Apache Cassandra, part 2 – data model example, machineryAndrey 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.
The document discusses three different buffer overflow exploits against three users (superuser, hyperuser, and masteruser) on a Linux machine. The superuser's program can be exploited through a simple buffer overflow. The hyperuser's program uses a canary but can still be exploited by overwriting a vulnerable function pointer. The masteruser's program must be exploited by overwriting the virtual pointer table. Code examples and steps are provided for each exploit.
Apache Cassandra is an open source, distributed, decentralized, scalable, highly available, fault-tolerant, and tunably consistent database. It is based on Amazon's Dynamo and Google's Bigtable models. Cassandra provides linear scalability, high availability with no single points of failure, and tunable consistency. It uses a distributed data model across a cluster with configurable replication for fault tolerance.
Realtime Communication Techniques with PHPWaterSpout
This document summarizes different techniques for real-time communication with PHP, including refresh, short polling, long polling, and WebSockets. It discusses the advantages and disadvantages of each technique in terms of timeliness, efficiency, and scalability. WebSockets provide the most efficient and real-time option by allowing two-way communication over a single TCP connection. The document also introduces WaterSpout, an open-source PHP server that implements long polling and WebSockets to enable real-time applications.
Is your crypto secure? Let's take a look at what main issues there are in modern cryptography that software developers and architects have to be aware of.
Kafka & Storm - FifthElephant 2015 by @bhaskerkode, HelpshiftBhasker Kode
The document discusses how Kafka's key distinguishing feature is its published protocol specification that defines how clients communicate with Kafka brokers. This allows different clients to integrate with Kafka by simply implementing the protocol over TCP, without relying on a specific client library. It also enables the ecosystem to develop rapidly due to wide adoption. The protocol focuses on efficiency through techniques like zero-copy transfer of message data directly from kernel space to sockets.
Is your crypto secure? Let's take a look at what main issues there are in modern cryptography that software developers and architects have to be aware of.
Apache Cassandra, part 2 – data model example, machineryAndrey 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.
The document discusses three different buffer overflow exploits against three users (superuser, hyperuser, and masteruser) on a Linux machine. The superuser's program can be exploited through a simple buffer overflow. The hyperuser's program uses a canary but can still be exploited by overwriting a vulnerable function pointer. The masteruser's program must be exploited by overwriting the virtual pointer table. Code examples and steps are provided for each exploit.
Apache Cassandra is an open source, distributed, decentralized, scalable, highly available, fault-tolerant, and tunably consistent database. It is based on Amazon's Dynamo and Google's Bigtable models. Cassandra provides linear scalability, high availability with no single points of failure, and tunable consistency. It uses a distributed data model across a cluster with configurable replication for fault tolerance.
Realtime Communication Techniques with PHPWaterSpout
This document summarizes different techniques for real-time communication with PHP, including refresh, short polling, long polling, and WebSockets. It discusses the advantages and disadvantages of each technique in terms of timeliness, efficiency, and scalability. WebSockets provide the most efficient and real-time option by allowing two-way communication over a single TCP connection. The document also introduces WaterSpout, an open-source PHP server that implements long polling and WebSockets to enable real-time applications.
Is your crypto secure? Let's take a look at what main issues there are in modern cryptography that software developers and architects have to be aware of.
Kafka & Storm - FifthElephant 2015 by @bhaskerkode, HelpshiftBhasker Kode
The document discusses how Kafka's key distinguishing feature is its published protocol specification that defines how clients communicate with Kafka brokers. This allows different clients to integrate with Kafka by simply implementing the protocol over TCP, without relying on a specific client library. It also enables the ecosystem to develop rapidly due to wide adoption. The protocol focuses on efficiency through techniques like zero-copy transfer of message data directly from kernel space to sockets.
Is your crypto secure? Let's take a look at what main issues there are in modern cryptography that software developers and architects have to be aware of.
1. There is no such thing as inherently thread-safe Ruby code as different Ruby implementations have different thread semantics.
2. Writing thread-safe code requires avoiding shared mutable state, and if needed, using concurrency primitives like Mutex and ConditionVariable to synchronize access to shared state.
3. Thoroughly testing concurrency involves testing with different Ruby implementations, a large number of threads, and synchronization patterns for precision.
