CN111143475B - State management method and device for Storm data analysis - Google Patents
State management method and device for Storm data analysis Download PDFInfo
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- CN111143475B CN111143475B CN201911412908.2A CN201911412908A CN111143475B CN 111143475 B CN111143475 B CN 111143475B CN 201911412908 A CN201911412908 A CN 201911412908A CN 111143475 B CN111143475 B CN 111143475B
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Abstract
The embodiment of the invention discloses a method and a device for state management of Storm data analysis, relates to the technical field of computer software application, and can reduce the probability of potential risk occurrence caused by frame function defects and improve the robustness of a whole calculation link. The invention comprises the following steps: saving the intermediate state data to a distributed database through a first Storm-Bolt component; when the process of storing the intermediate state data into the distributed database fails, the intermediate state data is sent to a message middleware; consuming data in the message middleware through a second Storm-Bolt component and storing the data in the distributed database; restoring the intermediate state data using data consumed by the second Storm-Bolt component when the first Storm-Bolt component performs a crash or restart. The method is suitable for maintaining the state data after the restarting task under the Storm-Bolt component.
Description
Technical Field
The invention relates to the technical field of computer software application, in particular to a method and a device for state management of Storm data analysis.
Background
With the rapid development of internet technology and the emergence of new technologies and industries such as 5G and the internet of things, the data volume is also exponentially increased, and the value of the data is highlighted. Storm is a distributed and fault-tolerant real-time computing system, and has the advantages of simple programming model, high fault tolerance, simplicity and convenience in expansion, rapidness, real-time performance and the like. The disadvantages are lack of state management and low throughput.
In the process of streaming, there is a very high demand for data state, because the input source in the streaming processing system is a borderless data stream, the task will run for a long time, even run for several days or months, and in the process, the intermediate state data needs to be well managed.
However, the Storm flow computing system does not support data state management, once the Storm-Bolt component needs to be modified or crash and other reasons, after the task is restarted, state data residing in a memory before is lost, which causes errors of subsequent computing results, and the problem can be avoided only by consuming data all day long, which causes the whole operation flow to be heavy and cumbersome, and risks to occur very easily, thereby causing major production accidents.
Disclosure of Invention
The embodiment of the invention provides a method and a device for state management of Storm data analysis, which can reduce the probability of potential risk occurrence caused by frame function defects and improve the robustness of the whole calculation link.
In order to achieve the above purpose, the embodiment of the invention adopts the following technical scheme:
in a first aspect, an embodiment of the present invention provides a method, including:
saving intermediate state data to a distributed database by a first Storm-Bolt component, wherein the intermediate state data is generated by a Storm task in a real-time computing process;
when the process of storing the intermediate state data into the distributed database fails, the intermediate state data is sent to a message middleware;
consuming data in the message middleware through a second Storm-Bolt component and storing the data in the distributed database;
restoring the intermediate state data using data consumed by the second Storm-Bolt component when the first Storm-Bolt component performs a crash or restart.
In a second aspect, an embodiment of the present invention provides an apparatus, including:
a first processing module, configured to save intermediate state data to a distributed database through a first Storm-Bolt component, where the intermediate state data is generated by a Storm task in a real-time calculation process;
the intermediate processing module is used for sending the intermediate state data to the message middleware when the process of storing the intermediate state data to the distributed database fails;
the second processing module is used for consuming the data in the message middleware through a second Storm-Bolt component and storing the data in the distributed database;
and the data recovery module is used for recovering the intermediate state data by using the data consumed by the second Storm-Bolt component when the first Storm-Bolt component performs crash or restart.
The method and the device for state management of Storm data analysis in the embodiment realize the intermediate state management function of the Storm system. The defects of the Storm system in real-time calculation are overcome, the potential risk occurrence probability caused by the frame function defects is reduced, and the robustness of the whole calculation link is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of a system architecture according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a method provided by an embodiment of the present invention;
fig. 3 is a schematic diagram of an embodiment provided in the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments. Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are exemplary only for explaining the present invention and are not construed as limiting the present invention. As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items. It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The method flow in this embodiment may be specifically executed on a system as shown in fig. 1, where the system includes: in a distributed database, a plurality of nodes generally exist, each node often generates a lot of state data during normal operation, and the nodes generating the state data can be called as data sources. The distributed database may be a server cluster for data processing, which is formed by multiple servers. The specific erection mode of the distributed database can adopt the database technology which is mature at present.
