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The Degraphmalizer!


The degraphmalizer is an application on top of Elasticsearch that can extract graph structure from your documents and use it to add computed attributes to your documents.

Computed attributes? Yes, the graph stores the "global" structure of your index. Then you can add some of this global structure to your documents. For instance you could add an "in-degree" attribute that would tell you how many incoming links there are to a document.

You can also ask the graph questions like: give me all documents that refer to this document. Then from those documents you can extract some information and store that in a computed attribute. The degraphmalizer's job is to keep such information up to date using attribute dependency tracking.

An example

A typical example probably makes this more clear:

Suppose we have two parties, Alice and Bob. Alice has an index of authors and Bob maintains an index of books and they refer to each other:


  "name": "William Ford Gibson",
  "books": ["book_id_1", "book_id_2"]


  "title": "Neuromancer"

  "title": "All Tomorrow's Parties"

In order to search for authors based on their book titles, one first has to find the id's of the books in which the words occur. Then we have to send a second query to find the the authors that wrote those books.

In many cases it would be nicer to have an index like this

  "name": "William Ford Gibson",
  "books": [ { "id": "book_id_1",
               "title": "Neuromancer"
             { "id": "book_id_2",
               "title": "All Tomorrow's Parties" } ] 

  "title": "Neuromancer",
  "authors": [ { "id": "gibson",
                 "name": "William Word Gibson" } ]

  "title": "All Tomorrow's Parties"
  "authors": [ { "id": "gibson",
                 "name": "William Word Gibson" } ]

So we duplicate the data of the separate indices into derived documents which we can directly query. This process is called "denormalization" and we are using a graph to do it, hence: degraphmalizer :)

Overview of all moving parts

The Degraphmalizer works by getting notifications from elasticsearch that a document has changed. You need four things:

  1. elasticsearch
  2. degraphmalizer-elasticsearch-plugin installed in elasticsearch
  3. degraphmalizer-core
  4. degraphmalizer configuration files that tell degraphmalizer-core what to do


Build requirements:

Run mvn package to build the artifacts. You'll find the resulting jar/zip files in the target directories of the subdirectories of the modules.


So you want to have your very own Degraphmalizer setup? You've come to the right place!

The quick way

The degraphmalizer provides a simple way to get started with the development mode. This mode means you will run the degraphmalizer in standalone mode with a builtin elasticsearch. It is meant to develop configurations on your local machine.

In your checkout directory create a conf directory. Then do: java -jar degraphmalizer-core/target/degraphmalizer-core-0.1-SNAPSHOT-jar-with-dependencies.jar -d -r

-d means development mode and -r means it will reload configurations while running

You can now connect to http://localhost:9200/ voor elastic search, and http://localhost:9821/ for the degraphmalizer

The full install

Install elasticsearch

If you don't have an elasticsearch installation yet you can download elasticsearch and extract the archive. Use /opt/elasticsearch/ for example.

Install degraphmalizer-elasticsearch-plugin

Unzip the degraphmalizer-elasticsearch-plugin jar with dependencies into a subdirectory in your elasticsearch plugins directory (e.g. /opt/elasticsearch/plugins/degraphmalizer). You should get something like this:

|-- commons-codec-1.6.jar
|-- commons-logging-1.1.1.jar
|-- elasticsearch-degraphmalizer-0.19.8-0.1-SNAPSHOT.jar
|-- httpclient-4.2.3.jar
`-- httpcore-4.2.2.jar

Install degraphmalizer-core

  • Pick a directory to install the degraphmalizer in, ie /opt/degraphmalizer
  • Create a configration directory in that directory mkdir /opt/degraphmazizer/conf
  • Put degraphmalizer-core jar with dependencies in that directory
  • Copy the logback.xml in degraphmalizer-core to that directory


Alright, all components have been installed. Now you might want to configure a few things. Read on.

Configure degraphmalizer-elasticsearch-plugin

You can configure the Degraphmalizer plugin using the following settings in the elasticsearch configuration file elasticsearch.yml:


  • URI scheme used to access the Degraphmalizer, either http or https
  • Default: http


  • Hostname used to access the Degraphmalizer
  • Default: localhost


  • Port used to access the Degraphmalizer
  • Default: 9821


  • Delay in milliseconds before retrying failed requests to the Degraphmalizer
  • Default: 10000


  • Number of updates to queue in memory per index before spooling to disk
  • Default: 100000


  • Path for error logs and overflow spool files
  • Default: /export/elasticsearch/degraphmalizer


  • Number of times to retry sending an update to the Degraphmalizer before considering it failed
  • Default: 10

Configure degraphmalizer-core

  • Add Degraphmalizer configuration files to tell degraphmalizer-core what to do

You can start with ones provided in this file at "The degraphmalizer configuration"


Everything has been installed, it's all configured, let's run this thing!

Running elasticsearch

For testing you can then start Elasticsearch with the "Run in foreground" option, like this: cd /opt/elasticsearch; bin/elasticsearch -f

You then see the log output on the console, ctrl+c to quit the elasticsearch node. Without -f elasticsearch starts as a background daemon, which is recommended for production.

Running degraphmalizer-core

To start the degraphmalizer with default options you do it like this: cd /opt/degraphmalizer ; java -jar degraphmalizer-core-0.1-SNAPSHOT-jar-with-dependencies.jar This will start the degraphmalizer connecting to the local elasticsearch (ie connecting to localhost:9300).

