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@ -5,33 +5,37 @@
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# `cozo`
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A general-purpose, transactional, relational database
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that uses Datalog for query and focuses on graph data and algorithms.
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that uses Datalog for query, is embeddable, and focuses on graph data and algorithms.
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## Features
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* Relational database with [Datalog](https://en.wikipedia.org/wiki/Datalog) as the query language
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* Recursive queries, especially recursion through (safe) aggregation, capable of expressing complex graph operations and algorithms
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* Fixed rules providing efficient whole-graph algorithms which integrate seamlessly with Datalog
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* Rich set of built-in functions and aggregations
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* Only a single executable, trivial to deploy and run
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* [Embeddable](https://cozodb.github.io/current/manual/setup.html#embedding-cozo), can run in the same process as the application
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* Easy to use from any programming language
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* Special support for [Jupyter](https://jupyter.org/) notebooks for integration with the Python DataScience ecosystem
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* Recursive queries, recursion through (safe) aggregations, capable of expressing complex graph operations and
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algorithms
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* Fixed rules for efficient whole-graph algorithms which integrate seamlessly with Datalog
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* Rich set of built-in functions and aggregations
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* Easy to use from any programming language, or as a standalone program
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* [Embeddable](https://cozodb.github.io/current/manual/setup.html#embedding-cozo), with ready-to-use bindings for
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Python, NodeJS and Java
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* Single executable, trivial to deploy and run
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* [Jupyter](https://jupyter.org/) notebooks integration, plays well with the DataScience ecosystem
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* Modern, clean, flexible syntax, informative error messages
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## Teasers
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Here `*route` is a relation with two columns `src` and `dst`,
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Here `*route` is a relation with two columns `src` and `dst`,
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representing a route between those airports.
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Find airports reachable by one stop from Frankfurt Airport (code `FRA`):
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TODO replace with images
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```js
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?[dst] := *route{src: 'FRA', dst: stop},
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*route{src: stop, dst}
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```
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Find airports reachable from Frankfurt with any number of stops
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Find airports reachable from Frankfurt with any number of stops
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with code starting with the letter `A`:
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```js
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@ -58,15 +62,35 @@ starting[airport] := airport = 'FRA'
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?[src, dst, cost, path] <~ ShortestPathDijkstra(*route[], starting[])
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```
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## Learning Cozo
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Nice error messages when things go wrong:
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xxx
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## Getting started
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First we need to get Cozo installed on the local machine. As Cozo is embeddable,
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there are lots of options for how we run Cozo. Follow one of the following guides that suits you best:
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* Start with the [Tutorial](https://nbviewer.org/github/cozodb/cozo/blob/main/docs/tutorial/tutorial.ipynb) to learn the basics;
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* Continue with the [Manual](https://cozodb.github.io/current/manual/) to understand the fine points.
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* Embedded in Python with JupyterLab (best interactive experience, recommended if you are comfortable with the Python
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ecosystem)
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* Embedded in Python
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* Embedded in NodeJS (Javascript)
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* Embedded in Java (or any JVM language)
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* Embedded in Rust
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* Client/server with HTTP API
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* Embedded in C/C++, or any language that has a C FFI
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(Golang, R, Haskell, CommonLisp, Julia, Fortran, C#, Swift, ...)
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After you have it installed, you can start learning CozoScript:
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* Start with the [Tutorial](https://nbviewer.org/github/cozodb/cozo/blob/main/docs/tutorial/tutorial.ipynb) to learn the
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basics;
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* Continue with the [Manual](https://cozodb.github.io/current/manual/) for the fine points.
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## Bug reports, discussions
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If you encounter a bug, first search for [past issues](https://github.com/cozodb/cozo/issues) to see
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if it has already been reported. If not, open a new issue.
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if it has already been reported. If not, open a new issue.
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Please provide sufficient information so that we can diagnose the problem faster.
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Other discussions about Cozo should be in [GitHub discussions](https://github.com/cozodb/cozo/discussions).
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@ -84,67 +108,68 @@ in specific situations:
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* You have a lot of interconnected relations
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and the usual queries need to relate many relations together.
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In other words, you need to query a complex graph.
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* An example is a system granting permissions to users for specific tasks.
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In this case, users may have roles,
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belong to an organization hierarchy, and tasks similarly have organizations
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and special provisions associated with them.
