CN113010392B - Big data platform testing method, device, equipment, storage medium and system - Google Patents
Big data platform testing method, device, equipment, storage medium and system Download PDFInfo
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- CN113010392B CN113010392B CN202110181643.0A CN202110181643A CN113010392B CN 113010392 B CN113010392 B CN 113010392B CN 202110181643 A CN202110181643 A CN 202110181643A CN 113010392 B CN113010392 B CN 113010392B
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Abstract
According to the method, the device, the equipment, the storage medium and the system for testing the big data platform, the target test index is one of one-key start-stop effectiveness, cluster effectiveness, service process self-pull-up effectiveness and overtime effectiveness, and according to the target test index, the pressure test tool and the monitoring tool are called to test the big data platform to obtain the performance parameters of the big data platform, so that automatic testing of the big data platform is realized, the testing accuracy and the testing efficiency of the big data platform are improved, and the reliability and the usability of the big data platform are improved.
Description
Technical Field
The embodiment of the application relates to the technical field of big data, in particular to a testing method, a testing device, testing equipment, a storage medium and testing system of a big data platform.
Background
The realization of the big data technology is independent of a big data platform, and the big data platform is usually composed of a server cluster, has the functions of collecting, processing, storing, mining and the like of the big data, and can mine out information and knowledge hidden in the big data, thereby being applied to aspects of human life. Therefore, in order to ensure that the big data platform operates effectively, it is necessary to perform non-functional testing on the big data platform.
In the prior art, a large data platform is subjected to nonfunctional test in a manual test mode, so that the problems of low test efficiency and high error rate exist.
Disclosure of Invention
The embodiment of the application provides a testing method, device, equipment, storage medium and system for a big data platform, so as to solve the problems of low testing efficiency and high error rate in the prior art.
In a first aspect, an embodiment of the present application provides a method for testing a big data platform, including:
acquiring a target test index, wherein the target test index is one of one-key start-stop effectiveness, cluster effectiveness, service process self-pull-up effectiveness and overtime effectiveness;
and according to the target test index, a pressure test tool and a monitoring tool are called to test the big data platform, so that the performance parameters of the big data platform are obtained.
Optionally, the calling the pressure testing tool and the monitoring tool to test the big data platform to obtain the performance parameters of the big data platform includes:
invoking a target test data packet which is pre-configured in the pressure test tool and matches with the target test index to simulate and send pressure, and testing the big data platform;
And calling a monitoring tool preset in the big data platform to monitor the running condition of the big data platform in the testing process, so as to obtain the performance parameters of the big data platform.
Optionally, the calling the target test data packet which is pre-configured in the pressure test tool and matches with the target test index to perform simulation and send pressure, and testing the big data platform includes:
according to the number of the thread groups pre-configured in the target test data packet, sending a data processing request to the big data platform;
and in the process that the big data platform executes data processing according to the data processing request, according to a pre-configured test instruction sending mechanism in the target test data packet, sending a test instruction related to the target test index to the big data platform so as to test the big data platform about the target test index.
Optionally, the target test indicator is a one-key start-stop validity, and the sending, according to a pre-configured test instruction sending mechanism in the target test data packet, a test instruction related to the target test indicator to the big data platform, so as to perform a test on the big data platform with respect to the target test indicator, includes:
Sending a process ending instruction to a target server in the big data platform; and after a preset time interval, sending a process recovery instruction to the target server so as to perform one-key start-stop effectiveness test on the big data platform.
Optionally, the performance parameters include: at least one of transaction number per second, resource utilization rate and process ending time consumption of the big data platform in the process ending process of the target server; and at least one of transaction per second, resource utilization and process recovery time consumption of the big data platform in the process recovery process of the target server.
Optionally, the target test index is cluster validity, and the sending, according to a pre-configured test instruction sending mechanism in the target test data packet, a test instruction related to the target test index to the big data platform, so as to perform a test on the big data platform about the target test index, includes:
sending a process suspension instruction to a target server in the big data platform; and after a preset time interval, sending a resume suspension instruction to a target server in the big data platform so as to carry out suspension test on the big data platform.
Optionally, the performance parameters include: at least one of transaction number per second, resource utilization rate, error reporting condition and process suspending time of the big data platform in the process suspending process of the target server; and at least one of transaction per second, resource utilization, error reporting condition and suspension recovery time of the big data platform in the process of recovering and suspending the process of the target server.
Optionally, the target test index is cluster validity, and the sending, according to a pre-configured test instruction sending mechanism in the target test data packet, a test instruction related to the target test index to the big data platform, so as to perform a test on the big data platform about the target test index, includes:
sending a server closing instruction to a target server in the big data platform; after a preset time interval, a server starting instruction is sent to the management equipment of the target server so as to carry out suspension test on the big data platform.
Optionally, the performance parameters include: at least one of transaction number per second, resource utilization rate, error reporting condition and server closing time consumption of the big data platform in the closing process of the target server; and at least one of transaction number per second, resource utilization rate, error reporting condition and time consumption for opening the server of the big data platform in the opening process of the target server.