The document provides an overview of the Perl 6/Raku programming language. It discusses the spec tests that define the language, the Rakudo compiler, MoarVM and JVM virtual machines, and Raku's support for multiple programming paradigms including functional, object-oriented, procedural, and event-based approaches. It also summarizes Raku's features including multi dispatch, signatures, subsets, roles, junctions, promises, channels, supplies, Unicode support, lazy evaluation, rational numbers, sets and bags, and its native call interface.
Devel::NYTProf is a profiler for Perl programs that provides per-line and per-subroutine timing information. It generates HTML and CSV reports that help identify inefficient code. To use it, run a Perl program with -d:NYTProf and then generate reports with nytprofhtml or nytprofcsv for analysis. The profiler is fast and its reports integrate well with other tools like Kcachegrind for call graph visualization.
This document provides 10 tips for improving Perl performance. Some key tips include using a profiler like Devel::NYTProf to identify bottlenecks, optimizing database queries with DBI, choosing fast hash storage like BerkeleyDB, avoiding serialization with Data::Dumper in favor of faster options like JSON::XS, and considering compiling Perl without threads for a potential 15% speed boost. Proper use of profiling is emphasized to avoid wasting time optimizing the wrong parts of code.
The document discusses PHP streams. It defines a stream as a resource that exhibits a flow or succession of data. A wrapper tells a stream how to handle specific protocols and encodings. A context is a set of parameters and options that tell a stream or filter how to behave. Common built-in PHP streams include file, http, and ftp streams. Filters perform operations on stream data and can be used to modify stream contents.
This document provides an introduction and overview of Cassandra and NoSQL databases. It discusses the challenges faced by modern web applications that led to the development of NoSQL databases. It then describes Cassandra's data model, API, consistency model, and architecture including write path, read path, compactions, and more. Key features of Cassandra like tunable consistency levels and high availability are also highlighted.
1. Phoenix can scale out servers easily by using load balancers and Kubernetes.
2. The default Phoenix.PubSub adapter broadcasts all messages to all servers, which does not scale.
3. Splitting the key-value store and pub-sub functionality across multiple Redis instances and using connection pooling helps scale Redis and the overall system.
The document discusses format string vulnerabilities and how they can be exploited to leak information from memory or write arbitrary values to memory by manipulating printf formatting strings. It provides an example of a simple vulnerable C program and how it can be exploited to leak variable values or overwrite values by precisely controlling the number of formatting characters printed. It then discusses how this technique can be used to exploit the Echoserver program by overwriting return addresses or loading shellcode into non-stack memory locations.
Cassandra is a distributed, column-oriented database designed to be highly scalable and fault-tolerant. It distributes data across nodes based on the partitioner, replicates data based on the replication strategy, and achieves consistency between replicas using a combination of hinted handoffs and read repair during reads and writes. Keyspaces contain column families which store rows of columns in a flexible schema-less data model that scales horizontally by adding more nodes.
Cassandra & Python - Springfield MO User GroupAdam Hutson
Adam Hutson gave an overview of Cassandra and how to use it with Python. Key points include:
- Cassandra is a distributed database with no single point of failure and linear scalability. It favors availability over consistency.
- The Python driver allows connecting to Cassandra clusters and executing queries using prepared statements, batches, and custom consistency levels.
- Best practices include reusing a single session object, specifying keyspaces, authorizing connections, and shutting down clusters to avoid resource leaks.
Design Patterns for Distributed Non-Relational Databasesguestdfd1ec
The document discusses design patterns for distributed non-relational databases, including consistent hashing for key placement, eventual consistency models, vector clocks for determining history, log-structured merge trees for storage layout, and gossip protocols for cluster management without a single point of failure. It raises questions to ask presenters about scalability, reliability, performance, consistency models, cluster management, data models, and real-life considerations for using such systems.
Design Patterns For Distributed NO-reational databaseslovingprince58
This document provides an overview of design patterns for distributed non-relational databases, including:
1) Consistent hashing for partitioning data across nodes, consistency models like eventual consistency, data models like key-value pairs and column families, and storage layouts like log-structured merge trees.
2) Cluster management patterns like the omniscient master and gossip protocols to distribute cluster state information.
3) The document discusses these patterns through examples and diagrams to illustrate how they work.
Fixed width data can be processed efficiently in Perl using forks and shared file handles. This talk describes the basic mechanism and alternatives for improving the performance in dealing with the records.
Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open-source and horizontally scalable. The original intention has been modern web-scale databases. The movement began early 2009 and is growing rapidly. Often more characteristics apply such as: schema-free, easy replication support, simple API, eventually consistent / BASE (not ACID), a huge amount of data and more. So the misleading term "nosql" (the community now translates it mostly with "not only sql") should be seen as an alias to something like the definition above.
This presentation explains how to get started with Apache Cassandra to provide a scale out, fault tolerant backend for inventory storage on OpenSimulator.
- Cassandra is an open source, distributed database management system designed to handle large amounts of data across many commodity servers. It was originally developed at Facebook in 2008 and is now an Apache project.
- Cassandra provides high availability with no single point of failure, linear scalability and performance of tens of thousands of queries per second. It is used by many large companies including Netflix, Twitter and eBay.
- Data is organized into tables within keyspaces. Tables must have a primary key which determines how data is partitioned and indexed. Cassandra uses a decentralized architecture with no single point of failure and automatic data distribution across nodes.
This document summarizes techniques for scaling MongoDB deployments, including:
- Single server read/write scaling using techniques like denormalization, indexing, and restricting fields
- Scaling reads using master-slave replication and replica sets for improved availability and read scaling
- Scaling reads and writes using sharding to partition data across multiple servers and distribute load
Cassandra is a distributed, column-oriented database that scales horizontally and is optimized for writes. It uses consistent hashing to distribute data across nodes and achieve high availability even when nodes join or leave the cluster. Cassandra offers flexible consistency options and tunable replication to balance availability and durability for read and write operations across the distributed database.
Cassandra's data model is more flexible than typically assumed.
Cassandra allows tuning of consistency levels to balance availability and consistency. It can be made consistently when certain replication conditions are met.
Cassandra uses a row-oriented model where rows are uniquely identified by keys and group columns and super columns. Super column families allow grouping columns under a common name and are often used for denormalizing data.
Cassandra's data model is query-based rather than domain-based. It focuses on answering questions through flexible querying rather than storing predefined objects. Design patterns like materialized views and composite keys can help support different types of queries.
MySQL 5.7 clustering: The developer perspectiveUlf Wendel
(Compiled from revised slides of previous presentations - skip if you know the old presentations)
A summary on clustering MySQL 5.7 with focus on the PHP clients view and the PHP driver. Which kinds on MySQL clusters are there, what are their goal, how does wich one scale, what extra work does which clustering technique put at the client and finally, how the PHP driver (PECL/mysqlnd_ms) helps you.
This document provides an overview of distributed key-value stores and Cassandra. It discusses key concepts like data partitioning, replication, and consistency models. It also summarizes Cassandra's features such as high availability, elastic scalability, and support for different data models. Code examples are given to demonstrate basic usage of the Cassandra client API for operations like insert, get, multiget and range queries.
1. There is no such thing as inherently thread-safe Ruby code as different Ruby implementations have different thread semantics.
2. Writing thread-safe code requires avoiding shared mutable state, and if needed, using concurrency primitives like Mutex and ConditionVariable to synchronize access to shared state.
3. Thoroughly testing concurrency involves testing with different Ruby implementations, a large number of threads, and synchronization patterns for precision.
The document provides an overview of the Perl 6/Raku programming language. It discusses the spec tests that define the language, the Rakudo compiler, MoarVM and JVM virtual machines, and Raku's support for multiple programming paradigms including functional, object-oriented, procedural, and event-based approaches. It also summarizes Raku's features including multi dispatch, signatures, subsets, roles, junctions, promises, channels, supplies, Unicode support, lazy evaluation, rational numbers, sets and bags, and its native call interface.
Devel::NYTProf is a profiler for Perl programs that provides per-line and per-subroutine timing information. It generates HTML and CSV reports that help identify inefficient code. To use it, run a Perl program with -d:NYTProf and then generate reports with nytprofhtml or nytprofcsv for analysis. The profiler is fast and its reports integrate well with other tools like Kcachegrind for call graph visualization.
This document provides 10 tips for improving Perl performance. Some key tips include using a profiler like Devel::NYTProf to identify bottlenecks, optimizing database queries with DBI, choosing fast hash storage like BerkeleyDB, avoiding serialization with Data::Dumper in favor of faster options like JSON::XS, and considering compiling Perl without threads for a potential 15% speed boost. Proper use of profiling is emphasized to avoid wasting time optimizing the wrong parts of code.
