Arches and Elasticsearch

Arches uses Elasticsearch as its search engine. A handful of settings.py variables point your Arches project to an Elasticsearch installation, in which your indexes will be created. An ELASTICSEARCH_PREFIX string is prepended to all of your project’s indexes, meaning that a single Elasticsearch installation can be used by multiple projects.

One important thing to remember: Elasticsearch indexes are replicable derivatives of your Arches database, meaning that they can safely be dropped and recreated at any time. Similarly, if you need to change or upgrade your Elasticsearch setup, you need only update some settings and then reindex your database.

You can install Elasticsearch locally alongside Arches–read on for how to do that. You can also use managed Elasticsearch solutions by cloud providers like AWS.

Installing Elasticsearch

The easiest way to install Elasticsearch is to download and unpack their archived releases. Archives are available at https://www.elastic.co/downloads/past-releases/elasticsearch-{release number}, e.g. https://www.elastic.co/downloads/past-releases/elasticsearch-8-5-1.

Download the release for your OS and architecture and then unpack/unzip it. For example, installing 8.5.1 on Ubuntu Linux looks like:

wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-8.5.1-linux-x86_64.tar.gz
tar -zxvf elasticsearch-8.5.1-linux-x86_64.tar.gz

A full installation is now in ./elasticsearch-8.5.1, which you can start by running ./elasticsearch-8.5.1/bin/elasticsearch (see below).

On Windows you will need the Windows release which is a ZIP archive, but the process is basically the same.

Development Configuration

Elasticsearch 8 introduced new security features. While you are working with Arches locally, i.e. during development, you can safely disable these features. Do not disable security features in production.

Make two changes:

  1. In your config file, find xpack.security.enabled = true and set it to xpack.security.enabled = false. Now start/restart Elasticsearch (see below).

    The config file is typically found at {path-to-elasticsearch}\config\elasticsearch.yml. If you installed the Debian package, you’ll find it at /etc/elasticsearch/elasticsearch.yml.

  2. In your Arches project’s settings.py or settings_local.py, add

    ELASTICSEARCH_HOSTS = [{“scheme”: “http”, “host”: “localhost”, “port”: ELASTICSEARCH_HTTP_PORT}]

    This overwrites the default ELASTICSEARCH_HOSTS variable, which has the scheme set to https.

Running Elasticsearch

Linux/macOS:

After unpacking the archive, use

{path-to-elasticsearch}/bin/elasticsearch

To rum in the background, add -d to that command. To stop the background process, use ps aux | grep elasticsearch to get the process id, and then sudo kill <process id> -9.

Windows:

On Windows, double-click the {path-to-elasticsearch}\bin\elasticsearch.bat batch file to run the process in a new console window.

To make sure Elasticsearch is running correctly, use

curl localhost:9200

You should get a JSON response that includes “You Know, For Search…”. You can also use the Chrome plugin ElasticSearch Head to view your instance in a browser at localhost:9200.

For more information, please visit the official Elasticsearch documentation.

Important

  1. By default, Elasticsearch uses 2GB of memory (RAM). For basic development purposes, we have found it to run well enough on 1GB. Use ES_JAVA_OPTS="-Xms1g -Xmx1g" ./bin/elasticsearch -d to set the memory allotment on startup (read more). You can use the same command to give more memory to Elasticsearch in a production setting.

Important

If you get an empty response from curl localhost:9200, this is likely because Elasticsearch security features are not probably set up, see Development Configuration above.

Reindexing The Database

You may need to reindex the entire database now and then. This can be helpful if a bulk load failed halfway through, or if you need to point your database at a different Elasticsearch installation.

Be warned that this process can take a long time if you have a lot of resources in your database. Also, if you are in DEBUG mode it can cause your server to run out of memory.

See reindex the database for the commands needed for reindexing.

Using Multiple Nodes

In production it’s advisable to have multiple Elasticsearch instances working together as nodes of a single cluster. To do this, you need to install a second Elasticsearch instance, and change the config/elasticsearch.yml file in each instance. Note that the cluster and node names can be whatever you want, as long as the cluster.name is the same in both instances and the node.name is unique to each one.

Master (Original) Node Config

http.port: 9200

cluster.name: arches-app
node.name: arches-app-node1

node.master: true
node.data: true

Secondary Node Config

http.port: 9201

cluster.name: arches-app
node.name: arches-app-node2

node.master: false
node.data: true

Leave all other parameters untouched.

You’ll need to start/stop each of these instances individually, but you should always have both running. When they are, the secondary node will automatically find the master node and the indices will be replicated between the two.

Nothing about your project’s settings.py should change; Arches need only connect to the original Elasticsearch instance as before. However, you’ll see now in the console output that the cluster health will be [GREEN] when you have two nodes running (it’s [YELLOW] if you only have one).

See also

Here’s some background and a stack overflow question with instructions for adding a node.

Adding a Custom Index

Arches allows you to create a custom index of resource data for your specific use case (for use in Kibana for example).

To add a new custom index create a new python module and add to it a class that inherits from arches.app.search.base_index.BaseIndex and implements the prepare_index and get_documents_to_index methods.

Example custom index:

from arches.app.search.base_index import BaseIndex

class SampleIndex(BaseIndex):
    def prepare_index(self):
        self.index_metadata = {"mappings": {"_doc": {"properties": {"tile_count": {"type": "keyword"}, "graph_id": {"type": "keyword"}}}}}
        super(SampleIndex, self).prepare_index()

    def get_documents_to_index(self, resourceinstance, tiles):
        return ({"tile_count": len(tiles), "graph_id": resourceinstance.graph_id}, str(resourceinstance.resourceinstanceid))

add this to your settings_local.py file

ELASTICSEARCH_CUSTOM_INDEXES = [{
    'module': '{path to file with SampleIndex class}.SampleIndex',
    'name': 'my_new_custom_index' <-- follow ES index naming rules, use this name to register in Elasticsearch
}]

Register your index in Elasticsearch: see register a custom index