Ingestion service queue runner between Plone RestAPI and ElasticSearch or OpenSearch.
Project description
Ingestion service queue runner between Plone RestAPI and ElasticSearch 8+ or OpenSearch 2+. Provides Celery-tasks to asynchronous index Plone content.
- auto-create Open-/ElasticSearch…
index
mapping from Plone schema using a flexible conversions file (JSON),
ingest-attachment pipelines using (same as above) file.
- task to
index a content object with all data given plus allowedRolesAndUsers and section (primary path)
unindex an content object
- configure from environment variables:
celery,
elasticsearch or opensearch
sentry logging (optional)
Installation
We recommended to use a Python virtual environment, create one with python3 -m venv venv, and activate it in the current terminal session with source venv/bin/activate.
Install collective.elastic.ingest ready to use with redis and opensearch:
pip install collective.elastic.ingest[redis,opensearch]
Depending on the queue server and index server used, the extra requirements vary:
queue server: redis or rabbitmq.
index server: opensearch or elasticsearch.
Configuration
Configuration is done via environment variables and JSON files.
Environment
Environment variables are:
- INDEX_OPENSEARCH
Whether to use OpenSearch or ElasticSearch.
Default: 1
- INDEX_SERVER
The URL of the ElasticSearch or OpenSearch server.
Default: localhost:9200
- INDEX_USE_SSL
Whether to use a secure TLS connection or not.
Default: 0
- INDEX_VERIFY_CERTS
Whether to verify TLS certificates on secure connection or not.
Default: 0
- INDEX_SSL_SHOW_WARN
Whether to warn for unverified TLS request is made or not.
Default: 0
- INDEX_SSL_ASSERT_HOSTNAME
Whether to assert the hostname in TLS request or not.
Default: 0
- INDEX_LOGIN
Username for the ElasticSearch 8+ or OpenSearch server.
Default: admin
- INDEX_PASSWORD
Password for the ElasticSearch 8+ or OpenSearch server.
Default: admin
- CELERY_BROKER
The broker URL for Celery. See docs.celeryq.dev for details.
Default: redis://localhost:6379/0
- PLONE_SERVICE
Base URL of the Plone Server
Default: http://localhost:8080
- PLONE_SITE_PREFIX_PATH
Path to the site to index at the Plone Server.
Default: Plone
- PLONE_SITE_PREFIX_METHOD
Wether to keep the prefix path while requesting the content from the Plone Server or not. Allowed values: strip, keep
On keep, the prefix path is kept in the index/schema path. If the PLONE_SERVICE runs under http://localhost:8080 and the PLONE_SITE_PREFIX_PATH is Plone, the index/schema base path where the ingest service fetches its data and schema from is http://localhost:8080/Plone.
On strip, the prefix path is stripped from the index/schema path. If the PLONE_SERVICE runs under https://www.mydomain.tld and the PLONE_SITE_PREFIX_PATH is Plone, the index/schema base path is https://www.mydomain.tld.
Default: keep
- PLONE_USER
Username for the Plone Server, needs to have at least Site Administrator role.
Default: admin
- PLONE_PASSWORD
Password for the Plone Server.
Default: admin
- MAPPINGS_FILE
Absolute path to the mappings configuration file. Configures field mappings from Plone schema to ElasticSearch.
No default, must be given.
- PREPROCESSINGS_FILE
Configures preprocessing of field values before indexing.
Default: Uses a defaults file of this package.
- ANALYSIS_FILE
(optional) Absolute path to the analysis configuration file.
- SENTRY_DSN
(optional) Sentry DSN for error reporting.
Default: disabled
- SENTRY_INGEST
(optional) Enable sentry reporting in Celery. Reason behind this is, SENTRY_DSN_DSN is possibly provided in a Plone environment when this package is used as a library. To not override any existing sentry-sdk initialization, this flag is used to enable sentry reporting specifically in ingest mode. Allowed values: true, false
Default: false
Upgrade
Coming from version 1.x of this package, in 2.x you need to change some names of the environment variables.
ELASTICSEARCH_INGEST_* to INDEX_*
OPENSEARCH* to INDEX_OPENSEARCH
PLONE_PATH to PLONE_SITE_PREFIX_PATH. Additional a new option PLONE_SITE_PREFIX_METHOD=strip to strip the path prefix from the index/schema path is available. See above for details.
If you use Sentry, additional SENTRY_INGEST=true is needed.
JSON-Files
mappings.json
The mappings file is a JSON file with the following structure:
First level: Key: Value Pairs
The key is - either a fully qualified field name (path) to the field in the schema (behaviors/... or types/...), like behaviors/plone.basic/title. - or the dotted name of a zope.schema based field type, like plone.namedfile.field.NamedBlobImage.
The value is an instruction how to map this specific field or field type to OpenSearch or ElasticSearch. The actual mapping send to the index server is generated from this instruction and the full schema fetched from Plone. At generation time, the process iterates over the full schema and applies the mapping instructions to each field.
At first the instruction lookup is done by the fully qualified field name. If no instruction is found, the dotted name of the field type is used.
There are two types of instructions: Simple ones and complex ones.
