Presentation on Cassandra indexing techniques at Cassandra Summit SF 2011.
See video at http://blip.tv/datastax/indexing-in-cassandra-5495633
Indexes are references to documents that are efficiently ordered by key and maintained in a tree structure for fast lookup. They improve the speed of document retrieval, range scanning, ordering, and other operations by enabling the use of the index instead of a collection scan. While indexes improve query performance, they can slow down document inserts and updates since the indexes also need to be maintained. The query optimizer aims to select the best index for each query but can sometimes be overridden.
Building Real-Time BI Systems with Kafka, Spark, and Kudu: Spark Summit East ...Spark Summit
One of the key challenges in working with real-time and streaming data is that the data format for capturing data is not necessarily the optimal format for ad hoc analytic queries. For example, Avro is a convenient and popular serialization service that is great for initially bringing data into HDFS. Avro has native integration with Flume and other tools that make it a good choice for landing data in Hadoop. But columnar file formats, such as Parquet and ORC, are much better optimized for ad hoc queries that aggregate over large number of similar rows.
MongoDB is an open-source, document-oriented database that provides high performance and horizontal scalability. It uses a document-model where data is organized in flexible, JSON-like documents rather than rigidly defined rows and tables. Documents can contain multiple types of nested objects and arrays. MongoDB is best suited for applications that need to store large amounts of unstructured or semi-structured data and benefit from horizontal scalability and high performance.
Slidedeck presented at http://devternity.com/ around MongoDB internals. We review the usage patterns of MongoDB, the different storage engines and persistency models as well has the definition of documents and general data structures.
Scylla Summit 2022: Scylla 5.0 New Features, Part 1ScyllaDB
Discover the new features and capabilities of Scylla Open Source 5.0 directly from the engineers who developed it. This second block of lightning talks will cover the following topics:
- New IO Scheduler and Disk Parallelism
- Per-Service-Level Timeouts
- Better Workload Estimation for Backpressure and Out-of-Memory Conditions
- Large Partition Handling Improvements
- Optimizing Reverse Queries
To watch all of the recordings hosted during Scylla Summit 2022 visit our website here: https://www.scylladb.com/summit.
MongoDB is a document-oriented NoSQL database written in C++. It uses a document data model and stores data in BSON format, which is a binary form of JSON that is lightweight, traversable, and efficient. MongoDB is schema-less, supports replication and high availability, auto-sharding for scaling, and rich queries. It is suitable for big data, content management, mobile and social applications, and user data management.
Redis is an in-memory key-value store that is often used as a database, cache, and message broker. It supports various data structures like strings, hashes, lists, sets, and sorted sets. While data is stored in memory for fast access, Redis can also persist data to disk. It is widely used by companies like GitHub, Craigslist, and Engine Yard to power applications with high performance needs.
PostgreSQL (or Postgres) began its life in 1986 as POSTGRES, a research project of the University of California at Berkeley.
PostgreSQL isn't just relational, it's object-relational.it's object-relational. This gives it some advantages over other open source SQL databases like MySQL, MariaDB and Firebird.
Introduction to memcached, a caching service designed for optimizing performance and scaling in the web stack, seen from perspective of MySQL/PHP users. Given for 2nd year students of professional bachelor in ICT at Kaho St. Lieven, Gent.
This presentation will demonstrate how you can use the aggregation pipeline with MongoDB similar to how you would use GROUP BY in SQL and the new stage operators coming 3.4. MongoDB’s Aggregation Framework has many operators that give you the ability to get more value out of your data, discover usage patterns within your data, or use the Aggregation Framework to power your application. Considerations regarding version, indexing, operators, and saving the output will be reviewed.
This document summarizes a presentation about optimizing performance between PostgreSQL and JDBC.
The presenter discusses several strategies for improving query performance such as using prepared statements, avoiding closing statements, setting fetch sizes appropriately, and using batch inserts with COPY for large amounts of data. Some potential issues that can cause performance degradation are also covered, such as parameter type changes invalidating prepared statements and unexpected plan changes after repeated executions.
The presentation includes examples and benchmarks demonstrating the performance impact of different approaches. The overall message is that prepared statements are very important for performance but must be used carefully due to edge cases that can still cause issues.
The document discusses MongoDB concepts including:
- MongoDB uses a document-oriented data model with dynamic schemas and supports embedding and linking of related data.
- Replication allows for high availability and data redundancy across multiple nodes.
- Sharding provides horizontal scalability by distributing data across nodes in a cluster.
- MongoDB supports both eventual and immediate consistency models.
ClickHouse Deep Dive, by Aleksei MilovidovAltinity Ltd
This document provides an overview of ClickHouse, an open source column-oriented database management system. It discusses ClickHouse's ability to handle high volumes of event data in real-time, its use of the MergeTree storage engine to sort and merge data efficiently, and how it scales through sharding and distributed tables. The document also covers replication using the ReplicatedMergeTree engine to provide high availability and fault tolerance.
Under The Hood Of A Shard-Per-Core Database ArchitectureScyllaDB
This document summarizes the key design decisions behind ScyllaDB's shard-per-core database architecture. It discusses how ScyllaDB addresses the challenges of scaling databases across hundreds of CPU cores by utilizing an asynchronous task model with one thread and one data shard per CPU core. This allows for linear scalability. It also overhauls the I/O scheduling to prioritize workloads and maximize throughput from SSDs under mixed read/write workloads. Benchmark results show ScyllaDB's architecture can handle petabyte-scale databases with high performance and low latency even on commodity hardware.
This presentation covers all aspects of PostgreSQL administration, including installation, security, file structure, configuration, reporting, backup, daily maintenance, monitoring activity, disk space computations, and disaster recovery. It shows how to control host connectivity, configure the server, find the query being run by each session, and find the disk space used by each database.