Optionally, the target test indicator is service process self-pulling validity, and the sending, according to a pre-configured test instruction sending mechanism in the target test data packet, a test instruction related to the target test indicator to the big data platform, so as to perform a test on the big data platform about the target test indicator, includes:
and sending a process ending instruction to a target server in the big data platform so as to perform service process self-pulling effectiveness test on the big data platform.
Optionally, the performance parameters include: at least one of transactions per second, resource utilization, process end time consumption, and self-start condition of the process for the big data platform.
Optionally, the target test indicator is timeout validity, and the sending, according to a pre-configured test instruction sending mechanism in the target test data packet, a test instruction related to the target test indicator to the big data platform, so as to perform a test on the big data platform about the target test indicator, includes:
sending an instruction that the overtime configuration is smaller than the baffle delay time to the big data platform; and after a preset time interval, sending a timeout configuration recovery instruction to the big data platform so as to perform timeout validity test on the big data platform.
Optionally, the performance parameters include: the overtime configuration is smaller than the baffle delay time, and at least one of the transaction number per second, the resource utilization rate and the error reporting condition of the big data platform is realized; and under the condition that the overtime configuration is not smaller than the baffle delay time, at least one of the transaction number per second, the resource utilization rate and the error reporting condition of the big data platform.
Optionally, the method further comprises:
and summarizing the performance parameters to generate a test report.
In a second aspect, an embodiment of the present application provides a testing apparatus for a big data platform, including:
the acquisition module is used for acquiring a target test index, wherein the target test index is one of one-key start-stop effectiveness, cluster effectiveness, service process self-pulling effectiveness and overtime effectiveness;
and the processing module is used for calling a pressure testing tool and a monitoring tool to test the big data platform according to the target testing index to obtain the performance parameters of the big data platform.
In a third aspect, an embodiment of the present application provides a compression server, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor implements the testing method of the big data platform according to the first aspect when executing the program.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method for testing a big data platform as described in the first aspect above.
In a fifth aspect, an embodiment of the present application provides a testing system for a big data platform, including: a big data platform and a compression server as described in the third aspect above.
According to the testing method, the device, the equipment, the storage medium and the system for the big data platform, the target testing index is one of one-key start-stop effectiveness, cluster effectiveness, service process self-pull-up effectiveness and overtime effectiveness, and according to the target testing index, the pressure testing tool and the monitoring tool are called to test the big data platform to obtain the performance parameters of the big data platform, so that automatic testing of the big data platform is achieved, testing accuracy and testing efficiency of the big data platform are improved, and reliability and usability of the big data platform are improved.
Drawings
Fig. 1 is a flow chart of a testing method of a big data platform according to an embodiment of the present application;
Fig. 2 is a schematic structural diagram of a testing device of a big data platform according to a second embodiment of the present application;
fig. 3 is a schematic structural diagram of a compression server according to a third embodiment of the present application;
fig. 4 is a schematic structural diagram of a testing system of a big data platform according to a fourth embodiment of the present application;
fig. 5 is a structural view showing the structure of a test system of a kylin big data platform according to the fourth embodiment of the present application.
Detailed Description
The present application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the application and not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present application are shown in the drawings.
The main idea of the technical scheme of the application is as follows: based on the technical problems existing in the prior art, the embodiment of the application provides an automatic testing scheme of a big data platform, a special data packet for automatic testing of the big data platform is independently researched and developed based on a pressure testing tool, the thread group numbers for different testing indexes are preset to simulate the concurrent numbers of users, a monitor is preset in an application server of the big data platform to detect the performance parameters corresponding to the relevant testing indexes, and the evaluation of the relevant performance indexes of the big data platform is realized by analyzing the obtained performance parameters, so that the testing efficiency and the testing accuracy of the big data platform are improved, the reliability and the usability of data processing of the big data platform are improved, the big data platform is optimized and maintained, and the benign operation of the big data platform is ensured.
Example 1
Fig. 1 is a flow chart of a testing method for a big data platform according to an embodiment of the present application, where the method of the embodiment may be performed by a testing device for a big data platform according to an embodiment of the present application, and the device may be implemented by software and/or hardware, and may be integrated in a compression server such as a server and an intelligent terminal, where the compression server is used as a compression machine, and a compression testing tool is pre-installed on the compression server. As shown in fig. 1, the testing method of the big data platform of the present embodiment includes:
s101, acquiring target test indexes.
In this step, the target test index is obtained according to a selection operation or an input operation of the user on the man-machine interface. The target test index is an index to be tested in the test and is a test index selected from candidate test indexes.
In order to realize non-functional test of a large data platform, in this embodiment, the one-key start-stop effectiveness, the cluster effectiveness, the service process self-pull-up effectiveness and the overtime effectiveness are used as candidate test indexes, and test data packets matched with each candidate test index are configured in a pressure test tool in advance so as to be selected and used when different candidate test indexes are tested. Accordingly, the target test index in this embodiment is any one of one-key start-stop validity, cluster validity, service process self-pull-up validity and timeout validity.