The document discusses PHP streams. It defines a stream as a resource that exhibits a flow or succession of data. A wrapper tells a stream how to handle specific protocols and encodings. A context is a set of parameters and options that tell a stream or filter how to behave. Common built-in PHP streams include file, http, and ftp streams. Filters perform operations on stream data and can be used to modify stream contents.
This document provides an introduction and overview of Cassandra and NoSQL databases. It discusses the challenges faced by modern web applications that led to the development of NoSQL databases. It then describes Cassandra's data model, API, consistency model, and architecture including write path, read path, compactions, and more. Key features of Cassandra like tunable consistency levels and high availability are also highlighted.
1. Phoenix can scale out servers easily by using load balancers and Kubernetes.
2. The default Phoenix.PubSub adapter broadcasts all messages to all servers, which does not scale.
3. Splitting the key-value store and pub-sub functionality across multiple Redis instances and using connection pooling helps scale Redis and the overall system.
The document discusses format string vulnerabilities and how they can be exploited to leak information from memory or write arbitrary values to memory by manipulating printf formatting strings. It provides an example of a simple vulnerable C program and how it can be exploited to leak variable values or overwrite values by precisely controlling the number of formatting characters printed. It then discusses how this technique can be used to exploit the Echoserver program by overwriting return addresses or loading shellcode into non-stack memory locations.
Cassandra is a distributed, column-oriented database designed to be highly scalable and fault-tolerant. It distributes data across nodes based on the partitioner, replicates data based on the replication strategy, and achieves consistency between replicas using a combination of hinted handoffs and read repair during reads and writes. Keyspaces contain column families which store rows of columns in a flexible schema-less data model that scales horizontally by adding more nodes.
Cassandra & Python - Springfield MO User GroupAdam Hutson
Adam Hutson gave an overview of Cassandra and how to use it with Python. Key points include:
- Cassandra is a distributed database with no single point of failure and linear scalability. It favors availability over consistency.
- The Python driver allows connecting to Cassandra clusters and executing queries using prepared statements, batches, and custom consistency levels.
- Best practices include reusing a single session object, specifying keyspaces, authorizing connections, and shutting down clusters to avoid resource leaks.
Design Patterns for Distributed Non-Relational Databasesguestdfd1ec
The document discusses design patterns for distributed non-relational databases, including consistent hashing for key placement, eventual consistency models, vector clocks for determining history, log-structured merge trees for storage layout, and gossip protocols for cluster management without a single point of failure. It raises questions to ask presenters about scalability, reliability, performance, consistency models, cluster management, data models, and real-life considerations for using such systems.
Design Patterns For Distributed NO-reational databaseslovingprince58
This document provides an overview of design patterns for distributed non-relational databases, including:
1) Consistent hashing for partitioning data across nodes, consistency models like eventual consistency, data models like key-value pairs and column families, and storage layouts like log-structured merge trees.
2) Cluster management patterns like the omniscient master and gossip protocols to distribute cluster state information.
3) The document discusses these patterns through examples and diagrams to illustrate how they work.
Fixed width data can be processed efficiently in Perl using forks and shared file handles. This talk describes the basic mechanism and alternatives for improving the performance in dealing with the records.
Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open-source and horizontally scalable. The original intention has been modern web-scale databases. The movement began early 2009 and is growing rapidly. Often more characteristics apply such as: schema-free, easy replication support, simple API, eventually consistent / BASE (not ACID), a huge amount of data and more. So the misleading term "nosql" (the community now translates it mostly with "not only sql") should be seen as an alias to something like the definition above.
This presentation explains how to get started with Apache Cassandra to provide a scale out, fault tolerant backend for inventory storage on OpenSimulator.
- Cassandra is an open source, distributed database management system designed to handle large amounts of data across many commodity servers. It was originally developed at Facebook in 2008 and is now an Apache project.
- Cassandra provides high availability with no single point of failure, linear scalability and performance of tens of thousands of queries per second. It is used by many large companies including Netflix, Twitter and eBay.
- Data is organized into tables within keyspaces. Tables must have a primary key which determines how data is partitioned and indexed. Cassandra uses a decentralized architecture with no single point of failure and automatic data distribution across nodes.