Scalar DB is an open source library released under Apache 2 which realizes ACID-compliant transactions on Cassandra, without requiring any modifications to Cassandra itself. It achieves strongly-consistent, linearly scalable, and highly available transactions. This talk will present the theory and practice behind Scalar DB, as well as providing some benchmark results and use cases.
This document discusses different C++ STL containers and their usage. It describes the key properties and use cases of common sequential containers like vector, deque, and list, as well as associative containers like set and map. It also covers container adaptors like stack and queue. The document provides guidance on choosing the right container based on needs like random access, insertion/deletion frequency, and data structure type (e.g. LIFO for stack). It highlights when certain containers may not be suitable or have disadvantages.
In this presentation, Raghavendra BM of Valuebound has discussed the basics of MongoDB - an open-source document database and leading NoSQL database.
----------------------------------------------------------
Get Socialistic
Our website: http://valuebound.com/
LinkedIn: http://bit.ly/2eKgdux
Facebook: https://www.facebook.com/valuebound/
Twitter: http://bit.ly/2gFPTi8
Storm is a distributed and fault-tolerant realtime computation system. It was created at BackType/Twitter to analyze tweets, links, and users on Twitter in realtime. Storm provides scalability, reliability, and ease of programming. It uses components like Zookeeper, ØMQ, and Thrift. A Storm topology defines the flow of data between spouts that read data and bolts that process data. Storm guarantees processing of all data through its reliability APIs and guarantees no data loss even during failures.
These are slides from our Big Data Warehouse Meetup in April. We talked about NoSQL databases: What they are, how they’re used and where they fit in existing enterprise data ecosystems.
Mike O’Brian from 10gen, introduced the syntax and usage patterns for a new aggregation system in MongoDB and give some demonstrations of aggregation using the new system. The new MongoDB aggregation framework makes it simple to do tasks such as counting, averaging, and finding minima or maxima while grouping by keys in a collection, complementing MongoDB’s built-in map/reduce capabilities.
For more information, visit our website at http://casertaconcepts.com/ or email us at [email protected].
Inside MongoDB: the Internals of an Open-Source DatabaseMike Dirolf
The document discusses MongoDB, including how it stores and indexes data, handles queries and replication, and supports sharding and geospatial indexing. Key points covered include how MongoDB stores data in BSON format across data files that grow in size, uses memory-mapped files for data access, supports indexing with B-trees, and replicates operations through an oplog.
Apache Spark Data Source V2 with Wenchen Fan and Gengliang WangDatabricks
As a general computing engine, Spark can process data from various data management/storage systems, including HDFS, Hive, Cassandra and Kafka. For flexibility and high throughput, Spark defines the Data Source API, which is an abstraction of the storage layer. The Data Source API has two requirements.
1) Generality: support reading/writing most data management/storage systems.
2) Flexibility: customize and optimize the read and write paths for different systems based on their capabilities.
Data Source API V2 is one of the most important features coming with Spark 2.3. This talk will dive into the design and implementation of Data Source API V2, with comparison to the Data Source API V1. We also demonstrate how to implement a file-based data source using the Data Source API V2 for showing its generality and flexibility.
In-memory OLTP storage with persistence and transaction supportAlexander Korotkov
Nowadays it becomes evident that single storage engine can't be "one size fits all". PostgreSQL community starts its movement towards pluggable storages. Significant restriction which is imposed in the current approach is compatibility. We consider pluggable storages to be compatible with (at least some) existing index access methods. That means we've long way to go, because we have to extend our index AMs before we can add corresponding features in the pluggable storages themselves.
In this talk we would like look this problem from another angle, and see what can we achieve if we try to make storage completely from scratch (using FDW interface for prototyping). Thus, we would show you a prototype of in-memory OLTP storage with transaction support and snapshot isolation. Internally it's implemented as index-organized table (B-tree) with undo log and optional persistence. That means it's quite different from what we have in PostgreSQL now.
The proved by benchmarks advantages of this in-memory storage are: better multicore scalability (thanks to no buffer manager), reduced bloat (thanks to undo log) and optimized IO (thank to logical WAL logging).
Building a Mobile Data Platform with Cassandra - Apigee Under the Hood (Webcast)Apigee | Google Cloud
The document discusses Usergrid, an open source mobile backend platform built on Apache Cassandra. It provides capabilities like API management, analytics, and tools. Usergrid allows building mobile and rich client apps without needing a web stack. It highlights key features like being platform agnostic, flexible data modeling, and multi-tenancy using virtual keyspaces in Cassandra. The document also discusses how Usergrid implements shared schemas and keyspaces in Cassandra to provide isolation and scale for multiple teams and applications.
This document discusses Elasticsearch and how to implement it beyond basic usage covered in Railscasts episodes. It covers Elasticsearch features like being schemaless, distributed, and RESTful. It then discusses how to configure mappings and analyzers for indexing partial words. Examples are given for searching, sorting results, and keeping the index in sync with database changes. Resources for further reading are also provided.
Redis is an in-memory key-value store that is often used as a database, cache, and message broker. It supports various data structures like strings, hashes, lists, sets, and sorted sets. While data is stored in memory for fast access, Redis can also persist data to disk. It is widely used by companies like GitHub, Craigslist, and Engine Yard to power applications with high performance needs.
PostgreSQL (or Postgres) began its life in 1986 as POSTGRES, a research project of the University of California at Berkeley.
PostgreSQL isn't just relational, it's object-relational.it's object-relational. This gives it some advantages over other open source SQL databases like MySQL, MariaDB and Firebird.