In this step, the user may input identification information such as a name, a number, or an icon of the test index in the man-machine interaction interface, and the pressure generating server identifies the target test index by identifying the identification information input by the user, so as to obtain the target test index.
S102, according to the target test index, a pressure test tool and a monitoring tool are called to test the big data platform, and the performance parameters of the big data platform are obtained.
In the step, based on the target test index determined in the step S101, a target test data packet which is preset in the pressure test tool and matched with the target test index is called to simulate the pressure, the big data platform is tested, meanwhile, a monitoring tool which is preset in the big data platform is called to monitor the running condition of the big data platform in the test process, and the performance parameters of the big data platform are obtained.
Different test data packets aiming at different candidate test indexes are preconfigured in the pressure test tool, and are used for implementing different tests aiming at the different candidate test indexes, wherein the test data packets comprise parameters required by testing each test index, such as simulated user concurrency number, performance parameters required to be monitored and the like.
Alternatively, the pressure testing tool used in this embodiment is a Jmeter tool. The Jmeter tool is a Java-based open source pressure testing tool developed by the Apache organization, which allows a developer to develop different test packets in advance.
The monitoring tool may be various components with data monitoring functions, such as probes or monitors, and it can be understood that the pressure generating server in this embodiment has the capability of calling the monitoring tool and controlling the monitoring tool to perform data collection.
The target test data packet is a test data packet matched with the target test index in the pressure test tool, and the target test data packet contains data required for testing the target test index.
The big data platform is a server cluster formed by a plurality of application servers and is used for carrying out a series of processing operations such as analysis, storage, calculation and the like on batch or massive data according to the needs.
In one possible implementation manner, in this embodiment, the number of concurrent threads preset according to the target test data packet simulates the number of users, a data processing request is sent to the big data platform, and in the process that the big data platform performs data processing according to the received data processing request, a test instruction related to the target test index is sent to the big data platform, so that the big data platform is tested with respect to the target test index.
The thread group number is used for simulating the concurrent number of users and controlling the number of data processing requests sent to the big data platform. In this embodiment, since the big data platform is mainly used for processing batch or massive data, in order to restore the data processing process of the big data platform more truly, a certain number of thread groups are preset in the test data packet, the concurrency number of users is simulated, and a data processing request with the same number as the concurrency number of users is sent to the big data platform, so that the big data platform enters the big data processing flow according to the data processing request, and preparation is made for implementing subsequent tests. It will be appreciated that the data processing request may be a data query request or a data construction request, without limitation.
Because the requirements on the big data platform are different when testing different test indexes, the target test data packet in the embodiment also comprises a test instruction sending mechanism related to the target test indexes, such as the type of the sent test instruction, the sending time of the test instruction and the like, after the big data platform enters the big data processing flow, the test instruction related to the target test indexes is sent to the big data platform according to the test instruction sending mechanism pre-configured in the target test data packet so as to control the work of an application server in the big data platform, thereby realizing one-key start-stop validity test, cluster validity test, service process self-pull validity test or overtime validity test on the big data platform. The test principle and the test instruction sending mechanism of different test indexes are described below:
One-touch start-stop validity test
The one-key start-stop effectiveness test is mainly used for testing the data processing performance of a big data platform when a process on an application server in the big data platform is stopped and started after the stopping.
The key start-stop effectiveness test principle is as follows: when a process end instruction (called a fault node) is received on an application server, the transaction sent to the node fails, the response time becomes long, and the transactions of other nodes in the cluster are not affected, so that the total transaction number per second (transaction per second, TPS) of the large data platform is reduced, after a period of time (such as 2 minutes), the other cluster nodes take over the fault node, and the transaction response time and the total TPS are recovered to be normal. When the fault node receives the process recovery instruction, the transaction is transferred back to the recovered node, and the big data platform is recovered to be normal.
In one possible implementation, the process end instruction is sent to a target server in the big data platform; after a preset time interval, a process recovery instruction is sent to the target server, and performance parameters of the big data platform in the process of ending the process of the target server and in the process of recovering the process of the target server are respectively monitored through monitoring tools arranged in all nodes, so that one-key start-stop effectiveness test of the big data platform is realized.
The target server is any one server in the big data platform, and the performance test of the one-key start-stop effectiveness of the whole big data platform is completed by respectively selecting one server from different types of servers as the target server and sequentially executing one-key start-stop effectiveness tests on different target servers.
The preset duration can be set in advance according to the transaction response time and the time required by the total TPS to recover to normal when the target server process is finished.
Optionally, during the process ending of the target server, the performance parameter monitored by the listening tool may be one or more of a transaction per second (total TPS of each node), a resource utilization (such as CPU utilization of each node), and a time consumed for process ending (a time consumed by the target server from receiving the process ending instruction to actually completing the process ending) of the large data platform.