The Storm system is a distributed, fault-tolerant, real-time computing system that can be used in "stream processing" to process messages and update databases in real-time. The Storm system can also be used for "continuous computing" (continuous computing), where continuous queries are made on a data stream and the results are streamed to the user at the time of computing. The Storm system disclosed in this embodiment may be specifically deployed on devices such as a workstation and a super computer, and a server, or a server cluster for Storm system operation, which is composed of a plurality of servers, on a hardware level.
An embodiment of the present invention provides a method for state management of Storm data analysis, as shown in fig. 2, including:
s101, storing the intermediate state data to a distributed database through a first Storm-Bolt component.
Wherein the intermediate state data is generated by the Storm-Bolt component during the real-time computation process and generated by the Storm task during the real-time computation process. The second Storm-Bolt component is specifically a Storm real-time task. The first Storm-Bolt component is also a real-time task, but the operation of storing the calculation result and the intermediate state data by the first Storm-Bolt component is completed by a timing task, so that the database pressure can be relieved by batch storage, and the database pressure is too large because the data is not stored in one piece.
The Storm-Bolt component described in this embodiment may be understood as a task program or a script program executed by the Bolt component in Storm.
The intermediate state data is used to record the state condition in the memory at a certain time, for example: the number of the duplicate removal visitors in the whole day is counted in real time, and the number of the visitors is assumed to be 1000 currently, so that the 1000 is the intermediate state data. In the existing solution, if the task is suspended during the processing, the state of the task in the memory is lost, and all data needs to be calculated from 0.
S102, when the process of storing the intermediate state data into the distributed database fails, the intermediate state data is sent to a message middleware.
Specifically, a Storm timing task is configured for a data source of the distributed database in advance, and intermediate state data in the calculation process is stored in the distributed database through the Storm timing task. If the data fails to be stored in the storage process, a fault tolerance mechanism is triggered, the data which is not stored is sent to the message middleware, and then another real-time task consumes and stores the data. Therefore, the integrity of the intermediate state data is ensured, and the error of the calculation result of the whole link caused by the loss of the state data is avoided.
It should be noted that the message middleware is a commonly-used name for those skilled in the art, and refers to performing platform-independent data communication by using an efficient and reliable message delivery mechanism, and performing integration of a distributed system based on data communication. By providing a message passing and message queuing model, the method can provide functions of application decoupling, elastic expansion, redundant storage, traffic peak clipping, asynchronous communication, data synchronization and the like in a distributed environment. There are generally two modes of delivery for message middleware: point-to-point (P2P) and publish-subscribe (Pub/Sub) modes.
And S103, consuming the data in the message middleware through a second Storm-Bolt component and storing the data in the distributed database.
When the second Storm-Bolt component crash or manual restart is carried out, whether the state in the current worker memory is empty or not is judged, if the state in the current worker memory is empty, the corresponding state value is searched from the distributed database and restored to the current memory, and the subsequent calculation result is directly carried out on the basis of the state.
And the data in the message middleware is specifically stored to a special server in the distributed database.
S104, when the first Storm-Bolt component is subjected to crash or restart, restoring the intermediate state data by using the data consumed by the second Storm-Bolt component.
In the traditional mode, due to the lack of a state management mechanism in Storm, once a task is crash for various reasons or is restarted, an intermediate state calculated before is completely lost because the intermediate state is stored in a memory, so that a subsequent calculation result is wrong, the problem can be avoided only by consuming data from the beginning, and a large amount of time cost is consumed in the process, and a new risk is introduced into the system. By adopting the technical scheme, the problems are not worried, once the task is restarted, the state data before restarting is recovered, and the accuracy of the subsequent calculation data is ensured.
In this embodiment, a combined mode of the Storm system + the distributed database + the message middleware is adopted to implement an intermediate state management function of the Storm system. The defects of the Storm system in real-time calculation are overcome, the potential risk occurrence probability caused by the frame function defects is reduced, the maintainability of the system is improved, and the availability and the robustness of the whole calculation link are improved. By means of the real-time processing capacity of the real-time computing function of the Storm system on mass data, the defects in the computing process are overcome, the robustness of the system is improved, the risk probability is greatly reduced, and powerful guarantee is provided for data production. Particularly, when the method is used for managing and recovering the state information of massive multi-dimensional data, the deployment can be conveniently implemented, and the stability is high.