Introduction to memcached, a caching service designed for optimizing performance and scaling in the web stack, seen from perspective of MySQL/PHP users. Given for 2nd year students of professional bachelor in ICT at Kaho St. Lieven, Gent.
This presentation will demonstrate how you can use the aggregation pipeline with MongoDB similar to how you would use GROUP BY in SQL and the new stage operators coming 3.4. MongoDB’s Aggregation Framework has many operators that give you the ability to get more value out of your data, discover usage patterns within your data, or use the Aggregation Framework to power your application. Considerations regarding version, indexing, operators, and saving the output will be reviewed.
This document summarizes a presentation about optimizing performance between PostgreSQL and JDBC.
The presenter discusses several strategies for improving query performance such as using prepared statements, avoiding closing statements, setting fetch sizes appropriately, and using batch inserts with COPY for large amounts of data. Some potential issues that can cause performance degradation are also covered, such as parameter type changes invalidating prepared statements and unexpected plan changes after repeated executions.
The presentation includes examples and benchmarks demonstrating the performance impact of different approaches. The overall message is that prepared statements are very important for performance but must be used carefully due to edge cases that can still cause issues.
The document discusses MongoDB concepts including:
- MongoDB uses a document-oriented data model with dynamic schemas and supports embedding and linking of related data.
- Replication allows for high availability and data redundancy across multiple nodes.
- Sharding provides horizontal scalability by distributing data across nodes in a cluster.
- MongoDB supports both eventual and immediate consistency models.
ClickHouse Deep Dive, by Aleksei MilovidovAltinity Ltd
This document provides an overview of ClickHouse, an open source column-oriented database management system. It discusses ClickHouse's ability to handle high volumes of event data in real-time, its use of the MergeTree storage engine to sort and merge data efficiently, and how it scales through sharding and distributed tables. The document also covers replication using the ReplicatedMergeTree engine to provide high availability and fault tolerance.
Under The Hood Of A Shard-Per-Core Database ArchitectureScyllaDB
This document summarizes the key design decisions behind ScyllaDB's shard-per-core database architecture. It discusses how ScyllaDB addresses the challenges of scaling databases across hundreds of CPU cores by utilizing an asynchronous task model with one thread and one data shard per CPU core. This allows for linear scalability. It also overhauls the I/O scheduling to prioritize workloads and maximize throughput from SSDs under mixed read/write workloads. Benchmark results show ScyllaDB's architecture can handle petabyte-scale databases with high performance and low latency even on commodity hardware.
This presentation covers all aspects of PostgreSQL administration, including installation, security, file structure, configuration, reporting, backup, daily maintenance, monitoring activity, disk space computations, and disaster recovery. It shows how to control host connectivity, configure the server, find the query being run by each session, and find the disk space used by each database.
Scalar DB is an open source library released under Apache 2 which realizes ACID-compliant transactions on Cassandra, without requiring any modifications to Cassandra itself. It achieves strongly-consistent, linearly scalable, and highly available transactions. This talk will present the theory and practice behind Scalar DB, as well as providing some benchmark results and use cases.
This document discusses different C++ STL containers and their usage. It describes the key properties and use cases of common sequential containers like vector, deque, and list, as well as associative containers like set and map. It also covers container adaptors like stack and queue. The document provides guidance on choosing the right container based on needs like random access, insertion/deletion frequency, and data structure type (e.g. LIFO for stack). It highlights when certain containers may not be suitable or have disadvantages.
In this presentation, Raghavendra BM of Valuebound has discussed the basics of MongoDB - an open-source document database and leading NoSQL database.
----------------------------------------------------------
Get Socialistic
Our website: http://valuebound.com/
LinkedIn: http://bit.ly/2eKgdux
Facebook: https://www.facebook.com/valuebound/
Twitter: http://bit.ly/2gFPTi8
Storm is a distributed and fault-tolerant realtime computation system. It was created at BackType/Twitter to analyze tweets, links, and users on Twitter in realtime. Storm provides scalability, reliability, and ease of programming. It uses components like Zookeeper, ØMQ, and Thrift. A Storm topology defines the flow of data between spouts that read data and bolts that process data. Storm guarantees processing of all data through its reliability APIs and guarantees no data loss even during failures.
These are slides from our Big Data Warehouse Meetup in April. We talked about NoSQL databases: What they are, how they’re used and where they fit in existing enterprise data ecosystems.
Mike O’Brian from 10gen, introduced the syntax and usage patterns for a new aggregation system in MongoDB and give some demonstrations of aggregation using the new system. The new MongoDB aggregation framework makes it simple to do tasks such as counting, averaging, and finding minima or maxima while grouping by keys in a collection, complementing MongoDB’s built-in map/reduce capabilities.
For more information, visit our website at http://casertaconcepts.com/ or email us at [email protected].
Inside MongoDB: the Internals of an Open-Source DatabaseMike Dirolf
The document discusses MongoDB, including how it stores and indexes data, handles queries and replication, and supports sharding and geospatial indexing. Key points covered include how MongoDB stores data in BSON format across data files that grow in size, uses memory-mapped files for data access, supports indexing with B-trees, and replicates operations through an oplog.
Apache Spark Data Source V2 with Wenchen Fan and Gengliang WangDatabricks
As a general computing engine, Spark can process data from various data management/storage systems, including HDFS, Hive, Cassandra and Kafka. For flexibility and high throughput, Spark defines the Data Source API, which is an abstraction of the storage layer. The Data Source API has two requirements.
1) Generality: support reading/writing most data management/storage systems.
2) Flexibility: customize and optimize the read and write paths for different systems based on their capabilities.
