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Huawei cloud database DDS products are deeply enabled

2022-07-04 15:07:00 【InfoQ】

  Statement ： This article references from 《 Huawei cloud developer school 》, Huawei cloud database DDS Deep product empowerment , Main orientation DA、 deliver 、 A line of 、 Second line database practitioners , in the light of DDS Common problems and product features , involve ： Introduction to product features 、 Product usage scenarios 、 Product FAQs 、DDS Example usage specification and best practice introduction 、DDS Introduction to the internal mechanism of replica set and the internal principle of partitioned cluster .

This article is suitable for database solution engineers （DA）、 Database Delivery Engineer 、 Database frontline & Second line practitioners 、 And right DDS Interested users , It is hoped that readers can use Huawei cloud database through this article DDS Learning of product depth empowerment courses , To strengthen DA、 deliver 、 A line of 、 Second line understanding and skill improvement of database products .

This article is divided into 5 There are four chapters to explain ：

The first 1 Chapter Huawei cloud database DDS Product introduction

The first 2 Chapter DDS Business development and use foundation

The first 3 Chapter understand DDS Kernel principle

The first 4 Chapter Use sharded clusters quickly

One 、 Huawei cloud database DDS Product introduction

1. DDS summary

First of all, we need to understand DDS Some basic information about ：

Document database DDS(Document Database Service)

Fully compatible with MongoDB agreement

, It's normal MongoDB How to use ,DDS How to use

High performance in Huawei cloud 、 High availability 、 High security 、 On the basis of elastic expansion , Provides one click deployment , The elastic expansion , disaster , Backup , recovery , Monitoring and other service capabilities . At present, we support

Fragmentation cluster (Sharding)、 Replica set (ReplicaSet)、 A single node (Single)

Three deployment architectures .

MongoDB Data structure of

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MongoDB Storage structure

• file (Document):MongoDB The most basic unit in , from BSON Key value pair (key-value) form .    

Equivalent to rows in a relational database (Row).    

• aggregate (Collection): A set can contain multiple      file , Equivalent to tables in a relational database (Table)

• database (Database): Equivalent to the database concept in relational database , A database can contain multiple collections . You can go to MongoDB Create multiple databases in .

2. DDS Deployment form and key features

2.1DDS Service deployment form - A single node (Single)

Architectural features

1. Ultra low cost , Just pay for one node

2. Support 10GB-1000GB Data storage ;

3. Compare replica sets / The cluster availability is not high : When the node fails , Business is not available ;

Applicable scenario

Non core data storage

Learning practice ;

The business of the test environment ;

2.2 DDS Service deployment form -- Replica set (Replica Set)

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The figure above shows a classic three copy form

Architectural features

1. Three node high availability architecture : When the primary node fails , The system automatically selects

New master

2. Support 10GB-3000GB data storage ;    

3. Have the ability to extend to 5 node ,7 Node replica set capabilities .    

Applicable scenario

High availability requirements , data storage <3T

2.3 DDS Service deployment form -- colony (sharding)

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Architectural features

1. Components : from mongos( route )、config( To configure )、shard( Fragmentation ) Three types of nodes make up

2. Shard Fragmentation : Every shard Is a replica set architecture , Responsible for storing business data . Can create 2-16 A shard , Each slice 10GB-2000GB. therefore , Cluster space range ( 2-16)*(10GB-2000GB)

3. Expand capabilities : Online specification change 、 Scale out online

Applicable scenario

High availability required , Large amount of data and future horizontal expansion requirements

2.4 DDS Key features

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Document database DDS Major feature sets

•

Database type and version :

compatible MongoDB 3.4/4.0 edition

•

Data security

: Multiple security policies protect database and user privacy , for example :VPC、 subnet 、 Security group 、SSL etc.

•

Data is highly reliable

: Database storage supports triple copy redundancy , High reliability of database data ; High reliability of backup data

•

High service availability ( Disaster tolerance in the same city ):

colony / Replica set instances support cross AZ Deploy , High service availability

•

Instance access :

Multiple access methods , Include : Intranet IP visit 、 Public network IP visit

•

Instance management :

Support the creation of instances 、 Delete 、 Specification change 、 Node expansion 、 Disk expansion 、 Restart and other life cycle management

•

Instance monitoring :

Support monitoring database instances OS And DB Key performance indicators of the engine , Include CPU/ Memory / Storage capacity utilization 、1/O Activities 、 The number of database connections, etc

•

Stretch and stretch :

Horizontal expansion : Add or delete shard Fragmentation ( most 16 individual ); Vertical expansion : Instance specification change , Storage expansion ( Maximum n*2TB)

