Programmable Pregel Algorithms

This feature is experimental and under active development. The naming and interfaces may change at any time. Execution times are not representative of the final product.

Pregel is a system for large scale graph processing. It is already implemented in ArangoDB and can be used with predefined algorithms, e.g. PageRank, Single-Source Shortest Path and Connected components.

Programmable Pregel Algorithms (PPA) are based on the already existing ArangoDB Pregel engine. The big change here is the possibility to write and execute your own defined algorithms.

The best part: You can write, develop and execute your custom algorithms without having to plug C++ code into ArangoDB (and re-compile). Algorithms can be defined and executed in a running ArangoDB instance without the need of restarting your instance.


PPAs can be run on a single-server instance but as it is designed to run in parallel in a distributed environment, you will only be able to add computing power in a clustered environment. Also PPAs do require proper graph sharding to be efficient. Using SmartGraphs is the recommend way to run Pregel algorithms.

As this is an extension of the native Pregel framework, the same prerequisites and requirements apply.


A Pregel computation consists of a sequence of iterations, each one of them is called a superstep. During a superstep, the custom algorithm will be executed for each vertex. This is happening in parallel, as the vertices are communicating via messages and not with shared memory.

The basic methods are (we are in superstep round S here):

  • Read messages which are sent to the vertex V in the previous superstep (S-1)
  • Send messages to other vertices that will be received in the next superstep (S+1)
  • Modify the state of the vertex V
  • Vote to Halt (mark a vertex V as “done”. V will be inactive in S+1, but it is possible to re-activate)

Definition of a custom algorithm

The format of a custom algorithm right now is based on a JSON object.

Algorithm skeleton

  "resultField": "<string>",
  "maxGSS": "<number>",
  "dataAccess": {
    "writeVertex": "<program>",
    "readVertex": "<array>",
    "readEdge": "<array>"
  "vertexAccumulators": "<object>",
  "globalAccumulators": "<object>",
  "customAccumulators": "<object>",
  "phases": "<array>"

Algorithm parameters

  • resultField (string, optional): Name of the document attribute to store the result in. The system replaces the attributes value with an object, mapping accumulator names to their values.

  • maxGSS (number, required): The max amount of global supersteps.

    After the amount of max defined supersteps is reached, the Pregel execution will stop.

  • dataAccess (object, optional): Allows to define writeVertex, readVertex and readEdge.

    • writeVertex: An AIR program that is used to write the results into vertices. If writeVertex is used, the resultField must not be set.

    • readVertex: An array that consists of strings and/or additional arrays (that represents a path).
      • string: Represents a single attribute at the top level.
      • array of strings: Represents a nested path
    • readEdge: An array that consists of strings and/or additional arrays (that represents a path).
      • string: Represents a single attribute at the top level.
      • array of strings: Represents a nested path

    readVertex and readEdge are used to modify the associated data for a vertex or edge. If not provided the default behavior is to load the whole document.

  • vertexAccumulators (object, optional): Definition of all used vertex accumulators.

  • globalAccumulators (object, optional): Definition all used global accumulators. Global Accumulators are able to access variables at shared global level.

  • customAccumulators (object, optional): Definition of all used custom accumulators.

  • phases (array): Array of a single or multiple phase definitions.

  • debug (optional): See Debugging.


Phases will run sequentially during your Pregel computation. The definition of multiple phases is allowed. Each phase requires instructions based on the operations you want to perform. The initialization program (1) will run a single time in the very first round. All upcoming rounds will execute the update program (2).

In each phase, the Pregel program execution will follow the order:

Step 1: Initialization

  1. onPreStep (Coordinator, executed on Coordinator instances)
  2. initProgram (Worker, executed on DB-Server instances)
  3. onPostStep (Coordinator)

Step 2 (+n): Computation

  1. onPreStep (Coordinator)
  2. updateProgram (Worker)
  3. onPostStep (Coordinator)

Phase parameters

  • name (string, required): Phase name.

    The given name of the defined phase.

  • onPreStep: Program to be executed.

    The onPreStep program will run once before each Pregel execution round.

  • initProgram: Program to be executed.

    The init program will run initially once per all vertices that are part of your graph.

  • updateProgram: Program to be executed.

    The updateProgram will be executed during every Pregel execution round and each per vertex.

  • onPostStep: Program to be executed.

    The onPostStep program will run once after each Pregel execution round.

All programs are specified as AIR programs.

The return value of initProgram resp. updateProgram is inspected. It must be one of the following:

  • "vote-halt" or false: indicates that this vertex voted halt.
  • "vote-active" or true: indicates that this vertex voted active and is active in the next round.


