CN113010392A - Testing method, device, equipment, storage medium and system for big data platform - Google Patents
Testing method, device, equipment, storage medium and system for big data platform Download PDFInfo
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Abstract
The embodiment of the application provides a test method, a test device, equipment, a storage medium and a test system for a big data platform, wherein a target test index is obtained, the target test index is one of one-key start-stop validity, cluster validity, service process self-pull validity and overtime validity, and according to the target test index, a pressure test tool and a monitoring tool are called to test the big data platform to obtain performance parameters of the big data platform, so that the automatic test on 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 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 method, a device, equipment, a storage medium and a system for testing a big data platform.
Background
The realization of big data technology can not be separated from a big data platform, the big data platform is generally formed by a server cluster, has the functions of collecting, processing, storing, mining and the like of large-scale data, and can mine information and knowledge hidden in the large-scale data, thereby being applied to the aspects of human life. Therefore, it is necessary to perform non-functional tests on the big data platform in order to ensure the effective operation of the big data platform.
In the prior art, a manual testing mode is adopted to perform non-functional testing on a big data platform, so that the problems of low testing efficiency and high error rate exist.
Disclosure of Invention
The embodiment of the application provides a testing method, a testing device, equipment, a storage medium and a testing system for a big data platform, and aims 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 validity, cluster validity, service process self-pull-up validity and overtime validity;
and 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.
Optionally, the invoking a pressure testing tool and a monitoring tool to test the big data platform to obtain the performance parameters of the big data platform includes:
calling a target test data packet which is configured in advance in the pressure test tool and matched with the target test index to perform simulated pressure sending, and testing the big data platform;
and calling a monitoring tool preset in the big data platform, and monitoring the running state of the big data platform in the test process to obtain the performance parameters of the big data platform.
Optionally, the invoking a target test data packet configured in advance in the pressure test tool and matched with the target test index to perform simulated pressure sending to test the big data platform includes:
sending a data processing request to the big data platform according to the thread group number configured in advance in the target test data packet;
and in the process that the big data platform executes data processing according to the data processing request, sending a test instruction related to the target test index to the big data platform according to a test instruction sending mechanism configured in advance in the target test data packet so as to test the big data platform about the target test index.
Optionally, the target test indicator is one-key start-stop validity, and the sending of the test instruction related to the target test indicator to the big data platform according to a test instruction sending mechanism pre-configured in the target test data packet so as to test the big data platform about the target test indicator includes:
sending a process ending instruction to a target server in the big data platform; and after the interval preset time length, sending a process recovery instruction to the target server so as to carry out one-key start-stop validity test on the big data platform.
Optionally, the performance parameters include: at least one of the transaction number per second, the resource utilization rate and the process ending time consumption of the big data platform in the process ending process of the target server; and at least one of the number of transactions per second, the resource utilization rate and the process recovery time consumption of the big data platform in the process recovery process of the target server.
Optionally, the target test indicator is cluster validity, and the sending, according to a test instruction sending mechanism configured in advance in the target test data packet, a test instruction related to the target test indicator to the big data platform so as to test the big data platform about 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 suspension recovery 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 the number of transactions per second, the resource utilization rate, the error reporting condition and the process suspension time consumption of the big data platform in the process of suspending the target server process; and at least one of the number of transactions per second, the resource utilization rate, the error reporting condition and the time consumed for the suspension recovery of the big data platform in the process of suspending the process recovery of the target server.
Optionally, the target test indicator is cluster validity, and the sending, according to a test instruction sending mechanism configured in advance in the target test data packet, a test instruction related to the target test indicator to the big data platform so as to test the big data platform about the target test indicator includes:
sending a server closing instruction to a target server in the big data platform; and after the preset time interval, sending a server starting instruction 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 the number of transactions per second, the resource utilization rate, the error reporting condition and the time consumed for closing the server of the big data platform in the closing process of the target server; and at least one of the number of transactions per second, the resource utilization rate, the error reporting condition and the time consumed for starting the server of the big data platform in the starting process of the target server.
