CN107766123B - JVM (JVM) tuning method - Google Patents
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Abstract
A JVM tuning method comprises the following steps: the recommendation system acquires a process number of a target service to acquire a corresponding JVM (Java virtual machine) instance; the system combines a JVM environment viewing tool provided by a JDK environment to regularly acquire spatial information and log information of target services, wherein the spatial information comprises JVM heap space, new generation space, old generation space and permanent generation memory information, and the log information comprises JVM garbage collection log and JVM running log information; storing the obtained information into a database, and performing mean value operation on the JVM heap space, the new generation space, the old generation space and the permanent memory respectively to generate an optimization result; and adjusting JVM parameters in the optimization system according to the optimization result. The JVM tuning process is generalized, so that the professional tuning process is simple and feasible, and a large amount of labor of workers is reduced.
Description
Technical Field
The invention relates to a computer running system optimization method, in particular to a JVM tuning method.
Background
In IT services, programmers use a programming language to convert software design ideas into program codes, and among many programming languages, the Java language is a very important one, and Java programs run on a Java virtual machine JVM. The JVM provides memory management and a runtime environment for Java programs. In general, in order to enable a service developed by Java language to run faster and more stably, developers need to adjust JVM parameters according to the characteristics of programs, such as processing of memory allocation and garbage object recovery, and if the JVM parameters can be optimized well, performance improvement will be obtained.
JVM tuning is an important loop in service optimization. Usually, a technician makes observation for a period of time according to the service running condition, and tentatively provides a JVM tuning scheme according to the meaning of each parameter of the JVM; after the service is operated for a period of time, checking whether the scheme is reasonable, and when the scheme is not reasonable or needs to be optimized, giving a new adjustment optimization scheme. The steps are repeated so as to obtain the adjusting and optimizing result.
In this prior art mode, although with constant debugging, the optimization results can eventually be obtained. The defects include that firstly, workers need to have deep knowledge about the JVM, and the significance of each parameter of the JVM on the optimization needs to be mastered; second, specialized personnel are also required to perform long-term follow-up maintenance, protocol changes, and retries for the service. The above problems are all easily added to the labor, time and cost of the work.
Disclosure of Invention
The invention provides a JVM tuning method, which is used for solving the problem that the optimization operation process of the JVM is complex in the prior art, and can quickly obtain tuning results by setting an automatic optimization step.
The invention is realized by the following technical scheme:
a JVM tuning method comprises the following steps:
the recommendation system acquires a process number of a target service to acquire a corresponding JVM (Java virtual machine) instance;
the system combines a JVM environment viewing tool provided by a JDK environment to regularly acquire spatial information and log information of target services, wherein the spatial information comprises JVM heap space, new generation space, old generation space and permanent generation memory information, and the log information comprises JVM garbage collection log and JVM running log information;
storing the obtained information into a database, and performing mean value operation on the JVM heap space, the new generation space, the old generation space and the permanent memory respectively to generate an optimization result;
and adjusting JVM parameters in the optimization system according to the optimization result.
According to the JVM tuning method, the range of the spatial tuning stride a of the optimization result is 20-60%, when the variation range of the space is larger than a, the amount of the original value is increased or decreased according to the optimization result, otherwise, the parameters of the JVM are kept unchanged.
According to the JVM tuning method, the JVM performs mean operation on the acquired spatial information and information data in the set interval time b according to the set interval time b, and the optimization system regenerates the optimization result.
The JVM tuning method as described above, wherein the JVM heap space optimization process includes: acquiring heap space information and log information; judging whether a permanent memory overflow error exists or not; if yes, the permanent memory space is increased, and if no, the process is ended.
The JVM tuning method as described above, wherein the old age space optimization process includes: acquiring the utilization rate of the memory of the old age; and judging whether the memory utilization rate is continuously high, if so, increasing the space of the old generation, then ending, if not, entering the next step to judge whether the memory utilization rate of the old generation is continuously low, if so, reducing the space of the old generation, then ending, if not, judging whether the difference value of the two new generation utilization rates at the interval time b falls within the range of the space adjustment step a by acquiring the change condition of the memory utilization rates of the old generation before and after Full GC, if so, increasing the space of the new generation, then ending, and if not, directly ending.
