CN111752795A - Full-process monitoring alarm platform and method thereof - Google Patents
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Abstract
A full-flow monitoring alarm platform comprises a client, a load balancer, computing nodes, an information collector, a monitoring data module and an early warning module, wherein the client is connected with the load balancer through a unified standard interface, data of the load balancer is distributed to the computing nodes through gateway degradation, the information collector automatically collects response data of the computing nodes, when a certain service meets a degradation fusing condition, the service immediately enters the degradation and fusing response, the degradation fusing response is sent to an alarm queue of the monitoring data module in real time, the monitoring data module stores a server state, a process state and a database state, when the degradation fusing condition is met, the early warning module can send alarm information in real time, when the fusing degradation condition is met for the first time, early warning is carried out in a mail mode, and when the degradation fusing condition is met for the second time, the early warning is carried out through short messages, And early warning is carried out by WeChat and a mode of sending an emergency mail to a general responsible person.
Description
Technical Field
The invention relates to process monitoring. In particular to a full-process monitoring alarm platform and a method thereof.
Background
In the current medical and American industry, fund transactions are sensitive, risks are high, once any dispute exists in the follow-up process, trace marks are left in the whole transaction process and are very important, but due to the particularity of the medical and American industry, the system cannot monitor and process all transaction records and exception handling information in the whole process, and further cannot give an alarm in real time.
The technology realizes the full-flow operation monitoring of the full roles and the full scenes related to the whole platform, and performs full trace recording and real-time alarm on any abnormal and abnormal authorized operation of fund transaction, thereby providing a feasible technical scheme for guaranteeing the security of the transaction fund.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, focuses on the medical and American industry, performs full-flow operation monitoring on the full roles and the full scenes related to the whole platform, performs full trace recording and real-time alarm on any abnormal fund transaction and abnormal authorized operation, and is mainly applied to additional POS machines, APPs and service platforms.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a full-flow monitoring alarm platform comprises a client, a load balancer, a computing node, an information collector, a monitoring data module and an early warning module,
the client is connected with the load balancer through a unified standard interface, data of the load balancer are distributed to the computing nodes through gateway degradation, the information collector automatically collects response data of the computing nodes, when a certain service meets degradation fusing conditions, the service immediately enters degradation and fusing responses, the degradation fusing responses are simultaneously sent to an alarm queue of the monitoring data module in real time, the monitoring data module stores server states, process states and database states, when the degradation fusing conditions are met, the early warning module can send alarm information in real time, when the degradation fusing conditions are met for the first time, early warning is carried out in a mail mode, and when the degradation fusing conditions are met for the second time, early warning is carried out in a short message, micro-mail and emergency mail sending mode to general responsible persons.
A full-process monitoring alarm method specifically comprises the following steps:
s1, a user starts to configure service through a client login platform;
s2, selecting different services in configuration, setting alarm rules, and adding the alarm rules into a monitoring and alarm range;
s2.1, selecting different nodes in the service, and adding the nodes into a monitoring and alarming range;
s2.2, increasing an alarm rule according to needs;
s2.3, after the user confirms, the newly added alarm rule takes effect;
s2.4, informing whether the alarm rule is effective or not, returning the state whether the alarm rule is effective or not to the client, and if the state which is not started is returned, modifying the rule and re-submitting the rule;
s2.5, returning a new result: if the S2.4 rule is effective, returning a result of the newly added rule to the client;
s3, after the rule configuration is effective, the platform formally starts the corresponding rule and service;
s3.1, expanding the volume through a CHD computing node, increasing the management of a cluster server, and configuring and managing the cluster server node through a clouderaManager;
s4, collecting service logs and system logs from an ElasticSearch through Filebeat and Logstash, and starting a log collector;
s4.1, establishing a storage rule and an index in an ElasticSearch, wherein the ElasticSearch stores all information and establishes a corresponding index;
s5, configuring a Prometheus data source by using REST and API of elastic search, capturing data and storing time sequence data by configuring Prometheus, and providing the data to a GPE early warning module;
s5.1. an Exporter registration component acquires the resource use condition of the current host;
s5.2. a server is pulled by Prometous, and a server actively pulls data to a client;
s5.3, Grafana realizes monitoring and early warning, is a cross-platform open-source measurement analysis and visualization tool, and can inquire and visually display acquired data and timely notify the acquired data;
s6, the GPE early warning module gives an alarm through the mail and the WeChat at the same time;
and S7, monitoring the server, the process and the database by the monitoring data module, and adding the server IP, the process and the database into a monitoring range.
Has the advantages that:
(1) carrying out full-flow operation monitoring on full roles and full scenes related to the whole platform, and carrying out full trace recording and real-time alarm on any abnormal fund transaction and abnormal authorized operation;
(2) the health condition of the cluster is controlled, various set indexes and the specific running condition of the system are comprehensively monitored, and the space and the capacity of cluster management are increased;
(3) storing, searching and analyzing a large amount of data in an extremely short time;
(4) the integrated monitoring program is smoother and more complete;
(5) the service monitors the message accurately and in real time;
(6) the stable availability of the automatic switching service of the payment link is protected;
(7) service communication is not blocked;
(8) early warning mechanism and heartbeat detection mechanism
(9) The data transmission safety in the credit data sharing exchange process is ensured through a hybrid encryption mode;
(10) the load balancer is distributed to the service nodes according to the strategy, so that the load balancer can really and accurately acquire the real-time processing capacity of the service nodes, and the influence of a real-time parameter process on the performance of the system is avoided;
drawings
FIG. 1 is a diagram of a full process monitoring alarm platform architecture of the present invention;
FIG. 2 is a flow chart of a full process monitoring alarm method of the present invention;
Detailed Description
The invention is further described with reference to the following figures and examples.
