This document summarizes Cassandra, an open source distributed database. It describes Cassandra's history starting at Facebook, then being taken over by Apache. It provides details on Cassandra's architecture as a massively scalable, distributed, structured data store with tunable consistency levels and fast reads/writes. The document outlines that values in Cassandra are structured and indexed by columns and supercolumns with slicing queries supported. Key features like hinted handoff, Thrift API, data center awareness, pluggable comparators, and enumeration/range queries are also summarized.
This document discusses using Apache Cassandra to store and manage time series data in OpenNMS. It describes some limitations of the existing RRDTool-based data storage, such as high I/O requirements for updating and aggregating data. Cassandra is presented as an alternative that is optimized for write throughput, flexible data modeling, high availability, and ability to perform aggregations at read time rather than write time. The Newts project is introduced as a standalone time series data store built on Cassandra that aims to provide fast storage and retrieval of raw samples along with flexible aggregation capabilities.
Whether it's statistics, weather forecasting, astronomy, finance, or network management, time series data plays a critical role in analytics and forecasting. Unfortunately, while many tools exist for time series storage and analysis, few are able to scale past memory limits, or provide rich query and analytics capabilities outside what is necessary to produce simple plots; For those challenged by large volumes of data, there is much room for improvement.
Apache Cassandra is a fully distributed second-generation database. Cassandra stores data in key-sorted order making it ideal for time series, and its high throughput and linear scalability make it well suited to very large data sets.
This talk will cover some of the requirements and challenges of large scale time series storage and analysis. Cassandra data and query modeling for this use-case will be discussed, and Newts, an open source Cassandra-based time series store under development at The OpenNMS Group will be introduced.
Presented at Cassandra London (April 7, 2014); The challenges of time-series storage and analytics in OpenNMS, with an introduction to Newts, a new Cassandra-based time-series data store.
Cassandra by Example: Data Modelling with CQL3Eric Evans
This document summarizes a presentation about modeling data with Cassandra Query Language (CQL) using examples from a Twitter-like application called Twissandra. It introduces CQL as an alternative to Thrift for querying Cassandra and describes how to model users, followers, tweets, timelines and other social media data structures in Cassandra tables. The presentation emphasizes denormalizing data and using materialized views to optimize queries, and concludes by noting that applications can be built in various languages thanks to Cassandra drivers.
Cassandra By Example: Data Modelling with CQL3Eric Evans
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.
Rethinking Topology In Cassandra (ApacheCon NA)Eric Evans
The document discusses topology and partitioning in Cassandra distributed hash tables (DHTs). It describes issues with poor load distribution and data distribution in traditional DHT designs. It proposes using virtual nodes, where each physical node is assigned multiple tokens, to better distribute partitions and improve performance. Configuration options for Cassandra are presented that implement virtual nodes using a random token assignment strategy.
Virtual Nodes: Rethinking Topology in CassandraEric Evans
The document discusses Cassandra's topology and how it is moving from a single token per node model to a virtual node model where each node is assigned multiple tokens. This improves load balancing and data distribution in the cluster. Specifically, it addresses problems with the single token approach like poor load distribution when nodes fail and inefficient data movement when adding or replacing nodes. The virtual node model with random token assignment provides better scaling properties as the number of nodes and data size increases.
Castle is an open-source project that provides an alternative to the lower layers of the storage stack -- RAID and POSIX filesystems -- for big data workloads, and distributed data stores such as Apache Cassandra.
This presentation from Berlin Buzzwords 2012 provides a high-level overview of Castle and how it is used with Cassandra to improve performance and predictability.
This document discusses CQL, the Cassandra Query Language. CQL is designed to be similar to SQL but with some differences to account for Cassandra's data model. The presentation provides an overview of CQL's syntax and capabilities, discusses why CQL was created to provide a more stable interface than Cassandra's native protocol, and analyzes CQL's performance compared to the native protocol. Future roadmap items for CQL are also presented, including prepared statements and custom transports. Available CQL drivers for languages like Java, Python, Ruby, and Node.js are also briefly mentioned.
