Feature List
Feature |
Feature Description |
Constraint |
Performance Metric |
Software Package |
Supported on VMs |
Remarks |
|---|---|---|---|---|---|---|
The SSD atomic write feature eliminates the doublewrite redundancy to improve database performance. |
|
The performance is expected to improve by 15% in write-intensive scenarios. |
- |
No |
This feature depends on the SSD hardware feature and is not suitable for virtualization. |
|
This feature introduces the shadow FD mechanism and cross-thread asynchronous communication mechanism. It accelerates the network protocol stack to improve application performance. |
|
The TPC-C comprehensive performance is expected to improve by 10%. |
- |
Yes |
This software feature can be used on both VMs and physical machines. The performance metrics are related to the test environment specifications so that the actual performance improvement result is subject to the VM specifications. |
|
In MySQL OLTP applications, a large number of DML statements (INSERT, UPDATE, and DELETE) are concurrently executed on key data structures in the trx_sys global structure, causing resource contention and synchronization bottlenecks in the critical region. The lock-free hash table is used to maintain transaction units to reduce lock conflicts and improve concurrency. |
|
The sysbench write performance is improved by 20%. |
Yes |
Though this software feature can be used on both VMs and physical machines, it is mainly designed to alleviate lock contention when many DML statements are concurrently executed in OLTP scenarios. This rarely happens on VMs. Therefore, the actual performance improvement result is subject to the VM specifications. |
||
In MySQL OLTP applications, a large number of DML statements (INSERT, UPDATE, and DELETE) are concurrently executed on the key data structures protected by the lock_sys->mutex global lock, causing severe lock contention and performance deterioration. A fine-grained hash bucket lock is used to reduce lock conflicts and improve concurrency. |
|
The TPC-C comprehensive performance is expected to improve by 10%. |
Yes |
Though this software feature can be used on both VMs and physical machines, it is mainly designed to alleviate lock contention when many DML statements are concurrently executed in OLTP scenarios. This rarely happens on VMs. Therefore, the actual performance improvement result is subject to the VM specifications. |
||
Only one thread can be scheduled for a single SQL query in the MySQL database, and the multi-core CPU cannot be used. The performance of a single query is poor and cannot meet the performance requirements in query scenarios. Parallel query is used to improve the query performance. |
|
The query performance is more than doubled. (The performance improvement result is related to the degree of parallelism.) |
Yes |
This software feature can be used on both VMs and physical machines. The performance metrics are related to the test environment specifications so that the actual performance improvement result is subject to the VM specifications. |
||
In high-concurrency MySQL OLTP applications, the default thread scheduling of the system causes frequent cross-NUMA access of threads. In this case, the CPU overhead increases and the performance deteriorates. Therefore, the foreground threads need to be dynamically bound to fixed NUMA CPUs to reduce cross-NUMA access and ensure that the CPU access load is balanced. Background threads need to be statically bound to fixed NUMA CPUs to reduce cross-NUMA access and improve background thread efficiency. |
|
The performance is improved by 10% in OLTP scenarios. |
Yes |
This software feature can be used on both VMs and physical machines. The performance metrics are related to the test environment specifications so that the actual performance improvement result is subject to the VM specifications. |
||
In high-concurrency MySQL OLTP applications, there are many threads, and the CPU is consumed by resource contention and frequent switchovers. By enabling the thread pool, all tasks are queued for execution based on the system execution capability. The number of tasks processed by each CPU at a time is limited (2 to 5 for the best), which is to ensure stable service processing capabilities. |
|
In the OLTP TPC-C scenario, before enabling the thread pool, the MySQL performance of running 10,000 concurrent tasks is only about 10% of the optimal. After the thread pool is enabled, the performance is maintained at 85%. |
MySQL 5.7.27 thread pool patch |
Yes |
This software feature can be used on both VMs and physical machines. The performance metrics are related to the test environment specifications so that the actual performance improvement result is subject to the VM specifications. |
|
Kunpeng CRC32 hardware instructions are used to replace the software implementation of the CRC32 algorithm, thereby improving system performance. |
|
The sysbench write performance is expected to improve by 5%. |
Yes |
This feature can be used on VMs. The performance metrics are related to the test environment specifications so that the actual performance improvement result is subject to the VM specifications. |
||
This feature provides continuous optimization for multi-modal objects (source code, assembly code, and binary) in more lifecycle phases (compile time, link time, and post-link time), to generate target programs with better performance. |
|
This feature can improve the overall database performance in TPC-C tests by 10% on servers and that in sysbench tests by 30% (based on optimal results) in VMs (8 vCPUs, 32 GB memory specifications). |
- |
Yes |
This software feature can be used on both VMs and physical machines. The performance metrics are related to the test environment specifications so that the actual performance improvement result is subject to the VM specifications. |
