| commit | d5d282d42077fd83fece9dc56a2af28c2095b55f | [log] [tgz] |
|---|---|---|
| author | Antonio Barone <syntonyze@gmail.com> | Sat Dec 29 17:04:44 2018 +0100 |
| committer | Antonio Barone <syntonyze@gmail.com> | Fri Jan 04 10:25:43 2019 +0000 |
| tree | 811134721779c10490d369069dfb704ce623c165 | |
| parent | 17fff45c899f9ebf7b88f246fa3f3e2c9559bd62 [diff] |
Add auditlog result field to elasticsearch Collect and index result values for ssh, http and rest api commands. Feature: Issue 10228 Change-Id: I86550224886e4117efa2e0c4b57b2cba411951c1
This repository provides a set of spark ETL jobs able to extract, transform and persist data from gerrit projects with the purpose of performing analytics tasks.
Each job focuses on a specific dataset and it knows how to extract it, filter it, aggregate it, transform it and then persist it.
The persistent storage of choice is elasticsearch, which plays very well with the kibana dashboard for visualizing the analytics.
All jobs are configured as separate sbt projects and have in common just a thin layer of core dependencies, such as spark, elasticsearch client, test utils, etc.
Each job can be built and published independently, both as a fat jar artifact or a docker image.
Here below an exhaustive list of all the spark jobs provided by this repo, along with their documentation.
Extracts and aggregates git commits data from Gerrit Projects.
Requires a Gerrit 2.13.x or later with the analytics plugin installed and Apache Spark 2.11 or later.
Job can be launched with the following parameters:
bin/spark-submit \ --class com.gerritforge.analytics.gitcommits.job.Main \ --conf spark.es.nodes=es.mycompany.com \ $JARS/analytics-etl-gitcommits.jar \ --since 2000-06-01 \ --aggregate email_hour \ --url http://gerrit.mycompany.com \ -e gerrit \ --username gerrit-api-username \ --password gerrit-api-password
You can also run this job in docker:
docker run -ti --rm \ -e ES_HOST="es.mycompany.com" \ -e GERRIT_URL="http://gerrit.mycompany.com" \ -e ANALYTICS_ARGS="--since 2000-06-01 --aggregate email_hour -e gerrit" \ gerritforge/gerrit-analytics-etl-gitcommits:latest
since, until, aggregate are the same defined in Gerrit Analytics plugin see: https://gerrit.googlesource.com/plugins/analytics/+/master/README.md
-u --url Gerrit server URL with the analytics plugins installed
-p --prefix (optional) Projects prefix. Limit the results to those projects that start with the specified prefix.
-e --elasticIndex Elastic Search index name. If not provided no ES export will be performed
-o --out folder location for storing the output as JSON files if not provided data is saved to /analytics- where is the system temporary directory
-a --email-aliases (optional) “emails to author alias” input data path.
-k --ignore-ssl-cert allows to proceed even for server connections otherwise considered insecure.
CSVs with 3 columns are expected in input.
Here an example of the required files structure:
author,email,organization John Smith,john@email.com,John's Company John Smith,john@anotheremail.com,John's Company David Smith,david.smith@email.com,Indipendent David Smith,david@myemail.com,Indipendent
You can use the following command to quickly extract the list of authors and emails to create part of an input CSV file:
echo -e "author,email\n$(git log --pretty="%an,%ae%n%cn,%ce"|sort |uniq )" > /tmp/my_aliases.csv
Once you have it, you just have to add the organization column.
NOTE:
To build the jar file, simply use
sbt analyticsETLGitCommits/assembly
To build the gerritforge/gerrit-analytics-etl-gitcommits docker container just run:
sbt analyticsETLGitCommits/docker.
If you want to distribute use:
sbt analyticsETLGitCommits/dockerBuildAndPush.
The build and distribution override the latest image tag too Remember to create an annotated tag for a release. The tag is used to define the docker image tag too
Extract, aggregate and persist auditLog entries produced by Gerrit via the audit-sl4j plugin. AuditLog entries are an immutable trace of what happened on Gerrit and this ETL can leverage that to answer questions such as:
and many others questions related to the usage of Gerrit.
