LogPy - Facts and Relations a quick and dirty in-memory database
As an example we’ll look at the 50 states in the US. We know two things about each state.
- Is it coastal? For example California (CA) is coastal because it is next to the Pacific Ocean, Arizona (AZ) is not.
- To which other states is it adjacent? For example California (CA) is adjacent to Oregon (OR), Arizona (AZ) and Nevada (NV).
We express data in LogPy using relations and facts
here we have asserted the fact that
'CA' is coastal. Lets quickly do this for all of the coastal states
Adjacency is only slightly more complex to express. The following code asserts that California (CA) is adjacent to Arizona (AZ) and that California (CA) is adjacent to Oregon (OR).
Now we need a list of all adjacent pairs of states. Fortunately someone else has already compiled such a list. His data looks like this
AK AL,MS,TN,GA,FL AR,MO,TN,MS,LA,TX,OK AZ,CA,NV,UT,CO,NM CA,OR,NV,AZ CO,WY,NE,KS,OK,NM,AZ,UT ...
Each line says that the first element is adjacent to the following ones. So for example Alaska (AK) is adjacent to no states and California (CA) is adjacent to Oregon (OR), Nevada (NV) and Arizona (AZ). We can parse this file and assert the relevant facts with fairly standard Python code
Once have asserted the relevant facts we can run queries with the logical expressions of LogPy. Recall from the last post that we can use relations to express logical goals and use
run to search for cases that satisfy those goals. Here are two simple queries
We can construct more complex queries with multiple goals. In SQL the following queries would require a
Facts and relations are currently indexed by default, yielding relatively fast query times.
LogPy provides a declarative interface to query complex data. Data is stored as facts/tuples and queries are expressed as logical goals. This system is expressive and can match SQL in many respects. The use of Logic programming languages for database queries has roots in Datalog a subset of Prolog designed for databases.
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