Riptide: Client-side Response Routing for Spring

Riptide is a library that implements client-side response routing. It tries to fill the gap between the HTTP protocol and Java. Riptide allows users to leverage the power of HTTP with its unique API.

  • Technology stack: Based on spring-web and uses the same foundation as Spring's RestTemplate.
  • Status: Actively maintained and used in production.
  • Riptide is unique in the way that it doesn't abstract HTTP away, but rather embrace it!

:rotating_light: Upgrading from 2.x to 3.x? Please refer to the Migration Guide.

Example

Usage typically looks like this:

http.get("/repos/{org}/{repo}/contributors", "zalando", "riptide")
    .dispatch(series(),
        on(SUCCESSFUL).call(listOf(User.class), users -> 
            users.forEach(System.out::println)));

Feel free to compare this e.g. to Feign or Retrofit.

Features

Origin

Most modern clients try to adapt HTTP to a single-return paradigm as shown in the following example. Even though this may be perfectly suitable for most applications it takes away a lot of the power that comes with HTTP. It's not easy to support multiple different return values, i.e. distinct happy cases. Access to response headers or manual content negotiation are also harder to do.

@GET
@Path("/repos/{org}/{repo}/contributors")
List<User> getContributors(@PathParam String org, @PathParam String repo);

Riptide tries to counter this by providing a different approach to leverage the power of HTTP. Go checkout the concept document for more details.

Dependencies

  • Spring 4.1 or higher
    • :warning: Spring Boot integration requires Spring 5

Installation

Add the following dependency to your project:

<dependency>
    <groupId>org.zalando</groupId>
    <artifactId>riptide-core</artifactId>
    <version>${riptide.version}</version>
</dependency>

Additional modules/artifacts of Riptide always share the same version number.

Alternatively, you can import our bill of materials...

<dependencyManagement>
  <dependencies>
    <dependency>
      <groupId>org.zalando</groupId>
      <artifactId>riptide-bom</artifactId>
      <version>${riptide.version}</version>
      <type>pom</type>
      <scope>import</scope>
    </dependency>
  </dependencies>
</dependencyManagement>

... which allows you to omit versions:

<dependencies>
  <dependency>
      <groupId>org.zalando</groupId>
      <artifactId>riptide-core</artifactId>
  </dependency>
  <dependency>
      <groupId>org.zalando</groupId>
      <artifactId>riptide-failsafe</artifactId>
  </dependency>
  <dependency>
      <groupId>org.zalando</groupId>
      <artifactId>riptide-faults</artifactId>
  </dependency>
</dependencies>

Configuration

Integration of your typical Spring Boot Application with Riptide, Logbook and Tracer can be greatly simplified by using the Riptide: Spring Boot Starter. Go check it out!

Http.builder()
    .executor(Executors.newCachedThreadPool())
    .requestFactory(new HttpComponentsClientHttpRequestFactory())
    .baseUrl("https://api.github.com")
    .converter(new MappingJackson2HttpMessageConverter())
    .converter(new Jaxb2RootElementHttpMessageConverter())
    .plugin(new OriginalStackTracePlugin())
    .build();

The following code is the bare minimum, since a request factory is required:

Http.builder()
    .executor(Executors.newCachedThreadPool())
    .requestFactory(new HttpComponentsClientHttpRequestFactory())
    .build();

This defaults to:

Thread Pool

All off the standard Executors.new*Pool() implementations only support the queue-first style, i.e. the pool scales up to the core pool size, then fills the queue and only then will scale up to the maximum pool size.

Riptide provides a ThreadPoolExecutors.builder() which also offers a scale-first style where thread pools scale up to the maximum pool size before they queue any tasks. That usually leads to higher throughput, lower latency on the expense of having to maintain more threads.

The following table shows which combination of properties are supported

ConfigurationSupported
Without queue, fixed size¹:heavy_check_mark:
Without queue, elastic size²:heavy_check_mark:
Bounded queue, fixed size:heavy_check_mark:
Bounded queue, elastic size:heavy_check_mark:
Unbounded queue, fixed size:heavy_check_mark:
Unbounded queue, elastic size:x:³
Scale first, without queue, fixed size:x:⁴
Scale first, without queue, elastic size:x:⁴
Scale first, bounded queue, fixed size:x:⁵
Scale first, bounded queue, elastic size:heavy_check_mark:⁶
Scale first, unbounded queue, fixed size:x:⁵
Scale first, unbounded queue, elastic size:heavy_check_mark:⁶

¹ Core pool size = maximum pool size
² Core pool size < maximum pool size
³ Pool can't grow past core pool size due to unbounded queue
⁴ Scale first has no meaning without a queue
⁵ Fixed size pools are already scaled up
⁶ Elastic, but only between 0 and maximum pool size

Examples

  1. Without queue, elastic size
ThreadPoolExecutors.builder()
    .withoutQueue()
    .elasticSize(5, 20)
    .keepAlive(1, MINUTES)
    .build()

2.   Bounded queue, fixed size

ThreadPoolExecutors.builder()
    .boundedQueue(20)
    .fixedSize(20)
    .keepAlive(1, MINUTES)
    .build()

3.   Scale-first, unbounded queue, elastic size

ThreadPoolExecutors.builder()
    .scaleFirst()
    .unboundedQueue()
    .elasticSize(20)   
    .keepAlive(1, MINUTES)
    .build()

You can read more about scale-first here:

In order to configure the thread pool correctly, please refer to How to set an ideal thread pool size.

Non-blocking IO

Riptide supports two different kinds of request factories:

ClientHttpRequestFactory

The following implementations offer blocking IO:

AsyncClientHttpRequestFactory

The following implementations offer non-blocking IO:

Non-blocking IO is asynchronous by nature. In order to provide asynchrony for blocking IO you need to register an executor. Not passing an executor will make all network communication synchronous, i.e. all futures returned by Riptide will already be completed.

 SynchronousAsynchronous
Blocking IOClientHttpRequestFactoryExecutor + ClientHttpRequestFactory
Non-blocking IOn/aAsyncClientHttpRequestFactory

Usage

Requests

A full-blown request may contain any of the following aspects: HTTP method, request URI, query parameters, headers and a body:

http.post("/sales-order")
    .queryParam("async", "false")
    .contentType(CART)
    .accept(SALES_ORDER)
    .header("Client-IP", "127.0.0.1")
    .body(cart)
    //...

Riptide supports the following HTTP methods: get, head, post, put, patch, delete, options and trace respectively. Query parameters can either be provided individually using queryParam(String, String) or multiple at once with queryParams(Multimap<String, String>).