A full-flow monitoring alarm platform comprises a client, a load balancer, a computing node, an information collector, a monitoring data module and an early warning module,
the client is connected with the load balancer through a unified standard interface, data of the load balancer are distributed to the computing nodes through gateway degradation, the information collector automatically collects response data of the computing nodes, when a certain service meets degradation fusing conditions, the service immediately enters degradation and fusing responses, the degradation fusing responses are simultaneously sent to an alarm queue of the monitoring data module in real time, the monitoring data module stores server states, process states and database states, when the degradation fusing conditions are met, the early warning module can send alarm information in real time, when the degradation fusing conditions are met for the first time, early warning is carried out in a mail mode, and when the degradation fusing conditions are met for the second time, early warning is carried out in a short message, micro-mail and emergency mail sending mode to general responsible persons.
The client is a mobile phone, a PC or a POS machine.
Wherein, the load balancer is an Nginx load balancer;
the method comprises the following steps that degradation fuses are configured between a load balancer and computing nodes and are in a starting state, the degradation fuses adopt Sentinel to form degradation fusing of services, the Sentinel forms a linked list based on a plurality of (7) different computing nodes, each computing node plays its own role, after doing internal matters, the Sentinl transmits a request to the next computing node until the service of one computing node meets the degradation fusing condition, blocking abnormity is thrown out to terminate transmission, the Sentinl completes calling of an entry method of each computing node through a computing node chain, each computing node performs logic processing according to a created rule, and when a statistical result reaches a set threshold value, degradation and fusing events are triggered; such as throwing an occlusion exception.
By means of an adapter, the Sentinel and a Spring cloud framework (Spring service suite) are integrated together, relevant degradation and fusing codes of the Sentinel are added to an extension point of the Spring cloud framework, and when a certain server or servers in a cluster server have overlarge load or do not have a health response for a long time, a Sentinel fusing mechanism starts to be started, so that the purposes of protecting high stability and high availability of automatic payment link switching service are achieved.
The method for achieving the degraded fusing condition comprises the following steps of: average response time, abnormal constants and abnormal proportion, when one of the conditions is met, the system triggers a degradation or fusing mechanism to avoid more cascading faults;
average response time: when the average response time of the resource exceeds the threshold value, the resource enters a quasi-downgrade state, and then if 5 requests are continuously entered, the response time of the requests continuously exceeds the threshold value, the calls to the resource automatically return to downgrade within the next time window;
abnormal ratio: when the exception ratio of the total exception amount of the resource per second to the throughput exceeds a threshold value, the resource enters a degraded state, namely within a next time window, the calling of the method is automatically returned, and the threshold value range of the exception ratio is 0-100%;
iso-constants are as follows: fusing when the abnormal number of the resources in about 1 minute exceeds a threshold value;
the CDH computing nodes are tools with cluster server automatic installation, centralized management, cluster server monitoring and alarming functions, are installed and managed through a cloudera manager, monitor the health condition of the cluster servers, comprehensively monitor various set indexes and the specific running condition of a system, and increase the space and the capacity for managing the cluster servers through capacity expansion of the CDH computing nodes;
the Cloudera Manager is a large data cluster installation and deployment sharer, so that the time of installing a cluster is shortened within a few hours from a few days, operation and maintenance personnel are reduced to within a few persons from tens of persons, and the cluster management efficiency is greatly improved. The application in the case of the scheme is as follows: platform deployment all clusters need to be managed with Cloudera Manager.
The ClouderaManager is a big data cluster server installation and deployment system, and has the functions of cluster server automatic installation, centralized management, cluster server monitoring and alarming, so that the time for installing the cluster server is shortened within several hours from several days, operation and maintenance personnel are reduced to within several persons from tens of persons, and the management efficiency of the cluster server is greatly improved.
The information collector is deployed with Filebeat, Logstash and elastic search, response data of resources are automatically collected, when a certain resource in the system meets a threshold value of degradation fusing, the resource immediately enters the degradation and fusing response, meanwhile, the response data is sent to an alarm queue of the monitoring system in real time, the system can send alarm information in real time, and the alarm information comprises logs, mails and short messages; filebeat is embedded into each cluster server, and service data on each cluster server is collected through filtering, and Logstash is responsible for storing log information into ElasticSearch through a data collection engine and a data pipeline, and the ElasticSearch stores the collected system information and constructs a search index database;
the method comprises the following steps that Filebeat is a lightweight log collector, is one of Beats, collects log file data, starts one or more input sources to monitor the position of a log file designated in advance when the Filebeat is started, starts a collector (harvester) for each log file located by the Filebeat, reads incremental data of the log file in real time by each collector, sends the incremental data to a libpeak, aggregates all the incremental data, and sends the data to a preset output source uniformly;
the Logstash is a data collection engine and has real-time pipeline processing capacity, and the Logstash is used as a bridge between a data source and a data storage analysis tool, and is combined with ElasticSearch and Kibana, so that the data can be processed and analyzed conveniently;
the method comprises the steps that an ElasticSearch is based on a RESTful web interface and is constructed on an open-source distributed search engine on an Apache license, the ElasticSearch is provided with a distributed document database, a cluster server is deployed, each field of the distributed document database can be indexed, data of each field can be searched, and the ElasticSearch can be transversely expanded to hundreds of servers to store and process PB-level data. The ElasticSearch can store, search and analyze a large amount of data in an extremely short time.
