Monolith systems utilize one processor that controls all logic. The microservice architecture consists of numerous independent processors connected by shared services that form part of an enterprise application.
- user interface
- Database
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Every change to the system will result in deploying a brand-new server version. Consider the concept more closely.
What Does Microservices Architecture Mean?
This definition of microservices is vague. Microservers usually have four main features:
- Meeting a particular business need.
- Automatic deployment.
- Endpoints are used to identify endpoints.
- Decentralization of language and data control.
What Does Scalability Mean In Microservices?
Scalability is among the key benefits microservices offer, enabling services to grow without changing an entire system and saving resources. Netflix serves as an example of this; they needed an efficient solution for scaling their ever-increasing database of subscribers; microservices proved perfect!
Microservice architecture greatly accelerates app development. Microservice deployment becomes much more straightforward by installing each part independently. Each microservice needs its database to take full advantage of modularization's benefits; otherwise, their diversity poses reporting problems that must be solved separately. Read on for solutions!
What Other Benefits Can Microservices Offer?
- Agile teams can work with smaller groups and have a more agile approach.
- Flexible continuous integration and deployment.
- It is possible to scale your system horizontally.
- Productivity increases for development teams.
- Simplifying the maintenance and debugging processes.
What Are The Advantages And Disadvantages Of Microservices Usage?
Microservice architecture has some drawbacks, including operating multiple systems in an unevenly distributed environment and having to complete different tasks simultaneously. In our opinion, critical microservice pitfalls include the following:
Management Issues
Microservices are complex and require developers to be more careful in their planning.
Security Risks
Microservice architectures that use external API communications to communicate with other microservices are more vulnerable.
Diverse Programming Languages
It is difficult to change between the two in development and deployment.
The Issue of Custom Reports
Freshcode worked on an EdTech project which consisted of over 10,000 Coldfusion files running off an MS SQL database and dating back seven years, comprising an intricate system with many microservices, its main components being:
- Financial and billing system with sophisticated features.
- multi-organization structure for large group entities.
- Workflow management software for business processes.
- Live chat, bulk SMS, and integrated email.
- Online system for surveys, quizzes, and examinations.
- Flexible assessment and Learning Management System.
FreshCode was working on a project at its transition into an entirely new interface in preparation for the global launch of microservices for handling massive data loads; its app targeted its audience specifically.
- Large education networks managing hundreds of campuses.
- Governments with up to 200k colleges, universities, and schools.
The EdTech App design suits large education networks and smaller schools of up to 100 students.
Freshcode's development team was tasked with improving and managing the performance of intricate microservice architecture. To meet this objective, they implemented technical solutions allowing clients to build both SaaS-based systems and self-hosted solutions.
How to Improve Microserver Performance?
Freshcode recognized that report generation involved multiple services and decided to improve performance by creating a reporting microservice. This microservice would receive all data, store it securely, transform it, and then provide customized reports for display.
Yellow color serves to identify all microservices within a system and their databases, with messaging tracking changes via reporting module before saving updated data into separate storage databases.
Implementation of the Reporting Module in 6 Steps
We will go through each of the six components comprising an effective reporting system and examine available technologies and potential solutions.
Step 1. Is To Change The Data Capture (Cdc)
CDC applies logic for every change made (insert update delete). Three technologies were suitable for the initial step of microservice reporting's system implementation.
1. Apache NiFi
No coding knowledge is needed to set up a simple CDC quickly. Apache NiFi offers multiple processors for data routing, system mediation, transformation, and other logic operations.
Pros:
- Support for cluster mode and simple scaling
- PutToKafka activities and PutToKinesis built-in
- Custom actions can be implemented in JVM languages.
- User-friendly interface
Cons:
- There is no standard format of data for messages between activities
- Only JVM-supported languages
- It could be better.
- Oracle CDC Activity
2. StreamSets Data Collection
This open-source, widely adopted solution for ingesting big data into reporting microservice systems offers several critical advantages over alternatives: its ease of use in creating pipelines and support for popular technologies are its hallmarks of excellence.
Pros:
- Built-in AWS S3, Kinesis, Kafka, Oracle, Postgres processors
- You can customize open-source software to meet your specific needs
- Easy to use and simple UI
- The most popular tools are supported
Cons:
- This is a brand-new solution that is currently under development.
- The learning curve for StreamSets is steep.
3. Matillion
It has a simple-to-use user interface. The ELT is designed specifically for Amazon Redshift and Google BigQuery.
Pros:
- The proprietary tool
- Support the Development Team
- Tested solution
Cons:
- This tool can only be used in conjunction with specific databases
- ELT is not appropriate for every project
Oracle was our central database for microservice reporting. Freshcode chose StreamSets Data Collector because it offered Oracle CDC out-of-the-box.