This document provides an overview and history of the Cassandra Query Language (CQL) and discusses changes between versions 1.0 and 2.0. It notes that CQL was introduced in Cassandra 0.8.0 to provide a more stable and user-friendly interface than the native Cassandra API. Major changes in CQL 2.0 included data type changes and additional functionality like named keys, counters, and timestamps. The document outlines the roadmap for future CQL features and lists several third-party driver projects supporting CQL connectivity.
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.
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.
This document provides an overview and introduction to Cassandra, an open source distributed database management system designed to handle large amounts of data across many commodity servers. It discusses Cassandra's origins from influential papers on Bigtable and Dynamo, its properties including flexibility, scalability and high availability. The document also covers Cassandra's data model using keyspaces and column families, its consistency options, API including Thrift and language drivers, and provides examples of usage for an address book app and storing timeseries data.
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.
Understanding Insider Security Threats: Types, Examples, Effects, and Mitigat...Bert Blevins
Today’s digitally connected world presents a wide range of security challenges for enterprises. Insider security threats are particularly noteworthy because they have the potential to cause significant harm. Unlike external threats, insider risks originate from within the company, making them more subtle and challenging to identify. This blog aims to provide a comprehensive understanding of insider security threats, including their types, examples, effects, and mitigation techniques.
INDIAN AIR FORCE FIGHTER PLANES LIST.pdfjackson110191
These fighter aircraft have uses outside of traditional combat situations. They are essential in defending India's territorial integrity, averting dangers, and delivering aid to those in need during natural calamities. Additionally, the IAF improves its interoperability and fortifies international military alliances by working together and conducting joint exercises with other air forces.
Comparison Table of DiskWarrior Alternatives.pdfAndrey Yasko
To help you choose the best DiskWarrior alternative, we've compiled a comparison table summarizing the features, pros, cons, and pricing of six alternatives.
Measuring the Impact of Network Latency at TwitterScyllaDB
Widya Salim and Victor Ma will outline the causal impact analysis, framework, and key learnings used to quantify the impact of reducing Twitter's network latency.
Paradigm Shifts in User Modeling: A Journey from Historical Foundations to Em...Erasmo Purificato
Slide of the tutorial entitled "Paradigm Shifts in User Modeling: A Journey from Historical Foundations to Emerging Trends" held at UMAP'24: 32nd ACM Conference on User Modeling, Adaptation and Personalization (July 1, 2024 | Cagliari, Italy)
TrustArc Webinar - 2024 Data Privacy Trends: A Mid-Year Check-InTrustArc
Six months into 2024, and it is clear the privacy ecosystem takes no days off!! Regulators continue to implement and enforce new regulations, businesses strive to meet requirements, and technology advances like AI have privacy professionals scratching their heads about managing risk.
What can we learn about the first six months of data privacy trends and events in 2024? How should this inform your privacy program management for the rest of the year?
Join TrustArc, Goodwin, and Snyk privacy experts as they discuss the changes we’ve seen in the first half of 2024 and gain insight into the concrete, actionable steps you can take to up-level your privacy program in the second half of the year.
This webinar will review:
- Key changes to privacy regulations in 2024
- Key themes in privacy and data governance in 2024
- How to maximize your privacy program in the second half of 2024
Choose our Linux Web Hosting for a seamless and successful online presencerajancomputerfbd
Our Linux Web Hosting plans offer unbeatable performance, security, and scalability, ensuring your website runs smoothly and efficiently.
Visit- https://onliveserver.com/linux-web-hosting/
Transcript: Details of description part II: Describing images in practice - T...BookNet Canada
This presentation explores the practical application of image description techniques. Familiar guidelines will be demonstrated in practice, and descriptions will be developed “live”! If you have learned a lot about the theory of image description techniques but want to feel more confident putting them into practice, this is the presentation for you. There will be useful, actionable information for everyone, whether you are working with authors, colleagues, alone, or leveraging AI as a collaborator.
Link to presentation recording and slides: https://bnctechforum.ca/sessions/details-of-description-part-ii-describing-images-in-practice/
Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.
Coordinate Systems in FME 101 - Webinar SlidesSafe Software
If you’ve ever had to analyze a map or GPS data, chances are you’ve encountered and even worked with coordinate systems. As historical data continually updates through GPS, understanding coordinate systems is increasingly crucial. However, not everyone knows why they exist or how to effectively use them for data-driven insights.