Raw data are input into an ElasticSearch from a plurality of sources (including logs, system indexes and network applications), the ElasticSearch carries out data acquisition, the raw data are analyzed, standardized and enriched before being indexed in the ElasticSearch, after the raw data are indexed in the ElasticSearch, a user can run complex queries and use aggregation to retrieve complex summaries of the own data, the aggregation can also be used to retrieve the complex summaries of the own data, and in Kibana, the user can create powerful visualizations based on the own data, share instrument panels and manage the ElasticStack.
The Elastic Stack is a core component of the Elastic search, and is a set of open source tools suitable for data acquisition, enrichment, storage, analysis and visualization.
The information collector is also deployed with Kafka, Listener, and rocktmq, to ensure service listening message accuracy and real-time,
kafka is a distributed message system which supports partitioning, is multi-copy and is based on Zookeeper coordination, and can process a large amount of data in real time to meet various demand scenarios;
the Listener monitors the Kafka message by using the @ Kafka Listener annotation, then the consumption is carried out in the method, the specific realization of the Kafka is not required to be concerned, and the method can be realized by adding Topics to the configuration value file;
the RocktMQ is an MQ message middleware product, is a distributed message middleware of a queue model, can process the processing and message processing of asynchronous distributed transactions, and can ensure a strict message sequence; providing a message pull mode; subscriber horizontal extension; a real-time message subscription mechanism; billions of message heaps; a Docker image is provided for isolation testing and cloud cluster deployment.
The Kafka collects logs of the platform and logs of various services, and the logs are opened to various users for consumption in a uniform interface service mode through the Kafka;
the RocktMQ monitors the port state of the payment channel, completes message notification, state updating, sending and receiving local transaction messages through messages, and simultaneously adopts Seata and RocktMQ to realize transaction control for independent data updating among a plurality of services.
The early warning module monitors and alarms through a GPE (general purpose engine), the GPE is short for Grafana, Prometous and Exporter, a registration center is added into a platform for service discovery, dynamic service addition is realized, and abnormal warning is realized by using mails, nails and Webhooks, wherein Grafana is a visualization tool which can be used after unpacking; prometheus is an open source service monitoring system, collects data from remote machines through the HTTP protocol and stores the data in a local time sequence database; the Exporter is an important component in prometheus monitoring, is responsible for collecting data indexes and is deployed in an agent of a client;
the GPE workflow is as follows, an Exporter component is registered in a registration center; the Prometheus pulls a server in the registration center; an Exporter component acquires indexes of server or system software; grafana configures a Prometoxus data source to acquire acquired data thereof and combines a user-defined panel to realize large monitoring screen; grafana realizes monitoring and early warning by setting Alerting.
The GPE realizes an early warning mechanism and a heartbeat detection mechanism of the platform.
Wherein, the communication between the client and the platform adopts Socket long connection of Netty and Nio unimpeded transmission,
the Netty using Socket long connection can save more TCP establishment/closing operations, reduce waste and save time, and is more suitable for long connection for customers frequently requesting resources; there is a problem with clients and servers if they are not turned off for a long time: as more and more clients are used, the resources of the Server are consumed.
Aiming at the defect of long connection, the method can be solved by establishing a distribution server, and the specific scheme is as follows:
s1, equipment requests a distribution server, the distribution server returns an effective Socket server IP and a Port, and then the connection is disconnected;
s2, after the equipment obtains the IP and the Port, the equipment is connected with a Socket server and then carries out protocol communication with the Socket server;
s3, if the equipment does not receive a connection success response, the connection is tried again, and if the three requests still do not successfully establish the connection, the equipment needs to request a distribution server and then re-operates the operation;
the problem of frequent communication between the equipment and the server can be easily solved.
IO processing whether Nio is blocked mainly has three major core parts: channel, Buffer, Selector, and Nio operate based on the Channel and Buffer, and data is always read from the Channel into the Buffer or written from the Buffer into the Channel. The Selector (select area) is used to listen to events of multiple channels. A single thread may listen to multiple data channels.
When any effective payment channel is started, in the initialization process of the payment channel, a communication relation is established between the Socket long link and the corresponding payment channel, relevant parameters of current equipment are obtained, and relevant data are obtained between the Socket long link and an interface of a platform server, wherein IO processing is performed through Nio.