Optionally, the target test indicator is service process self-pull validity, and the sending of the test instruction related to the target test indicator to the big data platform according to a test instruction sending mechanism pre-configured in the target test data packet so as to test 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 carry out self-pull validity test on the service process of the big data platform.
Optionally, the performance parameters include: at least one of the number of transactions per second, the resource utilization rate, the process end time consumption and the process self-starting condition of the big data platform.
Optionally, the sending, by the target test indicator being timeout validity, a test instruction related to the target test indicator to the big data platform according to a test instruction sending mechanism pre-configured in the target test data packet, so as to test 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 the interval preset time length, sending an overtime configuration recovery instruction to the big data platform so as to test the overtime effectiveness of the big data platform.
Optionally, the performance parameters include: when the overtime configuration is smaller than the baffle delay time, at least one of the number of transactions per second, the resource utilization rate and the error reporting condition of the big data platform; and under the condition that the overtime configuration is not less 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 system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring a target test index, and the target test index is one of one-key start-stop validity, cluster validity, service process self-pull-up validity and overtime validity;
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 voltage sending server, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the method for testing a big data platform according to the first aspect when executing the program.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the 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 test system for a big data platform, including: a big data platform and a sending and pressing server as described in the third aspect above.
According to the testing method, the testing device, the testing equipment, the testing medium and the testing system for the big data platform, the target testing index is one of one-key start-stop validity, cluster validity, service process self-pull validity and overtime validity, and according to the target testing index, a pressure testing tool and a monitoring tool are called to test the big data platform to obtain the performance parameters of the big data platform, so that the 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.
Drawings
Fig. 1 is a schematic flowchart of a testing method for a big data platform according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a testing apparatus of a big data platform according to a second embodiment of the present application;
fig. 3 is a schematic structural diagram of a voltage generating server according to a third embodiment of the present application;
fig. 4 is a schematic structural diagram of a test system of a big data platform according to a fourth embodiment of the present application;
fig. 5 is a schematic structural diagram of a test system of the kylin big data platform according to the fourth embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be further noted that, for the convenience of description, only some of the structures related to the present application are shown in the drawings, not all of the structures.
The main ideas of the technical scheme are as follows: based on the technical problems in the prior art, the embodiment of the application provides an automatic testing scheme for 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 number of thread groups for different testing indexes is preset to simulate the concurrent number of users, performance parameters corresponding to relevant testing indexes detected by a monitor are preset in an application server of the big data platform, evaluation of the relevant performance indexes of the big data platform is realized through analyzing the obtained performance parameters, 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 optimization and the maintenance of the big data platform are facilitated, and the benign operation of the big data platform is ensured.
Example one
Fig. 1 is a schematic flow chart of a testing method for a big data platform according to an embodiment of the present disclosure, where the testing method of the present disclosure may be executed by a testing apparatus for a big data platform according to an embodiment of the present disclosure, and the testing apparatus may be implemented in a software and/or hardware manner, and may be integrated in a pressure-sending server, such as a server and an intelligent terminal, where the pressure-sending server is used as a pressure-sending machine, and a pressure testing tool is pre-installed thereon. As shown in fig. 1, the method for testing a big data platform of this embodiment includes:
and S101, acquiring a target test index.
In this step, the target test index is obtained according to the selection operation or the input operation of the user on the man-machine interaction 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 testing of the big data platform, in this embodiment, the one-key start-stop validity, the cluster validity, the service process self-pull-up validity and the overtime validity are used as candidate test indexes, and test data packets matched with the candidate test indexes are configured in the pressure test tool in advance, so that different candidate test indexes can be selected and used during testing. Correspondingly, the target test index in this embodiment is any one of one-key start-stop validity, cluster validity, service process self-pull validity, and timeout validity.
In this step, for example, the user may input identification information such as a name, a number, or an icon of the test index on the human-computer interaction interface, and the pressure 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 indexes, a pressure test tool and a monitoring tool are called to test the big data platform, and performance parameters of the big data platform are obtained.
