Avro is a data serialization system that provides dynamic typing, a schema-based design, and efficient encoding. It supports serialization, RPC, and has implementations in many languages with first-class support for Hadoop. The project aims to make data serialization tools more useful and "sexy" for distributed systems.
This document provides an overview of microservices in the enterprise. It discusses factors driving the rise of microservices like SOA fatigue and the need for faster innovation. Examples of microservice architectures from companies like Netflix, Twitter and Gilt are presented. Key capabilities for building enterprise-ready microservices are described, including service discovery, description, deployment isolation using containers, data/verb partitioning, lightweight middleware, API gateways and observability. Open source technologies that support implementing these capabilities are also outlined. The document concludes that microservices are the future of distributed systems and enterprises should implement solutions from first principles using inspiration from internet companies.
RPC protocols like SOAP, XML-RPC, JSON-RPC, and Thrift allow for remote procedure calls between a client and server. RPC works by having a client call an RPC function, which then calls the service and executes the request. The service returns a reply and the program continues. Interface layers can be high-level existing API calls, middle-level custom API calls generated by a compiler, or low-level direct RPC API calls. Popular data formats used include XML, JSON, HTTP, and TCP/IP. Thrift is an open-source protocol developed by Facebook that supports many programming languages. It generates stubs for easy client-server communication. Avro and Protocol Buffers also generate stubs and are supported by languages like
The document discusses Apache Avro, a data serialization framework. It provides an overview of Avro's history and capabilities. Key points include that Avro supports schema evolution, multiple languages, and interoperability with other formats like Protobuf and Thrift. The document also covers implementing Avro, including using the generic, specific and reflect data types, and examples of writing and reading data. Performance is addressed, finding that Avro size is competitive while speed is in the top half.
The document discusses using protocol buffers (Protobuf) and code generation to reduce boilerplate code when developing microservices with the Go kit framework. It provides an example of defining a service in Protobuf and generating Go code for the service endpoints, transports, and registration functions using a custom code generation tool called protoc-gen-gotemplate. The tool reads Protobuf service definitions and template files to generate standardized, up-to-date Go code that implements the services while avoiding much of the repetitive code normally required.
This document discusses open microservice architecture with an emphasis on loosely coupled service-oriented architecture and its components, including users, products, and payments. It highlights various technologies and tools for deployment, communication, load balancing, and monitoring such as Docker, Kubernetes, and Kafka. Key metrics to monitor include success rates, error rates, and resource usage, among others.
Avro is a data serialization system that provides data interchange and interoperability between systems. It allows for efficient encoding of data and schema evolution. Avro defines data using JSON schemas and provides dynamic typing which allows data to be read without code generation. It includes a file format and supports MapReduce workflows. Avro aims to become the standard data format for Hadoop applications by providing rich data types, interoperability between languages, and compatibility between versions.
This document provides instructions for creating a gRPC Hello World sample in C# using .NET Core. It describes creating client and server projects with protobuf definition files. The server project implements a Greeter service that returns a greeting message. The client project calls the SayHello method to get a response from the server. Running the projects demonstrates a basic gRPC communication.
RPC allows a program to call a subroutine that resides on a remote machine. When a call is made, the calling process is suspended and execution takes place on the remote machine. The results are then returned. This makes the remote call appear local to the programmer. RPC uses message passing to transmit information between machines and allows communication between processes on different machines or the same machine. It provides a simple interface like local procedure calls but involves more overhead due to network communication.
gRPC is a modern open source RPC framework that enables client and server applications to communicate transparently. It is based on HTTP/2 for its transport mechanism and Protocol Buffers as its interface definition language. Some benefits of gRPC include being fast due to its use of HTTP/2, supporting multiple programming languages, and enabling server push capabilities. However, it also has some downsides such as potential issues with load balancing of persistent connections and requiring external services for service discovery.
Building High Performance APIs In Go Using gRPC And Protocol BuffersShiju Varghese
?
The document discusses building high performance APIs in Go using gRPC and Protocol Buffers. It describes how gRPC is a high performance, open-source RPC framework that uses Protocol Buffers for serialization. It provides an overview of building APIs with gRPC by defining services and messages with Protobuf, generating code, implementing servers and clients. The workflow allows building APIs that are efficient, strongly typed and work across languages.
Apache Avro is a data serialization system and RPC framework that provides rich data structures and compact, fast binary data formats. It uses JSON for schemas and relies on schemas stored with data to allow serialization without per-value overhead. Avro supports Java, C, C++, C#, Python and Ruby and is commonly used in Hadoop for data persistence and communication between nodes. It compares similarly to Protobuf and Thrift but with some differences in implementation, error handling and extensibility.
Enabling Googley microservices with HTTP/2 and gRPC.Alex Borysov
?
The document discusses gRPC as a high-performance, open-source RPC framework that leverages HTTP/2 for efficient communication, enabling applications to handle tens of billions of requests per second. It outlines the advantages of gRPC over traditional HTTP/1.x, such as reduced latency and CPU resource consumption, highlighting the protocol's features like multiplexing, header compression, and binary framing. Additionally, it provides implementation examples, technical specifications, and comparisons to prior HTTP standards.
Introduction to gRPC - Mete Atamel - Codemotion Rome 2017Codemotion
?
This document provides an overview of gRPC, a high-performance, open-source framework for remote procedure calls, highlighting its motivation, design goals, and basic implementation. It discusses the efficiency of gRPC using HTTP/2 and protocol buffers, as well as connection options such as unary, server-side streaming, and bi-directional streaming. Additionally, the document includes practical examples and code snippets demonstrating how to create gRPC services in various programming languages.
The document discusses the benefits of using gRPC and microservices at Square, highlighting its preference for modern communication methods over traditional monoliths. It outlines the features of gRPC, including support for various programming languages, efficient payload formats using Protocol Buffers, and performance improvements over JSON/HTTP. The presentation also indicates the use of microservices architecture for better scalability and efficiency in distributed systems.
REST and RPC are common approaches for building APIs. REST uses HTTP and focuses on resources, using verbs like GET, PUT and DELETE to manipulate representations of resources. It favors loose coupling between services. RPC relies on requests and returns structured data, often using protocols like Thrift, Protobuf or JSON-RPC. It allows for tight integration but is less flexible. The document also discusses pub/sub, event-driven architectures, reactive programming and other approaches relevant to distributed systems.
Robert Kubis - gRPC - boilerplate to high-performance scalable APIs - code.t...AboutYouGmbH
?
The document discusses gRPC, an open source framework for building microservices. It was created by Google to be a high performance, open source universal RPC framework. gRPC uses HTTP/2 for transport, Protocol Buffers as the interface definition language, and generated client/server code for many languages to make cross-platform communications simple and efficient. The document provides an overview of gRPC's goals and architecture, how to define a service using .proto files, and examples of common RPC patterns like unary, streaming, and bidirectional calls.
This document discusses Apache Kafka, an open-source distributed event streaming platform. It provides an introduction to Kafka's design and capabilities including:
1) Kafka is a distributed publish-subscribe messaging system that can handle high throughput workloads with low latency.
2) It is designed for real-time data pipelines and activity streaming and can be used for transporting logs, metrics collection, and building real-time applications.
3) Kafka supports distributed, scalable, fault-tolerant storage and processing of streaming data across multiple producers and consumers.
Apache big data 2016 - Speaking the language of Big Datatechmaddy
?
The document discusses various data interchange protocols in big data, focusing on their characteristics and the need for efficient data serialization. Protocols such as Protocol Buffers, Apache Thrift, and Apache Avro are highlighted for their flexibility, ease of integration, and ability to handle schema evolution. The author emphasizes the importance of language-neutral schemas and efficient data handling in distributed systems.
Driving containerd operations with gRPCDocker, Inc.
?
containerd uses gRPC and protocol buffers to define its API. gRPC provides benefits like code generation, performance, and common standards. The containerd gRPC services include Execution, Shim, and Content, which define methods like Create, Start, and List. Protocol buffer definitions generate client and server code in various languages. Developers can build clients and extend containerd with new services. The containerd API is under active development and aims to stabilize before the 1.0 release while allowing backwards compatibility through gRPC versioning.
This document provides an introduction and overview of GRPC, a remote procedure call framework developed by Google. It discusses the history and limitations of previous RPC technologies like CORBA, DCOM, RMI, SOAP, and REST. GRPC aims to provide a simple, high performance, language-agnostic, and extensible RPC framework. It uses protocol buffers for defining service interfaces and HTTP/2 for transport, allowing for features like bi-directional streaming. The document also covers how GRPC supports versioning of services and error handling.
This document discusses open microservice architecture with an emphasis on loosely coupled service-oriented architecture and its components, including users, products, and payments. It highlights various technologies and tools for deployment, communication, load balancing, and monitoring such as Docker, Kubernetes, and Kafka. Key metrics to monitor include success rates, error rates, and resource usage, among others.
Avro is a data serialization system that provides data interchange and interoperability between systems. It allows for efficient encoding of data and schema evolution. Avro defines data using JSON schemas and provides dynamic typing which allows data to be read without code generation. It includes a file format and supports MapReduce workflows. Avro aims to become the standard data format for Hadoop applications by providing rich data types, interoperability between languages, and compatibility between versions.
This document provides instructions for creating a gRPC Hello World sample in C# using .NET Core. It describes creating client and server projects with protobuf definition files. The server project implements a Greeter service that returns a greeting message. The client project calls the SayHello method to get a response from the server. Running the projects demonstrates a basic gRPC communication.
RPC allows a program to call a subroutine that resides on a remote machine. When a call is made, the calling process is suspended and execution takes place on the remote machine. The results are then returned. This makes the remote call appear local to the programmer. RPC uses message passing to transmit information between machines and allows communication between processes on different machines or the same machine. It provides a simple interface like local procedure calls but involves more overhead due to network communication.
gRPC is a modern open source RPC framework that enables client and server applications to communicate transparently. It is based on HTTP/2 for its transport mechanism and Protocol Buffers as its interface definition language. Some benefits of gRPC include being fast due to its use of HTTP/2, supporting multiple programming languages, and enabling server push capabilities. However, it also has some downsides such as potential issues with load balancing of persistent connections and requiring external services for service discovery.
Building High Performance APIs In Go Using gRPC And Protocol BuffersShiju Varghese
?
The document discusses building high performance APIs in Go using gRPC and Protocol Buffers. It describes how gRPC is a high performance, open-source RPC framework that uses Protocol Buffers for serialization. It provides an overview of building APIs with gRPC by defining services and messages with Protobuf, generating code, implementing servers and clients. The workflow allows building APIs that are efficient, strongly typed and work across languages.
Apache Avro is a data serialization system and RPC framework that provides rich data structures and compact, fast binary data formats. It uses JSON for schemas and relies on schemas stored with data to allow serialization without per-value overhead. Avro supports Java, C, C++, C#, Python and Ruby and is commonly used in Hadoop for data persistence and communication between nodes. It compares similarly to Protobuf and Thrift but with some differences in implementation, error handling and extensibility.
Enabling Googley microservices with HTTP/2 and gRPC.Alex Borysov
?
The document discusses gRPC as a high-performance, open-source RPC framework that leverages HTTP/2 for efficient communication, enabling applications to handle tens of billions of requests per second. It outlines the advantages of gRPC over traditional HTTP/1.x, such as reduced latency and CPU resource consumption, highlighting the protocol's features like multiplexing, header compression, and binary framing. Additionally, it provides implementation examples, technical specifications, and comparisons to prior HTTP standards.
Introduction to gRPC - Mete Atamel - Codemotion Rome 2017Codemotion
?
This document provides an overview of gRPC, a high-performance, open-source framework for remote procedure calls, highlighting its motivation, design goals, and basic implementation. It discusses the efficiency of gRPC using HTTP/2 and protocol buffers, as well as connection options such as unary, server-side streaming, and bi-directional streaming. Additionally, the document includes practical examples and code snippets demonstrating how to create gRPC services in various programming languages.
The document discusses the benefits of using gRPC and microservices at Square, highlighting its preference for modern communication methods over traditional monoliths. It outlines the features of gRPC, including support for various programming languages, efficient payload formats using Protocol Buffers, and performance improvements over JSON/HTTP. The presentation also indicates the use of microservices architecture for better scalability and efficiency in distributed systems.
REST and RPC are common approaches for building APIs. REST uses HTTP and focuses on resources, using verbs like GET, PUT and DELETE to manipulate representations of resources. It favors loose coupling between services. RPC relies on requests and returns structured data, often using protocols like Thrift, Protobuf or JSON-RPC. It allows for tight integration but is less flexible. The document also discusses pub/sub, event-driven architectures, reactive programming and other approaches relevant to distributed systems.
Robert Kubis - gRPC - boilerplate to high-performance scalable APIs - code.t...AboutYouGmbH
?
The document discusses gRPC, an open source framework for building microservices. It was created by Google to be a high performance, open source universal RPC framework. gRPC uses HTTP/2 for transport, Protocol Buffers as the interface definition language, and generated client/server code for many languages to make cross-platform communications simple and efficient. The document provides an overview of gRPC's goals and architecture, how to define a service using .proto files, and examples of common RPC patterns like unary, streaming, and bidirectional calls.
This document discusses Apache Kafka, an open-source distributed event streaming platform. It provides an introduction to Kafka's design and capabilities including:
1) Kafka is a distributed publish-subscribe messaging system that can handle high throughput workloads with low latency.
2) It is designed for real-time data pipelines and activity streaming and can be used for transporting logs, metrics collection, and building real-time applications.
3) Kafka supports distributed, scalable, fault-tolerant storage and processing of streaming data across multiple producers and consumers.
Apache big data 2016 - Speaking the language of Big Datatechmaddy
?
The document discusses various data interchange protocols in big data, focusing on their characteristics and the need for efficient data serialization. Protocols such as Protocol Buffers, Apache Thrift, and Apache Avro are highlighted for their flexibility, ease of integration, and ability to handle schema evolution. The author emphasizes the importance of language-neutral schemas and efficient data handling in distributed systems.
Driving containerd operations with gRPCDocker, Inc.
?
containerd uses gRPC and protocol buffers to define its API. gRPC provides benefits like code generation, performance, and common standards. The containerd gRPC services include Execution, Shim, and Content, which define methods like Create, Start, and List. Protocol buffer definitions generate client and server code in various languages. Developers can build clients and extend containerd with new services. The containerd API is under active development and aims to stabilize before the 1.0 release while allowing backwards compatibility through gRPC versioning.
This document provides an introduction and overview of GRPC, a remote procedure call framework developed by Google. It discusses the history and limitations of previous RPC technologies like CORBA, DCOM, RMI, SOAP, and REST. GRPC aims to provide a simple, high performance, language-agnostic, and extensible RPC framework. It uses protocol buffers for defining service interfaces and HTTP/2 for transport, allowing for features like bi-directional streaming. The document also covers how GRPC supports versioning of services and error handling.