Using the debug field in the algorithm specification you instruct the Pregel system to generate additional tracing information for debugging purpose. Currently, only sent messages can be traced but in future this will be expanded as needed.

  debug: {
    traceMessages: {
      "my-vertex-id": {}

This will generate a report for every message that is sent to the vertex my-vertex-id. Additionally you can specify a filter by adding a filter field.

  debug: {
    traceMessages: {
      "my-vertex-id": {
        filter: {
          bySender: ["my-sender-vertex"],
          byAccumulator: ["some-accumulator"]

This for example only generates trace reports for messages that were sent by my-sender-vertex and use the some-accumulator accumulator. You can add more than one vertex or accumulator to that list. The filters are combined using and semantics, i.e. only those messages that pass all filters are traced.


  • traceMessages (optional) a mapping from vertex-id to a dict described below
    • filter (optional)
      • bySender (optional) a list of vertex document ids. Only messages sent by those vertices are traced.
      • byAccumulator (optional) a list of accumulator names. Only messages sent to those accumulators are traced.

AIR Program

As the name already indicates, the AIR program is the part where the actual algorithmic action takes place. An AIR program is represented with the Arango Intermediate Representation (AIR).

Arango Intermediate Representation

We developed a Lisp-like intermediate representation to be able to transport programs into the existing Pregel implementation in ArangoDB. These programs are executed using the interpreter inside the AIR Pregel algorithm.

At the moment this interpreter is a prototype and hence not optimized and (probably) slow. It is very flexible in terms of what we can implement, provide and test: We can provide any function as a primitive in the language, and all basic operations are available as it is customary in the LISP tradition.

The intention is not that this language is presented to users as is. This is only the representation we are using in our early stage of that experimental feature state.

It is merely an intermediate representation which is very flexible for good prototyping. A surface syntax is subject to development and even flexible in terms of providing more than one. In particular this way we get a better feeling for which functionality is needed by clients and users of graph analytics.

A surface language / syntax will be available later.

AIR specification

The following list of functions and special forms is available in all contexts. AIR is based on Lisp, but represented in JSON and supports its data types.

Strings, numeric constants, booleans and objects (dicts) are self-evaluating, i.e. the result of the evaluation of those values is the value itself. Arrays are not self-evaluating. In general you should read an array like a function call:

["foo", 1, 2, "bar"] // read as foo(1, 2, "bar")

The first element of a list specifies the function. This can either be a string containing the function name, or a lambda object.

To prevent unwanted evaluation or to actually write down a list there are multiple options:

  • list
  • quote
  • quasi-quote
["list", 1, 2, ["foo", "bar"]] // evaluates to [1, 2, foo("bar")] -- evaluated parameters
["quote", 1, 2, ["foo", "bar"]] // evaluates to [1, 2, ["foo", "bar"]] -- unevaluated parameters

They are described in more detail below.

The documentation refers to an array of length two as pair. The first entry is called first and the second entry second.

Truthiness of values

A value is considered false if it is boolean false or absent (null) All other values are considered true.

Special forms

A special form is special in the sense that it does not necessarily evaluate its parameters.

let statement

binding values to variables

["let", [[name, value]...], expr...]

Expects as first parameter a list of name-value-pairs. Both members of each pair are evaluated. first has to evaluate to a string. Declared names become visible at the first expr. The following expressions are then evaluated in a context where the named variables are assigned to their given values. When evaluating the expression, let behaves like seq.

Variables can be dereference using var-ref.

> ["let", [["x", 12], ["y", 5]], ["+", ["var-ref", "x"], ["var-ref", "y"]]]
 = 17

seq statement

sequence of commands

["seq", expr ...]

seq evaluates expr in order. The result value is the result value of the last expression. An empty seq evaluates to null.

> ["seq", ["report", "Hello World!"], 2, 3]
Hello World!
 = 3

if statement

classical if-elseif-else-statement

["if", [cond, body], ...]

Takes pairs [cond, body] of conditions cond and expression body and evaluates the first body for which cond evaluates to a value that is considered true. It does not evaluate the other conds. If no condition matches, it evaluates to null. To simulate an else statement, set the last condition to true.

> ["if", [
        ["lt?", ["var-ref", "x"], 0],
        ["-", 0, ["var-ref", "x"]]
    ], [
        true, // else
        ["var-ref", "x"]
 = 5

match statement

not-so-classical switch-statement

["match", proto, [c, body]...]

First evaluates proto, then evaluates each c until ["eq?", val, c] is considered true. Then the corresponding body is evaluated and its return value is returned. If no branch matches, null is returned. This is a C-like switch statement except that its case-values are not treated as constants.