Data Source API V2 is one of the most important features coming with Spark 2.3. This talk will dive into the design and implementation of Data Source API V2, with comparison to the Data Source API V1. We also demonstrate how to implement a file-based data source using the Data Source API V2 for showing its generality and flexibility.
In-memory OLTP storage with persistence and transaction supportAlexander Korotkov
Nowadays it becomes evident that single storage engine can't be "one size fits all". PostgreSQL community starts its movement towards pluggable storages. Significant restriction which is imposed in the current approach is compatibility. We consider pluggable storages to be compatible with (at least some) existing index access methods. That means we've long way to go, because we have to extend our index AMs before we can add corresponding features in the pluggable storages themselves.
In this talk we would like look this problem from another angle, and see what can we achieve if we try to make storage completely from scratch (using FDW interface for prototyping). Thus, we would show you a prototype of in-memory OLTP storage with transaction support and snapshot isolation. Internally it's implemented as index-organized table (B-tree) with undo log and optional persistence. That means it's quite different from what we have in PostgreSQL now.
The proved by benchmarks advantages of this in-memory storage are: better multicore scalability (thanks to no buffer manager), reduced bloat (thanks to undo log) and optimized IO (thank to logical WAL logging).
Building a Mobile Data Platform with Cassandra - Apigee Under the Hood (Webcast)Apigee | Google Cloud
The document discusses Usergrid, an open source mobile backend platform built on Apache Cassandra. It provides capabilities like API management, analytics, and tools. Usergrid allows building mobile and rich client apps without needing a web stack. It highlights key features like being platform agnostic, flexible data modeling, and multi-tenancy using virtual keyspaces in Cassandra. The document also discusses how Usergrid implements shared schemas and keyspaces in Cassandra to provide isolation and scale for multiple teams and applications.
This document discusses Elasticsearch and how to implement it beyond basic usage covered in Railscasts episodes. It covers Elasticsearch features like being schemaless, distributed, and RESTful. It then discusses how to configure mappings and analyzers for indexing partial words. Examples are given for searching, sorting results, and keeping the index in sync with database changes. Resources for further reading are also provided.
This document provides an overview of Elasticsearch and how to use it with .NET. It discusses what Elasticsearch is, how to install it, how Elasticsearch provides scalability through its architecture of clusters, nodes, shards and replicas. It also covers topics like indexing and querying data through the REST API or NEST client for .NET, performing searches, aggregations, highlighting hits, handling human language through analyzers, and using suggesters.
MongoDB.local DC 2018: Tips and Tricks for Avoiding Common Query PitfallsMongoDB
Query performance can either be a constant headache or the unsung hero of an application. MongoDB provides extremely powerful querying capabilities when used properly. As a member of the solutions architecture team, I will share common mistakes observed as well as tips and tricks to avoiding them.
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.
C# is a component-oriented programming language that builds on the .NET framework. It has a familiar C-like syntax that is easy for developers familiar with C, C++, Java, and Visual Basic to adopt. C# is fully object-oriented and optimized for building .NET applications. Everything in C# belongs to a class, with basic data types including integers, floats, booleans, characters, and strings. C# supports common programming constructs like variables, conditional statements, loops, methods, and classes. C# can be easily combined with ASP.NET for building web applications in a powerful, fast, and high-level way.
The document provides an overview of the C++ programming language. It discusses that C++ was designed by Bjarne Stroustrup to provide Simula's facilities for program organization together with C's efficiency and flexibility for systems programming. It outlines key C++ features such as classes, operator overloading, references, templates, exceptions, and input/output streams. It also covers topics like class definitions, constructors, destructors, friend functions, and operator overloading. The document provides examples of basic C++ programs and explains concepts like compiling, linking, and executing C++ programs.
An overview and discussion on indexing data in Redis to facilitate fast and efficient data retrieval. Presented on September 22nd, 2014 to the Redis Tel Aviv Meetup.
In this talk most of the C++11 features will be uncovered and examples from real world use will be presented from my personal experience in writing software systems using C++11 using GCC compiler. Also I will compare open source implementation with proprietary implementation of C++11. This is basically a C++11 talk to give audience a glimpse on what is going on in the C++ world.
This document provides an overview of key Java concepts including:
- Java is an object-oriented, platform-independent programming language similar to C++ in syntax. It was developed by Sun Microsystems.
- Java features include automatic memory management, type safety, multi-threading, and network programming capabilities. Code is compiled to bytecode that runs on the Java Virtual Machine.
- Core Java concepts discussed include primitive types, variables, operators, control flow statements, methods, classes, objects, arrays, inheritance, polymorphism and encapsulation.
- Additional topics covered are packages, access modifiers, constructors, overloading, overriding, and inner classes.
The document discusses several advanced C++ programming concepts including abstract classes, exception handling, standard libraries, templates, and containers. It defines abstract classes as classes that contain pure virtual functions and cannot be instantiated. Exception handling allows programs to continue running or terminate gracefully after errors using try, catch, and throw blocks. The standard library provides common functions and classes for input/output, strings, and containers. Templates allow writing generic and reusable code for different data types, including class templates and function templates. The Standard Template Library includes common containers like vectors, lists, and maps that store and organize data using templates and iterators.
Elasticsearch is an open-source, distributed, real-time document indexer with support for online analytics. It has features like a powerful REST API, schema-less data model, full distribution and high availability, and advanced search capabilities. Documents are indexed into indexes which contain mappings and types. Queries retrieve matching documents from indexes. Analysis converts text into searchable terms using tokenizers, filters, and analyzers. Documents are distributed across shards and replicas for scalability and fault tolerance. The REST APIs can be used to index, search, and inspect the cluster.
AMC Squarelearning Bangalore is the best training institute for a career development. it had students from various parts of the country and even few were from West African countries.
