CN108737566B - Distributed real-time message filtering system - Google Patents
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/56—Provisioning of proxy services
- H04L67/565—Conversion or adaptation of application format or content
- H04L67/5651—Reducing the amount or size of exchanged application data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/14—Error detection or correction of the data by redundancy in operation
- G06F11/1402—Saving, restoring, recovering or retrying
- G06F11/1415—Saving, restoring, recovering or retrying at system level
- G06F11/142—Reconfiguring to eliminate the error
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/54—Interprogram communication
- G06F9/546—Message passing systems or structures, e.g. queues
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/54—Interprogram communication
- G06F9/547—Remote procedure calls [RPC]; Web services
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0654—Management of faults, events, alarms or notifications using network fault recovery
- H04L41/0663—Performing the actions predefined by failover planning, e.g. switching to standby network elements
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/10—Flow control; Congestion control
- H04L47/26—Flow control; Congestion control using explicit feedback to the source, e.g. choke packets
- H04L47/263—Rate modification at the source after receiving feedback
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/56—Provisioning of proxy services
- H04L67/566—Grouping or aggregating service requests, e.g. for unified processing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2209/00—Indexing scheme relating to G06F9/00
- G06F2209/54—Indexing scheme relating to G06F9/54
- G06F2209/541—Client-server
Abstract
The invention discloses a distributed real-time message filtering system, which comprises a main server, a standby server, a zookeeper module, a database, a Web module, a proxy server and a consumption end, wherein the main server is connected with the standby server; the method comprises the steps that a main server starts a resident process on a proxy server, wherein the resident process comprises a filter, a resource manager and a monitor; the method comprises the steps that a main server registers information to a server heartbeat node of a zookeeper module, a heartbeat node change event is registered at the same time, a standby server registers the server heartbeat node change event of the zookeeper module, and when the server heartbeat node change of the zookeeper module is found, the main server and the standby server automatically complete role switching in a mode of informing the main server and the standby server; the database stores filtering metadata information and filtering monitoring information. The invention intelligently combines the filtering conditions, self-adjusts the filtering rate, can switch states between active and standby, and has reasonable resource distribution.
Description
Technical Field
The present invention relates to a message filtering system, and more particularly, to a distributed real-time message filtering system.
Background
Kafka becomes a preferred component for mass data production and consumption subscription by virtue of the characteristics of high throughput, high availability and the like, but Kafka is used as a data stream component, data does not have a schema, only can be subscribed by simple data, and cannot be subjected to operations such as filtering and the like; if data stream needs to be processed simply, the common processing mode is that all data are consumed by a data subscriber, and then the real-time filtering operation of the full data is performed at a consumption end, so that the problem that: 1. the network io pressure of the subscriber is high, and the filtering is only needed to obtain a small amount of data from the marine data stream; 2. the consumption and the filtering condition of data cannot be known due to the lack of an effective monitoring means; 3. service filtering requirements of consumption terminals with different conditions of the same data source cannot be combined, so that the same data source needs to consume data for multiple times in a full amount.
The existing technical scheme for solving the problem of message filtering has the following two kinds, and has respective advantages and disadvantages:
spark Streaming + kafka: the Spark Streaming is based on a Spark engine, is an extension of a core API, is a micro batch processing model, and can realize the processing of real-time Streaming data with high throughput and a fault-tolerant mechanism. Support to obtain data from kafka, after obtaining data from a data source, simple processing of complex algorithms can be carried out by using high-level functions such as map, reduce and the like, and the integration with kafka is mature. The method has the disadvantages that 1, a spark, a third-party framework, is introduced, and certain learning cost and maintenance cost are realized; 2. the processing result is written back to the kafka, a plurality of topics need to be created, and the operation and maintenance of the kafka machine are burdened; 3. lack of effective consumption filtering monitoring; 4. spark has higher requirement on the memory; 5. spark belongs to micro batch processing, and data delay is high when data flows through a plurality of machines.
Kafka Streams: kafka Streams is a set of Kafka native class libraries that allow Apache Kafka to have stream processing capabilities, with business logic processing using the Kafka Streams API and eventual write back to Kakfa or other systems. The threshold for developers using Kafka Stream is very low, such as a single machine performing some small data amount of functional verification without starting some services on other machines, while having a dependency on lightweight, low-latency message processing and necessary Stream processing api. The disadvantages are that: 1. increasing topoic data, redundant data and heavy load of cluster operation and maintenance; 2. lack of resource scheduling causes uneven resource allocation; 3. message queues are combined with stream processing mashups, and a resource isolation mechanism is lacked.