> ["match", 5,
  [1, ["A"]],
  [2, ["B"]],
  [3, ["C"]],
  [4, ["D"]],
  [5, ["E"]]
  = "E"

for-each statement

["for-each", [[var, list]...] expr...]

Behaves similar to let but expects a list as value for each variable. It then produces the cartesian product of all lists and evaluates its expression for each n-tuple. The return value is always null. The order is guaranteed to be lexicographic order. If the list of variables is empty, the expressions are evaluated once. If one list is empty, nothing is evaluated.

> ["for-each", [["x", ["list", 1, 2]], ["y", ["list", 3, 4]]], ["report", ["var-ref", "x"], ["var-ref", "y"]]]
1 3
1 4
2 3
2 4
(no value)

quote and quote-splice statements

escape sequences for lisp

["quote", expr]
["quote-splice", list]

quote/quote-splice copies/splices its parameter verbatim into its output, without evaluating them. quote-splice fails if it is called in a context where it can not splice into something, for example at top-level.

> ["quote", ["foo"]]
 = ["foo"]
> ["list", "foo", ["quote-splice", ["bar"]] ]
 = ["foo","bar"]

quasi-quote, unquote and unquote-splice statements

like quote but can be unquoted

["quasi-quote", expr]
["unquote", expr]
["unquote-splice", expr]

quasi-quote is like quote but can be unquoted using unquote/unquote-splice.

Unlike quote, quasi-quote scans all the unevaluated values passed as parameters but copies them. When it finds a unquote or unquote-splice it evaluates its parameters and copies/splices the resulting value into the output.

["quasi-quote", [
  ["this", "list", "is", "copied"], // this is not evaluated as call
  ["this", "is",                    // this neither
    ["unquote-splice", ["f", 2]]    // this will splice f(2) into the result

= [["this", "list", "is", "copied"], ["this", "is", f(2)]]
> ["quasi-quote", [["foo"], ["unquote", ["list", 1, 2]], ["unquote-splice", ["list", 1, 2]]]]
 = [["foo"],[1,2],1,2]

cons statement

constructor for lists

["cons", value, list]

Classical lisp instruction that prepends value to the list list.

> ["cons", 1, [2, 3]]
 = [1, 2, 3]

and and or statements

basic logical operations

["and", expr...]
["or", expr...]

Computes the logical and/or expression of the given expression. As they are special forms, those expression shortcut, i.e. and/or terminates the evaluation on the first value considered false/true. The empty list evaluates as true/false. The rules for truthiness are applied.

There is also a not, but it is not a special form.

Language Primitives

Language primitives are methods which can be used in any context. As those are functions, all parameters are always evaluated before passed to the function.

Basic Algebraic Operators

left-fold with algebraic operators and the first value as initial value

["+", ...]
["-", ...]
["*", ...]
["/", ...]

All operators accept multiple parameters. The commutative operators +/* calculate the sum/product of all values passed. The empty list evaluates to 0/1. The operator - subtracts the remaining operands from the first, while / divides the first operand by the remaining. Again empty lists evaluate to 0/1.

> ["+", 1, 2, 3]
 = 6
> ["-", 5, 3, 2]
 = 0

Logical operators

convert values to booleans according to their truthiness

["true?", expr]
["false?", expr]
["not", expr]
  • true? returns true if expr is considered true, returns false otherwise.
  • false? returns true if expr is considered false, returns true otherwise.
  • not is an alias for false?.
> ["true?", 5]
 = true
> ["true?", 0]
 = true
> ["true?", false]
 = false
> ["true?", "Hello world!"]
 = true
> ["false?", 5]
 = false

Comparison operators

compares on value to other values

["eq?", proto, expr...]
["gt?", proto, expr...]
["ge?", proto, expr...]
["le?", proto, expr...]
["lt?", proto, expr...]
["ne?", proto, expr...]

Compares proto to all other expressions according to the selected operator. Returns true if all comparisons are true. Returns true for the empty list. Relational operators are only available for numeric values. If proto is a boolean value the other values are first converted to booleans using true?, i.e. you compare their truthiness.

The operator names translate as follows:

  • eq? -- ["eq?", left, right] evaluates to true if left is equal to right
  • gt? -- ["gt?", left, right] evaluates to true if left is greater than right
  • ge? -- ["ge?", left, right] evaluates to true if left is greater than or equal toright
  • le? -- ["le?", left, right] evaluates to true if left is less than or equal to right
  • lt? -- ["lt?", left, right] evaluates to true if left is less than right
  • ne? -- ["ne?", left, right] evaluates to true if left is not equal to right

Given more than two parameters

[<op>, proto, expr_1, expr_2, ...]

is equivalent to

["and", [<op>, proto, expr_1], [<op>, proto, expr_2], ...]

except that proto is only evaluated once.

