CN112286875A - System framework for processing real-time data stream and real-time data stream processing method - Google Patents
System framework for processing real-time data stream and real-time data stream processing method Download PDFInfo
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- CN112286875A CN112286875A CN202011149931.XA CN202011149931A CN112286875A CN 112286875 A CN112286875 A CN 112286875A CN 202011149931 A CN202011149931 A CN 202011149931A CN 112286875 A CN112286875 A CN 112286875A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/10—File systems; File servers
- G06F16/18—File system types
- G06F16/182—Distributed file systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/10—File systems; File servers
- G06F16/14—Details of searching files based on file metadata
- G06F16/148—File search processing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/10—File systems; File servers
- G06F16/17—Details of further file system functions
- G06F16/178—Techniques for file synchronisation in file systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/10—File systems; File servers
- G06F16/18—File system types
- G06F16/1805—Append-only file systems, e.g. using logs or journals to store data
- G06F16/1815—Journaling file systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/242—Query formulation
- G06F16/2433—Query languages
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/958—Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
Abstract
The embodiment of the invention discloses a system framework and a method for processing real-time data stream, wherein the system framework comprises the following components: the distributed publishing and subscribing message system is used for receiving the real-time data stream and pushing the real-time data stream into the folder; the data transmission system is used for monitoring the folder and pushing the real-time data stream to the HDFS when the real-time data stream enters the folder; and the HDFS is used for cleaning the real-time data stream to obtain target data, and pushing the target data to the storage system for storage for back-end query. By implementing the embodiment of the invention, a new real-time data stream system framework is built, which comprises a distributed publish-subscribe message system, a data transmission system, an HDFS (Hadoop distributed File System) and a storage system, and the real-time data stream can be processed and stored by the system framework.
Description
Technical Field
The invention relates to the technical field of computer software, in particular to a system framework for processing a real-time data stream and a real-time data stream processing method.
Background
With the advent of the big data era, the data value is more and more emphasized by people.
At present, the existing big data technology stacks are basically big data systems formed around Hadoop ecocircles. When a set of standard big data system is to be established, the problem of data storage is solved firstly, and in order to ensure the integrity and flexibility of data, the stored data should be based on the principle of 'multiple storage and close connection'. The prior art has the problem that once a server where a certain component is located is down, data loss or system breakdown occurs. These problems may be caused by physical conditions, such as insufficient support of the server memory, or slow execution of the application due to too high server temperature. The data storage problem can be solved by a higher server configuration, but results in an increase in cost.
Disclosure of Invention
In view of the technical defects in the prior art, an embodiment of the present invention is directed to providing a system framework for processing a real-time data stream and a real-time data stream processing method.
In order to achieve the above object, in a first aspect, an embodiment of the present invention provides a system framework for processing a real-time data stream, including a distributed publish-subscribe message system, a data transmission system, a Hadoop distributed file system, and a storage system;
the distributed publishing and subscribing message system is used for receiving a real-time data stream and pushing the real-time data stream into a folder;
the data transmission system is used for monitoring the folder and pushing a real-time data stream to the Hadoop distributed file system when the real-time data stream enters the folder;
the Hadoop distributed file system is used for cleaning the real-time data stream to obtain target data, and pushing the target data to the storage system to be stored for back-end query.
In some preferred embodiments of the present application, the real-time data stream includes online data and offline data, and the distributed publish-subscribe message system is further configured to uniformly process the online data and the offline data through a parallel loading mechanism of Hadoop.
Specifically, in some embodiments of the present application, the Hadoop distributed file system is configured to classify the real-time data stream to obtain the target data;
the Hadoop distributed file system is further used for forming a hive table according to the target data and storing the hive table to the storage system.
Preferably, in certain preferred embodiments of the present application, the data transmission system comprises a flash; the storage system includes Redis or Mysql.
In a second aspect, an embodiment of the present invention provides a real-time data stream processing method, including:
acquiring a real-time data stream, and pushing the real-time data stream into a distributed publish-subscribe message system;
receiving the real-time data stream through the distributed publishing and subscribing message system, and pushing the real-time data stream into a folder;
monitoring the folder through a data transmission system, and pushing a real-time data stream to a Hadoop distributed file system when the real-time data stream enters the folder;
and cleaning the real-time data stream through the Hadoop distributed file system to obtain target data, and synchronizing the target data to the storage system for storage for back-end query.
Further, in certain preferred embodiments of the present application, the method further comprises:
classifying the real-time data stream through the Hadoop distributed file system to obtain the target data;
and forming a hive table according to the target data through the Hadoop distributed file system, and synchronizing the hive table to the storage system.
Further, in certain preferred embodiments of the present application, the method further comprises:
receiving a query request of a user, querying the storage system according to the query request to obtain a query result, and pushing the query result to the user.