In this step, based on the target test index determined in S101, a target test data packet configured in advance in the pressure test tool and matched with the target test index is called to perform analog pressure sending to test the big data platform, and meanwhile, a monitoring tool preset in the big data platform is called to monitor the operation state of the big data platform in the test process, so as to obtain the performance parameter of the big data platform.
Different test data packets aiming at different candidate test indexes are pre-configured in the pressure test tool, different test data packets are used for implementing different tests aiming at different candidate test indexes, and the test data packets comprise parameters required for testing each test index, such as the number of simulated user concurrences, performance parameters required to be monitored and the like.
Optionally, the pressure testing tool used in the present embodiment is a meter tool. The Jmeter tool is a Java-based open source stress test tool developed by Apache organization, which may allow developers to develop different test packets in advance.
The monitoring tool may be various probes or components with a data monitoring function, such as a monitor, and it can be understood that the pressure server in this embodiment has the capability of calling the monitoring tool and controlling the monitoring tool to perform data acquisition.
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 analyzing, storing, calculating and other series of processing operations on batch or mass data according to needs.
In a possible implementation manner, in this embodiment, a user concurrency number is simulated according to a preset thread group number of a target test data packet, a data processing request is sent to a big data platform, and in a process that the big data platform performs data processing according to the received data processing request, a test instruction related to a target test index is sent to the big data platform, so that a test on the target test index on the big data platform is realized.
And the number of the thread groups is used for simulating the concurrency number of the users and controlling the number of the data processing requests sent to the big data platform. In this embodiment, since the big data platform is mainly used for processing batch or mass data, in order to more truly restore the data processing process of the big data platform, a certain number of thread groups are preset in the test data packet, the concurrent number of users is simulated, and data processing requests with the same number as the concurrent number of users are sent to the big data platform, so that the big data platform enters a big data processing flow according to the data processing requests, and preparation is made for implementing subsequent tests. It is to be understood that the data processing request may be a data query request or a data construction request, and is not limited herein.
Because the requirements for the big data platform are different when testing different test indexes, the target test data packet in this embodiment further includes a test instruction sending mechanism related to the target test index, such as the type of the sent test instruction and the sending time of the test instruction, and after the big data platform enters the big data processing flow, the test instruction related to the target test index is sent to the big data platform according to the test instruction sending mechanism pre-configured in the target test data packet to control the work of the application server in the big data platform, thereby implementing the one-key start-stop validity test, the cluster validity test, the self-pull-up validity test of the service process or the timeout validity test of the big data platform. The following will introduce the test principles and the test instruction sending mechanism for different test indexes:
one-key start-stop validity test
The one-key start-stop validity test is mainly used for testing the data processing performance of a big data platform when a process on a certain application server in the big data platform is stopped and started after the process is stopped.
The principle of the one-key start-stop validity test is as follows: when a process ending instruction (the application server is called as a fault node) is received on one application server, the transaction sent to the node fails, the response time is prolonged, and the transactions of other nodes in the cluster are not affected, so that the total 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 ending instruction is sent to a target server in the big data platform; and after the interval preset time length, sending a process recovery instruction to the target server, and monitoring the performance parameters of the big data platform in the process of finishing the process of the target server and the process of recovering the process of the target server respectively through monitoring tools arranged in all the nodes to realize the one-key start-stop validity test of the big data platform.
The target server is any one server in the big data platform, one server is selected from different types of servers to serve as the target server, one-key start-stop validity tests are sequentially executed on the different target servers, and performance tests of one-key start-stop validity of the whole big data platform are completed.
The preset duration can be set in advance according to the transaction response time when the target server process is finished and the time required for the total TPS to recover to normal.
Optionally, during the process termination process of the target server, the performance parameter monitored by the monitoring tool may be one or more of the number of transactions per second (total TPS of each node) of the big data platform, a resource utilization rate (for example, a CPU utilization rate of each node), and a process termination time (time consumed by the target server from receiving a process termination instruction to actually completing the process termination).
Optionally, in the process recovery process of the target server, the performance parameter monitored by the monitoring tool may be one or more of the number of transactions per second (total TPS of each node) of the big data platform, a resource utilization rate (for example, a CPU utilization rate of each node), and a process recovery time (time consumed by the target server from receiving the process recovery instruction to actually completing the process recovery).
