of course. there are a what we call SQL (relational) databases and NoSQL (non relational) databases. SQL is pretty set theoretic with joins, unions etc across tables, NoSQL is more open with four types:
- document store
- key value
- column store
- graph
of course. there are a what we call SQL (relational) databases and NoSQL (non relational) databases. SQL is pretty set theoretic with joins, unions etc across tables, NoSQL is more open with four types:
mongo is a document store database, basically its a complex key value pair. mongo was created to store JSON objects over a decade ago during the rise of javascript. despite its popularity and ease of use, its a poor data storage mechanism at scale
Scylla and cassandra like nosql would also be appropriate. I think the best is a combination of multiple different stacks, even transactional maybe on top. I wouldn’t recommend mongo it doesn’t scale. Xoken vega looks promising.
Fields the transaction did not carry are omitted. Open the payload to see the bytes as stored.
1F8LZF7xb8a7aSnGV226bXECALFP9u8gsQ Verifiedneo4j is a graph database. this is based off of graph theory and handles every object as nodes and edges. this was kind of pioneered by the sparql movement awhile back, but wikidata uses graph databases as their backend for many things.
though neo is quite interesting, i personally have never seen it scale it while outperforming relational databases. there is decades of knowledge for how to scale relational databases, the same can't be said for non relational (excluding columnstore/kvp)
a graph db is merely abstraction
document store databases allow you to "basically store anything". this freedom makes it extremely difficult to analyze the results, especially with large amounts of data. graph databases handle wildly recursive relationships, but that often isn't good
if you really care about graph like data, geospacial indices within relational systems are probably one of the most interesting methods you could use to scale
i would love to see someone pull off a graph db equivalent using spacial indices in SQL. really wouldn't be that hard, could probs beat neo4j performance on high d recursion pretty easily