|
This feature adopts a parallel processing mechanism. Each operator contains multiple worker threads, which significantly shortens the execution time of SQL statements. The columnstore structure improves data processing efficiency, and a secondary execution engine is provided as a plugin which is pluggable and can be dynamically loaded. |
|
The OLAP query performance is improved by more than three times. |
Yes |
This software feature can be used on both VMs and physical machines. The performance metrics are related to the test environment specifications so that the actual performance improvement result is subject to the VM specifications. |
||
This feature uses the Kunpeng hardware acceleration module to implement compression and decompression algorithms and works with a lossless userspace driver framework to improve query performance. |
The OLAP query performance can be improved by 10% in heavy I/O scenarios where only one request is processed at a time. |
Yes |
This feature can be used on VMs. The performance metrics are related to the test environment specifications so that the actual performance improvement result is subject to the VM specifications. |
|||
MySQL KAEzstd Page Compression and Decompression Optimization |
The feature uses the Kunpeng hardware acceleration module to accelerate the zstd-related compression and decompression algorithm, improving the performance of transparent page compression. |
|
Around half of the drive space can be saved; the TPS in sysbench tests drops by no more than 15% in 8 vCPUs, 32 GB memory specifications. |
Patch package: MySQL KAEZstd page compression and decompression optimization patch KAE package: |
Yes |
This feature can be used on VMs. The performance metrics are related to the test environment specifications so that the actual performance improvement result is subject to the VM specifications. |
This feature applies vector instructions and prefetch operations to improve the similarity query performance. |
When the recall rate of the HNSW algorithm reaches 0.99 and that of the ScaNN algorithm reaches 0.95, the QPS performance on the GIST dataset can be improved by 20%. |
Patch package: |
Yes |
This feature can be used on VMs. The performance metrics are related to the test environment specifications so that the actual performance improvement result is subject to the VM specifications. |
||
The KBest algorithm in the Kunpeng recall algorithm library is supported to improve the query efficiency of graph algorithms. |
When the recall rate of the KBest algorithms is higher than 0.99, the QPS performance is 30% higher than that of the HNSW algorithm. |
Patch packages: |
Yes |
This feature can be used on VMs. This software feature can be used on both VMs and physical machines. The performance metrics are related to the test environment specifications so that the actual performance improvement result is subject to the VM specifications. |
||
The KScaNN algorithm in the Kunpeng recall algorithm library is supported to improve the query efficiency of vector retrieval algorithms. |
When the recall rate of the KScaNN algorithms is higher than 0.95, the QPS performance is 30% higher than that of the ScaNN algorithm. |
Patch packages: |
Yes |
This feature can be used on VMs. This software feature can be used on both VMs and physical machines. The performance metrics are related to the test environment specifications so that the actual performance improvement result is subject to the VM specifications. |
||
Network Multipathing and Domain-based Scheduling Optimization |
This feature identifies the traffic characteristics of specific service processes and implements the affinity between service process network requests and network interruptions. |
|
In the scenario where a MySQL container occupies half of the resources of a machine (8 vCPUs and 32 GB memory), the overall (optimal) Sysbench performance shows a 10% improvement. For a Redis container using 2 vCPUs and 10 GB memory, the comprehensive performance of redis-benchmark is improved by 80% in the IPVLAN+bond4 networking. |
No |
This feature depends on the NIC driver interface, which is not supported by virtual NICs. |
|
This feature enhances performance by offloading network I/O operations to KBAIO for asynchronous batch processing. This reduces system calls and context switching, enabling non-blocking Redis operations. |
|
In the Docker+bond4+IPVLAN networking with 2 vCPUs and 10 GB memory, the redis-benchmark performance is 20% higher than that of the open source Redis. |
Yes |
This feature can be used on VMs. The performance metrics are related to the test environment specifications so that the actual performance improvement result is subject to the VM specifications. |
||
Record matching optimization, SIMD-based character set processing optimization, and migration of unaligned memory access policies from x86 to the Arm architecture are used to improve the query, comparison, and calculation efficiency. |
|
Sysbench read-only tests show that the performance of Percona-Server 5.7.44-53 running on a container with 8 vCPU and 16 GB memory can be improved by about 10%. |
Yes |
This feature can be used on VMs. The performance metrics are related to the test environment specifications so that the actual performance improvement result is subject to the VM specifications. |
||
The binlog pre-allocation, lock splitting, and writeset_history data structure optimization are used to improve system performance. |
|
Sysbench write-only tests show that the feature can improve the performance of Percona-Server 5.7.44-53 running on a container with 8 vCPU and 16 GB memory by 13%. |
Yes |
This feature can be used on VMs. The performance metrics are related to the test environment specifications so that the actual performance improvement result is subject to the VM specifications. |