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- Optimizing Indexes to Break Through Read/Write Performance Bottlenecks
- Troubleshooting Mongos Load Imbalance in Sharded Cluster
- Troubleshooting Excessive Slow Queries
- Fixing High CPU Utilization in MongoDB Instance Based on DBbrain
- Troubleshooting Excessive Connections
- Troubleshooting High Request Latency Based on Slow Log
- Troubleshooting High CPU Utilization
- Considerations for Using Shard Clusters
- Sample of Reading and Writing Data in MongoDB Instance
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- DescribeSlowLogs
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- Data Types
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- Instance Connection
- FAQs
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- Glossary
- Contact Us
- Release Notes and Announcements
- User Guide
- Product Introduction
- Purchase Guide
- Getting Started
- Operation Guide
- Access Management
- Instance Management
- Node Management
- Version Upgrade
- Monitoring
- Backup and Rollback
- Data Security
- SSL Authentication
- Database Management
- Multi-AZ Deployment
- Public Network Access
- Disaster Recovery/Read-Only Instances
- Parameter Configuration
- Recycle Bin
- Task Management
- Performance Optimization
- Data Migration Guide
- Best Practice
- Optimizing Indexes to Break Through Read/Write Performance Bottlenecks
- Troubleshooting Mongos Load Imbalance in Sharded Cluster
- Troubleshooting Excessive Slow Queries
- Fixing High CPU Utilization in MongoDB Instance Based on DBbrain
- Troubleshooting Excessive Connections
- Troubleshooting High Request Latency Based on Slow Log
- Troubleshooting High CPU Utilization
- Considerations for Using Shard Clusters
- Sample of Reading and Writing Data in MongoDB Instance
- Export and Import
- Troubleshooting MongoDB Connection Failures
- Performance Fine-Tuning
- Ops and Development Guide
- Troubleshooting
- API Documentation
- History
- Introduction
- API Category
- Making API Requests
- Instance APIs
- RenameInstance
- OfflineIsolatedDBInstance
- ModifyDBInstanceSpec
- IsolateDBInstance
- DescribeSpecInfo
- DescribeDBInstances
- DescribeClientConnections
- CreateDBInstanceHour
- AssignProject
- RenewDBInstances
- DescribeSlowLogs
- DescribeSlowLogPatterns
- InquirePriceRenewDBInstances
- InquirePriceModifyDBInstanceSpec
- InquirePriceCreateDBInstances
- DescribeDBInstanceDeal
- DescribeSecurityGroup
- DescribeInstanceParams
- ModifyDBInstanceSecurityGroup
- ModifyDBInstanceNetworkAddress
- Backup APIs
- Account APIs
- Task APIs
- Other APIs
- Data Types
- Error Codes
- Instance Connection
- FAQs
- Service Agreement
- Glossary
- Contact Us
This document describes how to perform standard performance testing on TencentDB for MongoDB instances. The result data is for your reference only.
Test Environment
Test date: August 2020.
Client specification: The client is installed in an 8-core 32 GB CVM instance. Tests show that if the TencentDB for MongoDB instance has a low specification, the CPU utilization of its replica instance can reach 100% under the test pressure of an 8-core 32 GB CVM instance. In this case, one CVM instance achieves even better test results than multiple CVM instances. However, if the CPU utilization can't reach 100% when only one CVM instance is used, four CVM instances can be used to share the concurrent threads.
Test object: TencentDB for MongoDB 4.0 replica set instance.
Test Tool
Test Scenario
Prepare about 10 GB of data. Then, run 100 and 200 concurrent threads and use YCSB to test the throughput (ops/sec), read average latency (RAL in μs), and write average latency (WAL in μs) of instances with different specifications under the pressure of 50% read requests and 50% update requests as well as 95% read requests and 5% update requests.
Latency
The average latency from the CVM instance to the TencentDB for MongoDB instance is 0.35 ms.
