This repository serves as a base for our data modeling work. You can easily connect to graphing engine (Gremlin + DynamoDB) running remotely in containers and import models to explore the graphs representing Fabric8-Analytics data.
See our contributing guidelines for more info.
Keep proper configuration values handy ( preferably in .env file for docker compose):
AWS_S3_ACCESS_KEY_ID=some_key
AWS_S3_SECRET_ACCESS_KEY=some_secret
AWS_EPV_BUCKET=epv_bucket_name
AWS_PKG_BUCKET=pkg_bucket_name
DYNAMODB_PREFIX=prefix_allocated_to_you
DYNAMODB_CLIENT_CREDENTIALS_CLASS_NAME=com.amazonaws.auth.DefaultAWSCredentialsProviderChain
AWS_ACCESS_KEY_ID=<dynamodb-access-key-id>
AWS_SECRET_ACCESS_KEY=<dynamodb-secret-access-key>
URL: /api/v1/readiness
Description: Generates response for the GET request to /api/v1/readiness. To check if the service is ready.
Method: GET
Input: -None-
Output: {}
HTTP Status Code: 200 success
URL: /api/v1/liveness
Description: Generate response for the GET request to /api/v1/liveness. To check if the service is up and running.
Method: GET
Input: -None-
Output: {}
HTTP Status Code: 200 success
URL: /api/v1/pending
Description: Get request to enlist all the EPVs which are not yet synced to Graph. Gets the pending list from RDS.
Method: GET
Input: Request (Query) Params -> ecosystem, package, version, limit, offset
Output:
{ "pending_list":[{
"ecosystem": "maven",
"package": "org.json:json",
"version": "20180130"
}
],
"all_counts" : 1
}
HTTP Status Code: 200 success
URL: /api/v1/sync_all
Description: Generate response for the GET request to /api/v1/sync_all. Get the pending list from RDS and
get the data from S3 and populate in graph and mark the epv as synched in RDS.
Method: GET
Input: Request (Query) Params -> ecosystem, package, version, limit, offset
Output:
Sample1
{
"epv": [],
"message": "Nothing to be synced to Graph!",
"count_imported_EPVs": 0
}
Sample2
{
"epv": [
{
"ecosystem": "maven",
"package": "org.json:json",
"version": "20180130"
}
],
"message": "The import finished successfully!",
"count_imported_EPVs": 1
}
HTTP Status Code: 200 success
500 in case of exception or when sync is not successful.
URL: /api/v1/ingest_to_graph
Description: Import epv data and generate response for the POST request to /api/v1/ingest_to_graph. Unlike sync_all,
it takes the list of epv from the user and then gets the data from S3 and populate to the graph and mark it as synched in RDS.
Method: POST
Input:
[
{
"ecosystem": "pypi",
"name": "access_points",
"version": "0.4.59"
}
]
Output:
Sample1
{
"count_imported_EPVs": 1,
"epv": [
{
"ecosystem": "pypi",
"name": "access_points",
"version": "0.4.59"
}
],
"message": "The import finished successfully!"
}
Sample2
{
"epv": [],
"message": "Nothing to be synced to Graph!",
"count_imported_EPVs": 0
}
HTTP Status Code: 200 success
400 when the input keys are invalid
500 in case of exception or when ingest is not successful.
URL: /api/v1/selective_ingest
Description: Import epv data and generate response for the POST request to /api/v1/selective. Its similar to ingest_to_graph, with a difference,
that in this, the user also provides a list which indicates which packages to synch from the list of package list provided.