Therefore, the current technology is in need of improvement and improvement.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a distributed real-time message filtering system, which solves the problems of message filtering and filtering condition monitoring.
The technical scheme adopted by the invention for solving the technical problems is to provide a distributed real-time message filtering system, which comprises a main server, a standby server, a zookeeper module, a database, a Web module, a proxy server and a consumption end; the main server starts a resident process on the proxy server, wherein the resident process comprises a filter, a resource manager and a monitor; the method comprises the steps that a main server registers information to a server heartbeat node of a zookeeper module, a heartbeat node change event is registered at the same time, a standby server registers the server heartbeat node change event of the zookeeper module, and when a main server fault, namely the server heartbeat node of the zookeeper module changes, is found, the main server and the standby server automatically complete role switching in a mode of informing the main server and the standby server; the database stores filtering metadata information and filtering monitoring information; the Web module reads filtering and executing information from a database and displays the information on a Web page; the filter is for a topic on a consumption proxy server; the resource manager: storing the resource load information of the filter to a resource manager node of the zookeeper module; the monitor monitors the operating state of the filter.
Further, the main server synchronizes resident process running information and filter monitoring information from the zookeeper module; then summarizing filter operation information, finding out filter conditions consuming the same source data in a resident process for merging, then calling and closing the original filters with a plurality of conditions through a remote process, starting the filters with the merged conditions for filtering, and finally distributing the filtering results according to different consumption ends; when the filter bears too many data streams and the data consumed by the filter is far more than the data transmitted to the consumption end, triggering a backpressure threshold value, intervening by the main server, and controlling the consumption rate of the filter through remote process call until the consumed data and the transmitted data are recovered to be normal and then recovering the consumption rate of the filter.
Further, the main server comprises a flow scheduling module, the flow scheduling module synchronously acquires resident process resources and load information from a resident process resource management node of the zookeeper module in real time, counts load weights of all resident processes in real time according to a weighted polling algorithm, and updates the load weights in real time; the consumption end carries out data consumption, obtains the minimum weight from the stream scheduling module, loads the data stream to the resident process with the minimum weight, then starts the filter by the stream scheduling module, and returns the data stream interface to the consumption end, thereby realizing the data stream load balance of the resident process.
Further, the consumption end registers a data mode and a filtering condition through an interface function, transmits the filtering condition and the data mode to the flow scheduling module and the filter, and after the flow scheduling module returns a data flow interface to the consumption end, the consumption end is remotely connected with the interface for consumption.
Further, the monitor monitors the filter by using an observer mode, acquires running information, filtering monitoring information and compensation information from the filter, stores the filter monitoring information and the compensation information to a resident process monitor node of the zookeeper module, and then stores the filtering monitoring information and the running information to a database at regular time; and sends the heartbeat to the zookeeper's resident process heartbeat node.
Further, when the main controller detects that the filter consumes the data stream failure through a resident process monitor node of the zookeeper module, the main controller firstly closes the failed filter, then reads metadata information and filtering condition information from a database, reads latest compensation information from the zookeeper module, and finally restarts the filter for consumption.
Further, the monitoring information is mainly service information, including data mode, filtering information, filtering rate and filtering data ratio of the subject line data.
Compared with the prior art, the invention has the following beneficial effects: the distributed real-time message filtering system provided by the invention has the advantages that the filtering conditions are intelligently combined, the data stream load is balanced, the resident process pressure is relieved, the filtering rate is self-regulated, the dispatching system can actively and waiting for state switching, the system is friendly to a consumer, the resource distribution is reasonable, and the learning cost of the consumer is low.
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FIG. 1 is an architecture diagram of a distributed real-time message filtering system in an embodiment of the present invention;
Detailed Description
The invention is further described below with reference to the figures and examples.
Fig. 1 is a structural diagram of a distributed real-time message filtering system according to an embodiment of the present invention.