A full-process monitoring alarm method specifically comprises the following steps:
s1, a user starts to configure service through a client login platform;
s2, selecting different services in configuration, setting alarm rules, and adding the alarm rules into a monitoring and alarm range;
s2.1, selecting different nodes in the service, and adding the nodes into a monitoring and alarming range;
s2.2, increasing an alarm rule according to needs;
s2.3, after the user confirms, the newly added alarm rule takes effect;
s2.4, informing whether the alarm rule is effective or not, returning the state whether the alarm rule is effective or not to the client, and if the state which is not started is returned, modifying the rule and re-submitting the rule;
s2.5, returning a new result: if the S2.4 rule is effective, returning a result of the newly added rule to the client;
s3, after the rule configuration is effective, the platform formally starts the corresponding rule and service;
s3.1, expanding the volume through a CHD computing node, increasing the management of a cluster server, and configuring and managing the cluster server node through a clouderaManager;
s4, collecting service logs and system logs from an ElasticSearch through Filebeat and Logstash, and starting a log collector;
s4.1, establishing a storage rule and an index in an ElasticSearch, wherein the ElasticSearch stores all information and establishes a corresponding index;
s5, configuring a Prometheus data source by using REST and API of elastic search, capturing data and storing time sequence data by configuring Prometheus, and providing the data to a GPE early warning module;
s5.1. an Exporter registration component acquires the resource use condition of the current host;
s5.2. a server is pulled by Prometous, and a server actively pulls data to a client;
s5.3, Grafana realizes monitoring and early warning, is a cross-platform open-source measurement analysis and visualization tool, and can inquire and visually display acquired data and timely notify the acquired data;
s6, the GPE early warning module gives an alarm through the mail and the WeChat at the same time;
and S7, monitoring the server, the process and the database by the monitoring data module, and adding the server IP, the process and the database into a monitoring range.
When a user logs in a platform, a service gateway with unified authentication authorization is used for authentication and authentication, a JWT/Oath2 security protocol is adopted, and a JWT carries out digital signature by using a public key/secret key pair algorithm; the Oath2 is an authorization protocol, and all users and third parties on the platform adopt the JWT/Oath2 protocol for authorization.
The SCgateway is a service component of the service gateway, provides routing and filtering for an application program, forwards a request of a receiving client to a service module, and completes cross-domain functions such as service security, log recording and user tracking, the SCgateway serves as a single policy enforcement point, and all calls are routed through the SCgateway service gateway and then reach a final destination. Since the SCGateway service gateway is located between all calls from clients to each service, it is also the central policy enforcement point for servicing calls.
The SCgateway service gateway is provided with a fuse, the fuse is used for interactive overtime processing and fault tolerance, the fuse consists of a fuse and a thread pool, if the switch is in an open state, the thread pool is not called but degraded service is called, the fuse generates corresponding action according to the state, the fuse is in a closed state, and if the calling failure times are accumulated to a threshold (or a certain proportion), the fuse is started; the fuse is in an open state, and the calling of the downstream service in the open state directly returns the exception without going through the network, but a clock option is set, the average fault processing time is generally set, and the fuse enters a semi-fusing state after the time; in the semi-blown state, a certain number of service requests are allowed, if the calling fails, the service requests are considered to be recovered, and the fuse is closed.
The fuse plays a thread isolation role, the thread pool is positioned between the user request and the service, the user request accesses the service through idle threads in the thread pool, if the thread pool has no idle threads, the user request is subjected to degradation processing, the degradation processing is used for preventing the user request from entering endless waiting to cause system crash, the request is not blocked, and an execution result can be seen.
The fusing is realized by the following steps:
s1, introducing a breaker assembly dependence item into maven;
s2, adding a fusing annotation on the main boot starting program;
s3, marking the remote calling method needing to be protected with an annotation;
and S4, judging whether service degradation is needed, and if so, implementing a degradation strategy.
The SCgateway service gateway starts current limiting control in a highly concurrent scene at the moment when the user quantity is large, and the function of current limiting is to adopt a refusal measure to the exceeded request under the condition that the system cannot process more requests, so as to ensure that the load does not exceed the upper limit of system processing.
The public key/secret key pair algorithm introduces two classic encryption algorithms, namely an iterative block encryption algorithm and an asymmetric encryption algorithm, integrates the advantages of the two algorithms, and realizes credit data exchange transmission based on a mixed encryption mode of the two encryption algorithms so as to ensure the safety of data transmission in the credit data sharing exchange process.
The public key/secret key pair algorithm specifically comprises the following steps:
s1: carrying out data encryption on a plaintext P of information to be transmitted by utilizing an iterative block encryption algorithm to form a ciphertext;
s2: generating a pair of secret keys by using an asymmetric encryption algorithm, encrypting the secret keys of the iterative block encryption algorithm by using a public key generated by the asymmetric encryption algorithm by a sender, and transmitting the secret keys of the iterative block encryption algorithm encrypted by the asymmetric encryption algorithm and a ciphertext encrypted by the iterative block encryption algorithm to a receiver;
s3: the receiver decrypts the secret key of the iterative block encryption algorithm encrypted by the asymmetric encryption algorithm by using the private key generated by the asymmetric encryption algorithm, and then decrypts the ciphertext by using the secret key of the iterative block encryption algorithm to form a plaintext, so that the aim of safe transmission is fulfilled.
