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The vast majority of requests are for read access. Data is updated in fairly large batches (> 1000 rows), not by single rows; or it is not updated at all. Data is added to the DB but is not modified. For reads, quite a large number of rows are extracted from the DB, but only a small subset of columns. Tables are “wide,” meaning they contain a large number of columns. Queries are relatively rare (usually hundreds of queries per server or less per second). For simple queries, latencies around 50 ms are allowed. Column values are fairly small: numbers and short strings (for example, 60 bytes per URL). Requires high throughput when processing a single query (up to billions of rows per second per server). Transactions are not necessary. Low requirements for data consistency. There is one large table per query. All tables are small, except for one. A query result is significantly smaller than the source data. In other words, data is filtered or aggregated, so the result fits in a single server’s RAM.

Collection of Go packages Have a version and use semantic versioning Specify which code is included in a build See https://roberto.selbach.ca/intro-to-go- modules/ for a great worked example

This book is for anyone who has heard about Apache Kafka and is curious to learn more but keeps getting lost in advanced documentation sites around the Apache Kafka community. We feel you, we hear you and we want to say: Look no further! Give this a read and we look forward to meeting you in the community chats in the future!

Introduction to RabbitMQ ....................................................10 Microservices and RabbitMQ ..................................... 12 What is RabbitMQ?....................................................... 16 Exchanges, Routing Keys and Bindings................... 28 RabbitMQ and client libraries.................................... 38 Rabbitmq with Ruby and bunny ............................... 40 RabbitMQ and Node.js with Amqplib .......................46 RabbitMQ and Python with Pika............................... 56 The Management Interface ....................................... 62

As architects and engineers have sought to exploit cloud computing, the essential drivers of cloud native architecture have become the following:Although the Cloud has its benefits, for many large organizations, it’s difficult to lift and shift all workloads completely to the Cloud. This difficulty results in multiple different environments to maintain. Since security is easier to enforce with visibility and consistency across systems, the hybrid model poses a challenge since consistency is not always achievable

Part 1: Opportunity and ResponsibilityIntroductionSecurity with a Builder’s Mentality Part 2: Mixed EnvironmentsResponsibility in the CloudHybrid CloudMulti-Cloud Part 3: Administrative ControlsPolicy as an Innovation Governance, Legal Issues and ComplianceManagement Plane and Business Continuity Part 4: Technical Controls Infrastructure Security Identity and Access Management Data Protection Part 5: Unified Strategy

PART 1 A NEW HOLISTIC APPROACH...........................................1 1 ■ Introducing streaming data 3 2 ■ Getting data from clients: data ingestion 14 3 ■ Transporting the data from collection tier: decoupling the data pipeline 38 4 ■ Analyzing streaming data 58 5 ■ Algorithms for data analysis 77 6 ■ Storing the analyzed or collected data 95 7 ■ Making the data available 113 8 ■ Consumer device capabilities and limitations accessing the data 135 PART 2 TAKING IT REAL WORLD.............................................157 9 ■ Analyzing Meetup RSVPs in real time 159

PART 1: GETTING STARTED WITH KAFKA STREAMS 1 Welcome to Kafka Streams 2 Kafka Quickly PART 2: KAFKA STREAMS DEVELOPMENT 3 Developing Kafka Streams 4 Streams and State 5 The KTable API 6 The Processor API PART 3: ADMINISTERING KAFKA STREAMS 7 Performance Monitoring 8 Testing PART 4: ADVANCED CONCEPTS WITH KAFKA STREAMS 9 Advanced Applications with Kafka Streams 10 Comparisons to Other Streaming Frameworks

PART 1: CASE STUDY: APPLYING LAYERS OF SECURITY TO A SIMPLE DEVOPS PIPELINE 2 Building a barebones DevOps pipeline to secure 3 Security layer 1: protecting web applications 4 Security layer 2: protecting cloud infrastructures 5 Security layer 3: securing communications 6 Security layer 4: securing the delivery pipeline PART 2: WATCHING FOR ANOMALIES AND PROTECTING SERVICES AGAINST ATTACKS 7 Collecting and storing logs 8 Analyzing logs for fraud and attacks 9 Detecting intrusions 10 The Caribbean breach: a case study in incident response PART 3: MATURING A DEVOPS SECURITY 11 Assessing risks 12 Testing security 13 Continuous security

PART 1: THE OVERVIEW 1 Introducing Kubernetes 2 First steps with Docker and Kubernetes PART 2: CORE CONCEPTS 3 Pods: running containers in Kubernetes 4 Replication & other controllers: deploying managed pods 5 Services: enabling clients to discover and talk to pods 6 Volumes: attaching disk storage to containers 7 ConfigMaps & Secrets: configuring applications 8 Accessing Pod metadata and other resources from applications 9 Deployments: updating applications declaratively 10 StatefulSets: deploying replicated stateful applications PART 3: BEYOND THE BASICS 11 Understanding Kubernetes internals 12 Securing clusters using authentication and authorization 13 Securing cluster nodes and the network 14 Managing computational resources 15 Automatic scaling of pods & cluster nodes 16 Advanced scheduling 17 Best practices for developing apps 18 Extending Kubernetes

微服务设计模式 英文版 1 Escaping monolithic hell 2 Decomposition strategies 3 Inter-process communication in a microservice architecture 4 Managing transactions with sagas 5 Designing business logic in a microservice architecture 6 Developing business logic withevent sourcing 7 Implementing queries in a microservice architecture 8 External API patterns 9 Testing microservices 10 Microservices in production 11 Refactoring to microservices