In this embodiment, in step S103, the consuming data in the message middleware through the second Storm-Bolt component and storing the data in the distributed database includes: and acquiring upstream data source information and consuming real-time streaming data through the second Storm-Bolt component. And extracting dimension fields from the real-time streaming data and generating dimension keys. The upstream data source described in this embodiment generally refers to message middleware such as kafka, and the upstream data source information generally refers to configuration information such as zookeeper address, ZKRoot, topic name, and kafka consumerid. Real-time streaming data refers to the flow of data in an upstream data source. And the dimension field is extracted from the upstream real-time streaming data.
For example: in the storage and recovery of state data, the process performed by the real-time task includes: storm acquires upstream data source information and consumes real-time stream data; preprocessing, cleaning and processing the data; dimension fields in the data are extracted and assembled into dimension keys, such as in the format dimension 1-dimension 2-dimension n-date. The timing task is mainly to synchronize the calculation result into the database and to backup the state data.
In this embodiment, in step S104, the recovering the intermediate state data by using the data consumed by the second Storm-Bolt component includes: and detecting whether the state in the jvm memory of the current Bolt instance is empty. And if the distributed database is empty, reading data consumed by the second Storm-Bolt component from the distributed database, and recovering the intermediate state data in a jvm memory of the current Bolt instance. It should be noted that the Bolt example is a term of expertise in Storm technology, the JVM is a Java Virtual Machine (Java Virtual Machine), and JVM memory refers to contents occupied by a currently running Java Virtual Machine. The nouns such as Bolt example, jvm memory and the like are all common nouns and technical concepts in the field of big data and cloud computing at present.
The specific way of reading the data consumed by the second Storm-Bolt component from the distributed database and recovering the intermediate state data in the jvm memory of the current Bolt instance is as follows: and when the condition value corresponding to the dimension key does not exist in the current worker memory, acquiring the state data of the latest version. Writing the latest version of state data into a jvm memory of a current Bolt instance, and backing up the latest version of state data into the distributed database through the first Storm-Bolt component.
For example, as shown in fig. 3, the process of storing and recovering the state data includes:
1) Storm acquires upstream data source information and consumes real-time stream data;
2) Carrying out pretreatment cleaning processing on the data;
3) Extracting dimension fields in the data and assembling the dimension fields into dimension keys, wherein the dimension keys are in a format of dimension 1-dimension 2-dimension n-date;
4) Inquiring whether a corresponding state value exists in a current worker memory according to the dimension key, if so, skipping 6), and if not, performing the following processing:
5) Acquiring state data of the latest version from a distributed database according to the dimension key, acquiring the latest state data if the state data exists, and if the state data does not exist, indicating that the dimension key is calculated for the first time without recovering the previous state;
6) Performing operation with state calculation behaviors such as accumulation and duplicate removal on the current state value;
7) Writing the current latest state data into a worker memory of the current process;
8) Meanwhile, the latest state data is backed up to a distributed database through a Storm timing task;
wherein, the fault-tolerant mechanism of state data storage is provided, which includes: if the Storm timed task fails in the process of backing up the Storm timed task to the database, a state fault tolerance mechanism is triggered, and failed state data are sent to a message middleware system; and consuming the data in the middleware through the real-time task and writing the data into the distributed database again.
An embodiment of the present invention further provides a device for state management of Storm data analysis, including:
a first processing module for saving intermediate state data to a distributed database through a first Storm-Bolt component, wherein the intermediate state data is generated by a Storm task in a real-time computing process;
the intermediate processing module is used for sending the intermediate state data to the message middleware when the process of storing the intermediate state data to the distributed database fails;
the second processing module is used for consuming the data in the message middleware through a second Storm-Bolt component and storing the data in the distributed database;
and the data recovery module is used for recovering the intermediate state data by using the data consumed by the second Storm-Bolt component when the first Storm-Bolt component performs crash or restart.
Wherein the first Storm-Bolt component is a Storm timed task;
the second Storm-Bolt component is a Storm real-time task.
The second processing module is specifically configured to acquire upstream data source information and consume real-time stream data through the second Storm-Bolt component; dimension fields are extracted from the real-time stream data and dimension keys are generated.
The data recovery module is specifically configured to detect whether a state in a jvm memory of the current Bolt instance is empty;
and if the distributed database is empty, reading data consumed by the second Storm-Bolt component from the distributed database, and recovering the intermediate state data in a jvm memory of the current Bolt instance.
