Cassandra Explained

This document discusses using Redis and the Redis::Client Perl module to build scalable distributed job queues. It provides an overview of Redis, describing it as a key-value store that is simple, fast, and open-source. It then covers the various Redis data types like strings, lists, hashes, sets and sorted sets. Examples are given of how to work with these types using Redis::Client. The document discusses using Redis lists to implement job queues, with jobs added via RPUSH and popped via BLPOP. Benchmark results show the Redis-based job queue approach significantly outperforms using a MySQL jobs table with polling. Some caveats are provided about the benchmarks.

This document summarizes a presentation about building a negative lookup caching translator for GlusterFS. The presentation demonstrates adding caching functionality to speed up lookups by caching previous misses. It shows the steps to hook the translator together, build it, configure it, debug it, and test its performance. Finally, it briefly introduces glupy, a new project for writing GlusterFS translators in Python, and demonstrates a Python implementation of the negative lookup cache.

This document discusses binary files and CSV (comma separated value) files in Python. It covers creating and reading binary files using the pickle module's dump() and load() methods. It also covers various binary file operations like inserting/appending, searching, updating and deleting records. For CSV files, it describes the characteristics and advantages/disadvantages of CSV format. It provides examples of writing to and reading from CSV files in Python using the csv module.

Redis is a networked data structure server that provides fast, simple access to various data types like Strings, Lists, Sets, Sorted Sets and Hashes. It uses an abstract data type interface where operations take a key as the first parameter and match the type of object stored. For example, list operations like LPUSH take a key and value, and the LRANGE operation takes a key and range to return elements in a list. Redis supports multiple programming language clients and can be used for tasks like leader boards, shopping carts and user profiles.

This document discusses using Redis as a work queue for distributing tasks across worker processes. It provides an overview of Redis, describes how to implement a basic work queue using Redis lists, and shows various work queue patterns like synchronous and asynchronous producer-consumer models. It also covers options for scaling out queues and ensuring high availability and reliability. Code examples are provided using the Redis.pm Perl module.

- Reika is a domain-specific language for querying time series databases built on ANTLR. It aims to provide a SQL-like syntax that supports multiple backends. - The current implementation includes a lexer, parser, AST generation using ANTLR, and an interpreter. Symbol and type checking are also implemented. - Lessons learned include checking library source code before using, problems can cascade, and deeper understanding comes after initial implementation. Related work includes InfluxQL and other query languages for time series data.

GlusterFS uses "translators" to modify and route file requests between users and storage bricks. Translators can convert request types, modify request properties like paths or flags, intercept or block requests, and spawn new requests. This allows GlusterFS to provide features like replication, caching, and integration with other systems, but also enables custom file systems to be built by modifying the translators. The asynchronous programming model and shared context objects allow translators to cooperate complex workflows across multiple servers.

Whether running load tests or migrating historic data, loading data directly into Cassandra can be very useful to bypass the system’s write path. In this webinar, we will look at how data is stored on disk in sstables, how to generate these structures directly, and how to load this data rapidly into your cluster using sstableloader. We'll also review different use cases for when you should and shouldn't use this method.

Abstract: Nowadays it’s only a lazy one who haven’t written his own metric storage and aggregation system. I am lazy, and that’s why I have to choose what to use and how to use. I don’t want you to do the same job, so I decided to share my considerations concerning architectures and test results.

The document summarizes a presentation about HTTP clients in Common Lisp. Eitaro Fukamachi discusses several Common Lisp HTTP client libraries, including Drakma and his own library called Dexador. He notes some pitfalls of Drakma, such as forcing URL encoding and poor error handling. Dexador is presented as an alternative with simpler APIs, better language support, and improved error handling including automatic retrying. Benchmarks show that Dexador is faster than Drakma for local requests and comparable for remote requests, but connection pooling in Dexador can further improve performance for multiple requests.