The JVM tuning method as described above, wherein the new generation space optimization process includes: acquiring the utilization rate of a new generation memory; and judging whether the memory utilization rate is continuously high, if so, increasing the new generation space, then ending, if not, entering the next step to judge whether the memory utilization rate is continuously low, if so, reducing the new generation space, then ending, if not, judging whether the new generation utilization rate difference value of two times in the interval time b falls within the range of the space adjustment step a by obtaining the new generation space utilization rate change conditions before and after Minor GC, if so, increasing the new generation space, then ending, and if not, directly ending.
The JVM tuning method further includes a new generation space-old generation space ratio optimization step, including obtaining heap space information, determining whether a heap memory overflow error exists, if yes, increasing the size of the heap memory and then ending, if not, further determining whether a difference between two heap memory usage rates in an interval time b falls within a range of a space adjustment step a, if yes, increasing the size of the heap memory and then ending, if not, obtaining a Full GC result before the heap memory overflow, determining whether the new generation space usage rate is smaller than a set value C, if not, increasing the size of the heap memory and then ending, and if yes, directly ending.
The JVM tuning method as described above, where the new generation space optimization process further includes an age threshold optimization step, and the method includes obtaining and counting information of large objects before and after the Minor GC, determining whether a living object exists all the time, and if not, directly ending the process, if so, obtaining the number of times of the Minor GC in a set time period, obtaining an age parameter of the JVM object entering the old generation, and then determining an average usage rate of the old generation space in an interval time b, and if the usage rate is less than the space adjustment stride a, reducing the age parameter, and if the usage rate is greater than the space adjustment stride a, increasing the age parameter, and then ending the process.
The JVM tuning method as described above, where the new generation space optimization process further includes a step of directly performing old generation object size threshold optimization, including obtaining and counting large object information before and after Minor GC, determining whether a live object exists all the time, if not, directly ending the new generation space optimization, if yes, obtaining a maximum object size a and an old generation space utilization rate during Full GC twice within an interval time b, if the utilization rate is greater than a space adjustment step a, directly ending the new generation space optimization, if the utilization rate is less than the space adjustment step a, further obtaining an object size parameter P directly entering the old generation, then comparing P with a, if P is greater than a, directly ending the new generation space optimization method, if P is less than a, assigning a value to P, and then ending the new generation space optimization method.
Compared with the prior art, the invention has the advantages that:
the JVM tuning process is generalized, so that the professional tuning process is simple and feasible, and a large amount of labor of workers is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below.
FIG. 1 is a system flow diagram of the present invention;
FIG. 2 is a flow chart of JVM heap space optimization of FIG. 1;
FIG. 3 is a flow chart of the spatial optimization of the senior generation of FIG. 1;
FIG. 4 is a flowchart of the new generation space optimization of FIG. 1;
FIG. 5 is a flow chart of the new generation space to old generation space ratio optimization of FIG. 1;
FIG. 6 is a flowchart of age threshold optimization of FIG. 1;
FIG. 7 is a flow diagram of the direct-entry old age object size threshold optimization of FIG. 1.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention.
Wherein the technical terms involved have the following meanings:
JVM is an abbreviation for Java Virtual Machine (Java Virtual Machine), a specification for computing devices, implemented by emulating various computer functions on a real computer. The Java virtual machine includes a set of bytecode instruction sets, a set of registers, a stack, a heap, and a storage method domain.
Secondly, the new generation space is a component of the JVM heap space, and the storage position of the newly created object in the JVM operation process
The old generation space is another part of the JVM heap space and is used for being matched with a recycling algorithm to store objects which are survived for multiple times of local garbage recycling to the region
Minor GC is a small garbage reclamation action of JVM to reclaim invalid objects in the new generation space. Minor GC action is triggered when the new generation space is insufficient.
Fifthly, Full GC is that JVM retrieves the garbage of the heap space in a Full heap once, and the recovery is triggered when the space of the old generation is insufficient to retrieve the invalid objects in the heap space. Full GC efficiency was much lower than Minor GC.