> ["eq?", "foo", "foo"]
 = true
> ["lt?", 1, 2, 3]
 = true
> ["lt?", 1, 3, 0]
 = false
> ["ne?", "foo", "bar"]
 = true


sequential container of inhomogeneous values

["list", expr...]
["list-cat", lists...]
["list-append", list, expr...]
["list-ref", list, index]
["list-set", arr, index, value]
["list-empty?", value]
["list-length", list]

list constructs a new list using the evaluated exprs. list-cat concatenates given lists. list-append returns a new list, consisting of the old list and the evaluated exprs. list-ref returns the value at index. Accessing out of bound is an error. Offsets are zero based. list-set returns a copy of the old list, where the entry and index index is replaced by value. Writing an index that is out of bounds is an error. list-empty? returns true if and only if the given value is an empty list. list-length returns the length of the list.


sort a list

["sort", compare, list]
`sort` sorts a list in ascending order by using the compare function. `compare`
is called with two parameters `a` and `b`. `a` is considered less than `b` is
the return value of this call is considered true. The sort is **not** stable.

> ["sort", "lt?", ["list", 3, 1, 2]]
 = [1, 2, 3]


["dict", [key, value]...]
["dict-merge", dict...]
["dict-keys", dict]
["dict-directory", dict]

["attrib-ref", dict, key]
["attrib-ref", dict, path]
["attrib-ref-or", dict, key, default]
["attrib-ref-or", dict, path, default]
["attrib-ref-or-fail", dict, key]
["attrib-ref-or-fail", dict, path]
["attrib-set", dict, key, value]
["attrib-set", dict, path, value]

dict creates a new dict using the specified key-value pairs. It is undefined behavior to specify a key more than once. dict-merge merges two or more dicts, keeping the latest occurrence of each key. dict-keys returns a list of all top level keys. dict-directory returns a list of all available paths in preorder, intended to be used with nested directories.

attrib-ref returns the value of key in dict. If key is not present null is returned. attrib-set returns a copy of dict but with key set to value. Both functions have a variant that accepts a path. A path is a list of strings. The function will recurse into the dict using that path. attrib-set returns the whole dict but with updated subdict.

attrib-ref-or is similar to attrib-ref except that it returns default if the key was not present. attrib-ref-or-fail returns an error instead.

> ["attrib-ref", {"foo": "bar"}, "foo"]
 = "bar"
> ["dict", ["quote", "foo", "bar"], ["quote", "x", 2]]
 = {"foo":"bar", "x": 2}
> ["attrib-ref-or", {"foo": "bar"}, "baz", 5]
 = 5


["lambda", captures, parameters, body]

lambda create a new function object. captures is a list of variables that are copied into the lambda at creating time. parameters is a list of names that the parameters are bound to. Both can be accessed using their name via var-ref. body is evaluated when the lambda is called.

Lambdas can be used wherever a function is expected.

> [["lambda", ["quote", []], ["quote", ["x"]], ["quote", ["+", ["var-ref", "x"], 4]]], 6]
 = 10


["reduce", value, lambda, initialValue]

The reduce method executes a reducer function (lambda - required) on each element of the array resp. object in natural resp. undefined order. In general, it is being used to generate a single output value, yet it can be used to generate any supported type.

The lambda function accepts three parameters, the current index (which is either the position in an array, or the current key in case of an object), the value and the current reduced value.


Addition of all array elements, start value set to 100.

  ["list", 1, 2, 3],
      ["quote", []],
      ["quote", ["key", "value", "accum" ]],
        ["+", ["var-ref", "value"], ["var-ref", "accum"] ]

Will produce:

 => 106


  • Iteration 1:
    • Take 100 as the initial accumulator value
    • Calculate and return the sum of 100 and 1
  • Iteration 2:
    • Take result of the first iteration as accumulator value
    • Calculate and return the sum of 101 and 2
  • Iteration 3:
    • Take result of the second iteration as accumulator value
    • Calculate and return the sum of 103 and 3
    • Return 106 as we’ve reached the end of our array

Advanced example:

Calculate the sum of all available object values

  {"a": 1, "b": 2, "c": 3},
    ["quote", []],
    ["quote", ["key", "value", "accum" ]],
          ["var-ref", "accum"],
          ["var-ref", "key"],
          ["+", ["var-ref", "value"], ["attrib-ref", ["var-ref", "accum"], ["var-ref", "key"]] ]
  {"a": 1, "b": 2, "c": 3, "d": 4}

Will produce:

=> {"a":2, "b":4, "c":6, "d":4}


random functions that fit no other category

["string-cat", strings...]
["int-to-str", int]
["min", numbers...]
["max", numbers...]
["avg", numbers...]
["rand-range", min, max]

string-cat concatenates the given strings. int-to-string converts an integer to its decimal representation. min/max/avg computes the minimum/maximum/average of its values. rand/rand-range produces a pseudo random number uniformly distributed in [0,1]/[min,max].

