differential-datalog | programming language for incremental computation | Database library
kandi X-RAY | differential-datalog Summary
kandi X-RAY | differential-datalog Summary
DDlog is a programming language for incremental computation. It is well suited for writing programs that continuously update their output in response to input changes. With DDlog, the programmer does not need to worry about writing incremental algorithms. Instead they specify the desired input-output mapping in a declarative manner, using a dialect of Datalog. The DDlog compiler then synthesizes an efficient incremental implementation. DDlog is based on Frank McSherry's excellent differential dataflow library.
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Top functions reviewed by kandi - BETA
- Visit a join .
- Generates the DDlog program .
- Convert DDlogRecord to an object .
- Converts this object to a typed object .
- Translates the create index statement .
- Creates an iterator that iterates over all elements that satisfy the given predicate .
- Retrieves the list of entries matching the given indexes .
- Checks if the given column reference is defined in the left relation .
- Assigns a variable assignment .
- Generates the string representation of the create table .
differential-datalog Key Features
differential-datalog Examples and Code Snippets
Community Discussions
Trending Discussions on differential-datalog
QUESTION
C newbie here. My use case is to give a header file posted here to the python library cffi
so that I can bind to a C library. The header file in the previous link has macros. cffi
only accepts a header file without macros, I think.
- Is there a way to give a header file with macros to
cffi
by specifying any options? - If not, how do I preprocess that file so that I can give it to
cffi
? I triedgcc -E ddlog.h > ddlog-processed.h
but when I ran the following code withcffi
, it errors out.
Steps to reproduce:
- Download ddlog.h
pip install cffi
gcc -E ddlog.h > ddlog-processed.h
- In a file
build.py
in the same folder asddlog.h
, place
ANSWER
Answered 2020-Apr-09 at 15:58There are two steps to using CFFI. The first one is to decide if you're using the ABI or the API mode---ffi.dlopen()
versus ffi.set_source()
; see https://cffi.readthedocs.io/en/latest/overview.html#abi-versus-api for details. Then you need to write the call to ffi.cdef()
accordingly. I generally recommend to use the API mode, which is much more flexible at the cost of requiring a C compiler at install-time (just like if you wrote a standard CPython C extension module). In both modes, you should copy manually parts of the C header inside the call to ffi.cdef()
; in the API mode you can leave many more details out and replace them with ...
(dot-dot-dot).
In neither of the two modes can you just paste a random C header making use of all the standard C features. Calling gcc -E
just makes the problem even harder (but has been done in some cases with very large libraries, with lots of custom post-processing). The point of ffi.cdef()
is that normally, you paste a simplified version of just the features you need.
To answer your precise question:
macros that are just constants, like an integer, are directly supported. In API mode you can also write
#define MY_CONSTANT ...
with dot-dot-dot.macros that work like functions are supported in the API mode (only) by using the following trick: write them in
ffi.cdef()
as if they were regular functions. That's enough to be able to call them.for more complex cases, you might have to write a real C function that wraps the usage of the macro or macros as needed. Just invent a new function name, declare the function in
ffi.cdef()
, and finally implement the function directly inffi.set_source()
.
In the ABI mode, the last two cases cannot be supported at all---macros don't exist in the ABI any more.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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No vulnerabilities reported
Install differential-datalog
You can use differential-datalog like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the differential-datalog component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
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