The following operations are applied to URI Templates (get(String, Object...)) and URIs (get(URI)) respectively:

URI Template

URI

  • none, used as is
  • expected to be already encoded

Both

  • after respective transformation
  • resolved against Base URL (if present)
  • Query String (merged with existing)
  • Normalization

The URI Resolution table shows some examples how URIs are resolved against Base URLs, based on the chosen resolution strategy.

The Content-Type- and Accept-header have type-safe methods in addition to the generic support that is header(String, String) and headers(HttpHeaders).

Responses

Riptide is special in the way it handles responses. Rather than having a single return value, you need to register callbacks. Traditionally you would attach different callbacks for different response status codes, alternatively there are also built-in routing capabilities on status code families (called series in Spring) as well as on content types.

http.post("/sales-order")
    // ...
    .dispatch(series(),
        on(SUCCESSFUL).dispatch(contentType(),
            on(SALES_ORDER).call(SalesOrder.class, this::persist),
        on(CLIENT_ERROR).dispatch(status(),
            on(CONFLICT).call(this::retry),
            on(PRECONDITION_FAILED).call(this::readAgainAndRetry),
            anyStatus().call(problemHandling())),
        on(SERVER_ERROR).dispatch(status(),
            on(SERVICE_UNAVAILABLE).call(this::scheduleRetryLater))));

The callbacks can have the following signatures:

persist(SalesOrder)
retry(ClientHttpResponse)
scheduleRetryLater()

Futures

Riptide will return a CompletableFuture<ClientHttpResponse>. That means you can choose to chain transformations/callbacks or block on it.

If you need proper return values take a look at Riptide: Capture.

Exceptions

The only special custom exception you may get is UnexpectedResponseException, if and only if there was no matching condition and no wildcard condition either.

Plugins

Riptide comes with a way to register extensions in the form of plugins.

  • OriginalStackTracePlugin, preserves stack traces when executing requests asynchronously
  • AuthorizationPlugin, adds Authorization support
  • FailsafePlugin, adds retries, circuit breaker, backup requests and timeout support
  • MicrometerPlugin, adds metrics for request duration
  • TransientFaults, detects transient faults, e.g. network issues Whenever you encounter the need to perform some repetitive task on the futures returned by a remote call, you may consider implementing a custom Plugin for it.

Plugins are executed in phases:

Plugin phases

Please consult the Plugin documentation for details.

Testing

Riptide is built on the same foundation as Spring's RestTemplate and AsyncRestTemplate. That allows us, with a small trick, to use the same testing facilities, the MockRestServiceServer:

RestTemplate template = new RestTemplate();
MockRestServiceServer server = MockRestServiceServer.createServer(template);
ClientHttpRequestFactory requestFactory = template.getRequestFactory();

Http.builder()
    .requestFactory(requestFactory)
    // continue configuration

We basically use an intermediate RestTemplate as a holder of the special ClientHttpRequestFactory that the MockRestServiceServer manages.

If you are using the Spring Boot Starter the test setup is provided by a convenient annotation @RiptideClientTest, see here.

Getting help

If you have questions, concerns, bug reports, etc., please file an issue in this repository's Issue Tracker.

Getting involved/Contributing

To contribute, simply make a pull request and add a brief description (1-2 sentences) of your addition or change. For more details check the contribution guidelines.

Credits and references

Download Details:
Author: zalando
Source Code: https://github.com/zalando/riptide
License: MIT license

#java #HTTPClient

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Riptide: Client-side Response Routing for Spring

Enhance Amazon Aurora Read/Write Capability with ShardingSphere-JDBC

1. Introduction

Amazon Aurora is a relational database management system (RDBMS) developed by AWS(Amazon Web Services). Aurora gives you the performance and availability of commercial-grade databases with full MySQL and PostgreSQL compatibility. In terms of high performance, Aurora MySQL and Aurora PostgreSQL have shown an increase in throughput of up to 5X over stock MySQL and 3X over stock PostgreSQL respectively on similar hardware. In terms of scalability, Aurora achieves enhancements and innovations in storage and computing, horizontal and vertical functions.

Aurora supports up to 128TB of storage capacity and supports dynamic scaling of storage layer in units of 10GB. In terms of computing, Aurora supports scalable configurations for multiple read replicas. Each region can have an additional 15 Aurora replicas. In addition, Aurora provides multi-primary architecture to support four read/write nodes. Its Serverless architecture allows vertical scaling and reduces typical latency to under a second, while the Global Database enables a single database cluster to span multiple AWS Regions in low latency.

Aurora already provides great scalability with the growth of user data volume. Can it handle more data and support more concurrent access? You may consider using sharding to support the configuration of multiple underlying Aurora clusters. To this end, a series of blogs, including this one, provides you with a reference in choosing between Proxy and JDBC for sharding.

1.1 Why sharding is needed

AWS Aurora offers a single relational database. Primary-secondary, multi-primary, and global database, and other forms of hosting architecture can satisfy various architectural scenarios above. However, Aurora doesn’t provide direct support for sharding scenarios, and sharding has a variety of forms, such as vertical and horizontal forms. If we want to further increase data capacity, some problems have to be solved, such as cross-node database Join, associated query, distributed transactions, SQL sorting, page turning, function calculation, database global primary key, capacity planning, and secondary capacity expansion after sharding.

1.2 Sharding methods

It is generally accepted that when the capacity of a MySQL table is less than 10 million, the time spent on queries is optimal because at this time the height of its BTREE index is between 3 and 5. Data sharding can reduce the amount of data in a single table and distribute the read and write loads to different data nodes at the same time. Data sharding can be divided into vertical sharding and horizontal sharding.

1. Advantages of vertical sharding

  • Address the coupling of business system and make clearer.
  • Implement hierarchical management, maintenance, monitoring, and expansion to data of different businesses, like micro-service governance.
  • In high concurrency scenarios, vertical sharding removes the bottleneck of IO, database connections, and hardware resources on a single machine to some extent.

2. Disadvantages of vertical sharding

  • After splitting the library, Join can only be implemented by interface aggregation, which will increase the complexity of development.
  • After splitting the library, it is complex to process distributed transactions.
  • There is a large amount of data on a single table and horizontal sharding is required.

3. Advantages of horizontal sharding

  • There is no such performance bottleneck as a large amount of data on a single database and high concurrency, and it increases system stability and load capacity.
  • The business modules do not need to be split due to minor modification on the application client.

4. Disadvantages of horizontal sharding

  • Transaction consistency across shards is hard to be guaranteed;
  • The performance of associated query in cross-library Join is poor.
  • It’s difficult to scale the data many times and maintenance is a big workload.

Based on the analysis above, and the available studis on popular sharding middleware, we selected ShardingSphere, an open source product, combined with Amazon Aurora to introduce how the combination of these two products meets various forms of sharding and how to solve the problems brought by sharding.