(II) Cluster validation test
The cluster validity test is mainly used for testing the data processing capacity of the big data platform when a process on a certain application server in the big data platform is suspended and recovered or when the certain application server stops working and recovers after stopping. In this embodiment, the cluster validity may be tested by applying a suspend test or a downtime test.
(1) Application hang test
The test principle of applying the hang test is: when a certain application server receives a process suspension instruction (the application server is called as a fault node), the transaction sent to the node fails, the response time is prolonged, the total TPS of the large data platform is reduced, and the transactions of other nodes of the cluster are not influenced; after a period of time (e.g., 1 minute), other nodes take over the failed node and the transaction returns to normal. When the fault node receives the recovery suspension instruction, the transaction is transferred back to the recovered node, and the large 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 the preset time interval, sending a suspension recovery instruction to a target server in the big data platform, and monitoring performance parameters of the big data platform in the process of suspending the process of the target server and the process of recovering and suspending the process of the target server respectively through monitoring tools arranged in all nodes 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 is not described herein again.
The preset duration can be set in advance according to the time required for the transaction processing to be recovered to normal after the target server process is suspended.
Optionally, in the process of suspending the process of the target server, the performance parameter monitored by the monitoring tool may be one or more of the number of transactions per second (total TPS of each node) of the big data platform, a resource utilization rate (for example, a CPU utilization rate of each node), an error reporting condition (for example, the number of errors occurring in each node, a time period during which an error occurs, and the like), and a process suspension time (a time consumed by the target server from receiving the process suspension instruction to actually completing the process suspension).
Optionally, during the process resumption suspension process of the target server, the performance parameter monitored by the monitoring tool may be one or more of the number of transactions per second (total TPS of each node) of the big data platform, resource utilization rate (for example, CPU utilization rate of each node), error reporting condition (for example, the number of errors occurring in each node, time period during which an error occurs, and the like), and time consumed for resumption suspension (time consumed by the target server from receiving the instruction to resume suspension to actually completing the resumption suspension).
(2) Downtime test
The testing principle of the downtime test is as follows: when an application server receives a server closing instruction (the application server is called as a fault node), all transactions sent to the node are invalid, the total TPS value is reduced, and the transactions of other nodes of the cluster are not influenced; after a period of time (e.g., 1 minute), the transaction is transferred to another node and the transaction returns to normal. When the management equipment of the fault node receives the server starting instruction, the closed node server is opened, 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 the big data platform; after the preset time interval, sending a server starting instruction to the management equipment of the target server, and monitoring the performance parameters of the large data platform in the closing process and the opening process of the target server respectively through monitoring tools arranged in all the nodes to realize the downtime test of the large 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 is not described herein again.
The preset duration can be set in advance according to the time required for the transaction to recover to normal after the target server is closed.
Optionally, during the target server shutdown process, the performance parameters monitored by the monitoring tool may be one or more of the number of transactions per second (total TPS of each node) of the big data platform, resource utilization rate (for example, CPU utilization rate of each node), error reporting condition (for example, the number of errors occurring in each node, the time period during which an error occurs, and the like), and time consumed by the server shutdown (time consumed by the target server from receiving the server shutdown instruction to actually completing the shutdown).
Optionally, in the starting process of the target server, the performance parameter monitored by the monitoring tool may be one or more of the number of transactions per second (total TPS of each node) of the big data platform, a resource utilization rate (for example, a CPU utilization rate of each node), an error reporting condition (for example, the number of errors occurring in each node, a time period during which an error occurs, and the like), and a server starting time (time consumed from receiving a server starting instruction from the management device of the target server to completing the server starting).
It can be understood that the management device of the target server is an upper-level server of the target server, and is a server for managing the target server, and a monitoring tool is also arranged on the management device, and the management device can control the target server to be turned off or on according to a turn-off instruction or a turn-on instruction received from the target server.