During this webinar, you’ll learn exactly what coordinate systems are and how you can use FME to maintain and transform your data’s coordinate systems in an easy-to-digest way, accurately representing the geographical space that it exists within. During this webinar, you will have the chance to:
- Enhance Your Understanding: Gain a clear overview of what coordinate systems are and their value
- Learn Practical Applications: Why we need datams and projections, plus units between coordinate systems
- Maximize with FME: Understand how FME handles coordinate systems, including a brief summary of the 3 main reprojectors
- Custom Coordinate Systems: Learn how to work with FME and coordinate systems beyond what is natively supported
- Look Ahead: Gain insights into where FME is headed with coordinate systems in the future
Don’t miss the opportunity to improve the value you receive from your coordinate system data, ultimately allowing you to streamline your data analysis and maximize your time. See you there!
Blockchain technology is transforming industries and reshaping the way we conduct business, manage data, and secure transactions. Whether you're new to blockchain or looking to deepen your knowledge, our guidebook, "Blockchain for Dummies", is your ultimate resource.
Scaling Connections in PostgreSQL Postgres Bangalore(PGBLR) Meetup-2 - MydbopsMydbops
This presentation, delivered at the Postgres Bangalore (PGBLR) Meetup-2 on June 29th, 2024, dives deep into connection pooling for PostgreSQL databases. Aakash M, a PostgreSQL Tech Lead at Mydbops, explores the challenges of managing numerous connections and explains how connection pooling optimizes performance and resource utilization.
Key Takeaways:
* Understand why connection pooling is essential for high-traffic applications
* Explore various connection poolers available for PostgreSQL, including pgbouncer
* Learn the configuration options and functionalities of pgbouncer
* Discover best practices for monitoring and troubleshooting connection pooling setups
* Gain insights into real-world use cases and considerations for production environments
This presentation is ideal for:
* Database administrators (DBAs)
* Developers working with PostgreSQL
* DevOps engineers
* Anyone interested in optimizing PostgreSQL performance
Contact info@mydbops.com for PostgreSQL Managed, Consulting and Remote DBA Services
Quality Patents: Patents That Stand the Test of TimeAurora Consulting
Is your patent a vanity piece of paper for your office wall? Or is it a reliable, defendable, assertable, property right? The difference is often quality.
Is your patent simply a transactional cost and a large pile of legal bills for your startup? Or is it a leverageable asset worthy of attracting precious investment dollars, worth its cost in multiples of valuation? The difference is often quality.
Is your patent application only good enough to get through the examination process? Or has it been crafted to stand the tests of time and varied audiences if you later need to assert that document against an infringer, find yourself litigating with it in an Article 3 Court at the hands of a judge and jury, God forbid, end up having to defend its validity at the PTAB, or even needing to use it to block pirated imports at the International Trade Commission? The difference is often quality.
Quality will be our focus for a good chunk of the remainder of this season. What goes into a quality patent, and where possible, how do you get it without breaking the bank?
** Episode Overview **
In this first episode of our quality series, Kristen Hansen and the panel discuss:
⦿ What do we mean when we say patent quality?
⦿ Why is patent quality important?
⦿ How to balance quality and budget
⦿ The importance of searching, continuations, and draftsperson domain expertise
⦿ Very practical tips, tricks, examples, and Kristen’s Musts for drafting quality applications
https://www.aurorapatents.com/patently-strategic-podcast.html
An invited talk given by Mark Billinghurst on Research Directions for Cross Reality Interfaces. This was given on July 2nd 2024 as part of the 2024 Summer School on Cross Reality in Hagenberg, Austria (July 1st - 7th)
論文紹介:A Systematic Survey of Prompt Engineering on Vision-Language Foundation ...Toru Tamaki
Jindong Gu, Zhen Han, Shuo Chen, Ahmad Beirami, Bailan He, Gengyuan Zhang, Ruotong Liao, Yao Qin, Volker Tresp, Philip Torr "A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models" arXiv2023
https://arxiv.org/abs/2307.12980
Best Practices for Effectively Running dbt in Airflow.pdfTatiana Al-Chueyr
As a popular open-source library for analytics engineering, dbt is often used in combination with Airflow. Orchestrating and executing dbt models as DAGs ensures an additional layer of control over tasks, observability, and provides a reliable, scalable environment to run dbt models.