This document provides an overview of key Kubernetes concepts including containers, pods, volumes, deployments, services, configmaps, secrets, replica sets, and horizontal pod autoscaling. It describes the basic building blocks in Kubernetes like pods, containers, volumes, labels and selectors. It also covers different types of services, deployments for declarative updates, replica sets for scaling pods, and horizontal pod autoscaling for autoscaling based on CPU utilization.

Since a couple of years, the NoSQL movement has developed a variety of open-source document stores. Most of them focus on high availability, horizontal scalability, and are designed to run on commodity hardware. These products have gained great traction in the industry to store large amounts of flexible data (mostly JSON). In the meantime, XQuery has evolved to a standardized, full-fledged programming language for XML with native support for complex queries, indexes, updates, full-text search, and scripting. Moreover, JSON has recently been added as a first-level datatype into the language. As of today, it is without doubt the most robust and productive technology to process flexible data. The aim of this talk is to showcase the benefits that can be achieved by integrating the Zorba XQuery Processor with MongoDB. We will introduce the 28msec platform that seamlessly stores, indexes, and manages flexible data entirely in XQuery. The data itself is stored in MongoDB. The platform leverages MongoDB’s indexes, sharding, and consistency guarantees to scale-out horizontally. The talk will conclude by showing a benchmark of the platform and discuss perspectives of the outlined approach.

This document discusses using Fluentd and AWS together. It provides an overview of how Treasure Data uses Fluentd to collect log data from applications on AWS and forwards it to various AWS services like S3, DynamoDB, and Redshift for storage and analysis. It also describes how Fluentd can be used to collect logs from EC2 instances to monitor them and address issues. The document highlights Fluentd's pluggable architecture and some of its core plugins for buffering, routing, and input/output of log data.

This document discusses integrating Bareos backups with the Gluster distributed file system for scalable backups. It begins with an agenda that covers the Gluster integration in Bareos, an introduction to GlusterFS, a quick start guide, an example configuration and demo, and future plans. It then provides more details on GlusterFS architecture including concepts like bricks, volumes, peers and site replication. The remainder of the document outlines quick start instructions for setting up Gluster and configuring Bareos to use the Gluster backend for scalable backups across multiple servers.

This document provides an introduction and overview of Gluster, an open source scale-out network-attached storage file system. It discusses what Gluster is, its architecture using distributed and replicated volumes, a quick start guide, use cases, features, and how to get involved in the community. The presentation aims to explain the benefits and capabilities of Gluster for scalable, high performance storage.

The document discusses GlusterD 2.0, a redesign of the Gluster distributed file system management daemon. Some key points: - GlusterD 1.0 had scalability and consistency issues that limited it to hundreds of nodes. GlusterD 2.0 was rewritten from scratch in Go for better performance. - GlusterD 2.0 uses etcd for centralized management and configuration storage. It has REST APIs and plugins for modularity. - Components include REST interfaces, etcd backend, RPC framework, transaction system, and a flexible volume generator. - Upgrades from Gluster 3.x to 4.x will be disruptive but provide a migration path. Gluster

This document discusses the architecture and technical challenges of handling a large volume of requests for an online advertising platform. It summarizes three key projects handled by the platform that delivered 3 billion, 14 billion, and 20 billion requests per month respectively. It describes the technologies used, including Solr, Redis, MySQL, Hadoop and Amazon Web Services instances. It also outlines optimizations made to improve performance, such as data compression, query optimizations, and Java 7 improvements. The goal was to process over 11,000 requests per second on average while maintaining response times below 100ms.

1. The document discusses Cassandra Query Language (CQL), a new structured query language for Apache Cassandra that is similar to SQL. 2. CQL aims to provide a simpler alternative to Cassandra's existing Thrift API, which is difficult for clients to use and unstable due to its tight coupling to Cassandra's internal APIs. 3. The document outlines some benefits of CQL compared to the Thrift API, such as requiring less client-side abstraction and being more intuitive through its use of a familiar query/data model.

Cassandra presentation given at the 3rd annual Palmetto Open Source Software Conference (POSSCON 2010).

