How to set Trace Sampling rate for Jaeger in Istio?

The default trace sampling rate for Jaeger in Istio is 1%, meaning, to get a single trace in Jaeger you need to send at least 100 requests to your application. This is good for a production environment but in dev or QA you would want a better sampling rate.

Unfortunately, Istio doesn't give any UI or dashboard to view/edit lots of settings and options which it uses. The documentation related to this is also hard to find.

If you are installing using istioctl then keep an eye for 'values.pilot.traceSampling' option. See more details about this and other options here.

If istio is already installed and running, we need to edit the 'istio-pilot' deployment and change the PILOT_TRACE_SAMPLING environment variable by running below command:

$ kubectl -n istio-system edit deploy istio-pilot

This opens vim (or whatever your default text editor) window with the deployment config file and lets you edit the value (which ranges from 0.0 to 100.0)

Just make the change and save the file and the changes take effect almost immediately.

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How to upload to Google Cloud Storage buckets using CURL

Signed URLs are pretty nifty feature given by Google Cloud Platform to let anyone access your cloud storage (bucket or any file in the bucket) without need to sign in. Official documentation gives step by step details as to how to read/write to the bucket using gsutil or through a program. This article will tell you how to upload a file to the bucket using curl so that any client which doesn't have cloud SDK installed can do this using a simple script. This command creates a signed PUT URL for your bucket. gsutil signurl -c 'text/plain' -m PUT serviceAccount.json gs://test_bucket_location Here is my URL: https://storage.googleapis.com/test_sl?GoogleAccessId=my-project-id@appspot.gserviceaccount.com&Expires=1490266627&Signature=UfKBNHWtjLKSBEcUQUKDeQtSQV6YCleE9hGG%2BCxVEjDOmkDxwkC%2BPtEg63pjDBHyKhVOnhspP1%2FAVSr%2B%2Fty8Ps7MSQ0lM2YHkbPeqjTiUcAfsbdcuXUMbe3p8FysRUFMe2dSikehBJWtbYtjb%2BNCw3L09c7fLFyAoJafIcnoIz7iJGP%2Br6gAUkSnZXgbVjr6wjN%2FIaudXIqA

Running Apache Beam pipeline using Spark Runner on a local standalone Spark Cluster

The best thing about Apache Beam ( B atch + Str eam ) is that multiple runners can be plugged in and same pipeline can be run using Spark, Flink or Google Cloud Dataflow. If you are a beginner like me and want to run a simple pipeline using Spark Runner then whole setup may be tad daunting. Start with Beam's WordCount examples which help you quickstart with running pipelines using different types of runners. There are code snippets for running the same pipeline using different types of runners but here the code is running on your local system using Spark libraries which is good for testing and debugging pipeline. If you want to run the pipeline on a Spark cluster you need to do a little more work! Let's start by setting up a simple standalone single-node cluster on our local machine. Extending the cluster is as easy as running a command on another machine, which you want to add to cluster. Start with the obvious: install spark on your machine! (Remember to have Java a

java.lang.IllegalArgumentException: Malformed \uxxxx encoding

I was getting this exception during build while running ant. Googling didn't help much and I was flummoxed because the same code was running fine till now. My code reads a text file and does some operations on the basis of values read. It was only when I saw the text files I understood the error. I had copied the text in wordpad and saved it as .txt file. Wordpad had put lot of formatting information before and after the content. Also there was "\par" after every line, which was giving this error. So moral of the story: if you get this exception check your properties file (or any other file that your code might be reading.)

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