CN111737226B - Method for optimizing HBase cluster performance based on Redis cluster - Google Patents
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Abstract
The invention provides a method for optimizing HBase cluster performance based on a Redis cluster, which comprises the following steps: the method comprises the following steps: establishing a data storage set group, and step two: the method for optimizing HBase cluster performance by setting and processing read and write of the cluster group specifically comprises the following steps: setting a cluster group write-in data and a timestamp rule, setting a reading and updating instruction timestamp judgment module for a client, and when a data timestamp is one day before a set running day, distributing a reading instruction to an HBase cluster; when the data timestamp to be read is the timestamp data of the latest day in the first Redis cluster data, the instruction is allocated to the first Redis cluster for query; and setting high availability of the double Redis clusters, and if the first Redis cluster has hardware problems or network fluctuation, the second Redis cluster replaces the first Redis cluster to receive data and trigger maintenance alarm.
Description
Technical Field
The invention relates to the technical field of database performance optimization, in particular to a method for optimizing HBase cluster performance based on a Redis cluster.
Background
With the development of the current society, the data volume is increasing at an explosion speed, the traditional data warehouse can not meet the requirement of the company for increasing data volume, even NoSQL such as Redis and HBase is beginning to face the challenge, and the current challenges of the warehouse mainly focus on how to meet the requirement of high performance and the persistent and reliable storage of mass data.
For the existing NoSQL and HBase, the advantages are that common commercial machines are combined together to form reliable storage of mass data, but the performance is slightly insufficient. Redis, which is a NoSQL based on a memory, has the performance about 10 to hundreds times that of HBase, but cannot ensure the reliability of data based on memory representation, is not suitable for long-time storage, and cannot store large-scale data. As for data sources, mass data of the modern society are generated by human behaviors, data generated by people are closely related to the work and rest of people, and the data represent that most of new data are generated in the period of human activity. For the underlying server of the database, when the machine pressure is the greatest when the underlying server is subjected to frequent requests of multiple clients and undertakes throughput of new data, the performance is also obviously reduced.
Disclosure of Invention
Aiming at the problem that when the bottom layer server of a database faces frequent requests of multiple clients and bears the throughput of new data, the machine pressure is the maximum, and the performance is obviously reduced, the invention provides a method for optimizing the performance of an HBase cluster based on a Redis cluster.
The technical scheme of the invention is as follows:
the technical scheme of the invention provides a method for optimizing HBase cluster performance based on a Redis cluster, which comprises the following steps:
the method comprises the following steps: establishing a data storage set group, specifically comprising:
building an HBase cluster;
building a Redis cluster on the basis of the HBase cluster, wherein the Redis cluster comprises a first Redis cluster and a second Redis cluster; the effective utilization of the double Redis cluster system provides load balancing capability for the bottom layer of the server;
step two: the method for optimizing HBase cluster performance by setting and processing read and write of the cluster group specifically comprises the following steps:
setting a cluster group write data and a time stamp rule, wherein the method comprises the following steps: setting a data writing rule of the active time section of the cluster group; setting data writing and data synchronization of the inactive time of the cluster group;
setting a reading and updating instruction time stamp judging module for the client, and when the data time stamp is in the day before the set running days, distributing the reading instruction to the HBase cluster; when the timestamp of the data to be read is the timestamp data of the latest day in the first Redis cluster, the instruction is allocated to the first Redis cluster for inquiring;
setting double Redis clusters to realize high cluster availability, setting a journal module, and setting an active time period, wherein a key value is reserved in the journal module when the first Redis cluster writes each piece of data into the second Redis cluster; and judging whether the clusters are abnormal or not according to the time of the returned key value, and if the first Redis cluster has hardware problems or network fluctuation, the second Redis cluster replaces the first Redis cluster to receive data and trigger maintenance alarm.
Further, the step one of establishing the data storage set group further includes:
a cluster group client active time period and a cluster group client inactive time period, or,
and creating an active time automatic judging mechanism, and judging the active time period of the client according to the actual throughput of the cluster group. The cluster group supports automatic judgment of the active time period, and the time period is set manually by default.
