cozo
A general-purpose, transactional, relational database that uses Datalog for query and focuses on graph data and algorithms.
Features
- Relational database with Datalog as the query language
- Recursive queries, especially recursion through (safe) aggregation, capable of expressing complex graph operations and algorithms
- Fixed rules providing efficient whole-graph algorithms which integrate seamlessly with Datalog
- Rich set of built-in functions and aggregations
- Only a single executable, trivial to deploy and run
- Embeddable, can run in the same process as the application
- Easy to use from any programming language
- Special support for Jupyter notebooks for integration with the Python DataScience ecosystem
- Modern, clean, flexible syntax, informative error messages
Teasers
Here *route is a relation with two columns src and dst, representing a route between those airports.
Find airports reachable by one stop from Frankfurt Airport (code FRA):
?[dst] := *route{src: 'FRA', dst: stop},
*route{src: stop, dst}
Find airports reachable from Frankfurt with any number of stops with code starting with the letter A:
reachable[dst] := *route{src: 'FRA', dst}
reachable[dst] := reachable[src], *route{src, dst}
?[airport] := reachable[airport], starts_with(airport, 'A')
Compute the shortest path between Frankfurt and all airports in the world:
shortest_paths[dst, shortest(path)] := *route{src: 'FRA', dst},
path = ['FRA', dst]
shortest_paths[dst, shortest(path)] := shortest_paths[stop, prev_path],
*route{src: stop, dst},
path = append(prev_path, dst)
?[dst, path] := shortest_paths[dst, path]
Compute the shortest path again, but with built-in algorithm:
starting[airport] := airport = 'FRA'
?[src, dst, cost, path] <~ ShortestPathDijkstra(*route[], starting[])
Learning Cozo
- Start with the Tutorial to learn the basics;
- Continue with the Manual to understand the fine points.
Bug reports, discussions
If you encounter a bug, first search for past issues to see if it has already been reported. If not, open a new issue. Please provide sufficient information so that we can diagnose the problem faster.
Other discussions about Cozo should be in GitHub discussions.
Use cases
As Cozo is a general-purpose database, it can be used in situations where traditional databases such as PostgreSQL and SQLite are used. However, Cozo is designed to overcome several shortcomings of traditional databases, and hence fares especially well in specific situations:
- You have a lot of interconnected relations and the usual queries need to relate many relations together. In other words, you need to query a complex graph.
- An example is a system granting permissions to users for specific tasks. In this case, users may have roles, belong to an organization hierarchy, and tasks similarly have organizations and special provisions associated with them. The granting process itself may also be a complicated rule encoded as data within the database.
- With a traditional database, the corresponding SQL tend to become an entangled web of nested queries, with many tables joined together, and maybe even with some recursive CTE thrown in. This is hard to maintain, and worse, the performance is unpredictable since query optimizers in general fail when you have over twenty tables joined together.
- With Cozo, on the other hand, Horn clauses make it easy to break the logic into smaller pieces and write clear, easily testable queries. Furthermore, the deterministic evaluation order makes identifying and solving performance problems easier.
- Your data may be simple, even a single table, but it is inherently a graph.
- We have seen an example in the Tutorial: the air route dataset, where the key relation contains the routes connecting airports.
- In traditional databases, when you are given a new relation, you try to understand it by running aggregations on it to collect statistics: what is the distribution of values, how are the columns correlated, etc.
- In Cozo you can do the same exploratory analysis, except now you also have graph algorithms that you can easily apply to understand things such as: what is the most connected entity, how are the nodes connected, and what are the communities structure within the nodes.
- Your data contains hidden structures that only become apparent when you identify the scales of the relevant structures.
- Examples are most real networks, such as social networks, which have a very rich hierarchy of structures
- In a traditional database, you are limited to doing nested aggregations and filtering, i.e. a form of multifaceted data analysis. For example, you can analyze by gender, geography, job or combinations of them. For structures hidden in other ways, or if such categorizing tags are not already present in your data, you are out of luck.
- With Cozo, you can now deal with emergent and fuzzy structures by using e.g. community detection algorithms, and collapse the original graph into a coarse-grained graph consisting of super-nodes and super-edges. The process can be iterated to gain insights into even higher-order emergent structures. This is possible in a social network with only edges and no categorizing tags associated with nodes at all, and the discovered structures almost always have meanings correlated to real-world events and organizations, for example, forms of collusion and crime rings. Also, from a performance perspective, coarse-graining is a required step in analyzing the so-called big data, since many graph algorithms have high complexity and are only applicable to the coarse-grained small or medium networks.
- You want to understand your live business data better by augmenting it into a knowledge graph.
- For example, your sales database contains product, buyer, inventory, and invoice tables. The augmentation is external data about the entities in your data in the form of taxonomies and ontologies in layers.
- This is inherently a graph-theoretic undertaking and traditional databases are not suitable. Usually, a dedicated graph processing engine is used, separate from the main database.
- With Cozo, it is possible to keep your live data and knowledge graph analysis together, and importing new external data and doing analysis is just a few lines of code away. This ease of use means that you will do the analysis much more often, with a perhaps much wider scope.
Status of the project
Cozo is very young and not production-ready yet, but we encourage you to try it out for your use case. Any feedback is welcome.
Versions before 1.0 do not promise syntax/API stability or storage compatibility. We promise that when you try to open database files created with an incompatible version, Cozo will at least refuse to start instead of silently corrupting your data.
Plans for development
In the near term, before we reach version 1.0:
- Backup/restore functionality
- Many, many more tests to ensure correctness
- Benchmarks
Further down the road:
- More tuning options
- Streaming/reactive data
- Extension system
- The core of Cozo should be kept small at all times. Additional functionalities should be in extensions for the user to choose from.
- What can be extended: datatypes, functions, aggregations, and fixed algorithms.
- 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.
- There will probably be a few "official" extension bundles, such as
- arbitrary precision arithmetic
- full-text "indexing" and searching
- relations that can emulate spatial and other types of non-lexicographic indices
- reading from external databases directly
- more exotic graph algorithms
Ideas and discussions are welcome.
Storage engine
Cozo is written in Rust, with RocksDB as the storage engine (this may change in the future). We manually wrote the C++/Rust bindings for RocksDB with cxx.
Licensing
The contents of this project are licensed under AGPL-3.0 or later, except:
- Files under
cozorocks/are licensed under MIT, or Apache-2.0, or BSD-3-Clause; - Files under
docs/are licensed under CC BY-SA 4.0.
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