•

Backup and recovery :

Backup : Automatic backup 、 Manual backup , Full volume backup 、 Incremental backup , Addition of backup files 、 Delete 、 check 、 Lifecycle management such as replication . recovery : Replica sets support recovery to any point in time within the backup retention period (Point-In-Time Recovery, PITR)/ A full backup point in time , Restore to new instance / The original example . The backup storage cycle is up to 732 God

•

Log management :

Slow support SQL journal 、 Check the error log 、 And audit log download

•

Parameter configuration :

According to monitoring and log information , Customize the parameters of the database instance through the management console , To optimize the database . In addition, the management console provides an add function for parameter groups 、 Delete 、 Change 、 check 、 Reset 、 Compare 、 The ability to manage a series of parameters such as replication .

3. DDS Product advantages and application scenarios

3.1 DDS Product advantages

MongoDB

• 100% compatible MongoDB

• There is no need for business transformation , The ability to migrate directly to the cloud

• Support the community 3.4/4.0 edition

3 Architecture

• colony 、 Replica set 、 A single node

• colony :nTB Storage 、 Online expansion

• Replica set :2TB Storage

• A single node : High cost performance

High availability

• High availability of Architecture 、 Span AZ Deploy

• Support for replica sets ,Shard High availability Architecture ( colony )

• Replica set multi node ( 3、 ... and 、 5、 ... and 、 7、 ... and )

• colony 、 Replica sets support cross AZ Deploy

Highly reliable

• Automatically / Manual backup , Data recovery     

• Automatic backup every day , Retain 732 God     

• Manual backup , Keep forever     

• Backup recovery     

High security

• With multi-layer safety protection     

• The Internet :VPC Network isolation

• transmission :SSL A secure connection

• visit : Security group out 、 Entry restrictions

management 、 monitor

• Visual monitoring :CPU、 Memory 、10、 Network, etc     

• Instance one click expansion 、 Specification change     

• Error log 、 Slow log management     

• Parameter group configuration

3.2 DDS Application scenarios

Flexible business scenarios

DDS use No-Schema The way , Avoid the pain of changing the table structure , Ideal for start-up business needs .

Game applications

The database as the game server stores user information , User's game equipment 、 Integration and so on are directly stored in the form of embedded documents , Easy to query and update .

Live video

Store user information , Gift information, etc

3.3 DDS Use scenario example ： Business elasticity extension , Flexible data structure

Business and data characteristics of the game industry

1. User information and transaction data are stored in MySQL in .

2. The character equipment data and the game process log are stored in DDS in

3. The game business changes frequently , The data table needs to be changed DDS Modifying the table structure has no impact on business .

The customer is in pain ( Private cloud deployment )

1. Resources do not scale elastically , You need to stop taking it and then operate it manually , High risk .

2. There is no automatic database fail over mechanism or insufficient capability , Primary instance failure , Modify application configuration , It takes a long time to stop taking it .

3. It's rare to set up a full-time DBA Position , Encounter the scenario of data archiving , It's hard to meet the demands of operations .

4. Game play changes quickly , The data model is flexible

Huawei cloud database solution

1. High performance

RDS MySQL and DDS Performance exceeds that of Alibaba cloud .

2. Stretch and stretch

RDS and DDS Support elastic expansion of disk , No business impact .

3. One click back ( Game business data demands )

RDS and DDS It supports table level and instance level file retrieval at any time point .

4. Read the service quickly

RDS and DDS, You can use backup to create a new instance , Realize fast service opening .

5. Fail over

RDS and DDS Switching between active and standby faults at second level , Transparent to business , The application configuration does not need to be changed .

4. DDS Management console and operation and maintenance guide

DDS Service home page

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DDS Instance creation -1

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DDS Instance creation -2

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DDS Example details

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Two 、 DDS Business development and use foundation

1. DDS High availability connection mode

Shard cluster high availability address ：

mongodb://wuser;****@192.168.0.204:8635.192.168.0.94:8635/test?authSource=admin

Multiple mongosIP Configure on the client Driver Load balancing

Single mongos fault , other mongos The normal operation

Scenario of connection failure between replica set and partitioned cluster instance :

• The network port is blocked , Security group , Span vpc

• Open or not SSL A secure connection

• Connection parameter error , user name 、 Wrong password

• DDS The instance connection is full , Unable to create new connection

• DDS Instance memory 、CPU Too high

• Refer to the official guide for more details :

https://support.huaweicloud.com/dds fa/dds_faq_0218.html

2. DDS User authentication and creation

2.1 User authentication

user , role , jurisdiction , object Model ---- be based on admin Libraries create and manage user roles

user

:userA,userB,userC,

role

:role1,role2,role3    

jurisdiction

:userAdmin,readWrite.…

object

:db,collection(action)

