In this session you will learn:
Zookeeper
To know more, click here: https://www.mindsmapped.com/courses/big-data-hadoop/big-data-and-hadoop-training-for-beginners/
The document provides an overview of MySQL Performance Schema. It discusses what Performance Schema is, how it works, key terminology like instruments and consumers, and how instruments collect data. It also covers the different types of tables in Performance Schema, how instruments and available metrics have evolved in different MySQL versions, and how the sys schema presents Performance Schema data to users.
CrateDB is a distributed SQL database that combines the familiarity of SQL with the scalability and flexibility of NoSQL. It offers features like simple scalability through automatic data rebalancing, transactional capabilities, real-time data ingestion with millisecond query performance, and time series analysis through automatic table partitioning. CrateDB can be run anywhere, connected to from various languages and applications, and extended through plugins. It is well-suited for IoT applications involving millions of data points per second with real-time queries.
About Logical Backups
Available Backup Tools for taking Logical backup
Mydumper/Myloader with Options
MySQL Shell utility with Options
Working with MySQL Shell utility taking logical backup
and restore.
Tales From the Field: The Wrong Way of Using Cassandra (Carlos Rolo, Pythian)...DataStax
Cassandra is a distributed database with features included but not limited to Secundary Indexes, UDF, Materialized Views, etc. and not so strict hardware requirements.
It is important to use those features and select hardware correctly to make sure the use of Cassandra in your business can be as painless as possible.
I will address how these features are used in the wrong way, how hardware should be selected, and how to make Cassandra work in the best possible way.
Learning Objective #1:
Learn that Cassandra hardware requirements exist (and why) and the shortcomings in some of features(Secundary Indexes, Compaction Strategies, etc).
Learning Objective #2:
The most misused features and common hardware errors. How they might seem harmeless at first (either small cluster or even single node).
Learning Objective #3:
How to correctly use Cassandra and it's features and go for perfect operation.
About the Speaker
Carlos Rolo Cassandra Consultant, Pythian
Carlos Rolo is a Cassandra MVP, and has deep expertise with distributed architecture technologies. Carlos is driven by challenge, and enjoys the opportunities to discover new things.. He has become known and trusted by customers and colleagues for his ability to understand complex problems, and to work well under pressure. When Carlos isn't working he can be found playing water polo or enjoying the his local community.
Scylla Summit 2018: Adventures in AdTech: Processing 50 Billion User Profiles...ScyllaDB
AdTech requires high speed at massive scale. Sizmek serves millions of requests every second. Requests need to be processed in tens of milliseconds, while involving 10 simultaneous lookups into a database that contains tens of billions of profiles. In this presentation, you will discover how Scylla enables Sizmek’s real-time bidders to query a gigantic user profile store quickly and reliably with only a few nodes. We’ll discuss data modeling, server and driver configuration, techniques to minimize disk access, as well as considerations for leveraging Spark while migrating from HBase.
How Orange Financial combat financial frauds over 50M transactions a day usin...JinfengHuang3
You will learn how Orange Financial combats financial fraud over 50M transactions a day using Apache Pulsar. The presentation is shared at Strata Data Conference at New York, US, 2019/09.
While large enterprises often require complex database systems that support thousands of concurrent users, terabytes of data, and a highly-trained support staff, the needs of most businesses and applications are more modest. InterBase SMP is a proven, highly-reliable, low-cost database that can easily support hundreds of concurrent users and gigabytes of data with no support during normal operation.
The document provides an overview of MySQL Performance Schema. It discusses what Performance Schema is, how it works, key terminology like instruments and consumers, and how instruments collect data. It also covers the different types of tables in Performance Schema, how instruments and available metrics have evolved in different MySQL versions, and how the sys schema presents Performance Schema data to users.
CrateDB is a distributed SQL database that combines the familiarity of SQL with the scalability and flexibility of NoSQL. It offers features like simple scalability through automatic data rebalancing, transactional capabilities, real-time data ingestion with millisecond query performance, and time series analysis through automatic table partitioning. CrateDB can be run anywhere, connected to from various languages and applications, and extended through plugins. It is well-suited for IoT applications involving millions of data points per second with real-time queries.
About Logical Backups
Available Backup Tools for taking Logical backup
Mydumper/Myloader with Options
MySQL Shell utility with Options
Working with MySQL Shell utility taking logical backup
and restore.
Tales From the Field: The Wrong Way of Using Cassandra (Carlos Rolo, Pythian)...DataStax
Cassandra is a distributed database with features included but not limited to Secundary Indexes, UDF, Materialized Views, etc. and not so strict hardware requirements.
