Software: Uniffle 0.2.0 Hadoop 2.8.5 Spark 2.4.6
Hardware: Machine 176 cores, 265G memory, 4T * 12 HDD, network bandwidth 10GB/s
Hadoop Yarn Cluster: 1 * ResourceManager + 6 * NodeManager, every machine 4T * 10 HDD
Uniffle Cluster: 1 * Coordinator + 6 * Shuffle Server, every machine 4T * 10 HDD
Spark's configuration
spark.executor.instances 100
spark.executor.cores 4
spark.executor.memory 9g
spark.executor.memoryOverhead 1024
spark.shuffle.manager org.apache.spark.shuffle.RssShuffleManager
spark.rss.storage.type MEMORY_LOCALFILE
Shuffle Server's configuration
rss.storage.type MEMORY_LOCALFILE
rss.server.buffer.capacity 50g
We used spark-sql-perf to generate 1TB data.
query name | vanilla | uniffle |
---|---|---|
query1 | 16 | 18 |
query10 | 30 | 35 |
query11 | 86 | 96 |
query12 | 14 | 17 |
query13 | 102 | 77 |
query14a | 239 | 254 |
query14b | 226 | 232 |
query15 | 44 | 48 |
query16 | 50 | 59 |
query17 | 83 | 97 |
query18 | 31 | 35 |
query19 | 15 | 17 |
query2 | 21 | 25 |
query20 | 15 | 16 |
query21 | 8 | 8 |
query22 | 21 | 22 |
query23a | 288 | 366 |
query23b | 366 | 422 |
query24a | 181 | 198 |
query24b | 167 | 187 |
query25 | 93 | 113 |
query26 | 15 | 15 |
query27 | 16 | 17 |
query28 | 38 | 41 |
query29 | 80 | 102 |
query3 | 9 | 11 |
query30 | 21 | 26 |
query31 | 30 | 40 |
query32 | 14 | 15 |
query33 | 26 | 30 |
query34 | 12 | 16 |
query35 | 34 | 39 |
query36 | 15 | 18 |
query37 | 16 | 20 |
query38 | 27 | 36 |
query39 | 15 | 19 |
query39a | 16 | 20 |
query39b | 14 | 19 |
query4 | 205 | 227 |
query40 | 38 | 38 |
query41 | 5 | 6 |
query42 | 9 | 10 |
query43 | 13 | 13 |
query44 | 20 | 22 |
query45 | 30 | 36 |
query46 | 16 | 18 |
query47 | 22 | 25 |
query48 | 25 | 24 |
query49 | 58 | 66 |
query5 | 56 | 59 |
query50 | 56 | 61 |
query51 | 23 | 28 |
query52 | 9 | 10 |
query53 | 12 | 13 |
query54 | 52 | 62 |
query55 | 9 | 10 |
query56 | 25 | 27 |
query57 | 20 | 22 |
query58 | 23 | 26 |
query59 | 22 | 22 |
query6 | 33 | 41 |
query60 | 25 | 28 |
query61 | 25 | 28 |
query62 | 10 | 11 |
query63 | 12 | 12 |
query64 | 176 | 185 |
query65 | 32 | 37 |
query66 | 23 | 24 |
query67 | 697 | 775 |
query68 | 17 | 19 |
query69 | 31 | 34 |
query7 | 17 | 17 |
query70 | 24 | 27 |
query71 | 23 | 24 |
query72 | 335 | 350 |
query73 | 12 | 14 |
query74 | 68 | 99 |
query75 | 58 | 67 |
query76 | 21 | 21 |
query77 | 35 | 37 |
query78 | 151 | 169 |
query79 | 16 | 16 |
query8 | 15 | 20 |
query80 | 146 | 163 |
query81 | 18 | 26 |
query82 | 28 | 31 |
query83 | 21 | 24 |
query84 | 16 | 19 |
query85 | 45 | 49 |
query86 | 14 | 17 |
query87 | 29 | 37 |
query88 | 29 | 29 |
query89 | 11 | 13 |
query9 | 37 | 37 |
query90 | 11 | 11 |
query91 | 17 | 21 |
query92 | 12 | 12 |
query93 | 86 | 86 |
query94 | 40 | 42 |
query95 | 94 | 94 |
query96 | 10 | 10 |
query97 | 29 | 34 |
query98 | 17 | 21 |
query99 | 13 | 12 |
total | 5821 | 6494 |
Uniffle is a little 9% slower than vanilla Spark. Because the amount of shuffle is tiny.
We generate 1TB data, we use the code of the repo
Overall Time: Write Time: Read Time:
Overall Time: Write Time: Read Time: Uniffle is 30%+ much faster than vanilla Spark when there is a large shuffle.