Method for effectively solving containerized JVM (Java Virtual Machine) memory overflow

Abstract

The invention discloses a method for effectively solving containerized JVM (Java Virtual Machine) memory overflow, which comprises the following steps of: S1, deploying a JVM index acquisition agent in a container, pulling index data at a fixed frequency by adopting a Prometheus time sequence database, and monitoring the use condition of a JVM memory in the container; s2, the JVM index in the container is inquired by the JVM controller from the Prometheus time sequence database in real time, and the size of a JVM heap in the container is dynamically adjusted; and S3, modifying a JVM configuration file, and optimizing a JVM garbage collection mechanism. According to the scheme, the problem of JVM memory overflow in a containerized environment is solved, and the stability and performance of the application program are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud computing and containerization, and in particular to a method for effectively solving containerized JVM memory overflow. Background Art

[0002] With the rapid development of cloud computing and containerization technology, more and more applications are deployed in containerized environments. However, in containerized environments, JVM (Java Virtual Machine) memory overflow has become a common problem. Memory overflow can cause application crashes or performance degradation, seriously affecting the stability and reliability of applications. Therefore, how to effectively solve the JVM memory overflow problem in containerized environments has become an urgent problem to be solved. Summary of the invention

[0003] In view of the above technical problems, the present invention provides a method for effectively solving the memory overflow of containerized JVM.

[0004] The present invention is implemented by adopting the following technical solution: a method for effectively solving the memory overflow of containerized JVM, comprising the following steps: Step S1: deploy a JVM indicator collection agent inside the container, use the Prometheus time series database to pull indicator data at a fixed frequency, and monitor the JVM memory usage in the container; Step S2: The JVM-controller controller queries the JVM indicators in the container from the Prometheus time series database in real time and dynamically adjusts the JVM heap size in the container; Step S3: Modify the JVM configuration file to optimize the garbage collection mechanism of the JVM.

[0005] Specifically, the JVM indicator collection agent collects JVM indicator data in real time.

[0006] Specifically, the indicator data includes: usage of heap memory and non-heap memory, JVM container memory quota, and frequency and duration of garbage collection.

[0007] Specifically, the step S2 of dynamically adjusting the JVM heap size in the container specifically includes: Calculate and compare the monitored JVM memory usage with the memory quota of the container where the JVM is located; When the JVM's memory usage exceeds the preset maximum usage, and the JVM's maximum stack memory divided by the JVM's container memory quota is less than 90% for 5 consecutive minutes, the JVM-Controller automatically increases the maximum stack memory of the corresponding JVM running container; When the memory usage of the JVM is lower than the preset minimum usage, the JVM-Controller automatically reduces the maximum heap memory and the memory quota of the container where the JVM is running, thereby reducing memory waste.

[0008] Specifically, the preset maximum usage rate is set to 90%, and the preset minimum usage rate is set to 50%.

[0009] Specifically, the step S3 of optimizing the garbage collection mechanism of the JVM further includes: optimizing using the configuration management function of the container platform.

[0010] Specifically, the optimization of the garbage collection mechanism of the JVM specifically includes: adjusting the garbage collection algorithm, increasing the number of garbage collection threads and optimizing the object allocation strategy.

[0011] The beneficial effects of the present invention are: by collecting the memory usage data of the JVM in real time, the present invention can timely discover memory usage anomalies and avoid the occurrence of memory overflow problems. The election mechanism is used to select the master node, which ensures the automatic repair capability of the system.

[0012] Dynamically adjusting the JVM heap size based on the monitored JVM memory usage can better meet the memory requirements of the application and avoid memory waste.

[0013] By optimizing the JVM's garbage collection mechanism, you can reduce the frequency and duration of garbage collection, improve the efficiency of garbage collection, and further improve the performance and stability of the application.

[0014] The technical solution of the present invention can be easily deployed on various container platforms, has good scalability, and can be customized and expanded according to actual needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0016] Figure 1 This is a flow chart of the method for effectively solving containerized JVM memory overflow in the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, 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 in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0018] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0019] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0020] The present invention proposes a method for effectively solving the memory overflow of containerized JVM, such as Figure 1 As shown, the following steps are included: Step S1: deploy a JVM indicator collection agent inside the container, use the Prometheus time series database to pull indicator data at a fixed frequency, and monitor the JVM memory usage in the container; Step S2: The JVM-controller controller queries the JVM indicators in the container from the Prometheus time series database in real time and dynamically adjusts the JVM heap size in the container; Step S3: Modify the JVM configuration file to optimize the garbage collection mechanism of the JVM.

[0021] In one embodiment, the JVM metric collection agent collects JVM metric data in real time, including: usage of heap memory and non-heap memory, JVM container memory quota, and frequency and duration of garbage collection.

[0022] In another embodiment, step S2 dynamically adjusting the JVM heap size in the container specifically includes: Calculate and compare the monitored JVM memory usage with the memory quota of the container where the JVM is located; When the JVM's memory usage exceeds the preset maximum usage, and the JVM's maximum stack memory divided by the JVM's container memory quota is less than 90% for 5 consecutive minutes, the JVM-Controller automatically increases the maximum stack memory of the corresponding JVM running container; When the memory usage of the JVM is lower than the preset minimum usage, the JVM-Controller automatically reduces the maximum heap memory and the memory quota of the container where the JVM is running, thereby reducing memory waste.