ShardingSphere is an open source ecosystem including a set of distributed database middleware solutions, including 3 independent products, Sharding-JDBC, Sharding-Proxy & Sharding-Sidecar.

2. ShardingSphere introduction:

The characteristics of Sharding-JDBC are:

  1. With the client end connecting directly to the database, it provides service in the form of jar and requires no extra deployment and dependence.
  2. It can be considered as an enhanced JDBC driver, which is fully compatible with JDBC and all kinds of ORM frameworks.
  3. Applicable in any ORM framework based on JDBC, such as JPA, Hibernate, Mybatis, Spring JDBC Template or direct use of JDBC.
  4. Support any third-party database connection pool, such as DBCP, C3P0, BoneCP, Druid, HikariCP;
  5. Support any kind of JDBC standard database: MySQL, Oracle, SQLServer, PostgreSQL and any databases accessible to JDBC.
  6. Sharding-JDBC adopts decentralized architecture, applicable to high-performance light-weight OLTP application developed with Java

Hybrid Structure Integrating Sharding-JDBC and Applications

Sharding-JDBC’s core concepts

Data node: The smallest unit of a data slice, consisting of a data source name and a data table, such as ds_0.product_order_0.

Actual table: The physical table that really exists in the horizontal sharding database, such as product order tables: product_order_0, product_order_1, and product_order_2.

Logic table: The logical name of the horizontal sharding databases (tables) with the same schema. For instance, the logic table of the order product_order_0, product_order_1, and product_order_2 is product_order.

Binding table: It refers to the primary table and the joiner table with the same sharding rules. For example, product_order table and product_order_item are sharded by order_id, so they are binding tables with each other. Cartesian product correlation will not appear in the multi-tables correlating query, so the query efficiency will increase greatly.

Broadcast table: It refers to tables that exist in all sharding database sources. The schema and data must consist in each database. It can be applied to the small data volume that needs to correlate with big data tables to query, dictionary table and configuration table for example.

3. Testing ShardingSphere-JDBC

3.1 Example project

Download the example project code locally. In order to ensure the stability of the test code, we choose shardingsphere-example-4.0.0 version.

git clone https://github.com/apache/shardingsphere-example.git

Project description:

shardingsphere-example
  ├── example-core
  │   ├── config-utility
  │   ├── example-api
  │   ├── example-raw-jdbc
  │   ├── example-spring-jpa #spring+jpa integration-based entity,repository
  │   └── example-spring-mybatis
  ├── sharding-jdbc-example
  │   ├── sharding-example
  │   │   ├── sharding-raw-jdbc-example
  │   │   ├── sharding-spring-boot-jpa-example #integration-based sharding-jdbc functions
  │   │   ├── sharding-spring-boot-mybatis-example
  │   │   ├── sharding-spring-namespace-jpa-example
  │   │   └── sharding-spring-namespace-mybatis-example
  │   ├── orchestration-example
  │   │   ├── orchestration-raw-jdbc-example
  │   │   ├── orchestration-spring-boot-example #integration-based sharding-jdbc governance function
  │   │   └── orchestration-spring-namespace-example
  │   ├── transaction-example
  │   │   ├── transaction-2pc-xa-example #sharding-jdbc sample of two-phase commit for a distributed transaction
  │   │   └──transaction-base-seata-example #sharding-jdbc distributed transaction seata sample
  │   ├── other-feature-example
  │   │   ├── hint-example
  │   │   └── encrypt-example
  ├── sharding-proxy-example
  │   └── sharding-proxy-boot-mybatis-example
  └── src/resources
        └── manual_schema.sql  

Configuration file description:

application-master-slave.properties #read/write splitting profile
application-sharding-databases-tables.properties #sharding profile
application-sharding-databases.properties       #library split profile only
application-sharding-master-slave.properties    #sharding and read/write splitting profile
application-sharding-tables.properties          #table split profile
application.properties                         #spring boot profile

Code logic description:

The following is the entry class of the Spring Boot application below. Execute it to run the project.

The execution logic of demo is as follows:

3.2 Verifying read/write splitting

As business grows, the write and read requests can be split to different database nodes to effectively promote the processing capability of the entire database cluster. Aurora uses a reader/writer endpoint to meet users' requirements to write and read with strong consistency, and a read-only endpoint to meet the requirements to read without strong consistency. Aurora's read and write latency is within single-digit milliseconds, much lower than MySQL's binlog-based logical replication, so there's a lot of loads that can be directed to a read-only endpoint.

Through the one primary and multiple secondary configuration, query requests can be evenly distributed to multiple data replicas, which further improves the processing capability of the system. Read/write splitting can improve the throughput and availability of system, but it can also lead to data inconsistency. Aurora provides a primary/secondary architecture in a fully managed form, but applications on the upper-layer still need to manage multiple data sources when interacting with Aurora, routing SQL requests to different nodes based on the read/write type of SQL statements and certain routing policies.

ShardingSphere-JDBC provides read/write splitting features and it is integrated with application programs so that the complex configuration between application programs and database clusters can be separated from application programs. Developers can manage the Shard through configuration files and combine it with ORM frameworks such as Spring JPA and Mybatis to completely separate the duplicated logic from the code, which greatly improves the ability to maintain code and reduces the coupling between code and database.

3.2.1 Setting up the database environment

Create a set of Aurora MySQL read/write splitting clusters. The model is db.r5.2xlarge. Each set of clusters has one write node and two read nodes.

3.2.2 Configuring Sharding-JDBC

application.properties spring boot Master profile description:

You need to replace the green ones with your own environment configuration.

# Jpa automatically creates and drops data tables based on entities
spring.jpa.properties.hibernate.hbm2ddl.auto=create-drop
spring.jpa.properties.hibernate.dialect=org.hibernate.dialect.MySQL5Dialect
spring.jpa.properties.hibernate.show_sql=true

#spring.profiles.active=sharding-databases
#spring.profiles.active=sharding-tables
#spring.profiles.active=sharding-databases-tables
#Activate master-slave configuration item so that sharding-jdbc can use master-slave profile
spring.profiles.active=master-slave
#spring.profiles.active=sharding-master-slave

application-master-slave.properties sharding-jdbc profile description:

spring.shardingsphere.datasource.names=ds_master,ds_slave_0,ds_slave_1
# data souce-master
spring.shardingsphere.datasource.ds_master.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_master.password=Your master DB password
spring.shardingsphere.datasource.ds_master.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_master.jdbc-url=Your primary DB data sourceurl spring.shardingsphere.datasource.ds_master.username=Your primary DB username
# data source-slave
spring.shardingsphere.datasource.ds_slave_0.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_slave_0.password= Your slave DB password
spring.shardingsphere.datasource.ds_slave_0.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_slave_0.jdbc-url=Your slave DB data source url
spring.shardingsphere.datasource.ds_slave_0.username= Your slave DB username
# data source-slave
spring.shardingsphere.datasource.ds_slave_1.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_slave_1.password= Your slave DB password
spring.shardingsphere.datasource.ds_slave_1.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_slave_1.jdbc-url= Your slave DB data source url
spring.shardingsphere.datasource.ds_slave_1.username= Your slave DB username
# Routing Policy Configuration
spring.shardingsphere.masterslave.load-balance-algorithm-type=round_robin
spring.shardingsphere.masterslave.name=ds_ms
spring.shardingsphere.masterslave.master-data-source-name=ds_master
spring.shardingsphere.masterslave.slave-data-source-names=ds_slave_0,ds_slave_1
# sharding-jdbc configures the information storage mode
spring.shardingsphere.mode.type=Memory
# start shardingsphere log,and you can see the conversion from logical SQL to actual SQL from the print
spring.shardingsphere.props.sql.show=true

 

3.2.3 Test and verification process description

  • Test environment data initialization: Spring JPA initialization automatically creates tables for testing.