The simple instruction has the type defined as a top level key. The type is the mapping type defined by the index server for the mapping, like text or boolean. For some types this is enough, others take additional keys. The nested type is such a type. Here the keys properties and dynamic are required. Those keys are provided on top level.
The complex instruction has the type defined in the definition key. The definition key is a mapping with the type key and the same additional keys for the definition of the field type as for the simple one. There are two other possible top-level keys for complex instructions: detections and pipelines.
A detection is a method to do something based on the schemas field parameters. At the moment this is only used to detect a value_type of a Plone list field or similar. This detector is registered as replace.
A pipeline is a method to add a processing pipeline to the field. Those are used to ingest binary data like images or PDFs, but any other pipeline can be configured. The pipeline is registered and executed. The configuration of a pipeline consists of a source, a target, type as above for defining the target data, processors, and an expansion.
source is the field name with the input data for the pipeline.
target is the field name for the output data of the pipeline.
type is the definition of the target field.
processors are a list of processors to apply to the data.
expansion not directly mapping related, but configured here as it defines where in a postprocessing step the data is fetched from. Binary data is not provided in the content data, only a link where to download.
preprocessings.json
Pre-processings are steps done before anything else is processed. They run on the raw data from the Plone REST API, the full schema fetched from the Plone backend, and the full content object fetched from the Plone backend. Each preprocessing is a function that takes the data and modifies the full schema or full content.
The pre-processings-file consists of list a processing instructions records.
Each record is a mapping with a match, an action and a configuration.
The match call an function that returns a boolean value. If the value is true, the action is executed, otherwise skipped.
There are two matches available
- always
Always matches.
Example configuration {"match": {"type": "always"}, ...}
This is the default if no match is given.
- content_exists
Matches if the field configuration["path"] is present in the content data. Path can point to a field foo or check for its sub entries like foo/bar/baz.
Example configuration {"match": {"type": "content_exists", "path": "foo"}, ...}
The action is a function that takes the full schema and content data, the configuration, and then modifies the full schema or full content.
These actions ar available:
- additional_schema
Adds an additional schema to the full schema. The configuration must a valid schema, i.e. with name and field, to add. In the resulting mapping the schema is added to a (new) section additional under the subsection preprocessed.
- rewrite
Moves content data from one position in the field-tree to another. The configuration must be a mapping with source and target keys. The value of source is the path to the data to move. The value of target is the path to the new location of the data (missing containers are created). The value of enforce is a boolean value (default: False). If True, the source must exist, otherwise an error is raised.
Example: "configuration": {"source": "@components/blocks_plaintext", "target": "blocks_plaintext", "enforce": false}
- remove
Deletes a field or sub-field from the content data. The value of target is the path to the data to delete.
- field_remove
Deletes a field from the full schema and its field value from the content. The value of section is the section (one of behaviors or types) The value of name is the name of the behavior or type. The value of field is the name of the field to delete.
- full_remove
Deletes a full behavior or type with all its fields from the full schema and its fields values from the content. The value of section is the section (one of behaviors or types) The value of name is the name of the behavior or type.
- remove_empty
Deletes all empty fields from the content data. A field is considered empty if it is None, [], {} or ""
analysis.json
This file provides the index with analyzers to be used in the mappings.json files different definition sections (top-level, nested, complex or pipeline target).
Read more on this topic in the dedicated section below.
Start up
Run celery worker:
celery -A collective.elastic.ingest.celery.app worker -c 1 -l info
Or with debug information:
celery -A collective.elastic.ingest.celery.app worker -c 1 -l debug
The number is the concurrency of the worker. For production use, it should be set to the number of Plone backends available for indexing load.
OCI Image usage
For use in Docker, Podman, Kubernetes, …, an OCI image is provided at the Github Container Registry.
The environment variables above are used as configuration.
Additional the following environment variables are used:
- CELERY_CONCURRENCY
The number of concurrent tasks to run.
Default: 1
- CELERY_LOGLEVEL
The log level for celery.
Default: info
The MAPPINGS_FILE variable defaults to /configuration/mappings.json. By default no file is present.
When a mount is provided to /configuration, the mappings file can be placed there. Alternatively, the mappings file can be provided as a configs element in docker compose or as a configmap in Kubernetes.
Examples
Example configuration files are provided in the ./examples directory.
OpenSearch with Docker Compose
Location: examples/docker-os/*
A docker-compose file docker-compose.yml and a Dockerfile to start an Ingest, Redis and an OpenSearch server with dashboard is provided.
Precondition:
Docker and docker-compose are installed.
Max virtual memory map needs increase to run this: sudo sysctl -w vm.max_map_count=262144 (not permanent, see StackOverflow post).
enter the directory cd examples/docker
Steps to start the example OpenSearch Server with the ingest-attachment plugin installed:
locally build the custom OpenSearch Docker image enriched with the plugin using:
docker buildx use default docker buildx build --tag opensearch-ingest-attachment:latest Dockerfile
start the cluster with docker-compose up.
Now you have an OpenSearch server running on http://localhost:9200 and an OpenSearch Dashboard running on http://localhost:5601 (user/pass: admin/admin). The OpenSearch server has the ingest-attachment plugin installed. The plugin enables OpenSearch to extract text from binary files like PDFs.