View the current user and role :

    use admin

db.runCommand({rolesinfo: 1});db.runCommand({ userslnfo: 1} );

Take several common user roles for example :

Manage user and role accounts :userAdminAnyDatabasedb.createUser({user:"user_admin,pwd:"YourPwd_234",roles:[{role:"userAdminAnyDatabase",db:"admin") Manage database accounts :dbAdminAnyDatabasedb.createUser(fuser:"dbadmin"pwd:"YourPwd 234"roles:[{role:"dbAdminAnyDatabase",db:"admin"}l})· Specify the management account of the library :dbAdmindb.createUser({user:"db_admin_db1",pwd:"YourPwd_234",roles:[{role:"dbAdmin",db:"db1"}I}) Read only account of the whole database :readAnyDatabasedb.createUser({user:"db_read",pwd:"YourPwd_234",roles:{role: "readAnyDatabase",db:"admin"}})· Specify the read / write account number of the library :readWritedb.createUser({user:"db_rw_db2",pwd:"YourPwd_234",roles:[{role:"readWrite",db:"db2"}l})

2.2 Create a privileged user

The management user can manage the unreadable and writable data of the library table after logging in

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Read only users can read but not write after logging in

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3. DDS Use the connection parameters to separate the read and write

3.1 Selection of read / write separation scenarios

•

Highly reliable operation of the production environment , High availability of core components , Improve the overall availability and service quality of the system

•

Understand in combination with node deployment patterns :

•

https://supporthuaweicloud.com/bestpractice-dds/dds_0003.html

•

Usage scenario of read-write separation

•

How to select read parameters Read Preference

primary( Only master ) Only from primary Nodes read data , This is the default setting primaryPreferred( First lord, then slave ) Priority from primary Read ,primary No service , from secondary read secondary( Only from ) Only from scondary Nodes read data secondaryPreferred ( First from then on ) Priority from secondary Read , No, secondary Members , from primary Read nearest( Nearby ) Read nearby according to network distance , According to the client and server PingTime Realization

3.2 An example of a read / write separation configuration

An example of a read / write separation configuration :

Can pass uri Connection parameter configuration , You can also configure the read preferences for a single query operation mongodb://db_rw_db2:[email protected]:port1,IP2:port2/db2?

authSource=admin&replicaSet=replica&readPreference=secondaryPreferred

replica:PRIMARY> db.coll1.find().readPref("secondary");

2021-12-29T11:56:09.435+0000 I NETWORK [js] Successfully connected to 192.168.2

"id":0bjectId("61cc2b7270efbd76daa54891"),"age": 13 }

id"    ObjectId("61cc2b7970efbd76daa54892"),"age": 14 }

4. DDS Write policy configuration

A battle of consistency and availability ----- Adjustable consistency parameter

• Write strategy writeConcern Parameter usage and default values

·db.collection.insert({x:1},{writeConcern:{w:1}})

·mongodb://db_rw_db2:YourPwd [email protected]:port1P2:port2/db2?

authSource=admin&replicaSet=replica&w=majority&wtimeoutMS=5000

• Partition fault tolerance is inevitable

• Business data consistency : Real time consistency , Final consistency {w:n} {w:“majority"}w The default is 1

• System service availability : The delay tolerance of read / write operations depends on multi node data synchronization oplog Copy to the standby node

• Select the above parameter values based on the business characteristics

The write operation policy is specified in the connection parameter

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The write operation policy is specified in the single operation parameter

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3、 ... and 、 understand DDS Kernel principle

1. DDS Overview of service deployment model

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Architectural features

1. High availability service mode , Automatic failover , Read write separation scenario

2. Support 10GB-3000GB data storage ;

3. Have the ability to extend to 5 node ,7 Node replica set capabilities .

Applicable scenario

High availability requirements , data storage <3T

2. DDS Heartbeat and election

2.1 DDS Heartbeat and election

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• Within the replica set , Between nodes

• By default , interval 2s

• 10s The primary node did not respond to the active election

• Automatic failover

victory :

The node with the latest data gets most of the node votes

2.2 DDS Automatic failover

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Replica set node role :

• Primary

• Secondary

• Hidden

• Arbiter

Fail over

• Primary Something goes wrong ( Node heartbeat is not normal )

