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The document discusses problems with the existing Redis implementation and requirements for a new solution. It evaluates Aerospike and Apache Ignite as potential replacements. Aerospike is highlighted as a good fit due to its support for sharding, ACID compliance, and ability to store data on disk or RAM for high availability and reduced costs compared to RAM-only solutions.
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in-memory database system and low latency
This document discusses in-memory database systems and high performance computing. It begins with an overview of domestic and international in-memory databases like Altibase, Oracle TimesTen, McObject ExtremeDB, and KX Systems KDB+. It then discusses the advantages of in-memory architectures for achieving ultra-low latency. The rest of the document covers technologies that enable high performance like NUMA, SSDs, InfiniBand, message queues, and complex event processing. It concludes by discussing in-memory computing and high performance computing technologies.
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- MongoDB 3.0 introduces pluggable storage engines, with WiredTiger as the first integrated engine, providing document-level locking, compression, and improved concurrency over MMAPv1.
- WiredTiger uses a B+tree structure on disk and stores each collection and index in its own file, with no padding or in-place updates. It includes a write ahead transaction log for durability.
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This is from a 2 hour talk introducing in-memory databases. First a look at traditional RDBMS architecture and some of it's limitations, then a look at some in-memory products and finally a closer look at OrigoDB, the open source in-memory database toolkit for NET/Mono.
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- WiredTiger uses a B+tree structure on disk and stores each collection and index in its own file, with no padding or in-place updates. It includes a write ahead transaction log for durability.
- To use WiredTiger, launch mongod with the --storageEngine=wiredTiger option, and upgrade existing deployments through mongodump/mongorestore or initial sync of a replica member. Some MMAPv1 options do not apply to WiredTiger.
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"Dear Students,
Greetings from www.etraining.guru
We provide BEST online training for IBM DB2 LUW/UDB DBA by a database architect. Our DB2 Trainer comes with a working experience of 11+ years, 9+ years in DB2 and a DB2 certified professional.
DB2 LUW DBA Course Content: http://www.etraining.guru/course/dba/online-training-db2-luw-udb-dba
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Number of Hours: 30-35 hours
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"Dear Students,
Greetings from www.etraining.guru
We provide BEST online training for IBM DB2 LUW/UDB DBA by a database architect. Our DB2 Trainer comes with a working experience of 11+ years, 9+ years in DB2 and a DB2 certified professional.
DB2 LUW DBA Course Content: http://www.etraining.guru/course/dba/online-training-db2-luw-udb-dba
Course Cost: USD 350 (or) INR 21000
Number of Hours: 30-35 hours
Regards,
Karthik
www.etraining.guru"
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Introduction to AWS Big Data
a comprehensive good introduction to the the Big data world in AWS cloud, hadoop, Streaming, batch, Kinesis, DynamoDB, Hbase, EMR, Athena, Hive, Spark, Piq, Impala, Oozie, Data pipeline, Security , Cost, Best practices
Apache Spark™ is a multi-language engine for executing data-S5.ppt
Apache Spark™ is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.
Boston Apache Spark User Group (the Spahk group) - Introduction to Spark - 15...
This document provides an introduction to Apache Spark, including:
- A brief history of Spark, which started at UC Berkeley in 2009 and was donated to the Apache Foundation in 2013.
- An overview of what Spark is - an open-source, efficient, and productive cluster computing system that is interoperable with Hadoop.
- Descriptions of Spark's core abstractions including Resilient Distributed Datasets (RDDs), transformations, actions, and how it allows loading and saving data.
- Mentions of Spark's machine learning, SQL, streaming, and graph processing capabilities through projects like MLlib, Spark SQL, Spark Streaming, and GraphX.
Introduction to Spark
Spark is an in-memory cluster computing framework that allows processing of large datasets across clusters of computers using simple programming models. It was developed at UC Berkeley in 2009 and became an Apache project in 2013. Spark is now the most active big data project within the Apache Software Foundation and provides APIs for Scala, Java, Python and an interface for SQL queries. Spark is up to 100 times faster than Hadoop for iterative/interactive jobs and can run up to 10 times faster on disk due to its in-memory computing capabilities.
Ingesting Over Four Million Rows Per Second With QuestDB Timeseries Database ...
How would you build a database to support sustained ingestion of several hundreds of thousands rows per second while running near real-time queries on top?
In this session I will go over some of the technical decisions and trade-offs we applied when building QuestDB, an open source time-series database developed mainly in JAVA, and how we can achieve over four million row writes per second on a single instance without blocking or slowing down the reads. There will be code and demos, of course.
We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Apache Spark II (SparkSQL)
In this second part, we'll continue the Spark's review and introducing SparkSQL which allows to use data frames in Python, Java, and Scala; read and write data in a variety of structured formats; and query Big Data with SQL.