[0023] In this embodiment, the preset maximum usage rate is set to 90%, and the preset minimum usage rate is set to 50%.

[0024] In one embodiment, step S3 of optimizing the garbage collection mechanism of the JVM further includes: optimizing using the configuration management function of the container platform. Optimizing the garbage collection mechanism of the JVM specifically includes: adjusting the garbage collection algorithm, increasing the number of garbage collection threads, and optimizing the object allocation strategy.

[0025] In one embodiment, a method for effectively solving a containerized JVM memory overflow includes the following steps: Monitor JVM memory usage in the container: Deploy a JVM metric collection agent inside the container to collect JVM metric data in real time, and use the Prometheus time series database to pull the metric data at a fixed frequency, including the usage of heap memory and non-heap memory, the memory quota of the JVM container, and the frequency and duration of garbage collection.

[0026] Dynamically adjust the JVM heap size in the container: Develop a JVM-controller controller to query the JVM indicators in the container from the Prometheus time series database in real time, and calculate and compare the monitored JVM memory usage and the memory quota of the container where the JVM is located. When the JVM memory usage exceeds 90% and the JVM maximum stack memory divided by the JVM container memory quota is less than 90% for 5 consecutive minutes, the JVM-Controller automatically increases the maximum stack memory of the corresponding JVM container; when the JVM memory usage is less than 50%, the JVM-Controller automatically reduces the maximum heap memory and the memory quota of the container where the JVM is located to reduce memory waste.

[0027] Optimize the JVM garbage collection mechanism: By optimizing the JVM's garbage collection mechanism, reduce the frequency and duration of garbage collection, and improve the efficiency of garbage collection. Specifically, the garbage collection mechanism can be optimized by adjusting the garbage collection algorithm, increasing the number of garbage collection threads, optimizing the object allocation strategy, etc. This step can be achieved by modifying the JVM configuration file or using the configuration management function of the container platform.

[0028] Through the above method, dynamic JVM stack memory size configuration can be realized through automatic monitoring data collection under the container orchestration system, thereby smoothing dynamic perception and effectively preventing memory overflow problems.

[0029] The present invention solves the problem of JVM memory overflow in a containerized environment, improves the stability and performance of the application, and has the following advantages: Real-time monitoring: By collecting JVM memory usage data in real time, you can promptly detect memory usage anomalies and avoid memory overflow problems.

[0030] Dynamic adjustment: Dynamically adjust the JVM heap size based on the monitored JVM memory usage to better meet the memory requirements of the application and avoid memory waste.

[0031] Optimize garbage collection: By optimizing the JVM's garbage collection mechanism, you can reduce the frequency and duration of garbage collection, improve the efficiency of garbage collection, and further improve the performance and stability of the application.

[0032] Easy to deploy and expand: The technical solution of the present invention can be easily deployed on various container platforms, has good scalability, and can be customized and expanded according to actual needs.

[0033] For the aforementioned embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily required by the present application.

[0034] The above embodiments describe the basic principles and main features of the present invention and the advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the changes and modifications made by those skilled in the art shall be within the scope of protection of the appended claims of the present invention without departing from the spirit and scope of the present invention.

Claims

1. A method for effectively solving containerized JVM memory overflow, characterized in that: The following steps are involved: Step S1: deploy a JVM indicator collection agent inside the container, use the Prometheus time series database to pull indicator data at a fixed frequency, and monitor the JVM memory usage in the container; Step S2: The JVM-controller controller queries the JVM indicators in the container from the Prometheus time series database in real time and dynamically adjusts the JVM heap size in the container; Step S3: Modify the JVM configuration file to optimize the garbage collection mechanism of the JVM.

2. A method for effectively solving containerized JVM memory overflow as described in claim 1, characterized in that: The JVM indicator collection agent collects JVM indicator data in real time.

3. A method for effectively solving containerized JVM memory overflow as described in claim 2, characterized in that: The indicator data includes: usage of heap memory and non-heap memory, JVM container memory quota, and frequency and duration of garbage collection.

4. A method for effectively solving containerized JVM memory overflow as described in claim 1, characterized in that: The step S2 of dynamically adjusting the JVM heap size in the container specifically includes: Calculate and compare the monitored JVM memory usage with the memory quota of the container where the JVM is located; When the JVM's memory usage exceeds the preset maximum usage, and the JVM's maximum stack memory divided by the JVM's container memory quota is less than 90% for 5 consecutive minutes, the JVM-Controller automatically increases the maximum stack memory of the corresponding JVM running container; When the memory usage of the JVM is lower than the preset minimum usage, the JVM-Controller automatically reduces the maximum heap memory and the memory quota of the container where the JVM is running, thereby reducing memory waste.

5. A method for effectively solving containerized JVM memory overflow as described in claim 4, characterized in that: The preset maximum usage rate is set to 90%, and the preset minimum usage rate is set to 50%.

6. A method for effectively solving containerized JVM memory overflow as described in claim 1, characterized in that: The step S3 of optimizing the garbage collection mechanism of the JVM also includes: using the configuration management function of the container platform for optimization.

7. A method for effectively solving containerized JVM memory overflow as claimed in claim 6, characterized in that: The optimization of the garbage collection mechanism of the JVM specifically includes: adjusting the garbage collection algorithm, increasing the number of garbage collection threads and optimizing the object allocation strategy.

Images