  • Write data to the master instance

As shown in the ShardingSphere-SQL log figure below, the write SQL is executed on the ds_master data source.

  • Data query operations are performed on the slave library.

As shown in the ShardingSphere-SQL log figure below, the read SQL is executed on the ds_slave data source in the form of polling.

[INFO ] 2022-04-02 19:43:39,376 --main-- [ShardingSphere-SQL] Rule Type: master-slave 
[INFO ] 2022-04-02 19:43:39,376 --main-- [ShardingSphere-SQL] SQL: select orderentit0_.order_id as order_id1_1_, orderentit0_.address_id as address_2_1_, 
orderentit0_.status as status3_1_, orderentit0_.user_id as user_id4_1_ from t_order orderentit0_ ::: DataSources: ds_slave_0 
---------------------------- Print OrderItem Data -------------------
Hibernate: select orderiteme1_.order_item_id as order_it1_2_, orderiteme1_.order_id as order_id2_2_, orderiteme1_.status as status3_2_, orderiteme1_.user_id 
as user_id4_2_ from t_order orderentit0_ cross join t_order_item orderiteme1_ where orderentit0_.order_id=orderiteme1_.order_id
[INFO ] 2022-04-02 19:43:40,898 --main-- [ShardingSphere-SQL] Rule Type: master-slave 
[INFO ] 2022-04-02 19:43:40,898 --main-- [ShardingSphere-SQL] SQL: select orderiteme1_.order_item_id as order_it1_2_, orderiteme1_.order_id as order_id2_2_, orderiteme1_.status as status3_2_, 
orderiteme1_.user_id as user_id4_2_ from t_order orderentit0_ cross join t_order_item orderiteme1_ where orderentit0_.order_id=orderiteme1_.order_id ::: DataSources: ds_slave_1 

Note: As shown in the figure below, if there are both reads and writes in a transaction, Sharding-JDBC routes both read and write operations to the master library. If the read/write requests are not in the same transaction, the corresponding read requests are distributed to different read nodes according to the routing policy.

@Override
@Transactional // When a transaction is started, both read and write in the transaction go through the master library. When closed, read goes through the slave library and write goes through the master library
public void processSuccess() throws SQLException {
    System.out.println("-------------- Process Success Begin ---------------");
    List<Long> orderIds = insertData();
    printData();
    deleteData(orderIds);
    printData();
    System.out.println("-------------- Process Success Finish --------------");
}

3.2.4 Verifying Aurora failover scenario

The Aurora database environment adopts the configuration described in Section 2.2.1.

3.2.4.1 Verification process description

  1. Start the Spring-Boot project

2. Perform a failover on Aurora’s console

3. Execute the Rest API request

4. Repeatedly execute POST (http://localhost:8088/save-user) until the call to the API failed to write to Aurora and eventually recovered successfully.

5. The following figure shows the process of executing code failover. It takes about 37 seconds from the time when the latest SQL write is successfully performed to the time when the next SQL write is successfully performed. That is, the application can be automatically recovered from Aurora failover, and the recovery time is about 37 seconds.

3.3 Testing table sharding-only function

3.3.1 Configuring Sharding-JDBC

application.properties spring boot master profile description

# Jpa automatically creates and drops data tables based on entities
spring.jpa.properties.hibernate.hbm2ddl.auto=create-drop
spring.jpa.properties.hibernate.dialect=org.hibernate.dialect.MySQL5Dialect
spring.jpa.properties.hibernate.show_sql=true
#spring.profiles.active=sharding-databases
#Activate sharding-tables configuration items
#spring.profiles.active=sharding-tables
#spring.profiles.active=sharding-databases-tables
# spring.profiles.active=master-slave
#spring.profiles.active=sharding-master-slave

application-sharding-tables.properties sharding-jdbc profile description

## configure primary-key policy
spring.shardingsphere.sharding.tables.t_order.key-generator.column=order_id
spring.shardingsphere.sharding.tables.t_order.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order.key-generator.props.worker.id=123
spring.shardingsphere.sharding.tables.t_order_item.actual-data-nodes=ds.t_order_item_$->{0..1}
spring.shardingsphere.sharding.tables.t_order_item.table-strategy.inline.sharding-column=order_id
spring.shardingsphere.sharding.tables.t_order_item.table-strategy.inline.algorithm-expression=t_order_item_$->{order_id % 2}
spring.shardingsphere.sharding.tables.t_order_item.key-generator.column=order_item_id
spring.shardingsphere.sharding.tables.t_order_item.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order_item.key-generator.props.worker.id=123
# configure the binding relation of t_order and t_order_item
spring.shardingsphere.sharding.binding-tables[0]=t_order,t_order_item
# configure broadcast tables
spring.shardingsphere.sharding.broadcast-tables=t_address
# sharding-jdbc mode
spring.shardingsphere.mode.type=Memory
# start shardingsphere log
spring.shardingsphere.props.sql.show=true

 

3.3.2 Test and verification process description

1. DDL operation

JPA automatically creates tables for testing. When Sharding-JDBC routing rules are configured, the client executes DDL, and Sharding-JDBC automatically creates corresponding tables according to the table splitting rules. If t_address is a broadcast table, create a t_address because there is only one master instance. Two physical tables t_order_0 and t_order_1 will be created when creating t_order.

2. Write operation

As shown in the figure below, Logic SQL inserts a record into t_order. When Sharding-JDBC is executed, data will be distributed to t_order_0 and t_order_1 according to the table splitting rules.

When t_order and t_order_item are bound, the records associated with order_item and order are placed on the same physical table.

3. Read operation

As shown in the figure below, perform the join query operations to order and order_item under the binding table, and the physical shard is precisely located based on the binding relationship.

The join query operations on order and order_item under the unbound table will traverse all shards.

