Series Index Generics Part 01: Basic Syntax Generics Part 02: Underlying Types Generics Part 03: Struct Types and Data Semantics Introduction In the previous post, I showed you how to declare a user-defined type, based on an underlying type. I did this through the progression of writing different versions of the same type using concrete types, the empty interface and then finally, generics. I also provided information on how the compiler was limited in its ability to infer the substitution for the generic type during zero value construction, but it could with initialized construction.
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Series Index Python and Go: Part I - gRPC Python and Go: Part II - Extending Python With Go Python and Go: Part III - Packaging Python Code Python and Go: Part IV - Using Python in Memory Introduction In a previous post we used gRPC to call Python code from Go. gRPC is a great framework, but there is a performance cost to it. Every function call needs to marshal the arguments using protobuf, make a network call over HTTP/2, and then un-marshal the result using protobuf.
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Changes The draft is a living document which means these posts will need to change over time. This section documents when changes have taken place to this post. 21/08/20 : Moving forward with the generics design draft Series Index Generics Part 01: Basic Syntax Generics Part 02: Underlying Types Generics Part 03: Struct Types and Data Semantics Introduction In the previous post, I showed you how to write a generic function in Go using the proposed syntax from the generics draft document.
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Series Index Python and Go: Part I - gRPC Python and Go: Part II - Extending Python With Go Python and Go: Part III - Packaging Python Code Python and Go: Part IV - Using Python in Memory Introduction In the previous post we compiled Go code to a shared library and used it from the Python interactive shell. In this post we’re going to finish the development process by writing a Python module that hides the low level details of working with a shared library and then package this code as a Python package.
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Changes The draft is a living document which means these posts will need to change over time. This section documents when changes have taken place to this post. 21/08/20 : Moving forward with the generics design draft Series Index Generics Part 01: Basic Syntax Generics Part 02: Underlying Types Generics Part 03: Struct Types and Data Semantics Introduction In this series of posts about generics in Go, I will present code and teach to the different aspects of the current generics draft.
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Series Index Python and Go: Part I - gRPC Python and Go: Part II - Extending Python With Go Python and Go: Part III - Packaging Python Code Python and Go: Part IV - Using Python in Memory Introduction In the previous post we saw how a Go service can call a Python service using gRPC. Using gRPC to connect a Go and Python program together can be a great choice, but there’s a complexity price that goes with it.
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Introduction In this paper written by Manish Jain (the founder of Dgraph) he describes Dgraph as: a distributed graph database which provides horizontal scalability, distributed cluster-wide ACID transactions, low-latency arbitrary-depth joins, synchronous replication, high availability, and crash resilience. There are many claims being stated here which frankly I didn’t understand the meaning of the first time I read this sentence. I decided I wanted to better understand these claims with the hope that it would help me understand Dgraph’s architecture and engineering decisions better.
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Series Index Python and Go: Part I - gRPC Python and Go: Part II - Extending Python With Go Python and Go: Part III - Packaging Python Code Python and Go: Part IV - Using Python in Memory Introduction Like tools, programming languages tend to solve problems they are designed to. You can use a knife to tighten a screw, but it’s better to use a screwdriver. Plus there is less chance of you getting hurt in the process.
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Introduction In most of the reviews for this post, I was asked why choose a graph database over something else? This is a hard question to answer since my experience right now is limited on the graph database side. My guess is you’re wondering the same thing, so this is my best answer to date. At this point in my career, I would only choose a relational database if I was writing financial software.
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Introduction I have spent a career building business applications that work with databases, starting with SQL and then moving to No-SQL. I’ve been curious how Graph databases work and how to build applications with them. It seems the sweet spot for a Graph database is when your application needs to model the interrelationships of complex and different datasets. I’ve been using No-SQL databases for these types of applications so I am super interested in knowing how Graph databases differ.
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