Method: POST
Input:
sample1
{
"package_list": [
{
"version": "0.4.59",
"name": "access_points",
"ecosystem": "pypi"
}
],
"select_ingest": []
}
sample2
{
"package_list": [],
"select_ingest": []
}
sample3
{
"package_list": [{}],
"select_ingest": [{}]
}
Output:
sample1
{
"epv": {
"select_ingest": [],
"package_list": [
{
"ecosystem": "pypi",
"version": "0.4.59",
"name": "access_points"
}
]
},
"message": "The import finished successfully!",
"count_imported_EPVs": 1
}
sample2
{
"message": "No Packages provided. Nothing to be ingested",
}
sample3
{
"message": "Invalid keys found in input:"
}
HTTP Status Code: 200 success
400 when the input keys are invalid or when no packages are in input
500 in case of exception or when ingest is not successful
URL: /api/v1/vertex/string:ecosystem/string:package/string:version/properties
Sample URL: /api/v1/vertex/maven/net.iharder:base64/2.3.9/properties
Description: Handle delete properties associated with given EPV. Search for the record via the given values from the graph
and then perform a delete operation on it.
Method: DELETE
Input:
{ "properties": [
{
"name": "cve_ids"
}
]
}
Output: {}
HTTP Status Code: 200 success
400 when operation fails or encounters error/exception
URL: /api/v1/vertex/string:ecosystem/string:package/string:version/properties
Sample URL: /api/v1/vertex/maven/net.iharder:base64/2.3.9/properties
Description: Handle update properties associated with given EPV. Search for the record via the given values from the graph
and then update the entry with the new value provided.
Method: PUT
Input:
{ "properties": [
{
"name": "cve_ids",
"value": "CVE-3005-0001:10"
}
]
}
Output: {}
HTTP Status Code: 200 success
400 when operation fails or encounters error/exception
cd local-setup
docker-compose up
Now you can run the tests or the REST API
This REST API serves as a language-independent interface to data-model functionality such as importing the given list of EPVs (ecosystem+package+version). It enables the consumers like UI-component to easily invoke data-model APIs.
virtualenv --python /usr/bin/python2.7 env
source env/bin/activate
pip install -r requirements.txt
cp src/config.py.template src/config.py
#make necessary changes in config.py
gunicorn --pythonpath src/ -b 0.0.0.0:5001 rest_api:app
The HTTP-based endpoint to populate the graph is /api/v1/ingest_to_graph
This endpoint creates a minimalistic graph having only P(Package) and V(Version).
You can POST the following list as body of the request with Content-Type: application/json in the header:
[
{
"ecosystem":"maven",
"name":"commons-collections:commons-collections",
"version":"3.2.1"
}
]
NOTE: Make sure to perform test on a fresh instance of graph. i.e g.V().count == 0
virtualenv --python /usr/bin/python2.7 env
source env/bin/activate
pip install -r requirements.txt
cp src/config.py.template src/config.py
PYTHONPATH=`pwd`/src py.test test/ -s
To run on OpenShift see Data Importer section below in this document.
sudo docker-compose -f docker-compose-server.yml up
oc process -f gremlin-server-openshift-template.yaml -v DYNAMODB_PREFIX=<dynamodb_prefix> -v REST_VALUE=1 -v CHANNELIZER=http | oc create -f -
gremlin-httpdeploys with the HTTP channelizer
To deploy these locally, set the AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables in your local environment or store the same in the .env file in the same directory as docker-compose-server.yml
These variables will contain the required AWS credentials to connect on a remote DynamoDB instance. In OpenShift environment, the credentials are stored as OpenShift secrets.
To specify a dynamodb_prefix use $DYNAMODB_PREFIX environment variable. This will allow you to create your own graph and not collide with others in a DynamoDB instance.
Now, to deploy:
-
Docker Compose, run -
docker-compose -f docker-compose-server.yml up <service name, optional>Get the endpoint from
docker inspect <container name> -
OpenShift, run -
-
kompose -f docker-compose-server.yml --provider openshift up -
Alternatively, you can deploy the gremlin server on OpenShift using the OpenShift template file present in the root directory.
oc process -f gremlin-server-openshift-template.yaml -v DYNAMODB_PREFIX=<dynamodb_prefix> | oc create -f -To deploy with the HTTP channelizer, add
-v REST_VALUE=1to the above command.