> ["string-cat", "hello", " ", "world"]
 = "hello world"
> ["min", 1, 2, 3]
 = 1
> ["max", 1, 2, 3]
 = 3
> ["avg", 1, 2, 3]
 = 2
> ["rand"]
 = 0.8401877171547095
> ["rand-range", 5, 7]
 = 5.788765853638186


["id", value]
["apply", func, list]
["map", func, list]
["map", func, dict]
["filter", func, list]
["filter", func, dict]

id returns its argument. apply invokes func using the values from list as arguments. map invokes func for every value/key-value-pair in the list/dict. func should accept two parameters (index, value)/(key, value). filter returns a new list/dict that contains all entries for which the return value of func invoked with (index, value)/(key, value) is considered true.

> ["id", 12]
 = 12
> ["apply", "min", ["quote", 1, 2, 3]]
 = 1
> ["map", ["lambda", ["list"], ["list", "idx", "value"], ["quote", ["int-to-str", ["var-ref", "value"]]]], ["list", 1, 2, 3, 4]]
 = ["1", "2", "3", "4"]
> ["filter", ["lambda",
        ["list", "idx", "value"],
        ["quote", ["gt?", ["var-ref", "value"], 3]]
      ], ["list", 1, 2, 3, 4, 5, 6]]
 = [4,5,6]


["var-ref", name]
["bind-ref", name]

var-ref evaluates to the current value of the variable with name name. It is an error to reference a variable that is not defined in the current context. bind-ref is an alias of var-ref.

Debug operators

["report", values...]
["error", msg...]
["assert", cond, msg...]

report print in a context dependent way the string representation of its arguments joined by spaces. Strings represent themselves, numbers are converted to decimal representation, booleans are represented as true or false. Dicts and lists are converted to JSON.

This function is not supported in all contexts, yet.

error creates an error and aborts execution immediately. Errors are reported in a context dependent way. The error message is constructed from the remaining parameters like print, except that it is not printed but associated with the error. This like a panic or an uncaught exception.

assert checks if cond is considered true if it an error with the remaining parameters as message is raised. It is equivalent to ["if", [cond, ["error", msg...]]].

Math Library

The following mathematical functions are available in all contexts. They all interpret the data as a double and directly forward their input to the respective C/C++ library implementation.

  • abs
  • acos
  • acosh
  • asin
  • asinh
  • atan
  • atan2
  • atanh
  • cbrt
  • ceil
  • cos
  • cosh
  • exp
  • exp2
  • expm1
  • floor
  • fmod
  • hypot
  • log
  • log10
  • log1p
  • log2
  • pow
  • round
  • sin
  • sinh
  • sqrt
  • tan
  • tanh
  • trunc

Foreign calls in Vertex Computation context

The following functions are only available when running as a vertex computation (i.e. as a initProgram, updateProgram, …). this usually refers to the vertex we are attached to.

Vertex Accumulators

["accum-ref", name]
["accum-set!", name, value]
["accum-clear!", name]
  • accum-ref evaluates to the current value of the accumulator name.
  • accum-set! sets the current value of the accumulator name to value.
  • accum-clear! resets the current value of the accumulator name to a well-known one. Currently numeric limits for max and min accumulators, 0 for sum, false for or, true for and, and empty for list and VelocyPack.

Global Accumulators

["global-accum-ref", name]
["send-to-global-accum", name, value]
  • global-accum-ref evaluates the global accumulator name.
  • send-to-global-accum sends value to the global accumulator name.

Also see the remarks about update visibility.

Message Passing

["send-to-accum", name, to-pregel-id, value]
["send-to-all-neighbors", name, value]
  • send-to-accum send the value value to the accumulator name at vertex with pregel-id to-pregel-vertex. There is not edge required between the sender and the receiver.
  • send-to-all-neighbors sends the value value to the accumulator name in all neighbors reachable by an edge, i.e. along outbound edges. Note that if there are multiple edges from us to the neighbor, the value is sent multiple times.

This Vertex

  • this-doc returns the data associated with the vertex.
  • this-outdegree returns the number of outgoing edges.
  • this-outbound-edges-count alias for this-outdegree.
  • this-outbound-edges returns a list of outbound edges of the form
      "document": <edge-document>,
      "to-pregel-id": <to-vertex-pregel-id>
  • this-vertex-id returns the vertex document identifier.
  • this-unique-id returns a unique but opaque numeric value associated with this vertex.
  • this-pregel-id returns an identifier used by Pregel to send messages.


