Greatly improves performance of plan making, because we don't consider
candidates in other racks, most of which will fail to be selected due
to replication constraints (no rack overload). Also (but minor)
reduces the overhead of candidate evaluation, as we don't have to
evaluate rack load.
Enabled only for rf_rack_valid_keyspaces because such setups guarantee
that we will not need (because we must not) move tablets across racks,
and we don't need to execute the general algorithm for the whole DC.
Tested with perf-load-balancing, which performs a single scale-out
operation on a cluster which initially has 10 nodes 88 shards each, 2
racks, RF=2, 70 tables, 256 tablets per table. Scale out adds 6 new
nodes (same shard count). Time to reballance the cluster (plan making
only, sum of all iterations, no streaming):
Before: 16 min 25 s
After: 0 min 25 s
Before, plan making cost (single incremental iteration) alternated
between fast (0.1 [s]) and slow (14.1 [s]):
testlog - Rebalance iteration 7 took 14.156 [s]: mig=88, bad=88, first_bad=17741, eval=93874484, skiplist=0, skip: (load=0, rack=17653, node=0)
testlog - Rebalance iteration 8 took 0.143 [s]: mig=88, bad=88, first_bad=88, eval=865407, skiplist=0, skip: (load=0, rack=0, node=0)
The slow run chose min and max nodes in different racks, hence the
fast path failed to find any candidates and we switched to exhaustive
search of candidates in other nodes.
After, all iterations are fast (0.1 [s] per rack, 0.2 [s] per plan-making). The plan is twice as large because it combines the output of two subsequent (pre-patch) plan-making calls.
Fixes#26016Closesscylladb/scylladb#26017
* github.com:scylladb/scylladb:
test: perf: perf-load-balancing: Add parallel-scaleout scenario
test: perf: perf-load-balancing: Convert to tool_app_template
tablets: scheduler: Balance racks separately when rf_rack_valid_keyspaces is true
2239474a87