This webinar will cover a step-by-step guide to Cosmos, an open source package from Astronomer that helps you easily run your dbt Core projects as Airflow DAGs and Task Groups, all with just a few lines of code. We’ll walk through:
- Standard ways of running dbt (and when to utilize other methods)
- How Cosmos can be used to run and visualize your dbt projects in Airflow
- Common challenges and how to address them, including performance, dependency conflicts, and more
- How running dbt projects in Airflow helps with cost optimization
Webinar given on 9 July 2024
Best Practices for Effectively Running dbt in Airflow.pdf
Cassandra In A Nutshell
1. History
Description
Who
Cassandra In A Nutshell
Eric Evans
eevans@rackspace.com
NoSQL Oakland
November 2, 2009
Eric Evans eevans@rackspace.com Cassandra In A Nutshell
2. History
Description
Who
A prophetess in Troy during the Trojan War. Her predictions were
always true, but never believed.
Eric Evans eevans@rackspace.com Cassandra In A Nutshell
3. History
Description
Who
A massively scalable, distributed (peer-to-peer), structured data
store (aka database).
Eric Evans eevans@rackspace.com Cassandra In A Nutshell
4. History
Description
Who
Outline
1 History
2 Description
3 Who
Eric Evans eevans@rackspace.com Cassandra In A Nutshell
5. History
Description
Who
Facebook
Eric Evans eevans@rackspace.com Cassandra In A Nutshell
6. History
Description
Who
Google Code
Eric Evans eevans@rackspace.com Cassandra In A Nutshell
7. History
Description
Who
Apache
Eric Evans eevans@rackspace.com Cassandra In A Nutshell
8. History
Description
Who
Outline
1 History
2 Description
3 Who
Eric Evans eevans@rackspace.com Cassandra In A Nutshell
9. History
Description
Who
Cassandra is...
O(1) DHT
Eric Evans eevans@rackspace.com Cassandra In A Nutshell
10. History
Description
Who
Cassandra is...
O(1) DHT
Eventual consistency
Eric Evans eevans@rackspace.com Cassandra In A Nutshell
11. History
Description
Who
Cassandra is...
O(1) DHT
Eventual consistency
Tunable trade-offs, consistency vs. latency
Eric Evans eevans@rackspace.com Cassandra In A Nutshell
12. History
Description
Who
But...
Values are structured, indexed
Eric Evans eevans@rackspace.com Cassandra In A Nutshell
13. History
Description
Who
But...
Values are structured, indexed
Columns, Supercolumns
Eric Evans eevans@rackspace.com Cassandra In A Nutshell
14. History
Description
Who
But...
Values are structured, indexed
Columns, Supercolumns
Slicing w/ predicates
Eric Evans eevans@rackspace.com Cassandra In A Nutshell
15. History
Description
Who
And...
Hinted hand-off
Eric Evans eevans@rackspace.com Cassandra In A Nutshell
16. History
Description
Who
And...
Hinted hand-off
Thrift API
Eric Evans eevans@rackspace.com Cassandra In A Nutshell
17. History
Description
Who
And...
Hinted hand-off
Thrift API
Rack/data-center aware partitioning
Eric Evans eevans@rackspace.com Cassandra In A Nutshell
18. History
Description
Who
And...
Hinted hand-off
Thrift API
Rack/data-center aware partitioning
Pluggable comparators
Eric Evans eevans@rackspace.com Cassandra In A Nutshell
19. History
Description
Who
And...
Hinted hand-off
Thrift API
Rack/data-center aware partitioning
Pluggable comparators
Key enumeration, range queries
Eric Evans eevans@rackspace.com Cassandra In A Nutshell
20. History
Description
Who
And...
Hinted hand-off
Thrift API
Rack/data-center aware partitioning
Pluggable comparators
Key enumeration, range queries
Reads are fast, writes are nutty fast
Eric Evans eevans@rackspace.com Cassandra In A Nutshell
21. History
Description
Who
Outline
1 History
2 Description
3 Who
Eric Evans eevans@rackspace.com Cassandra In A Nutshell
22. History
Description
Who
Droppin’ Names
Facebook
Digg
Rackspace
Twitter
IBM Research
Eric Evans eevans@rackspace.com Cassandra In A Nutshell