  • ["vertex-count"] returns the total number of vertices in the graph under consideration.
  • ["global-superstep"] the current superstep the algorithm is in.
  • ["phase-superstep"] the current superstep the current phase is in.
  • ["current-phase"] the current phase name.

Foreign calls in Coordinator context

The following functions are only available when running in the Coordinator context to coordinate phases and phase changes and to access and modify global accumulators.

Phase Management

["goto-phase", phase]

goto-phase sets the current phase to phase. finish finishes the Pregel computation.

Global Accumulators

["global-accum-ref", name]
["global-accum-set!", name, value]
["global-accum-clear!", name]

global-accum-ref, global-accum-set!, global-accum-clear! like for accumulators but for global accumulators.

Foreign calls in Custom Accumulator context

The following functions are only available when running inside a custom accumulator.

  • ["parameters"] returns the object passed as parameter to the accumulator definition
  • ["current-value"] returns the current value of the accumulator
  • ["get-current-value"] returns the current value but calls the getProgram to do so.
  • ["input-value"] returns the input value. This is the value received as update in updateProgram. Or the value the accumulator is set to in setProgram.
  • ["input-sender"] returns the vertex-id of the sending vertex. This is only available in updateProgram.
  • ["input-state"] return the input state for a merge operation. This is only available in aggregateStateProgram.
  • ["this-set!", value] set the new value of the accumulator to value.
  • ["this-set-value!", value] set the new value of the accumulator but calls the setProgram to do so.


In PPAs there are special types, called: Accumulators. There are two types of Accumulators:

  • VertexAccumulators: one instance per vertex.
  • GlobalAccumulators: a single instance globally.

Accumulators are used to consume and process messages which are being sent to them during the computational steps (initProgram, updateProgram, onPreStep, onPostStep) of a superstep. After a superstep is done, all messages will be processed.

The manner on how they are going to be processed depends on their accumulatorType.

Vertex Accumulators

Vertex Accumulators are following the general definition of an Accumulator. There is only one exception: A vertex is able to modify their own local accumulator directly during the computational steps, but only their own.

In short: Modifications which will be done via messages, will be visible in the next superstep round. Changes done locally, are visible directly - but cannot be done from one vertex to another.


Imagine a simple part of a graph like this:

       B  ←  E
  A →  C

The vertex A has edges pointing to the vertices B, C and D. Additionally, the vertex E is pointing to the vertex B. If we want to calculate now, how many incoming edges B, C and D have, we need to sent a message with the value 1, which represents an incoming edge, along all outgoing edges of our vertices. As only A and E do have outgoing edges, only those two vertices will send messages:

  1. Phase - Computation (Superstep S)

    Vertex A:

    • Sending 1 to B
    • Sending 1 to C
    • Sending 1 to D

    Vertex E:

    • Sending 1 to B

    As we want to sum up all received values, the sumAccumulator needs to be used. It will automatically compute the value out of all received messages:

  2. Phase - Aggregation

    • Vertex B receives two messages
      • Result is: 2. (1+1)
    • Vertex C receives one messages
      • Result is: 1. (1)
    • Vertex D receives one messages
      • Result is: 1. (1)
  3. Phase - onPostStep (Superstep S)

    Aggregated Accumulators are visible now. Additional modifications can be implemented here.

  4. Phase - onPreStep (Superstep S+1)

    Aggregated Accumulators are visible now. They could be modified in the previous onPostStep routine. Latest changes will be visible here as well. Further modifications can be done here.

  5. Phase - Computation (Superstep S+1)

    The latest Accumulator states are visible. New messages can be sent. They will be visible in the next round.

Vertex Accumulator Definition

Each vertex accumulator requires a name as string:

  "<name>": {
    "accumulatorType": "<accumulator-type>",
    "valueType": "<valueType>",
    "customType": "<custom-accumulator-type>"

Vertex Accumulator Parameters

  • accumulatorType (string, required): The name of the used accumulator type. Valid values are:
    • max: stores the maximum of all messages received.
    • min: stores the minimum of all messages received.
    • sum: sums up all messages received.
    • and: computes and on all messages received.
    • or: computes or and all messages received.
    • store: holds the last received value (non-deterministic).
    • list: stores all received values in list (order is non-deterministic).
    • custom: see below.
  • valueType (string, required): The name of the value type. Valid value types are:
    • any (JSON data)
    • int (Integer type)
    • double: (Double type)
    • bool: (Boolean type)
    • string: (String type)
  • customType (string, optional): The name of the used custom accumulator type. Has to be set if and only if accumulatorType == custom.