This document summarizes techniques for scaling MongoDB deployments, including:
- Single server read/write scaling using techniques like denormalization, indexing, and restricting fields
- Scaling reads using master-slave replication and replica sets for improved availability and read scaling
- Scaling reads and writes using sharding to partition data across multiple servers and distribute load
Cassandra is a distributed, column-oriented database that scales horizontally and is optimized for writes. It uses consistent hashing to distribute data across nodes and achieve high availability even when nodes join or leave the cluster. Cassandra offers flexible consistency options and tunable replication to balance availability and durability for read and write operations across the distributed database.
Cassandra's data model is more flexible than typically assumed.
Cassandra allows tuning of consistency levels to balance availability and consistency. It can be made consistently when certain replication conditions are met.
Cassandra uses a row-oriented model where rows are uniquely identified by keys and group columns and super columns. Super column families allow grouping columns under a common name and are often used for denormalizing data.
Cassandra's data model is query-based rather than domain-based. It focuses on answering questions through flexible querying rather than storing predefined objects. Design patterns like materialized views and composite keys can help support different types of queries.
MySQL 5.7 clustering: The developer perspectiveUlf Wendel
(Compiled from revised slides of previous presentations - skip if you know the old presentations)
A summary on clustering MySQL 5.7 with focus on the PHP clients view and the PHP driver. Which kinds on MySQL clusters are there, what are their goal, how does wich one scale, what extra work does which clustering technique put at the client and finally, how the PHP driver (PECL/mysqlnd_ms) helps you.
This document provides an overview of distributed key-value stores and Cassandra. It discusses key concepts like data partitioning, replication, and consistency models. It also summarizes Cassandra's features such as high availability, elastic scalability, and support for different data models. Code examples are given to demonstrate basic usage of the Cassandra client API for operations like insert, get, multiget and range queries.
This document provides an overview of distributed key-value stores and summarizes Cassandra in particular. It discusses how distributed key-value stores address the scalability limitations of relational databases by partitioning and replicating data across multiple servers. The document outlines some common distributed key-value store architectures and algorithms, such as Amazon's Dynamo, and describes how Cassandra implements these approaches. Examples of typical applications of distributed key-value stores and an overview of Cassandra's features and code samples are also provided.
Apache Cassandra operations have the reputation to be quite simple against single datacenter clusters and / or low volume clusters but they become way more complex against high latency multi-datacenter clusters: basic operations such as repair, compaction or hints delivery can have dramatic consequences even on a healthy cluster.
In this presentation, Julien will go through Cassandra operations in details: bootstrapping new nodes and / or datacenter, repair strategies, compaction strategies, GC tuning, OS tuning, large batch of data removal and Apache Cassandra upgrade strategy.
Julien will give you tips and techniques on how to anticipate issues inherent to multi-datacenter cluster: how and what to monitor, hardware and network considerations as well as data model and application level bad design / anti-patterns that can affect your multi-datacenter cluster performances.
Serialization is the process of converting an object into a byte stream for transmission or storage. It allows structured objects to be sent over networks or written to files. Deserialization reverses this process. In Hadoop, serialization is used for interprocess communication between nodes, where messages are serialized into binary streams for transmission, then deserialized on the receiving end. An effective serialization format should be compact, fast, extensible, and interoperable. Hadoop uses its own serialization protocol which implements these properties. The Writable interface defines how Java objects can be serialized and deserialized, with classes like IntWritable and Text providing concrete implementations for common types.
DIY: A distributed database cluster, or: MySQL ClusterUlf Wendel
Live from the International PHP Conference 2013: MySQL Cluster is a distributed, auto-sharding database offering 99,999% high availability. It runs on Rasperry PI as well as on a cluster of multi-core machines. A 30 node cluster was able to deliver 4.3 billion (not million) read transactions per second in 2012. Take a deeper look into the theory behind all the MySQL replication/clustering solutions (including 3rd party) and learn how they differ.
Big Data & NoSQL - EFS'11 (Pavlo Baron)Pavlo Baron
That's the slides of my half day workshop at the EFS'11 in Stuttgart where I covered some theoretical aspects of NoSQL data stores relevant for dealing with large data amounts
Cassandra is a highly scalable, distributed, and fault-tolerant NoSQL database. It partitions data across nodes through consistent hashing of row keys, and replicates data for fault tolerance based on a replication factor. Cassandra provides tunable consistency levels for reads and writes. It uses a gossip protocol for node discovery and a commit log with memtables and SSTables for write durability and reads.