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The granting process itself may also be a complicated rule encoded as data
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within the database.
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* With a traditional database,
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the corresponding SQL tend to become
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an entangled web of nested queries, with many tables joined together,
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and maybe even with some recursive CTE thrown in. This is hard to maintain,
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and worse, the performance is unpredictable since query optimizers in general
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fail when you have over twenty tables joined together.
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* With Cozo, on the other hand, [Horn clauses](https://en.wikipedia.org/wiki/Horn_clause)
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make it easy to break
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the logic into smaller pieces and write clear, easily testable queries.
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Furthermore, the deterministic evaluation order makes identifying and solving
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performance problems easier.
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* An example is a system granting permissions to users for specific tasks.
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In this case, users may have roles,
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belong to an organization hierarchy, and tasks similarly have organizations
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and special provisions associated with them.
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The granting process itself may also be a complicated rule encoded as data
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within the database.
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* With a traditional database,
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the corresponding SQL tend to become
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an entangled web of nested queries, with many tables joined together,
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and maybe even with some recursive CTE thrown in. This is hard to maintain,
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and worse, the performance is unpredictable since query optimizers in general
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fail when you have over twenty tables joined together.
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* With Cozo, on the other hand, [Horn clauses](https://en.wikipedia.org/wiki/Horn_clause)
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make it easy to break
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the logic into smaller pieces and write clear, easily testable queries.
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Furthermore, the deterministic evaluation order makes identifying and solving
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performance problems easier.
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* Your data may be simple, even a single table, but it is inherently a graph.
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* We have seen an example in the [Tutorial](https://nbviewer.org/github/cozodb/cozo/blob/main/docs/tutorial/tutorial.ipynb):
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the air route dataset, where the key relation contains the routes connecting airports.
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* In traditional databases, when you are given a new relation,
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you try to understand it by running aggregations on it to collect statistics:
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what is the distribution of values, how are the columns correlated, etc.
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* In Cozo you can do the same exploratory analysis,
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except now you also have graph algorithms that you can
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easily apply to understand things such as: what is the most _connected_ entity,
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how are the nodes connected, and what are the _communities_ structure within the nodes.
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* We have seen an example in
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the [Tutorial](https://nbviewer.org/github/cozodb/cozo/blob/main/docs/tutorial/tutorial.ipynb):
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the air route dataset, where the key relation contains the routes connecting airports.
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* In traditional databases, when you are given a new relation,
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you try to understand it by running aggregations on it to collect statistics:
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what is the distribution of values, how are the columns correlated, etc.
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* In Cozo you can do the same exploratory analysis,
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except now you also have graph algorithms that you can
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easily apply to understand things such as: what is the most _connected_ entity,
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how are the nodes connected, and what are the _communities_ structure within the nodes.
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* Your data contains hidden structures that only become apparent when you
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identify the _scales_ of the relevant structures.
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* Examples are most real networks, such as social networks,
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which have a very rich hierarchy of structures
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* In a traditional database, you are limited to doing nested aggregations and filtering,
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i.e. a form of multifaceted data analysis. For example, you can analyze by gender, geography,
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job or combinations of them. For structures hidden in other ways,
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or if such categorizing tags are not already present in your data,
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you are out of luck.
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* With Cozo, you can now deal with emergent and fuzzy structures by using e.g.
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community detection algorithms, and collapse the original graph into a coarse-grained
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graph consisting of super-nodes and super-edges.
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The process can be iterated to gain insights into even higher-order emergent structures.
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This is possible in a social network with only edges and _no_ categorizing tags
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associated with nodes at all,
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and the discovered structures almost always have meanings correlated to real-world events and
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organizations, for example, forms of collusion and crime rings.
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Also, from a performance perspective,
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coarse-graining is a required step in analyzing the so-called big data,
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since many graph algorithms have high complexity and are only applicable to
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the coarse-grained small or medium networks.
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* Examples are most real networks, such as social networks,
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which have a very rich hierarchy of structures
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* In a traditional database, you are limited to doing nested aggregations and filtering,
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i.e. a form of multifaceted data analysis. For example, you can analyze by gender, geography,
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job or combinations of them. For structures hidden in other ways,
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or if such categorizing tags are not already present in your data,
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you are out of luck.
