GitHub: Ready, Set, Go!

31 Aug 2016 | , , ,
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By Fintan Quill



Looking through the newly enhanced Kx GitHub index recently my attention was caught by the kdbgo project.

Released in 2009, the Go programming language created by Google has been increasing in popularity year on year. The compiled language, which has many of its roots in C, recently jumped from 95 to 20 in the TIOBE programming language index in 2016.

Google has several secretive internal projects developed in Go. There are also many large scale projects being implemented outside of Google, with the most notable being the container software Docker.

Created by Kx tech team member Sergey Vidyuk, the kdbgo project allows bi-directional communication between kdb+ & Go, allowing Kx technology to be accessed more easily by thousands of programmers in different geographies & industries.

To get started, ensure Go is installed and correctly configured. For kdbgo first install the glog & gouuid dependent libraries as follows:

go get

go get

Then finally install the kdbgo package itself:

go get

There you have it, you’re ready to Go.

There are sample test files within the kdbgo project. I have also created a separate sample test file.

This test file shows how to:

  • create a connection
  • return different kdb+ data types
  • async call
  • single insert
  • bulk insert
  • primitive prettyprint functions for dictionaries, tables and keyed tables

To try this, simply run the following command:

go run test.go

I have also created a listener script at GitHub for subscribing to a ticker plant in kdb+tick.

This can be run using the command:

go run listener.go


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Subject matter experts are needed for ML projects since generalist data scientists cannot be expected to be fully conversant with the context, details, and specifics of problems across all industries. The challenges are often domain-specific and require considerable industry background to fully contextualize and address. For that reason, successful projects are typically those that adopt a teamwork approach bringing together the strengths of data scientists and subject matter experts. Where data scientists bring generic analytics and coding capabilities, Subject matter experts provide specialized insights in three crucial areas: identifying the right problem, using the right data, and getting the right answers.

Transitive Comparison

Kdb+ Transitive Comparisons

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By Hugh Hyndman, Director, Industrial IoT Solutions. A direct comparison of the performance of kdb+ against InfluxData and, by transitivity, against Cassandra, ElasticSearch, MongoDB, and OpenTSDB