Cassandra is an open source, distributed, decentralized, elastically scalable, highly available, and fault-tolerant database. It originated at Facebook in 2007 to solve their inbox search problem. Some key companies using Cassandra include Twitter, Facebook, Digg, and Rackspace. Cassandra's data model is based on Google's Bigtable and its distribution design is based on Amazon's Dynamo.
Introduction to Apache Cassandra (September 2014). Design principles, replication, consistency, clusters, CQL.
Cassandra is the dominant data store used at Netflix and it's health is critical to many of its services. In this talk we will share details of the recent redesign of our health monitoring system and how we leveraged a reactive stream processing system to give us a real-time view our entire fleet while dramatically improving accuracy and reducing false alarms in our alerting. About the Speaker Jason Cacciatore Senior Software Engineer, Netflix Jason Cacciatore is a Senior Software Engineer at Netflix, where he's been working for the past several years. He's interested in stateful distributed systems and has a diverse background in technology. In his spare time he enjoys spending time with his wife and two sons, reading non-fiction, and watching Netflix documentaries.
Cassandra is used for real-time bidding in online advertising. It processes billions of bid requests per day with low latency requirements. Segment data, which assigns product or service affinity to user groups, is stored in Cassandra to reduce calculations and allow users to be bid on sooner. Tuning the cache size and understanding the active dataset helps optimize performance.
Apache Cassandra operations have the reputation to be simple on single datacenter deployments and / or low volume clusters but they become way more complex on high latency multi-datacenter clusters with high volume and / or high throughout: basic Apache Cassandra operations such as repairs, compactions or hints delivery can have dramatic consequences even on a healthy high latency multi-datacenter cluster. In this presentation, Julien will go through Apache Cassandra mutli-datacenter concepts first then show multi-datacenter operations essentials in details: bootstrapping new nodes and / or datacenter, repairs strategy, Java GC tuning, OS tuning, Apache Cassandra configuration and monitoring. Based on his 3 years experience managing a multi-datacenter cluster against Apache Cassandra 2.0, 2.1, 2.2 and 3.0, Julien will give you tips on how to anticipate and prevent / mitigate issues related to basic Apache Cassandra operations with a multi-datacenter cluster. About the Speaker Julien Anguenot VP Software Engineering, iland Internet Solutions, Corp Julien currently serves as iland's Vice President of Software Engineering. Prior to joining iland, Mr. Anguenot held tech leadership positions at several open source content management vendors and tech startups in Europe and in the U.S. Julien is a long time Open Source software advocate, contributor and speaker: Zope, ZODB, Nuxeo contributor, Zope and OpenStack foundations member, his talks includes Apache Con, Cassandra summit, OpenStack summit, The WWW Conference or still EuroPython.
Running a Cassandra cluster in AWS that can store petabytes worth of data can be costly. This talk will detail the novel approach of using approximate data structures to keep costs low, yet retain insightful, and up to date query results. The talk will explore a number of real world examples from our environment to demonstrate the power of approximate data. It will cover: determining how many IP addresses are on a network, ranking IPs by traffic, and finally determining approximate min, max, and averages on values. The talk will also cover how this data is laid out in Cassandra, so that a query always returns up to date data, without burdening the compactor. About the Speaker Ben Kornmeier Engineer, ProtectWise Ben is a Staff Engineer at ProtectWise. When he is not building realtime processing pipelines, he enjoys hiking, biking, and keeping his dog out of trouble.
iland has built a global data warehouse across multiple data centers, collecting and aggregating data from core cloud services including compute, storage and network as well as chargeback and compliance. iland's warehouse brings actionable intelligence that customers can use to manipulate resources, analyze trends, define alerts and share information. In this session, we would like to present the lessons learned around Cassandra, both at the development and operations level, but also the technology and architecture we put in action on top of Cassandra such as Redis, syslog-ng, RabbitMQ, Java EE, etc. Finally, we would like to share insights on how we are currently extending our platform with Spark and Kafka and what our motivations are.
