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Usage Statistics
General
When you bring multiple Shiny apps to your end users, it may be interesting to track usage of the different applications over time. It may a.o. help to understand your user base and to prioritize maintenance work for the different applications.
ShinyProxy has a general model on board that allows one to store Shiny app events and for different storage back-ends, different plug-ins are available. Currently, ShinyProxy ships with plug-ins that can store events in the following back-ends:
Alternative plug-ins can easily be developed and are on the roadmap.
Starting from ShinyProxy 2.5.0 an extra plug-in was added that supports Micrometer.io. Micrometer itself is a modular library that allows to support many popular monitoring system. Our focus is to export Prometheus Metrics, however, any Micrometer supported monitoring system is supported.
For Kubernetes, a complete monitoring stack for ShinyProxy can be found on GitHub. This provides metrics using Prometheus, log aggregation using Loki and dashboards using Grafana. Everything has been prepared to easily deploy this on your infrastructure.
InfluxDB
Installation
InfluxDB installation is very simple: on an Ubuntu 16.04 LTS system one just needs to download and install the package using something along
wget https://dl.influxdata.com/influxdb/releases/influxdb_1.0.2_amd64.deb'
sudo dpkg -i influxdb_1.0.2_amd64.deb
To start the database one can use
sudo service influxdb start
and querying its status can be done with
sudo service influxdb status
Database creation
In order to store the usage statistics, a specific database needs to be created
on the InfluxDB instance. In this example we will give it the
name shinyproxy_usagestats
.
To do so one can use the CLI client:
influx
then, at the database prompt, enter
CREATE DATABASE shinyproxy_usagestats
quit
Configuration
Once the database is created, we can configure ShinyProxy to post all usage
events to the shinyproxy_usagestats
database. It is sufficient to add a single
property in the application.yml
file under the shiny / proxy section:
proxy:
[ ... ]
usage-stats-url: http://localhost:8086/write?db=shinyproxy_usagestats
[ ... ]
As can be seen, InfluxDB has the advantage that the storage of usage events can
be done on a separate server and should not necessarily happen on localhost
i.e. the machine on which the ShinyProxy server itself is running.
Note:
- If authentication on the
shinyproxy_usagestats
database is required, one can use theusage-stats-username
andusage-stats-password
properties. - More detailed information on configuring ShinyProxy can be found here
SQL Based Databases (JDBC)
Installation
Please refer to the official documentation of your database system for installation. For example, on an Ubuntu 16.04 system MonetDB can be installed from the official Debian package repository that is available here.
Database creation
When posting events to a database, it is assumed that an event
table exists
that should be created upfront.
For MonetDB. MySQL, MariaDB or PostgreSQL use:
create table event
(
event_time timestamp,
username varchar(128),
type varchar(128),
data text
);
For Microsoft SQL Server, use:
create table event
(
event_time datetime,
username varchar(128),
type varchar(128),
data text
);
Configuration
Once the database is created, we can configure ShinyProxy to post all usage
events to the database. It is sufficient to only add a JDBC url in
the usage-stats-url
field in the shiny.proxy
section of
the application.yml
file:
proxy:
[ ... ]
# for MonetDB
usage-stats-url: jdbc:monetdb://localhost:50000/usage_stats
# for MySQL/MariaDB
usage-stats-url: jdbc:mysql://localhost/shinyproxy
# for SQLite
usage-stats-url: jdbc:sqlite:shinyproxy.sql
# for PostgreSQL
usage-stats-url: jdbc:postgresql://localhost/postgres
# for MS SQL Server:
usage-stats-url: jdbc:sqlserver://localhost;databaseName=shinyproxy
[ ... ]
Similarly to InfluxDB, MonetDB can be used to store events on a separate server
and event logging should not necessarily happen on localhost
i.e. the machine
on which the ShinyProxy server itself is running.
Note:
- The default login and password that will be used to log into the database
are
monetdb
andmonetdb
respectively. If other credentials have been set on the database, one can enter theusage-stats-username
andusage-stats-password
properties below theusage-stats-url
field. - More detailed information on configuring ShinyProxy can be found here.
Micrometer
This section describes on how to set up the Micrometer monitoring backend using
Prometheus. It assumes that ShinyProxy, Prometheus and Grafana (see later) are
all running on localhost
.
Configuration
Start by changing your ShinyProxy configuration so that it includes the following configuration:
proxy:
[ ... ]
usage-stats-url: micrometer
management:
metrics:
export:
prometheus:
enabled: true
[ ... ]
The Prometheus metrics are now available at the
localhost:9090/actuator/prometheus
endpoint. Note that you should not include
any context-path in this URL.