(III) service Process self-Pull validation test
The service process self-pull effectiveness test is mainly used for testing the data processing performance and the self-pull capability of a big data platform when the process on a certain application server in the big data platform is finished.
The testing principle of the self-pull effectiveness test of the service process is as follows: when a certain application server receives a process ending instruction (the application server is called a failure node), whether the process of the failure node is automatically pulled up or not (namely, automatic recovery) and the automatic pulling up consumption (the time from actual ending to automatic recovery of the process) are observed, and the error rate, the processing capacity, the response time, the resource use condition and the like of each node in the large data platform are observed.
In a possible implementation manner, a process ending instruction is sent to a target server in a big data platform, and performance parameters of the big data platform are monitored through monitoring tools arranged in nodes, so that a service process self-pull effectiveness test of the big 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 is not described herein again.
Optionally, the performance parameters in this embodiment include one or more of the number of transactions per second (total TPS of each node) of the big data platform, resource utilization rate (for example, CPU utilization rate of each node), process end time (time consumed by the target server from receiving the process end instruction to actually completing the process end), and self-starting condition of the process (whether to pull itself or time consumed by pulling itself).
(IV) timeout validity test
The overtime validity test is mainly used for testing the data processing performance of a big data platform when an application server in the big data platform is overtime and overtime is recovered.
The testing principle of the timeout validity test is as follows: after the large data platform stably operates for a period of time, modifying overtime configuration of a certain application server to be less than baffle delay time (the application server is called as a fault node), refreshing to take effect, making a transaction error, displaying log information to be overtime, and observing error rate, processing capacity, response time, resource use condition and the like of each node in each large data platform. After the operation is carried out for a period of time, the overtime configuration of the modified fault node is greater than or equal to the baffle delay time, the refreshing is effective, the transaction execution is normal, and a new overtime error does not appear in the log any more; and (5) continuing to stably operate the scene for a period of time, and ending the test.
In one possible implementation, the timeout configuration less than baffle delay time instruction is sent to the big data platform; and after the interval preset time is long, sending an overtime configuration recovery instruction to the big data platform, and monitoring performance parameters of the target server under the condition that the overtime configuration is less than the baffle delay time and under the condition that the overtime configuration is not less than the baffle delay time respectively through monitoring tools arranged in all the nodes so as to realize the overtime validity 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 is not described herein again.
The preset duration can be determined according to the time required by stable operation of the big data platform after the modification of the overtime configuration.
It can be understood that, in this embodiment, the timeout configuration of the target server is modified, that is, the timeout configuration of the target server is modified to be smaller than the baffle delay time, so that the target server generates timeout, thereby implementing the timeout validity test on the large data platform.
Optionally, when the timeout configuration is less than the barrier delay time or the timeout configuration is not less than the barrier delay time, the performance parameter monitored by the monitoring tool may be one or more of the number of transactions per second (total TPS of each node), the resource utilization rate (for example, the CPU usage rate of each node), and the error reporting condition (for example, the number of errors occurring in each node, the time period during which the error occurs, etc.) of the big data platform.
Optionally, after S102, the method of this embodiment further includes: the obtained performance parameters are collected and compared with the standard performance parameters to generate a test report, and the test report is visually displayed, so that a tester can conveniently check the test result.
In this embodiment, by obtaining a target test index, which is one of one-key start-stop validity, cluster validity, service process self-pull validity and timeout validity, and calling a pressure test tool and a monitoring tool to test the big data platform according to the target test index, performance parameters of the big data platform are obtained, 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.
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 disclosure, and as shown in fig. 2, the testing apparatus 10 for a big data platform in this embodiment includes:
an acquisition module 11 and a processing module 12.
The acquiring module 11 is configured to acquire a target test index, where the target test index is one of one-key start-stop validity, cluster validity, service process self-pull-up validity, and timeout validity;
and the processing module 12 is configured to invoke a pressure test tool and a monitoring tool to test the big data platform according to the target test index, so as to obtain a performance parameter of the big data platform.
Optionally, the processing module 12 is specifically configured to:
calling a target test data packet which is configured in advance in the pressure test tool and matched with the target test index to perform simulated pressure sending, and testing the big data platform;
and calling a monitoring tool preset in the big data platform, and monitoring the running state of the big data platform in the test process to obtain the performance parameters of the big data platform.
