GideonPotok commented on code in PR #46917:
URL: https://github.com/apache/spark/pull/46917#discussion_r1638766832
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sql/core/benchmarks/CollationBenchmark-results.txt:
##########
Review Comment:
@uros-db now the benchmark results are updated (jdk17 only at the moment) .
Relative to each other (collations), it looks good. It is about as performant
as the other approach.
@uros-db @dbatomic Which approach should we go with?
This PR:
```
OpenJDK 64-Bit Server VM 17.0.11+9-LTS on Linux 6.5.0-1021-azure
AMD EPYC 7763 64-Core Processor
collation e2e benchmarks - mode - 10000 elements: Best Time(ms) Avg
Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
---------------------------------------------------------------------------------------------------------------------------------
mode df column with collation - UTF8_BINARY_LCASE 58
69 7 0.2 5757.5 1.0X
mode df column with collation - UNICODE 52
58 5 0.2 5233.2 1.1X
mode df column with collation - UTF8_BINARY 45
50 5 0.2 4462.9 1.3X
mode df column with collation - UNICODE_CI 46
50 5 0.2 4570.9 1.3X
```
# The original approach
(GroupMapReduce)[https://github.com/apache/spark/pull/46597]:
```
OpenJDK 64-Bit Server VM 17.0.11+9-LTS on Linux 6.5.0-1021-azure
AMD EPYC 7763 64-Core Processor
collation e2e benchmarks - mode - 10000 elements: Best Time(ms) Avg
Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
---------------------------------------------------------------------------------------------------------------------------------
mode df column with collation - UTF8_BINARY_LCASE 56
68 7 0.2 5571.2 1.0X
mode df column with collation - UNICODE 47
52 5 0.2 4659.6 1.2X
mode df column with collation - UTF8_BINARY 44
48 3 0.2 4423.5 1.3X
mode df column with collation - UNICODE_CI 43
47 4 0.2 4316.9 1.3X
```
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