By implementing the embodiment of the invention, a new real-time data stream system framework is built, and the new real-time data stream system framework comprises a distributed publishing and subscribing message system, a data transmission system, a Hadoop distributed file system and a storage system.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below.
FIG. 1 is a block diagram of a system framework for processing real-time data streams provided by an embodiment of the present invention;
fig. 2 is a flowchart of a real-time data stream processing method according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The working principle of the invention is that a Zookeeper technology stack under Apache is used, and the bottom layer judges the availability of a node through a lock and heartbeat mechanism. The heartbeat mechanism means that zookeeper will request the master node every 3 seconds, and if the master node responds, the node is proved to be in a healthy state. If the node does not respond, the node is proved to be broken, the Zookeeper immediately releases the lock in the hand, other nodes return to preempt the Znood node, and the program is quickly recovered to be normal.
Referring to fig. 1, a system framework for processing a real-time data stream according to an embodiment of the present invention is shown. As shown, the system framework includes:
a distributed publish-subscribe message system 100 configured to receive a real-time data stream and push the real-time data stream into a folder; the source of the real-time data stream is not limited at all, and may be online data from a network, or offline data from other places, etc.;
the data transmission system 200 is used for monitoring the folder and pushing a real-time data stream to the Hadoop distributed file system when the real-time data stream enters the folder;
the Hadoop distributed file system 300 is used for cleaning the real-time data stream to obtain target data;
the storage system 400 is configured to receive the target data from the Hadoop distributed file system 300, and store the target data for back-end query.
Specifically, in this embodiment, the real-time data stream is pushed into the distributed publish-subscribe message system Kafka, and finally the real-time data stream is pushed into the folder. Kafka, among other things, is a high-throughput distributed publish-subscribe messaging system that can handle all the action flow data of a consumer in a web site. This action (web browsing, searching and other user actions) is a key factor in many social functions on modern networks. These data are typically addressed by handling logs and log aggregations due to throughput requirements. This is a viable solution to the limitations of Hadoop-like log data and offline analysis systems, but which require real-time processing. The purpose of Kafka is to unify online and offline message processing through the parallel loading mechanism of Hadoop, and also to provide real-time messages through clustering. Furthermore, Kafka may well combine the push approach of multiple data streams. And Kafka needs to configure at least three servers and guarantee high availability thereof.
Specifically, in this embodiment, the data transmission system is Flume. And adopting the flash to monitor the folder, and pushing the real-time data stream to the Hadoop distributed file system in real time, thereby not only ensuring the integrity of the data, but also storing mass data. Among them, Flume is a highly available, highly reliable, distributed system for collecting, aggregating and transmitting mass logs provided by Cloudera. The Flume supports various data senders customized in the log system for collecting data; at the same time, flash provides the ability to simply process data and write to various data recipients (customizable).
Specifically, in this embodiment, the Hadoop Distributed File System (HDFS) is configured to classify real-time data streams to obtain target data; the Hadoop distributed file system is also used for forming a hive table according to the target data and storing the hive table to the storage system.
Among them, the Hadoop Distributed File System (HDFS) refers to a Distributed File System (Distributed File System) designed to be suitable for running on general purpose hardware (comfort hardware). It has many similarities with existing distributed file systems. But at the same time, its distinction from other distributed file systems is also clear. HDFS is a highly fault tolerant system suitable for deployment on inexpensive machines. HDFS provides high throughput data access and is well suited for application on large-scale data sets. HDFS relaxes a portion of the POSIX constraints to achieve the goal of streaming file system data. HDFS was originally developed as an infrastructure for the Apache Nutch search engine project. HDFS is part of the Apache Hadoop Core project.
Further, hive is a set of data warehouse analysis system constructed based on Hadoop, and provides rich SQL query ways to analyze data stored in the Hadoop distributed file system: the structured data file can be mapped into a database table, and a complete SQL query function is provided; SQL sentences can be converted into MapReduce tasks to run, needed contents are inquired and analyzed through the own SQL, the set of SQL is called Hive SQL for short, and users unfamiliar with MapReduce can conveniently inquire, summarize and analyze data by using SQL language. And mapreduce developers can use mappers and reducers written by themselves as plug-ins to support hive for more complex data analysis. It is slightly different from the SQL of relational databases, but supports most statements such as DDL, DML and common aggregation functions, join queries, conditional queries. The system also provides a series of devices for data extraction, transformation and loading, which are used for storing, inquiring and analyzing a large-scale data set stored in Hadoop, and supports UDF (User-Defined Function), UDAF (User-Defined aggregation Function) and UDTF (User-Defined Table-Generating Function), can also realize the customization of map and reduce functions, and provides good flexibility and extensibility for data operation.