• Secondary Launch an election , Take over actively

• Driver Connect replica set for normal business read / write

2.3 DDS Replica set management

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Replica set management :

• Replica set configuration initialization rs.initiate()

• Replica set add members rs.add()

• Replica set delete member rs.remove()

• Replica set configuration view rs.config() Replica set configuration reset rs.reconfig()

• Replica set status view rs.status()

3. DDS Data synchronization and replication

3.1 DDS Data synchronization

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Data synchronization within multiple replica sets :

• Within the replica set , One Primary, Multiple Secondary

• The standby node can carry the read capacity

• The standby node selects the synchronization source : Master node or Other nodes that are newer than their own data

• Allow chain copying , Reduce the pressure on the main node

• Data is replayed on the standby node through operation records

• The latest operation timestamp of the standby node as the data replication progresses

3.2 DDS Detailed explanation of data synchronization

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Key technical points of data replication :

• After the master node writes data , Record oplog

• Standby node pull oplog√ Standby node application oplog

• voplog Is a set of fixed size , Write in natural order , No index

• Multithreaded parallel applications oplog:

• A thread internal operation is executed serially

• A collection of oplog Will be handled by a thread

• Of different sets oplog Will be allocated to different threads for processing

• Update database state , Update the latest timestamp

3.3 DDS Data synchronization considerations

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Notes on replication :

• Within the replica set oplogsize Fix , Writing data will eliminate the old oplog data

• Database instance of no-load service , adopt noop Special operation progress

• noop Every operation interval 10s write in oplog, Less than 10s No writing

• Business overload , Write speed is too fast, causing the replication to be disjointed

• Can pass write Concern Make sure to write to most nodes

• Read write separation can be specified readPreference

4. DDS Query plans and other mechanisms

4.1 DDS Query plan and index suggestions

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Index type :

• Single field index     

• Combined field index

{userid: 1,score:-1}    

• Nested field index         

{“addr.zip”}    

• Geolocation index     

2dsphere indexes

• Text index

• hash Indexes

Index attribute :

• unique index     

• Partial index     

• Sparse index

• TTL Indexes (Time to Live)        

Diagnose the query effect through the query plan :

• explain Plan analysis : Return to document 、 Scan document 、 Whether to overwrite the index 、 Whether to sort in memory

• queryPlanCache View the query plan scheme cache

4.2 DDS Subscribe to data change scenarios

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Subscription data change :

• adopt watch Listen for data changes in a set

• adopt cursor Of next Continue to return the data changes of the set

• full_document='updateLookup’ return update Full text file

• db.bar.watch(0.{“resumeAfter”:<\_id>}) Recover from breakpoints

• readConcern=true

• Breakpoint time can be recovered , The same goes for oplog Available Windows

Change Stream Limit :

• part DDL Operation does not support

• fullDocument Back to update The full text may be changed by subsequent operations

• Full information ( Under the premise of frequent and rapid changes )

• change stream Return body limit 16M, The original data modification needs to be smaller

4.3 DDS Other mechanism considerations

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file & Gather suggestions :

• JSON Data model ( Object model cohesive data association )

• file Size Limit 16MB

• It is not recommended to store excessive data in a collection

• Use fixed sets effectively (size,max)

• Delete the database to quickly free memory

• The total number of sets is too large, which leads to high memory consumption

Four 、 Use sharded clusters quickly

1.DDS Overview of service deployment model

DDS Service set deployment form

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Architectural features

1. Components : from mongos( route )、config( To configure )、shard( Fragmentation ) Three types of nodes make up

2.Shard Fragmentation : Every shard Is a replica set architecture , Be responsible for keeping

Store business data . Can create 2-32 A shard , Each slice 10GB-2000GB. therefore , Cluster space range (2-32)*(10GB-2000GB)

3. Expand capabilities : Online specification change 、 Scale out online

Applicable scenario

High availability required , Large amount of data and future horizontal expansion requirements

2. DDS Partitioned cluster basically uses

2.1 DDS Select the cluster size

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• Fragment cluster deployment mode , Horizontal sub table , Scale out storage and read / write capabilities

• 2 individual mongos above , The routing module provides highly available access

• 2 individual shard above , It has the ability of fragment expansion

• One shard Internally, it serves as a replica set , Have all the properties of the replica set

• The user database creates non sharding by default , You need to specify the database and table splitting methods

2.2 DDS Select the slice key Association index

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Suggestions for slicing keys

Similar to index selection , Fields with strong value selectivity 、 It can be that there are too many single associated documents for the combined field index value , Easily lead to Jumbo chunk Make sure the query contains shard key, avoid scatter-gather Query slice key types HashedRange