Further, an automatic active time determination mechanism is created, and an active time period of the client is determined according to the actual throughput of the cluster group, specifically including:
creating an active time period automatic judgment module;
the active time period automatic judgment module acquires cluster throughput data through an interface, the throughput data is imported into a formula, and the HBase cluster reference throughput on the bottom-layer server is set to be H; actual throughput (user insertion data amount 1.3+ user reading data amount 0.6+ user update data amount 1.2)/time period;
when the actual throughput >0.8H, the active period is determined.
Further, the step one of establishing the data storage set group further includes:
and setting the number of nodes of the first Redis cluster and the second Redis cluster based on the data volume of the client.
Further, the same number of nodes of the first Redis cluster and the second Redis cluster is set to be n,
n1.2 x N1/(c-system memory)
Wherein N1 is the data volume inserted in the HBase cluster active time period every day;
c is the memory size of each node server of the Redis cluster;
the system memory is a system common memory.
Further, the step of setting the cluster group write data and the timestamp rule specifically includes:
s21: setting data writing rule of active time section of cluster group
In the active time period of the cluster group client, newly written data are written into a first Redis cluster by stamping a time stamp key value, the first Redis cluster in the cluster group is responsible for inserting new data in the latest day, and a second Redis cluster in the cluster group backs up the newly inserted data in the active time period;
s22: data write and data synchronization for setting cluster group inactive time
When the cluster group is in an inactive time period, the HBase cluster receives data synchronization of the latest one-day timestamp of the first Redis cluster while meeting the requirements of clients for reading and updating data; simultaneously with the data synchronization, the second Redis cluster temporarily receives the client's request to insert new data and synchronizes directly to the HBase cluster. When the Redis cluster pressure is large, the bgsave in the rdb mechanism is enabled for a short time to perform data persistence.
Further, the method further comprises:
setting a batch processing interface during data synchronization, and using a batch processing DML mode; and after the data synchronization is finished, the Redis cluster releases all the memory data. The batch DML processing mode can greatly reduce the interaction times with the server and reduce the end-to-end requests of multiple clients and times.
Further, the step of setting the dual Redis cluster to realize high cluster availability includes:
the method comprises the steps that double Redis clusters are set to be in a semi-persistent RDB mode for ensuring high performance, when a long-time data reading and writing task is carried out on data, in order to meet cooperative reliability, a journal module is set in a management node in each Redis cluster, and a key value is reserved in the journal module when a first Redis cluster writes each piece of data into a second Redis cluster in an active time period;
if the key value is not received within a set time period, data rewriting is started, three pieces of data are sent to a first Redis cluster, if the first Redis cluster, a second Redis cluster and the clusters are normally communicated, the second Redis cluster receives the key value returned twice, and if the first Redis cluster has hardware problems or network fluctuation, the second Redis cluster replaces the first Redis cluster to continuously receive the data and trigger maintenance alarm.
According to the technical scheme, the invention has the following advantages: 1. an HBase and Redis combined cluster group 2 based on the HBase cluster is established, the effective utilization of the double Redis clusters provides the load balancing capability of a server bottom layer, and the end-to-end requests of multiple clients for multiple times are reduced. 3. And establishing a judgment and high availability module to effectively finish the interaction of HBase and Redis. The method can solve the problems of difficult performance improvement of an independent HBase cluster and data volatility and difficult persistence of an independent Redis cluster, and relieves the end-to-end pressure of a plurality of clients on the bottom layer of the database.
In addition, the invention has reliable design principle, simple structure and very wide application prospect.
Therefore, compared with the prior art, the invention has prominent substantive features and remarkable progress, and the beneficial effects of the implementation are also obvious.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for optimizing performance of an HBase cluster based on a Redis cluster according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a method for optimizing performance of an HBase cluster based on a Redis cluster according to an embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all embodiments. 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.
