Cassandra is a structured storage system designed to run on a peer-to-peer network. It was created to handle large amounts of data and requests across many servers. Cassandra provides high availability, eventual consistency, incremental scalability and low administration costs. It uses a decentralized architecture with dynamic data modeling and optimized for writes. Data is replicated across multiple nodes for fault tolerance and linear read and write scalability.
Apache Cassandra, part 3 – machinery, work with CassandraAndrey Lomakin
Aim of this presentation to provide enough information for enterprise architect to choose whether Cassandra will be project data store. Presentation describes each nuance of Cassandra architecture and ways to design data and work with them.
Apache Cassandra, part 3 – machinery, work with CassandraAndrey Lomakin
Aim of this presentation to provide enough information for enterprise architect to choose whether Cassandra will be project data store. Presentation describes each nuance of Cassandra architecture and ways to design data and work with them.
This presentation introduces people to Cassandra and Column Family Datastores in general. I will discuss what Cassandra is, how and when it is useful, and how it integrates with Rails. I will also go in to lessons learned during our 3-month project, and the useful patterns that emerged. The discussion will be very technical, but targeted at developers who are not familiar with, or have not done a project with Cassandra.
C*ollege Credit: Data Modeling for Apache CassandraDataStax
Cassandra stores data differently than traditional RDBMS’s. It is these differences that allow for improvements in performance, availability and scalability. Aaron Morton, DataStax MVP for Apache Cassandra will present the basics of the data model and outline the differences clearly. This webinar is 101 level and is suitable for people who are coming from a relational background and just starting to get into Apache Cassandra.
This course is designed to be a “fast start” on the basics of data modeling with Cassandra. We will cover some basic Administration information upfront that is important to understand as you choose your data model. It is still important to take a proper Admin class if you are responsible for production instance. This course focuses on CQL3, but thrift shall not be ignored.
Redis is a NoSQL technology that rides a fine line between database and in-memory cache. Redis also offers "remote data structures", which gives it a significant advantage over other in-memory databases. This session will cover several PHP clients for Redis, and how to use them for caching, data modeling and generally improving application throughput.
Introduction to Apache ZooKeeper | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
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
Apache Cassandra is a scalable, fault-tolerant database that has found its way into more than 25% of the Fortune 100 and continues to enjoy significant adoption in the marketplace. In this talk we'll introduce you to Cassandra, explore some of its internals, and discuss CQL (the SQL-like query language for Cassandra). We'll finish by talking about how some companies are using it for services you probably interact with in your daily life. You'll leave with all the tools you need to start exploring Cassandra on your own.
Cassandra Community Webinar: Back to Basics with CQL3DataStax
Cassandra is a distributed, massively scalable, fault tolerant, columnar data store, and if you need the ability to make fast writes, the only thing faster than Cassandra is /dev/null! In this fast-paced presentation, we'll briefly describe big data, and the area of big data that Cassandra is designed to fill. We will cover Cassandra's unique, every-node-the-same architecture. We will reveal Cassandra's internal data structure and explain just why Cassandra is so darned fast. Finally, we'll wrap up with a discussion of data modeling using the new standard protocol: CQL (Cassandra Query Language).
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.
Summary of JDK10 and What will come into JDK11なおき きしだ
Newer version is here
https://www.slideshare.net/nowokay/summary-of-jdk10-and-what-will-come-into-jdk11-99363835
Summary of JDK10
and What will come into JDK11 so far
Cassandra's Sweet Spot - an introduction to Apache CassandraDave Gardner
Slides from my NoSQL Exchange 2011 talk introducing Apache Cassandra. This talk explained the fundamental concepts of Cassandra and then demonstrated how to build a simple ad-targeting application using PHP, with a focus on data modeling.
Video of talk: http://skillsmatter.com/podcast/home/cassandra/js-2880
SQL Track: Restoring databases with powershellITProceed
Build plan of approach to structured point in time restores of databases ( e.g. from Production to QA ) using Powershell as an easy helper tool to ensure all steps are being performed.