The iterative block encryption algorithm is specifically as follows:
the iterative block cipher algorithm produces 64-bit cipher text while processing 64-bit information plaintext blocks. In the iterative block encryption algorithm, eight bits are used as check bits, 8 th, 16 th, 24 th, 32 th, 40 th, 48 th, 56 th, 64 th bits, respectively, and the key significance used for encryption is 56 bits.
In 64-bit plaintext data input till 64-bit ciphertext output, 16 rounds of encryption are performed by the iterative block encryption algorithm, and 48 bits of secret key K exist in each round of encryptioniAnd 8 alternative mapping boxes SiThe method is characterized in that the input 64-bit plaintext data is split into two identical and independent 32-bit plaintexts, and the plaintexts are marked as L0And R0Performing the same round encryption for each round of algorithm, and using the 32-bit L of the previous roundi-1And Ri-1As an input parameter, a 32-bit parameter is output as LiAnd RiWherein the value range of i is more than or equal to 1 and less than or equal to 16.
The encryption calculation method for each round comprises the following specific steps:
s1, inputting 64-bit data;
s2, initially replacing IP;
s3. encrypt, Li＝Ri-1，
S4, finally replacing IP-1。
Wherein, f (R)i-1,Ki) Representing a mapping Box Algorithm, KiA key representing the ith round is represented by,
representing an exclusive-or operation, E () and P () representing an extension function and a mapping function, respectively, E () and P () pair Ri-1And
and carrying out bit number expansion mapping to expand 32 bits to 48 bits. For an iterative block cipher algorithm, an initial permutation IP and a final permutation IP-1The position replacement arrangement is carried out according to the corresponding rule;
wherein the mapping box algorithm f (R)i-1,Ki) The concrete implementation is as follows:
s1, adding Ri-1＝r1r2r3.......r32Extending from 32 bits to 48 bits, Ri-1Representing the ciphertext of a plaintext after i-1 rounds of encryption, riA character representing the ith bit;
s2. for Ri-1The expansion is carried out, and the expansion is carried out,
T1representing 8 6-bit character strings Bi，T1＝B1B2......B8；
S3.(S1(B1),S2(B2),...,S8(B8))→T2Wherein S isi(Bi) B is to bei＝b1b2......b6Is mapped as a mapping box SiR rows and c columns of (1), wherein r is 2b1+b6，b2b3b4b5Is a binary representation of c being greater than or equal to 0 and less than or equal to 15, biAs a string BiThe ith character of (1);
s4, pair T by replacing function P ()2Substitution is made, and is denoted as P (T)2)→T3By direct transposition, the T of 32 bits2＝t1t2......t32Substitution to T2＝t16t7......t25。
Wherein, the secret key K of the iterative block encryption algorithmiIs composed of 48-bit cipher length, each iteration of 16 rounds of iteration needs different secret key KiEncrypted with a secret key KiThe generation process is as follows:
s1, removing 8 secret key parity check bits, and using 64-bit initial secret key K0Down to 56 bits, the 56 bit key is divided into two blocks, 28 bits C respectively, according to the key permutation selection0And 28 bit D0；
S2, according to the turns, the C0And D0Circularly left-shifting, generating C after conversion1And D1Then, C is added1And D1Combining, and generating 48-bit key K by key replacement selection1；
S3.C1And D1Performing left shift conversion again to generate C2And D2And C is1And D1Merging, generating a 48-bit key K by selective permutation2；
S4, repeating the step S3 for 9 times to obtain a 48-bit secret key Ki，3≤i≤16。
The number of bits of the loop left shift is determined by the number of iteration rounds, the 1 st, 2 nd, 9 th and 16 th rounds are left shift by one bit, and the rest rounds are left shift by two bits.
The asymmetric encryption algorithm comprises the following steps:
s1, selecting two large prime numbers p and q, and requiring that p is not equal to q, and calculating to obtain the final product
n＝p×q；
S2, selecting a prime number e, wherein the requirement e meets the requirement
And greatest common divisor
S3, calculating
mod represents a remainder function;
s4, publishing a secret key, and sending KpublicAs public key parameter, K ═ e, nprivateAs private key parameter (d, n);
s5, using C ═ peThe plaintext is encrypted by a modn formula, and p is CdThe mod n formula decrypts the ciphertext; wherein the binary digit number of the prime numbers p and q is more than 1024, the decimal numerical value of the prime numbers p and q satisfies 1000 < | p-q | < 10000,
the load balancer distributes the request task to the service node according to a certain strategy, and in order to ensure that the load balancer can really and accurately acquire the real-time processing capacity of the service node, the processing capacity factors influencing the service node need to be analyzed. When the load balancing module collects parameters of the service node, if the parameters are too much, certain resource loss is caused to the load balancing node and the service node. In order to avoid the influence of the process of acquiring the real-time parameters of the service nodes on the performance of the system, two main core factors influencing the server nodes are determined as the indexes of the real-time load capacity, namely the utilization rate of the processor and the idle rate of the memory.