Example one
As shown in fig. 1, the technical solution of the present invention provides a method for optimizing HBase cluster performance based on a Redis cluster, including the following steps:
the method comprises the following steps: establishing a data storage set group, specifically comprising:
building an HBase cluster;
building a Redis cluster on the basis of the HBase cluster, wherein the Redis cluster comprises a first Redis cluster and a second Redis cluster; the effective utilization of the double Redis cluster system provides load balancing capability for the bottom layer of the server;
step two: the method for optimizing HBase cluster performance by setting and processing read and write of the cluster group specifically comprises the following steps:
setting a cluster group write data and a time stamp rule, wherein the method comprises the following steps: setting a data writing rule of the active time section of the cluster group; setting data writing and data synchronization of the inactive time of the cluster group;
setting a reading and updating instruction timestamp judgment module for the client, and distributing a reading instruction to an HBase cluster when a data timestamp is in the day before a set running day; when the timestamp of the data to be read is the timestamp data of the latest day in the first Redis cluster, the instruction is allocated to the first Redis cluster for inquiring;
setting double Redis clusters to realize high cluster availability, setting a journal module, and setting an active time period, wherein a key value is reserved in the journal module when the first Redis cluster writes each piece of data into the second Redis cluster; and judging whether the clusters are abnormal or not according to the time of the returned key value, and if the first Redis cluster has hardware problems or network fluctuation, the second Redis cluster replaces the first Redis cluster to receive data and trigger maintenance alarm.
Example two
As shown in fig. 2, the technical solution of the present invention provides a method for optimizing HBase cluster performance based on a Redis cluster, including the following steps:
the method comprises the following steps: establishing a data storage set group, which specifically comprises:
s1-1: building an HBase cluster; in this embodiment, building the HBase cluster is implemented as follows: (1) building an HBase cluster: and selecting and adding zookeeper and HDFS services as bottom layer interaction and storage of HBase through a cloud sea Insight platform management interface. (2) And selecting to add HBase service, and designating a host where the HBase master, the registers and the clients are located to perform installation and deployment, wherein the HBase master is designated on an HBase cluster management server, and the servers where the registers are located are all working servers and are responsible for data storage and response of client requests.
In addition, file configuration environment variables such as HBase-env.sh and HBase-site.xml can be modified manually by using an open source HBase official on-line HBase installation package, interaction with zookeeper and HDFS is completed, and an HBase cluster is formed.
S1-2: building a Redis cluster on the basis of the HBase cluster, wherein the Redis cluster comprises a first Redis cluster and a second Redis cluster; the effective utilization of the double Redis cluster system provides load balancing capability for the bottom layer of the server; in this embodiment, (1) a cloud sea Insight big data management platform is installed in the first Redis cluster, a Redis service is added, and a server that needs to build the first Redis cluster is specified. (2) And when a second Redis cluster is built, adding Redis services on a required host by using different cloud sea Insight management platforms.
S1-3: a cluster group client active time period and a cluster group client inactive time period, or,
and creating an active time automatic judging mechanism, and judging the active time period of the client according to the actual throughput of the cluster group. The cluster group supports automatic judgment of the active time period, and the time period is set manually by default.
Creating an automatic active time judging mechanism, judging the active time period of a client according to the actual throughput of the cluster group, and specifically comprising the following steps: creating an automatic judgment module of an active time period; the active time period automatic judgment module acquires cluster throughput data through an interface, the throughput data is imported into a formula, and the HBase cluster reference throughput on the bottom-layer server is set to be H; actual throughput (user insertion data amount 1.3+ user reading data amount 0.6+ user update data amount 1.2)/time period; when the actual throughput >0.8H, the active period is determined.
S1-4: and setting the number of nodes of the first Redis cluster and the second Redis cluster based on the data volume of the client.
It should be noted that the number of days of cluster group operation is set to a, the memory size of each node server of the Redis cluster is set to c, the cluster group client active time period is set to t1 (for example, it is simply selected to be daytime), the cluster group client inactive time period is set to t2 (for example, it is selected to be late night and early morning), the amount of data inserted by the HBase cluster per day t1 is N1, and the amount of data inserted by the t2 time period is N2. In the step, the same number of nodes of the first Redis cluster and the second Redis cluster is set as n,
n1.2 x N1/(c-system memory)
Wherein N1 is the data volume inserted in the HBase cluster active time period every day;
c is the memory size of each node server of the Redis cluster; the system memory refers to a common memory of the system, generally about 8G, and different systems have partial differences.
