Elasticsearch is a distributed RESTful search engine built for the cloud.
To use it in your application, add it to
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# .symfony/services.yaml mysearch: # supported versions: 6.5 and 7.2 # 0.90, 1.4, 1.7, 2.4, 5.2 and 5.4 are also available but not # maintained upstream type: elasticsearch:7.2 disk: 1024
And wire it in
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# .symfony.cloud.yaml relationships: elasticsearch: "mysearch:elasticsearch"
The configuration is exposed via the following environment variables (where
ELASTICSEARCH is the upper-cased version of the key defined in the
ELASTICSEARCH_HOST: The Elasticsearch host
ELASTICSEARCH_PORT: The Elasticsearch port
ELASTICSEARCH_SCHEME: The Elasticsearch protocol scheme (
When you create an index on Elasticsearch, you should not specify
number_of_replicas settings in your
Elasticsearch API call. These values will be set automatically based on
The Elasticsearch 2.4 and later services offer a number of plugins. To enable
them, list them under the
configuration.plugins key in your
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mysearch: type: "elasticsearch:7.2" disk: 1024 configuration: plugins: - analysis-icu - lang-python
This is the complete list of official Elasticsearch plugins that can be enabled:
|analysis-icu||Support ICU Unicode text analysis|
|analysis-nori||Integrates Lucene nori analysis module into Elasticsearch|
|analysis-kuromoji||Japanese language support|
|analysis-smartcn||Smart Chinese Analysis Plugins|
|analysis-stempel||Stempel Polish Analysis Plugin|
|analysis-ukrainian||Ukrainian language support|
|cloud-aws||AWS Cloud plugin, allows storing indices on AWS S3|
|delete-by-query||Support for deleting documents matching a given query|
|discovery-multicast||Ability to form a cluster using TCP/IP multicast messages|
|ingest-attachment||Extract file attachments in common formats (such as PPT, XLS, and PDF)|
|ingest-user-agent||Extracts details from the user agent string a browser sends with its web requests|
|lang-python||Python language plugin, allows the use of Python in Elasticsearch scripts|
|mapper-annotated-text||Adds support for text fields with markup used to inject annotation tokens into the index|
|mapper-attachments||Mapper attachments plugin for indexing common file types|
|mapper-murmur3||Murmur3 mapper plugin for computing hashes at index-time|
|mapper-size||Size mapper plugin, enables the _size meta field|
|repository-s3||Support for using S3 as a repository for Snapshot/Restore|
If there is a publicly available plugin you need that is not listed here, please contact our support team.
The Elasticsearch data format sometimes changes between versions in incompatible ways. Elasticsearch does not include a data upgrade mechanism as it is expected that all indexes can be regenerated from stable data if needed. To upgrade (or downgrade) Elasticsearch you will need to use a new service from scratch.
There are two ways of doing that.
.symfony/services.yaml file, change the version of your
Elasticsearch service and its name. Then update the name in the
.symfony.cloud.yaml relationships block.
When you deploy this change to SymfonyCloud, the old service will be deleted and a new one with the new name is created, with no data. You can then have your application reindex data as appropriate.
This approach is simple but has the downside of temporarily having an empty Elasticsearch instance, which your application may or may not handle gracefully, and needing to rebuild your index afterward. Depending on the size of your data that could take a while.
For a transitional approach you will temporarily have two Elasticsearch
services. Add a second Elasticsearch service with the new version and a new
name and give it a new relationship in
.symfony.cloud.yaml. You can
optionally run in that configuration for a while to allow your application to
populate indexes in the new service as well.
Once you're ready to cut over, remove the old Elasticsearch service and relationship. You may optionally have the new Elasticsearch service use the old relationship name if that's easier for your application to handle. Your application is now using the new Elasticsearch service.
This approach has the benefit of never being without a working Elasticsearch instance. On the downside, it requires two running Elasticsearch servers temporarily, each of which will consume resources and need adequate disk space. Depending on the size of your data that may be a lot of disk space.
This work, including the code samples, is licensed under a Creative Commons BY-SA 3.0 license.