Optionally, during the process recovery of the target server, the performance parameter monitored by the listening tool may be one or more of a transaction per second (total TPS of each node), a resource utilization (such as CPU utilization of each node), and a process recovery time (a time taken for the target server to actually complete the process recovery from receiving the process recovery instruction).
(II) Cluster validity test
The cluster validity test is mainly used for testing the data processing capacity of a big data platform when a process on one application server in the big data platform is suspended and suspended to be recovered or when one application server stops working and recovers after stopping. In this embodiment, the cluster validity may be tested by applying a suspension test or a downtime test.
(1) Application suspension test
The test principle of the application suspension test is as follows: when a certain application server receives a process suspension instruction (the application server is called a fault node), the transaction sent to the node fails, the response time is prolonged, the total TPS of a large data platform is reduced, and other node transactions of the cluster are not affected; after a period of time (e.g., 1 minute), the other nodes take over the failed node and the transaction resumes. When the fault node receives the recovery suspension instruction, the transaction is transferred back to the recovered node, and the big data platform is recovered to be normal.
In one possible implementation, the process suspension instruction is sent to a target server in the big data platform; after a preset time interval, sending a resume suspension instruction to a target server in the big data platform, and respectively monitoring performance parameters of the big data platform in the process of suspending the process of the target server and in the process of resuming the process of the target server by a monitoring tool arranged in each node to realize application suspension test of the big data platform.
The concept of the target server is the same as that of the target server in the one-key start-stop validity, and will not be described here again.
The preset duration can be set in advance according to the time required for the transaction to resume after the target server process is suspended.
Optionally, during the process suspension of the target server, the performance parameter monitored by the listening tool may be one or more of a transaction per second (total TPS of each node) of the large data platform, a resource utilization (such as CPU utilization of each node), an error reporting condition (such as the number of errors in each node, a time period when an error occurs, etc.), and a time consumed for suspending the process (a time taken by the target server from receiving the process suspension instruction to actually completing the process suspension).
Optionally, during the process recovery suspension of the target server, the performance parameter monitored by the listening tool may be one or more of a transaction per second (total TPS of each node) of the large data platform, a resource utilization (e.g., CPU utilization of each node), an error reporting condition (e.g., a number of errors in each node, a time period when an error occurs, etc.), and a suspension recovery time (a time taken for the target server to actually complete suspension recovery from receiving a suspension recovery instruction).
(2) Downtime test
The test principle of the downtime test is as follows: when a certain application server receives a server closing instruction (the application server is called as a fault node), all transactions sent to the node fail, the total TPS value is reduced, and other node transactions of the cluster are not affected; after a period of time (e.g., 1 minute), the transaction is transferred to the other node and the transaction resumes. When the management equipment of the fault node receives the server start instruction, the management equipment is opened by the closed node server, the transaction is transferred back to the recovery node, and the system is recovered to be normal.
In one possible implementation, a server shutdown instruction is sent to a target server in a big data platform; after a preset time interval, a server opening instruction is sent to management equipment of the target server, and performance parameters of the large data platform in the closing process and the opening process of the target server are respectively monitored through monitoring tools arranged in all nodes, so that downtime test of the large data platform is realized.
The concept of the target server is the same as that of the target server in the one-key start-stop validity, and will not be described here again.
The preset duration can be set in advance according to the time required for the transaction to resume after the target server is closed.
Optionally, during the shutdown of the target server, the performance parameter monitored by the listening tool may be one or more of a transaction per second (total TPS of each node) of the large data platform, a resource utilization (such as CPU usage of each node), an error reporting condition (such as the number of errors reported in each node, a time period when an error reporting occurs, etc.), and a time consuming (a time consumed by the target server from receiving the server shutdown instruction to actually completing the shutdown) of the server.
Optionally, during the target server opening process, the performance parameter monitored by the listening tool may be one or more of a transaction per second (total TPS of each node) of the large data platform, a resource utilization (such as CPU usage of each node), an error reporting condition (such as the number of errors in each node, a time period when an error occurs, etc.), and a time consuming for opening the server (a time consumed from receiving a server opening instruction from a management device of the target server to completing opening the server).
It can be understood that the management device of the target server is a superior server of the target server, is a server for managing the target server, is also provided with a monitoring tool, and can control the closing or opening of the target server according to the received closing or opening instruction of the target server.
(III) service process self-pull validity test
The service process self-pulling effectiveness test is mainly used for testing the data processing performance and self-pulling capacity of a big data platform when the process on one application server in the big data platform is finished.
The testing principle of the service process self-pull effectiveness test is as follows: when an application server receives a process end instruction (the application server is called a fault node), whether the process of the fault node is automatically pulled up (namely, automatically recovered) or not, and when the process is automatically pulled up and consumed (the time required from the actual end of the process to the automatic recovery of the process) are observed, the error rate, the processing capacity, the response time, the resource use condition and the like of each node in a big data platform are observed.