Cassandra South Bay Meetup - Backup And Restore For Apache Cassandraaaronmorton
This document discusses backup and restore strategies for Apache Cassandra. It covers committing data to the commit log, archiving commit log segments, taking table snapshots by backing up SSTables to S3, and restoring from backups. Commit log archiving allows restoring data from a point in time by replaying commit log segments. Table snapshots provide simple backups of SSTables to S3 but require manual restore. Backup is important for disaster recovery, cloning environments, and point-in-time recovery from bad deployments.
Cassandra SF Meetup - CQL Performance With Apache Cassandra 3.Xaaronmorton
The document discusses performance improvements in Apache Cassandra 3.0's storage engine. Key improvements include delta encoding, variable integer encoding, clustering columns written only once, aggregated cell metadata, and cell presence bitmaps. This reduces storage size and improves read performance. The write path involves committing to the commit log and merging into the memtable. The read path can use clustering index filters to short-circuit searching based on deletion times, column names, or clustering ranges to avoid reading unnecessary SSTables.
Cassandra Day Atlanta 2016 - Monitoring Cassandraaaronmorton
This document discusses monitoring Apache Cassandra using metrics. It describes various metric types like gauges, histograms, meters, and timers that can be used to monitor Cassandra. It provides examples of specific metrics that can be monitored for requests, latencies, memory usage, clients, errors, inconsistencies, compactions, thread pools, commit logs, and more. Reporting mechanisms like Graphite and JMX are also covered. The presentation aims to help understand how to gain insights into a Cassandra cluster through effective metric collection and monitoring.
Cassandra SF 2015 - Repeatable, Scalable, Reliable, Observable Cassandraaaronmorton
Slides from my talk at Cassandra Summit 2015
http://cassandrasummit-datastax.com/agenda/repeatable-scalable-reliable-observable-cassandra/
thelastpickle.com
Cassandra sf 2015 - Steady State Data Size With Compaction, Tombstones, and TTL aaronmorton
Slides from my talk at Cassandra Summit 2015
http://cassandrasummit-datastax.com/agenda/steady-state-data-size-with-compaction-tombstones-and-ttl/
thelastpickle.com
This document discusses operational and performance concerns with large node Cassandra deployments prior to version 1.2, and improvements made in versions 1.2 through 2.1 to better support large nodes. Memory structures like bloom filters and compression metadata that previously grew with data size are now stored off-heap. The number of token ranges per node was increased from 1 to 256 with virtual nodes. Disk I/O was improved through "JBOD" support and failure policies. Repair and compaction algorithms were enhanced. These changes alleviate many issues with large Cassandra nodes.
Cassandra Community Webinar August 29th 2013 - In Case Of Emergency, Break Glassaaronmorton
This document provides a summary of a webinar on Cassandra platforms and tools. It discusses the Cassandra storage engine and how it handles writes and reads in an optimized way for performance. It also covers logging, debugging, and monitoring Cassandra using tools like nodetool.
Cassandra Community Webinar - August 22 2013 - Cassandra Internalsaaronmorton
This document summarizes a webinar on Cassandra internals. It describes the key components of Cassandra's architecture including the API layer with services like Thrift, CQL, and JMX. It then discusses the Dynamo layer for managing nodes and messaging. Finally, it outlines the database layer for storage and retrieval of data using components like Memtables, SSTables, and filters. The webinar provided an overview of how these layers interact to handle requests and responses in Cassandra.
Cassandra SF 2013 - In Case Of Emergency Break Glassaaronmorton
The document provides information about a Cassandra summit including details about:
1) The Cassandra platform and how it achieves consistency, availability and partition tolerance through an eventual consistency model and replication.
2) The Cassandra storage engine which is optimized for writes using memtables, SSTables and compaction.
3) Tools for monitoring and troubleshooting Cassandra including logging, GC logging, nodetool commands for viewing cluster information and statistics.
Cassandra SF 2013 - Cassandra Internalsaaronmorton
Cassandra uses a layered architecture with an API layer, Dynamo layer, and database layer. The API layer supports Thrift, CQL, and JMX interfaces. The Dynamo layer handles ring operations and messaging. The database layer includes the memtable, SSTables, and components for data storage and access like Table and ColumnFamilyStore. Messages are processed asynchronously across stages like READ and MUTATION using handlers and callbacks.