Job can be launched, for example, with the following parameters:
spark-submit \ --class com.gerritforge.analytics.auditlog.job.Main \ --conf spark.es.nodes=es.mycompany.com \ --conf spark.es.port=9200 \ --conf spark.es.index.auto.create=true \ $JARS/analytics-etl-auditlog.jar \ --gerritUrl https://gerrit.mycompany.com \ --elasticSearchIndex gerrit \ --eventsPath /path/to/auditlogs \ --ignoreSSLCert false \ --since 2000-06-01 \ --until 2020-12-01
You can also run this job in docker:
docker run \ --volume <source>/audit_log:/app/events/audit_log -ti --rm \ -e ES_HOST="<elasticsearch_url>" \ -e GERRIT_URL="http://<gerrit_url>:<gerrit_port>" \ -e ANALYTICS_ARGS="--elasticSearchIndex gerrit --eventsPath /app/events/audit_log --ignoreSSLCert false --since 2000-06-01 --until 2020-12-01 -a hour" \ gerritforge/gerrit-analytics-etl-auditlog:latest
To build the jar file, simply use
sbt analyticsETLAuditLog/assembly
To build the gerritforge/gerrit-analytics-etl-auditlog docker image just run:
sbt analyticsETLAuditLog/docker.
If you want to distribute it use:
sbt analyticsETLAuditLog/dockerBuildAndPush.
The build and distribution override the latest image tag too.
A docker compose file is provided to spin up an instance of Elastisearch with Kibana locally. Just run docker-compose up.
If you want to run the git ETL job from within docker against containerized elasticsearch and/or gerrit instances, you need to make them reachable by the ETL container. You can do this by spinning the ETL within the same network used by your elasticsearch/gerrit container (use --network argument)
If elasticsearch or gerrit run on your host machine, then you need to make that reachable by the ETL container. You can do this by providing routing to the docker host machine (i.e. --add-host="gerrit:<your_host_ip_address>" --add-host="elasticsearch:<your_host_ip_address>")
For example:
HOST_IP=`ifconfig en0 | grep "inet " | awk '{print $2}'` \ docker run -ti --rm \ --add-host="gerrit:$HOST_IP" \ --network analytics-etl_ek \ -e ES_HOST="elasticsearch" \ -e GERRIT_URL="http://$HOST_IP:8080" \ -e ANALYTICS_ARGS="--since 2000-06-01 --aggregate email_hour -e gerrit" \ gerritforge/gerrit-analytics-etl-gitcommits:latest
HOST_IP=`ifconfig en0 | grep "inet " | awk '{print $2}'` \ docker run -ti --rm --volume <source>/audit_log:/app/events/audit_log \ --add-host="gerrit:$HOST_IP" \ --network analytics-wizard_ek \ -e ES_HOST="elasticsearch" \ -e GERRIT_URL="http://$HOST_IP:8181" \ -e ANALYTICS_ARGS="--elasticSearchIndex gerrit --eventsPath /app/events/audit_log --ignoreSSLCert true --since 2000-06-01 --until 2020-12-01 -a hour" \ gerritforge/gerrit-analytics-etl-auditlog:latest
If Elastisearch dies with exit code 137 you might have to give Docker more memory (check this article for more details)
Should ElasticSearch need authentication (i.e.: if X-Pack is enabled), credentials can be passed through the spark.es.net.http.auth.pass and spark.es.net.http.auth.user parameters.
If the dockerized spark job cannot connect to elasticsearch (also, running on docker) you might need to tell elasticsearch to publish the host to the cluster using the _site_ address.
elasticsearch:
...
environment:
...
- http.host=0.0.0.0
- network.host=_site_
- http.publish_host=_site_
...
See here for more info
To perform actions across all jobs simply run the relevant sbt task without specifying the job name. For example:
sbt testsbt assemblysbt docker