The document describes an agenda for a Cassandra training event on December 3rd and 4th, including an introduction to Cassandra, Spark, and related tools on the 3rd, and a Cassandra Summit conference on the 4th to learn how companies are using Cassandra to grow their businesses. It also provides information about DataStax as the main commercial backer of Cassandra and their Cassandra-based products and services.
This document discusses bulk loading and unloading data into and from Cassandra. It describes using CQL INSERT statements via Java drivers or CQLSH COPY FROM for loading, as well as using SSTable files via sstableloader or custom code. For unloading, it recommends using parallel CQL SELECT queries by splitting the token range across multiple connections. Testing showed Java asynchronous INSERTs to be the fastest loading method in most cases, while sstableloader requires all nodes be online. Batching INSERTs can improve throughput but increases latency.
This talk will provide a high-level overview of Cassandra, the Cassandra Query Language (CQL) and more specifically the DataStax CQL Java driver. This talk will aim to introduce Java developers tools, techniques and best practices for building Java application leveraging the Cassandra database using CQL3.
This document discusses how to size a Cassandra cluster based on replication factor, data size, and performance needs. It describes that replication factor, data size, data velocity, and hardware considerations like CPU, memory, and disk type should all be examined to determine the appropriate number of nodes. The goal is to have enough nodes to store data, achieve target throughput levels, and maintain performance and availability even if nodes fail.
Christian Johannsen presents on evaluating Apache Cassandra as a cloud database. Cassandra is optimized for cloud infrastructure with features like transparent elasticity, scalability, high availability, easy data distribution and redundancy. It supports multiple data types, is easy to manage, low cost, supports multiple infrastructures and has security features. A demo of DataStax OpsCenter and Apache Spark on Cassandra is shown.
An overview and lessons learned from developing a system to process 50,000 events per second with Cassandra and Spark.
In this talk we will walk through how Apache Kafka and Apache Accumulo can be used together to orchestrate a de-coupled, real-time distributed and reactive request/response system at massive scale. Multiple data pipelines can perform complex operations for each message in parallel at high volumes with low latencies. The final result will be inline with the initiating call. The architecture gains are immense. They allow for the requesting system to receive a response without the need for direct integration with the data pipeline(s) that messages must go through. By utilizing Apache Kafka and Apache Accumulo, these gains sustain at scale and allow for complex operations of different messages to be applied to each response in real-time.
Would you like to learn how to use Cassandra but don’t know where to begin? Want to get your feet wet but you’re lost in the desert? Longing for a cluster when you don’t even know how to set up a node? Then look no further! Rebecca Mills, Junior Evangelist at Datastax, will guide you in the webinar “Getting Started with Apache Cassandra...” You'll get an overview of Planet Cassandra’s resources to get you started quickly and easily. Rebecca will take you down the path that's right for you, whether you are a developer or administrator. Join if you are interested in getting Cassandra up and working in the way that suits you best.
EmoDB is an open source RESTful data store built on top of Cassandra that stores JSON documents and, most notably, offers a databus that allows subscribers to watch for changes to those documents in real time. It features massive non-blocking global writes, asynchronous cross data center communication, and schema-less json content. For non-blocking global writes, we created a ""JSON delta"" specification that defines incremental updates to any json document. Each row, in Cassandra, is thus a sequence of deltas that serves as a Conflict-free Replicated Datatype (CRDT) for EmoDB's system of record. We introduce the concept of ""distributed compactions"" to frequently compact these deltas for efficient reads. Finally, the databus forms a crucial piece of our data infrastructure and offers a change queue to real time streaming applications. About the Speaker Fahd Siddiqui Lead Software Engineer, Bazaarvoice Fahd Siddiqui is a Lead Software Engineer at Bazaarvoice in the data infrastructure team. His interests include highly scalable, and distributed data systems. He holds a Master's degree in Computer Engineering from the University of Texas at Austin, and frequently talks at Austin C* User Group. About Bazaarvoice: Bazaarvoice is a network that connects brands and retailers to the authentic voices of people where they shop. More at www.bazaarvoice.com
Swarnim Kulkarni (Cerner) Cerner has been an active consumer of HBase for a very long time, storing petabytes of healthcare data in its multiple isolated HBase clusters. This talk will walk through the design of Cerner's enterprise data hub with a focus on the multi-tenant HBase as a service offering within the hub.