As shown in fig. 1 to fig. 7, a JVM tuning method in this embodiment includes the following steps:
the recommendation system acquires a process number of a target service to acquire a corresponding JVM (Java virtual machine) instance; the system combines a JVM environment viewing tool provided by a JDK environment to regularly acquire spatial information and log information of target services, wherein the spatial information comprises JVM heap space, new generation space, old generation space and permanent generation memory information, and the log information comprises JVM garbage collection log and JVM running log information; storing the obtained information into a database, and performing mean value operation on the JVM heap space, the new generation space, the old generation space and the permanent memory respectively to generate an optimization result; and adjusting JVM parameters in the optimization system according to the optimization result.
In particular, JVM tuning mostly occurs on the tuning of the following parameters; JVM heap memory size, JVM maximum heap memory size, JVM new generation memory size, JVM old generation age threshold, size threshold for directly entering old generation objects, and JVM permanent generation memory size. Based on the situation, the method acquires relevant parameters by regularly inquiring the JVM state, and then acquires the variation range of the parameters through cluster analysis, thereby obtaining the JVM tuning scheme. And the user applies the tuning scheme to a specific example, and the tuning scheme makes new adjustment according to the change of time so as to make further recommendation for the user.
The embodiment is a service based on Web technology, and the system is deployed on a node where a JVM instance to be optimized is located. And sending a request to the recommendation system in an HTTP mode.
The specific deployment mode is as follows: setting JVM parameters of a program to be optimized, and enabling the JVM instance to output a garbage collection log; starting a program to be optimized, and starting a JVM (JVM) tuning recommendation system; acquiring a process number of a program to be optimized and acquiring a log output path of the program; inputting the process number and the log output path into a JVM (JVM) tuning recommendation system; and after the recommendation system gives the recommendation, applying the recommendation result to the JVM of the program to be optimized, restarting the JVM, and waiting for the tuning system to give the recommendation again. Until the user requirements are met.
The recommendation system acquires a process number of a target service, namely acquires a corresponding JVM (Java virtual machine) instance; the system combines JVM environment provided by JDK environment to view tools such as jmap, jstack, jps and the like, and regularly obtains the memory size of the JVM heap of the target service, the change condition of the new generation space, the old generation information, the permanent memory information and the JVM garbage collection log, and the JVM runs the log information; and storing the acquired information into a database. And taking the time length of five minutes as a unit, and carrying out mean value operation on the acquired information and the information in other five minutes. The set interval time b of this embodiment is 5 minutes, that is, every 5 minutes, the optimization system will regenerate the optimization result; and generating an optimization result according to the information in the time period. The adjustment step a of the space is set to 25%. That is, if the optimization result is an increase or decrease, the original value is increased or decreased by 25% according to the optimization result, otherwise, the parameter remains unchanged.
Further, in this embodiment, the JVM heap space optimization process includes: acquiring heap space information and log information; judging whether a permanent memory overflow error exists or not; if yes, the permanent memory space is increased, and if no, the process is ended.
Further, in this embodiment, the old age space optimization process includes: acquiring the utilization rate of the memory of the old age; and judging whether the memory utilization rate is continuously high, if so, increasing the space of the old generation, then ending, if not, entering the next step to judge whether the memory utilization rate of the old generation is continuously low, if so, reducing the space of the old generation, then ending, if not, judging whether the difference value of the two new generation utilization rates at the interval time b falls within the range of the space adjustment step a by acquiring the change condition of the memory utilization rates of the old generation before and after Full GC, if so, increasing the space of the new generation, then ending, and if not, directly ending.
Further, in this embodiment, the new generation space optimization process includes: acquiring the utilization rate of a new generation memory; and judging whether the memory utilization rate is continuously high, if so, increasing the new generation space, then ending, if not, entering the next step to judge whether the memory utilization rate is continuously low, if so, reducing the new generation space, then ending, if not, judging whether the new generation utilization rate difference value of two times in the interval time b falls within the range of the space adjustment step a by obtaining the new generation space utilization rate change conditions before and after Minor GC, if so, increasing the new generation space, then ending, and if not, directly ending.