If you start the degraphmalizer with --help it will show you the command line options:

-c, --config        Specify configuration directory
                    Default: conf
-b, --bind          Binds the listening ports and Elastic Search transport to this host/ip
                    Default: []
-d, --development   Run in development mode
                    Default: false
-f, --fixtures      Load fixtures on startup
                    Default: false
-g, --graphdb       Specify graph DB storage directory
                    Default: data/graphdb
-?, --help          Show commandline options
                    Default: false
-j, --jmx           Enable JMX monitoring bean
                    Default: false
-l, --jslib         Load Javascript library from this file
                    Default: []
-L, --logback       Specify logback configuration file
                    Default: logback.xml
-p, --port          Listening port
                    Default: 9821
-r, --reload        Enable automatic configuration reloading
                    Default: false
-t, --transport     Run against remote ES (host, port, cluster)
                    Default: [localhost, 9300, elasticsearch]

The degraphmalizer configuration

The degraphmalizer is configured through javascript, for each target index there is a directory containing javascript files. You make a javascript configuration file for each source index and type you want to process.


A javascript configuration has the following variables:

  • sourceIndex to tell which index this configuration will process
  • sourceType to tell which type of document this configuration will process
  • extract containing a function which gets passed a document and a subgraph. This function is called to extract edges from the document and put them in a subgraph.
  • filter containing a function which gets passed the document. This function is called and returns true if the document is to be extracted, transformed and walked. If you omit this function all documents will be processed.
  • walks which contains a list of objects each containing a 'walk' consisting of a direction variable and a properties variable containing a set of fields which will be put in the destination document. Each field has a function reduce which gets passed the document tree. The reduce function returns a single field or a complete JSON object to add to the destination document.
  • transform containing a function which gets passed the document, this function returns a new document which will get merged with the fields from the walks and form the destination document. Do note that the other functions get passed the original document, not the transformed one. In absence of this function the original document gets copied to the destination document.

Common functions

Common functions can be put in a separate javascript file which can be loaded through the --jslib option on the commandline. The fuctions defined in the library will be available in all configuration javascripts.

The Alice and Bob example

configuration that would transform Alice and Bob's index as above:


	// we specify which documents we want to use as input for our new document
	sourceIndex: "alice",
	sourceType: "author",

	// for each document of type author, extract subgraph relation
	extract: function(doc, subgraph) {

		/* so subgraph is a tiny graph with one node corresponding
		   to the raw document in ES, (so that is id AND version).
		   the total graph is composed of all the subgraphs */

		// so we add on edge for each book this author wrote
		if(doc.books && doc.books.length)
			doc.books.forEach(function(c) {
				// this constructs an edge from us to "/bob/books/c"
				// if that ID doesn't exist, a node is created for it.
				subgraph.addEdge("wrote_book", "bob", "books", c, false, {});
	// we now define some walks
	walks: [
			/* there are two options here: OUT or IN
			   OUT follows edges from tail to head, and vv.
			   In the future you can define your own walks here,
			   using a DSL that automatically gives reverse walk
			"bookwalk": {
                "direction": "OUT",

                /* for each walk, we can define a number of properties
                   that need to be computed based on the walk */
                "properties": {
                    "books": {

                        // this function is given a graph or tree
                        // of documents from which it should compute
                        // the property value
                        reduce: function(doc_tree)
                            // return a flat list of dicts with id and title keys
                            return bfs_walk(doc_tree).map(function(book) {
                                id: book['id']
                                title: book['title']

The internals

The rule that marks documents as 'dirty'

The rule: Suppose document x changes. We now want to find all documents affected by this change. Suppose we have a property depending on a forward walk, then all documents in a backward walk starting at x must have this attribute recomputed. (Because if I would start a forward walk at any of these document I would eventually hit x).

So this is why we need reversible graph walks.

The pipeline

We receive a document, then

  • Extract subgraph structure from the documents
    • We now want to find all document which are affected by this change:
    • Perform all defined walks on the graph, in reverse
      • Every node in these walk is marked as dirty
        • For this node, perform the walk
        • Retrieve all documents in the walk from ES
        • Then, for every property defined on this walk,
          • Compute reduce of the walk
          • Update the document with the new attribute value

We can be quite smart about which documents to fetch first etc, but we are not doing this ATM. patches welcome!

The graph database

The graph should be a DAG, should you have a cycle then this will cause a walk hitting the cycle to loop and explode. Boom. Currently there is no cycle detection, so be careful.

You can pick any graph database supported by Blueprints. Currently this runs on an embedded Neo4j graph.

The graph is a property graph model. That means that to each node or label a set of key/value pairs is associated. Furthermore to each edge we associate a label. Nodes are unique in the graph for (id,version). Edges are unique for (head-id, head-version, label, tail-id, tail-verion).

It is up to you what information you store in the graph. You might want to use this to restrict the graph walk. At the moment it doesn't really matter much, as we just walk the entire forward or backward tree from a node.

The ES plugin

The Elasticsearch plugin provides the Degraphmalizer with notifications of the changes in Elasticsearch. It consists of the following components:


3 Types:

  • One index lifecycle listener, which will watch for changes in the indexes and their shards and will register/deregister an indexshard listener for each indexshard.
  • An indexshard listener for every indexshard to watch for document changes in the index, it sends these to the manager
  • One cluster listener to look for the presence of a running Degrahphmalizer, and tell the manager about it.


For each index there will be an updater which will receive the document changes from the manager and forward them to the degraphmalizer. It maintains an internal queue for this for when the Degraphmalizer is not available. This queue will overflow to disk if it gets to large.


There is a manager which manages the updaters, and passes changes to the right updater. It will also pause the updaters when there is no Degraphmalizer active (it gets called by the cluster listener for this).

JMX Bean

For monitoring the queue sizes.

The future

  • Push configuration to /_degraphmalize/
  • Replicate the graph to some other machines
  • Watch every "index" request
  • Perform degraphmalizing on one machine


Elasticsearch Degraphmalizer Plugin




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