Redis in Practice: Scenarios, Performance and Practice with PHPChen Huang
Knowledge sharing about Redis, mainly focusing on:
Why to use Redis? Comparison of some in-memory storages and their scenarios
How to make Redis faster? Consider time complexity, communication latency and serialization
Practice of replication and sentinel in PHP
This presentation introduces people to Cassandra and Column Family Datastores in general. I will discuss what Cassandra is, how and when it is useful, and how it integrates with Rails. I will also go in to lessons learned during our 3-month project, and the useful patterns that emerged. The discussion will be very technical, but targeted at developers who are not familiar with, or have not done a project with Cassandra.
C*ollege Credit: Data Modeling for Apache CassandraDataStax
Cassandra stores data differently than traditional RDBMS’s. It is these differences that allow for improvements in performance, availability and scalability. Aaron Morton, DataStax MVP for Apache Cassandra will present the basics of the data model and outline the differences clearly. This webinar is 101 level and is suitable for people who are coming from a relational background and just starting to get into Apache Cassandra.
This course is designed to be a “fast start” on the basics of data modeling with Cassandra. We will cover some basic Administration information upfront that is important to understand as you choose your data model. It is still important to take a proper Admin class if you are responsible for production instance. This course focuses on CQL3, but thrift shall not be ignored.
Redis is a NoSQL technology that rides a fine line between database and in-memory cache. Redis also offers "remote data structures", which gives it a significant advantage over other in-memory databases. This session will cover several PHP clients for Redis, and how to use them for caching, data modeling and generally improving application throughput.
Introduction to Apache ZooKeeper | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
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
Apache Cassandra is a scalable, fault-tolerant database that has found its way into more than 25% of the Fortune 100 and continues to enjoy significant adoption in the marketplace. In this talk we'll introduce you to Cassandra, explore some of its internals, and discuss CQL (the SQL-like query language for Cassandra). We'll finish by talking about how some companies are using it for services you probably interact with in your daily life. You'll leave with all the tools you need to start exploring Cassandra on your own.
Cassandra Community Webinar: Back to Basics with CQL3DataStax
Cassandra is a distributed, massively scalable, fault tolerant, columnar data store, and if you need the ability to make fast writes, the only thing faster than Cassandra is /dev/null! In this fast-paced presentation, we'll briefly describe big data, and the area of big data that Cassandra is designed to fill. We will cover Cassandra's unique, every-node-the-same architecture. We will reveal Cassandra's internal data structure and explain just why Cassandra is so darned fast. Finally, we'll wrap up with a discussion of data modeling using the new standard protocol: CQL (Cassandra Query Language).
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.
Summary of JDK10 and What will come into JDK11なおき きしだ
Newer version is here
https://www.slideshare.net/nowokay/summary-of-jdk10-and-what-will-come-into-jdk11-99363835
Summary of JDK10
and What will come into JDK11 so far
Cassandra's Sweet Spot - an introduction to Apache CassandraDave Gardner
Slides from my NoSQL Exchange 2011 talk introducing Apache Cassandra. This talk explained the fundamental concepts of Cassandra and then demonstrated how to build a simple ad-targeting application using PHP, with a focus on data modeling.
Video of talk: http://skillsmatter.com/podcast/home/cassandra/js-2880
SQL Track: Restoring databases with powershellITProceed
Build plan of approach to structured point in time restores of databases ( e.g. from Production to QA ) using Powershell as an easy helper tool to ensure all steps are being performed.
Redis in Practice: Scenarios, Performance and Practice with PHPChen Huang
Knowledge sharing about Redis, mainly focusing on:
Why to use Redis? Comparison of some in-memory storages and their scenarios
How to make Redis faster? Consider time complexity, communication latency and serialization
Practice of replication and sentinel in PHP
This is a talk about Netflix's path to Cassandra. The first few slides may look similar to previous presentations, but they are just to set the context. Most the content is brand new!
Abstract:
Cassandra is a new kind of database: it is more than a single-machine system. It naturally runs in a High-Availability configuration. All nodes in the system are symmetric; there is no single point of failure. As you add machines, failure becomes routine, and Cassandra is built to tolerate that with no interruptions.