>sh.shardCollection("database.collection"{<field>:"hashed"})>sh.shardCollection("database.collection",{<shard key>})

Example of partition key : Massive device log type data

2.3 DDS preset chunk And other suggestions

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• preset chunk It can effectively reduce the performance consumption caused by balancing     

• chunksize Default 64MB、 The logical unit of data aggregation     

• shard key optimization , avoid Jumbo chunk    

• shard key The value does not exceed 512 Bytes    

• db.colname.getShardDistribution()# You can view the data distribution     

• scatter-gather Query generates mongos Input network card traffic is too high     

• hash Fragmentation mode , Create a set to specify the preset initialization chunks Number

mongos> sh.enableSharding("<database>") mongos> use admin

mongos>db.runCommand({shardcollection:“<database>.<collection>”,key:{“keyname”:“hashed”), numinitialChunks: 1000})

• range Fragmentation mode , Initialize by using the split point chunks Number

mongos>db.adminCommand({split:"test.coll2middle:{username:1900}})( Look for the right syncopation point many times , The proposal USES DRS Full migration functionality )

3. DDS config And management operation

3.1DDS config server And metadata

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• config database    # mongos> use config    

• config.shards    #db.shards.find()    

• config.databases    #db.databases.find()    

• config.collections    # Piecewise set     

• config.chunks    # all chunks The record of     

• config.settings    #shard cluster Configuration information     

• mongos>db.settings.find()

("id" : "chunksize”, "value": NumberLong(64)}

{"id" : "balancer", "stopped": false )

• config server Store the metadata of the entire partitioned cluster

• In replica sets 、 High availability deployment

3.2 DDS Cluster management and best practices

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• Add the shard

db.runCommand({addShard:"repl0/mongodb3examplenet:27327"})

• Delete fragment removeShard It will affect chunks The redistribution of 、 Too long running time

• View balance

mongos>sh.getBalancerState()# Check whether equalization is enabled

mongos>sh.isBalancerRunning0 # Check whether the migration is balanced chunk

• Stop during backup 、 Turn on equilibrium sh.stopBalancer()

sh.setBalancerState(true)

• Refresh routing information

db.adminCommand({flushRouterConfig:"<db.collection>"}) db.adminCommand({ flushRouterConfig: "<db>" } )

db.adminCommand( { flushRouterConfig: 1 })    

4.DDS The basic principles of division and equilibrium

4.1 DDS The basic principle of splitting

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• Split command

sh.splitFind("records.people"{"zipcode":"63109"})

# matching zipcode:63109 Of chunk, Split into 2 One or more chunk sh.splitAt( “records.people”,{ “zipcode”: “63109”))# With zipcode:63109 Is the split point , Split into 2 individual chunk

• The basic process of splitting

sharding.autoSplit Auto split is on by default

Insert 、 to update 、 When deleting data ,mongos Judge chunk Whether the threshold is exceeded , Trigger chunk split

chunk Splitting is a logical action , Mark the data boundary left open and right closed based on metadata [)

chunk Internal data shard key Is the same value , Cannot be split to form Jumbo chunk

4.2 DDS The basic principle of equilibrium

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•

Migration command

db.adminCommand{moveChunk:<namespace>    

find : <query>.

    to: <string>,    

_secondaryThrottle<boolean>,

writeConcern:<document>,

•

Migration command

db.adminCommand{moveChunk:<namespace>    

find : <query>.

to: <string>,    

_secondaryThrottle<boolean>,

writeConcern:<document>,

_waitForDelete<boolean>})    

•

The basic process of migration

• from config server Notify the sender shard, And specify the migration chunk Mission ( Move one at a time chunk)

• The sender shard Inform the recipient shard Actively copy data in batches 、 The increment is applied to the receiver     

• The sender judges chunk After data migration, enter the critical zone , The write operation is suspended until the critical area is exited     

• The receiver's last copy chunk The last incremental data is completed commit, After completion, the receiver exits the critical area

• The receiver finally deletes the local data asynchronously chunk( orphan chunk)

•

Equalization window

use config    

db.settings.update({id:"balancer}.{$set:

{activeWindow:{start:“02:00stop:"06:00"}}}    {upsert: true })

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当前位置：网站首页>Huawei cloud database DDS products are deeply enabled

Huawei cloud database DDS products are deeply enabled

One 、 Huawei cloud database DDS Product introduction

Two 、 DDS Business development and use foundation

3、 ... and 、 understand DDS Kernel principle

Four 、 Use sharded clusters quickly

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