Rails is a great Ruby-based framework for producing web sites quickly and effectively. Here are a bunch of tips and best practices aimed at the Ruby newbie.
ElasticSearch is a flexible and powerful open source, distributed real-time search and analytics engine for the cloud. It is JSON-oriented, uses a RESTful API, and has a schema-free design. Logstash is a tool for collecting, parsing, and storing logs and events in ElasticSearch for later use and analysis. It has many input, filter, and output plugins to collect data from various sources, parse it, and send it to destinations like ElasticSearch. Kibana works with ElasticSearch to visualize and explore stored logs and data.
This document provides an introduction to the CSE 326: Data Structures course. It discusses the following key points in 3 sentences or less:
The course will cover common data structures and algorithms, how to choose the appropriate data structure for different needs, and how to justify design decisions through formal reasoning. It aims to help students become better developers by understanding fundamental data structures and when to apply them. The document provides examples of stacks and queues to illustrate abstract data types, data structures, and their implementations in different programming languages.
This document provides an overview of a Data Structures course. The course will cover basic data structures and algorithms used in software development. Students will learn about common data structures like lists, stacks, and queues; analyze the runtime of algorithms; and practice implementing data structures. The goal is for students to understand which data structures are appropriate for different problems and be able to justify design decisions. Key concepts covered include abstract data types, asymptotic analysis to evaluate algorithms, and the tradeoffs involved in choosing different data structure implementations.
This document provides an introduction to the CSE 326: Data Structures course. It discusses the following key points in 3 sentences or less:
The course will cover common data structures and algorithms, how to choose the appropriate data structure for different needs, and how to justify design decisions through formal reasoning. It aims to help students become better developers by understanding fundamental data structures and when to apply them. The document provides examples of stacks and queues to illustrate abstract data types, data structures, and their implementations in different programming languages.
This document discusses Kotlin coroutines and how they can be used with the Spring Framework. It provides an overview of coroutines, explaining concepts like fibers, green threads, and suspendable computations. It also covers using coroutines with Spring features like the @Async annotation and asynchronous MVC return types. The document provides code examples of coroutines concepts like channels, jobs, and yielding in sequences.
Procurement Insights Cost To Value Guide.pptxJon Hansen
Procurement Insights integrated Historic Procurement Industry Archives, serves as a powerful complement — not a competitor — to other procurement industry firms. It fills critical gaps in depth, agility, and contextual insight that most traditional analyst and association models overlook.
Learn more about this value- driven proprietary service offering here.
DevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptxJustin Reock
Building 10x Organizations with Modern Productivity Metrics
10x developers may be a myth, but 10x organizations are very real, as proven by the influential study performed in the 1980s, ‘The Coding War Games.’
Right now, here in early 2025, we seem to be experiencing YAPP (Yet Another Productivity Philosophy), and that philosophy is converging on developer experience. It seems that with every new method we invent for the delivery of products, whether physical or virtual, we reinvent productivity philosophies to go alongside them.
But which of these approaches actually work? DORA? SPACE? DevEx? What should we invest in and create urgency behind today, so that we don’t find ourselves having the same discussion again in a decade?
Increasing Retail Store Efficiency How can Planograms Save Time and Money.pptxAnoop Ashok
In today's fast-paced retail environment, efficiency is key. Every minute counts, and every penny matters. One tool that can significantly boost your store's efficiency is a well-executed planogram. These visual merchandising blueprints not only enhance store layouts but also save time and money in the process.
This is the keynote of the Into the Box conference, highlighting the release of the BoxLang JVM language, its key enhancements, and its vision for the future.
Dev Dives: Automate and orchestrate your processes with UiPath MaestroUiPathCommunity
This session is designed to equip developers with the skills needed to build mission-critical, end-to-end processes that seamlessly orchestrate agents, people, and robots.
📕 Here's what you can expect:
- Modeling: Build end-to-end processes using BPMN.
- Implementing: Integrate agentic tasks, RPA, APIs, and advanced decisioning into processes.
- Operating: Control process instances with rewind, replay, pause, and stop functions.
- Monitoring: Use dashboards and embedded analytics for real-time insights into process instances.
This webinar is a must-attend for developers looking to enhance their agentic automation skills and orchestrate robust, mission-critical processes.
👨🏫 Speaker:
Andrei Vintila, Principal Product Manager @UiPath
This session streamed live on April 29, 2025, 16:00 CET.
Check out all our upcoming Dev Dives sessions at https://community.uipath.com/dev-dives-automation-developer-2025/.
HCL Nomad Web – Best Practices und Verwaltung von Multiuser-Umgebungenpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-nomad-web-best-practices-und-verwaltung-von-multiuser-umgebungen/
HCL Nomad Web wird als die nächste Generation des HCL Notes-Clients gefeiert und bietet zahlreiche Vorteile, wie die Beseitigung des Bedarfs an Paketierung, Verteilung und Installation. Nomad Web-Client-Updates werden “automatisch” im Hintergrund installiert, was den administrativen Aufwand im Vergleich zu traditionellen HCL Notes-Clients erheblich reduziert. Allerdings stellt die Fehlerbehebung in Nomad Web im Vergleich zum Notes-Client einzigartige Herausforderungen dar.
Begleiten Sie Christoph und Marc, während sie demonstrieren, wie der Fehlerbehebungsprozess in HCL Nomad Web vereinfacht werden kann, um eine reibungslose und effiziente Benutzererfahrung zu gewährleisten.