Step 2. The Messaging System
This allows for the sending of messages and publishing standards between computers.
1. Apache Kafka
Apache Kafka is a popular tool for real-time analysis. It has high reliability and throughput.
Pros:
- Durability, high throughput, and fault tolerance.
- High concurrency, great scalability.
- Native computation in batch mode over streams.
- An excellent choice for a microservices reporting system on premises.
Cons:
- DevOps is required for the correct installation.
- There is no built-in monitoring device.
2. AWS Kinesis
Amazon Kinesis simplifies the collection, processing, and analysis of streaming data. This allows for a cost-effective solution at any scale.
Pros:
- Scalable and easy to manage.
- Integrate seamlessly with AWS Services.
- Almost no DevOps effort.
- Monitor and Alert System Built-in.
Cons:
- Cost optimization is required.
- There is no way to install on-premises software.
Apache Kafka was a more complex solution to set up and deploy, but it is a good on-premises option.
Step 3: Streaming Computation Systems
It is possible to prepare data before ingestion so that it can be denormalized/joined and added any info if needed. The data can be prepared before ingestion, so it's possible to join/denormalize it and add additional information if necessary.
1. Spark Streaming
Apache Spark's integrated language API allows you to write streaming jobs in the same manner as batch jobs.
Pros:
- It's easy to use once you get the semantics of "stateful" out of your box.
- Fault-tolerance, scalability
- Memory-based computation
Cons:
- The cost of using the product is high
- Manual Optimization
- There is no built-in management system
2. Apache Flink
This is an excellent tool for performing stateful computations on unbounded or bounded data streams. Apache Flink can be used in all cluster environments. It performs calculations at high speed.
Pros:
- Consistency of a state that is exactly-once.
- SQL Stream & Batch Data.
- Scalability and fault tolerance.
- Support for substantial states.
Cons:
- We Need deep programming knowledge.
- Complex architecture.
- Flink's community is small compared to Spark but is still growing.
3. Apache Samza
This scalable engine can be integrated into a reporting microservice system to provide real-time data analytics.
Pros:
- Can maintain a large state.
- High throughput and low latency. Tested at scale.
- High-performance and fault-tolerant.
Cons:
- Guarantee of at least one processing.
- The lack of advanced streaming (triggers, watermarks, sessions).
4. AWS Kinesis Services
Data Firehose is part of the set, along with Data Analytics and Data Streams. It allows for powerful stream processing to be built without the need to write custom code.
Pros:
- Only pay for the services you use.
- SQL is the easiest way to handle data streams in real time.
- Streaming data of any size can be taken.
Cons:
- Use on-premises?
- Costs in high-load environments will be higher than other solutions. However, development and maintenance costs may be less.
- Customized Hard.
AWS offers excellent tools for ETL, data processing, and other tasks. Although it's a perfect starting point, there needs to be a way to install it on customized servers. It could be more suitable for on-premises solutions. Apache Flink offers the highest performance and most features. The application can store large amounts of data (multi-terabytes). It requires more people to develop it.
Step 4. Data Lake
Data lakes are a central repository for storing current and historical data. Data lakes can be used to create analytical reports, machine-learning solutions, and much more.
1. AWS S3
The object storage services offer industry-leading scalability and data security.
Pros:
- Integrate easily with AWS services.
- Data durability is 99.999999999% (1 11 9's).
- Cost-effective solution for data that is rarely used.
- Open-source implementations with API support.
Cons:
- Pricing for High Networks.
- S3 had issues with availability in the past, but this is not an issue for Data Lakes.
2. Apache Hadoop
Hadoop's primary storage system allows for the processing and storing of vast volumes of data.
Pros:
- Work efficiently with large amounts of data.
- Integrate with other analytical tools and operations (Impala Hive HBase Kuda Kyle and others).
Cons:
- It is challenging to manage and deploy.
- Monitoring and high availability are required.
The open-source AWS S3 implementation allowed us to integrate the system with our on-premise reporting microservices.
Step 5. Report Databases
1. AWS Aurora
The database is 5x faster than standard MySQL and 3x faster than PostgreSQL.
Pros:
- Fast SQL Database.
- High durability and availability.
- Fully managed.
- Scale up to any height.
Cons:
- Analytical reports are not performing well in big data projects.
- PostgreSQL can easily replace the minimally-available instance if it is too large.
2. AWS Redshift
Redshift is ten times faster than any other data warehouse. Redshift uses columnar storage, high-performance disks, and massively parallel query processing.
Pros:
- May run queries on external S3 files.
