GraphFrames with HGraphDB.
HGraphDB provides support for analyzing graphs with Apache Flink Gelly. First the vertices and edges need to be wrapped with Flink DataSets by importing graph data with instances of
HBaseEdgeInputFormat. After obtaining the DataSets, a Gelly graph can be created as follows:
ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment(); Graph gelly = Graph.fromTupleDataSet(vertices, edges, env);
See this blog post for more details on using Flink Gelly with HGraphDB.
HGraphDB can be used with Google Cloud Bigtable. Since Bigtable does not support namespaces, we set the name of the graph as the table prefix below.
Configuration cfg = new HBaseGraphConfiguration() .setInstanceType(InstanceType.BIGTABLE) .setGraphTablePrefix("mygraph") .setCreateTables(true) .set("hbase.client.connection.impl", "com.google.cloud.bigtable.hbase2_x.BigtableConnection") .set("google.bigtable.instance.id", "my-instance-id") .set("google.bigtable.project.id", "my-project-id"); HBaseGraph graph = (HBaseGraph) GraphFactory.open(cfg);
One benefit of having a TinkerPop layer to HBase is that a number of graph-related tools become available, which are all part of the TinkerPop ecosystem. These tools include the Gremlin DSL and the Gremlin console. To use HGraphDB in the Gremlin console, run the following commands:
\,,,/ (o o) -----oOOo-(3)-oOOo----- plugin activated: tinkerpop.server plugin activated: tinkerpop.utilities plugin activated: tinkerpop.tinkergraph gremlin> :install org.apache.hbase hbase-client 2.2.1 gremlin> :install org.apache.hbase hbase-common 2.2.1 gremlin> :install org.apache.hadoop hadoop-common 2.7.4 gremlin> :install io.hgraphdb hgraphdb 3.0.0 gremlin> :plugin use io.hgraphdb
Then restart the Gremlin console and run the following:
gremlin> graph = HBaseGraph.open("mygraph", "127.0.0.1", "/hbase-unsecure")
HGraphDB provides two kinds of caches, global caches and relationship caches. Global caches contain both vertices and edges. Relationship caches are specific to a vertex and cache the edges that are incident to the vertex. Both caches can be controlled through
HBaseGraphConfiguration by specifying a maximum size for each type of cache as well as a TTL for elements after they have been accessed via the cache. Specifying a maximum size of 0 will disable caching.
By default, vertices and edges are eagerly loaded. In some failure conditions, it may be possible for indices to point to vertices or edges which have been deleted. By eagerly loading graph elements, stale data can be filtered out and removed before it reaches the client. However, this incurs a slight performance penalty. As an alternative, lazy loading can be enabled. This can be done by calling
HBaseGraphConfiguration.setLazyLoading(true). However, if there are stale indices in the graph, the client will need to handle the exception that is thrown when an attempt is made to access a non-existent vertex or edge.
HGraphDB also provides an
HBaseBulkLoader class for more performant loading of vertices and edges. The bulk loader will not attempt to check if elements with the same ID already exist when adding new elements.
HGraphDB uses a tall table schema. The schema is created in the namespace specified to the
HBaseGraphConfiguration. The tables look as follows:
|Row Key||Column: label||Column: createdAt||Column: [property1 key]||Column: [property2 key]||...|
|[vertex ID]||[label value]||[createdAt value]||[property1 value]||[property2 value]||...|
|Row Key||Column: label||Column: fromVertex||Column: toVertex||Column: createdAt||Column: [property1 key]||Column: [property2 key]||...|
|[edge ID]||[label value]||[fromVertex ID ]||[toVertex ID]||[createdAt value]||[property1 value]||[property2 value]||...|
|Row Key||Column: createdAt||Column: vertexID|
|[vertex label, isUnique, property key, property value, vertex ID (if not unique)]||[createdAt value]||[vertex ID (if unique)]|
|Row Key||Column: createdAt||Column: vertexID||Column: edgeID|
|[vertex1 ID, direction, isUnique, property key, edge label, property value, vertex2 ID (if not unique), edge ID (if not unique)]||[createdAt value]||[vertex2 ID (if unique)]||[edge ID (if unique)]|
|Row Key||Column: createdAt||Column: isUnique||Column: state|
|[label, property key, element type]||[createdAt value]||[isUnique value]||[state value]|
Note that in the index tables, if the index is a unique index, then the indexed IDs are stored in the column values; otherwise they are stored in the row key.
If schema management is enabled, two additional tables are used:
|Row Key||Column: id||Column: createdAt||Column: [property1 key]||Column: [property2 key]||...|
|[label, element type]||[id type]||[createdAt value]||[property1 type]||[property2 type]||...|
|Row Key||Column: createdAt|
|[from vertex label, edge label, to vertex label]||[createdAt value]|
HGraphDB was designed to support the features mentioned here.
Possible future enhancements include MapReduce jobs for the following: