High-Performance Java, Or How JVector Happened

An airhacks.fm conversation with Jonathan Ellis (@spyced) about: discussion of JVector 3 features and improvements, compression techniques for vector indexes, binary quantization vs product quantization, anisotropic product quantization for improved accuracy, indexing Wikipedia example, Cassandra integration, SIMD acceleration with Fused ADC, optimization with Chronicle Map, E5 embedding models, comparison of CPU vs GPU for vector search, implementation details and low-level optimizations in Java, use of Java Panama API and foreign function interface, JVector's performance advantages, memory footprint reduction, integration with Cassandra and Astra DB, challenges of vector search at scale, trade-offs between RAM usage and CPU performance, Eventual Consistency in distributed vector search, comparison of different embedding models and their accuracy, importance of re-ranking in vector search, advantages of JVector over other vector search implementations Jonathan Ellis on twitter: @spyced

An airhacks.fm conversation with Stephan Janssen (@Stephan007) about: Stephan previously appeared on "#254 How JavaPolis and Devoxx Happened", discussion on the AI revolution in programming, development of an AI-assisted photo sharing application, creation of the Devoxx Genie IntelliJ plugin for AI-augmented programming, advantages of Claude 3.5 from Anthropic, use of local AI models in development environments, integration of AI in Java development, langchain4j and its adoption by Red Hat, development of Java-based AI tools like Lama3.java, jlama and JVector, potential for specialized AI models in software development, challenges and opportunities for junior and senior developers in AI-augmented programming, importance of understanding cloud services and cost structures when using AI, potential future of prompt-based programming and code generation, discussion on maintaining and improving AI-generated code, exciting developments in Java for AI including project valhalla and tornadovm, potential for running AI models directly on Java without external dependencies, considerations for enterprise AI adoption and integration, the need for promoting Java's capabilities in AI development, potential for Visual Studio Code port of Devoxx Genie, the challenge of maintaining AI-generated code versus keeping prompts, the concept of "prompt ops" for software development, the use of AI for code review and improvement, the potential for AI to lower the barrier to entry for new developers, and the exciting future of AI in software development Stephan Janssen on twitter: @Stephan007

An airhacks.fm conversation with Jake Luciani (@tjake) about: from Commodore 64 to cloud databases, early programming experiences with Basic and Excel macros, studying cognitive science and its influence on his career, transition to computer science, working at Bell Labs on R language, developing open-source projects like Night Rider MP3 player, creating a NoSQL database that led to involvement with Cassandra, building search API on top of Cassandra, joining datastax as an early employee, working on various aspects of Cassandra including compaction and streaming, challenges of byte buffer implementation, development of CQL (Cassandra Query Language), transition from NoSQL to SQL-like interfaces, separation of compute and storage in cloud databases, using S3 as the source of truth for Astra DB, implementing a Java file system abstraction for S3 integration, using etcd as a transactional cache for metadata, offering multiple APIs including REST and CQL drivers for astra DB, implementing JSON document storage and querying capabilities, cross-AZ cost considerations in cloud deployments, Java as a language for database development, future plans for jlama (Java-based LLM inference engine), the importance of open-source in cloud technologies, cost-driven architectures in cloud deployments, serverless vs. traditional deployments trade-offs, integration of AstraDB with cloud marketplaces and security considerations Jake Luciani on twitter: @tjake

An airhacks.fm conversation with Alfonso Peterssen (@TheMukel) about: Alfonso previously appeared on "#294 LLama2.java: LLM integration with A 100% Pure Java file", discussion of llama2.java and llama3.java projects for running LLMs in Java, performance comparison between Java and C implementations, use of Vector API in Java for matrix multiplication, challenges and potential improvements in Vector API implementation, integration of various LLM models like Mistral, phi, qwen or gemma, differences in model sizes and capabilities, tokenization and chat format challenges across different models, potential for Java Community Process (JCP) standardization of gguf parsing, quantization techniques and their impact on performance, plans for integrating with langchain4j, advantages of pure Java implementations for AI models, potential for GraalVM and native image optimizations, discussion on the future of specialized AI models for specific tasks, challenges in training models with language capabilities but limited world knowledge, importance of SIMD instructions and vector operations for performance optimization, potential improvements in Java's handling of different float formats like float16 and bfloat16, discussion on the role of smaller, specialized AI models in enterprise applications and development tools Alfonso Peterssen on twitter: @TheMukel

An airhacks.fm conversation with Georgios Andrianakis (@geoand86) about: discussion on JAX-RS and reactive programming in quarkus, comparison of blocking vs non-blocking approaches, performance considerations for different use cases, Quarkus underlying architecture using Vert.x, handling of HTTP requests and responses, thread management in Quarkus, reactive vs traditional programming models, integration with databases using Hibernate and Hibernate Reactive, JSON serialization options (Jackson, JSON-B), balancing act between supporting standards and providing modern features, documentation challenges for a large project like Quarkus, detecting blocked event loop threads, CPU-intensive tasks in reactive programming, non-blocking database drivers for reactive programming, historical perspective on messaging systems and their challenges, use cases for reactive programming, performance characteristics of blocking vs non-blocking systems under high load, brief mention of LangChain for Java and its similarity to JPA for LLMs Georgios Andrianakis on twitter: @geoand86