The method comprises the following steps:
s1, a Socket interface is adopted for communication of a load balancer and a service node, and UDP with less resource requirements is adopted as a transmission protocol for reducing the problem of resource loss caused by communication;
s2, the load balancer periodically sends an acquisition request to the service node, and the processor utilization rate and the memory vacancy rate of the service node are obtained through calculation;
the processor is divided into three operation states, namely an idle state, a user state and a system kernel state, the operation time of the idle state is represented by T1, the operation time of the user state is represented by T2, the operation time of the kernel state is represented by T3, and the utilization rate C of the processor is obtainediThe calculation formula is as follows:
the total memory size, the buffer size, the cached size, the free memory size and the memory vacancy rate M of the current service node can be found out through the system fileiThe calculation is as follows:
s3, calculating the weight of the service node,
for a plurality of service nodes NiUsing the set Node ═ N1,N2,...,Ni,...,NnDenotes, for the service node NiProcessor utilization of CiIndicating that memory is free by MiMeans that weight is reused by WiRepresenting, a service node NiWeight expression function FiThe following were used: fi＝λ1(1-Ci)+λ2Mi；
Wherein λ1+λ2＝1，λ1And λ2Representing the impact factor of the processor and memory, CiAnd MiAll values of (A) are in the interval [0,1 ]]In the range of 1-CiAnd MiWhen both are 0, FiA value of 0 indicates that the service node is in an unavailable state and will not be assigned a task, and when the load weight difference is satisfied, Wi＝Fi；
Among them, λ is preferred1＝0.6，λ2＝0.4；
S4, carrying out boundary condition analysis on the new weight value and the old value, and setting the weight W of the service node under the boundary value PiThe update of (2) is to satisfy the following equation:
when the boundary condition is met, a setsockopt function is called to write the new weight into a load configuration table of the load balancer and redistribute the load task, otherwise, the task is forwarded and distributed according to the previous load balance
The above-described embodiment merely represents one embodiment of the present invention, but is not to be construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.
Claims (10)
1. A full-flow monitoring alarm platform comprises a client, a load balancer, a computing node, an information collector, a monitoring data module and an early warning module,
the client is connected with the load balancer through a unified standard interface, data of the load balancer are distributed to the computing nodes through gateway degradation, the information collector automatically collects response data of the computing nodes, when a certain service meets degradation fusing conditions, the service immediately enters degradation and fusing responses, the degradation fusing responses are simultaneously sent to an alarm queue of the monitoring data module in real time, the monitoring data module stores server states, process states and database states, when the degradation fusing conditions are met, the early warning module can send alarm information in real time, when the degradation fusing conditions are met for the first time, early warning is carried out in a mail mode, and when the degradation fusing conditions are met for the second time, early warning is carried out in a short message, micro-mail and emergency mail sending mode to general responsible persons.
2. The full process monitoring and warning platform of claim 1, wherein: the client is a mobile phone, a PC or a POS machine; the load balancer is an Nginx load balancer;
a degradation fuse is configured between the load balancer and the computing nodes and is in a starting state, the degradation fuse adopts Sentiel to form degradation fusing of service, the Sentiel forms a linked list based on a plurality of (7) different computing nodes, each computing node plays its own role, after doing internal matters, the Sentiel transmits a request to the next computing node until the service of a certain computing node meets the degradation fusing condition, a blocking exception is thrown out to terminate the transmission, the Sentiel completes the calling of an entry method of each computing node through a computing node chain, each computing node performs own logic processing according to the created rule, and when the statistical result reaches a set threshold value, degradation and fusing events are triggered; such as throwing an occlusion exception;
by means of an adapter, the Sentinel and a Spring cloud framework (Spring service suite) are integrated together, relevant degradation and fusing codes of the Sentinel are added to an extension point of the Spring cloud framework, and when a certain server or servers in a cluster server have overlarge load or do not have a health response for a long time, a Sentinel fusing mechanism starts to be started, so that the purposes of protecting high stability and high availability of automatic payment link switching service are achieved.
3. The full process monitoring and warning platform of claim 1, wherein: the degraded fusing condition is configured on a configuration file and comprises the following steps: average response time, abnormal constants and abnormal proportion, when one of the conditions is met, the system triggers a degradation or fusing mechanism to avoid more cascading faults;
average response time: when the average response time of the resource exceeds the threshold value, the resource enters a quasi-downgrade state, and then if 5 requests are continuously entered, the response time of the requests continuously exceeds the threshold value, the calls to the resource automatically return to downgrade within the next time window;
abnormal ratio: when the exception ratio of the total exception amount of the resource per second to the throughput exceeds a threshold value, the resource enters a degraded state, namely within a next time window, the calling of the method is automatically returned, and the threshold value range of the exception ratio is 0-100%;
iso-constants are as follows: the fusing is performed when the number of the anomalies of the resource in the last 1 minute exceeds the threshold value.