Specifically, in the present embodiment, the storage system may be Redis or Mysql. Where redis is a key-value storage system. Similar to Memcached, it supports relatively more stored value types, including string, list, set, zset, and hash. These data types all support push/pop, add/remove, and intersect union and difference, and richer operations, and these operations are all atomic. On this basis, redis supports various different ways of ordering. Like memcached, data is cached in memory to ensure efficiency. The difference is that the redis can periodically write updated data into a disk or write modification operation into an additional recording file, and master-slave synchronization is realized on the basis of the update.
By implementing the embodiment of the invention, a new real-time data stream system framework is built, and the new real-time data stream system framework comprises a distributed publishing and subscribing message system, a data transmission system, a Hadoop distributed file system and a storage system.
Based on the same inventive concept, the embodiment of the invention provides a real-time data stream processing method. This method is mounted on the system frame described above. As shown in fig. 2, the method may include:
s101, acquiring a real-time data stream, and pushing the real-time data stream into a distributed publish-subscribe message system.
S102, receiving the real-time data stream through the distributed publish-subscribe message system, and pushing the real-time data stream into a folder.
S103, monitoring the folder through the data transmission system, and pushing the real-time data stream to the Hadoop distributed file system when the real-time data stream enters the folder.
And S104, cleaning the real-time data stream through the Hadoop distributed file system to obtain target data, and synchronizing the target data to the storage system for storage for back-end query.
Further, the method further comprises:
classifying the real-time data stream through the Hadoop distributed file system to obtain the target data;
and forming a hive table according to the target data through the Hadoop distributed file system, and synchronizing the hive table to the storage system.
Further, the method further comprises:
receiving a query request of a user, querying the storage system according to the query request to obtain a query result, and pushing the query result to the user.
It should be noted that, in the embodiment of the present method, the data transmission system is preferably a flash; the storage system is preferably Redis or Mysql. For a more detailed description of the embodiments of the method, please refer to the foregoing embodiments, which are not repeated herein.
By implementing the real-time data stream processing method provided by the embodiment of the invention, after the data stream is pushed into kafka, a subsequent program can automatically process the data stream, and finally, the data can be orderly output to the Mysql or Redis database according to the requirement. In addition, the configuration of the server required by the method is not high, and the data processing logic is simple and feasible.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (9)
1. A system framework for processing real-time data streams is characterized by comprising a distributed publishing and subscribing message system, a data transmission system, a Hadoop distributed file system and a storage system;
the distributed publishing and subscribing message system is used for receiving a real-time data stream and pushing the real-time data stream into a folder;
the data transmission system is used for monitoring the folder and pushing a real-time data stream to the Hadoop distributed file system when the real-time data stream enters the folder;
the Hadoop distributed file system is used for cleaning the real-time data stream to obtain target data, and pushing the target data to the storage system to be stored for back-end query.
2. The system framework for processing a real-time data stream as recited in claim 1, wherein the real-time data stream includes online data and offline data, the distributed publish-subscribe message system further for uniformly processing the online data and the offline data through a parallel load mechanism of Hadoop.
3. The system framework for processing a real-time data stream according to claim 1, wherein the Hadoop distributed file system is configured to classify the real-time data stream to obtain the target data;
the Hadoop distributed file system is further used for forming a hive table according to the target data and storing the hive table to the storage system.
4. The system framework for processing a real-time data stream as recited in claim 1, wherein the data transmission system comprises a flash.
5. The system framework for processing a real-time data stream according to claim 1, wherein the storage system comprises Redis or Mysql.
6. A method for processing a real-time data stream, comprising:
acquiring a real-time data stream, and pushing the real-time data stream into a distributed publish-subscribe message system;
receiving the real-time data stream through the distributed publishing and subscribing message system, and pushing the real-time data stream into a folder;
monitoring the folder through a data transmission system, and pushing a real-time data stream to a Hadoop distributed file system when the real-time data stream enters the folder;
and cleaning the real-time data stream through the Hadoop distributed file system to obtain target data, and synchronizing the target data to the storage system for storage for back-end query.
7. The real-time data stream processing method of claim 6, wherein the method further comprises:
classifying the real-time data stream through the Hadoop distributed file system to obtain the target data;
and forming a hive table according to the target data through the Hadoop distributed file system, and synchronizing the hive table to the storage system.
8. The real-time data stream processing method of claim 6 or 7, wherein the method further comprises:
receiving a query request of a user, querying the storage system according to the query request to obtain a query result, and pushing the query result to the user.
9. The real-time data stream processing method of claim 8, wherein the data transmission system comprises a flash; the storage system includes Redis or Mysql.
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Application publication date: 20210129 |