It is important to use those features and select hardware correctly to make sure the use of Cassandra in your business can be as painless as possible.
I will address how these features are used in the wrong way, how hardware should be selected, and how to make Cassandra work in the best possible way.
Learning Objective #1:
Learn that Cassandra hardware requirements exist (and why) and the shortcomings in some of features(Secundary Indexes, Compaction Strategies, etc).
Learning Objective #2:
The most misused features and common hardware errors. How they might seem harmeless at first (either small cluster or even single node).
Learning Objective #3:
How to correctly use Cassandra and it's features and go for perfect operation.
About the Speaker
Carlos Rolo Cassandra Consultant, Pythian
Carlos Rolo is a Cassandra MVP, and has deep expertise with distributed architecture technologies. Carlos is driven by challenge, and enjoys the opportunities to discover new things.. He has become known and trusted by customers and colleagues for his ability to understand complex problems, and to work well under pressure. When Carlos isn't working he can be found playing water polo or enjoying the his local community.
Scylla Summit 2018: Adventures in AdTech: Processing 50 Billion User Profiles...ScyllaDB
AdTech requires high speed at massive scale. Sizmek serves millions of requests every second. Requests need to be processed in tens of milliseconds, while involving 10 simultaneous lookups into a database that contains tens of billions of profiles. In this presentation, you will discover how Scylla enables Sizmek’s real-time bidders to query a gigantic user profile store quickly and reliably with only a few nodes. We’ll discuss data modeling, server and driver configuration, techniques to minimize disk access, as well as considerations for leveraging Spark while migrating from HBase.
How Orange Financial combat financial frauds over 50M transactions a day usin...JinfengHuang3
You will learn how Orange Financial combats financial fraud over 50M transactions a day using Apache Pulsar. The presentation is shared at Strata Data Conference at New York, US, 2019/09.
While large enterprises often require complex database systems that support thousands of concurrent users, terabytes of data, and a highly-trained support staff, the needs of most businesses and applications are more modest. InterBase SMP is a proven, highly-reliable, low-cost database that can easily support hundreds of concurrent users and gigabytes of data with no support during normal operation.
Building a Multi-Region Cluster at Target (Aaron Ploetz, Target) | Cassandra ...DataStax
Lessons learned from a year spent building a Cassandra cluster over multiple regions, data centers, and providers. Will discuss our successes and learnings on replication, operations, and application development.
About the Speaker
Aaron Ploetz Lead Technical Architect, Target
Aaron is a Lead Technical Architect for Target, where he coaches development teams on modeling and building applications for Cassandra. He is active in the Cassandra tags on StackOverflow, and has also contributed patches to cqlsh. Aaron holds a B.S. in Management/Computer Systems from the University of Wisconsin-Whitewater, a M.S. in Software Engineering and Database Technologies from Regis University, and is a 2x DataStax MVP for Apache Cassandra.
NoSQL databases were created to handle large and growing datasets for web applications. They are non-tabular, distributed, open source, and designed for high performance, scalability, and availability. The document focuses on key characteristics of NoSQL like schema flexibility, horizontal scaling, and the BASE consistency model. It also covers major NoSQL types (key-value, document, and column-oriented), queries, and compares NoSQL to SQL databases in terms of features, performance, and cost.
Stream or segment : what is the best way to access your events in Pulsar_NengStreamNative
Infinite event streams are the core data abstraction in Apache Pulsar. Pulsar provides two-level reading APIs for accessing events in Pulsar topics, one is pub/sub and the other one is segment readers. The pub/sub API provides a unified messaging API for accessing events in a streaming way. People can choose different subscription modes for consuming events. The segment API provides a way to access events directly from Apache BookKeeper and tiered storage, which is more suitable for batch-oriented workloads. You can combine both pub/sub API and segment API to create a unified data processing experience as well.
In the past year, we at StreamNative have been helping with many customers running Pulsar for different use cases from online queuing, event sourcing to stream and batch processing. We also worked on integrating Pulsar with different components in the big data ecosystem. In this talk, we will share our experiences and best practices of choosing the right API for accessing your event streams in Pulsar for different use cases.
Mesos is a platform that enables sharing of cluster resources between different frameworks. It achieves this through a two-level resource sharing approach: 1) Mesos manages coarse-grained sharing of resources like CPUs and memory between frameworks; 2) Frameworks control fine-grained sharing of tasks within their allocated resources. Mesos's use of resource offers allows frameworks to dynamically accept or reject resources based on their needs, improving cluster utilization. It has been used successfully at large companies to share resources between frameworks like Hadoop and Spark.