Further, in the JVM heap space optimization process in this embodiment, a new generation space and old generation space ratio optimization step is further provided, including obtaining heap space information, and determining whether a heap memory overflow error exists, if yes, increasing the size of the heap memory and then ending, if not, further determining whether a difference between the two heap memory usage rates at an interval time b falls within a range of the space adjustment stride a, if yes, increasing the size of the heap memory and then ending, if not, obtaining a Full GC result before the heap memory overflow, determining whether the new generation space usage rate is smaller than a set value C, if not, increasing the size of the heap memory and then ending, and if yes, directly ending.
Further, in this embodiment, the new generation space optimization process further includes an age threshold optimization step, which includes obtaining and counting large object information before and after the Minor GC, determining whether a live object exists all the time, and if not, directly ending the process, if so, obtaining the number of times of the Minor GC in a set time period, obtaining an age parameter of the JVM object entering the old generation, and then determining an average usage rate of the old generation space within an interval time b, if the usage rate is less than the space adjustment step a, reducing the age parameter, and if the usage rate is greater than the space adjustment step a, increasing the age parameter, and then ending the process.
Further, in the embodiment, the new generation space optimization process further includes a step of directly performing old generation object size threshold optimization, including obtaining and counting large object information before and after Minor GC, determining whether a live object exists all the time, if not, directly ending the process, if yes, obtaining the size a of the largest object and the old generation space utilization rate during two Full GC intervals within b, if the utilization rate is greater than the space adjustment step a, directly ending the process, if the utilization rate is less than the space adjustment step a, further obtaining an object size parameter P directly entering the old generation, then comparing the size parameter P with the size parameter a, if P is greater than a, directly ending the process, if P is less than a, assigning the value of a to P, and then ending the process.
The technical contents not described in detail in the present invention are all known techniques.
Claims (6)
1. A JVM tuning method is characterized by comprising the following steps:
the recommendation system acquires a process number of a target service to acquire a corresponding JVM (Java virtual machine) instance;
the system combines a JVM environment viewing tool provided by a JDK environment to regularly acquire spatial information and log information of target services, wherein the spatial information comprises JVM heap space, new generation space, old generation space and permanent generation memory information, and the log information comprises JVM garbage collection log and JVM running log information;
storing the obtained information into a database, and performing mean value operation on the JVM heap space, the new generation space, the old generation space and the permanent memory respectively to generate an optimization result; the range of the space adjustment stride a of the optimization result is 20-60%, when the change range of the space is larger than a, the amount of the original value is increased or decreased according to the optimization result, otherwise, the parameters of the JVM are kept unchanged;
adjusting JVM parameters in the optimization system according to the optimization result; the JVM performs mean operation on the acquired spatial information and information data in the set interval time b according to the set interval time b, and the optimization system regenerates an optimization result;
the old age space optimization process comprises the following steps: acquiring the utilization rate of the memory of the old age; judging whether the memory utilization rate is continuously high, if so, increasing the old generation space, then ending, if not, entering the next step to judge whether the memory utilization rate of the old generation is continuously low, if so, reducing the old generation space, then ending, if not, judging whether the difference value of the new generation utilization rate of two times in the interval time b falls within the range of the space adjustment step a by acquiring the change condition of the old generation space utilization rate before and after the Ful GC, if so, increasing the new generation space, then ending, and if not, directly ending.
2. The JVM tuning method of claim 1, wherein the JVM heap space optimization process comprises: acquiring heap space information and log information; judging whether a permanent memory overflow error exists or not; if yes, the permanent memory space is increased, and if no, the process is ended.
3. The JVM tuning method of claim 1, wherein the generative space optimization process comprises: acquiring the utilization rate of a new generation memory; and judging whether the memory utilization rate is continuously high, if so, increasing the new generation space, then ending, if not, entering the next step to judge whether the memory utilization rate is continuously low, if so, reducing the new generation space, then ending, if not, judging whether the difference value of the new generation utilization rate of two times in the interval time b falls within the range of the space adjustment step a by obtaining the change condition of the new generation space utilization rate before and after MinorgC, if so, increasing the new generation space, then ending, and if not, directly ending.