Latency: Minimum = 0.30 ms; maximum = 0.44 ms; average = 0.35 ms
Relevant commands
1. Prepare data (about 10 GB)
nohup ./ycsb-0.15.0/bin/ycsb load mongodb -s -P workloads/workloada-p mongodb.url=mongodb://mongouser:password@10.xx.xx.30:27017,10.xx.xx.28:27017,10.xx.xx.5:27017/admin?w=0 -p table=test -threads 300 -p recordcount=10000000>loadlog.txt &
2. Test with 50% read requests and 50% update requests
nohup ./ycsb-0.15.0/bin/ycsb run mongodb -s -P workloads/workloada -p mongodb.url=mongodb://mongouser: password @10.xx.xx.30:27017,10.xx.xx.28:27017,10.xx.xx.5:27017/admin?w=0 -p table=test -p recordcount=10000000 -p readproportion=0.5 -p updateproportion=0.5 -p insertproportion=0 -p operationcount=100000 -threads 100 >runlog.txt &
3. Test with 95% read requests and 5% update requests
nohup ./ycsb-0.15.0/bin/ycsb run mongodb -s -P workloads/workloada -p mongodb.url=mongodb://mongouser: password @10.xx.xx.30:27017,10.xx.xx.28:27017,10.xx.xx.5:27017/admin?w=0 -p table=test -p recordcount=10000000 -p readproportion=0.95 -p updateproportion=0.05 -p insertproportion=0 -p operationcount=100000 -threads 100 >runlog.txt &
Note:
You need to adjust
-p operationcount=100000 dynamically according to the specific execution time to ensure that the execution time is longer than 20 minutes; otherwise, the result data will not be representative.w in ?w=0 represents write concern.w:1 (acknowledged write) requires an acknowledgment that the write operation has propagated to the specified mongod instance or the primary node in the replica set. The default value is 1.w:0 (unacknowledged write) returns no response and therefore will not report whether the write operation succeeds. However, it may return the exception information when data is written to a closed socket or the network is abnormal.w:>1 (used in a replica set) is used to set the number of nodes where data is to be written, including the primary node.Test Data
50:50 read/update request ratio
MongoDB Specification | Threads | Throughput (Ops/Sec) | RAL (μs) | WAL (μs) | CPU Utilization |
2-core 4 GB | 100 | 3188 | 24091 | 38254 | 100% |
2-core 4 GB | 200 | 5510 | 34475 | 38022 | 100% |
4-core 8 GB | 100 | 7058 | 8355 | 19887 | 100% |
4-core 8 GB | 200 | 13590 | 14391 | 14983 | 100% |
6-core 16 GB | 100 | 8970 | 22132 | 51 | 100% |
6-core 16 GB | 200 | 10041 | 28696 | 10966 | 100% |
12-core 32 GB | 100 | 29462 | 6727 | 35 | 100% |
12-core 32 GB | 200 | 47815 | 4673 | 3681 | 100% |
24-core 64 GB | 100 | 107047 | 1826 | 33 | 100% |
24-core 64 GB | 200 | 51046 | 7802 | 27 | 100% |
24-core 128 GB | 100 | 130811 | 1486 | 32 | 100% |
24-core 128 GB | 200 | 49274 | 8054 | 27 | 100% |
32-core 240 GB | 100 | 154253 | 1254 | 32 | 100% |
32-core 240 GB | 200 | 52148 | 8243 | 1108 | 100% |
48-core 512 GB | 100 | 174284 | 1103 | 28 | 100% |
48-core 512 GB | 200 | 121713 | 3237 | 32 | 100% |
95:5 read/update request ratio
MongoDB Specification | Threads | Throughput (Ops/Sec) | RAL (μs) | WAL (μs) | CPU Utilization |
2-core 4 GB | 100 | 2738 | 38216 | 178 | 100% |
2-core 4 GB | 200 | 10093 | 20178 | 11561 | 100% |
4-core 8 GB | 100 | 14380 | 6864 | 7631 | 100% |
4-core 8 GB | 200 | 26459 | 7651 | 5369 | 100% |
6-core 16 GB | 100 | 13707 | 7650 | 56 | 100% |
6-core 16 GB | 200 | 45796 | 4383 | 3928 | 100% |
12-core 32 GB | 100 | 115529 | 902 | 37 | 100% |
12-core 32 GB | 200 | 56751 | 3658 | 31 | 100% |
24-core 64 GB | 100 | 160227 | 668 | 29 | 100% |
24-core 64 GB | 200 | 112755 | 1876 | 32 | 100% |
24-core 128 GB | 100 | 159130 | 659 | 26 | 100% |
24-core 128 GB | 200 | 112993 | 1936 | 32 | 100% |
32-core 240 GB | 100 | 167518 | 634 | 28 | 74% |
32-core 240 GB | 200 | 172424 | 1244 | 35 | 100% |
48-core 512 GB | 100 | 173768 | 608 | 31 | 50% |
48-core 512 GB | 200 | 211986 | 1012 | 33 | 85% |
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