- Simple to install, manage and use.
- Columnar storage.
Cons:
- It doesn't enforce uniqueness.
- It can't be used to store live apps.
- This is mainly useful when averaging a lot of data.
3. Kinetica
This database is designed to handle analytical workloads (OLAP). Kinetica distributes workloads across CPUs, GPUs, and more for the best results.
Pros:
- GPU-based aggregation with high performance.
- Materialized join views can be updated incrementally.
Cons:
- GPU instances are still expensive.
- There is no way to combine data from different partitions.
4. Apache Druid
This works with most event-driven, clickstream data. It also does well when using time series or streaming Apache Kafka datasets. Druid is a popular sink that provides Apache Kafka semantics for exactly-once consumption.
Pros:
- The Druid application can run on any commodity hardware in any *NIX environment.
- The best dashboards are interactive dashboards that allow complete drilling down.
- Only pre-aggregated information is stored.
Cons:
- It's not perfect for users to build custom reports.
- It only works on time series data.
- Support for no full-join support.
The databases were terrific, but the client wanted to generate reports using data from each microservice. The development team recommended AWS Aurora as it simplifies the workflow.
Step 6: Microservice Report
The report microservice was responsible for storing information on data objects and their relationships. The report microservice was accountable for managing the security of data objects and creating reports based on them.
SaaS and Self Hosted Technological Stacks
Two technological stacks were prepared for the Microservice Reporting System. We used the following for our SaaS on AWS solution:
- StreamSets for CDC.
- Apache Kafka is a messaging tool.
- AWS DataLake S3.
- AWS Aurora is a database for reports.
- AWS ElasticCache is an in-memory storage system.
The infrastructure chosen was best suited to the needs of the client. The main benefit was that it made replacing AWS with self-hosted services easy. We were able to eliminate code/logic duplicates for various deployment schemes.
Minio, PostgreSQL, and Redis were used similarly for our on-premises solution. Both APIs were compatible, and we experienced no problems with the microservice reporting.
The Advantages Of Microservices
Microservices design is getting very popular nowadays. Microservices architecture is gaining popularity among application teams.
Remember that microservices are more than just a way to break down a monolithic app into smaller applications. Microservices are based on creating an independent piece of functionality with clear interfaces that could also have internal components.
Here are some of the high-level benefits that come with a microservices architecture.
- Cohesion and loose coupling.
- Easy to upsize and downscale.
- The technology stack that is modern and diverse: Each microservice may be developed in different languages and deployed across heterogeneous clouds or servers.
- Distributed and modular: Microservices that are small and organized by business capability.
- The time-to-market for monolithic services is slower.
- It is easy to test, deploy and produce.
Microservices Best Practices
Microservices provide several advantages. However, their management, upkeep, and performance may prove challenging; often, software engineers need help creating high-performance Microservices.
Maintaining the performance of an application or project becomes more complicated as the number of microservices expands. Troubleshooting performance issues between them is also tricky.
Since working with microservices, no direct and definite ways of measuring their efficiency or performance have existed. Good development should not just be seen as a one-off task - instead, we should follow some recommendations and best practices when writing or reviewing code (be it microservice, simple program, or any other kind). Here are a few suggestions and best practices we use when writing or reviewing code:
Design, Communications & Security
- Choose and Follow Best Practices of Microservice Development Technology: When developing microservices, engineers should select technology according to business requirements and functionalities, not their personal biases (biasing). However, engineers tend to form strong opinions regarding one particular technology developing an entire service stack and developing accurate services based on these biases (bias). It took me quite some time before changing this mindset - AI/ML services are typically built using Python-esque languages like R or Python+Java; when creating AI/ML models using Java instead, they may perform significantly below expected, so we should follow its best practices once selecting our most suitable tech stack.
- Create Microservices by Applying SOLID Design Principles. When designing microservices, we suggest following SOLID Design Principles as they form the cornerstone for efficient and effective object-oriented programs or applications. They help bring focus when planning each service component individually.
- Optimizing Microservice Architecture to Achieve Performance and Security. APIs and security concerns of microservices must be included during their early design, not as final components. Unsecured services harm consumers more than benefit; OAUTH, Kerberos, and similar security concepts are widely employed across programming languages, and libraries provide easy access.
- Microservice Communication: Use non-blocking requests (asynchronous requests) if possible. Synchronous requests can become performance bottlenecks. Asynchronous communication refers to exchanging data/information/messages among two or more services without all being active at once in response; messaging queues or database polling provide this functionality; Kafka has become a trendy solution for microservice communication.