Mesos - A Platform for Fine-Grained Resource Sharing in the Data CenterAnkur Chauhan
Papers we Love @ Seattle, 08/14/2015
Abstract
We present Mesos, a platform for sharing commodity clusters between multiple diverse cluster computing frameworks, such as Hadoop and MPI. Sharing improves cluster utilization and avoids per-framework data replication. Mesos shares resources in a fine-grained manner, allowing frameworks to achieve data locality by taking turns reading data stored on each machine. To support the sophisticated schedulers of today's frameworks, Mesos introduces a distributed two-level scheduling mechanism called resource offers. Mesos decides how many resources to offer each framework, while frameworks decide which resources to accept and which computations to run on them. Our results show that Mesos can achieve near-optimal data locality when sharing the cluster among diverse frameworks, can scale to 50,000 (emulated) nodes, and is resilient to failures.
Using cassandra as a distributed logging to store pb dataRamesh Veeramani
This document discusses using Cassandra for big data event logging. It notes that Cassandra scales incrementally, is highly available, and is well suited for OLTP workloads where write throughput is prioritized over reads. It covers Cassandra's internal workings including token assignment, replication, and compaction strategies. Setup instructions are provided along with benchmarking results. Maintenance tools like Nodetool and stress testing tools are also mentioned. The document concludes that Cassandra is a good candidate for logging systems due to its scalability and ease of adding nodes.
Disney+ Hotstar: Scaling NoSQL for Millions of Video On-Demand UsersScyllaDB
Disney+ Hotstar is the fastest growing branch of Disney+. Join Disney+ Hotstar Architect Vamsi Subhash and senior data engineer Balakrishnan Kaliyamoorthy to learn…
How Disney+ Hotstar architected their systems to handle massive data loads
Why they chose to replace both Redis and Elasticsearch
Their requirements for massively scalable data infrastructure and evolving data models
How they migrated their data to Scylla Cloud, ScyllaDB’s fully managed NoSQL database-as-a-service, without suffering downtime
Migrating from a Relational Database to Cassandra: Why, Where, When and HowAnant Corporation
Everything you need to know about moving from a relational database to Cassandra.
You may be very familiar with what Cassandra is, or the name might just be a buzzword you've heard used when discussing databases. Regardless of your familiarity with Cassandra, this database should be the first tool you consider when you need scalability and high availability without compromising performance.
Webtech Conference: NoSQL and Web scalabilityLuca Bonmassar
NoSQL databases provide an alternative to SQL databases that can improve performance and scalability. Memcached is an in-memory key-value store that is commonly used to cache database queries for improved performance. It uses a simple get/set interface and does not provide persistent storage. Data is stored by key and expires from the cache. Memcached can be used to cache database query results in front of an SQL database to improve response times. Data can also be sharded or partitioned across multiple servers in a NoSQL system like Memcached to improve scalability for large datasets or high query volumes.
MongoDB vs Scylla: Production Experience from Both Dev & Ops Standpoint at Nu...ScyllaDB
This document compares MongoDB and ScyllaDB databases. It discusses their histories, architectures, data models, querying capabilities, consistency handling, and scaling approaches. It also provides takeaways for operations teams and developers, noting that ScyllaDB favors consistent performance over flexibility while MongoDB is more flexible but sacrifices some performance. The document also outlines how a company called Numberly uses both MongoDB and ScyllaDB for different use cases.
MySQL Cluster provides a way to scale MySQL beyond a single server by using a shared-nothing architecture with data partitioning and replication across multiple database nodes. It allows for high availability and automatic failover. The tradeoff is that all data and indexes must reside entirely in main memory.
Empowering the AWS DynamoDB™ application developer with AlternatorScyllaDB
Getting started with AWS DynamoDB™ is famously easy, but as an application grows and evolves it often starts to struggle with DynamoDB’s limitations. We introduce Scylla’s Alternator, which provides the same API as DynamoDB but aims to empower the application developer. In this presentation we will survey some of Alternator’s developer-centered features: Alternator lets you test and eventually deploy your application anywhere, on any public cloud or private cluster. It efficiently supports multiple tables so it does not require difficult single-table design. Finally, Alternator provides the developer with strong observability tools. The insights provided by these tools can detect bottlenecks, improve performance and even lower its cost.
This document discusses Cassandra and techniques for inserting data into Cassandra using the Cassandra driver. It describes three methods for inserting data - execute (blocks until response), execute async (returns immediately without blocking), and batch insert (combines multiple statements). It also covers pagination in Cassandra using fetch size, saving the paging state, and offset queries. Performance comparisons show execute async has lower execution time than execute/sync for the same number of entries.