3.4 Testing database sharding-only function

3.4.1 Setting up the database environment

Create two instances on Aurora: ds_0 and ds_1

When the sharding-spring-boot-jpa-example project is started, tables t_order, t_order_itemt_address will be created on two Aurora instances.

3.4.2 Configuring Sharding-JDBC

application.properties springboot master profile description

# Jpa automatically creates and drops data tables based on entities
spring.jpa.properties.hibernate.hbm2ddl.auto=create
spring.jpa.properties.hibernate.dialect=org.hibernate.dialect.MySQL5Dialect
spring.jpa.properties.hibernate.show_sql=true

# Activate sharding-databases configuration items
spring.profiles.active=sharding-databases
#spring.profiles.active=sharding-tables
#spring.profiles.active=sharding-databases-tables
#spring.profiles.active=master-slave
#spring.profiles.active=sharding-master-slave

application-sharding-databases.properties sharding-jdbc profile description

spring.shardingsphere.datasource.names=ds_0,ds_1
# ds_0
spring.shardingsphere.datasource.ds_0.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_0.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_0.jdbc-url= spring.shardingsphere.datasource.ds_0.username= 
spring.shardingsphere.datasource.ds_0.password=
# ds_1
spring.shardingsphere.datasource.ds_1.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_1.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_1.jdbc-url= 
spring.shardingsphere.datasource.ds_1.username= 
spring.shardingsphere.datasource.ds_1.password=
spring.shardingsphere.sharding.default-database-strategy.inline.sharding-column=user_id
spring.shardingsphere.sharding.default-database-strategy.inline.algorithm-expression=ds_$->{user_id % 2}
spring.shardingsphere.sharding.binding-tables=t_order,t_order_item
spring.shardingsphere.sharding.broadcast-tables=t_address
spring.shardingsphere.sharding.default-data-source-name=ds_0

spring.shardingsphere.sharding.tables.t_order.actual-data-nodes=ds_$->{0..1}.t_order
spring.shardingsphere.sharding.tables.t_order.key-generator.column=order_id
spring.shardingsphere.sharding.tables.t_order.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order.key-generator.props.worker.id=123
spring.shardingsphere.sharding.tables.t_order_item.actual-data-nodes=ds_$->{0..1}.t_order_item
spring.shardingsphere.sharding.tables.t_order_item.key-generator.column=order_item_id
spring.shardingsphere.sharding.tables.t_order_item.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order_item.key-generator.props.worker.id=123
# sharding-jdbc mode
spring.shardingsphere.mode.type=Memory
# start shardingsphere log
spring.shardingsphere.props.sql.show=true

 

3.4.3 Test and verification process description

1. DDL operation

JPA automatically creates tables for testing. When Sharding-JDBC’s library splitting and routing rules are configured, the client executes DDL, and Sharding-JDBC will automatically create corresponding tables according to table splitting rules. If t_address is a broadcast table, physical tables will be created on ds_0 and ds_1. The three tables, t_address, t_order and t_order_item will be created on ds_0 and ds_1 respectively.

2. Write operation

For the broadcast table t_address, each record written will also be written to the t_address tables of ds_0 and ds_1.

The tables t_order and t_order_item of the slave library are written on the table in the corresponding instance according to the slave library field and routing policy.

3. Read operation

Query order is routed to the corresponding Aurora instance according to the routing rules of the slave library .

Query Address. Since address is a broadcast table, an instance of address will be randomly selected and queried from the nodes used.

As shown in the figure below, perform the join query operations to order and order_item under the binding table, and the physical shard is precisely located based on the binding relationship.

3.5 Verifying sharding function

3.5.1 Setting up the database environment

As shown in the figure below, create two instances on Aurora: ds_0 and ds_1

When the sharding-spring-boot-jpa-example project is started, physical tables t_order_01, t_order_02, t_order_item_01,and t_order_item_02 and global table t_address will be created on two Aurora instances.

3.5.2 Configuring Sharding-JDBC

application.properties springboot master profile description

# Jpa automatically creates and drops data tables based on entities
spring.jpa.properties.hibernate.hbm2ddl.auto=create
spring.jpa.properties.hibernate.dialect=org.hibernate.dialect.MySQL5Dialect
spring.jpa.properties.hibernate.show_sql=true
# Activate sharding-databases-tables configuration items
#spring.profiles.active=sharding-databases
#spring.profiles.active=sharding-tables
spring.profiles.active=sharding-databases-tables
#spring.profiles.active=master-slave
#spring.profiles.active=sharding-master-slave

application-sharding-databases.properties sharding-jdbc profile description

spring.shardingsphere.datasource.names=ds_0,ds_1
# ds_0
spring.shardingsphere.datasource.ds_0.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_0.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_0.jdbc-url= 306/dev?useSSL=false&characterEncoding=utf-8
spring.shardingsphere.datasource.ds_0.username= 
spring.shardingsphere.datasource.ds_0.password=
spring.shardingsphere.datasource.ds_0.max-active=16
# ds_1
spring.shardingsphere.datasource.ds_1.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_1.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_1.jdbc-url= 
spring.shardingsphere.datasource.ds_1.username= 
spring.shardingsphere.datasource.ds_1.password=
spring.shardingsphere.datasource.ds_1.max-active=16
# default library splitting policy
spring.shardingsphere.sharding.default-database-strategy.inline.sharding-column=user_id
spring.shardingsphere.sharding.default-database-strategy.inline.algorithm-expression=ds_$->{user_id % 2}
spring.shardingsphere.sharding.binding-tables=t_order,t_order_item
spring.shardingsphere.sharding.broadcast-tables=t_address
# Tables that do not meet the library splitting policy are placed on ds_0
spring.shardingsphere.sharding.default-data-source-name=ds_0
# t_order table splitting policy
spring.shardingsphere.sharding.tables.t_order.actual-data-nodes=ds_$->{0..1}.t_order_$->{0..1}
spring.shardingsphere.sharding.tables.t_order.table-strategy.inline.sharding-column=order_id
spring.shardingsphere.sharding.tables.t_order.table-strategy.inline.algorithm-expression=t_order_$->{order_id % 2}
spring.shardingsphere.sharding.tables.t_order.key-generator.column=order_id
spring.shardingsphere.sharding.tables.t_order.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order.key-generator.props.worker.id=123
# t_order_item table splitting policy
spring.shardingsphere.sharding.tables.t_order_item.actual-data-nodes=ds_$->{0..1}.t_order_item_$->{0..1}
spring.shardingsphere.sharding.tables.t_order_item.table-strategy.inline.sharding-column=order_id
spring.shardingsphere.sharding.tables.t_order_item.table-strategy.inline.algorithm-expression=t_order_item_$->{order_id % 2}
spring.shardingsphere.sharding.tables.t_order_item.key-generator.column=order_item_id
spring.shardingsphere.sharding.tables.t_order_item.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order_item.key-generator.props.worker.id=123
# sharding-jdbc mdoe
spring.shardingsphere.mode.type=Memory
# start shardingsphere log
spring.shardingsphere.props.sql.show=true

 

3.5.3 Test and verification process description

1. DDL operation

JPA automatically creates tables for testing. When Sharding-JDBC’s sharding and routing rules are configured, the client executes DDL, and Sharding-JDBC will automatically create corresponding tables according to table splitting rules. If t_address is a broadcast table, t_address will be created on both ds_0 and ds_1. The three tables, t_address, t_order and t_order_item will be created on ds_0 and ds_1 respectively.