In one possible implementation manner, by sending a process ending instruction to a target server in the big data platform, monitoring performance parameters of the big data platform through monitoring tools arranged in all nodes, and realizing the service process self-pulling effectiveness test of the big data platform.
The concept of the target server is the same as that of the target server in the one-key start-stop validity, and will not be described here again.
Optionally, the performance parameters in this embodiment include one or more of transactions per second (total TPS of each node), resource utilization (e.g., CPU utilization of each node), process end time (time taken by the target server from receiving the process end instruction to actually completing the process end), and self-start condition of the process (whether self-pulled or self-pulled time consuming).
(IV) super time validity test
The super-time validity test is mainly used for testing the data processing performance of the big data platform when a certain application server in the big data platform is overtime and overtime recovery occurs.
The test principle of the timeout validity test is as follows: after the big data platform stably operates for a period of time, modifying that the timeout duration of a certain application server is smaller than the baffle delay time (the application server is called as a fault node), refreshing to be effective, making a transaction error, displaying the timeout of log information, and observing the error rate, processing capacity, response time, resource use condition and the like of each node in each big data platform. After the operation is carried out for a period of time, the time-out time of the fault node is modified to be longer than or equal to the baffle delay time, refreshing is effective, the transaction execution is normal, and no new time-out error occurs in the log; and the scene continues to stably run for a period of time, and the test is ended.
In one possible implementation, the method includes sending an instruction to the big data platform that a timeout period is less than a baffle delay time; after a preset time interval, sending an overtime time recovery instruction to the big data platform, and respectively monitoring performance parameters of the target server under the condition that the overtime time of the target server is smaller than the baffle delay time and the overtime time of the target server is not smaller than the baffle delay time by a monitoring tool arranged in each node so as to realize the overtime effectiveness test of the big data platform.
The concept of the target server is the same as that of the target server in the one-key start-stop validity, and will not be described here again.
The preset time length can be determined according to the time required by the stable operation of the big data platform after being modified in advance according to the timeout time length.
It can be understood that in this embodiment, the timeout validity test for the big data platform is implemented by modifying the timeout configuration of the target server, that is, modifying that the timeout duration of the target server is smaller than the baffle delay time, so that the target server generates timeout.
Optionally, the performance parameter monitored by the monitoring tool may be one or more of transaction per second (total TPS of each node), resource utilization (e.g. CPU utilization of each node), and error reporting (e.g. number of errors in each node, time period when an error occurs, etc.) of the large data platform when the timeout period is less than or not less than the baffle delay time.
Optionally, after S102, the method of the present embodiment further includes: the acquired performance parameters are summarized, the acquired performance parameters are compared with the standard performance parameters, a test report is generated, and the test report is visually displayed, so that a tester can check test results conveniently.
In this embodiment, the target test index is one of one-key start-stop validity, cluster validity, service process self-pull validity and timeout validity, and according to the target test index, the pressure test tool and the monitoring tool are called to test the big data platform to obtain the performance parameters of the big data platform, so that the automatic test of the big data platform is realized, the test accuracy and the test efficiency of the big data platform are improved, and the reliability and the availability of the big data platform are improved.
Example two
Fig. 2 is a schematic structural diagram of a testing apparatus for a big data platform according to a second embodiment of the present application, and as shown in fig. 2, a testing apparatus 10 for a big data platform according to the present embodiment includes:
an acquisition module 11 and a processing module 12.
The acquiring module 11 is configured to acquire a target test indicator, where the target test indicator is one of one-key start-stop validity, cluster validity, service process self-pulling validity and timeout validity;
and the processing module 12 is used for calling a pressure testing tool and a monitoring tool to test the big data platform according to the target test index so as to obtain the performance parameters of the big data platform.
Optionally, the processing module 12 is specifically configured to:
invoking a target test data packet which is pre-configured in the pressure test tool and matches with the target test index to simulate and send pressure, and testing the big data platform;
and calling a monitoring tool preset in the big data platform to monitor the running condition of the big data platform in the testing process, so as to obtain the performance parameters of the big data platform.
Optionally, the processing module 12 is specifically configured to:
according to the number of the thread groups pre-configured in the target test data packet, sending a data processing request to the big data platform;
and in the process that the big data platform executes data processing according to the data processing request, according to a pre-configured test instruction sending mechanism in the target test data packet, sending a test instruction related to the target test index to the big data platform so as to test the big data platform about the target test index.
Optionally, the target test indicator is a one-key start-stop validity, and the processing module 12 is specifically configured to:
sending a process ending instruction to a target server in the big data platform; and after a preset time interval, sending a process recovery instruction to the target server so as to perform one-key start-stop effectiveness test on the big data platform.
Optionally, the performance parameters include: at least one of transaction number per second, resource utilization rate and process ending time consumption of the big data platform in the process ending process of the target server; and at least one of transaction per second, resource utilization and process recovery time consumption of the big data platform in the process recovery process of the target server.
