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[#1607] docs: Performance Benchmark Report using TPC-DS
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| <!-- | ||
| ~ Licensed to the Apache Software Foundation (ASF) under one or more | ||
| ~ contributor license agreements. See the NOTICE file distributed with | ||
| ~ this work for additional information regarding copyright ownership. | ||
| ~ The ASF licenses this file to You under the Apache License, Version 2.0 | ||
| ~ (the "License"); you may not use this file except in compliance with | ||
| ~ the License. You may obtain a copy of the License at | ||
| ~ | ||
| ~ http://www.apache.org/licenses/LICENSE-2.0 | ||
| ~ | ||
| ~ Unless required by applicable law or agreed to in writing, software | ||
| ~ distributed under the License is distributed on an "AS IS" BASIS, | ||
| ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| ~ See the License for the specific language governing permissions and | ||
| ~ limitations under the License. | ||
| --> | ||
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| ## Environment | ||
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| ### Software | ||
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| Uniffle 0.9.0, Hadoop 2.8.5, Spark 3.3.1 | ||
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| ### Hardware | ||
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| #### Uniffle Cluster | ||
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| | Cluster Type | Memory | CPU Cores | Disk Configuration for Every Shuffle Server | Max IO Read/Write Speed | Quantity | Network Bandwidth | | ||
| |--------------|--------|-----------|---------------------------------------------|-------------------------|---------------------------------------|-------------------| | ||
| | HDD | 250G | 96 | 10 * 4T HDD | 150MB/s | 2 * Coordinator + 10 * Shuffle Server | 25GB/s | | ||
| | SSD | 250G | 96 | 1 * 6T NVME | 3GB/s | 2 * Coordinator + 10 * Shuffle Server | 25GB/s | | ||
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| #### Hadoop Yarn Cluster | ||
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| 2 * ResourceManager + 750 * NodeManager, every machine 12 * 4T HDD | ||
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| ## Configuration | ||
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| Spark's configuration: | ||
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| ```` | ||
| spark.speculation false | ||
| spark.executor.instances 1400 | ||
| spark.executor.cores 2 | ||
| spark.executor.memory 20g | ||
| spark.executor.memoryOverhead 1024 | ||
| spark.shuffle.manager org.apache.spark.shuffle.RssShuffleManager | ||
| spark.sql.shuffle.partitions 20000 | ||
| spark.sql.files.maxPartitionBytes 107374182 | ||
| spark.rss.storage.type MEMORY_LOCALFILE | ||
| spark.rss.writer.buffer.spill.size 1g | ||
| spark.rss.writer.buffer.size 16m | ||
| spark.rss.client.send.size.limit 32m | ||
| spark.rss.client.rpc.maxAttempts 50 | ||
| spark.rss.resubmit.stage false | ||
| # Enable Netty mode | ||
| spark.rss.client.type GRPC_NETTY | ||
| spark.rss.client.netty.io.mode EPOLL | ||
| ```` | ||
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| Shuffle Server's configuration: | ||
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| ```` | ||
| rss.storage.type MEMORY_LOCALFILE | ||
| rss.server.buffer.capacity 140g | ||
| rss.server.read.buffer.capacity 20g | ||
| rss.rpc.executor.size 1000 | ||
| # Enable Netty mode | ||
| rss.rpc.server.type GRPC_NETTY | ||
| rss.server.netty.epoll.enable true | ||
| rss.server.netty.port 17000 | ||
| rss.server.netty.connect.backlog 128 | ||
| ```` | ||
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| ## TPC-DS | ||
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| We use [spark-sql-perf](https://github.com/databricks/spark-sql-perf) to generate 10TB data. | ||
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| We use the following special SQL to perform stress testing, it mainly focuses on shuffle, with no data skewness, and has | ||
| no practical business implications: | ||
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| ```` | ||