Optionally, the processing module 12 is specifically configured to:
sending a data processing request to the big data platform according to the thread group number configured in advance in the target test data packet;
and in the process that the big data platform executes data processing according to the data processing request, sending a test instruction related to the target test index to the big data platform according to a test instruction sending mechanism configured in advance in the target test data packet so as to test the big data platform about the target test index.
Optionally, the target test indicator is 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 the interval preset time length, sending a process recovery instruction to the target server so as to carry out one-key start-stop validity test on the big data platform.
Optionally, the performance parameters include: at least one of the transaction number per second, the resource utilization rate and the process ending time consumption of the big data platform in the process ending process of the target server; and at least one of the number of transactions per second, the resource utilization rate and the process recovery time consumption of the big data platform in the process recovery process of the target server.
Optionally, the target test indicator 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 suspension recovery 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 the number of transactions per second, the resource utilization rate, the error reporting condition and the process suspension time consumption of the big data platform in the process of suspending the target server process; and at least one of the number of transactions per second, the resource utilization rate, the error reporting condition and the time consumed for the suspension recovery of the big data platform in the process of suspending the process recovery of the target server.
Optionally, the target test indicator 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; and after the preset time interval, sending a server starting instruction 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 the number of transactions per second, the resource utilization rate, the error reporting condition and the time consumed for closing the server of the big data platform in the closing process of the target server; and at least one of the number of transactions per second, the resource utilization rate, the error reporting condition and the time consumed for starting the server of the big data platform in the starting process of the target server.
Optionally, the target test indicator is self-pull effectiveness of the service process, 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 carry out self-pull validity test on the service process of the big data platform.
Optionally, the performance parameters include: at least one of the number of transactions per second, the resource utilization rate, the process end time consumption and the process self-starting condition of the big data platform.
Optionally, the target test indicator 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 the interval preset time length, sending an overtime configuration recovery instruction to the big data platform so as to test the overtime effectiveness of the big data platform.
Optionally, the performance parameters include: when the overtime configuration is smaller than the baffle delay time, at least one of the number of transactions per second, the resource utilization rate and the error reporting condition of the big data platform; and under the condition that the overtime configuration is not less 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 embodiment of the method, and has corresponding functional modules and beneficial effects of the execution method. The implementation principle and technical effect of this embodiment are similar to those of the above method embodiments, and are not described in detail here.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a voltage generating server according to a third embodiment of the present invention, as shown in fig. 3, the voltage generating server 20 includes a memory 21, a processor 22, and a computer program stored in the memory and executable on the processor; the number of the processors 22 in the transmitting server 20 may be one or more, and one processor 22 is taken as an example in fig. 3; the processor 22 and the memory 21 in the transmitting server 20 may be connected by a bus or other means, and fig. 3 illustrates the connection by the bus as an example.
The memory 21 is a computer-readable storage medium, and can be used for storing software programs, computer-executable programs, 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 the software program, instructions and modules stored in the memory 21, so as to issue various functional applications and data processing of the server, that is, to implement the test method of the big 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, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the 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 located remotely from processor 22, which may be connected to the voltage-generating server through a grid. Examples of such a mesh include, but are not limited to, the internet, an intranet, a local area network, a mobile communications network, and combinations thereof.
Example four
Fig. 4 is a schematic structural diagram of a test system of a big data platform according to a fourth embodiment of the present disclosure, and as shown in fig. 4, a test system 30 of a big data platform according to the present disclosure includes a big data platform 31 and a voltage generating server 20 as described in the third embodiment.
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 mass data according to needs. The application server in the big data platform 31 is provided with a listening tool.
For example, taking the big data platform 31 as an kylin big data platform as an example, fig. 5 is a schematic structural view of a testing system of the kylin big data platform provided in the fourth embodiment of the present application, as shown in fig. 5, the kylin big data platform includes an kylin query server cluster and an kylin build server cluster, the kylin query server cluster is composed of a plurality of kylin query servers, and the kylin build server cluster is composed of a plurality of kylin build servers.