Optionally, the target test index is cluster validity, and the processing module 12 is specifically configured to:
sending a process suspension instruction to a target server in the big data platform; and after a preset time interval, sending a resume suspension instruction to a target server in the big data platform so as to carry out suspension test on the big data platform.
Optionally, the performance parameters include: at least one of transaction number per second, resource utilization rate, error reporting condition and process suspending time of the big data platform in the process suspending process of the target server; and at least one of transaction per second, resource utilization, error reporting condition and suspension recovery time of the big data platform in the process of recovering and suspending the process of the target server.
Optionally, the target test index is cluster validity, and the processing module 12 is specifically configured to:
Sending a server closing instruction to a target server in the big data platform; after a preset time interval, a server starting instruction is sent to the management equipment of the target server so as to carry out suspension test on the big data platform.
Optionally, the performance parameters include: at least one of transaction number per second, resource utilization rate, error reporting condition and server closing time consumption of the big data platform in the closing process of the target server; and at least one of transaction number per second, resource utilization rate, error reporting condition and time consumption for opening the server of the big data platform in the opening process of the target server.
Optionally, the target test indicator is service process self-pulling validity, and the processing module 12 is specifically configured to:
and sending a process ending instruction to a target server in the big data platform so as to perform service process self-pulling effectiveness test on the big data platform.
Optionally, the performance parameters include: at least one of transactions per second, resource utilization, process end time consumption, and self-start condition of the process for the big data platform.
Optionally, the target test index is timeout validity, and the processing module 12 is specifically configured to:
Sending an instruction that the overtime configuration is smaller than the baffle delay time to the big data platform; and after a preset time interval, sending a timeout configuration recovery instruction to the big data platform so as to perform timeout validity test on the big data platform.
Optionally, the performance parameters include: the overtime configuration is smaller than the baffle delay time, and at least one of the transaction number per second, the resource utilization rate and the error reporting condition of the big data platform is realized; and under the condition that the overtime configuration is not smaller than the baffle delay time, at least one of the transaction number per second, the resource utilization rate and the error reporting condition of the big data platform.
Optionally, the processing module 12 is further configured to:
and summarizing the performance parameters to generate a test report.
The testing device for the big data platform provided by the embodiment can execute the testing method for the big data platform provided by the method embodiment, and has the corresponding functional modules and beneficial effects of the executing method. The implementation principle and technical effect of the present embodiment are similar to those of the above method embodiment, and are not described here again.
Example III
Fig. 3 is a schematic structural diagram of a compression server according to a third embodiment of the present application, and as shown in fig. 3, the compression server 20 includes a memory 21, a processor 22, and a computer program stored in the memory and capable of running on the processor; the number of processors 22 in the compression server 20 may be one or more, one processor 22 being taken as an example in fig. 3; the processor 22, the memory 21 in the compression server 20 may be connected by a bus or other means, for example in fig. 3.
The memory 21 is a computer readable storage medium that can be used to store a software program, a computer executable program, and modules, such as program instructions/modules corresponding to the acquisition module 11 and the processing module 12 in the embodiment of the present application. The processor 22 executes software programs, instructions and modules stored in the memory 21 to thereby generate various functional applications and data processing of the server, i.e., to implement the above-described testing method of the large data platform.
The memory 21 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for functions; the storage data area may store data created according to the use of the terminal, etc. In addition, memory 21 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some examples, memory 21 may further include memory remotely located with respect to processor 22, which may be connected to the compression server through a grid. Examples of such grids include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Example IV
Fig. 4 is a schematic structural diagram of a testing system for a big data platform according to the fourth embodiment of the present application, and as shown in fig. 4, a testing system 30 for a big data platform according to the present embodiment includes a big data platform 31 and a compression server 20 as described in embodiment three.
The big data platform 31 is composed of a plurality of application servers, and is used for processing, cleaning, storing, analyzing and the like of batch or massive data according to the needs. The application server in the big data platform 31 is provided with listening tools.
Taking the big data platform 31 as an example, fig. 5 is a structural diagram showing the opinion of the test system of the big data platform according to the fourth embodiment of the present application, and as shown in fig. 5, the big data platform includes a kylin query server cluster and a kylin construction server cluster, where the kylin query server cluster is composed of a plurality of kylin query servers, and the kylin construction server cluster is composed of a plurality of kylin construction servers.
Optionally, the test system for the kylin big data platform can further comprise: the system comprises an index query server cluster, an online application server cluster, an asynchronous application server cluster, a database and a hosting server cluster, wherein the compression server cluster (comprising a plurality of compression servers, each compression server is provided with a compression testing tool) is respectively connected with the index query server cluster and the online application server cluster, the index query server cluster is connected with a kylin query server cluster, the online application server cluster is connected with an asynchronous application server cluster, the asynchronous application server cluster is connected with a kylin construction server cluster, the kylin construction server cluster is connected with the hosting server cluster, and the database is respectively connected with the index query server cluster, the online application server cluster and the asynchronous application server cluster.
