graphframes.lib package

Collection of utilities usable with message aggregation

class graphframes.lib.AggregateMessages

Collection of utilities usable with graphframes.GraphFrame.aggregateMessages().

dst

Reference for destination column, used for specifying messages.

edge

Reference for edge column, used for specifying messages.

static getCachedDataFrame(df: DataFrame) → DataFrame

Create a new cached copy of a DataFrame.

This utility method is useful for iterative DataFrame-based algorithms. See Scala documentation for more details.

WARNING: This is NOT the same as DataFrame.cache().

The original DataFrame will NOT be cached.

msg

Reference for message column, used for specifying aggregation function.

src

Reference for source column, used for specifying messages.

class graphframes.lib.Pregel(graph: GraphFrame)

Implements a Pregel-like bulk-synchronous message-passing API based on DataFrame operations.

See Malewicz et al., Pregel: a system for large-scale graph processing for a detailed description of the Pregel algorithm.

You can construct a Pregel instance using either this constructor or graphframes.GraphFrame.pregel, then use builder pattern to describe the operations, and then call run() to start a run. It returns a DataFrame of vertices from the last iteration.

When a run starts, it expands the vertices DataFrame using column expressions defined by withVertexColumn(). Those additional vertex properties can be changed during Pregel iterations. In each Pregel iteration, there are three phases:

• Given each edge triplet, generate messages and specify target vertices to send, described by sendMsgToDst() and sendMsgToSrc().

• Aggregate messages by target vertex IDs, described by aggMsgs().

• Update additional vertex properties based on aggregated messages and states from previous iteration, described by withVertexColumn().

Please find what columns you can reference at each phase in the method API docs.

You can control the number of iterations by setMaxIter() and check API docs for advanced controls.

Parameters:

graph – a graphframes.GraphFrame object holding a graph with vertices and edges stored as DataFrames.

>>> from graphframes import GraphFrame
>>> from pyspark.sql.functions import coalesce, col, lit, sum, when
>>> from graphframes.lib import Pregel
>>> edges = spark.createDataFrame([[0, 1],
...                                [1, 2],
...                                [2, 4],
...                                [2, 0],
...                                [3, 4], # 3 has no in-links
...                                [4, 0],
...                                [4, 2]], ["src", "dst"])
>>> edges.cache()
>>> vertices = spark.createDataFrame([[0], [1], [2], [3], [4]], ["id"])
>>> numVertices = vertices.count()
>>> vertices = GraphFrame(vertices, edges).outDegrees
>>> vertices.cache()
>>> graph = GraphFrame(vertices, edges)
>>> alpha = 0.15
>>> ranks = graph.pregel \
...     .setMaxIter(5) \
...     .withVertexColumn("rank", lit(1.0 / numVertices), \
...         coalesce(Pregel.msg(), lit(0.0)) * lit(1.0 - alpha) + lit(alpha / numVertices)) \
...     .sendMsgToDst(Pregel.src("rank") / Pregel.src("outDegree")) \
...     .aggMsgs(sum(Pregel.msg())) \
...     .run()

aggMsgs(aggExpr: Any) → Pregel

Defines how messages are aggregated after grouped by target vertex IDs.

Parameters:

aggExpr – the message aggregation expression, such as sum(Pregel.msg()). You can reference the message column by msg() and the vertex ID by col(“id”), while the latter is usually not used.

static dst(colName: str) → Any

References a destination vertex column in generating messages to send.

See sendMsgToSrc() and sendMsgToDst()

Parameters:

colName – the vertex column name.

static edge(colName: str) → Any

References an edge column in generating messages to send.

See sendMsgToSrc() and sendMsgToDst()

Parameters:

colName – the edge column name.

static msg() → Any

References the message column in aggregating messages and updating additional vertex columns.

See aggMsgs() and withVertexColumn()

run() → DataFrame

Runs the defined Pregel algorithm.

Returns:

the result vertex DataFrame from the final iteration including both original and additional columns.

sendMsgToDst(msgExpr: Any) → Pregel

Defines a message to send to the destination vertex of each edge triplet.

You can call it multiple times to send more than one messages.

See method sendMsgToSrc().

Parameters:

msgExpr – the message expression to send to the destination vertex given a (src, edge, dst) triplet. Source/destination vertex properties and edge properties are nested under columns src, dst, and edge, respectively. You can reference them using src(), dst(), and edge(). Null messages are not included in message aggregation.

sendMsgToSrc(msgExpr: Any) → Pregel

Defines a message to send to the source vertex of each edge triplet.

You can call it multiple times to send more than one messages.

See method sendMsgToDst().

Parameters:

msgExpr – the expression of the message to send to the source vertex given a (src, edge, dst) triplet. Source/destination vertex properties and edge properties are nested under columns src, dst, and edge, respectively. You can reference them using src(), dst(), and edge(). Null messages are not included in message aggregation.

setCheckpointInterval(value: int) → Pregel

Sets the number of iterations between two checkpoints (default: 2).

This is an advanced control to balance query plan optimization and checkpoint data I/O cost. In most cases, you should keep the default value.

Checkpoint is disabled if this is set to 0.

setEarlyStopping(value: bool) → Pregel

Set should Pregel stop earlier in case of no new messages to send or not.

Early stopping allows to terminate Pregel before reaching maxIter by checking if there are any non-null messages. While in some cases it may gain significant performance boost, in other cases it can lead to performance degradation, because checking if the messages DataFrame is empty or not is an action and requires materialization of the Spark Plan with some additional computations.

In the case when the user can assume a good value of maxIter, it is recommended to leave this value to the default “false”. In the case when it is hard to estimate the number of iterations required for convergence, it is recommended to set this value to “false” to avoid iterating over convergence until reaching maxIter. When this value is “true”, maxIter can be set to a bigger value without risks.

setMaxIter(value: int) → Pregel

Sets the max number of iterations (default: 10).

static src(colName: str) → Any

References a source vertex column in generating messages to send.

See sendMsgToSrc() and sendMsgToDst()

Parameters:

colName – the vertex column name.

withVertexColumn(colName: str, initialExpr: Any, updateAfterAggMsgsExpr: Any) → Pregel

Defines an additional vertex column at the start of run and how to update it in each iteration.

You can call it multiple times to add more than one additional vertex columns.

Parameters:

• colName – the name of the additional vertex column. It cannot be an existing vertex column in the graph.

• initialExpr – the expression to initialize the additional vertex column. You can reference all original vertex columns in this expression.

• updateAfterAggMsgsExpr – the expression to update the additional vertex column after messages aggregation. You can reference all original vertex columns, additional vertex columns, and the aggregated message column using msg(). If the vertex received no messages, the message column would be null.