One of our presentation which was given on Cassandra Database. Aruman implement big-data projects for its multiple client. RDBMS to Cassandra conversion is task which is taken by ARUMAN.
A database is an organized collection of data, generally stored and accessed electronically from a computer system. Where databases are more complex they are often developed using formal design and modeling techniques.
Use Cases for Oacle Pluggable Databases in Development Environmentsclaudegex
Oracle pluggable databases allow for the dynamic creation and deletion of portable database instances called pluggable databases (PDBs) within a multitenant container database (CDB). This enables several use cases for development environments including: 1) Each developer can have multiple PDBs for different features/releases, 2) Teams can easily share database states by cloning PDBs, and 3) PDBs can be snapshotted to repeatedly test against a specific database state or test data set.
Apache Cassandra Lunch #70: Basics of Apache CassandraAnant Corporation
In Cassandra Lunch #70, we discuss the Basics of Apache Cassandra and setup a stand-alone Apache Cassandra.
Accompanying Blog: https://blog.anant.us/cassandra-launch-70-basics-of-apache-cassandra
Accompanying YouTube: https://youtu.be/o-yU0mi4nzc
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Cassandra Lunch Weekly at 12 PM EST Every Wednesday: https://www.meetup.com/Cassandra-DataStax-DC/events/
Cassandra.Link:
https://cassandra.link/
Follow Us and Reach Us At:
Anant:
https://www.anant.us/
Awesome Cassandra:
https://github.com/Anant/awesome-cassandra
Cassandra.Lunch:
https://github.com/Anant/Cassandra.Lunch
Email:
solutions@anant.us
LinkedIn:
https://www.linkedin.com/company/anant/
Twitter:
https://twitter.com/anantcorp
Eventbrite:
https://www.eventbrite.com/o/anant-1072927283
Facebook:
https://www.facebook.com/AnantCorp/
Join The Anant Team:
https://www.careers.anant.us
Apache ZooKeeper is an open-source distributed coordination service that helps manage large sets of hosts. It implements coordination protocols to provide a consistent view of shared state across distributed applications or servers. ZooKeeper uses a hierarchical namespacing system called znodes to store configuration data and other information. It ensures highly reliable distributed coordination through features like leader election, group membership, and notifications.
Cassandra is used for real-time bidding in online advertising. It processes billions of bid requests per day with low latency requirements. Segment data, which assigns product or service affinity to user groups, is stored in Cassandra to reduce calculations and allow users to be bid on sooner. Tuning the cache size and understanding the active dataset helps optimize performance.
Building a Multi-Region Cluster at Target (Aaron Ploetz, Target) | Cassandra ...DataStax
Lessons learned from a year spent building a Cassandra cluster over multiple regions, data centers, and providers. Will discuss our successes and learnings on replication, operations, and application development.
About the Speaker
Aaron Ploetz Lead Technical Architect, Target
Aaron is a Lead Technical Architect for Target, where he coaches development teams on modeling and building applications for Cassandra. He is active in the Cassandra tags on StackOverflow, and has also contributed patches to cqlsh. Aaron holds a B.S. in Management/Computer Systems from the University of Wisconsin-Whitewater, a M.S. in Software Engineering and Database Technologies from Regis University, and is a 2x DataStax MVP for Apache Cassandra.
NoSQL databases were created to handle large and growing datasets for web applications. They are non-tabular, distributed, open source, and designed for high performance, scalability, and availability. The document focuses on key characteristics of NoSQL like schema flexibility, horizontal scaling, and the BASE consistency model. It also covers major NoSQL types (key-value, document, and column-oriented), queries, and compares NoSQL to SQL databases in terms of features, performance, and cost.
Stream or segment : what is the best way to access your events in Pulsar_NengStreamNative
Infinite event streams are the core data abstraction in Apache Pulsar. Pulsar provides two-level reading APIs for accessing events in Pulsar topics, one is pub/sub and the other one is segment readers. The pub/sub API provides a unified messaging API for accessing events in a streaming way. People can choose different subscription modes for consuming events. The segment API provides a way to access events directly from Apache BookKeeper and tiered storage, which is more suitable for batch-oriented workloads. You can combine both pub/sub API and segment API to create a unified data processing experience as well.
In the past year, we at StreamNative have been helping with many customers running Pulsar for different use cases from online queuing, event sourcing to stream and batch processing. We also worked on integrating Pulsar with different components in the big data ecosystem. In this talk, we will share our experiences and best practices of choosing the right API for accessing your event streams in Pulsar for different use cases.