The index query server cluster is used for processing front-end data query service.
And the database is used for storing the index definition data. The database may be an Oracle or Neo4j database.
The kylin big data platform and the managed server cluster are used for preprocessing index data, constructing CUBE and storing the preprocessed index data so as to improve query performance. The hosting server cluster may be a Hadoop server cluster.
And the asynchronous application server cluster is used for triggering index construction.
And the online application server cluster is used for processing the index definition query service.
Specifically, in the process of testing the kylin big data platform, the process of performing data processing by the pressure initiation and the kylin big data platform can be divided into two scenarios:
(1) The method comprises the steps that a usable compression server is selected as a compression machine by the compression server cluster, a compression testing tool is called to send a CUBE query request (data query request) to the index query server cluster, the index query server cluster processes the request, index rules corresponding to the request are searched, a corresponding structured query language (structured query language, SQL) query statement is constructed according to the searched index rules and sent to the kylin query server cluster, the kylin query server cluster analyzes SQL, and the corresponding query operation is carried out by matching the CUBE.
(2) The method comprises the steps that a usable pressure server is selected as a pressure generator in the pressure server cluster, a pressure testing tool is called to send a CUBE construction request (data construction request) to the online application server cluster, the online application server cluster forwards the request to the asynchronous application server cluster, the asynchronous application server cluster calls related application program interfaces (application programming interface, APIs), an instruction is sent to the kylin construction server cluster, the kylin construction server cluster processes the received request, a CUBE construction instruction is generated, and the CUBE construction instruction is sent to the hosting server cluster to execute a CUBE construction flow.
In the test system of the big data platform, the compression server can send the data processing request to the big data platform in an indirect mode.
Example five
A fifth embodiment of the present application also provides a computer-readable storage medium having stored thereon a computer program for performing a method of testing a large data platform when executed by a computer processor, the method comprising:
acquiring a target test index, wherein the target test index is one of one-key start-stop effectiveness, cluster effectiveness, service process self-pull-up effectiveness and overtime effectiveness;
And according to the target test index, a pressure test tool and a monitoring tool are called to test the big data platform, so that the performance parameters of the big data platform are obtained.
Of course, the computer program of the computer readable storage medium provided in the embodiments of the present application is not limited to the method operations described above, but may also perform related operations in the testing method of the large data platform provided in any embodiment of the present application.
From the above description of embodiments, it will be clear to a person skilled in the art that the present application may be implemented by means of software and necessary general purpose hardware, but of course also by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a FLASH Memory (FLASH), a hard disk, or an optical disk of a computer, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a grid device, etc.) to perform the method described in the embodiments of the present application.
It should be noted that, in the embodiment of the testing device of the big data platform, each unit and module included are only divided according to the functional logic, but not limited to the above-mentioned division, so long as the corresponding functions can be implemented; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present application.
Note that the above is only a preferred embodiment of the present application and the technical principle applied. Those skilled in the art will appreciate that the present application is not limited to the particular embodiments described herein, but is capable of numerous obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the present application. Therefore, while the present application has been described in connection with the above embodiments, the present application is not limited to the above embodiments, but may include many other equivalent embodiments without departing from the spirit of the present application, the scope of which is defined by the scope of the appended claims.
Claims (15)
1. A method for testing a big data platform, comprising:
acquiring a target test index, wherein the target test index is one of one-key start-stop effectiveness, cluster effectiveness, service process self-pull-up effectiveness and overtime effectiveness;
According to the target test index, a pressure test tool and a monitoring tool are called to test the big data platform, so that performance parameters of the big data platform are obtained;
the calling pressure testing tool and the monitoring tool test the big data platform to obtain the performance parameters of the big data platform, and the calling pressure testing tool and the monitoring tool comprise:
invoking a target test data packet which is pre-configured in the pressure test tool and matches with the target test index to simulate and send pressure, and testing the big data platform;
calling a monitoring tool preset in the big data platform, and monitoring the running condition of the big data platform in the testing process to obtain the performance parameters of the big data platform;
and invoking a target test data packet which is pre-configured in the pressure test tool and matches with the target test index to perform simulation pressure generation, and testing the big data platform, wherein the method comprises the following steps:
according to the number of the thread groups pre-configured in the target test data packet, sending a data processing request to the big data platform;
in the process that the big data platform executes data processing according to the data processing request, according to a pre-configured test instruction sending mechanism in the target test data packet, sending a test instruction related to the target test index to the big data platform so as to test the big data platform about the target test index;
The target test index is a one-key start-stop validity, and the sending, according to a pre-configured test instruction sending mechanism in the target test data packet, a test instruction related to the target test index to the big data platform so as to test the big data platform with respect to the target test index includes:
sending a process ending instruction to a target server in the big data platform; and after a preset time interval, sending a process recovery instruction to the target server so as to perform one-key start-stop effectiveness test on the big data platform.