Get the endpoint from
oc get servicesoroc get routesif you have a router configured. -
To run Data Importer in a container, we can use following command:
$ sudo docker-compose -f docker-compose-importer.yml build
Building data-model
Step 1 : FROM centos:7
---> 970633036444
Step 2 : MAINTAINER Saleem Ansari <sansari@redhat.com>
---> Using cache
---> 73bf6ff1b3eb
Step 3 : RUN yum install -y epel-release && yum install -y python-pip postgresql-devel python-devel python-matplotlib gcc && mkdir -p /src
---> Using cache
---> e279a98a6cd6
Step 4 : COPY ./requirements.txt /
---> Using cache
---> 22b1fc5544e9
Step 5 : RUN pip install -r requirements.txt && rm requirements.txt
---> Using cache
---> 9dae7686ad3a
Step 6 : COPY ./src /src
---> Using cache
---> f405b86b1e82
Step 7 : RUN cp /src/config.py.template /src/config.py
---> Using cache
---> bcd9853b9921
Step 8 : ADD scripts/entrypoint.sh /bin/entrypoint.sh
---> Using cache
---> 85408b91857a
Step 9 : ENTRYPOINT /bin/entrypoint.sh
---> Using cache
---> 0c3eefc9ee72
Successfully built 0c3eefc9ee72
$ sudo docker-compose -f docker-compose-importer.yml up
Starting datamodel_data-model_1
Attaching to datamodel_data-model_1
data-model_1 | No source provided
data-model_1 | READ from S3
datamodel_data-model_1 exited with code 0
For this to work, update docker-compose-import.yml with correct values of AWS / S3 credentials and this should begin the import:
AWS_S3_ACCESS_KEY_ID
AWS_S3_SECRET_ACCESS_KEY
AWS_BUCKET
When starting fresh, create secrets first:
set +o history
# replace 'myKey' and 'mySecretKey' in the following command
oc process -v AWS_S3_ACCESS_KEY_ID=`echo -n 'myKey' | base64` -v AWS_S3_SECRET_ACCESS_KEY=`echo -n 'mySecretKey' | base64` \
-v AWS_DYNAMODB_ACCESS_KEY_ID=`echo -n 'myKey' | base64` -v AWS_DYNAMODB_SECRET_ACCESS_KEY=`echo -n 'mySecretKey' | base64` \
-f secrets-openshift-template.yaml | oc apply -f -
set -o history
To deploy this on an OpenShift cluster, use the following command:
oc process -f data-model-importer-openshift-template.yaml -v AWS_BUCKET=<value> | oc create -f -
Dev Cluster and OpenShift setup
-
Local setup instructions and files can be found at
local-setupdirectory, in case anyone wants local DynamoDB support. Note that, the container images used in the backup directory are not the same that are used to host the project in OpenShift. This directory should be removed once the local development support for DynamoDB makes its way in the current master branch. -
For any queries related to building of container images on registry.centos.org, or if some modifications are required in the Dockerfiles, then the CentOS Container Pipeline team can be contacted on the
container-buildchannel under CentOS on Mattermost. -
For any queries regarding deployment of the application on OpenShift, contact the Deploy team at devtools-deploy@redhat.com or at
devtools-deploychannel under RH-DevTools on Mattermost. -
You can use scripts
run-linter.shandcheck-docstyle.shto check if the code follows PEP 8 and PEP 257 coding standards. These scripts can be run w/o any arguments:
./run-linter.sh
./check-docstyle.sh
The first script checks the indentation, line lengths, variable names, white space around operators etc. The second script checks all documentation strings - its presence and format. Please fix any warnings and errors reported by these scripts.
The scripts measure-cyclomatic-complexity.sh and measure-maintainability-index.sh are used to measure code complexity. These scripts can be run w/o any arguments:
./measure-cyclomatic-complexity.sh
./measure-maintainability-index.sh
The first script measures cyclomatic complexity of all Python sources found in the repository. Please see this table for further explanation how to comprehend the results.
The second script measures maintainability index of all Python sources found in the repository. Please see the following link with explanation of this measurement.