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Distributed Iterative Graph Processing (Pregel)

Pregel enables you to do online analytical processing directly on graphs stored in ArangoDB.

Distributed graph processing enables you to do online analytical processing directly on graphs stored in ArangoDB. This is intended to help you gain analytical insights on your data, without having to use external processing systems. Examples of algorithms to execute are PageRank, Vertex Centrality, Vertex Closeness, Connected Components, Community Detection. For more details, see all available algorithms in ArangoDB.

Check out the hands-on ArangoDB Pregel Tutorial to learn more.

The processing system inside ArangoDB is based on: Pregel: A System for Large-Scale Graph Processing – Malewicz et al. (Google), 2010. This concept enables us to perform distributed graph processing, without the need for distributed global locking.

This system is not useful for typical online queries, where you just work on a small set of vertices. These kind of tasks are better suited for AQL traversals.


If you run a single ArangoDB instance in single-server mode, there are no requirements regarding the modeling of your data. All you need is at least one vertex collection and one edge collection.

In cluster mode, the collections need to be sharded in a specific way to ensure correct results: The outgoing edges of a vertex need to be on the same DB-Server as the vertex. This is guaranteed by SmartGraphs.

SmartGraphs (and thus Pregel in cluster deployments) are only available in the Enterprise Edition.

Note that the performance may be better, if the number of your shards / collections matches the number of CPU cores.

arangosh API

Starting an Algorithm Execution

The Pregel API is accessible through the @arangodb/pregel package.

To start an execution you need to specify the algorithm name and a named graph (SmartGraph in cluster). Alternatively, you can specify the vertex and edge collections. Additionally, you can specify custom parameters which vary for each algorithm. The start() method always returns a unique ID (a number) which can be used to interact with the algorithm and later on.

The below variant of the start() method can be used for named graphs:

var pregel = require("@arangodb/pregel");
var params = {};
var execution = pregel.start("<algorithm>", "<graphname>", params);

params needs to be an object, the valid keys are mentioned in the page Available Algorithms.

Alternatively, you might want to specify the vertex and edge collections directly. The call syntax of the start() method changes in this case. The second argument must be an object with the keys vertexCollections and edgeCollections.

var pregel = require("@arangodb/pregel");
var params = {};
var execution = pregel.start("<algorithm>", {vertexCollections:["vertices"], edgeCollections:["edges"]}, params);

The last argument is still the parameter object. See below for a list of algorithms and parameters.

Status of an Algorithm Execution

The code returned by the pregel.start(...) method can be used to track the status of your algorithm.

var execution = pregel.start("sssp", "demograph", {source: "vertices/V"});
var status = pregel.status(execution);

It tells you the current state of the execution, the current global superstep, the runtime, the global aggregator values as well as the number of send and received messages.

The state field has one of the following values:

State Description
"running" Algorithm is executing normally.
"in error" The execution is in an error state. This can be caused by primary DB-Servers being not reachable or being non responsive. The execution might recover later, or switch to “canceled” if it was not able to recover successfully
"recovering" The execution is actively recovering, will switch back to “running” if the recovery was successful
"canceled" The execution was permanently canceled, either by the user or by an error.
"storing" The algorithm finished, but the results are still being written back into the collections. Occurs if the store parameter is set to true only.
"done" The execution is done. In version 3.7.1 and later, this means that storing is also done. In earlier versions, the results may not be written back into the collections yet. This event is announced in the server log (requires at least info log level for the pregel topic).

The object returned by the status() method might for example look something like this:

  "state" : "running",
  "gss" : 12,
  "totalRuntime" : 123.23,
  "aggregators" : {
    "converged" : false,
    "max" : true,
    "phase" : 2
  "sendCount" : 3240364978,
  "receivedCount" : 3240364975

Canceling an Execution / Discarding results

To cancel an execution which is still running, and discard any intermediate results you can use the cancel() method. This will immediately free all memory taken up by the execution, and will make you lose all intermediary data.

// start a single source shortest path job
var execution = pregel.start("sssp", "demograph", {source: "vertices/V"});

You might get inconsistent results if you requested to store the results and then cancel an execution when it is already in its storing state (or done state in versions prior to 3.7.1). The data is written multi-threaded into all collection shards at once. This means there are multiple transactions simultaneously. A transaction might already be committed when you cancel the execution job. Therefore, you might see some updated documents, while other documents have no or stale results from a previous execution.

AQL integration

When the graph processing subsystem finishes executing an algorithm, the results can either be written back into documents or kept in memory only. If the data is persisted, then you can query the documents normally to get access to the results.

If you do not want to store results, then they are only held temporarily, until you call the cancel() method. The in-memory results can be accessed via the PREGEL_RESULT() AQL function.

The result field names depend on the algorithm in both cases.

For example, you might want to query only nodes with the highest rank from the result set of a PageRank execution:

  FILTER v.result >= 0.01
  RETURN v._key

By default, the PREGEL_RESULT() AQL function returns the _key of each vertex plus the result of the computation. In case the computation was done for vertices from different vertex collections, just the _key values may not be sufficient to distinguish vertices from different collections. In this case, PREGEL_RESULT() can be given a second parameter withId, which will make it return the _id values of the vertices as well:

FOR v IN PREGEL_RESULT(<handle>, true)
  FILTER v.result >= 0.01
  RETURN v._id

Algorithm Parameters

There are a number of general parameters which apply to almost all algorithms:

  • store (bool): Defaults to true. If true, the Pregel engine will write results back to the database. If the value is false then you can query the results with PREGEL_RESULT() in AQL. See AQL integration
  • maxGSS (number): Maximum number of global iterations for this algorithm
  • parallelism (number): Number of parallel threads to use per worker. Does not influence the number of threads used to load or store data from the database (this depends on the number of shards).
  • async (bool): Algorithms which support asynchronous mode will run without synchronized global iterations. Might lead to performance increases if you have load imbalances.
  • resultField (string): Most algorithms use this as attribute name for the result. Some use it as prefix for multiple result attributes. Defaults to "result".
  • useMemoryMaps (bool): Use disk based files to store temporary results. This might make the computation disk-bound, but allows you to run computations which would not fit into main memory. It is recommended to set this flag for larger datasets.
  • shardKeyAttribute (string): shard key that edge collections are sharded after (default: "vertex")


Pregel algorithms in ArangoDB will by default store temporary vertex and edge data in main memory. For large datasets this is going to cause problems, as servers may run out of memory while loading the data.

To avoid servers from running out of memory while loading the dataset, a Pregel job can be started with the attribute useMemoryMaps set to true. This will make the algorithm use memory-mapped files as a backing storage in case of huge datasets. Falling back to memory-mapped files might make the computation disk-bound, but may be the only way to complete the computation at all.

Parts of the Pregel temporary results (aggregated messages) may also be stored in main memory, and currently the aggregation cannot fall back to memory-mapped files. That means if an algorithm needs to store a lot of result messages temporarily, it may consume a lot of main memory.

In general it is also recommended to set the store attribute of Pregel jobs to true to make a job store its value on disk and not just in main memory. This way the results are removed from main memory once a Pregel job completes. If the store attribute is explicitly set to false, result sets of completed Pregel runs will not be removed from main memory until the result set is explicitly discarded by a call to the cancel() method (or a shutdown of the server).