4. The full process monitoring and warning platform of claim 1, wherein: the CDH computing node is provided with tools with cluster server automatic installation, centralized management, cluster server monitoring and alarming functions, is installed and managed through the cloudera manager, monitors the health condition of the cluster server, comprehensively monitors various set indexes and the specific running condition of a system, and increases the space and the capacity for managing the cluster server through capacity expansion of the CDH computing node;
the Cloudera Manager is a large data cluster installation and deployment sharer, so that the time of installing a cluster is shortened within a few hours from a few days, operation and maintenance personnel are reduced to within a few persons from tens of persons, and the cluster management efficiency is greatly improved. The application in the case of the scheme is as follows: all clusters deployed by the platform need to be managed by a Cloudera Manager;
5. the full process monitoring and warning platform of claim 1, wherein: the information collector is deployed with Filebeat, Logstash and elastic search, response data of resources are automatically collected, when a certain resource in the system meets a threshold value of degradation fusing, the resource immediately enters the degradation and fusing response, meanwhile, the response data is sent to an alarm queue of the monitoring system in real time, the system can send alarm information in real time, and the alarm information comprises logs, mails and short messages; filebeat is embedded into each cluster server, and service data on each cluster server is collected through filtering, and Logstash is responsible for storing log information into ElasticSearch through a data collection engine and a data pipeline, and the ElasticSearch stores the collected system information and constructs a search index database;
the method comprises the following steps that Filebeat is a lightweight log collector, is one of Beats, collects log file data, starts one or more input sources to monitor the position of a log file designated in advance when the Filebeat is started, starts a collector (harvester) for each log file located by the Filebeat, reads incremental data of the log file in real time by each collector, sends the incremental data to a libpeak, aggregates all the incremental data, and sends the data to a preset output source uniformly;
the Logstash is a data collection engine and has real-time pipeline processing capacity, and the Logstash is used as a bridge between a data source and a data storage analysis tool, and is combined with ElasticSearch and Kibana, so that the data can be processed and analyzed conveniently;
the method comprises the steps that an ElasticSearch is based on a RESTful web interface and is constructed on an open-source distributed search engine on an Apache license, the ElasticSearch is provided with a distributed document database, a cluster server is deployed, each field of the distributed document database can be indexed, data of each field can be searched, and the ElasticSearch can be transversely expanded to hundreds of servers to store and process PB-level data. The ElasticSearch can store, search and analyze a large amount of data in a very short time;
raw data are input into an ElasticSearch from a plurality of sources (including logs, system indexes and network application programs), the ElasticSearch carries out data acquisition, the analysis, standardization and enrichment before indexing are carried out in the ElasticSearch, after the indexing of the raw data in the ElasticSearch is completed, a user can run complex queries, and uses aggregation to retrieve complex summaries of the own data, and can also use aggregation to retrieve the complex summaries of the own data, and in Kibana, the user can create powerful visualizations based on the own data, share instrument panels and manage the ElasticStack;
the Elastic Stack is a core component of the Elastic search, and is a set of open source tools suitable for data acquisition, enrichment, storage, analysis and visualization.
6. The full process monitoring and warning platform of claim 1, wherein:
the information collector is also deployed with Kafka, Listener, and rocktmq, to ensure service listening message accuracy and real-time,
kafka is a distributed message system which supports partitioning, is multi-copy and is based on Zookeeper coordination, and can process a large amount of data in real time to meet various demand scenarios;
the Listener monitors the Kafka message by using the @ Kafka Listener annotation, then the consumption is carried out in the method, the specific realization of the Kafka is not required to be concerned, and the method can be realized by adding Topics to the configuration value file;
the RocktMQ is an MQ message middleware product, is a distributed message middleware of a queue model, can process the processing and message processing of asynchronous distributed transactions, and can ensure a strict message sequence; providing a message pull mode; subscriber horizontal extension; a real-time message subscription mechanism; billions of message heaps; a Docker image is provided for isolation testing and cloud cluster deployment.
The Kafka collects logs of the platform and logs of various services, and the logs are opened to various users for consumption in a uniform interface service mode through the Kafka;
the RocktMQ monitors the port state of the payment channel, completes message notification, state updating, sending and receiving local transaction messages through messages, and simultaneously adopts Seata and RocktMQ to realize transaction control for independent data updating among a plurality of services.
7. The full process monitoring and warning platform of claim 1, wherein: the early warning module monitors and alarms through a GPE (general purpose equipment), wherein the GPE is short for Grafana, Prometous and Exporter, a platform is added into a registration center to find services, dynamic service addition is realized, and abnormal warning is realized by using mails, nails and Webhooks, wherein Grafana is a visualization tool which can be used after opening a box; prometheus is an open source service monitoring system, collects data from remote machines through the HTTP protocol and stores the data in a local time sequence database; the Exporter is an important component in prometheus monitoring, is responsible for collecting data indexes and is deployed in an agent of a client;
the GPE workflow is as follows, an Exporter component is registered in a registration center; the Prometheus pulls a server in the registration center; an Exporter component acquires indexes of server or system software; grafana configures a Prometoxus data source to acquire acquired data thereof and combines a user-defined panel to realize large monitoring screen; grafana realizes monitoring and early warning by setting Alerting;
the GPE realizes an early warning mechanism and a heartbeat detection mechanism of the platform.