Cassandra Community Webinar - Introduction To Apache Cassandra 1.2aaronmorton
This document provides an introduction to Apache Cassandra, including an overview of key concepts like the cluster, nodes, data model, and data modeling best practices. It discusses Cassandra's origins and popularity. The presentation covers the cluster architecture with consistent hashing and token ranges, replication strategies, consistency levels, and more. It also summarizes the Cassandra data model including tables, columns, SSTables, caching, compaction and discusses building a Twitter-like data model in CQL.
Apache Cassandra in Bangalore - Cassandra Internals and Performanceaaronmorton
Cassandra internals and performance was presented. The key points covered include:
1) Cassandra has a layered architecture with APIs, a Dynamo layer, and a database layer. The Dynamo layer implements the Dynamo paper and handles replication and failure handling.
2) The database layer includes the memtable, SSTables, commit log and more. It handles writes, flushes, compactions and reads from storage.
3) A number of performance tests were shown measuring the impact of configuration parameters like memtable flush queue size, commit log sync period, and secondary indexes on write and read latency. Bloom filters, compactions and concurrency were also discussed.
Apache Con NA 2013 - Cassandra Internalsaaronmorton
The document provides an overview of the architecture and internals of Apache Cassandra. It discusses the client-facing API layer including Thrift, CQL, JMX, and CLI. It then covers the Dynamo layer which handles messaging, distributed hash tables, replication strategies, and gossip protocols. Finally, it summarizes the database layer for managing tables, columns, memtables, SSTables, and read/write paths.
My talk from http://wdcnz.com 2012.
I took a brief look at Cassandra and then stepped through building a twitter clone. Very rough code is at https://github.com/amorton/wdcnz-2012-site
Cassandra is a distributed, highly available, and scalable database. It uses a column-oriented data model and is optimized for writes. Data is stored across nodes in a cluster and is automatically replicated for fault tolerance. Cassandra provides a fast and scalable data store through its distributed architecture and data model.
Building a distributed Key-Value store with Cassandraaaronmorton
Slides from my talk at Kiwi Pycon in 2010.
Covers why we chose Cassandra, overview of it's feature and data model, and how we implemented our application.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
Nunit vs XUnit vs MSTest Differences Between These Unit Testing Frameworks.pdfflufftailshop
When it comes to unit testing in the .NET ecosystem, developers have a wide range of options available. Among the most popular choices are NUnit, XUnit, and MSTest. These unit testing frameworks provide essential tools and features to help ensure the quality and reliability of code. However, understanding the differences between these frameworks is crucial for selecting the most suitable one for your projects.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Dive into the realm of operating systems (OS) with Pravash Chandra Das, a seasoned Digital Forensic Analyst, as your guide. 🚀 This comprehensive presentation illuminates the core concepts, types, and evolution of OS, essential for understanding modern computing landscapes.
Beginning with the foundational definition, Das clarifies the pivotal role of OS as system software orchestrating hardware resources, software applications, and user interactions. Through succinct descriptions, he delineates the diverse types of OS, from single-user, single-task environments like early MS-DOS iterations, to multi-user, multi-tasking systems exemplified by modern Linux distributions.
Crucial components like the kernel and shell are dissected, highlighting their indispensable functions in resource management and user interface interaction. Das elucidates how the kernel acts as the central nervous system, orchestrating process scheduling, memory allocation, and device management. Meanwhile, the shell serves as the gateway for user commands, bridging the gap between human input and machine execution. 💻
The narrative then shifts to a captivating exploration of prominent desktop OSs, Windows, macOS, and Linux. Windows, with its globally ubiquitous presence and user-friendly interface, emerges as a cornerstone in personal computing history. macOS, lauded for its sleek design and seamless integration with Apple's ecosystem, stands as a beacon of stability and creativity. Linux, an open-source marvel, offers unparalleled flexibility and security, revolutionizing the computing landscape. 🖥️
Moving to the realm of mobile devices, Das unravels the dominance of Android and iOS. Android's open-source ethos fosters a vibrant ecosystem of customization and innovation, while iOS boasts a seamless user experience and robust security infrastructure. Meanwhile, discontinued platforms like Symbian and Palm OS evoke nostalgia for their pioneering roles in the smartphone revolution.
The journey concludes with a reflection on the ever-evolving landscape of OS, underscored by the emergence of real-time operating systems (RTOS) and the persistent quest for innovation and efficiency. As technology continues to shape our world, understanding the foundations and evolution of operating systems remains paramount. Join Pravash Chandra Das on this illuminating journey through the heart of computing. 🌟
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.