Hadoop Architecture_Cluster_Cap_Plan
Hadoop is an open-source framework that processes large datasets in a distributed manner across commodity hardware. It uses a distributed file system (HDFS) and MapReduce programming model to store and process data. Hadoop is highly scalable, fault-tolerant, and reliable. It can handle data volumes and variety including structured, semi-structured and unstructured data.
MySQL HA
MySQL Cluster provides high availability through data replication across multiple nodes, automatic failover, and synchronous replication to ensure data integrity, but it has limitations in that the entire database must reside in memory and database size is restricted by available memory. Other options for high availability with MySQL include using MySQL proxy to split reads and writes across nodes, replication with multi-master setups, and technologies like DRBD to replicate data for recovery. Planning for failures, keeping implementations simple, and separating data and connectivity high availability are important principles for highly available MySQL architectures.
Apache Spark
This document provides an overview of the Apache Spark framework. It discusses how Spark allows distributed processing of large datasets across computer clusters using simple programming models. It also describes how Spark can scale from single servers to thousands of machines. Spark is designed to provide high availability by detecting and handling failures at the application layer. The document also summarizes Resilient Distributed Datasets (RDDs), which are Spark's fundamental data abstraction, and transformations and actions that can be performed on RDDs.
Advanced Spark Programming - Part 1 | Big Data Hadoop Spark Tutorial | CloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2Jab9wX
This CloudxLab Advanced Spark Programming tutorial helps you to understand Advanced Spark Programming in detail. Below are the topics covered in this tutorial:
1) Persistence (Caching) in Spark
2) Persistence Storage Level
3) Which Storage Level to Choose?
4) Data Partitioning in Spark
Introduction to Apache Spark Ecosystem
This document introduces Apache Spark, an open-source cluster computing system that provides fast, general execution engines for large-scale data processing. It summarizes key Spark concepts including resilient distributed datasets (RDDs) that let users spread data across a cluster, transformations that operate on RDDs, and actions that return values to the driver program. Examples demonstrate how to load data from files, filter and transform it using RDDs, and run Spark programs on a local or cluster environment.
Crimson: Ceph for the Age of NVMe and Persistent Memory
Ceph is a mature open source software-defined storage solution that was created over a decade ago.
During that time new faster storage technologies have emerged including NVMe and Persistent memory.
The crimson project aim is to create a better Ceph OSD that is more well suited to those faster devices. The crimson OSD is built on the Seastar C++ framework and can leverage these devices by minimizing latency, cpu overhead, and cross-core communication. This talk will discuss the project design, our current status, and our future plans.
JDD 2016 - Michal Matloka - Small Intro To Big Data
Pig, Hive, Flink, Kafka, Zeppelin... if you now wonder if someone just tried to offend you or are those just Pokemon names, then this talk is just for you! Big Data is everywhere and new tools for it are released almost at the speed of new JavaScript frameworks. During this entry level presentation we will walk though the challenges which Big Data presents, reflect how big is big and introduce currently most fancy and popular (mostly open source) tools. We'll try to spark off interest in Big Data by showing application areas and by throwing ideas where you can later dive into.
Spark Study Notes
This document discusses Apache Spark, an open-source cluster computing framework. It provides an overview of Spark, including its main concepts like RDDs (Resilient Distributed Datasets) and transformations. Spark is presented as a faster alternative to Hadoop for iterative jobs and machine learning through its ability to keep data in-memory. Example code is shown for Spark's programming model in Scala and Python. The document concludes that Spark offers a rich API to make data analytics fast, achieving speedups of up to 100x over Hadoop in real applications.
Spark SQL
The document summarizes Spark SQL, which is a Spark module for structured data processing. It introduces key concepts like RDDs, DataFrames, and interacting with data sources. The architecture of Spark SQL is explained, including how it works with different languages and data sources through its schema RDD abstraction. Features of Spark SQL are covered such as its integration with Spark programs, unified data access, compatibility with Hive, and standard connectivity.
Masterclass Live: Amazon EMR
Abhishek Sinha is a senior product manager at Amazon for Amazon EMR. Amazon EMR allows customers to easily run data frameworks like Hadoop, Spark, and Presto on AWS. It provides a managed platform and tools to launch clusters in minutes that leverage the elasticity of AWS. Customers can customize clusters and choose from different applications, instances types, and access methods. Amazon EMR allows separating compute and storage where the low-cost S3 can be used for persistent storage while clusters are dynamically scaled based on workload.
Similar to In-memory database (20)
Apache Spark™ is a multi-language engine for executing data-S5.ppt
Apache Spark™ is a multi-language engine for executing data-S5.ppt
Boston Apache Spark User Group (the Spahk group) - Introduction to Spark - 15...
