semvec/pypi-packages huggingface dataset:
NeoMetaTracker 0.0.11
Track and visualize changes in a Neo4j database schema
License: MIT
Requires Python: >=3.9

# neo4j-meta-logger

A python module to log and track your neo4j graph schema transformations.

`call db.schema.visualization` + ⏲️ a time machine + some helpful 🔧 tools and 📈 statistics

Works fast with neo4j graphs of any size 💪

Maintainer: Tim Bleimehl  
Status: Pre-alpha


# Example

Lets create a sample graph with python and Neo4J

```python

import py2neo
from NeoMetaTracker import NeoMetaTracker

g = py2neo.Graph(name="test_graph")

g.run(
    "CREATE p = (:Human{name:'Amina Okujewa'})-[:LIVES_ON]->(:World {name: 'Earth'})"
)
g.run(
    "CREATE p = (:Cat{name:'Grumpy Cat'})-[:LIVES_ON]->(:World {name: 'Internet'})"
)
g.run(
    "MATCH (wI:World{name:'Internet'}),(wE:World{name:'Earth'}) CREATE (wI)-[:EXISTS_ON]->(wE)"
)
```

Our graph looks like this:

!["docs/01_test_base_graph.png"](docs/01_test_base_graph.png)

and the schema will look like this:

!["docs/03_schema.png"](docs/03_schema.png)

Lets capture the current status to analyse the changes later.

```python
meta_logger = NeoMetaTracker(test_graph)
meta_logger.capture()
```

Now lets do some changes to our Graphs content

```python
g.run(
    "MATCH (wI:World{name:'Internet'}),(wE:World{name:'Earth'}) CREATE (as:Human{name:'Aaron Swartz'})-[:LIVES_ON]->(wI), (as)-[:LIVES_ON]->(wE)"
)
```

Our graph now looks like this

!["docs/docs/02_graph_extended.png"](docs/02_graph_extended.png)

The schema still looks the same. We wont be able to recognize changes without any help:

`call db.schema.visualization`

!["docs/03_schema.png"](docs/03_schema.png)

Here come `NeoMetaTracker` for the rescue

Lets do another capture to compare the changes we did:

```python
meta_logger.capture()
```

Now we can analyze the changes in the graph:


```python
meta_logger.get_numeric_last_changes()
```

This outputs: 

```
{'labels': {'Human': 1}, 'relations': {'LIVES_ON': 2}}
```

We can see we created one new Node with the Label `Human` and 2 new relations named `LIVES_ON`.  
This allready can be a valuable meta information, but wait there is more...

Lets visualize the schema changes in another graph.

```python
changes_subgraph = meta_logger.get_schemagraph_last_changes()
schema_g = py2neo.Graph(name="test_graph")
schema_g.merge(changes_subgraph)
```

Now we only see the part of our schema that changed:

!["docs/04_schema_changes.png"](docs/04_schema_changes.png)

We can also recall any old state of our schema on any point in time where we did a `capture`  

```python
meta_logger.capture_points[0].schema
```

This will return a `py2neo.Subgraph` of the schema from the beginning of our script. Same as `call db.schema.visualization` but with a timemachine :)