2. Write operation

For the broadcast table t_address, each record written will also be written to the t_address tables of ds_0 and ds_1.

The tables t_order and t_order_item of the sub-library are written to the table on the corresponding instance according to the slave library field and routing policy.

3. Read operation

The read operation is similar to the library split function verification described in section2.4.3.

3.6 Testing database sharding, table sharding and read/write splitting function

3.6.1 Setting up the database environment

The following figure shows the physical table of the created database instance.

3.6.2 Configuring Sharding-JDBC

application.properties spring boot master profile description

# Jpa automatically creates and drops data tables based on entities
spring.jpa.properties.hibernate.hbm2ddl.auto=create
spring.jpa.properties.hibernate.dialect=org.hibernate.dialect.MySQL5Dialect
spring.jpa.properties.hibernate.show_sql=true

# activate sharding-databases-tables configuration items
#spring.profiles.active=sharding-databases
#spring.profiles.active=sharding-tables
#spring.profiles.active=sharding-databases-tables
#spring.profiles.active=master-slave
spring.profiles.active=sharding-master-slave

application-sharding-master-slave.properties sharding-jdbc profile description

The url, name and password of the database need to be changed to your own database parameters.

spring.shardingsphere.datasource.names=ds_master_0,ds_master_1,ds_master_0_slave_0,ds_master_0_slave_1,ds_master_1_slave_0,ds_master_1_slave_1
spring.shardingsphere.datasource.ds_master_0.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_master_0.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_master_0.jdbc-url= spring.shardingsphere.datasource.ds_master_0.username= 
spring.shardingsphere.datasource.ds_master_0.password=
spring.shardingsphere.datasource.ds_master_0.max-active=16
spring.shardingsphere.datasource.ds_master_0_slave_0.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_master_0_slave_0.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_master_0_slave_0.jdbc-url= spring.shardingsphere.datasource.ds_master_0_slave_0.username= 
spring.shardingsphere.datasource.ds_master_0_slave_0.password=
spring.shardingsphere.datasource.ds_master_0_slave_0.max-active=16
spring.shardingsphere.datasource.ds_master_0_slave_1.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_master_0_slave_1.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_master_0_slave_1.jdbc-url= spring.shardingsphere.datasource.ds_master_0_slave_1.username= 
spring.shardingsphere.datasource.ds_master_0_slave_1.password=
spring.shardingsphere.datasource.ds_master_0_slave_1.max-active=16
spring.shardingsphere.datasource.ds_master_1.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_master_1.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_master_1.jdbc-url= 
spring.shardingsphere.datasource.ds_master_1.username= 
spring.shardingsphere.datasource.ds_master_1.password=
spring.shardingsphere.datasource.ds_master_1.max-active=16
spring.shardingsphere.datasource.ds_master_1_slave_0.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_master_1_slave_0.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_master_1_slave_0.jdbc-url=
spring.shardingsphere.datasource.ds_master_1_slave_0.username=
spring.shardingsphere.datasource.ds_master_1_slave_0.password=
spring.shardingsphere.datasource.ds_master_1_slave_0.max-active=16
spring.shardingsphere.datasource.ds_master_1_slave_1.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_master_1_slave_1.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_master_1_slave_1.jdbc-url= spring.shardingsphere.datasource.ds_master_1_slave_1.username=admin
spring.shardingsphere.datasource.ds_master_1_slave_1.password=
spring.shardingsphere.datasource.ds_master_1_slave_1.max-active=16
spring.shardingsphere.sharding.default-database-strategy.inline.sharding-column=user_id
spring.shardingsphere.sharding.default-database-strategy.inline.algorithm-expression=ds_$->{user_id % 2}
spring.shardingsphere.sharding.binding-tables=t_order,t_order_item
spring.shardingsphere.sharding.broadcast-tables=t_address
spring.shardingsphere.sharding.default-data-source-name=ds_master_0
spring.shardingsphere.sharding.tables.t_order.actual-data-nodes=ds_$->{0..1}.t_order_$->{0..1}
spring.shardingsphere.sharding.tables.t_order.table-strategy.inline.sharding-column=order_id
spring.shardingsphere.sharding.tables.t_order.table-strategy.inline.algorithm-expression=t_order_$->{order_id % 2}
spring.shardingsphere.sharding.tables.t_order.key-generator.column=order_id
spring.shardingsphere.sharding.tables.t_order.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order.key-generator.props.worker.id=123
spring.shardingsphere.sharding.tables.t_order_item.actual-data-nodes=ds_$->{0..1}.t_order_item_$->{0..1}
spring.shardingsphere.sharding.tables.t_order_item.table-strategy.inline.sharding-column=order_id
spring.shardingsphere.sharding.tables.t_order_item.table-strategy.inline.algorithm-expression=t_order_item_$->{order_id % 2}
spring.shardingsphere.sharding.tables.t_order_item.key-generator.column=order_item_id
spring.shardingsphere.sharding.tables.t_order_item.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order_item.key-generator.props.worker.id=123
# master/slave data source and slave data source configuration
spring.shardingsphere.sharding.master-slave-rules.ds_0.master-data-source-name=ds_master_0
spring.shardingsphere.sharding.master-slave-rules.ds_0.slave-data-source-names=ds_master_0_slave_0, ds_master_0_slave_1
spring.shardingsphere.sharding.master-slave-rules.ds_1.master-data-source-name=ds_master_1
spring.shardingsphere.sharding.master-slave-rules.ds_1.slave-data-source-names=ds_master_1_slave_0, ds_master_1_slave_1
# sharding-jdbc mode
spring.shardingsphere.mode.type=Memory
# start shardingsphere log
spring.shardingsphere.props.sql.show=true

 

3.6.3 Test and verification process description

1. DDL operation

JPA automatically creates tables for testing. When Sharding-JDBC’s library splitting and routing rules are configured, the client executes DDL, and Sharding-JDBC will automatically create corresponding tables according to table splitting rules. If t_address is a broadcast table, t_address will be created on both ds_0 and ds_1. The three tables, t_address, t_order and t_order_item will be created on ds_0 and ds_1 respectively.