Global Accumulator

Global Accumulators are following the general definition of an Accumulator. Compared to a Vertex Accumulator they do not have local access to the Accumulator. Changes can only take place when sending messages or in pre-step and post-step programs and therefore can only be visible in the next superstep round (or in the onPostStep routine in the current round).

Custom Accumulator

Because the above list of accumulators feels limited and may not suite your case best you can create your own custom accumulator. You can define a custom accumulator in the customAccumulators field of the algorithm, which is an object, mapping the name of the custom accumulator to its definition. To use it, set the accumulatorType to custom and the valueType to any. In customType put the name of the custom accumulator.

The definition of a custom vertex accumulator contains the following fields:

  • updateProgram this code is executed whenever the accumulator receives a message. The input-value and input-sender functions are available here. This program should either return "hot" when the accumulator changed, i.e. its vertex will be activated in the next step, or "cold" if not.
  • clearProgram this code is executed whenever the accumulator is cleared, for example when accum-clear is called.
  • setProgram this code is executed whenever the accumulator is set to a specific value. The input-value function is available to receive the new value, for example when accum-set! is called. By default this program replaces the internal state of the accumulator with the given value.
  • getProgram this code is executed whenever the accumulator is read from. Its return value is the actual value that will be returned by for example accum-ref. By default this program returns the internal state of the accumulator.
  • finalizeProgram this code is executed when the value of the accumulator is written back into the vertex document. It defaults to getProgram.

Each custom accumulator has an internal buffer. You can access this buffer using the current-value function. To set a new value use this-set!. Note that this-set! will not invoke the setProgram but instead copy the provided value to the internal buffer.

A simple sum accumulator could look like this:

  "updateProgram": ["if",
      [["eq?", ["input-value"], 0],
              ["this-set!", ["+", ["current-value"], ["input-value"]]],
  "clearProgram": ["this-set!", 0],
  "getProgram": ["current-value"],
  "setProgram": ["this-set!", ["input-value"]],
  "finalizeProgram": ["current-value"],

Global Custom Accumulators

You can upgrade a custom vertex accumulator to a global accumulator as follows. Before a new superstep begins the global accumulators are distributed to the DB-Servers by the coordinator. During the superstep, vertex programs can read from those accumulators and send messages to them. Those messages are then accumulated per DB-Server in a cleared version of the accumulator, i.e. sending a message does call updateProgram but the write accumulator is cleared when the superstep begins.

After the superstep the accumulated values are collected by the coordinator and then aggregated. Finally the new value of the global accumulator is available in the onPostStep program.

There are more fields, some of them required, involved in when using accumulator as global accumulator.

  • setStateProgram this code is executed when the DB-Server receives a new value for the global accumulator. input-state is available in this context. The default implementation replaces the internal state of the accumulator with input-state.
  • getStateProgram this code is executed when the coordinator serializes the value of the global accumulator before distributing it to the DB-Servers. The default implementation just copies the internal state.
  • getStateUpdateProgram this code is executed when the DB-Server serializes the value of the accumulator during the collect phase, sending its result back to the Coordinator. The default implementation is to call getStateProgram.
  • aggregateStateProgram this code is executed on the coordinator after it received the update states. This code merges the different aggregates.

Coming back to our sum accumulator we would expand it like so:

  updateProgram: ["if",
      [["eq?", ["input-value"], 0],
              ["this-set!", ["+", ["current-value"], ["input-value"]]],
  clearProgram: ["this-set!", 0],
  getProgram: ["current-value"],
  setProgram: ["this-set!", ["input-value"]],
  finalizeProgram: ["current-value"],
  aggregateStateProgram: ["seq",
    ["this-set!", ["+", ["current-value"], ["input-state"]]],

Execute a PPA

Except the precondition to have your custom defined algorithm, the execution of a PPA follows the basic Pregel implementation. To start a PPA, you need to require the Pregel module in arangosh.

const pregel = require("@arangodb/pregel");
return pregel.start("air", "<graphName>", "<custom-algorithm>");

Status of a PPA

Executing a PPA using the pregel.start() method will deliver unique ID to the status of the algorithm execution.

let pregelID = pregel.start("air", graphName, "<custom-algorithm>");
var status = pregel.status(pregelID);

The result will tell you the current status of the algorithm execution. It will tell 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. Also see Status of an algorithm execution.

Additionally, the status objects for custom algorithms is extended and contains more info as the general pregel one. More details in the next section.