Step two: the method for optimizing HBase cluster performance by setting and processing read and write of the cluster group specifically comprises the following steps:
s2-1: it should be noted that the step of setting the cluster group write data and the timestamp rule specifically includes:
s21: setting data writing rule of active time section of cluster group
The key-value key value pairs of the data of the database refer to the key values with a timestamp, and in the cluster group client active time period t1, the newly written data are written with the timestamp key value into the first Redis cluster, which in the cluster group is only responsible for the insertion of new data of the latest day. A second Redis cluster in the cluster group performs a backup of the newly inserted data during the active time period.
S22: data write and data synchronization for setting cluster group inactive time
At the time of the cluster group inactive time period t2, the HBase cluster receives the data synchronization of the latest one-day timestamp of the first Redis cluster while satisfying the client reading and updating data request; while the data is synchronized, the second Redis cluster temporarily receives the client's request to insert new data and synchronizes directly to the HBase cluster. Setting a batch processing interface during data synchronization, and using a batch processing DML mode; the difference with the data writing of the active time period client is that the active time period is that each client of the multiple clients submits several pieces of data respectively, and the scenario after optimization is that the Redis cluster inserts the same amount of data into the HBase cluster in batch every time. The batch DML processing mode can greatly reduce the interaction times with the server and reduce the end-to-end requests of multiple clients and times.
And after the data synchronization is finished, releasing all the memory data by the Redis cluster. When the Redis cluster pressure is large, the bgsave in the rdb mechanism is enabled for a short time to perform data persistence.
S2-2: setting a reading and updating instruction time stamp judging module for the client, and when the data time stamp is in A-1 day of the set running days, distributing the reading instruction to the HBase cluster; when the data timestamp to be read by the first Redis cluster is the timestamp data of the latest day, the instruction is allocated to the first Redis cluster for query.
S2-3: the method comprises the following steps of setting a double Redis cluster to realize high availability of the cluster, and specifically comprises the following steps:
the double Redis clusters are set to be in a semi-persistent RDB mode for ensuring high performance, when long-time data reading and writing tasks are carried out on data, in order to meet cooperative reliability, a journal module is set in a management node in each Redis cluster, a data receiver is set, a time period t3 is set, data reception exists in the time period t3, and no return value exists. And if 0 piece of data is received within the time period t3, sending a data write-back instruction to the first Redis cluster, and triggering a maintenance alarm if no data is written after the instruction is sent. Setting an active time period, and leaving a key value in a journal module when a first Redis cluster writes each piece of data into a second Redis cluster;
if the key value is not received within the set time period t3, data rewriting is started, three pieces of data are sent to the first Redis cluster, if the first Redis cluster, the second Redis cluster and the clusters are normally communicated, the second Redis cluster receives the key value returned twice, and if the first Redis cluster has hardware problems or network fluctuation, the second Redis cluster replaces the first Redis cluster to continuously receive the data and trigger maintenance alarm.
Although the present invention has been described in detail by referring to the drawings in connection with the preferred embodiments, the present invention is not limited thereto. Various equivalent modifications or substitutions can be made on the embodiments of the present invention by those skilled in the art without departing from the spirit and scope of the present invention, and these modifications or substitutions are within the scope of the present invention/any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (7)
1. A method for optimizing HBase cluster performance based on a Redis cluster is characterized by comprising the following steps:
the method comprises the following steps: establishing a data storage set group, specifically comprising:
building an HBase cluster;
building a Redis cluster on the basis of the HBase cluster, wherein the Redis cluster comprises a first Redis cluster and a second Redis cluster;
step two: the method for optimizing the HBase cluster performance by setting and processing the cluster group read-write specifically comprises the following steps:
setting a cluster group write data and a time stamp rule, wherein the method comprises the following steps: setting a data writing rule of the active time section of the cluster group; setting data writing and data synchronization of the inactive time of the cluster group;
setting a reading and updating instruction time stamp judging module for the client, and when the data time stamp is in the day before the set running days, distributing the reading instruction to the HBase cluster; when the data timestamp to be read is the timestamp data of the latest day in the first Redis cluster data, the instruction is allocated to the first Redis cluster for query;
setting double Redis clusters to realize high cluster availability, setting a journal module, and setting an active time period, wherein a key value is reserved in the journal module when the first Redis cluster writes each piece of data into the second Redis cluster; judging whether the cluster is abnormal according to the time of the returned key value, and if the first Redis cluster has a hardware problem or network fluctuation, replacing the first Redis cluster by the second Redis cluster to receive data and trigger a maintenance alarm;
the step of setting the cluster group write data and the timestamp rule specifically includes:
s21: setting a data writing rule of the active time section of the cluster group;
in the active time period of the cluster group client, newly written data are written into a first Redis cluster by stamping a time stamp key value, the first Redis cluster in the cluster group is responsible for inserting new data in the latest day, and a second Redis cluster in the cluster group backs up the newly inserted data in the active time period;
s22: setting data writing and data synchronization of the inactive time of the cluster group;
when the cluster group is in an inactive time period, the HBase cluster receives data synchronization of the latest one-day timestamp of the first Redis cluster while meeting the requirements of clients for reading and updating data; simultaneously with the data synchronization, the second Redis cluster temporarily receives the client's request to insert new data and synchronizes directly to the HBase cluster.
