Skip to main content

Evaluating Pinpoint APM (Part 3)

Having completed all of the steps described in the first two posts of this series you should be able to start and use Pinpoint. To test that everything is working fine you can use the testapp web application which is part of its quickstart bundle.
For this purpose you could start the collector and the web UI from the quickstart as well:

%PINPOINT_HOME%\quickstart\bin\start-collector.cmd
%PINPOINT_HOME%\quickstart\bin\start-web.cmd


Then start the testapp application:

%PINPOINT_HOME%\quickstart\bin\start-testapp.cmd

Check that everything is fine connecting to the web UIs:
    Pinpoint Web - http://localhost:28080
    TestApp - http://localhost:28081

Start to do some actions in the testapp application and see through the web UI which information are sent to Pinpoint.
Now you can profile any Java web and standalone application of yours. You need to download the agent jar in any location in the application hosting machine. Then, for standalone applications, you need to run them adding the following arguments for the JVM:

-javaagent:%PINPOINT_HOME%\quickstart\agent\target\pinpoint-agent\pinpoint-bootstrap-1.6.1-SNAPSHOT.jar -Dpinpoint.agentId=myapp-agent -Dpinpoint.applicationName=myapps 

where:
  • javaagent is is the location of the agent jar;
  • agentId is is a unique name that identifies the application instance;
  • applicationName is the name to group application instances as a single service.
The same JVM arguments should be added to the CATALINA_OPTS variable in the Tomcat startup script (cataline.bat or catalina.sh):

CATALINA_OPTS="$CATALINA_OPTS -javaagent:$AGENT_PATH/pinpoint-bootstrap-$VERSION.jar"
CATALINA_OPTS="$CATALINA_OPTS -Dpinpoint.agentId=$AGENT_ID"
CATALINA_OPTS="$CATALINA_OPTS -Dpinpoint.applicationName=$APPLICATION_NAME"


What's next

In the next posts we will have a detailed look at the Pinpoint internal model and how to implement a custom plugin using the available Java APIs.

Comments

Popular posts from this blog

Streamsets Data Collector log shipping and analysis using ElasticSearch, Kibana and... the Streamsets Data Collector

One common use case scenario for the Streamsets Data Collector (SDC) is the log shipping to some system, like ElasticSearch, for real-time analysis. To build a pipeline for this particular purpose in SDC is really simple and fast and doesn't require coding at all. For this quick tutorial I will use the SDC logs as example. The log data will be shipped to Elasticsearch and then visualized through a Kibana dashboard. Basic knowledge of SDC, Elasticsearch and Kibana is required for a better understanding of this post. These are the releases I am referring to for each system involved in this tutorial: JDK 8 Streamsets Data Collector 1.4.0 ElasticSearch 2.3.3 Kibana 4.5.1 Elasticsearch and Kibana installation You should have your Elasticsearch cluster installed and configured and a Kibana instance pointing to that cluster in order to go on with this tutorial. Please refer to the official documentation for these two products in order to complete their installation (if you do

Exporting InfluxDB data to a CVS file

Sometimes you would need to export a sample of the data from an InfluxDB table to a CSV file (for example to allow a data scientist to do some offline analysis using a tool like Jupyter, Zeppelin or Spark Notebook). It is possible to perform this operation through the influx command line client. This is the general syntax: sudo /usr/bin/influx -database '<database_name>' -host '<hostname>' -username '<username>'  -password '<password>' -execute 'select_statement' -format '<format>' > <file_path>/<file_name>.csv where the format could be csv , json or column . Example: sudo /usr/bin/influx -database 'telegraf' -host 'localhost' -username 'admin'  -password '123456789' -execute 'select * from mem' -format 'csv' > /home/googlielmo/influxdb-export/mem-export.csv

Using Rapids cuDF in a Colab notebook

During last Spark+AI Summit Europe 2019 I had a chance to attend a talk from Miguel Martinez  who was presenting Rapids , the new Open Source framework from NVIDIA for GPU accelerated end-to-end Data Science and Analytics. Fig. 1 - Overview of the Rapids eco-system Rapids is a suite of Open Source libraries: cuDF cuML cuGraph cuXFilter I enjoied the presentation and liked the idea of this initiative, so I wanted to start playing with the Rapids libraries in Python on Colab , starting from cuDF, but the first attempt came with an issue that I eventually solved. So in this post I am going to share how I fixed it, with the hope it would be useful to someone else running into the same blocker. I am assuming here you are already familiar with Google Colab. I am using Python 3.x as Python 2 isn't supported by Rapids. Once you have created a new notebook in Colab, you need to check if the runtime for it is set to use Python 3 and uses a GPU as hardware accelerator. You