Distributed Coordination with Python

This is a talk that I gave at the San Francisco DevOps meetup on 9/29/15. I talk about how Yelp performs service discovery using SmartStack and Docker.

This document discusses integrating the Python driver for Cassandra into Python applications. It covers connecting to Cassandra, executing queries, prepared statements, asynchronous queries, object mapping with cqlengine, and best practices for application development including using virtual environments. The presentation aims to make working with Cassandra from Python straightforward and high performing.

ZooKeeper is a distributed coordination service that allows distributed applications to synchronize data and configuration information. It uses a data model of directories and files, called znodes, that can contain small amounts of structured data. ZooKeeper maintains data consistency through a leader election process and quorum-based consensus algorithm called Paxos. It provides applications with synchronization primitives and configuration maintenance in a highly-available and reliable way.

ZooKeeper is a service for coordinating processes within distributed systems. Stress test of the tool was applied. Reliable Multicast and Dynamic LogBack system Configuration management were implemented with ZooKeeper. More details: http://proskurnia.in.ua/wiki/zookeeper_research

Zookeeper is a distributed coordination service that provides naming, configuration, synchronization, and group services. It allows distributed processes to coordinate with each other through a shared hierarchical namespace of data registers called znodes. Zookeeper follows a leader-elected consensus protocol to guarantee atomic broadcast of state updates from the leader to followers. It uses a hierarchical namespace of znodes similar to a file system to store configuration data and other application-defined metadata. Zookeeper provides services like leader election, group membership, synchronization, and configuration management that are essential for distributed systems.

ZooKeeper is a coordination service for distributed systems that allows nodes to perform tasks correctly. It provides features like atomic operations, order guarantees, and high availability. ZooKeeper uses the Zab protocol to elect a leader and achieve consensus. It stores data in a hierarchical structure of znodes that can be persistent or ephemeral. Software load balancers can use ZooKeeper to store cluster configuration and direct traffic. The Apache Curator library provides a higher-level API for common ZooKeeper patterns like leader election and locks.

This is Apache ZooKeeper session. ZooKeeper is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services. By the end of this presentation you should be fairly clear about Apache ZooKeeper. To watch the video or know more about the course, please visit http://www.knowbigdata.com/page/big-data-and-hadoop-online-instructor-led-training

Big Data with Hadoop & Spark Training: http://bit.ly/2kvXlPd This CloudxLab Introduction to Apache ZooKeeper tutorial helps you to understand ZooKeeper in detail. Below are the topics covered in this tutorial: 1) Data Model 2) Znode Types 3) Persistent Znode 4) Sequential Znode 5) Architecture 6) Election & Majority Demo 7) Why Do We Need Majority? 8) Guarantees - Sequential consistency, Atomicity, Single system image, Durability, Timeliness 9) ZooKeeper APIs 10) Watches & Triggers 11) ACLs - Access Control Lists 12) Usecases 13) When Not to Use ZooKeeper

This document discusses ZooKeeper, an open-source server that enables distributed coordination. It provides instructions for installing ZooKeeper, describes ZooKeeper's data tree and API, and exercises for interacting with ZooKeeper including creating znodes, using watches, and setting up an ensemble across multiple servers.

Presentation on MaestroNG, an orchestration and management tool for multi-host container deployments with Docker. #lspe meetup, February 20th, 2014 at Yahoo!'s URL café.

The document provides an overview and summary of Curator, a client library for Apache ZooKeeper. Curator aims to simplify ZooKeeper development by providing a friendlier API, handling retries, and implementing common patterns ("recipes") like leader election and locks. It consists of a client, framework, and recipes components. The framework handles connection management and retries, while recipes implement distributed primitives. Details about common recipes like locks and leader election are provided.

The document discusses the .NET driver for Cassandra. It provides an overview of the driver and how to connect to Cassandra and execute queries from .NET applications. Key points covered include how to connect to a cluster, execute queries using simple and prepared statements, handle paging of large result sets, and map query results to .NET objects. Examples are provided showing common operations like creating a session, executing queries, and updating data using batches in a .NET application connecting to Cassandra.

From talk given at Spring One 2gx Dallas, 2014 Application configuration is an evolution. It starts as a hard-coded strings in your application and hopefully progresses to something external, such as a file or system property that can be changed without deployment. But what happens when other enterprise concerns enter the mix, such as audit requirements or access control around who can make changes? How do you maintain the consistency of values across too many application servers to manage at one time from a terminal window? The next step in the application configuration evolution is centralized configuration that can be accessed by your applications as they move through your various environments on their way to production. Such a service transfers the ownership of configuration from the last developer who touched the code to a well-versed application owner who is responsible for the configuration of the application across all environments. At Dealer.com, we have created one such solution that relies on Apache ZooKeeper to handle the storage and coordination of the configuration data and Spring to handle to the retrieval, creation and registration of configured objects in each application. The end result is a transparent framework that provides the same configured objects that could have been created using a Spring configuration, configuration file and property value wiring. This talk will cover both the why and how of our solution, with a focus on how we leveraged the powerful attributes of both Apache ZooKeeper and Spring to rid our application of local configuration files and provide a consistent mechanism for application configuration in our enterprise.

At DBC we are running docker and other container types in a mesos/marathon cluster environment. I will demonstrate how we collect statistics, logs etc. and monitor this environment, showing configuration examples, data flows and templates. Some of the covered topics: - Mesos master and agents - Marathon Framework - Docker engine - Containers - Zookeeper - Elasticserach/ELK

In this talk, Evan describes the glue that Yelp has created around Mesos, Marathon, and Docker to make it easy to deploy services.

Reactive Programming, Traits and Principles. What is Reactive, where does it come from, and what is it good for? How does it differ from event driven programming? It only functional?

This document provides a guide for developing distributed applications that use ZooKeeper. It discusses ZooKeeper's data model including znodes, ephemeral nodes, and sequence nodes. It describes ZooKeeper sessions, watches, consistency guarantees, and available bindings. It provides an overview of common ZooKeeper operations like connecting, reads, writes, and handling watches. It also discusses program structure, common problems, and troubleshooting. The guide is intended to help developers understand key ZooKeeper concepts and how to integrate ZooKeeper coordination services into their distributed applications.

Storm is a scalable distributed real-time computation system. It provides a simple programming model through topologies containing spouts that emit streams and bolts that process streams. Storm guarantees processing of all messages through anchoring and tracking tuples in distributed worker processes. It offers fault tolerance through mechanisms like acking tuples and replaying failed tasks. Exactly-once processing can be achieved through techniques like transaction IDs.

The document describes InfectNet, a massively multiplayer online strategy game powered by code written by players. It discusses the game's architecture, which includes separate server and client repositories on GitHub. The game uses a domain-specific language for players to write code that gets executed on the server. Key aspects of the architecture include entity component systems, action/request queues to handle asynchronous behavior, and various game systems that power different aspects like spawning and movement.

- Firmware Slap is a tool that automates the discovery of exploitable vulnerabilities in firmware using concolic analysis and function clustering. It recovers function prototypes from firmware binaries, runs automated analysis on the functions in parallel to find bugs, and visualizes the results in JSON and Elasticsearch/Kibana. - The document discusses challenges with concolic analysis like memory usage and underconstraining symbolic values. It proposes techniques like starting analysis after initialization, modeling functions individually, and tracking memory more precisely. - Function clustering is used to find similar functions that may contain similar bugs. Features are extracted from functions and k-means clustering is applied to group similar functions.

The document discusses best practices for deploying MongoDB including sizing hardware with sufficient memory, CPU and I/O; using an appropriate operating system and filesystem; installing and upgrading MongoDB; ensuring durability with replication and backups; implementing security, monitoring performance with tools, and considerations for deploying on Amazon EC2.

Engaging users in real-time is the topic of our times. Whether it’s a game, a shop, or a content-network, the aim remains the same: providing a personalized experience. In this workshop we will look under the hood of Apache Storm and lay a firm foundation on how to use it with PHP. By that, you can leverage your existing codebase and PHP expertise for an entirely new world: real-time analytics and business logic working on message streams. During the course of the workshop, we will introduce Apache Storm and take a look at all of its components. We will then skyrocket the applicability of Storm by showing you how to implement their components with PHP. All exercises will be conducted using an example project, the infamous and most exhilarating lolcat kitten game ever conceived: Plan 9 From Outer Kitten. In order to follow the hands-on excercises, you will need a development VM prepared by us with all relevant system components and our project repositories. To make the workshop experience as smooth as possible for all participants, please bring a prepared computer to the workshop, as there will be no time to deal with installation and setup issues. Please download all prerequisites and install them as described: VM, Plan 9 webapp, Plan 9 storm backend, (Tutorial: https://github.com/DECK36/plan9_workshop_tutorial ).

Today most networks present one “gateway” to the whole network – The SSL-VPN. A vector that is often overlooked and considered “secure”, we decided to take apart an industry leading SSL-VPN appliance and analyze it to bits to thoroughly understand how secure it really is. During this talk we will examine the internals of the F5 FirePass SSL-VPN Appliance. We discover that even though many security protections are in-place, the internals of the appliance hides interesting vulnerabilities we can exploit. Through processes ranging from reverse engineering to binary planting, we decrypt the file-system and begin examining the environment. As we go down the rabbit hole, our misconceptions about “security appliances” are revealed. Using a combination of web vulnerabilities, format string vulnerabilities and a bunch of frustration, we manage to overcome the multiple limitations and protections presented by the appliance to gain a remote unauthenticated root shell. Due to the magnitude of this vulnerability and the potential for impact against dozens of fortune 500 companies, we contacted F5 and received one of the best vendor responses we’ve experienced – EVER! https://www.hackitoergosum.org

Slides from talk given on Java/Scala Lab 2014 in Odessa, Ukraine. Describes of how Java can be used as platform for latency-restricted applications such as High Frequency Trading and demonstrates how latencies 15-30µsec can be achieved on vanilla Oracle JDK.

This document discusses troubleshooting Oracle WebLogic performance issues. It outlines various tools that can be used for troubleshooting including operating system tools like sar and vmstat, Java tools like jps and jstat, and WebLogic-specific tools like the WebLogic Diagnostics Framework. It also covers taking thread dumps, configuring WebLogic logging and debugging options, and using the Oracle Diagnostic Logging framework.

This document discusses a presentation on practical Windows kernel exploitation. It covers the basics of kernel exploitation, common vulnerability classes like write-what-where and use-after-free, techniques for executing code, mitigation technologies, writing Windows kernel exploits for Metasploit, and improving reliability. The speaker works at SecureState researching and developing kernel exploits and is an open source contributor to projects like Metasploit.

This document discusses quantifying the scalability of software. It recommends instrumenting code from the beginning to collect monitoring data on application health, the entire cluster, and individual nodes' system resources. This allows measuring how well a system can handle increasing load and evolving constraints.

This document provides an overview of patterns for scalability, availability, and stability in distributed systems. It discusses general recommendations like immutability and referential transparency. It covers scalability trade-offs around performance vs scalability, latency vs throughput, and availability vs consistency. It then describes various patterns for scalability including managing state through partitioning, caching, sharding databases, and using distributed caching. It also covers patterns for managing behavior through event-driven architecture, compute grids, load balancing, and parallel computing. Availability patterns like fail-over, replication, and fault tolerance are discussed. The document provides examples of popular technologies that implement many of these patterns.

This document discusses the importance of instrumentation for effective fuzzing. It notes that while fuzzing may seem simple, it actually requires significant effort, target code adaptation, and input corpus minimization. Instrumentation is key to determining code coverage, finding new paths, and prioritizing inputs that lead to crashes or new code coverage. The document provides examples of instrumentation techniques using binary rewriting and hardware features and discusses how to set up fuzzing when source code is available versus when it is not. It also outlines some current gaps in fuzzing techniques.

This document discusses quantifying the scalability of software. It recommends instrumenting code from the beginning to collect monitoring data on application health, the entire cluster, and individual nodes' system resources. This allows measuring how well a system can handle increasing load and evolving constraints.