Referring to fig. 1, the distributed real-time message filtering system provided by the present invention includes a main server, a standby server, a zookeeper module, a database, a Web module, a proxy server, and a consumption end; the main server starts a resident process on the proxy server, wherein the resident process comprises a filter, a resource manager and a monitor; the method comprises the steps that a main server registers information to a server heartbeat node of a zookeeper module, and meanwhile, a heartbeat node change event is registered, a standby server registers the server heartbeat node change event of the zookeeper module, and when a main server fault, namely the server heartbeat node of the zookeeper module changes, is found, the main server and the standby server automatically complete role switching in a mode of informing the main server and the standby server; the database stores filtering metadata information and filtering monitoring information; the Web module reads filtering and executing information from a database and displays the information on a Web page; the filter is for a topic on a consumption proxy server; the resource manager: storing the resource load information of the filter to a resource manager node of the zookeeper module; the monitor monitors the operating state of the filter.
The main server synchronizes resident process running information and filter monitoring information from the zookeeper module; then summarizing filter operation information, finding out filtering conditions consuming the same source data in a resident process for merging, then calling and closing filters with a plurality of original conditions through a remote process, starting the filters with the merged conditions for filtering, and finally distributing filtering results according to different consumption ends, so that the waste of network bandwidth caused by the fact that each consumption end consumes the source data in full when different consumption ends have service requirements on different filtering conditions of the same source data is avoided; when the filter bears too many data flows and the data consumed by the filter is far more than the data transmitted to the consumption end, the counter pressure threshold value is triggered, the main server intervenes, the consumption rate of the filter is controlled through remote process call until the consumed data and the transmitted data are recovered to be normal and then the consumption rate of the filter is recovered, and the problems that the consumed data of the filter is accumulated and waits for processing, the normal operation of a filter program is influenced and even an avalanche is caused are avoided.
The main server comprises a flow scheduling module, the flow scheduling module synchronously acquires resident process resources and load information from a resident process resource management node of the zookeeper module in real time, counts load weights of all resident processes in real time according to a weighted polling algorithm, and updates the load weights in real time; the consumption end carries out data consumption, obtains the minimum weight from the stream scheduling module, loads the data stream to the resident process with the minimum weight, then starts the filter by the stream scheduling module, and returns the data stream interface to the consumption end, thereby realizing the data stream load balance of the resident process. The consumption end registers a data mode and a filtering condition through an interface function, transmits the filtering condition and the data mode to the flow scheduling module and the filter, and is remotely connected with the interface for consumption after the flow scheduling module returns a data flow interface to the consumption end.
The monitor monitors the filter by using an observer mode, acquires running information, filtering monitoring information and compensation information from the filter, stores the filter monitoring information and the compensation information to a resident process monitor node of the zookeeper module, and then stores the filtering monitoring information and the running information to a database at regular time; and sends the heartbeat to the zookeeper's resident process heartbeat node. When the main controller detects that the filter consumes the data stream failure through a resident process monitor node of the zookeeper module, the main controller closes the failed filter, reads metadata information and filtering condition information from a database, reads latest compensation information from the zookeeper module, and restarts the filter for consumption.
The monitoring information is mainly business information, and comprises a data mode, filtering information, a filtering rate and a filtering data ratio of the subject line data.
And the standby server acquires resident process running state information from the zookeeper module and monitors the health state of the main server, and if the main server fails, the standby server is switched to the server. The main server needs to send heartbeat information to the zookeeper at regular time, if the heartbeat information is not sent after timeout or the node updating state is dead, the server is judged to fail, the script is called remotely to kill the process, then the standby server is started remotely, the state is registered again on the zookeeper to be in an active state, and the process scheduling service is continued to reside.
The distributed real-time message filtering system provided by the invention adopts a cgroups hard isolation mode, provides exclusive standards for different consumption ends, does not interfere with each other and occupy each other, and avoids the problem that some resources are occupied by intensive data streams for a long time due to unpredictable data streams, so that bottlenecks exist in other data streams and later-added data stream resources, and the data execution of the whole system is influenced.
In summary, the distributed real-time message filtering system provided by the present invention has the following advantages: 1. the intelligent combination of the filtering conditions reduces the consumption of the full source data for multiple times; 2. data flow load is balanced, and resident process pressure is relieved; 3. the data flow realizes back pressure and self-regulates the filtering rate; 4. the dispatching system realizes high availability and can switch states during active and standby; 5. restarting the data loss; 6. the resident process is resident in the agent, so that the step of network transmission is omitted, and the data delay is low; 6. providing a web page to monitor the filtering condition, and being friendly to a consumption end; 7. resource scheduling evaluation is provided, and resource allocation is reasonable; 8. the system bottom layer is transparent to users, the interface function is easy to use, and the learning cost of the consumer side is low.