2. The method of claim 1, wherein the performance parameter comprises, when the target test indicator is a one-touch start-stop validity: at least one of transaction number per second, resource utilization rate and process ending time consumption of the big data platform in the process ending process of the target server; and at least one of transaction per second, resource utilization and process recovery time consumption of the big data platform in the process recovery process of the target server.
3. The method according to claim 1, wherein the target test indicator is cluster validity, and the sending, according to a pre-configured test instruction sending mechanism in the target test data packet, a test instruction related to the target test indicator to the big data platform to perform a test on the big data platform with respect to the target test indicator includes:
Sending a process suspension instruction to a target server in the big data platform; and after a preset time interval, sending a resume suspension instruction to a target server in the big data platform so as to carry out suspension test on the big data platform.
4. The method of claim 3, wherein when the target test indicator is cluster availability, the performance parameters include: at least one of transaction number per second, resource utilization rate, error reporting condition and process suspending time of the big data platform in the process suspending process of the target server; and at least one of transaction per second, resource utilization, error reporting condition and suspension recovery time of the big data platform in the process of recovering and suspending the process of the target server.
5. The method according to claim 1, wherein the target test indicator is cluster validity, and the sending, according to a pre-configured test instruction sending mechanism in the target test data packet, a test instruction related to the target test indicator to the big data platform to perform a test on the big data platform with respect to the target test indicator includes:
Sending a server closing instruction to a target server in the big data platform; after a preset time interval, a server starting instruction is sent to the management equipment of the target server so as to carry out suspension test on the big data platform.
6. The method of claim 5, wherein when the target test indicator is cluster validity, the performance parameters include: at least one of transaction number per second, resource utilization rate, error reporting condition and server closing time consumption of the big data platform in the closing process of the target server; and at least one of transaction number per second, resource utilization rate, error reporting condition and time consumption for opening the server of the big data platform in the opening process of the target server.
7. The method according to claim 1, wherein the target test indicator is a service process self-pulling validity, and the sending, according to a pre-configured test instruction sending mechanism in the target test data packet, a test instruction related to the target test indicator to the big data platform to perform a test on the big data platform with respect to the target test indicator includes:
And sending a process ending instruction to a target server in the big data platform so as to perform service process self-pulling effectiveness test on the big data platform.
8. The method of claim 7, wherein the performance parameters when the target test indicator is service process self-pull availability include: at least one of transactions per second, resource utilization, process end time consumption, and self-start condition of the process for the big data platform.
9. The method according to claim 1, wherein the target test indicator is a timeout validity, and the sending, according to a pre-configured test instruction sending mechanism in the target test data packet, a test instruction related to the target test indicator to the big data platform to perform a test on the big data platform with respect to the target test indicator includes:
sending an instruction with the timeout duration smaller than the baffle delay time to the big data platform; and after a preset time interval, sending a timeout time recovery instruction to the big data platform so as to perform the timeout effectiveness test on the big data platform.
10. The method of claim 9, wherein the performance parameter comprises, when the target test indicator is validity over time: the overtime time is smaller than the baffle delay time, and at least one of the transaction number per second, the resource utilization rate and the error reporting condition of the big data platform is realized; and the overtime time is not smaller than the baffle delay time, and at least one of the transaction number per second, the resource utilization rate and the error reporting condition of the big data platform is realized.
11. The method according to any one of claims 1-10, further comprising:
and summarizing the performance parameters to generate a test report.
12. A test device for a big data platform, comprising:
the acquisition module is used for acquiring a target test index, wherein the target test index is one of one-key start-stop effectiveness, cluster effectiveness, service process self-pulling effectiveness and overtime effectiveness;
the processing module is used for calling a pressure testing tool and a monitoring tool to test the big data platform according to the target testing index to obtain the performance parameters of the big data platform;
the processing module is specifically configured to:
invoking a target test data packet which is pre-configured in the pressure test tool and matches with the target test index to simulate and send pressure, and testing the big data platform;
calling a monitoring tool preset in the big data platform, and monitoring the running condition of the big data platform in the testing process to obtain the performance parameters of the big data platform;
the processing module is specifically configured to:
according to the number of the thread groups pre-configured in the target test data packet, sending a data processing request to the big data platform;
In the process that the big data platform executes data processing according to the data processing request, according to a pre-configured test instruction sending mechanism in the target test data packet, sending a test instruction related to the target test index to the big data platform so as to test the big data platform about the target test index;
the target test index is one-key start-stop effectiveness, and the processing module is specifically used for:
sending a process ending instruction to a target server in the big data platform; and after a preset time interval, sending a process recovery instruction to the target server so as to perform one-key start-stop effectiveness test on the big data platform.
13. A compression server comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements a method of testing a big data platform according to any of claims 1-11 when executing the program.
14. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements a method for testing a big data platform according to any of the claims 1-11.
15. A test system for a large data platform, comprising: a big data platform and the streaming server of claim 13.
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