| select SUM(IFNULL(CAST(ss_sold_time_sk AS DECIMAL(10, 2)), 0) + IFNULL(CAST(ss_item_sk AS DECIMAL(10, 2)), 0) + IFNULL(CAST(ss_cdemo_sk AS DECIMAL(10, 2)), 0) + IFNULL(CAST(ss_hdemo_sk AS DECIMAL(10, 2)), 0) + IFNULL(CAST(ss_addr_sk AS DECIMAL(10, 2)), 0) + IFNULL(CAST(ss_store_sk AS DECIMAL(10, 2)), 0) + IFNULL(CAST(ss_promo_sk AS DECIMAL(10, 2)), 0) + IFNULL(CAST(ss_ticket_number AS DECIMAL(10, 2)), 0) + IFNULL(CAST(ss_quantity AS DECIMAL(10, 2)), 0) + IFNULL(ss_wholesale_cost, 0) + IFNULL(ss_list_price, 0) + IFNULL(ss_sales_price, 0) + IFNULL(ss_ext_discount_amt, 0) + IFNULL(ss_ext_sales_price, 0) + IFNULL(ss_ext_wholesale_cost, 0) + IFNULL(ss_ext_list_price, 0) + IFNULL(ss_ext_tax, 0) + IFNULL(ss_coupon_amt, 0) + IFNULL(ss_net_paid, 0) + IFNULL(ss_net_paid_inc_tax, 0) + IFNULL(ss_net_profit, 0)) as sum_all_fields from (select * from (select s.*,c.* from (select *,floor(rand(123)*82857000) as sr from store_sales) s join (select*,floor(rand(123)*82857000)as cr from customer) c on s.sr=c.cr) sc DISTRIBUTE BY sc.ss_customer_sk,sc.ss_item_sk) | ||
| ```` | ||
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| ## Read-Write Performance | ||
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| Total: Read 10.7TiB, Write 6.4TiB | ||
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| | Concurrent Tasks | Type | Single Shuffle Server Write Speed | Single Shuffle Server Read Speed | Tasks Total Time | Netty(SSD) Performance Improvement | Notes | | ||
| |------------------|---------------|-----------------------------------|----------------------------------|------------------|------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | ||
| | 1400 | Netty(SSD) | 0.93GB/s | 1.56GB/s | 268.7h | - | | | ||
| | | gRPC(SSD) | 0.75GB/s | 1.25GB/s | 330.4h | 18.67% | | | ||
| | | Netty(HDD) | 0.24GB/s | 0.4GB/s | 1024.4h | 73.77% | | | ||
| | | Spark ESS | 0.5GB/s | 0.82GB/s | 525.5h | 48.88% | | | ||
| | | Vanilla Spark | - | - | __*Failed*__ | - | | | ||
| | 2800 | Netty(SSD) | 1.02GB/s | 1.70GB/s | 450.7h | - | | | ||
| | | gRPC(SSD) | 0.86GB/s | 1.44GB/s | 566.4h | 20.42% | | | ||
| | | Netty(HDD) | 0.24GB/s | 0.4GB/s | 2009.9h | 77.6% | | | ||
| | | Spark ESS | 0.5GB/s | 0.68GB/s | 672.3h | 32.96% | | | ||
| | | Vanilla Spark | - | - | __*Failed*__ | - | | | ||
| | 5600 | Netty(SSD) | 1.02GB/s | 1.70GB/s | 896.2h | - | | | ||
| | | gRPC(SSD) | 0.80GB/s | 1.34GB/s | 1145.1h | 21.72% | | | ||
| | | Netty(HDD) | 0.22GB/s | 0.36GB/s | 4671.3h | 80.8% | | | ||
| | | Spark ESS | - | - | __*Failed*__ | - | | | ||
| | | Vanilla Spark | - | - | __*Failed*__ | - | | | ||
| | 11200 | Netty(SSD) | 0.86GB/s | 1.44GB/s | 1783.1h | - | | | ||
| | | gRPC(SSD) | 0.62GB/s | 1.04GB/s | 2028.2h | 12.08% | At 11200 concurrency, gRPC requires reducing `rss.rpc.executor.size` to 200 to run tasks successfully. Shuffle Server memory usage and CPU load are higher in gRPC mode than in Netty mode. Not recommended. | | ||
| | | Netty(HDD) | 0.20GB/s | 0.34GB/s | 8716.5h | 79.5% | | | ||
| | | Spark ESS | - | - | __*Failed*__ | - | | | ||
| | | Vanilla Spark | - | - | __*Failed*__ | - | | | ||
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| Note: | ||
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| 1. Read and write operations are essentially happening simultaneously. | ||
| 2. The calculation formula for `Netty(SSD) Performance Improvement` is as follows: | ||
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| ```` | ||
| Netty(SSD) Performance Improvement = (Tasks Total Time - Tasks Total Time( Netty(SSD) )) / Tasks Total Time * 100% | ||
| ```` | ||
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| ## Conclusion | ||
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| We can draw the following conclusions: | ||
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| 1. At 1400 concurrency, Vanilla Spark is already unable to complete tasks successfully, and at 5600 concurrency, Spark | ||
| ESS also fails to complete tasks. However, whether it is HDD or SSD, and whether it is gRPC mode or Netty mode, | ||
| Uniffle can all run normally. **Uniffle can significantly improve job stability in high-pressure scenarios**. | ||
| 2. When comparing using SSDs, **Netty mode brings about a 20% performance improvement compared to gRPC mode**. | ||
| 3. When comparing with Netty mode turned on, **SSD brings about an 80% performance improvement compared to HDD**. | ||
| 4. **Above 11200 concurrency, it is not recommended to use gRPC mode**, as gRPC mode will cause the machine's load | ||
| to be much higher than Netty mode, and the Shuffle Server's process will consume more memory on the machine. |