Optionally, the test system for the kylin big data platform may further include: as shown in fig. 5, a pressure server cluster (composed of a plurality of pressure servers, each of which is provided with a pressure 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 an 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 an kylin construction server cluster, the kylin construction server cluster is connected with a 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 the 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 hosting server cluster are used for preprocessing index data, constructing CUBE, and storing the preprocessed index data to improve query performance. The hosted 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 processes of pressure initiation and data processing performed by the kylin big data platform can be divided into two scenarios:
(1) the method comprises the steps that usable pressure servers are selected as pressure generators by a pressure generating server cluster, a pressure testing tool is called to send CUBE query requests (data query requests) to an index query server cluster, the index query server cluster processes the requests, index rules corresponding to the requests are found, corresponding Structured Query Language (SQL) query sentences are constructed according to the found index rules and sent to an kylin query server cluster, then the kylin query server cluster analyzes SQL, and the CUBE is matched to conduct corresponding query operation.
(2) The method comprises the steps that the pressure sending server cluster selects an available pressure sending server as a pressure sending machine, a pressure testing tool is called to send a CUBE construction request (data construction request) to an online application server cluster, the online application server cluster forwards the request to an asynchronous application server cluster, the asynchronous application server cluster calls a related Application Program Interface (API), an instruction is sent to an kylin construction server cluster, the kylin construction server cluster processes the received request to generate a CUBE construction instruction, and the CUBE construction instruction is sent to a hosting server cluster to execute a CUBE construction process.
According to the two scenarios, in the test system of the big data platform, the pressure server can also send a data processing request to the big data platform in an indirect manner.
EXAMPLE five
A fifth embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, the computer program, when executed by a computer processor, is configured to perform a method for testing a big data platform, the method including:
acquiring a target test index, wherein the target test index is one of one-key start-stop validity, cluster validity, service process self-pull-up validity and overtime validity;
and 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.
Of course, the computer program of the computer-readable storage medium provided in this embodiment of the present application is not limited to the method operations described above, and may also perform related operations in the test method for a large data platform provided in any embodiment of the present application.
From the above description of the embodiments, it is obvious for those skilled in the art that the present application can be implemented by software and necessary general hardware, and certainly can be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present application may be embodied 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 (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a grid device) to execute the methods described in the embodiments of the present application.
It should be noted that, in the embodiment of the testing apparatus for a big data platform, each unit and each module included in the testing apparatus are only divided according to functional logic, but are not limited to the above division, as long as the corresponding function can be implemented; in addition, specific names of the functional units are only used for distinguishing one functional unit from another, and are not used for limiting the protection scope of the application.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present application and the technical principles employed. It will be understood by those skilled in the art that the present application is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the application. Therefore, although the present application has been described in more detail with reference to the above embodiments, the present application is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present application, and the scope of the present application is determined by the scope of the appended claims.
Claims (18)
1. A test method for a big data platform is characterized by comprising the following steps:
acquiring a target test index, wherein the target test index is one of one-key start-stop validity, cluster validity, service process self-pull-up validity and overtime validity;
and 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.
2. The method of claim 1, wherein the invoking a stress testing tool and a listening tool to test the big data platform to obtain performance parameters of the big data platform comprises:
calling a target test data packet which is configured in advance in the pressure test tool and matched with the target test index to perform simulated pressure sending, and testing the big data platform;
and calling a monitoring tool preset in the big data platform, and monitoring the running state of the big data platform in the test process to obtain the performance parameters of the big data platform.
3. The method of claim 2, wherein the invoking of a target test data packet that matches the target test index and is pre-configured in the stress test tool to perform a simulated stress on the big data platform comprises:
sending a data processing request to the big data platform according to the thread group number configured in advance in the target test data packet;
and in the process that the big data platform executes data processing according to the data processing request, sending a test instruction related to the target test index to the big data platform according to a test instruction sending mechanism configured in advance in the target test data packet so as to test the big data platform about the target test index.