4. The JVM tuning method of claim 2, wherein the JVM heap space optimization process is further provided with a new generation space and old generation space ratio optimization step, including obtaining heap space information, determining whether a heap memory overflow error exists, if yes, increasing the heap memory size and then ending, if not, further determining whether a difference between two heap memory usage rates in an interval time b falls within a range of a space tuning stride a, if yes, increasing the heap memory size and then ending, if not, obtaining a Full GC result before the heap memory overflow, determining whether the new generation space usage rate is smaller than a set value C, if not, increasing the heap memory size and then ending, and if yes, directly ending.
5. The JVM tuning method of claim 3, wherein the new generation space optimization process further comprises an age threshold optimization step, which includes obtaining information of large objects before and after MinorGC, performing statistics, determining whether a living object exists all the time, if not, directly ending, if yes, obtaining Minor GC times in a set time period, obtaining an age parameter of the JVM object entering the old generation, then determining an average usage rate of the old generation space within an interval time b, if the usage rate is less than a space adjustment step a, reducing the age parameter, if the usage rate is greater than the space adjustment step a, increasing the age parameter, and then ending.
6. The JVM tuning method of claim 3, wherein the new generation space optimization process further comprises a step of directly performing old generation object size threshold optimization, including obtaining and counting large object information before and after MinorGC, determining whether a live object exists all the time, if not, directly ending the optimization, if yes, obtaining the size A of the largest object and the old generation space utilization rate during two Full GC intervals within b, if the utilization rate is greater than a space adjustment step a, directly ending the optimization, if the utilization rate is less than the space adjustment step a, further obtaining an object size parameter P directly entering the old generation, then comparing the values of P and A, if P is greater than A, directly ending the optimization, if P is less than A, assigning the value of A to P, and then ending the optimization.
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| CN109144671A (en) * | 2018-08-21 | 2019-01-04 | 郑州云海信息技术有限公司 | The management method and device of virtual machine in cloud data system |
| CN109597676A (en) * | 2018-10-25 | 2019-04-09 | 平安科技（深圳）有限公司 | Monitoring and early warning method, apparatus and storage medium based on JVM |
| US11451285B2 (en) * | 2019-10-29 | 2022-09-20 | Mediatek Singapore Pte. Ltd. | Efficient extreme high-throughput sounding feedback supporting JMAP and CMAP transmissions |
| CN111352698B (en) * | 2020-02-25 | 2023-12-22 | 北京奇艺世纪科技有限公司 | JVM parameter adjusting method and device |
| CN111427667B (en) * | 2020-04-03 | 2021-03-26 | 帆软软件有限公司 | JVM load quantification and optimization method |
| CN112000428B (en) * | 2020-07-30 | 2024-03-22 | 杭州安恒信息技术股份有限公司 | JVM optimization method and device based on machine learning and electronic device |
| CN112000471B (en) * | 2020-08-10 | 2023-10-27 | Vidaa(荷兰)国际控股有限公司 | Memory optimization method and device |
| CN111949377B (en) * | 2020-08-25 | 2021-08-31 | 深圳市广通软件有限公司 | JVM parameter optimization method, device, server and storage medium for WEB middleware |
| CN112579259B (en) * | 2020-12-14 | 2022-07-15 | 中国科学院软件研究所 | GC self-adaptive adjustment method and device for big data processing framework |
| CN112905418A (en) * | 2021-03-02 | 2021-06-04 | 湖南快乐阳光互动娱乐传媒有限公司 | JVM (JVM) tuning method and device |
| CN113608798A (en) * | 2021-08-06 | 2021-11-05 | 中航金网(北京)电子商务有限公司 | Method and device for configuring resources used by non-JAVA application and cloud-native application |
| CN113778740B (en) * | 2021-11-10 | 2022-08-02 | 中航金网(北京)电子商务有限公司 | Exception handling method and device based on garbage collection log |
| CN114816760B (en) * | 2022-05-13 | 2023-04-28 | 兰考堌阳医院有限公司 | Interactive nursing billboard system and storage medium |
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