11–20 of 55 posts
In the preface you list: - Generate high-performance code - Specialize on multiple arguments - Evaluate code interactively - Exploit parallel hardware > This list of requirements eliminates Python, R and other mainstream languages used for data science. Can you elaborate on why/how? Awesome work by the way
I have a passion project 4x4anarchy.com that operates with a Python-MariaDB system for querying map data by latitude and longitude, transforming it into GeoJSON for map display. The website deals with sizable tables, approximately 1 GB in size. I've made extensive optimizations, relying on well-structured indexes, caching mechanisms, and query optimization to enhance performance. Given these circumstances, how might…
Interesting! I work on a very similar product. I don't know Julia well, but I definitely would suggest exploring whether PostGIS can help improve the speed of your DB queries. I'd also consider how you deliver your geospatial data to your clients -- I'm not sure GeoJSON is your best bet. Protobuf tiles might be better for your use-case (e.g. the Mapbox Vector Tiles spec).
For anyone working with GIS data, it's absolutely worth investigating what PostGIS provides and the ease of integration to your existing application!
In the preface you list: - Generate high-performance code - Specialize on multiple arguments - Evaluate code interactively - Exploit parallel hardware > This list of requirements eliminates Python, R and other mainstream languages used for data science. Can you elaborate on why/how? Awesome work by the way
Python and R do not generate high performing code. At best they generate calls to high performing code.
It should be noted that this is usually sufficient. But particularly for earth scale problems it can often not be.
In the preface you list: - Generate high-performance code - Specialize on multiple arguments - Evaluate code interactively - Exploit parallel hardware > This list of requirements eliminates Python, R and other mainstream languages used for data science. Can you elaborate on why/how? Awesome work by the way
Python and R do not generate high performing code. At best they generate calls to high performing code.
In the preface you list: - Generate high-performance code - Specialize on multiple arguments - Evaluate code interactively - Exploit parallel hardware > This list of requirements eliminates Python, R and other mainstream languages used for data science. Can you elaborate on why/how? Awesome work by the way
Earlier quoted context omitted.
Python and R do not generate high performing code. At best they generate calls to high performing code.
Julia is designed to seem to win arguments as best I can tell... If you complain about the need to break abstractions and the lack of general purpose application you're accused of not understanding. When you say it slow they say you can inline assembler, and when you say that's dumb why have a high level language then, they then say well you don't have to it is fast as is and everyone else is slow, and it just devolv…
Earlier quoted context omitted.
Python and R do not generate high performing code. At best they generate calls to high performing code.
Julia is designed to seem to win arguments as best I can tell... If you complain about the need to break abstractions and the lack of general purpose application you're accused of not understanding. When you say it slow they say you can inline assembler, and when you say that's dumb why have a high level language then, they then say well you don't have to it is fast as is and everyone else is slow, and it just devolv…
In the preface you list: - Generate high-performance code - Specialize on multiple arguments - Evaluate code interactively - Exploit parallel hardware > This list of requirements eliminates Python, R and other mainstream languages used for data science. Can you elaborate on why/how? Awesome work by the way
Python and R do not generate high performing code. At best they generate calls to high performing code.
I have a passion project 4x4anarchy.com that operates with a Python-MariaDB system for querying map data by latitude and longitude, transforming it into GeoJSON for map display. The website deals with sizable tables, approximately 1 GB in size. I've made extensive optimizations, relying on well-structured indexes, caching mechanisms, and query optimization to enhance performance. Given these circumstances, how might…
PostGIS gives you the benefit of spatial indexes which are extremely performant.
I've seen Python GeoSpatial applications taking hours to finish processing which only took a few minutes when shifted onto PostGIS.
If you're also doing a lot of processing in Python, exploring other languages could also help. In the case of Julia you get a typed language that's also JIT compiled.
I have a passion project 4x4anarchy.com that operates with a Python-MariaDB system for querying map data by latitude and longitude, transforming it into GeoJSON for map display. The website deals with sizable tables, approximately 1 GB in size. I've made extensive optimizations, relying on well-structured indexes, caching mechanisms, and query optimization to enhance performance. Given these circumstances, how might…
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