A Redis server is running on localhost:6379.
Additional the ingest worker runs and is ready to index content from a Plone backend.
Open another terminal.
In another terminal window run a Plone backend at http://localhost:8080/Plone with the add-on collective.elastic.plone installed. There, create an item or modify an existing one. You should see the indexing task in the celery worker terminal window.
For production use, please check that the port 9200 is not exposed to the internet. For a good measure block it with a firewall rule.
ElasticSearch with Docker Compose
Location: examples/docker-es/*
A docker-compose file docker-compose.yml to start an Ingest, Redis and an ElasticSearch server with Dejavu dashboard is provided.
Precondition:
Docker and docker-compose are installed.
Max virtual memory map needs increase to run this: sudo sysctl -w vSITE_PREFIXm.max_map_count=262144 (not permanent, see StackOverflow post).
enter the directory cd examples/docker-es
Run the cluster with:
source .env docker compose up
First you need to set the passwords for the ElasticSearch, execute the following command and note the passwords printed in a new terminal:
docker exec -it docker-es-elasticsearch-1 /usr/share/elasticsearch/bin/elasticsearch-setup-passwords auto
Find the password for the user elastic and set it in the environment variable INDEX_PASSWORD in the .env file. Stop the cluster (Ctrl-C), source .env with the new settings and start it again (as above).
Now you have an ElasticSearch server running on http://localhost:9200 and an Dejavu Dashboard running on http://localhost:1358. (The ElasticSearch server has the ingest-attachment plugin installed by default).
A Redis server is running on localhost:6379.
Additional the ingest worker runs and is ready to index content from a Plone backend.
Open another terminal.
In another terminal window run a Plone backend at http://localhost:8080/Plone with the add-on collective.elastic.plone installed. There, create an item or modify an existing one. You should see the indexing task in the celery worker terminal window.
For production use, please check that the port 9200 is not exposed to the internet. For a good measure block it with a firewall rule.
Local/ Development
Location: examples/docker/local/*
A very basic mappings file examples/docker/local/mappings.json is provided. To use it set MAPPINGS_FILE=examples/mappings-basic.json and then start the celery worker. An environment file examples/docker/local/.env is provided with the environment variables ready to use for local startup.
Run source examples/.env to load the environment variables. Then start the celery worker with celery -A collective.elastic.ingest.celery.app worker -l debug.
Complex Mapping With German Text Analysis
Location: examples/docker/analysis/*
A complex mappings file with german text analysis configured, mappings-german-analysis.json is provided. It comes together with the matching analysis configuration file analysis-german.json and a stub lexicon file elasticsearch-lexicon-german.txt. Read the next section for more information about text analysis.
Text Analysis
Test analysis is optional. Skip this on a first installation.
Search results can be enhanced with a tailored text analysis. The simple fuzzy search, which can be used without any analysis configuration, has its limits. This is even more true in complex languages like German.
This is an advanced topic.
You can find detailed information about text analysis in the ElasticSearch documentation. We provide an example analysis configuration for a better search for German compounded words.
Example: A document with the string ‘Lehrstellenbörse’ can be found by querying ‘Lehrstelle’. It shall be found too by querying ‘Börse’ using a decompounder with a word list ‘Lehrstelle, Börse’ and an additional stemmer. The example analyzer configuration applies a stemmer, which can handle inflections of words. This is an important enhancement for better search results.
The analysis configuration is a configuration of analyzers. The example provided here uses two of them: german_analyzer and german_exact.
The first decompounds words according the word list in lexicon.txt. A stemmer is added.
The second one is to allow also exact queries with a quoted search string.
These two analyzers are to be applied to fields. You can apply them in your mapping.
Example:
"behaviors/plone.basic/title": {
"type": "text",
"analyzer": "german_analyzer",
"fields": {
"exact": {
"type": "text",
"analyzer": "german_exact_analyzer"
}
}
},
Check your configured analysis with:
POST {{elasticsearchserver}}/_analyze
{
"text": "Lehrstellenbörse",
"tokenizer": "standard",
"filter": [
"lowercase",
"custom_dictionary_decompounder",
"light_german_stemmer",
"unique"
]
}
The response delivers the tokens for the analyzed text ‘Lehrstellenbörse’.
Note: The file elasticsearch-lexicon.txt with the word list used by the decompounder of the sample analysis configuration in analysis.json.example has to be located in the configuration directory of your elasticsearch server.
Source Code
The sources are in a GIT DVCS with its main branches at github. There you can report issues too.
We’d be happy to see many forks and pull-requests to make this addon even better.
Maintainers are Jens Klein, Katja Suess and the BlueDynamics Alliance developer team. We appreciate any contribution and if a release is needed to be done on PyPI, please just contact one of us. We also offer commercial support if any training, coaching, integration or adaptions are needed.
Installation for development
clone source code repository,
enter repository directory
recommended: create a Virtualenv python -mvenv env
development install ./bin/env/pip install -e .[test,redis,opensearch]
load environment configuration source examples/.env.
License
The project is licensed under the GPLv2.
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