CQL is a structured query language for Apache Cassandra that is similar to SQL. It provides an alternative interface to the existing Thrift API, with the goals of being more stable, easier to use, and providing a better mental model for querying and data. The document outlines the motivations for developing CQL, including limitations of the existing Thrift API, and provides details on CQL specification, drivers, and additional resources.

This document is an introduction to Cassandra presented by Eric Evans. It provides an outline that covers the project history, description of Cassandra as a massively scalable and decentralized structured data store, and lists some of the people and companies involved in Cassandra including Facebook, Digg, IBM Research, Rackspace and Twitter. The document discusses Cassandra's capabilities such as tunable consistency levels, structured columns and supercolumns, querying, updates, client APIs and performance compared to MySQL.

This document summarizes Cassandra, an open source distributed database management system designed to handle large amounts of data across many commodity servers. It discusses Cassandra's history, key features like tunable consistency levels and support for structured and indexed columns. Case studies describe how companies like Digg, Twitter, Facebook and Mahalo use Cassandra to handle terabytes of data and high transaction volumes. The roadmap outlines upcoming releases that will improve features like compaction, management tools, and support for dynamic schema changes.

Cassandra is a distributed database management system designed to handle large amounts of data across many commodity servers. It provides high availability with no single points of failure and linear scalability as nodes are added. Cassandra uses a peer-to-peer distributed architecture and tunable consistency levels to achieve high performance and availability without requiring strong consistency. It is based on Amazon's Dynamo and Google's Bigtable papers and provides a combination of their features.

This document discusses Apache Cassandra, a distributed database management system designed to handle large amounts of data across many commodity servers. It summarizes Cassandra's origins from Amazon Dynamo and Google Bigtable, describes its data model and client APIs. The document also provides examples of using Cassandra and discusses considerations around operations and performance.

This document provides an overview of Apache Cassandra, a distributed database designed for managing large amounts of structured data across commodity servers. It discusses Cassandra's data model, which is based on Dynamo and Bigtable, as well as its client API and operational benefits like easy scaling and high availability. The document uses a Twitter-like application called StatusApp to illustrate Cassandra's data model and provide examples of common operations.

This document provides an overview of various AWS big data services including Athena, Redshift Spectrum, EMR, and Hive. It discusses how Athena allows users to run SQL queries directly on data stored in S3 using Presto. Redshift Spectrum enables querying data in S3 using standard SQL from Amazon Redshift. EMR is a managed Hadoop framework that can run Hive, Spark, and other big data applications. Hive provides a SQL-like interface to query data stored in various formats like Parquet and ORC on distributed storage systems. The document demonstrates features and provides best practices for working with these AWS big data services.

Cassandra is a highly scalable, eventually consistent, distributed, structured columnfamily store with no single points of failure, initially open-sourced by Facebook and now part of the Apache Incubator. These slides are from Jonathan Ellis's OSCON 09 talk: http://en.oreilly.com/oscon2009/public/schedule/detail/7975

Enterprise applications are complex making it difficult to fit everything in one model. NoSQL is taking a leading role in the next generation database technologies and polyglot persistence a good option to leverage the strength of multiple data stores. This talk will introduce the Spring Data project, an umbrella project that provides a familiar and consistent Spring-based programming model for a wide range of data access technologies such as Redis, MongoDB, HBase, Neo4j...while retaining store-specific features and capabilities.

JNoSQL is an open source project that provides a common API for working with different NoSQL databases. It includes Diana, which defines a common communication layer, and Artemis, a CDI-based annotation framework. The goal is to simplify development of NoSQL applications by handling differences in data models and query languages between databases in a standardized way.

To date, Hadoop usage has focused primarily on offline analysis--making sense of web logs, parsing through loads of unstructured data in HDFS, etc. But what if you want to run map/reduce against your live data set without affecting online performance? Combining Hadoop with Cassandra's multi-datacenter replication capabilities makes this possible. If you're interested in getting value from your data without the hassle and latency of first moving it into Hadoop, this talk is for you. I'll show you how to connect all the parts, enabling you to write map/reduce jobs or run Pig queries against your live data. As a bonus I'll cover writing map/reduce in Scala, which is particularly well-suited for the task.