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This repo holds a library for low overhead zipkin instrumentation based on the LTTng toolkit
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BlkKin is a project that enables you to trace low-overhead applications using LTTng following the tracing semantics that are described in Google's Dapper Paper

According to this paper the logged information is called Annotation and belongs to a specific span and trace. Each trace is comprised of multiple spans which are related with each other with causal relationships. So, the BlkKin library gives the user the API to easily instrument C/C++ applications. In order to instrument applications you should take a look at blkin-lib/tests for some testcases and at the blkin-lib/zipkin_c.h file

As a tracing backend BlkKin uses LTTng. So you must have LTTng installed.

In order to build and install the lib, go to blkin-lib folder and:

make install

You should take a look at the examples to find out how to link the blkin lib with your instrumented application.

In order to visualize the aggregated information you can use Twitter's Zipkin and send the data that you created, by running the equivalent babeltrace plugin. In order to do you can run

./zipkin/src/ </path/to/lttng/traces> -s <server_ip>
-p <port_number>

within the babeltrace-plugins directory.

In case you have not used the blkin-lib to instrument your application, you can still send your data to a Scribe server. To do that you can use another Babeltrace plugin. This plugin tranforms LTTng trace data to a JSON format and sends them to a Scribe sever.To do so we can equivalently run

./json/src/ </path/to/lttng/traces> -s <server_ip>
-p <port_number>

within the babeltrace-plugins directory

Both of these plugins require that you have installed Babeltrace with its Python bindings enabled. The path to the lttng traces should not be the root directory but the directory where the channel directories are included.


  • libboost-all-dev
  • lttng-tools

Note that BlkKin is tested only with LTTng2.4

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