In diesem Webinar werden wir effektive Strategien zur Diagnose und Lösung häufiger Probleme in HCL Nomad Web untersuchen, einschließlich
- Zugriff auf die Konsole
- Auffinden und Interpretieren von Protokolldateien
- Zugriff auf den Datenordner im Cache des Browsers (unter Verwendung von OPFS)
- Verständnis der Unterschiede zwischen Einzel- und Mehrbenutzerszenarien
- Nutzung der Client Clocking-Funktion
Linux Support for SMARC: How Toradex Empowers Embedded DevelopersToradex
Toradex brings robust Linux support to SMARC (Smart Mobility Architecture), ensuring high performance and long-term reliability for embedded applications. Here’s how:
• Optimized Torizon OS & Yocto Support – Toradex provides Torizon OS, a Debian-based easy-to-use platform, and Yocto BSPs for customized Linux images on SMARC modules.
• Seamless Integration with i.MX 8M Plus and i.MX 95 – Toradex SMARC solutions leverage NXP’s i.MX 8 M Plus and i.MX 95 SoCs, delivering power efficiency and AI-ready performance.
• Secure and Reliable – With Secure Boot, over-the-air (OTA) updates, and LTS kernel support, Toradex ensures industrial-grade security and longevity.
• Containerized Workflows for AI & IoT – Support for Docker, ROS, and real-time Linux enables scalable AI, ML, and IoT applications.
• Strong Ecosystem & Developer Support – Toradex offers comprehensive documentation, developer tools, and dedicated support, accelerating time-to-market.
With Toradex’s Linux support for SMARC, developers get a scalable, secure, and high-performance solution for industrial, medical, and AI-driven applications.
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UiPath Community Berlin: Orchestrator API, Swagger, and Test Manager APIUiPathCommunity
Join this UiPath Community Berlin meetup to explore the Orchestrator API, Swagger interface, and the Test Manager API. Learn how to leverage these tools to streamline automation, enhance testing, and integrate more efficiently with UiPath. Perfect for developers, testers, and automation enthusiasts!
📕 Agenda
Welcome & Introductions
Orchestrator API Overview
Exploring the Swagger Interface
Test Manager API Highlights
Streamlining Automation & Testing with APIs (Demo)
Q&A and Open Discussion
Perfect for developers, testers, and automation enthusiasts!
👉 Join our UiPath Community Berlin chapter: https://community.uipath.com/berlin/
This session streamed live on April 29, 2025, 18:00 CET.
Check out all our upcoming UiPath Community sessions at https://community.uipath.com/events/.
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Webinar Recording: https://www.panagenda.com/webinars/hcl-nomad-web-best-practices-and-managing-multiuser-environments/
HCL Nomad Web is heralded as the next generation of the HCL Notes client, offering numerous advantages such as eliminating the need for packaging, distribution, and installation. Nomad Web client upgrades will be installed “automatically” in the background. This significantly reduces the administrative footprint compared to traditional HCL Notes clients. However, troubleshooting issues in Nomad Web present unique challenges compared to the Notes client.
Join Christoph and Marc as they demonstrate how to simplify the troubleshooting process in HCL Nomad Web, ensuring a smoother and more efficient user experience.
In this webinar, we will explore effective strategies for diagnosing and resolving common problems in HCL Nomad Web, including
- Accessing the console
- Locating and interpreting log files
- Accessing the data folder within the browser’s cache (using OPFS)
- Understand the difference between single- and multi-user scenarios
- Utilizing Client Clocking
TrustArc Webinar: Consumer Expectations vs Corporate Realities on Data Broker...TrustArc
Most consumers believe they’re making informed decisions about their personal data—adjusting privacy settings, blocking trackers, and opting out where they can. However, our new research reveals that while awareness is high, taking meaningful action is still lacking. On the corporate side, many organizations report strong policies for managing third-party data and consumer consent yet fall short when it comes to consistency, accountability and transparency.
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Attendees will learn:
- Consumer awareness around data brokers and what consumers are doing to limit data collection
- How businesses assess third-party vendors and their consent management operations
- Where business preparedness needs improvement
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This discussion is essential for privacy, risk, and compliance professionals who want to ground their strategies in current data and prepare for what’s next in the privacy landscape.
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Precision in data labeling = Precision on the production floor.
Special Meetup Edition - TDX Bengaluru Meetup #52.pptxshyamraj55
We’re bringing the TDX energy to our community with 2 power-packed sessions:
🛠️ Workshop: MuleSoft for Agentforce
Explore the new version of our hands-on workshop featuring the latest Topic Center and API Catalog updates.
📄 Talk: Power Up Document Processing
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2. Agenda
• Background
• Basics of Indexes
• Native Secondary Indexes
• "Wide rows" and CF-based Indexes
• Inverted-indexes Using SuperColumns
• Inverted-indexes Using Composite Columns
• Q&A
3. Background
This presentation is based on:
• What we learned over the last year in building a
highly indexed system on Cassandra
• Participation in the Hector client project
• Common questions and issues posted to the
Cassandra mailing lists
4. Brief History - Cassandra 0.6
• No built-in secondary indexes
• All indexes were custom-built, usually using
super-columns
• Pros
– Forced you to learn how Cassandra works
• Cons
– Lots of work
– Super-columns proved a dead-end
5. Brief History - Cassandra 0.7
• Built-in secondary indexes
• New users flocked to these
• Pros
– Easy to use, out of the box
• Cons
– Deceptively similar to SQL indexes but not the same
– Reinforce data modeling that plays against Cassandra’s
strengths
6. Present Day
• New users can now get started with Cassandra
without really understanding it (CQL, etc.)