- Micro-batching involves grouping statements into small batches to improve throughput and reduce network overhead when writing to Cassandra. - A benchmark was conducted to compare individual statements, regular batches, and partition-aware batches when inserting 1 million rows into Cassandra. - The results showed that partition-aware batches had shorter runtime, lower client and cluster CPU usage, and was more performant overall compared to individual statements and regular batches. However, it may have higher latency which is better suited for bulk data processing rather than real-time workloads.
This presentation shortly describes key features of Apache Cassandra. It was held at the Apache Cassandra Meetup in Vienna in January 2014. You can access the meetup here: http://www.meetup.com/Vienna-Cassandra-Users/
Apache Cassandra is a free, distributed, open source, and highly scalable NoSQL database that is designed to handle large amounts of data across many commodity servers. It provides high availability with no single point of failure, linear scalability, and tunable consistency. Cassandra's architecture allows it to spread data across a cluster of servers and replicate across multiple data centers for fault tolerance. It is used by many large companies for applications that require high performance, scalability, and availability.
This document outlines an online course on Cassandra that covers its key concepts and features. The course contains 8 modules that progress from introductory topics to more advanced ones like integrating Cassandra with Hadoop. It teaches students how to model and query data in Cassandra, configure and maintain Cassandra clusters, and build a sample application. The course includes live classes, recordings, quizzes, assignments, and an online certification exam to help students learn Cassandra.
An introduction to the Apache Cassandra database, as presented at the Northern Illinois Coders user group on 20141022.
The database industry has been abuzz over the past year about NoSQL databases. Apache Cassandra, which has quickly emerged as a best-of-breed solution in this space, is used at many companies to achieve unprecedented scale while maintaining streamlined operations. This presentation goes beyond the hype, buzzwords, and rehashed slides and actually presents the attendees with a hands-on, step-by-step tutorial on how to write a Java application on top of Apache Cassandra. It focuses on concepts such as idempotence, tunable consistency, and shared-nothing clusters to help attendees get started with Apache Cassandra quickly while avoiding common pitfalls.
Apache Cassandra is an open source NoSQL database that provides high performance and scalability across many servers. It was originally developed at Facebook in 2008 and released as an open source project on Google Code before becoming an Apache project in 2009. Cassandra uses a decentralized architecture and replication strategy to ensure there is no single point of failure and the system remains operational as long as one node remains up.
Dağıtık bilgisayar sistemleri ve dağıtık yazılımlar ile ilgili araştırmam
WSO2 is an open source software company founded in 2005 that produces an entire middleware platform under the Apache license. Their business model involves selling comprehensive support and maintenance for their products. They have over 150 employees with offices globally. The document discusses using Apache Cassandra as a NoSQL database with WSO2's Column Store Service, including how to install the Cassandra feature, manage keyspaces and column families, and develop applications using the Java API Hector.
Cursos Big Data Open Source: Introduccion al Big Data y Cursos técnicos para ingenieros Data Scientists Hadoop, spark, Cassandra, MongoDB, NOSql,
CQL is the query language for Apache Cassandra that provides an SQL-like interface. The document discusses the evolution from the older Thrift RPC interface to CQL and provides examples of modeling tweet data in Cassandra using tables like users, tweets, following, followers, userline, and timeline. It also covers techniques like denormalization, materialized views, and batch loading of related data to optimize for common queries.