Cassandra is linearly scalable with good performance characteristics for very small and very large data stores. Unlike earlier efforts, Cassandra is more than just a key-value store; it is a structured data store which can facilitate complex use cases and queries. Cassandra allows for random access to your data organized into rows and columns.
Cassandra is different, and exciting. This presentation will discuss the pros and cons of using Cassandra, and why it has seen such amazing adoption in the past year.
Bio:
Ben Coverston is Director of Operations at DataStax (formerly knows as Riptano), a provider of software, support, services, training, resources and help for Cassandra. He has been involved in enterprise software his entire career. Working in the airline industry, he helped to build some of the highest volume online booking sites in the world. He saw first hand the consequences of trying to solve real world scalability problems at the limit of what traditional relational databases are capable of.
An introduction to Cassandra, including replication + partitioning options, data center awareness, local storage model, data modeling example. Presented by Andrew Byde on 25th August 2011 at NoSQLNow! in San Jose , California
Inside Cassandra – C* is an interesting piece of software for many reasons, but it is especially interesting in its use of elegant data structures and algorithms. This talk will focus on the data structures and algorithms that make C* such a scalable and performant database. We will walk along the write, read and delete paths exploring the low-level details of how each of these operations work. We will also explore some of the background processes that maintain availability and performance. The goal of this talk is to gain a deeper understanding of C* by exploring the low-level details of its implementation.
A Deep Dive into Apache Cassandra for .NET DevelopersLuke Tillman
.NET developers have a lot of options when it comes to databases these days. Apache Cassandra is a scalable, fault-tolerant database that has already found its way into more than 25% of the Fortune 100 and continues to grow in popularity. But what makes it different from the myriad of other options available? In this talk, we’ll take a deep dive into Cassandra and learn about:
- Cassandra’s internals and how it works
- CQL (the SQL-like query language for Cassandra)
- Data Modeling like a pro
- Tools available for developers
- Writing .NET code that talks to Cassandra
If there’s time and interest, we’ll finish up with how some companies are already using Cassandra to power services you probably interact with in your daily life. You’ll leave with all the tools you need to start build highly available .NET applications and services on top of Cassandra.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
2. Why Cassandra?
• Lots of data
– Copies of messages, reverse indices of
messages, per user data.
• Many incoming requests resulting in a lot
of random reads and random writes.
• No existing production ready solutions in
the market meet these requirements.
3. Design Goals
• High availability
• Eventual consistency
– trade-off strong consistency in favor of high
availability
• Incremental scalability
• Optimistic Replication
• “Knobs” to tune tradeoffs between consistency,
durability and latency
• Low total cost of ownership
• Minimal administration
4. Data Model Columns are
added and
ColumnFamily1 Name : MailList modified
Type : Simple Sort : Name
KEY Name : tid1 Name : tid2 Name : tid3 dynamically
Name : tid4
Value : <Binary> Value : <Binary> Value : <Binary> Value : <Binary>
TimeStamp : t1 TimeStamp : t2 TimeStamp : t3 TimeStamp : t4
ColumnFamily2 Name : WordList Type : Super Sort : Time
Column Families Name : aloha Name : dude
are declared C1 C2 C3 C4 C2 C6
upfront
SuperColumns V1 V2 V3 V4 V2 V6
are added and T1 T2 T3 T4 T2 T6
modified
Columns are
dynamically
added and
modified ColumnFamily3 Name : System Type : Super Sort : Name
dynamically Name : hint1 Name : hint2 Name : hint3 Name : hint4
<Column List> <Column List> <Column List> <Column List>
5. Write Operations
• A client issues a write request to a random
node in the Cassandra cluster.
• The “Partitioner” determines the nodes
responsible for the data.
• Locally, write operations are logged and
then applied to an in-memory version.
• Commit log is stored on a dedicated disk
local to the machine.