The data recovery module is specifically used for acquiring state data of the latest version when the state value corresponding to the dimension key does not exist in the current worker memory;
writing the state data of the latest version into a jvm memory of the current Bolt instance, and backing up the state data of the latest version into the distributed database through the first Storm-Bolt component.
In this embodiment, a combined mode of the Storm system + the distributed database + the message middleware is adopted to implement an intermediate state management function of the Storm system. The defects of the Storm system in real-time calculation are overcome, the potential risk occurrence probability caused by the frame function defects is reduced, the maintainability of the system is improved, and the availability and the robustness of the whole calculation link are improved. By means of the real-time processing capability of the Storm system on mass data, the defects in the calculation process are overcome, the robustness of the system is improved, the risk probability is greatly reduced, and powerful guarantee is provided for data production. Particularly, when the method is used for managing and recovering the state information of massive multi-dimensional data, the deployment can be conveniently implemented, and the stability is high.
All the embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the apparatus embodiment, since it is substantially similar to the method embodiment, it is relatively simple to describe, and reference may be made to some descriptions of the method embodiment for relevant points. The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (2)
1. A method of state management for data analysis of Storm, comprising:
saving intermediate state data to a distributed database by a first Storm-Bolt component, wherein the intermediate state data is generated by a Storm task in a real-time computing process;
when the process of storing the intermediate state data into the distributed database fails, the intermediate state data is sent to a message middleware;
consuming data in the message middleware through a second Storm-Bolt component and storing the data in the distributed database;
restoring the intermediate state data using data consumed by the second Storm-Bolt component when the first Storm-Bolt component performs a crash or restart;
the second Storm-Bolt component is a Storm real-time task;
the first Storm-Bolt component completes the operation on the intermediate state data and the calculation result through a timing task;
the consuming, by the second Storm-Bolt component, data in the message middleware and storing to the distributed database, comprising:
configuring upstream data source information and consuming real-time streaming data through the second Storm-Bolt component;
extracting dimension fields from the real-time stream data and generating dimension keys;
the restoring the intermediate state data using data consumed by the second Storm-Bolt component, comprising:
detecting whether the state in the jvm memory of the current Bolt instance is empty;
if the distributed database is empty, reading data consumed by the second Storm-Bolt component from the distributed database, and recovering the intermediate state data in a jvm memory of the current Bolt instance;
the reading data consumed by the second Storm-Bolt component from the distributed database and restoring the intermediate state data in jvm memory of the current Bolt instance comprises:
when the condition value corresponding to the dimension key does not exist in the current worker memory is inquired, obtaining the condition data of the latest version, wherein the dimension key is obtained by assembling dimension fields;
writing the state data of the latest version into a jvm memory of the current Bolt instance, and backing up the state data of the latest version into the distributed database through the first Storm-Bolt component.
2. An apparatus for state management for data analysis of Storm, comprising:
a first processing module for saving intermediate state data to a distributed database through a first Storm-Bolt component, wherein the intermediate state data is generated by a Storm task in a real-time computing process;
the intermediate processing module is used for sending the intermediate state data to the message middleware when the process of storing the intermediate state data to the distributed database fails;
the second processing module is used for consuming the data in the message middleware through a second Storm-Bolt component and storing the data in the distributed database;
a data recovery module for recovering the intermediate state data using data consumed by the second Storm-Bolt component when the first Storm-Bolt component performs a crash or restart;
the first Storm-Bolt component is a Storm timing task;
the second Storm-Bolt component is a Storm real-time task;
the second processing module is specifically configured to acquire upstream data source information and consume real-time stream data through the second Storm-Bolt component; extracting dimension fields from the real-time stream data and generating dimension keys;
the data recovery module is specifically configured to detect whether a state in a jvm memory of the current Bolt instance is empty;
if the distributed database is empty, reading data consumed by the second Storm-Bolt component from the distributed database, and recovering the intermediate state data in a jvm memory of the current Bolt instance;
the data recovery module is specifically used for acquiring state data of the latest version when the state value corresponding to the dimension key does not exist in the current worker memory, wherein the dimension key is obtained by assembling dimension fields;
writing the state data of the latest version into a jvm memory of the current Bolt instance, and backing up the state data of the latest version into the distributed database through the first Storm-Bolt component.
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