Mesos is a platform that enables sharing of cluster resources between different frameworks. It achieves this through a two-level resource sharing approach: 1) Mesos manages coarse-grained sharing of resources like CPUs and memory between frameworks; 2) Frameworks control fine-grained sharing of tasks within their allocated resources. Mesos's use of resource offers allows frameworks to dynamically accept or reject resources based on their needs, improving cluster utilization. It has been used successfully at large companies to share resources between frameworks like Hadoop and Spark.
Mesos - A Platform for Fine-Grained Resource Sharing in the Data CenterAnkur Chauhan
Papers we Love @ Seattle, 08/14/2015
Abstract
We present Mesos, a platform for sharing commodity clusters between multiple diverse cluster computing frameworks, such as Hadoop and MPI. Sharing improves cluster utilization and avoids per-framework data replication. Mesos shares resources in a fine-grained manner, allowing frameworks to achieve data locality by taking turns reading data stored on each machine. To support the sophisticated schedulers of today's frameworks, Mesos introduces a distributed two-level scheduling mechanism called resource offers. Mesos decides how many resources to offer each framework, while frameworks decide which resources to accept and which computations to run on them. Our results show that Mesos can achieve near-optimal data locality when sharing the cluster among diverse frameworks, can scale to 50,000 (emulated) nodes, and is resilient to failures.
Using cassandra as a distributed logging to store pb dataRamesh Veeramani
This document discusses using Cassandra for big data event logging. It notes that Cassandra scales incrementally, is highly available, and is well suited for OLTP workloads where write throughput is prioritized over reads. It covers Cassandra's internal workings including token assignment, replication, and compaction strategies. Setup instructions are provided along with benchmarking results. Maintenance tools like Nodetool and stress testing tools are also mentioned. The document concludes that Cassandra is a good candidate for logging systems due to its scalability and ease of adding nodes.
Disney+ Hotstar: Scaling NoSQL for Millions of Video On-Demand UsersScyllaDB
Disney+ Hotstar is the fastest growing branch of Disney+. Join Disney+ Hotstar Architect Vamsi Subhash and senior data engineer Balakrishnan Kaliyamoorthy to learn…
How Disney+ Hotstar architected their systems to handle massive data loads
Why they chose to replace both Redis and Elasticsearch
Their requirements for massively scalable data infrastructure and evolving data models
How they migrated their data to Scylla Cloud, ScyllaDB’s fully managed NoSQL database-as-a-service, without suffering downtime
Migrating from a Relational Database to Cassandra: Why, Where, When and HowAnant Corporation
Everything you need to know about moving from a relational database to Cassandra.
You may be very familiar with what Cassandra is, or the name might just be a buzzword you've heard used when discussing databases. Regardless of your familiarity with Cassandra, this database should be the first tool you consider when you need scalability and high availability without compromising performance.
Webtech Conference: NoSQL and Web scalabilityLuca Bonmassar
NoSQL databases provide an alternative to SQL databases that can improve performance and scalability. Memcached is an in-memory key-value store that is commonly used to cache database queries for improved performance. It uses a simple get/set interface and does not provide persistent storage. Data is stored by key and expires from the cache. Memcached can be used to cache database query results in front of an SQL database to improve response times. Data can also be sharded or partitioned across multiple servers in a NoSQL system like Memcached to improve scalability for large datasets or high query volumes.
MongoDB vs Scylla: Production Experience from Both Dev & Ops Standpoint at Nu...ScyllaDB
This document compares MongoDB and ScyllaDB databases. It discusses their histories, architectures, data models, querying capabilities, consistency handling, and scaling approaches. It also provides takeaways for operations teams and developers, noting that ScyllaDB favors consistent performance over flexibility while MongoDB is more flexible but sacrifices some performance. The document also outlines how a company called Numberly uses both MongoDB and ScyllaDB for different use cases.
MySQL Cluster provides a way to scale MySQL beyond a single server by using a shared-nothing architecture with data partitioning and replication across multiple database nodes. It allows for high availability and automatic failover. The tradeoff is that all data and indexes must reside entirely in main memory.
Empowering the AWS DynamoDB™ application developer with AlternatorScyllaDB
Getting started with AWS DynamoDB™ is famously easy, but as an application grows and evolves it often starts to struggle with DynamoDB’s limitations. We introduce Scylla’s Alternator, which provides the same API as DynamoDB but aims to empower the application developer. In this presentation we will survey some of Alternator’s developer-centered features: Alternator lets you test and eventually deploy your application anywhere, on any public cloud or private cluster. It efficiently supports multiple tables so it does not require difficult single-table design. Finally, Alternator provides the developer with strong observability tools. The insights provided by these tools can detect bottlenecks, improve performance and even lower its cost.
