Disagree, though in practice it depends on the query, cardinality of the various columns across table, indices, and RDBMS implementation (so, everything).
A simple equijoin with high cardinality and indexed columns will usually be extremely fast. The same join in a 1:M might be fast, or it might result in a massive fanout. In the case of the latter, if your RDBMS uses a clustering index, and if you’ve designed your schemata to exploit this fact (e.g. a table called UserPurchase that has a PK of (user_id, purchase_id)) can still be quite fast.
Aggregations often imply large amounts of data being retrieved, though this is not necessarily true.
That level of database optimization is rare in practice. As soon as a non-database person gets decision making authority there goes your data model and disk layout.
And many important datasets never make it into any kind of database like that. Very few people provide "index columns" in their CSV files. Or they use long variable length strings as their primary key.
OP pertains to that kind of data. Some stuff in text files.
unconvinced. any join needs some kind of seek on the secondary relation index, or a bunch of state if ur stream joining to build temporary index sizes O(n) until end of batch. on the other hand summing N numbers needs O(1) memory and if your data is column shaped it’s like one CPU instruction to process 8 rows. in “big data” context usually there’s no traditional b-tree index to join either. For jobs that process every row in the input set Mr Join is horrible for perf to the point people end up with a dedicated join job/materialized view so downstream jobs don’t have to re do the work
Disagree, though in practice it depends on the query, cardinality of the various columns across table, indices, and RDBMS implementation (so, everything).
A simple equijoin with high cardinality and indexed columns will usually be extremely fast. The same join in a 1:M might be fast, or it might result in a massive fanout. In the case of the latter, if your RDBMS uses a clustering index, and if you’ve designed your schemata to exploit this fact (e.g. a table called UserPurchase that has a PK of (user_id, purchase_id)) can still be quite fast.
Aggregations often imply large amounts of data being retrieved, though this is not necessarily true.
That level of database optimization is rare in practice. As soon as a non-database person gets decision making authority there goes your data model and disk layout.
And many important datasets never make it into any kind of database like that. Very few people provide "index columns" in their CSV files. Or they use long variable length strings as their primary key.
OP pertains to that kind of data. Some stuff in text files.
unconvinced. any join needs some kind of seek on the secondary relation index, or a bunch of state if ur stream joining to build temporary index sizes O(n) until end of batch. on the other hand summing N numbers needs O(1) memory and if your data is column shaped it’s like one CPU instruction to process 8 rows. in “big data” context usually there’s no traditional b-tree index to join either. For jobs that process every row in the input set Mr Join is horrible for perf to the point people end up with a dedicated join job/materialized view so downstream jobs don’t have to re do the work