• Veteran users are using advanced techniques
(Composites, etc.) that aren’t really documented
anywhere*
• New user panic mode when they try to go to the
next level and suddenly find themselves in the
deep end
*Actually, they are, Google is your friend…
8. A Quick Review
There are essentially two ways of finding rows:
The Primary Index
(row keys)
Alternate Indexes
(everything else)
9. The “Primary Index”
• The “primary index” is your row key*
• Sometimes it’s meaningful (“natural id”):
Users = {
"edanuff" : {
email: "[email protected]"
}
}
*Yeah. No. But if it helps, yes.
10. The “Primary Index”
• The “primary index” is your row key*
• But usually it’s not:
Users = {
"4e3c0423-aa84-11e0-a743-58b0356a4c0a" : {
username: "edanuff",
email: "[email protected]"
}
}
*Yeah. No. But if it helps, yes.
11. Get vs. Find
• Using the row key is the best way to retrieve
something if you’ve got a precise and
immutable 1:1 mapping
• If you find yourself ever planning to iterate
over keys, you’re probably doing something
wrong
– i.e. avoid the Order Preserving Partitioner*
• Use alternate indexes to find (search) things
*Feel free to disregard, but don’t complain later
12. Alternate Indexes
Anything other than using the row key:
• Native secondary indexes
• Wide rows as lookup and grouping tables
• Custom secondary indexes
Remember, there is no magic here…
13. Native Secondary Indexes
• Easy to use
• Look (deceptively) like SQL indexes, especially
when used with CQL
CREATE INDEX ON Users(username);
SELECT * FROM Users WHERE
username="edanuff";
14. Under The Hood
• Every index is stored as its own "hidden" CF
• Nodes index the rows they store
• When you issue a query, it gets sent to all
nodes
• Currently does equality operations, the range
operations get performed by in memory by
coordinator node
15. Some Limitations
• Not recommended for high cardinality values (i.e.
timestamps, birthdates, keywords, etc.)
• Requires at least one equality comparison in a
query – not great for less-than/greater-than/range
queries
• Unsorted - results are in token order, not query
value order
• Limited to search on data types Cassandra
natively understands
16. I can’t live with those limitations,
what are my options?
Complain on the mailing list
Switch to Mongo
Build my indexes in my application
18. Wide Rows
“Why would a row need 2B columns”?
• Basis of all indexing, organizing, and
relationships in Cassandra
• If your data model has no rows with over a
hundred columns, you’re either doing
something wrong or shouldn’t be using
Cassandra*
*IMHO J
22. Column Families As Indexes
• CF column operations very fast
• Column slices can be retrieved by range, are
always sorted, can be reversed, etc.
• If target key a TimeUUID you get both grouping
and sort by timestamp
– Good for inboxes, feeds, logs, etc. (Twissandra)
• Best option when you need to combine groups,
sort, and search
– Search friends list, inbox, etc.
23. But, only works for 1:1
What happens when I’ve got one to many?
Indexes = {
"User_Keys_By_Last_Name" : {
"adams" : "e5d61f2b-…",
"alden" : "e80a17ba-…",
"anderson" : "e5d61f2b-…",
✖! "anderson" : "e719962b-…",
Not Allowed
"doe" : "e78ece0f-…",
"franks" : "e66afd40-…",
… : …,
}
}
25. Use with caution
• Not officially deprecated, but not highly
recommended either
• Sorts only on the supercolumn, not subcolumn
• Some performance issues
• What if I want more nesting?
– Can subcolumns have subcolumns? NO!
• Anecdotally, many projects have moved away
from using supercolumns
26. So, let’s revisit regular CF’s
What happens when I’ve got one to many?
Indexes = {
"User_Keys_By_Last_Name" : {
"adams" : "e5d61f2b-…",
"alden" : "e80a17ba-…",
"anderson" : "e5d61f2b-…",
✖! "anderson" : "e719962b-…",
Not Allowed
"doe" : "e78ece0f-…",
"franks" : "e66afd40-…",
… : …,
}
}
27. So, let’s revisit regular CF’s
What if we could turn it back to one to one?
Indexes = {
"User_Keys_By_Last_Name" : {
{"adams", 1} : "e5d…",
{"alden", 1} : "e80…",
{"anderson", 1} : "e5f…",
✔! {"anderson", 2} : "e71…",
Allowed!!!
{"doe", 1} : "e78…",
{"franks", 1} : "e66…",
… : …,
}
}
28. Composite Column Names
Comparator = “CompositeType” or “DynamicCompositeType”
{"anderson", 1, 1, 1, …} : "e5f…"
1..N Components
Build your column name out of one or more
“component” values which can be of any of the
columns types Cassandra supports
(i.e. UTF8, IntegerType, UUIDType, etc.)
29. Composite Column Names
Comparator = “CompositeType” or “DynamicCompositeType”
{"anderson", 1, 1, 1, …} : "e5f…"
{"anderson", 1, 1, 2, …} : "e5f…"
{"anderson", 1, 2, 1, …} : "e5f…"
{"anderson", 1, 2, 2, …} : "e5f…"
Sorts by component values using each
component type’s sort order
Retrieve using normal column slice technique
30. Two Types Of Composites
- column_families:
- name: My_Composite_Index_CF
- compare_with:
CompositeType(UTF8Type, UUIDType)
- name: My_Dynamic_Composite_Index_CF
- compare_with:
DynamicCompositeType(s=>UTF8Type, u=>UUIDType)
Main difference for use in indexes is whether you
need to create one CF per index vs one CF for
all indexes with one row per index
31. Static Composites
- column_families:
- name: My_Composite_Index_CF
- compare_with:
CompositeType(UTF8Type, UUIDType)
{"anuff", "e5f…"} : "e5f…"
Fixed # and order
defined in column
configuration
32. Dynamic Composites
- column_families:
- name: My_Dynamic_Composite_Index_CF
- compare_with:
DynamicCompositeType(s=>UTF8Type, u=>UUIDType)
{"anuff", "e5f…", "e5f…"} : "…"
Any # and
order of
types at
runtime
33. Typical Composite Index Entry
{<term 1>, …, <term N>, <key>, <ts>}
<term 1…N> - terms to query on (i.e. last_name, first_name)
<key> - target row key
<ts> - unique timestamp, usually time-based UUID
34. How does this work?
• Queries are easy
– regular column slice operations
• Updates are harder
– Need to remove old value and insert the new
value
– Uh oh, read before write??!!!