Boston Apache Spark User Group (the Spahk group) - Introduction to Spark - 15...
Ingesting Over Four Million Rows Per Second With QuestDB Timeseries Database ...
Ingesting Over Four Million Rows Per Second With QuestDB Timeseries Database ...
Advanced Spark Programming - Part 1 | Big Data Hadoop Spark Tutorial | CloudxLab
Advanced Spark Programming - Part 1 | Big Data Hadoop Spark Tutorial | CloudxLab
Crimson: Ceph for the Age of NVMe and Persistent Memory
Crimson: Ceph for the Age of NVMe and Persistent Memory
JDD 2016 - Michal Matloka - Small Intro To Big Data
JDD 2016 - Michal Matloka - Small Intro To Big Data
Recently uploaded
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
AppSec PNW: Android and iOS Application Security with MobSF
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
Introduction of Cybersecurity with OSS at Code Europe 2024
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Choosing The Best AWS Service For Your Website + API.pptx
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
June Patch Tuesday
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
"Choosing proper type of scaling", Olena Syrota
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
JavaLand 2024: Application Development Green Masterplan
My presentation slides I used at JavaLand 2024
Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays, June 2024 with Maria Copot 20
What is an RPA CoE? Session 1 – CoE Vision
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.Astute Business Solutions | Oracle Cloud Partner |
Your goto partner for Oracle Cloud, PeopleSoft, E-Business Suite, and Ellucian Banner. We are a firm specialized in managed services and consulting.
Skybuffer SAM4U tool for SAP license adoption
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
GNSS spoofing via SDR (Criptored Talks 2024)
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
Apps Break Data
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
Recently uploaded (20)
Nordic Marketo Engage User Group_June 13_ 2024.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptx
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...
9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
AppSec PNW: Android and iOS Application Security with MobSF
AppSec PNW: Android and iOS Application Security with MobSF
Introduction of Cybersecurity with OSS at Code Europe 2024
Introduction of Cybersecurity with OSS at Code Europe 2024
Choosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptx
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
JavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green Masterplan
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Astute Business Solutions | Oracle Cloud Partner |
Astute Business Solutions | Oracle Cloud Partner |
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
In-memory database
- 2. Problems - Multiple Redis instances on 1 server - Each instance contains some types of data - 200000 users active /s - About 5 milions records in memory - Master-Slave structures * Redis 1 instances → 1 core → Bottleneck read/write * No cluster * Scale out ???
- 4. Requirements ● Horizontal Scale ● Fast read for real time access ● Use full core of CPU ● High availability
- 5. Replacements ● Aerospike ● Redis cluster ● Apache ignite
- 6. Aerospike ● Written in C ● Opensource ● Community Edition (free) and Enterprise Edititon ● 1177 stars on Github
- 7. Aerospike ● Sharding supported ● ACID (Atomicity, Consistency, Isolation, Durability) ● Data can be saved to disk or RAM or hybrid storage (indexes in RAM, data in disk) ● Self managing ● Optimized for SSD ● Reduce money (Less server, SSD is more cheaper than RAM) ● Proved by AppNexus, Inmobi,...
- 9. Aerospike ● Multicast ● No master/slave node. Each node contains some different primary shards and some different replica shards
- 10. Storage ● Pure DRAM without persistence ● DRAM with persistence ● Flash storage (SSDs)
- 11. Namespace – 1 database = many namespaces – How data is stored: RAM or DISK – How many replicas – When record expire
- 12. Sets ● Group of records ● 1 namespace = many sets
- 13. Record ● Key ● Meta-data (generation, time to live) ● Bins (1 or more bins) → as rows and their value in DBMS
- 14. Apache Ignite Many features ● Written in java ● In-memory database ● Data grid ● Accelator for Hadoop and Spark ….
- 17. Shared RDDs among Spark jobs
- 18. Indexed SQL Query ● SparkSQL does not index RDD ● IgniteSQL can index its cached data by field or using lucene index for text. Examples: val cacheRdd = igniteContext.fromCache("partitioned") cacheRdd.savePairs(sparkContext.parallelize(1 to 10000, 10).map(i => (i, i))) val cacheRdd = igniteContext.fromCache("partitioned") val result = cacheRdd.sql( "select _val from Integer where val > ? and val < ?",10,100)
- 19. public class Person implements Serializable { /** Person ID (indexed). */ @QuerySqlField(index = true) private long id; /** First name (not-indexed). */ @QuerySqlField private String name; /** Resume text (create LUCENE-based TEXT index for this field). */ @QueryTextField private String resume; /** Salary (indexed). */ @QuerySqlField(index = true) private double salary; ….. }