Mastering Java Persistence Best Practices for Cloud-Native Applications and Modernization

Join us for an insightful presentation as we explore the world of Java persistence in the context of cloud-native applications and modernization. Discover the fundamental principles, challenges, and strategies. From uncovering database patterns to harnessing the power of cloud-native technologies, this session will provide valuable insights for developers, engineers, and architects seeking to optimize their application's persistence layer. Take advantage of this opportunity to better understand how to create maintainable architectures, tackle data integration challenges, and embark on successful modernization journeys. Whether you're new to the concept or looking to enhance your expertise, this presentation is your guide to mastering Java persistence for the future of software development.

Transcript

1. Mastering Java Persistence
   Best Practices for Cloud-Native
   Applications and Modernization
   

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2. Endures knowledge
   Throughout humanity
   Temples, Caves as database
   Why do we need
   databases anyway?
   

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3. Database evolutions
   

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4. Why Do Modern
   Applications
   Need Data Storage?
   The new opportunities
   The current opportunity
   It's where the state is
   

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5. Maturity Model
   Database flavors
   Paradigms
   Persistence landscape
   State of affairs
   https://survey.stackoverflow.co/2023/
   

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6. Development lifecycle very
   often starts with immature
   data structures
   Changing a database type in
   existing applications is
   complex and expensive.
   Over time, maintenance of
   data and schema evolution
   gets challenging
   First thing we do
   Persistence landscape
   Change Maintenance
   State of affairs
   Evolutionary Data
   

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7. The rules of Software Architecture
   Everything in Software Architecture
   is a trade-off
   Why is more important than How
   

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8. Business Isolation Performance
   The success at Software Architecture
   

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9. SQL Database
   Database solutions
   Polyglot Persistence
   key value
   key
   key
   key
   value
   value
   value
   Wide-Column Graph Document Key Value
   NoSQL Database
   

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10. Apollo
    Key-value
    Structure
    Ares
    Love Beauty
    War
    Sun
    Aphrodite
    Key Value
    

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11. Use Case Description
    Caching Speeds up data retrieval by storing frequently accessed data in
    memory, reducing the need to fetch it from the backend.
    Session Management Stores and manages user sessions, enhancing application
    performance and scalability.
    Real-time Analytics Powers real-time dashboards and analytics by swiftly processing and
    aggregating data.
    Pub/Sub Messaging Facilitates real-time communication between components through
    publish-subscribe messaging patterns.
    Redis
    Use cases
    

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12. Simplify Schema
    Identify Key Patterns
    Use Contextual Keys
    Batching Operations
    Cache-friendly Design
    Avoid Complex Joins
    Beware of
    Over-Normalization
    Avoid Bloated Values
    Limit Indexes
    Redis
    Do and Don'ts
    

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13. Redis
    Clusters
    

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14. Apollo
    Aphrodite
    Ares
    Kratos
    Duty
    Dead Gods
    Love, happy
    Sun
    War
    13
    Color
    Sword
    Row-key Columns
    Wide-Column
    Structure
    Duty
    Duty
    weapon
    

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15. Apache Cassandra
    User Cases
    Use Case Description
    Online Retail
    Managing e-commerce platforms, handling product catalogs, user profiles,
    and transaction records.
    Social Media Analytics
    Monitoring and analyzing social media interactions, tracking trends, and user
    engagement.
    Real-Time Analytics
    Enabling quick analysis of data streams for instant insights, critical in financial
    and logistics sectors.
    Logging and Monitoring
    Centralized storage and analysis of logs and monitoring data from applications
    and servers.
    

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16. Apache Cassandra
    Modeling tips
    Denormalize When Necessary
    Design for Query Patterns
    Choose Optimal Column Families
    Utilize Secondary Indexes
    Compression and Bloom Filters
    Avoid Over-Denormalization
    Limit Column Count
    Avoid Overusing Secondary Indexes
    

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17. 1
    2
    3
    4
    5
    6
    7
    8
    9
    11
    12
    10
    Client
    R1
    R2
    R3
    1
    2
    3
    4
    5
    6
    7
    8
    9
    11
    12
    10
    R4
    R5
    R6
    DC1 DC2
    Apache Cassandra
    Cluster
    

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18. {
    "name":"Diana",
    "duty":[
    "Hunt",
    "Moon",
    "Nature"
    ],
    "siblings":{
    "Apollo":"brother"
    }
    }
    Document
    Structure
    

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19. MongoDB
    User Cases
    Use Case Description
    Content Management
    Store and manage diverse content types like articles, images, and videos with
    schema flexibility.
    Catalog Management
    Efficiently organize and categorize products or items with changing attributes
    and metadata.
    Mobile Applications
    Provide offline capabilities and sync data seamlessly once online, enhancing
    user experience.
    Social Media Platforms
    Facilitate rapid storage and retrieval of user-generated content, profiles, and
    social interactions.
    

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20. MongoDB
    Modeling tips
    Leverage Embedded Documents
    Design Around Use Cases
    Employ Indexes Judiciously
    Normalize When Logical
    Stay Flexible with Arrays
    Avoid Over-Embedding
    Steer Clear of Monolithic Documents
    Don’t Over-Index
    Beware of the One-Size-Fits-All Approach
    

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21. MongoDB
    Cluster
    

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22. MongoDB
    Cluster
    

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23. Apollo Ares
    Kratos
    was killed by was killed by
    killed killed
    Graph
    Structure
    

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24. Neo4J
    Modeling tips
    Avoid Over-Reliance on Joins
    Steer Clear of Over-Connecting Nodes
    Don’t Overcomplicate Graph Design
    Beware of Property Index Overuse
    Embrace Relationship-Driven Design
    Craft Efficient Traversal Paths
    Use Labels and Types Wisely
    Leverage Indexing for Nodes
    Utilize Property Indexes
    

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25. Neo4J
    User Cases
    Use Case Description
    Social Networks Modeling user profiles, friendships, and interactions for effective social
    networking platforms.
    Recommendation
    Engines
    Powering personalized recommendations by analyzing connections and
    preferences.
    Knowledge Graphs Organizing and querying complex relationships in fields like healthcare,
    finance, and research.
    Fraud Detection Uncovering hidden patterns and connections indicative of fraudulent
    activities.
    Network Analysis Analyzing intricate relationships in data networks, such as transportation
    and communication.
    