35. Example – Users By Location
• We need 3 Column Families (not 2)
• First 2 CF’s are obvious:
– Users
– Indexes
• We also need a third CF:
– Users_Index_Entries
41. Updating The Index
• We read previous index values from the
Users_Index_Entries CF rather than the
Users CF to deal with concurrency
• Columns in Index CF and
Users_Index_Entries CF are timestamped so
no locking is needed for concurrent updates
42. Get Old Values For Column
SELECT {"location"}..{"location",*}
FROM Users_Index_Entries WHERE KEY = <user_key>;
BEGIN BATCH
DELETE {"location", ts1}, {"location", ts2}, …
FROM Users_Index_Entries WHERE KEY = <user_key>;
DELETE {<value1>, <user_key>, ts1}, {<value2>, <user_key>, ts2}, …
FROM Users_By_Location WHERE KEY = <user_key>;
UPDATE Users_Index_Entries SET {"location", ts3} = <value3>
WHERE KEY = <user_key>;
UPDATE Indexes SET {<value3>, <user_key>, ts3) = null
WHERE KEY = "Users_By_Location";
UPDATE Users SET location = <value3>
WHERE KEY = <user_key>;
APPLY BATCH
43. Remove Old Column Values
SELECT {"location"}..{"location",*}
FROM Users_Index_Entries WHERE KEY = <user_key>;
BEGIN BATCH
DELETE {"location", ts1}, {"location", ts2}, …
FROM Users_Index_Entries WHERE KEY = <user_key>;
DELETE {<value1>, <user_key>, ts1}, {<value2>, <user_key>, ts2}, …
FROM Users_By_Location WHERE KEY = <user_key>;
UPDATE Users_Index_Entries SET {"location", ts3} = <value3>
WHERE KEY = <user_key>;
UPDATE Indexes SET {<value3>, <user_key>, ts3) = null
WHERE KEY = "Users_By_Location";
UPDATE Users SET location = <value3>
WHERE KEY = <user_key>;
APPLY BATCH
44. Insert New Column Values In Index
SELECT {"location"}..{"location",*}
FROM Users_Index_Entries WHERE KEY = <user_key>;
BEGIN BATCH
DELETE {"location", ts1}, {"location", ts2}, …
FROM Users_Index_Entries WHERE KEY = <user_key>;
DELETE {<value1>, <user_key>, ts1}, {<value2>, <user_key>, ts2}, …
FROM Users_By_Location WHERE KEY = <user_key>;
UPDATE Users_Index_Entries SET {"location", ts3} = <value3>
WHERE KEY = <user_key>;
UPDATE Indexes SET {<value3>, <user_key>, ts3) = null
WHERE KEY = "Users_By_Location";
UPDATE Users SET location = <value3>
WHERE KEY = <user_key>;
APPLY BATCH
45. Set New Value For User
SELECT {"location"}..{"location",*}
FROM Users_Index_Entries WHERE KEY = <user_key>;
BEGIN BATCH
DELETE {"location", ts1}, {"location", ts2}, …
FROM Users_Index_Entries WHERE KEY = <user_key>;
DELETE {<value1>, <user_key>, ts1}, {<value2>, <user_key>, ts2}, …
FROM Users_By_Location WHERE KEY = <user_key>;
UPDATE Users_Index_Entries SET {"location", ts3} = <value3>
WHERE KEY = <user_key>;
UPDATE Indexes SET {<value3>, <user_key>, ts3) = null
WHERE KEY = "Users_By_Location";
UPDATE Users SET location = <value3>
WHERE KEY = <user_key>;
APPLY BATCH
46. Frequently Asked Questions
• Do I need locking for concurrency?
– No, the index will always be eventually consistent
• What if something goes wrong?
– You will have to have provisions to repeat the batch operation
until it completes, but its idempotent, so it’s ok
• Can’t I get a false positive?
– Depending on updates being in-flight, you might get a false
positive, if this is a problem, filter on read
• Who else is using this approach?
– Actually very common with lots of variations, this isn’t the only
way to do this but at least composite format is now standard
47. Some Things To Think About
• Indexes can be derived from column values
– Create a “last_name, first_name” index from a
“fullname” column
– Unroll a JSON object to construct deep indexes of
serialized JSON structures
• Include additional denormalized values in the
index for faster lookups
• Use composites for column values too not just
column names
48. How can I learn more?
Sample implementation using Hector:
https://github.com/edanuff/CassandraIndexedCollections
JPA implementation using this for Hector:
https://github.com/riptano/hector-jpa
Jira entry on native composites for Cassandra:
https://issues.apache.org/jira/browse/CASSANDRA-2231
Background blog posts:
http://www.anuff.com/2011/02/indexing-in-cassandra.html
http://www.anuff.com/2010/07/secondary-indexes-in-cassandra.html
49. What is Usergrid?
• Cloud PaaS backend for mobile and rich-client applications
• Powered by Cassandra
• In private Beta now
• Entire stack to be open-sourced in August so people can
run their own
• Sign up to get admitted to Beta and to be notified when
about source code
http://www.usergrid.com