2. The method according to claim 1, wherein the step of establishing the data storage set group further comprises:
a cluster group client active time period and a cluster group client inactive time period, or,
and creating an active time automatic judging mechanism, and judging the active time period of the client according to the actual throughput of the cluster group.
3. The method for optimizing HBase cluster performance based on Redis cluster according to claim 2, wherein an active time automatic determination mechanism is created, and a client active time period is determined according to actual throughput of a cluster group, specifically comprising:
creating an automatic judgment module of an active time period;
the active time period automatic judgment module acquires cluster throughput data through an interface, the throughput data is imported into a formula, and the HBase cluster reference throughput on the bottom-layer server is set to be H; actual throughput = (user insertion data amount × 1.3+ user reading data amount × 0.6+ user update data amount × 1.2)/time period;
when the actual throughput >0.8H, the active period is determined.
4. The method according to claim 2, wherein the step of establishing the data storage set group further comprises:
and setting the number of nodes of the first Redis cluster and the second Redis cluster based on the data volume of the client.
5. The method for optimizing HBase cluster performance based on Redis cluster as claimed in claim 4, wherein the number of nodes of the first Redis cluster and the second Redis cluster are set to be n,
n =1.2 x N1/(c-system memory)
Wherein N1 is the data volume inserted in the HBase cluster active time period every day;
c is the memory size of each node server of the Redis cluster;
the system memory is a system common memory.
6. The method for optimizing HBase cluster performance based on Redis cluster as claimed in claim 1, wherein the method further comprises:
setting a batch processing interface during data synchronization, and using a batch processing DML mode; and after the data synchronization is finished, the Redis cluster releases all the memory data.
7. The method according to claim 1, wherein the step of setting dual Redis clusters to realize cluster high availability comprises:
setting a journal module in a management node in each Redis cluster, and setting an active time period when each piece of data is written into a second Redis cluster by a first Redis cluster, and simultaneously leaving a key value in the journal module;
if the key value is not received within a set time period, data rewriting is started, three pieces of data are sent to a first Redis cluster, if the first Redis cluster, a second Redis cluster and the clusters are normally communicated, the second Redis cluster receives the key value returned twice, and if the first Redis cluster has hardware problems or network fluctuation, the second Redis cluster replaces the first Redis cluster to continuously receive the data and trigger a maintenance alarm.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108183961A (en) * | 2018-01-04 | 2018-06-19 | 中电福富信息科技有限公司 | A kind of distributed caching method based on Redis |
| CN108733808A (en) * | 2018-05-21 | 2018-11-02 | 试金石信用服务有限公司 | Big data software systems switching method, system, terminal device and storage medium |
| CN109871367A (en) * | 2019-02-28 | 2019-06-11 | 江苏实达迪美数据处理有限公司 | A kind of distributed cold and heat data separation method based on Redis and HBase |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108183961A (en) * | 2018-01-04 | 2018-06-19 | 中电福富信息科技有限公司 | A kind of distributed caching method based on Redis |
| CN108733808A (en) * | 2018-05-21 | 2018-11-02 | 试金石信用服务有限公司 | Big data software systems switching method, system, terminal device and storage medium |
| CN109871367A (en) * | 2019-02-28 | 2019-06-11 | 江苏实达迪美数据处理有限公司 | A kind of distributed cold and heat data separation method based on Redis and HBase |
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