- Limit memory usage by microservices. Their business logic should be minimal; each microservice should only address one use case at any given time - this maximizes performance while attenuating methods or functions which do not directly relate (for instance, between Sales and Procurement). Typically we wouldn't call a model a microservice if only 30-40% or fewer features of it are utilized by us; these mini-services have similar performance limitations when used regularly as opposed to discrete microservices.
- Caching OAuth and Kerberos Tokens: Security tokens can be costly to generate and require considerable time before use, which makes caching OAuth or Kerberos Tokens beneficial in avoiding frequent calls to APIs that create them. We recommend catching them using systems for between 60 to 180 minutes, depending on our desired level of microservice security - we use Spring integration for non-blocking processes to cache/refresh tokens; additionally, if distributed messaging systems (Kafka, etc.) are utilized within applications, its authentication tokens should also be cached to increase performance.
Database Related
- Select an Appropriate Database Type/Technology. Microservice response times depend heavily upon their data source and database used. Therefore, selecting and modeling such databases are critical to creating microservices with fast response times - either RDBMS, key-value store or unstructured (such as images/ videos, etc.). Optimally stored unstructured data should use NoSQL databases such as MongoDB or Cassandra for optimal performance.
Selecting an appropriate database type - no-SQL or RDBMS) depends entirely on your use case; we should never try and "fit all into one box."
- Database Caching: If data in a database changes slowly (such as references), queries and responses should be cached to keep questions from overloading and reduce queries. An open-source tool such as Hibernate Spring and JPA work well when caching is used as well as spending time to determine optimal indexing/partitioning strategies; an ideal ratio would be an 80/20 or 70/30 split; this indicates keeping 20%-30% more data accesses will improve performance.
- Optimize database calls/questions: Avoid fetching the entire row/tuple from the database when making API calls to hit tables in that database that return ten attributes; that table could have up to 40 or more points, and using "Select All," we would get back the entire row/tuple which could eat into network costs and speed of execution time significantly. Instead, send only what attributes will be necessary by API call to save network costs while speeding execution speed.
- Database Connection Pooling (DBCP): DBCP helps reduce costs associated with opening and closing connections through its pool system by reusing connections across multiple requests without incurring an extra connection fee or time delay for starting/closing them individually. Furthermore, taking steps such as this allows efficient use of resources.
- Implement Database Clustering. Combined with load balancing, database clustering will speed up response times when answering queries from clients and customers. There are various strategies you can employ here to accomplish this feat:
- It could be that the enslaved person is read-only and then becomes consistent.
- The Master-Master is slower than the master-slave configuration.
- Choose a good Index or Partition strategy. Fine-tune the disk, table, and user spaces. It is essential to choose the correct indexes and partition strategies. Good indexes or partition strategies can improve query times, while bad selections of indexes can reduce performance.
It is good to review and log the JPA-generated queries in the early stages of development since these queries can add joins or self-joins that are not necessary.
The Server-side Cache & Scaling:
- Caching on the server side: An excellent caching microservices strategy leads to higher performance. A flawed caching approach will degrade performance. If you want to improve performance, we recommend caching microservice responses based on request parameters. The answer can be cached based on the input parameters if it isn't frequently changed (responses such as image, video, and item details). It will increase performance, as business logic/computing does not need to be performed for the exact/similar requests. Memcached or Redis can store critical values between the application and the database. Redis, an advanced, in-memory caching system that is distributed and allows for backups and restores, is a tool with both these features. Both integrate well with Spring microservices. CDN can be an excellent solution for Videos (clips and movies). The wiki page for content delivery networks is here.
- Two recommended approaches are Vertical Scaling and Horizontal scaling to handle increased microservice load. Vertical scaling is the process of increasing memory for a specific service. Vertical scaling depends on storage and requires restarting microservices (downtime).
The horizontal scaling is the addition of a new node to serve requests. This can either be performed on the same host/cloud or different hosts/clouds. Auto-scalers are a ubiquitous feature that most cloud service providers offer. Auto-scaling can be configured based on memory, Http throughput, and other factors.
A Load balancer will be required for different hosts to direct traffic toward microservices running across multiple cloud pools/nodes. Load balancers can be Pure Geographic, Round Robin, or customized.
The API Gateway, Rate Limiters, and Proxy:
- A Rate Limiter: API Gateways or an in-house API Rate Limiter will protect the APIs against overuse and increase the availability of microservices. The Load balancer can also help with Throttling or fix the number of requests hitting a service in a given time. I'd suggest adding a load balancer to distribute the requests after enabling auto-scaling and multiple node deployments.
Wrapping Up
Good development is not Supposemething that you do once. It's a way of life. If we follow some recommendations and best practices when developing code. In that case, if it is a microservice or simple program, then we can end up with an efficient and performant code.