2. Write operation

For the broadcast table t_address, each record written will also be written to the t_address tables of ds_0 and ds_1.

The tables t_order and t_order_item of the slave library are written to the table on the corresponding instance according to the slave library field and routing policy.

3. Read operation

The join query operations on order and order_item under the binding table are shown below.

3. Conclusion

As an open source product focusing on database enhancement, ShardingSphere is pretty good in terms of its community activitiy, product maturity and documentation richness.

Among its products, ShardingSphere-JDBC is a sharding solution based on the client-side, which supports all sharding scenarios. And there’s no need to introduce an intermediate layer like Proxy, so the complexity of operation and maintenance is reduced. Its latency is theoretically lower than Proxy due to the lack of intermediate layer. In addition, ShardingSphere-JDBC can support a variety of relational databases based on SQL standards such as MySQL/PostgreSQL/Oracle/SQL Server, etc.

However, due to the integration of Sharding-JDBC with the application program, it only supports Java language for now, and is strongly dependent on the application programs. Nevertheless, Sharding-JDBC separates all sharding configuration from the application program, which brings relatively small changes when switching to other middleware.

In conclusion, Sharding-JDBC is a good choice if you use a Java-based system and have to to interconnect with different relational databases — and don’t want to bother with introducing an intermediate layer.

Author

Sun Jinhua

A senior solution architect at AWS, Sun is responsible for the design and consult on cloud architecture. for providing customers with cloud-related design and consulting services. Before joining AWS, he ran his own business, specializing in building e-commerce platforms and designing the overall architecture for e-commerce platforms of automotive companies. He worked in a global leading communication equipment company as a senior engineer, responsible for the development and architecture design of multiple subsystems of LTE equipment system. He has rich experience in architecture design with high concurrency and high availability system, microservice architecture design, database, middleware, IOT etc.

Spring: A Static Web Site Generator Written By GitHub Issues

Spring

Spring is a blog engine written by GitHub Issues, or is a simple, static web site generator. No more server and database, you can setup it in free hosting with GitHub Pages as a repository, then post the blogs in the repository Issues.

You can add some labels in your repository Issues as the blog category, and create Issues for writing blog content through Markdown.

Spring has responsive templates, looking good on mobile, tablet, and desktop.Gracefully degrading in older browsers. Compatible with Internet Explorer 10+ and all modern browsers.

Get up and running in seconds.

中文介绍

Quick start guide

For the impatient, here's how to get a Spring blog site up and running.

First of all

  • Fork the Spring repository as yours.
  • Goto your repository settings page to rename Repository Name.
  • Hosted directly on GitHub Pages from your project repository, you can take it as User or organization site or Project site(create a gh-pages branch).
  • Also, you can set up a custom domain with Pages.

Secondly

  • Open the index.html file to edit the config variables with yours below.
$.extend(spring.config, {
  // my blog title
  title: 'Spring',
  // my blog description
  desc: "A blog engine written by github issues [Fork me on GitHub](https://github.com/zhaoda/spring)",
  // my github username
  owner: 'zhaoda',
  // creator's username
  creator: 'zhaoda',
  // the repository name on github for writting issues
  repo: 'spring',
  // custom page
  pages: [
  ]
})
  • Put your domain into the CNAME file if you have.
  • Commit your change and push it.

And then

  • Goto your repository settings page to turn on the Issues feature.
  • Browser this repository's issues page, like this https://github.com/your-username/your-repo-name/issues?state=open.
  • Click the New Issue button to just write some content as a new one blog.

Finally

  • Browser this repository's GitHub Pages url, like this http://your-username.github.io/your-repo-name, you will see your Spring blog, have a test.
  • And you're done!

Custom development

Installation

  • You will need a web server installed on your system, for example, Nginx, Apache etc.
  • Configure your spring project to your local web server directory.
  • Run and browser it, like http://localhost/spring/dev.html .
  • dev.html is used to develop, index.html is used to runtime.

Folder Structure

spring/
├── css/
|    ├── boot.less  #import other less files
|    ├── github.less  #github highlight style
|    ├── home.less  #home page style
|    ├── issuelist.less #issue list widget style
|    ├── issues.less #issues page style
|    ├── labels.less #labels page style
|    ├── main.less #commo style
|    ├── markdown.less #markdown format style
|    ├── menu.less #menu panel style
|    ├── normalize.less #normalize style
|    ├── pull2refresh.less #pull2refresh widget style
|    └── side.html  #side panel style
├── dist/
|    ├── main.min.css  #css for runtime
|    └── main.min.js  #js for runtime
├── img/  #some icon, startup images
├── js/
|    ├── lib/  #some js librarys need to use
|    ├── boot.js  #boot
|    ├── home.js  #home page
|    ├── issuelist.js #issue list widget
|    ├── issues.js #issues page
|    ├── labels.js #labels page
|    ├── menu.js #menu panel
|    ├── pull2refresh.less #pull2refresh widget
|    └── side.html  #side panel
├── css/
|    ├── boot.less  #import other less files
|    ├── github.less  #github highlight style
|    ├── home.less  #home page style
|    ├── issuelist.less #issue list widget style
|    ├── issues.less #issues page style
|    ├── labels.less #labels page style
|    ├── main.less #commo style
|    ├── markdown.less #markdown format style
|    ├── menu.less #menu panel style
|    ├── normalize.less #normalize style
|    ├── pull2refresh.less #pull2refresh widget style
|    └── side.html  #side panel style
├── dev.html #used to develop
├── favicon.ico #website icon
├── Gruntfile.js #Grunt task config
├── index.html #used to runtime
└── package.json  #nodejs install config

Customization

  • Browser http://localhost/spring/dev.html, enter the development mode.
  • Changes you want to modify the source code, like css, js etc.
  • Refresh dev.html view change.

Building

  • You will need Node.js installed on your system.
  • Installation package.
bash

$ npm install

*   Run grunt task.

    ```bash
$ grunt
  • Browser http://localhost/spring/index.html, enter the runtime mode.
  • If there is no problem, commit and push the code.
  • Don't forget to merge master branch into gh-pages branch if you have.
  • And you're done! Good luck!

Report a bug

Who used

If you are using, please tell me.

Download Details:
Author: zhaoda
Source Code: https://github.com/zhaoda/spring
License: MIT License

#spring #spring-framework #spring-boot #java 

Zachary Palmer

Zachary Palmer

1555901576

CSS Flexbox Tutorial | Build a Chat Application

Creating the conversation sidebar and main chat section

In this article we are going to focus on building a basic sidebar, and the main chat window inside our chat shell. See below.