This document discusses Cassandra and techniques for inserting data into Cassandra using the Cassandra driver. It describes three methods for inserting data - execute (blocks until response), execute async (returns immediately without blocking), and batch insert (combines multiple statements). It also covers pagination in Cassandra using fetch size, saving the paging state, and offset queries. Performance comparisons show execute async has lower execution time than execute/sync for the same number of entries.
One of our presentation which was given on Cassandra Database. Aruman implement big-data projects for its multiple client. RDBMS to Cassandra conversion is task which is taken by ARUMAN.
A database is an organized collection of data, generally stored and accessed electronically from a computer system. Where databases are more complex they are often developed using formal design and modeling techniques.
Use Cases for Oacle Pluggable Databases in Development Environmentsclaudegex
Oracle pluggable databases allow for the dynamic creation and deletion of portable database instances called pluggable databases (PDBs) within a multitenant container database (CDB). This enables several use cases for development environments including: 1) Each developer can have multiple PDBs for different features/releases, 2) Teams can easily share database states by cloning PDBs, and 3) PDBs can be snapshotted to repeatedly test against a specific database state or test data set.
Apache Cassandra Lunch #70: Basics of Apache CassandraAnant Corporation
In Cassandra Lunch #70, we discuss the Basics of Apache Cassandra and setup a stand-alone Apache Cassandra.
Accompanying Blog: https://blog.anant.us/cassandra-launch-70-basics-of-apache-cassandra
Accompanying YouTube: https://youtu.be/o-yU0mi4nzc
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Cassandra Lunch Weekly at 12 PM EST Every Wednesday: https://www.meetup.com/Cassandra-DataStax-DC/events/
Cassandra.Link:
https://cassandra.link/
Follow Us and Reach Us At:
Anant:
https://www.anant.us/
Awesome Cassandra:
https://github.com/Anant/awesome-cassandra
Cassandra.Lunch:
https://github.com/Anant/Cassandra.Lunch
Email:
solutions@anant.us
LinkedIn:
https://www.linkedin.com/company/anant/
Twitter:
https://twitter.com/anantcorp
Eventbrite:
https://www.eventbrite.com/o/anant-1072927283
Facebook:
https://www.facebook.com/AnantCorp/
Join The Anant Team:
https://www.careers.anant.us
Apache ZooKeeper is an open-source distributed coordination service that helps manage large sets of hosts. It implements coordination protocols to provide a consistent view of shared state across distributed applications or servers. ZooKeeper uses a hierarchical namespacing system called znodes to store configuration data and other information. It ensures highly reliable distributed coordination through features like leader election, group membership, and notifications.
Cassandra is used for real-time bidding in online advertising. It processes billions of bid requests per day with low latency requirements. Segment data, which assigns product or service affinity to user groups, is stored in Cassandra to reduce calculations and allow users to be bid on sooner. Tuning the cache size and understanding the active dataset helps optimize performance.
Designing your SaaS Database for Scale with PostgresOzgun Erdogan
If you’re building a SaaS application, you probably already have the notion of tenancy built in your data model. Typically, most information relates to tenants / customers / accounts and your database tables capture this natural relation.
With smaller amounts of data, it’s easy to throw more hardware at the problem and scale up your database. As these tables grow however, you need to think about ways to scale your multi-tenant (B2B) database across dozens or hundreds of machines.
In this talk, we're first going to talk about motivations behind scaling your SaaS (multi-tenant) database and several heuristics we found helpful on deciding when to scale. We'll then describe three design patterns that are common in scaling SaaS databases: (1) Create one database per tenant, (2) Create one schema per tenant, and (3) Have all tenants share the same table(s). Next, we'll highlight the tradeoffs involved with each design pattern and focus on one pattern that scales to hundreds of thousands of tenants. We'll also share an example architecture from the industry that describes this pattern in more detail.
Last, we'll talk about key PostgreSQL properties, such as semi-structured data types, that make building multi-tenant applications easy. We'll also mention Citus as a method to scale out your multi-tenant database. We'll conclude by answering frequently asked questions on multi-tenant databases and Q&A.
Scylla Summit 2016: Compose on Containing the DatabaseScyllaDB
This document discusses how Compose applies containerization best practices to provide database services. It outlines the "Twelve Factors of Stateful Apps" that guide Compose's architecture. These include running databases and data in separate containers, using environment variables for configuration, scaling containers vertically before adding nodes, and collecting logs and metrics within the deployment. By applying these factors, Compose can reliably deploy a range of database technologies like MongoDB, PostgreSQL, and now ScyllaDB across its platform.
