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This is so users can replicate the example correctly. Ignore the sqlquerygraph.py file from code coverage. This is because it is a collection of other code that has been tested.
This is so we can load data into the neo4j graph database.
This is so we can get some data into the database in the case where there is a rogue line that aborts the import. This is useful when the .csv files have about hundreds or thousands of rows. This will also reduce the memory overhead of the transaction state.
Codecov Report
@@ Coverage Diff @@
## main #12 +/- ##
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+ Coverage 49.03% 76.38% +27.35%
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Files 3 3
Lines 155 144 -11
Branches 29 32 +3
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+ Hits 76 110 +34
+ Misses 69 24 -45
Partials 10 10
Continue to review full report at Codecov.
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This is so we can move that into the neo4j container. Also update Cypher queries so they use :auto to avoid issues with manual importing. May not need this :auto call when using Cypher shell though.
This is so we can reference where the queries in this repo are based off. Also innclude image of graph database that visualises the table dependencies.
This is so we can create nodes and constraints for each dataset.
This is so we can ensure they work.
This is so we can then read these scripts and create in Cypher shell.
This is so we do not create unecessary import of files that do not exist in the Cypher code. Also simplify the aliasing to deal with cases where have two datasets that start with the same letter.
This is so the Cypher scripts are also passed into the Docker container.
This is to adhere to neo4j node naming standards.
This is so others can reproduce the graph.
This is so the tests pass. The idea is tha the user generates the example .csv file through following the README.md.
This is so we can automate import of data.
This is to run queries in the database.
This is so we can have a separate container handling the importing of data into the database.
This is so we can apply the Python loader module on the Cypher scripts.
This is because we only need py2neo package for docker container.
This is with the understanding that docker-compose is an orchestrator platform in the vein of kubernetes and that the stuff trying to be done with respect to calling the module will be better done in a Dockerfile. Currently Python module cannot connect to neo4j docker container because of hosting. Need to specify the Docker container's IP address in the module.
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Summary
This branch establishes the functions that write the Cypher queries to take the created data and manually imports into the neo4j graph database.
Changes
The high-level changes made in this PR are:
README.md.Please see the commit history for more detailed changes.
Check
README.md.Note
This "fixes #4" and "fixes #6".
This branch does not quite achieve the objective of being able to run commands from the terminal into the neo4j Docker container but you can achieve this through Python itself. This is to do with the call to
bolt://localhost:7687being incorrect. We should swaplocalhostwith the IP address of the Docker container.