8. The full process monitoring and warning platform of claim 1, wherein: the communication between the client and the platform adopts Socket long connection of Netty and Nio unimpeded transmission,
the Netty using Socket long connection can save more TCP establishment/closing operations, reduce waste and save time, and is more suitable for long connection for customers frequently requesting resources; there is a problem with clients and servers if they are not turned off for a long time: with more and more clients, the resources of the Server are consumed;
aiming at the defect of long connection, the method can be solved by establishing a distribution server, and the specific scheme is as follows:
s1, equipment requests a distribution server, the distribution server returns an effective Socket server IP and a Port, and then the connection is disconnected;
s2, after the equipment obtains the IP and the Port, the equipment is connected with a Socket server and then carries out protocol communication with the Socket server;
s3, if the equipment does not receive a connection success response, the connection is tried again, and if the three requests still do not successfully establish the connection, the equipment needs to request a distribution server and then re-operates the operation;
IO processing whether Nio is blocked mainly has three major core parts: channel, Buffer, Selector, and Nio operate based on the Channel and Buffer, and data is always read from the Channel into the Buffer or written from the Buffer into the Channel. The Selector (select area) is used to listen to events of multiple channels. A single thread may listen to multiple data channels.
When any effective payment channel is started, in the initialization process of the payment channel, a communication relation is established between the Socket long link and the corresponding payment channel, relevant parameters of current equipment are obtained, and relevant data are obtained between the Socket long link and an interface of a platform server, wherein IO processing is performed through Nio.
9. The full-process monitoring and alarming method of the full-process monitoring and alarming platform as claimed in any one of claims 1 to 8, wherein:
s1, a user starts to configure service through a client login platform;
s2, selecting different services in configuration, setting alarm rules, and adding the alarm rules into a monitoring and alarm range;
s2.1, selecting different nodes in the service, and adding the nodes into a monitoring and alarming range;
s2.2, increasing an alarm rule according to needs;
s2.3, after the user confirms, the newly added alarm rule takes effect;
s2.4, informing whether the alarm rule is effective or not, returning the state whether the alarm rule is effective or not to the client, and if the state which is not started is returned, modifying the rule and re-submitting the rule;
s2.5, returning a new result: if the S2.4 rule is effective, returning a result of the newly added rule to the client;
s3, after the rule configuration is effective, the platform formally starts the corresponding rule and service;
s3.1, expanding the volume through a CHD computing node, increasing the management of a cluster server, and configuring and managing the cluster server node through a clouderaManager;
s4, collecting service logs and system logs from an ElasticSearch through Filebeat and Logstash, and starting a log collector;
s4.1, establishing a storage rule and an index in an ElasticSearch, wherein the ElasticSearch stores all information and establishes a corresponding index;
s5, configuring a Prometheus data source by using REST and API of elastic search, capturing data and storing time sequence data by configuring Prometheus, and providing the data to a GPE early warning module;
s5.1. an Exporter registration component acquires the resource use condition of the current host;
s5.2. a server is pulled by Prometous, and a server actively pulls data to a client;
s5.3, Grafana realizes monitoring and early warning, is a cross-platform open-source measurement analysis and visualization tool, and can inquire and visually display acquired data and timely notify the acquired data;
s6, the GPE early warning module gives an alarm through the mail and the WeChat at the same time;
and S7, monitoring the server, the process and the database by the monitoring data module, and adding the server IP, the process and the database into a monitoring range.
10. The full-flow monitoring alarm method according to claim 9, characterized in that: when a user logs in the platform, the user uses a service gateway with unified authentication authorization to carry out authentication and authentication, a JWT/Oath2 security protocol is adopted, and the JWT carries out digital signature by using a public key/secret key pair algorithm; the Oath2 is an authorization protocol, and all users and third parties on the platform adopt JWT/Oath2 protocol for authorization;
the SCgateway is a service component of the service gateway, provides routing and filtering for an application program, forwards a request of a receiving client to a service module, and completes cross-domain functions such as service security, log recording and user tracking, the SCgateway serves as a single policy enforcement point, and all calls are routed through the SCgateway service gateway and then reach a final destination. Since the SCGateway service gateway is located between all calls from clients to each service, it is also the central policy enforcement point for servicing calls;
the SCgateway service gateway is provided with a fuse, the fuse is used for interactive overtime processing and fault tolerance, the fuse consists of a fuse and a thread pool, if the switch is in an open state, the thread pool is not called but degraded service is called, the fuse generates corresponding action according to the state, the fuse is in a closed state, and if the calling failure times are accumulated to a threshold (or a certain proportion), the fuse is started; the fuse is in an open state, and the calling of the downstream service in the open state directly returns the exception without going through the network, but a clock option is set, the average fault processing time is generally set, and the fuse enters a semi-fusing state after the time; in a semi-fusing state, allowing a certain number of service requests, if the calling fails, considering that the service requests are recovered, and closing the fuse;
the fuse plays a thread isolation role, the thread pool is positioned between the user request and the service, the user request accesses the service through an idle thread in the thread pool, if the thread pool has no idle thread, the user request can be subjected to degradation processing, the degradation processing has the function of preventing the user request from entering endless waiting to cause system crash, the request cannot be blocked, and an execution result can be seen;
the fusing is realized by the following steps:
s1, introducing a breaker assembly dependence item into maven;
s2, adding a fusing annotation on the main boot starting program;
s3, marking the remote calling method needing to be protected with an annotation;
and S4, judging whether service degradation is needed, and if so, implementing a degradation strategy.
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Application publication date: 20201009 |