Big Data Storage Concepts from the "Big Data concepts Technology and Architec...raghdooosh
The document discusses big data storage concepts including cluster computing, distributed file systems, and different database types. It covers cluster structures like symmetric and asymmetric, distribution models like sharding and replication, and database types like relational, non-relational and NewSQL. Sharding partitions large datasets across multiple machines while replication stores duplicate copies of data to improve fault tolerance. Distributed file systems allow clients to access files stored across cluster nodes. Relational databases are schema-based while non-relational databases like NoSQL are schema-less and scale horizontally.
Cloud computing UNIT 2.1 presentation inRahulBhole12
Cloud storage allows users to store files online through cloud storage providers like Apple iCloud, Dropbox, Google Drive, Amazon Cloud Drive, and Microsoft SkyDrive. These providers offer various amounts of free storage and options to purchase additional storage. They allow files to be securely uploaded, accessed, and synced across devices. The best cloud storage provider depends on individual needs and preferences regarding storage space requirements and features offered.
SpringPeople - Introduction to Cloud ComputingSpringPeople
Cloud computing is no longer a fad that is going around. It is for real and is perhaps the most talked about subject. Various players in the cloud eco-system have provided a definition that is closely aligned to their sweet spot –let it be infrastructure, platforms or applications.
This presentation will provide an exposure of a variety of cloud computing techniques, architecture, technology options to the participants and in general will familiarize cloud fundamentals in a holistic manner spanning all dimensions such as cost, operations, technology etc
Data has a better idea the in-memory data gridBogdan Dina
The document discusses the In-Memory Data Grid (IMDG) and Hazelcast IMDG. It begins with an introduction to IMDGs and their benefits for performance, data handling, and operations. It then covers topics like replication vs partitioning, deployment options, and features of Hazelcast IMDG like its rich APIs, ease of use, and ability to function as a distributed data store. The document outlines a business scenario using Hazelcast IMDG and highlights features like client-server deployment, TCP/IP discovery, replicated and partitioned maps, user code deployment, and integration with Spring. It concludes with an overview of the demo.
The document discusses various techniques for scaling databases and applications, including caching, replication, functional partitioning, sharding, batching, buffering, queuing, and background processing. It provides examples of when and how to implement these techniques, as well as considerations around caching policies, data distribution strategies, and managing asynchronous replication. The goal is to optimize performance and scalability through techniques that reduce round trips, parallelize operations, and distribute load across servers and databases.
The document provides an overview of Hadoop including:
- A brief history of Hadoop and its origins from Nutch.
- An overview of the Hadoop architecture including HDFS and MapReduce.
- Examples of how companies like Yahoo, Facebook and Amazon use Hadoop at large scales to process petabytes of data.
MongoDB World 2018: Solving Your Backup Needs Using MongoDB Ops Manager, Clou...MongoDB
This document discusses MongoDB's cloud database offerings including MongoDB Atlas, Ops Manager, and Cloud Manager. It provides an overview of key features such as automated backups, point-in-time restore, queryable snapshots, global availability, security, and elastic scaling. The document also demonstrates MongoDB's managed backup capabilities in Atlas including cloud provider snapshots on AWS and Azure, as well as a roadmap for future disaster recovery features.
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostZilliz
If you are building a RAG application that serves millions of users, you should consider how to scale your system seamlessly and cost-efficiently. The Zilliz Serverless tier represents a significant innovation in the field of vector search, enabling you to rapidly scale to millions of tenants and billions of vectors, while fully leveraging the hot/cold characteristics across tenants to reduce data storage costs. It enables vector storage at costs comparable to S3 and facilitates vector search times in the hundreds of milliseconds for tens of millions of data points!
In this talk, we will delve into the implementation details, usage patterns, and performance metrics of Zilliz Serverless. We will discuss how it empowers AI-native applications to achieve rapid business growth by providing a cost-effective and scalable vector storage and search solution.
Oracle Clusterware is software that provides services for managing and maintaining Oracle clusters. It allows clusters to be managed as a single system and provides high availability, resource management, and workload balancing. Clusterware uses a shared disk architecture and provides services like cluster management, node monitoring, and time synchronization. It requires nodes to have two network adapters, one for a private interconnect and one for a public network, and supports features like fencing, Single Client Access Name (SCAN), and Grid Naming Service (GNS) for cluster domain name resolution and load balancing.
ZooKeeper is a distributed coordination service that allows distributed applications to synchronize data and configuration information. It uses a data model of directories and files, called znodes, that can contain small amounts of structured data. ZooKeeper maintains data consistency through a leader election process and quorum-based consensus algorithm called Paxos. It provides applications with synchronization primitives and configuration maintenance in a highly-available and reliable way.
