Re-implement `CATEGORIZE` in a way that works for multi-node clusters.
This requires that data is first categorized on each data node in a first pass, then the categorizers from each data node are merged on the coordinator node and previously categorized rows are re-categorized.
BlockHashes, used in HashAggregations, already work in a very similar way. E.g. for queries like `... | STATS ... BY field1, field2` they map values for `field1` and `field2` to unique integer ids that are then passed to the actual aggregate functions to identify which "bucket" a row belongs to. When passed from the data nodes to the coordinator, the BlockHashes are also merged to obtain unique ids for every value in `field1, field2` that is seen on the coordinator (not only on the local data nodes).
Therefore, we re-implement `CATEGORIZE` as a special BlockHash.
To choose the correct BlockHash when a query plan is mapped to physical operations, the `AggregateExec` query plan node needs to know that we will be categorizing the field `message` in a query containing `... | STATS ... BY c = CATEGORIZE(message)`. For this reason, _we do not extract the expression_ `c = CATEGORIZE(message)` into an `EVAL` node, in contrast to e.g. `STATS ... BY b = BUCKET(field, 10)`. The expression `c = CATEGORIZE(message)` simply remains inside the `AggregateExec`'s groupings.
**Important limitation:** For now, to use `CATEGORIZE` in a `STATS` command, there can be only 1 grouping (the `CATEGORIZE`) overall.
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