An airhacks.fm conversation with Gerald Venzl (@GeraldVenzl) about: from a 386 computer with SimCity to Oracle's database evangelist, early interest in computer hardware and software, apprenticeship as a programmer in Austria, work experience with Oracle database and PLSQL, Steven Feuerstein, PLSQL expert, career moves to New York, London, and San Francisco, role as product manager and team leader at Oracle, efforts to attract developers to Oracle technologies, involvement in Oracle ACE Program, work on docker files for Oracle Database, challenges with ARM port for Mac, popular JavaOne talk on optimizing Java code for database performance, discussion of Oracle's various database technologies including NoSQL and TimesTen, importance of educating developers on database best practices, evolution of database performance techniques, future topics for discussion including Oracle architecture, Java integration, and business logic in databases, Gerald's team of evangelists across Europe, ways to contact Gerald and his team for speaking engagements or information Gerald Venzl on twitter: @GeraldVenzl

An airhacks.fm conversation with Nicolai Parlog (@nipafx) about: Java 22 and 23 new features overview, including unnamed variables with underscore, multi-source file launching, G1 region pinning, Foreign Function & Memory API finalization, Markdown Javadoc support, ZGC generational collector by default, discussion on Java installation and beginner-friendliness, debate on proper use of LTS terminology for Java releases, potential for Java in AI/ML space with new vector APIs and native performance, comparison of Java to python for AI workloads, challenges and opportunities for Java adoption in data science and machine learning domains, importance of specialized AI models vs general models for enterprise use cases, trade-offs between developer experience and operational efficiency for different languages and runtimes, potential future directions for Java in high-performance computing and AI acceleration, previously, Nicolai appeared on "#300 Object-Oriented Programming (OOP) vs. Data-Oriented Programming (DOP) in Java" Nicolai Parlog on twitter: @nipafx

An airhacks.fm conversation with Georgios Andrianakis (@geoand86) about: early experiences with computers and programming, transition from Pascal and C to Java in university, early career working with WebLogic and EJB, move to Spring development, joining Red Hat and discovering quarkus, developing Spring compatibility layer for Quarkus, Vodafone Greece case study showing benefits of migrating from Spring to Quarkus, current work on RESTEasy Reactive and langchain4j, exploration of future AI integration in Java with projects like Llama3.java, comparison of Spring, Quarkus, and Micronaut, discussion on the evolution of Spring and its perceived bloat, potential for Quarkus and LangChain4j to revolutionize enterprise AI integration, importance of pure Java solutions for AI inference and integration with existing enterprise applications Georgios Andrianakis on twitter: @geoand86

An airhacks.fm conversation with Jonathan Schneider (@jon_k_schneider) about: Spinnaker's role in continuous delivery and multi-cloud deployments, multi-cloud architectures, Micrometer's origin and design as a vendor-neutral metrics abstraction library, comparison of micrometer to other metrics solutions like opentelemetry and MicroProfile Metrics, exploration of Micrometer's architecture including registries and meter types, debate on static vs dependency-injected registries, explanation of distribution summaries and their use cases, consideration of unit testing metrics, examination of Micrometer's support for multiple monitoring systems simultaneously, discussion of meter filters for customizing metric output, reflection on the trade-offs between language support and monitoring system support in metrics libraries, insights into the separation of application and runtime metrics, Jonathan's experience developing Micrometer at Netflix and Pivotal, current usage of Micrometer and prometheus in Modern's multi-tenant SaaS architecture, comparison of serverless and EC2-based deployments for different use cases, OpenRewrite's growing popularity in Europe Jonathan Schneider on twitter: @jon_k_schneider

An airhacks.fm conversation with Gil Tene (@giltene) about: discussion of Azul Systems' Vega chip, a custom-designed processor optimized for Java workloads, Vega's architecture and features including multiple generations (Vega 1, 2, and 3), high core count (up to 54 cores per chip), custom instruction set, hardware-managed register windows, type-aware pointers for efficient method calls, fully symmetric multiprocessing with up to 16 chips (864 cores total), memory striping across controllers for even distribution, Hardware Transactional Memory support for concurrent Java operations, custom coherency and memory ordering instructions, comparison with contemporary processors from Intel and Sun, challenges in chip design and manufacturing, impact on Java performance and concurrency, evolution of the technology and its influence on modern processor designs, Gil Tene's role in developing the Vega chip and related software technologies at Azul Systems, Gil's blog: Stuff Gil Says Gil Tene on twitter: @giltene