Although the present invention has been described with respect to the preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (6)
1. A distributed real-time message filtering system is characterized by comprising a main server, a standby server, a zookeeper module, a database, a Web module, a proxy server and a consumption end; the main server starts a resident process on the proxy server, wherein the resident process comprises a filter, a resource manager and a monitor; the method comprises the steps that a main server registers information to a server heartbeat node of a zookeeper module, a heartbeat node change event is registered at the same time, a standby server registers the server heartbeat node change event of the zookeeper module, and when a main server fault, namely the server heartbeat node of the zookeeper module changes, is found, the main server and the standby server automatically complete role switching in a mode of informing the main server and the standby server; the database stores filtering metadata information and filtering monitoring information; the Web module reads filtering and executing information from a database and displays the information on a Web page; the filter is for a topic on a consumption proxy server; the resource manager: storing the resource load information of the filter to a resource manager node of the zookeeper module; the monitor monitors the operating state of the filter;
the monitor monitors the filter by using an observer mode, acquires running information, filtering monitoring information and compensation information from the filter, stores the filter monitoring information and the compensation information to a resident process monitor node of the zookeeper module, and then stores the filtering monitoring information and the running information to a database at regular time; and regularly sending the monitoring information to a zookeeper resident process heartbeat node.
2. The distributed real-time message filtering system of claim 1, wherein the master server synchronizes resident process run information and filter monitoring information from the zookeeper module; then summarizing filter operation information, finding out filter conditions consuming the same source data in a resident process for merging, then calling and closing the original filters with a plurality of conditions through a remote process, starting the filters with the merged conditions for filtering, and finally distributing the filtering results according to different consumption ends; when the filter bears too many data streams and the data consumed by the filter is far more than the data transmitted to the consumption end, triggering a backpressure threshold value, intervening by the main server, and controlling the consumption rate of the filter through remote process call until the consumed data and the transmitted data are recovered to be normal and then recovering the consumption rate of the filter.
3. The distributed real-time message filtering system according to claim 1, wherein the main server comprises a stream scheduling module, the stream scheduling module synchronously acquires resident process resources and load information from a resident process resource management node of the zookeeper module in real time, then counts load weights of all resident processes in real time according to a weighted polling algorithm, and updates the load weights in real time; the consumption end carries out data consumption, obtains the minimum weight from the stream scheduling module, loads the data stream to the resident process with the minimum weight, then starts the filter by the stream scheduling module, and returns the data stream interface to the consumption end, thereby realizing the data stream load balance of the resident process.
4. The distributed real-time message filtering system of claim 3, wherein the consuming side registers the data pattern and the filtering condition through the interface function, transmits the filtering condition and the data pattern to the stream scheduling module and the filter, and the consuming side connects the interface remotely for consumption after the stream scheduling module returns the data stream interface to the consuming side.
5. The distributed real-time message filtering system according to claim 1, wherein when the host server detects failure of filter consumption data stream through a resident process monitor node of the zookeeper module, the host server first closes the failed filter, then reads metadata information and filtering condition information from a database, reads latest compensation information from the zookeeper module, and finally restarts the filter for consumption.
6. The distributed real-time message filtering system of claim 1, wherein the monitoring information is traffic information including data patterns, filtering information, filtering rate, and filtering data fraction of subject line data.
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| CN110166524B (en) * | 2019-04-12 | 2023-04-07 | 未鲲(上海)科技服务有限公司 | Data center switching method, device, equipment and storage medium |
| CN111953713A (en) * | 2019-05-14 | 2020-11-17 | 上海博泰悦臻网络技术服务有限公司 | Kafka data display method and device, computer readable storage medium and terminal |
| CN111464368B (en) * | 2020-04-27 | 2022-04-15 | 东方通信股份有限公司 | Device and method for quickly realizing signaling tracking in network management system |
| CN112394662A (en) * | 2020-11-11 | 2021-02-23 | 许继集团有限公司 | Transformer substation monitoring system server role determination method and system |
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