Chat shell with a fixed width sidebar and expanded chat window

This is the second article in this series. You can check out the previous article for setting up the shell OR you can just check out the chat-shell branch from the following repository.

https://github.com/lyraddigital/flexbox-chat-app.git

Open up the chat.html file. You should have the following HTML.

<!DOCTYPE html>
<html>
<head>
    <meta charset="utf-8" />
    <title>Chat App</title>
    <link rel="stylesheet" type="text/css" media="screen" href="css/chat.css" />
</head>
<body>
    <div id="chat-container">
    </div>
</body>
</html>

Now inside of the chat-container div add the following HTML.

<div id="side-bar">
</div>
<div id="chat-window">
</div>

Now let’s also add the following CSS under the #chat-container selector in the chat.css file.

#side-bar {
    background: #0048AA;
    border-radius: 10px 0 0 10px;
}
#chat-window {
    background: #999;
    border-radius: 0 10px 10px 0;
}

Now reload the page. You should see the following:-

So what happened? Where is our sidebar and where is our chat window? I expected to see a blue side bar and a grey chat window, but it’s no where to be found. Well it’s all good. This is because we have no content inside of either element, so it can be 0 pixels wide.

Sizing Flex Items

So now that we know that our items are 0 pixels wide, let’s attempt to size them. We’ll attempt to try this first using explicit widths.

Add the following width property to the #side-bar rule, then reload the page.

width: 275px;

Hmm. Same result. It’s still a blank shell. Oh wait I have to make sure the height is 100% too. So we better do that too. Once again add the following property to the #side-bar rule, then reload the page.

height: 100%;

So now we have our sidebar that has grown to be exactly 275 pixels wide, and is 100% high. So that’s it. We’re done right? Wrong. Let me ask you a question. How big is the chat window? Let’s test that by adding some text to it. Try this yourself just add some text. You should see something similar to this.

So as you can see the chat window is only as big as the text that’s inside of it, and it is not next to the side bar. And this makes sense because up until now the chat shell is not a flex container, and just a regular block level element.

So let’s make our chat shell a flex container. Set the following display property for the #chat-window selector. Then reload the page.

display: flex;

So as you can see by the above illustration, we can see it’s now next to the side bar, and not below it. But as you can see currently it’s only as wide as the text that’s inside of it.

But we want it to take up the remaining space of the chat shell. Well we know how to do this, as we did it in the previous article. Set the flex-grow property to 1 on the #chat-window selector. Basically copy and paste the property below and reload the page.

flex-grow: 1;

So now we have the chat window taking up the remaining space of the chat shell. Next, let’s remove the background property, and also remove all text inside the chat-window div if any still exists. You should now see the result below.

But are we done? Technically yes, but before we move on, let’s improve things a little bit.

Understanding the default alignment

If you remember, before we had defined our chat shell to be a flex container, we had to make sure we set the height of the side bar to be 100%. Otherwise it was 0 pixels high, and as a result nothing was displayed. With that said, try removing the height property from the #side-bar selector and see what happens when you reload the page. Yes that’s right, it still works. The height of the sidebar is still 100% high.

So what happened here? Why do we no longer have to worry about setting the height to 100%? Well this is one of the cool things Flexbox gives you for free. By default every flex item will stretch vertically to fill in the entire height of the flex container. We can in fact change this behaviour, and we will see how this is done in a future article.

Setting the size of the side bar properly

So another feature of Flexbox is being able to set the size of a flex item by using the flex-basis property. The flex-basis property allows you to specify an initial size of a flex item, before any growing or shrinking takes place. We’ll understand more about this in an upcoming article.

For now I just want you to understand one important thing. And that is using width to specify the size of the sidebar is not a good idea. Let’s see why.

Say that potentially, if the screen is mobile we want the side bar to now appear across the top of the chat shell, acting like a top bar instead. We can do this by changing the direction flex items can flex inside a flex container. For example, add the following CSS to the #chat-container selector. Then reload the page.

flex-direction: column;

So as you can see we are back to a blank shell. So firstly let’s understand what we actually did here. By setting the flex-direction property to column, we changed the direction of how the flex items flex. By default flex items will flex from left to right. However when we set flex-direction to column, it changes this behaviour forcing flex items to flex from top to bottom instead. On top of this, when the direction of flex changes, the sizing and alignment of flex items changes as well.

When flexing from left to right, we get a height of 100% for free as already mentioned, and then we made sure the side bar was set to be 275 pixels wide, by setting the width property.

However now that we a flexing from top to bottom, the width of the flex item by default would be 100% wide, and you would need to specify the height instead. So try this. Add the following property to the #side-bar selector to set the height of the side bar. Then reload the page.

height: 275px;

Now we are seeing the side bar again, as we gave it a fixed height too. But we still have that fixed width. That’s not what we wanted. We want the side bar (ie our new top bar) here to now be 100% wide. Comment out the width for a moment and reload the page again.

So now we were able to move our side bar so it appears on top instead, acting like a top bar. Which as previously mentioned might be suited for mobile device widths. But to do this we had to swap the value of width to be the value of height. Wouldn’t it be great if this size was preserved regardless of which direction our items are flexing.

Try this, remove all widths and height properties from the #side-bar selector and write the following instead. Then reload the page.

flex-basis: 275px;

As you can see we get the same result. Now remove the flex-direction property from the #chat-container selector. Then once again reload the page.

Once again we are back to our final output. But now we also have the flexibility to easily change the side bar to be a top bar if we need to, by just changing the direction items can flow. Regardless of the direction of flex, the size of our side bar / top bar is preserved.

Conclusion

Ok so once again we didn’t build much, but we did cover a lot of concepts about Flexbox around sizing. 

#css #programming #webdev 

Spring vs Spring BooDifference Between Spring and Spring Boot

As an extension of the Spring Framework, Spring Boot is widely used to make development on Spring faster, more efficient and convenient. In this article, we will look at some of the parameters were using Spring Boot can drastically reduce the time and effort required in application development.

What is Spring?

Spring Boot

Difference between Spring and Spring Boot

Advantages of Spring Boot over Spring

Conclusion

#full stack development #spring #spring and spring boot #spring boot

Spring Native turns Spring apps into native executables

Spring Native beta release leverages GraalVM to compile Spring Java and Kotlin applications to native images, reducing startup time and memory overhead compared to the JVM.

Spring Native, for compiling Spring Java applications to standalone executables called native images, is now available as a beta release. Native images promise faster startup times and lower runtime memory overhead compared to the JVM.

Launched March 11 and available on start.spring.io, the Spring Native beta compiles Spring applications to native images using the GraalVM multi-language runtime. These standalone executables offer benefits including nearly instant startup (typically fewer than 100ms), instant peak performance, and lower memory consumption, at the cost of longer build times and fewer runtime optimizations than the JVM.

#spring native turns spring apps into native executables #spring native #spring #native executables #spring apps