Error reporting

Before the execution of a PPAs starts, it will be validated and checked for potential errors. This helps a lot during development. If a PPA fails, the status will be “fatal error”. In that case there will be an additional field called reports. All debugging messages and errors will be listed there. Also you’ll get detailed information when, where and why the error occurred.


  "reports": [{
    "msg": "in phase `init` init-program failed: pregel program returned \"vote-halts\", expecting one of `true`, `false`, `\"vote-halt\", or `\"vote-active\"`\n",
    "level": "error",
    "annotations": {
      "vertex": "LineGraph10_V/0:4020479",
      "pregel-id": {
        "key": "0:4020479",
        "shard": 1
      "phase-step": 0,
      "phase": "init",
      "global-superstep": 0

Also we have added a few debugging primitives to help you increase your developing speed. For example, there is the possibility to add “prints” to your program. Furthermore have a look at the documentation of the debug field for the algorithm. See Debug operators.

Developing a PPA

There are two ways of developing your PPA. You can either run and develop in the ArangoShell (as shown above), or you can use the Foxx Service “Pregelator”. The Pregelator can be installed separately and provides a nice UI to write a PPA, execute it and get direct feedback in both “success” and “error” cases.


The Pregelator Service is available on GitHub:

The bundled ZIP files are kept in the directory: zippedBuilds and can be installed via foxx-cli, the standard web-ui or via arangosh.


As there are almost no limits regarding the definition of a PPA, here we will provide a basic example of the “vertex-degree” algorithm and demonstrate how the implementation would look like.

Note: We have implemented also more complex algorithms in PPA to demonstrate advanced usage. As those are complex algorithms, they are not included as examples in the documentation. But for the curious ones, they can be found here:

Vertex Degree

The algorithm calculates the vertex degree for incoming and outgoing edges. First, take a look at the complete vertex degree implementation. Afterwards we will split things up and go into more details per each individual section.

    "maxGSS": 1,
    "vertexAccumulators": {
      "outDegree": {
        "accumulatorType": "store",
        "valueType": "ints"
      "inDegree": {
        "accumulatorType": "store",
        "valueType": "ints"
    "phases": [{
      "name": "main",
      "initProgram": ["seq",
        ["accum-set!", "outDegree", ["this-outbound-edges-count"]],
        ["accum-set!", "inDegree", 0],
        ["send-to-all-neighbours", "inDegree", 1]
      "updateProgram": ["seq",
    "dataAccess": {
      "writeVertex": [
        "attrib-set", ["attrib-set", ["dict"], "inDegree", ["accum-ref", "inDegree"]],
        "outDegree", ["accum-ref", "outDegree"]

Used Accumulators

In the example, we are using two vertex accumulators: outDegree and inDegree, as we want to calculate and store two values.

"outDegree": {
  "accumulatorType": "store",
  "valueType": "ints"

A vertex knows exactly how many outgoing edges it has by definition. Therefore we only have to set the amount to an accumulator once and not multiple times. With that knowledge it makes sense to set the accumulatorType to store, as no further calculations need to take place. As the possible amount of outgoing edges is integral, we are setting valueType to ints.


What a vertex does not know is, how many incoming edges it has. Therefore we need to get them to know that value.

"inDegree": {
  "accumulatorType": "sum",
  "valueType": "ints"

The choice for valueType is equal compared to “outDegree” because of the same reason. But as you can see, the accumulatorType is now set to sum. As our vertices do not know how many incoming edges there are, each vertex needs to send a message to all outgoing. In our program, a message will be sent to every neighbor. That means a vertex with n neighbors, will send n messages. As in our case, a single message represents a single incoming edge, we need to add “+1” to our accumulator per each message, and therefor set the accumulatorType to sum.


initProgram: [

  // Set our outDegree accumulator to ["this-outbound-edges-count"]
  ["accum-set!", "outDegree", ["this-outbound-edges-count"]],

  // Initializes our inDegree (sum) accumulator to 0
  ["accum-set!", "inDegree", 0],

  // Send value: "1" to all neighbors, so their inDegree can be raised next round!
  ["send-to-all-neighbours", "inDegree", 1]
updateProgram: ["seq",

As in our case, we do not need an update program. We are just inserting a dummy (void) method (will be improved in further state). But currently it is necessary, because our update program needs to run once to accumulate the inDegrees values that have been sent out in our initProgram. Therefore maxGSS is set to 2.

Storing the result

To be able to store the result, we either need to define a resultField (which will just store all accumulators into the given resultField attribute) or create a <program> which will take care of our store procedure. The next code snippet demonstrates how a store program could look like:

"dataAccess": {
  "writeVertex": [
    ["list", "inDegree", ["accum-ref", "inDegree"]],
    ["list", "outDegree", ["accum-ref", "outDegree"]]