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26. Neo4J
    Leader
    Follower
    Follower
    Read Replica
    Read Replica
    

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27. Scalability
    Flexibility
    key-value
    Column
    Document
    Graph
    Time-series
    Scalability vs Flexibility
    Query and speed
    

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28. Application vs. Database
    

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29. Application
    (Object Oriented Language)
    Mismatch
    Database
    (Relational)
    Object
    Challenges in a database land
    Different paradigms: Apps x DBMS
    Tables
    

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30. Mapping
    Mismatch
    1
    *
    Addresses
    Inheritance
    Polymorphism
    Encapsulation
    Types Normalization
    Denormalization
    Structure
    Application Database
    key value
    key
    key
    key
    value
    value
    value
    wide-Column Graph
    Document Key Value
    Challenges in a database land
    Different paradigms: Apps x DBMS
    

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31. Driver
    Data Mapper
    DAO
    Database integration
    Data Access Patterns
    Handling Data Integration in Java
    Active Record
    Repository
    DTO
    

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32. Driver
    Database integration
    Data Mapping
    Database Application
    Data Mapper
    Active Record
    Repository
    DAO
    Data-oriented
    Programing
    Object Oriented
    Programming
    

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33. Database-oriented Programming
    4 Principles
    Database
    Data-oriented
    Programing
    1. Separating code (behavior) from data.
    2. Representing data with generic data structures.
    3. Treating data as immutable.
    4. Separating data schema from data
    representation.
    

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34. Object Oriented Programming
    Principles
    Application
    Object Oriented
    Programming
    1. Expose behavior and Hide data
    2. Abstraction
    3. Polymorphism
    

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35. Driver
    Data Mapping
    try(Connection conn = DriverManager.getConnection(DB_URL, USER, PASS){
    Statement stmt = conn.createStatement();
    ResultSet rs = stmt.executeQuery(QUERY);) {
    // Extract data from result set
    while (rs.next()) {
    // Retrieve by column name
    System.out.print("ID: " + rs.getInt("id"));
    System.out.print(",name: " + rs.getInt("name"));
    System.out.print(",birthday: " + rs.getString("birthday"));
    System.out.println(",city: " + rs.getString("city"));
    System.out.println(",street: " + rs.getString("street"));
    …}
    }
    Complex relation
    w/ business logic
    Code flexibility
    

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36. Data Mapper
    Data Mapping
    @Entity
    public class Person {
    @Id @GeneratedValue(strategy = AUTO) Long id;
    String name;
    LocalDate birthday;
    @ManyToOne List address;
    …
    }
    impedance mismatch
    centralize mapper
    responsibility
    public class PersonRowMapper implements RowMapper {
    @Override
    public Person mapRow(ResultSet rs, int rowNum) throws
    SQLException {
    Person person = new Person();
    person.setId(rs.getInt("ID"));
    return person;
    }
    }
    

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37. Data Access Object
    Data Mapping
    public interface PersonDAO {
    List getAll();
    void update(Person person);
    void delete(Person person);
    void insert(Person person);
    }
    Centralize Data
    operations
    impedance mismatch
    

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38. Active Record
    Data Mapping
    @Entity
    public class Person extends PanacheEntity {
    public String name;
    public LocalDate birthday;
    public List addresses;
    }
    Person person =...;
    // persist it
    person.persist();
    List people = Person.listAll();
    // finding a specific person by ID
    person = Person.findById(personId);
    SOLID breaking
    Higher domain's
    responsibility
    

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39. Repository
    Data Mapping
    Far from database
    Domain oriented
    @Entity
    public class Person {
    private @Id Long id;
    private @Column String name;
    private @Column LocalDate birthday;
    private @ManyToOne List addresses;
    }
    public interface PersonRepository extends {}
    Person person =...;
    // persist it
    repository.save(person);
    List people = repository.findAll();
    // finding a specific person by ID
    person = repository.findById(personId);
    

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40. Database integration
    Data Mapping
    Client
    Database Database
    Client
    Mapper
    DAO
    Repository
    Data mapping
    and conversion
    

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41. Flexibility vs Complexity, Use with Caution
    Database Layers
    

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42. DTO
    Entity
    Resource
    DTO
    Flexibility vs Complexity, Use with Caution
    Database
    

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43. Database metrics
    Biz. transactions
    Oversized /
    downsized
    Invalid/stale
    connections
    Apps on await state
    Fixed cache size
    No cache usage
    Consistency
    impacts w/
    distributed cache
    Complex mapping
    Auto-generated
    schemas
    On-prem x Cloud
    Many NoSQL types
    SQL x
    NoSQL x NewSQL
    Eager x Lazy
    loading
    N+1 Problem
    Hard to change
    db types
    Persistence
    Config.
    Data storage
    Data
    Manipulation
    Cache Conn. Pool
    Framework
    What's next?
    

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44. books.a4j.dev/mastering-persistence
    Expand your Knowledge
    Deliver high-performance Java solutions
    Get your copy today!
    @otaviojava @kvarel4
    

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