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* With Cozo, you can now deal with emergent and fuzzy structures by using e.g.
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community detection algorithms, and collapse the original graph into a coarse-grained
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graph consisting of super-nodes and super-edges.
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The process can be iterated to gain insights into even higher-order emergent structures.
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This is possible in a social network with only edges and _no_ categorizing tags
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associated with nodes at all,
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and the discovered structures almost always have meanings correlated to real-world events and
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organizations, for example, forms of collusion and crime rings.
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Also, from a performance perspective,
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coarse-graining is a required step in analyzing the so-called big data,
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since many graph algorithms have high complexity and are only applicable to
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the coarse-grained small or medium networks.
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* You want to understand your live business data better by augmenting it into a _knowledge graph_.
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* For example, your sales database contains product, buyer, inventory, and invoice tables.
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The augmentation is external data about the entities in your data in the form of _taxonomies_
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and _ontologies_ in layers.
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* This is inherently a graph-theoretic undertaking and traditional databases are not suitable.
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Usually, a dedicated graph processing engine is used, separate from the main database.
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* With Cozo, it is possible to keep your live data and knowledge graph analysis together,
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and importing new external data and doing analysis is just a few lines of code away.
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This ease of use means that you will do the analysis much more often, with a perhaps much wider scope.
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* For example, your sales database contains product, buyer, inventory, and invoice tables.
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The augmentation is external data about the entities in your data in the form of _taxonomies_
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and _ontologies_ in layers.
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* This is inherently a graph-theoretic undertaking and traditional databases are not suitable.
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Usually, a dedicated graph processing engine is used, separate from the main database.
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* With Cozo, it is possible to keep your live data and knowledge graph analysis together,
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and importing new external data and doing analysis is just a few lines of code away.
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This ease of use means that you will do the analysis much more often, with a perhaps much wider scope.
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## Status of the project
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Cozo is very young and **not** production-ready yet,
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Cozo is very young and **not** production-ready yet,
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but we encourage you to try it out for your use case.
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Any feedback is welcome.
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@ -165,26 +190,29 @@ Further down the road:
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* More tuning options
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* Streaming/reactive data
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* Extension system
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* The core of Cozo should be kept small at all times. Additional functionalities should be in extensions for the user to choose from.
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* What can be extended: datatypes, functions, aggregations, and fixed algorithms.
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* Extensions should be written in a compiled language such as Rust or C++ and compiled into a dynamic library, to be loaded by Cozo at runtime.
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* There will probably be a few "official" extension bundles, such as
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* arbitrary precision arithmetic
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* full-text "indexing" and searching
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* relations that can emulate spatial and other types of non-lexicographic indices
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* reading from external databases directly
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* more exotic graph algorithms
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* The core of Cozo should be kept small at all times. Additional functionalities should be in extensions for the
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user to choose from.
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* What can be extended: datatypes, functions, aggregations, and fixed algorithms.
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* Extensions should be written in a compiled language such as Rust or C++ and compiled into a dynamic library, to be
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loaded by Cozo at runtime.
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* There will probably be a few "official" extension bundles, such as
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* arbitrary precision arithmetic
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* full-text "indexing" and searching
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* relations that can emulate spatial and other types of non-lexicographic indices
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* reading from external databases directly
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* more exotic graph algorithms
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Ideas and discussions are welcome.
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## Storage engine
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Cozo is written in Rust, with [RocksDB](http://rocksdb.org/) as the storage engine
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Cozo is written in Rust, with [RocksDB](http://rocksdb.org/) as the storage engine
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(this may change in the future).
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We manually wrote the C++/Rust bindings for RocksDB with [cxx](https://cxx.rs/).
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We manually wrote the C++/Rust bindings for RocksDB with [cxx](https://cxx.rs/).
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## Licensing
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The contents of this project are licensed under AGPL-3.0 or later, except:
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* Files under `cozorocks/` are licensed under MIT, or Apache-2.0, or BSD-3-Clause;
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* Files under `cozorocks/`, `python/`, `nodejs/`, `java/` and `c/` are licensed under MIT, or Apache-2.0, or BSD-3-Clause;
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* Files under `docs/` are licensed under CC BY-SA 4.0.
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Ziyang Hu
2 years ago