6. Write cont’d
Key (CF1 , CF2 , CF3) • Data size
• Number of Objects
Memtable ( CF1)
• Lifetime
Commit Log Memtable ( CF2)
Binary serialized
Key ( CF1 , CF2 , CF3 ) Memtable ( CF2)
Data file on disk
<Key name><Size of key Data><Index of columns/supercolumns><
Serialized column family>
K128 Offset ---
---
K256 Offset BLOCK Index <Key Name> Offset, <Key Name> Offset
Dedicated Disk
---
K384 Offset ---
<Key name><Size of key Data><Index of columns/supercolumns><
Bloom Filter Serialized column family>
(Index in memory)
7. Compactions
K2 < Serialized data > K4 < Serialized data >
K1 < Serialized data >
K10 < Serialized data > K5 < Serialized data >
K2 < Serialized data >
K30 < Serialized data > K10 < Serialized data >
K3 < Serialized data >
DELETED
-- --
--
Sorted -- Sorted --
Sorted --
-- --
--
MERGE SORT
Index File
K1 < Serialized data >
Loaded in memory K2 < Serialized data >
K3 < Serialized data >
K1 Offset
K4 < Serialized data >
K5 Offset Sorted
K5 < Serialized data >
K30 Offset
K10 < Serialized data >
Bloom Filter
K30 < Serialized data >
Data File
8. Write Properties
• No locks in the critical path
• Sequential disk access
• Behaves like a write back Cache
• Append support without read ahead
• Atomicity guarantee for a key
• “Always Writable”
– accept writes during failure scenarios
9. Read
Client
Query Result
Cassandra Cluster
Closest replica Result Read repair if
digests differ
Replica A
Digest Query
Digest Response Digest Response
Replica B Replica C
11. Cluster Membership and Failure
Detection
• Gossip protocol is used for cluster membership.
• Super lightweight with mathematically provable properties.
• State disseminated in O(logN) rounds where N is the number of
nodes in the cluster.
• Every T seconds each member increments its heartbeat counter
and selects one other member to send its list to.
• A member merges the list with its own list .
12.
13.
14.
15.
16. Accrual Failure Detector
• Valuable for system management, replication, load balancing etc.
• Defined as a failure detector that outputs a value, PHI, associated
with each process.
• Also known as Adaptive Failure detectors - designed to adapt to
changing network conditions.
• The value output, PHI, represents a suspicion level.
• Applications set an appropriate threshold, trigger suspicions and
perform appropriate actions.
• In Cassandra the average time taken to detect a failure is 10-15
seconds with the PHI threshold set at 5.
17. Properties of the Failure Detector
• If a process p is faulty, the suspicion level
Φ(t) ∞as t ∞.
• If a process p is faulty, there is a time after which Φ(t) is monotonic
increasing.
• A process p is correct Φ(t) has an ub over an infinite execution.
• If process p is correct, then for any time T,
Φ(t) = 0 for t >= T.
18. Implementation
• PHI estimation is done in three phases
– Inter arrival times for each member are stored in a sampling
window.
– Estimate the distribution of the above inter arrival times.
– Gossip follows an exponential distribution.
– The value of PHI is now computed as follows:
• Φ(t) = -log10( P(tnow – tlast) )
where P(t) is the CDF of an exponential distribution. P(t) denotes the
probability that a heartbeat will arrive more than t units after the previous
one. P(t) = ( 1 – e-tλ )
The overall mechanism is described in the figure below.
20. Performance Benchmark
• Loading of data - limited by network
bandwidth.
• Read performance for Inbox Search in
production:
Search Interactions Term Search
Min 7.69 ms 7.78 ms
Median 15.69 ms 18.27 ms
Average 26.13 ms 44.41 ms
21. MySQL Comparison
• MySQL > 50 GB Data
Writes Average : ~300 ms
Reads Average : ~350 ms
• Cassandra > 50 GB Data
Writes Average : 0.12 ms
Reads Average : 15 ms
22. Lessons Learnt
• Add fancy features only when absolutely
required.
• Many types of failures are possible.
• Big systems need proper systems-level
monitoring.
• Value simple designs
23. Future work
• Atomicity guarantees across multiple keys
• Analysis support via Map/Reduce
• Distributed transactions
• Compression support
• Granular security via ACL’s