Setup Prometheus
The next step is to ensure that Prometheus scrapes your ShinyProxy server. For
demonstrating purposes, it is enough
to download it and extract it on your server.
The next step is to adapt your configuration (by editing the
included prometheus.yml
file), so that Prometheus scrapes the ShinyProxy
server:
scrape_configs:
- job_name: 'shinyproxy'
metrics_path: '/actuator/prometheus'
static_configs:
# note: this is the port of ShinyProxy Actuator service, not the port of Prometheus which is by default also 9090
- targets: [ 'localhost:9090' ]
labels:
namespace: local
Now you can start Prometheus. Make sure to run it on a different port than 9090, since that port is already being used by ShinyProxy:
./prometheus --web.listen-address="0.0.0.0:7070"
Now go to your browser and open
http://localhost:7070/targets, this page should
contain one target called shinyproxy
with the UP state.
Setup Grafana
Now that Prometheus scrapes the metrics, we need a way to visualize these metrics. An excellent tool for this is Grafana. Start by downloading Grafana, again you can use the standalone binaries and simple extract them. Then start Grafana using:
./bin/grafana-server
Next, access Grafana on http://localhost:3000 and login
using admin:admin
. Next add Prometheus as datasource, remember that Prometheus
is running at http://localhost:7070
.
The final step is to import the dashboard. Download the JSON file (right-click and choose save as) and import it into Grafana. If everything is correctly setup, you should see the graph changing when you log in, start an app etc.
In order to provide more accurate graphs in Grafana, the scrape interval used by Prometheus should correspond to the one specified in the configuration of Grafana. The dashboard makes use of this option in order to perform certain calculations. This settings can be configured at the datasource settings in Grafana. See the Grafana docs.
Note: with the release of ShinyProxy 2.6.0 and 3.0.0 the dashboard has been updated and is incompatible with previous releases.
Exposed Metrics
All metrics exposed by ShinyProxy are tagged with the shinyproxy_instance
tag,
containing the instance id of the ShinyProxy server.
ShinyProxy keeps track of the following actions:
- User Login (
userLogins
) - User Logout (also by expiring session) (
userLogouts
) - Authentication failure
authFailed
- Application start (
appStarts
, contains aspec_id
tag) - Application stop (
appStops
, contains aspec_id
tag) - Duration of application startup (
startupTime
, contains aspec_id
tag) - Duration of application usage (
usageTime
, contains aspec_id
tag) - Time between the start of the application process and when the app is
reachable by ShinyProxy. For example, in the case of the Shiny app, this is
the time the R process takes to startup and be reachable on its webserver.
(
applicationStartupTime
, contains aspec_id
tag) - Time needed for the container to get assigned to a node on Kubernetes.
Includes the time to launch a new node if the cluster needs to
scale-up. (
containerScheduleTime
, contains aspec_id
tag) - Duration of pulling the image (only available on Docker and
Kubernetes,
imagePullTime
, contains aspec_id
tag) - Duration of container startup (before the process itself
starts) (
containerStartupTime
, contains aspec_id
tag) - Application start failures (
startFailed
) - Absolute number of running apps (
absolute_apps_running
, contains aspec_id
tag) - Absolute number of logged-in users (
absolute_users_logged_in
) - Absolute number of active users. A user is counted as active if they were
active in the last minute. (
absolute_users_active
)
Screenshot
Metric name prefix
It is a good practice to differentiate the metrics generated by ShinyProxy with
any other metric stored in Prometheus. Of course, Prometheus attaches some tags
(a.k.a labels) to all metrics that can be used for this purpose. Nevertheless,
it can be useful to prefix all metrics exposed by ShinyProxy. This can be done
by setting the proxy.usage-stats-micrometer-prefix
option to a self-chosen
string.
For example, the following code will prefix every metric exposed by ShinyProxy
with shinyproxy_
. It is not required to end the prefix with _
or any other
separator symbol. The reason is that ShinyProxy automatically determines the
correct separator for the used metric backend.
proxy:
usage-stats-micrometer-prefix: shinyproxy
Using multiple statistics backends
In some cases it can be useful to log statistics to multiple databases. For
example, logging infrastructure related metrics to Prometheus (i.e.
observability) and keeping an audit trail in a SQL database. This is possible (
since ShinyProxy 3.0.0) using the proxy.usage-stats
property. This property
takes a list of configuration blocks. Each block should contain at least the
parameter url
which is explained above. Each block can also have a username
and password
property if needed for the statistics backend. For example, to
log to Micrometer (for Prometheus) and PostgreSQL:
proxy:
usage-stats:
- url: micrometer
- url: jdbc:postgresql://localhost/postgres
username: postgres
password: mysecretpassword