4. The method according to claim 3, wherein the target test indicator is one-key start-stop validity, and the sending of the test instruction related to the target test indicator to the big data platform according to a test instruction sending mechanism configured in advance in the target test data packet so as to test the big data platform about the target test indicator comprises:
sending a process ending instruction to a target server in the big data platform; and after the interval preset time length, sending a process recovery instruction to the target server so as to carry out one-key start-stop validity test on the big data platform.
5. The method of claim 4, wherein the performance parameters comprise: at least one of the transaction number per second, the resource utilization rate and the process ending time consumption of the big data platform in the process ending process of the target server; and at least one of the number of transactions per second, the resource utilization rate and the process recovery time consumption of the big data platform in the process recovery process of the target server.
6. The method according to claim 3, wherein the target test indicator is cluster validity, and the sending of the test instruction related to the target test indicator to the big data platform according to a test instruction sending mechanism pre-configured in the target test data packet to test the big data platform for the target test indicator comprises:
sending a process suspension instruction to a target server in the big data platform; and after a preset time interval, sending a suspension recovery instruction to a target server in the big data platform so as to carry out suspension test on the big data platform.
7. The method of claim 6, wherein the performance parameters comprise: at least one of the number of transactions per second, the resource utilization rate, the error reporting condition and the process suspension time consumption of the big data platform in the process of suspending the target server process; and at least one of the number of transactions per second, the resource utilization rate, the error reporting condition and the time consumed for the suspension recovery of the big data platform in the process of suspending the process recovery of the target server.
8. The method according to claim 3, wherein the target test indicator is cluster validity, and the sending of the test instruction related to the target test indicator to the big data platform according to a test instruction sending mechanism pre-configured in the target test data packet to test the big data platform for the target test indicator comprises:
sending a server closing instruction to a target server in the big data platform; and after the preset time interval, sending a server starting instruction to the management equipment of the target server so as to carry out suspension test on the big data platform.
9. The method of claim 8, wherein the performance parameters comprise: at least one of the number of transactions per second, the resource utilization rate, the error reporting condition and the time consumed for closing the server of the big data platform in the closing process of the target server; and at least one of the number of transactions per second, the resource utilization rate, the error reporting condition and the time consumed for starting the server of the big data platform in the starting process of the target server.
10. The method according to claim 3, wherein the target test indicator is self-pull validity of a service process, and the sending of the test instruction related to the target test indicator to the big data platform according to a test instruction sending mechanism configured in advance in the target test data packet to test the big data platform for the target test indicator comprises:
and sending a process ending instruction to a target server in the big data platform so as to carry out self-pull validity test on the service process of the big data platform.
11. The method of claim 10, wherein the performance parameters comprise: at least one of the number of transactions per second, the resource utilization rate, the process end time consumption and the process self-starting condition of the big data platform.
12. The method according to claim 3, wherein the target test indicator is validity of timeout, and the sending of the test instruction related to the target test indicator to the big data platform according to the test instruction sending mechanism configured in advance in the target test data packet to test the big data platform for the target test indicator comprises:
sending an instruction that the overtime configuration is smaller than the baffle delay time to the big data platform; and after the interval preset time length, sending an overtime configuration recovery instruction to the big data platform so as to test the overtime effectiveness of the big data platform.
13. The method of claim 12, wherein the performance parameters comprise: when the overtime configuration is smaller than the baffle delay time, at least one of the number of transactions per second, the resource utilization rate and the error reporting condition of the big data platform; and under the condition that the overtime configuration is not less 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.
14. The method according to any one of claims 1-13, further comprising:
and summarizing the performance parameters to generate a test report.
15. A big data platform's testing arrangement, its characterized in that includes:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring a target test index, and the target test index is one of one-key start-stop validity, cluster validity, service process self-pull-up validity and overtime validity;
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.
16. A voltage-generating server comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the program, implements a method of testing a big data platform according to any of claims 1 to 14.
17. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method for testing a big data platform according to any of claims 1 to 14.
18. A big data platform's test system characterized in that includes: a big data platform and a sending server as claimed in claim 16.
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