Organizations continue to adopt Solr because of its ability to scale to meet even the most demanding workflows. Recently, LucidWorks has been leading the effort to identify, measure, and expand the limits of Solr. As part of this effort, we've learned a few things along the way that should prove useful for any organization wanting to scale Solr. Attendees will come away with a better understanding of how sharding and replication impact performance. Also, no benchmark is useful without being repeatable; Tim will also cover how to perform similar tests using the Solr-Scale-Toolkit in Amazon EC2.
Development of concurrent services using In-Memory Data Gridsjlorenzocima
As part of OTN Tour 2014 believes this presentation which is intented for covers the basic explanation of a solution of IMDG, explains how it works and how it can be used within an architecture and shows some use cases. Enjoy
Managing Security At 1M Events a Second using ElasticsearchJoe Alex
The document discusses managing security events at scale using Elasticsearch. Some key points:
- The author manages security logs for customers, collecting, correlating, storing, indexing, analyzing, and monitoring over 1 million events per second.
- Before Elasticsearch, traditional databases couldn't scale to billions of logs, searches took days, and advanced analytics weren't possible. Elasticsearch allows customers to access and search logs in real-time and perform analytics.
- Their largest Elasticsearch cluster has 128 nodes indexing over 20 billion documents per day totaling 800 billion documents. They use Hadoop for long term storage and Spark and Kafka for real-time analytics.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
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.
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.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
3. Page 3Classification: Restricted
Zookeeper
•ZooKeeper is a distributed Co-ordinated service.
•Partial failures are intrinsic in distributed systems.
•ZooKeeper gives you a set of tools to build distributed applications that can
safely handle partial failures
4. Page 4Classification: Restricted
A scenario
•A group of servers provides services to the clients. To maintain the list of
these servers at a certain place is a challenge – Can’t be stored on a single
node; even if stored on multiple machines, removing a certain entity from
the list is challenging.
•ZooKeeper provides a group membership service to achieve the above
requirement.
5. Page 5Classification: Restricted
Group membership in ZK
•ZK provides a high availability file system.
•It doesn’t have files & directories though – but, znodes
•Znodes - contain data (as a file) & also contain other znodes(as directories)
•Znodes form a hierarchical namespace, and a natural way to build a
membership list is to create a parent znode with the name of the group and
child znodes with the names of the group members (servers)
7. Page 7Classification: Restricted
ZK data model
•ZK maintains a hierarchical tree of nodes called znodes. A znode stores data
and has a corresponding ACL(access list)
•ZooKeeper is designed for coordination (which typically uses small
datafiles), not high-volume data storage, so there is a limit of 1 MB on the
amount of data that may be stored in any znode.
•Data access is atomic
•A write will replace all the data associated with a znode. A Write will either
succeed or fail. ZooKeeper does not support an append operation
8. Page 8Classification: Restricted
ZK data model – node types
•Znodes – ephemeral & persistent
•A znode’s type is set at creation time and may not be changed later
•An ephemeral znode is deleted by ZooKeeper when the creating client’s
session ends
•a persistent znode is not tied to the client’s session and is deleted only
when explicitly deleted by a client (not necessarily the one that created it)
•An ephemeral znode may not have children, not even ephemeral ones.
•Even though ephemeral nodes are tied to a client session, they are visible to
all clients (subject to their ACL policies, of course)
•Ephemeral znodes are ideal for building applications that need to know
when certain distributed resources are available
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ZK data model – sequence numbers
•A sequential znode is given a sequence number by ZooKeeper as a part of its
name
•If a znode is created with the sequential flag set, then the value of a
monotonically increasing counter (maintained by the parent znode) is
appended to its name.
•If a client asks to create a sequential znode with the name /a/b-, for
example, the znode created may actually have the name /a/b-3. If, later on,
another sequential znode with the name /a/b- is created, it will be given a
unique name with a larger value of the counter—for example, /a/b-5
•Sequence numbers can be used to impose a global ordering on events in a
distributed system and may be used by the client to infer the ordering. You
can use this in Lock sevice
10. Page 10Classification: Restricted
ZK data model – Watches
•Watches allow clients to get notifications when a znode changes in some
way
•Watches are set by operations on the ZooKeeper service and are triggered
by other operations on the service
•For example, a client might call the exists operation on a znode, placing a
watch on it at the same time. If the znode doesn’t exist, the exists operation
will return false. If, some time later, the znode is created by a second client,
the watch is triggered, notifying the first client of the znode’s creation
•Watchers are triggered only once